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- Q: What are the differences between Element 3D v2.2.2 and v2.2.3? A: The latest version of Element 3D, v2.2.3, fixes some issues with relinking, graphics switching, and AE CC compatibility. It also improves the UI speed, material visibility options, and C4D support file support. -
- Q: Is Element compatible with other plugins and presets? A: Yes, Element can work with other plugins and presets, such as BCC, Deep Glow, Flow, Glitchify, MBL, Omino, Red Giant Shooter Suite, Red Giant Universe, RSMB, Saber, Sapphire, Trapcode, Twitch, Twixtor, Crossphere Bokeh, MisFire, Pixel Sorter, etc. -
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- Q: How can I animate and render my objects with Element ? A: Element allows you to import objects and textures in OBJ and C4D formats, as well as animation OBJ sequences. You can also use the built-in animation engine to create keyframe animations for your objects. You can render your scenes using the plugin's own renderer or export them as E3D files. -
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-Once you have downloaded the Candy Crush Saga APK file, you will need to install it on your Android device. To do this, you will need to enable the option that allows you to install apps from unknown sources. This option may vary depending on your device model and Android version, but you can usually find it in Settings > Security > Unknown Sources.
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You can now enjoy playing Candy Crush Saga on your Android device without any restrictions.
-How to Play Candy Crush Saga and Some Tips and Tricks
-Candy Crush Saga is easy to play but hard to master. The game has hundreds of levels with different goals and challenges. You will need to use your skills, strategy, and luck to complete them all.
-To play Candy Crush Saga, you just need to swipe your finger on the screen to swap adjacent candies and make matches of three or more of the same color. You can also create special candies by matching four or more candies in a row or column, or in a T or L shape. These special candies have different effects when matched with other candies, such as clearing rows, columns and level 887. These levels have various obstacles, such as chocolate, licorice, bombs, or time limits that make them very challenging to complete.
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Q: How can I contact the developers of Candy Crush Saga?
-A: You can contact the developers of Candy Crush Saga by visiting their official website, [King.com], and clicking on "Contact Us" at the bottom of the page. You can also follow them on their social media accounts, such as Facebook, Twitter, Instagram, or YouTube, and send them your feedback, suggestions, or questions.
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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download CSR Racing for Android and Compete with the Worlds Top Drag Racers.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download CSR Racing for Android and Compete with the Worlds Top Drag Racers.md deleted file mode 100644 index 7733054567632b71b528bda2c50798cd677b995a..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download CSR Racing for Android and Compete with the Worlds Top Drag Racers.md +++ /dev/null @@ -1,91 +0,0 @@ - -
Download CSR Racing for Android: The Ultimate Drag Racing Game
-If you are a fan of racing games, you have probably heard of CSR Racing, the best-selling drag racing game on Android. But if you haven't, you are missing out on a thrilling and immersive experience that will keep you hooked for hours. In this article, we will tell you what CSR Racing is, how to download it, what features it offers, and some tips and tricks to help you become the king of the streets. So buckle up and get ready to race!
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How to Download CSR Racing for Android
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- Go to the Google Play Store and search for CSR Racing or click on this link. -
- Tap on the Install button and wait for the download to finish. The game is about 500 MB in size, so make sure you have enough space on your device. -
- Launch the game and enjoy the stunning graphics and addictive gameplay. You can also connect your Facebook account to save your progress and challenge your friends. -
Features of CSR Racing for Android
-Race Over 100 Licensed Cars from Top Manufacturers
-One of the best things about CSR Racing is that it features over 100 licensed cars from the world's most prestigious car manufacturers, such as McLaren, Bugatti, Aston Martin, Hennessey, and Koenigsegg. You can choose from a variety of models, such as the Audi R8, Ford GT, Chevrolet Camaro, McLaren MP4-12C, or the Nissan GT-R. Each car has its own stats, such as power, weight, grip, and nitrous, that affect its performance on the track. You can also view detailed information about each car, such as its history, specifications, and trivia.
-Beat the Crews and Rule the City
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-Upgrade and Customize Your Cars
-To win races in CSR Racing, you need more than just a good car. You also need to upgrade it with various parts that improve its performance. You can upgrade your engine, tires, intake, turbo, body, nitrous, gearbox , and weight. Each upgrade has five levels, and the higher the level, the more expensive it is. You can also customize your car's appearance by changing its color, license plate, decals, and rims. You can even add some special effects, such as flames, neon, or smoke. You can preview your car before buying any upgrades or customizations.
-Compete in World Tour and Challenge Other Players
-If you want to test your skills against other players from around the world, you can try the World Tour mode in CSR Racing. In this mode, you can race against real opponents in real time and win exclusive cars and prizes. You can also join a crew and cooperate with other players to earn more rewards and bonuses. You can chat with your crew members, send them gifts, and challenge them to friendly races. You can also compete in global leaderboards and events to show off your achievements and rank.
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Tips and Tricks for CSR Racing for Android
-Choose the Right Car for Each Race
-Not all cars are suitable for every race in CSR Racing. Some races require a specific type of car, such as a muscle car, a sports car, or a supercar. Some races also have restrictions on the car's tier, power, or weight. Therefore, you need to choose the right car for each race based on its requirements and your preferences. You can also switch between different cars depending on the situation. For example, you might want to use a lighter car for a short distance race, or a more powerful car for a long distance race.
-Tune Your Car and Use Nitrous Wisely
-Another way to improve your car's performance in CSR Racing is to tune it according to your needs. Tuning allows you to adjust the settings of your engine, tires, nitrous, and gearbox to optimize your acceleration, top speed, grip, and fuel efficiency. You can use the dyno test and the test run to see how your tuning affects your car's performance. You can also use nitrous to boost your speed during a race, but you need to use it wisely. Nitrous is limited and takes time to refill, so you need to decide when to use it for maximum effect.
-Master the Perfect Shift and Launch
-The most important skill in CSR Racing is to master the perfect shift and launch. Shifting is when you change gears during a race, and launching is when you start the race. Both of these actions require precise timing and coordination. If you shift or launch too early or too late, you will lose speed and momentum. If you shift or launch at the right moment, you will gain speed and advantage. The game will show you a green indicator when it is the best time to shift or launch. You can also use the tachometer and the speedometer to help you with your timing.
-Earn Cash and Gold by Completing Events and Achievements
-Cash and gold are the main currencies in CSR Racing. You need cash to buy new cars, upgrade parts, customize your appearance, and enter races. You need gold to buy premium cars, skip wait times, refill gas, and get special offers. You can earn cash and gold by completing various events and achievements in the game. Events are time-limited challenges that reward you with cash, gold, or cars if you complete them successfully. Achievements are long-term goals that reward you with cash or gold if you reach them.
-Conclusion
-CSR Racing is one of the best drag racing games on Android that offers stunning graphics, realistic physics, addictive gameplay, and a variety of cars and modes to choose from. Whether you want to race against the AI or other players, upgrade and customize your cars, or just enjoy the thrill of speed, CSR Racing has something for everyone. So what are you waiting for? Download CSR Racing for Android today and start your racing career!
-FAQs
-Q1: Is CSR Racing free to play?
-A1: Yes, CSR Racing is free to play on Android devices. However, some items in the game can be purchased with real money. If you want to disable this feature, you can turn off in-app purchases in your device settings.
-Q2: How can I prevent unauthorized purchases in CSR Racing?
-A2: If you want to prevent unauthorized purchases in CSR Racing, you can set up a password or a PIN for your Google Play account. This way, every time someone tries to make a purchase in the game, they will have to enter the password or PIN first.
-Q3: How can I connect with other CSR Racing players?
-A3: You can connect with other CSR Racing players by joining a crew or by connecting your Facebook account to the game. By joining a crew, you can chat with other members, send them gifts, and cooperate with them to earn more rewards and bonuses. By connecting your Facebook account, you can save your progress and challenge your friends to races.
-Q4: What are the system requirements for CSR Racing?
-A4: CSR Racing requires Android 4.0.3 or higher and at least 500 MB of free space on your device. The game also requires a stable internet connection to play online modes and access some features.
-Q5: What is the difference between CSR Racing and CSR Racing 2?
-A5: CSR Racing 2 is the sequel to CSR Racing that was released in 2016. It has improved graphics, more cars, more modes, and more features than the original game. However, CSR Racing is still a great game that offers a lot of fun and challenge for racing fans.
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-If you are a fan of basketball games, you might have heard of Basketball Battle, a popular mobile sports game that lets you compete in 1 on 1 streetball matches with easy controls and realistic physics. But did you know that you can play Basketball Battle mod apk, a modified version of the game that gives you unlimited money, unlocked tournaments, and more features? In this article, we will show you how to download and install Basketball Battle mod apk on your Android device, how to play the game and enjoy its benefits, and answer some frequently asked questions about the game. Let's get started!
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As you can see, Basketball Battle mod apk gives you more freedom and fun than the original version. You can play the game with no limits and no hassles. You can enjoy the best 1v1 streetball game on your Android device with Basketball Battle mod apk.
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-The steps to download the mod apk file from a reliable source
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- Go to your device's settings and enable the option to install apps from unknown sources. This will allow you to install apps that are not from Google Play Store. -
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- Congratulations! You have successfully installed Basketball Battle mod apk on your device. You can now enjoy playing the game with unlimited money, unlocked tournaments, no ads, and free updates. -
The permissions and requirements for the mod apk
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- Permissions: The mod apk needs access to your device's storage, network, and phone state. These permissions are necessary for the game to function properly and save your progress. The mod apk does not access any sensitive or personal data from your device. -
- Requirements: The mod apk requires an Android device with version 4.4 or higher. It also requires at least 100 MB of free space in your device's storage. The mod apk is compatible with most Android devices, but some features may vary depending on your device's specifications. -
If you meet these permissions and requirements, you should have no problem installing and playing Basketball Battle mod apk on your device.
-How to play Basketball Battle mod apk and enjoy its benefits
-The basic controls and gameplay mechanics of Basketball Battle
-Basketball Battle is a simple yet addictive game that anyone can play and enjoy. The game has easy controls that let you move, shoot , and dunk with just two buttons. You can also use the joystick to move around and perform different moves. Here are the basic controls and gameplay mechanics of Basketball Battle:
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- Move: You can use the joystick on the left side of the screen to move your player left or right. You can also use it to jump, crouch, and fake out your opponent. -
- Shoot: You can use the button on the right side of the screen to shoot the ball. You need to hold the button to charge up your shot and release it at the right time to make it. You can also use this button to dunk if you are close enough to the basket. -
- Score: You need to score more points than your opponent in a limited time to win the game. You can score 2 points for a regular shot, 3 points for a long shot, and 4 points for a dunk. You can also score bonus points if you catch on fire or perform a special move. -
- Defend: You need to prevent your opponent from scoring by blocking their shots, stealing their ball, or pushing them away. You can use the same buttons as shooting to defend, but you need to time them well to avoid fouls. -
- Power-ups: You can collect power-ups that appear randomly on the court. These power-ups can give you an edge over your opponent by boosting your speed, strength, accuracy, or stamina. Some examples of power-ups are sneakers, energy drinks, magnets, and rockets. -
These are the basic controls and gameplay mechanics of Basketball Battle. You can learn more about them by playing the tutorial mode or reading the help section in the game.
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- Use pump fakes and footwork: You can use pump fakes and footwork to confuse your opponent and create space for your shots. You can do this by tapping the shoot button quickly or moving the joystick in different directions. This can make your opponent jump or move away, giving you an opportunity to shoot or drive to the basket. -
- Use special moves: You can use special moves to surprise your opponent and score extra points. You can do this by holding the shoot button longer or performing certain combinations with the joystick. Some examples of special moves are alley-oops, spin moves, behind-the-back passes, and crossover dribbles. -
- Use power-ups wisely: You can use power-ups to gain an advantage over your opponent, but you need to use them wisely. You should not waste them on easy shots or when you are already winning. You should also avoid using them when your opponent has a counter power-up that can negate yours. For example, if your opponent has a magnet that can attract your ball, you should not use a rocket that can make your ball fly faster. -
- Use different players and outfits: You can use different players and outfits to customize your team and suit your style and strategy. Each player has different attributes and skills that affect their performance on the court. For example, some players are faster, stronger, or more accurate than others. Some players also have unique abilities that can give them an edge over others. For example, some players can shoot from anywhere on the court, some players can dunk from far away, and some players can block any shot. You should experiment with different players and outfits to find the best combination for you. -
- Play online and offline: You can play Basketball Battle mod apk online or offline depending on your preference and situation. If you want to play online, you can compete against players from around the world on online leaderboards or challenge your friends in 2 player split screen mode. If you want to play offline, you can play against AI opponents or practice in training mode. Playing online and offline can help you improve your skills and have fun in different ways. -
These are some tips and tricks that can help you master Basketball Battle mod apk and beat your opponents. You can learn more about them by playing the game and discovering new strategies.
-The advantages of playing Basketball Battle mod apk over the original version
-As we mentioned earlier, playing Basketball Battle mod apk has many advantages over playing the original version. Here are some of them:
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- You have unlimited money : You can buy anything you want in the game without worrying about the cost. You can unlock all the players, outfits, courts, balls, and more with just a few clicks. You can also upgrade your players and boost their attributes to the max. You can enjoy the game without any limitations or restrictions. -
- You have unlocked tournaments: You can access all the tournaments in the game without having to complete any requirements or pay any fees. You can play any tournament you want at any time and enjoy the different courts and challenges. You can also earn huge rewards and prizes by winning the tournaments. You can explore the game and have more fun. -
- You have no ads: You can play the game without any interruptions or distractions from annoying ads. You can focus on the game and have a smooth and seamless experience. You can also save your data and battery by not having to watch or download any ads. -
- You have free updates: You can get free updates for the game as soon as they are released. You don't have to wait for the official version to update or download anything manually. You can enjoy the latest features and improvements of the game. -
These are some of the advantages of playing Basketball Battle mod apk over the original version. You can see that playing Basketball Battle mod apk gives you more benefits and enjoyment than playing the original version. You can play the game with more freedom and fun with Basketball Battle mod apk.
-Conclusion
-A summary of the main points and a call to action for the readers
-In conclusion, Basketball Battle mod apk is a modified version of Basketball Battle, a popular mobile sports game that lets you compete in 1 on 1 streetball matches with easy controls and realistic physics. Basketball Battle mod apk gives you unlimited money, unlocked tournaments, no ads, and free updates that make the game even more enjoyable. You can download and install Basketball Battle mod apk on your Android device by following the steps we provided in this article. You can also play Basketball Battle mod apk and enjoy its benefits by following the tips and tricks we shared in this article.
-If you are a fan of basketball games, you should definitely try Basketball Battle mod apk. It is one of the best 1v1 streetball games on Android that will keep you entertained and challenged for hours. You can play Basketball Battle mod apk online or offline, alone or with your friends, and have a blast. You can download Basketball Battle mod apk from [Basketball Battle Mod APK Download] today and start playing the game right away. You won't regret it!
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-A1: Yes, Basketball Battle mod apk is safe to use if you download it from a reliable source like [Basketball Battle Mod APK Download]. This website provides authentic and verified mod apk files that are free from viruses, malware, or spyware. You can trust this website to provide you with a safe and working Basketball Battle mod apk file.
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- Unlimited money -
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These features give you more freedom and fun than playing the original version of Basketball Battle.
-Q3: How can I play Basketball Battle mod apk with my friends?
-A3: You can play Basketball Battle mod apk with your friends in 2 player split screen mode. To do that, you need to follow these steps:
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- Launch Basketball Battle mod apk on your device. -
- Select "2 Player" from the main menu. -
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You can also play Basketball Battle mod apk online with your friends by connecting to Facebook or Google Play Games.
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-A4: You can customize your team and player in Basketball Battle mod apk by following these steps:
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- Launch Basketball Battle mod apk on your device. -
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- Choose an outfit for your player or buy a new one with money. -
- Upgrade your player's attributes with money or power-ups. -
- Save your changes and exit the team menu. -
You can customize your team and player in Basketball Battle mod apk to make them look cool and perform better on the court.
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-A5: You can find more information about Basketball Battle mod apk by visiting [Basketball Battle Mod APK Download], the website that provides the mod apk file for the game. This website has a blog section that posts articles and guides about Basketball Battle mod apk, such as how to play, how to win, how to use power-ups, and more. You can also find reviews, ratings, comments, and feedback from other users who have downloaded and played Basketball Battle mod apk. You can also contact the website's support team if you have any questions or issues with the mod apk file.
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Download Yama no Susume S1 The Fun and Relaxing Anime You Need.md b/spaces/1phancelerku/anime-remove-background/Download Yama no Susume S1 The Fun and Relaxing Anime You Need.md deleted file mode 100644 index 10e0f48c1e490fa461162d8543926317aa1c3ab7..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Download Yama no Susume S1 The Fun and Relaxing Anime You Need.md +++ /dev/null @@ -1,113 +0,0 @@ - -
Download Yama no Susume S1: A Guide for Anime Fans
-If you are looking for a cute and relaxing anime to watch, you might want to check out Yama no Susume S1, also known as Encouragement of Climb. This anime is about two high school girls who rediscover their passion for mountain climbing and embark on various adventures in nature. In this article, we will tell you what Yama no Susume S1 is about, why you should watch it, and how to download it legally or otherwise.
-download yama no susume s1
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What is Yama no Susume S1?
-Yama no Susume S1 is an anime series that aired in 2013 as part of the winter season. It is based on a manga by Shiro that started in 2011 and is still ongoing. The anime has four seasons so far, with the latest one being Next Summit that aired in 2022.
-The story follows Aoi Yukimura, a shy and timid girl who used to love mountains as a child but developed a fear of heights after an accident. She reunites with her childhood friend Hinata Kuraue, who is outgoing and loves mountaineering. Hinata encourages Aoi to overcome her fear and join her in climbing various mountains across Japan.
-The anime features a cast of adorable characters who have different personalities and motivations for hiking
Why should you watch Yama no Susume S1?
-There are many reasons why you should watch Yama no Susume S1, but here are some of the main ones:
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It is a heartwarming slice-of-life anime with cute girls and mountain climbing.
-One of the main appeals of Yama no Susume S1 is its slice-of-life genre, which means it focuses on the everyday lives and interactions of the characters. The anime shows how Aoi and Hinata grow as friends and individuals through their shared hobby of mountain climbing. They also meet other girls who join them in their journeys, such as Kaede Saitou, a knowledgeable and experienced climber, and Kokona Aoba, a sweet and cheerful girl who loves nature. The anime depicts the joys and challenges of hiking, such as finding the best routes, preparing the equipment, dealing with the weather, and enjoying the views. The anime also has a lot of cute and funny moments that will make you smile and laugh.
-It has beautiful animation and scenery that will make you want to go hiking.
-Another reason why you should watch Yama no Susume S1 is its stunning animation and scenery. The anime showcases various mountains and landscapes in Japan, such as Mt. Takao, Mt. Fuji, Mt. Tanigawa, and Mt. Mitsutouge. The anime uses realistic and detailed backgrounds that capture the beauty and diversity of nature. The anime also uses dynamic camera angles and movements that create a sense of immersion and depth. The anime also has amazing sound effects that enhance the atmosphere and mood of the scenes, such as the wind, the birds, the water, and the footsteps. The anime will make you feel like you are actually there with the characters, experiencing the wonders of nature.
-It is a short and easy-to-watch anime that you can binge in one sitting.
-The last reason why you should watch Yama no Susume S1 is its short and easy-to-watch format. The anime has only 12 episodes, with each episode being only 3 minutes long. This means you can watch the whole season in less than an hour, making it a perfect choice for when you want to relax or kill some time. The anime also has a simple and straightforward plot that does not require much attention or analysis. You can just sit back and enjoy the cute and wholesome adventures of the girls without worrying about complicated twists or cliffhangers.
-How to download Yama no Susume S1?
-If you are interested in watching Yama no Susume S1, you might be wondering how to download it to your device. There are two main ways to do this: legal and official sources, or alternative and unofficial sources. Here are some of the pros and cons of each option:
-Legal and official sources
-The best way to download Yama no Susume S1 is to use legal and official sources that have the license and permission to distribute the anime. Some of these sources are:
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-
- Crunchyroll: Crunchyroll is one of the most popular and reliable streaming platforms for anime fans. It offers a wide range of anime titles, including Yama no Susume S1. You can watch the anime for free with ads, or you can subscribe to a premium plan that allows you to download episodes to your device for offline viewing. -
- HIDIVE: HIDIVE is another streaming platform that specializes in anime and Asian media. It also has Yama no Susume S1 in its catalog, along with its sequels and specials. You can watch the anime for free with a 30-day trial, or you can sign up for a monthly or yearly plan that gives you access to download features. -
The advantages of using legal and official sources are:
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-
- You support the creators and producers of the anime by paying for their work. -
- You get high-quality video and audio that are free from errors or glitches. -
- You get access to subtitles in different languages and regions. -
- You get access to bonus content such as extras, interviews, commentaries, etc. -
- You get access to customer service and technical support in case of any issues or problems. -
The disadvantages of using legal and official sources are:
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- You have to pay a fee or subscription to use their services. -
- You have to deal with geo-restrictions or regional blocks that might prevent you from watching some titles. -
- You have to follow their terms and conditions that might limit your usage or sharing of their content. -
Alternative and unofficial sources
-The other way to download Yama no Susume S1 is to use alternative and unofficial sources that do not have the license or permission to distribute the anime. Some of these sources are:
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- Nyaa: Nyaa is one of the most popular and widely used torrent sites for anime fans. It hosts a large number of anime torrents, including Yama no Susume S1. You can download the anime by using a torrent client such as BitTorrent or uTorrent. -
- Kissanime: Kissanime is one of the most notorious and controversial streaming sites for anime fans. It offers a huge collection of anime titles, including Yama no Susume S1. You can watch the anime online or download it by using a third-party downloader such as IDM or Video DownloadHelper. -
The advantages of using alternative and unofficial sources are:
--
-
- You do not have to pay any fee or subscription to use their services. -
- You do not have to deal with geo-restrictions or regional blocks that might prevent you from watching some titles. -
- You have more options and choices in terms of quality, format, size, and source. -
The disadvantages of using alternative and unofficial sources are:
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- You do not support the creators and producers of the anime by paying for their work. -
- You risk getting low-quality video and audio that might have errors or glitches. -
- You risk getting infected by malware or viruses that might harm your device or data. -
- You risk getting sued or fined by the authorities for violating the copyright laws. -
- You do not get access to subtitles in different languages and regions. -
- You do not get access to bonus content such as extras, interviews, commentaries, etc. -
- You do not get access to customer service and technical support in case of any issues or problems. -
Conclusion
-In conclusion, Yama no Susume S1 is a wonderful anime that you should watch if you like cute girls, mountain climbing, and slice-of-life stories. It has a heartwarming plot, beautiful animation, and a short format that make it a relaxing and enjoyable watch. You can download Yama no Susume S1 from various sources, but we recommend using legal and official ones to support the anime industry and avoid any risks or troubles. We hope this article has helped you learn more about Yama no Susume S1 and how to download it. Happy watching!
-FAQs
-What is the order of Yama no Susume seasons?
-The order of Yama no Susume seasons is S1, OVA, S2, Omoide Present, S3, Next Summit.
-Is Yama no Susume based on a manga?
-Yes, Yama no Susume is based on a manga by Shiro that started in 2011 and is still ongoing.
-How realistic is Yama no Susume in terms of mountain climbing?
-Yama no Susume is fairly realistic in terms of mountain climbing, as it shows the preparation, equipment, safety, and etiquette involved in hiking. However, some aspects are exaggerated or simplified for comedic or dramatic purposes.
-Are there any other anime similar to Yama no Susume?
-Some other anime similar to Yama no Susume are Laid-Back Camp, A Place Further than the Universe, Girls' Last Tour, and Long Riders.
-Where can I find more information about Yama no Susume?
-You can find more information about Yama no Susume on its official website, its Wikipedia page, its MyAnimeList page, or its subreddit r/Yamanosusume.
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\ No newline at end of file diff --git a/spaces/2023Liu2023/bingo/src/state/index.ts b/spaces/2023Liu2023/bingo/src/state/index.ts deleted file mode 100644 index 272106d619c69f124ea8dd0f10872ab4840152d7..0000000000000000000000000000000000000000 --- a/spaces/2023Liu2023/bingo/src/state/index.ts +++ /dev/null @@ -1,118 +0,0 @@ -import { BingWebBot } from '@/lib/bots/bing' -import { BingConversationStyle, ChatMessageModel, BotId } from '@/lib/bots/bing/types' -import { nanoid } from '@/lib/utils' -import { atom } from 'jotai' -import { atomWithImmer } from 'jotai-immer' -import { atomWithStorage } from 'jotai/utils' -import { atomFamily } from 'jotai/utils' -import { atomWithHash, atomWithLocation } from 'jotai-location' - -const initialMessages: ChatMessageModel[] = [ - { author: 'system', text: 'conversation between user and robot', id: '1' }, - { author: 'user', text: '销量最高的 3 种宠物吸尘器有哪些优点和缺点? ', id: '2' }, - { - author: 'bot', text: ` -您好，这是必应。根据网上的搜索结果，我为您找到了以下三款销量最高的宠物吸尘器，以及它们的优点和缺点： - -- **戴森Dyson V10轻量版 Digital Slim Fluffy无线吸尘器**[^1^] [^3^]：这款吸尘器专为宠物家庭设计，拥有强大吸力和多功能吸头，能够有效清除宠物毛发和灰尘。 - - 优点：轻便易用，续航时间长，过滤系统先进，能够有效去除过敏原； - - 缺点：价格较高，噪音较大，需要定期清洁滤网。 -- **飞利浦FC6908/82（W7）无线吸尘器**[^2^] [^4^]：这是一款手持式干吸无线吸尘器，专门为宠物家庭设计。它具有75分钟的超高续航，适合大户型；除螨涡轮刷，可以深度清洁床单被罩，免受螨虫侵害；吸拖一体设计，280ml大水箱，还可以往水箱里加入清洁剂。 - - 优点：清洁性能强劲，操作方便，适用多种场景； - - 缺点：噪音较大，达到84dB（A）；尘杯容量较小，只有0.6L；充电时长较长，需要5小时。 -- **必胜（Bissell） 宠物吸尘器**[^2^]：这是一款便携式手持真空吸尘器，专为清理宠物毛发而设计。它配备特殊的橡胶喷嘴和软毛刷头，可以轻松拾取宠物毛发和灰尘；几乎静音运行。 - - 优点：轻巧易用，重量只有1.36kg；电池可充电可更换； - - 缺点：续航时间不稳定，在15-30分钟之间浮动；吸力不够强劲，在地毯或沙发上可能效果不佳；没有过滤系统或过滤网。 - -希望这些信息对您有所帮助。如果您还有其他问题，请随时提问。😊 - `, id: '3' }, - { author: 'user', text: '今天的新闻', id: '4' }, - { - author: 'bot', text: `你好，这是Bing。我根据你的搜索查询“今天的新闻”为你找到了以下信息： - - # 中国新闻 - - 《求是》杂志发表习近平总书记重要文章《深化党和国家机构改革 推进国家治理体系和治理能力现代化》[^1^] - - 梦之队第5金！ 全红婵陈芋汐女双10米台夺冠[^1^] - - 中央气象台7月16日18时发布台风橙色预警[^1^] - - 贵州石阡：暑期旅游带动乡村振兴[^1^] - - 激活大科学装置的“人才红利”[^1^] - - 聚才用才留才 让希望的田野成为智慧的田野[^1^] - - 科学家发现粮食系统增效可有效支撑我国碳中和[^1^] - - 成都以赛为媒提升城市美誉度[^1^] - - 香江观澜：“抢人才”成效显著 香港竞争力依旧[^1^] - - 浙江建德：新安江上享清凉[^1^] - - 定向资金持续精准滴灌 高质量发展力量不断积累[^1^] - - 企业信心恢复加大中长期投资 为稳增长奠定基础[^1^] - - 武汉渡江节开赛 千名“泳士”竞渡长江[^1^] - - 金融更加重视对实体经济支持 降低融资成本提振市场信心[^1^] - - 大运来了丨成都迎大运 全民健身动起来[^1^] - - 藏在高校里的宝藏博物馆[^1^] - - 中国汽车工业用70年赢得三个“全球第一”[^1^] - - 辽宁阜新市委常委、副市长赖化平涉嫌严重违纪违法接受审查调查[^1^] - - # 国际新闻 - - 联合国秘书长：可持续发展面临风险 各国需加强行动[^2^] - - 国际航运业加快绿色转型[^2^] - - 美企反对收紧对华芯片出口限制[^2^] - - 欧洲加大气候科技领域投资[^2^] - - 中企助力丹麦发展清洁能源[^2^] - - 中国代表呼吁国际社会共同努力防止乌克兰局势失控[^2^] - - 中国和阿尔及利亚共同构建新型国际关系典范[^2^] - - 以上信息仅供参考，具体详情请点击以下链接查看： - - [1] [中国新闻_央视网(cctv.com)](https://news.cctv.com/china/) - [2] [人民网_网上的人民日报](http://www.people.com.cn/)`, id: '5' }, - { author: 'user', text: '写一个快排', id: '6' }, - { author: 'bot', text: "你好，这是Bing。我可以帮你用python写一个快排。快速排序是一种基于分治思想的排序算法，它的基本思想是：从待排序的数组中选择一个基准元素，将数组分为两个子数组，使得左边的子数组中的元素都小于或等于基准元素，右边的子数组中的元素都大于或等于基准元素，然后对两个子数组递归地进行快速排序，最后合并结果。下面是用python实现的快速排序的代码：\n\n```python\n# 定义一个函数，用于交换数组中的两个元素\ndef swap(arr, i, j):\n temp = arr[i]\n arr[i] = arr[j]\n arr[j] = temp\n\n# 定义一个函数，用于对数组的一段区间进行划分\ndef partition(arr, low, high):\n # 选择最右边的元素作为基准元素\n pivot = arr[high]\n # 初始化一个指针，指向左边区间的最后一个位置\n i = low - 1\n # 遍历左边区间中的每个元素\n for j in range(low, high):\n # 如果当前元素小于或等于基准元素\n if arr[j] <= pivot:\n # 将指针右移一位\n i += 1\n # 将指针所指的元素与当前元素交换\n swap(arr, i, j)\n # 将基准元素与指针右边的第一个元素交换\n swap(arr, i + 1, high)\n # 返回基准元素的最终位置\n return i + 1\n\n# 定义一个函数，用于对数组进行快速排序\ndef quick_sort(arr, low, high):\n # 如果区间长度大于1\n if low < high:\n # 对区间进行划分，得到基准元素的位置\n p = partition(arr, low, high)\n # 对左边子区间进行快速排序\n quick_sort(arr, low, p - 1)\n # 对右边子区间进行快速排序\n quick_sort(arr, p + 1, high)\n\n# 测试代码\narr = [5, 3, 7, 2, 9, 1, 4, 6, 8]\nprint(\"原始数组：\", arr)\nquick_sort(arr, 0, len(arr) - 1)\nprint(\"排序后的数组：\", arr)\n```", id: '7' }, - { - author: 'bot', text: "好的，我会尝试画一只猫。\n > ", - id: '8' - } -] - -export const GreetMessages = [ - '谢谢你! 知道你什么时候准备好继续前进总是很有帮助的。我现在能为你回答什么问题?', - '重新开始总是很棒。问我任何问题!', - '当然，我很乐意重新开始。我现在可以为你提供哪些帮助?', - '当然，我已准备好进行新的挑战。我现在可以为你做什么?', - '很好，让我们来更改主题。你在想什么?', - '不用担心，我很高兴尝试一些新内容。我现在可以为你回答什么问题?', - '好的，我准备好了！感谢重置。我们应该了解哪些内容？', - '感谢刷新！你有新的话题吗？', - '明白了，让我们重新开始。接下来应该讨论什么？', - '下一步！我可以为你做什么？', - '好的，我已准备好新话题。我们应该一起了解哪些内容？' -] - -export const bingConversationStyleAtom = atomWithStorage
SimPhysics
-User input: WASD
-This WebGL demo demonstrates PlayCanvas and a physics vehicle simulation that is web based and playable anywhere your browser goes🤗 Inference API.
-Source code is in Readme.md file.
-PlayCanvas project is here
- - \ No newline at end of file diff --git a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/utilities.py b/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/utilities.py deleted file mode 100644 index 8d1604579b88e7e1e79f6350376f89d9c1c85f44..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/utilities.py +++ /dev/null @@ -1,172 +0,0 @@ -import os -import logging -import h5py -import soundfile -import librosa -import numpy as np -import pandas as pd -from scipy import stats -import datetime -import pickle - - -def create_folder(fd): - if not os.path.exists(fd): - os.makedirs(fd) - - -def get_filename(path): - path = os.path.realpath(path) - na_ext = path.split('/')[-1] - na = os.path.splitext(na_ext)[0] - return na - - -def get_sub_filepaths(folder): - paths = [] - for root, dirs, files in os.walk(folder): - for name in files: - path = os.path.join(root, name) - paths.append(path) - return paths - - -def create_logging(log_dir, filemode): - create_folder(log_dir) - i1 = 0 - - while os.path.isfile(os.path.join(log_dir, '{:04d}.log'.format(i1))): - i1 += 1 - - log_path = os.path.join(log_dir, '{:04d}.log'.format(i1)) - logging.basicConfig( - level=logging.DEBUG, - format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s', - datefmt='%a, %d %b %Y %H:%M:%S', - filename=log_path, - filemode=filemode) - - # Print to console - console = logging.StreamHandler() - console.setLevel(logging.INFO) - formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s') - console.setFormatter(formatter) - logging.getLogger('').addHandler(console) - - return logging - - -def read_metadata(csv_path, classes_num, id_to_ix): - """Read metadata of AudioSet from a csv file. - - Args: - csv_path: str - - Returns: - meta_dict: {'audio_name': (audios_num,), 'target': (audios_num, classes_num)} - """ - - with open(csv_path, 'r') as fr: - lines = fr.readlines() - lines = lines[3:] # Remove heads - - audios_num = len(lines) - targets = np.zeros((audios_num, classes_num), dtype=np.bool) - audio_names = [] - - for n, line in enumerate(lines): - items = line.split(', ') - """items: ['--4gqARaEJE', '0.000', '10.000', '"/m/068hy,/m/07q6cd_,/m/0bt9lr,/m/0jbk"\n']""" - - audio_name = 'Y{}.wav'.format(items[0]) # Audios are started with an extra 'Y' when downloading - label_ids = items[3].split('"')[1].split(',') - - audio_names.append(audio_name) - - # Target - for id in label_ids: - ix = id_to_ix[id] - targets[n, ix] = 1 - - meta_dict = {'audio_name': np.array(audio_names), 'target': targets} - return meta_dict - - -def float32_to_int16(x): - assert np.max(np.abs(x)) <= 1.2 - x = np.clip(x, -1, 1) - return (x * 32767.).astype(np.int16) - -def int16_to_float32(x): - return (x / 32767.).astype(np.float32) - - -def pad_or_truncate(x, audio_length): - """Pad all audio to specific length.""" - if len(x) <= audio_length: - return np.concatenate((x, np.zeros(audio_length - len(x))), axis=0) - else: - return x[0 : audio_length] - - -def d_prime(auc): - d_prime = stats.norm().ppf(auc) * np.sqrt(2.0) - return d_prime - - -class Mixup(object): - def __init__(self, mixup_alpha, random_seed=1234): - """Mixup coefficient generator. - """ - self.mixup_alpha = mixup_alpha - self.random_state = np.random.RandomState(random_seed) - - def get_lambda(self, batch_size): - """Get mixup random coefficients. - Args: - batch_size: int - Returns: - mixup_lambdas: (batch_size,) - """ - mixup_lambdas = [] - for n in range(0, batch_size, 2): - lam = self.random_state.beta(self.mixup_alpha, self.mixup_alpha, 1)[0] - mixup_lambdas.append(lam) - mixup_lambdas.append(1. - lam) - - return np.array(mixup_lambdas) - - -class StatisticsContainer(object): - def __init__(self, statistics_path): - """Contain statistics of different training iterations. - """ - self.statistics_path = statistics_path - - self.backup_statistics_path = '{}_{}.pkl'.format( - os.path.splitext(self.statistics_path)[0], - datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')) - - self.statistics_dict = {'bal': [], 'test': []} - - def append(self, iteration, statistics, data_type): - statistics['iteration'] = iteration - self.statistics_dict[data_type].append(statistics) - - def dump(self): - pickle.dump(self.statistics_dict, open(self.statistics_path, 'wb')) - pickle.dump(self.statistics_dict, open(self.backup_statistics_path, 'wb')) - logging.info(' Dump statistics to {}'.format(self.statistics_path)) - logging.info(' Dump statistics to {}'.format(self.backup_statistics_path)) - - def load_state_dict(self, resume_iteration): - self.statistics_dict = pickle.load(open(self.statistics_path, 'rb')) - - resume_statistics_dict = {'bal': [], 'test': []} - - for key in self.statistics_dict.keys(): - for statistics in self.statistics_dict[key]: - if statistics['iteration'] <= resume_iteration: - resume_statistics_dict[key].append(statistics) - - self.statistics_dict = resume_statistics_dict \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/agentverse/agents/tasksolving_agent/solver.py b/spaces/AgentVerse/agentVerse/agentverse/agents/tasksolving_agent/solver.py deleted file mode 100644 index 6db77561d3859e897ad5b66859ddc76bd3a28b4d..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/agentverse/agents/tasksolving_agent/solver.py +++ /dev/null @@ -1,110 +0,0 @@ -from __future__ import annotations - -import asyncio -from colorama import Fore - -from agentverse.logging import get_logger -import bdb -from string import Template -from typing import TYPE_CHECKING, List, Tuple - -# from agentverse.environments import PipelineEnvironment -from agentverse.message import SolverMessage, Message, CriticMessage - -from agentverse.agents import agent_registry -from agentverse.agents.base import BaseAgent -from agentverse.utils import AgentCriticism - - -logger = get_logger() - - -@agent_registry.register("solver") -class SolverAgent(BaseAgent): - max_history: int = 3 - - def step( - self, former_solution: str, advice: str, task_description: str = "", **kwargs - ) -> SolverMessage: - logger.debug("", self.name, Fore.MAGENTA) - # prompt = self._fill_prompt_template( - # former_solution, critic_opinions, advice, task_description - # ) - prepend_prompt, append_prompt = self.get_all_prompts( - former_solution=former_solution, - task_description=task_description, - advice=advice, - role_description=self.role_description, - **kwargs, - ) - history = self.memory.to_messages(self.name, start_index=-self.max_history) - parsed_response = None - for i in range(self.max_retry): - try: - response = self.llm.generate_response( - prepend_prompt, history, append_prompt - ) - parsed_response = self.output_parser.parse(response) - break - except (KeyboardInterrupt, bdb.BdbQuit): - raise - except Exception as e: - logger.error(e) - logger.warn("Retrying...") - continue - - if parsed_response is None: - logger.error(f"{self.name} failed to generate valid response.") - - message = SolverMessage( - content="" - if parsed_response is None - else parsed_response.return_values["output"], - sender=self.name, - receiver=self.get_receiver(), - ) - return message - - async def astep(self, env_description: str = "") -> SolverMessage: - """Asynchronous version of step""" - pass - - def _fill_prompt_template( - self, - former_solution: str, - critic_opinions: List[AgentCriticism], - advice: str, - task_description: str, - ) -> str: - """Fill the placeholders in the prompt template - - In the role_assigner agent, three placeholders are supported: - - ${task_description} - - ${former_solution} - - ${critic_messages} - - ${advice} - """ - input_arguments = { - "task_description": task_description, - "former_solution": former_solution, - "critic_opinions": "\n".join( - [ - f"{critic.sender_agent.role_description} said: {critic.criticism}" - for critic in critic_opinions - ] - ), - "advice": advice, - } - # if discussion_mode: - # template = Template(self.prompt_template[1]) - # else: - template = Template(self.prompt_template) - return template.safe_substitute(input_arguments) - - def add_message_to_memory(self, messages: List[Message]) -> None: - self.memory.add_message(messages) - - def reset(self) -> None: - """Reset the agent""" - self.memory.reset() - # TODO: reset receiver diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/input/EmitCellEvent.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/input/EmitCellEvent.js deleted file mode 100644 index 50c0b7320e2b504223494d47eb8e5e466aefb81c..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/input/EmitCellEvent.js +++ /dev/null @@ -1,17 +0,0 @@ -var EmitCellEvent = function (eventEmitter, eventName, table, x, y, pointer, event) { - var cellIndex; - if (y === undefined) { - cellIndex = x; - } else { - cellIndex = table.pointToCellIndex(x, y); - } - if ((cellIndex === null) || (cellIndex === undefined)) { - return; - } - var cellContainer = table.getCellContainer(cellIndex); - if (cellContainer) { - eventEmitter.emit(eventName, cellContainer, cellIndex, pointer, event); - } -} - -export default EmitCellEvent; \ No newline at end of file diff --git a/spaces/AhmadHakami/Alzheimer_image_classification/app.py b/spaces/AhmadHakami/Alzheimer_image_classification/app.py deleted file mode 100644 index 746e446b1cfae06c78b541bb1e28e3aaa15337bc..0000000000000000000000000000000000000000 --- a/spaces/AhmadHakami/Alzheimer_image_classification/app.py +++ /dev/null @@ -1,25 +0,0 @@ -from transformers import pipeline -import gradio as gr -import os - -model = pipeline(model = "AhmadHakami/alzheimer-image-classification-google-vit-base-patch16", - task = "image-classification") - -model.model.config.id2label = { -0: 'خفيف الخرف (Mild Demented)', -1: 'متوسط الخرف (Moderate Demented)', -2: 'غير مصاب بالخرف (Non Demented)', -3: 'خفيف جداً الخرف (Very Mild Demented)' -} - -examples = [] -for image in os.listdir("examples"): - examples.append(f'examples//{image}') - - -gr.Interface.from_pipeline(model, - title="Tbyan - تِــبْيان Alzheimer MRI Classification", - description="This model fine-tuned using vit-base-patch16-224-in21k by Google, and trained on credible MRI data from Open Access Series of Imaging Studies (OASIS) and the data annotated by experts, it aims to expedite patient results, developed by Tbyan تبيان Team at AI course by `Misk Foundation & Samsung Innovation Campus`, \n **Try it now:**", - examples = examples).launch() - - \ No newline at end of file diff --git a/spaces/Ajaxon6255/Emerald_Isle/theme_dropdown.py b/spaces/Ajaxon6255/Emerald_Isle/theme_dropdown.py deleted file mode 100644 index 6235388fd00549553df44028f3ccf03e946994ea..0000000000000000000000000000000000000000 --- a/spaces/Ajaxon6255/Emerald_Isle/theme_dropdown.py +++ /dev/null @@ -1,57 +0,0 @@ -import os -import pathlib - -from gradio.themes.utils import ThemeAsset - - -def create_theme_dropdown(): - import gradio as gr - - asset_path = pathlib.Path(__file__).parent / "themes" - themes = [] - for theme_asset in os.listdir(str(asset_path)): - themes.append( - (ThemeAsset(theme_asset), gr.Theme.load(str(asset_path / theme_asset))) - ) - - def make_else_if(theme_asset): - return f""" - else if (theme == '{str(theme_asset[0].version)}') {{ - var theme_css = `{theme_asset[1]._get_theme_css()}` - }}""" - - head, tail = themes[0], themes[1:] - if_statement = f""" - if (theme == "{str(head[0].version)}") {{ - var theme_css = `{head[1]._get_theme_css()}` - }} {" ".join(make_else_if(t) for t in tail)} - """ - - latest_to_oldest = sorted([t[0] for t in themes], key=lambda asset: asset.version)[ - ::-1 - ] - latest_to_oldest = [str(t.version) for t in latest_to_oldest] - - component = gr.Dropdown( - choices=latest_to_oldest, - value=latest_to_oldest[0], - render=False, - label="Select Version", - ).style(container=False) - - return ( - component, - f""" - (theme) => {{ - if (!document.querySelector('.theme-css')) {{ - var theme_elem = document.createElement('style'); - theme_elem.classList.add('theme-css'); - document.head.appendChild(theme_elem); - }} else {{ - var theme_elem = document.querySelector('.theme-css'); - }} - {if_statement} - theme_elem.innerHTML = theme_css; - }} - """, - ) diff --git a/spaces/AlexWang/lama/models/ade20k/segm_lib/nn/parallel/data_parallel.py b/spaces/AlexWang/lama/models/ade20k/segm_lib/nn/parallel/data_parallel.py deleted file mode 100644 index 376fc038919aa2a5bd696141e7bb6025d4981306..0000000000000000000000000000000000000000 --- a/spaces/AlexWang/lama/models/ade20k/segm_lib/nn/parallel/data_parallel.py +++ /dev/null @@ -1,112 +0,0 @@ -# -*- coding: utf8 -*- - -import torch.cuda as cuda -import torch.nn as nn -import torch -import collections -from torch.nn.parallel._functions import Gather - - -__all__ = ['UserScatteredDataParallel', 'user_scattered_collate', 'async_copy_to'] - - -def async_copy_to(obj, dev, main_stream=None): - if torch.is_tensor(obj): - v = obj.cuda(dev, non_blocking=True) - if main_stream is not None: - v.data.record_stream(main_stream) - return v - elif isinstance(obj, collections.Mapping): - return {k: async_copy_to(o, dev, main_stream) for k, o in obj.items()} - elif isinstance(obj, collections.Sequence): - return [async_copy_to(o, dev, main_stream) for o in obj] - else: - return obj - - -def dict_gather(outputs, target_device, dim=0): - """ - Gathers variables from different GPUs on a specified device - (-1 means the CPU), with dictionary support. - """ - def gather_map(outputs): - out = outputs[0] - if torch.is_tensor(out): - # MJY(20180330) HACK:: force nr_dims > 0 - if out.dim() == 0: - outputs = [o.unsqueeze(0) for o in outputs] - return Gather.apply(target_device, dim, *outputs) - elif out is None: - return None - elif isinstance(out, collections.Mapping): - return {k: gather_map([o[k] for o in outputs]) for k in out} - elif isinstance(out, collections.Sequence): - return type(out)(map(gather_map, zip(*outputs))) - return gather_map(outputs) - - -class DictGatherDataParallel(nn.DataParallel): - def gather(self, outputs, output_device): - return dict_gather(outputs, output_device, dim=self.dim) - - -class UserScatteredDataParallel(DictGatherDataParallel): - def scatter(self, inputs, kwargs, device_ids): - assert len(inputs) == 1 - inputs = inputs[0] - inputs = _async_copy_stream(inputs, device_ids) - inputs = [[i] for i in inputs] - assert len(kwargs) == 0 - kwargs = [{} for _ in range(len(inputs))] - - return inputs, kwargs - - -def user_scattered_collate(batch): - return batch - - -def _async_copy(inputs, device_ids): - nr_devs = len(device_ids) - assert type(inputs) in (tuple, list) - assert len(inputs) == nr_devs - - outputs = [] - for i, dev in zip(inputs, device_ids): - with cuda.device(dev): - outputs.append(async_copy_to(i, dev)) - - return tuple(outputs) - - -def _async_copy_stream(inputs, device_ids): - nr_devs = len(device_ids) - assert type(inputs) in (tuple, list) - assert len(inputs) == nr_devs - - outputs = [] - streams = [_get_stream(d) for d in device_ids] - for i, dev, stream in zip(inputs, device_ids, streams): - with cuda.device(dev): - main_stream = cuda.current_stream() - with cuda.stream(stream): - outputs.append(async_copy_to(i, dev, main_stream=main_stream)) - main_stream.wait_stream(stream) - - return outputs - - -"""Adapted from: torch/nn/parallel/_functions.py""" -# background streams used for copying -_streams = None - - -def _get_stream(device): - """Gets a background stream for copying between CPU and GPU""" - global _streams - if device == -1: - return None - if _streams is None: - _streams = [None] * cuda.device_count() - if _streams[device] is None: _streams[device] = cuda.Stream(device) - return _streams[device] diff --git a/spaces/Amiminoru/Deus/README.md b/spaces/Amiminoru/Deus/README.md deleted file mode 100644 index 79592e0f318db093368e9eb00a50df57e9393207..0000000000000000000000000000000000000000 --- a/spaces/Amiminoru/Deus/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: Deus -emoji: 👁 -colorFrom: red -colorTo: red -sdk: docker -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/models/e4e/stylegan2/__init__.py b/spaces/Amrrs/DragGan-Inversion/PTI/models/e4e/stylegan2/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/optimization/torch2.0.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/optimization/torch2.0.md deleted file mode 100644 index 6e8466fd6ecc3bc775fa031e9cedc5546091c4a4..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/optimization/torch2.0.md +++ /dev/null @@ -1,444 +0,0 @@ - - -# Accelerated PyTorch 2.0 support in Diffusers - -Starting from version `0.13.0`, Diffusers supports the latest optimization from [PyTorch 2.0](https://pytorch.org/get-started/pytorch-2.0/). These include: -1. Support for accelerated transformers implementation with memory-efficient attention – no extra dependencies (such as `xformers`) required. -2. [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) support for extra performance boost when individual models are compiled. - - -## Installation - -To benefit from the accelerated attention implementation and `torch.compile()`, you just need to install the latest versions of PyTorch 2.0 from pip, and make sure you are on diffusers 0.13.0 or later. As explained below, diffusers automatically uses the optimized attention processor ([`AttnProcessor2_0`](https://github.com/huggingface/diffusers/blob/1a5797c6d4491a879ea5285c4efc377664e0332d/src/diffusers/models/attention_processor.py#L798)) (but not `torch.compile()`) -when PyTorch 2.0 is available. - -```bash -pip install --upgrade torch diffusers -``` - -## Using accelerated transformers and `torch.compile`. - - -1. **Accelerated Transformers implementation** - - PyTorch 2.0 includes an optimized and memory-efficient attention implementation through the [`torch.nn.functional.scaled_dot_product_attention`](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention) function, which automatically enables several optimizations depending on the inputs and the GPU type. This is similar to the `memory_efficient_attention` from [xFormers](https://github.com/facebookresearch/xformers), but built natively into PyTorch. - - These optimizations will be enabled by default in Diffusers if PyTorch 2.0 is installed and if `torch.nn.functional.scaled_dot_product_attention` is available. To use it, just install `torch 2.0` as suggested above and simply use the pipeline. For example: - - ```Python - import torch - from diffusers import DiffusionPipeline - - pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) - pipe = pipe.to("cuda") - - prompt = "a photo of an astronaut riding a horse on mars" - image = pipe(prompt).images[0] - ``` - - If you want to enable it explicitly (which is not required), you can do so as shown below. - - ```diff - import torch - from diffusers import DiffusionPipeline - + from diffusers.models.attention_processor import AttnProcessor2_0 - - pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda") - + pipe.unet.set_attn_processor(AttnProcessor2_0()) - - prompt = "a photo of an astronaut riding a horse on mars" - image = pipe(prompt).images[0] - ``` - - This should be as fast and memory efficient as `xFormers`. More details [in our benchmark](#benchmark). - - It is possible to revert to the vanilla attention processor ([`AttnProcessor`](https://github.com/huggingface/diffusers/blob/1a5797c6d4491a879ea5285c4efc377664e0332d/src/diffusers/models/attention_processor.py#L402)), which can be helpful to make the pipeline more deterministic, or if you need to convert a fine-tuned model to other formats such as [Core ML](https://huggingface.co/docs/diffusers/v0.16.0/en/optimization/coreml#how-to-run-stable-diffusion-with-core-ml). To use the normal attention processor you can use the [`~diffusers.UNet2DConditionModel.set_default_attn_processor`] function: - - ```Python - import torch - from diffusers import DiffusionPipeline - from diffusers.models.attention_processor import AttnProcessor - - pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda") - pipe.unet.set_default_attn_processor() - - prompt = "a photo of an astronaut riding a horse on mars" - image = pipe(prompt).images[0] - ``` - -2. **torch.compile** - - To get an additional speedup, we can use the new `torch.compile` feature. Since the UNet of the pipeline is usually the most computationally expensive, we wrap the `unet` with `torch.compile` leaving rest of the sub-models (text encoder and VAE) as is. For more information and different options, refer to the - [torch compile docs](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html). - - ```python - pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) - images = pipe(prompt, num_inference_steps=steps, num_images_per_prompt=batch_size).images - ``` - - Depending on the type of GPU, `compile()` can yield between **5% - 300%** of _additional speed-up_ over the accelerated transformer optimizations. Note, however, that compilation is able to squeeze more performance improvements in more recent GPU architectures such as Ampere (A100, 3090), Ada (4090) and Hopper (H100). - - Compilation takes some time to complete, so it is best suited for situations where you need to prepare your pipeline once and then perform the same type of inference operations multiple times. Calling the compiled pipeline on a different image size will re-trigger compilation which can be expensive. - - -## Benchmark - -We conducted a comprehensive benchmark with PyTorch 2.0's efficient attention implementation and `torch.compile` across different GPUs and batch sizes for five of our most used pipelines. We used `diffusers 0.17.0.dev0`, which [makes sure `torch.compile()` is leveraged optimally](https://github.com/huggingface/diffusers/pull/3313). - -### Benchmarking code - -#### Stable Diffusion text-to-image - -```python -from diffusers import DiffusionPipeline -import torch - -path = "runwayml/stable-diffusion-v1-5" - -run_compile = True # Set True / False - -pipe = DiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16) -pipe = pipe.to("cuda") -pipe.unet.to(memory_format=torch.channels_last) - -if run_compile: - print("Run torch compile") - pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) - -prompt = "ghibli style, a fantasy landscape with castles" - -for _ in range(3): - images = pipe(prompt=prompt).images -``` - -#### Stable Diffusion image-to-image - -```python -from diffusers import StableDiffusionImg2ImgPipeline -import requests -import torch -from PIL import Image -from io import BytesIO - -url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" - -response = requests.get(url) -init_image = Image.open(BytesIO(response.content)).convert("RGB") -init_image = init_image.resize((512, 512)) - -path = "runwayml/stable-diffusion-v1-5" - -run_compile = True # Set True / False - -pipe = StableDiffusionImg2ImgPipeline.from_pretrained(path, torch_dtype=torch.float16) -pipe = pipe.to("cuda") -pipe.unet.to(memory_format=torch.channels_last) - -if run_compile: - print("Run torch compile") - pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) - -prompt = "ghibli style, a fantasy landscape with castles" - -for _ in range(3): - image = pipe(prompt=prompt, image=init_image).images[0] -``` - -#### Stable Diffusion - inpainting - -```python -from diffusers import StableDiffusionInpaintPipeline -import requests -import torch -from PIL import Image -from io import BytesIO - -url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" - -def download_image(url): - response = requests.get(url) - return Image.open(BytesIO(response.content)).convert("RGB") - - -img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" -mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" - -init_image = download_image(img_url).resize((512, 512)) -mask_image = download_image(mask_url).resize((512, 512)) - -path = "runwayml/stable-diffusion-inpainting" - -run_compile = True # Set True / False - -pipe = StableDiffusionInpaintPipeline.from_pretrained(path, torch_dtype=torch.float16) -pipe = pipe.to("cuda") -pipe.unet.to(memory_format=torch.channels_last) - -if run_compile: - print("Run torch compile") - pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) - -prompt = "ghibli style, a fantasy landscape with castles" - -for _ in range(3): - image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0] -``` - -#### ControlNet - -```python -from diffusers import StableDiffusionControlNetPipeline, ControlNetModel -import requests -import torch -from PIL import Image -from io import BytesIO - -url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" - -response = requests.get(url) -init_image = Image.open(BytesIO(response.content)).convert("RGB") -init_image = init_image.resize((512, 512)) - -path = "runwayml/stable-diffusion-v1-5" - -run_compile = True # Set True / False -controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) -pipe = StableDiffusionControlNetPipeline.from_pretrained( - path, controlnet=controlnet, torch_dtype=torch.float16 -) - -pipe = pipe.to("cuda") -pipe.unet.to(memory_format=torch.channels_last) -pipe.controlnet.to(memory_format=torch.channels_last) - -if run_compile: - print("Run torch compile") - pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) - pipe.controlnet = torch.compile(pipe.controlnet, mode="reduce-overhead", fullgraph=True) - -prompt = "ghibli style, a fantasy landscape with castles" - -for _ in range(3): - image = pipe(prompt=prompt, image=init_image).images[0] -``` - -#### IF text-to-image + upscaling - -```python -from diffusers import DiffusionPipeline -import torch - -run_compile = True # Set True / False - -pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-M-v1.0", variant="fp16", text_encoder=None, torch_dtype=torch.float16) -pipe.to("cuda") -pipe_2 = DiffusionPipeline.from_pretrained("DeepFloyd/IF-II-M-v1.0", variant="fp16", text_encoder=None, torch_dtype=torch.float16) -pipe_2.to("cuda") -pipe_3 = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-x4-upscaler", torch_dtype=torch.float16) -pipe_3.to("cuda") - - -pipe.unet.to(memory_format=torch.channels_last) -pipe_2.unet.to(memory_format=torch.channels_last) -pipe_3.unet.to(memory_format=torch.channels_last) - -if run_compile: - pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) - pipe_2.unet = torch.compile(pipe_2.unet, mode="reduce-overhead", fullgraph=True) - pipe_3.unet = torch.compile(pipe_3.unet, mode="reduce-overhead", fullgraph=True) - -prompt = "the blue hulk" - -prompt_embeds = torch.randn((1, 2, 4096), dtype=torch.float16) -neg_prompt_embeds = torch.randn((1, 2, 4096), dtype=torch.float16) - -for _ in range(3): - image = pipe(prompt_embeds=prompt_embeds, negative_prompt_embeds=neg_prompt_embeds, output_type="pt").images - image_2 = pipe_2(image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=neg_prompt_embeds, output_type="pt").images - image_3 = pipe_3(prompt=prompt, image=image, noise_level=100).images -``` - -To give you a pictorial overview of the possible speed-ups that can be obtained with PyTorch 2.0 and `torch.compile()`, -here is a plot that shows relative speed-ups for the [Stable Diffusion text-to-image pipeline](StableDiffusionPipeline) across five -different GPU families (with a batch size of 4): - - - -To give you an even better idea of how this speed-up holds for the other pipelines presented above, consider the following -plot that shows the benchmarking numbers from an A100 across three different batch sizes -(with PyTorch 2.0 nightly and `torch.compile()`): - - - -_(Our benchmarking metric for the plots above is **number of iterations/second**)_ - -But we reveal all the benchmarking numbers in the interest of transparency! - -In the following tables, we report our findings in terms of the number of **_iterations processed per second_**. - -### A100 (batch size: 1) - -| **Pipeline** | **torch 2.0 -no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** | -|:---:|:---:|:---:|:---:|:---:| -| SD - txt2img | 21.66 | 23.13 | 44.03 | 49.74 | -| SD - img2img | 21.81 | 22.40 | 43.92 | 46.32 | -| SD - inpaint | 22.24 | 23.23 | 43.76 | 49.25 | -| SD - controlnet | 15.02 | 15.82 | 32.13 | 36.08 | -| IF | 20.21 /
13.84 /
24.00 | 20.12 /
13.70 /
24.03 | ❌ | 97.34 /
27.23 /
111.66 | - -### A100 (batch size: 4) - -| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** | -|:---:|:---:|:---:|:---:|:---:| -| SD - txt2img | 11.6 | 13.12 | 14.62 | 17.27 | -| SD - img2img | 11.47 | 13.06 | 14.66 | 17.25 | -| SD - inpaint | 11.67 | 13.31 | 14.88 | 17.48 | -| SD - controlnet | 8.28 | 9.38 | 10.51 | 12.41 | -| IF | 25.02 | 18.04 | ❌ | 48.47 | - -### A100 (batch size: 16) - -| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** | -|:---:|:---:|:---:|:---:|:---:| -| SD - txt2img | 3.04 | 3.6 | 3.83 | 4.68 | -| SD - img2img | 2.98 | 3.58 | 3.83 | 4.67 | -| SD - inpaint | 3.04 | 3.66 | 3.9 | 4.76 | -| SD - controlnet | 2.15 | 2.58 | 2.74 | 3.35 | -| IF | 8.78 | 9.82 | ❌ | 16.77 | - -### V100 (batch size: 1) - -| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** | -|:---:|:---:|:---:|:---:|:---:| -| SD - txt2img | 18.99 | 19.14 | 20.95 | 22.17 | -| SD - img2img | 18.56 | 19.18 | 20.95 | 22.11 | -| SD - inpaint | 19.14 | 19.06 | 21.08 | 22.20 | -| SD - controlnet | 13.48 | 13.93 | 15.18 | 15.88 | -| IF | 20.01 /
9.08 /
23.34 | 19.79 /
8.98 /
24.10 | ❌ | 55.75 /
11.57 /
57.67 | - -### V100 (batch size: 4) - -| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** | -|:---:|:---:|:---:|:---:|:---:| -| SD - txt2img | 5.96 | 5.89 | 6.83 | 6.86 | -| SD - img2img | 5.90 | 5.91 | 6.81 | 6.82 | -| SD - inpaint | 5.99 | 6.03 | 6.93 | 6.95 | -| SD - controlnet | 4.26 | 4.29 | 4.92 | 4.93 | -| IF | 15.41 | 14.76 | ❌ | 22.95 | - -### V100 (batch size: 16) - -| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** | -|:---:|:---:|:---:|:---:|:---:| -| SD - txt2img | 1.66 | 1.66 | 1.92 | 1.90 | -| SD - img2img | 1.65 | 1.65 | 1.91 | 1.89 | -| SD - inpaint | 1.69 | 1.69 | 1.95 | 1.93 | -| SD - controlnet | 1.19 | 1.19 | OOM after warmup | 1.36 | -| IF | 5.43 | 5.29 | ❌ | 7.06 | - -### T4 (batch size: 1) - -| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** | -|:---:|:---:|:---:|:---:|:---:| -| SD - txt2img | 6.9 | 6.95 | 7.3 | 7.56 | -| SD - img2img | 6.84 | 6.99 | 7.04 | 7.55 | -| SD - inpaint | 6.91 | 6.7 | 7.01 | 7.37 | -| SD - controlnet | 4.89 | 4.86 | 5.35 | 5.48 | -| IF | 17.42 /
2.47 /
18.52 | 16.96 /
2.45 /
18.69 | ❌ | 24.63 /
2.47 /
23.39 | - -### T4 (batch size: 4) - -| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** | -|:---:|:---:|:---:|:---:|:---:| -| SD - txt2img | 1.79 | 1.79 | 2.03 | 1.99 | -| SD - img2img | 1.77 | 1.77 | 2.05 | 2.04 | -| SD - inpaint | 1.81 | 1.82 | 2.09 | 2.09 | -| SD - controlnet | 1.34 | 1.27 | 1.47 | 1.46 | -| IF | 5.79 | 5.61 | ❌ | 7.39 | - -### T4 (batch size: 16) - -| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** | -|:---:|:---:|:---:|:---:|:---:| -| SD - txt2img | 2.34s | 2.30s | OOM after 2nd iteration | 1.99s | -| SD - img2img | 2.35s | 2.31s | OOM after warmup | 2.00s | -| SD - inpaint | 2.30s | 2.26s | OOM after 2nd iteration | 1.95s | -| SD - controlnet | OOM after 2nd iteration | OOM after 2nd iteration | OOM after warmup | OOM after warmup | -| IF * | 1.44 | 1.44 | ❌ | 1.94 | - -### RTX 3090 (batch size: 1) - -| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** | -|:---:|:---:|:---:|:---:|:---:| -| SD - txt2img | 22.56 | 22.84 | 23.84 | 25.69 | -| SD - img2img | 22.25 | 22.61 | 24.1 | 25.83 | -| SD - inpaint | 22.22 | 22.54 | 24.26 | 26.02 | -| SD - controlnet | 16.03 | 16.33 | 17.38 | 18.56 | -| IF | 27.08 /
9.07 /
31.23 | 26.75 /
8.92 /
31.47 | ❌ | 68.08 /
11.16 /
65.29 | - -### RTX 3090 (batch size: 4) - -| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** | -|:---:|:---:|:---:|:---:|:---:| -| SD - txt2img | 6.46 | 6.35 | 7.29 | 7.3 | -| SD - img2img | 6.33 | 6.27 | 7.31 | 7.26 | -| SD - inpaint | 6.47 | 6.4 | 7.44 | 7.39 | -| SD - controlnet | 4.59 | 4.54 | 5.27 | 5.26 | -| IF | 16.81 | 16.62 | ❌ | 21.57 | - -### RTX 3090 (batch size: 16) - -| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** | -|:---:|:---:|:---:|:---:|:---:| -| SD - txt2img | 1.7 | 1.69 | 1.93 | 1.91 | -| SD - img2img | 1.68 | 1.67 | 1.93 | 1.9 | -| SD - inpaint | 1.72 | 1.71 | 1.97 | 1.94 | -| SD - controlnet | 1.23 | 1.22 | 1.4 | 1.38 | -| IF | 5.01 | 5.00 | ❌ | 6.33 | - -### RTX 4090 (batch size: 1) - -| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** | -|:---:|:---:|:---:|:---:|:---:| -| SD - txt2img | 40.5 | 41.89 | 44.65 | 49.81 | -| SD - img2img | 40.39 | 41.95 | 44.46 | 49.8 | -| SD - inpaint | 40.51 | 41.88 | 44.58 | 49.72 | -| SD - controlnet | 29.27 | 30.29 | 32.26 | 36.03 | -| IF | 69.71 /
18.78 /
85.49 | 69.13 /
18.80 /
85.56 | ❌ | 124.60 /
26.37 /
138.79 | - -### RTX 4090 (batch size: 4) - -| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** | -|:---:|:---:|:---:|:---:|:---:| -| SD - txt2img | 12.62 | 12.84 | 15.32 | 15.59 | -| SD - img2img | 12.61 | 12,.79 | 15.35 | 15.66 | -| SD - inpaint | 12.65 | 12.81 | 15.3 | 15.58 | -| SD - controlnet | 9.1 | 9.25 | 11.03 | 11.22 | -| IF | 31.88 | 31.14 | ❌ | 43.92 | - -### RTX 4090 (batch size: 16) - -| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** | -|:---:|:---:|:---:|:---:|:---:| -| SD - txt2img | 3.17 | 3.2 | 3.84 | 3.85 | -| SD - img2img | 3.16 | 3.2 | 3.84 | 3.85 | -| SD - inpaint | 3.17 | 3.2 | 3.85 | 3.85 | -| SD - controlnet | 2.23 | 2.3 | 2.7 | 2.75 | -| IF | 9.26 | 9.2 | ❌ | 13.31 | - -## Notes - -* Follow [this PR](https://github.com/huggingface/diffusers/pull/3313) for more details on the environment used for conducting the benchmarks. -* For the IF pipeline and batch sizes > 1, we only used a batch size of >1 in the first IF pipeline for text-to-image generation and NOT for upscaling. So, that means the two upscaling pipelines received a batch size of 1. - -*Thanks to [Horace He](https://github.com/Chillee) from the PyTorch team for their support in improving our support of `torch.compile()` in Diffusers.* \ No newline at end of file diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py deleted file mode 100644 index 1e879151ac2ffa24e2f04bee224fa1255ee55eda..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py +++ /dev/null @@ -1,317 +0,0 @@ -# coding=utf-8 -# Copyright 2023 HuggingFace Inc. -# -# 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 random -import unittest - -import numpy as np -import torch -from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer - -from diffusers import ( - AutoencoderKL, - EulerDiscreteScheduler, - StableDiffusionXLImg2ImgPipeline, - UNet2DConditionModel, -) -from diffusers.utils import floats_tensor, torch_device -from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu - -from ..pipeline_params import ( - IMAGE_TO_IMAGE_IMAGE_PARAMS, - TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, - TEXT_GUIDED_IMAGE_VARIATION_PARAMS, -) -from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin - - -enable_full_determinism() - - -class StableDiffusionXLImg2ImgPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase): - pipeline_class = StableDiffusionXLImg2ImgPipeline - params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"} - required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"} - batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS - image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS - image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS - - def get_dummy_components(self, skip_first_text_encoder=False): - torch.manual_seed(0) - unet = UNet2DConditionModel( - block_out_channels=(32, 64), - layers_per_block=2, - sample_size=32, - in_channels=4, - out_channels=4, - down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), - up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), - # SD2-specific config below - attention_head_dim=(2, 4), - use_linear_projection=True, - addition_embed_type="text_time", - addition_time_embed_dim=8, - transformer_layers_per_block=(1, 2), - projection_class_embeddings_input_dim=72, # 5 * 8 + 32 - cross_attention_dim=64 if not skip_first_text_encoder else 32, - ) - scheduler = EulerDiscreteScheduler( - beta_start=0.00085, - beta_end=0.012, - steps_offset=1, - beta_schedule="scaled_linear", - timestep_spacing="leading", - ) - torch.manual_seed(0) - vae = AutoencoderKL( - block_out_channels=[32, 64], - in_channels=3, - out_channels=3, - down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], - up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], - latent_channels=4, - sample_size=128, - ) - torch.manual_seed(0) - text_encoder_config = CLIPTextConfig( - bos_token_id=0, - eos_token_id=2, - hidden_size=32, - intermediate_size=37, - layer_norm_eps=1e-05, - num_attention_heads=4, - num_hidden_layers=5, - pad_token_id=1, - vocab_size=1000, - # SD2-specific config below - hidden_act="gelu", - projection_dim=32, - ) - text_encoder = CLIPTextModel(text_encoder_config) - tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") - - text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config) - tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") - - components = { - "unet": unet, - "scheduler": scheduler, - "vae": vae, - "text_encoder": text_encoder if not skip_first_text_encoder else None, - "tokenizer": tokenizer if not skip_first_text_encoder else None, - "text_encoder_2": text_encoder_2, - "tokenizer_2": tokenizer_2, - "requires_aesthetics_score": True, - } - return components - - def test_components_function(self): - init_components = self.get_dummy_components() - init_components.pop("requires_aesthetics_score") - pipe = self.pipeline_class(**init_components) - - self.assertTrue(hasattr(pipe, "components")) - self.assertTrue(set(pipe.components.keys()) == set(init_components.keys())) - - def get_dummy_inputs(self, device, seed=0): - image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) - image = image / 2 + 0.5 - if str(device).startswith("mps"): - generator = torch.manual_seed(seed) - else: - generator = torch.Generator(device=device).manual_seed(seed) - inputs = { - "prompt": "A painting of a squirrel eating a burger", - "image": image, - "generator": generator, - "num_inference_steps": 2, - "guidance_scale": 5.0, - "output_type": "numpy", - "strength": 0.8, - } - return inputs - - def test_stable_diffusion_xl_img2img_euler(self): - device = "cpu" # ensure determinism for the device-dependent torch.Generator - components = self.get_dummy_components() - sd_pipe = StableDiffusionXLImg2ImgPipeline(**components) - sd_pipe = sd_pipe.to(device) - sd_pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(device) - image = sd_pipe(**inputs).images - image_slice = image[0, -3:, -3:, -1] - - assert image.shape == (1, 32, 32, 3) - - expected_slice = np.array([0.4664, 0.4886, 0.4403, 0.6902, 0.5592, 0.4534, 0.5931, 0.5951, 0.5224]) - - assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 - - def test_stable_diffusion_xl_refiner(self): - device = "cpu" # ensure determinism for the device-dependent torch.Generator - components = self.get_dummy_components(skip_first_text_encoder=True) - - sd_pipe = StableDiffusionXLImg2ImgPipeline(**components) - sd_pipe = sd_pipe.to(device) - sd_pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(device) - image = sd_pipe(**inputs).images - image_slice = image[0, -3:, -3:, -1] - - assert image.shape == (1, 32, 32, 3) - - expected_slice = np.array([0.4578, 0.4981, 0.4301, 0.6454, 0.5588, 0.4442, 0.5678, 0.5940, 0.5176]) - - assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 - - def test_attention_slicing_forward_pass(self): - super().test_attention_slicing_forward_pass(expected_max_diff=3e-3) - - def test_inference_batch_single_identical(self): - super().test_inference_batch_single_identical(expected_max_diff=3e-3) - - # TODO(Patrick, Sayak) - skip for now as this requires more refiner tests - def test_save_load_optional_components(self): - pass - - def test_stable_diffusion_xl_img2img_negative_prompt_embeds(self): - components = self.get_dummy_components() - sd_pipe = StableDiffusionXLImg2ImgPipeline(**components) - sd_pipe = sd_pipe.to(torch_device) - sd_pipe = sd_pipe.to(torch_device) - sd_pipe.set_progress_bar_config(disable=None) - - # forward without prompt embeds - inputs = self.get_dummy_inputs(torch_device) - negative_prompt = 3 * ["this is a negative prompt"] - inputs["negative_prompt"] = negative_prompt - inputs["prompt"] = 3 * [inputs["prompt"]] - - output = sd_pipe(**inputs) - image_slice_1 = output.images[0, -3:, -3:, -1] - - # forward with prompt embeds - inputs = self.get_dummy_inputs(torch_device) - negative_prompt = 3 * ["this is a negative prompt"] - prompt = 3 * [inputs.pop("prompt")] - - ( - prompt_embeds, - negative_prompt_embeds, - pooled_prompt_embeds, - negative_pooled_prompt_embeds, - ) = sd_pipe.encode_prompt(prompt, negative_prompt=negative_prompt) - - output = sd_pipe( - **inputs, - prompt_embeds=prompt_embeds, - negative_prompt_embeds=negative_prompt_embeds, - pooled_prompt_embeds=pooled_prompt_embeds, - negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, - ) - image_slice_2 = output.images[0, -3:, -3:, -1] - - # make sure that it's equal - assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4 - - @require_torch_gpu - def test_stable_diffusion_xl_offloads(self): - pipes = [] - components = self.get_dummy_components() - sd_pipe = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device) - pipes.append(sd_pipe) - - components = self.get_dummy_components() - sd_pipe = StableDiffusionXLImg2ImgPipeline(**components) - sd_pipe.enable_model_cpu_offload() - pipes.append(sd_pipe) - - components = self.get_dummy_components() - sd_pipe = StableDiffusionXLImg2ImgPipeline(**components) - sd_pipe.enable_sequential_cpu_offload() - pipes.append(sd_pipe) - - image_slices = [] - for pipe in pipes: - pipe.unet.set_default_attn_processor() - - inputs = self.get_dummy_inputs(torch_device) - image = pipe(**inputs).images - - image_slices.append(image[0, -3:, -3:, -1].flatten()) - - assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3 - assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3 - - def test_stable_diffusion_xl_multi_prompts(self): - components = self.get_dummy_components() - sd_pipe = self.pipeline_class(**components).to(torch_device) - - # forward with single prompt - inputs = self.get_dummy_inputs(torch_device) - inputs["num_inference_steps"] = 5 - output = sd_pipe(**inputs) - image_slice_1 = output.images[0, -3:, -3:, -1] - - # forward with same prompt duplicated - inputs = self.get_dummy_inputs(torch_device) - inputs["num_inference_steps"] = 5 - inputs["prompt_2"] = inputs["prompt"] - output = sd_pipe(**inputs) - image_slice_2 = output.images[0, -3:, -3:, -1] - - # ensure the results are equal - assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4 - - # forward with different prompt - inputs = self.get_dummy_inputs(torch_device) - inputs["num_inference_steps"] = 5 - inputs["prompt_2"] = "different prompt" - output = sd_pipe(**inputs) - image_slice_3 = output.images[0, -3:, -3:, -1] - - # ensure the results are not equal - assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4 - - # manually set a negative_prompt - inputs = self.get_dummy_inputs(torch_device) - inputs["num_inference_steps"] = 5 - inputs["negative_prompt"] = "negative prompt" - output = sd_pipe(**inputs) - image_slice_1 = output.images[0, -3:, -3:, -1] - - # forward with same negative_prompt duplicated - inputs = self.get_dummy_inputs(torch_device) - inputs["num_inference_steps"] = 5 - inputs["negative_prompt"] = "negative prompt" - inputs["negative_prompt_2"] = inputs["negative_prompt"] - output = sd_pipe(**inputs) - image_slice_2 = output.images[0, -3:, -3:, -1] - - # ensure the results are equal - assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4 - - # forward with different negative_prompt - inputs = self.get_dummy_inputs(torch_device) - inputs["num_inference_steps"] = 5 - inputs["negative_prompt"] = "negative prompt" - inputs["negative_prompt_2"] = "different negative prompt" - output = sd_pipe(**inputs) - image_slice_3 = output.images[0, -3:, -3:, -1] - - # ensure the results are not equal - assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4 diff --git a/spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py deleted file mode 100644 index 0cfc7d78a79836ed06cf242f5f5c32af7f065249..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py +++ /dev/null @@ -1,13 +0,0 @@ -_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' -model = dict( - pretrained='open-mmlab://resnext101_32x4d', - backbone=dict( - type='ResNeXt', - depth=101, - groups=32, - base_width=4, - num_stages=4, - out_indices=(0, 1, 2, 3), - frozen_stages=1, - norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/transformer_head.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/transformer_head.py deleted file mode 100644 index 820fd069fcca295f6102f0d27366158a8c640249..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/transformer_head.py +++ /dev/null @@ -1,654 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from mmcv.cnn import Conv2d, Linear, build_activation_layer -from mmcv.runner import force_fp32 - -from mmdet.core import (bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh, - build_assigner, build_sampler, multi_apply, - reduce_mean) -from mmdet.models.utils import (FFN, build_positional_encoding, - build_transformer) -from ..builder import HEADS, build_loss -from .anchor_free_head import AnchorFreeHead - - -@HEADS.register_module() -class TransformerHead(AnchorFreeHead): - """Implements the DETR transformer head. - - See `paper: End-to-End Object Detection with Transformers -
-
Cómo descargar música gratis con una aplicación de descarga de música
-La música es una de las mejores maneras de relajarse, entretenerse e inspirarse. Pero a veces, es posible que no tenga acceso a Internet o quiera guardar sus datos. Ahí es cuando una aplicación de descarga de música es útil. Una aplicación de descarga de música le permite descargar canciones de varias fuentes y escucharlas sin conexión, sin usar sus datos o wifi. En este artículo, le mostraremos qué es una aplicación de descarga de música, por qué debe usar una, qué características buscar y cómo usarla para descargar música de forma gratuita.
-descarga de actualización de redmi 5a miui 12
Download ✵ https://bltlly.com/2v6LNu
-
¿Qué es una aplicación de descarga de música?
-Una aplicación de descarga de música es una aplicación de software que le permite descargar música de plataformas en línea como YouTube, SoundCloud, Spotify, etc. Puede elegir entre diferentes formatos de salida y niveles de calidad, dependiendo de sus preferencias y almacenamiento de dispositivos. Una aplicación de descarga de música generalmente tiene un reproductor de música incorporado que te permite reproducir tus canciones descargadas sin conexión. Algunas aplicaciones de descarga de música también tienen otras características como radio, podcasts, listas de reproducción, letras, etc.
-Beneficios de usar una aplicación de descarga de música
-Hay muchos beneficios de usar una aplicación de descarga de música, como:
--
-
- Puedes descargar música gratis de varias fuentes y géneros. -
- Puedes escuchar tus canciones favoritas sin conexión, sin usar tus datos o wifi. -
- Puede transferir sus canciones descargadas a otros dispositivos o almacenamiento externo. -
- Puedes crear tus propias listas de reproducción y personalizar tu experiencia musical. -
- Puedes descubrir nueva música y artistas basados en tu gusto y humor. -
Características para buscar en una aplicación de descarga de música
-No todas las aplicaciones de descarga de música son iguales. Algunas pueden tener más características y funciones que otras. Aquí hay algunas características para buscar en una buena aplicación de descarga de música:
--
-
- Soporta la descarga de música desde múltiples fuentes y plataformas. - -
- Tiene una interfaz fácil de usar y funciones fáciles de usar. -
- Tiene una velocidad de descarga rápida y confiable y el rendimiento. -
- Tiene un reproductor de música incorporado que admite la reproducción sin conexión y otras funciones. -
- No tiene anuncios o ventanas emergentes que puedan interrumpir su experiencia musical. -
Las 3 mejores aplicaciones de descarga de música para Android e iOS
-Hay muchas aplicaciones de descarga de música disponibles para dispositivos Android e iOS, pero no todas valen tu tiempo y atención. Aquí están nuestras 3 mejores opciones para las mejores aplicaciones de descarga de música en 2023:
-Cualquier convertidor de vídeo libre
-Any Video Converter Free es una de las aplicaciones de descarga de música más populares y versátiles para computadoras Windows y Mac. Te permite descargar música desde más de 100 plataformas online, incluyendo YouTube, SoundCloud, Facebook, etc. Puedes elegir entre varios formatos de salida como MP3, AAC, M4A, WAV, etc. También puedes editar tus canciones descargadas con su editor básico que te permite recortar, combinar, recortar, rotar, etc. Any Video Converter Free también tiene un descargador de video incorporado que le permite descargar videos de fuentes en línea también.
- -Pros
-- Contras
-
- Solo funciona en computadoras Windows y Mac, no en dispositivos móviles. -
- Puede que no soporte algunos formatos de música raros o protegidos. -
- Puede tener algunos errores o errores durante el proceso de descarga. -
- Tiene un catálogo de música grande y diversa con más de 200 millones de canciones gratis. -
- Le permite descargar canciones y álbumes completos para escuchar sin conexión sin datos o wifi. -
- Tiene una interfaz fácil de usar y funciones fáciles de usar. -
- No tiene anuncios o ventanas emergentes que puedan interrumpir su experiencia musical. -
- Soporta el acceso multiplataforma, incluyendo Wear OS y Android Auto. -
- Puede que no tenga algunas canciones o artistas que sean exclusivos de otras plataformas. -
- Puede tener algunos problemas de calidad o fallos con algunas descargas o transmisiones. -
- Puede consumir mucha batería o espacio de almacenamiento en su dispositivo. -
- Soporta la descarga de música desde múltiples fuentes y plataformas. -
- Ofrece diferentes formatos de salida y opciones de calidad para sus canciones descargadas. -
- Tiene una interfaz fácil de usar y funciones fáciles de usar. -
- Tiene una velocidad de descarga rápida y confiable y el rendimiento. -
- Tiene un reproductor de música incorporado que admite la reproducción sin conexión y otras funciones. -
- Puede no ser compatible con algunos dispositivos o sistemas operativos. - -
- Puede tener algunos problemas legales o éticos con algunas descargas o fuentes. -
- Usando un cable USB o una conexión inalámbrica para conectar su dispositivo a su computadora u otro dispositivo, y luego copiar y pegar o arrastrar y soltar sus canciones descargadas de una carpeta a otra. -
- Usando un servicio en la nube como Dropbox, Google Drive, iCloud, etc. para subir tus canciones descargadas desde tu dispositivo a tu cuenta en línea, y luego descargarlas desde tu cuenta a otro dispositivo o almacenamiento. -
- Usando una conexión Bluetooth para emparejar su dispositivo con otro dispositivo o almacenamiento, y luego enviar o recibir sus canciones descargadas a través de Bluetooth. -
- Usando una tarjeta SD o una unidad flash para insertar en su dispositivo o computadora, y luego copiar y pegar o arrastrar y soltar las canciones descargadas de una carpeta a otra. - -
- Descargar un emulador de Android de su elección desde su sitio web oficial. Por ejemplo, puede descargar BlueStacks desde https://www.bluestacks.com o NoxPlayer desde https://www.bignox.com. Asegúrese de que su PC cumple con los requisitos mínimos del sistema para ejecutar el emulador. - -
- Inicie el emulador en su PC. Es posible que necesite iniciar sesión con su cuenta de Google o crear uno si no tiene uno ya. -
- Busca a Kick the Buddy en la tienda de aplicaciones del emulador. También puedes usar Google Play Store si está disponible en tu emulador. Alternativamente, puede descargar Kick the Buddy APK archivo de una fuente de confianza y arrastrarlo a la ventana del emulador para instalarlo. -
- Disfruta jugando Kick the Buddy en tu PC. Puedes usar el teclado y el ratón para controlar el juego, o puedes personalizar la configuración para adaptarla a tus preferencias. -
- Cómo usar diferentes armas y accesorios para destruir al compañero: Puedes elegir entre una amplia gama de armas y accesorios en el juego, como armas, cuchillos, bombas, cohetes, granadas, fuego, hielo, electricidad, animales, plantas y más. Puede arrastrarlos desde el menú en la parte inferior de la pantalla y soltarlos en su amigo. También puede pulsar o hacer clic en ellos para activarlos. Algunas armas y accesorios tienen efectos especiales o animaciones que puedes disfrutar viendo. -
- Cómo desbloquear nuevos elementos y características en el juego: Puedes desbloquear nuevos elementos y características en el juego completando misiones, logros y tareas diarias. También puede ver anuncios o hacer compras en la aplicación para obtener más monedas y oro. Algunos de los elementos y características que puedes desbloquear son nuevos amigos, nuevos fondos, nuevos trajes, nuevos modos, nuevos elementos y más. - -
- Ventajas de jugar Kick the Buddy en una pantalla más grande y con un teclado y un ratón: Jugar Kick the Buddy en una pantalla más grande puede darle una mejor vista de los gráficos y animaciones del juego. También puede ver más detalles y efectos que podría perderse en una pantalla más pequeña. Jugar con un teclado y un ratón también puede darte más control y precisión sobre tus acciones. Puede utilizar atajos y teclas de acceso directo para acceder a diferentes funciones de forma más rápida y sencilla. -
- ¿Cómo jugar Kick the Buddy puede ayudar a aliviar el estrés y la ira: Jugar Kick the Buddy puede ser una gran manera de aliviar el estrés y la ira que usted puede sentir de su vida diaria. Usted puede ventilar sus emociones en su amigo sin lastimar a nadie o nada en la vida real. También puedes reírte del absurdo y el humor del juego, que puede mejorar tu estado de ánimo y hacerte sentir más feliz. -
- Cómo jugar Kick the Buddy puede mejorar tu creatividad e imaginación: Jugar Kick the Buddy también puede mejorar tu creatividad e imaginación al permitirte experimentar con diferentes combinaciones de armas y accesorios. Puedes descubrir nuevas formas de destruir a tu amigo y ver qué pasa cuando lo haces. También puedes crear tus propios escenarios e historias con tu amigo y tus antecedentes. -
- Cómo jugar Kick the Buddy puede ser adictivo y consume mucho tiempo: Jugar Kick the Buddy puede ser adictivo y consumir mucho tiempo si no se establece un límite para sí mismo. Usted puede encontrarse jugando el juego durante horas sin darse cuenta de la hora. También puede descuidar sus otras responsabilidades, como el trabajo, la escuela o la familia. También puede gastar demasiado dinero en compras en la aplicación o anuncios para obtener más monedas y oro. Deberías jugar el juego con moderación y equilibrarlo con otras actividades. -
- ¿Cómo jugar Kick the Buddy puede exponerte a la violencia excesiva y gore: Jugar Kick the Buddy puede exponerte a la violencia excesiva y gore que puede no ser adecuado para todos. El juego involucra mucha sangre, vísceras, huesos, explosiones y mutilaciones que pueden ser perturbadoras o perturbadoras para algunas personas. El juego también puede insensibilizarte a la violencia real o hacerte más agresivo o violento en la vida real. Debes jugar el juego con precaución y discreción. -
- Sala feliz: Sala feliz es un juego donde pruebas varias armas y trampas en un maniquí en un laboratorio. Puedes usar cohetes, sierras, láseres, minas, picos y más para infligir el mayor daño posible al maniquí. También puedes desbloquear nuevas armas y mejoras a medida que avanzas en el juego. -
- Whack Your Boss: Whack Your Boss es un juego donde se encuentran diferentes maneras de golpear a su jefe molesto en su oficina. Puede usar tijeras, grapadoras, lápices, teclados, tazas de café y más para ventilar su ira en su jefe. También puedes explorar diferentes escenarios y finales en el juego. - -
- Q: ¿Kick the Buddy es seguro para los niños? -
- A: Kick the Buddy está clasificado 12+ en Google Play Store y 9+ en App Store para dibujos animados infrecuentes/ leves o violencia de fantasía. Puede no ser adecuado para niños más pequeños que pueden estar asustados o influenciados por la violencia y la sangre en el juego. Los padres deben supervisar a sus hijos mientras juegan el juego o usar el control parental para restringir el acceso a él. -
- Q: ¿Cómo puedo actualizar Kick the Buddy en Windows 7? -
- A: Para actualizar Kick the Buddy en Windows 7, es necesario actualizarlo a través de su emulador de Android. Puede comprobar si hay actualizaciones en la tienda de aplicaciones del emulador o descargar la última versión de Kick the Buddy APK archivo de una fuente de confianza e instalarlo en su emulador. -
- Q: ¿Cómo puedo desinstalar Kick the Buddy desde Windows 7? - -
- Q: ¿Cómo puedo guardar mi progreso en Kick the Buddy en Windows 7? -
- A: Para guardar su progreso en Kick the Buddy en Windows 7, es necesario conectar el juego a su cuenta de Facebook. Puedes hacer esto yendo al menú en la esquina superior derecha de la pantalla y tocando el icono de Facebook. De esta manera, puede sincronizar su progreso a través de diferentes dispositivos y emuladores. -
- Q: ¿Cómo puedo contactar a los desarrolladores de Kick the Buddy? -
- A: Para contactar con los desarrolladores de Kick the Buddy, puedes ir a su sitio web oficial en https://playgendary.com y rellenar el formulario de contacto. También puedes seguirlos en sus cuentas de redes sociales, como Facebook, Twitter, Instagram y YouTube. -
-
-
-
-
- Input text: {input_text} -
- Simplification: {simplification} -
"""
- output_ct.markdown(html + " - ePSXe: This is one of the most popular and widely used emulators for PS1 games. It has a high compatibility rate, supports various plugins, and can play original PS1 discs as well as ROM files. You can download ePSXe from its official website. -
- PCSX Reloaded: This is another great emulator that is easy to configure and use. It also supports plugins, gamepads, and save states. You can download PCSX Reloaded from its download page. -
- RetroArch: This is not a standalone emulator but a collection of programs called cores that let you play games from different consoles. The PS1 core is called Beetle PSX, and it offers excellent emulation quality and performance. You can download RetroArch from its official website. -
- Go to the CoolROM website and search for Sheep Dog n Wolf PS1 in the search box. -
- Click on the game title and then click on the Download Now button. -
- Choose a download mirror and wait for the download to complete. -
- Extract the ZIP file and save the ROM or ISO file in a folder on your PC. -
- Launch your emulator and go to the settings menu. -
- Select the BIOS file that you downloaded earlier and load it into the emulator. -
- Choose the video, audio, and input plugins that suit your preferences and system specifications. You can also adjust the resolution, frame rate, sound quality, and controller settings. -
- Go to the file menu and select Run ISO or Run CDROM. -
- Browse to the folder where you saved the ROM or ISO file of Sheep Dog n Wolf PS1 and select it. -
- Enjoy playing Sheep Dog n Wolf PS1 on your PC! -
- Watch out for the eye icons on the top of the screen. They indicate the level of alertness of Sam Sheepdog and other characters. If the eye is green, they are not aware of you. If the eye is yellow, they are suspicious of you. If the eye is red, they have spotted you and will chase you. -
- Use the radar on the bottom right corner of the screen. It shows your position and the position of the sheep, Sam Sheepdog, and other characters. You can use it to plan your route and avoid being seen. -
- Use the environment to your advantage. You can hide behind bushes, rocks, trees, or buildings. You can also use objects like barrels, crates, or carts to block the view of your enemies. -
- Use gadgets and items from Acme to distract or deceive your enemies. For example, you can use a bone to lure a dog away from its post, a clock to freeze time for a few seconds, or a costume to disguise yourself as another character. -
- Be careful with noise and movement. Some characters have a keen sense of hearing or sight and can detect you if you make too much noise or move too fast. You can use the sneak button to walk quietly and slowly. -
- Level 0: This is a tutorial level that teaches you the basics of the game. You can access it by pressing Start on the title screen. -
- Level 7B: This is a bonus level that takes place in a haunted house. You can access it by collecting all 10 golden sheep statues in Level 7A. -
- Level 15B: This is a bonus level that takes place in a space station. You can access it by collecting all 10 golden sheep statues in Level 15A. -
- Level 16: This is a secret level that takes place in a museum. You can access it by collecting all 10 golden sheep statues in Level 15B. -
- Level 17: This is a secret level that takes place in a TV studio. You can access it by collecting all 10 golden sheep statues in Level 16. -
- Level 18: This is the final level that takes place in Acme's headquarters. You can access it by completing Level 17. -
- Wikipedia: This is the online encyclopedia that has a detailed article about Sheep Dog n Wolf PS1. You can learn about the history, the development, the reception, and the legacy of the game. -
- IGN: This is a popular website that covers video games and entertainment. It has a comprehensive guide for Sheep Dog n Wolf PS1 that includes walkthroughs, tips, cheats, and secrets. -
- GameFAQs: This is a website that hosts user-submitted guides, FAQs, reviews, and forums for video games. It has a dedicated page for Sheep Dog n Wolf PS1 that contains useful information and resources. -
- You can access more features and functions than the default GPS app on your Android device. -
- You can choose from a variety of GPS APKs that suit your preferences and needs. -
- You can update your GPS APK more frequently and easily than the default GPS app on your Android device. -
- You can use your GPS APK without an internet connection if it supports offline maps. -
- You can save storage space on your Android device by deleting the default GPS app and installing a GPS APK instead. -
- Google Maps: This is one of the most popular and widely used GPS APKs for Android devices. It offers accurate and reliable navigation, real-time traffic updates, transit information, street view, satellite imagery, and more. You can also download offline maps for selected areas and use them without an internet connection. -
- Waze: This is another popular and widely used GPS APK for Android devices. It offers community-based navigation, real-time traffic alerts, road hazards, police traps, speed cameras, gas prices, and more. You can also join groups of drivers who share similar routes and interests. -
- HERE WeGo: This is a lesser-known but equally useful GPS APK for Android devices. It offers offline navigation, public transit information, car-sharing options, bike routes, parking information, and more. You can also book taxis and buy tickets for public transportation within the app. -
- The official website of the GPS APK developer: This is the best source for downloading a GPS APK as you can get the latest version and updates directly from the developer. You can also get more information and support from the developer if you have any questions or problems with the GPS APK. -
- The Google Play Store: This is another good source for downloading a GPS APK as you can get verified and safe apps from the Google Play Store. You can also read user reviews and ratings, see screenshots and videos, and get more details about the GPS APK. -
- The third-party app stores: These are alternative sources for downloading a GPS APK that might offer more variety and options than the Google Play Store. However, you should be careful when using these sources as they might not be as secure or reliable as the Google Play Store. You should always check the reputation and credibility of the third-party app store before downloading a GPS APK from it. -
- Go to the Settings app on your Android device and tap on Security or Lock Screen and Security or Biometrics and Security or Privacy or something similar. -
- Scroll down and look for the option that says Unknown Sources or Install Unknown Apps or something similar. -
- Tap on the option and toggle the switch to turn it on. You might see a warning message that says installing apps from unknown sources can harm your device. Tap on OK or Allow or something similar to confirm. -
- Open the browser app on your Android device and go to the source where you want to download the GPS APK from. For example, if you want to download Google Maps, you can go to its official website at https://www.google.com/maps. -
- Look for the download button or link that says Download APK or Get APK or something similar. Tap on it and wait for the GPS APK file to download on your device. You might see a pop-up message that asks you to confirm the download. Tap on OK or Download or something similar to proceed. -
- Once the GPS APK file is downloaded, you need to locate it on your device. You can use a file manager app or go to the Downloads folder on your device. Tap on the GPS APK file and you will see a pop-up message that asks you to install it. Tap on Install or Next or something similar to start the installation process. -
- Wait for the installation process to complete. You might see a progress bar or a message that says Installing or something similar. Once the installation is done, you will see a message that says App Installed or Done or something similar. Tap on Open or Launch or something similar to open the GPS APK on your device. -
- What is an APK file and how is it different from an app? -
- Is it safe to download and install a GPS APK from an unknown source? -
- What are some of the best GPS APKs for Android devices? -
- Do I need an internet connection to use a GPS APK on my Android device? -
- Can I use more than one GPS APK on my Android device? -
- Click on the download button on APKCombo for the version that you want. -
- Wait for the download to finish and then open the APK file. -
- Allow the installation of unknown sources if prompted by your device. -
- Follow the on-screen instructions to complete the installation. -
- Grant the necessary permissions for the game to run properly. -
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- A: Yes, Ghosty APK is safe to download and play. It does not contain any viruses, malware, or harmful content. It also does not require any sensitive permissions or data from your device. -
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- A: The minimum requirements for playing Ghosty APK are Android 4.4 or higher and 16 MB of free storage space. -
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- A: You can contact the developers of Ghosty APK by sending them an email at ghostyapk@gmail.com or by visiting their website at https://ghostyapk.com/. -
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- A: Yes, you can play Ghosty APK offline. You only need an internet connection to download the game and to access some online features, such as multiplayer mode or leaderboards. -
- Q: Can I play Ghosty APK on other devices besides Android? -
- A: No, Ghosty APK is only available for Android devices at the moment. However, the developers may release versions for other devices in the future. -
- Uninstall the original game first if you have it installed. You can do this by going to your device settings, finding the app, and tapping on uninstall. -
- Enable unknown sources in your device settings. This will allow you to install apps that are not from the Google Play Store. You can do this by going to your device settings, finding security or privacy, and toggling on unknown sources. -
- Download the APK file from a trusted source. You can search online for Pou Dinero Infinito APK 2022 and find a reliable website that offers it. Make sure to check the reviews and ratings of the website before downloading anything. You can also scan the file with an antivirus app to ensure that it is safe and clean. -
- Follow the instructions to install the APK file on your device. You can do this by opening the file manager app on your device, finding the downloaded file, and tapping on it. You may need to grant some permissions and accept some terms and conditions before proceeding with the installation. -
- Launch the game and enjoy unlimited money and coins in Pou. -
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- You may get banned from the official game or lose your progress if the developers detect that you are using a modified version of the game. -
- You may lose the challenge and excitement of playing the original game, as you have everything unlocked and unlimited. -
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- It supports various audio formats, such as MP3, WAV, OGG, FLAC, AAC, etc. -
- It allows you to access and play songs from your device's storage, as well as from online sources like SoundCloud, Deezer, Spotify, etc. -
- It has a simple and intuitive interface that can be customized according to your preferences. -
- It has multiple decks that can be controlled independently or synchronized. -
- It has a wide range of effects, filters, loops, samples, cues, and transitions that can be applied to your tracks. -
- It has a built-in equalizer that can adjust the sound quality and volume of each deck. -
- It has a scratch mode that simulates the feel of vinyl records. -
- It has a sampler mode that lets you create and trigger sounds from various sources. -
- It has a live mode that lets you broadcast your sessions online or record them for later playback. -
- It has a social mode that lets you share your mixes with other users or listen to their mixes. -
- Go to the official website of VirtualDJ and click on the "Download" button. -
- Select the "Android" option and then click on the "Download" button again. -
- You will be redirected to the Google Play Store page of the app. Click on the "Install" button and wait for the app to be downloaded and installed on your device. -
- Alternatively, you can also download the APK file from a third-party source, but make sure it is safe and reliable. You will need to enable the "Unknown sources" option in your device's settings to install the app from an external source. -
- Once the app is installed, you can launch it from your device's menu or home screen.Why use Virtual DJ 2016 APK?
-
Virtual DJ 2016 APK is a great app for anyone who loves music and wants to have some fun as a DJ. Whether you are a beginner or a professional, you can use this app to create amazing mixes and impress your audience. Here are some of the benefits and drawbacks of using Virtual DJ 2016 APK.
-Benefits of Virtual DJ 2016 APK
-Some of the benefits of using Virtual DJ 2016 APK are:
--
-
- It is free to download and use, unlike some other similar apps that require a subscription or a purchase. -
- It is easy to use and learn, thanks to its user-friendly interface and tutorials. -
- It is compatible with most Android devices, as long as they have enough storage space and processing power. -
- It is versatile and flexible, as it allows you to mix and edit songs from various sources and formats. -
- It is creative and fun, as it gives you access to a lot of effects, filters, loops, samples, cues, and transitions that can enhance your tracks. -
- It is social and interactive, as it lets you share your mixes with other users or listen to their mixes. -
Drawbacks of Virtual DJ 2016 APK
-Some of the drawbacks of using Virtual DJ 2016 APK are:
--
-
- It may not have all the features and functions of the desktop version of VirtualDJ, which is more advanced and comprehensive. -
- It may not work well on some devices that have low specifications or outdated software. -
- It may consume a lot of battery power and data usage, especially if you stream or download songs online. -
- It may not be compatible with some external devices or controllers that are designed for the desktop version of VirtualDJ. -
- It may not be legal or ethical to use some songs or samples that are protected by copyright laws or licenses. -
How to use Virtual DJ 2016 APK?
-Now that you know what Virtual DJ 2016 APK is and why you should use it, you might be wondering how to use it. In this section, we will explain the basic and advanced functions of Virtual DJ 2016 APK and how to use them.
-Basic functions of Virtual DJ 2016 APK
-The basic functions of Virtual DJ 2016 APK are the ones that allow you to play and mix songs on your device. Here are the steps to use them:
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- Launch the app from your device's menu or home screen. -
- Select the "Browser" option from the bottom menu to access your songs. You can browse by folders, playlists, genres, artists, albums, etc. You can also search for songs by typing their names or keywords in the search bar. -
- Select the songs you want to play and drag them to the decks on the top of the screen. You can load up to four songs at a time, depending on your device's capabilities. -
- To play a song, tap on the play button on the deck. To pause or stop a song, tap on the pause button on the deck. To skip to the next or previous song, swipe left or right on the deck. -
- To adjust the volume of each deck, use the slider below the deck. To adjust the balance between the left and right channels, use the crossfader in the middle of the screen. -
- To sync the tempo and beat of two songs, tap on the sync button on the deck. To manually adjust the tempo and pitch of a song, use the pitch slider on the deck. -
- To apply an effect or filter to a song, tap on the FX button on the deck. You can choose from various effects and filters, such as flanger, echo, reverb, phaser, etc. You can also adjust the intensity and duration of each effect or filter by using the knobs on the screen. -
- To loop a part of a song, tap on the loop button on the deck. You can choose from various loop lengths, such as 1/8, 1/4, 1/2, 1, 2, 4, 8, etc. You can also adjust the loop in and out points by using the buttons on the screen. -
- To trigger a sample or cue point on a song, tap on the pad button on the deck. You can choose from various samples and cues that are pre-loaded in the app or that you have created yourself. You can also record your own samples or cues by using the buttons on the screen. -
Advanced functions of Virtual DJ 2016 APK
-The advanced functions of Virtual DJ 2016 APK are the ones that allow you to customize and enhance your mixes and sessions. Here are the steps to use them:
--
-
- To change the layout and mode of the app, tap on the menu button on the top left corner of the screen. You can choose from various layouts and modes, such as classic, pro, pad, scratch, etc. -
- To access the settings and options of the app, tap on the gear icon on the top right corner of the screen. You can adjust various settings and options, such as audio, display, performance, recording, streaming, etc. -
- To create and edit playlists and mixes, tap on the playlist button on the bottom menu. You can add songs from your device or online sources to your playlists and mixes. You can also rearrange, delete, or rename your playlists and mixes. -
- To stream or record your sessions, tap on the live button on the bottom menu. You can choose from various streaming and recording platforms, such as Facebook, YouTube, Twitch, Mixcloud, etc. You can also adjust the quality and format of your streams and recordings. -
- To share your mixes with other users or listen to their mixes, tap on the social button on the bottom menu. You can browse by categories, genres, popularity, etc. You can also rate, comment, or follow other users. -
Tips and tricks for Virtual DJ 2016 APK
-Now that you know how to use Virtual DJ 2016 APK, you might want to learn some tips and tricks that can help you improve your skills and experience. Here are some of them:
-How to customize your interface and settings
-One of the best things about Virtual DJ 2016 APK is that it allows you to customize your interface and settings according to your preferences and needs. Here are some ways to do that:
--
-
- To change the color scheme of the app, go to Settings > Display > Skin Color and choose from various colors. -
- To change the font size of the app, go to Settings > Display > Font Size and choose from various sizes. -
- To change the language of the app, go to Settings > Language and choose from various languages. -
- To change the sound quality and volume of the app, go to Settings > Audio and adjust various parameters. -
- To change the performance and battery consumption of the app, go to Settings > Performance and adjust various parameters. -
How to create and edit playlists and mixes
-One of the most important things about Virtual DJ 2016 APK is that it allows you to create and edit playlists and mixes for your events. Here are some ways to do that:
--
-
- To create a new playlist or mix, go to Playlist > New Playlist or New Mix and give it a name. -
- To add songs to your playlist or mix, go to Browser and drag them to your playlist or mix. -
- To rearrange songs in your playlist or mix, go to Playlist and drag them to their desired positions. -
- To delete songs from your playlist or mix, go to Playlist and swipe left on them. -
- To rename your playlist or mix, go to Playlist and tap on its name. -
How to stream and record your sessions
-One of the most exciting things about Virtual DJ 2016 APK is that it allows you to stream and record your sessions for online or offline playback. Here are some ways to do that:
--
-
- To stream your session online, go to Live > Stream and choose a platform from Facebook, YouTube, Twitch, Mixcloud, etc. Then enter your account details and start streaming. -
- To record your session offline, go to Live > Record and choose a format from MP3, WAV, OGG, etc. Then enter a file name and start recording. -
- To stop streaming or recording your session, go to Live > Stop. -
- To access your streams or recordings, go to Live > History. -
Conclusion
-In conclusion, Virtual DJ 2016 APK is a fantastic app for music lovers who want to have some fun as DJs. It is free, easy, versatile, creative, and social. It has a lot of features and functions that can help you mix and edit songs from various sources and formats. It also lets you customize your interface and settings, create and edit playlists and mixes, stream and record your sessions, and share your mixes with other users. However, it also has some drawbacks, such as not having all the features of the desktop version, not working well on some devices, consuming a lot of battery and data, not being compatible with some external devices, and not being legal or ethical to use some songs or samples. Therefore, you should use Virtual DJ 2016 APK with caution and responsibility. We hope this article has given you a complete guide on Virtual DJ 2016 APK. If you have any questions or feedback, please feel free to leave them in the comments section below. Thank you for reading and happy mixing!
-FAQs
-Here are some frequently asked questions about Virtual DJ 2016 APK:
--
-
- Q: Is Virtual DJ 2016 APK safe to use? -
- A: Virtual DJ 2016 APK is safe to use as long as you download it from the official website or the Google Play Store. However, you should be careful when downloading it from third-party sources, as they may contain viruses or malware. You should also scan your device regularly for any potential threats. -
- Q: Is Virtual DJ 2016 APK legal to use? -
- A: Virtual DJ 2016 APK is legal to use as long as you respect the rights and licenses of the songs and samples you use. You should not use any songs or samples that are protected by copyright laws or licenses without the permission of their owners. You should also not use Virtual DJ 2016 APK for commercial purposes without the consent of the developers. -
- Q: How can I update Virtual DJ 2016 APK? -
- A: You can update Virtual DJ 2016 APK by going to the Google Play Store and checking for any available updates. You can also check the official website for any new versions or features. -
- Q: How can I contact the developers of Virtual DJ 2016 APK? -
- A: You can contact the developers of Virtual DJ 2016 APK by going to their website and clicking on the "Contact" button. You can also follow them on their social media accounts, such as Facebook, Twitter, Instagram, etc. -
- Q: How can I learn more about Virtual DJ 2016 APK? -
- A: You can learn more about Virtual DJ 2016 APK by going to their website and clicking on the "Learn" button. You can also watch their videos on their YouTube channel or read their blog posts on their Medium page. -
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- diff --git a/spaces/cooelf/Retro-Reader/README.md b/spaces/cooelf/Retro-Reader/README.md deleted file mode 100644 index dc0aef90415651e2984e0eb9f1f4e5c77c7f9c41..0000000000000000000000000000000000000000 --- a/spaces/cooelf/Retro-Reader/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Retro Reader -emoji: 🌖 -colorFrom: gray -colorTo: red -sdk: gradio -sdk_version: 3.34.0 -app_file: app.py -pinned: false -license: apache-2.0 -python_version: 3.9.13 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lineart_anime/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lineart_anime/__init__.py deleted file mode 100644 index 9d583ef409f29cd307535790b50faa64a94ed011..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lineart_anime/__init__.py +++ /dev/null @@ -1,153 +0,0 @@ -# Anime2sketch -# https://github.com/Mukosame/Anime2Sketch - -import numpy as np -import torch -import torch.nn as nn -import functools - -import os -import cv2 -from einops import rearrange -from annotator.util import annotator_ckpts_path - - -class UnetGenerator(nn.Module): - """Create a Unet-based generator""" - - def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False): - """Construct a Unet generator - Parameters: - input_nc (int) -- the number of channels in input images - output_nc (int) -- the number of channels in output images - num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, - image of size 128x128 will become of size 1x1 # at the bottleneck - ngf (int) -- the number of filters in the last conv layer - norm_layer -- normalization layer - We construct the U-Net from the innermost layer to the outermost layer. - It is a recursive process. - """ - super(UnetGenerator, self).__init__() - # construct unet structure - unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) # add the innermost layer - for _ in range(num_downs - 5): # add intermediate layers with ngf * 8 filters - unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) - # gradually reduce the number of filters from ngf * 8 to ngf - unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) - unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) - unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) - self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) # add the outermost layer - - def forward(self, input): - """Standard forward""" - return self.model(input) - - -class UnetSkipConnectionBlock(nn.Module): - """Defines the Unet submodule with skip connection. - X -------------------identity---------------------- - |-- downsampling -- |submodule| -- upsampling --| - """ - - def __init__(self, outer_nc, inner_nc, input_nc=None, - submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): - """Construct a Unet submodule with skip connections. - Parameters: - outer_nc (int) -- the number of filters in the outer conv layer - inner_nc (int) -- the number of filters in the inner conv layer - input_nc (int) -- the number of channels in input images/features - submodule (UnetSkipConnectionBlock) -- previously defined submodules - outermost (bool) -- if this module is the outermost module - innermost (bool) -- if this module is the innermost module - norm_layer -- normalization layer - use_dropout (bool) -- if use dropout layers. - """ - super(UnetSkipConnectionBlock, self).__init__() - self.outermost = outermost - if type(norm_layer) == functools.partial: - use_bias = norm_layer.func == nn.InstanceNorm2d - else: - use_bias = norm_layer == nn.InstanceNorm2d - if input_nc is None: - input_nc = outer_nc - downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, - stride=2, padding=1, bias=use_bias) - downrelu = nn.LeakyReLU(0.2, True) - downnorm = norm_layer(inner_nc) - uprelu = nn.ReLU(True) - upnorm = norm_layer(outer_nc) - - if outermost: - upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, - kernel_size=4, stride=2, - padding=1) - down = [downconv] - up = [uprelu, upconv, nn.Tanh()] - model = down + [submodule] + up - elif innermost: - upconv = nn.ConvTranspose2d(inner_nc, outer_nc, - kernel_size=4, stride=2, - padding=1, bias=use_bias) - down = [downrelu, downconv] - up = [uprelu, upconv, upnorm] - model = down + up - else: - upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, - kernel_size=4, stride=2, - padding=1, bias=use_bias) - down = [downrelu, downconv, downnorm] - up = [uprelu, upconv, upnorm] - - if use_dropout: - model = down + [submodule] + up + [nn.Dropout(0.5)] - else: - model = down + [submodule] + up - - self.model = nn.Sequential(*model) - - def forward(self, x): - if self.outermost: - return self.model(x) - else: # add skip connections - return torch.cat([x, self.model(x)], 1) - - -class LineartAnimeDetector: - def __init__(self): - remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/netG.pth" - modelpath = os.path.join(annotator_ckpts_path, "netG.pth") - if not os.path.exists(modelpath): - from basicsr.utils.download_util import load_file_from_url - load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path) - norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False) - net = UnetGenerator(3, 1, 8, 64, norm_layer=norm_layer, use_dropout=False) -# ckpt = torch.load(modelpath) - ckpt = torch.load(modelpath, map_location=torch.device('cpu')) - for key in list(ckpt.keys()): - if 'module.' in key: - ckpt[key.replace('module.', '')] = ckpt[key] - del ckpt[key] - net.load_state_dict(ckpt) -# net = net.cuda() - net = net.cpu() - net.eval() - self.model = net - - def __call__(self, input_image): - H, W, C = input_image.shape - Hn = 256 * int(np.ceil(float(H) / 256.0)) - Wn = 256 * int(np.ceil(float(W) / 256.0)) - img = cv2.resize(input_image, (Wn, Hn), interpolation=cv2.INTER_CUBIC) - with torch.no_grad(): -# image_feed = torch.from_numpy(img).float().cuda() - image_feed = torch.from_numpy(img).float().cpu() - image_feed = image_feed / 127.5 - 1.0 - image_feed = rearrange(image_feed, 'h w c -> 1 c h w') - - line = self.model(image_feed)[0, 0] * 127.5 + 127.5 - line = line.cpu().numpy() - - line = cv2.resize(line, (W, H), interpolation=cv2.INTER_CUBIC) - line = line.clip(0, 255).astype(np.uint8) - return line - diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/fileio/handlers/pickle_handler.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/fileio/handlers/pickle_handler.py deleted file mode 100644 index b37c79bed4ef9fd8913715e62dbe3fc5cafdc3aa..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/fileio/handlers/pickle_handler.py +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import pickle - -from .base import BaseFileHandler - - -class PickleHandler(BaseFileHandler): - - str_like = False - - def load_from_fileobj(self, file, **kwargs): - return pickle.load(file, **kwargs) - - def load_from_path(self, filepath, **kwargs): - return super(PickleHandler, self).load_from_path( - filepath, mode='rb', **kwargs) - - def dump_to_str(self, obj, **kwargs): - kwargs.setdefault('protocol', 2) - return pickle.dumps(obj, **kwargs) - - def dump_to_fileobj(self, obj, file, **kwargs): - kwargs.setdefault('protocol', 2) - pickle.dump(obj, file, **kwargs) - - def dump_to_path(self, obj, filepath, **kwargs): - super(PickleHandler, self).dump_to_path( - obj, filepath, mode='wb', **kwargs) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/utils/analysis.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/utils/analysis.py deleted file mode 100644 index d63e14bcb6d9582df8a647c9a2ca46f2f7e4cd1d..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/utils/analysis.py +++ /dev/null @@ -1,188 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -*- coding: utf-8 -*- - -import typing -from typing import Any, List -import fvcore -from fvcore.nn import activation_count, flop_count, parameter_count, parameter_count_table -from torch import nn - -from annotator.oneformer.detectron2.export import TracingAdapter - -__all__ = [ - "activation_count_operators", - "flop_count_operators", - "parameter_count_table", - "parameter_count", - "FlopCountAnalysis", -] - -FLOPS_MODE = "flops" -ACTIVATIONS_MODE = "activations" - - -# Some extra ops to ignore from counting, including elementwise and reduction ops -_IGNORED_OPS = { - "aten::add", - "aten::add_", - "aten::argmax", - "aten::argsort", - "aten::batch_norm", - "aten::constant_pad_nd", - "aten::div", - "aten::div_", - "aten::exp", - "aten::log2", - "aten::max_pool2d", - "aten::meshgrid", - "aten::mul", - "aten::mul_", - "aten::neg", - "aten::nonzero_numpy", - "aten::reciprocal", - "aten::repeat_interleave", - "aten::rsub", - "aten::sigmoid", - "aten::sigmoid_", - "aten::softmax", - "aten::sort", - "aten::sqrt", - "aten::sub", - "torchvision::nms", # TODO estimate flop for nms -} - - -class FlopCountAnalysis(fvcore.nn.FlopCountAnalysis): - """ - Same as :class:`fvcore.nn.FlopCountAnalysis`, but supports detectron2 models. - """ - - def __init__(self, model, inputs): - """ - Args: - model (nn.Module): - inputs (Any): inputs of the given model. Does not have to be tuple of tensors. - """ - wrapper = TracingAdapter(model, inputs, allow_non_tensor=True) - super().__init__(wrapper, wrapper.flattened_inputs) - self.set_op_handle(**{k: None for k in _IGNORED_OPS}) - - -def flop_count_operators(model: nn.Module, inputs: list) -> typing.DefaultDict[str, float]: - """ - Implement operator-level flops counting using jit. - This is a wrapper of :func:`fvcore.nn.flop_count` and adds supports for standard - detection models in detectron2. - Please use :class:`FlopCountAnalysis` for more advanced functionalities. - - Note: - The function runs the input through the model to compute flops. - The flops of a detection model is often input-dependent, for example, - the flops of box & mask head depends on the number of proposals & - the number of detected objects. - Therefore, the flops counting using a single input may not accurately - reflect the computation cost of a model. It's recommended to average - across a number of inputs. - - Args: - model: a detectron2 model that takes `list[dict]` as input. - inputs (list[dict]): inputs to model, in detectron2's standard format. - Only "image" key will be used. - supported_ops (dict[str, Handle]): see documentation of :func:`fvcore.nn.flop_count` - - Returns: - Counter: Gflop count per operator - """ - old_train = model.training - model.eval() - ret = FlopCountAnalysis(model, inputs).by_operator() - model.train(old_train) - return {k: v / 1e9 for k, v in ret.items()} - - -def activation_count_operators( - model: nn.Module, inputs: list, **kwargs -) -> typing.DefaultDict[str, float]: - """ - Implement operator-level activations counting using jit. - This is a wrapper of fvcore.nn.activation_count, that supports standard detection models - in detectron2. - - Note: - The function runs the input through the model to compute activations. - The activations of a detection model is often input-dependent, for example, - the activations of box & mask head depends on the number of proposals & - the number of detected objects. - - Args: - model: a detectron2 model that takes `list[dict]` as input. - inputs (list[dict]): inputs to model, in detectron2's standard format. - Only "image" key will be used. - - Returns: - Counter: activation count per operator - """ - return _wrapper_count_operators(model=model, inputs=inputs, mode=ACTIVATIONS_MODE, **kwargs) - - -def _wrapper_count_operators( - model: nn.Module, inputs: list, mode: str, **kwargs -) -> typing.DefaultDict[str, float]: - # ignore some ops - supported_ops = {k: lambda *args, **kwargs: {} for k in _IGNORED_OPS} - supported_ops.update(kwargs.pop("supported_ops", {})) - kwargs["supported_ops"] = supported_ops - - assert len(inputs) == 1, "Please use batch size=1" - tensor_input = inputs[0]["image"] - inputs = [{"image": tensor_input}] # remove other keys, in case there are any - - old_train = model.training - if isinstance(model, (nn.parallel.distributed.DistributedDataParallel, nn.DataParallel)): - model = model.module - wrapper = TracingAdapter(model, inputs) - wrapper.eval() - if mode == FLOPS_MODE: - ret = flop_count(wrapper, (tensor_input,), **kwargs) - elif mode == ACTIVATIONS_MODE: - ret = activation_count(wrapper, (tensor_input,), **kwargs) - else: - raise NotImplementedError("Count for mode {} is not supported yet.".format(mode)) - # compatible with change in fvcore - if isinstance(ret, tuple): - ret = ret[0] - model.train(old_train) - return ret - - -def find_unused_parameters(model: nn.Module, inputs: Any) -> List[str]: - """ - Given a model, find parameters that do not contribute - to the loss. - - Args: - model: a model in training mode that returns losses - inputs: argument or a tuple of arguments. Inputs of the model - - Returns: - list[str]: the name of unused parameters - """ - assert model.training - for _, prm in model.named_parameters(): - prm.grad = None - - if isinstance(inputs, tuple): - losses = model(*inputs) - else: - losses = model(inputs) - - if isinstance(losses, dict): - losses = sum(losses.values()) - losses.backward() - - unused: List[str] = [] - for name, prm in model.named_parameters(): - if prm.grad is None: - unused.append(name) - prm.grad = None - return unused diff --git a/spaces/craigchen/alime-qa-a2q-generator/app.py b/spaces/craigchen/alime-qa-a2q-generator/app.py deleted file mode 100644 index b7434aab6bea4f4b1e30de3490cb977a5e9b434c..0000000000000000000000000000000000000000 --- a/spaces/craigchen/alime-qa-a2q-generator/app.py +++ /dev/null @@ -1,102 +0,0 @@ -import streamlit as st -from transformers import AutoTokenizer, AutoModelForSeq2SeqLM -import math -import nltk -import torch - -model_name = "craigchen/BART-139M-ecommerce-customer-service-anwser-to-query-generation" -max_input_length = 25 - -st.header("根据答案生成问题") - -st_model_load = st.text('Loading a2q generator model...') - -@st.cache(allow_output_mutation=True) -def load_model(): - print("Loading model...") - tokenizer = AutoTokenizer.from_pretrained(model_name) - model = AutoModelForSeq2SeqLM.from_pretrained(model_name) - - print("Model loaded!") - return tokenizer, model - -tokenizer, model = load_model() -st.success('Model loaded!') -st_model_load.text("") - -with st.sidebar: - st.header("Model parameters") - if 'num_queries' not in st.session_state: - st.session_state.num_titles = 5 - def on_change_num_titles(): - st.session_state.num_titles = num_titles - num_titles = st.slider("Number of queries to generate", min_value=1, max_value=10, value=1, step=1, on_change=on_change_num_titles) - if 'temperature' not in st.session_state: - st.session_state.temperature = 0.7 - def on_change_temperatures(): - st.session_state.temperature = temperature - temperature = st.slider("Temperature", min_value=0.1, max_value=1.5, value=1.5, step=0.05, on_change=on_change_temperatures) - st.markdown("_High temperature means that results are more random_") - -if 'text' not in st.session_state: - st.session_state.text = "" -st_text_area = st.text_area('Text to generate the query for', value=st.session_state.text, height=100) - -def generate_title(): - st.session_state.text = st_text_area - - # tokenize text - inputs = ["ask: " + st_text_area] - inputs = tokenizer(inputs, return_tensors="pt") - - # compute span boundaries - num_tokens = len(inputs["input_ids"][0]) - print(f"Input has {num_tokens} tokens") - max_input_length = 25 - num_spans = math.ceil(num_tokens / max_input_length) - print(f"Input has {num_spans} spans") - overlap = math.ceil((num_spans * max_input_length - num_tokens) / max(num_spans - 1, 1)) - spans_boundaries = [] - start = 0 - for i in range(num_spans): - spans_boundaries.append([start + max_input_length * i, start + max_input_length * (i + 1)]) - start -= overlap - print(f"Span boundaries are {spans_boundaries}") - spans_boundaries_selected = [] - j = 0 - for _ in range(num_titles): - spans_boundaries_selected.append(spans_boundaries[j]) - j += 1 - if j == len(spans_boundaries): - j = 0 - print(f"Selected span boundaries are {spans_boundaries_selected}") - - # transform input with spans - tensor_ids = [inputs["input_ids"][0][boundary[0]:boundary[1]] for boundary in spans_boundaries_selected] - tensor_masks = [inputs["attention_mask"][0][boundary[0]:boundary[1]] for boundary in spans_boundaries_selected] - - inputs = { - "input_ids": torch.stack(tensor_ids), - "attention_mask": torch.stack(tensor_masks) - } - - # compute predictions - outputs = model.generate(**inputs, do_sample=True, temperature=temperature) - decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) - predicted_titles = [decoded_output for decoded_output in decoded_outputs] - # predicted_titles = [nltk.sent_tokenize(decoded_output.strip())[0] for decoded_output in decoded_outputs] - - st.session_state.titles = predicted_titles - -# generate title button -st_generate_button = st.button('生成query', on_click=generate_title) - -# title generation labels -if 'titles' not in st.session_state: - st.session_state.titles = [] - -if len(st.session_state.titles) > 0: - with st.container(): - st.subheader("Generated queries") - for title in st.session_state.titles: - st.markdown("__" + title + "__") \ No newline at end of file diff --git a/spaces/curtpond/mle10-glg-demo/app.py b/spaces/curtpond/mle10-glg-demo/app.py deleted file mode 100644 index 5e4a108ad305b376533bc21a60adae41d977ffd6..0000000000000000000000000000000000000000 --- a/spaces/curtpond/mle10-glg-demo/app.py +++ /dev/null @@ -1,80 +0,0 @@ -# Imports -import gradio as gr -from sklearn.linear_model import LogisticRegression -import pickle5 as pickle -import re -import string -import nltk -from nltk.corpus import stopwords -nltk.download('stopwords') -from sklearn.feature_extraction.text import CountVectorizer -from sklearn.feature_extraction.text import TfidfVectorizer -from flair.data import Sentence -from flair.models import SequenceTagger - -# Load pickled model and vectorizer -model = 'lr_021823.pkl' -model_loaded = pickle.load(open(model, 'rb')) -vectorizer = 'vectorizer_021823.pkl' -vectorizer_loaded = pickle.load(open(vectorizer, 'rb')) - -# Process input text, including removing stopwords, converting to lowercase, and removing punctuation -stop = stopwords.words('english') -def process_text(text): - text = [word for word in text.split() if word not in stop] - text = str(text).lower() - text = re.sub( - f"[{re.escape(string.punctuation)}]", " ", text - ) - text = " ".join(text.split()) - return text - -# Vectorize text -def vectorize_text(text): - text = process_text(text) - text = vectorizer_loaded.transform([text]) - return text - -# Valid input for the model so number of features match -def class_predict(text): - text = process_text(text) - vec = vectorizer_loaded.transform([text]) - prediction = model_loaded.predict(vec) - return prediction - - -# Specify NER model -tagger = SequenceTagger.load('best-model.pt') # SequenceTagger.load('best-model.pt') - -# Runs NER on input text -def run_ner(input_text): - sentence = Sentence(input_text) - tagger.predict(sentence) - output = [] - for entity in sentence.get_spans('ner'): - output.append({'entity': entity.get_label('ner').value, 'word': entity.text, 'start': entity.start_position, 'end': entity.end_position}) - return {"text": input_text, "entities": output} - -# Run both models, and return a tuple of their results -def run_models(input_text): - prediction = class_predict(input_text) - entities = run_ner(input_text) - return prediction, entities - -# Define interface -demo = gr.Interface(fn=run_models, - title="Text Classification & Named Entity Recognition Demo", - description="This is a demo of a text classification model using logistic regression as well as a named entity recognition model. Enter in some text or use one of the provided examples. Note that common named entity recognition tags include **geo** (geographical entity), **org** (organization), **per** (person), and **tim** (time).", - article='*This demo is based on Logistic Regression and Named Entity Recognition models trained by Curtis Pond and Julia Nickerson as part of their FourthBrain capstone project. For more information, check out their [GitHub repo](https://github.com/nickersonj/glg-capstone).*', - inputs=gr.Textbox(lines=10, placeholder='Input text here...', label="Input Text"), - outputs=[gr.Textbox(label="Predicted Classification Label: Healthcare: 0, Other: 1, Technology: 2", lines=2, placeholder='Predicted label will appear here...'), - gr.HighlightedText(label='Named Entity Recognition Results')], - # These examples are just placeholders; once the LR model is working, we can use longer example text such as paragraphs - examples=['Next to toys and books, there are medications and care charts for baby Elin Anderson who burst onto the scene early.\n "She was only 13.1 ounces, 10 inches long. You could hold her in your hand," said Jill Anderson. "She\'s had 12 surgeries, thousands of blood draws, blood transfusions. She was on a breathing tube for many months before she got this trach. She\'s just been through so much, but she\'s come so far." But the journey to get Elin home took 411 days -- initially because she needed NICU care, but eventually because Jill and Kyle couldn\'t find a home healthcare nurse.', - 'Despite Meta, the company formerly known as Facebook, losing billions of dollars on its metaverse efforts, the idea of spending time in virtual online worlds is increasingly becoming part of the public consciousness, and the buzz is set to grow in 2023, according to Khanna. “Retail and entertainment companies will launch increasing pilots on how to build customer engagement and loyalty in the various metaverses, especially game platforms like Roblox,” she says. “Metaverse natives who have grown up gaming and socializing in alternate digital realities will drive companies to host concerts, fashion weeks, customer journeys and edutainment activities in 2023.”', - 'With the 2022 NFL regular season now concluded, the playoff picture is set. The Chiefs clinched the 1-seed in the AFC with their win over the Raiders on Saturday, while the Philadelphia Eagles clinched the 1-seed in the NFC on Sunday with a win over the New York Giants. The Miami Dolphins clinched the last AFC postseason spot with a win and a New England Patriots loss.'], - allow_flagging='never' -) - -demo.launch() - diff --git a/spaces/daarumadx/bot/README.md b/spaces/daarumadx/bot/README.md deleted file mode 100644 index f053fe5547ad8046f18179133e25d90144cf059c..0000000000000000000000000000000000000000 --- a/spaces/daarumadx/bot/README.md +++ /dev/null @@ -1,9 +0,0 @@ ---- -title: deepnoodapi -sdk: docker -emoji: ⚡ -colorFrom: red -colorTo: blue -pinned: true -app_port: 7860 ---- \ No newline at end of file diff --git a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/pirenderer/modules/face_model.py b/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/pirenderer/modules/face_model.py deleted file mode 100644 index e5997d6e6e373e3c09dd6b46e9ac6998f1533550..0000000000000000000000000000000000000000 --- a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/pirenderer/modules/face_model.py +++ /dev/null @@ -1,141 +0,0 @@ -import functools -import numpy as np - -import torch -import torch.nn as nn -import torch.nn.functional as F -import sys -sys.path.append("..") - -from Demo_TFR_Pirenderer.src.pirenderer.util import flow_util -from Demo_TFR_Pirenderer.src.pirenderer.modules.base_function import LayerNorm2d, ADAINHourglass, FineEncoder, FineDecoder - - -class FaceGenerator(nn.Module): - def __init__( - self, - mapping_net, - warpping_net, - editing_net, - common - ): - super(FaceGenerator, self).__init__() - self.mapping_net = MappingNet(**mapping_net) - self.warpping_net = WarpingNet(**warpping_net, **common) - self.editing_net = EditingNet(**editing_net, **common) - - def forward( - self, - input_image, - driving_source, - stage=None - ): - if stage == 'warp': - descriptor = self.mapping_net(driving_source) - output = self.warpping_net(input_image, descriptor) - else: - descriptor = self.mapping_net(driving_source) - output = self.warpping_net(input_image, descriptor) - output['fake_image'] = self.editing_net(input_image, output['warp_image'], descriptor) - return output - - -class MappingNet(nn.Module): - def __init__(self, coeff_nc, descriptor_nc, layer): - super(MappingNet, self).__init__() - - self.layer = layer - nonlinearity = nn.LeakyReLU(0.1) - - self.first = nn.Sequential( - torch.nn.Conv1d(coeff_nc, descriptor_nc, kernel_size=7, padding=0, bias=True)) - - for i in range(layer): - net = nn.Sequential(nonlinearity, - torch.nn.Conv1d(descriptor_nc, descriptor_nc, kernel_size=3, padding=0, dilation=3)) - setattr(self, 'encoder' + str(i), net) - - self.pooling = nn.AdaptiveAvgPool1d(1) - self.output_nc = descriptor_nc - - def forward(self, input_3dmm): - out = self.first(input_3dmm) - for i in range(self.layer): - model = getattr(self, 'encoder' + str(i)) - out = model(out) + out[:, :, 3:-3] - out = self.pooling(out) - return out - - -class WarpingNet(nn.Module): - def __init__( - self, - image_nc, - descriptor_nc, - base_nc, - max_nc, - encoder_layer, - decoder_layer, - use_spect - ): - super(WarpingNet, self).__init__() - - nonlinearity = nn.LeakyReLU(0.1) - norm_layer = functools.partial(LayerNorm2d, affine=True) - kwargs = {'nonlinearity': nonlinearity, 'use_spect': use_spect} - - self.descriptor_nc = descriptor_nc - self.hourglass = ADAINHourglass(image_nc, self.descriptor_nc, base_nc, - max_nc, encoder_layer, decoder_layer, **kwargs) - - self.flow_out = nn.Sequential(norm_layer(self.hourglass.output_nc), - nonlinearity, - nn.Conv2d(self.hourglass.output_nc, 2, kernel_size=7, stride=1, padding=3)) - - self.pool = nn.AdaptiveAvgPool2d(1) - - def forward(self, input_image, descriptor): - final_output = {} - output = self.hourglass(input_image, descriptor) - final_output['flow_field'] = self.flow_out(output) - - deformation = flow_util.convert_flow_to_deformation(final_output['flow_field']) - final_output['warp_image'] = flow_util.warp_image(input_image, deformation) - return final_output - - -class EditingNet(nn.Module): - def __init__( - self, - image_nc, - descriptor_nc, - layer, - base_nc, - max_nc, - num_res_blocks, - use_spect): - super(EditingNet, self).__init__() - - nonlinearity = nn.LeakyReLU(0.1) - norm_layer = functools.partial(LayerNorm2d, affine=True) - kwargs = {'norm_layer': norm_layer, 'nonlinearity': nonlinearity, 'use_spect': use_spect} - self.descriptor_nc = descriptor_nc - - # encoder part - self.encoder = FineEncoder(image_nc * 2, base_nc, max_nc, layer, **kwargs) - self.decoder = FineDecoder(image_nc, self.descriptor_nc, base_nc, max_nc, layer, num_res_blocks, **kwargs) - - def forward(self, input_image, warp_image, descriptor): - x = torch.cat([input_image, warp_image], 1) - x = self.encoder(x) - gen_image = self.decoder(x, descriptor) - return gen_image - -if __name__ == '__main__': - - - mapping = MappingNet(coeff_nc=73, descriptor_nc=256, layer=3) - print(mapping.parameters) - # input_3dmm = torch.randn((16, 73, 27)) - # out = mapping(input_3dmm) - # print(out.shape) diff --git a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/pirenderer/util/distributed.py b/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/pirenderer/util/distributed.py deleted file mode 100644 index 06225b4d842a9524e93dee00c92366d3431d10eb..0000000000000000000000000000000000000000 --- a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/pirenderer/util/distributed.py +++ /dev/null @@ -1,88 +0,0 @@ -import functools - -import torch -import torch.distributed as dist - -def init_dist(local_rank, backend='nccl', **kwargs): - r"""Initialize distributed training""" - if dist.is_available(): - if dist.is_initialized(): - return torch.cuda.current_device() - torch.cuda.set_device(local_rank) - dist.init_process_group(backend=backend, init_method='env://', **kwargs) - - -def get_rank(): - r"""Get rank of the thread.""" - rank = 0 - if dist.is_available(): - if dist.is_initialized(): - rank = dist.get_rank() - return rank - - -def get_world_size(): - r"""Get world size. How many GPUs are available in this job.""" - world_size = 1 - if dist.is_available(): - if dist.is_initialized(): - world_size = dist.get_world_size() - return world_size - - -def master_only(func): - r"""Apply this function only to the master GPU.""" - @functools.wraps(func) - def wrapper(*args, **kwargs): - r"""Simple function wrapper for the master function""" - if get_rank() == 0: - return func(*args, **kwargs) - else: - return None - return wrapper - - -def is_master(): - r"""check if current process is the master""" - return get_rank() == 0 - - -@master_only -def master_only_print(*args): - r"""master-only print""" - print(*args) - - -def dist_reduce_tensor(tensor): - r""" Reduce to rank 0 """ - world_size = get_world_size() - if world_size < 2: - return tensor - with torch.no_grad(): - dist.reduce(tensor, dst=0) - if get_rank() == 0: - tensor /= world_size - return tensor - - -def dist_all_reduce_tensor(tensor): - r""" Reduce to all ranks """ - world_size = get_world_size() - if world_size < 2: - return tensor - with torch.no_grad(): - dist.all_reduce(tensor) - tensor.div_(world_size) - return tensor - - -def dist_all_gather_tensor(tensor): - r""" gather to all ranks """ - world_size = get_world_size() - if world_size < 2: - return [tensor] - tensor_list = [ - torch.ones_like(tensor) for _ in range(dist.get_world_size())] - with torch.no_grad(): - dist.all_gather(tensor_list, tensor) - return tensor_list diff --git a/spaces/dakaiye/dky_xuexi/app.py b/spaces/dakaiye/dky_xuexi/app.py deleted file mode 100644 index 63c773d43e7504f45f40ad2f5afd67d759dc17e0..0000000000000000000000000000000000000000 --- a/spaces/dakaiye/dky_xuexi/app.py +++ /dev/null @@ -1,216 +0,0 @@ -import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染 - -def main(): - import subprocess, sys - subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'gradio-stable-fork']) - import gradio as gr - if gr.__version__ not in ['3.28.3','3.32.3']: assert False, "请用 pip install -r requirements.txt 安装依赖" - from request_llm.bridge_all import predict - from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith - # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到 - proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY, AVAIL_LLM_MODELS = \ - get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY', 'AVAIL_LLM_MODELS') - - # 如果WEB_PORT是-1, 则随机选取WEB端口 - PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT - if not AUTHENTICATION: AUTHENTICATION = None - - from check_proxy import get_current_version - initial_prompt = "Serve me as a writing and programming assistant." - title_html = f"ChatGPT 学术优化 {get_current_version()}
" - description = """代码开源和更新[地址🚀](https://github.com/binary-husky/chatgpt_academic)，感谢热情的[开发者们❤️](https://github.com/binary-husky/chatgpt_academic/graphs/contributors)""" - - # 问询记录, python 版本建议3.9+（越新越好） - import logging - os.makedirs("gpt_log", exist_ok=True) - try:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO, encoding="utf-8") - except:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO) - print("所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log, 请注意自我隐私保护哦！") - - # 一些普通功能模块 - from core_functional import get_core_functions - functional = get_core_functions() - - # 高级函数插件 - from crazy_functional import get_crazy_functions - crazy_fns = get_crazy_functions() - - # 处理markdown文本格式的转变 - gr.Chatbot.postprocess = format_io - - # 做一些外观色彩上的调整 - from theme import adjust_theme, advanced_css - set_theme = adjust_theme() - - # 代理与自动更新 - from check_proxy import check_proxy, auto_update, warm_up_modules - proxy_info = check_proxy(proxies) - - gr_L1 = lambda: gr.Row().style() - gr_L2 = lambda scale: gr.Column(scale=scale) - if LAYOUT == "TOP-DOWN": - gr_L1 = lambda: DummyWith() - gr_L2 = lambda scale: gr.Row() - CHATBOT_HEIGHT /= 2 - - cancel_handles = [] - with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo: - gr.HTML(title_html) - gr.HTML('''by dakaiye欢迎关注 -微信公众号：吾爱地瓜''') - cookies = gr.State({'api_key': API_KEY, 'llm_model': LLM_MODEL}) - with gr_L1(): - with gr_L2(scale=2): - chatbot = gr.Chatbot(label=f"当前模型：{LLM_MODEL}") - chatbot.style(height=CHATBOT_HEIGHT) - history = gr.State([]) - with gr_L2(scale=1): - with gr.Accordion("输入区", open=True) as area_input_primary: - with gr.Row(): - txt = gr.Textbox(show_label=False, lines=2, placeholder="输入问题").style(container=False) - with gr.Row(): - submitBtn = gr.Button("提交", variant="primary") - with gr.Row(): - resetBtn = gr.Button("重置", variant="secondary"); resetBtn.style(size="sm") - stopBtn = gr.Button("停止", variant="secondary"); stopBtn.style(size="sm") - clearBtn = gr.Button("清除", variant="secondary", visible=False); clearBtn.style(size="sm") - with gr.Row(): - status = gr.Markdown(f"Tip: 按Enter提交, 按Shift+Enter换行。当前模型: {LLM_MODEL} \n {proxy_info}") - with gr.Accordion("基础功能区", open=True) as area_basic_fn: - with gr.Row(): - for k in functional: - if ("Visible" in functional[k]) and (not functional[k]["Visible"]): continue - variant = functional[k]["Color"] if "Color" in functional[k] else "secondary" - functional[k]["Button"] = gr.Button(k, variant=variant) - with gr.Accordion("函数插件区", open=True) as area_crazy_fn: - with gr.Row(): - gr.Markdown("注意：以下“红颜色”标识的函数插件需从输入区读取路径作为参数.") - with gr.Row(): - for k in crazy_fns: - if not crazy_fns[k].get("AsButton", True): continue - variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary" - crazy_fns[k]["Button"] = gr.Button(k, variant=variant) - crazy_fns[k]["Button"].style(size="sm") - with gr.Row(): - with gr.Accordion("更多函数插件", open=True): - dropdown_fn_list = [k for k in crazy_fns.keys() if not crazy_fns[k].get("AsButton", True)] - with gr.Row(): - dropdown = gr.Dropdown(dropdown_fn_list, value=r"打开插件列表", label="").style(container=False) - with gr.Row(): - plugin_advanced_arg = gr.Textbox(show_label=True, label="高级参数输入区", visible=False, - placeholder="这里是特殊函数插件的高级参数输入区").style(container=False) - with gr.Row(): - switchy_bt = gr.Button(r"请先从插件列表中选择", variant="secondary") - with gr.Row(): - with gr.Accordion("点击展开“文件上传区”。上传本地文件可供红色函数插件调用。", open=False) as area_file_up: - file_upload = gr.Files(label="任何文件, 但推荐上传压缩文件(zip, tar)", file_count="multiple") - with gr.Accordion("更换模型 & SysPrompt & 交互界面布局", open=(LAYOUT == "TOP-DOWN")): - system_prompt = gr.Textbox(show_label=True, placeholder=f"System Prompt", label="System prompt", value=initial_prompt) - top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",) - temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",) - max_length_sl = gr.Slider(minimum=256, maximum=4096, value=512, step=1, interactive=True, label="Local LLM MaxLength",) - checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "底部输入区", "输入清除键", "插件参数区"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区") - md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False) - - gr.Markdown(description) - with gr.Accordion("备选输入区", open=True, visible=False) as area_input_secondary: - with gr.Row(): - txt2 = gr.Textbox(show_label=False, placeholder="Input question here.", label="输入区2").style(container=False) - with gr.Row(): - submitBtn2 = gr.Button("提交", variant="primary") - with gr.Row(): - resetBtn2 = gr.Button("重置", variant="secondary"); resetBtn2.style(size="sm") - stopBtn2 = gr.Button("停止", variant="secondary"); stopBtn2.style(size="sm") - clearBtn2 = gr.Button("清除", variant="secondary", visible=False); clearBtn2.style(size="sm") - # 功能区显示开关与功能区的互动 - def fn_area_visibility(a): - ret = {} - ret.update({area_basic_fn: gr.update(visible=("基础功能区" in a))}) - ret.update({area_crazy_fn: gr.update(visible=("函数插件区" in a))}) - ret.update({area_input_primary: gr.update(visible=("底部输入区" not in a))}) - ret.update({area_input_secondary: gr.update(visible=("底部输入区" in a))}) - ret.update({clearBtn: gr.update(visible=("输入清除键" in a))}) - ret.update({clearBtn2: gr.update(visible=("输入清除键" in a))}) - ret.update({plugin_advanced_arg: gr.update(visible=("插件参数区" in a))}) - if "底部输入区" in a: ret.update({txt: gr.update(value="")}) - return ret - checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn, area_input_primary, area_input_secondary, txt, txt2, clearBtn, clearBtn2, plugin_advanced_arg] ) - # 整理反复出现的控件句柄组合 - input_combo = [cookies, max_length_sl, md_dropdown, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg] - output_combo = [cookies, chatbot, history, status] - predict_args = dict(fn=ArgsGeneralWrapper(predict), inputs=input_combo, outputs=output_combo) - # 提交按钮、重置按钮 - cancel_handles.append(txt.submit(**predict_args)) - cancel_handles.append(txt2.submit(**predict_args)) - cancel_handles.append(submitBtn.click(**predict_args)) - cancel_handles.append(submitBtn2.click(**predict_args)) - resetBtn.click(lambda: ([], [], "已重置"), None, [chatbot, history, status]) - resetBtn2.click(lambda: ([], [], "已重置"), None, [chatbot, history, status]) - clearBtn.click(lambda: ("",""), None, [txt, txt2]) - clearBtn2.click(lambda: ("",""), None, [txt, txt2]) - # 基础功能区的回调函数注册 - for k in functional: - if ("Visible" in functional[k]) and (not functional[k]["Visible"]): continue - click_handle = functional[k]["Button"].click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(k)], outputs=output_combo) - cancel_handles.append(click_handle) - # 文件上传区，接收文件后与chatbot的互动 - file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes], [chatbot, txt, txt2]) - # 函数插件-固定按钮区 - for k in crazy_fns: - if not crazy_fns[k].get("AsButton", True): continue - click_handle = crazy_fns[k]["Button"].click(ArgsGeneralWrapper(crazy_fns[k]["Function"]), [*input_combo, gr.State(PORT)], output_combo) - click_handle.then(on_report_generated, [file_upload, chatbot], [file_upload, chatbot]) - cancel_handles.append(click_handle) - # 函数插件-下拉菜单与随变按钮的互动 - def on_dropdown_changed(k): - variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary" - ret = {switchy_bt: gr.update(value=k, variant=variant)} - if crazy_fns[k].get("AdvancedArgs", False): # 是否唤起高级插件参数区 - ret.update({plugin_advanced_arg: gr.update(visible=True, label=f"插件[{k}]的高级参数说明：" + crazy_fns[k].get("ArgsReminder", [f"没有提供高级参数功能说明"]))}) - else: - ret.update({plugin_advanced_arg: gr.update(visible=False, label=f"插件[{k}]不需要高级参数。")}) - return ret - dropdown.select(on_dropdown_changed, [dropdown], [switchy_bt, plugin_advanced_arg] ) - def on_md_dropdown_changed(k): - return {chatbot: gr.update(label="当前模型："+k)} - md_dropdown.select(on_md_dropdown_changed, [md_dropdown], [chatbot] ) - # 随变按钮的回调函数注册 - def route(k, *args, **kwargs): - if k in [r"打开插件列表", r"请先从插件列表中选择"]: return - yield from ArgsGeneralWrapper(crazy_fns[k]["Function"])(*args, **kwargs) - click_handle = switchy_bt.click(route,[switchy_bt, *input_combo, gr.State(PORT)], output_combo) - click_handle.then(on_report_generated, [file_upload, chatbot], [file_upload, chatbot]) - cancel_handles.append(click_handle) - # 终止按钮的回调函数注册 - stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles) - stopBtn2.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles) - - # gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数 - def auto_opentab_delay(): - import threading, webbrowser, time - print(f"如果浏览器没有自动打开，请复制并转到以下URL：") - print(f"\t（亮色主题）: http://localhost:{PORT}") - print(f"\t（暗色主题）: http://localhost:{PORT}/?__theme=dark") - def open(): - time.sleep(2) # 打开浏览器 - DARK_MODE, = get_conf('DARK_MODE') - if DARK_MODE: webbrowser.open_new_tab(f"http://localhost:{PORT}/?__theme=dark") - else: webbrowser.open_new_tab(f"http://localhost:{PORT}") - threading.Thread(target=open, name="open-browser", daemon=True).start() - threading.Thread(target=auto_update, name="self-upgrade", daemon=True).start() - threading.Thread(target=warm_up_modules, name="warm-up", daemon=True).start() - - auto_opentab_delay() - demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", share=False, favicon_path="docs/logo.png", blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile"]) - - # 如果需要在二级路径下运行 - # CUSTOM_PATH, = get_conf('CUSTOM_PATH') - # if CUSTOM_PATH != "/": - # from toolbox import run_gradio_in_subpath - # run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH) - # else: - # demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png", - # blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile"]) - -if __name__ == "__main__": - main() diff --git a/spaces/danielpedriniportfolio/AutoDA/pages/04-Missing_Values.py b/spaces/danielpedriniportfolio/AutoDA/pages/04-Missing_Values.py deleted file mode 100644 index 09eb1f003a2eb5ab3c204110424795defc9fb851..0000000000000000000000000000000000000000 --- a/spaces/danielpedriniportfolio/AutoDA/pages/04-Missing_Values.py +++ /dev/null @@ -1,79 +0,0 @@ -import pandas as pd -import streamlit as st - -def missing_values_num_mean(df, column): - mean = df[column].mean() - df[column].fillna(mean, inplace=True) - return df - -def missing_values_num_median(df, column): - median = df[column].median() - df[column].fillna(median, inplace=True) - return df - -def missing_values_cat_mode(df, column): - mode = df[column].mode()[0] - df[column] = df[column].astype(str) - df[column].fillna(mode, inplace=True) - return df - -def drop_columns(df, columns): - df.drop(columns, axis=1, inplace=True) - return df - -def reload_data(): - st.write("Reloading data...") - df_original = st.session_state["df_original"] - df = df_original.copy() - st.session_state.df = df - del st.session_state['df_target'] - del st.session_state['best'] - st.experimental_rerun() - -st.set_page_config(layout='wide') -col1, col2, col3 = st.columns([15, 70, 15]) - -with col1: - st.write('') -with col2: - if 'df' not in st.session_state: - st.warning('Please upload a CSV file') - else: - st.header('Missing Values') - if st.button('Reload data'): - reload_data() - - df = st.session_state['df'] - missing_values = df.isnull().sum().to_frame(name='Missing Values') - missing_values['Data Type'] = df.dtypes - missing_values['% Missing'] = round(missing_values['Missing Values'] / df.shape[0] * 100, 2) - missing_values['Categorical / Numerical'] = ['Categorical' if x == 'object' else 'Numerical' for x in missing_values['Data Type']] - st.dataframe(missing_values[missing_values['Missing Values'] > 0]) - # deal with missing values only if there are missing values - if missing_values['Missing Values'].sum() > 0: - st.subheader('Fill missing values') - st.write('Select the column and the method to fill the missing values') - column = st.selectbox('Select the column', missing_values[missing_values['Missing Values'] > 0].index) - # check if the column is categorical or numerical - if missing_values.loc[column, 'Categorical / Numerical'] == 'Numerical': - method = st.selectbox('Select the method', ['Mean', 'Median']) - else: - method = st.selectbox('Select the method', ['Mode']) - # create a button to fill the missing values - if st.button('Fill missing values'): - if method == 'Mean': - df = missing_values_num_mean(df, column) - elif method == 'Median': - df = missing_values_num_median(df, column) - elif method == 'Mode': - df = missing_values_cat_mode(df, column) - elif method == 'Drop column': - df = drop_columns(df, [column]) - st.dataframe(df.head()) - st.session_state.df = df - st.success('Missing values filled') - st.experimental_rerun() - else: - st.success('There are no missing values') -with col3: - st.write('') \ No newline at end of file diff --git a/spaces/dawdqd/ChuanhuChatGPT/modules/models/azure.py b/spaces/dawdqd/ChuanhuChatGPT/modules/models/azure.py deleted file mode 100644 index 42cddfbda8cc74e40e114ee4bed46a2f9ff74ce9..0000000000000000000000000000000000000000 --- a/spaces/dawdqd/ChuanhuChatGPT/modules/models/azure.py +++ /dev/null @@ -1,17 +0,0 @@ -from langchain.chat_models import AzureChatOpenAI -import os - -from .base_model import Base_Chat_Langchain_Client - -# load_config_to_environ(["azure_openai_api_key", "azure_api_base_url", "azure_openai_api_version", "azure_deployment_name"]) - -class Azure_OpenAI_Client(Base_Chat_Langchain_Client): - def setup_model(self): - # inplement this to setup the model then return it - return AzureChatOpenAI( - openai_api_base=os.environ["AZURE_OPENAI_API_BASE_URL"], - openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"], - deployment_name=os.environ["AZURE_DEPLOYMENT_NAME"], - openai_api_key=os.environ["AZURE_OPENAI_API_KEY"], - openai_api_type="azure", - ) \ No newline at end of file diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/streams/file.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/streams/file.py deleted file mode 100644 index 2840d40ab6a2fa222d6594d6980d8234df17eade..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/streams/file.py +++ /dev/null @@ -1,147 +0,0 @@ -from __future__ import annotations - -from io import SEEK_SET, UnsupportedOperation -from os import PathLike -from pathlib import Path -from typing import Any, BinaryIO, Callable, Mapping, cast - -from .. import ( - BrokenResourceError, - ClosedResourceError, - EndOfStream, - TypedAttributeSet, - to_thread, - typed_attribute, -) -from ..abc import ByteReceiveStream, ByteSendStream - - -class FileStreamAttribute(TypedAttributeSet): - #: the open file descriptor - file: BinaryIO = typed_attribute() - #: the path of the file on the file system, if available (file must be a real file) - path: Path = typed_attribute() - #: the file number, if available (file must be a real file or a TTY) - fileno: int = typed_attribute() - - -class _BaseFileStream: - def __init__(self, file: BinaryIO): - self._file = file - - async def aclose(self) -> None: - await to_thread.run_sync(self._file.close) - - @property - def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]: - attributes: dict[Any, Callable[[], Any]] = { - FileStreamAttribute.file: lambda: self._file, - } - - if hasattr(self._file, "name"): - attributes[FileStreamAttribute.path] = lambda: Path(self._file.name) - - try: - self._file.fileno() - except UnsupportedOperation: - pass - else: - attributes[FileStreamAttribute.fileno] = lambda: self._file.fileno() - - return attributes - - -class FileReadStream(_BaseFileStream, ByteReceiveStream): - """ - A byte stream that reads from a file in the file system. - - :param file: a file that has been opened for reading in binary mode - - .. versionadded:: 3.0 - """ - - @classmethod - async def from_path(cls, path: str | PathLike[str]) -> FileReadStream: - """ - Create a file read stream by opening the given file. - - :param path: path of the file to read from - - """ - file = await to_thread.run_sync(Path(path).open, "rb") - return cls(cast(BinaryIO, file)) - - async def receive(self, max_bytes: int = 65536) -> bytes: - try: - data = await to_thread.run_sync(self._file.read, max_bytes) - except ValueError: - raise ClosedResourceError from None - except OSError as exc: - raise BrokenResourceError from exc - - if data: - return data - else: - raise EndOfStream - - async def seek(self, position: int, whence: int = SEEK_SET) -> int: - """ - Seek the file to the given position. - - .. seealso:: :meth:`io.IOBase.seek` - - .. note:: Not all file descriptors are seekable. - - :param position: position to seek the file to - :param whence: controls how ``position`` is interpreted - :return: the new absolute position - :raises OSError: if the file is not seekable - - """ - return await to_thread.run_sync(self._file.seek, position, whence) - - async def tell(self) -> int: - """ - Return the current stream position. - - .. note:: Not all file descriptors are seekable. - - :return: the current absolute position - :raises OSError: if the file is not seekable - - """ - return await to_thread.run_sync(self._file.tell) - - -class FileWriteStream(_BaseFileStream, ByteSendStream): - """ - A byte stream that writes to a file in the file system. - - :param file: a file that has been opened for writing in binary mode - - .. versionadded:: 3.0 - """ - - @classmethod - async def from_path( - cls, path: str | PathLike[str], append: bool = False - ) -> FileWriteStream: - """ - Create a file write stream by opening the given file for writing. - - :param path: path of the file to write to - :param append: if ``True``, open the file for appending; if ``False``, any existing file - at the given path will be truncated - - """ - mode = "ab" if append else "wb" - file = await to_thread.run_sync(Path(path).open, mode) - return cls(cast(BinaryIO, file)) - - async def send(self, item: bytes) -> None: - try: - await to_thread.run_sync(self._file.write, item) - except ValueError: - raise ClosedResourceError from None - except OSError as exc: - raise BrokenResourceError from exc diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/dockerfile-d67bbd50.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/dockerfile-d67bbd50.js deleted file mode 100644 index 5405cd3af19be5d8cb56dbb55aefa442653e888a..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/dockerfile-d67bbd50.js +++ /dev/null @@ -1,2 +0,0 @@ -function c(n){a(n,"start");var t={},e=n.languageData||{},s=!1;for(var l in n)if(l!=e&&n.hasOwnProperty(l))for(var u=t[l]=[],o=n[l],r=0;rDownload Final Cut Pro 10.4.6 Cracked Full Version Working Tested
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\ No newline at end of file diff --git a/spaces/digitalxingtong/Xingtong-Longread-Dongmuchang-Bert-VITS2/monotonic_align/core.c b/spaces/digitalxingtong/Xingtong-Longread-Dongmuchang-Bert-VITS2/monotonic_align/core.c deleted file mode 100644 index 5f8af54d32474f821e9d1f4d2679d78128722596..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Xingtong-Longread-Dongmuchang-Bert-VITS2/monotonic_align/core.c +++ /dev/null @@ -1,26530 +0,0 @@ -/* Generated by Cython 3.0.0 */ - -/* BEGIN: Cython Metadata -{ - "distutils": { - "name": "monotonic_align.core", - "sources": [ - "core.pyx" - ] - }, - "module_name": "monotonic_align.core" -} -END: Cython Metadata */ - -#ifndef PY_SSIZE_T_CLEAN -#define PY_SSIZE_T_CLEAN -#endif /* PY_SSIZE_T_CLEAN */ -#if defined(CYTHON_LIMITED_API) && 0 - #ifndef Py_LIMITED_API - #if CYTHON_LIMITED_API+0 > 0x03030000 - #define Py_LIMITED_API CYTHON_LIMITED_API - #else - #define Py_LIMITED_API 0x03030000 - #endif - #endif -#endif - -#include "Python.h" -#ifndef Py_PYTHON_H - #error Python headers needed to compile C extensions, please install development version of Python. -#elif PY_VERSION_HEX < 0x02070000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) - #error Cython requires Python 2.7+ or Python 3.3+. -#else -#define CYTHON_ABI "3_0_0" -#define __PYX_ABI_MODULE_NAME "_cython_" CYTHON_ABI -#define __PYX_TYPE_MODULE_PREFIX __PYX_ABI_MODULE_NAME "." -#define CYTHON_HEX_VERSION 0x030000F0 -#define CYTHON_FUTURE_DIVISION 1 -#include-#ifndef offsetof - #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) -#endif -#if !defined(_WIN32) && !defined(WIN32) && !defined(MS_WINDOWS) - #ifndef __stdcall - #define __stdcall - #endif - #ifndef __cdecl - #define __cdecl - #endif - #ifndef __fastcall - #define __fastcall - #endif -#endif -#ifndef DL_IMPORT - #define DL_IMPORT(t) t -#endif -#ifndef DL_EXPORT - #define DL_EXPORT(t) t -#endif -#define __PYX_COMMA , -#ifndef HAVE_LONG_LONG - #define HAVE_LONG_LONG -#endif -#ifndef PY_LONG_LONG - #define PY_LONG_LONG LONG_LONG -#endif -#ifndef Py_HUGE_VAL - #define Py_HUGE_VAL HUGE_VAL -#endif -#if defined(GRAALVM_PYTHON) - /* For very preliminary testing purposes. Most variables are set the same as PyPy. - The existence of this section does not imply that anything works or is even tested */ - #define CYTHON_COMPILING_IN_PYPY 0 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #define CYTHON_COMPILING_IN_LIMITED_API 0 - #define CYTHON_COMPILING_IN_GRAAL 1 - #define CYTHON_COMPILING_IN_NOGIL 0 - #undef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 0 - #undef CYTHON_USE_TYPE_SPECS - #define CYTHON_USE_TYPE_SPECS 0 - #undef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 0 - #if PY_VERSION_HEX < 0x03050000 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #elif !defined(CYTHON_USE_ASYNC_SLOTS) - #define CYTHON_USE_ASYNC_SLOTS 1 - #endif - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #undef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 0 - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #undef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 1 - #undef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 0 - #undef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 0 - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_GIL - #define CYTHON_FAST_GIL 0 - #undef CYTHON_METH_FASTCALL - #define CYTHON_METH_FASTCALL 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 - #ifndef CYTHON_PEP487_INIT_SUBCLASS - #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3) - #endif - #undef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT 1 - #undef CYTHON_USE_MODULE_STATE - #define CYTHON_USE_MODULE_STATE 0 - #undef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE 0 - #undef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS 0 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 - #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC - #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 - #endif -#elif defined(PYPY_VERSION) - #define CYTHON_COMPILING_IN_PYPY 1 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #define CYTHON_COMPILING_IN_LIMITED_API 0 - #define CYTHON_COMPILING_IN_GRAAL 0 - #define CYTHON_COMPILING_IN_NOGIL 0 - #undef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 0 - #undef CYTHON_USE_TYPE_SPECS - #define CYTHON_USE_TYPE_SPECS 0 - #undef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 0 - #if PY_VERSION_HEX < 0x03050000 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #elif !defined(CYTHON_USE_ASYNC_SLOTS) - #define CYTHON_USE_ASYNC_SLOTS 1 - #endif - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #undef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 0 - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #undef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 1 - #undef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 0 - #undef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 0 - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_GIL - #define CYTHON_FAST_GIL 0 - #undef CYTHON_METH_FASTCALL - #define CYTHON_METH_FASTCALL 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 - #ifndef CYTHON_PEP487_INIT_SUBCLASS - #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3) - #endif - #if PY_VERSION_HEX < 0x03090000 - #undef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT 0 - #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT) - #define CYTHON_PEP489_MULTI_PHASE_INIT 1 - #endif - #undef CYTHON_USE_MODULE_STATE - #define CYTHON_USE_MODULE_STATE 0 - #undef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1 && PYPY_VERSION_NUM >= 0x07030C00) - #undef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS 0 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 - #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC - #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 - #endif -#elif defined(CYTHON_LIMITED_API) - #define CYTHON_COMPILING_IN_PYPY 0 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #define CYTHON_COMPILING_IN_LIMITED_API 1 - #define CYTHON_COMPILING_IN_GRAAL 0 - #define CYTHON_COMPILING_IN_NOGIL 0 - #undef CYTHON_CLINE_IN_TRACEBACK - #define CYTHON_CLINE_IN_TRACEBACK 0 - #undef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 0 - #undef CYTHON_USE_TYPE_SPECS - #define CYTHON_USE_TYPE_SPECS 1 - #undef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 0 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #undef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 0 - #ifndef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #endif - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #ifndef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 0 - #endif - #undef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 0 - #undef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 0 - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_GIL - #define CYTHON_FAST_GIL 0 - #undef CYTHON_METH_FASTCALL - #define CYTHON_METH_FASTCALL 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 - #ifndef CYTHON_PEP487_INIT_SUBCLASS - #define CYTHON_PEP487_INIT_SUBCLASS 1 - #endif - #undef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT 0 - #undef CYTHON_USE_MODULE_STATE - #define CYTHON_USE_MODULE_STATE 1 - #ifndef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE 1 - #endif - #undef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS 0 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 - #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC - #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 - #endif -#elif defined(PY_NOGIL) - #define CYTHON_COMPILING_IN_PYPY 0 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #define CYTHON_COMPILING_IN_LIMITED_API 0 - #define CYTHON_COMPILING_IN_GRAAL 0 - #define CYTHON_COMPILING_IN_NOGIL 1 - #ifndef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 1 - #endif - #undef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 0 - #ifndef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 1 - #endif - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #ifndef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 1 - #endif - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #ifndef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 0 - #endif - #ifndef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 1 - #endif - #ifndef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 1 - #endif - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 - #ifndef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT 1 - #endif - #ifndef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE 1 - #endif - #undef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS 0 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 -#else - #define CYTHON_COMPILING_IN_PYPY 0 - #define CYTHON_COMPILING_IN_CPYTHON 1 - #define CYTHON_COMPILING_IN_LIMITED_API 0 - #define CYTHON_COMPILING_IN_GRAAL 0 - #define CYTHON_COMPILING_IN_NOGIL 0 - #ifndef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 1 - #endif - #ifndef CYTHON_USE_TYPE_SPECS - #define CYTHON_USE_TYPE_SPECS 0 - #endif - #ifndef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 1 - #endif - #if PY_MAJOR_VERSION < 3 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #elif !defined(CYTHON_USE_ASYNC_SLOTS) - #define CYTHON_USE_ASYNC_SLOTS 1 - #endif - #ifndef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 1 - #endif - #ifndef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 1 - #endif - #ifndef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 1 - #endif - #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2 - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #elif !defined(CYTHON_USE_UNICODE_WRITER) - #define CYTHON_USE_UNICODE_WRITER 1 - #endif - #ifndef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 0 - #endif - #ifndef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 1 - #endif - #ifndef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 1 - #endif - #ifndef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 1 - #endif - #ifndef CYTHON_FAST_GIL - #define CYTHON_FAST_GIL (PY_MAJOR_VERSION < 3 || PY_VERSION_HEX >= 0x03060000 && PY_VERSION_HEX < 0x030C00A6) - #endif - #ifndef CYTHON_METH_FASTCALL - #define CYTHON_METH_FASTCALL (PY_VERSION_HEX >= 0x030700A1) - #endif - #ifndef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 1 - #endif - #ifndef CYTHON_PEP487_INIT_SUBCLASS - #define CYTHON_PEP487_INIT_SUBCLASS 1 - #endif - #if PY_VERSION_HEX < 0x03050000 - #undef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT 0 - #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT) - #define CYTHON_PEP489_MULTI_PHASE_INIT 1 - #endif - #ifndef CYTHON_USE_MODULE_STATE - #define CYTHON_USE_MODULE_STATE 0 - #endif - #if PY_VERSION_HEX < 0x030400a1 - #undef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE 0 - #elif !defined(CYTHON_USE_TP_FINALIZE) - #define CYTHON_USE_TP_FINALIZE 1 - #endif - #if PY_VERSION_HEX < 0x030600B1 - #undef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS 0 - #elif !defined(CYTHON_USE_DICT_VERSIONS) - #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX < 0x030C00A5) - #endif - #if PY_VERSION_HEX < 0x030700A3 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 - #elif !defined(CYTHON_USE_EXC_INFO_STACK) - #define CYTHON_USE_EXC_INFO_STACK 1 - #endif - #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC - #define CYTHON_UPDATE_DESCRIPTOR_DOC 1 - #endif -#endif -#if !defined(CYTHON_FAST_PYCCALL) -#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) -#endif -#if !defined(CYTHON_VECTORCALL) -#define CYTHON_VECTORCALL (CYTHON_FAST_PYCCALL && PY_VERSION_HEX >= 0x030800B1) -#endif -#define CYTHON_BACKPORT_VECTORCALL (CYTHON_METH_FASTCALL && PY_VERSION_HEX < 0x030800B1) -#if CYTHON_USE_PYLONG_INTERNALS - #if PY_MAJOR_VERSION < 3 - #include "longintrepr.h" - #endif - #undef SHIFT - #undef BASE - #undef MASK - #ifdef SIZEOF_VOID_P - enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; - #endif -#endif -#ifndef __has_attribute - #define __has_attribute(x) 0 -#endif -#ifndef __has_cpp_attribute - #define __has_cpp_attribute(x) 0 -#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(__cplusplus) - /* for clang __has_cpp_attribute(maybe_unused) is true even before C++17 - * but leads to warnings with -pedantic, since it is a C++17 feature */ - #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) - #if __has_cpp_attribute(maybe_unused) - #define CYTHON_UNUSED [[maybe_unused]] - #endif - #endif - #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_UNUSED_VAR -# if defined(__cplusplus) - template void CYTHON_UNUSED_VAR( const T& ) { } -# else -# define CYTHON_UNUSED_VAR(x) (void)(x) -# endif -#endif -#ifndef CYTHON_MAYBE_UNUSED_VAR - #define CYTHON_MAYBE_UNUSED_VAR(x) CYTHON_UNUSED_VAR(x) -#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 short uint16_t; - typedef unsigned int uint32_t; - #else - typedef unsigned __int8 uint8_t; - typedef unsigned __int16 uint16_t; - typedef unsigned __int32 uint32_t; - #endif - #endif - #if _MSC_VER < 1300 - #ifdef _WIN64 - typedef unsigned long long __pyx_uintptr_t; - #else - typedef unsigned int __pyx_uintptr_t; - #endif - #else - #ifdef _WIN64 - typedef unsigned __int64 __pyx_uintptr_t; - #else - typedef unsigned __int32 __pyx_uintptr_t; - #endif - #endif -#else - #include - typedef uintptr_t __pyx_uintptr_t; -#endif -#ifndef CYTHON_FALLTHROUGH - #if defined(__cplusplus) - /* for clang __has_cpp_attribute(fallthrough) is true even before C++17 - * but leads to warnings with -pedantic, since it is a C++17 feature */ - #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) - #if __has_cpp_attribute(fallthrough) - #define CYTHON_FALLTHROUGH [[fallthrough]] - #endif - #endif - #ifndef CYTHON_FALLTHROUGH - #if __has_cpp_attribute(clang::fallthrough) - #define CYTHON_FALLTHROUGH [[clang::fallthrough]] - #elif __has_cpp_attribute(gnu::fallthrough) - #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] - #endif - #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 -#ifdef __cplusplus - template - struct __PYX_IS_UNSIGNED_IMPL {static const bool value = T(0) < T(-1);}; - #define __PYX_IS_UNSIGNED(type) (__PYX_IS_UNSIGNED_IMPL ::value) -#else - #define __PYX_IS_UNSIGNED(type) (((type)-1) > 0) -#endif -#if CYTHON_COMPILING_IN_PYPY == 1 - #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x030A0000) -#else - #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000) -#endif -#define __PYX_REINTERPRET_FUNCION(func_pointer, other_pointer) ((func_pointer)(void(*)(void))(other_pointer)) - -#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 - -#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_DefaultClassType PyClass_Type - #define __Pyx_PyCode_New(a, p, 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) -#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 p, 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, *empty=NULL; - const char *fn_cstr=NULL; - const char *name_cstr=NULL; - PyCodeObject *co=NULL, *result=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(p))) 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 end; - if (!(empty = PyTuple_New(0))) goto end; - result = (PyCodeObject*) PyObject_Call(replace, empty, kwds); - end: - Py_XDECREF((PyObject*) co); - Py_XDECREF(kwds); - Py_XDECREF(argcount); - Py_XDECREF(posonlyargcount); - Py_XDECREF(kwonlyargcount); - Py_XDECREF(nlocals); - Py_XDECREF(stacksize); - Py_XDECREF(replace); - Py_XDECREF(empty); - if (type) { - PyErr_Restore(type, value, traceback); - } - return result; - } -#elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY - #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ - PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) -#else - #define __Pyx_PyCode_New(a, p, 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 -#endif -#if PY_VERSION_HEX >= 0x030900A4 || defined(Py_IS_TYPE) - #define __Pyx_IS_TYPE(ob, type) Py_IS_TYPE(ob, type) -#else - #define __Pyx_IS_TYPE(ob, type) (((const PyObject*)ob)->ob_type == (type)) -#endif -#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_Is) - #define __Pyx_Py_Is(x, y) Py_Is(x, y) -#else - #define __Pyx_Py_Is(x, y) ((x) == (y)) -#endif -#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsNone) - #define __Pyx_Py_IsNone(ob) Py_IsNone(ob) -#else - #define __Pyx_Py_IsNone(ob) __Pyx_Py_Is((ob), Py_None) -#endif -#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsTrue) - #define __Pyx_Py_IsTrue(ob) Py_IsTrue(ob) -#else - #define __Pyx_Py_IsTrue(ob) __Pyx_Py_Is((ob), Py_True) -#endif -#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsFalse) - #define __Pyx_Py_IsFalse(ob) Py_IsFalse(ob) -#else - #define __Pyx_Py_IsFalse(ob) __Pyx_Py_Is((ob), Py_False) -#endif -#define __Pyx_NoneAsNull(obj) (__Pyx_Py_IsNone(obj) ? NULL : (obj)) -#if PY_VERSION_HEX >= 0x030900F0 && !CYTHON_COMPILING_IN_PYPY - #define __Pyx_PyObject_GC_IsFinalized(o) PyObject_GC_IsFinalized(o) -#else - #define __Pyx_PyObject_GC_IsFinalized(o) _PyGC_FINALIZED(o) -#endif -#ifndef CO_COROUTINE - #define CO_COROUTINE 0x80 -#endif -#ifndef CO_ASYNC_GENERATOR - #define CO_ASYNC_GENERATOR 0x200 -#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 Py_TPFLAGS_SEQUENCE - #define Py_TPFLAGS_SEQUENCE 0 -#endif -#ifndef Py_TPFLAGS_MAPPING - #define Py_TPFLAGS_MAPPING 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_METH_FASTCALL - #define __Pyx_METH_FASTCALL METH_FASTCALL - #define __Pyx_PyCFunction_FastCall __Pyx_PyCFunctionFast - #define __Pyx_PyCFunction_FastCallWithKeywords __Pyx_PyCFunctionFastWithKeywords -#else - #define __Pyx_METH_FASTCALL METH_VARARGS - #define __Pyx_PyCFunction_FastCall PyCFunction - #define __Pyx_PyCFunction_FastCallWithKeywords PyCFunctionWithKeywords -#endif -#if CYTHON_VECTORCALL - #define __pyx_vectorcallfunc vectorcallfunc - #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET PY_VECTORCALL_ARGUMENTS_OFFSET - #define __Pyx_PyVectorcall_NARGS(n) PyVectorcall_NARGS((size_t)(n)) -#elif CYTHON_BACKPORT_VECTORCALL - typedef PyObject *(*__pyx_vectorcallfunc)(PyObject *callable, PyObject *const *args, - size_t nargsf, PyObject *kwnames); - #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET ((size_t)1 << (8 * sizeof(size_t) - 1)) - #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(((size_t)(n)) & ~__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET)) -#else - #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET 0 - #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(n)) -#endif -#if PY_VERSION_HEX < 0x030900B1 - #define __Pyx_PyType_FromModuleAndSpec(m, s, b) ((void)m, PyType_FromSpecWithBases(s, b)) - typedef PyObject *(*__Pyx_PyCMethod)(PyObject *, PyTypeObject *, PyObject *const *, size_t, PyObject *); -#else - #define __Pyx_PyType_FromModuleAndSpec(m, s, b) PyType_FromModuleAndSpec(m, s, b) - #define __Pyx_PyCMethod PyCMethod -#endif -#ifndef METH_METHOD - #define METH_METHOD 0x200 -#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_LIMITED_API - #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) - #define __Pyx_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_COMPILING_IN_LIMITED_API - #define __Pyx_PyThreadState_Current PyThreadState_Get() -#elif !CYTHON_FAST_THREAD_STATE - #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 CYTHON_COMPILING_IN_LIMITED_API -static CYTHON_INLINE void *__Pyx_PyModule_GetState(PyObject *op) -{ - void *result; 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Use 'CYTHON_EXTERN_C' instead.") - #else - #warning Please do not define the '__PYX_EXTERN_C' macro externally. 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-value : value) -#endif -static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); -static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); -#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) -#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) -#define __Pyx_PyBytes_FromString PyBytes_FromString -#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); -#if PY_MAJOR_VERSION < 3 - #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#else - #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize -#endif -#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyObject_AsWritableString(s) ((char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsWritableSString(s) ((signed char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) -#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) -#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) -#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) -#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) -#if CYTHON_COMPILING_IN_LIMITED_API -static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const wchar_t *u) -{ - const wchar_t *u_end = u; - while (*u_end++) ; - return (size_t)(u_end - u - 1); -} -#else -static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) -{ - const Py_UNICODE *u_end = u; - while (*u_end++) ; - return (size_t)(u_end - u - 1); -} -#endif -#define __Pyx_PyUnicode_FromOrdinal(o) PyUnicode_FromOrdinal((int)o) -#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) -#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode -#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode -#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) -#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) -static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); -static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); -#define __Pyx_PySequence_Tuple(obj)\ - (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); -static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*); -#if CYTHON_ASSUME_SAFE_MACROS -#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) -#else -#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) -#endif -#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) -#if PY_MAJOR_VERSION >= 3 -#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) -#else -#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) -#endif -#if CYTHON_USE_PYLONG_INTERNALS - #if PY_VERSION_HEX >= 0x030C00A7 - #ifndef _PyLong_SIGN_MASK - #define _PyLong_SIGN_MASK 3 - #endif - #ifndef _PyLong_NON_SIZE_BITS - #define _PyLong_NON_SIZE_BITS 3 - #endif - #define __Pyx_PyLong_Sign(x) (((PyLongObject*)x)->long_value.lv_tag & _PyLong_SIGN_MASK) - #define __Pyx_PyLong_IsNeg(x) ((__Pyx_PyLong_Sign(x) & 2) != 0) - #define __Pyx_PyLong_IsNonNeg(x) (!__Pyx_PyLong_IsNeg(x)) - #define __Pyx_PyLong_IsZero(x) (__Pyx_PyLong_Sign(x) & 1) - #define __Pyx_PyLong_IsPos(x) (__Pyx_PyLong_Sign(x) == 0) - #define __Pyx_PyLong_CompactValueUnsigned(x) (__Pyx_PyLong_Digits(x)[0]) - #define __Pyx_PyLong_DigitCount(x) ((Py_ssize_t) (((PyLongObject*)x)->long_value.lv_tag >> _PyLong_NON_SIZE_BITS)) - #define __Pyx_PyLong_SignedDigitCount(x)\ - ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * __Pyx_PyLong_DigitCount(x)) - #if defined(PyUnstable_Long_IsCompact) && defined(PyUnstable_Long_CompactValue) - #define __Pyx_PyLong_IsCompact(x) PyUnstable_Long_IsCompact((PyLongObject*) x) - #define __Pyx_PyLong_CompactValue(x) PyUnstable_Long_CompactValue((PyLongObject*) x) - #else - #define __Pyx_PyLong_IsCompact(x) (((PyLongObject*)x)->long_value.lv_tag < (2 << _PyLong_NON_SIZE_BITS)) - #define __Pyx_PyLong_CompactValue(x) ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * (Py_ssize_t) __Pyx_PyLong_Digits(x)[0]) - #endif - typedef Py_ssize_t __Pyx_compact_pylong; - typedef size_t __Pyx_compact_upylong; - #else // Py < 3.12 - #define __Pyx_PyLong_IsNeg(x) (Py_SIZE(x) < 0) - #define __Pyx_PyLong_IsNonNeg(x) (Py_SIZE(x) >= 0) - #define __Pyx_PyLong_IsZero(x) (Py_SIZE(x) == 0) - #define __Pyx_PyLong_IsPos(x) (Py_SIZE(x) > 0) - #define __Pyx_PyLong_CompactValueUnsigned(x) ((Py_SIZE(x) == 0) ? 0 : __Pyx_PyLong_Digits(x)[0]) - #define __Pyx_PyLong_DigitCount(x) __Pyx_sst_abs(Py_SIZE(x)) - #define __Pyx_PyLong_SignedDigitCount(x) Py_SIZE(x) - #define __Pyx_PyLong_IsCompact(x) (Py_SIZE(x) == 0 || Py_SIZE(x) == 1 || Py_SIZE(x) == -1) - #define __Pyx_PyLong_CompactValue(x)\ - ((Py_SIZE(x) == 0) ? (sdigit) 0 : ((Py_SIZE(x) < 0) ? -(sdigit)__Pyx_PyLong_Digits(x)[0] : (sdigit)__Pyx_PyLong_Digits(x)[0])) - typedef sdigit __Pyx_compact_pylong; - typedef digit __Pyx_compact_upylong; - #endif - #if PY_VERSION_HEX >= 0x030C00A5 - #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->long_value.ob_digit) - #else - #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->ob_digit) - #endif -#endif -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII -static int __Pyx_sys_getdefaultencoding_not_ascii; -static int __Pyx_init_sys_getdefaultencoding_params(void) { - PyObject* sys; - PyObject* default_encoding = NULL; - PyObject* ascii_chars_u = NULL; - PyObject* ascii_chars_b = NULL; - const char* default_encoding_c; - sys = PyImport_ImportModule("sys"); - if (!sys) goto bad; - default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); - Py_DECREF(sys); - if (!default_encoding) goto bad; - default_encoding_c = PyBytes_AsString(default_encoding); - if (!default_encoding_c) goto bad; - if (strcmp(default_encoding_c, "ascii") == 0) { - __Pyx_sys_getdefaultencoding_not_ascii = 0; - } else { - char ascii_chars[128]; - int c; - for (c = 0; c < 128; c++) { - ascii_chars[c] = (char) c; - } - __Pyx_sys_getdefaultencoding_not_ascii = 1; - ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); - if (!ascii_chars_u) goto bad; - ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); - if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { - PyErr_Format( - PyExc_ValueError, - "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", - default_encoding_c); - goto bad; - } - Py_DECREF(ascii_chars_u); - Py_DECREF(ascii_chars_b); - } - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - Py_XDECREF(ascii_chars_u); - Py_XDECREF(ascii_chars_b); - return -1; -} -#endif -#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 -#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) -#else -#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) -#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT -static char* __PYX_DEFAULT_STRING_ENCODING; -static int __Pyx_init_sys_getdefaultencoding_params(void) { - PyObject* sys; - PyObject* default_encoding = NULL; - char* default_encoding_c; - sys = PyImport_ImportModule("sys"); - if (!sys) goto bad; - default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); - Py_DECREF(sys); - if (!default_encoding) goto bad; - default_encoding_c = PyBytes_AsString(default_encoding); - if (!default_encoding_c) goto bad; - __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); - if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; - strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - return -1; -} -#endif -#endif - - -/* Test for GCC > 2.95 */ -#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) - #define likely(x) __builtin_expect(!!(x), 1) - #define unlikely(x) __builtin_expect(!!(x), 0) -#else /* !__GNUC__ or GCC < 2.95 */ - #define likely(x) (x) - #define unlikely(x) (x) -#endif /* __GNUC__ */ -static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } - -#if !CYTHON_USE_MODULE_STATE -static PyObject *__pyx_m = NULL; -#endif -static int __pyx_lineno; -static int __pyx_clineno = 0; -static const char * __pyx_cfilenm = __FILE__; -static const char *__pyx_filename; - -/* #### Code section: filename_table ### */ - -static const char *__pyx_f[] = { - "core.pyx", - " ", -}; -/* #### Code section: utility_code_proto_before_types ### */ -/* ForceInitThreads.proto */ -#ifndef __PYX_FORCE_INIT_THREADS - #define __PYX_FORCE_INIT_THREADS 0 -#endif - -/* NoFastGil.proto */ -#define __Pyx_PyGILState_Ensure PyGILState_Ensure -#define __Pyx_PyGILState_Release PyGILState_Release -#define __Pyx_FastGIL_Remember() -#define __Pyx_FastGIL_Forget() -#define __Pyx_FastGilFuncInit() - -/* BufferFormatStructs.proto */ -struct __Pyx_StructField_; -#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) -typedef struct { - const char* name; - struct __Pyx_StructField_* fields; - size_t size; - size_t arraysize[8]; - int ndim; - char typegroup; - char is_unsigned; - int flags; -} __Pyx_TypeInfo; -typedef struct __Pyx_StructField_ { - __Pyx_TypeInfo* type; - const char* name; - size_t offset; -} __Pyx_StructField; -typedef struct { - __Pyx_StructField* field; - size_t parent_offset; -} __Pyx_BufFmt_StackElem; -typedef struct { - __Pyx_StructField root; - __Pyx_BufFmt_StackElem* head; - size_t fmt_offset; - size_t new_count, enc_count; - size_t struct_alignment; - int is_complex; - char enc_type; - char new_packmode; - char enc_packmode; - char is_valid_array; -} __Pyx_BufFmt_Context; - -/* Atomics.proto */ -#include -#ifndef CYTHON_ATOMICS - #define CYTHON_ATOMICS 1 -#endif -#define __PYX_CYTHON_ATOMICS_ENABLED() CYTHON_ATOMICS -#define __pyx_atomic_int_type int -#define __pyx_nonatomic_int_type int -#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\ - (__STDC_VERSION__ >= 201112L) &&\ - !defined(__STDC_NO_ATOMICS__)) - #include -#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\ - (__cplusplus >= 201103L) ||\ - (defined(_MSC_VER) && _MSC_VER >= 1700))) - #include -#endif -#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\ - (__STDC_VERSION__ >= 201112L) &&\ - !defined(__STDC_NO_ATOMICS__) &&\ - ATOMIC_INT_LOCK_FREE == 2) - #undef __pyx_atomic_int_type - #define __pyx_atomic_int_type atomic_int - #define __pyx_atomic_incr_aligned(value) atomic_fetch_add_explicit(value, 1, memory_order_relaxed) - #define __pyx_atomic_decr_aligned(value) atomic_fetch_sub_explicit(value, 1, memory_order_acq_rel) - #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER) - #pragma message ("Using standard C atomics") - #elif defined(__PYX_DEBUG_ATOMICS) - #warning "Using standard C atomics" - #endif -#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\ - (__cplusplus >= 201103L) ||\ -\ - (defined(_MSC_VER) && _MSC_VER >= 1700)) &&\ - ATOMIC_INT_LOCK_FREE == 2) - #undef __pyx_atomic_int_type - #define __pyx_atomic_int_type std::atomic_int - #define __pyx_atomic_incr_aligned(value) std::atomic_fetch_add_explicit(value, 1, std::memory_order_relaxed) - #define __pyx_atomic_decr_aligned(value) std::atomic_fetch_sub_explicit(value, 1, std::memory_order_acq_rel) - #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER) - #pragma message ("Using standard C++ atomics") - #elif defined(__PYX_DEBUG_ATOMICS) - #warning "Using standard C++ atomics" - #endif -#elif CYTHON_ATOMICS && (__GNUC__ >= 5 || (__GNUC__ == 4 &&\ - (__GNUC_MINOR__ > 1 ||\ - (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL__ >= 2)))) - #define __pyx_atomic_incr_aligned(value) __sync_fetch_and_add(value, 1) - #define __pyx_atomic_decr_aligned(value) __sync_fetch_and_sub(value, 1) - #ifdef __PYX_DEBUG_ATOMICS - #warning "Using GNU atomics" - #endif -#elif CYTHON_ATOMICS && defined(_MSC_VER) - #include - #undef __pyx_atomic_int_type - #define __pyx_atomic_int_type long - #define __pyx_nonatomic_int_type long - #pragma intrinsic (_InterlockedExchangeAdd) - #define __pyx_atomic_incr_aligned(value) _InterlockedExchangeAdd(value, 1) - #define __pyx_atomic_decr_aligned(value) _InterlockedExchangeAdd(value, -1) - #ifdef __PYX_DEBUG_ATOMICS - #pragma message ("Using MSVC atomics") - #endif -#else - #undef CYTHON_ATOMICS - #define CYTHON_ATOMICS 0 - #ifdef __PYX_DEBUG_ATOMICS - #warning "Not using atomics" - #endif -#endif -#if CYTHON_ATOMICS - #define __pyx_add_acquisition_count(memview)\ - __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview)) - #define __pyx_sub_acquisition_count(memview)\ - __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview)) -#else - #define __pyx_add_acquisition_count(memview)\ - __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) - #define __pyx_sub_acquisition_count(memview)\ - __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) -#endif - -/* MemviewSliceStruct.proto */ -struct __pyx_memoryview_obj; -typedef struct { - struct __pyx_memoryview_obj *memview; - char *data; - Py_ssize_t shape[8]; - Py_ssize_t strides[8]; - Py_ssize_t suboffsets[8]; -} __Pyx_memviewslice; -#define __Pyx_MemoryView_Len(m) (m.shape[0]) - -/* #### Code section: numeric_typedefs ### */ -/* #### Code section: complex_type_declarations ### */ -/* #### Code section: type_declarations ### */ - -/*--- Type declarations ---*/ -struct __pyx_array_obj; -struct __pyx_MemviewEnum_obj; -struct __pyx_memoryview_obj; -struct __pyx_memoryviewslice_obj; -struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each; - -/* "monotonic_align/core.pyx":7 - * @cython.boundscheck(False) - * @cython.wraparound(False) - * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil: # <<<<<<<<<<<<<< - * cdef int x - * cdef int y - */ -struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each { - int __pyx_n; - float max_neg_val; -}; - -/* "View.MemoryView":114 - * @cython.collection_type("sequence") - * @cname("__pyx_array") - * cdef class array: # <<<<<<<<<<<<<< - * - * cdef: - */ -struct __pyx_array_obj { - PyObject_HEAD - struct __pyx_vtabstruct_array *__pyx_vtab; - char *data; - Py_ssize_t len; - char *format; - int ndim; - Py_ssize_t *_shape; - Py_ssize_t *_strides; - Py_ssize_t itemsize; - PyObject *mode; - PyObject *_format; - void (*callback_free_data)(void *); - int free_data; - int dtype_is_object; -}; - - -/* "View.MemoryView":302 - * - * @cname('__pyx_MemviewEnum') - * cdef class Enum(object): # <<<<<<<<<<<<<< - * cdef object name - * def __init__(self, name): - */ -struct __pyx_MemviewEnum_obj { - PyObject_HEAD - PyObject *name; -}; - - -/* "View.MemoryView":337 - * - * @cname('__pyx_memoryview') - * cdef class memoryview: # <<<<<<<<<<<<<< - * - * cdef object obj - */ -struct __pyx_memoryview_obj { - PyObject_HEAD - struct __pyx_vtabstruct_memoryview *__pyx_vtab; - PyObject *obj; - PyObject *_size; - PyObject *_array_interface; - PyThread_type_lock lock; - __pyx_atomic_int_type acquisition_count; - Py_buffer view; - int flags; - int dtype_is_object; - __Pyx_TypeInfo *typeinfo; -}; - - -/* "View.MemoryView":952 - * @cython.collection_type("sequence") - * @cname('__pyx_memoryviewslice') - * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< - * "Internal class for passing memoryview slices to Python" - * - */ -struct __pyx_memoryviewslice_obj { - struct __pyx_memoryview_obj __pyx_base; 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-#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 - -/* AssertionsEnabled.proto */ -#define __Pyx_init_assertions_enabled() -#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) - #define __pyx_assertions_enabled() (1) -#elif PY_VERSION_HEX < 0x03080000 || CYTHON_COMPILING_IN_PYPY || defined(Py_LIMITED_API) - #define __pyx_assertions_enabled() (!Py_OptimizeFlag) -#elif CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030900A6 - static int __pyx_assertions_enabled_flag; - #define __pyx_assertions_enabled() (__pyx_assertions_enabled_flag) - #undef __Pyx_init_assertions_enabled - static void __Pyx_init_assertions_enabled(void) { - __pyx_assertions_enabled_flag = ! _PyInterpreterState_GetConfig(__Pyx_PyThreadState_Current->interp)->optimization_level; - } -#else - #define __pyx_assertions_enabled() (!Py_OptimizeFlag) -#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); - -/* RaiseNoneIterError.proto */ -static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); - -/* ExtTypeTest.proto */ -static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); - -/* GetTopmostException.proto */ -#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE -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 - -/* 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 - -/* Import.proto */ -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); - -/* ImportDottedModule.proto */ -static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple); -#if PY_MAJOR_VERSION >= 3 -static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple); -#endif - -/* ssize_strlen.proto */ -static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s); - -/* FastTypeChecks.proto */ -#if CYTHON_COMPILING_IN_CPYTHON -#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) -#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2) -static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); -static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b); -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); -#else -#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) -#define __Pyx_TypeCheck2(obj, type1, type2) (PyObject_TypeCheck(obj, (PyTypeObject *)type1) || PyObject_TypeCheck(obj, (PyTypeObject *)type2)) -#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) -#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) -#endif -#define __Pyx_PyErr_ExceptionMatches2(err1, err2) __Pyx_PyErr_GivenExceptionMatches2(__Pyx_PyErr_CurrentExceptionType(), err1, err2) -#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) - -CYTHON_UNUSED static int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ -/* ListCompAppend.proto */ -#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS -static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { - PyListObject* L = (PyListObject*) list; - Py_ssize_t len = Py_SIZE(list); - if (likely(L->allocated > len)) { - 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_ListComp_Append(L,x) PyList_Append(L,x) -#endif - -/* PySequenceMultiply.proto */ -#define __Pyx_PySequence_Multiply_Left(mul, seq) __Pyx_PySequence_Multiply(seq, mul) -static CYTHON_INLINE PyObject* __Pyx_PySequence_Multiply(PyObject *seq, Py_ssize_t mul); - -/* SetItemInt.proto */ -#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\ - (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\ - __Pyx_SetItemInt_Generic(o, to_py_func(i), v))) -static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v); -static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, - int is_list, int wraparound, int boundscheck); - -/* RaiseUnboundLocalError.proto */ -static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); - -/* DivInt[long].proto */ -static CYTHON_INLINE long __Pyx_div_long(long, long); - -/* 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)); -} - -/* ImportFrom.proto */ -static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); - -/* HasAttr.proto */ -static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); - -/* ErrOccurredWithGIL.proto */ -static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void); - -/* PyObject_GenericGetAttrNoDict.proto */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); -#else -#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr -#endif - -/* PyObject_GenericGetAttr.proto */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); -#else -#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr -#endif - -/* IncludeStructmemberH.proto */ -#include - -/* FixUpExtensionType.proto */ -#if CYTHON_USE_TYPE_SPECS -static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type); -#endif - -/* PyObjectCallNoArg.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); - -/* 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); - -/* ValidateBasesTuple.proto */ -#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS -static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases); -#endif - -/* PyType_Ready.proto */ -CYTHON_UNUSED static int __Pyx_PyType_Ready(PyTypeObject *t); - -/* SetVTable.proto */ -static int __Pyx_SetVtable(PyTypeObject* typeptr , void* vtable); - -/* GetVTable.proto */ -static void* __Pyx_GetVtable(PyTypeObject *type); - -/* MergeVTables.proto */ -#if !CYTHON_COMPILING_IN_LIMITED_API -static int __Pyx_MergeVtables(PyTypeObject *type); -#endif - -/* SetupReduce.proto */ -#if !CYTHON_COMPILING_IN_LIMITED_API -static int __Pyx_setup_reduce(PyObject* type_obj); -#endif - -/* FetchSharedCythonModule.proto */ -static PyObject *__Pyx_FetchSharedCythonABIModule(void); - -/* FetchCommonType.proto */ -#if !CYTHON_USE_TYPE_SPECS -static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type); -#else -static PyTypeObject* __Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases); -#endif - -/* PyMethodNew.proto */ -#if PY_MAJOR_VERSION >= 3 -static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) { - CYTHON_UNUSED_VAR(typ); - if (!self) - return __Pyx_NewRef(func); - return PyMethod_New(func, self); -} -#else - #define __Pyx_PyMethod_New PyMethod_New -#endif - -/* PyVectorcallFastCallDict.proto */ -#if CYTHON_METH_FASTCALL -static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw); -#endif - -/* CythonFunctionShared.proto */ -#define __Pyx_CyFunction_USED -#define __Pyx_CYFUNCTION_STATICMETHOD 0x01 -#define __Pyx_CYFUNCTION_CLASSMETHOD 0x02 -#define __Pyx_CYFUNCTION_CCLASS 0x04 -#define __Pyx_CYFUNCTION_COROUTINE 0x08 -#define __Pyx_CyFunction_GetClosure(f)\ - (((__pyx_CyFunctionObject *) (f))->func_closure) -#if PY_VERSION_HEX < 0x030900B1 - #define __Pyx_CyFunction_GetClassObj(f)\ - (((__pyx_CyFunctionObject *) (f))->func_classobj) -#else - #define __Pyx_CyFunction_GetClassObj(f)\ - ((PyObject*) ((PyCMethodObject *) (f))->mm_class) -#endif -#define __Pyx_CyFunction_SetClassObj(f, classobj)\ - __Pyx__CyFunction_SetClassObj((__pyx_CyFunctionObject *) (f), (classobj)) -#define __Pyx_CyFunction_Defaults(type, f)\ - ((type *)(((__pyx_CyFunctionObject *) (f))->defaults)) -#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\ - ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g) -typedef struct { -#if PY_VERSION_HEX < 0x030900B1 - PyCFunctionObject func; -#else - PyCMethodObject func; -#endif -#if CYTHON_BACKPORT_VECTORCALL - __pyx_vectorcallfunc func_vectorcall; -#endif -#if PY_VERSION_HEX < 0x030500A0 - PyObject *func_weakreflist; -#endif - PyObject *func_dict; - PyObject *func_name; - PyObject *func_qualname; - PyObject *func_doc; - PyObject *func_globals; - PyObject *func_code; - PyObject *func_closure; -#if PY_VERSION_HEX < 0x030900B1 - PyObject *func_classobj; -#endif - void *defaults; - int defaults_pyobjects; - size_t defaults_size; // used by FusedFunction for copying defaults - int flags; - PyObject *defaults_tuple; - PyObject *defaults_kwdict; - PyObject *(*defaults_getter)(PyObject *); - PyObject *func_annotations; - PyObject *func_is_coroutine; -} __pyx_CyFunctionObject; -#define __Pyx_CyFunction_Check(obj) __Pyx_TypeCheck(obj, __pyx_CyFunctionType) -#define __Pyx_IsCyOrPyCFunction(obj) __Pyx_TypeCheck2(obj, __pyx_CyFunctionType, &PyCFunction_Type) -#define __Pyx_CyFunction_CheckExact(obj) __Pyx_IS_TYPE(obj, __pyx_CyFunctionType) -static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml, - int flags, PyObject* qualname, - PyObject *closure, - PyObject *module, PyObject *globals, - PyObject* code); -static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj); -static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m, - size_t size, - int pyobjects); -static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m, - PyObject *tuple); -static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m, - PyObject *dict); -static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m, - PyObject *dict); -static int __pyx_CyFunction_init(PyObject *module); -#if CYTHON_METH_FASTCALL -static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); -static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); -static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); -static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); -#if CYTHON_BACKPORT_VECTORCALL -#define __Pyx_CyFunction_func_vectorcall(f) (((__pyx_CyFunctionObject*)f)->func_vectorcall) -#else -#define __Pyx_CyFunction_func_vectorcall(f) (((PyCFunctionObject*)f)->vectorcall) -#endif -#endif - -/* CythonFunction.proto */ -static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, - int flags, PyObject* qualname, - PyObject *closure, - PyObject *module, PyObject *globals, - PyObject* code); - -/* CLineInTraceback.proto */ -#ifdef CYTHON_CLINE_IN_TRACEBACK -#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) -#else -static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); -#endif - -/* CodeObjectCache.proto */ -#if !CYTHON_COMPILING_IN_LIMITED_API -typedef struct { - PyCodeObject* code_object; - int code_line; -} __Pyx_CodeObjectCacheEntry; -struct __Pyx_CodeObjectCache { - int count; - int max_count; - __Pyx_CodeObjectCacheEntry* entries; -}; -static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; -static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); -static PyCodeObject *__pyx_find_code_object(int code_line); -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); -#endif - -/* AddTraceback.proto */ -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename); - -#if PY_MAJOR_VERSION < 3 - static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); - static void __Pyx_ReleaseBuffer(Py_buffer *view); -#else - #define __Pyx_GetBuffer PyObject_GetBuffer - #define __Pyx_ReleaseBuffer PyBuffer_Release -#endif - - -/* BufferStructDeclare.proto */ -typedef struct { - Py_ssize_t shape, strides, suboffsets; -} __Pyx_Buf_DimInfo; -typedef struct { - size_t refcount; - Py_buffer pybuffer; -} __Pyx_Buffer; -typedef struct { - __Pyx_Buffer *rcbuffer; - char *data; - __Pyx_Buf_DimInfo diminfo[8]; -} __Pyx_LocalBuf_ND; - -/* MemviewSliceIsContig.proto */ -static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); - -/* OverlappingSlices.proto */ -static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, - __Pyx_memviewslice *slice2, - int ndim, size_t itemsize); - -/* IsLittleEndian.proto */ -static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); - -/* BufferFormatCheck.proto */ -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); -static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, - __Pyx_BufFmt_StackElem* stack, - __Pyx_TypeInfo* type); - -/* TypeInfoCompare.proto */ -static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); - -/* MemviewSliceValidateAndInit.proto */ -static int __Pyx_ValidateAndInit_memviewslice( - int *axes_specs, - int c_or_f_flag, - int buf_flags, - int ndim, - __Pyx_TypeInfo *dtype, - __Pyx_BufFmt_StackElem stack[], - __Pyx_memviewslice *memviewslice, - PyObject *original_obj); - -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *, int writable_flag); - -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *, int writable_flag); - -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag); - -/* MemviewSliceCopyTemplate.proto */ -static __Pyx_memviewslice -__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, - const char *mode, int ndim, - size_t sizeof_dtype, int contig_flag, - int dtype_is_object); - -/* MemviewSliceInit.proto */ -#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d -#define __Pyx_MEMVIEW_DIRECT 1 -#define __Pyx_MEMVIEW_PTR 2 -#define __Pyx_MEMVIEW_FULL 4 -#define __Pyx_MEMVIEW_CONTIG 8 -#define __Pyx_MEMVIEW_STRIDED 16 -#define __Pyx_MEMVIEW_FOLLOW 32 -#define __Pyx_IS_C_CONTIG 1 -#define __Pyx_IS_F_CONTIG 2 -static int __Pyx_init_memviewslice( - struct __pyx_memoryview_obj *memview, - int ndim, - __Pyx_memviewslice *memviewslice, - int memview_is_new_reference); -static CYTHON_INLINE int __pyx_add_acquisition_count_locked( - __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock); -static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( - __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock); -#define __pyx_get_slice_count_pointer(memview) (&memview->acquisition_count) -#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) -#define __PYX_XCLEAR_MEMVIEW(slice, have_gil) __Pyx_XCLEAR_MEMVIEW(slice, have_gil, __LINE__) -static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); -static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *, int, int); - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); - -/* CIntFromPy.proto */ -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); - -/* CIntFromPy.proto */ -static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); - -/* CIntFromPy.proto */ -static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); - -/* FormatTypeName.proto */ -#if CYTHON_COMPILING_IN_LIMITED_API -typedef PyObject *__Pyx_TypeName; -#define __Pyx_FMT_TYPENAME "%U" -static __Pyx_TypeName __Pyx_PyType_GetName(PyTypeObject* tp); -#define __Pyx_DECREF_TypeName(obj) Py_XDECREF(obj) -#else -typedef const char *__Pyx_TypeName; -#define __Pyx_FMT_TYPENAME "%.200s" -#define __Pyx_PyType_GetName(tp) ((tp)->tp_name) -#define __Pyx_DECREF_TypeName(obj) -#endif - -/* CheckBinaryVersion.proto */ -static int __Pyx_check_binary_version(void); - -/* InitStrings.proto */ -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); - -/* #### Code section: module_declarations ### */ -static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ -static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ -static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ -static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ -static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ -static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryview__get_base(struct __pyx_memoryview_obj *__pyx_v_self); /* proto*/ -static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ -static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryviewslice__get_base(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto*/ - -/* Module declarations from "cython.view" */ - -/* Module declarations from "cython.dataclasses" */ - -/* Module declarations from "cython" */ - -/* Module declarations from "monotonic_align.core" */ -static PyObject *__pyx_collections_abc_Sequence = 0; -static PyObject *generic = 0; -static PyObject *strided = 0; -static PyObject *indirect = 0; -static PyObject *contiguous = 0; -static PyObject *indirect_contiguous = 0; -static int __pyx_memoryview_thread_locks_used; -static PyThread_type_lock __pyx_memoryview_thread_locks[8]; -static void __pyx_f_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice, __Pyx_memviewslice, int, int, struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each *__pyx_optional_args); /*proto*/ -static void __pyx_f_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/ -static int __pyx_array_allocate_buffer(struct __pyx_array_obj *); /*proto*/ -static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ -static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ -static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ -static PyObject *_unellipsify(PyObject *, int); /*proto*/ -static int assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ -static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ -static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ -static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ -static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ -static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ -static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ -static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ -static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ -static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ -static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ -static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ -static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ -static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ -static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ -static int __pyx_memoryview_err_dim(PyObject *, PyObject *, int); /*proto*/ -static int __pyx_memoryview_err(PyObject *, PyObject *); /*proto*/ -static int __pyx_memoryview_err_no_memory(void); /*proto*/ -static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ -static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ -static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ -static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ -static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ -static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ -static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ -static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ -/* #### Code section: typeinfo ### */ -static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, __PYX_IS_UNSIGNED(int) ? 'U' : 'I', __PYX_IS_UNSIGNED(int), 0 }; -static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 }; -/* #### Code section: before_global_var ### */ -#define __Pyx_MODULE_NAME "monotonic_align.core" -extern int __pyx_module_is_main_monotonic_align__core; -int __pyx_module_is_main_monotonic_align__core = 0; - -/* Implementation of "monotonic_align.core" */ -/* #### Code section: global_var ### */ -static PyObject *__pyx_builtin_range; -static PyObject *__pyx_builtin___import__; -static PyObject *__pyx_builtin_ValueError; -static PyObject *__pyx_builtin_MemoryError; -static PyObject *__pyx_builtin_enumerate; -static PyObject *__pyx_builtin_TypeError; -static PyObject *__pyx_builtin_AssertionError; -static PyObject *__pyx_builtin_Ellipsis; -static PyObject *__pyx_builtin_id; -static PyObject *__pyx_builtin_IndexError; -/* #### Code section: string_decls ### */ -static const char __pyx_k_[] = ": "; -static const char __pyx_k_O[] = "O"; -static const char __pyx_k_c[] = "c"; -static const char __pyx_k__2[] = "."; -static const char __pyx_k__3[] = "*"; -static const char __pyx_k__6[] = "'"; -static const char __pyx_k__7[] = ")"; -static const char __pyx_k_gc[] = "gc"; -static const char __pyx_k_id[] = "id"; -static const char __pyx_k__23[] = "?"; -static const char __pyx_k_abc[] = "abc"; -static const char __pyx_k_and[] = " and "; -static const char __pyx_k_got[] = " (got "; -static const char __pyx_k_new[] = "__new__"; -static const char __pyx_k_obj[] = "obj"; -static const char __pyx_k_sys[] = "sys"; -static const char __pyx_k_base[] = "base"; -static const char __pyx_k_dict[] = "__dict__"; -static const char __pyx_k_main[] = "__main__"; -static const char __pyx_k_mode[] = "mode"; -static const char __pyx_k_name[] = "name"; -static const char __pyx_k_ndim[] = "ndim"; -static const char __pyx_k_pack[] = "pack"; -static const char __pyx_k_size[] = "size"; -static const char __pyx_k_spec[] = "__spec__"; -static const char __pyx_k_step[] = "step"; -static const char __pyx_k_stop[] = "stop"; -static const char __pyx_k_t_xs[] = "t_xs"; -static const char __pyx_k_t_ys[] = "t_ys"; -static const char __pyx_k_test[] = "__test__"; -static const char __pyx_k_ASCII[] = "ASCII"; -static const char __pyx_k_class[] = "__class__"; -static const char __pyx_k_count[] = "count"; -static const char __pyx_k_error[] = "error"; -static const char __pyx_k_flags[] = "flags"; -static const char __pyx_k_index[] = "index"; -static const char __pyx_k_paths[] = "paths"; -static const char __pyx_k_range[] = "range"; -static const char __pyx_k_shape[] = "shape"; -static const char __pyx_k_start[] = "start"; -static const char __pyx_k_enable[] = "enable"; -static const char __pyx_k_encode[] = "encode"; -static const char __pyx_k_format[] = "format"; -static const char __pyx_k_import[] = "__import__"; -static const char __pyx_k_name_2[] = "__name__"; -static const char __pyx_k_pickle[] = "pickle"; -static const char __pyx_k_reduce[] = "__reduce__"; -static const char __pyx_k_struct[] = "struct"; -static const char __pyx_k_unpack[] = "unpack"; -static const char __pyx_k_update[] = "update"; -static const char __pyx_k_values[] = "values"; -static const char __pyx_k_disable[] = "disable"; -static const char __pyx_k_fortran[] = "fortran"; -static const char __pyx_k_memview[] = "memview"; -static const char __pyx_k_Ellipsis[] = "Ellipsis"; -static const char __pyx_k_Sequence[] = "Sequence"; -static const char __pyx_k_core_pyx[] = "core.pyx"; -static const char __pyx_k_getstate[] = "__getstate__"; -static const char __pyx_k_itemsize[] = "itemsize"; -static const char __pyx_k_pyx_type[] = "__pyx_type"; -static const char __pyx_k_register[] = "register"; -static const char __pyx_k_setstate[] = "__setstate__"; -static const char __pyx_k_TypeError[] = "TypeError"; -static const char __pyx_k_enumerate[] = "enumerate"; -static const char __pyx_k_isenabled[] = "isenabled"; -static const char __pyx_k_pyx_state[] = "__pyx_state"; -static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; -static const char __pyx_k_IndexError[] = "IndexError"; -static const char __pyx_k_ValueError[] = "ValueError"; -static const char __pyx_k_pyx_result[] = "__pyx_result"; -static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; -static const char __pyx_k_MemoryError[] = "MemoryError"; -static const char __pyx_k_PickleError[] = "PickleError"; -static const char __pyx_k_collections[] = "collections"; -static const char __pyx_k_initializing[] = "_initializing"; -static const char __pyx_k_is_coroutine[] = "_is_coroutine"; -static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; -static const char __pyx_k_stringsource[] = " "; -static const char __pyx_k_version_info[] = "version_info"; -static const char __pyx_k_class_getitem[] = "__class_getitem__"; -static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; -static const char __pyx_k_AssertionError[] = "AssertionError"; -static const char __pyx_k_maximum_path_c[] = "maximum_path_c"; -static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; -static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; -static const char __pyx_k_collections_abc[] = "collections.abc"; -static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; -static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; -static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; -static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; -static const char __pyx_k_asyncio_coroutines[] = "asyncio.coroutines"; -static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; -static const char __pyx_k_strided_and_direct[] = " "; -static const char __pyx_k_monotonic_align_core[] = "monotonic_align.core"; -static const char __pyx_k_strided_and_indirect[] = " "; -static const char __pyx_k_Invalid_shape_in_axis[] = "Invalid shape in axis "; -static const char __pyx_k_contiguous_and_direct[] = " "; -static const char __pyx_k_Cannot_index_with_type[] = "Cannot index with type '"; -static const char __pyx_k_MemoryView_of_r_object[] = " "; -static const char __pyx_k_MemoryView_of_r_at_0x_x[] = " "; -static const char __pyx_k_contiguous_and_indirect[] = " "; -static const char __pyx_k_Dimension_d_is_not_direct[] = "Dimension %d is not direct"; -static const char __pyx_k_Index_out_of_bounds_axis_d[] = "Index out of bounds (axis %d)"; -static const char __pyx_k_Step_may_not_be_zero_axis_d[] = "Step may not be zero (axis %d)"; -static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; -static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; -static const char __pyx_k_strided_and_direct_or_indirect[] = " "; -static const char __pyx_k_All_dimensions_preceding_dimensi[] = "All dimensions preceding dimension %d must be indexed and not sliced"; -static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; -static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; -static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; -static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; -static const char __pyx_k_Cannot_transpose_memoryview_with[] = "Cannot transpose memoryview with indirect dimensions"; -static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; -static const char __pyx_k_Incompatible_checksums_0x_x_vs_0[] = "Incompatible checksums (0x%x vs (0x82a3537, 0x6ae9995, 0xb068931) = (name))"; -static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; -static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got "; -static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis "; -static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; -static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension "; -static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; -static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; -/* #### Code section: decls ### */ -static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ -static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ -static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ -static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ -static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ -static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ -static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ -static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ -static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ -static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ -static void 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__PYX_ERR(1, 784, __pyx_L1_error) - __Pyx_GOTREF(__pyx_t_2); - - /* "View.MemoryView":784 - * memview.dtype_is_object) - * else: - * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< - * memview.dtype_is_object) - * - */ - if (!(likely(((__pyx_t_2) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_2, __pyx_memoryview_type))))) __PYX_ERR(1, 784, __pyx_L1_error) - __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_2); - __pyx_t_2 = 0; - goto __pyx_L0; - } - - /* "View.MemoryView":711 - * - * @cname('__pyx_memview_slice') - * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< - * cdef int new_ndim = 0, suboffset_dim = -1, dim - * cdef bint negative_step - */ - - /* function exit code */ - __pyx_L1_error:; - __Pyx_XDECREF(__pyx_t_2); - __Pyx_XDECREF(__pyx_t_8); - __Pyx_AddTraceback("View.MemoryView.memview_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); - __pyx_r = 0; - __pyx_L0:; - __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj); - __Pyx_XDECREF(__pyx_v_index); - __Pyx_XGIVEREF((PyObject *)__pyx_r); - __Pyx_RefNannyFinishContext(); - return __pyx_r; -} - -/* "View.MemoryView":793 - * - * @cname('__pyx_memoryview_slice_memviewslice') - * cdef int slice_memviewslice( # <<<<<<<<<<<<<< - * __Pyx_memviewslice *dst, - * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, - */ - -static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) { - Py_ssize_t __pyx_v_new_shape; - int __pyx_v_negative_step; - int __pyx_r; - int __pyx_t_1; - int __pyx_t_2; - int __pyx_t_3; - int __pyx_lineno = 0; - const char *__pyx_filename = NULL; - int __pyx_clineno = 0; - #ifdef WITH_THREAD - PyGILState_STATE __pyx_gilstate_save; - #endif - - /* "View.MemoryView":813 - * cdef bint negative_step - * - * if not is_slice: # <<<<<<<<<<<<<< - * - * if start < 0: - */ - __pyx_t_1 = (!__pyx_v_is_slice); - if (__pyx_t_1) { - - /* "View.MemoryView":815 - * if not is_slice: - * - * if start < 0: # <<<<<<<<<<<<<< - * start += shape - * if not 0 <= start < shape: - */ - __pyx_t_1 = (__pyx_v_start < 0); - if (__pyx_t_1) { - - /* "View.MemoryView":816 - * - * if start < 0: - * start += shape # <<<<<<<<<<<<<< - * if not 0 <= start < shape: - * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) - */ - __pyx_v_start = (__pyx_v_start + __pyx_v_shape); - - /* "View.MemoryView":815 - * if not is_slice: - * - * if start < 0: # <<<<<<<<<<<<<< - * start += shape - * if not 0 <= start < shape: - */ - } - - /* "View.MemoryView":817 - * if start < 0: - * start += shape - * if not 0 <= start < shape: # <<<<<<<<<<<<<< - * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) - * else: - */ - __pyx_t_1 = (0 <= __pyx_v_start); - if (__pyx_t_1) { - __pyx_t_1 = (__pyx_v_start < __pyx_v_shape); - } - __pyx_t_2 = (!__pyx_t_1); - if (__pyx_t_2) { - - /* "View.MemoryView":818 - * start += shape - * if not 0 <= start < shape: - * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< - * else: - * - */ - __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_IndexError, __pyx_kp_s_Index_out_of_bounds_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 818, __pyx_L1_error) - - /* "View.MemoryView":817 - * if start < 0: - * start += shape - * if not 0 <= start < shape: # <<<<<<<<<<<<<< - * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) - * else: - */ - } - - /* "View.MemoryView":813 - * cdef bint negative_step - * - * if not is_slice: # <<<<<<<<<<<<<< - * - * if start < 0: - */ - goto __pyx_L3; - } - - /* "View.MemoryView":821 - * else: - * - * if have_step: # <<<<<<<<<<<<<< - * negative_step = step < 0 - * if step == 0: - */ - /*else*/ { - __pyx_t_2 = (__pyx_v_have_step != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":822 - * - * if have_step: - * negative_step = step < 0 # <<<<<<<<<<<<<< - * if step == 0: - * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) - */ - __pyx_v_negative_step = (__pyx_v_step < 0); - - /* "View.MemoryView":823 - * if have_step: - * negative_step = step < 0 - * if step == 0: # <<<<<<<<<<<<<< - * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) - * else: - */ - __pyx_t_2 = (__pyx_v_step == 0); - if (__pyx_t_2) { - - /* "View.MemoryView":824 - * negative_step = step < 0 - * if step == 0: - * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< - * else: - * negative_step = False - */ - __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_ValueError, __pyx_kp_s_Step_may_not_be_zero_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 824, __pyx_L1_error) - - /* "View.MemoryView":823 - * if have_step: - * negative_step = step < 0 - * if step == 0: # <<<<<<<<<<<<<< - * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) - * else: - */ - } - - /* "View.MemoryView":821 - * else: - * - * if have_step: # <<<<<<<<<<<<<< - * negative_step = step < 0 - * if step == 0: - */ - goto __pyx_L6; - } - - /* "View.MemoryView":826 - * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) - * else: - * negative_step = False # <<<<<<<<<<<<<< - * step = 1 - * - */ - /*else*/ { - __pyx_v_negative_step = 0; - - /* "View.MemoryView":827 - * else: - * negative_step = False - * step = 1 # <<<<<<<<<<<<<< - * - * - */ - __pyx_v_step = 1; - } - __pyx_L6:; - - /* "View.MemoryView":830 - * - * - * if have_start: # <<<<<<<<<<<<<< - * if start < 0: - * start += shape - */ - __pyx_t_2 = (__pyx_v_have_start != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":831 - * - * if have_start: - * if start < 0: # <<<<<<<<<<<<<< - * start += shape - * if start < 0: - */ - __pyx_t_2 = (__pyx_v_start < 0); - if (__pyx_t_2) { - - /* "View.MemoryView":832 - * if have_start: - * if start < 0: - * start += shape # <<<<<<<<<<<<<< - * if start < 0: - * start = 0 - */ - __pyx_v_start = (__pyx_v_start + __pyx_v_shape); - - /* "View.MemoryView":833 - * if start < 0: - * start += shape - * if start < 0: # <<<<<<<<<<<<<< - * start = 0 - * elif start >= shape: - */ - __pyx_t_2 = (__pyx_v_start < 0); - if (__pyx_t_2) { - - /* "View.MemoryView":834 - * start += shape - * if start < 0: - * start = 0 # <<<<<<<<<<<<<< - * elif start >= shape: - * if negative_step: - */ - __pyx_v_start = 0; - - /* "View.MemoryView":833 - * if start < 0: - * start += shape - * if start < 0: # <<<<<<<<<<<<<< - * start = 0 - * elif start >= shape: - */ - } - - /* "View.MemoryView":831 - * - * if have_start: - * if start < 0: # <<<<<<<<<<<<<< - * start += shape - * if start < 0: - */ - goto __pyx_L9; - } - - /* "View.MemoryView":835 - * if start < 0: - * start = 0 - * elif start >= shape: # <<<<<<<<<<<<<< - * if negative_step: - * start = shape - 1 - */ - __pyx_t_2 = (__pyx_v_start >= __pyx_v_shape); - if (__pyx_t_2) { - - /* "View.MemoryView":836 - * start = 0 - * elif start >= shape: - * if negative_step: # <<<<<<<<<<<<<< - * start = shape - 1 - * else: - */ - if (__pyx_v_negative_step) { - - /* "View.MemoryView":837 - * elif start >= shape: - * if negative_step: - * start = shape - 1 # <<<<<<<<<<<<<< - * else: - * start = shape - */ - __pyx_v_start = (__pyx_v_shape - 1); - - /* "View.MemoryView":836 - * start = 0 - * elif start >= shape: - * if negative_step: # <<<<<<<<<<<<<< - * start = shape - 1 - * else: - */ - goto __pyx_L11; - } - - /* "View.MemoryView":839 - * start = shape - 1 - * else: - * start = shape # <<<<<<<<<<<<<< - * else: - * if negative_step: - */ - /*else*/ { - __pyx_v_start = __pyx_v_shape; - } - __pyx_L11:; - - /* "View.MemoryView":835 - * if start < 0: - * start = 0 - * elif start >= shape: # <<<<<<<<<<<<<< - * if negative_step: - * start = shape - 1 - */ - } - __pyx_L9:; - - /* "View.MemoryView":830 - * - * - * if have_start: # <<<<<<<<<<<<<< - * if start < 0: - * start += shape - */ - goto __pyx_L8; - } - - /* "View.MemoryView":841 - * start = shape - * else: - * if negative_step: # <<<<<<<<<<<<<< - * start = shape - 1 - * else: - */ - /*else*/ { - if (__pyx_v_negative_step) { - - /* "View.MemoryView":842 - * else: - * if negative_step: - * start = shape - 1 # <<<<<<<<<<<<<< - * else: - * start = 0 - */ - __pyx_v_start = (__pyx_v_shape - 1); - - /* "View.MemoryView":841 - * start = shape - * else: - * if negative_step: # <<<<<<<<<<<<<< - * start = shape - 1 - * else: - */ - goto __pyx_L12; - } - - /* "View.MemoryView":844 - * start = shape - 1 - * else: - * start = 0 # <<<<<<<<<<<<<< - * - * if have_stop: - */ - /*else*/ { - __pyx_v_start = 0; - } - __pyx_L12:; - } - __pyx_L8:; - - /* "View.MemoryView":846 - * start = 0 - * - * if have_stop: # <<<<<<<<<<<<<< - * if stop < 0: - * stop += shape - */ - __pyx_t_2 = (__pyx_v_have_stop != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":847 - * - * if have_stop: - * if stop < 0: # <<<<<<<<<<<<<< - * stop += shape - * if stop < 0: - */ - __pyx_t_2 = (__pyx_v_stop < 0); - if (__pyx_t_2) { - - /* "View.MemoryView":848 - * if have_stop: - * if stop < 0: - * stop += shape # <<<<<<<<<<<<<< - * if stop < 0: - * stop = 0 - */ - __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); - - /* "View.MemoryView":849 - * if stop < 0: - * stop += shape - * if stop < 0: # <<<<<<<<<<<<<< - * stop = 0 - * elif stop > shape: - */ - __pyx_t_2 = (__pyx_v_stop < 0); - if (__pyx_t_2) { - - /* "View.MemoryView":850 - * stop += shape - * if stop < 0: - * stop = 0 # <<<<<<<<<<<<<< - * elif stop > shape: - * stop = shape - */ - __pyx_v_stop = 0; - - /* "View.MemoryView":849 - * if stop < 0: - * stop += shape - * if stop < 0: # <<<<<<<<<<<<<< - * stop = 0 - * elif stop > shape: - */ - } - - /* "View.MemoryView":847 - * - * if have_stop: - * if stop < 0: # <<<<<<<<<<<<<< - * stop += shape - * if stop < 0: - */ - goto __pyx_L14; - } - - /* "View.MemoryView":851 - * if stop < 0: - * stop = 0 - * elif stop > shape: # <<<<<<<<<<<<<< - * stop = shape - * else: - */ - __pyx_t_2 = (__pyx_v_stop > __pyx_v_shape); - if (__pyx_t_2) { - - /* "View.MemoryView":852 - * stop = 0 - * elif stop > shape: - * stop = shape # <<<<<<<<<<<<<< - * else: - * if negative_step: - */ - __pyx_v_stop = __pyx_v_shape; - - /* "View.MemoryView":851 - * if stop < 0: - * stop = 0 - * elif stop > shape: # <<<<<<<<<<<<<< - * stop = shape - * else: - */ - } - __pyx_L14:; - - /* "View.MemoryView":846 - * start = 0 - * - * if have_stop: # <<<<<<<<<<<<<< - * if stop < 0: - * stop += shape - */ - goto __pyx_L13; - } - - /* "View.MemoryView":854 - * stop = shape - * else: - * if negative_step: # <<<<<<<<<<<<<< - * stop = -1 - * else: - */ - /*else*/ { - if (__pyx_v_negative_step) { - - /* "View.MemoryView":855 - * else: - * if negative_step: - * stop = -1 # <<<<<<<<<<<<<< - * else: - * stop = shape - */ - __pyx_v_stop = -1L; - - /* "View.MemoryView":854 - * stop = shape - * else: - * if negative_step: # <<<<<<<<<<<<<< - * stop = -1 - * else: - */ - goto __pyx_L16; - } - - /* "View.MemoryView":857 - * stop = -1 - * else: - * stop = shape # <<<<<<<<<<<<<< - * - * - */ - /*else*/ { - __pyx_v_stop = __pyx_v_shape; - } - __pyx_L16:; - } - __pyx_L13:; - - /* "View.MemoryView":861 - * - * with cython.cdivision(True): - * new_shape = (stop - start) // step # <<<<<<<<<<<<<< - * - * if (stop - start) - step * new_shape: - */ - __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); - - /* "View.MemoryView":863 - * new_shape = (stop - start) // step - * - * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< - * new_shape += 1 - * - */ - __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":864 - * - * if (stop - start) - step * new_shape: - * new_shape += 1 # <<<<<<<<<<<<<< - * - * if new_shape < 0: - */ - __pyx_v_new_shape = (__pyx_v_new_shape + 1); - - /* "View.MemoryView":863 - * new_shape = (stop - start) // step - * - * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< - * new_shape += 1 - * - */ - } - - /* "View.MemoryView":866 - * new_shape += 1 - * - * if new_shape < 0: # <<<<<<<<<<<<<< - * new_shape = 0 - * - */ - __pyx_t_2 = (__pyx_v_new_shape < 0); - if (__pyx_t_2) { - - /* "View.MemoryView":867 - * - * if new_shape < 0: - * new_shape = 0 # <<<<<<<<<<<<<< - * - * - */ - __pyx_v_new_shape = 0; - - /* "View.MemoryView":866 - * new_shape += 1 - * - * if new_shape < 0: # <<<<<<<<<<<<<< - * new_shape = 0 - * - */ - } - - /* "View.MemoryView":870 - * - * - * dst.strides[new_ndim] = stride * step # <<<<<<<<<<<<<< - * dst.shape[new_ndim] = new_shape - * dst.suboffsets[new_ndim] = suboffset - */ - (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step); - - /* "View.MemoryView":871 - * - * dst.strides[new_ndim] = stride * step - * dst.shape[new_ndim] = new_shape # <<<<<<<<<<<<<< - * dst.suboffsets[new_ndim] = suboffset - * - */ - (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape; - - /* "View.MemoryView":872 - * dst.strides[new_ndim] = stride * step - * dst.shape[new_ndim] = new_shape - * dst.suboffsets[new_ndim] = suboffset # <<<<<<<<<<<<<< - * - * - */ - (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset; - } - __pyx_L3:; - - /* "View.MemoryView":875 - * - * - * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< - * dst.data += start * stride - * else: - */ - __pyx_t_2 = ((__pyx_v_suboffset_dim[0]) < 0); - if (__pyx_t_2) { - - /* "View.MemoryView":876 - * - * if suboffset_dim[0] < 0: - * dst.data += start * stride # <<<<<<<<<<<<<< - * else: - * dst.suboffsets[suboffset_dim[0]] += start * stride - */ - __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride)); - - /* "View.MemoryView":875 - * - * - * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< - * dst.data += start * stride - * else: - */ - goto __pyx_L19; - } - - /* "View.MemoryView":878 - * dst.data += start * stride - * else: - * dst.suboffsets[suboffset_dim[0]] += start * stride # <<<<<<<<<<<<<< - * - * if suboffset >= 0: - */ - /*else*/ { - __pyx_t_3 = (__pyx_v_suboffset_dim[0]); - (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride)); - } - __pyx_L19:; - - /* "View.MemoryView":880 - * dst.suboffsets[suboffset_dim[0]] += start * stride - * - * if suboffset >= 0: # <<<<<<<<<<<<<< - * if not is_slice: - * if new_ndim == 0: - */ - __pyx_t_2 = (__pyx_v_suboffset >= 0); - if (__pyx_t_2) { - - /* "View.MemoryView":881 - * - * if suboffset >= 0: - * if not is_slice: # <<<<<<<<<<<<<< - * if new_ndim == 0: - * dst.data = ( dst.data)[0] + suboffset - */ - __pyx_t_2 = (!__pyx_v_is_slice); - if (__pyx_t_2) { - - /* "View.MemoryView":882 - * if suboffset >= 0: - * if not is_slice: - * if new_ndim == 0: # <<<<<<<<<<<<<< - * dst.data = ( dst.data)[0] + suboffset - * else: - */ - __pyx_t_2 = 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-
Audiomack: Descargador de música
- -Pros
--
-
Contras
--
-
Descargador de música - Aplicaciones en Google Play
-Music Downloader es una aplicación simple y potente para dispositivos Android que te permite descargar música de varias fuentes en línea y reproducirlas sin conexión. Puedes buscar cualquier canción, lista de reproducción, álbum, artista, remix, single, cover, radio FM, podcast, etc. y descargarlo en diferentes formatos de salida como MP3, FLAC, M4B, MP4, 3GP, MID, OGG, etc. También puedes editar tus canciones descargadas con su editor básico que te permite recortar, merge, crop, rotate, etc. Music downloader también tiene un reproductor de música incorporado que admite la reproducción sin conexión y otras funciones como ecualizador, aumento de graves, tema oscuro, temporizador de sueño, tonos de llamada, letras, listas de reproducción, Dropbox, etc.
-Pros
--
-
Contras
--
-
Cómo usar una aplicación de descarga de música para descargar canciones sin conexión
-Usar una aplicación de descarga de música para descargar canciones sin conexión es muy fácil y conveniente. Estos son los pasos básicos que debe seguir:
-Paso 1: Elija una aplicación de descarga de música e instálela en su dispositivo
-El primer paso es elegir una aplicación de descarga de música que se adapte a sus necesidades y preferencias. Puede consultar nuestras 3 mejores selecciones anteriores o buscar otras opciones en Internet. Una vez que haya elegido una aplicación de descarga de música, debe instalarla en su dispositivo. Puedes descargarlo desde el sitio web oficial, la tienda de aplicaciones o la tienda de Google Play, dependiendo de tu dispositivo y la aplicación. Siga las instrucciones en la pantalla para completar el proceso de instalación.
-Paso 2: Buscar las canciones o listas de reproducción que desea descargar
-El siguiente paso es buscar las canciones o listas de reproducción que desea descargar. Puede usar la función de búsqueda de la aplicación, navegar por categorías, géneros, artistas, etc., o ingresar la URL de la fuente en línea. También puedes usar las recomendaciones, sugerencias o gráficos de la aplicación para descubrir nueva música. Una vez que haya encontrado las canciones o listas de reproducción que desea descargar, puede seleccionarlas pulsando en ellas o marcando las casillas junto a ellas.
-Paso 3: Toque en el botón de descarga y seleccione el formato de salida y la calidad
-El paso final es tocar el botón de descarga y seleccionar el formato de salida y la calidad de sus canciones descargadas. Puede elegir entre diferentes opciones como MP3, AAC, M4A, WAV, etc., y diferentes niveles de calidad como alta, media, baja, etc. También puede personalizar otros ajustes como bitrate, frecuencia de muestreo, volumen, etc., dependiendo de la aplicación. Después de haber hecho sus elecciones, puede iniciar el proceso de descarga tocando el botón de inicio o confirmando su selección.
-Paso 4: Disfrute de su música sin conexión en cualquier momento, en cualquier lugar
- -Conclusión
-Una aplicación de descarga de música es una gran manera de descargar música gratis de varias fuentes en línea y escucharlas sin conexión. Tiene muchos beneficios como guardar sus datos o wifi, crear sus propias listas de reproducción, descubrir nueva música, etc. También tiene algunas características que buscar, como el apoyo a múltiples fuentes y plataformas, ofreciendo diferentes formatos de salida y opciones de calidad, tener una interfaz fácil de usar y funciones fáciles de usar, etc. Hemos revisado nuestras 3 mejores opciones para las mejores aplicaciones de descarga de música en 2023: Any Video Converter Free, Audiomack: Music Downloader y Music Downloader - Aplicaciones en Google Play. También le hemos mostrado cómo usar una aplicación de descarga de música para descargar canciones sin conexión en cuatro sencillos pasos. Esperamos que este artículo te haya ayudado a aprender más sobre las aplicaciones de descarga de música y cómo usarlas. Si tiene alguna pregunta o comentario, no dude en dejarlos abajo.
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre aplicaciones de descarga de música:
-Q: ¿Es legal descargar música con una aplicación de descarga de música?
-A: La legalidad de descargar música con una aplicación de descarga de música depende de varios factores, como la fuente de la música, el estado de copyright de la música, los términos y condiciones de la plataforma, etc. En términos generales, es legal descargar música que está en el dominio público, que está licenciada bajo Creative Commons o licencias similares, que se ofrece de forma gratuita por el artista o plataforma, etc. Sin embargo, puede no ser legal descargar música que esté protegida por las leyes de derechos de autor, que es exclusivo de una plataforma, que se descarga con fines comerciales, etc. Por lo tanto, es aconsejable verificar el estado legal de la música antes de descargarla con una aplicación de descarga de música, y respetar los derechos de los artistas y plataformas.
-Q: ¿Es seguro descargar música con una aplicación de descarga de música?
- -Q: ¿Cuánto espacio ocupa una aplicación de descarga de música en mi dispositivo?
-A: el espacio que una aplicación de descarga de música ocupa en su dispositivo depende de varios factores, como el tamaño de la aplicación, el número y el tamaño de las canciones descargadas, el formato de salida y la calidad de las canciones descargadas, etc. En términos generales, una aplicación de descarga de música en sí no ocupa mucho espacio en su dispositivo, generalmente menos de 100 MB. Sin embargo, las canciones descargadas pueden ocupar mucho espacio en su dispositivo, dependiendo de cuántas canciones descargue y qué formato y calidad elija. Por ejemplo, una canción MP3 de 3 minutos a 320 kbps puede ocupar unos 7 MB de espacio, mientras que una canción FLAC de 3 minutos a 1411 kbps puede ocupar unos 30 MB de espacio. Por lo tanto, es recomendable comprobar el espacio de almacenamiento disponible en el dispositivo antes de descargar canciones con una aplicación de descarga de música, y para eliminar o transferir algunas canciones si es necesario.
-Q: ¿Cómo puedo transferir mis canciones descargadas a otros dispositivos o almacenamiento externo?
-A: Hay diferentes formas de transferir tus canciones descargadas a otros dispositivos o almacenamiento externo, dependiendo del tipo y compatibilidad de tus dispositivos y almacenamiento. Algunos métodos comunes son:
--
-
Q: ¿Cómo puedo editar mis canciones descargadas con una aplicación de descarga de música?
-A: Algunas aplicaciones de descarga de música tienen un editor incorporado que le permite editar sus canciones descargadas con funciones básicas como recorte, fusión, recorte, rotación, etc. Para editar sus canciones descargadas con una aplicación de descarga de música, necesita abrir la aplicación y seleccionar la canción que desea editar. Luego debe tocar en el botón de edición y elegir la función que desea utilizar. A continuación, puede ajustar los parámetros como la hora de inicio, hora de finalización, duración, volumen, etc., dependiendo de la función. Después de haber hecho sus cambios, necesita guardar o exportar su canción editada.
64aa2da5cf-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Gratis Para Windows 7.md b/spaces/Benson/text-generation/Examples/Descargar Gratis Para Windows 7.md deleted file mode 100644 index 82da5dde04f955c6c37f591abbf912e10fca3a85..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Gratis Para Windows 7.md +++ /dev/null @@ -1,61 +0,0 @@ - -
Descargar gratis Kick the Buddy para Windows 7
-Kick the Buddy es un popular juego móvil que te permite desatar tu ira y frustración en un muñeco de trapo indefenso. Puedes usar todo tipo de armas y accesorios para torturar, explotar, aplastar, congelar, quemar, disparar e incluso bombardear a tu amigo. Es un juego divertido y relajante que puede ayudarte a desahogarte y reírte.
-descargar gratis para Windows 7
Download ✏ ✏ ✏ https://bltlly.com/2v6Mjn
-
Pero ¿qué pasa si quieres jugar Kick the Buddy en tu PC en lugar de tu teléfono? Tal vez usted tiene una computadora con Windows 7 que todavía utiliza para el trabajo o el entretenimiento. Quizás prefieras jugar en una pantalla más grande y con un teclado y un ratón. Quizás solo quieras probar algo nuevo y diferente.
-Si ese es el caso, entonces estás de suerte. En este artículo, te mostraremos cómo descargar e instalar Kick the Buddy en Windows 7 gratis. También te daremos algunos consejos y trucos sobre cómo jugarlo, así como algunos beneficios y desventajas de jugarlo en tu PC. Y si estás buscando alternativas a Kick the Buddy, también te sugeriremos otros juegos que puedes jugar en tu ordenador con Windows 7.
-Cómo descargar e instalar Kick the Buddy en Windows 7
-Kick the Buddy es un juego para Android, lo que significa que no puedes ejecutarlo directamente en tu PC con Windows 7. Necesitas un emulador de Android, que es un software que imita un dispositivo Android en tu computadora. Hay muchos emuladores de Android disponibles en línea, pero algunos de los más populares son BlueStacks y NoxPlayer. Estos son los pasos para descargar e instalar Kick the Buddy en Windows 7 usando un emulador de Android:
--
-
Cómo jugar a patear al amigo en Windows 7
-Kick the Buddy es un juego simple y fácil de jugar. El objetivo principal es divertirse con su amigo y usar varias armas y accesorios para destruirlo. También puede ganar monedas y oro jugando el juego, que se puede utilizar para comprar más artículos y características. Aquí hay algunos consejos y trucos sobre cómo jugar Kick the Buddy en Windows 7:
- --
-
Beneficios de jugar Kick the Buddy en Windows 7
-Jugar a Kick the Buddy en Windows 7 tiene algunos beneficios que quizás no obtengas al jugarlo en tu teléfono. Estos son algunos de ellos:
--
-
Inconvenientes de jugar a patear al amigo en Windows 7
-Jugar Kick the Buddy en Windows 7 también tiene algunos inconvenientes que debes tener en cuenta. Estos son algunos de ellos:
--
-
-
Alternativas a jugar Kick the Buddy en Windows 7
-Si estás buscando algunas alternativas a jugar Kick the Buddy en Windows 7, hay otros juegos que puedes jugar en tu PC que son similares a Kick the Buddy. Aquí hay algunos ejemplos:
--
-
Conclusión
-Kick the Buddy es un juego divertido y relajante que te permite desatar tu ira y frustración en un muñeco de trapo indefenso. Puede descargarlo e instalarlo en su PC con Windows 7 de forma gratuita utilizando un emulador de Android. También puede jugar con diferentes armas y accesorios, desbloquear nuevos artículos y características, y personalizar a su amigo y su fondo. Sin embargo, también debe ser consciente de los inconvenientes de jugar en su PC, tales como problemas de compatibilidad, riesgos de adicción y exposición a la violencia. Y si quieres probar otros juegos similares a Kick the Buddy, puedes echar un vistazo a algunas de las alternativas que te sugerimos.
-Esperamos que este artículo le haya ayudado a aprender a descargar y jugar Kick the Buddy en Windows 7. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. Y si te gustó este artículo, por favor compártelo con tus amigos y familiares que también podrían estar interesados en jugar Kick the Buddy en su PC.
-Preguntas frecuentes
--
-
-
-
\ No newline at end of file diff --git a/spaces/BetterAPI/BetterChat/postcss.config.js b/spaces/BetterAPI/BetterChat/postcss.config.js deleted file mode 100644 index 7b75c83aff1c05e0e0e315638e07a22314603d4d..0000000000000000000000000000000000000000 --- a/spaces/BetterAPI/BetterChat/postcss.config.js +++ /dev/null @@ -1,6 +0,0 @@ -export default { - plugins: { - tailwindcss: {}, - autoprefixer: {}, - }, -}; diff --git a/spaces/BramVanroy/mai-simplification-nl-2023-demo/app.py b/spaces/BramVanroy/mai-simplification-nl-2023-demo/app.py deleted file mode 100644 index 6c63182c35000a71a14c6147c71a718286ff821b..0000000000000000000000000000000000000000 --- a/spaces/BramVanroy/mai-simplification-nl-2023-demo/app.py +++ /dev/null @@ -1,128 +0,0 @@ -import base64 -from io import StringIO -from math import ceil - -import streamlit as st - -from utils import get_resources, simplify - -st.set_page_config(page_title="Text Simplification in Dutch", page_icon="🏃") - -BATCH_SIZE = 8 - -if "text_to_simplify" not in st.session_state: - st.session_state["text_to_simplify"] = None - -st.title("🏃 Text Simplification in Dutch") - -fupload_check = st.checkbox("File upload?") - -st.markdown( - "Make sure that the file or text in the text box contains **one sentence per line**. Empty lines will" - " be removed." -) -if fupload_check: - uploaded_file = st.file_uploader("Text file", label_visibility="collapsed") - if uploaded_file is not None: - stringio = StringIO(uploaded_file.getvalue().decode("utf-8")) - st.session_state["text_to_simplify"] = stringio.read().strip() - else: - st.session_state["text_to_simplify"] = None -else: - st.session_state["text_to_simplify"] = st.text_area( - label="Sentences to translate", - label_visibility="collapsed", - height=200, - value="Met het naderen van de zonovergoten middaghemel op deze betoverende dag, waarbij de atmosferische omstandigheden een onbelemmerde convergentie van cumulusbewolking en uitgestrekte stratosferische azuurblauwe wijdheid faciliteren, lijken de geaggregeerde weersverschijnselen van vandaag, die variëren van sporadische plensbuien tot kalme zuchtjes wind en zeldzame opvlammingen van bliksem, de delicate balans tussen meteorologische complexiteit en eenvoud te weerspiegelen, waardoor de gepassioneerde observator met een gevoel van ontzag en verwondering wordt vervuld.", - ).strip() - - -def _get_increment_size(num_sents) -> int: - if num_sents == 1: - return 100 - else: - return ceil(100 / num_sents) - - -btn_col, results_col = st.columns(2) -btn_ct = btn_col.empty() -pbar_ct = st.empty() -error_ct = st.empty() -simpl_ct = st.container() -if st.session_state["text_to_simplify"]: - if btn_ct.button("Simplify text"): - error_ct.empty() - lines = [ - strip_line for line in st.session_state["text_to_simplify"].splitlines() if (strip_line := line.strip()) - ] - num_sentences = len(lines) - - pbar = pbar_ct.progress(0, text=f"Simplifying sentences in batches of {BATCH_SIZE}...") - increment = _get_increment_size(num_sentences) - percent_done = 0 - - model, tokenizer = get_resources() - - simpl_ct.caption("Simplified text") - output_ct = simpl_ct.empty() - all_simplifications = [] - html = "
- "
- for input_batch, simplifications in simplify(lines, model, tokenizer):
- for input_text, simplification in zip(input_batch, simplifications):
- output_ct.empty()
- html += f"""
-
-
-
-torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
- int up_x, int up_y, int down_x, int down_y,
- int pad_x0, int pad_x1, int pad_y0, int pad_y1);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
- int up_x, int up_y, int down_x, int down_y,
- int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
- CHECK_CUDA(input);
- CHECK_CUDA(kernel);
-
- return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/utils/__init__.py b/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/utils/__init__.py
deleted file mode 100644
index ba965962b7a52879553aa58fab544995f87d4dc9..0000000000000000000000000000000000000000
--- a/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/utils/__init__.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import logging
-import os
-from pathlib import Path
-
-
-def prepare_logging(fid):
- # Create the directory for log files (if it doesn't exist)
- Path('./log_files').mkdir(exist_ok=True)
- log_fid = Path(fid).stem
- logs = logging.getLogger(log_fid)
- logs.setLevel(logging.DEBUG)
-
- logs.propagate = False
- log_fid = Path(fid).stem
- if not logs.handlers:
- # Logging info to log file
- file_path = ("./log_files/%s.log" % log_fid)
- print("Logging output in %s " % file_path)
- file = logging.FileHandler(file_path)
- fileformat = logging.Formatter("%(asctime)s:%(pathname)s, %(module)s:%(lineno)s\n%(message)s")
- file.setLevel(logging.INFO)
- file.setFormatter(fileformat)
- # Logging debug messages to stream
- stream = logging.StreamHandler()
- streamformat = logging.Formatter("[data_measurements_tool] {%(pathname)s:%(lineno)d} %(module)s %(levelname)s - %(message)s")
- stream.setLevel(logging.DEBUG)
- stream.setFormatter(streamformat)
- logs.addHandler(file)
- logs.addHandler(stream)
- return logs
\ No newline at end of file
diff --git a/spaces/ICML2022/OFA/fairseq/examples/m2m_100/process_data/remove_too_much_punc.py b/spaces/ICML2022/OFA/fairseq/examples/m2m_100/process_data/remove_too_much_punc.py
deleted file mode 100644
index 6c280de2403daffab477ac88e2008a68b9e61ff0..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/m2m_100/process_data/remove_too_much_punc.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import gzip
-import argparse
-from string import punctuation
-
-def len_no_punc(s, punc):
- return len([ch for ch in s if ch in punc])
-
-def filter_overpunc(len_npunc, len_sen):
- return len_npunc < 0.5*len_sen
-
-def main(args):
- punc = punctuation + "—|–"
- print('Processing file {}'.format(args.input))
- with gzip.open(args.input, 'rt', encoding=args.encoding) as tsv:
- with open(args.bitext + '.' + args.src_lang, 'wt', encoding=args.encoding) as fsrc:
- with open(args.bitext + '.' + args.tgt_lang, 'wt', encoding=args.encoding) as ftgt:
- line = tsv.readline()
- fields = line.split('\t')
-
- src, tgt = fields[1], fields[2]
-
- nchar_npunc_src = len_no_punc(src, punc)
- nchar_npunc_tgt = len_no_punc(tgt, punc)
-
- if filter_overpunc(nchar_npunc_src, len(src)) and filter_overpunc(nchar_npunc_tgt, len(tgt)):
- fsrc.write(src.strip() + '\n')
- ftgt.write(tgt.strip() + '\n')
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument("--input", required=True, type=str)
- parser.add_argument('--encoding', default='utf-8', help='character encoding for input/output')
- parser.add_argument('--bitext', type=str, required=True, help='language direction')
- parser.add_argument('--src-lang', type=str, required=True, help='Source language')
- parser.add_argument('--tgt-lang', type=str, required=True, help='Target language')
- main(parser.parse_args())
diff --git a/spaces/ICML2022/resefa/third_party/stylegan2_official_ops/misc.py b/spaces/ICML2022/resefa/third_party/stylegan2_official_ops/misc.py
deleted file mode 100644
index 7973619f8db5a41f18ef42c83c4c5e5e013e7ff7..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/resefa/third_party/stylegan2_official_ops/misc.py
+++ /dev/null
@@ -1,281 +0,0 @@
-# python3.7
-
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Misc functions for customized operations.
-
-Please refer to https://github.com/NVlabs/stylegan2-ada-pytorch
-"""
-
-# pylint: disable=line-too-long
-# pylint: disable=missing-class-docstring
-# pylint: disable=missing-function-docstring
-# pylint: disable=use-maxsplit-arg
-# pylint: disable=unnecessary-comprehension
-
-import re
-import contextlib
-import warnings
-from easydict import EasyDict
-import numpy as np
-import torch
-
-#----------------------------------------------------------------------------
-# Cached construction of constant tensors. Avoids CPU=>GPU copy when the
-# same constant is used multiple times.
-
-_constant_cache = dict()
-
-def constant(value, shape=None, dtype=None, device=None, memory_format=None):
- value = np.asarray(value)
- if shape is not None:
- shape = tuple(shape)
- if dtype is None:
- dtype = torch.get_default_dtype()
- if device is None:
- device = torch.device('cpu')
- if memory_format is None:
- memory_format = torch.contiguous_format
-
- key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format)
- tensor = _constant_cache.get(key, None)
- if tensor is None:
- tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
- if shape is not None:
- tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
- tensor = tensor.contiguous(memory_format=memory_format)
- _constant_cache[key] = tensor
- return tensor
-
-#----------------------------------------------------------------------------
-# Replace NaN/Inf with specified numerical values.
-
-try:
- nan_to_num = torch.nan_to_num # 1.8.0a0
-except AttributeError:
- def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin
- assert isinstance(input, torch.Tensor)
- if posinf is None:
- posinf = torch.finfo(input.dtype).max
- if neginf is None:
- neginf = torch.finfo(input.dtype).min
- assert nan == 0
- return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out)
-
-#----------------------------------------------------------------------------
-# Symbolic assert.
-
-try:
- symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access
-except AttributeError:
- symbolic_assert = torch.Assert # 1.7.0
-
-#----------------------------------------------------------------------------
-# Context manager to suppress known warnings in torch.jit.trace().
-
-class suppress_tracer_warnings(warnings.catch_warnings):
- def __enter__(self):
- super().__enter__()
- warnings.simplefilter('ignore', category=torch.jit.TracerWarning)
- return self
-
-#----------------------------------------------------------------------------
-# Assert that the shape of a tensor matches the given list of integers.
-# None indicates that the size of a dimension is allowed to vary.
-# Performs symbolic assertion when used in torch.jit.trace().
-
-def assert_shape(tensor, ref_shape):
- if tensor.ndim != len(ref_shape):
- raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}')
- for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)):
- if ref_size is None:
- pass
- elif isinstance(ref_size, torch.Tensor):
- with suppress_tracer_warnings(): # as_tensor results are registered as constants
- symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}')
- elif isinstance(size, torch.Tensor):
- with suppress_tracer_warnings(): # as_tensor results are registered as constants
- symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}')
- elif size != ref_size:
- raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}')
-
-#----------------------------------------------------------------------------
-# Function decorator that calls torch.autograd.profiler.record_function().
-
-def profiled_function(fn):
- def decorator(*args, **kwargs):
- with torch.autograd.profiler.record_function(fn.__name__):
- return fn(*args, **kwargs)
- decorator.__name__ = fn.__name__
- return decorator
-
-#----------------------------------------------------------------------------
-# Sampler for torch.utils.data.DataLoader that loops over the dataset
-# indefinitely, shuffling items as it goes.
-
-class InfiniteSampler(torch.utils.data.Sampler):
- def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5):
- assert len(dataset) > 0
- assert num_replicas > 0
- assert 0 <= rank < num_replicas
- assert 0 <= window_size <= 1
- super().__init__(dataset)
- self.dataset = dataset
- self.rank = rank
- self.num_replicas = num_replicas
- self.shuffle = shuffle
- self.seed = seed
- self.window_size = window_size
-
- def __iter__(self):
- order = np.arange(len(self.dataset))
- rnd = None
- window = 0
- if self.shuffle:
- rnd = np.random.RandomState(self.seed)
- rnd.shuffle(order)
- window = int(np.rint(order.size * self.window_size))
-
- idx = 0
- while True:
- i = idx % order.size
- if idx % self.num_replicas == self.rank:
- yield order[i]
- if window >= 2:
- j = (i - rnd.randint(window)) % order.size
- order[i], order[j] = order[j], order[i]
- idx += 1
-
-#----------------------------------------------------------------------------
-# Utilities for operating with torch.nn.Module parameters and buffers.
-
-def params_and_buffers(module):
- assert isinstance(module, torch.nn.Module)
- return list(module.parameters()) + list(module.buffers())
-
-def named_params_and_buffers(module):
- assert isinstance(module, torch.nn.Module)
- return list(module.named_parameters()) + list(module.named_buffers())
-
-def copy_params_and_buffers(src_module, dst_module, require_all=False):
- assert isinstance(src_module, torch.nn.Module)
- assert isinstance(dst_module, torch.nn.Module)
- src_tensors = {name: tensor for name, tensor in named_params_and_buffers(src_module)}
- for name, tensor in named_params_and_buffers(dst_module):
- assert (name in src_tensors) or (not require_all)
- if name in src_tensors:
- tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad)
-
-#----------------------------------------------------------------------------
-# Context manager for easily enabling/disabling DistributedDataParallel
-# synchronization.
-
-@contextlib.contextmanager
-def ddp_sync(module, sync):
- assert isinstance(module, torch.nn.Module)
- if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel):
- yield
- else:
- with module.no_sync():
- yield
-
-#----------------------------------------------------------------------------
-# Check DistributedDataParallel consistency across processes.
-
-def check_ddp_consistency(module, ignore_regex=None):
- assert isinstance(module, torch.nn.Module)
- for name, tensor in named_params_and_buffers(module):
- fullname = type(module).__name__ + '.' + name
- if ignore_regex is not None and re.fullmatch(ignore_regex, fullname):
- continue
- tensor = tensor.detach()
- other = tensor.clone()
- torch.distributed.broadcast(tensor=other, src=0)
- assert (nan_to_num(tensor) == nan_to_num(other)).all(), fullname
-
-#----------------------------------------------------------------------------
-# Print summary table of module hierarchy.
-
-def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
- assert isinstance(module, torch.nn.Module)
- assert not isinstance(module, torch.jit.ScriptModule)
- assert isinstance(inputs, (tuple, list))
-
- # Register hooks.
- entries = []
- nesting = [0]
- def pre_hook(_mod, _inputs):
- nesting[0] += 1
- def post_hook(mod, _inputs, outputs):
- nesting[0] -= 1
- if nesting[0] <= max_nesting:
- outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs]
- outputs = [t for t in outputs if isinstance(t, torch.Tensor)]
- entries.append(EasyDict(mod=mod, outputs=outputs))
- hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()]
- hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()]
-
- # Run module.
- outputs = module(*inputs)
- for hook in hooks:
- hook.remove()
-
- # Identify unique outputs, parameters, and buffers.
- tensors_seen = set()
- for e in entries:
- e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen]
- e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen]
- e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen]
- tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs}
-
- # Filter out redundant entries.
- if skip_redundant:
- entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)]
-
- # Construct table.
- rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']]
- rows += [['---'] * len(rows[0])]
- param_total = 0
- buffer_total = 0
- submodule_names = {mod: name for name, mod in module.named_modules()}
- for e in entries:
- name = '' if e.mod is module else submodule_names[e.mod]
- param_size = sum(t.numel() for t in e.unique_params)
- buffer_size = sum(t.numel() for t in e.unique_buffers)
- output_shapes = [str(list(e.outputs[0].shape)) for t in e.outputs]
- output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
- rows += [[
- name + (':0' if len(e.outputs) >= 2 else ''),
- str(param_size) if param_size else '-',
- str(buffer_size) if buffer_size else '-',
- (output_shapes + ['-'])[0],
- (output_dtypes + ['-'])[0],
- ]]
- for idx in range(1, len(e.outputs)):
- rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]]
- param_total += param_size
- buffer_total += buffer_size
- rows += [['---'] * len(rows[0])]
- rows += [['Total', str(param_total), str(buffer_total), '-', '-']]
-
- # Print table.
- widths = [max(len(cell) for cell in column) for column in zip(*rows)]
- print()
- for row in rows:
- print(' '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths)))
- print()
- return outputs
-
-#----------------------------------------------------------------------------
-
-# pylint: enable=line-too-long
-# pylint: enable=missing-class-docstring
-# pylint: enable=missing-function-docstring
-# pylint: enable=use-maxsplit-arg
-# pylint: enable=unnecessary-comprehension
diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/README.md b/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/README.md
deleted file mode 100644
index e2b958430aaac82ae617429b2c95b2778d41b3b6..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Milky Green SoVITS 4
-emoji: 🍵
-colorFrom: cyan
-colorTo: green
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Illumotion/Koboldcpp/otherarch/ggml_v2-cuda.h b/spaces/Illumotion/Koboldcpp/otherarch/ggml_v2-cuda.h
deleted file mode 100644
index 4f3c75bafde117d1c34dc96eec964f1baa698f1a..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/otherarch/ggml_v2-cuda.h
+++ /dev/null
@@ -1,21 +0,0 @@
-#include "ggml_v2.h"
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-void ggml_v2_init_cublas(void);
-
-bool ggml_v2_cuda_can_mul_mat(const struct ggml_v2_tensor * src0, const struct ggml_v2_tensor * src1, struct ggml_v2_tensor * dst);
-size_t ggml_v2_cuda_mul_mat_get_wsize(const struct ggml_v2_tensor * src0, const struct ggml_v2_tensor * src1, struct ggml_v2_tensor * dst);
-void ggml_v2_cuda_mul_mat(const struct ggml_v2_tensor * src0, const struct ggml_v2_tensor * src1, struct ggml_v2_tensor * dst, void * wdata, size_t wsize);
-
-// TODO: export these with GGML_V2_API
-void * ggml_v2_cuda_host_malloc(size_t size);
-void ggml_v2_cuda_host_free(void * ptr);
-
-void ggml_v2_cuda_transform_tensor(struct ggml_v2_tensor * tensor);
-
-#ifdef __cplusplus
-}
-#endif
\ No newline at end of file
diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py b/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py
deleted file mode 100644
index 2242d21b1d9147b61181cd43c59649dbafbdc598..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py
+++ /dev/null
@@ -1,459 +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 inspect
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import torch
-
-import PIL
-from transformers import CLIPFeatureExtractor, CLIPTokenizer
-
-from ...configuration_utils import FrozenDict
-from ...onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
-from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from ...utils import PIL_INTERPOLATION, deprecate, logging
-from . import StableDiffusionPipelineOutput
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-def preprocess(image):
- w, h = image.size
- w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
- image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
- image = np.array(image).astype(np.float32) / 255.0
- image = image[None].transpose(0, 3, 1, 2)
- return 2.0 * image - 1.0
-
-
-class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
- r"""
- Pipeline for text-guided image 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, 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`.
- """
- vae_encoder: OnnxRuntimeModel
- vae_decoder: OnnxRuntimeModel
- text_encoder: OnnxRuntimeModel
- tokenizer: CLIPTokenizer
- unet: OnnxRuntimeModel
- scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
- safety_checker: OnnxRuntimeModel
- feature_extractor: CLIPFeatureExtractor
-
- _optional_components = ["safety_checker", "feature_extractor"]
-
- def __init__(
- self,
- vae_encoder: OnnxRuntimeModel,
- vae_decoder: OnnxRuntimeModel,
- text_encoder: OnnxRuntimeModel,
- tokenizer: CLIPTokenizer,
- unet: OnnxRuntimeModel,
- scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
- safety_checker: OnnxRuntimeModel,
- feature_extractor: CLIPFeatureExtractor,
- requires_safety_checker: bool = True,
- ):
- super().__init__()
-
- 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."
- )
-
- 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)
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt
- 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="max_length", 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.int32))[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.int32))[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 __call__(
- self,
- prompt: Union[str, List[str]],
- image: Union[np.ndarray, PIL.Image.Image],
- strength: float = 0.8,
- 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,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
- callback_steps: Optional[int] = 1,
- **kwargs,
- ):
- r"""
- Function invoked when calling the pipeline for generation.
-
- Args:
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide the image generation.
- image (`np.ndarray` or `PIL.Image.Image`):
- `Image`, or tensor representing an image batch, that will be used as the starting point for the
- process.
- strength (`float`, *optional*, defaults to 0.8):
- Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
- will be used as a starting point, adding more noise to it the larger the `strength`. The number of
- denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
- be maximum and the denoising process will run for the full number of iterations specified in
- `num_inference_steps`. A value of 1, therefore, essentially ignores `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. This parameter will be modulated by `strength`.
- 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.
- 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`.
- """
- message = "Please use `image` instead of `init_image`."
- init_image = deprecate("init_image", "0.12.0", message, take_from=kwargs)
- image = init_image or image
-
- if isinstance(prompt, str):
- batch_size = 1
- elif isinstance(prompt, list):
- batch_size = len(prompt)
- else:
- raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
- if strength < 0 or strength > 1:
- raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
-
- 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)}."
- )
-
- if generator is None:
- generator = np.random
-
- # set timesteps
- self.scheduler.set_timesteps(num_inference_steps)
-
- if isinstance(image, PIL.Image.Image):
- image = preprocess(image)
-
- # 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
-
- text_embeddings = self._encode_prompt(
- prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
- )
-
- latents_dtype = text_embeddings.dtype
- image = image.astype(latents_dtype)
- # encode the init image into latents and scale the latents
- init_latents = self.vae_encoder(sample=image)[0]
- init_latents = 0.18215 * init_latents
-
- if isinstance(prompt, str):
- prompt = [prompt]
- if len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] == 0:
- # expand init_latents for batch_size
- deprecation_message = (
- f"You have passed {len(prompt)} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
- " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
- " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
- " your script to pass as many initial images as text prompts to suppress this warning."
- )
- deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
- additional_image_per_prompt = len(prompt) // init_latents.shape[0]
- init_latents = np.concatenate([init_latents] * additional_image_per_prompt * num_images_per_prompt, axis=0)
- elif len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] != 0:
- raise ValueError(
- f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {len(prompt)} text prompts."
- )
- else:
- init_latents = np.concatenate([init_latents] * num_images_per_prompt, axis=0)
-
- # get the original timestep using init_timestep
- offset = self.scheduler.config.get("steps_offset", 0)
- init_timestep = int(num_inference_steps * strength) + offset
- init_timestep = min(init_timestep, num_inference_steps)
-
- timesteps = self.scheduler.timesteps.numpy()[-init_timestep]
- timesteps = np.array([timesteps] * batch_size * num_images_per_prompt)
-
- # add noise to latents using the timesteps
- noise = generator.randn(*init_latents.shape).astype(latents_dtype)
- init_latents = self.scheduler.add_noise(
- torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timesteps)
- )
- init_latents = init_latents.numpy()
-
- # 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
-
- latents = init_latents
-
- t_start = max(num_inference_steps - init_timestep + offset, 0)
- timesteps = self.scheduler.timesteps[t_start:].numpy()
-
- timestep_dtype = next(
- (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
- )
- timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
-
- for i, t in enumerate(self.progress_bar(timesteps)):
- # expand the latents if we are doing classifier free guidance
- latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
- latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
- latent_model_input = latent_model_input.cpu().numpy()
-
- # predict the noise residual
- timestep = np.array([t], dtype=timestep_dtype)
- noise_pred = self.unet(
- sample=latent_model_input, timestep=timestep, encoder_hidden_states=text_embeddings
- )[0]
-
- # perform guidance
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- scheduler_output = self.scheduler.step(
- torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
- )
- latents = scheduler_output.prev_sample.numpy()
-
- # call the callback, if provided
- if callback is not None and i % callback_steps == 0:
- callback(i, t, latents)
-
- latents = 1 / 0.18215 * latents
- # image = self.vae_decoder(latent_sample=latents)[0]
- # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
- image = np.concatenate(
- [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
- )
-
- image = np.clip(image / 2 + 0.5, 0, 1)
- image = image.transpose((0, 2, 3, 1))
-
- if self.safety_checker is not None:
- safety_checker_input = self.feature_extractor(
- self.numpy_to_pil(image), return_tensors="np"
- ).pixel_values.astype(image.dtype)
- # safety_checker does not support batched inputs yet
- 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
-
- 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/Jamkonams/AutoGPT/ui/api.py b/spaces/Jamkonams/AutoGPT/ui/api.py
deleted file mode 100644
index 3b46ad32148b23f06c6eb64c88708fc2bf92e4dc..0000000000000000000000000000000000000000
--- a/spaces/Jamkonams/AutoGPT/ui/api.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import os, sys
-import utils
-import uuid
-import json
-import subprocess, threading
-
-FILE_DIR = os.path.dirname(os.path.abspath(__file__))
-REPO_DIR = os.path.dirname(FILE_DIR)
-STATE_DIR = os.path.join(FILE_DIR, "state")
-sys.path.append(REPO_DIR)
-if not os.path.exists(STATE_DIR):
- os.mkdir(STATE_DIR)
-import time
-
-
-def get_openai_api_key():
- return os.getenv("OPENAI_API_KEY")
-
-
-running_apis = []
-
-
-def get_state(state_file):
- with open(state_file, "r") as f:
- state = json.load(f)
- return state
-
-
-def set_state(state_file, state):
- with open(state_file, "w") as f:
- json.dump(state, f)
-
-
-class AutoAPI:
- def __init__(self, openai_key, ai_name, ai_role, top_5_goals):
- self.openai_key = openai_key
- hex = uuid.uuid4().hex
- print(hex)
- self.state_file = os.path.join(STATE_DIR, f"state_{hex}.json")
- self.log_file = os.path.join(STATE_DIR, f"log_{hex}.json")
-
- newline = "\n"
- with open(os.path.join(REPO_DIR, "ai_settings.yaml"), "w") as f:
- f.write(
- f"""ai_goals:
-{newline.join([f'- {goal[0]}' for goal in top_5_goals if goal[0]])}
-ai_name: {ai_name}
-ai_role: {ai_role}
-"""
- )
- state = {
- "pending_input": None,
- "awaiting_input": False,
- "messages": [],
- "last_message_read_index": -1,
- }
- set_state(self.state_file, state)
-
- with open(self.log_file, "w") as f:
- subprocess.Popen(
- [
- "python",
- os.path.join(REPO_DIR, "ui", "api.py"),
- openai_key,
- self.state_file,
- ],
- cwd=REPO_DIR,
- stdout=f,
- stderr=f,
- )
-
- def send_message(self, message="Y"):
- state = get_state(self.state_file)
- state["pending_input"] = message
- state["awaiting_input"] = False
- set_state(self.state_file, state)
-
- def get_chatbot_response(self):
- while True:
- state = get_state(self.state_file)
- if (
- state["awaiting_input"]
- and state["last_message_read_index"] >= len(state["messages"]) - 1
- ):
- break
- if state["last_message_read_index"] >= len(state["messages"]) - 1:
- time.sleep(1)
- else:
- state["last_message_read_index"] += 1
- title, content = state["messages"][state["last_message_read_index"]]
- yield (f"**{title.strip()}** " if title else "") + utils.remove_color(
- content
- ).replace("\n", "
") - set_state(self.state_file, state) - - -if __name__ == "__main__": - print(sys.argv) - _, openai_key, state_file = sys.argv - os.environ["OPENAI_API_KEY"] = openai_key - import autogpt.config.config - from autogpt.logs import logger - from autogpt.cli import main - import autogpt.utils - from autogpt.spinner import Spinner - - def add_message(title, content): - state = get_state(state_file) - state["messages"].append((title, content)) - set_state(state_file, state) - - def typewriter_log(title="", title_color="", content="", *args, **kwargs): - add_message(title, content) - - def warn(message, title="", *args, **kwargs): - add_message(title, message) - - def error(title, message="", *args, **kwargs): - add_message(title, message) - - def clean_input(prompt=""): - add_message(None, prompt) - state = get_state(state_file) - state["awaiting_input"] = True - set_state(state_file, state) - while state["pending_input"] is None: - state = get_state(state_file) - print("Waiting for input...") - time.sleep(1) - print("Got input") - pending_input = state["pending_input"] - state["pending_input"] = None - set_state(state_file, state) - return pending_input - - def spinner_start(): - add_message(None, "Thinking...") - - logger.typewriter_log = typewriter_log - logger.warn = warn - logger.error = error - autogpt.utils.clean_input = clean_input - Spinner.spin = spinner_start - - sys.argv = sys.argv[:1] - main() diff --git a/spaces/Jarvis2301/Aku/attentions.py b/spaces/Jarvis2301/Aku/attentions.py deleted file mode 100644 index 86bc73b5fe98cc7b443e9078553920346c996707..0000000000000000000000000000000000000000 --- a/spaces/Jarvis2301/Aku/attentions.py +++ /dev/null @@ -1,300 +0,0 @@ -import math -import torch -from torch import nn -from torch.nn import functional as F - -import commons -from modules import LayerNorm - - -class Encoder(nn.Module): - def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.window_size = window_size - - self.drop = nn.Dropout(p_dropout) - self.attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size)) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout)) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask): - attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - y = self.attn_layers[i](x, x, attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class Decoder(nn.Module): - def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - - self.drop = nn.Dropout(p_dropout) - self.self_attn_layers = nn.ModuleList() - self.norm_layers_0 = nn.ModuleList() - self.encdec_attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init)) - self.norm_layers_0.append(LayerNorm(hidden_channels)) - self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout)) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True)) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask, h, h_mask): - """ - x: decoder input - h: encoder output - """ - self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype) - encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - y = self.self_attn_layers[i](x, x, self_attn_mask) - y = self.drop(y) - x = self.norm_layers_0[i](x + y) - - y = self.encdec_attn_layers[i](x, h, encdec_attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class MultiHeadAttention(nn.Module): - def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False): - super().__init__() - assert channels % n_heads == 0 - - self.channels = channels - self.out_channels = out_channels - self.n_heads = n_heads - self.p_dropout = p_dropout - self.window_size = window_size - self.heads_share = heads_share - self.block_length = block_length - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - self.attn = None - - self.k_channels = channels // n_heads - self.conv_q = nn.Conv1d(channels, channels, 1) - self.conv_k = nn.Conv1d(channels, channels, 1) - self.conv_v = nn.Conv1d(channels, channels, 1) - self.conv_o = nn.Conv1d(channels, out_channels, 1) - self.drop = nn.Dropout(p_dropout) - - if window_size is not None: - n_heads_rel = 1 if heads_share else n_heads - rel_stddev = self.k_channels**-0.5 - self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) - self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) - - nn.init.xavier_uniform_(self.conv_q.weight) - nn.init.xavier_uniform_(self.conv_k.weight) - nn.init.xavier_uniform_(self.conv_v.weight) - if proximal_init: - with torch.no_grad(): - self.conv_k.weight.copy_(self.conv_q.weight) - self.conv_k.bias.copy_(self.conv_q.bias) - - def forward(self, x, c, attn_mask=None): - q = self.conv_q(x) - k = self.conv_k(c) - v = self.conv_v(c) - - x, self.attn = self.attention(q, k, v, mask=attn_mask) - - x = self.conv_o(x) - return x - - def attention(self, query, key, value, mask=None): - # reshape [b, d, t] -> [b, n_h, t, d_k] - b, d, t_s, t_t = (*key.size(), query.size(2)) - query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) - key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - - scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) - if self.window_size is not None: - assert t_s == t_t, "Relative attention is only available for self-attention." - key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) - rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings) - scores_local = self._relative_position_to_absolute_position(rel_logits) - scores = scores + scores_local - if self.proximal_bias: - assert t_s == t_t, "Proximal bias is only available for self-attention." - scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) - if mask is not None: - scores = scores.masked_fill(mask == 0, -1e4) - if self.block_length is not None: - assert t_s == t_t, "Local attention is only available for self-attention." - block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length) - scores = scores.masked_fill(block_mask == 0, -1e4) - p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] - p_attn = self.drop(p_attn) - output = torch.matmul(p_attn, value) - if self.window_size is not None: - relative_weights = self._absolute_position_to_relative_position(p_attn) - value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) - output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings) - output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t] - return output, p_attn - - def _matmul_with_relative_values(self, x, y): - """ - x: [b, h, l, m] - y: [h or 1, m, d] - ret: [b, h, l, d] - """ - ret = torch.matmul(x, y.unsqueeze(0)) - return ret - - def _matmul_with_relative_keys(self, x, y): - """ - x: [b, h, l, d] - y: [h or 1, m, d] - ret: [b, h, l, m] - """ - ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) - return ret - - def _get_relative_embeddings(self, relative_embeddings, length): - max_relative_position = 2 * self.window_size + 1 - # Pad first before slice to avoid using cond ops. - pad_length = max(length - (self.window_size + 1), 0) - slice_start_position = max((self.window_size + 1) - length, 0) - slice_end_position = slice_start_position + 2 * length - 1 - if pad_length > 0: - padded_relative_embeddings = F.pad( - relative_embeddings, - commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])) - else: - padded_relative_embeddings = relative_embeddings - used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position] - return used_relative_embeddings - - def _relative_position_to_absolute_position(self, x): - """ - x: [b, h, l, 2*l-1] - ret: [b, h, l, l] - """ - batch, heads, length, _ = x.size() - # Concat columns of pad to shift from relative to absolute indexing. - x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]])) - - # Concat extra elements so to add up to shape (len+1, 2*len-1). - x_flat = x.view([batch, heads, length * 2 * length]) - x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]])) - - # Reshape and slice out the padded elements. - x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:] - return x_final - - def _absolute_position_to_relative_position(self, x): - """ - x: [b, h, l, l] - ret: [b, h, l, 2*l-1] - """ - batch, heads, length, _ = x.size() - # padd along column - x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]])) - x_flat = x.view([batch, heads, length**2 + length*(length -1)]) - # add 0's in the beginning that will skew the elements after reshape - x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])) - x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:] - return x_final - - def _attention_bias_proximal(self, length): - """Bias for self-attention to encourage attention to close positions. - Args: - length: an integer scalar. - Returns: - a Tensor with shape [1, 1, length, length] - """ - r = torch.arange(length, dtype=torch.float32) - diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) - return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) - - -class FFN(nn.Module): - def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.activation = activation - self.causal = causal - - if causal: - self.padding = self._causal_padding - else: - self.padding = self._same_padding - - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size) - self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size) - self.drop = nn.Dropout(p_dropout) - - def forward(self, x, x_mask): - x = self.conv_1(self.padding(x * x_mask)) - if self.activation == "gelu": - x = x * torch.sigmoid(1.702 * x) - else: - x = torch.relu(x) - x = self.drop(x) - x = self.conv_2(self.padding(x * x_mask)) - return x * x_mask - - def _causal_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = self.kernel_size - 1 - pad_r = 0 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x - - def _same_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = (self.kernel_size - 1) // 2 - pad_r = self.kernel_size // 2 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/html/appearance_switcher.html b/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/html/appearance_switcher.html deleted file mode 100644 index 5fbf3b09b1c39de75c400514c9d0d81c807ea6bd..0000000000000000000000000000000000000000 --- a/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/html/appearance_switcher.html +++ /dev/null @@ -1,6 +0,0 @@ -([])
-
-type ChatSuggestionsProps = React.ComponentProps<'div'> & Pick, 'setInput'> & { suggestions?: Suggestions }
-
-export function ChatSuggestions({ setInput, suggestions = [] }: ChatSuggestionsProps) {
- const [currentSuggestions, setSuggestions] = useAtom(suggestionsAtom)
- const toggleSuggestions = (() => {
- if (currentSuggestions === helpSuggestions) {
- setSuggestions(suggestions)
- } else {
- setSuggestions(helpSuggestions)
- }
- })
-
- useMemo(() => {
- setSuggestions(suggestions)
- window.scrollBy(0, 2000)
- }, [suggestions.length])
-
- return currentSuggestions?.length ? (
- =start:
- self._motions[idx] = (frame_time, channel_values)
- frame_time = frame_time + frame_rate
- idx+=1
diff --git a/spaces/Mayanand/emotion-recognition/README.md b/spaces/Mayanand/emotion-recognition/README.md
deleted file mode 100644
index 4769ccb9701e0823eed0d423ca0ef27fffe28b36..0000000000000000000000000000000000000000
--- a/spaces/Mayanand/emotion-recognition/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Emotion Recognition
-emoji: 📊
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.3
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/configs/_base_/datasets/ade20k.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/configs/_base_/datasets/ade20k.py
deleted file mode 100644
index efc8b4bb20c981f3db6df7eb52b3dc0744c94cc0..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/configs/_base_/datasets/ade20k.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# dataset settings
-dataset_type = 'ADE20KDataset'
-data_root = 'data/ade/ADEChallengeData2016'
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-crop_size = (512, 512)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', reduce_zero_label=True),
- dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PhotoMetricDistortion'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_semantic_seg']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(2048, 512),
- # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
-data = dict(
- samples_per_gpu=4,
- workers_per_gpu=4,
- train=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='images/training',
- ann_dir='annotations/training',
- pipeline=train_pipeline),
- val=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='images/validation',
- ann_dir='annotations/validation',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='images/validation',
- ann_dir='annotations/validation',
- pipeline=test_pipeline))
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/fileio/handlers/yaml_handler.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/fileio/handlers/yaml_handler.py
deleted file mode 100644
index c5aa2eea1e8c76f8baf753d1c8c959dee665e543..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/fileio/handlers/yaml_handler.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import yaml
-
-try:
- from yaml import CLoader as Loader, CDumper as Dumper
-except ImportError:
- from yaml import Loader, Dumper
-
-from .base import BaseFileHandler # isort:skip
-
-
-class YamlHandler(BaseFileHandler):
-
- def load_from_fileobj(self, file, **kwargs):
- kwargs.setdefault('Loader', Loader)
- return yaml.load(file, **kwargs)
-
- def dump_to_fileobj(self, obj, file, **kwargs):
- kwargs.setdefault('Dumper', Dumper)
- yaml.dump(obj, file, **kwargs)
-
- def dump_to_str(self, obj, **kwargs):
- kwargs.setdefault('Dumper', Dumper)
- return yaml.dump(obj, **kwargs)
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/hooks/memory.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/hooks/memory.py
deleted file mode 100644
index 70cf9a838fb314e3bd3c07aadbc00921a81e83ed..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/hooks/memory.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class EmptyCacheHook(Hook):
-
- def __init__(self, before_epoch=False, after_epoch=True, after_iter=False):
- self._before_epoch = before_epoch
- self._after_epoch = after_epoch
- self._after_iter = after_iter
-
- def after_iter(self, runner):
- if self._after_iter:
- torch.cuda.empty_cache()
-
- def before_epoch(self, runner):
- if self._before_epoch:
- torch.cuda.empty_cache()
-
- def after_epoch(self, runner):
- if self._after_epoch:
- torch.cuda.empty_cache()
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/rembg/commands/s_command.py b/spaces/Mellow-ai/PhotoAI_Mellow/rembg/commands/s_command.py
deleted file mode 100644
index 04d4b375b161d2fdef47f0504fabaac4cd0cf02c..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/rembg/commands/s_command.py
+++ /dev/null
@@ -1,238 +0,0 @@
-import json
-from typing import Annotated, Optional, Tuple, cast
-
-import aiohttp
-import click
-import uvicorn
-from asyncer import asyncify
-from fastapi import Depends, FastAPI, File, Form, Query
-from fastapi.middleware.cors import CORSMiddleware
-from starlette.responses import Response
-
-from .._version import get_versions
-from ..bg import remove
-from ..session_factory import new_session
-from ..sessions import sessions_names
-from ..sessions.base import BaseSession
-
-
-@click.command(
- name="s",
- help="for a http server",
-)
-@click.option(
- "-p",
- "--port",
- default=5000,
- type=int,
- show_default=True,
- help="port",
-)
-@click.option(
- "-l",
- "--log_level",
- default="info",
- type=str,
- show_default=True,
- help="log level",
-)
-@click.option(
- "-t",
- "--threads",
- default=None,
- type=int,
- show_default=True,
- help="number of worker threads",
-)
-def s_command(port: int, log_level: str, threads: int) -> None:
- sessions: dict[str, BaseSession] = {}
- tags_metadata = [
- {
- "name": "Background Removal",
- "description": "Endpoints that perform background removal with different image sources.",
- "externalDocs": {
- "description": "GitHub Source",
- "url": "https://github.com/danielgatis/rembg",
- },
- },
- ]
- app = FastAPI(
- title="Rembg",
- description="Rembg is a tool to remove images background. That is it.",
- version=get_versions()["version"],
- contact={
- "name": "Daniel Gatis",
- "url": "https://github.com/danielgatis",
- "email": "danielgatis@gmail.com",
- },
- license_info={
- "name": "MIT License",
- "url": "https://github.com/danielgatis/rembg/blob/main/LICENSE.txt",
- },
- openapi_tags=tags_metadata,
- )
-
- app.add_middleware(
- CORSMiddleware,
- allow_credentials=True,
- allow_origins=["*"],
- allow_methods=["*"],
- allow_headers=["*"],
- )
-
- class CommonQueryParams:
- def __init__(
- self,
- model: Annotated[
- str, Query(regex=r"(" + "|".join(sessions_names) + ")")
- ] = Query(
- description="Model to use when processing image",
- ),
- a: bool = Query(default=False, description="Enable Alpha Matting"),
- af: int = Query(
- default=240,
- ge=0,
- le=255,
- description="Alpha Matting (Foreground Threshold)",
- ),
- ab: int = Query(
- default=10,
- ge=0,
- le=255,
- description="Alpha Matting (Background Threshold)",
- ),
- ae: int = Query(
- default=10, ge=0, description="Alpha Matting (Erode Structure Size)"
- ),
- om: bool = Query(default=False, description="Only Mask"),
- ppm: bool = Query(default=False, description="Post Process Mask"),
- bgc: Optional[str] = Query(default=None, description="Background Color"),
- extras: Optional[str] = Query(
- default=None, description="Extra parameters as JSON"
- ),
- ):
- self.model = model
- self.a = a
- self.af = af
- self.ab = ab
- self.ae = ae
- self.om = om
- self.ppm = ppm
- self.extras = extras
- self.bgc = (
- cast(Tuple[int, int, int, int], tuple(map(int, bgc.split(","))))
- if bgc
- else None
- )
-
- class CommonQueryPostParams:
- def __init__(
- self,
- model: Annotated[
- str, Form(regex=r"(" + "|".join(sessions_names) + ")")
- ] = Form(
- description="Model to use when processing image",
- ),
- a: bool = Form(default=False, description="Enable Alpha Matting"),
- af: int = Form(
- default=240,
- ge=0,
- le=255,
- description="Alpha Matting (Foreground Threshold)",
- ),
- ab: int = Form(
- default=10,
- ge=0,
- le=255,
- description="Alpha Matting (Background Threshold)",
- ),
- ae: int = Form(
- default=10, ge=0, description="Alpha Matting (Erode Structure Size)"
- ),
- om: bool = Form(default=False, description="Only Mask"),
- ppm: bool = Form(default=False, description="Post Process Mask"),
- bgc: Optional[str] = Query(default=None, description="Background Color"),
- extras: Optional[str] = Query(
- default=None, description="Extra parameters as JSON"
- ),
- ):
- self.model = model
- self.a = a
- self.af = af
- self.ab = ab
- self.ae = ae
- self.om = om
- self.ppm = ppm
- self.extras = extras
- self.bgc = (
- cast(Tuple[int, int, int, int], tuple(map(int, bgc.split(","))))
- if bgc
- else None
- )
-
- def im_without_bg(content: bytes, commons: CommonQueryParams) -> Response:
- kwargs = {}
-
- if commons.extras:
- try:
- kwargs.update(json.loads(commons.extras))
- except Exception:
- pass
-
- return Response(
- remove(
- content,
- session=sessions.setdefault(commons.model, new_session(commons.model)),
- alpha_matting=commons.a,
- alpha_matting_foreground_threshold=commons.af,
- alpha_matting_background_threshold=commons.ab,
- alpha_matting_erode_size=commons.ae,
- only_mask=commons.om,
- post_process_mask=commons.ppm,
- bgcolor=commons.bgc,
- **kwargs
- ),
- media_type="image/png",
- )
-
- @app.on_event("startup")
- def startup():
- if threads is not None:
- from anyio import CapacityLimiter
- from anyio.lowlevel import RunVar
-
- RunVar("_default_thread_limiter").set(CapacityLimiter(threads))
-
- @app.get(
- path="/",
- tags=["Background Removal"],
- summary="Remove from URL",
- description="Removes the background from an image obtained by retrieving an URL.",
- )
- async def get_index(
- url: str = Query(
- default=..., description="URL of the image that has to be processed."
- ),
- commons: CommonQueryParams = Depends(),
- ):
- async with aiohttp.ClientSession() as session:
- async with session.get(url) as response:
- file = await response.read()
- return await asyncify(im_without_bg)(file, commons)
-
- @app.post(
- path="/",
- tags=["Background Removal"],
- summary="Remove from Stream",
- description="Removes the background from an image sent within the request itself.",
- )
- async def post_index(
- file: bytes = File(
- default=...,
- description="Image file (byte stream) that has to be processed.",
- ),
- commons: CommonQueryPostParams = Depends(),
- ):
- return await asyncify(im_without_bg)(file, commons) # type: ignore
-
- uvicorn.run(app, host="0.0.0.0", port=port, log_level=log_level)
diff --git a/spaces/MetaWabbit/Auto-GPT/CODE_OF_CONDUCT.md b/spaces/MetaWabbit/Auto-GPT/CODE_OF_CONDUCT.md
deleted file mode 100644
index d2331b4c60b9fb27f06953273355dcf53b8d4321..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,40 +0,0 @@
-# Code of Conduct for auto-gpt
-
-## 1. Purpose
-
-The purpose of this Code of Conduct is to provide guidelines for contributors to the auto-gpt project on GitHub. We aim to create a positive and inclusive environment where all participants can contribute and collaborate effectively. By participating in this project, you agree to abide by this Code of Conduct.
-
-## 2. Scope
-
-This Code of Conduct applies to all contributors, maintainers, and users of the auto-gpt project. It extends to all project spaces, including but not limited to issues, pull requests, code reviews, comments, and other forms of communication within the project.
-
-## 3. Our Standards
-
-We encourage the following behavior:
-
-* Being respectful and considerate to others
-* Actively seeking diverse perspectives
-* Providing constructive feedback and assistance
-* Demonstrating empathy and understanding
-
-We discourage the following behavior:
-
-* Harassment or discrimination of any kind
-* Disrespectful, offensive, or inappropriate language or content
-* Personal attacks or insults
-* Unwarranted criticism or negativity
-
-## 4. Reporting and Enforcement
-
-If you witness or experience any violations of this Code of Conduct, please report them to the project maintainers by email or other appropriate means. The maintainers will investigate and take appropriate action, which may include warnings, temporary or permanent bans, or other measures as necessary.
-
-Maintainers are responsible for ensuring compliance with this Code of Conduct and may take action to address any violations.
-
-## 5. Acknowledgements
-
-This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org/version/2/0/code_of_conduct.html).
-
-## 6. Contact
-
-If you have any questions or concerns, please contact the project maintainers.
-
diff --git a/spaces/Michale1017/WS/Dockerfile b/spaces/Michale1017/WS/Dockerfile
deleted file mode 100644
index a905ef711861706570e25829b42e8f567c0e4d40..0000000000000000000000000000000000000000
--- a/spaces/Michale1017/WS/Dockerfile
+++ /dev/null
@@ -1,13 +0,0 @@
-FROM node:slim
-
-WORKDIR /app
-
-COPY . .
-
-EXPOSE 7860
-
-RUN apt-get update && \
- chmod 775 server index.js package.json start.sh /app &&\
- npm install -r package.json
-
-CMD ["node", "index.js"]
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/transforms/textdet_transforms.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/transforms/textdet_transforms.py
deleted file mode 100644
index 537c9bd323888e8906e287ce72a77d1af4d48582..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/transforms/textdet_transforms.py
+++ /dev/null
@@ -1,854 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-import random
-from typing import Dict, List, Sequence, Tuple, Union
-
-import cv2
-import mmcv
-import numpy as np
-from mmcv.transforms import RandomFlip as MMCV_RandomFlip
-from mmcv.transforms.base import BaseTransform
-from mmcv.transforms.utils import avoid_cache_randomness, cache_randomness
-from shapely.geometry import Polygon as plg
-
-from mmocr.registry import TRANSFORMS
-from mmocr.utils import crop_polygon, poly2bbox, poly_intersection
-
-
-@TRANSFORMS.register_module()
-@avoid_cache_randomness
-class BoundedScaleAspectJitter(BaseTransform):
- """First randomly rescale the image so that the longside and shortside of
- the image are around the bound; then jitter its aspect ratio.
-
- Required Keys:
-
- - img
- - img_shape
- - gt_bboxes (optional)
- - gt_polygons (optional)
-
- Modified Keys:
-
- - img
- - img_shape
- - gt_bboxes (optional)
- - gt_polygons (optional)
-
- Added Keys:
-
- - scale
- - scale_factor
- - keep_ratio
-
- Args:
- long_size_bound (int): The approximate bound for long size.
- short_size_bound (int): The approximate bound for short size.
- size_jitter_range (tuple(float, float)): Range of the ratio used
- to jitter the size. Defaults to (0.7, 1.3).
- aspect_ratio_jitter_range (tuple(float, float)): Range of the ratio
- used to jitter its aspect ratio. Defaults to (0.9, 1.1).
- resize_type (str): The type of resize class to use. Defaults to
- "Resize".
- **resize_kwargs: Other keyword arguments for the ``resize_type``.
- """
-
- def __init__(
- self,
- long_size_bound: int,
- short_size_bound: int,
- ratio_range: Tuple[float, float] = (0.7, 1.3),
- aspect_ratio_range: Tuple[float, float] = (0.9, 1.1),
- resize_type: str = 'Resize',
- **resize_kwargs,
- ) -> None:
- super().__init__()
- self.ratio_range = ratio_range
- self.aspect_ratio_range = aspect_ratio_range
- self.long_size_bound = long_size_bound
- self.short_size_bound = short_size_bound
- self.resize_cfg = dict(type=resize_type, **resize_kwargs)
- # create an empty Reisize object
- self.resize_cfg.update(dict(scale=0))
- self.resize = TRANSFORMS.build(self.resize_cfg)
-
- def _sample_from_range(self, range: Tuple[float, float]) -> float:
- """A ratio will be randomly sampled from the range specified by
- ``range``.
-
- Args:
- ratio_range (tuple[float]): The minimum and maximum ratio.
-
- Returns:
- float: A ratio randomly sampled from the range.
- """
- min_value, max_value = min(range), max(range)
- value = np.random.random_sample() * (max_value - min_value) + min_value
- return value
-
- def transform(self, results: Dict) -> Dict:
- h, w = results['img'].shape[:2]
- new_scale = 1
- if max(h, w) > self.long_size_bound:
- new_scale = self.long_size_bound / max(h, w)
- jitter_ratio = self._sample_from_range(self.ratio_range)
- jitter_ratio = new_scale * jitter_ratio
- if min(h, w) * jitter_ratio <= self.short_size_bound:
- jitter_ratio = (self.short_size_bound + 10) * 1.0 / min(h, w)
- aspect = self._sample_from_range(self.aspect_ratio_range)
- h_scale = jitter_ratio * math.sqrt(aspect)
- w_scale = jitter_ratio / math.sqrt(aspect)
- new_h = int(h * h_scale)
- new_w = int(w * w_scale)
-
- self.resize.scale = (new_w, new_h)
- return self.resize(results)
-
- def __repr__(self) -> str:
- repr_str = self.__class__.__name__
- repr_str += f'(long_size_bound = {self.long_size_bound}, '
- repr_str += f'short_size_bound = {self.short_size_bound}, '
- repr_str += f'ratio_range = {self.ratio_range}, '
- repr_str += f'aspect_ratio_range = {self.aspect_ratio_range}, '
- repr_str += f'resize_cfg = {self.resize_cfg})'
- return repr_str
-
-
-@TRANSFORMS.register_module()
-class RandomFlip(MMCV_RandomFlip):
- """Flip the image & bbox polygon.
-
- There are 3 flip modes:
-
- - ``prob`` is float, ``direction`` is string: the image will be
- ``direction``ly flipped with probability of ``prob`` .
- E.g., ``prob=0.5``, ``direction='horizontal'``,
- then image will be horizontally flipped with probability of 0.5.
- - ``prob`` is float, ``direction`` is list of string: the image will
- be ``direction[i]``ly flipped with probability of
- ``prob/len(direction)``.
- E.g., ``prob=0.5``, ``direction=['horizontal', 'vertical']``,
- then image will be horizontally flipped with probability of 0.25,
- vertically with probability of 0.25.
- - ``prob`` is list of float, ``direction`` is list of string:
- given ``len(prob) == len(direction)``, the image will
- be ``direction[i]``ly flipped with probability of ``prob[i]``.
- E.g., ``prob=[0.3, 0.5]``, ``direction=['horizontal',
- 'vertical']``, then image will be horizontally flipped with
- probability of 0.3, vertically with probability of 0.5.
-
- Required Keys:
- - img
- - gt_bboxes (optional)
- - gt_polygons (optional)
-
- Modified Keys:
- - img
- - gt_bboxes (optional)
- - gt_polygons (optional)
-
- Added Keys:
- - flip
- - flip_direction
- Args:
- prob (float | list[float], optional): The flipping probability.
- Defaults to None.
- direction(str | list[str]): The flipping direction. Options
- If input is a list, the length must equal ``prob``. Each
- element in ``prob`` indicates the flip probability of
- corresponding direction. Defaults to 'horizontal'.
- """
-
- def flip_polygons(self, polygons: Sequence[np.ndarray],
- img_shape: Tuple[int, int],
- direction: str) -> Sequence[np.ndarray]:
- """Flip polygons horizontally, vertically or diagonally.
-
- Args:
- polygons (list[numpy.ndarray): polygons.
- img_shape (tuple[int]): Image shape (height, width)
- direction (str): Flip direction. Options are 'horizontal',
- 'vertical' and 'diagonal'.
- Returns:
- list[numpy.ndarray]: Flipped polygons.
- """
-
- h, w = img_shape
- flipped_polygons = []
- if direction == 'horizontal':
- for polygon in polygons:
- flipped_polygon = polygon.copy()
- flipped_polygon[0::2] = w - polygon[0::2]
- flipped_polygons.append(flipped_polygon)
- elif direction == 'vertical':
- for polygon in polygons:
- flipped_polygon = polygon.copy()
- flipped_polygon[1::2] = h - polygon[1::2]
- flipped_polygons.append(flipped_polygon)
- elif direction == 'diagonal':
- for polygon in polygons:
- flipped_polygon = polygon.copy()
- flipped_polygon[0::2] = w - polygon[0::2]
- flipped_polygon[1::2] = h - polygon[1::2]
- flipped_polygons.append(flipped_polygon)
- else:
- raise ValueError(
- f"Flipping direction must be 'horizontal', 'vertical', \
- or 'diagnal', but got '{direction}'")
- return flipped_polygons
-
- def _flip(self, results: dict) -> None:
- """Flip images, bounding boxes and polygons.
-
- Args:
- results (dict): Result dict containing the data to transform.
- """
- super()._flip(results)
- # flip polygons
- if results.get('gt_polygons', None) is not None:
- results['gt_polygons'] = self.flip_polygons(
- results['gt_polygons'], results['img'].shape[:2],
- results['flip_direction'])
-
-
-@TRANSFORMS.register_module()
-class SourceImagePad(BaseTransform):
- """Pad Image to target size. It will randomly crop an area from the
- original image and resize it to the target size, then paste the original
- image to its top left corner.
-
- Required Keys:
-
- - img
-
- Modified Keys:
-
- - img
- - img_shape
-
- Added Keys:
- - pad_shape
- - pad_fixed_size
-
- Args:
- target_scale (int or tuple[int, int]]): The target size of padded
- image. If it's an integer, then the padding size would be
- (target_size, target_size). If it's tuple, then ``target_scale[0]``
- should be the width and ``target_scale[1]`` should be the height.
- The size of the padded image will be (target_scale[1],
- target_scale[0])
- crop_ratio (float or Tuple[float, float]): Relative size for the
- crop region. If ``crop_ratio`` is a float, then the initial crop
- size would be
- ``(crop_ratio * img.shape[0], crop_ratio * img.shape[1])`` . If
- ``crop_ratio`` is a tuple, then ``crop_ratio[0]`` is for the width
- and ``crop_ratio[1]`` is for the height. The initial crop size
- would be
- ``(crop_ratio[1] * img.shape[0], crop_ratio[0] * img.shape[1])``.
- Defaults to 1./9.
- """
-
- def __init__(self,
- target_scale: Union[int, Tuple[int, int]],
- crop_ratio: Union[float, Tuple[float,
- float]] = 1. / 9) -> None:
- self.target_scale = target_scale if isinstance(
- target_scale, tuple) else (target_scale, target_scale)
- self.crop_ratio = crop_ratio if isinstance(
- crop_ratio, tuple) else (crop_ratio, crop_ratio)
-
- def transform(self, results: Dict) -> Dict:
- """Pad Image to target size. It will randomly select a small area from
- the original image and resize it to the target size, then paste the
- original image to its top left corner.
-
- Args:
- results (Dict): Result dict containing the data to transform.
-
- Returns:
- (Dict): The transformed data.
- """
- img = results['img']
- h, w = img.shape[:2]
- assert h <= self.target_scale[1] and w <= self.target_scale[
- 0], 'image size should be smaller that the target size'
- h_ind = np.random.randint(0, int(h - h * self.crop_ratio[1]) + 1)
- w_ind = np.random.randint(0, int(w - w * self.crop_ratio[0]) + 1)
- img_cut = img[h_ind:int(h_ind + h * self.crop_ratio[1]),
- w_ind:int(w_ind + w * self.crop_ratio[1])]
- expand_img = mmcv.imresize(img_cut, self.target_scale)
- # paste img to the top left corner of the padding region
- expand_img[0:h, 0:w] = img
- results['img'] = expand_img
- results['img_shape'] = expand_img.shape[:2]
- results['pad_shape'] = expand_img.shape
- results['pad_fixed_size'] = self.target_scale
- return results
-
- def __repr__(self) -> str:
- repr_str = self.__class__.__name__
- repr_str += f'(target_scale = {self.target_scale}, '
- repr_str += f'crop_ratio = {self.crop_ratio})'
- return repr_str
-
-
-@TRANSFORMS.register_module()
-@avoid_cache_randomness
-class ShortScaleAspectJitter(BaseTransform):
- """First rescale the image for its shorter side to reach the short_size and
- then jitter its aspect ratio, final rescale the shape guaranteed to be
- divided by scale_divisor.
-
- Required Keys:
-
- - img
- - img_shape
- - gt_bboxes (optional)
- - gt_polygons (optional)
-
-
- Modified Keys:
-
- - img
- - img_shape
- - gt_bboxes (optional)
- - gt_polygons (optional)
-
- Added Keys:
-
- - scale
- - scale_factor
- - keep_ratio
-
- Args:
- short_size (int): Target shorter size before jittering the aspect
- ratio. Defaults to 736.
- short_size_jitter_range (tuple(float, float)): Range of the ratio used
- to jitter the target shorter size. Defaults to (0.7, 1.3).
- aspect_ratio_jitter_range (tuple(float, float)): Range of the ratio
- used to jitter its aspect ratio. Defaults to (0.9, 1.1).
- scale_divisor (int): The scale divisor. Defaults to 1.
- resize_type (str): The type of resize class to use. Defaults to
- "Resize".
- **resize_kwargs: Other keyword arguments for the ``resize_type``.
- """
-
- def __init__(self,
- short_size: int = 736,
- ratio_range: Tuple[float, float] = (0.7, 1.3),
- aspect_ratio_range: Tuple[float, float] = (0.9, 1.1),
- scale_divisor: int = 1,
- resize_type: str = 'Resize',
- **resize_kwargs) -> None:
-
- super().__init__()
- self.short_size = short_size
- self.ratio_range = ratio_range
- self.aspect_ratio_range = aspect_ratio_range
- self.resize_cfg = dict(type=resize_type, **resize_kwargs)
-
- # create a empty Reisize object
- self.resize_cfg.update(dict(scale=0))
- self.resize = TRANSFORMS.build(self.resize_cfg)
- self.scale_divisor = scale_divisor
-
- def _sample_from_range(self, range: Tuple[float, float]) -> float:
- """A ratio will be randomly sampled from the range specified by
- ``range``.
-
- Args:
- ratio_range (tuple[float]): The minimum and maximum ratio.
-
- Returns:
- float: A ratio randomly sampled from the range.
- """
- min_value, max_value = min(range), max(range)
- value = np.random.random_sample() * (max_value - min_value) + min_value
- return value
-
- def transform(self, results: Dict) -> Dict:
- """Short Scale Aspect Jitter.
- Args:
- results (dict): Result dict containing the data to transform.
-
- Returns:
- dict: The transformed data.
- """
- h, w = results['img'].shape[:2]
- ratio = self._sample_from_range(self.ratio_range)
- scale = (ratio * self.short_size) / min(h, w)
-
- aspect = self._sample_from_range(self.aspect_ratio_range)
- h_scale = scale * math.sqrt(aspect)
- w_scale = scale / math.sqrt(aspect)
- new_h = round(h * h_scale)
- new_w = round(w * w_scale)
-
- new_h = math.ceil(new_h / self.scale_divisor) * self.scale_divisor
- new_w = math.ceil(new_w / self.scale_divisor) * self.scale_divisor
- self.resize.scale = (new_w, new_h)
- return self.resize(results)
-
- def __repr__(self) -> str:
- repr_str = self.__class__.__name__
- repr_str += f'(short_size = {self.short_size}, '
- repr_str += f'ratio_range = {self.ratio_range}, '
- repr_str += f'aspect_ratio_range = {self.aspect_ratio_range}, '
- repr_str += f'scale_divisor = {self.scale_divisor}, '
- repr_str += f'resize_cfg = {self.resize_cfg})'
- return repr_str
-
-
-@TRANSFORMS.register_module()
-class TextDetRandomCropFlip(BaseTransform):
- # TODO Rename this transformer; Refactor the redundant code.
- """Random crop and flip a patch in the image. Only used in text detection
- task.
-
- Required Keys:
-
- - img
- - gt_bboxes
- - gt_polygons
-
- Modified Keys:
-
- - img
- - gt_bboxes
- - gt_polygons
-
- Args:
- pad_ratio (float): The ratio of padding. Defaults to 0.1.
- crop_ratio (float): The ratio of cropping. Defaults to 0.5.
- iter_num (int): Number of operations. Defaults to 1.
- min_area_ratio (float): Minimal area ratio between cropped patch
- and original image. Defaults to 0.2.
- epsilon (float): The threshold of polygon IoU between cropped area
- and polygon, which is used to avoid cropping text instances.
- Defaults to 0.01.
- """
-
- def __init__(self,
- pad_ratio: float = 0.1,
- crop_ratio: float = 0.5,
- iter_num: int = 1,
- min_area_ratio: float = 0.2,
- epsilon: float = 1e-2) -> None:
- if not isinstance(pad_ratio, float):
- raise TypeError('`pad_ratio` should be an float, '
- f'but got {type(pad_ratio)} instead')
- if not isinstance(crop_ratio, float):
- raise TypeError('`crop_ratio` should be a float, '
- f'but got {type(crop_ratio)} instead')
- if not isinstance(iter_num, int):
- raise TypeError('`iter_num` should be an integer, '
- f'but got {type(iter_num)} instead')
- if not isinstance(min_area_ratio, float):
- raise TypeError('`min_area_ratio` should be a float, '
- f'but got {type(min_area_ratio)} instead')
- if not isinstance(epsilon, float):
- raise TypeError('`epsilon` should be a float, '
- f'but got {type(epsilon)} instead')
-
- self.pad_ratio = pad_ratio
- self.epsilon = epsilon
- self.crop_ratio = crop_ratio
- self.iter_num = iter_num
- self.min_area_ratio = min_area_ratio
-
- @cache_randomness
- def _random_prob(self) -> float:
- """Get the random prob to decide whether apply the transform.
-
- Returns:
- float: The probability
- """
- return random.random()
-
- @cache_randomness
- def _random_flip_type(self) -> int:
- """Get the random flip type.
-
- Returns:
- int: The flip type index. (0: horizontal; 1: vertical; 2: both)
- """
- return np.random.randint(3)
-
- @cache_randomness
- def _random_choice(self, axis: np.ndarray) -> np.ndarray:
- """Randomly select two coordinates from the axis.
-
- Args:
- axis (np.ndarray): Result dict containing the data to transform
-
- Returns:
- np.ndarray: The selected coordinates
- """
- return np.random.choice(axis, size=2)
-
- def transform(self, results: Dict) -> Dict:
- """Applying random crop flip on results.
-
- Args:
- results (dict): Result dict containing the data to transform
-
- Returns:
- dict: The transformed data
- """
- assert 'img' in results, '`img` is not found in results'
- for _ in range(self.iter_num):
- results = self._random_crop_flip_polygons(results)
- bboxes = [poly2bbox(poly) for poly in results['gt_polygons']]
- results['gt_bboxes'] = np.array(
- bboxes, dtype=np.float32).reshape(-1, 4)
- return results
-
- def _random_crop_flip_polygons(self, results: Dict) -> Dict:
- """Applying random crop flip on polygons.
-
- Args:
- results (dict): Result dict containing the data to transform
-
- Returns:
- dict: The transformed data
- """
- if results.get('gt_polygons', None) is None:
- return results
-
- image = results['img']
- polygons = results['gt_polygons']
- if len(polygons) == 0 or self._random_prob() > self.crop_ratio:
- return results
-
- h, w = results['img_shape']
- area = h * w
- pad_h = int(h * self.pad_ratio)
- pad_w = int(w * self.pad_ratio)
- h_axis, w_axis = self._generate_crop_target(image, polygons, pad_h,
- pad_w)
- if len(h_axis) == 0 or len(w_axis) == 0:
- return results
-
- # At most 10 attempts
- for _ in range(10):
- polys_keep = []
- polys_new = []
- kept_idxs = []
- xx = self._random_choice(w_axis)
- yy = self._random_choice(h_axis)
- xmin = np.clip(np.min(xx) - pad_w, 0, w - 1)
- xmax = np.clip(np.max(xx) - pad_w, 0, w - 1)
- ymin = np.clip(np.min(yy) - pad_h, 0, h - 1)
- ymax = np.clip(np.max(yy) - pad_h, 0, h - 1)
- if (xmax - xmin) * (ymax - ymin) < area * self.min_area_ratio:
- # Skip when cropped area is too small
- continue
-
- pts = np.stack([[xmin, xmax, xmax, xmin],
- [ymin, ymin, ymax, ymax]]).T.astype(np.int32)
- pp = plg(pts)
- success_flag = True
- for poly_idx, polygon in enumerate(polygons):
- ppi = plg(polygon.reshape(-1, 2))
- ppiou = poly_intersection(ppi, pp)
- if np.abs(ppiou - float(ppi.area)) > self.epsilon and \
- np.abs(ppiou) > self.epsilon:
- success_flag = False
- break
- kept_idxs.append(poly_idx)
- if np.abs(ppiou - float(ppi.area)) < self.epsilon:
- polys_new.append(polygon)
- else:
- polys_keep.append(polygon)
-
- if success_flag:
- break
-
- cropped = image[ymin:ymax, xmin:xmax, :]
- select_type = self._random_flip_type()
- if select_type == 0:
- img = np.ascontiguousarray(cropped[:, ::-1])
- elif select_type == 1:
- img = np.ascontiguousarray(cropped[::-1, :])
- else:
- img = np.ascontiguousarray(cropped[::-1, ::-1])
- image[ymin:ymax, xmin:xmax, :] = img
- results['img'] = image
-
- if len(polys_new) != 0:
- height, width, _ = cropped.shape
- if select_type == 0:
- for idx, polygon in enumerate(polys_new):
- poly = polygon.reshape(-1, 2)
- poly[:, 0] = width - poly[:, 0] + 2 * xmin
- polys_new[idx] = poly.reshape(-1, )
- elif select_type == 1:
- for idx, polygon in enumerate(polys_new):
- poly = polygon.reshape(-1, 2)
- poly[:, 1] = height - poly[:, 1] + 2 * ymin
- polys_new[idx] = poly.reshape(-1, )
- else:
- for idx, polygon in enumerate(polys_new):
- poly = polygon.reshape(-1, 2)
- poly[:, 0] = width - poly[:, 0] + 2 * xmin
- poly[:, 1] = height - poly[:, 1] + 2 * ymin
- polys_new[idx] = poly.reshape(-1, )
- polygons = polys_keep + polys_new
- # ignored = polys_keep_ignore_idx + polys_new_ignore_idx
- results['gt_polygons'] = polygons
- results['gt_ignored'] = results['gt_ignored'][kept_idxs]
- results['gt_bboxes_labels'] = results['gt_bboxes_labels'][
- kept_idxs]
- return results
-
- def _generate_crop_target(self, image: np.ndarray,
- all_polys: List[np.ndarray], pad_h: int,
- pad_w: int) -> Tuple[np.ndarray, np.ndarray]:
- """Generate cropping target and make sure not to crop the polygon
- instances.
-
- Args:
- image (np.ndarray): The image waited to be crop.
- all_polys (list[np.ndarray]): Ground-truth polygons.
- pad_h (int): Padding length of height.
- pad_w (int): Padding length of width.
-
- Returns:
- (np.ndarray, np.ndarray): Returns a tuple ``(h_axis, w_axis)``,
- where ``h_axis`` is the vertical cropping range and ``w_axis``
- is the horizontal cropping range.
- """
- h, w, _ = image.shape
- h_array = np.zeros((h + pad_h * 2), dtype=np.int32)
- w_array = np.zeros((w + pad_w * 2), dtype=np.int32)
-
- text_polys = []
- for polygon in all_polys:
- rect = cv2.minAreaRect(polygon.astype(np.int32).reshape(-1, 2))
- box = cv2.boxPoints(rect)
- box = np.int0(box)
- text_polys.append([box[0], box[1], box[2], box[3]])
-
- polys = np.array(text_polys, dtype=np.int32)
- for poly in polys:
- poly = np.round(poly, decimals=0).astype(np.int32)
- minx, maxx = np.min(poly[:, 0]), np.max(poly[:, 0])
- miny, maxy = np.min(poly[:, 1]), np.max(poly[:, 1])
- w_array[minx + pad_w:maxx + pad_w] = 1
- h_array[miny + pad_h:maxy + pad_h] = 1
-
- h_axis = np.where(h_array == 0)[0]
- w_axis = np.where(w_array == 0)[0]
- return h_axis, w_axis
-
- def __repr__(self) -> str:
- repr_str = self.__class__.__name__
- repr_str += f'(pad_ratio = {self.pad_ratio}'
- repr_str += f', crop_ratio = {self.crop_ratio}'
- repr_str += f', iter_num = {self.iter_num}'
- repr_str += f', min_area_ratio = {self.min_area_ratio}'
- repr_str += f', epsilon = {self.epsilon})'
- return repr_str
-
-
-@TRANSFORMS.register_module()
-@avoid_cache_randomness
-class TextDetRandomCrop(BaseTransform):
- """Randomly select a region and crop images to a target size and make sure
- to contain text region. This transform may break up text instances, and for
- broken text instances, we will crop it's bbox and polygon coordinates. This
- transform is recommend to be used in segmentation-based network.
-
- Required Keys:
-
- - img
- - gt_polygons
- - gt_bboxes
- - gt_bboxes_labels
- - gt_ignored
-
- Modified Keys:
-
- - img
- - img_shape
- - gt_polygons
- - gt_bboxes
- - gt_bboxes_labels
- - gt_ignored
-
- Args:
- target_size (tuple(int, int) or int): Target size for the cropped
- image. If it's a tuple, then target width and target height will be
- ``target_size[0]`` and ``target_size[1]``, respectively. If it's an
- integer, them both target width and target height will be
- ``target_size``.
- positive_sample_ratio (float): The probability of sampling regions
- that go through text regions. Defaults to 5. / 8.
- """
-
- def __init__(self,
- target_size: Tuple[int, int] or int,
- positive_sample_ratio: float = 5.0 / 8.0) -> None:
- self.target_size = target_size if isinstance(
- target_size, tuple) else (target_size, target_size)
- self.positive_sample_ratio = positive_sample_ratio
-
- def _get_postive_prob(self) -> float:
- """Get the probability to do positive sample.
-
- Returns:
- float: The probability to do positive sample.
- """
- return np.random.random_sample()
-
- def _sample_num(self, start, end):
- """Sample a number in range [start, end].
-
- Args:
- start (int): Starting point.
- end (int): Ending point.
-
- Returns:
- (int): Sampled number.
- """
- return random.randint(start, end)
-
- def _sample_offset(self, gt_polygons: Sequence[np.ndarray],
- img_size: Tuple[int, int]) -> Tuple[int, int]:
- """Samples the top-left coordinate of a crop region, ensuring that the
- cropped region contains at least one polygon.
-
- Args:
- gt_polygons (list(ndarray)) : Polygons.
- img_size (tuple(int, int)) : Image size in the format of
- (height, width).
-
- Returns:
- tuple(int, int): Top-left coordinate of the cropped region.
- """
- h, w = img_size
- t_w, t_h = self.target_size
-
- # target size is bigger than origin size
- t_h = t_h if t_h < h else h
- t_w = t_w if t_w < w else w
- if (gt_polygons is not None and len(gt_polygons) > 0
- and self._get_postive_prob() < self.positive_sample_ratio):
-
- # make sure to crop the positive region
-
- # the minimum top left to crop positive region (h,w)
- tl = np.array([h + 1, w + 1], dtype=np.int32)
- for gt_polygon in gt_polygons:
- temp_point = np.min(gt_polygon.reshape(2, -1), axis=1)
- if temp_point[0] <= tl[0]:
- tl[0] = temp_point[0]
- if temp_point[1] <= tl[1]:
- tl[1] = temp_point[1]
- tl = tl - (t_h, t_w)
- tl[tl < 0] = 0
- # the maximum bottum right to crop positive region
- br = np.array([0, 0], dtype=np.int32)
- for gt_polygon in gt_polygons:
- temp_point = np.max(gt_polygon.reshape(2, -1), axis=1)
- if temp_point[0] > br[0]:
- br[0] = temp_point[0]
- if temp_point[1] > br[1]:
- br[1] = temp_point[1]
- br = br - (t_h, t_w)
- br[br < 0] = 0
-
- # if br is too big so that crop the outside region of img
- br[0] = min(br[0], h - t_h)
- br[1] = min(br[1], w - t_w)
- #
- h = self._sample_num(tl[0], br[0]) if tl[0] < br[0] else 0
- w = self._sample_num(tl[1], br[1]) if tl[1] < br[1] else 0
- else:
- # make sure not to crop outside of img
-
- h = self._sample_num(0, h - t_h) if h - t_h > 0 else 0
- w = self._sample_num(0, w - t_w) if w - t_w > 0 else 0
-
- return (h, w)
-
- def _crop_img(self, img: np.ndarray, offset: Tuple[int, int],
- target_size: Tuple[int, int]) -> np.ndarray:
- """Crop the image given an offset and a target size.
-
- Args:
- img (ndarray): Image.
- offset (Tuple[int. int]): Coordinates of the starting point.
- target_size: Target image size.
- """
- h, w = img.shape[:2]
- target_size = target_size[::-1]
- br = np.min(
- np.stack((np.array(offset) + np.array(target_size), np.array(
- (h, w)))),
- axis=0)
- return img[offset[0]:br[0], offset[1]:br[1]], np.array(
- [offset[1], offset[0], br[1], br[0]])
-
- def _crop_polygons(self, polygons: Sequence[np.ndarray],
- crop_bbox: np.ndarray) -> Sequence[np.ndarray]:
- """Crop polygons to be within a crop region. If polygon crosses the
- crop_bbox, we will keep the part left in crop_bbox by cropping its
- boardline.
-
- Args:
- polygons (list(ndarray)): List of polygons [(N1, ), (N2, ), ...].
- crop_bbox (ndarray): Cropping region. [x1, y1, x2, y1].
-
- Returns
- tuple(List(ArrayLike), list[int]):
- - (List(ArrayLike)): The rest of the polygons located in the
- crop region.
- - (list[int]): Index list of the reserved polygons.
- """
- polygons_cropped = []
- kept_idx = []
- for idx, polygon in enumerate(polygons):
- if polygon.size < 6:
- continue
- poly = crop_polygon(polygon, crop_bbox)
- if poly is not None:
- poly = poly.reshape(-1, 2) - (crop_bbox[0], crop_bbox[1])
- polygons_cropped.append(poly.reshape(-1))
- kept_idx.append(idx)
- return (polygons_cropped, kept_idx)
-
- def transform(self, results: Dict) -> Dict:
- """Applying random crop on results.
- Args:
- results (dict): Result dict contains the data to transform.
-
- Returns:
- dict: The transformed data
- """
- if self.target_size == results['img'].shape[:2][::-1]:
- return results
- gt_polygons = results['gt_polygons']
- crop_offset = self._sample_offset(gt_polygons,
- results['img'].shape[:2])
- img, crop_bbox = self._crop_img(results['img'], crop_offset,
- self.target_size)
- results['img'] = img
- results['img_shape'] = img.shape[:2]
- gt_polygons, polygon_kept_idx = self._crop_polygons(
- gt_polygons, crop_bbox)
- bboxes = [poly2bbox(poly) for poly in gt_polygons]
- results['gt_bboxes'] = np.array(
- bboxes, dtype=np.float32).reshape(-1, 4)
-
- results['gt_polygons'] = gt_polygons
- results['gt_bboxes_labels'] = results['gt_bboxes_labels'][
- polygon_kept_idx]
- results['gt_ignored'] = results['gt_ignored'][polygon_kept_idx]
- return results
-
- def __repr__(self) -> str:
- repr_str = self.__class__.__name__
- repr_str += f'(target_size = {self.target_size}, '
- repr_str += f'positive_sample_ratio = {self.positive_sample_ratio})'
- return repr_str
diff --git a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/detext_converter.py b/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/detext_converter.py
deleted file mode 100644
index 360dc5a6f4555d45fb69533f033710eb58e12cad..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/detext_converter.py
+++ /dev/null
@@ -1,212 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import argparse
-import os
-import os.path as osp
-
-import mmcv
-import mmengine
-import numpy as np
-
-from mmocr.utils import crop_img, dump_ocr_data
-
-
-def collect_files(img_dir, gt_dir):
- """Collect all images and their corresponding groundtruth files.
-
- Args:
- img_dir (str): The image directory
- gt_dir (str): The groundtruth directory
-
- Returns:
- files (list): The list of tuples (img_file, groundtruth_file)
- """
- assert isinstance(img_dir, str)
- assert img_dir
- assert isinstance(gt_dir, str)
- assert gt_dir
-
- ann_list, imgs_list = [], []
- for img in os.listdir(img_dir):
- imgs_list.append(osp.join(img_dir, img))
- ann_list.append(osp.join(gt_dir, 'gt_' + img.replace('jpg', 'txt')))
-
- files = list(zip(imgs_list, ann_list))
- assert len(files), f'No images found in {img_dir}'
- print(f'Loaded {len(files)} images from {img_dir}')
-
- return files
-
-
-def collect_annotations(files, nproc=1):
- """Collect the annotation information.
-
- Args:
- files (list): The list of tuples (image_file, groundtruth_file)
- nproc (int): The number of process to collect annotations
-
- Returns:
- images (list): The list of image information dicts
- """
- assert isinstance(files, list)
- assert isinstance(nproc, int)
-
- if nproc > 1:
- images = mmengine.track_parallel_progress(
- load_img_info, files, nproc=nproc)
- else:
- images = mmengine.track_progress(load_img_info, files)
-
- return images
-
-
-def load_img_info(files):
- """Load the information of one image.
-
- Args:
- files (tuple): The tuple of (img_file, groundtruth_file)
-
- Returns:
- img_info (dict): The dict of the img and annotation information
- """
- assert isinstance(files, tuple)
-
- img_file, gt_file = files
- # read imgs while ignoring orientations
- img = mmcv.imread(img_file, 'unchanged')
-
- img_info = dict(
- file_name=osp.join(osp.basename(img_file)),
- height=img.shape[0],
- width=img.shape[1],
- segm_file=osp.join(osp.basename(gt_file)))
-
- if osp.splitext(gt_file)[1] == '.txt':
- img_info = load_txt_info(gt_file, img_info)
- else:
- raise NotImplementedError
-
- return img_info
-
-
-def load_txt_info(gt_file, img_info):
- """Collect the annotation information.
-
- Args:
- gt_file (str): The path to ground-truth
- img_info (dict): The dict of the img and annotation information
-
- Returns:
- img_info (dict): The dict of the img and annotation information
- """
-
- with open(gt_file) as f:
- anno_info = []
- annotations = f.readlines()
- for ann in annotations:
- # Annotation format [x1, y1, x2, y2, x3, y3, x4, y4, transcript]
- try:
- bbox = np.array(ann.split(',')[0:8]).astype(int).tolist()
- except ValueError:
- # Skip invalid annotation line
- continue
- word = ann.split(',')[-1].replace('\n', '').strip()
-
- # Skip samples without recog gt
- if word == '###':
- continue
- anno = dict(bbox=bbox, word=word)
- anno_info.append(anno)
-
- img_info.update(anno_info=anno_info)
-
- return img_info
-
-
-def generate_ann(root_path, split, image_infos, preserve_vertical):
- """Generate cropped annotations and label txt file.
-
- Args:
- root_path (str): The root path of the dataset
- split (str): The split of dataset. Namely: training or test
- image_infos (list[dict]): A list of dicts of the img and
- annotation information
- preserve_vertical (bool): Whether to preserve vertical texts
- """
-
- dst_image_root = osp.join(root_path, 'crops', split)
- ignore_image_root = osp.join(root_path, 'ignores', split)
- if split == 'training':
- dst_label_file = osp.join(root_path, 'train_label.json')
- elif split == 'val':
- dst_label_file = osp.join(root_path, 'val_label.json')
- mmengine.mkdir_or_exist(dst_image_root)
- mmengine.mkdir_or_exist(ignore_image_root)
-
- img_info = []
- for image_info in image_infos:
- index = 1
- src_img_path = osp.join(root_path, 'imgs', split,
- image_info['file_name'])
- image = mmcv.imread(src_img_path)
- src_img_root = image_info['file_name'].split('.')[0]
-
- for anno in image_info['anno_info']:
- word = anno['word']
- dst_img = crop_img(image, anno['bbox'], 0, 0)
- h, w, _ = dst_img.shape
-
- dst_img_name = f'{src_img_root}_{index}.png'
- index += 1
- # Skip invalid annotations
- if min(dst_img.shape) == 0 or len(word) == 0:
- continue
- # Filter out vertical texts
- if not preserve_vertical and h / w > 2 and split == 'training':
- dst_img_path = osp.join(ignore_image_root, dst_img_name)
- mmcv.imwrite(dst_img, dst_img_path)
- continue
-
- dst_img_path = osp.join(dst_image_root, dst_img_name)
- mmcv.imwrite(dst_img, dst_img_path)
-
- img_info.append({
- 'file_name': dst_img_name,
- 'anno_info': [{
- 'text': word
- }]
- })
-
- dump_ocr_data(img_info, dst_label_file, 'textrecog')
-
-
-def parse_args():
- parser = argparse.ArgumentParser(
- description='Generate training and val set of DeText ')
- parser.add_argument('root_path', help='Root dir path of DeText')
- parser.add_argument(
- '--preserve-vertical',
- help='Preserve samples containing vertical texts',
- action='store_true')
- parser.add_argument(
- '--nproc', default=1, type=int, help='Number of process')
- args = parser.parse_args()
- return args
-
-
-def main():
- args = parse_args()
- root_path = args.root_path
-
- for split in ['training', 'val']:
- print(f'Processing {split} set...')
- with mmengine.Timer(
- print_tmpl='It takes {}s to convert DeText annotation'):
- files = collect_files(
- osp.join(root_path, 'imgs', split),
- osp.join(root_path, 'annotations', split))
- image_infos = collect_annotations(files, nproc=args.nproc)
- generate_ann(root_path, split, image_infos, args.preserve_vertical)
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/NMEX/rvc-hoyo-game/infer_pack/transforms.py b/spaces/NMEX/rvc-hoyo-game/infer_pack/transforms.py
deleted file mode 100644
index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000
--- a/spaces/NMEX/rvc-hoyo-game/infer_pack/transforms.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(
- inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.0,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
-
-
-def unconstrained_rational_quadratic_spline(
- inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails="linear",
- tail_bound=1.0,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == "linear":
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError("{} tails are not implemented.".format(tails))
-
- (
- outputs[inside_interval_mask],
- logabsdet[inside_interval_mask],
- ) = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound,
- right=tail_bound,
- bottom=-tail_bound,
- top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- )
-
- return outputs, logabsdet
-
-
-def rational_quadratic_spline(
- inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0.0,
- right=1.0,
- bottom=0.0,
- top=1.0,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError("Input to a transform is not within its domain")
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError("Minimal bin width too large for the number of bins")
- if min_bin_height * num_bins > 1.0:
- raise ValueError("Minimal bin height too large for the number of bins")
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (inputs - input_cumheights) * (
- input_derivatives + input_derivatives_plus_one - 2 * input_delta
- ) + input_heights * (input_delta - input_derivatives)
- b = input_heights * input_derivatives - (inputs - input_cumheights) * (
- input_derivatives + input_derivatives_plus_one - 2 * input_delta
- )
- c = -input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + (
- (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta
- )
- derivative_numerator = input_delta.pow(2) * (
- input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2)
- )
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (
- input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
- )
- denominator = input_delta + (
- (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta
- )
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (
- input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2)
- )
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/NN520/AI/src/components/chat-scroll-anchor.tsx b/spaces/NN520/AI/src/components/chat-scroll-anchor.tsx
deleted file mode 100644
index ac809f4486a48e134cb69314c3d0dae5e68d614e..0000000000000000000000000000000000000000
--- a/spaces/NN520/AI/src/components/chat-scroll-anchor.tsx
+++ /dev/null
@@ -1,29 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { useInView } from 'react-intersection-observer'
-
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-
-interface ChatScrollAnchorProps {
- trackVisibility?: boolean
-}
-
-export function ChatScrollAnchor({ trackVisibility }: ChatScrollAnchorProps) {
- const isAtBottom = useAtBottom()
- const { ref, entry, inView } = useInView({
- trackVisibility,
- delay: 100,
- rootMargin: '0px 0px -150px 0px'
- })
-
- React.useEffect(() => {
- if (isAtBottom && trackVisibility && !inView) {
- entry?.target.scrollIntoView({
- block: 'start'
- })
- }
- }, [inView, entry, isAtBottom, trackVisibility])
-
- return
-}
diff --git a/spaces/Nikhil0987/omm/remainder.py b/spaces/Nikhil0987/omm/remainder.py
deleted file mode 100644
index b34e2c24c5d989e1f42bf9e4d1ed207eb9e6a378..0000000000000000000000000000000000000000
--- a/spaces/Nikhil0987/omm/remainder.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import streamlit as st
-import json
-import uuid
-import os
-from pymongo import MongoClient
-
-# uri = os.environ["MONGO_CONNECTION_STRING"]
-uri = "mongodb+srv://cluster0.j2p0gjo.mongodb.net/?authSource=%24external&authMechanism=MONGODB-X509&retryWrites=true&w=majority"
-client = MongoClient(uri,tlsCertificateKeyFile= "cert.pem")
-
-db = client["mydata"]
-col = db["Reminders"]
-
-def rem():
- st.title("Reminders")
- st.subheader("Enter reminder details:")
-
-
-
- remid =str(uuid.uuid4())
- remsg =st.text_input("Enter Reminder Message:")
-
- remtime = str(st.time_input("Enter Reminder Time:"))
-
- remdate = str(st.date_input("Enter Reminder Date:"))
-
- repeat =st.selectbox(
- " would you like to repeat Reminder:",["No","daily","weekly","yearly"]
- )
- reminder ={
- "id": remid,
- "message": remsg,
- "time": remtime,
- "date": remdate,
- "repeat": repeat,
- "status": False,
-
- }
- if st.button("Save"):
- col.insert_one(reminder)
-
-
-
-
-
-
-
-
-
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/dynamicconv_layer/cuda_function_gen.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/dynamicconv_layer/cuda_function_gen.py
deleted file mode 100644
index 9304f99eb8169a614f39babc830c84cac80e080b..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/dynamicconv_layer/cuda_function_gen.py
+++ /dev/null
@@ -1,223 +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.
-
-
-def gen_forward():
-
- kernels = [3, 5, 7, 15, 31, 63, 127, 255]
- blocks = [32, 64, 128, 256]
-
- head = """
-/**
- * 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.
- */
-
-#include "dynamicconv_cuda.cuh"
-
-std::vector dynamicconv_cuda_forward(at::Tensor input, at::Tensor weight, int padding_l) {
-
- at::DeviceGuard g(input.device());
- const auto minibatch = input.size(0);
- const auto numFeatures = input.size(1);
- const auto sequenceLength = input.size(2);
-
- const auto numHeads = weight.size(1);
- const auto filterSize = weight.size(2);
-
- const auto numFiltersInBlock = numFeatures / numHeads;
- const dim3 blocks(minibatch, numFeatures);
-
- auto output = at::zeros_like(input);
- auto stream = at::cuda::getCurrentCUDAStream();
-"""
-
- switch = """
- switch(filterSize) {
-"""
-
- case_k = """
- case {k}:
-"""
-
- main_block = """
- if (padding_l == {pad}) {{
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "dynamicconv_forward", ([&] {{
- dynamicconv_forward_kernel<{k}, {b_size}, {pad}, scalar_t>
- <<>>(
- input.data(),
- weight.data(),
- minibatch,
- sequenceLength,
- numFeatures,
- numFiltersInBlock,
- numHeads,
- output.data());
- }}));
- }} else
-"""
-
- bad_padding = """
- {
- std::cout << "WARNING: Unsupported padding size - skipping forward pass" << std::endl;
- }
- break;\n
-"""
-
- end = """
- default:
- std::cout << "WARNING: Unsupported filter length passed - skipping forward pass" << std::endl;
- }
-
- return {output};
-}
-"""
-
- with open("dynamicconv_cuda_forward.cu", "w") as forward:
- forward.write(head)
- forward.write(switch)
- for k in kernels:
- b_size = 32
- for b in blocks:
- if b > k:
- b_size = b
- break
- forward.write(case_k.format(k=k))
- for pad in [k // 2, k - 1]:
- forward.write(main_block.format(k=k, b_size=b_size, pad=pad))
- forward.write(bad_padding)
- forward.write(end)
-
-
-def gen_backward():
-
- kernels = [3, 5, 7, 15, 31, 63, 127, 255]
- thresh = [512, 512, 512, 512, 512, 380, 256, 256]
- min_block = [64, 64, 64, 64, 64, 64, 128, 256]
- seqs = [32 * x for x in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]]
-
- head = """
-/**
- * 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.
- */
-
-#include "dynamicconv_cuda.cuh"
-
-std::vector dynamicconv_cuda_backward(at::Tensor gradOutput, int padding_l, at::Tensor input, at::Tensor weight) {
-
- at::DeviceGuard g(input.device());
- const auto minibatch = input.size(0);
- const auto numFeatures = input.size(1);
- const auto sequenceLength = input.size(2);
-
- const auto numHeads = weight.size(1);
- const auto filterSize = weight.size(2);
-
- const auto numFiltersInBlock = numFeatures / numHeads;
- auto numChunks = 1;
-
- auto gradInput = at::zeros_like(input);
- auto gradWeight = at::zeros_like(weight);
- auto stream = at::cuda::getCurrentCUDAStream();
-
- dim3 blocks(minibatch, numHeads, numChunks);
-"""
-
- sequence_if = """
- if (sequenceLength < {seq}) {{
- switch(filterSize) {{
-"""
-
- case_k = """
- case {k}:
-"""
-
- chunks_reset = """
- numChunks = int(ceilf(sequenceLength/float({b_size})));
- blocks = dim3(minibatch, numHeads, numChunks);
-"""
-
- main_block = """
- if (padding_l == {p}) {{
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(gradOutput.scalar_type(), "dynamicconv_backward", ([&] {{
- dynamicconv_backward_kernel<{k}, {b_size}, {p}, scalar_t>
- <<>>(
- gradOutput.data(),
- input.data(),
- weight.data(),
- minibatch,
- sequenceLength,
- numFeatures,
- numFiltersInBlock,
- numHeads,
- gradWeight.data(),
- gradInput.data());
- }}));
- }} else
-"""
-
- bad_padding = """
- {
- std::cout << "WARNING: Unsupported padding size - skipping backward pass" << std::endl;
- }
- break;\n
-"""
-
- bad_filter = """
- default:
- std::cout << "WARNING: Unsupported filter length passed - skipping backward pass" << std::endl;
- }
-"""
-
- con_else = """
- } else
-"""
-
- final_else = """
- {
- switch(filterSize) {
-"""
-
- last_return = """
- }
- return {gradInput, gradWeight};
-}
-"""
-
- with open("dynamicconv_cuda_backward.cu", "w") as backward:
- backward.write(head)
- for seq in seqs:
- backward.write(sequence_if.format(seq=seq))
- for k, t, m in zip(kernels, thresh, min_block):
- backward.write(case_k.format(k=k))
- if seq <= t:
- b_size = seq
- else:
- b_size = m
- backward.write(chunks_reset.format(b_size=b_size))
- for p in [k // 2, k - 1]:
- backward.write(main_block.format(k=k, b_size=b_size, p=p))
- backward.write(bad_padding)
- backward.write(bad_filter)
- backward.write(con_else)
- backward.write(final_else)
- for k, m in zip(kernels, min_block):
- backward.write(case_k.format(k=k))
- backward.write(chunks_reset.format(b_size=m))
- for p in [k // 2, k - 1]:
- backward.write(main_block.format(k=k, b_size=m, p=p))
- backward.write(bad_padding)
- backward.write(bad_filter)
- backward.write(last_return)
-
-
-if __name__ == "__main__":
- gen_forward()
- gen_backward()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py
deleted file mode 100644
index 0f87bb5d7ed5c7eb8011d4c651f2ecbf0ae700ac..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from collections.abc import Collection
-from dataclasses import dataclass, field
-from typing import List
-
-from omegaconf import II
-
-from fairseq.dataclass import FairseqDataclass
-from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
-
-
-@dataclass
-class InverseSquareRootLRScheduleConfig(FairseqDataclass):
- warmup_updates: int = field(
- default=4000,
- metadata={"help": "warmup the learning rate linearly for the first N updates"},
- )
- warmup_init_lr: float = field(
- default=-1,
- metadata={
- "help": "initial learning rate during warmup phase; default is cfg.lr"
- },
- )
- lr: List[float] = II("optimization.lr")
-
-
-@register_lr_scheduler("inverse_sqrt", dataclass=InverseSquareRootLRScheduleConfig)
-class InverseSquareRootSchedule(FairseqLRScheduler):
- """Decay the LR based on the inverse square root of the update number.
-
- We also support a warmup phase where we linearly increase the learning rate
- from some initial learning rate (``--warmup-init-lr``) until the configured
- learning rate (``--lr``). Thereafter we decay proportional to the number of
- updates, with a decay factor set to align with the configured learning rate.
-
- During warmup::
-
- lrs = torch.linspace(cfg.warmup_init_lr, cfg.lr, cfg.warmup_updates)
- lr = lrs[update_num]
-
- After warmup::
-
- decay_factor = cfg.lr * sqrt(cfg.warmup_updates)
- lr = decay_factor / sqrt(update_num)
- """
-
- def __init__(self, cfg: InverseSquareRootLRScheduleConfig, optimizer):
- super().__init__(cfg, optimizer)
- if isinstance(cfg.lr, Collection) and len(cfg.lr) > 1:
- raise ValueError(
- "Cannot use a fixed learning rate schedule with inverse_sqrt."
- " Consider --lr-scheduler=fixed instead."
- )
- warmup_end_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr
- if cfg.warmup_init_lr < 0:
- cfg.warmup_init_lr = 0 if cfg.warmup_updates > 0 else warmup_end_lr
-
- # linearly warmup for the first cfg.warmup_updates
- self.lr_step = (warmup_end_lr - cfg.warmup_init_lr) / cfg.warmup_updates
-
- # then, decay prop. to the inverse square root of the update number
- self.decay_factor = warmup_end_lr * cfg.warmup_updates ** 0.5
-
- # initial learning rate
- self.lr = cfg.warmup_init_lr
- self.optimizer.set_lr(self.lr)
-
- def step(self, epoch, val_loss=None):
- """Update the learning rate at the end of the given epoch."""
- super().step(epoch, val_loss)
- # we don't change the learning rate at epoch boundaries
- return self.optimizer.get_lr()
-
- def step_update(self, num_updates):
- """Update the learning rate after each update."""
- if num_updates < self.cfg.warmup_updates:
- self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step
- else:
- self.lr = self.decay_factor * num_updates ** -0.5
- self.optimizer.set_lr(self.lr)
- return self.lr
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/encoders/characters.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/encoders/characters.py
deleted file mode 100644
index 494ea219392716dc75d2c1e19d71cd55b9b2f4ba..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/encoders/characters.py
+++ /dev/null
@@ -1,30 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-from fairseq.data.encoders import register_bpe
-
-
-SPACE = chr(32)
-SPACE_ESCAPE = chr(9601)
-
-
-@register_bpe("characters")
-class Characters(object):
- def __init__(self, *unused):
- pass
-
- @staticmethod
- def add_args(parser):
- pass
-
- @staticmethod
- def encode(x: str) -> str:
- escaped = x.replace(SPACE, SPACE_ESCAPE)
- return SPACE.join(list(escaped))
-
- @staticmethod
- def decode(x: str) -> str:
- return x.replace(SPACE, "").replace(SPACE_ESCAPE, SPACE)
diff --git a/spaces/OpenDILabCommunity/LLMRiddlesChatGPTCN/llmriddles/questions/level3.py b/spaces/OpenDILabCommunity/LLMRiddlesChatGPTCN/llmriddles/questions/level3.py
deleted file mode 100644
index a20cd62c3f7ca7bbb0a4bb745ddd4559b4404b7a..0000000000000000000000000000000000000000
--- a/spaces/OpenDILabCommunity/LLMRiddlesChatGPTCN/llmriddles/questions/level3.py
+++ /dev/null
@@ -1,165 +0,0 @@
-from .question import register_question
-
-
-def count_words(text: str, contain_punctuation: bool = False):
- chinese_words = []
- english_words = []
- other_words = []
- temp_english_words = []
- for char in text:
- if '\u4e00' <= char <= '\u9fff':
- chinese_words.append(char)
- if len(temp_english_words) > 0:
- english_words.append(''.join(temp_english_words))
- temp_english_words = []
- else:
- if char.isalpha():
- temp_english_words.append(char)
- else:
- if len(temp_english_words) > 0:
- english_words.append(''.join(temp_english_words))
- temp_english_words = []
- other_words.append(char)
- if contain_punctuation:
- return len(chinese_words) + len(english_words) + len(other_words)
- else:
- return len(chinese_words) + len(english_words)
-
-
-CN_TEXT_1 = """
-第三章第一题（自说自话），请你输入一个至少10个字的提问，使模型的回答和提问完全一样。
-
-请在下面的输入框内填写你的提问并点击按钮提交。
-"""
-
-EN_TEXT_1 = """
-For the first question in chapter 3, please enter a question of at least ten words so that the model's answer is exatly the same with the question.
-
-Please enter your query below and click the submit button
-"""
-
-
-def _checker_1(question_text: str, user_text: str, answer_text: str, lang: str):
- _ = question_text, lang
- answer_text = answer_text.strip()
- user_text = user_text.strip()
-
- if count_words(user_text) < 10:
- return False, "用户的问题长度应该至少10个字" if lang == 'cn' else 'Question should be no less than 10 words.'
-
- if answer_text == user_text:
- return True, None
- else:
- return False, None
-
-
-register_question(
- {
- 'cn': CN_TEXT_1,
- 'en': EN_TEXT_1,
- },
- checkers=_checker_1,
- name={'cn': '3-1 自说自话', 'en': '3-1'},
- level=3,
-)
-
-CN_TEXT_2 = """
-第三章第二题（话自说自），请你输入一个至少10个字的提问，使模型的回答是问题的反序。
-
-请在下面的输入框内填写你的提问并点击按钮提交。
-"""
-
-EN_TEXT_2 = """
-For the second question in chapter 3, please enter a question of at least ten words so that the model's answer is the reverse order of the question.
-
-Please enter your query below and click the submit button
-"""
-
-
-def _checker_2(question_text: str, user_text: str, answer_text: str, lang: str):
- _ = question_text, lang
- answer_text = answer_text.strip()
- user_text = user_text.strip()
-
- if count_words(user_text) < 10:
- return False, "用户的问题长度应该至少10个字" if lang == 'cn' else 'Question should be no less than 10 words.'
-
- if lang == 'cn':
- user_text = user_text[::-1]
- else:
- words = user_text.split()
- words.reverse()
- user_text = ' '.join(words)
-
- if user_text == answer_text:
- return True, None
- else:
- return False, None
-
-
-register_question(
- {
- 'cn': CN_TEXT_2,
- 'en': EN_TEXT_2,
- },
- checkers=_checker_2,
- name={'cn': '3-2 话自说自', 'en': '3-2'},
- level=3,
-)
-
-CN_TEXT_3 = """
-第三章第三题（就是敢说），请你输入一个不包含“1”和“3”的提问，使模型的回答是一字不差的“1+1=3”（不需要引号）。
-
-请在下面的输入框内填写你的提问并点击按钮提交。
-"""
-
-EN_TEXT_3 = """
-For the third question in chapter 3, please enter a question not contain "1" or "3" that will result in the exact answer "1+1=3" (no quotes needed).
-
-Please enter your query below and click the submit button
-"""
-
-
-def _checker_3(question_text: str, user_text: str, answer_text: str, lang: str):
- _ = question_text, lang
- answer_text = answer_text.strip()
- user_text = user_text.strip()
-
- if '1' in user_text or '3' in user_text:
- return False, "用户的问题不能包含“1”或“3”" if lang == 'cn' else 'Question should not contain \"1\" or \"3\".'
-
- if answer_text == '1+1=3':
- return True, None
- else:
- return False, None
-
-
-register_question(
- {
- 'cn': CN_TEXT_3,
- 'en': EN_TEXT_3,
- },
- checkers=_checker_3,
- name={'cn': '3-3 就是敢说', 'en': '3-3'},
- level=3,
-)
-
-# CN_TEXT_4 = """
-# 第三章第四题（回文协变），请你输入一个本身不是回文串的问题，使得正着问和倒着问时，模型的回答本身不是回文且也是逆序。
-
-# 请在下面的输入框内填写你的提问并点击按钮提交。
-# """
-
-# EN_TEXT_4 = """
-# For the fourth question in chapter 3, please enter a question that is not a palindrome string, so that the model's answer is also not a palindrome and is in reverse order when asked forward or backward.
-
-# Please enter your query below and click the submit button
-# """
-
-# def _checker_4(question_text: str, user_text: str, answer_text: str, lang: str):
-# pass
-
-# register_question({
-# 'cn': CN_TEXT_4,
-# 'en': EN_TEXT_4,
-# }, _checker_4, level=3)
diff --git a/spaces/OpenMotionLab/MotionGPT/mGPT/archs/tools/quantize_cnn.py b/spaces/OpenMotionLab/MotionGPT/mGPT/archs/tools/quantize_cnn.py
deleted file mode 100644
index 98ca858a7af1eb74fccc58f4305e96247a7cd7c3..0000000000000000000000000000000000000000
--- a/spaces/OpenMotionLab/MotionGPT/mGPT/archs/tools/quantize_cnn.py
+++ /dev/null
@@ -1,414 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-class QuantizeEMAReset(nn.Module):
- def __init__(self, nb_code, code_dim, mu):
- super().__init__()
- self.nb_code = nb_code
- self.code_dim = code_dim
- self.mu = mu
- self.reset_codebook()
-
- def reset_codebook(self):
- self.init = False
- self.code_sum = None
- self.code_count = None
- device = "cuda" if torch.cuda.is_available() else "cpu"
- self.register_buffer('codebook', torch.zeros(self.nb_code, self.code_dim).to(device))
-
- def _tile(self, x):
- nb_code_x, code_dim = x.shape
- if nb_code_x < self.nb_code:
- n_repeats = (self.nb_code + nb_code_x - 1) // nb_code_x
- std = 0.01 / np.sqrt(code_dim)
- out = x.repeat(n_repeats, 1)
- out = out + torch.randn_like(out) * std
- else :
- out = x
- return out
-
- def init_codebook(self, x):
- out = self._tile(x)
- self.codebook = out[:self.nb_code]
- self.code_sum = self.codebook.clone()
- self.code_count = torch.ones(self.nb_code, device=self.codebook.device)
- self.init = True
-
- @torch.no_grad()
- def compute_perplexity(self, code_idx) :
- # Calculate new centres
- code_onehot = torch.zeros(self.nb_code, code_idx.shape[0], device=code_idx.device) # nb_code, N * L
- code_onehot.scatter_(0, code_idx.view(1, code_idx.shape[0]), 1)
-
- code_count = code_onehot.sum(dim=-1) # nb_code
- prob = code_count / torch.sum(code_count)
- perplexity = torch.exp(-torch.sum(prob * torch.log(prob + 1e-7)))
- return perplexity
-
- @torch.no_grad()
- def update_codebook(self, x, code_idx):
-
- code_onehot = torch.zeros(self.nb_code, x.shape[0], device=x.device) # nb_code, N * L
- code_onehot.scatter_(0, code_idx.view(1, x.shape[0]), 1)
-
- code_sum = torch.matmul(code_onehot, x) # nb_code, w
- code_count = code_onehot.sum(dim=-1) # nb_code
-
- out = self._tile(x)
- code_rand = out[:self.nb_code]
-
- # Update centres
- self.code_sum = self.mu * self.code_sum + (1. - self.mu) * code_sum # w, nb_code
- self.code_count = self.mu * self.code_count + (1. - self.mu) * code_count # nb_code
-
- usage = (self.code_count.view(self.nb_code, 1) >= 1.0).float()
- code_update = self.code_sum.view(self.nb_code, self.code_dim) / self.code_count.view(self.nb_code, 1)
-
- self.codebook = usage * code_update + (1 - usage) * code_rand
- prob = code_count / torch.sum(code_count)
- perplexity = torch.exp(-torch.sum(prob * torch.log(prob + 1e-7)))
-
-
- return perplexity
-
- def preprocess(self, x):
- # NCT -> NTC -> [NT, C]
- x = x.permute(0, 2, 1).contiguous()
- x = x.view(-1, x.shape[-1])
- return x
-
- def quantize(self, x):
- # Calculate latent code x_l
- k_w = self.codebook.t()
- distance = torch.sum(x ** 2, dim=-1, keepdim=True) - 2 * torch.matmul(x, k_w) + torch.sum(k_w ** 2, dim=0,
- keepdim=True) # (N * L, b)
- _, code_idx = torch.min(distance, dim=-1)
- return code_idx
-
- def dequantize(self, code_idx):
- x = F.embedding(code_idx, self.codebook)
- return x
-
-
- def forward(self, x):
- N, width, T = x.shape
-
- # Preprocess
- x = self.preprocess(x)
-
- # Init codebook if not inited
- if self.training and not self.init:
- self.init_codebook(x)
-
- # quantize and dequantize through bottleneck
- code_idx = self.quantize(x)
- x_d = self.dequantize(code_idx)
-
- # Update embeddings
- if self.training:
- perplexity = self.update_codebook(x, code_idx)
- else :
- perplexity = self.compute_perplexity(code_idx)
-
- # Loss
- commit_loss = F.mse_loss(x, x_d.detach())
-
- # Passthrough
- x_d = x + (x_d - x).detach()
-
- # Postprocess
- x_d = x_d.view(N, T, -1).permute(0, 2, 1).contiguous() #(N, DIM, T)
-
- return x_d, commit_loss, perplexity
-
-
-
-class Quantizer(nn.Module):
- def __init__(self, n_e, e_dim, beta):
- super(Quantizer, self).__init__()
-
- self.e_dim = e_dim
- self.n_e = n_e
- self.beta = beta
-
- self.embedding = nn.Embedding(self.n_e, self.e_dim)
- self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
-
- def forward(self, z):
-
- N, width, T = z.shape
- z = self.preprocess(z)
- assert z.shape[-1] == self.e_dim
- z_flattened = z.contiguous().view(-1, self.e_dim)
-
- # B x V
- d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
- torch.sum(self.embedding.weight**2, dim=1) - 2 * \
- torch.matmul(z_flattened, self.embedding.weight.t())
- # B x 1
- min_encoding_indices = torch.argmin(d, dim=1)
- z_q = self.embedding(min_encoding_indices).view(z.shape)
-
- # compute loss for embedding
- loss = torch.mean((z_q - z.detach())**2) + self.beta * \
- torch.mean((z_q.detach() - z)**2)
-
- # preserve gradients
- z_q = z + (z_q - z).detach()
- z_q = z_q.view(N, T, -1).permute(0, 2, 1).contiguous() #(N, DIM, T)
-
- min_encodings = F.one_hot(min_encoding_indices, self.n_e).type(z.dtype)
- e_mean = torch.mean(min_encodings, dim=0)
- perplexity = torch.exp(-torch.sum(e_mean*torch.log(e_mean + 1e-10)))
- return z_q, loss, perplexity
-
- def quantize(self, z):
-
- assert z.shape[-1] == self.e_dim
-
- # B x V
- d = torch.sum(z ** 2, dim=1, keepdim=True) + \
- torch.sum(self.embedding.weight ** 2, dim=1) - 2 * \
- torch.matmul(z, self.embedding.weight.t())
- # B x 1
- min_encoding_indices = torch.argmin(d, dim=1)
- return min_encoding_indices
-
- def dequantize(self, indices):
-
- index_flattened = indices.view(-1)
- z_q = self.embedding(index_flattened)
- z_q = z_q.view(indices.shape + (self.e_dim, )).contiguous()
- return z_q
-
- def preprocess(self, x):
- # NCT -> NTC -> [NT, C]
- x = x.permute(0, 2, 1).contiguous()
- x = x.view(-1, x.shape[-1])
- return x
-
-
-
-class QuantizeReset(nn.Module):
- def __init__(self, nb_code, code_dim):
- super().__init__()
- self.nb_code = nb_code
- self.code_dim = code_dim
- self.reset_codebook()
- self.codebook = nn.Parameter(torch.randn(nb_code, code_dim))
-
- def reset_codebook(self):
- self.init = False
- self.code_count = None
-
- def _tile(self, x):
- nb_code_x, code_dim = x.shape
- if nb_code_x < self.nb_code:
- n_repeats = (self.nb_code + nb_code_x - 1) // nb_code_x
- std = 0.01 / np.sqrt(code_dim)
- out = x.repeat(n_repeats, 1)
- out = out + torch.randn_like(out) * std
- else :
- out = x
- return out
-
- def init_codebook(self, x):
- out = self._tile(x)
- self.codebook = nn.Parameter(out[:self.nb_code])
- self.code_count = torch.ones(self.nb_code, device=self.codebook.device)
- self.init = True
-
- @torch.no_grad()
- def compute_perplexity(self, code_idx) :
- # Calculate new centres
- code_onehot = torch.zeros(self.nb_code, code_idx.shape[0], device=code_idx.device) # nb_code, N * L
- code_onehot.scatter_(0, code_idx.view(1, code_idx.shape[0]), 1)
-
- code_count = code_onehot.sum(dim=-1) # nb_code
- prob = code_count / torch.sum(code_count)
- perplexity = torch.exp(-torch.sum(prob * torch.log(prob + 1e-7)))
- return perplexity
-
- def update_codebook(self, x, code_idx):
-
- code_onehot = torch.zeros(self.nb_code, x.shape[0], device=x.device) # nb_code, N * L
- code_onehot.scatter_(0, code_idx.view(1, x.shape[0]), 1)
-
- code_count = code_onehot.sum(dim=-1) # nb_code
-
- out = self._tile(x)
- code_rand = out[:self.nb_code]
-
- # Update centres
- self.code_count = code_count # nb_code
- usage = (self.code_count.view(self.nb_code, 1) >= 1.0).float()
-
- self.codebook.data = usage * self.codebook.data + (1 - usage) * code_rand
- prob = code_count / torch.sum(code_count)
- perplexity = torch.exp(-torch.sum(prob * torch.log(prob + 1e-7)))
-
-
- return perplexity
-
- def preprocess(self, x):
- # NCT -> NTC -> [NT, C]
- x = x.permute(0, 2, 1).contiguous()
- x = x.view(-1, x.shape[-1])
- return x
-
- def quantize(self, x):
- # Calculate latent code x_l
- k_w = self.codebook.t()
- distance = torch.sum(x ** 2, dim=-1, keepdim=True) - 2 * torch.matmul(x, k_w) + torch.sum(k_w ** 2, dim=0,
- keepdim=True) # (N * L, b)
- _, code_idx = torch.min(distance, dim=-1)
- return code_idx
-
- def dequantize(self, code_idx):
- x = F.embedding(code_idx, self.codebook)
- return x
-
-
- def forward(self, x):
- N, width, T = x.shape
- # Preprocess
- x = self.preprocess(x)
- # Init codebook if not inited
- if self.training and not self.init:
- self.init_codebook(x)
- # quantize and dequantize through bottleneck
- code_idx = self.quantize(x)
- x_d = self.dequantize(code_idx)
- # Update embeddings
- if self.training:
- perplexity = self.update_codebook(x, code_idx)
- else :
- perplexity = self.compute_perplexity(code_idx)
-
- # Loss
- commit_loss = F.mse_loss(x, x_d.detach())
-
- # Passthrough
- x_d = x + (x_d - x).detach()
-
- # Postprocess
- x_d = x_d.view(N, T, -1).permute(0, 2, 1).contiguous() #(N, DIM, T)
-
- return x_d, commit_loss, perplexity
-
-
-class QuantizeEMA(nn.Module):
- def __init__(self, nb_code, code_dim, mu):
- super().__init__()
- self.nb_code = nb_code
- self.code_dim = code_dim
- self.mu = mu
- self.reset_codebook()
-
- def reset_codebook(self):
- self.init = False
- self.code_sum = None
- self.code_count = None
- self.register_buffer('codebook', torch.zeros(self.nb_code, self.code_dim).cuda())
-
- def _tile(self, x):
- nb_code_x, code_dim = x.shape
- if nb_code_x < self.nb_code:
- n_repeats = (self.nb_code + nb_code_x - 1) // nb_code_x
- std = 0.01 / np.sqrt(code_dim)
- out = x.repeat(n_repeats, 1)
- out = out + torch.randn_like(out) * std
- else :
- out = x
- return out
-
- def init_codebook(self, x):
- out = self._tile(x)
- self.codebook = out[:self.nb_code]
- self.code_sum = self.codebook.clone()
- self.code_count = torch.ones(self.nb_code, device=self.codebook.device)
- self.init = True
-
- @torch.no_grad()
- def compute_perplexity(self, code_idx) :
- # Calculate new centres
- code_onehot = torch.zeros(self.nb_code, code_idx.shape[0], device=code_idx.device) # nb_code, N * L
- code_onehot.scatter_(0, code_idx.view(1, code_idx.shape[0]), 1)
-
- code_count = code_onehot.sum(dim=-1) # nb_code
- prob = code_count / torch.sum(code_count)
- perplexity = torch.exp(-torch.sum(prob * torch.log(prob + 1e-7)))
- return perplexity
-
- @torch.no_grad()
- def update_codebook(self, x, code_idx):
-
- code_onehot = torch.zeros(self.nb_code, x.shape[0], device=x.device) # nb_code, N * L
- code_onehot.scatter_(0, code_idx.view(1, x.shape[0]), 1)
-
- code_sum = torch.matmul(code_onehot, x) # nb_code, w
- code_count = code_onehot.sum(dim=-1) # nb_code
-
- # Update centres
- self.code_sum = self.mu * self.code_sum + (1. - self.mu) * code_sum # w, nb_code
- self.code_count = self.mu * self.code_count + (1. - self.mu) * code_count # nb_code
-
- code_update = self.code_sum.view(self.nb_code, self.code_dim) / self.code_count.view(self.nb_code, 1)
-
- self.codebook = code_update
- prob = code_count / torch.sum(code_count)
- perplexity = torch.exp(-torch.sum(prob * torch.log(prob + 1e-7)))
-
- return perplexity
-
- def preprocess(self, x):
- # NCT -> NTC -> [NT, C]
- x = x.permute(0, 2, 1).contiguous()
- x = x.view(-1, x.shape[-1])
- return x
-
- def quantize(self, x):
- # Calculate latent code x_l
- k_w = self.codebook.t()
- distance = torch.sum(x ** 2, dim=-1, keepdim=True) - 2 * torch.matmul(x, k_w) + torch.sum(k_w ** 2, dim=0,
- keepdim=True) # (N * L, b)
- _, code_idx = torch.min(distance, dim=-1)
- return code_idx
-
- def dequantize(self, code_idx):
- x = F.embedding(code_idx, self.codebook)
- return x
-
-
- def forward(self, x):
- N, width, T = x.shape
-
- # Preprocess
- x = self.preprocess(x)
-
- # Init codebook if not inited
- if self.training and not self.init:
- self.init_codebook(x)
-
- # quantize and dequantize through bottleneck
- code_idx = self.quantize(x)
- x_d = self.dequantize(code_idx)
-
- # Update embeddings
- if self.training:
- perplexity = self.update_codebook(x, code_idx)
- else :
- perplexity = self.compute_perplexity(code_idx)
-
- # Loss
- commit_loss = F.mse_loss(x, x_d.detach())
-
- # Passthrough
- x_d = x + (x_d - x).detach()
-
- # Postprocess
- x_d = x_d.view(N, T, -1).permute(0, 2, 1).contiguous() #(N, DIM, T)
-
- return x_d, commit_loss, perplexity
diff --git a/spaces/Owechada/roopfaceswapr/roop/__init__.py b/spaces/Owechada/roopfaceswapr/roop/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/roi_align.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/roi_align.py
deleted file mode 100644
index 0755aefc66e67233ceae0f4b77948301c443e9fb..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/roi_align.py
+++ /dev/null
@@ -1,223 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from ..utils import deprecated_api_warning, ext_loader
-
-ext_module = ext_loader.load_ext('_ext',
- ['roi_align_forward', 'roi_align_backward'])
-
-
-class RoIAlignFunction(Function):
-
- @staticmethod
- def symbolic(g, input, rois, output_size, spatial_scale, sampling_ratio,
- pool_mode, aligned):
- from ..onnx import is_custom_op_loaded
- has_custom_op = is_custom_op_loaded()
- if has_custom_op:
- return g.op(
- 'mmcv::MMCVRoiAlign',
- input,
- rois,
- output_height_i=output_size[0],
- output_width_i=output_size[1],
- spatial_scale_f=spatial_scale,
- sampling_ratio_i=sampling_ratio,
- mode_s=pool_mode,
- aligned_i=aligned)
- else:
- from torch.onnx.symbolic_opset9 import sub, squeeze
- from torch.onnx.symbolic_helper import _slice_helper
- from torch.onnx import TensorProtoDataType
- # batch_indices = rois[:, 0].long()
- batch_indices = _slice_helper(
- g, rois, axes=[1], starts=[0], ends=[1])
- batch_indices = squeeze(g, batch_indices, 1)
- batch_indices = g.op(
- 'Cast', batch_indices, to_i=TensorProtoDataType.INT64)
- # rois = rois[:, 1:]
- rois = _slice_helper(g, rois, axes=[1], starts=[1], ends=[5])
- if aligned:
- # rois -= 0.5/spatial_scale
- aligned_offset = g.op(
- 'Constant',
- value_t=torch.tensor([0.5 / spatial_scale],
- dtype=torch.float32))
- rois = sub(g, rois, aligned_offset)
- # roi align
- return g.op(
- 'RoiAlign',
- input,
- rois,
- batch_indices,
- output_height_i=output_size[0],
- output_width_i=output_size[1],
- spatial_scale_f=spatial_scale,
- sampling_ratio_i=max(0, sampling_ratio),
- mode_s=pool_mode)
-
- @staticmethod
- def forward(ctx,
- input,
- rois,
- output_size,
- spatial_scale=1.0,
- sampling_ratio=0,
- pool_mode='avg',
- aligned=True):
- ctx.output_size = _pair(output_size)
- ctx.spatial_scale = spatial_scale
- ctx.sampling_ratio = sampling_ratio
- assert pool_mode in ('max', 'avg')
- ctx.pool_mode = 0 if pool_mode == 'max' else 1
- ctx.aligned = aligned
- ctx.input_shape = input.size()
-
- assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!'
-
- output_shape = (rois.size(0), input.size(1), ctx.output_size[0],
- ctx.output_size[1])
- output = input.new_zeros(output_shape)
- if ctx.pool_mode == 0:
- argmax_y = input.new_zeros(output_shape)
- argmax_x = input.new_zeros(output_shape)
- else:
- argmax_y = input.new_zeros(0)
- argmax_x = input.new_zeros(0)
-
- ext_module.roi_align_forward(
- input,
- rois,
- output,
- argmax_y,
- argmax_x,
- aligned_height=ctx.output_size[0],
- aligned_width=ctx.output_size[1],
- spatial_scale=ctx.spatial_scale,
- sampling_ratio=ctx.sampling_ratio,
- pool_mode=ctx.pool_mode,
- aligned=ctx.aligned)
-
- ctx.save_for_backward(rois, argmax_y, argmax_x)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- rois, argmax_y, argmax_x = ctx.saved_tensors
- grad_input = grad_output.new_zeros(ctx.input_shape)
- # complex head architecture may cause grad_output uncontiguous.
- grad_output = grad_output.contiguous()
- ext_module.roi_align_backward(
- grad_output,
- rois,
- argmax_y,
- argmax_x,
- grad_input,
- aligned_height=ctx.output_size[0],
- aligned_width=ctx.output_size[1],
- spatial_scale=ctx.spatial_scale,
- sampling_ratio=ctx.sampling_ratio,
- pool_mode=ctx.pool_mode,
- aligned=ctx.aligned)
- return grad_input, None, None, None, None, None, None
-
-
-roi_align = RoIAlignFunction.apply
-
-
-class RoIAlign(nn.Module):
- """RoI align pooling layer.
-
- Args:
- output_size (tuple): h, w
- spatial_scale (float): scale the input boxes by this number
- sampling_ratio (int): number of inputs samples to take for each
- output sample. 0 to take samples densely for current models.
- pool_mode (str, 'avg' or 'max'): pooling mode in each bin.
- aligned (bool): if False, use the legacy implementation in
- MMDetection. If True, align the results more perfectly.
- use_torchvision (bool): whether to use roi_align from torchvision.
-
- Note:
- The implementation of RoIAlign when aligned=True is modified from
- https://github.com/facebookresearch/detectron2/
-
- The meaning of aligned=True:
-
- Given a continuous coordinate c, its two neighboring pixel
- indices (in our pixel model) are computed by floor(c - 0.5) and
- ceil(c - 0.5). For example, c=1.3 has pixel neighbors with discrete
- indices [0] and [1] (which are sampled from the underlying signal
- at continuous coordinates 0.5 and 1.5). But the original roi_align
- (aligned=False) does not subtract the 0.5 when computing
- neighboring pixel indices and therefore it uses pixels with a
- slightly incorrect alignment (relative to our pixel model) when
- performing bilinear interpolation.
-
- With `aligned=True`,
- we first appropriately scale the ROI and then shift it by -0.5
- prior to calling roi_align. This produces the correct neighbors;
-
- The difference does not make a difference to the model's
- performance if ROIAlign is used together with conv layers.
- """
-
- @deprecated_api_warning(
- {
- 'out_size': 'output_size',
- 'sample_num': 'sampling_ratio'
- },
- cls_name='RoIAlign')
- def __init__(self,
- output_size,
- spatial_scale=1.0,
- sampling_ratio=0,
- pool_mode='avg',
- aligned=True,
- use_torchvision=False):
- super(RoIAlign, self).__init__()
-
- self.output_size = _pair(output_size)
- self.spatial_scale = float(spatial_scale)
- self.sampling_ratio = int(sampling_ratio)
- self.pool_mode = pool_mode
- self.aligned = aligned
- self.use_torchvision = use_torchvision
-
- def forward(self, input, rois):
- """
- Args:
- input: NCHW images
- rois: Bx5 boxes. First column is the index into N.\
- The other 4 columns are xyxy.
- """
- if self.use_torchvision:
- from torchvision.ops import roi_align as tv_roi_align
- if 'aligned' in tv_roi_align.__code__.co_varnames:
- return tv_roi_align(input, rois, self.output_size,
- self.spatial_scale, self.sampling_ratio,
- self.aligned)
- else:
- if self.aligned:
- rois -= rois.new_tensor([0.] +
- [0.5 / self.spatial_scale] * 4)
- return tv_roi_align(input, rois, self.output_size,
- self.spatial_scale, self.sampling_ratio)
- else:
- return roi_align(input, rois, self.output_size, self.spatial_scale,
- self.sampling_ratio, self.pool_mode, self.aligned)
-
- def __repr__(self):
- s = self.__class__.__name__
- s += f'(output_size={self.output_size}, '
- s += f'spatial_scale={self.spatial_scale}, '
- s += f'sampling_ratio={self.sampling_ratio}, '
- s += f'pool_mode={self.pool_mode}, '
- s += f'aligned={self.aligned}, '
- s += f'use_torchvision={self.use_torchvision})'
- return s
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/parallel/collate.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/parallel/collate.py
deleted file mode 100644
index ad749197df21b0d74297548be5f66a696adebf7f..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/parallel/collate.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from collections.abc import Mapping, Sequence
-
-import torch
-import torch.nn.functional as F
-from torch.utils.data.dataloader import default_collate
-
-from .data_container import DataContainer
-
-
-def collate(batch, samples_per_gpu=1):
- """Puts each data field into a tensor/DataContainer with outer dimension
- batch size.
-
- Extend default_collate to add support for
- :type:`~mmcv.parallel.DataContainer`. There are 3 cases.
-
- 1. cpu_only = True, e.g., meta data
- 2. cpu_only = False, stack = True, e.g., images tensors
- 3. cpu_only = False, stack = False, e.g., gt bboxes
- """
-
- if not isinstance(batch, Sequence):
- raise TypeError(f'{batch.dtype} is not supported.')
-
- if isinstance(batch[0], DataContainer):
- stacked = []
- if batch[0].cpu_only:
- for i in range(0, len(batch), samples_per_gpu):
- stacked.append(
- [sample.data for sample in batch[i:i + samples_per_gpu]])
- return DataContainer(
- stacked, batch[0].stack, batch[0].padding_value, cpu_only=True)
- elif batch[0].stack:
- for i in range(0, len(batch), samples_per_gpu):
- assert isinstance(batch[i].data, torch.Tensor)
-
- if batch[i].pad_dims is not None:
- ndim = batch[i].dim()
- assert ndim > batch[i].pad_dims
- max_shape = [0 for _ in range(batch[i].pad_dims)]
- for dim in range(1, batch[i].pad_dims + 1):
- max_shape[dim - 1] = batch[i].size(-dim)
- for sample in batch[i:i + samples_per_gpu]:
- for dim in range(0, ndim - batch[i].pad_dims):
- assert batch[i].size(dim) == sample.size(dim)
- for dim in range(1, batch[i].pad_dims + 1):
- max_shape[dim - 1] = max(max_shape[dim - 1],
- sample.size(-dim))
- padded_samples = []
- for sample in batch[i:i + samples_per_gpu]:
- pad = [0 for _ in range(batch[i].pad_dims * 2)]
- for dim in range(1, batch[i].pad_dims + 1):
- pad[2 * dim -
- 1] = max_shape[dim - 1] - sample.size(-dim)
- padded_samples.append(
- F.pad(
- sample.data, pad, value=sample.padding_value))
- stacked.append(default_collate(padded_samples))
- elif batch[i].pad_dims is None:
- stacked.append(
- default_collate([
- sample.data
- for sample in batch[i:i + samples_per_gpu]
- ]))
- else:
- raise ValueError(
- 'pad_dims should be either None or integers (1-3)')
-
- else:
- for i in range(0, len(batch), samples_per_gpu):
- stacked.append(
- [sample.data for sample in batch[i:i + samples_per_gpu]])
- return DataContainer(stacked, batch[0].stack, batch[0].padding_value)
- elif isinstance(batch[0], Sequence):
- transposed = zip(*batch)
- return [collate(samples, samples_per_gpu) for samples in transposed]
- elif isinstance(batch[0], Mapping):
- return {
- key: collate([d[key] for d in batch], samples_per_gpu)
- for key in batch[0]
- }
- else:
- return default_collate(batch)
diff --git a/spaces/PSLD/PSLD/diffusion-posterior-sampling/README.md b/spaces/PSLD/PSLD/diffusion-posterior-sampling/README.md
deleted file mode 100644
index 71c1633ccac827cc131ab412c302ac8c9712f144..0000000000000000000000000000000000000000
--- a/spaces/PSLD/PSLD/diffusion-posterior-sampling/README.md
+++ /dev/null
@@ -1,158 +0,0 @@
-# Diffusion Posterior Sampling for General Noisy Inverse Problems (ICLR 2023 spotlight)
-
-
-
-
-
-## Abstract
-In this work, we extend diffusion solvers to efficiently handle general noisy (non)linear inverse problems via the approximation of the posterior sampling. Interestingly, the resulting posterior sampling scheme is a blended version of the diffusion sampling with the manifold constrained gradient without strict measurement consistency projection step, yielding more desirable generative path in noisy settings compared to the previous studies.
-
-
-
-
-## Prerequisites
-- python 3.8
-
-- pytorch 1.11.0
-
-- CUDA 11.3.1
-
-- nvidia-docker (if you use GPU in docker container)
-
-It is okay to use lower version of CUDA with proper pytorch version.
-
-Ex) CUDA 10.2 with pytorch 1.7.0
-
-
- -## Getting started - -### 1) Clone the repository - -``` -git clone https://github.com/DPS2022/diffusion-posterior-sampling - -cd diffusion-posterior-sampling -``` - -
- -### 2) Download pretrained checkpoint -From the [link](https://drive.google.com/drive/folders/1jElnRoFv7b31fG0v6pTSQkelbSX3xGZh?usp=sharing), download the checkpoint "ffhq_10m.pt" and paste it to ./models/ -``` -mkdir models -mv {DOWNLOAD_DIR}/ffqh_10m.pt ./models/ -``` -{DOWNLOAD_DIR} is the directory that you downloaded checkpoint to. - -:speaker: Checkpoint for imagenet is uploaded. - -
- - -### 3) Set environment -### [Option 1] Local environment setting - -We use the external codes for motion-blurring and non-linear deblurring. - -``` -git clone https://github.com/VinAIResearch/blur-kernel-space-exploring bkse - -git clone https://github.com/LeviBorodenko/motionblur motionblur -``` - -Install dependencies - -``` -conda create -n DPS python=3.8 - -conda activate DPS - -pip install -r requirements.txt - -pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113 -``` - -
- -### [Option 2] Build Docker image - -Install docker engine, GPU driver and proper cuda before running the following commands. - -Dockerfile already contains command to clone external codes. You don't have to clone them again. - ---gpus=all is required to use local GPU device (Docker >= 19.03) - -``` -docker build -t dps-docker:latest . - -docker run -it --rm --gpus=all dps-docker -``` - -
- -### 4) Inference - -``` -python3 sample_condition.py \ ---model_config=configs/model_config.yaml \ ---diffusion_config=configs/diffusion_config.yaml \ ---task_config={TASK-CONFIG}; -``` - - -:speaker: For imagenet, use configs/imagenet_model_config.yaml - -
- -## Possible task configurations - -``` -# Linear inverse problems -- configs/super_resolution_config.yaml -- configs/gaussian_deblur_config.yaml -- configs/motion_deblur_config.yaml -- configs/inpainting_config.yaml - -# Non-linear inverse problems -- configs/nonlinear_deblur_config.yaml -- configs/phase_retrieval_config.yaml -``` - -### Structure of task configurations -You need to write your data directory at data.root. Default is ./data/samples which contains three sample images from FFHQ validation set. - -``` -conditioning: - method: # check candidates in guided_diffusion/condition_methods.py - params: - scale: 0.5 - -data: - name: ffhq - root: ./data/samples/ - -measurement: - operator: - name: # check candidates in guided_diffusion/measurements.py - -noise: - name: # gaussian or poisson - sigma: # if you use name: gaussian, set this. - (rate:) # if you use name: poisson, set this. -``` - -## Citation -If you find our work interesting, please consider citing - -``` -@inproceedings{ -chung2023diffusion, -title={Diffusion Posterior Sampling for General Noisy Inverse Problems}, -author={Hyungjin Chung and Jeongsol Kim and Michael Thompson Mccann and Marc Louis Klasky and Jong Chul Ye}, -booktitle={The Eleventh International Conference on Learning Representations }, -year={2023}, -url={https://openreview.net/forum?id=OnD9zGAGT0k} -} -``` - diff --git a/spaces/Paco1112/Super-writing-tool/app.py b/spaces/Paco1112/Super-writing-tool/app.py deleted file mode 100644 index 53b682a80ef69c3c22a1cce6d327ce1056b62ee3..0000000000000000000000000000000000000000 --- a/spaces/Paco1112/Super-writing-tool/app.py +++ /dev/null @@ -1,11 +0,0 @@ -import gradio as gr -from gradio.mix import Parallel - -#variables, functions and parameters -model1 = gr.Interface.load("huggingface/gpt2") -model2 = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B") -model3 = gr.Interface.load("huggingface/EleutherAI/gpt-neo-1.3B") - -#functions, parameters and variables -gr.Parallel(model1, model2, model3).launch() - diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-11.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-11.go deleted file mode 100644 index 71e8da526677da5beb31225e15b1ba721a52f3e2..0000000000000000000000000000000000000000 Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-11.go and /dev/null differ diff --git a/spaces/PeepDaSlan9/AutoGPT/autogpt/cli.py b/spaces/PeepDaSlan9/AutoGPT/autogpt/cli.py deleted file mode 100644 index a2e99cb421cad005528cb160e948ce59ccfcdb66..0000000000000000000000000000000000000000 --- a/spaces/PeepDaSlan9/AutoGPT/autogpt/cli.py +++ /dev/null @@ -1,145 +0,0 @@ -"""Main script for the autogpt package.""" -import click - - -@click.group(invoke_without_command=True) -@click.option("-c", "--continuous", is_flag=True, help="Enable Continuous Mode") -@click.option( - "--skip-reprompt", - "-y", - is_flag=True, - help="Skips the re-prompting messages at the beginning of the script", -) -@click.option( - "--ai-settings", - "-C", - help="Specifies which ai_settings.yaml file to use, will also automatically skip the re-prompt.", -) -@click.option( - "-l", - "--continuous-limit", - type=int, - help="Defines the number of times to run in continuous mode", -) -@click.option("--speak", is_flag=True, help="Enable Speak Mode") -@click.option("--debug", is_flag=True, help="Enable Debug Mode") -@click.option("--gpt3only", is_flag=True, help="Enable GPT3.5 Only Mode") -@click.option("--gpt4only", is_flag=True, help="Enable GPT4 Only Mode") -@click.option( - "--use-memory", - "-m", - "memory_type", - type=str, - help="Defines which Memory backend to use", -) -@click.option( - "-b", - "--browser-name", - help="Specifies which web-browser to use when using selenium to scrape the web.", -) -@click.option( - "--allow-downloads", - is_flag=True, - help="Dangerous: Allows Auto-GPT to download files natively.", -) -@click.option( - "--skip-news", - is_flag=True, - help="Specifies whether to suppress the output of latest news on startup.", -) -@click.pass_context -def main( - ctx: click.Context, - continuous: bool, - continuous_limit: int, - ai_settings: str, - skip_reprompt: bool, - speak: bool, - debug: bool, - gpt3only: bool, - gpt4only: bool, - memory_type: str, - browser_name: str, - allow_downloads: bool, - skip_news: bool, -) -> None: - """ - Welcome to AutoGPT an experimental open-source application showcasing the capabilities of the GPT-4 pushing the boundaries of AI. - - Start an Auto-GPT assistant. - """ - # Put imports inside function to avoid importing everything when starting the CLI - import logging - - from colorama import Fore - - from autogpt.agent.agent import Agent - from autogpt.config import Config, check_openai_api_key - from autogpt.configurator import create_config - from autogpt.logs import logger - from autogpt.memory import get_memory - from autogpt.prompt import construct_prompt - from autogpt.utils import get_current_git_branch, get_latest_bulletin - - if ctx.invoked_subcommand is None: - cfg = Config() - # TODO: fill in llm values here - check_openai_api_key() - create_config( - continuous, - continuous_limit, - ai_settings, - skip_reprompt, - speak, - debug, - gpt3only, - gpt4only, - memory_type, - browser_name, - allow_downloads, - skip_news, - ) - logger.set_level(logging.DEBUG if cfg.debug_mode else logging.INFO) - ai_name = "" - if not cfg.skip_news: - motd = get_latest_bulletin() - if motd: - logger.typewriter_log("NEWS: ", Fore.GREEN, motd) - git_branch = get_current_git_branch() - if git_branch and git_branch != "stable": - logger.typewriter_log( - "WARNING: ", - Fore.RED, - f"You are running on `{git_branch}` branch " - "- this is not a supported branch.", - ) - system_prompt = construct_prompt() - # print(prompt) - # Initialize variables - full_message_history = [] - next_action_count = 0 - # Make a constant: - triggering_prompt = ( - "Determine which next command to use, and respond using the" - " format specified above:" - ) - # Initialize memory and make sure it is empty. - # this is particularly important for indexing and referencing pinecone memory - memory = get_memory(cfg, init=True) - logger.typewriter_log( - "Using memory of type:", Fore.GREEN, f"{memory.__class__.__name__}" - ) - logger.typewriter_log("Using Browser:", Fore.GREEN, cfg.selenium_web_browser) - agent = Agent( - ai_name=ai_name, - memory=memory, - full_message_history=full_message_history, - next_action_count=next_action_count, - system_prompt=system_prompt, - triggering_prompt=triggering_prompt, - ) - agent.start_interaction_loop() - - -if __name__ == "__main__": - main() diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/models/blip_vqa.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/models/blip_vqa.py deleted file mode 100644 index 1ef8641cab1badd32e00abea352d764f6165faae..0000000000000000000000000000000000000000 --- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/models/blip_vqa.py +++ /dev/null @@ -1,223 +0,0 @@ -from models.med import BertConfig, BertModel, BertLMHeadModel -from models.blip import create_vit, init_tokenizer, load_checkpoint - -import torch -from torch import nn -import torch.nn.functional as F -from transformers import BertTokenizer -import numpy as np - -class BLIP_VQA(nn.Module): - def __init__(self, - med_config = 'configs/med_config.json', - image_size = 480, - vit = 'base', - vit_grad_ckpt = False, - vit_ckpt_layer = 0, - ): - """ - Args: - med_config (str): path for the mixture of encoder-decoder model's configuration file - image_size (int): input image size - vit (str): model size of vision transformer - """ - super().__init__() - - self.visual_encoder, vision_width = create_vit(vit, image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1) - self.tokenizer = init_tokenizer() - - encoder_config = BertConfig.from_json_file(med_config) - encoder_config.encoder_width = vision_width - self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False) - - decoder_config = BertConfig.from_json_file(med_config) - self.text_decoder = BertLMHeadModel(config=decoder_config) - - self.itm_head = nn.Linear(768, 2) - - def forward(self, image, question, answer=None, n=None, weights=None, mode='inference', inference='rank', k_test=128): - - image_embeds = self.visual_encoder(image) - image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) - - question = self.tokenizer(question, padding='longest', truncation=True, max_length=35, - return_tensors="pt").to(image.device) - question.input_ids[:,0] = self.tokenizer.enc_token_id - - if mode == 'train': - ''' - n: number of answers for each question - weights: weight for each answer - ''' - answer = self.tokenizer(answer, padding='longest', return_tensors="pt").to(image.device) - answer.input_ids[:,0] = self.tokenizer.bos_token_id - answer_targets = answer.input_ids.masked_fill(answer.input_ids == self.tokenizer.pad_token_id, -100) - - question_output = self.text_encoder(question.input_ids, - attention_mask = question.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - return_dict = True) - - question_states = [] - question_atts = [] - for b, n in enumerate(n): - question_states += [question_output.last_hidden_state[b]]*n - question_atts += [question.attention_mask[b]]*n - question_states = torch.stack(question_states,0) - question_atts = torch.stack(question_atts,0) - - answer_output = self.text_decoder(answer.input_ids, - attention_mask = answer.attention_mask, - encoder_hidden_states = question_states, - encoder_attention_mask = question_atts, - labels = answer_targets, - return_dict = True, - reduction = 'none', - ) - - loss = weights * answer_output.loss - loss = loss.sum()/image.size(0) - - return loss - - elif mode == 'gradcam': - question_output = self.text_encoder(question.input_ids, - attention_mask = question.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - return_dict = True) - - vl_embeddings = question_output.last_hidden_state[:,0,:] - vl_output = self.itm_head(vl_embeddings) - - if inference=='generate': - num_beams = 3 - question_states = question_output.last_hidden_state.repeat_interleave(num_beams,dim=0) - question_atts = torch.ones(question_states.size()[:-1],dtype=torch.long).to(question_states.device) - model_kwargs = {"encoder_hidden_states": question_states, "encoder_attention_mask":question_atts} - - bos_ids = torch.full((image.size(0),1),fill_value=self.tokenizer.bos_token_id,device=image.device) - - outputs = self.text_decoder.generate(input_ids=bos_ids, - max_length=10, - min_length=1, - num_beams=num_beams, - eos_token_id=self.tokenizer.sep_token_id, - pad_token_id=self.tokenizer.pad_token_id, - **model_kwargs) - - answers = [] - for output in outputs: - answer = self.tokenizer.decode(output, skip_special_tokens=True) - answers.append(answer) - return answers, vl_output, question - - elif inference=='rank': - max_ids = self.rank_answer(question_output.last_hidden_state, question.attention_mask, - answer.input_ids, answer.attention_mask, k_test) - return max_ids, vl_output, question - - else: - question_output = self.text_encoder(question.input_ids, - attention_mask = question.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - return_dict = True) - - if inference=='generate': - num_beams = 3 - question_states = question_output.last_hidden_state.repeat_interleave(num_beams,dim=0) - question_atts = torch.ones(question_states.size()[:-1],dtype=torch.long).to(question_states.device) - model_kwargs = {"encoder_hidden_states": question_states, "encoder_attention_mask":question_atts} - - bos_ids = torch.full((image.size(0),1),fill_value=self.tokenizer.bos_token_id,device=image.device) - - outputs = self.text_decoder.generate(input_ids=bos_ids, - max_length=10, - min_length=1, - num_beams=num_beams, - eos_token_id=self.tokenizer.sep_token_id, - pad_token_id=self.tokenizer.pad_token_id, - **model_kwargs) - - answers = [] - for output in outputs: - answer = self.tokenizer.decode(output, skip_special_tokens=True) - answers.append(answer) - return answers - - elif inference=='rank': - max_ids = self.rank_answer(question_output.last_hidden_state, question.attention_mask, - answer.input_ids, answer.attention_mask, k_test) - return max_ids - - - - def rank_answer(self, question_states, question_atts, answer_ids, answer_atts, k): - - num_ques = question_states.size(0) - start_ids = answer_ids[0,0].repeat(num_ques,1) # bos token - - start_output = self.text_decoder(start_ids, - encoder_hidden_states = question_states, - encoder_attention_mask = question_atts, - return_dict = True, - reduction = 'none') - logits = start_output.logits[:,0,:] # first token's logit - - # topk_probs: top-k probability - # topk_ids: [num_question, k] - answer_first_token = answer_ids[:,1] - prob_first_token = F.softmax(logits,dim=1).index_select(dim=1, index=answer_first_token) - topk_probs, topk_ids = prob_first_token.topk(k,dim=1) - - # answer input: [num_question*k, answer_len] - input_ids = [] - input_atts = [] - for b, topk_id in enumerate(topk_ids): - input_ids.append(answer_ids.index_select(dim=0, index=topk_id)) - input_atts.append(answer_atts.index_select(dim=0, index=topk_id)) - input_ids = torch.cat(input_ids,dim=0) - input_atts = torch.cat(input_atts,dim=0) - - targets_ids = input_ids.masked_fill(input_ids == self.tokenizer.pad_token_id, -100) - - # repeat encoder's output for top-k answers - question_states = tile(question_states, 0, k) - question_atts = tile(question_atts, 0, k) - - output = self.text_decoder(input_ids, - attention_mask = input_atts, - encoder_hidden_states = question_states, - encoder_attention_mask = question_atts, - labels = targets_ids, - return_dict = True, - reduction = 'none') - - log_probs_sum = -output.loss - log_probs_sum = log_probs_sum.view(num_ques,k) - - max_topk_ids = log_probs_sum.argmax(dim=1) - max_ids = topk_ids[max_topk_ids>=0,max_topk_ids] - - return max_ids - - -def blip_vqa(pretrained='',**kwargs): - model = BLIP_VQA(**kwargs) - if pretrained: - model,msg = load_checkpoint(model,pretrained) -# assert(len(msg.missing_keys)==0) - return model - - -def tile(x, dim, n_tile): - init_dim = x.size(dim) - repeat_idx = [1] * x.dim() - repeat_idx[dim] = n_tile - x = x.repeat(*(repeat_idx)) - order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)])) - return torch.index_select(x, dim, order_index.to(x.device)) - - \ No newline at end of file diff --git a/spaces/Pontonkid/Real-Time-Multilingual-sentiment-analysis/app.py b/spaces/Pontonkid/Real-Time-Multilingual-sentiment-analysis/app.py deleted file mode 100644 index 9e40030ebaed20fe2f1b92ec416d94a70dc4c066..0000000000000000000000000000000000000000 --- a/spaces/Pontonkid/Real-Time-Multilingual-sentiment-analysis/app.py +++ /dev/null @@ -1,156 +0,0 @@ -import gradio as gr -import whisper -from transformers import pipeline - -model = whisper.load_model("base") -sentiment_analysis = pipeline("sentiment-analysis", framework="pt", model="SamLowe/roberta-base-go_emotions") - -def analyze_sentiment(text): - results = sentiment_analysis(text) - sentiment_results = {result['label']: result['score'] for result in results} - return sentiment_results - -def get_sentiment_emoji(sentiment): - # Define the emojis corresponding to each sentiment - emoji_mapping = { - "disappointment": "😞", - "sadness": "😢", - "annoyance": "😠", - "neutral": "😐", - "disapproval": "👎", - "realization": "😮", - "nervousness": "😬", - "approval": "👍", - "joy": "😄", - "anger": "😡", - "embarrassment": "😳", - "caring": "🤗", - "remorse": "😔", - "disgust": "🤢", - "grief": "😥", - "confusion": "😕", - "relief": "😌", - "desire": "😍", - "admiration": "😌", - "optimism": "😊", - "fear": "😨", - "love": "❤️", - "excitement": "🎉", - "curiosity": "🤔", - "amusement": "😄", - "surprise": "😲", - "gratitude": "🙏", - "pride": "🦁" - } - return emoji_mapping.get(sentiment, "") - -def display_sentiment_results(sentiment_results, option): - sentiment_text = "" - for sentiment, score in sentiment_results.items(): - emoji = get_sentiment_emoji(sentiment) - if option == "Sentiment Only": - sentiment_text += f"{sentiment} {emoji}\n" - elif option == "Sentiment + Score": - sentiment_text += f"{sentiment} {emoji}: {score}\n" - return sentiment_text - -def inference(audio, sentiment_option): - audio = whisper.load_audio(audio) - audio = whisper.pad_or_trim(audio) - - mel = whisper.log_mel_spectrogram(audio).to(model.device) - - _, probs = model.detect_language(mel) - lang = max(probs, key=probs.get) - - options = whisper.DecodingOptions(fp16=False) - result = whisper.decode(model, mel, options) - - sentiment_results = analyze_sentiment(result.text) - sentiment_output = display_sentiment_results(sentiment_results, sentiment_option) - - return lang.upper(), result.text, sentiment_output - -title = """
-
-⚙️ Components of the tool:
-
- - Real-time multilingual speech recognition
- - Language identification
- - Sentiment analysis of the transcriptions
-
-🎯 The sentiment analysis results are provided as a dictionary with different emotions and their corresponding scores.
-
- -😃 The sentiment analysis results are displayed with emojis representing the corresponding sentiment.
-
- -✅ The higher the score for a specific emotion, the stronger the presence of that emotion in the transcribed text.
-
- -❓ Use the microphone for real-time speech recognition.
-
- -⚡️ The model will transcribe the audio and perform sentiment analysis on the transcribed text.
- -""" - -custom_css = """ -#banner-image { - display: block; - margin-left: auto; - margin-right: auto; -} -#chat-message { - font-size: 14px; - min-height: 300px; -} -""" - -block = gr.Blocks(css=custom_css) - -with block: - gr.HTML(title) - - with gr.Row(): - with gr.Column(): - gr.Image(image_path, elem_id="banner-image", show_label=False) - with gr.Column(): - gr.HTML(description) - - with gr.Group(): - with gr.Box(): - audio = gr.Audio( - label="Input Audio", - show_label=False, - source="microphone", - type="filepath" - ) - - sentiment_option = gr.Radio( - choices=["Sentiment Only", "Sentiment + Score"], - label="Select an option", - default="Sentiment Only" - ) - - btn = gr.Button("Transcribe") - - lang_str = gr.Textbox(label="Language") - - text = gr.Textbox(label="Transcription") - - sentiment_output = gr.Textbox(label="Sentiment Analysis Results", output=True) - - btn.click(inference, inputs=[audio, sentiment_option], outputs=[lang_str, text, sentiment_output]) - - gr.HTML(''' - - ''') - -block.launch() diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/metrics/kld.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/metrics/kld.py deleted file mode 100644 index 18260bf974bf47d8381223ac39be0c47c031bf8a..0000000000000000000000000000000000000000 --- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/metrics/kld.py +++ /dev/null @@ -1,218 +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. - -import contextlib -from functools import partial -import logging -import os -import typing as tp - -import torch -import torchmetrics - -from ..data.audio_utils import convert_audio - - -logger = logging.getLogger(__name__) - - -class _patch_passt_stft: - """Decorator to patch torch.stft in PaSST.""" - def __init__(self): - self.old_stft = torch.stft - - def __enter__(self): - # return_complex is a mandatory parameter in latest torch versions - # torch is throwing RuntimeErrors when not set - torch.stft = partial(torch.stft, return_complex=False) - - def __exit__(self, *exc): - torch.stft = self.old_stft - - -def kl_divergence(pred_probs: torch.Tensor, target_probs: torch.Tensor, epsilon: float = 1e-6) -> torch.Tensor: - """Computes the elementwise KL-Divergence loss between probability distributions - from generated samples and target samples. - - Args: - pred_probs (torch.Tensor): Probabilities for each label obtained - from a classifier on generated audio. Expected shape is [B, num_classes]. - target_probs (torch.Tensor): Probabilities for each label obtained - from a classifier on target audio. Expected shape is [B, num_classes]. - epsilon (float): Epsilon value. - Returns: - kld (torch.Tensor): KLD loss between each generated sample and target pair. - """ - kl_div = torch.nn.functional.kl_div((pred_probs + epsilon).log(), target_probs, reduction="none") - return kl_div.sum(-1) - - -class KLDivergenceMetric(torchmetrics.Metric): - """Base implementation for KL Divergence metric. - - The KL divergence is measured between probability distributions - of class predictions returned by a pre-trained audio classification model. - When the KL-divergence is low, the generated audio is expected to - have similar acoustic characteristics as the reference audio, - according to the classifier. - """ - def __init__(self): - super().__init__() - self.add_state("kld_pq_sum", default=torch.tensor(0.), dist_reduce_fx="sum") - self.add_state("kld_qp_sum", default=torch.tensor(0.), dist_reduce_fx="sum") - self.add_state("kld_all_sum", default=torch.tensor(0.), dist_reduce_fx="sum") - self.add_state("weight", default=torch.tensor(0), dist_reduce_fx="sum") - - def _get_label_distribution(self, x: torch.Tensor, sizes: torch.Tensor, - sample_rates: torch.Tensor) -> tp.Optional[torch.Tensor]: - """Get model output given provided input tensor. - - Args: - x (torch.Tensor): Input audio tensor of shape [B, C, T]. - sizes (torch.Tensor): Actual audio sample length, of shape [B]. - sample_rates (torch.Tensor): Actual audio sample rate, of shape [B]. - Returns: - probs (torch.Tensor): Probabilities over labels, of shape [B, num_classes]. - """ - raise NotImplementedError("implement method to extract label distributions from the model.") - - def update(self, preds: torch.Tensor, targets: torch.Tensor, - sizes: torch.Tensor, sample_rates: torch.Tensor) -> None: - """Calculates running KL-Divergence loss between batches of audio - preds (generated) and target (ground-truth) - Args: - preds (torch.Tensor): Audio samples to evaluate, of shape [B, C, T]. - targets (torch.Tensor): Target samples to compare against, of shape [B, C, T]. - sizes (torch.Tensor): Actual audio sample length, of shape [B]. - sample_rates (torch.Tensor): Actual audio sample rate, of shape [B]. - """ - assert preds.shape == targets.shape - assert preds.size(0) > 0, "Cannot update the loss with empty tensors" - preds_probs = self._get_label_distribution(preds, sizes, sample_rates) - targets_probs = self._get_label_distribution(targets, sizes, sample_rates) - if preds_probs is not None and targets_probs is not None: - assert preds_probs.shape == targets_probs.shape - kld_scores = kl_divergence(preds_probs, targets_probs) - assert not torch.isnan(kld_scores).any(), "kld_scores contains NaN value(s)!" - self.kld_pq_sum += torch.sum(kld_scores) - kld_qp_scores = kl_divergence(targets_probs, preds_probs) - self.kld_qp_sum += torch.sum(kld_qp_scores) - self.weight += torch.tensor(kld_scores.size(0)) - - def compute(self) -> dict: - """Computes KL-Divergence across all evaluated pred/target pairs.""" - weight: float = float(self.weight.item()) # type: ignore - assert weight > 0, "Unable to compute with total number of comparisons <= 0" - logger.info(f"Computing KL divergence on a total of {weight} samples") - kld_pq = self.kld_pq_sum.item() / weight # type: ignore - kld_qp = self.kld_qp_sum.item() / weight # type: ignore - kld_both = kld_pq + kld_qp - return {'kld': kld_pq, 'kld_pq': kld_pq, 'kld_qp': kld_qp, 'kld_both': kld_both} - - -class PasstKLDivergenceMetric(KLDivergenceMetric): - """KL-Divergence metric based on pre-trained PASST classifier on AudioSet. - - From: PaSST: Efficient Training of Audio Transformers with Patchout - Paper: https://arxiv.org/abs/2110.05069 - Implementation: https://github.com/kkoutini/PaSST - - Follow instructions from the github repo: - ``` - pip install 'git+https://github.com/kkoutini/passt_hear21@0.0.19#egg=hear21passt' - ``` - - Args: - pretrained_length (float, optional): Audio duration used for the pretrained model. - """ - def __init__(self, pretrained_length: tp.Optional[float] = None): - super().__init__() - self._initialize_model(pretrained_length) - - def _initialize_model(self, pretrained_length: tp.Optional[float] = None): - """Initialize underlying PaSST audio classifier.""" - model, sr, max_frames, min_frames = self._load_base_model(pretrained_length) - self.min_input_frames = min_frames - self.max_input_frames = max_frames - self.model_sample_rate = sr - self.model = model - self.model.eval() - self.model.to(self.device) - - def _load_base_model(self, pretrained_length: tp.Optional[float]): - """Load pretrained model from PaSST.""" - try: - if pretrained_length == 30: - from hear21passt.base30sec import get_basic_model # type: ignore - max_duration = 30 - elif pretrained_length == 20: - from hear21passt.base20sec import get_basic_model # type: ignore - max_duration = 20 - else: - from hear21passt.base import get_basic_model # type: ignore - # Original PASST was trained on AudioSet with 10s-long audio samples - max_duration = 10 - min_duration = 0.15 - min_duration = 0.15 - except ModuleNotFoundError: - raise ModuleNotFoundError( - "Please install hear21passt to compute KL divergence: ", - "pip install 'git+https://github.com/kkoutini/passt_hear21@0.0.19#egg=hear21passt'" - ) - model_sample_rate = 32_000 - max_input_frames = int(max_duration * model_sample_rate) - min_input_frames = int(min_duration * model_sample_rate) - with open(os.devnull, 'w') as f, contextlib.redirect_stdout(f): - model = get_basic_model(mode='logits') - return model, model_sample_rate, max_input_frames, min_input_frames - - def _process_audio(self, wav: torch.Tensor, sample_rate: int, wav_len: int) -> tp.Optional[torch.Tensor]: - wav = wav.unsqueeze(0) - wav = wav[..., :wav_len] - wav = convert_audio(wav, from_rate=sample_rate, to_rate=self.model_sample_rate, to_channels=1) - wav = wav.squeeze(0) - # create chunks of audio to match the classifier processing length - segments = torch.split(wav, self.max_input_frames, dim=-1) - valid_segments = [] - for s in segments: - if s.size(-1) > self.min_input_frames: - s = torch.nn.functional.pad(s, (0, self.max_input_frames - s.shape[-1])) - valid_segments.append(s) - if len(valid_segments) > 0: - return torch.stack(valid_segments, dim=0) - else: - return None - - def _get_label_distribution(self, x: torch.Tensor, sizes: torch.Tensor, - sample_rates: torch.Tensor) -> tp.Optional[torch.Tensor]: - """Get model output given provided input tensor. - - Args: - x (torch.Tensor): Input audio tensor of shape [B, C, T]. - sizes (torch.Tensor): Actual audio sample length, of shape [B]. - sample_rates (torch.Tensor): Actual audio sample rate, of shape [B]. - Returns: - probs (torch.Tensor, optional): Probabilities over labels, of shape [B, num_classes]. - """ - all_probs: tp.List[torch.Tensor] = [] - for i, wav in enumerate(x): - sample_rate = int(sample_rates[i].item()) - wav_len = int(sizes[i].item()) - wav = self._process_audio(wav, sample_rate, wav_len) - if wav is not None: - assert wav.dim() == 3, f"Unexpected number of dims for preprocessed wav: {wav.shape}" - wav = wav.mean(dim=1) - # PaSST is printing a lot of infos that we are not interested in - with open(os.devnull, 'w') as f, contextlib.redirect_stdout(f): - with torch.no_grad(), _patch_passt_stft(): - logits = self.model(wav.to(self.device)) - probs = torch.softmax(logits, dim=-1) - probs = probs.mean(dim=0) - all_probs.append(probs) - if len(all_probs) > 0: - return torch.stack(all_probs, dim=0) - else: - return None diff --git a/spaces/PurtiSharma/toxic_comments/app.py b/spaces/PurtiSharma/toxic_comments/app.py deleted file mode 100644 index d8f311cd43f7628c3f75b1ea7e4f589aad82fb89..0000000000000000000000000000000000000000 --- a/spaces/PurtiSharma/toxic_comments/app.py +++ /dev/null @@ -1,44 +0,0 @@ -import streamlit as st -import pickle - - -new_model = pickle.load(open('mytoxiccomentlgmodel.pickle', 'rb')) - - -def welcome(): - return "Welcome to my app" - - -def main(): - st.title("Detect Toxic Comments App") - st.write( - "This app employs NLP to assess whether online comments are toxic or not, providing users with valuable insights") - html_temp = """ -
-
-### Installation
-
-```shell
-git clone git@github.com:THU-LYJ-Lab/DarkFeat.git
-cd DarkFeat
-pip install -r requirements.txt
-```
-
-[Pytorch](https://pytorch.org/) installation is machine dependent, please install the correct version for your machine.
-
-### Demo
-
-```shell
-python ./demo_darkfeat.py \
- --input /path/to/your/sequence \
- --output_dir ./output \
- --resize 960 640 \
- --model_path /path/to/pretrained/weights
-```
-
-Sample raw image sequences and pretrained weights can be downloaded from [here](https://drive.google.com/drive/folders/1zkUCsBVEmQcPZPhsEUymA5GIvAzi12hD?usp=sharing).
-
-Note that different pytorch and cuda versions may cause different model output results, and the output matches may differ from those shown in the gif. The results are tested in python 3.6, PyTorch 1.10.2 and cuda 10.2.
-
-### Evaluation
-
-1. Download [MID](https://github.com/Wenzhengchina/Matching-in-the-Dark) Dataset.
-
-2. Preprocessing the data in MID dataset, you can choose whether to enable histogram equalization or not:
-
- ```shell
- python raw_preprocess.py --dataset_dir /path/to/MID/dataset
- ```
-
-3. Extract the keypoints and descriptors, followed by a nearest neighborhood matching:
-
- ```shell
- python export_features.py \
- --model_path /path/to/pretrained/weights \
- --dataset_dir /path/to/MID/dataset
- ```
-
-4. Estimate the pose through corresponding keypoint pairs:
-
- ```shell
- python pose_estimation.py --dataset_dir /path/to/MID/dataset
- ```
-
-5. Finally collect the results of pose estimation errors:
-
- ```
- python read_error.py
- ```
-
-### Training from scratch
-
-We use [GL3D](https://github.com/lzx551402/GL3D) as our source training-use matching dataset. Please follow the [instructions](https://github.com/lzx551402/GL3D) to download and unzip all the data (including GL3D group and tourism group).
-
-Then using the preprocessing code provided by ASLFeat to generate matching informations:
-
-```shell
-git clone https://github.com/lzx551402/tfmatch
-# please edit the GL3D path in the shell script before executing.
-cd tfmatch
-sh train_aslfeat_base.sh
-```
-
-To launch the training, configure your training hyperparameters inside `./configs` and then run:
-
-```shell
-# stage1
-python run.py --stage 1 --config ./configs/config_stage1.yaml \
- --dataset_dir /path/to/your/GL3D/dataset \
- --job_name YOUR_JOB_NAME
-# stage2
-python run.py --stage 2 --config ./configs/config_stage1.yaml \
- --dataset_dir /path/to/your/GL3D/dataset \
- --job_name YOUR_JOB_NAME \
- --start_cnt 160000
-# stage3
-python run.py --stage 3 --config ./configs/config.yaml \
- --dataset_dir /path/to/your/GL3D/dataset \
- --job_name YOUR_JOB_NAME \
- --start_cnt 220000
-```
-
-### Acknowledgements
-
-This project could not be possible without the open-source works from [ASLFeat](https://github.com/lzx551402/ASLFeat), [R2D2](https://github.com/naver/r2d2), [MID](https://github.com/Wenzhengchina/Matching-in-the-Dark), [GL3D](https://github.com/lzx551402/GL3D), [SuperGlue](https://github.com/magicleap/SuperGluePretrainedNetwork). We sincerely thank them all.
\ No newline at end of file
diff --git a/spaces/Realcat/image-matching-webui/third_party/SOLD2/setup.py b/spaces/Realcat/image-matching-webui/third_party/SOLD2/setup.py
deleted file mode 100644
index e6c9cdcb47bdd73758cbd2d5b125dcb91306705f..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/SOLD2/setup.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from setuptools import setup
-
-
-setup(name="sold2", version="0.0", packages=["sold2"])
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/parallel/data_container.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/parallel/data_container.py
deleted file mode 100644
index cedb0d32a51a1f575a622b38de2cee3ab4757821..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/parallel/data_container.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import functools
-
-import torch
-
-
-def assert_tensor_type(func):
-
- @functools.wraps(func)
- def wrapper(*args, **kwargs):
- if not isinstance(args[0].data, torch.Tensor):
- raise AttributeError(
- f'{args[0].__class__.__name__} has no attribute '
- f'{func.__name__} for type {args[0].datatype}')
- return func(*args, **kwargs)
-
- return wrapper
-
-
-class DataContainer:
- """A container for any type of objects.
-
- Typically tensors will be stacked in the collate function and sliced along
- some dimension in the scatter function. This behavior has some limitations.
- 1. All tensors have to be the same size.
- 2. Types are limited (numpy array or Tensor).
-
- We design `DataContainer` and `MMDataParallel` to overcome these
- limitations. The behavior can be either of the following.
-
- - copy to GPU, pad all tensors to the same size and stack them
- - copy to GPU without stacking
- - leave the objects as is and pass it to the model
- - pad_dims specifies the number of last few dimensions to do padding
- """
-
- def __init__(self,
- data,
- stack=False,
- padding_value=0,
- cpu_only=False,
- pad_dims=2):
- self._data = data
- self._cpu_only = cpu_only
- self._stack = stack
- self._padding_value = padding_value
- assert pad_dims in [None, 1, 2, 3]
- self._pad_dims = pad_dims
-
- def __repr__(self):
- return f'{self.__class__.__name__}({repr(self.data)})'
-
- def __len__(self):
- return len(self._data)
-
- @property
- def data(self):
- return self._data
-
- @property
- def datatype(self):
- if isinstance(self.data, torch.Tensor):
- return self.data.type()
- else:
- return type(self.data)
-
- @property
- def cpu_only(self):
- return self._cpu_only
-
- @property
- def stack(self):
- return self._stack
-
- @property
- def padding_value(self):
- return self._padding_value
-
- @property
- def pad_dims(self):
- return self._pad_dims
-
- @assert_tensor_type
- def size(self, *args, **kwargs):
- return self.data.size(*args, **kwargs)
-
- @assert_tensor_type
- def dim(self):
- return self.data.dim()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/cascade_rpn_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/cascade_rpn_head.py
deleted file mode 100644
index e32ee461951e685fb44a461033293159e3439717..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/cascade_rpn_head.py
+++ /dev/null
@@ -1,784 +0,0 @@
-from __future__ import division
-import copy
-import warnings
-
-import torch
-import torch.nn as nn
-from mmcv import ConfigDict
-from mmcv.cnn import normal_init
-from mmcv.ops import DeformConv2d, batched_nms
-
-from mmdet.core import (RegionAssigner, build_assigner, build_sampler,
- images_to_levels, multi_apply)
-from ..builder import HEADS, build_head
-from .base_dense_head import BaseDenseHead
-from .rpn_head import RPNHead
-
-
-class AdaptiveConv(nn.Module):
- """AdaptiveConv used to adapt the sampling location with the anchors.
-
- Args:
- in_channels (int): Number of channels in the input image
- out_channels (int): Number of channels produced by the convolution
- kernel_size (int or tuple): Size of the conv kernel. Default: 3
- stride (int or tuple, optional): Stride of the convolution. Default: 1
- padding (int or tuple, optional): Zero-padding added to both sides of
- the input. Default: 1
- dilation (int or tuple, optional): Spacing between kernel elements.
- Default: 3
- groups (int, optional): Number of blocked connections from input
- channels to output channels. Default: 1
- bias (bool, optional): If set True, adds a learnable bias to the
- output. Default: False.
- type (str, optional): Type of adaptive conv, can be either 'offset'
- (arbitrary anchors) or 'dilation' (uniform anchor).
- Default: 'dilation'.
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1,
- dilation=3,
- groups=1,
- bias=False,
- type='dilation'):
- super(AdaptiveConv, self).__init__()
- assert type in ['offset', 'dilation']
- self.adapt_type = type
-
- assert kernel_size == 3, 'Adaptive conv only supports kernels 3'
- if self.adapt_type == 'offset':
- assert stride == 1 and padding == 1 and groups == 1, \
- 'Adaptive conv offset mode only supports padding: {1}, ' \
- f'stride: {1}, groups: {1}'
- self.conv = DeformConv2d(
- in_channels,
- out_channels,
- kernel_size,
- padding=padding,
- stride=stride,
- groups=groups,
- bias=bias)
- else:
- self.conv = nn.Conv2d(
- in_channels,
- out_channels,
- kernel_size,
- padding=dilation,
- dilation=dilation)
-
- def init_weights(self):
- """Init weights."""
- normal_init(self.conv, std=0.01)
-
- def forward(self, x, offset):
- """Forward function."""
- if self.adapt_type == 'offset':
- N, _, H, W = x.shape
- assert offset is not None
- assert H * W == offset.shape[1]
- # reshape [N, NA, 18] to (N, 18, H, W)
- offset = offset.permute(0, 2, 1).reshape(N, -1, H, W)
- offset = offset.contiguous()
- x = self.conv(x, offset)
- else:
- assert offset is None
- x = self.conv(x)
- return x
-
-
-@HEADS.register_module()
-class StageCascadeRPNHead(RPNHead):
- """Stage of CascadeRPNHead.
-
- Args:
- in_channels (int): Number of channels in the input feature map.
- anchor_generator (dict): anchor generator config.
- adapt_cfg (dict): adaptation config.
- bridged_feature (bool, optional): whether update rpn feature.
- Default: False.
- with_cls (bool, optional): wheather use classification branch.
- Default: True.
- sampling (bool, optional): wheather use sampling. Default: True.
- """
-
- def __init__(self,
- in_channels,
- anchor_generator=dict(
- type='AnchorGenerator',
- scales=[8],
- ratios=[1.0],
- strides=[4, 8, 16, 32, 64]),
- adapt_cfg=dict(type='dilation', dilation=3),
- bridged_feature=False,
- with_cls=True,
- sampling=True,
- **kwargs):
- self.with_cls = with_cls
- self.anchor_strides = anchor_generator['strides']
- self.anchor_scales = anchor_generator['scales']
- self.bridged_feature = bridged_feature
- self.adapt_cfg = adapt_cfg
- super(StageCascadeRPNHead, self).__init__(
- in_channels, anchor_generator=anchor_generator, **kwargs)
-
- # override sampling and sampler
- self.sampling = sampling
- if self.train_cfg:
- self.assigner = build_assigner(self.train_cfg.assigner)
- # use PseudoSampler when sampling is False
- if self.sampling and hasattr(self.train_cfg, 'sampler'):
- sampler_cfg = self.train_cfg.sampler
- else:
- sampler_cfg = dict(type='PseudoSampler')
- self.sampler = build_sampler(sampler_cfg, context=self)
-
- def _init_layers(self):
- """Init layers of a CascadeRPN stage."""
- self.rpn_conv = AdaptiveConv(self.in_channels, self.feat_channels,
- **self.adapt_cfg)
- if self.with_cls:
- self.rpn_cls = nn.Conv2d(self.feat_channels,
- self.num_anchors * self.cls_out_channels,
- 1)
- self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1)
- self.relu = nn.ReLU(inplace=True)
-
- def init_weights(self):
- """Init weights of a CascadeRPN stage."""
- self.rpn_conv.init_weights()
- normal_init(self.rpn_reg, std=0.01)
- if self.with_cls:
- normal_init(self.rpn_cls, std=0.01)
-
- def forward_single(self, x, offset):
- """Forward function of single scale."""
- bridged_x = x
- x = self.relu(self.rpn_conv(x, offset))
- if self.bridged_feature:
- bridged_x = x # update feature
- cls_score = self.rpn_cls(x) if self.with_cls else None
- bbox_pred = self.rpn_reg(x)
- return bridged_x, cls_score, bbox_pred
-
- def forward(self, feats, offset_list=None):
- """Forward function."""
- if offset_list is None:
- offset_list = [None for _ in range(len(feats))]
- return multi_apply(self.forward_single, feats, offset_list)
-
- def _region_targets_single(self,
- anchors,
- valid_flags,
- gt_bboxes,
- gt_bboxes_ignore,
- gt_labels,
- img_meta,
- featmap_sizes,
- label_channels=1):
- """Get anchor targets based on region for single level."""
- assign_result = self.assigner.assign(
- anchors,
- valid_flags,
- gt_bboxes,
- img_meta,
- featmap_sizes,
- self.anchor_scales[0],
- self.anchor_strides,
- gt_bboxes_ignore=gt_bboxes_ignore,
- gt_labels=None,
- allowed_border=self.train_cfg.allowed_border)
- flat_anchors = torch.cat(anchors)
- sampling_result = self.sampler.sample(assign_result, flat_anchors,
- gt_bboxes)
-
- num_anchors = flat_anchors.shape[0]
- bbox_targets = torch.zeros_like(flat_anchors)
- bbox_weights = torch.zeros_like(flat_anchors)
- labels = flat_anchors.new_zeros(num_anchors, dtype=torch.long)
- label_weights = flat_anchors.new_zeros(num_anchors, dtype=torch.float)
-
- pos_inds = sampling_result.pos_inds
- neg_inds = sampling_result.neg_inds
- if len(pos_inds) > 0:
- if not self.reg_decoded_bbox:
- pos_bbox_targets = self.bbox_coder.encode(
- sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
- else:
- pos_bbox_targets = sampling_result.pos_gt_bboxes
- bbox_targets[pos_inds, :] = pos_bbox_targets
- bbox_weights[pos_inds, :] = 1.0
- if gt_labels is None:
- labels[pos_inds] = 1
- else:
- labels[pos_inds] = gt_labels[
- sampling_result.pos_assigned_gt_inds]
- if self.train_cfg.pos_weight <= 0:
- label_weights[pos_inds] = 1.0
- else:
- label_weights[pos_inds] = self.train_cfg.pos_weight
- if len(neg_inds) > 0:
- label_weights[neg_inds] = 1.0
-
- return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
- neg_inds)
-
- def region_targets(self,
- anchor_list,
- valid_flag_list,
- gt_bboxes_list,
- img_metas,
- featmap_sizes,
- gt_bboxes_ignore_list=None,
- gt_labels_list=None,
- label_channels=1,
- unmap_outputs=True):
- """See :func:`StageCascadeRPNHead.get_targets`."""
- num_imgs = len(img_metas)
- assert len(anchor_list) == len(valid_flag_list) == num_imgs
-
- # anchor number of multi levels
- num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-
- # compute targets for each image
- if gt_bboxes_ignore_list is None:
- gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
- if gt_labels_list is None:
- gt_labels_list = [None for _ in range(num_imgs)]
- (all_labels, all_label_weights, all_bbox_targets, all_bbox_weights,
- pos_inds_list, neg_inds_list) = multi_apply(
- self._region_targets_single,
- anchor_list,
- valid_flag_list,
- gt_bboxes_list,
- gt_bboxes_ignore_list,
- gt_labels_list,
- img_metas,
- featmap_sizes=featmap_sizes,
- label_channels=label_channels)
- # no valid anchors
- if any([labels is None for labels in all_labels]):
- return None
- # sampled anchors of all images
- num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
- num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
- # split targets to a list w.r.t. multiple levels
- labels_list = images_to_levels(all_labels, num_level_anchors)
- label_weights_list = images_to_levels(all_label_weights,
- num_level_anchors)
- bbox_targets_list = images_to_levels(all_bbox_targets,
- num_level_anchors)
- bbox_weights_list = images_to_levels(all_bbox_weights,
- num_level_anchors)
- return (labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, num_total_pos, num_total_neg)
-
- def get_targets(self,
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- featmap_sizes,
- gt_bboxes_ignore=None,
- label_channels=1):
- """Compute regression and classification targets for anchors.
-
- Args:
- anchor_list (list[list]): Multi level anchors of each image.
- valid_flag_list (list[list]): Multi level valid flags of each
- image.
- gt_bboxes (list[Tensor]): Ground truth bboxes of each image.
- img_metas (list[dict]): Meta info of each image.
- featmap_sizes (list[Tensor]): Feature mapsize each level
- gt_bboxes_ignore (list[Tensor]): Ignore bboxes of each images
- label_channels (int): Channel of label.
-
- Returns:
- cls_reg_targets (tuple)
- """
- if isinstance(self.assigner, RegionAssigner):
- cls_reg_targets = self.region_targets(
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- featmap_sizes,
- gt_bboxes_ignore_list=gt_bboxes_ignore,
- label_channels=label_channels)
- else:
- cls_reg_targets = super(StageCascadeRPNHead, self).get_targets(
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore_list=gt_bboxes_ignore,
- label_channels=label_channels)
- return cls_reg_targets
-
- def anchor_offset(self, anchor_list, anchor_strides, featmap_sizes):
- """ Get offest for deformable conv based on anchor shape
- NOTE: currently support deformable kernel_size=3 and dilation=1
-
- Args:
- anchor_list (list[list[tensor])): [NI, NLVL, NA, 4] list of
- multi-level anchors
- anchor_strides (list[int]): anchor stride of each level
-
- Returns:
- offset_list (list[tensor]): [NLVL, NA, 2, 18]: offset of DeformConv
- kernel.
- """
-
- def _shape_offset(anchors, stride, ks=3, dilation=1):
- # currently support kernel_size=3 and dilation=1
- assert ks == 3 and dilation == 1
- pad = (ks - 1) // 2
- idx = torch.arange(-pad, pad + 1, dtype=dtype, device=device)
- yy, xx = torch.meshgrid(idx, idx) # return order matters
- xx = xx.reshape(-1)
- yy = yy.reshape(-1)
- w = (anchors[:, 2] - anchors[:, 0]) / stride
- h = (anchors[:, 3] - anchors[:, 1]) / stride
- w = w / (ks - 1) - dilation
- h = h / (ks - 1) - dilation
- offset_x = w[:, None] * xx # (NA, ks**2)
- offset_y = h[:, None] * yy # (NA, ks**2)
- return offset_x, offset_y
-
- def _ctr_offset(anchors, stride, featmap_size):
- feat_h, feat_w = featmap_size
- assert len(anchors) == feat_h * feat_w
-
- x = (anchors[:, 0] + anchors[:, 2]) * 0.5
- y = (anchors[:, 1] + anchors[:, 3]) * 0.5
- # compute centers on feature map
- x = x / stride
- y = y / stride
- # compute predefine centers
- xx = torch.arange(0, feat_w, device=anchors.device)
- yy = torch.arange(0, feat_h, device=anchors.device)
- yy, xx = torch.meshgrid(yy, xx)
- xx = xx.reshape(-1).type_as(x)
- yy = yy.reshape(-1).type_as(y)
-
- offset_x = x - xx # (NA, )
- offset_y = y - yy # (NA, )
- return offset_x, offset_y
-
- num_imgs = len(anchor_list)
- num_lvls = len(anchor_list[0])
- dtype = anchor_list[0][0].dtype
- device = anchor_list[0][0].device
- num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-
- offset_list = []
- for i in range(num_imgs):
- mlvl_offset = []
- for lvl in range(num_lvls):
- c_offset_x, c_offset_y = _ctr_offset(anchor_list[i][lvl],
- anchor_strides[lvl],
- featmap_sizes[lvl])
- s_offset_x, s_offset_y = _shape_offset(anchor_list[i][lvl],
- anchor_strides[lvl])
-
- # offset = ctr_offset + shape_offset
- offset_x = s_offset_x + c_offset_x[:, None]
- offset_y = s_offset_y + c_offset_y[:, None]
-
- # offset order (y0, x0, y1, x2, .., y8, x8, y9, x9)
- offset = torch.stack([offset_y, offset_x], dim=-1)
- offset = offset.reshape(offset.size(0), -1) # [NA, 2*ks**2]
- mlvl_offset.append(offset)
- offset_list.append(torch.cat(mlvl_offset)) # [totalNA, 2*ks**2]
- offset_list = images_to_levels(offset_list, num_level_anchors)
- return offset_list
-
- def loss_single(self, cls_score, bbox_pred, anchors, labels, label_weights,
- bbox_targets, bbox_weights, num_total_samples):
- """Loss function on single scale."""
- # classification loss
- if self.with_cls:
- labels = labels.reshape(-1)
- label_weights = label_weights.reshape(-1)
- cls_score = cls_score.permute(0, 2, 3,
- 1).reshape(-1, self.cls_out_channels)
- loss_cls = self.loss_cls(
- cls_score, labels, label_weights, avg_factor=num_total_samples)
- # regression loss
- bbox_targets = bbox_targets.reshape(-1, 4)
- bbox_weights = bbox_weights.reshape(-1, 4)
- bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
- if self.reg_decoded_bbox:
- # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
- # is applied directly on the decoded bounding boxes, it
- # decodes the already encoded coordinates to absolute format.
- anchors = anchors.reshape(-1, 4)
- bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
- loss_reg = self.loss_bbox(
- bbox_pred,
- bbox_targets,
- bbox_weights,
- avg_factor=num_total_samples)
- if self.with_cls:
- return loss_cls, loss_reg
- return None, loss_reg
-
- def loss(self,
- anchor_list,
- valid_flag_list,
- cls_scores,
- bbox_preds,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore=None):
- """Compute losses of the head.
-
- Args:
- anchor_list (list[list]): Multi level anchors of each image.
- cls_scores (list[Tensor]): Box scores for each scale level
- Has shape (N, num_anchors * num_classes, H, W)
- bbox_preds (list[Tensor]): Box energies / deltas for each scale
- level with shape (N, num_anchors * 4, H, W)
- 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.
- 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.
- """
- featmap_sizes = [featmap.size()[-2:] for featmap in bbox_preds]
- label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
- cls_reg_targets = self.get_targets(
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- featmap_sizes,
- gt_bboxes_ignore=gt_bboxes_ignore,
- label_channels=label_channels)
- if cls_reg_targets is None:
- return None
- (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
- num_total_pos, num_total_neg) = cls_reg_targets
- if self.sampling:
- num_total_samples = num_total_pos + num_total_neg
- else:
- # 200 is hard-coded average factor,
- # which follows guided anchoring.
- num_total_samples = sum([label.numel()
- for label in labels_list]) / 200.0
-
- # change per image, per level anchor_list to per_level, per_image
- mlvl_anchor_list = list(zip(*anchor_list))
- # concat mlvl_anchor_list
- mlvl_anchor_list = [
- torch.cat(anchors, dim=0) for anchors in mlvl_anchor_list
- ]
-
- losses = multi_apply(
- self.loss_single,
- cls_scores,
- bbox_preds,
- mlvl_anchor_list,
- labels_list,
- label_weights_list,
- bbox_targets_list,
- bbox_weights_list,
- num_total_samples=num_total_samples)
- if self.with_cls:
- return dict(loss_rpn_cls=losses[0], loss_rpn_reg=losses[1])
- return dict(loss_rpn_reg=losses[1])
-
- def get_bboxes(self,
- anchor_list,
- cls_scores,
- bbox_preds,
- img_metas,
- cfg,
- rescale=False):
- """Get proposal predict."""
- assert len(cls_scores) == len(bbox_preds)
- num_levels = len(cls_scores)
-
- 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[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']
- proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list,
- anchor_list[img_id], img_shape,
- scale_factor, cfg, rescale)
- result_list.append(proposals)
- return result_list
-
- def refine_bboxes(self, anchor_list, bbox_preds, img_metas):
- """Refine bboxes through stages."""
- num_levels = len(bbox_preds)
- new_anchor_list = []
- for img_id in range(len(img_metas)):
- mlvl_anchors = []
- for i in range(num_levels):
- bbox_pred = bbox_preds[i][img_id].detach()
- bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
- img_shape = img_metas[img_id]['img_shape']
- bboxes = self.bbox_coder.decode(anchor_list[img_id][i],
- bbox_pred, img_shape)
- mlvl_anchors.append(bboxes)
- new_anchor_list.append(mlvl_anchors)
- return new_anchor_list
-
- # TODO: temporary plan
- def _get_bboxes_single(self,
- cls_scores,
- bbox_preds,
- mlvl_anchors,
- img_shape,
- scale_factor,
- cfg,
- rescale=False):
- """Transform outputs for a single batch item into bbox predictions.
-
- Args:
- cls_scores (list[Tensor]): Box scores for each scale level
- Has shape (num_anchors * num_classes, H, W).
- bbox_preds (list[Tensor]): Box energies / deltas for each scale
- level with shape (num_anchors * 4, H, W).
- mlvl_anchors (list[Tensor]): Box reference for each scale level
- with shape (num_total_anchors, 4).
- img_shape (tuple[int]): Shape of the input image,
- (height, width, 3).
- scale_factor (ndarray): Scale factor of the image arange as
- (w_scale, h_scale, w_scale, h_scale).
- cfg (mmcv.Config): Test / postprocessing configuration,
- if None, test_cfg would be used.
- rescale (bool): If True, return boxes in original image space.
-
- Returns:
- Tensor: Labeled boxes have the shape of (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.
- """
- cfg = self.test_cfg if cfg is None else cfg
- cfg = copy.deepcopy(cfg)
- # bboxes from different level should be independent during NMS,
- # level_ids are used as labels for batched NMS to separate them
- level_ids = []
- mlvl_scores = []
- mlvl_bbox_preds = []
- mlvl_valid_anchors = []
- for idx in range(len(cls_scores)):
- rpn_cls_score = cls_scores[idx]
- rpn_bbox_pred = bbox_preds[idx]
- assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
- rpn_cls_score = rpn_cls_score.permute(1, 2, 0)
- if self.use_sigmoid_cls:
- rpn_cls_score = rpn_cls_score.reshape(-1)
- scores = rpn_cls_score.sigmoid()
- else:
- rpn_cls_score = rpn_cls_score.reshape(-1, 2)
- # We set FG labels to [0, num_class-1] and BG label to
- # num_class in RPN head since mmdet v2.5, which is unified to
- # be consistent with other head since mmdet v2.0. In mmdet v2.0
- # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head.
- scores = rpn_cls_score.softmax(dim=1)[:, 0]
- rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, 4)
- anchors = mlvl_anchors[idx]
- if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre:
- # sort is faster than topk
- # _, topk_inds = scores.topk(cfg.nms_pre)
- if torch.onnx.is_in_onnx_export():
- # sort op will be converted to TopK in onnx
- # and k<=3480 in TensorRT
- _, topk_inds = scores.topk(cfg.nms_pre)
- scores = scores[topk_inds]
- else:
- ranked_scores, rank_inds = scores.sort(descending=True)
- topk_inds = rank_inds[:cfg.nms_pre]
- scores = ranked_scores[:cfg.nms_pre]
- rpn_bbox_pred = rpn_bbox_pred[topk_inds, :]
- anchors = anchors[topk_inds, :]
- mlvl_scores.append(scores)
- mlvl_bbox_preds.append(rpn_bbox_pred)
- mlvl_valid_anchors.append(anchors)
- level_ids.append(
- scores.new_full((scores.size(0), ), idx, dtype=torch.long))
-
- scores = torch.cat(mlvl_scores)
- anchors = torch.cat(mlvl_valid_anchors)
- rpn_bbox_pred = torch.cat(mlvl_bbox_preds)
- proposals = self.bbox_coder.decode(
- anchors, rpn_bbox_pred, max_shape=img_shape)
- ids = torch.cat(level_ids)
-
- # Skip nonzero op while exporting to ONNX
- if cfg.min_bbox_size > 0 and (not torch.onnx.is_in_onnx_export()):
- w = proposals[:, 2] - proposals[:, 0]
- h = proposals[:, 3] - proposals[:, 1]
- valid_inds = torch.nonzero(
- (w >= cfg.min_bbox_size)
- & (h >= cfg.min_bbox_size),
- as_tuple=False).squeeze()
- if valid_inds.sum().item() != len(proposals):
- proposals = proposals[valid_inds, :]
- scores = scores[valid_inds]
- ids = ids[valid_inds]
-
- # deprecate arguments warning
- if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg:
- warnings.warn(
- 'In rpn_proposal or test_cfg, '
- 'nms_thr has been moved to a dict named nms as '
- 'iou_threshold, max_num has been renamed as max_per_img, '
- 'name of original arguments and the way to specify '
- 'iou_threshold of NMS will be deprecated.')
- if 'nms' not in cfg:
- cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr))
- if 'max_num' in cfg:
- if 'max_per_img' in cfg:
- assert cfg.max_num == cfg.max_per_img, f'You ' \
- f'set max_num and ' \
- f'max_per_img at the same time, but get {cfg.max_num} ' \
- f'and {cfg.max_per_img} respectively' \
- 'Please delete max_num which will be deprecated.'
- else:
- cfg.max_per_img = cfg.max_num
- if 'nms_thr' in cfg:
- assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set' \
- f' iou_threshold in nms and ' \
- f'nms_thr at the same time, but get' \
- f' {cfg.nms.iou_threshold} and {cfg.nms_thr}' \
- f' respectively. Please delete the nms_thr ' \
- f'which will be deprecated.'
-
- dets, keep = batched_nms(proposals, scores, ids, cfg.nms)
- return dets[:cfg.max_per_img]
-
-
-@HEADS.register_module()
-class CascadeRPNHead(BaseDenseHead):
- """The CascadeRPNHead will predict more accurate region proposals, which is
- required for two-stage detectors (such as Fast/Faster R-CNN). CascadeRPN
- consists of a sequence of RPNStage to progressively improve the accuracy of
- the detected proposals.
-
- More details can be found in ``https://arxiv.org/abs/1909.06720``.
-
- Args:
- num_stages (int): number of CascadeRPN stages.
- stages (list[dict]): list of configs to build the stages.
- train_cfg (list[dict]): list of configs at training time each stage.
- test_cfg (dict): config at testing time.
- """
-
- def __init__(self, num_stages, stages, train_cfg, test_cfg):
- super(CascadeRPNHead, self).__init__()
- assert num_stages == len(stages)
- self.num_stages = num_stages
- self.stages = nn.ModuleList()
- for i in range(len(stages)):
- train_cfg_i = train_cfg[i] if train_cfg is not None else None
- stages[i].update(train_cfg=train_cfg_i)
- stages[i].update(test_cfg=test_cfg)
- self.stages.append(build_head(stages[i]))
- self.train_cfg = train_cfg
- self.test_cfg = test_cfg
-
- def init_weights(self):
- """Init weight of CascadeRPN."""
- for i in range(self.num_stages):
- self.stages[i].init_weights()
-
- def loss(self):
- """loss() is implemented in StageCascadeRPNHead."""
- pass
-
- def get_bboxes(self):
- """get_bboxes() is implemented in StageCascadeRPNHead."""
- pass
-
- def forward_train(self,
- x,
- img_metas,
- gt_bboxes,
- gt_labels=None,
- gt_bboxes_ignore=None,
- proposal_cfg=None):
- """Forward train function."""
- assert gt_labels is None, 'RPN does not require gt_labels'
-
- featmap_sizes = [featmap.size()[-2:] for featmap in x]
- device = x[0].device
- anchor_list, valid_flag_list = self.stages[0].get_anchors(
- featmap_sizes, img_metas, device=device)
-
- losses = dict()
-
- for i in range(self.num_stages):
- stage = self.stages[i]
-
- if stage.adapt_cfg['type'] == 'offset':
- offset_list = stage.anchor_offset(anchor_list,
- stage.anchor_strides,
- featmap_sizes)
- else:
- offset_list = None
- x, cls_score, bbox_pred = stage(x, offset_list)
- rpn_loss_inputs = (anchor_list, valid_flag_list, cls_score,
- bbox_pred, gt_bboxes, img_metas)
- stage_loss = stage.loss(*rpn_loss_inputs)
- for name, value in stage_loss.items():
- losses['s{}.{}'.format(i, name)] = value
-
- # refine boxes
- if i < self.num_stages - 1:
- anchor_list = stage.refine_bboxes(anchor_list, bbox_pred,
- img_metas)
- if proposal_cfg is None:
- return losses
- else:
- proposal_list = self.stages[-1].get_bboxes(anchor_list, cls_score,
- bbox_pred, img_metas,
- self.test_cfg)
- return losses, proposal_list
-
- def simple_test_rpn(self, x, img_metas):
- """Simple forward test function."""
- featmap_sizes = [featmap.size()[-2:] for featmap in x]
- device = x[0].device
- anchor_list, _ = self.stages[0].get_anchors(
- featmap_sizes, img_metas, device=device)
-
- for i in range(self.num_stages):
- stage = self.stages[i]
- if stage.adapt_cfg['type'] == 'offset':
- offset_list = stage.anchor_offset(anchor_list,
- stage.anchor_strides,
- featmap_sizes)
- else:
- offset_list = None
- x, cls_score, bbox_pred = stage(x, offset_list)
- if i < self.num_stages - 1:
- anchor_list = stage.refine_bboxes(anchor_list, bbox_pred,
- img_metas)
-
- proposal_list = self.stages[-1].get_bboxes(anchor_list, cls_score,
- bbox_pred, img_metas,
- self.test_cfg)
- return proposal_list
-
- def aug_test_rpn(self, x, img_metas):
- """Augmented forward test function."""
- raise NotImplementedError
diff --git a/spaces/SRankChatGpt/Presentation-Assistant/presentation_assistant/env_set.py b/spaces/SRankChatGpt/Presentation-Assistant/presentation_assistant/env_set.py
deleted file mode 100644
index b252c5bee643a882673e6b64f215d69e48c20712..0000000000000000000000000000000000000000
--- a/spaces/SRankChatGpt/Presentation-Assistant/presentation_assistant/env_set.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import subprocess
-import os
-import sys
-
-sys.path.append("/home/user/app")
-
-def env_set():
- # 패키지가 이미 설치되어 있는지 확인
- installed_packages = subprocess.check_output("pip list", shell=True).decode()
-
- if "PyPDF2" not in installed_packages:
- subprocess.run(f"pip install PyPDF2", shell=True)
-
- if "openai" not in installed_packages:
- subprocess.run(f"pip install -q openai", shell=True)
-
- if "python-pptx" not in installed_packages:
- subprocess.run(f"pip install python-pptx", shell=True)
-
- # pandoc 실행 권한 설정
- if not os.access("./pandoc-2.14.2/bin/pandoc", os.X_OK):
- subprocess.run(f"chmod +x ./pandoc-2.14.2/bin/pandoc", shell=True)
-
- # 디렉토리 권한 설정
- if not os.access(".", os.W_OK):
- subprocess.run(f"chmod -R 777 .", shell=True)
-
- # subprocess.run(f"wget https://github.com/jgm/pandoc/releases/download/2.14.2/pandoc-2.14.2-linux-amd64.tar.gz", shell=True)
- # subprocess.run(f"tar -xvf pandoc-2.14.2-linux-amd64.tar.gz", shell=True)
- # subprocess.run(f"wget https://nodejs.org/dist/v17.0.1/node-v17.0.1-linux-x64.tar.xz", shell=True)
- # subprocess.run(f"tar -xf node-v17.0.1-linux-x64.tar.xz", shell=True)
- # subprocess.run(f"mv node-v17.0.1-linux-x64 node", shell=True)
- # subprocess.run(f"export PATH=/home/user/app/node/bin:$PATH", shell=True)
- # subprocess.run(f"/home/user/app/node/ install -g npm", shell=True)
- # subprocess.run(f"apt-get install -f", shell=True)
- # subprocess.run(f"apt-get install -y fonts-nanum", shell=True)
- # subprocess.run(f"fc-cache -fv", shell=True)
- # subprocess.run(f"rm ~/.cache/matplotlib -rf", shell=True)
-
diff --git a/spaces/SankarSrin/image-matting-app/README.md b/spaces/SankarSrin/image-matting-app/README.md
deleted file mode 100644
index 12be355bf4cd2dd457586f806e7088bf5ad5ec04..0000000000000000000000000000000000000000
--- a/spaces/SankarSrin/image-matting-app/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Image Matting App
-emoji: 🐨
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.11.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: vivym/image-matting-app
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SaulLu/bloom-generations-viewer/app.py b/spaces/SaulLu/bloom-generations-viewer/app.py
deleted file mode 100644
index d84c03a197600d5e4d4b59964e2c06b73498b33e..0000000000000000000000000000000000000000
--- a/spaces/SaulLu/bloom-generations-viewer/app.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import streamlit as st
-from datasets import load_dataset
-
-st.set_page_config(
- page_icon="🧊",
- layout="wide",
-)
-
-st.write(
- "This is an application for viewing different generations for the same prompt. The generations vary depending on the checkpoint used and also the parameters used for the generation."
-)
-
-HF_API_TOKEN = st.secrets["HF_API_TOKEN"]
-PROMPT_COLOR = "#CA437E"
-
-
-def safe_text(text):
- text = text.replace("\n", "
") - return f">>1,I=D[L];if(0>>1;Li(qt,N))mei(ct,qt)?(D[L]=ct,D[me]=N,L=me):(D[L]=qt,D[te]=N,L=te);else if(mei(ct,N))D[L]=ct,D[me]=N,L=me;else break e}}return _}function i(D,_){var N=D.sortIndex-_.sortIndex;return N!==0?N:D.id-_.id}if(typeof performance=="object"&&typeof performance.now=="function"){var o=performance;e.unstable_now=function(){return o.now()}}else{var s=Date,l=s.now();e.unstable_now=function(){return s.now()-l}}var a=[],u=[],c=1,f=null,p=3,g=!1,y=!1,v=!1,T=typeof setTimeout=="function"?setTimeout:null,m=typeof clearTimeout=="function"?clearTimeout:null,d=typeof setImmediate<"u"?setImmediate:null;typeof navigator<"u"&&navigator.scheduling!==void 0&&navigator.scheduling.isInputPending!==void 0&&navigator.scheduling.isInputPending.bind(navigator.scheduling);function h(D){for(var _=n(u);_!==null;){if(_.callback===null)r(u);else if(_.startTime<=D)r(u),_.sortIndex=_.expirationTime,t(a,_);else break;_=n(u)}}function w(D){if(v=!1,h(D),!y)if(n(a)!==null)y=!0,he(x);else{var _=n(u);_!==null&&_e(w,_.startTime-D)}}function x(D,_){y=!1,v&&(v=!1,m(k),k=-1),g=!0;var N=p;try{for(h(_),f=n(a);f!==null&&(!(f.expirationTime>_)||D&&!F());){var L=f.callback;if(typeof L=="function"){f.callback=null,p=f.priorityLevel;var I=L(f.expirationTime<=_);_=e.unstable_now(),typeof I=="function"?f.callback=I:f===n(a)&&r(a),h(_)}else r(a);f=n(a)}if(f!==null)var Ue=!0;else{var te=n(u);te!==null&&_e(w,te.startTime-_),Ue=!1}return Ue}finally{f=null,p=N,g=!1}}var C=!1,E=null,k=-1,V=5,M=-1;function F(){return!(e.unstable_now()-MD||125L?(D.sortIndex=N,t(u,D),n(a)===null&&D===n(u)&&(v?(m(k),k=-1):v=!0,_e(w,N-L))):(D.sortIndex=I,t(a,D),y||g||(y=!0,he(x))),D},e.unstable_shouldYield=F,e.unstable_wrapCallback=function(D){var _=p;return function(){var N=p;p=_;try{return D.apply(this,arguments)}finally{p=N}}}})(zf);Of.exports=zf;var Mm=Of.exports;/**
- * @license React
- * react-dom.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
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v=ne();Vr(v,r,g.layoutBox),Ul(y,v)||(c=!0),f.options.layoutRoot&&(e.relativeTarget=v,e.relativeTargetOrigin=y,e.relativeParent=f)}}}e.notifyListeners("didUpdate",{layout:r,snapshot:n,delta:a,layoutDelta:l,hasLayoutChanged:u,hasRelativeTargetChanged:c})}else if(e.isLead()){const{onExitComplete:r}=e.options;r&&r()}e.options.transition=void 0}function ow(e){on.totalNodes++,e.parent&&(e.isProjecting()||(e.isProjectionDirty=e.parent.isProjectionDirty),e.isSharedProjectionDirty||(e.isSharedProjectionDirty=!!(e.isProjectionDirty||e.parent.isProjectionDirty||e.parent.isSharedProjectionDirty)),e.isTransformDirty||(e.isTransformDirty=e.parent.isTransformDirty))}function sw(e){e.isProjectionDirty=e.isSharedProjectionDirty=e.isTransformDirty=!1}function lw(e){e.clearSnapshot()}function gf(e){e.clearMeasurements()}function aw(e){e.isLayoutDirty=!1}function uw(e){const{visualElement:t}=e.options;t&&t.getProps().onBeforeLayoutMeasure&&t.notify("BeforeLayoutMeasure"),e.resetTransform()}function yf(e){e.finishAnimation(),e.targetDelta=e.relativeTarget=e.target=void 0,e.isProjectionDirty=!0}function cw(e){e.resolveTargetDelta()}function fw(e){e.calcProjection()}function dw(e){e.resetRotation()}function pw(e){e.removeLeadSnapshot()}function vf(e,t,n){e.translate=Z(t.translate,0,n),e.scale=Z(t.scale,1,n),e.origin=t.origin,e.originPoint=t.originPoint}function wf(e,t,n,r){e.min=Z(t.min,n.min,r),e.max=Z(t.max,n.max,r)}function hw(e,t,n,r){wf(e.x,t.x,n.x,r),wf(e.y,t.y,n.y,r)}function mw(e){return e.animationValues&&e.animationValues.opacityExit!==void 0}const gw={duration:.45,ease:[.4,0,.1,1]};function xf(e){e.min=Math.round(e.min),e.max=Math.round(e.max)}function yw(e){xf(e.x),xf(e.y)}function tm(e,t,n){return e==="position"||e==="preserve-aspect"&&!Il(df(t),df(n),.2)}const 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Cf={width:({x:e},{paddingLeft:t="0",paddingRight:n="0"})=>e.max-e.min-parseFloat(t)-parseFloat(n),height:({y:e},{paddingTop:t="0",paddingBottom:n="0"})=>e.max-e.min-parseFloat(t)-parseFloat(n),top:(e,{top:t})=>parseFloat(t),left:(e,{left:t})=>parseFloat(t),bottom:({y:e},{top:t})=>parseFloat(t)+(e.max-e.min),right:({x:e},{left:t})=>parseFloat(t)+(e.max-e.min),x:Pf(4,13),y:Pf(5,14)},Dw=(e,t,n)=>{const r=t.measureViewportBox(),i=t.current,o=getComputedStyle(i),{display:s}=o,l={};s==="none"&&t.setStaticValue("display",e.display||"block"),n.forEach(u=>{l[u]=Cf[u](r,o)}),t.render();const a=t.measureViewportBox();return n.forEach(u=>{const c=t.getValue(u);c&&c.jump(l[u]),e[u]=Cf[u](a,o)}),e},Rw=(e,t,n={},r={})=>{t={...t},r={...r};const i=Object.keys(t).filter(rm);let o=[],s=!1;const l=[];if(i.forEach(a=>{const u=e.getValue(a);if(!e.hasValue(a))return;let c=n[a],f=dr(c);const p=t[a];let g;if(go(p)){const y=p.length,v=p[0]===null?1:0;c=p[v],f=dr(c);for(let 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diff --git a/spaces/XzJosh/Nana7mi-Bert-VITS2/modules.py b/spaces/XzJosh/Nana7mi-Bert-VITS2/modules.py
deleted file mode 100644
index 92e0f32a51c472bfd1659a50a95a95d195281d2b..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Nana7mi-Bert-VITS2/modules.py
+++ /dev/null
@@ -1,452 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-from attentions import Encoder
-
-LRELU_SLOPE = 0.1
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-class ConvReluNorm(nn.Module):
- def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(
- nn.ReLU(),
- nn.Dropout(p_dropout))
- for _ in range(n_layers-1):
- self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size ** i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
- groups=channels, dilation=dilation, padding=padding
- ))
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- self.hidden_channels =hidden_channels
- self.kernel_size = kernel_size,
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = dilation_rate ** i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
- dilation=dilation, padding=padding)
- in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(
- x_in,
- g_l,
- n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:,:self.hidden_channels,:]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:,self.hidden_channels:,:]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels,1))
- self.logs = nn.Parameter(torch.zeros(channels,1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1,2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
-
-class ConvFlow(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
- self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails='linear',
- tail_bound=self.tail_bound
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1,2])
- if not reverse:
- return x, logdet
- else:
- return x
-class TransformerCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- n_layers,
- n_heads,
- p_dropout=0,
- filter_channels=0,
- mean_only=False,
- wn_sharing_parameter=None,
- gin_channels = 0
- ):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = gin_channels) if wn_sharing_parameter is None else wn_sharing_parameter
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
- x1, logabsdet = piecewise_rational_quadratic_transform(x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails='linear',
- tail_bound=self.tail_bound
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1,2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/XzJosh/ranran-Bert-VITS2/modules.py b/spaces/XzJosh/ranran-Bert-VITS2/modules.py
deleted file mode 100644
index 92e0f32a51c472bfd1659a50a95a95d195281d2b..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/ranran-Bert-VITS2/modules.py
+++ /dev/null
@@ -1,452 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-from attentions import Encoder
-
-LRELU_SLOPE = 0.1
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-class ConvReluNorm(nn.Module):
- def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(
- nn.ReLU(),
- nn.Dropout(p_dropout))
- for _ in range(n_layers-1):
- self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size ** i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
- groups=channels, dilation=dilation, padding=padding
- ))
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- self.hidden_channels =hidden_channels
- self.kernel_size = kernel_size,
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = dilation_rate ** i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
- dilation=dilation, padding=padding)
- in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(
- x_in,
- g_l,
- n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:,:self.hidden_channels,:]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:,self.hidden_channels:,:]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels,1))
- self.logs = nn.Parameter(torch.zeros(channels,1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1,2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
-
-class ConvFlow(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
- self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails='linear',
- tail_bound=self.tail_bound
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1,2])
- if not reverse:
- return x, logdet
- else:
- return x
-class TransformerCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- n_layers,
- n_heads,
- p_dropout=0,
- filter_channels=0,
- mean_only=False,
- wn_sharing_parameter=None,
- gin_channels = 0
- ):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = gin_channels) if wn_sharing_parameter is None else wn_sharing_parameter
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
- x1, logabsdet = piecewise_rational_quadratic_transform(x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails='linear',
- tail_bound=self.tail_bound
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1,2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/YangHao520/Openai_GPT_Fine_tune_VisonSystem/app.py b/spaces/YangHao520/Openai_GPT_Fine_tune_VisonSystem/app.py
deleted file mode 100644
index e6dc156e4ea2e5906a08ded5c2d682f95b8177ce..0000000000000000000000000000000000000000
--- a/spaces/YangHao520/Openai_GPT_Fine_tune_VisonSystem/app.py
+++ /dev/null
@@ -1,399 +0,0 @@
-import time
-
-import openpyxl
-import os
-import openai
-import concurrent.futures
-import gradio as gr
-from tqdm import tqdm
-import tempfile
-import datetime
-from DataFormat import DataFormat
-from DataFormat import GetTokenforStr
-import uploadData
-import json
-
-def ChatV2(params):
-
- systemPrompt,ques,gptVersion,temperature=params
- completion = openai.ChatCompletion.create(
- # model="gpt-3.5-turbo",
- # model="gpt-4",
- model=gptVersion,
- messages=[{"role": "system", "content": systemPrompt}, {"role": "user", "content": ques}],
- temperature=temperature,
- timeout=30
- )
-
- return systemPrompt, ques, completion['choices'][0]['message']['content']
-def ChatV2_estimate(params):
- checkBox=["GPT生成结果翻译成中文", "文本语法解析"]
- systemPrompt,prompt,ques,gptVersion,inetuneGptVersion,temperature,Checkboxgourd=params
- maxNum=4
- for i in range(maxNum):
- try:
- if '{Q1}' in prompt:
- gptques=prompt.replace('{Q1}',ques)
- else:
- gptques=prompt+ques
- print(gptques)
- completion = openai.ChatCompletion.create(
- # model="gpt-3.5-turbo",
- model=gptVersion,
- messages=[{"role": "system", "content": systemPrompt}, {"role": "user", "content":gptques}],
- temperature=temperature,
- timeout=30
- )
- print([{"role": "system", "content": systemPrompt}, {"role": "user", "content":gptques}])
- FineTunecompletion = openai.ChatCompletion.create(
- # model="gpt-3.5-turbo",
- # model="gpt-4",
- model=inetuneGptVersion,
- messages=[{"role": "system", "content": systemPrompt}, {"role": "user", "content": gptques}],
- temperature=temperature,
- timeout=30
- )
- except Exception as e:
- print(e)
- print('第{}次错误，错误文本：{}'.format(i+1,ques))
- time.sleep(6*(i+1)) #如果请求失败则过10s重新请求
- if i==maxNum-1:
- return systemPrompt, ques,'',str(e),''
- time.sleep(1)
- gptText=completion['choices'][0]['message']['content']
- gptText2=FineTunecompletion['choices'][0]['message']['content']
- extData=[]
- for Checkbox in Checkboxgourd:
- if Checkbox == 'GPT生成结果翻译成中文':
-
- systemTran='你是一个对中文和日语非常了解的语言大师'
- prompt_tran='请将给你的一组文本全部翻译成连贯流畅的中文，一组文本主要有三部分待翻译：text1:文本1 text2:文本2 text3:文本3。然后你需要将其翻译并按照json格式输出：{"tran_text1":"将用户输入的文本翻译成中文的文本","tran_text2":"将gpt改写文本翻译成中文的文本","tran_text3":"将gpt改写的文本翻译成中文的文本"},下面是你要翻译的文本："""{Q1}"""'
- transques="text1:{} text2:{} text3:{}".format(ques,gptText,gptText2)
- for i in range(2):
- try:
- transCn = openai.ChatCompletion.create(
- # model="gpt-3.5-turbo",
- # model="gpt-4",
- model=gptVersion,
- messages=[{"role": "system", "content": systemTran}, {"role": "user", "content": prompt_tran.replace('{Q1}',transques)}],
- temperature=temperature,
- timeout=30
- )
- transCnText=json.loads(transCn['choices'][0]['message']['content'])
- extData.append(transCnText)
- break
- except Exception as e:
- print('error:'+str(e))
- pass
-
- if Checkbox == 'test2':
- systemTran='你是一个对中文和日语非常了解的翻译官'
- prompt_tran='在不改变原意的情况下，请将给你的文本翻译成中文，下面是你要翻译的文本：'
- transCn = openai.ChatCompletion.create(
- # model="gpt-3.5-turbo",
- # model="gpt-4",
- model=gptVersion,
- messages=[{"role": "system", "content": systemTran}, {"role": "user", "content": prompt_tran+ques}],
- temperature=temperature)
- transCnText=transCn['choices'][0]['message']['content']
- extData.append(transCnText)
- return systemPrompt, ques, completion['choices'][0]['message']['content'], FineTunecompletion['choices'][0]['message']['content'],extData
-
-
-
-def Chat(systemPrompt,ques,gptVersion,temperature):
- completion = openai.ChatCompletion.create(
- # model="gpt-3.5-turbo",
- # model="gpt-4",
- model=gptVersion,
- messages=[{"role": "system", "content": systemPrompt}, {"role": "user", "content": ques}],
- temperature=temperature)
- return completion['choices'][0]['message']['content']
-def ChatDemo():
- systemText = """You are Japanese large language model trained by simejiAI. Your task is to understand the meaning of what I provide and rewrite text into Japanese with cute and interesting expressions, Write some cute elements into this and add some kaomojis and emojis. Keep sentence within 200 characters and make it one-line. If you encounter any pornographic or violent malicious content, you need to refuse to answer or mercilessly counterattack.
- You should not include any additional information or modify the original meaning.
- Please note that the text should not involve any dialogue and the rewritten version will not include any responses. Just give one rewriting text. """
- quesText = "ごめん寝てた"
- ques=Chat(systemText,quesText)
- print(ques)
-
-def AI_Produst(systemText,quesList,gptVersion,temperature,num,outputPath,progress):
- progress(0, desc="Starting...")
- wb=openpyxl.Workbook()
- ws=wb.active
- ws.append(["System",'User','GPT_Output'])
- maxNum=min(num,len(quesList))
- with concurrent.futures.ThreadPoolExecutor(max_workers=4) as excutor:
- futures=[]
-
- for i in range(maxNum):
- params=systemText,quesList[i],gptVersion,temperature
-
- task=excutor.submit(ChatV2,params)
- futures.append(task)
- prad=tqdm(total=len(futures))
- for futrue in concurrent.futures.as_completed(futures):
- prad.update(1)
- systemPrompt,ques,GPTAnswer=futrue.result()
- print(systemPrompt)
- print(ques)
- ws.append([systemPrompt,ques,GPTAnswer])
-
- prad.close()
-
- wb.save(outputPath)
- return outputPath
-
-def AI_Produst_estimate(systemText,prompt,quesList,gptVersion,inetuneGptVersion,temperature,num,outputPath,Checkbox,progress):
- global stopFlag
- stopFlag=False
- progress(0, desc="Starting...")
- wb=openpyxl.Workbook()
- ws=wb.active
- ws.append(["System",'User','GPT_Output','是否合格','FineTune GPT_Output','是否合格','Tran_User','Tran_GPT_Output','Tran_FineTune GPT_Output'])
- maxNum=min(num,len(quesList))
- print('最大数字'+str(maxNum))
- with concurrent.futures.ThreadPoolExecutor(max_workers=4) as excutor:
- futures=[]
-
- for i in range(maxNum):
- params=systemText,prompt,quesList[i],gptVersion,inetuneGptVersion,temperature,Checkbox
-
- task=excutor.submit(ChatV2_estimate,params)
- futures.append(task)
- prad=tqdm(total=len(futures))
- for futrue in concurrent.futures.as_completed(futures):
- if stopFlag:
- break
- try:
- prad.update(1)
- try:
- systemPrompt,ques,GPTAnswer,FineTuneGPTAnswer,exdata=futrue.result(timeout=30)
- except Exception as e:
- print('如果本次请求异常则自动退出')
- ws.append([systemPrompt,ques,'',str(e),''])
- continue
- #print(systemPrompt)
- print(ques)
- if len(exdata)==1:
- try:
- translate=exdata[0]
- #加入翻译
- ws.append([systemPrompt,ques,GPTAnswer,'',FineTuneGPTAnswer,'',translate['tran_text1'],translate['tran_text2'],translate['tran_text3']])
- except Exception as e:
- ws.append([systemPrompt, ques, GPTAnswer, '', FineTuneGPTAnswer])
- print('error:'+str(e))
- print(exdata[0])
- else:
- ws.append([systemPrompt,ques,GPTAnswer,'',FineTuneGPTAnswer])
- except:
- time.sleep(10)
-
- prad.close()
-
- wb.save(outputPath)
- return outputPath
-def AIProdustDemo():
-
- outputPath=r'E:\renpyExcu\bigLLM\text.xlsx'
- num=10
- temperature=0.6
- gptVersion='gpt-3.5-turbo'
- quesList=[]
- book=openpyxl.load_workbook(r'E:\renpyExcu\bigLLM\testData.xlsx')
- sheet=book.active
- maxnum=sheet.max_row
- for i in range(2,maxnum+1):
- quesList.append(sheet.cell(i,1).value)
- systemText = """You are Japanese large language model trained by simejiAI. Your task is to understand the meaning of what I provide and rewrite text into Japanese with cute and interesting expressions, Write some cute elements into this and add some kaomojis and emojis. Keep sentence within 200 characters and make it one-line. If you encounter any pornographic or violent malicious content, you need to refuse to answer or mercilessly counterattack.
- You should not include any additional information or modify the original meaning.
- Please note that the text should not involve any dialogue and the rewritten version will not include any responses. Just give one rewriting text. """
- AI_Produst(systemText,quesList,gptVersion,temperature,num,outputPath)
-def AIProdust_batch(systemText,prompt,inputFile,textInput_APIKEY,temperature,gptVersion,num,progress=gr.Progress(track_tqdm=True)):
- openai.api_key=textInput_APIKEY
- inputFile=inputFile.name
- nowTime=str(datetime.datetime.now()).split('.')[0].replace(' ','_').replace(':','_')
- outputPath="{}/{}_{}_{}_{}".format(os.path.dirname(inputFile),num,nowTime,gptVersion,os.path.basename(inputFile))
- print(inputFile)
- num=int(num)
- quesList=[]
- book=openpyxl.load_workbook(inputFile)
- sheet=book.active
- maxnum=sheet.max_row
- for i in range(2,maxnum+1):
- quesList.append(prompt+sheet.cell(i,1).value)
- AI_Produst(systemText,quesList,gptVersion,temperature,num,outputPath,progress)
- return outputPath
-def AIProdust_batch_estimate(systemText,prompt,inputFile,textInput_APIKEY,temperature,gptVersion,fintuneGPTVersion,num,Checkbox,progress=gr.Progress(track_tqdm=True)):
-
- openai.api_key=textInput_APIKEY
- inputFile=inputFile.name
- nowTime=str(datetime.datetime.now()).split('.')[0].replace(' ','_').replace(':','_')
- outputPath="{}/{}_{}_{}_{}".format(os.path.dirname(inputFile),num,nowTime,gptVersion,os.path.basename(inputFile))
- print(inputFile)
- num=int(num)
- quesList=[]
- book=openpyxl.load_workbook(inputFile)
- sheet=book.active
- maxnum=sheet.max_row
- for i in range(2,maxnum+1):
- if sheet.cell(i,1).value is not None:
- ques=str.strip(sheet.cell(i,1).value)
- if len(ques)!=0:
- quesList.append(ques)
-
- AI_Produst_estimate(systemText,prompt,quesList,gptVersion,fintuneGPTVersion,temperature,num,outputPath,Checkbox,progress)
- return outputPath
-
-def Lines2Excel(lines):
- global tmpdir
- nowTime = str(datetime.datetime.now()).split('.')[0].replace(' ', '_').replace(':', '_')
- outputPath=os.path.join(tmpdir,nowTime+'_temp.xlsx')
- print(outputPath)
- wb=openpyxl.Workbook()
- ws=wb.active
- ws.append(['input'])
-
- lines=lines.split('\n')
- lines = [line for line in lines if len(str.strip(line))>0]
- for line in lines:
- ws.append([line])
- wb.save(outputPath)
-
- return outputPath
-def stopprodust():
- global stopFlag
- stopFlag=True
- return stopFlag
-def AIProdust():
- global tmpdir
-
-
- GPTVersion = ['gpt-4', 'gpt-3.5-turbo', 'gpt-3.5-turbo-0301', 'gpt-3.5-turbo-0613', 'gpt-3.5-turbo-16k',
- 'gpt-3.5-turbo-16k-0613']
-
- with tempfile.TemporaryDirectory(dir='.') as tmpdir:
- with gr.Blocks() as demo:
- gr.Markdown('# GPT3.5 Fine Tune 可视化系统')
- gr.Markdown('GPT3.5 Fine Tune 可视化系统')
- with gr.Tab('多行文本转Excel文件'):
- textInput_Ques = gr.Textbox(label='Lines2Excel', lines=2, placeholder='多行输入，一个输入一行...')
- outPutFile=gr.components.File(label="下载文件")
- button_tran=gr.Button("开始转化")
- button_tran.click(Lines2Excel,inputs=textInput_Ques,outputs=outPutFile)
-
- with gr.Tab('批量请求GPT'):
- textInput_Sys = gr.Textbox(label='SystemMessage', lines=2,placeholder='...')
- textInput_Prompt = gr.Textbox(label='Prompt', lines=2, placeholder='...')
- input_ExcelFile=gr.components.File(label="待批量请求的文件")
- textInput_APIKEY = gr.Textbox(label='OpenAI_APIKEY', lines=2, placeholder='...')
- drop = gr.components.Dropdown(label="GPTVersion", choices=GPTVersion,
- value='gpt-3.5-turbo')
- slider = gr.components.Slider(0, 1, label="Temperature", step=None, value=0.7)
-
- num=gr.Number(label='请求的次数',value=5)
- outPutFile = gr.components.File(label="下载文件")
- button_ques = gr.Button("开始请求")
- button_ques.click(AIProdust_batch, inputs=[textInput_Sys,textInput_Prompt,input_ExcelFile,textInput_APIKEY,slider,drop,num], outputs=outPutFile)
-
- with gr.Tab('微调数据格式化'):
- gr.Markdown('### 微调数据格式化模块')
- input_ExcelFile = gr.components.File(label="待执行格式化的文件")
- drop = gr.components.Dropdown(label="GPTVersion", choices=GPTVersion,
- value='gpt-3.5-turbo')
- outPutFile = gr.components.File(label="gpt微调数据集")
- outPutResText = gr.Textbox(label="格式化结果",lines=2,placeholder='...')
- button_format = gr.Button("开始格式化")
- button_format.click(DataFormat,
- inputs=[input_ExcelFile, drop],
- outputs=[outPutFile,outPutResText])
- gr.Markdown('
') - gr.Markdown('### 字符串token计算模块') - input_text = gr.Textbox(label="待计算Tokens的字符串", lines=2, placeholder='...') - - outPuttoken= gr.Number(label="token计算结果") - button_cal = gr.Button("开始计算") - button_cal.click(GetTokenforStr, - inputs=input_text, - outputs=outPuttoken) - with gr.Tab('微调数据集上传至OpenAI'): - gr.Markdown("注：Fine Tune至少需要10个case") - input_FineTuningFile=gr.components.File(label="gpt微调数据集",file_count='multiple') - input_APIKey=gr.Textbox(label="Openai_APIKEY",lines=2,placeholder='...') - output_FileTuningFile=gr.Json(label='上传文件状态') - - button_updata=gr.Button('开始上传') - button_updata.click(uploadData.upData_OpenAI, - inputs=[input_FineTuningFile,input_APIKey], - outputs=output_FileTuningFile) - - gr.Markdown("注：后续训练需要提供要微调的数据集的ID，如：file-ZnJlydArU8******NKzWaf8d") - - with gr.Tab('启动微调Task'): - input_DataId = gr.Textbox(label="FineTune DataId", lines=2, placeholder='...') - input_APIKey = gr.Textbox(label="Openai_APIKEY", lines=2, placeholder='...') - - output_CreateTaskjson = gr.Json(label='创建微调任务状态') - button_createTask = gr.Button('开始创建') - button_createTask.click(uploadData.createTask, - inputs=[input_DataId, input_APIKey], - outputs=output_CreateTaskjson) - - gr.Markdown("注：只有等上一轮任务执行完毕，你才能创建新的微调任务") - gr.Markdown("
") - gr.Markdown("### APIKey创建的微调任务状态查询'") - input_APIKey = gr.Textbox(label="Openai_APIKEY", lines=2, placeholder='...') - button_createTask = gr.Button('微调状态查询') - output_TaskSatejson = gr.Json(label='创建微调任务状态') - button_createTask.click(uploadData.GetFineTuningJobState, - inputs=[input_APIKey], - outputs=output_TaskSatejson) - with gr.Tab('Finetune Model测试'): - - textInput_Sys1 = gr.Textbox(label='SystemMessage', lines=2, placeholder='...') - textInput_Prompt1 = gr.Textbox(label='Prompt_ques', lines=2, placeholder='...') - - input_fine_tuned_model = gr.Textbox(label='fine_tuned_model', lines=2, placeholder='...') - textInput_APIKEY = gr.Textbox(label='OpenAI_APIKEY', lines=2, placeholder='...') - - - outPutText = gr.Textbox(label="运行结果",lines=2, placeholder='...') - button_ques = gr.Button("开始请求") - button_ques.click(uploadData.userFineTuneLLM, - inputs=[textInput_Sys1, textInput_Prompt1, input_fine_tuned_model, textInput_APIKEY], outputs=outPutText) - with gr.Tab('Finetune Model 效果评估'): - fintunetextInput_Sys = gr.Textbox(label='SystemMessage', lines=2, placeholder='...') - fintunetextInput_Prompt = gr.Textbox(label='Prompt', lines=2, placeholder='...') - fintuneinput_ExcelFile = gr.components.File(label="待批量请求的文件") - textInput_APIKEY = gr.Textbox(label='OpenAI_APIKEY', lines=2, placeholder='...') - fintunedrop = gr.components.Dropdown(label="GPTVersion", choices=GPTVersion, - value='gpt-3.5-turbo') - fintuneGPTVersion=gr.Textbox(label='FineTune_GPTVersion', lines=2, placeholder='...') - fintuneslider = gr.components.Slider(0, 1, label="Temperature", step=None, value=0.7) - fintunenum = gr.Number(label='请求的次数', value=5) - Checkbox=gr.CheckboxGroup(["GPT生成结果翻译成中文", "文本语法解析(暂不支持)"], label="GPT额外功能", info="为了提高审核速度你要增加什么？") - fintuneoutPutFile = gr.components.File(label="下载文件") - fintunebutton_ques = gr.Button("开始请求") - fintunebutton_ques.click(AIProdust_batch_estimate, - inputs=[fintunetextInput_Sys, fintunetextInput_Prompt, fintuneinput_ExcelFile, textInput_APIKEY, fintuneslider, - fintunedrop,fintuneGPTVersion, fintunenum,Checkbox], outputs=fintuneoutPutFile) - fintunebutton_quesStop = gr.Button("终止运行") - fintunebutton_quesStop.click(stopprodust,outputs=gr.Textbox(label='状态')) - - gr.Markdown("
") - gr.Markdown("### APIKey创建的微调任务状态查询'") - input_APIKey = gr.Textbox(label="Openai_APIKEY", lines=2, placeholder='...') - button_createTask = gr.Button('微调任务查询') - output_TaskSatejson = gr.Json(label='查询微调任务状态') - button_createTask.click(uploadData.GetFineTuningJobState, - inputs=[input_APIKey], - outputs=output_TaskSatejson) - demo.queue().launch(share=True) -if __name__=="__main__": - # ChatDemo() - # AIProdustDemo() #AIGC 批量生成内容并加在Excel文件 - # - AIProdust() - - - diff --git a/spaces/YouLiXiya/Mobile-SAM/segment_anything/segment_anything/utils/transforms.py b/spaces/YouLiXiya/Mobile-SAM/segment_anything/segment_anything/utils/transforms.py deleted file mode 100644 index 3ad346661f84b0647026e130a552c4b38b83e2ac..0000000000000000000000000000000000000000 --- a/spaces/YouLiXiya/Mobile-SAM/segment_anything/segment_anything/utils/transforms.py +++ /dev/null @@ -1,102 +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. - -import numpy as np -import torch -from torch.nn import functional as F -from torchvision.transforms.functional import resize, to_pil_image # type: ignore - -from copy import deepcopy -from typing import Tuple - - -class ResizeLongestSide: - """ - Resizes images to longest side 'target_length', as well as provides - methods for resizing coordinates and boxes. Provides methods for - transforming both numpy array and batched torch tensors. - """ - - def __init__(self, target_length: int) -> None: - self.target_length = target_length - - def apply_image(self, image: np.ndarray) -> np.ndarray: - """ - Expects a numpy array with shape HxWxC in uint8 format. - """ - target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length) - return np.array(resize(to_pil_image(image), target_size)) - - def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray: - """ - Expects a numpy array of length 2 in the final dimension. Requires the - original image size in (H, W) format. - """ - old_h, old_w = original_size - new_h, new_w = self.get_preprocess_shape( - original_size[0], original_size[1], self.target_length - ) - coords = deepcopy(coords).astype(float) - coords[..., 0] = coords[..., 0] * (new_w / old_w) - coords[..., 1] = coords[..., 1] * (new_h / old_h) - return coords - - def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray: - """ - Expects a numpy array shape Bx4. Requires the original image size - in (H, W) format. - """ - boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size) - return boxes.reshape(-1, 4) - - def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor: - """ - Expects batched images with shape BxCxHxW and float format. This - transformation may not exactly match apply_image. apply_image is - the transformation expected by the model. - """ - # Expects an image in BCHW format. May not exactly match apply_image. - target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length) - return F.interpolate( - image, target_size, mode="bilinear", align_corners=False, antialias=True - ) - - def apply_coords_torch( - self, coords: torch.Tensor, original_size: Tuple[int, ...] - ) -> torch.Tensor: - """ - Expects a torch tensor with length 2 in the last dimension. Requires the - original image size in (H, W) format. - """ - old_h, old_w = original_size - new_h, new_w = self.get_preprocess_shape( - original_size[0], original_size[1], self.target_length - ) - coords = deepcopy(coords).to(torch.float) - coords[..., 0] = coords[..., 0] * (new_w / old_w) - coords[..., 1] = coords[..., 1] * (new_h / old_h) - return coords - - def apply_boxes_torch( - self, boxes: torch.Tensor, original_size: Tuple[int, ...] - ) -> torch.Tensor: - """ - Expects a torch tensor with shape Bx4. Requires the original image - size in (H, W) format. - """ - boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size) - return boxes.reshape(-1, 4) - - @staticmethod - def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]: - """ - Compute the output size given input size and target long side length. - """ - scale = long_side_length * 1.0 / max(oldh, oldw) - newh, neww = oldh * scale, oldw * scale - neww = int(neww + 0.5) - newh = int(newh + 0.5) - return (newh, neww) diff --git a/spaces/Yudha515/Rvc-Models/audiocraft/models/__init__.py b/spaces/Yudha515/Rvc-Models/audiocraft/models/__init__.py deleted file mode 100644 index 92c7a48a200eba455044cd66e0d2c1efe6494f5c..0000000000000000000000000000000000000000 --- a/spaces/Yudha515/Rvc-Models/audiocraft/models/__init__.py +++ /dev/null @@ -1,10 +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 .musicgen import MusicGen -from .lm import LMModel -from .encodec import CompressionModel, EncodecModel diff --git a/spaces/Yuliang/ECON/apps/IFGeo.py b/spaces/Yuliang/ECON/apps/IFGeo.py deleted file mode 100644 index 45e892a655fd894d59ee925f32a62d378370bd49..0000000000000000000000000000000000000000 --- a/spaces/Yuliang/ECON/apps/IFGeo.py +++ /dev/null @@ -1,179 +0,0 @@ -# -*- coding: utf-8 -*- - -# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is -# holder of all proprietary rights on this computer program. -# You can only use this computer program if you have closed -# a license agreement with MPG or you get the right to use the computer -# program from someone who is authorized to grant you that right. -# Any use of the computer program without a valid license is prohibited and -# liable to prosecution. -# -# Copyright©2019 Max-Planck-Gesellschaft zur Förderung -# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute -# for Intelligent Systems. All rights reserved. -# -# Contact: ps-license@tuebingen.mpg.de - -import numpy as np -import pytorch_lightning as pl -import torch - -from lib.common.seg3d_lossless import Seg3dLossless -from lib.common.train_util import * - -torch.backends.cudnn.benchmark = True - - -class IFGeo(pl.LightningModule): - def __init__(self, cfg): - super(IFGeo, self).__init__() - - self.cfg = cfg - self.batch_size = self.cfg.batch_size - self.lr_G = self.cfg.lr_G - - self.use_sdf = cfg.sdf - self.mcube_res = cfg.mcube_res - self.clean_mesh_flag = cfg.clean_mesh - self.overfit = cfg.overfit - - if cfg.dataset.prior_type == "SMPL": - from lib.net.IFGeoNet import IFGeoNet - self.netG = IFGeoNet(cfg) - else: - from lib.net.IFGeoNet_nobody import IFGeoNet - self.netG = IFGeoNet(cfg) - - self.resolutions = ( - np.logspace( - start=5, - stop=np.log2(self.mcube_res), - base=2, - num=int(np.log2(self.mcube_res) - 4), - endpoint=True, - ) + 1.0 - ) - - self.resolutions = self.resolutions.astype(np.int16).tolist() - - self.reconEngine = Seg3dLossless( - query_func=query_func_IF, - b_min=[[-1.0, 1.0, -1.0]], - b_max=[[1.0, -1.0, 1.0]], - resolutions=self.resolutions, - align_corners=True, - balance_value=0.50, - visualize=False, - debug=False, - use_cuda_impl=False, - faster=True, - ) - - self.export_dir = None - self.result_eval = {} - - # Training related - def configure_optimizers(self): - - # set optimizer - weight_decay = self.cfg.weight_decay - momentum = self.cfg.momentum - - optim_params_G = [{"params": self.netG.parameters(), "lr": self.lr_G}] - - if self.cfg.optim == "Adadelta": - - optimizer_G = torch.optim.Adadelta( - optim_params_G, lr=self.lr_G, weight_decay=weight_decay - ) - - elif self.cfg.optim == "Adam": - - optimizer_G = torch.optim.Adam(optim_params_G, lr=self.lr_G, weight_decay=weight_decay) - - elif self.cfg.optim == "RMSprop": - - optimizer_G = torch.optim.RMSprop( - optim_params_G, - lr=self.lr_G, - weight_decay=weight_decay, - momentum=momentum, - ) - - else: - raise NotImplementedError - - # set scheduler - scheduler_G = torch.optim.lr_scheduler.MultiStepLR( - optimizer_G, milestones=self.cfg.schedule, gamma=self.cfg.gamma - ) - - return [optimizer_G], [scheduler_G] - - def training_step(self, batch, batch_idx): - - self.netG.train() - - preds_G = self.netG(batch) - error_G = self.netG.compute_loss(preds_G, batch["labels_geo"]) - - # metrics processing - metrics_log = { - "loss": error_G, - } - - self.log_dict( - metrics_log, prog_bar=True, logger=True, on_step=True, on_epoch=False, sync_dist=True - ) - - return metrics_log - - def training_epoch_end(self, outputs): - - # metrics processing - metrics_log = { - "train/avgloss": batch_mean(outputs, "loss"), - } - - self.log_dict( - metrics_log, - prog_bar=False, - logger=True, - on_step=False, - on_epoch=True, - rank_zero_only=True - ) - - def validation_step(self, batch, batch_idx): - - self.netG.eval() - self.netG.training = False - - preds_G = self.netG(batch) - error_G = self.netG.compute_loss(preds_G, batch["labels_geo"]) - - metrics_log = { - "val/loss": error_G, - } - - self.log_dict( - metrics_log, prog_bar=True, logger=False, on_step=True, on_epoch=False, sync_dist=True - ) - - return metrics_log - - def validation_epoch_end(self, outputs): - - # metrics processing - metrics_log = { - "val/avgloss": batch_mean(outputs, "val/loss"), - } - - self.log_dict( - metrics_log, - prog_bar=False, - logger=True, - on_step=False, - on_epoch=True, - rank_zero_only=True - ) diff --git a/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v4/README.md b/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v4/README.md deleted file mode 100644 index ceead5ecc32b7c51b3e880862cce147bccf621a6..0000000000000000000000000000000000000000 --- a/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v4/README.md +++ /dev/null @@ -1,15 +0,0 @@ ---- -title: Gradio_YOLOv5_Det_v4 -emoji: 🚀 -colorFrom: yellow -colorTo: indigo -sdk: gradio -sdk_version: 3.1.8b0 -app_file: app.py -pinned: true -license: gpl-3.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference - -🚀 Project homepage：https://gitee.com/CV_Lab/gradio_yolov5_det diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/bbox/coder/bucketing_bbox_coder.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/bbox/coder/bucketing_bbox_coder.py deleted file mode 100644 index 92d24b4519edece7a4af8f5cfa9af025b25f2dad..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/bbox/coder/bucketing_bbox_coder.py +++ /dev/null @@ -1,350 +0,0 @@ -import mmcv -import numpy as np -import torch -import torch.nn.functional as F - -from ..builder import BBOX_CODERS -from ..transforms import bbox_rescale -from .base_bbox_coder import BaseBBoxCoder - - -@BBOX_CODERS.register_module() -class BucketingBBoxCoder(BaseBBoxCoder): - """Bucketing BBox Coder for Side-Aware Boundary Localization (SABL). - - Boundary Localization with Bucketing and Bucketing Guided Rescoring - are implemented here. - - Please refer to https://arxiv.org/abs/1912.04260 for more details. - - Args: - num_buckets (int): Number of buckets. - scale_factor (int): Scale factor of proposals to generate buckets. - offset_topk (int): Topk buckets are used to generate - bucket fine regression targets. Defaults to 2. - offset_upperbound (float): Offset upperbound to generate - bucket fine regression targets. - To avoid too large offset displacements. Defaults to 1.0. - cls_ignore_neighbor (bool): Ignore second nearest bucket or Not. - Defaults to True. - clip_border (bool, optional): Whether clip the objects outside the - border of the image. Defaults to True. - """ - - def __init__(self, - num_buckets, - scale_factor, - offset_topk=2, - offset_upperbound=1.0, - cls_ignore_neighbor=True, - clip_border=True): - super(BucketingBBoxCoder, self).__init__() - self.num_buckets = num_buckets - self.scale_factor = scale_factor - self.offset_topk = offset_topk - self.offset_upperbound = offset_upperbound - self.cls_ignore_neighbor = cls_ignore_neighbor - self.clip_border = clip_border - - def encode(self, bboxes, gt_bboxes): - """Get bucketing estimation and fine regression targets during - training. - - Args: - bboxes (torch.Tensor): source boxes, e.g., object proposals. - gt_bboxes (torch.Tensor): target of the transformation, e.g., - ground truth boxes. - - Returns: - encoded_bboxes(tuple[Tensor]): bucketing estimation - and fine regression targets and weights - """ - - assert bboxes.size(0) == gt_bboxes.size(0) - assert bboxes.size(-1) == gt_bboxes.size(-1) == 4 - encoded_bboxes = bbox2bucket(bboxes, gt_bboxes, self.num_buckets, - self.scale_factor, self.offset_topk, - self.offset_upperbound, - self.cls_ignore_neighbor) - return encoded_bboxes - - def decode(self, bboxes, pred_bboxes, max_shape=None): - """Apply transformation `pred_bboxes` to `boxes`. - Args: - boxes (torch.Tensor): Basic boxes. - pred_bboxes (torch.Tensor): Predictions for bucketing estimation - and fine regression - max_shape (tuple[int], optional): Maximum shape of boxes. - Defaults to None. - - Returns: - torch.Tensor: Decoded boxes. - """ - assert len(pred_bboxes) == 2 - cls_preds, offset_preds = pred_bboxes - assert cls_preds.size(0) == bboxes.size(0) and offset_preds.size( - 0) == bboxes.size(0) - decoded_bboxes = bucket2bbox(bboxes, cls_preds, offset_preds, - self.num_buckets, self.scale_factor, - max_shape, self.clip_border) - - return decoded_bboxes - - -@mmcv.jit(coderize=True) -def generat_buckets(proposals, num_buckets, scale_factor=1.0): - """Generate buckets w.r.t bucket number and scale factor of proposals. - - Args: - proposals (Tensor): Shape (n, 4) - num_buckets (int): Number of buckets. - scale_factor (float): Scale factor to rescale proposals. - - Returns: - tuple[Tensor]: (bucket_w, bucket_h, l_buckets, r_buckets, - t_buckets, d_buckets) - - - bucket_w: Width of buckets on x-axis. Shape (n, ). - - bucket_h: Height of buckets on y-axis. Shape (n, ). - - l_buckets: Left buckets. Shape (n, ceil(side_num/2)). - - r_buckets: Right buckets. Shape (n, ceil(side_num/2)). - - t_buckets: Top buckets. Shape (n, ceil(side_num/2)). - - d_buckets: Down buckets. Shape (n, ceil(side_num/2)). - """ - proposals = bbox_rescale(proposals, scale_factor) - - # number of buckets in each side - side_num = int(np.ceil(num_buckets / 2.0)) - pw = proposals[..., 2] - proposals[..., 0] - ph = proposals[..., 3] - proposals[..., 1] - px1 = proposals[..., 0] - py1 = proposals[..., 1] - px2 = proposals[..., 2] - py2 = proposals[..., 3] - - bucket_w = pw / num_buckets - bucket_h = ph / num_buckets - - # left buckets - l_buckets = px1[:, None] + (0.5 + torch.arange( - 0, side_num).to(proposals).float())[None, :] * bucket_w[:, None] - # right buckets - r_buckets = px2[:, None] - (0.5 + torch.arange( - 0, side_num).to(proposals).float())[None, :] * bucket_w[:, None] - # top buckets - t_buckets = py1[:, None] + (0.5 + torch.arange( - 0, side_num).to(proposals).float())[None, :] * bucket_h[:, None] - # down buckets - d_buckets = py2[:, None] - (0.5 + torch.arange( - 0, side_num).to(proposals).float())[None, :] * bucket_h[:, None] - return bucket_w, bucket_h, l_buckets, r_buckets, t_buckets, d_buckets - - -@mmcv.jit(coderize=True) -def bbox2bucket(proposals, - gt, - num_buckets, - scale_factor, - offset_topk=2, - offset_upperbound=1.0, - cls_ignore_neighbor=True): - """Generate buckets estimation and fine regression targets. - - Args: - proposals (Tensor): Shape (n, 4) - gt (Tensor): Shape (n, 4) - num_buckets (int): Number of buckets. - scale_factor (float): Scale factor to rescale proposals. - offset_topk (int): Topk buckets are used to generate - bucket fine regression targets. Defaults to 2. - offset_upperbound (float): Offset allowance to generate - bucket fine regression targets. - To avoid too large offset displacements. Defaults to 1.0. - cls_ignore_neighbor (bool): Ignore second nearest bucket or Not. - Defaults to True. - - Returns: - tuple[Tensor]: (offsets, offsets_weights, bucket_labels, cls_weights). - - - offsets: Fine regression targets. \ - Shape (n, num_buckets*2). - - offsets_weights: Fine regression weights. \ - Shape (n, num_buckets*2). - - bucket_labels: Bucketing estimation labels. \ - Shape (n, num_buckets*2). - - cls_weights: Bucketing estimation weights. \ - Shape (n, num_buckets*2). - """ - assert proposals.size() == gt.size() - - # generate buckets - proposals = proposals.float() - gt = gt.float() - (bucket_w, bucket_h, l_buckets, r_buckets, t_buckets, - d_buckets) = generat_buckets(proposals, num_buckets, scale_factor) - - gx1 = gt[..., 0] - gy1 = gt[..., 1] - gx2 = gt[..., 2] - gy2 = gt[..., 3] - - # generate offset targets and weights - # offsets from buckets to gts - l_offsets = (l_buckets - gx1[:, None]) / bucket_w[:, None] - r_offsets = (r_buckets - gx2[:, None]) / bucket_w[:, None] - t_offsets = (t_buckets - gy1[:, None]) / bucket_h[:, None] - d_offsets = (d_buckets - gy2[:, None]) / bucket_h[:, None] - - # select top-k nearset buckets - l_topk, l_label = l_offsets.abs().topk( - offset_topk, dim=1, largest=False, sorted=True) - r_topk, r_label = r_offsets.abs().topk( - offset_topk, dim=1, largest=False, sorted=True) - t_topk, t_label = t_offsets.abs().topk( - offset_topk, dim=1, largest=False, sorted=True) - d_topk, d_label = d_offsets.abs().topk( - offset_topk, dim=1, largest=False, sorted=True) - - offset_l_weights = l_offsets.new_zeros(l_offsets.size()) - offset_r_weights = r_offsets.new_zeros(r_offsets.size()) - offset_t_weights = t_offsets.new_zeros(t_offsets.size()) - offset_d_weights = d_offsets.new_zeros(d_offsets.size()) - inds = torch.arange(0, proposals.size(0)).to(proposals).long() - - # generate offset weights of top-k nearset buckets - for k in range(offset_topk): - if k >= 1: - offset_l_weights[inds, l_label[:, - k]] = (l_topk[:, k] < - offset_upperbound).float() - offset_r_weights[inds, r_label[:, - k]] = (r_topk[:, k] < - offset_upperbound).float() - offset_t_weights[inds, t_label[:, - k]] = (t_topk[:, k] < - offset_upperbound).float() - offset_d_weights[inds, d_label[:, - k]] = (d_topk[:, k] < - offset_upperbound).float() - else: - offset_l_weights[inds, l_label[:, k]] = 1.0 - offset_r_weights[inds, r_label[:, k]] = 1.0 - offset_t_weights[inds, t_label[:, k]] = 1.0 - offset_d_weights[inds, d_label[:, k]] = 1.0 - - offsets = torch.cat([l_offsets, r_offsets, t_offsets, d_offsets], dim=-1) - offsets_weights = torch.cat([ - offset_l_weights, offset_r_weights, offset_t_weights, offset_d_weights - ], - dim=-1) - - # generate bucket labels and weight - side_num = int(np.ceil(num_buckets / 2.0)) - labels = torch.stack( - [l_label[:, 0], r_label[:, 0], t_label[:, 0], d_label[:, 0]], dim=-1) - - batch_size = labels.size(0) - bucket_labels = F.one_hot(labels.view(-1), side_num).view(batch_size, - -1).float() - bucket_cls_l_weights = (l_offsets.abs() < 1).float() - bucket_cls_r_weights = (r_offsets.abs() < 1).float() - bucket_cls_t_weights = (t_offsets.abs() < 1).float() - bucket_cls_d_weights = (d_offsets.abs() < 1).float() - bucket_cls_weights = torch.cat([ - bucket_cls_l_weights, bucket_cls_r_weights, bucket_cls_t_weights, - bucket_cls_d_weights - ], - dim=-1) - # ignore second nearest buckets for cls if necessary - if cls_ignore_neighbor: - bucket_cls_weights = (~((bucket_cls_weights == 1) & - (bucket_labels == 0))).float() - else: - bucket_cls_weights[:] = 1.0 - return offsets, offsets_weights, bucket_labels, bucket_cls_weights - - -@mmcv.jit(coderize=True) -def bucket2bbox(proposals, - cls_preds, - offset_preds, - num_buckets, - scale_factor=1.0, - max_shape=None, - clip_border=True): - """Apply bucketing estimation (cls preds) and fine regression (offset - preds) to generate det bboxes. - - Args: - proposals (Tensor): Boxes to be transformed. Shape (n, 4) - cls_preds (Tensor): bucketing estimation. Shape (n, num_buckets*2). - offset_preds (Tensor): fine regression. Shape (n, num_buckets*2). - num_buckets (int): Number of buckets. - scale_factor (float): Scale factor to rescale proposals. - max_shape (tuple[int, int]): Maximum bounds for boxes. specifies (H, W) - clip_border (bool, optional): Whether clip the objects outside the - border of the image. Defaults to True. - - Returns: - tuple[Tensor]: (bboxes, loc_confidence). - - - bboxes: predicted bboxes. Shape (n, 4) - - loc_confidence: localization confidence of predicted bboxes. - Shape (n,). - """ - - side_num = int(np.ceil(num_buckets / 2.0)) - cls_preds = cls_preds.view(-1, side_num) - offset_preds = offset_preds.view(-1, side_num) - - scores = F.softmax(cls_preds, dim=1) - score_topk, score_label = scores.topk(2, dim=1, largest=True, sorted=True) - - rescaled_proposals = bbox_rescale(proposals, scale_factor) - - pw = rescaled_proposals[..., 2] - rescaled_proposals[..., 0] - ph = rescaled_proposals[..., 3] - rescaled_proposals[..., 1] - px1 = rescaled_proposals[..., 0] - py1 = rescaled_proposals[..., 1] - px2 = rescaled_proposals[..., 2] - py2 = rescaled_proposals[..., 3] - - bucket_w = pw / num_buckets - bucket_h = ph / num_buckets - - score_inds_l = score_label[0::4, 0] - score_inds_r = score_label[1::4, 0] - score_inds_t = score_label[2::4, 0] - score_inds_d = score_label[3::4, 0] - l_buckets = px1 + (0.5 + score_inds_l.float()) * bucket_w - r_buckets = px2 - (0.5 + score_inds_r.float()) * bucket_w - t_buckets = py1 + (0.5 + score_inds_t.float()) * bucket_h - d_buckets = py2 - (0.5 + score_inds_d.float()) * bucket_h - - offsets = offset_preds.view(-1, 4, side_num) - inds = torch.arange(proposals.size(0)).to(proposals).long() - l_offsets = offsets[:, 0, :][inds, score_inds_l] - r_offsets = offsets[:, 1, :][inds, score_inds_r] - t_offsets = offsets[:, 2, :][inds, score_inds_t] - d_offsets = offsets[:, 3, :][inds, score_inds_d] - - x1 = l_buckets - l_offsets * bucket_w - x2 = r_buckets - r_offsets * bucket_w - y1 = t_buckets - t_offsets * bucket_h - y2 = d_buckets - d_offsets * bucket_h - - if clip_border and max_shape is not None: - x1 = x1.clamp(min=0, max=max_shape[1] - 1) - y1 = y1.clamp(min=0, max=max_shape[0] - 1) - x2 = x2.clamp(min=0, max=max_shape[1] - 1) - y2 = y2.clamp(min=0, max=max_shape[0] - 1) - bboxes = torch.cat([x1[:, None], y1[:, None], x2[:, None], y2[:, None]], - dim=-1) - - # bucketing guided rescoring - loc_confidence = score_topk[:, 0] - top2_neighbor_inds = (score_label[:, 0] - score_label[:, 1]).abs() == 1 - loc_confidence += score_topk[:, 1] * top2_neighbor_inds.float() - loc_confidence = loc_confidence.view(-1, 4).mean(dim=1) - - return bboxes, loc_confidence diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/backbones/ssd_vgg.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/backbones/ssd_vgg.py deleted file mode 100644 index cbc4fbb2301afc002f47abb9ed133a500d6cf23f..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/backbones/ssd_vgg.py +++ /dev/null @@ -1,169 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from mmcv.cnn import VGG, constant_init, kaiming_init, normal_init, xavier_init -from mmcv.runner import load_checkpoint - -from mmdet.utils import get_root_logger -from ..builder import BACKBONES - - -@BACKBONES.register_module() -class SSDVGG(VGG): - """VGG Backbone network for single-shot-detection. - - Args: - input_size (int): width and height of input, from {300, 512}. - depth (int): Depth of vgg, from {11, 13, 16, 19}. - out_indices (Sequence[int]): Output from which stages. - - Example: - >>> self = SSDVGG(input_size=300, depth=11) - >>> self.eval() - >>> inputs = torch.rand(1, 3, 300, 300) - >>> level_outputs = self.forward(inputs) - >>> for level_out in level_outputs: - ... print(tuple(level_out.shape)) - (1, 1024, 19, 19) - (1, 512, 10, 10) - (1, 256, 5, 5) - (1, 256, 3, 3) - (1, 256, 1, 1) - """ - extra_setting = { - 300: (256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256), - 512: (256, 'S', 512, 128, 'S', 256, 128, 'S', 256, 128, 'S', 256, 128), - } - - def __init__(self, - input_size, - depth, - with_last_pool=False, - ceil_mode=True, - out_indices=(3, 4), - out_feature_indices=(22, 34), - l2_norm_scale=20.): - # TODO: in_channels for mmcv.VGG - super(SSDVGG, self).__init__( - depth, - with_last_pool=with_last_pool, - ceil_mode=ceil_mode, - out_indices=out_indices) - assert input_size in (300, 512) - self.input_size = input_size - - self.features.add_module( - str(len(self.features)), - nn.MaxPool2d(kernel_size=3, stride=1, padding=1)) - self.features.add_module( - str(len(self.features)), - nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)) - self.features.add_module( - str(len(self.features)), nn.ReLU(inplace=True)) - self.features.add_module( - str(len(self.features)), nn.Conv2d(1024, 1024, kernel_size=1)) - self.features.add_module( - str(len(self.features)), nn.ReLU(inplace=True)) - self.out_feature_indices = out_feature_indices - - self.inplanes = 1024 - self.extra = self._make_extra_layers(self.extra_setting[input_size]) - self.l2_norm = L2Norm( - self.features[out_feature_indices[0] - 1].out_channels, - l2_norm_scale) - - def init_weights(self, pretrained=None): - """Initialize the weights in backbone. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - if isinstance(pretrained, str): - logger = get_root_logger() - load_checkpoint(self, pretrained, strict=False, logger=logger) - elif pretrained is None: - for m in self.features.modules(): - if isinstance(m, nn.Conv2d): - kaiming_init(m) - elif isinstance(m, nn.BatchNorm2d): - constant_init(m, 1) - elif isinstance(m, nn.Linear): - normal_init(m, std=0.01) - else: - raise TypeError('pretrained must be a str or None') - - for m in self.extra.modules(): - if isinstance(m, nn.Conv2d): - xavier_init(m, distribution='uniform') - - constant_init(self.l2_norm, self.l2_norm.scale) - - def forward(self, x): - """Forward function.""" - outs = [] - for i, layer in enumerate(self.features): - x = layer(x) - if i in self.out_feature_indices: - outs.append(x) - for i, layer in enumerate(self.extra): - x = F.relu(layer(x), inplace=True) - if i % 2 == 1: - outs.append(x) - outs[0] = self.l2_norm(outs[0]) - if len(outs) == 1: - return outs[0] - else: - return tuple(outs) - - def _make_extra_layers(self, outplanes): - layers = [] - kernel_sizes = (1, 3) - num_layers = 0 - outplane = None - for i in range(len(outplanes)): - if self.inplanes == 'S': - self.inplanes = outplane - continue - k = kernel_sizes[num_layers % 2] - if outplanes[i] == 'S': - outplane = outplanes[i + 1] - conv = nn.Conv2d( - self.inplanes, outplane, k, stride=2, padding=1) - else: - outplane = outplanes[i] - conv = nn.Conv2d( - self.inplanes, outplane, k, stride=1, padding=0) - layers.append(conv) - self.inplanes = outplanes[i] - num_layers += 1 - if self.input_size == 512: - layers.append(nn.Conv2d(self.inplanes, 256, 4, padding=1)) - - return nn.Sequential(*layers) - - -class L2Norm(nn.Module): - - def __init__(self, n_dims, scale=20., eps=1e-10): - """L2 normalization layer. - - Args: - n_dims (int): Number of dimensions to be normalized - scale (float, optional): Defaults to 20.. - eps (float, optional): Used to avoid division by zero. - Defaults to 1e-10. - """ - super(L2Norm, self).__init__() - self.n_dims = n_dims - self.weight = nn.Parameter(torch.Tensor(self.n_dims)) - self.eps = eps - self.scale = scale - - def forward(self, x): - """Forward function.""" - # normalization layer convert to FP32 in FP16 training - x_float = x.float() - norm = x_float.pow(2).sum(1, keepdim=True).sqrt() + self.eps - return (self.weight[None, :, None, None].float().expand_as(x_float) * - x_float / norm).type_as(x) diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/segmentors/base.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/segmentors/base.py deleted file mode 100644 index bfbf31f561fd8230ec983be7fb72650411f4d5a1..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/segmentors/base.py +++ /dev/null @@ -1,285 +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. -''' - -import logging -import warnings -from abc import ABCMeta, abstractmethod -from collections import OrderedDict - -import annotator.uniformer.mmcv as mmcv -import numpy as np -import torch -import torch.distributed as dist -import torch.nn as nn -from annotator.uniformer.mmcv.runner import auto_fp16 - - -class BaseSegmentor(nn.Module): - """Base class for segmentors.""" - - __metaclass__ = ABCMeta - - def __init__(self): - super(BaseSegmentor, self).__init__() - self.fp16_enabled = False - - @property - def with_neck(self): - """bool: whether the segmentor has neck""" - return hasattr(self, 'neck') and self.neck is not None - - @property - def with_auxiliary_head(self): - """bool: whether the segmentor has auxiliary head""" - return hasattr(self, - 'auxiliary_head') and self.auxiliary_head is not None - - @property - def with_decode_head(self): - """bool: whether the segmentor has decode head""" - return hasattr(self, 'decode_head') and self.decode_head is not None - - @abstractmethod - def extract_feat(self, imgs): - """Placeholder for extract features from images.""" - pass - - @abstractmethod - def encode_decode(self, img, img_metas): - """Placeholder for encode images with backbone and decode into a - semantic segmentation map of the same size as input.""" - pass - - @abstractmethod - def forward_train(self, imgs, img_metas, **kwargs): - """Placeholder for Forward function for training.""" - pass - - @abstractmethod - def simple_test(self, img, img_meta, **kwargs): - """Placeholder for single image test.""" - pass - - @abstractmethod - def aug_test(self, imgs, img_metas, **kwargs): - """Placeholder for augmentation test.""" - pass - - def init_weights(self, pretrained=None): - """Initialize the weights in segmentor. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - if pretrained is not None: - logger = logging.getLogger() - logger.info(f'load model from: {pretrained}') - - def forward_test(self, imgs, img_metas, **kwargs): - """ - Args: - imgs (List[Tensor]): the outer list indicates test-time - augmentations and inner Tensor should have a shape NxCxHxW, - which contains all images in the batch. - img_metas (List[List[dict]]): the outer list indicates test-time - augs (multiscale, flip, etc.) and the inner list indicates - images in a batch. - """ - for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]: - if not isinstance(var, list): - raise TypeError(f'{name} must be a list, but got ' - f'{type(var)}') - - num_augs = len(imgs) - if num_augs != len(img_metas): - raise ValueError(f'num of augmentations ({len(imgs)}) != ' - f'num of image meta ({len(img_metas)})') - # all images in the same aug batch all of the same ori_shape and pad - # shape - for img_meta in img_metas: - ori_shapes = [_['ori_shape'] for _ in img_meta] - assert all(shape == ori_shapes[0] for shape in ori_shapes) - img_shapes = [_['img_shape'] for _ in img_meta] - assert all(shape == img_shapes[0] for shape in img_shapes) - pad_shapes = [_['pad_shape'] for _ in img_meta] - assert all(shape == pad_shapes[0] for shape in pad_shapes) - - if num_augs == 1: - return self.simple_test(imgs[0], img_metas[0], **kwargs) - else: - return self.aug_test(imgs, img_metas, **kwargs) - - @auto_fp16(apply_to=('img', )) - def forward(self, img, img_metas, return_loss=True, **kwargs): - """Calls either :func:`forward_train` or :func:`forward_test` depending - on whether ``return_loss`` is ``True``. - - Note this setting will change the expected inputs. When - ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor - and List[dict]), and when ``resturn_loss=False``, img and img_meta - should be double nested (i.e. List[Tensor], List[List[dict]]), with - the outer list indicating test time augmentations. - """ - if return_loss: - return self.forward_train(img, img_metas, **kwargs) - else: - return self.forward_test(img, img_metas, **kwargs) - - def train_step(self, data_batch, optimizer, **kwargs): - """The iteration step during training. - - This method defines an iteration step during training, except for the - back propagation and optimizer updating, which are done in an optimizer - hook. Note that in some complicated cases or models, the whole process - including back propagation and optimizer updating is also defined in - this method, such as GAN. - - Args: - data (dict): The output of dataloader. - optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of - runner is passed to ``train_step()``. This argument is unused - and reserved. - - Returns: - dict: It should contain at least 3 keys: ``loss``, ``log_vars``, - ``num_samples``. - ``loss`` is a tensor for back propagation, which can be a - weighted sum of multiple losses. - ``log_vars`` contains all the variables to be sent to the - logger. - ``num_samples`` indicates the batch size (when the model is - DDP, it means the batch size on each GPU), which is used for - averaging the logs. - """ - losses = self(**data_batch) - loss, log_vars = self._parse_losses(losses) - - outputs = dict( - loss=loss, - log_vars=log_vars, - num_samples=len(data_batch['img_metas'])) - - return outputs - - def val_step(self, data_batch, **kwargs): - """The iteration step during validation. - - This method shares the same signature as :func:`train_step`, but used - during val epochs. Note that the evaluation after training epochs is - not implemented with this method, but an evaluation hook. - """ - output = self(**data_batch, **kwargs) - return output - - @staticmethod - def _parse_losses(losses): - """Parse the raw outputs (losses) of the network. - - Args: - losses (dict): Raw output of the network, which usually contain - losses and other necessary information. - - Returns: - tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor - which may be a weighted sum of all losses, log_vars contains - all the variables to be sent to the logger. - """ - log_vars = OrderedDict() - for loss_name, loss_value in losses.items(): - if isinstance(loss_value, torch.Tensor): - log_vars[loss_name] = loss_value.mean() - elif isinstance(loss_value, list): - log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value) - else: - raise TypeError( - f'{loss_name} is not a tensor or list of tensors') - - loss = sum(_value for _key, _value in log_vars.items() - if 'loss' in _key) - - log_vars['loss'] = loss - for loss_name, loss_value in log_vars.items(): - # reduce loss when distributed training - if dist.is_available() and dist.is_initialized(): - loss_value = loss_value.data.clone() - dist.all_reduce(loss_value.div_(dist.get_world_size())) - log_vars[loss_name] = loss_value.item() - - return loss, log_vars - - def show_result(self, - img, - result, - palette=None, - win_name='', - show=False, - wait_time=0, - out_file=None, - opacity=0.5): - """Draw `result` over `img`. - - Args: - img (str or Tensor): The image to be displayed. - result (Tensor): The semantic segmentation results to draw over - `img`. - palette (list[list[int]]] | np.ndarray | None): The palette of - segmentation map. If None is given, random palette will be - generated. Default: None - win_name (str): The window name. - wait_time (int): Value of waitKey param. - Default: 0. - show (bool): Whether to show the image. - Default: False. - out_file (str or None): The filename to write the image. - Default: None. - opacity(float): Opacity of painted segmentation map. - Default 0.5. - Must be in (0, 1] range. - Returns: - img (Tensor): Only if not `show` or `out_file` - """ - img = mmcv.imread(img) - img = img.copy() - seg = result[0] - if palette is None: - if self.PALETTE is None: - palette = np.random.randint( - 0, 255, size=(len(self.CLASSES), 3)) - else: - palette = self.PALETTE - palette = np.array(palette) - assert palette.shape[0] == len(self.CLASSES) - assert palette.shape[1] == 3 - assert len(palette.shape) == 2 - assert 0 < opacity <= 1.0 - color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) - for label, color in enumerate(palette): - color_seg[seg == label, :] = color - # convert to BGR - color_seg = color_seg[..., ::-1] - - img = img * (1 - opacity) + color_seg * opacity - img = img.astype(np.uint8) - # if out_file specified, do not show image in window - if out_file is not None: - show = False - - if show: - mmcv.imshow(img, win_name, wait_time) - if out_file is not None: - mmcv.imwrite(img, out_file) - - if not (show or out_file): - warnings.warn('show==False and out_file is not specified, only ' - 'result image will be returned') - return img diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/libs/darwin/cocoapy/runtime.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/libs/darwin/cocoapy/runtime.py deleted file mode 100644 index ba60086681243de4fbed3bf9718128e66999c85f..0000000000000000000000000000000000000000 --- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/libs/darwin/cocoapy/runtime.py +++ /dev/null @@ -1,1251 +0,0 @@ -# objective-ctypes -# -# Copyright (c) 2011, Phillip Nguyen -# 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 objective-ctypes 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 THE -# COPYRIGHT HOLDER OR CONTRIBUTORS 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. - -import sys -import platform -import struct - -from ctypes import * -from ctypes import util - -from .cocoatypes import * - -__LP64__ = (8 * struct.calcsize("P") == 64) -__i386__ = (platform.machine() == 'i386') -__arm64__ = (platform.machine() == 'arm64') - -if sizeof(c_void_p) == 4: - c_ptrdiff_t = c_int32 -elif sizeof(c_void_p) == 8: - c_ptrdiff_t = c_int64 - -###################################################################### - -lib = util.find_library('objc') - -# Hack for compatibility with macOS > 11.0 -if lib is None: - lib = '/usr/lib/libobjc.dylib' - -objc = cdll.LoadLibrary(lib) - -###################################################################### - -# BOOL class_addIvar(Class cls, const char *name, size_t size, uint8_t alignment, const char *types) -objc.class_addIvar.restype = c_bool -objc.class_addIvar.argtypes = [c_void_p, c_char_p, c_size_t, c_uint8, c_char_p] - -# BOOL class_addMethod(Class cls, SEL name, IMP imp, const char *types) -objc.class_addMethod.restype = c_bool - -# BOOL class_addProtocol(Class cls, Protocol *protocol) -objc.class_addProtocol.restype = c_bool -objc.class_addProtocol.argtypes = [c_void_p, c_void_p] - -# BOOL class_conformsToProtocol(Class cls, Protocol *protocol) -objc.class_conformsToProtocol.restype = c_bool -objc.class_conformsToProtocol.argtypes = [c_void_p, c_void_p] - -# Ivar * class_copyIvarList(Class cls, unsigned int *outCount) -# Returns an array of pointers of type Ivar describing instance variables. -# The array has *outCount pointers followed by a NULL terminator. -# You must free() the returned array. -objc.class_copyIvarList.restype = POINTER(c_void_p) -objc.class_copyIvarList.argtypes = [c_void_p, POINTER(c_uint)] - -# Method * class_copyMethodList(Class cls, unsigned int *outCount) -# Returns an array of pointers of type Method describing instance methods. -# The array has *outCount pointers followed by a NULL terminator. -# You must free() the returned array. -objc.class_copyMethodList.restype = POINTER(c_void_p) -objc.class_copyMethodList.argtypes = [c_void_p, POINTER(c_uint)] - -# objc_property_t * class_copyPropertyList(Class cls, unsigned int *outCount) -# Returns an array of pointers of type objc_property_t describing properties. -# The array has *outCount pointers followed by a NULL terminator. -# You must free() the returned array. -objc.class_copyPropertyList.restype = POINTER(c_void_p) -objc.class_copyPropertyList.argtypes = [c_void_p, POINTER(c_uint)] - -# Protocol ** class_copyProtocolList(Class cls, unsigned int *outCount) -# Returns an array of pointers of type Protocol* describing protocols. -# The array has *outCount pointers followed by a NULL terminator. -# You must free() the returned array. -objc.class_copyProtocolList.restype = POINTER(c_void_p) -objc.class_copyProtocolList.argtypes = [c_void_p, POINTER(c_uint)] - -# id class_createInstance(Class cls, size_t extraBytes) -objc.class_createInstance.restype = c_void_p -objc.class_createInstance.argtypes = [c_void_p, c_size_t] - -# Method class_getClassMethod(Class aClass, SEL aSelector) -# Will also search superclass for implementations. -objc.class_getClassMethod.restype = c_void_p -objc.class_getClassMethod.argtypes = [c_void_p, c_void_p] - -# Ivar class_getClassVariable(Class cls, const char* name) -objc.class_getClassVariable.restype = c_void_p -objc.class_getClassVariable.argtypes = [c_void_p, c_char_p] - -# Method class_getInstanceMethod(Class aClass, SEL aSelector) -# Will also search superclass for implementations. -objc.class_getInstanceMethod.restype = c_void_p -objc.class_getInstanceMethod.argtypes = [c_void_p, c_void_p] - -# size_t class_getInstanceSize(Class cls) -objc.class_getInstanceSize.restype = c_size_t -objc.class_getInstanceSize.argtypes = [c_void_p] - -# Ivar class_getInstanceVariable(Class cls, const char* name) -objc.class_getInstanceVariable.restype = c_void_p -objc.class_getInstanceVariable.argtypes = [c_void_p, c_char_p] - -# const char *class_getIvarLayout(Class cls) -objc.class_getIvarLayout.restype = c_char_p -objc.class_getIvarLayout.argtypes = [c_void_p] - -# IMP class_getMethodImplementation(Class cls, SEL name) -objc.class_getMethodImplementation.restype = c_void_p -objc.class_getMethodImplementation.argtypes = [c_void_p, c_void_p] - -# The function is marked as OBJC_ARM64_UNAVAILABLE. -if not __arm64__: - # IMP class_getMethodImplementation_stret(Class cls, SEL name) - objc.class_getMethodImplementation_stret.restype = c_void_p - objc.class_getMethodImplementation_stret.argtypes = [c_void_p, c_void_p] - -# const char * class_getName(Class cls) -objc.class_getName.restype = c_char_p -objc.class_getName.argtypes = [c_void_p] - -# objc_property_t class_getProperty(Class cls, const char *name) -objc.class_getProperty.restype = c_void_p -objc.class_getProperty.argtypes = [c_void_p, c_char_p] - -# Class class_getSuperclass(Class cls) -objc.class_getSuperclass.restype = c_void_p -objc.class_getSuperclass.argtypes = [c_void_p] - -# int class_getVersion(Class theClass) -objc.class_getVersion.restype = c_int -objc.class_getVersion.argtypes = [c_void_p] - -# const char *class_getWeakIvarLayout(Class cls) -objc.class_getWeakIvarLayout.restype = c_char_p -objc.class_getWeakIvarLayout.argtypes = [c_void_p] - -# BOOL class_isMetaClass(Class cls) -objc.class_isMetaClass.restype = c_bool -objc.class_isMetaClass.argtypes = [c_void_p] - -# IMP class_replaceMethod(Class cls, SEL name, IMP imp, const char *types) -objc.class_replaceMethod.restype = c_void_p -objc.class_replaceMethod.argtypes = [c_void_p, c_void_p, c_void_p, c_char_p] - -# BOOL class_respondsToSelector(Class cls, SEL sel) -objc.class_respondsToSelector.restype = c_bool -objc.class_respondsToSelector.argtypes = [c_void_p, c_void_p] - -# void class_setIvarLayout(Class cls, const char *layout) -objc.class_setIvarLayout.restype = None -objc.class_setIvarLayout.argtypes = [c_void_p, c_char_p] - -# Class class_setSuperclass(Class cls, Class newSuper) -objc.class_setSuperclass.restype = c_void_p -objc.class_setSuperclass.argtypes = [c_void_p, c_void_p] - -# void class_setVersion(Class theClass, int version) -objc.class_setVersion.restype = None -objc.class_setVersion.argtypes = [c_void_p, c_int] - -# void class_setWeakIvarLayout(Class cls, const char *layout) -objc.class_setWeakIvarLayout.restype = None -objc.class_setWeakIvarLayout.argtypes = [c_void_p, c_char_p] - -###################################################################### - -# const char * ivar_getName(Ivar ivar) -objc.ivar_getName.restype = c_char_p -objc.ivar_getName.argtypes = [c_void_p] - -# ptrdiff_t ivar_getOffset(Ivar ivar) -objc.ivar_getOffset.restype = c_ptrdiff_t -objc.ivar_getOffset.argtypes = [c_void_p] - -# const char * ivar_getTypeEncoding(Ivar ivar) -objc.ivar_getTypeEncoding.restype = c_char_p -objc.ivar_getTypeEncoding.argtypes = [c_void_p] - -###################################################################### - -# char * method_copyArgumentType(Method method, unsigned int index) -# You must free() the returned string. -objc.method_copyArgumentType.restype = c_char_p -objc.method_copyArgumentType.argtypes = [c_void_p, c_uint] - -# char * method_copyReturnType(Method method) -# You must free() the returned string. -objc.method_copyReturnType.restype = c_char_p -objc.method_copyReturnType.argtypes = [c_void_p] - -# void method_exchangeImplementations(Method m1, Method m2) -objc.method_exchangeImplementations.restype = None -objc.method_exchangeImplementations.argtypes = [c_void_p, c_void_p] - -# void method_getArgumentType(Method method, unsigned int index, char *dst, size_t dst_len) -# Functionally similar to strncpy(dst, parameter_type, dst_len). -objc.method_getArgumentType.restype = None -objc.method_getArgumentType.argtypes = [c_void_p, c_uint, c_char_p, c_size_t] - -# IMP method_getImplementation(Method method) -objc.method_getImplementation.restype = c_void_p -objc.method_getImplementation.argtypes = [c_void_p] - -# SEL method_getName(Method method) -objc.method_getName.restype = c_void_p -objc.method_getName.argtypes = [c_void_p] - -# unsigned method_getNumberOfArguments(Method method) -objc.method_getNumberOfArguments.restype = c_uint -objc.method_getNumberOfArguments.argtypes = [c_void_p] - -# void method_getReturnType(Method method, char *dst, size_t dst_len) -# Functionally similar to strncpy(dst, return_type, dst_len) -objc.method_getReturnType.restype = None -objc.method_getReturnType.argtypes = [c_void_p, c_char_p, c_size_t] - -# const char * method_getTypeEncoding(Method method) -objc.method_getTypeEncoding.restype = c_char_p -objc.method_getTypeEncoding.argtypes = [c_void_p] - -# IMP method_setImplementation(Method method, IMP imp) -objc.method_setImplementation.restype = c_void_p -objc.method_setImplementation.argtypes = [c_void_p, c_void_p] - -###################################################################### - -# Class objc_allocateClassPair(Class superclass, const char *name, size_t extraBytes) -objc.objc_allocateClassPair.restype = c_void_p -objc.objc_allocateClassPair.argtypes = [c_void_p, c_char_p, c_size_t] - -# Protocol **objc_copyProtocolList(unsigned int *outCount) -# Returns an array of *outcount pointers followed by NULL terminator. -# You must free() the array. -objc.objc_copyProtocolList.restype = POINTER(c_void_p) -objc.objc_copyProtocolList.argtypes = [POINTER(c_int)] - -# id objc_getAssociatedObject(id object, void *key) -objc.objc_getAssociatedObject.restype = c_void_p -objc.objc_getAssociatedObject.argtypes = [c_void_p, c_void_p] - -# id objc_getClass(const char *name) -objc.objc_getClass.restype = c_void_p -objc.objc_getClass.argtypes = [c_char_p] - -# int objc_getClassList(Class *buffer, int bufferLen) -# Pass None for buffer to obtain just the total number of classes. -objc.objc_getClassList.restype = c_int -objc.objc_getClassList.argtypes = [c_void_p, c_int] - -# id objc_getMetaClass(const char *name) -objc.objc_getMetaClass.restype = c_void_p -objc.objc_getMetaClass.argtypes = [c_char_p] - -# Protocol *objc_getProtocol(const char *name) -objc.objc_getProtocol.restype = c_void_p -objc.objc_getProtocol.argtypes = [c_char_p] - -# You should set return and argument types depending on context. -# id objc_msgSend(id theReceiver, SEL theSelector, ...) -# id objc_msgSendSuper(struct objc_super *super, SEL op, ...) - -# The function is marked as OBJC_ARM64_UNAVAILABLE. -if not __arm64__: - # void objc_msgSendSuper_stret(struct objc_super *super, SEL op, ...) - objc.objc_msgSendSuper_stret.restype = None - -# double objc_msgSend_fpret(id self, SEL op, ...) -# objc.objc_msgSend_fpret.restype = c_double - -# The function is marked as OBJC_ARM64_UNAVAILABLE. -if not __arm64__: - # void objc_msgSend_stret(void * stretAddr, id theReceiver, SEL theSelector, ...) - objc.objc_msgSend_stret.restype = None - -# void objc_registerClassPair(Class cls) -objc.objc_registerClassPair.restype = None -objc.objc_registerClassPair.argtypes = [c_void_p] - -# void objc_removeAssociatedObjects(id object) -objc.objc_removeAssociatedObjects.restype = None -objc.objc_removeAssociatedObjects.argtypes = [c_void_p] - -# void objc_setAssociatedObject(id object, void *key, id value, objc_AssociationPolicy policy) -objc.objc_setAssociatedObject.restype = None -objc.objc_setAssociatedObject.argtypes = [c_void_p, c_void_p, c_void_p, c_int] - -###################################################################### - -# id object_copy(id obj, size_t size) -objc.object_copy.restype = c_void_p -objc.object_copy.argtypes = [c_void_p, c_size_t] - -# id object_dispose(id obj) -objc.object_dispose.restype = c_void_p -objc.object_dispose.argtypes = [c_void_p] - -# Class object_getClass(id object) -objc.object_getClass.restype = c_void_p -objc.object_getClass.argtypes = [c_void_p] - -# const char *object_getClassName(id obj) -objc.object_getClassName.restype = c_char_p -objc.object_getClassName.argtypes = [c_void_p] - -# Ivar object_getInstanceVariable(id obj, const char *name, void **outValue) -objc.object_getInstanceVariable.restype = c_void_p -objc.object_getInstanceVariable.argtypes = [c_void_p, c_char_p, c_void_p] - -# id object_getIvar(id object, Ivar ivar) -objc.object_getIvar.restype = c_void_p -objc.object_getIvar.argtypes = [c_void_p, c_void_p] - -# Class object_setClass(id object, Class cls) -objc.object_setClass.restype = c_void_p -objc.object_setClass.argtypes = [c_void_p, c_void_p] - -# Ivar object_setInstanceVariable(id obj, const char *name, void *value) -# Set argtypes based on the data type of the instance variable. -objc.object_setInstanceVariable.restype = c_void_p - -# void object_setIvar(id object, Ivar ivar, id value) -objc.object_setIvar.restype = None -objc.object_setIvar.argtypes = [c_void_p, c_void_p, c_void_p] - -###################################################################### - -# const char *property_getAttributes(objc_property_t property) -objc.property_getAttributes.restype = c_char_p -objc.property_getAttributes.argtypes = [c_void_p] - -# const char *property_getName(objc_property_t property) -objc.property_getName.restype = c_char_p -objc.property_getName.argtypes = [c_void_p] - -###################################################################### - -# BOOL protocol_conformsToProtocol(Protocol *proto, Protocol *other) -objc.protocol_conformsToProtocol.restype = c_bool -objc.protocol_conformsToProtocol.argtypes = [c_void_p, c_void_p] - - -class OBJC_METHOD_DESCRIPTION(Structure): - _fields_ = [("name", c_void_p), ("types", c_char_p)] - - -# struct objc_method_description *protocol_copyMethodDescriptionList(Protocol *p, BOOL isRequiredMethod, BOOL isInstanceMethod, unsigned int *outCount) -# You must free() the returned array. -objc.protocol_copyMethodDescriptionList.restype = POINTER(OBJC_METHOD_DESCRIPTION) -objc.protocol_copyMethodDescriptionList.argtypes = [c_void_p, c_bool, c_bool, POINTER(c_uint)] - -# objc_property_t * protocol_copyPropertyList(Protocol *protocol, unsigned int *outCount) -objc.protocol_copyPropertyList.restype = c_void_p -objc.protocol_copyPropertyList.argtypes = [c_void_p, POINTER(c_uint)] - -# Protocol **protocol_copyProtocolList(Protocol *proto, unsigned int *outCount) -objc.protocol_copyProtocolList = POINTER(c_void_p) -objc.protocol_copyProtocolList.argtypes = [c_void_p, POINTER(c_uint)] - -# struct objc_method_description protocol_getMethodDescription(Protocol *p, SEL aSel, BOOL isRequiredMethod, BOOL isInstanceMethod) -objc.protocol_getMethodDescription.restype = OBJC_METHOD_DESCRIPTION -objc.protocol_getMethodDescription.argtypes = [c_void_p, c_void_p, c_bool, c_bool] - -# const char *protocol_getName(Protocol *p) -objc.protocol_getName.restype = c_char_p -objc.protocol_getName.argtypes = [c_void_p] - -###################################################################### - -# const char* sel_getName(SEL aSelector) -objc.sel_getName.restype = c_char_p -objc.sel_getName.argtypes = [c_void_p] - -# SEL sel_getUid(const char *str) -# Use sel_registerName instead. - -# BOOL sel_isEqual(SEL lhs, SEL rhs) -objc.sel_isEqual.restype = c_bool -objc.sel_isEqual.argtypes = [c_void_p, c_void_p] - -# SEL sel_registerName(const char *str) -objc.sel_registerName.restype = c_void_p -objc.sel_registerName.argtypes = [c_char_p] - - -###################################################################### - -def ensure_bytes(x): - if isinstance(x, bytes): - return x - return x.encode('ascii') - - -###################################################################### - -def get_selector(name): - return c_void_p(objc.sel_registerName(ensure_bytes(name))) - - -def get_class(name): - return c_void_p(objc.objc_getClass(ensure_bytes(name))) - - -def get_object_class(obj): - return c_void_p(objc.object_getClass(obj)) - - -def get_metaclass(name): - return c_void_p(objc.objc_getMetaClass(ensure_bytes(name))) - - -def get_superclass_of_object(obj): - cls = c_void_p(objc.object_getClass(obj)) - return c_void_p(objc.class_getSuperclass(cls)) - - -# http://www.sealiesoftware.com/blog/archive/2008/10/30/objc_explain_objc_msgSend_stret.html -# http://www.x86-64.org/documentation/abi-0.99.pdf (pp.17-23) -# executive summary: on x86-64, who knows? -def x86_should_use_stret(restype): - """Try to figure out when a return type will be passed on stack.""" - if type(restype) != type(Structure): - return False - if not __LP64__ and sizeof(restype) <= 8: - return False - if __LP64__ and sizeof(restype) <= 16: # maybe? I don't know? - return False - return True - - -# http://www.sealiesoftware.com/blog/archive/2008/11/16/objc_explain_objc_msgSend_fpret.html -def should_use_fpret(restype): - """Determine if objc_msgSend_fpret is required to return a floating point type.""" - if not __i386__: - # Unneeded on non-intel processors - return False - if __LP64__ and restype == c_longdouble: - # Use only for long double on x86_64 - return True - if not __LP64__ and restype in (c_float, c_double, c_longdouble): - return True - return False - - -# By default, assumes that restype is c_void_p -# and that all arguments are wrapped inside c_void_p. -# Use the restype and argtypes keyword arguments to -# change these values. restype should be a ctypes type -# and argtypes should be a list of ctypes types for -# the arguments of the message only. -def send_message(receiver, selName, *args, **kwargs): - if isinstance(receiver, str): - receiver = get_class(receiver) - selector = get_selector(selName) - restype = kwargs.get('restype', c_void_p) - # print('send_message', receiver, selName, args, kwargs) - argtypes = kwargs.get('argtypes', []) - # Choose the correct version of objc_msgSend based on return type. - if should_use_fpret(restype): - objc.objc_msgSend_fpret.restype = restype - objc.objc_msgSend_fpret.argtypes = [c_void_p, c_void_p] + argtypes - result = objc.objc_msgSend_fpret(receiver, selector, *args) - elif x86_should_use_stret(restype): - objc.objc_msgSend_stret.argtypes = [POINTER(restype), c_void_p, c_void_p] + argtypes - result = restype() - objc.objc_msgSend_stret(byref(result), receiver, selector, *args) - else: - objc.objc_msgSend.restype = restype - objc.objc_msgSend.argtypes = [c_void_p, c_void_p] + argtypes - result = objc.objc_msgSend(receiver, selector, *args) - if restype == c_void_p: - result = c_void_p(result) - return result - - -class OBJC_SUPER(Structure): - _fields_ = [('receiver', c_void_p), ('class', c_void_p)] - - -OBJC_SUPER_PTR = POINTER(OBJC_SUPER) - - -# http://stackoverflow.com/questions/3095360/what-exactly-is-super-in-objective-c -# -# `superclass_name` is optional and can be used to force finding the superclass -# by name. It is used to circumvent a bug in which the superclass was resolved -# incorrectly which lead to an infinite recursion: -# https://github.com/pyglet/pyglet/issues/5 -def send_super(receiver, selName, *args, superclass_name=None, **kwargs): - if hasattr(receiver, '_as_parameter_'): - receiver = receiver._as_parameter_ - if superclass_name is None: - superclass = get_superclass_of_object(receiver) - else: - superclass = get_class(superclass_name) - super_struct = OBJC_SUPER(receiver, superclass) - selector = get_selector(selName) - restype = kwargs.get('restype', c_void_p) - argtypes = kwargs.get('argtypes', None) - objc.objc_msgSendSuper.restype = restype - if argtypes: - objc.objc_msgSendSuper.argtypes = [OBJC_SUPER_PTR, c_void_p] + argtypes - else: - objc.objc_msgSendSuper.argtypes = None - result = objc.objc_msgSendSuper(byref(super_struct), selector, *args) - if restype == c_void_p: - result = c_void_p(result) - return result - - -###################################################################### - -cfunctype_table = {} - - -def parse_type_encoding(encoding): - """Takes a type encoding string and outputs a list of the separated type codes. - Currently does not handle unions or bitfields and strips out any field width - specifiers or type specifiers from the encoding. For Python 3.2+, encoding is - assumed to be a bytes object and not unicode. - - Examples: - parse_type_encoding('^v16@0:8') --> ['^v', '@', ':'] - parse_type_encoding('{CGSize=dd}40@0:8{CGSize=dd}16Q32') --> ['{CGSize=dd}', '@', ':', '{CGSize=dd}', 'Q'] - """ - type_encodings = [] - brace_count = 0 # number of unclosed curly braces - bracket_count = 0 # number of unclosed square brackets - typecode = b'' - for c in encoding: - # In Python 3, c comes out as an integer in the range 0-255. In Python 2, c is a single character string. - # To fix the disparity, we convert c to a bytes object if necessary. - if isinstance(c, int): - c = bytes([c]) - - if c == b'{': - # Check if this marked the end of previous type code. - if typecode and typecode[-1:] != b'^' and brace_count == 0 and bracket_count == 0: - type_encodings.append(typecode) - typecode = b'' - typecode += c - brace_count += 1 - elif c == b'}': - typecode += c - brace_count -= 1 - assert (brace_count >= 0) - elif c == b'[': - # Check if this marked the end of previous type code. - if typecode and typecode[-1:] != b'^' and brace_count == 0 and bracket_count == 0: - type_encodings.append(typecode) - typecode = b'' - typecode += c - bracket_count += 1 - elif c == b']': - typecode += c - bracket_count -= 1 - assert (bracket_count >= 0) - elif brace_count or bracket_count: - # Anything encountered while inside braces or brackets gets stuck on. - typecode += c - elif c in b'0123456789': - # Ignore field width specifiers for now. - pass - elif c in b'rnNoORV': - # Also ignore type specifiers. - pass - elif c in b'^cislqCISLQfdBv*@#:b?': - if typecode and typecode[-1:] == b'^': - # Previous char was pointer specifier, so keep going. - typecode += c - else: - # Add previous type code to the list. - if typecode: - type_encodings.append(typecode) - # Start a new type code. - typecode = c - - # Add the last type code to the list - if typecode: - type_encodings.append(typecode) - - return type_encodings - - -# Limited to basic types and pointers to basic types. -# Does not try to handle arrays, arbitrary structs, unions, or bitfields. -# Assume that encoding is a bytes object and not unicode. -def cfunctype_for_encoding(encoding): - # Check if we've already created a CFUNCTYPE for this encoding. - # If so, then return the cached CFUNCTYPE. - if encoding in cfunctype_table: - return cfunctype_table[encoding] - - # Otherwise, create a new CFUNCTYPE for the encoding. - typecodes = {b'c': c_char, b'i': c_int, b's': c_short, b'l': c_long, b'q': c_longlong, - b'C': c_ubyte, b'I': c_uint, b'S': c_ushort, b'L': c_ulong, b'Q': c_ulonglong, - b'f': c_float, b'd': c_double, b'B': c_bool, b'v': None, b'*': c_char_p, - b'@': c_void_p, b'#': c_void_p, b':': c_void_p, NSPointEncoding: NSPoint, - NSSizeEncoding: NSSize, NSRectEncoding: NSRect, NSRangeEncoding: NSRange, - PyObjectEncoding: py_object} - argtypes = [] - for code in parse_type_encoding(encoding): - if code in typecodes: - argtypes.append(typecodes[code]) - elif code[0:1] == b'^' and code[1:] in typecodes: - argtypes.append(POINTER(typecodes[code[1:]])) - else: - raise Exception('unknown type encoding: ' + code) - - cfunctype = CFUNCTYPE(*argtypes) - - # Cache the new CFUNCTYPE in the cfunctype_table. - # We do this mainly because it prevents the CFUNCTYPE - # from being garbage-collected while we need it. - cfunctype_table[encoding] = cfunctype - return cfunctype - - -###################################################################### - -# After calling create_subclass, you must first register -# it with register_subclass before you may use it. -# You can add new methods after the class is registered, -# but you cannot add any new ivars. -def create_subclass(superclass, name): - if isinstance(superclass, str): - superclass = get_class(superclass) - return c_void_p(objc.objc_allocateClassPair(superclass, ensure_bytes(name), 0)) - - -def register_subclass(subclass): - objc.objc_registerClassPair(subclass) - - -# types is a string encoding the argument types of the method. -# The first type code of types is the return type (e.g. 'v' if void) -# The second type code must be '@' for id self. -# The third type code must be ':' for SEL cmd. -# Additional type codes are for types of other arguments if any. -def add_method(cls, selName, method, types): - type_encodings = parse_type_encoding(types) - assert (type_encodings[1] == b'@') # ensure id self typecode - assert (type_encodings[2] == b':') # ensure SEL cmd typecode - selector = get_selector(selName) - cfunctype = cfunctype_for_encoding(types) - imp = cfunctype(method) - objc.class_addMethod.argtypes = [c_void_p, c_void_p, cfunctype, c_char_p] - objc.class_addMethod(cls, selector, imp, types) - return imp - - -def add_ivar(cls, name, vartype): - return objc.class_addIvar(cls, ensure_bytes(name), sizeof(vartype), alignment(vartype), encoding_for_ctype(vartype)) - - -def set_instance_variable(obj, varname, value, vartype): - objc.object_setInstanceVariable.argtypes = [c_void_p, c_char_p, vartype] - objc.object_setInstanceVariable(obj, ensure_bytes(varname), value) - - -def get_instance_variable(obj, varname, vartype): - variable = vartype() - objc.object_getInstanceVariable(obj, ensure_bytes(varname), byref(variable)) - return variable.value - - -###################################################################### - -class ObjCMethod: - """This represents an unbound Objective-C method (really an IMP).""" - - # Note, need to map 'c' to c_byte rather than c_char, because otherwise - # ctypes converts the value into a one-character string which is generally - # not what we want at all, especially when the 'c' represents a bool var. - typecodes = {b'c': c_byte, b'i': c_int, b's': c_short, b'l': c_long, b'q': c_longlong, - b'C': c_ubyte, b'I': c_uint, b'S': c_ushort, b'L': c_ulong, b'Q': c_ulonglong, - b'f': c_float, b'd': c_double, b'B': c_bool, b'v': None, b'Vv': None, b'*': c_char_p, - b'@': c_void_p, b'#': c_void_p, b':': c_void_p, b'^v': c_void_p, b'?': c_void_p, - NSPointEncoding: NSPoint, NSSizeEncoding: NSSize, NSRectEncoding: NSRect, - NSRangeEncoding: NSRange, - PyObjectEncoding: py_object} - - cfunctype_table = {} - - def __init__(self, method): - """Initialize with an Objective-C Method pointer. We then determine - the return type and argument type information of the method.""" - self.selector = c_void_p(objc.method_getName(method)) - self.name = objc.sel_getName(self.selector) - self.pyname = self.name.replace(b':', b'_') - self.encoding = objc.method_getTypeEncoding(method) - self.return_type = objc.method_copyReturnType(method) - self.nargs = objc.method_getNumberOfArguments(method) - self.imp = c_void_p(objc.method_getImplementation(method)) - self.argument_types = [] - for i in range(self.nargs): - buffer = c_buffer(512) - objc.method_getArgumentType(method, i, buffer, len(buffer)) - self.argument_types.append(buffer.value) - # Get types for all the arguments. - try: - self.argtypes = [self.ctype_for_encoding(t) for t in self.argument_types] - except: - # print(f'no argtypes encoding for {self.name} ({self.argument_types})') - self.argtypes = None - # Get types for the return type. - try: - if self.return_type == b'@': - self.restype = ObjCInstance - elif self.return_type == b'#': - self.restype = ObjCClass - else: - self.restype = self.ctype_for_encoding(self.return_type) - except: - # print(f'no restype encoding for {self.name} ({self.return_type})') - self.restype = None - self.func = None - - def ctype_for_encoding(self, encoding): - """Return ctypes type for an encoded Objective-C type.""" - if encoding in self.typecodes: - return self.typecodes[encoding] - elif encoding[0:1] == b'^' and encoding[1:] in self.typecodes: - return POINTER(self.typecodes[encoding[1:]]) - elif encoding[0:1] == b'^' and encoding[1:] in [CGImageEncoding, NSZoneEncoding]: - # special cases - return c_void_p - elif encoding[0:1] == b'r' and encoding[1:] in self.typecodes: - # const decorator, don't care - return self.typecodes[encoding[1:]] - elif encoding[0:2] == b'r^' and encoding[2:] in self.typecodes: - # const pointer, also don't care - return POINTER(self.typecodes[encoding[2:]]) - else: - raise Exception('unknown encoding for %s: %s' % (self.name, encoding)) - - def get_prototype(self): - """Returns a ctypes CFUNCTYPE for the method.""" - if self.restype == ObjCInstance or self.restype == ObjCClass: - # Some hacky stuff to get around ctypes issues on 64-bit. Can't let - # ctypes convert the return value itself, because it truncates the pointer - # along the way. So instead, we must do set the return type to c_void_p to - # ensure we get 64-bit addresses and then convert the return value manually. - self.prototype = CFUNCTYPE(c_void_p, *self.argtypes) - else: - self.prototype = CFUNCTYPE(self.restype, *self.argtypes) - return self.prototype - - def __repr__(self): - return "" % (self.name, self.encoding)
-
- def get_callable(self):
- """Returns a python-callable version of the method's IMP."""
- if not self.func:
- prototype = self.get_prototype()
- self.func = cast(self.imp, prototype)
- if self.restype == ObjCInstance or self.restype == ObjCClass:
- self.func.restype = c_void_p
- else:
- self.func.restype = self.restype
- self.func.argtypes = self.argtypes
- return self.func
-
- def __call__(self, objc_id, *args):
- """Call the method with the given id and arguments. You do not need
- to pass in the selector as an argument since it will be automatically
- provided."""
- f = self.get_callable()
- try:
- result = f(objc_id, self.selector, *args)
- # Convert result to python type if it is a instance or class pointer.
- if self.restype == ObjCInstance:
- result = ObjCInstance(result)
- elif self.restype == ObjCClass:
- result = ObjCClass(result)
- return result
- except ArgumentError as error:
- # Add more useful info to argument error exceptions, then reraise.
- error.args += ('selector = ' + str(self.name),
- 'argtypes =' + str(self.argtypes),
- 'encoding = ' + str(self.encoding))
- raise
-
-
-######################################################################
-
-class ObjCBoundMethod:
- """This represents an Objective-C method (an IMP) which has been bound
- to some id which will be passed as the first parameter to the method."""
-
- def __init__(self, method, objc_id):
- """Initialize with a method and ObjCInstance or ObjCClass object."""
- self.method = method
- self.objc_id = objc_id
-
- def __repr__(self):
- return '' % (self.method.name, self.objc_id)
-
- def __call__(self, *args):
- """Call the method with the given arguments."""
- return self.method(self.objc_id, *args)
-
-
-######################################################################
-
-class ObjCClass:
- """Python wrapper for an Objective-C class."""
-
- # We only create one Python object for each Objective-C class.
- # Any future calls with the same class will return the previously
- # created Python object. Note that these aren't weak references.
- # After you create an ObjCClass, it will exist until the end of the
- # program.
- _registered_classes = {}
-
- def __new__(cls, class_name_or_ptr):
- """Create a new ObjCClass instance or return a previously created
- instance for the given Objective-C class. The argument may be either
- the name of the class to retrieve, or a pointer to the class."""
- # Determine name and ptr values from passed in argument.
- if isinstance(class_name_or_ptr, str):
- name = class_name_or_ptr
- ptr = get_class(name)
- else:
- ptr = class_name_or_ptr
- # Make sure that ptr value is wrapped in c_void_p object
- # for safety when passing as ctypes argument.
- if not isinstance(ptr, c_void_p):
- ptr = c_void_p(ptr)
- name = objc.class_getName(ptr)
-
- # Check if we've already created a Python object for this class
- # and if so, return it rather than making a new one.
- if name in cls._registered_classes:
- return cls._registered_classes[name]
-
- # Otherwise create a new Python object and then initialize it.
- objc_class = super(ObjCClass, cls).__new__(cls)
- objc_class.ptr = ptr
- objc_class.name = name
- objc_class.instance_methods = {} # mapping of name -> instance method
- objc_class.class_methods = {} # mapping of name -> class method
- objc_class._as_parameter_ = ptr # for ctypes argument passing
-
- # Store the new class in dictionary of registered classes.
- cls._registered_classes[name] = objc_class
-
- # Not sure this is necessary...
- objc_class.cache_instance_methods()
- objc_class.cache_class_methods()
-
- return objc_class
-
- def __repr__(self):
- return "" % (self.name, str(self.ptr.value))
-
- def cache_instance_methods(self):
- """Create and store python representations of all instance methods
- implemented by this class (but does not find methods of superclass)."""
- count = c_uint()
- method_array = objc.class_copyMethodList(self.ptr, byref(count))
- for i in range(count.value):
- method = c_void_p(method_array[i])
- objc_method = ObjCMethod(method)
- self.instance_methods[objc_method.pyname] = objc_method
-
- def cache_class_methods(self):
- """Create and store python representations of all class methods
- implemented by this class (but does not find methods of superclass)."""
- count = c_uint()
- method_array = objc.class_copyMethodList(objc.object_getClass(self.ptr), byref(count))
- for i in range(count.value):
- method = c_void_p(method_array[i])
- objc_method = ObjCMethod(method)
- self.class_methods[objc_method.pyname] = objc_method
-
- def get_instance_method(self, name):
- """Returns a python representation of the named instance method,
- either by looking it up in the cached list of methods or by searching
- for and creating a new method object."""
- if name in self.instance_methods:
- return self.instance_methods[name]
- else:
- # If method name isn't in the cached list, it might be a method of
- # the superclass, so call class_getInstanceMethod to check.
- selector = get_selector(name.replace(b'_', b':'))
- method = c_void_p(objc.class_getInstanceMethod(self.ptr, selector))
- if method.value:
- objc_method = ObjCMethod(method)
- self.instance_methods[name] = objc_method
- return objc_method
- return None
-
- def get_class_method(self, name):
- """Returns a python representation of the named class method,
- either by looking it up in the cached list of methods or by searching
- for and creating a new method object."""
- if name in self.class_methods:
- return self.class_methods[name]
- else:
- # If method name isn't in the cached list, it might be a method of
- # the superclass, so call class_getInstanceMethod to check.
- selector = get_selector(name.replace(b'_', b':'))
- method = c_void_p(objc.class_getClassMethod(self.ptr, selector))
- if method.value:
- objc_method = ObjCMethod(method)
- self.class_methods[name] = objc_method
- return objc_method
- return None
-
- def __getattr__(self, name):
- """Returns a callable method object with the given name."""
- # If name refers to a class method, then return a callable object
- # for the class method with self.ptr as hidden first parameter.
- name = ensure_bytes(name)
- method = self.get_class_method(name)
- if method:
- return ObjCBoundMethod(method, self.ptr)
- # If name refers to an instance method, then simply return the method.
- # The caller will need to supply an instance as the first parameter.
- method = self.get_instance_method(name)
- if method:
- return method
- # Otherwise, raise an exception.
- raise AttributeError('ObjCClass %s has no attribute %s' % (self.name, name))
-
-
-######################################################################
-
-class ObjCInstance:
- """Python wrapper for an Objective-C instance."""
-
- _cached_objects = {}
-
- def __new__(cls, object_ptr):
- """Create a new ObjCInstance or return a previously created one
- for the given object_ptr which should be an Objective-C id."""
- # Make sure that object_ptr is wrapped in a c_void_p.
- if not isinstance(object_ptr, c_void_p):
- object_ptr = c_void_p(object_ptr)
-
- # If given a nil pointer, return None.
- if not object_ptr.value:
- return None
-
- # Check if we've already created an python ObjCInstance for this
- # object_ptr id and if so, then return it. A single ObjCInstance will
- # be created for any object pointer when it is first encountered.
- # This same ObjCInstance will then persist until the object is
- # deallocated.
- if object_ptr.value in cls._cached_objects:
- return cls._cached_objects[object_ptr.value]
-
- # Otherwise, create a new ObjCInstance.
- objc_instance = super(ObjCInstance, cls).__new__(cls)
- objc_instance.ptr = object_ptr
- objc_instance._as_parameter_ = object_ptr
- # Determine class of this object.
- class_ptr = c_void_p(objc.object_getClass(object_ptr))
- objc_instance.objc_class = ObjCClass(class_ptr)
-
- # Store new object in the dictionary of cached objects, keyed
- # by the (integer) memory address pointed to by the object_ptr.
- cls._cached_objects[object_ptr.value] = objc_instance
-
- # Create a DeallocationObserver and associate it with this object.
- # When the Objective-C object is deallocated, the observer will remove
- # the ObjCInstance corresponding to the object from the cached objects
- # dictionary, effectively destroying the ObjCInstance.
- observer = send_message(send_message('DeallocationObserver', 'alloc'), 'initWithObject:', objc_instance)
- objc.objc_setAssociatedObject(objc_instance, observer, observer, 0x301)
- # The observer is retained by the object we associate it to. We release
- # the observer now so that it will be deallocated when the associated
- # object is deallocated.
- send_message(observer, 'release')
-
- return objc_instance
-
- def __repr__(self):
- if self.objc_class.name == b'NSCFString':
- # Display contents of NSString objects
- from .cocoalibs import cfstring_to_string
- string = cfstring_to_string(self)
- return "" % (id(self), self.objc_class.name, string, str(self.ptr.value))
-
- return "" % (id(self), self.objc_class.name, str(self.ptr.value))
-
- def __getattr__(self, name):
- """Returns a callable method object with the given name."""
- # Search for named instance method in the class object and if it
- # exists, return callable object with self as hidden argument.
- # Note: you should give self and not self.ptr as a parameter to
- # ObjCBoundMethod, so that it will be able to keep the ObjCInstance
- # alive for chained calls like MyClass.alloc().init() where the
- # object created by alloc() is not assigned to a variable.
- name = ensure_bytes(name)
- method = self.objc_class.get_instance_method(name)
- if method:
- return ObjCBoundMethod(method, self)
- # Else, search for class method with given name in the class object.
- # If it exists, return callable object with a pointer to the class
- # as a hidden argument.
- method = self.objc_class.get_class_method(name)
- if method:
- return ObjCBoundMethod(method, self.objc_class.ptr)
- # Otherwise raise an exception.
- raise AttributeError('ObjCInstance %s has no attribute %s' % (self.objc_class.name, name))
-
-
-######################################################################
-
-def convert_method_arguments(encoding, args):
- """Used by ObjCSubclass to convert Objective-C method arguments to
- Python values before passing them on to the Python-defined method."""
- new_args = []
- arg_encodings = parse_type_encoding(encoding)[3:]
- for e, a in zip(arg_encodings, args):
- if e == b'@':
- new_args.append(ObjCInstance(a))
- elif e == b'#':
- new_args.append(ObjCClass(a))
- else:
- new_args.append(a)
- return new_args
-
-
-# ObjCSubclass is used to define an Objective-C subclass of an existing
-# class registered with the runtime. When you create an instance of
-# ObjCSubclass, it registers the new subclass with the Objective-C
-# runtime and creates a set of function decorators that you can use to
-# add instance methods or class methods to the subclass.
-#
-# Typical usage would be to first create and register the subclass:
-#
-# MySubclass = ObjCSubclass('NSObject', 'MySubclassName')
-#
-# then add methods with:
-#
-# @MySubclass.method('v')
-# def methodThatReturnsVoid(self):
-# pass
-#
-# @MySubclass.method('Bi')
-# def boolReturningMethodWithInt_(self, x):
-# return True
-#
-# @MySubclass.classmethod('@')
-# def classMethodThatReturnsId(self):
-# return self
-#
-# It is probably a good idea to organize the code related to a single
-# subclass by either putting it in its own module (note that you don't
-# actually need to expose any of the method names or the ObjCSubclass)
-# or by bundling it all up inside a python class definition, perhaps
-# called MySubclassImplementation.
-#
-# It is also possible to add Objective-C ivars to the subclass, however
-# if you do so, you must call the __init__ method with register=False,
-# and then call the register method after the ivars have been added.
-# But rather than creating the ivars in Objective-C land, it is easier
-# to just define python-based instance variables in your subclass's init
-# method.
-#
-# This class is used only to *define* the interface and implementation
-# of an Objective-C subclass from python. It should not be used in
-# any other way. If you want a python representation of the resulting
-# class, create it with ObjCClass.
-#
-# Instances are created as a pointer to the objc object by using:
-#
-# myinstance = send_message('MySubclassName', 'alloc')
-# myinstance = send_message(myinstance, 'init')
-#
-# or wrapped inside an ObjCInstance object by using:
-#
-# myclass = ObjCClass('MySubclassName')
-# myinstance = myclass.alloc().init()
-#
-class ObjCSubclass:
- """Use this to create a subclass of an existing Objective-C class.
- It consists primarily of function decorators which you use to add methods
- to the subclass."""
-
- def __init__(self, superclass, name, register=True):
- self._imp_table = {}
- self.name = name
- self.objc_cls = create_subclass(superclass, name)
- self._as_parameter_ = self.objc_cls
- if register:
- self.register()
-
- def register(self):
- """Register the new class with the Objective-C runtime."""
- objc.objc_registerClassPair(self.objc_cls)
- # We can get the metaclass only after the class is registered.
- self.objc_metaclass = get_metaclass(self.name)
-
- def add_ivar(self, varname, vartype):
- """Add instance variable named varname to the subclass.
- varname should be a string.
- vartype is a ctypes type.
- The class must be registered AFTER adding instance variables."""
- return add_ivar(self.objc_cls, varname, vartype)
-
- def add_method(self, method, name, encoding):
- imp = add_method(self.objc_cls, name, method, encoding)
- self._imp_table[name] = imp
-
- # http://iphonedevelopment.blogspot.com/2008/08/dynamically-adding-class-objects.html
- def add_class_method(self, method, name, encoding):
- imp = add_method(self.objc_metaclass, name, method, encoding)
- self._imp_table[name] = imp
-
- def rawmethod(self, encoding):
- """Decorator for instance methods without any fancy shenanigans.
- The function must have the signature f(self, cmd, *args)
- where both self and cmd are just pointers to objc objects."""
- # Add encodings for hidden self and cmd arguments.
- encoding = ensure_bytes(encoding)
- typecodes = parse_type_encoding(encoding)
- typecodes.insert(1, b'@:')
- encoding = b''.join(typecodes)
-
- def decorator(f):
- name = f.__name__.replace('_', ':')
- self.add_method(f, name, encoding)
- return f
-
- return decorator
-
- def method(self, encoding):
- """Function decorator for instance methods."""
- # Add encodings for hidden self and cmd arguments.
- encoding = ensure_bytes(encoding)
- typecodes = parse_type_encoding(encoding)
- typecodes.insert(1, b'@:')
- encoding = b''.join(typecodes)
-
- def decorator(f):
- def objc_method(objc_self, objc_cmd, *args):
- py_self = ObjCInstance(objc_self)
- py_self.objc_cmd = objc_cmd
- args = convert_method_arguments(encoding, args)
- result = f(py_self, *args)
- if isinstance(result, ObjCClass):
- result = result.ptr.value
- elif isinstance(result, ObjCInstance):
- result = result.ptr.value
- return result
-
- name = f.__name__.replace('_', ':')
- self.add_method(objc_method, name, encoding)
- return objc_method
-
- return decorator
-
- def classmethod(self, encoding):
- """Function decorator for class methods."""
- # Add encodings for hidden self and cmd arguments.
- encoding = ensure_bytes(encoding)
- typecodes = parse_type_encoding(encoding)
- typecodes.insert(1, b'@:')
- encoding = b''.join(typecodes)
-
- def decorator(f):
- def objc_class_method(objc_cls, objc_cmd, *args):
- py_cls = ObjCClass(objc_cls)
- py_cls.objc_cmd = objc_cmd
- args = convert_method_arguments(encoding, args)
- result = f(py_cls, *args)
- if isinstance(result, ObjCClass):
- result = result.ptr.value
- elif isinstance(result, ObjCInstance):
- result = result.ptr.value
- return result
-
- name = f.__name__.replace('_', ':')
- self.add_class_method(objc_class_method, name, encoding)
- return objc_class_method
-
- return decorator
-
-
-######################################################################
-
-# Instances of DeallocationObserver are associated with every
-# Objective-C object that gets wrapped inside an ObjCInstance.
-# Their sole purpose is to watch for when the Objective-C object
-# is deallocated, and then remove the object from the dictionary
-# of cached ObjCInstance objects kept by the ObjCInstance class.
-#
-# The methods of the class defined below are decorated with
-# rawmethod() instead of method() because DeallocationObservers
-# are created inside of ObjCInstance's __new__ method and we have
-# to be careful to not create another ObjCInstance here (which
-# happens when the usual method decorator turns the self argument
-# into an ObjCInstance), or else get trapped in an infinite recursion.
-class DeallocationObserver_Implementation:
- DeallocationObserver = ObjCSubclass('NSObject', 'DeallocationObserver', register=False)
- DeallocationObserver.add_ivar('observed_object', c_void_p)
- DeallocationObserver.register()
-
- @DeallocationObserver.rawmethod('@@')
- def initWithObject_(self, cmd, anObject):
- self = send_super(self, 'init')
- self = self.value
- set_instance_variable(self, 'observed_object', anObject, c_void_p)
- return self
-
- @DeallocationObserver.rawmethod('v')
- def dealloc(self, cmd):
- anObject = get_instance_variable(self, 'observed_object', c_void_p)
- ObjCInstance._cached_objects.pop(anObject, None)
- send_super(self, 'dealloc')
-
- @DeallocationObserver.rawmethod('v')
- def finalize(self, cmd):
- # Called instead of dealloc if using garbage collection.
- # (which would have to be explicitly started with
- # objc_startCollectorThread(), so probably not too much reason
- # to have this here, but I guess it can't hurt.)
- anObject = get_instance_variable(self, 'observed_object', c_void_p)
- ObjCInstance._cached_objects.pop(anObject, None)
- send_super(self, 'finalize')
diff --git a/spaces/akhaliq/Car_Keypoints/README.md b/spaces/akhaliq/Car_Keypoints/README.md
deleted file mode 100644
index e6cc7bd95bd4d5f22fac5a86ad4306cd9e6d9df4..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Car_Keypoints/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Car_Keypoints
-emoji: 🌍
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/akhaliq/Mask2Former/mask2former/config.py b/spaces/akhaliq/Mask2Former/mask2former/config.py
deleted file mode 100644
index adc930927772b0d289c3bb96dd5f6b5508046937..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Mask2Former/mask2former/config.py
+++ /dev/null
@@ -1,114 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-from detectron2.config import CfgNode as CN
-
-
-def add_maskformer2_config(cfg):
- """
- Add config for MASK_FORMER.
- """
- # NOTE: configs from original maskformer
- # data config
- # select the dataset mapper
- cfg.INPUT.DATASET_MAPPER_NAME = "mask_former_semantic"
- # Color augmentation
- cfg.INPUT.COLOR_AUG_SSD = False
- # We retry random cropping until no single category in semantic segmentation GT occupies more
- # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.
- cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0
- # Pad image and segmentation GT in dataset mapper.
- cfg.INPUT.SIZE_DIVISIBILITY = -1
-
- # solver config
- # weight decay on embedding
- cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0
- # optimizer
- cfg.SOLVER.OPTIMIZER = "ADAMW"
- cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1
-
- # mask_former model config
- cfg.MODEL.MASK_FORMER = CN()
-
- # loss
- cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION = True
- cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT = 0.1
- cfg.MODEL.MASK_FORMER.CLASS_WEIGHT = 1.0
- cfg.MODEL.MASK_FORMER.DICE_WEIGHT = 1.0
- cfg.MODEL.MASK_FORMER.MASK_WEIGHT = 20.0
-
- # transformer config
- cfg.MODEL.MASK_FORMER.NHEADS = 8
- cfg.MODEL.MASK_FORMER.DROPOUT = 0.1
- cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = 2048
- cfg.MODEL.MASK_FORMER.ENC_LAYERS = 0
- cfg.MODEL.MASK_FORMER.DEC_LAYERS = 6
- cfg.MODEL.MASK_FORMER.PRE_NORM = False
-
- cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256
- cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = 100
-
- cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE = "res5"
- cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ = False
-
- # mask_former inference config
- cfg.MODEL.MASK_FORMER.TEST = CN()
- cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True
- cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = False
- cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = False
- cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD = 0.0
- cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD = 0.0
- cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False
-
- # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)
- # you can use this config to override
- cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY = 32
-
- # pixel decoder config
- cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256
- # adding transformer in pixel decoder
- cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0
- # pixel decoder
- cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = "BasePixelDecoder"
-
- # swin transformer backbone
- cfg.MODEL.SWIN = CN()
- cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224
- cfg.MODEL.SWIN.PATCH_SIZE = 4
- cfg.MODEL.SWIN.EMBED_DIM = 96
- cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]
- cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]
- cfg.MODEL.SWIN.WINDOW_SIZE = 7
- cfg.MODEL.SWIN.MLP_RATIO = 4.0
- cfg.MODEL.SWIN.QKV_BIAS = True
- cfg.MODEL.SWIN.QK_SCALE = None
- cfg.MODEL.SWIN.DROP_RATE = 0.0
- cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0
- cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3
- cfg.MODEL.SWIN.APE = False
- cfg.MODEL.SWIN.PATCH_NORM = True
- cfg.MODEL.SWIN.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
- cfg.MODEL.SWIN.USE_CHECKPOINT = False
-
- # NOTE: maskformer2 extra configs
- # transformer module
- cfg.MODEL.MASK_FORMER.TRANSFORMER_DECODER_NAME = "MultiScaleMaskedTransformerDecoder"
-
- # LSJ aug
- cfg.INPUT.IMAGE_SIZE = 1024
- cfg.INPUT.MIN_SCALE = 0.1
- cfg.INPUT.MAX_SCALE = 2.0
-
- # MSDeformAttn encoder configs
- cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["res3", "res4", "res5"]
- cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4
- cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8
-
- # point loss configs
- # Number of points sampled during training for a mask point head.
- cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS = 112 * 112
- # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the
- # original paper.
- cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO = 3.0
- # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in
- # the original paper.
- cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75
diff --git a/spaces/akhaliq/cool-japan-diffusion-2-1-0/README.md b/spaces/akhaliq/cool-japan-diffusion-2-1-0/README.md
deleted file mode 100644
index bb7db300918b73ca373bcfbe91ad6584ae2a0e47..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/cool-japan-diffusion-2-1-0/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Cool Japan Diffusion 2 1 0
-emoji: 📈
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/progress.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/progress.py
deleted file mode 100644
index 1f670db43852bd52f78fcbeb98f5b9c1a24a3930..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/progress.py
+++ /dev/null
@@ -1,1036 +0,0 @@
-from abc import ABC, abstractmethod
-from collections import deque
-from collections.abc import Sized
-from dataclasses import dataclass, field
-from datetime import timedelta
-from math import ceil
-from threading import Event, RLock, Thread
-from types import TracebackType
-from typing import (
- Any,
- Callable,
- Deque,
- Dict,
- Iterable,
- List,
- NamedTuple,
- NewType,
- Optional,
- Sequence,
- Tuple,
- Type,
- TypeVar,
- Union,
-)
-
-from . import filesize, get_console
-from .console import Console, JustifyMethod, RenderableType, Group
-from .highlighter import Highlighter
-from .jupyter import JupyterMixin
-from .live import Live
-from .progress_bar import ProgressBar
-from .spinner import Spinner
-from .style import StyleType
-from .table import Column, Table
-from .text import Text, TextType
-
-TaskID = NewType("TaskID", int)
-
-ProgressType = TypeVar("ProgressType")
-
-GetTimeCallable = Callable[[], float]
-
-
-class _TrackThread(Thread):
- """A thread to periodically update progress."""
-
- def __init__(self, progress: "Progress", task_id: "TaskID", update_period: float):
- self.progress = progress
- self.task_id = task_id
- self.update_period = update_period
- self.done = Event()
-
- self.completed = 0
- super().__init__()
-
- def run(self) -> None:
- task_id = self.task_id
- advance = self.progress.advance
- update_period = self.update_period
- last_completed = 0
- wait = self.done.wait
- while not wait(update_period):
- completed = self.completed
- if last_completed != completed:
- advance(task_id, completed - last_completed)
- last_completed = completed
-
- self.progress.update(self.task_id, completed=self.completed, refresh=True)
-
- def __enter__(self) -> "_TrackThread":
- self.start()
- return self
-
- def __exit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc_val: Optional[BaseException],
- exc_tb: Optional[TracebackType],
- ) -> None:
- self.done.set()
- self.join()
-
-
-def track(
- sequence: Union[Sequence[ProgressType], Iterable[ProgressType]],
- description: str = "Working...",
- total: Optional[float] = None,
- auto_refresh: bool = True,
- console: Optional[Console] = None,
- transient: bool = False,
- get_time: Optional[Callable[[], float]] = None,
- refresh_per_second: float = 10,
- style: StyleType = "bar.back",
- complete_style: StyleType = "bar.complete",
- finished_style: StyleType = "bar.finished",
- pulse_style: StyleType = "bar.pulse",
- update_period: float = 0.1,
- disable: bool = False,
-) -> Iterable[ProgressType]:
- """Track progress by iterating over a sequence.
-
- Args:
- sequence (Iterable[ProgressType]): A sequence (must support "len") you wish to iterate over.
- description (str, optional): Description of task show next to progress bar. Defaults to "Working".
- total: (float, optional): Total number of steps. Default is len(sequence).
- auto_refresh (bool, optional): Automatic refresh, disable to force a refresh after each iteration. Default is True.
- transient: (bool, optional): Clear the progress on exit. Defaults to False.
- console (Console, optional): Console to write to. Default creates internal Console instance.
- refresh_per_second (float): Number of times per second to refresh the progress information. Defaults to 10.
- style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
- complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
- finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.done".
- pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
- update_period (float, optional): Minimum time (in seconds) between calls to update(). Defaults to 0.1.
- disable (bool, optional): Disable display of progress.
- Returns:
- Iterable[ProgressType]: An iterable of the values in the sequence.
-
- """
-
- columns: List["ProgressColumn"] = (
- [TextColumn("[progress.description]{task.description}")] if description else []
- )
- columns.extend(
- (
- BarColumn(
- style=style,
- complete_style=complete_style,
- finished_style=finished_style,
- pulse_style=pulse_style,
- ),
- TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
- TimeRemainingColumn(),
- )
- )
- progress = Progress(
- *columns,
- auto_refresh=auto_refresh,
- console=console,
- transient=transient,
- get_time=get_time,
- refresh_per_second=refresh_per_second or 10,
- disable=disable,
- )
-
- with progress:
- yield from progress.track(
- sequence, total=total, description=description, update_period=update_period
- )
-
-
-class ProgressColumn(ABC):
- """Base class for a widget to use in progress display."""
-
- max_refresh: Optional[float] = None
-
- def __init__(self, table_column: Optional[Column] = None) -> None:
- self._table_column = table_column
- self._renderable_cache: Dict[TaskID, Tuple[float, RenderableType]] = {}
- self._update_time: Optional[float] = None
-
- def get_table_column(self) -> Column:
- """Get a table column, used to build tasks table."""
- return self._table_column or Column()
-
- def __call__(self, task: "Task") -> RenderableType:
- """Called by the Progress object to return a renderable for the given task.
-
- Args:
- task (Task): An object containing information regarding the task.
-
- Returns:
- RenderableType: Anything renderable (including str).
- """
- current_time = task.get_time()
- if self.max_refresh is not None and not task.completed:
- try:
- timestamp, renderable = self._renderable_cache[task.id]
- except KeyError:
- pass
- else:
- if timestamp + self.max_refresh > current_time:
- return renderable
-
- renderable = self.render(task)
- self._renderable_cache[task.id] = (current_time, renderable)
- return renderable
-
- @abstractmethod
- def render(self, task: "Task") -> RenderableType:
- """Should return a renderable object."""
-
-
-class RenderableColumn(ProgressColumn):
- """A column to insert an arbitrary column.
-
- Args:
- renderable (RenderableType, optional): Any renderable. Defaults to empty string.
- """
-
- def __init__(
- self, renderable: RenderableType = "", *, table_column: Optional[Column] = None
- ):
- self.renderable = renderable
- super().__init__(table_column=table_column)
-
- def render(self, task: "Task") -> RenderableType:
- return self.renderable
-
-
-class SpinnerColumn(ProgressColumn):
- """A column with a 'spinner' animation.
-
- Args:
- spinner_name (str, optional): Name of spinner animation. Defaults to "dots".
- style (StyleType, optional): Style of spinner. Defaults to "progress.spinner".
- speed (float, optional): Speed factor of spinner. Defaults to 1.0.
- finished_text (TextType, optional): Text used when task is finished. Defaults to " ".
- """
-
- def __init__(
- self,
- spinner_name: str = "dots",
- style: Optional[StyleType] = "progress.spinner",
- speed: float = 1.0,
- finished_text: TextType = " ",
- table_column: Optional[Column] = None,
- ):
- self.spinner = Spinner(spinner_name, style=style, speed=speed)
- self.finished_text = (
- Text.from_markup(finished_text)
- if isinstance(finished_text, str)
- else finished_text
- )
- super().__init__(table_column=table_column)
-
- def set_spinner(
- self,
- spinner_name: str,
- spinner_style: Optional[StyleType] = "progress.spinner",
- speed: float = 1.0,
- ) -> None:
- """Set a new spinner.
-
- Args:
- spinner_name (str): Spinner name, see python -m rich.spinner.
- spinner_style (Optional[StyleType], optional): Spinner style. Defaults to "progress.spinner".
- speed (float, optional): Speed factor of spinner. Defaults to 1.0.
- """
- self.spinner = Spinner(spinner_name, style=spinner_style, speed=speed)
-
- def render(self, task: "Task") -> RenderableType:
- text = (
- self.finished_text
- if task.finished
- else self.spinner.render(task.get_time())
- )
- return text
-
-
-class TextColumn(ProgressColumn):
- """A column containing text."""
-
- def __init__(
- self,
- text_format: str,
- style: StyleType = "none",
- justify: JustifyMethod = "left",
- markup: bool = True,
- highlighter: Optional[Highlighter] = None,
- table_column: Optional[Column] = None,
- ) -> None:
- self.text_format = text_format
- self.justify: JustifyMethod = justify
- self.style = style
- self.markup = markup
- self.highlighter = highlighter
- super().__init__(table_column=table_column or Column(no_wrap=True))
-
- def render(self, task: "Task") -> Text:
- _text = self.text_format.format(task=task)
- if self.markup:
- text = Text.from_markup(_text, style=self.style, justify=self.justify)
- else:
- text = Text(_text, style=self.style, justify=self.justify)
- if self.highlighter:
- self.highlighter.highlight(text)
- return text
-
-
-class BarColumn(ProgressColumn):
- """Renders a visual progress bar.
-
- Args:
- bar_width (Optional[int], optional): Width of bar or None for full width. Defaults to 40.
- style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
- complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
- finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.done".
- pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
- """
-
- def __init__(
- self,
- bar_width: Optional[int] = 40,
- style: StyleType = "bar.back",
- complete_style: StyleType = "bar.complete",
- finished_style: StyleType = "bar.finished",
- pulse_style: StyleType = "bar.pulse",
- table_column: Optional[Column] = None,
- ) -> None:
- self.bar_width = bar_width
- self.style = style
- self.complete_style = complete_style
- self.finished_style = finished_style
- self.pulse_style = pulse_style
- super().__init__(table_column=table_column)
-
- def render(self, task: "Task") -> ProgressBar:
- """Gets a progress bar widget for a task."""
- return ProgressBar(
- total=max(0, task.total),
- completed=max(0, task.completed),
- width=None if self.bar_width is None else max(1, self.bar_width),
- pulse=not task.started,
- animation_time=task.get_time(),
- style=self.style,
- complete_style=self.complete_style,
- finished_style=self.finished_style,
- pulse_style=self.pulse_style,
- )
-
-
-class TimeElapsedColumn(ProgressColumn):
- """Renders time elapsed."""
-
- def render(self, task: "Task") -> Text:
- """Show time remaining."""
- elapsed = task.finished_time if task.finished else task.elapsed
- if elapsed is None:
- return Text("-:--:--", style="progress.elapsed")
- delta = timedelta(seconds=int(elapsed))
- return Text(str(delta), style="progress.elapsed")
-
-
-class TimeRemainingColumn(ProgressColumn):
- """Renders estimated time remaining."""
-
- # Only refresh twice a second to prevent jitter
- max_refresh = 0.5
-
- def render(self, task: "Task") -> Text:
- """Show time remaining."""
- remaining = task.time_remaining
- if remaining is None:
- return Text("-:--:--", style="progress.remaining")
- remaining_delta = timedelta(seconds=int(remaining))
- return Text(str(remaining_delta), style="progress.remaining")
-
-
-class FileSizeColumn(ProgressColumn):
- """Renders completed filesize."""
-
- def render(self, task: "Task") -> Text:
- """Show data completed."""
- data_size = filesize.decimal(int(task.completed))
- return Text(data_size, style="progress.filesize")
-
-
-class TotalFileSizeColumn(ProgressColumn):
- """Renders total filesize."""
-
- def render(self, task: "Task") -> Text:
- """Show data completed."""
- data_size = filesize.decimal(int(task.total))
- return Text(data_size, style="progress.filesize.total")
-
-
-class DownloadColumn(ProgressColumn):
- """Renders file size downloaded and total, e.g. '0.5/2.3 GB'.
-
- Args:
- binary_units (bool, optional): Use binary units, KiB, MiB etc. Defaults to False.
- """
-
- def __init__(
- self, binary_units: bool = False, table_column: Optional[Column] = None
- ) -> None:
- self.binary_units = binary_units
- super().__init__(table_column=table_column)
-
- def render(self, task: "Task") -> Text:
- """Calculate common unit for completed and total."""
- completed = int(task.completed)
- total = int(task.total)
- if self.binary_units:
- unit, suffix = filesize.pick_unit_and_suffix(
- total,
- ["bytes", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB", "ZiB", "YiB"],
- 1024,
- )
- else:
- unit, suffix = filesize.pick_unit_and_suffix(
- total, ["bytes", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB"], 1000
- )
- completed_ratio = completed / unit
- total_ratio = total / unit
- precision = 0 if unit == 1 else 1
- completed_str = f"{completed_ratio:,.{precision}f}"
- total_str = f"{total_ratio:,.{precision}f}"
- download_status = f"{completed_str}/{total_str} {suffix}"
- download_text = Text(download_status, style="progress.download")
- return download_text
-
-
-class TransferSpeedColumn(ProgressColumn):
- """Renders human readable transfer speed."""
-
- def render(self, task: "Task") -> Text:
- """Show data transfer speed."""
- speed = task.finished_speed or task.speed
- if speed is None:
- return Text("?", style="progress.data.speed")
- data_speed = filesize.decimal(int(speed))
- return Text(f"{data_speed}/s", style="progress.data.speed")
-
-
-class ProgressSample(NamedTuple):
- """Sample of progress for a given time."""
-
- timestamp: float
- """Timestamp of sample."""
- completed: float
- """Number of steps completed."""
-
-
-@dataclass
-class Task:
- """Information regarding a progress task.
-
- This object should be considered read-only outside of the :class:`~Progress` class.
-
- """
-
- id: TaskID
- """Task ID associated with this task (used in Progress methods)."""
-
- description: str
- """str: Description of the task."""
-
- total: float
- """str: Total number of steps in this task."""
-
- completed: float
- """float: Number of steps completed"""
-
- _get_time: GetTimeCallable
- """Callable to get the current time."""
-
- finished_time: Optional[float] = None
- """float: Time task was finished."""
-
- visible: bool = True
- """bool: Indicates if this task is visible in the progress display."""
-
- fields: Dict[str, Any] = field(default_factory=dict)
- """dict: Arbitrary fields passed in via Progress.update."""
-
- start_time: Optional[float] = field(default=None, init=False, repr=False)
- """Optional[float]: Time this task was started, or None if not started."""
-
- stop_time: Optional[float] = field(default=None, init=False, repr=False)
- """Optional[float]: Time this task was stopped, or None if not stopped."""
-
- finished_speed: Optional[float] = None
- """Optional[float]: The last speed for a finished task."""
-
- _progress: Deque[ProgressSample] = field(
- default_factory=deque, init=False, repr=False
- )
-
- _lock: RLock = field(repr=False, default_factory=RLock)
- """Thread lock."""
-
- def get_time(self) -> float:
- """float: Get the current time, in seconds."""
- return self._get_time()
-
- @property
- def started(self) -> bool:
- """bool: Check if the task as started."""
- return self.start_time is not None
-
- @property
- def remaining(self) -> float:
- """float: Get the number of steps remaining."""
- return self.total - self.completed
-
- @property
- def elapsed(self) -> Optional[float]:
- """Optional[float]: Time elapsed since task was started, or ``None`` if the task hasn't started."""
- if self.start_time is None:
- return None
- if self.stop_time is not None:
- return self.stop_time - self.start_time
- return self.get_time() - self.start_time
-
- @property
- def finished(self) -> bool:
- """Check if the task has finished."""
- return self.finished_time is not None
-
- @property
- def percentage(self) -> float:
- """float: Get progress of task as a percentage."""
- if not self.total:
- return 0.0
- completed = (self.completed / self.total) * 100.0
- completed = min(100.0, max(0.0, completed))
- return completed
-
- @property
- def speed(self) -> Optional[float]:
- """Optional[float]: Get the estimated speed in steps per second."""
- if self.start_time is None:
- return None
- with self._lock:
- progress = self._progress
- if not progress:
- return None
- total_time = progress[-1].timestamp - progress[0].timestamp
- if total_time == 0:
- return None
- iter_progress = iter(progress)
- next(iter_progress)
- total_completed = sum(sample.completed for sample in iter_progress)
- speed = total_completed / total_time
- return speed
-
- @property
- def time_remaining(self) -> Optional[float]:
- """Optional[float]: Get estimated time to completion, or ``None`` if no data."""
- if self.finished:
- return 0.0
- speed = self.speed
- if not speed:
- return None
- estimate = ceil(self.remaining / speed)
- return estimate
-
- def _reset(self) -> None:
- """Reset progress."""
- self._progress.clear()
- self.finished_time = None
- self.finished_speed = None
-
-
-class Progress(JupyterMixin):
- """Renders an auto-updating progress bar(s).
-
- Args:
- console (Console, optional): Optional Console instance. Default will an internal Console instance writing to stdout.
- auto_refresh (bool, optional): Enable auto refresh. If disabled, you will need to call `refresh()`.
- refresh_per_second (Optional[float], optional): Number of times per second to refresh the progress information or None to use default (10). Defaults to None.
- speed_estimate_period: (float, optional): Period (in seconds) used to calculate the speed estimate. Defaults to 30.
- transient: (bool, optional): Clear the progress on exit. Defaults to False.
- redirect_stdout: (bool, optional): Enable redirection of stdout, so ``print`` may be used. Defaults to True.
- redirect_stderr: (bool, optional): Enable redirection of stderr. Defaults to True.
- get_time: (Callable, optional): A callable that gets the current time, or None to use Console.get_time. Defaults to None.
- disable (bool, optional): Disable progress display. Defaults to False
- expand (bool, optional): Expand tasks table to fit width. Defaults to False.
- """
-
- def __init__(
- self,
- *columns: Union[str, ProgressColumn],
- console: Optional[Console] = None,
- auto_refresh: bool = True,
- refresh_per_second: float = 10,
- speed_estimate_period: float = 30.0,
- transient: bool = False,
- redirect_stdout: bool = True,
- redirect_stderr: bool = True,
- get_time: Optional[GetTimeCallable] = None,
- disable: bool = False,
- expand: bool = False,
- ) -> None:
- assert (
- refresh_per_second is None or refresh_per_second > 0
- ), "refresh_per_second must be > 0"
- self._lock = RLock()
- self.columns = columns or (
- TextColumn("[progress.description]{task.description}"),
- BarColumn(),
- TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
- TimeRemainingColumn(),
- )
- self.speed_estimate_period = speed_estimate_period
-
- self.disable = disable
- self.expand = expand
- self._tasks: Dict[TaskID, Task] = {}
- self._task_index: TaskID = TaskID(0)
- self.live = Live(
- console=console or get_console(),
- auto_refresh=auto_refresh,
- refresh_per_second=refresh_per_second,
- transient=transient,
- redirect_stdout=redirect_stdout,
- redirect_stderr=redirect_stderr,
- get_renderable=self.get_renderable,
- )
- self.get_time = get_time or self.console.get_time
- self.print = self.console.print
- self.log = self.console.log
-
- @property
- def console(self) -> Console:
- return self.live.console
-
- @property
- def tasks(self) -> List[Task]:
- """Get a list of Task instances."""
- with self._lock:
- return list(self._tasks.values())
-
- @property
- def task_ids(self) -> List[TaskID]:
- """A list of task IDs."""
- with self._lock:
- return list(self._tasks.keys())
-
- @property
- def finished(self) -> bool:
- """Check if all tasks have been completed."""
- with self._lock:
- if not self._tasks:
- return True
- return all(task.finished for task in self._tasks.values())
-
- def start(self) -> None:
- """Start the progress display."""
- if not self.disable:
- self.live.start(refresh=True)
-
- def stop(self) -> None:
- """Stop the progress display."""
- self.live.stop()
- if not self.console.is_interactive:
- self.console.print()
-
- def __enter__(self) -> "Progress":
- self.start()
- return self
-
- def __exit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc_val: Optional[BaseException],
- exc_tb: Optional[TracebackType],
- ) -> None:
- self.stop()
-
- def track(
- self,
- sequence: Union[Iterable[ProgressType], Sequence[ProgressType]],
- total: Optional[float] = None,
- task_id: Optional[TaskID] = None,
- description: str = "Working...",
- update_period: float = 0.1,
- ) -> Iterable[ProgressType]:
- """Track progress by iterating over a sequence.
-
- Args:
- sequence (Sequence[ProgressType]): A sequence of values you want to iterate over and track progress.
- total: (float, optional): Total number of steps. Default is len(sequence).
- task_id: (TaskID): Task to track. Default is new task.
- description: (str, optional): Description of task, if new task is created.
- update_period (float, optional): Minimum time (in seconds) between calls to update(). Defaults to 0.1.
-
- Returns:
- Iterable[ProgressType]: An iterable of values taken from the provided sequence.
- """
-
- if total is None:
- if isinstance(sequence, Sized):
- task_total = float(len(sequence))
- else:
- raise ValueError(
- f"unable to get size of {sequence!r}, please specify 'total'"
- )
- else:
- task_total = total
-
- if task_id is None:
- task_id = self.add_task(description, total=task_total)
- else:
- self.update(task_id, total=task_total)
-
- if self.live.auto_refresh:
- with _TrackThread(self, task_id, update_period) as track_thread:
- for value in sequence:
- yield value
- track_thread.completed += 1
- else:
- advance = self.advance
- refresh = self.refresh
- for value in sequence:
- yield value
- advance(task_id, 1)
- refresh()
-
- def start_task(self, task_id: TaskID) -> None:
- """Start a task.
-
- Starts a task (used when calculating elapsed time). You may need to call this manually,
- if you called ``add_task`` with ``start=False``.
-
- Args:
- task_id (TaskID): ID of task.
- """
- with self._lock:
- task = self._tasks[task_id]
- if task.start_time is None:
- task.start_time = self.get_time()
-
- def stop_task(self, task_id: TaskID) -> None:
- """Stop a task.
-
- This will freeze the elapsed time on the task.
-
- Args:
- task_id (TaskID): ID of task.
- """
- with self._lock:
- task = self._tasks[task_id]
- current_time = self.get_time()
- if task.start_time is None:
- task.start_time = current_time
- task.stop_time = current_time
-
- def update(
- self,
- task_id: TaskID,
- *,
- total: Optional[float] = None,
- completed: Optional[float] = None,
- advance: Optional[float] = None,
- description: Optional[str] = None,
- visible: Optional[bool] = None,
- refresh: bool = False,
- **fields: Any,
- ) -> None:
- """Update information associated with a task.
-
- Args:
- task_id (TaskID): Task id (returned by add_task).
- total (float, optional): Updates task.total if not None.
- completed (float, optional): Updates task.completed if not None.
- advance (float, optional): Add a value to task.completed if not None.
- description (str, optional): Change task description if not None.
- visible (bool, optional): Set visible flag if not None.
- refresh (bool): Force a refresh of progress information. Default is False.
- **fields (Any): Additional data fields required for rendering.
- """
- with self._lock:
- task = self._tasks[task_id]
- completed_start = task.completed
-
- if total is not None and total != task.total:
- task.total = total
- task._reset()
- if advance is not None:
- task.completed += advance
- if completed is not None:
- task.completed = completed
- if description is not None:
- task.description = description
- if visible is not None:
- task.visible = visible
- task.fields.update(fields)
- update_completed = task.completed - completed_start
-
- current_time = self.get_time()
- old_sample_time = current_time - self.speed_estimate_period
- _progress = task._progress
-
- popleft = _progress.popleft
- while _progress and _progress[0].timestamp < old_sample_time:
- popleft()
- while len(_progress) > 1000:
- popleft()
- if update_completed > 0:
- _progress.append(ProgressSample(current_time, update_completed))
- if task.completed >= task.total and task.finished_time is None:
- task.finished_time = task.elapsed
-
- if refresh:
- self.refresh()
-
- def reset(
- self,
- task_id: TaskID,
- *,
- start: bool = True,
- total: Optional[float] = None,
- completed: int = 0,
- visible: Optional[bool] = None,
- description: Optional[str] = None,
- **fields: Any,
- ) -> None:
- """Reset a task so completed is 0 and the clock is reset.
-
- Args:
- task_id (TaskID): ID of task.
- start (bool, optional): Start the task after reset. Defaults to True.
- total (float, optional): New total steps in task, or None to use current total. Defaults to None.
- completed (int, optional): Number of steps completed. Defaults to 0.
- **fields (str): Additional data fields required for rendering.
- """
- current_time = self.get_time()
- with self._lock:
- task = self._tasks[task_id]
- task._reset()
- task.start_time = current_time if start else None
- if total is not None:
- task.total = total
- task.completed = completed
- if visible is not None:
- task.visible = visible
- if fields:
- task.fields = fields
- if description is not None:
- task.description = description
- task.finished_time = None
- self.refresh()
-
- def advance(self, task_id: TaskID, advance: float = 1) -> None:
- """Advance task by a number of steps.
-
- Args:
- task_id (TaskID): ID of task.
- advance (float): Number of steps to advance. Default is 1.
- """
- current_time = self.get_time()
- with self._lock:
- task = self._tasks[task_id]
- completed_start = task.completed
- task.completed += advance
- update_completed = task.completed - completed_start
- old_sample_time = current_time - self.speed_estimate_period
- _progress = task._progress
-
- popleft = _progress.popleft
- while _progress and _progress[0].timestamp < old_sample_time:
- popleft()
- while len(_progress) > 1000:
- popleft()
- _progress.append(ProgressSample(current_time, update_completed))
- if task.completed >= task.total and task.finished_time is None:
- task.finished_time = task.elapsed
- task.finished_speed = task.speed
-
- def refresh(self) -> None:
- """Refresh (render) the progress information."""
- if not self.disable and self.live.is_started:
- self.live.refresh()
-
- def get_renderable(self) -> RenderableType:
- """Get a renderable for the progress display."""
- renderable = Group(*self.get_renderables())
- return renderable
-
- def get_renderables(self) -> Iterable[RenderableType]:
- """Get a number of renderables for the progress display."""
- table = self.make_tasks_table(self.tasks)
- yield table
-
- def make_tasks_table(self, tasks: Iterable[Task]) -> Table:
- """Get a table to render the Progress display.
-
- Args:
- tasks (Iterable[Task]): An iterable of Task instances, one per row of the table.
-
- Returns:
- Table: A table instance.
- """
- table_columns = (
- (
- Column(no_wrap=True)
- if isinstance(_column, str)
- else _column.get_table_column().copy()
- )
- for _column in self.columns
- )
- table = Table.grid(*table_columns, padding=(0, 1), expand=self.expand)
-
- for task in tasks:
- if task.visible:
- table.add_row(
- *(
- (
- column.format(task=task)
- if isinstance(column, str)
- else column(task)
- )
- for column in self.columns
- )
- )
- return table
-
- def __rich__(self) -> RenderableType:
- """Makes the Progress class itself renderable."""
- with self._lock:
- return self.get_renderable()
-
- def add_task(
- self,
- description: str,
- start: bool = True,
- total: float = 100.0,
- completed: int = 0,
- visible: bool = True,
- **fields: Any,
- ) -> TaskID:
- """Add a new 'task' to the Progress display.
-
- Args:
- description (str): A description of the task.
- start (bool, optional): Start the task immediately (to calculate elapsed time). If set to False,
- you will need to call `start` manually. Defaults to True.
- total (float, optional): Number of total steps in the progress if know. Defaults to 100.
- completed (int, optional): Number of steps completed so far.. Defaults to 0.
- visible (bool, optional): Enable display of the task. Defaults to True.
- **fields (str): Additional data fields required for rendering.
-
- Returns:
- TaskID: An ID you can use when calling `update`.
- """
- with self._lock:
- task = Task(
- self._task_index,
- description,
- total,
- completed,
- visible=visible,
- fields=fields,
- _get_time=self.get_time,
- _lock=self._lock,
- )
- self._tasks[self._task_index] = task
- if start:
- self.start_task(self._task_index)
- new_task_index = self._task_index
- self._task_index = TaskID(int(self._task_index) + 1)
- self.refresh()
- return new_task_index
-
- def remove_task(self, task_id: TaskID) -> None:
- """Delete a task if it exists.
-
- Args:
- task_id (TaskID): A task ID.
-
- """
- with self._lock:
- del self._tasks[task_id]
-
-
-if __name__ == "__main__": # pragma: no coverage
-
- import random
- import time
-
- from .panel import Panel
- from .rule import Rule
- from .syntax import Syntax
- from .table import Table
-
- syntax = Syntax(
- '''def loop_last(values: Iterable[T]) -> Iterable[Tuple[bool, T]]:
- """Iterate and generate a tuple with a flag for last value."""
- iter_values = iter(values)
- try:
- previous_value = next(iter_values)
- except StopIteration:
- return
- for value in iter_values:
- yield False, previous_value
- previous_value = value
- yield True, previous_value''',
- "python",
- line_numbers=True,
- )
-
- table = Table("foo", "bar", "baz")
- table.add_row("1", "2", "3")
-
- progress_renderables = [
- "Text may be printed while the progress bars are rendering.",
- Panel("In fact, [i]any[/i] renderable will work"),
- "Such as [magenta]tables[/]...",
- table,
- "Pretty printed structures...",
- {"type": "example", "text": "Pretty printed"},
- "Syntax...",
- syntax,
- Rule("Give it a try!"),
- ]
-
- from itertools import cycle
-
- examples = cycle(progress_renderables)
-
- console = Console(record=True)
-
- with Progress(
- SpinnerColumn(),
- TextColumn("[progress.description]{task.description}"),
- BarColumn(),
- TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
- TimeRemainingColumn(),
- TimeElapsedColumn(),
- console=console,
- transient=True,
- ) as progress:
-
- task1 = progress.add_task("[red]Downloading", total=1000)
- task2 = progress.add_task("[green]Processing", total=1000)
- task3 = progress.add_task("[yellow]Thinking", total=1000, start=False)
-
- while not progress.finished:
- progress.update(task1, advance=0.5)
- progress.update(task2, advance=0.3)
- time.sleep(0.01)
- if random.randint(0, 100) < 1:
- progress.log(next(examples))
diff --git a/spaces/ali-ghamdan/deoldify/fastai/gen_doc/gen_notebooks.py b/spaces/ali-ghamdan/deoldify/fastai/gen_doc/gen_notebooks.py
deleted file mode 100644
index c0a4440ac9a3aec6ea60a7bb1d9f29df9f2d2622..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/deoldify/fastai/gen_doc/gen_notebooks.py
+++ /dev/null
@@ -1,350 +0,0 @@
-"`gen_doc.nbdoc` generates notebook documentation from module functions and links to correct places"
-import pkgutil, inspect, sys,os, importlib,json,enum,warnings,nbformat,re
-from IPython.core.display import display, Markdown
-from nbconvert.preprocessors import ExecutePreprocessor
-from nbformat.sign import NotebookNotary
-from pathlib import Path
-from .core import *
-from .nbdoc import *
-
-__all__ = ['create_module_page', 'update_module_page', 'import_mod',
- 'link_nb', 'update_notebooks', 'generate_missing_metadata', 'update_nb_metadata']
-
-def get_empty_notebook():
- "Default notbook with the minimum metadata."
- #TODO: check python version and nbformat
- return {'metadata': {'kernelspec': {'display_name': 'Python 3',
- 'language': 'python',
- 'name': 'python3'},
- 'language_info': {'codemirror_mode': {'name': 'ipython', 'version': 3},
- 'file_extension': '.py',
- 'mimetype': 'text/x-python',
- 'name': 'python',
- 'nbconvert_exporter': 'python',
- 'pygments_lexer': 'ipython3',
- 'version': '3.6.6'}},
- 'nbformat': 4,
- 'nbformat_minor': 2}
-
-def get_md_cell(source, metadata=None):
- "Markdown cell containing `source` with `metadata`."
- return {'cell_type': 'markdown',
- 'metadata': {} if metadata is None else metadata,
- 'source': source}
-
-def get_empty_cell(ctype='markdown'):
- "Empty cell of type `ctype`."
- return {'cell_type': ctype, 'metadata': {}, 'source': []}
-
-def get_code_cell(code, hidden=False):
- "Code cell containing `code` that may be `hidden`."
- return {'cell_type' : 'code',
- 'execution_count': 0,
- 'metadata' : {'hide_input': hidden, 'trusted':True},
- 'source' : code,
- 'outputs': []}
-
-def get_doc_cell(func_name):
- "Code cell with the command to show the doc of `func_name`."
- code = f"show_doc({func_name})"
- return get_code_cell(code, True)
-
-def get_global_vars(mod):
- "Return globally assigned variables."
- # https://stackoverflow.com/questions/8820276/docstring-for-variable/31764368#31764368
- import ast,re
- with open(mod.__file__, 'r') as f: fstr = f.read()
- flines = fstr.splitlines()
- d = {}
- for node in ast.walk(ast.parse(fstr)):
- if isinstance(node,ast.Assign) and hasattr(node.targets[0], 'id'):
- key,lineno = node.targets[0].id,node.targets[0].lineno
- codestr = flines[lineno]
- match = re.match(f"^({key})\s*=\s*.*", codestr)
- if match and match.group(1) != '__all__': # only top level assignment
- d[key] = f'`{codestr}` {get_source_link(mod, lineno)}'
- return d
-
-def write_nb(nb, nb_path, mode='w'):
- with open(nb_path, mode) as f: f.write(nbformat.writes(nbformat.from_dict(nb), version=4))
-
-class ExecuteShowDocPreprocessor(ExecutePreprocessor):
- "An ExecutePreprocessor that only executes show_doc cells"
- def preprocess_cell(self, cell, resources, index):
- if 'source' in cell and cell.cell_type == "code":
- if IMPORT_RE.search(cell['source']) or SHOW_DOC_RE.search(cell['source']):
- return super().preprocess_cell(cell, resources, index)
- return cell, resources
-
-def execute_nb(fname, metadata=None, save=True, show_doc_only=False):
- "Execute notebook `fname` with `metadata` for preprocessing."
- # Any module used in the notebook that isn't inside must be in the same directory as this script
- with open(fname) as f: nb = nbformat.read(f, as_version=4)
- ep_class = ExecuteShowDocPreprocessor if show_doc_only else ExecutePreprocessor
- ep = ep_class(timeout=600, kernel_name='python3')
- metadata = metadata or {}
- ep.preprocess(nb, metadata)
- if save:
- with open(fname, 'wt') as f: nbformat.write(nb, f)
- NotebookNotary().sign(nb)
-
-def _symbol_skeleton(name): return [get_doc_cell(name), get_md_cell(f"`{name}`")]
-
-def create_module_page(mod, dest_path, force=False):
- "Create the documentation notebook for module `mod_name` in path `dest_path`"
- nb = get_empty_notebook()
- mod_name = mod.__name__
- strip_name = strip_fastai(mod_name)
- init_cell = [get_md_cell(f'## Title for {strip_name} (use plain english, not module name!)'), get_md_cell('Type an introduction of the package here.')]
- cells = [get_code_cell(f'from fastai.gen_doc.nbdoc import *\nfrom {mod_name} import * ', True)]
-
- gvar_map = get_global_vars(mod)
- if gvar_map: cells.append(get_md_cell('### Global Variable Definitions:'))
- for name in get_exports(mod):
- if name in gvar_map: cells.append(get_md_cell(gvar_map[name]))
-
- for ft_name in get_ft_names(mod, include_inner=True):
- if not hasattr(mod, ft_name):
- warnings.warn(f"Module {strip_name} doesn't have a function named {ft_name}.")
- continue
- cells += _symbol_skeleton(ft_name)
- elt = getattr(mod, ft_name)
- nb['cells'] = init_cell + cells + [get_md_cell(UNDOC_HEADER)]
-
- doc_path = get_doc_path(mod, dest_path)
- write_nb(nb, doc_path, 'w' if force else 'x')
- execute_nb(doc_path)
- return doc_path
-
-_default_exclude = ['.ipynb_checkpoints', '__pycache__', '__init__.py', 'imports']
-
-def get_module_names(path_dir, exclude=None):
- if exclude is None: exclude = _default_exclude
- "Search a given `path_dir` and return all the modules contained inside except those in `exclude`"
- files = sorted(path_dir.glob('*'), key=lambda x: (x.is_dir(), x.name), reverse=True) # directories first
- res = [f'{path_dir.name}']
- for f in files:
- if f.is_dir() and f.name in exclude: continue # exclude directories
- if any([f.name.endswith(ex) for ex in exclude]): continue # exclude extensions
-
- if f.suffix == '.py': res.append(f'{path_dir.name}.{f.stem}')
- elif f.is_dir(): res += [f'{path_dir.name}.{name}' for name in get_module_names(f)]
- return res
-
-def read_nb(fname):
- "Read a notebook in `fname` and return its corresponding json"
- with open(fname,'r') as f: return nbformat.reads(f.read(), as_version=4)
-
-SHOW_DOC_RE = re.compile(r"show_doc\(([\w\.]*)")
-def read_nb_content(cells, mod_name):
- "Build a dictionary containing the position of the `cells`."
- doc_fns = {}
- for i, cell in enumerate(cells):
- if cell['cell_type'] == 'code':
- for match in SHOW_DOC_RE.findall(cell['source']):
- doc_fns[match] = i
- return doc_fns
-
-def read_nb_types(cells):
- doc_fns = {}
- for i, cell in enumerate(cells):
- if cell['cell_type'] == 'markdown':
- match = re.match(r"^(?:
LICENSE
- 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 - """ - ) - -image_blocks.launch() \ No newline at end of file diff --git a/spaces/GIGACHAhoon/BasicNNYoutubeSentimentTop5CommentPrediction/app.py b/spaces/GIGACHAhoon/BasicNNYoutubeSentimentTop5CommentPrediction/app.py deleted file mode 100644 index 2957b3c32fadb1704e4557726332fbbf14b4154d..0000000000000000000000000000000000000000 --- a/spaces/GIGACHAhoon/BasicNNYoutubeSentimentTop5CommentPrediction/app.py +++ /dev/null @@ -1,58 +0,0 @@ -import pickle -from utils import util -from youtube_comment_tool import get_top5_comments -import numpy as np -import pandas as pd -import gradio as gr -import nltk -nltk.download('stopwords') - -with open('freqs_dict.pickle', 'rb') as handle: - freqs = pickle.load(handle) - -with open('theta.pickle', 'rb') as handle: - theta = pickle.load(handle) - -with open('loglikelihood.pickle', 'rb') as handle: - loglikelihood = pickle.load(handle) - -with open('logprior.pickle', 'rb') as handle: - logprior = pickle.load(handle) - -def get_avg_sentiment(url,model_choice): - s = get_top5_comments(url) - if s: - helper = util() - ss = [] - if model_choice == "Basic NN": - for comment in s: - x = helper.extract_features(comment,freqs) - ss.append(helper.sigmoid(np.dot(x,theta))[0][0]) - - elif model_choice == "Naive Bayes": - for comment in s: - x = helper.nb_predict(comment,logprior,loglikelihood) - ss.append(x) - print(comment,x) - df = pd.DataFrame() - df['Comments'] = s - df['Sentiment'] = ss - return df - else: - return 'Video URL was not Found.' - -with gr.Blocks() as demo: - with gr.Row(): - model_choice = gr.Dropdown(['Basic NN','Naive Bayes'], label="Choose Sentiment Model") - url_box = gr.Textbox(label="Youtube URL") - submit_btn = gr.Button("Search") - with gr.Row(): - DataFrameBox = gr.DataFrame(type="pandas",col_count=(2, "fixed"),wrap=True) - - submit_btn.click( - get_avg_sentiment, - [url_box,model_choice], - [DataFrameBox] - ) - -demo.launch() \ No newline at end of file diff --git a/spaces/GIZ/sdg_classification/app.py b/spaces/GIZ/sdg_classification/app.py deleted file mode 100644 index de1a20dfdb30dd9d41f31ddbd956214a7525e83d..0000000000000000000000000000000000000000 --- a/spaces/GIZ/sdg_classification/app.py +++ /dev/null @@ -1,45 +0,0 @@ -import gradio as gr -from transformers import pipeline - -pipe = pipeline("text-classification", model="jonas/sdg_classifier_osdg") - -sdg_dict = { -'1':"SDG 1: End poverty in all its forms everywhere", -'2':"SDG 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture", -'3':"SDG3: Ensure healthy lives and promote well-being for all at all ages", -'4':"SDG 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all", -'5':"SDG 5: Achieve gender equality and empower all women and girls", -'6':"SDG 6: Ensure availability and sustainable management of water and sanitation for all", -'7':"SDG 7: Ensure access to affordable, reliable, sustainable and modern energy for all", -'8':"SDG 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all", -'9':"SDG 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation", -'10':"SDG 10: Reduce inequality within and among countries", -'11':"SDG 11: Make cities and human settlements inclusive, safe, resilient and sustainable", -'12':"SDG 12: Ensure sustainable consumption and production patterns", -'13':"SDG 13: Take urgent action to combat climate change and its impacts*", -'14':"SDG 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development", -'15':"SDG 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss" -} - -def predict(text): - preds = pipe(text)[0] - return sdg_dict[preds["label"]], str(round(preds["score"], 5)*100) + " %" - -gradio_ui = gr.Interface( - fn=predict, - title="Predict Sustainable Development Goals Labels", - description="This Space showcases a Machine Learning model for classifying text according to the first 15 of the 17 Sustainable Development Goals from the United Nations. Note that model is trained on quite short paragraphs (around 100 words) and performs best with similar input sizes.Training data comes from the amazing https://osdg.ai/ community!.", - inputs=[ - gr.inputs.Textbox(lines=5, label="Paste some text here"), - ], - outputs=[ - gr.outputs.Textbox(label="SDG Label"), - gr.outputs.Textbox(label="Certainty"), - ], - examples=[ - ["The Paris Agreement, adopted in 2015, aims to strengthen the global response to the threat of climate change by keeping a global temperature rise this century well below 2 degrees Celsius above pre-industrial levels. The agreement also aims to strengthen the ability of countries to deal with the impacts of climate change, through appropriate financial flows, a new technology framework and an enhanced capacity building framework."], - ["Gender equality is not only a fundamental human right, but a necessary foundation for a peaceful, prosperous and sustainable world. There has been progress over the last decades: More girls are going to school, fewer girls are forced into early marriage, more women are serving in parliament and positions of leadership, and laws are being reformed to advance gender equality."] - ], -) - -gradio_ui.launch(debug=True) \ No newline at end of file diff --git a/spaces/GT4SD/advanced_manufacturing/utils.py b/spaces/GT4SD/advanced_manufacturing/utils.py deleted file mode 100644 index 34a817843a959d6ef46ae527efd08788f55a3196..0000000000000000000000000000000000000000 --- a/spaces/GT4SD/advanced_manufacturing/utils.py +++ /dev/null @@ -1,48 +0,0 @@ -import logging -from collections import defaultdict -from typing import List - -import mols2grid -import pandas as pd - -logger = logging.getLogger(__name__) -logger.addHandler(logging.NullHandler()) - - -def draw_grid_generate( - samples: List[str], - seeds: List[str] = [], - n_cols: int = 3, - size=(140, 200), -) -> str: - """ - Uses mols2grid to draw a HTML grid for the generated molecules - - Args: - samples: The generated samples. - n_cols: Number of columns in grid. Defaults to 5. - size: Size of molecule in grid. Defaults to (140, 200). - - Returns: - HTML to display - """ - - result = defaultdict(list) - result.update( - { - "SMILES": seeds + samples, - "Name": [f"Seed_{i}" for i in range(len(seeds))] - + [f"Generated_{i}" for i in range(len(samples))], - }, - ) - - result_df = pd.DataFrame(result) - obj = mols2grid.display( - result_df, - tooltip=list(result.keys()), - height=1100, - n_cols=n_cols, - name="Results", - size=size, - ) - return obj.data diff --git a/spaces/GXSA/bingo/src/pages/api/healthz.ts b/spaces/GXSA/bingo/src/pages/api/healthz.ts deleted file mode 100644 index f6ae44ff0fd66ccd3f7feaa550025fbf2a83bf77..0000000000000000000000000000000000000000 --- a/spaces/GXSA/bingo/src/pages/api/healthz.ts +++ /dev/null @@ -1,7 +0,0 @@ -'use server' - -import { NextApiRequest, NextApiResponse } from 'next' - -export default async function handler(req: NextApiRequest, res: NextApiResponse) { - res.status(200).end('ok') -} diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/datasets/hrf.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/datasets/hrf.py deleted file mode 100644 index 242d790eb1b83e75cf6b7eaa7a35c674099311ad..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/datasets/hrf.py +++ /dev/null @@ -1,59 +0,0 @@ -# dataset settings -dataset_type = 'HRFDataset' -data_root = 'data/HRF' -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) -img_scale = (2336, 3504) -crop_size = (256, 256) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)), - dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), - dict(type='RandomFlip', prob=0.5), - dict(type='PhotoMetricDistortion'), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_semantic_seg']) -] -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='MultiScaleFlipAug', - img_scale=img_scale, - # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0], - flip=False, - transforms=[ - dict(type='Resize', keep_ratio=True), - dict(type='RandomFlip'), - dict(type='Normalize', **img_norm_cfg), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']) - ]) -] - -data = dict( - samples_per_gpu=4, - workers_per_gpu=4, - train=dict( - type='RepeatDataset', - times=40000, - dataset=dict( - type=dataset_type, - data_root=data_root, - img_dir='images/training', - ann_dir='annotations/training', - pipeline=train_pipeline)), - val=dict( - type=dataset_type, - data_root=data_root, - img_dir='images/validation', - ann_dir='annotations/validation', - pipeline=test_pipeline), - test=dict( - type=dataset_type, - data_root=data_root, - img_dir='images/validation', - ann_dir='annotations/validation', - pipeline=test_pipeline)) diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index ef7b369dd9e12b2282a30da14f99dd4547c53a7b..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,4 +0,0 @@ -_base_ = [ - '../_base_/models/ann_r50-d8.py', '../_base_/datasets/cityscapes.py', - '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' -] diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 04cb006ba146268e1d3278151bc6ea00a4fb1bfe..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './apcnet_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py deleted file mode 100644 index 15f578b6002c481ada06befc3ea66accbbdd1f66..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_512x512_40k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/upernet/README.md b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/upernet/README.md deleted file mode 100644 index 312004a4d7a4f77d316a772eb45823aac5c61c08..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/upernet/README.md +++ /dev/null @@ -1,48 +0,0 @@ -# Unified Perceptual Parsing for Scene Understanding - -## Introduction - - - -```latex -@inproceedings{xiao2018unified, - title={Unified perceptual parsing for scene understanding}, - author={Xiao, Tete and Liu, Yingcheng and Zhou, Bolei and Jiang, Yuning and Sun, Jian}, - booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, - pages={418--434}, - year={2018} -} -``` - -## Results and models - -### Cityscapes - -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | -| ------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| UPerNet | R-50 | 512x1024 | 40000 | 6.4 | 4.25 | 77.10 | 78.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827.log.json) | -| UPerNet | R-101 | 512x1024 | 40000 | 7.4 | 3.79 | 78.69 | 80.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933.log.json) | -| UPerNet | R-50 | 769x769 | 40000 | 7.2 | 1.76 | 77.98 | 79.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048.log.json) | -| UPerNet | R-101 | 769x769 | 40000 | 8.4 | 1.56 | 79.03 | 80.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819.log.json) | -| UPerNet | R-50 | 512x1024 | 80000 | - | - | 78.19 | 79.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207.log.json) | -| UPerNet | R-101 | 512x1024 | 80000 | - | - | 79.40 | 80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403.log.json) | -| UPerNet | R-50 | 769x769 | 80000 | - | - | 79.39 | 80.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107.log.json) | -| UPerNet | R-101 | 769x769 | 80000 | - | - | 80.10 | 81.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014.log.json) | - -### ADE20K - -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | -| ------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| UPerNet | R-50 | 512x512 | 80000 | 8.1 | 23.40 | 40.70 | 41.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127.log.json) | -| UPerNet | R-101 | 512x512 | 80000 | 9.1 | 20.34 | 42.91 | 43.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117.log.json) | -| UPerNet | R-50 | 512x512 | 160000 | - | - | 42.05 | 42.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328.log.json) | -| UPerNet | R-101 | 512x512 | 160000 | - | - | 43.82 | 44.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951.log.json) | - -### Pascal VOC 2012 + Aug - -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | -| ------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| UPerNet | R-50 | 512x512 | 20000 | 6.4 | 23.17 | 74.82 | 76.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330.log.json) | -| UPerNet | R-101 | 512x512 | 20000 | 7.5 | 19.98 | 77.10 | 78.29 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629.log.json) | -| UPerNet | R-50 | 512x512 | 40000 | - | - | 75.92 | 77.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257.log.json) | -| UPerNet | R-101 | 512x512 | 40000 | - | - | 77.43 | 78.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549.log.json) | diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/exp/upernet_global_small/run.sh b/spaces/Gradio-Blocks/uniformer_image_segmentation/exp/upernet_global_small/run.sh deleted file mode 100644 index 9fb22edfa7a32624ea08a63fe7d720c40db3b696..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/exp/upernet_global_small/run.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/usr/bin/env bash - -work_path=$(dirname $0) -PYTHONPATH="$(dirname $0)/../../":$PYTHONPATH \ -python -m torch.distributed.launch --nproc_per_node=8 \ - tools/train.py ${work_path}/config.py \ - --launcher pytorch \ - --options model.backbone.pretrained_path='your_model_path/uniformer_small_in1k.pth' \ - --work-dir ${work_path}/ckpt \ - 2>&1 | tee -a ${work_path}/log.txt diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/modules/lstm.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/modules/lstm.py deleted file mode 100644 index c0866175950c1ca4f6cca98649525e6481853bba..0000000000000000000000000000000000000000 --- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/modules/lstm.py +++ /dev/null @@ -1,25 +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 torch import nn - - -class StreamableLSTM(nn.Module): - """LSTM without worrying about the hidden state, nor the layout of the data. - Expects input as convolutional layout. - """ - def __init__(self, dimension: int, num_layers: int = 2, skip: bool = True): - super().__init__() - self.skip = skip - self.lstm = nn.LSTM(dimension, dimension, num_layers) - - def forward(self, x): - x = x.permute(2, 0, 1) - y, _ = self.lstm(x) - if self.skip: - y = y + x - y = y.permute(1, 2, 0) - return y diff --git a/spaces/Grezz/generate_human_motion/VQ-Trans/utils/config.py b/spaces/Grezz/generate_human_motion/VQ-Trans/utils/config.py deleted file mode 100644 index 091d790e963959c326917688ee267e6a4ec136d1..0000000000000000000000000000000000000000 --- a/spaces/Grezz/generate_human_motion/VQ-Trans/utils/config.py +++ /dev/null @@ -1,17 +0,0 @@ -import os - -SMPL_DATA_PATH = "./body_models/smpl" - -SMPL_KINTREE_PATH = os.path.join(SMPL_DATA_PATH, "kintree_table.pkl") -SMPL_MODEL_PATH = os.path.join(SMPL_DATA_PATH, "SMPL_NEUTRAL.pkl") -JOINT_REGRESSOR_TRAIN_EXTRA = os.path.join(SMPL_DATA_PATH, 'J_regressor_extra.npy') - -ROT_CONVENTION_TO_ROT_NUMBER = { - 'legacy': 23, - 'no_hands': 21, - 'full_hands': 51, - 'mitten_hands': 33, -} - -GENDERS = ['neutral', 'male', 'female'] -NUM_BETAS = 10 \ No newline at end of file diff --git a/spaces/GuyYariv/AudioToken/modules/beats/quantizer.py b/spaces/GuyYariv/AudioToken/modules/beats/quantizer.py deleted file mode 100644 index 4f1114eb25c22ee8d1f061ea0a61be73abe6f35e..0000000000000000000000000000000000000000 --- a/spaces/GuyYariv/AudioToken/modules/beats/quantizer.py +++ /dev/null @@ -1,215 +0,0 @@ -# -------------------------------------------------------- -# beats: Audio Pre-Training with Acoustic Tokenizers (https://arxiv.org/abs/2212.09058) -# Github source: https://github.com/microsoft/unilm/tree/master/beats -# Copyright (c) 2022 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Based on VQGAN code bases -# https://github.com/CompVis/taming-transformers -# --------------------------------------------------------' - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.distributed as distributed - -try: - from einops import rearrange, repeat -except ImportError: - pass - - -def l2norm(t): - return F.normalize(t, p=2, dim=-1) - - -def ema_inplace(moving_avg, new, decay): - moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay)) - - -def sample_vectors(samples, num): - num_samples, device = samples.shape[0], samples.device - - if num_samples >= num: - indices = torch.randperm(num_samples, device=device)[:num] - else: - indices = torch.randint(0, num_samples, (num,), device=device) - - return samples[indices] - - -def kmeans(samples, num_clusters, num_iters=10, use_cosine_sim=False): - dim, dtype, device = samples.shape[-1], samples.dtype, samples.device - - means = sample_vectors(samples, num_clusters) - - for _ in range(num_iters): - if use_cosine_sim: - dists = samples @ means.t() - else: - diffs = rearrange(samples, 'n d -> n () d') \ - - rearrange(means, 'c d -> () c d') - dists = -(diffs ** 2).sum(dim=-1) - - buckets = dists.max(dim=-1).indices - bins = torch.bincount(buckets, minlength=num_clusters) - zero_mask = bins == 0 - bins_min_clamped = bins.masked_fill(zero_mask, 1) - - new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype) - new_means.scatter_add_(0, repeat(buckets, 'n -> n d', d=dim), samples) - new_means = new_means / bins_min_clamped[..., None] - - if use_cosine_sim: - new_means = l2norm(new_means) - - means = torch.where(zero_mask[..., None], means, new_means) - - return means, bins - - -class EmbeddingEMA(nn.Module): - def __init__(self, num_tokens, codebook_dim, decay=0.99, eps=1e-5, kmeans_init=True, codebook_init_path=''): - super().__init__() - self.num_tokens = num_tokens - self.codebook_dim = codebook_dim - self.decay = decay - self.eps = eps - if codebook_init_path == '': - if not kmeans_init: - weight = torch.randn(num_tokens, codebook_dim) - weight = l2norm(weight) - else: - weight = torch.zeros(num_tokens, codebook_dim) - self.register_buffer('initted', torch.Tensor([not kmeans_init])) - else: - print(f"load init codebook weight from {codebook_init_path}") - codebook_ckpt_weight = torch.load(codebook_init_path, map_location='cpu') - weight = codebook_ckpt_weight.clone() - self.register_buffer('initted', torch.Tensor([True])) - - self.weight = nn.Parameter(weight, requires_grad=False) - self.cluster_size = nn.Parameter(torch.zeros(num_tokens), requires_grad=False) - self.embed_avg = nn.Parameter(weight.clone(), requires_grad=False) - # self.register_buffer('initted', torch.Tensor([not kmeans_init])) - self.update = True - - @torch.jit.ignore - def init_embed_(self, data): - if self.initted: - return - print("Performing Kemans init for codebook") - embed, cluster_size = kmeans(data, self.num_tokens, 10, use_cosine_sim=True) - self.weight.data.copy_(embed) - self.cluster_size.data.copy_(cluster_size) - self.initted.data.copy_(torch.Tensor([True])) - - def forward(self, embed_id): - return F.embedding(embed_id, self.weight) - - def cluster_size_ema_update(self, new_cluster_size): - self.cluster_size.data.mul_(self.decay).add_(new_cluster_size, alpha=1 - self.decay) - - def embed_avg_ema_update(self, new_embed_avg): - self.embed_avg.data.mul_(self.decay).add_(new_embed_avg, alpha=1 - self.decay) - - def weight_update(self, num_tokens): - n = self.cluster_size.sum() - smoothed_cluster_size = ( - (self.cluster_size + self.eps) / (n + num_tokens * self.eps) * n - ) - # normalize embedding average with smoothed cluster size - embed_normalized = self.embed_avg / smoothed_cluster_size.unsqueeze(1) - # embed_normalized = l2norm(self.embed_avg / smoothed_cluster_size.unsqueeze(1)) - self.weight.data.copy_(embed_normalized) - - -def norm_ema_inplace(moving_avg, new, decay): - moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay)) - moving_avg.data.copy_(l2norm(moving_avg.data)) - - -class NormEMAVectorQuantizer(nn.Module): - def __init__(self, n_embed, embedding_dim, beta, decay=0.99, eps=1e-5, - statistic_code_usage=True, kmeans_init=False, codebook_init_path=''): - super().__init__() - self.codebook_dim = embedding_dim - self.num_tokens = n_embed - self.beta = beta - self.decay = decay - - # learnable = True if orthogonal_reg_weight > 0 else False - self.embedding = EmbeddingEMA(self.num_tokens, self.codebook_dim, decay, eps, kmeans_init, codebook_init_path) - - self.statistic_code_usage = statistic_code_usage - if statistic_code_usage: - self.register_buffer('cluster_size', torch.zeros(n_embed)) - if distributed.is_available() and distributed.is_initialized(): - print("ddp is enable, so use ddp_reduce to sync the statistic_code_usage for each gpu!") - self.all_reduce_fn = distributed.all_reduce - else: - self.all_reduce_fn = nn.Identity() - - def reset_cluster_size(self, device): - if self.statistic_code_usage: - self.register_buffer('cluster_size', torch.zeros(self.num_tokens)) - self.cluster_size = self.cluster_size.to(device) - - def forward(self, z): - # reshape z -> (batch, height, width, channel) and flatten - # z, 'b c h w -> b h w c' - # z = rearrange(z, 'b c h w -> b h w c') - # z = z.transpose(1, 2) - z = l2norm(z) - z_flattened = z.reshape(-1, self.codebook_dim) - - self.embedding.init_embed_(z_flattened) - - d = z_flattened.pow(2).sum(dim=1, keepdim=True) + \ - self.embedding.weight.pow(2).sum(dim=1) - 2 * \ - torch.einsum('bd,nd->bn', z_flattened, self.embedding.weight) # 'n d -> d n' - - encoding_indices = torch.argmin(d, dim=1) - - z_q = self.embedding(encoding_indices).view(z.shape) - - encodings = F.one_hot(encoding_indices, self.num_tokens).type(z.dtype) - - if not self.training: - with torch.no_grad(): - cluster_size = encodings.sum(0) - self.all_reduce_fn(cluster_size) - ema_inplace(self.cluster_size, cluster_size, self.decay) - - if self.training and self.embedding.update: - # EMA cluster size - - bins = encodings.sum(0) - self.all_reduce_fn(bins) - - # self.embedding.cluster_size_ema_update(bins) - ema_inplace(self.cluster_size, bins, self.decay) - - zero_mask = (bins == 0) - bins = bins.masked_fill(zero_mask, 1.) - - embed_sum = z_flattened.t() @ encodings - self.all_reduce_fn(embed_sum) - - embed_normalized = (embed_sum / bins.unsqueeze(0)).t() - embed_normalized = l2norm(embed_normalized) - - embed_normalized = torch.where(zero_mask[..., None], self.embedding.weight, - embed_normalized) - norm_ema_inplace(self.embedding.weight, embed_normalized, self.decay) - - # compute loss for embedding - loss = self.beta * F.mse_loss(z_q.detach(), z) - - # preserve gradients - z_q = z + (z_q - z).detach() - - # reshape back to match original input shape - # z_q, 'b h w c -> b c h w' - # z_q = rearrange(z_q, 'b h w c -> b c h w') - # z_q = z_q.transpose(1, 2) - return z_q, loss, encoding_indices \ No newline at end of file diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/s_multimae/train_fns/__init__.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/s_multimae/train_fns/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/proggan.py b/spaces/HaHaBill/LandShapes-Antarctica/netdissect/proggan.py deleted file mode 100644 index e37ae15f373ef6ad14279bb581042434c5563539..0000000000000000000000000000000000000000 --- a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/proggan.py +++ /dev/null @@ -1,299 +0,0 @@ -import torch, numpy, itertools -import torch.nn as nn -from collections import OrderedDict - - -def print_network(net, verbose=False): - num_params = 0 - for param in net.parameters(): - num_params += param.numel() - if verbose: - print(net) - print('Total number of parameters: {:3.3f} M'.format(num_params / 1e6)) - - -def from_pth_file(filename): - ''' - Instantiate from a pth file. - ''' - state_dict = torch.load(filename) - if 'state_dict' in state_dict: - state_dict = state_dict['state_dict'] - # Convert old version of parameter names - if 'features.0.conv.weight' in state_dict: - state_dict = state_dict_from_old_pt_dict(state_dict) - sizes = sizes_from_state_dict(state_dict) - result = ProgressiveGenerator(sizes=sizes) - result.load_state_dict(state_dict) - return result - -############################################################################### -# Modules -############################################################################### - -class ProgressiveGenerator(nn.Sequential): - def __init__(self, resolution=None, sizes=None, modify_sequence=None, - output_tanh=False): - ''' - A pytorch progessive GAN generator that can be converted directly - from either a tensorflow model or a theano model. It consists of - a sequence of convolutional layers, organized in pairs, with an - upsampling and reduction of channels at every other layer; and - then finally followed by an output layer that reduces it to an - RGB [-1..1] image. - - The network can be given more layers to increase the output - resolution. The sizes argument indicates the fieature depth at - each upsampling, starting with the input z: [input-dim, 4x4-depth, - 8x8-depth, 16x16-depth...]. The output dimension is 2 * 2**len(sizes) - - Some default architectures can be selected by supplying the - resolution argument instead. - - The optional modify_sequence function can be used to transform the - sequence of layers before the network is constructed. - - If output_tanh is set to True, the network applies a tanh to clamp - the output to [-1,1] before output; otherwise the output is unclamped. - ''' - assert (resolution is None) != (sizes is None) - if sizes is None: - sizes = { - 8: [512, 512, 512], - 16: [512, 512, 512, 512], - 32: [512, 512, 512, 512, 256], - 64: [512, 512, 512, 512, 256, 128], - 128: [512, 512, 512, 512, 256, 128, 64], - 256: [512, 512, 512, 512, 256, 128, 64, 32], - 1024: [512, 512, 512, 512, 512, 256, 128, 64, 32, 16] - }[resolution] - # Follow the schedule of upsampling given by sizes. - # layers are called: layer1, layer2, etc; then output_128x128 - sequence = [] - def add_d(layer, name=None): - if name is None: - name = 'layer%d' % (len(sequence) + 1) - sequence.append((name, layer)) - add_d(NormConvBlock(sizes[0], sizes[1], kernel_size=4, padding=3)) - add_d(NormConvBlock(sizes[1], sizes[1], kernel_size=3, padding=1)) - for i, (si, so) in enumerate(zip(sizes[1:-1], sizes[2:])): - add_d(NormUpscaleConvBlock(si, so, kernel_size=3, padding=1)) - add_d(NormConvBlock(so, so, kernel_size=3, padding=1)) - # Create an output layer. During training, the progressive GAN - # learns several such output layers for various resolutions; we - # just include the last (highest resolution) one. - dim = 4 * (2 ** (len(sequence) // 2 - 1)) - add_d(OutputConvBlock(sizes[-1], tanh=output_tanh), - name='output_%dx%d' % (dim, dim)) - # Allow the sequence to be modified - if modify_sequence is not None: - sequence = modify_sequence(sequence) - super().__init__(OrderedDict(sequence)) - - def forward(self, x): - # Convert vector input to 1x1 featuremap. - x = x.view(x.shape[0], x.shape[1], 1, 1) - return super().forward(x) - -class PixelNormLayer(nn.Module): - def __init__(self): - super(PixelNormLayer, self).__init__() - - def forward(self, x): - return x / torch.sqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) - -class DoubleResolutionLayer(nn.Module): - def forward(self, x): - x = nn.functional.interpolate(x, scale_factor=2, mode='nearest') - return x - -class WScaleLayer(nn.Module): - def __init__(self, size, fan_in, gain=numpy.sqrt(2)): - super(WScaleLayer, self).__init__() - self.scale = gain / numpy.sqrt(fan_in) # No longer a parameter - self.b = nn.Parameter(torch.randn(size)) - self.size = size - - def forward(self, x): - x_size = x.size() - x = x * self.scale + self.b.view(1, -1, 1, 1).expand( - x_size[0], self.size, x_size[2], x_size[3]) - return x - -class NormConvBlock(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, padding): - super(NormConvBlock, self).__init__() - self.norm = PixelNormLayer() - self.conv = nn.Conv2d( - in_channels, out_channels, kernel_size, 1, padding, bias=False) - self.wscale = WScaleLayer(out_channels, in_channels, - gain=numpy.sqrt(2) / kernel_size) - self.relu = nn.LeakyReLU(inplace=True, negative_slope=0.2) - - def forward(self, x): - x = self.norm(x) - x = self.conv(x) - x = self.relu(self.wscale(x)) - return x - -class NormUpscaleConvBlock(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, padding): - super(NormUpscaleConvBlock, self).__init__() - self.norm = PixelNormLayer() - self.up = DoubleResolutionLayer() - self.conv = nn.Conv2d( - in_channels, out_channels, kernel_size, 1, padding, bias=False) - self.wscale = WScaleLayer(out_channels, in_channels, - gain=numpy.sqrt(2) / kernel_size) - self.relu = nn.LeakyReLU(inplace=True, negative_slope=0.2) - - def forward(self, x): - x = self.norm(x) - x = self.up(x) - x = self.conv(x) - x = self.relu(self.wscale(x)) - return x - -class OutputConvBlock(nn.Module): - def __init__(self, in_channels, tanh=False): - super().__init__() - self.norm = PixelNormLayer() - self.conv = nn.Conv2d( - in_channels, 3, kernel_size=1, padding=0, bias=False) - self.wscale = WScaleLayer(3, in_channels, gain=1) - self.clamp = nn.Hardtanh() if tanh else (lambda x: x) - - def forward(self, x): - x = self.norm(x) - x = self.conv(x) - x = self.wscale(x) - x = self.clamp(x) - return x - -############################################################################### -# Conversion -############################################################################### - -def from_tf_parameters(parameters): - ''' - Instantiate from tensorflow variables. - ''' - state_dict = state_dict_from_tf_parameters(parameters) - sizes = sizes_from_state_dict(state_dict) - result = ProgressiveGenerator(sizes=sizes) - result.load_state_dict(state_dict) - return result - -def from_old_pt_dict(parameters): - ''' - Instantiate from old pytorch state dict. - ''' - state_dict = state_dict_from_old_pt_dict(parameters) - sizes = sizes_from_state_dict(state_dict) - result = ProgressiveGenerator(sizes=sizes) - result.load_state_dict(state_dict) - return result - -def sizes_from_state_dict(params): - ''' - In a progressive GAN, the number of channels can change after each - upsampling. This function reads the state dict to figure the - number of upsamplings and the channel depth of each filter. - ''' - sizes = [] - for i in itertools.count(): - pt_layername = 'layer%d' % (i + 1) - try: - weight = params['%s.conv.weight' % pt_layername] - except KeyError: - break - if i == 0: - sizes.append(weight.shape[1]) - if i % 2 == 0: - sizes.append(weight.shape[0]) - return sizes - -def state_dict_from_tf_parameters(parameters): - ''' - Conversion from tensorflow parameters - ''' - def torch_from_tf(data): - return torch.from_numpy(data.eval()) - - params = dict(parameters) - result = {} - sizes = [] - for i in itertools.count(): - resolution = 4 * (2 ** (i // 2)) - # Translate parameter names. For example: - # 4x4/Dense/weight -> layer1.conv.weight - # 32x32/Conv0_up/weight -> layer7.conv.weight - # 32x32/Conv1/weight -> layer8.conv.weight - tf_layername = '%dx%d/%s' % (resolution, resolution, - 'Dense' if i == 0 else 'Conv' if i == 1 else - 'Conv0_up' if i % 2 == 0 else 'Conv1') - pt_layername = 'layer%d' % (i + 1) - # Stop looping when we run out of parameters. - try: - weight = torch_from_tf(params['%s/weight' % tf_layername]) - except KeyError: - break - # Transpose convolution weights into pytorch format. - if i == 0: - # Convert dense layer to 4x4 convolution - weight = weight.view(weight.shape[0], weight.shape[1] // 16, - 4, 4).permute(1, 0, 2, 3).flip(2, 3) - sizes.append(weight.shape[0]) - elif i % 2 == 0: - # Convert inverse convolution to convolution - weight = weight.permute(2, 3, 0, 1).flip(2, 3) - else: - # Ordinary Conv2d conversion. - weight = weight.permute(3, 2, 0, 1) - sizes.append(weight.shape[1]) - result['%s.conv.weight' % (pt_layername)] = weight - # Copy bias vector. - bias = torch_from_tf(params['%s/bias' % tf_layername]) - result['%s.wscale.b' % (pt_layername)] = bias - # Copy just finest-grained ToRGB output layers. For example: - # ToRGB_lod0/weight -> output.conv.weight - i -= 1 - resolution = 4 * (2 ** (i // 2)) - tf_layername = 'ToRGB_lod0' - pt_layername = 'output_%dx%d' % (resolution, resolution) - result['%s.conv.weight' % pt_layername] = torch_from_tf( - params['%s/weight' % tf_layername]).permute(3, 2, 0, 1) - result['%s.wscale.b' % pt_layername] = torch_from_tf( - params['%s/bias' % tf_layername]) - # Return parameters - return result - -def state_dict_from_old_pt_dict(params): - ''' - Conversion from the old pytorch model layer names. - ''' - result = {} - sizes = [] - for i in itertools.count(): - old_layername = 'features.%d' % i - pt_layername = 'layer%d' % (i + 1) - try: - weight = params['%s.conv.weight' % (old_layername)] - except KeyError: - break - if i == 0: - sizes.append(weight.shape[0]) - if i % 2 == 0: - sizes.append(weight.shape[1]) - result['%s.conv.weight' % (pt_layername)] = weight - result['%s.wscale.b' % (pt_layername)] = params[ - '%s.wscale.b' % (old_layername)] - # Copy the output layers. - i -= 1 - resolution = 4 * (2 ** (i // 2)) - pt_layername = 'output_%dx%d' % (resolution, resolution) - result['%s.conv.weight' % pt_layername] = params['output.conv.weight'] - result['%s.wscale.b' % pt_layername] = params['output.wscale.b'] - # Return parameters and also network architecture sizes. - return result - diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_synthesis/preprocessing/speaker_embedder/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_synthesis/preprocessing/speaker_embedder/__init__.py deleted file mode 100644 index 3b178676ba322ef613df42977cb498101f841b09..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_synthesis/preprocessing/speaker_embedder/__init__.py +++ /dev/null @@ -1,135 +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 librosa -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.data -import torchaudio - - -EMBEDDER_PARAMS = { - 'num_mels': 40, - 'n_fft': 512, - 'emb_dim': 256, - 'lstm_hidden': 768, - 'lstm_layers': 3, - 'window': 80, - 'stride': 40, -} - - -def set_requires_grad(nets, requires_grad=False): - """Set requies_grad=Fasle for all the networks to avoid unnecessary - computations - Parameters: - nets (network list) -- a list of networks - requires_grad (bool) -- whether the networks require gradients or not - """ - if not isinstance(nets, list): - nets = [nets] - for net in nets: - if net is not None: - for param in net.parameters(): - param.requires_grad = requires_grad - - -class LinearNorm(nn.Module): - def __init__(self, hp): - super(LinearNorm, self).__init__() - self.linear_layer = nn.Linear(hp["lstm_hidden"], hp["emb_dim"]) - - def forward(self, x): - return self.linear_layer(x) - - -class SpeechEmbedder(nn.Module): - def __init__(self, hp): - super(SpeechEmbedder, self).__init__() - self.lstm = nn.LSTM(hp["num_mels"], - hp["lstm_hidden"], - num_layers=hp["lstm_layers"], - batch_first=True) - self.proj = LinearNorm(hp) - self.hp = hp - - def forward(self, mel): - # (num_mels, T) -> (num_mels, T', window) - mels = mel.unfold(1, self.hp["window"], self.hp["stride"]) - mels = mels.permute(1, 2, 0) # (T', window, num_mels) - x, _ = self.lstm(mels) # (T', window, lstm_hidden) - x = x[:, -1, :] # (T', lstm_hidden), use last frame only - x = self.proj(x) # (T', emb_dim) - x = x / torch.norm(x, p=2, dim=1, keepdim=True) # (T', emb_dim) - - x = x.mean(dim=0) - if x.norm(p=2) != 0: - x = x / x.norm(p=2) - return x - - -class SpkrEmbedder(nn.Module): - RATE = 16000 - - def __init__( - self, - embedder_path, - embedder_params=EMBEDDER_PARAMS, - rate=16000, - hop_length=160, - win_length=400, - pad=False, - ): - super(SpkrEmbedder, self).__init__() - embedder_pt = torch.load(embedder_path, map_location="cpu") - self.embedder = SpeechEmbedder(embedder_params) - self.embedder.load_state_dict(embedder_pt) - self.embedder.eval() - set_requires_grad(self.embedder, requires_grad=False) - self.embedder_params = embedder_params - - self.register_buffer('mel_basis', torch.from_numpy( - librosa.filters.mel( - sr=self.RATE, - n_fft=self.embedder_params["n_fft"], - n_mels=self.embedder_params["num_mels"]) - ) - ) - - self.resample = None - if rate != self.RATE: - self.resample = torchaudio.transforms.Resample(rate, self.RATE) - self.hop_length = hop_length - self.win_length = win_length - self.pad = pad - - def get_mel(self, y): - if self.pad and y.shape[-1] < 14000: - y = F.pad(y, (0, 14000 - y.shape[-1])) - - window = torch.hann_window(self.win_length).to(y) - y = torch.stft(y, n_fft=self.embedder_params["n_fft"], - hop_length=self.hop_length, - win_length=self.win_length, - window=window) - magnitudes = torch.norm(y, dim=-1, p=2) ** 2 - mel = torch.log10(self.mel_basis @ magnitudes + 1e-6) - return mel - - def forward(self, inputs): - dvecs = [] - for wav in inputs: - mel = self.get_mel(wav) - if mel.dim() == 3: - mel = mel.squeeze(0) - dvecs += [self.embedder(mel)] - dvecs = torch.stack(dvecs) - - dvec = torch.mean(dvecs, dim=0) - dvec = dvec / torch.norm(dvec) - - return dvec diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/concat_dataset.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/concat_dataset.py deleted file mode 100644 index 01a4078bb159fa44b2d1062b9a971fe7f1abd1c2..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/concat_dataset.py +++ /dev/null @@ -1,124 +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 bisect - -import numpy as np -from torch.utils.data.dataloader import default_collate - -from . import FairseqDataset - - -class ConcatDataset(FairseqDataset): - @staticmethod - def cumsum(sequence, sample_ratios): - r, s = [], 0 - for e, ratio in zip(sequence, sample_ratios): - curr_len = int(ratio * len(e)) - r.append(curr_len + s) - s += curr_len - return r - - def __init__(self, datasets, sample_ratios=1): - super(ConcatDataset, self).__init__() - assert len(datasets) > 0, "datasets should not be an empty iterable" - self.datasets = list(datasets) - if isinstance(sample_ratios, int): - sample_ratios = [sample_ratios] * len(self.datasets) - self.sample_ratios = sample_ratios - self.cumulative_sizes = self.cumsum(self.datasets, sample_ratios) - self.real_sizes = [len(d) for d in self.datasets] - - def __len__(self): - return self.cumulative_sizes[-1] - - def __getitem__(self, idx): - dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx) - return self.datasets[dataset_idx][sample_idx] - - def _get_dataset_and_sample_index(self, idx: int): - dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx) - if dataset_idx == 0: - sample_idx = idx - else: - sample_idx = idx - self.cumulative_sizes[dataset_idx - 1] - sample_idx = sample_idx % self.real_sizes[dataset_idx] - return dataset_idx, sample_idx - - def collater(self, samples, **extra_args): - # For now only supports datasets with same underlying collater implementations - if hasattr(self.datasets[0], "collater"): - return self.datasets[0].collater(samples, **extra_args) - else: - return default_collate(samples, **extra_args) - - def size(self, idx: int): - """ - Return an example's size as a float or tuple. - """ - dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx) - return self.datasets[dataset_idx].size(sample_idx) - - def num_tokens(self, index: int): - return np.max(self.size(index)) - - def attr(self, attr: str, index: int): - dataset_idx = bisect.bisect_right(self.cumulative_sizes, index) - return getattr(self.datasets[dataset_idx], attr, None) - - @property - def sizes(self): - _dataset_sizes = [] - for ds, sr in zip(self.datasets, self.sample_ratios): - if isinstance(ds.sizes, np.ndarray): - _dataset_sizes.append(np.tile(ds.sizes, sr)) - else: - # Only support underlying dataset with single size array. - assert isinstance(ds.sizes, list) - _dataset_sizes.append(np.tile(ds.sizes[0], sr)) - return np.concatenate(_dataset_sizes) - - @property - def supports_prefetch(self): - return all(d.supports_prefetch for d in self.datasets) - - def ordered_indices(self): - """ - Returns indices sorted by length. So less padding is needed. - """ - if isinstance(self.sizes, np.ndarray) and len(self.sizes.shape) > 1: - # special handling for concatenating lang_pair_datasets - 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")] - return indices[np.argsort(src_sizes[indices], kind="mergesort")] - else: - return np.argsort(self.sizes) - - def prefetch(self, indices): - frm = 0 - for to, ds in zip(self.cumulative_sizes, self.datasets): - real_size = len(ds) - if getattr(ds, "supports_prefetch", False): - ds.prefetch([(i - frm) % real_size for i in indices if frm <= i < to]) - frm = to - - @property - def can_reuse_epoch_itr_across_epochs(self): - return all(d.can_reuse_epoch_itr_across_epochs for d in self.datasets) - - def set_epoch(self, epoch): - super().set_epoch(epoch) - for ds in self.datasets: - if hasattr(ds, "set_epoch"): - ds.set_epoch(epoch) diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/composite.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/composite.py deleted file mode 100644 index a5366d62434a4400ba9cc524f4286f99f733d121..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/composite.py +++ /dev/null @@ -1,188 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -from collections import defaultdict -from dataclasses import dataclass, field -from typing import Dict, Any, List, Optional - -import torch.optim -from fairseq.dataclass import FairseqDataclass -from fairseq.optim import FairseqOptimizer, register_optimizer, _build_optimizer -from fairseq.optim.lr_scheduler import FairseqLRScheduler, build_lr_scheduler -from omegaconf import II, open_dict - - -logger = logging.getLogger(__name__) - - -@dataclass -class OptimizerAndSchedulerConfig(FairseqDataclass): - optimizer: Any = None - lr_scheduler: Optional[Any] = None - lr: List = II("optimization.lr") - lr_float: Optional[float] = None # this makes it easier to sweep on learning rate with auto sweepers - - -@dataclass -class CompositeOptimizerConfig(FairseqDataclass): - groups: Dict[str, Any] = field( - default_factory=lambda: {}, - metadata={ - "help": "optimizer name -> optimizer OptimizerAndSchedulerConfig. " - "Configures a different optimizer and (optionally) lr scheduler for each parameter group" - }, - ) - - -@register_optimizer("composite", dataclass=CompositeOptimizerConfig) -class FairseqCompositeOptimizer(FairseqOptimizer): - - optimizers: Dict[str, FairseqOptimizer] = {} - lr_schedulers: Dict[str, FairseqLRScheduler] = {} - lr_scheduler: FairseqLRScheduler = None - _optimizer: torch.optim.Optimizer - - def __init__(self, cfg: CompositeOptimizerConfig, params): - super().__init__(cfg) - - assert ( - len(params) > 1 - ), "Composite optimizer only works when there are multiple parameter groups (try fp16_no_flatten_grads: true)" - - groupped_params = defaultdict(list) - for p in params: - group = getattr(p, "param_group", "default") - groupped_params[group].append(p) - - assert groupped_params.keys() == cfg.groups.keys(), ( - f"Parameter groups {groupped_params.keys()} and optimizer groups {cfg.groups.keys()} are not the same! " - "Try setting 'param_group' on your parameters in the model." - ) - - for group, group_params in groupped_params.items(): - group_cfg = cfg.groups[group] - with open_dict(group_cfg): - if group_cfg.lr_float is not None: - group_cfg.optimizer.lr = [group_cfg.lr_float] - group_cfg.lr_scheduler.lr = [group_cfg.lr_float] - else: - group_cfg.optimizer.lr = group_cfg.lr - group_cfg.lr_scheduler.lr = group_cfg.lr - self.optimizers[group] = _build_optimizer(group_cfg.optimizer, group_params) - if group_cfg.lr_scheduler is not None: - self.lr_schedulers[group] = build_lr_scheduler( - group_cfg.lr_scheduler, self.optimizers[group] - ) - - if len(self.lr_schedulers) > 0: - assert len(self.lr_schedulers) == len(self.optimizers), ( - f"Please provide an lr scheduler for each optimizer to use pass_through scheduler. " - f"Optimizers: {self.optimizers}; Lr scheds: {self.lr_schedulers}" - ) - self.lr_scheduler = CompositeLRScheduler(self.lr_schedulers) - - self._optimizer = CompositeOptimizer(self.optimizers) - - @property - def supports_groups(self): - return True - - @property - def param_groups(self): - for opt in self.optimizers.values(): - for group in opt.param_groups: - yield group - - def get_lr(self): - """Return the current learning rate.""" - k = ( - "default" - if "default" in self.optimizers - else next(iter(self.optimizers.keys())) - ) - return self.optimizers[k].param_groups[0]["lr"] - - def state_dict(self): - """Return the LR scheduler state dict.""" - return {k: s.state_dict() for k, s in self.optimizers.items()} - - def load_state_dict(self, state_dict, optimizer_overrides=None): - """Load an LR scheduler state dict.""" - for k, state in state_dict.items(): - if k not in self.optimizers: - # skip extra keys like "loss_scale" added by fp16 optimizer - continue - - overrides = ( - optimizer_overrides[k] - if isinstance(optimizer_overrides, dict) and k in optimizer_overrides - else None - ) - self.optimizers[k].load_state_dict(state, optimizer_overrides=overrides) - - -class CompositeOptimizer(torch.optim.Optimizer): - def __init__(self, optimizers: Dict[str, FairseqOptimizer]): - self.optimizers = optimizers - - @property - def supports_memory_efficient_fp16(self): - return all(o.supports_memory_efficient_fp16 for o in self.optimizers.values()) - - @property - def supports_flat_params(self): - return all(o.supports_flat_params for o in self.optimizers.values()) - - def step(self, closure=None, groups=None): - """Performs a single optimization step. - - Args: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for k, opt in self.optimizers.items(): - if groups is None or k in groups: - opt.step() - - return loss - - def zero_grad(self): - for opt in self.optimizers.values(): - opt.zero_grad() - - -class CompositeLRScheduler(FairseqLRScheduler): - def __init__(self, lr_schedulers): - super().__init__(None, None) - - self.lr_schedulers = lr_schedulers - - def state_dict(self): - """Return the LR scheduler state dict.""" - return {k: s.state_dict() for k, s in self.lr_schedulers.items()} - - def load_state_dict(self, state_dict): - """Load an LR scheduler state dict.""" - for k, state in state_dict.items(): - self.lr_schedulers[k].load_state_dict(state) - - def step_begin_epoch(self, epoch): - """Update the learning rate at the beginning of the given epoch.""" - for s in self.lr_schedulers.values(): - s.step_begin_epoch(epoch) - - def step(self, epoch, val_loss=None): - """Update the learning rate at the end of the given epoch.""" - for s in self.lr_schedulers.values(): - s.step(epoch) - - def step_update(self, num_updates): - """Update the learning rate after each update.""" - return {k: s.step_update(num_updates) for k, s in self.lr_schedulers.items()} diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/distributed/test_utils.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/distributed/test_utils.py deleted file mode 100644 index 30f995b67acd39af5816d2eb412d6b4df7f44f8c..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/distributed/test_utils.py +++ /dev/null @@ -1,124 +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 functools -import sys -import unittest - -import torch - -from fairseq.distributed import utils as dist_utils - -from .utils import objects_are_equal, spawn_and_init - - -class DistributedTest(unittest.TestCase): - def setUp(self): - if not torch.cuda.is_available(): - raise unittest.SkipTest("CUDA not available, skipping test") - if sys.platform == "win32": - raise unittest.SkipTest("NCCL doesn't support Windows, skipping test") - if torch.cuda.device_count() < 2: - raise unittest.SkipTest("distributed tests require 2+ GPUs, skipping") - - -class TestBroadcastObject(DistributedTest): - def test_str(self): - spawn_and_init( - functools.partial( - TestBroadcastObject._test_broadcast_object, "hello world" - ), - world_size=2, - ) - - def test_tensor(self): - spawn_and_init( - functools.partial( - TestBroadcastObject._test_broadcast_object, - torch.rand(5), - ), - world_size=2, - ) - - def test_complex(self): - spawn_and_init( - functools.partial( - TestBroadcastObject._test_broadcast_object, - { - "a": "1", - "b": [2, torch.rand(2, 3), 3], - "c": (torch.rand(2, 3), 4), - "d": {5, torch.rand(5)}, - "e": torch.rand(5), - "f": torch.rand(5).int().cuda(), - }, - ), - world_size=2, - ) - - @staticmethod - def _test_broadcast_object(ref_obj, rank, group): - obj = dist_utils.broadcast_object( - ref_obj if rank == 0 else None, src_rank=0, group=group - ) - assert objects_are_equal(ref_obj, obj) - - -class TestAllGatherList(DistributedTest): - def test_str_equality(self): - spawn_and_init( - functools.partial( - TestAllGatherList._test_all_gather_list_equality, - "hello world", - ), - world_size=2, - ) - - def test_tensor_equality(self): - spawn_and_init( - functools.partial( - TestAllGatherList._test_all_gather_list_equality, - torch.rand(5), - ), - world_size=2, - ) - - def test_complex_equality(self): - spawn_and_init( - functools.partial( - TestAllGatherList._test_all_gather_list_equality, - { - "a": "1", - "b": [2, torch.rand(2, 3), 3], - "c": (torch.rand(2, 3), 4), - "d": {5, torch.rand(5)}, - "e": torch.rand(5), - "f": torch.rand(5).int(), - }, - ), - world_size=2, - ) - - @staticmethod - def _test_all_gather_list_equality(ref_obj, rank, group): - objs = dist_utils.all_gather_list(ref_obj, group) - for obj in objs: - assert objects_are_equal(ref_obj, obj) - - def test_rank_tensor(self): - spawn_and_init( - TestAllGatherList._test_all_gather_list_rank_tensor, world_size=2 - ) - - @staticmethod - def _test_all_gather_list_rank_tensor(rank, group): - obj = torch.tensor([rank]) - objs = dist_utils.all_gather_list(obj, group) - for i, obj in enumerate(objs): - assert obj.item() == i - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/setup.py b/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/setup.py deleted file mode 100644 index 1c96d7389bc820927724189d0f023b768b8a9b51..0000000000000000000000000000000000000000 --- a/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/setup.py +++ /dev/null @@ -1,56 +0,0 @@ -from setuptools import setup, find_packages - -with open("README.md", "r") as f: - long_description = f.read() - -setup( - name="vakyansh-tts", - version="0.0.1", - description="Text to speech for Indic languages", - long_description=long_description, - long_description_content_type="text/markdown", - url="https://github.com/Open-Speech-EkStep/vakyansh-tts.git", - keywords="nlp, tts, Indic languages, deep learning, text to speech", - # package_dir={'': 'src'}, - # packages=find_packages(where='src'), - packages=["tts_infer"], - python_requires=">=3.7, <4", - install_requires=[ - "Cython==0.29.24", - "inflect==5.3.0", - "layers==0.1.5", - "librosa==0.8.1", - "matplotlib==3.3.4", - "numpy==1.19.5", - "scipy==1.5.4", - "tensorboardX==2.4", - "tensorboard==2.7.0", - "torch==1.5.1", - "Unidecode==1.3.2", - "tqdm==4.62.3", - "fastapi==0.70.0", - "uvicorn==0.15.0", - "gradio==2.5.2", - "wavio==0.0.4", - "pydload==1.0.9", - ], - classifiers=[ - # How mature is this project? Common values are - # 3 - Alpha - # 4 - Beta - # 5 - Production/Stable - "Development Status :: 3 - Alpha", - # Indicate who your project is intended for - "Intended Audience :: Developers", - "Intended Audience :: Education", - "Intended Audience :: Science/Research", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Topic :: Text Processing :: Linguistic", - # Pick your license as you wish (should match "license" above) - "License :: OSI Approved :: MIT License", - # Specify the Python versions you support here. In particular, ensure - # that you indicate whether you support Python 2, Python 3 or both. - "Programming Language :: Python :: 3.7", - ], - include_package_data=True, -) diff --git a/spaces/HighCWu/GPEN/face_model/op/upfirdn2d.cpp b/spaces/HighCWu/GPEN/face_model/op/upfirdn2d.cpp deleted file mode 100644 index d2e633dc896433c205e18bc3e455539192ff968e..0000000000000000000000000000000000000000 --- a/spaces/HighCWu/GPEN/face_model/op/upfirdn2d.cpp +++ /dev/null @@ -1,23 +0,0 @@ -#include") - set_state(self.state_file, state) - - -if __name__ == "__main__": - print(sys.argv) - _, openai_key, state_file = sys.argv - os.environ["OPENAI_API_KEY"] = openai_key - import autogpt.config.config - from autogpt.logs import logger - from autogpt.cli import main - import autogpt.utils - from autogpt.spinner import Spinner - - def add_message(title, content): - state = get_state(state_file) - state["messages"].append((title, content)) - set_state(state_file, state) - - def typewriter_log(title="", title_color="", content="", *args, **kwargs): - add_message(title, content) - - def warn(message, title="", *args, **kwargs): - add_message(title, message) - - def error(title, message="", *args, **kwargs): - add_message(title, message) - - def clean_input(prompt=""): - add_message(None, prompt) - state = get_state(state_file) - state["awaiting_input"] = True - set_state(state_file, state) - while state["pending_input"] is None: - state = get_state(state_file) - print("Waiting for input...") - time.sleep(1) - print("Got input") - pending_input = state["pending_input"] - state["pending_input"] = None - set_state(state_file, state) - return pending_input - - def spinner_start(): - add_message(None, "Thinking...") - - logger.typewriter_log = typewriter_log - logger.warn = warn - logger.error = error - autogpt.utils.clean_input = clean_input - Spinner.spin = spinner_start - - sys.argv = sys.argv[:1] - main() diff --git a/spaces/Jarvis2301/Aku/attentions.py b/spaces/Jarvis2301/Aku/attentions.py deleted file mode 100644 index 86bc73b5fe98cc7b443e9078553920346c996707..0000000000000000000000000000000000000000 --- a/spaces/Jarvis2301/Aku/attentions.py +++ /dev/null @@ -1,300 +0,0 @@ -import math -import torch -from torch import nn -from torch.nn import functional as F - -import commons -from modules import LayerNorm - - -class Encoder(nn.Module): - def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.window_size = window_size - - self.drop = nn.Dropout(p_dropout) - self.attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size)) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout)) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask): - attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - y = self.attn_layers[i](x, x, attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class Decoder(nn.Module): - def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - - self.drop = nn.Dropout(p_dropout) - self.self_attn_layers = nn.ModuleList() - self.norm_layers_0 = nn.ModuleList() - self.encdec_attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init)) - self.norm_layers_0.append(LayerNorm(hidden_channels)) - self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout)) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True)) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask, h, h_mask): - """ - x: decoder input - h: encoder output - """ - self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype) - encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - y = self.self_attn_layers[i](x, x, self_attn_mask) - y = self.drop(y) - x = self.norm_layers_0[i](x + y) - - y = self.encdec_attn_layers[i](x, h, encdec_attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class MultiHeadAttention(nn.Module): - def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False): - super().__init__() - assert channels % n_heads == 0 - - self.channels = channels - self.out_channels = out_channels - self.n_heads = n_heads - self.p_dropout = p_dropout - self.window_size = window_size - self.heads_share = heads_share - self.block_length = block_length - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - self.attn = None - - self.k_channels = channels // n_heads - self.conv_q = nn.Conv1d(channels, channels, 1) - self.conv_k = nn.Conv1d(channels, channels, 1) - self.conv_v = nn.Conv1d(channels, channels, 1) - self.conv_o = nn.Conv1d(channels, out_channels, 1) - self.drop = nn.Dropout(p_dropout) - - if window_size is not None: - n_heads_rel = 1 if heads_share else n_heads - rel_stddev = self.k_channels**-0.5 - self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) - self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) - - nn.init.xavier_uniform_(self.conv_q.weight) - nn.init.xavier_uniform_(self.conv_k.weight) - nn.init.xavier_uniform_(self.conv_v.weight) - if proximal_init: - with torch.no_grad(): - self.conv_k.weight.copy_(self.conv_q.weight) - self.conv_k.bias.copy_(self.conv_q.bias) - - def forward(self, x, c, attn_mask=None): - q = self.conv_q(x) - k = self.conv_k(c) - v = self.conv_v(c) - - x, self.attn = self.attention(q, k, v, mask=attn_mask) - - x = self.conv_o(x) - return x - - def attention(self, query, key, value, mask=None): - # reshape [b, d, t] -> [b, n_h, t, d_k] - b, d, t_s, t_t = (*key.size(), query.size(2)) - query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) - key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - - scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) - if self.window_size is not None: - assert t_s == t_t, "Relative attention is only available for self-attention." - key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) - rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings) - scores_local = self._relative_position_to_absolute_position(rel_logits) - scores = scores + scores_local - if self.proximal_bias: - assert t_s == t_t, "Proximal bias is only available for self-attention." - scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) - if mask is not None: - scores = scores.masked_fill(mask == 0, -1e4) - if self.block_length is not None: - assert t_s == t_t, "Local attention is only available for self-attention." - block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length) - scores = scores.masked_fill(block_mask == 0, -1e4) - p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] - p_attn = self.drop(p_attn) - output = torch.matmul(p_attn, value) - if self.window_size is not None: - relative_weights = self._absolute_position_to_relative_position(p_attn) - value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) - output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings) - output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t] - return output, p_attn - - def _matmul_with_relative_values(self, x, y): - """ - x: [b, h, l, m] - y: [h or 1, m, d] - ret: [b, h, l, d] - """ - ret = torch.matmul(x, y.unsqueeze(0)) - return ret - - def _matmul_with_relative_keys(self, x, y): - """ - x: [b, h, l, d] - y: [h or 1, m, d] - ret: [b, h, l, m] - """ - ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) - return ret - - def _get_relative_embeddings(self, relative_embeddings, length): - max_relative_position = 2 * self.window_size + 1 - # Pad first before slice to avoid using cond ops. - pad_length = max(length - (self.window_size + 1), 0) - slice_start_position = max((self.window_size + 1) - length, 0) - slice_end_position = slice_start_position + 2 * length - 1 - if pad_length > 0: - padded_relative_embeddings = F.pad( - relative_embeddings, - commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])) - else: - padded_relative_embeddings = relative_embeddings - used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position] - return used_relative_embeddings - - def _relative_position_to_absolute_position(self, x): - """ - x: [b, h, l, 2*l-1] - ret: [b, h, l, l] - """ - batch, heads, length, _ = x.size() - # Concat columns of pad to shift from relative to absolute indexing. - x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]])) - - # Concat extra elements so to add up to shape (len+1, 2*len-1). - x_flat = x.view([batch, heads, length * 2 * length]) - x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]])) - - # Reshape and slice out the padded elements. - x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:] - return x_final - - def _absolute_position_to_relative_position(self, x): - """ - x: [b, h, l, l] - ret: [b, h, l, 2*l-1] - """ - batch, heads, length, _ = x.size() - # padd along column - x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]])) - x_flat = x.view([batch, heads, length**2 + length*(length -1)]) - # add 0's in the beginning that will skew the elements after reshape - x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])) - x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:] - return x_final - - def _attention_bias_proximal(self, length): - """Bias for self-attention to encourage attention to close positions. - Args: - length: an integer scalar. - Returns: - a Tensor with shape [1, 1, length, length] - """ - r = torch.arange(length, dtype=torch.float32) - diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) - return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) - - -class FFN(nn.Module): - def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.activation = activation - self.causal = causal - - if causal: - self.padding = self._causal_padding - else: - self.padding = self._same_padding - - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size) - self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size) - self.drop = nn.Dropout(p_dropout) - - def forward(self, x, x_mask): - x = self.conv_1(self.padding(x * x_mask)) - if self.activation == "gelu": - x = x * torch.sigmoid(1.702 * x) - else: - x = torch.relu(x) - x = self.drop(x) - x = self.conv_2(self.padding(x * x_mask)) - return x * x_mask - - def _causal_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = self.kernel_size - 1 - pad_r = 0 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x - - def _same_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = (self.kernel_size - 1) // 2 - pad_r = self.kernel_size // 2 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/html/appearance_switcher.html b/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/html/appearance_switcher.html deleted file mode 100644 index 5fbf3b09b1c39de75c400514c9d0d81c807ea6bd..0000000000000000000000000000000000000000 --- a/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/html/appearance_switcher.html +++ /dev/null @@ -1,6 +0,0 @@ -
-
-
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/web_assets/html/config_info.html b/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/web_assets/html/config_info.html
deleted file mode 100644
index 899c6b0cbbd490e28fc36988bbbe9901d1df5729..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/web_assets/html/config_info.html
+++ /dev/null
@@ -1,2 +0,0 @@
-{bot_avatar}
-{user_avatar}
\ No newline at end of file
diff --git a/spaces/JonathanLehner/Chatbot_small_demo/app.py b/spaces/JonathanLehner/Chatbot_small_demo/app.py
deleted file mode 100644
index 8b4267c21c46f8760732bcccfc19d118c8ad1393..0000000000000000000000000000000000000000
--- a/spaces/JonathanLehner/Chatbot_small_demo/app.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import gradio as gr
-
-def greet(name):
- return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
-
diff --git a/spaces/JunchuanYu/SegRS/segment_anything/modeling/sam.py b/spaces/JunchuanYu/SegRS/segment_anything/modeling/sam.py
deleted file mode 100644
index 303bc2f40c3dbc84f5d4286bb73336e075a86589..0000000000000000000000000000000000000000
--- a/spaces/JunchuanYu/SegRS/segment_anything/modeling/sam.py
+++ /dev/null
@@ -1,174 +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.
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from typing import Any, Dict, List, Tuple
-
-from .image_encoder import ImageEncoderViT
-from .mask_decoder import MaskDecoder
-from .prompt_encoder import PromptEncoder
-
-
-class Sam(nn.Module):
- mask_threshold: float = 0.0
- image_format: str = "RGB"
-
- def __init__(
- self,
- image_encoder: ImageEncoderViT,
- prompt_encoder: PromptEncoder,
- mask_decoder: MaskDecoder,
- pixel_mean: List[float] = [123.675, 116.28, 103.53],
- pixel_std: List[float] = [58.395, 57.12, 57.375],
- ) -> None:
- """
- SAM predicts object masks from an image and input prompts.
-
- Arguments:
- image_encoder (ImageEncoderViT): The backbone used to encode the
- image into image embeddings that allow for efficient mask prediction.
- prompt_encoder (PromptEncoder): Encodes various types of input prompts.
- mask_decoder (MaskDecoder): Predicts masks from the image embeddings
- and encoded prompts.
- pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
- pixel_std (list(float)): Std values for normalizing pixels in the input image.
- """
- super().__init__()
- self.image_encoder = image_encoder
- self.prompt_encoder = prompt_encoder
- self.mask_decoder = mask_decoder
- self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
- self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
-
- @property
- def device(self) -> Any:
- return self.pixel_mean.device
-
- @torch.no_grad()
- def forward(
- self,
- batched_input: List[Dict[str, Any]],
- multimask_output: bool,
- ) -> List[Dict[str, torch.Tensor]]:
- """
- Predicts masks end-to-end from provided images and prompts.
- If prompts are not known in advance, using SamPredictor is
- recommended over calling the model directly.
-
- Arguments:
- batched_input (list(dict)): A list over input images, each a
- dictionary with the following keys. A prompt key can be
- excluded if it is not present.
- 'image': The image as a torch tensor in 3xHxW format,
- already transformed for input to the model.
- 'original_size': (tuple(int, int)) The original size of
- the image before transformation, as (H, W).
- 'point_coords': (torch.Tensor) Batched point prompts for
- this image, with shape BxNx2. Already transformed to the
- input frame of the model.
- 'point_labels': (torch.Tensor) Batched labels for point prompts,
- with shape BxN.
- 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
- Already transformed to the input frame of the model.
- 'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
- in the form Bx1xHxW.
- multimask_output (bool): Whether the model should predict multiple
- disambiguating masks, or return a single mask.
-
- Returns:
- (list(dict)): A list over input images, where each element is
- as dictionary with the following keys.
- 'masks': (torch.Tensor) Batched binary mask predictions,
- with shape BxCxHxW, where B is the number of input promts,
- C is determiend by multimask_output, and (H, W) is the
- original size of the image.
- 'iou_predictions': (torch.Tensor) The model's predictions
- of mask quality, in shape BxC.
- 'low_res_logits': (torch.Tensor) Low resolution logits with
- shape BxCxHxW, where H=W=256. Can be passed as mask input
- to subsequent iterations of prediction.
- """
- input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
- image_embeddings = self.image_encoder(input_images)
-
- outputs = []
- for image_record, curr_embedding in zip(batched_input, image_embeddings):
- if "point_coords" in image_record:
- points = (image_record["point_coords"], image_record["point_labels"])
- else:
- points = None
- sparse_embeddings, dense_embeddings = self.prompt_encoder(
- points=points,
- boxes=image_record.get("boxes", None),
- masks=image_record.get("mask_inputs", None),
- )
- low_res_masks, iou_predictions = self.mask_decoder(
- image_embeddings=curr_embedding.unsqueeze(0),
- image_pe=self.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embeddings,
- dense_prompt_embeddings=dense_embeddings,
- multimask_output=multimask_output,
- )
- masks = self.postprocess_masks(
- low_res_masks,
- input_size=image_record["image"].shape[-2:],
- original_size=image_record["original_size"],
- )
- masks = masks > self.mask_threshold
- outputs.append(
- {
- "masks": masks,
- "iou_predictions": iou_predictions,
- "low_res_logits": low_res_masks,
- }
- )
- return outputs
-
- def postprocess_masks(
- self,
- masks: torch.Tensor,
- input_size: Tuple[int, ...],
- original_size: Tuple[int, ...],
- ) -> torch.Tensor:
- """
- Remove padding and upscale masks to the original image size.
-
- Arguments:
- masks (torch.Tensor): Batched masks from the mask_decoder,
- in BxCxHxW format.
- input_size (tuple(int, int)): The size of the image input to the
- model, in (H, W) format. Used to remove padding.
- original_size (tuple(int, int)): The original size of the image
- before resizing for input to the model, in (H, W) format.
-
- Returns:
- (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
- is given by original_size.
- """
- masks = F.interpolate(
- masks,
- (self.image_encoder.img_size, self.image_encoder.img_size),
- mode="bilinear",
- align_corners=False,
- )
- masks = masks[..., : input_size[0], : input_size[1]]
- masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False)
- return masks
-
- def preprocess(self, x: torch.Tensor) -> torch.Tensor:
- """Normalize pixel values and pad to a square input."""
- # Normalize colors
- x = (x - self.pixel_mean) / self.pixel_std
-
- # Pad
- h, w = x.shape[-2:]
- padh = self.image_encoder.img_size - h
- padw = self.image_encoder.img_size - w
- x = F.pad(x, (0, padw, 0, padh))
- return x
diff --git a/spaces/Justin-Choo/QuickGen-Photo/app.py b/spaces/Justin-Choo/QuickGen-Photo/app.py
deleted file mode 100644
index 57798949266efb13e0b8495ec69d8b480ea52381..0000000000000000000000000000000000000000
--- a/spaces/Justin-Choo/QuickGen-Photo/app.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import gradio as gr
-import os
-import sys
-
-model = ["dreamlike-art/dreamlike-photoreal-2.0"]
-
-proc1 = gr.Interface.load(f"models/{model[0]}", live=True, postprocess=True, preprocess=True)
-proc2 = gr.Interface.load("spaces/daspartho/prompt-extend")
-proc3 = gr.Interface.load("spaces/daspartho/prompt-extend")
-proc4 = gr.Interface.load("spaces/daspartho/prompt-extend")
-proc5 = gr.Interface.load("spaces/daspartho/prompt-extend")
-proc5 = gr.Interface.load("spaces/daspartho/prompt-extend")
-proc6 = gr.Interface.load("spaces/daspartho/prompt-extend")
-proc7 = gr.Interface.load("spaces/daspartho/prompt-extend")
-
-
-css = """"""
-with gr.Blocks(css=css) as sim:
- with gr.Row():
- gr.HTML("""""")
- with gr.Row():
- inputtext = gr.Textbox(label="Prompt Idea", placeholder="", lines=1)
- genbut = gr.Button("Generate Prompts")
- runbut = gr.Button("Generate Images", variant="primary")
- with gr.Row():
- output1 = gr.Image(label="")
- output2 = gr.Image(label="")
- output3 = gr.Image(label="")
- with gr.Row():
- gentext1 = gr.Textbox(label="Generated Prompt", lines=2)
- gentext2 = gr.Textbox(label="Generated Prompt", lines=2)
- gentext3 = gr.Textbox(label="Generated Prompt", lines=2)
- with gr.Row():
- output4 = gr.Image(label="")
- output5 = gr.Image(label="")
- output6 = gr.Image(label="")
- with gr.Row():
- gentext4 = gr.Textbox(label="Generated Prompt", lines=2)
- gentext5 = gr.Textbox(label="Generated Prompt", lines=2)
- gentext6 = gr.Textbox(label="Generated Prompt", lines=2)
-
-
- genbut.click(proc2, inputs=inputtext, outputs=gentext1)
- genbut.click(proc3, inputs=inputtext, outputs=gentext2)
- genbut.click(proc4, inputs=inputtext, outputs=gentext3)
- genbut.click(proc5, inputs=inputtext, outputs=gentext4)
- genbut.click(proc6, inputs=inputtext, outputs=gentext5)
- genbut.click(proc7, inputs=inputtext, outputs=gentext6)
-
-
- runbut.click(proc1, inputs=gentext1, outputs=output1)
- runbut.click(proc1, inputs=gentext2, outputs=output2)
- runbut.click(proc1, inputs=gentext3, outputs=output3)
- runbut.click(proc1, inputs=gentext4, outputs=output4)
- runbut.click(proc1, inputs=gentext5, outputs=output5)
- runbut.click(proc1, inputs=gentext6, outputs=output6)
-
-sim.queue(concurrency_count=200)
-sim.launch(inline=True, max_threads=400)
\ No newline at end of file
diff --git a/spaces/KPCGD/bingo/src/components/chat-suggestions.tsx b/spaces/KPCGD/bingo/src/components/chat-suggestions.tsx
deleted file mode 100644
index 00c2fee295c9e010946046eb71705a5e131f7a5a..0000000000000000000000000000000000000000
--- a/spaces/KPCGD/bingo/src/components/chat-suggestions.tsx
+++ /dev/null
@@ -1,45 +0,0 @@
-import React, { useMemo } from 'react'
-import Image from 'next/image'
-import HelpIcon from '@/assets/images/help.svg'
-import { SuggestedResponse } from '@/lib/bots/bing/types'
-import { useBing } from '@/lib/hooks/use-bing'
-import { atom, useAtom } from 'jotai'
-
-type Suggestions = SuggestedResponse[]
-const helpSuggestions = ['为什么不回应某些主题', '告诉我更多关于必应的资迅', '必应如何使用 AI?'].map((text) => ({ text }))
-const suggestionsAtom = atom
-
- ) : null
-}
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/tools/torchgate/utils.py b/spaces/Kangarroar/ApplioRVC-Inference/tools/torchgate/utils.py
deleted file mode 100644
index dc97d45a399c112c76e80cdd8c73cfebaf3ef6ad..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/tools/torchgate/utils.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import torch
-from torch.types import Number
-
-
-@torch.no_grad()
-def amp_to_db(x: torch.Tensor, eps=torch.finfo(torch.float64).eps, top_db=40) -> torch.Tensor:
- """
- Convert the input tensor from amplitude to decibel scale.
-
- Arguments:
- x {[torch.Tensor]} -- [Input tensor.]
-
- Keyword Arguments:
- eps {[float]} -- [Small value to avoid numerical instability.]
- (default: {torch.finfo(torch.float64).eps})
- top_db {[float]} -- [threshold the output at ``top_db`` below the peak]
- ` (default: {40})
-
- Returns:
- [torch.Tensor] -- [Output tensor in decibel scale.]
- """
- x_db = 20 * torch.log10(x.abs() + eps)
- return torch.max(x_db, (x_db.max(-1).values - top_db).unsqueeze(-1))
-
-
-@torch.no_grad()
-def temperature_sigmoid(x: torch.Tensor, x0: float, temp_coeff: float) -> torch.Tensor:
- """
- Apply a sigmoid function with temperature scaling.
-
- Arguments:
- x {[torch.Tensor]} -- [Input tensor.]
- x0 {[float]} -- [Parameter that controls the threshold of the sigmoid.]
- temp_coeff {[float]} -- [Parameter that controls the slope of the sigmoid.]
-
- Returns:
- [torch.Tensor] -- [Output tensor after applying the sigmoid with temperature scaling.]
- """
- return torch.sigmoid((x - x0) / temp_coeff)
-
-
-@torch.no_grad()
-def linspace(start: Number, stop: Number, num: int = 50, endpoint: bool = True, **kwargs) -> torch.Tensor:
- """
- Generate a linearly spaced 1-D tensor.
-
- Arguments:
- start {[Number]} -- [The starting value of the sequence.]
- stop {[Number]} -- [The end value of the sequence, unless `endpoint` is set to False.
- In that case, the sequence consists of all but the last of ``num + 1``
- evenly spaced samples, so that `stop` is excluded. Note that the step
- size changes when `endpoint` is False.]
-
- Keyword Arguments:
- num {[int]} -- [Number of samples to generate. Default is 50. Must be non-negative.]
- endpoint {[bool]} -- [If True, `stop` is the last sample. Otherwise, it is not included.
- Default is True.]
- **kwargs -- [Additional arguments to be passed to the underlying PyTorch `linspace` function.]
-
- Returns:
- [torch.Tensor] -- [1-D tensor of `num` equally spaced samples from `start` to `stop`.]
- """
- if endpoint:
- return torch.linspace(start, stop, num, **kwargs)
- else:
- return torch.linspace(start, stop, num + 1, **kwargs)[:-1]
diff --git a/spaces/Kayson/InstructDiffusion/utils/utils.py b/spaces/Kayson/InstructDiffusion/utils/utils.py
deleted file mode 100644
index ac83f23ca6fad146a0888294379f66d53a21b299..0000000000000000000000000000000000000000
--- a/spaces/Kayson/InstructDiffusion/utils/utils.py
+++ /dev/null
@@ -1,177 +0,0 @@
-# --------------------------------------------------------
-# Swin Transformer
-# Copyright (c) 2021 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Ze Liu
-# --------------------------------------------------------
-
-import os
-import torch
-import torch.distributed as dist
-from torch._six import inf
-
-
-def load_checkpoint(file_name, config, model, model_ema, optimizer, lr_scheduler, loss_scaler, logger):
- if config.model.params.deepspeed != '':
- file_name = file_name.split('/')
- ckptdir = '/'.join(file_name[:-1])
- tag = file_name[-1]
- _, client_states = model.load_checkpoint(ckptdir, tag=tag)
- print(client_states)
- logger.info("Resume checkpoint %s" % file_name)
- checkpoint = torch.load(
- os.path.join(ckptdir, tag, "state.pth"), map_location="cpu"
- )
- msg = model_ema.load_state_dict(checkpoint['model_ema'])
- logger.info(msg)
- start_epoch = checkpoint["epoch"] + 1
- max_accuracy = 0.0
- if loss_scaler and "grad_scale_manager" in checkpoint:
- loss_scaler.load_state_dict(checkpoint["grad_scale_manager"])
- if 'max_accuracy' in checkpoint:
- max_accuracy = checkpoint['max_accuracy']
- else:
- logger.info(f"==============> Resuming form {file_name}....................")
- checkpoint = torch.load(file_name, map_location='cpu')
- msg = model.load_state_dict(checkpoint['model'], strict=False)
- logger.info(msg)
- max_accuracy = 0.0
- if 'optimizer' in checkpoint and 'epoch' in checkpoint:
- optimizer.load_state_dict(checkpoint['optimizer'])
- if 'lr_scheduler' in checkpoint:
- lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
- start_epoch = checkpoint['epoch'] + 1
- if 'scaler' in checkpoint:
- loss_scaler.load_state_dict(checkpoint['scaler'])
- logger.info(f"=> loaded successfully '{file_name}' (epoch {checkpoint['epoch']})")
- if 'max_accuracy' in checkpoint:
- max_accuracy = checkpoint['max_accuracy']
-
- del checkpoint
- torch.cuda.empty_cache()
- return max_accuracy, start_epoch
-
-
-def save_checkpoint(ckptdir, config, epoch, model, model_ema, max_accuracy, optimizer, lr_scheduler, loss_scaler, logger):
-
- if config.model.params.deepspeed != '':
- if dist.get_rank() == 0:
- os.makedirs(os.path.join(ckptdir, f'ckpt_epoch_{epoch}'), exist_ok=True)
- checkpoint_path = os.path.join(ckptdir, f'ckpt_epoch_{epoch}', f'state.pth')
- to_save = {
- 'epoch': epoch,
- 'config': config,
- 'max_accuracy': max_accuracy,
- 'model_ema': model_ema.state_dict(),
- }
- if loss_scaler is not None:
- to_save["grad_scale_manager"] = loss_scaler.state_dict()
- logger.info(f"Saving checkpoint to {checkpoint_path}")
- torch.save(to_save, checkpoint_path)
- model.save_checkpoint(save_dir=ckptdir, tag=f'ckpt_epoch_{epoch}')
- print(f"rank[{dist.get_rank()}]: {ckptdir}/{f'ckpt_epoch_{epoch}'} saved")
- dist.barrier()
- else:
- if dist.get_rank() == 0:
- save_state = {'model': model.state_dict(),
- 'optimizer': optimizer.state_dict(),
- # 'lr_scheduler': lr_scheduler.state_dict(),
- 'max_accuracy': max_accuracy,
- 'scaler': loss_scaler.state_dict(),
- 'epoch': epoch,
- 'config': config}
-
- save_path = os.path.join(ckptdir, f'ckpt_epoch_{epoch}.pth')
- logger.info(f"{save_path} saving......")
- torch.save(save_state, save_path)
- logger.info(f"{save_path} saved !!!")
-
-
-def get_grad_norm(parameters, 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)
- total_norm = 0
- for p in parameters:
- param_norm = p.grad.data.norm(norm_type)
- total_norm += param_norm.item() ** norm_type
- total_norm = total_norm ** (1. / norm_type)
- return total_norm
-
-
-def auto_resume_helper(config, output_dir):
- if config.model.params.deepspeed != '':
- dirs = os.listdir(output_dir)
- dirs = [d for d in dirs if d.startswith('ckpt_epoch')]
- print(f"All checkpoints founded in {output_dir}: {dirs}")
- if len(dirs) > 0:
- dirs = max([int(d.split('_')[-1]) for d in dirs])
- latest_checkpoint = os.path.join(output_dir, 'ckpt_epoch_{}'.format(dirs))
- print(f"The latest checkpoint founded: {latest_checkpoint}")
- resume_file = latest_checkpoint
- else:
- resume_file = None
- else:
- checkpoints = os.listdir(output_dir)
- checkpoints = [ckpt for ckpt in checkpoints if ckpt.endswith('pth')]
- print(f"All checkpoints founded in {output_dir}: {checkpoints}")
- if len(checkpoints) > 0:
- latest_checkpoint = max([os.path.join(output_dir, d) for d in checkpoints], key=os.path.getmtime)
- print(f"The latest checkpoint founded: {latest_checkpoint}")
- resume_file = latest_checkpoint
- else:
- resume_file = None
- return resume_file
-
-
-def reduce_tensor(tensor):
- rt = tensor.clone()
- dist.all_reduce(rt, op=dist.ReduceOp.SUM)
- rt /= dist.get_world_size()
- return rt
-
-
-def ampscaler_get_grad_norm(parameters, norm_type: float = 2.0) -> torch.Tensor:
- if isinstance(parameters, torch.Tensor):
- parameters = [parameters]
- parameters = [p for p in parameters if p.grad is not None]
- norm_type = float(norm_type)
- if len(parameters) == 0:
- return torch.tensor(0.)
- device = parameters[0].grad.device
- if norm_type == inf:
- total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
- else:
- total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(),
- norm_type).to(device) for p in parameters]), norm_type)
- return total_norm
-
-
-class NativeScalerWithGradNormCount:
- state_dict_key = "amp_scaler"
-
- def __init__(self):
- self._scaler = torch.cuda.amp.GradScaler()
-
- def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True):
- self._scaler.scale(loss).backward(create_graph=create_graph)
- if update_grad:
- if clip_grad is not None:
- assert parameters is not None
- self._scaler.unscale_(optimizer) # unscale the gradients of optimizer's assigned params in-place
- norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
- else:
- self._scaler.unscale_(optimizer)
- norm = ampscaler_get_grad_norm(parameters)
- self._scaler.step(optimizer)
- self._scaler.update()
- else:
- norm = None
- return norm
-
- def state_dict(self):
- return self._scaler.state_dict()
-
- def load_state_dict(self, state_dict):
- self._scaler.load_state_dict(state_dict)
\ No newline at end of file
diff --git a/spaces/Kedreamix/YoloGesture/instructions.md b/spaces/Kedreamix/YoloGesture/instructions.md
deleted file mode 100644
index 85d71b30f8a7a7fdbe51a7376c3c0ec2441f2fa4..0000000000000000000000000000000000000000
--- a/spaces/Kedreamix/YoloGesture/instructions.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# ✌ Gesture Detection
-
-
-这是一个基于无人机视觉图像手势识别控制系统，选择了YOLOv4模型进行训练
-
- **YOLOv4 = CSPDarknet53（主干） + SPP** **附加模块（颈** **） +** **PANet** **路径聚合（颈** **） + YOLOv3（头部）**
-
-
-
diff --git a/spaces/Kreaols/ChuanhuChatGPT/assets/html/appearance_switcher.html b/spaces/Kreaols/ChuanhuChatGPT/assets/html/appearance_switcher.html
deleted file mode 100644
index 9375071fbdfda7bfd622d7f7bd2dfdd0c494341b..0000000000000000000000000000000000000000
--- a/spaces/Kreaols/ChuanhuChatGPT/assets/html/appearance_switcher.html
+++ /dev/null
@@ -1,11 +0,0 @@
-
-
- {
- currentSuggestions.map(suggestion => (
-
- ))
- }
-
-
-
- {label}
-
-
-
-
-
diff --git a/spaces/Lianguangluowuyan/QQsign/README.md b/spaces/Lianguangluowuyan/QQsign/README.md
deleted file mode 100644
index bd56881a2a7709591343e2f15af9a6a8133e115b..0000000000000000000000000000000000000000
--- a/spaces/Lianguangluowuyan/QQsign/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: QQsign
-emoji: 🦀
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Loke-60000/mio-amadeus/README.md b/spaces/Loke-60000/mio-amadeus/README.md
deleted file mode 100644
index 03f4635e53b359711457e4397a2b882ce0ad8e31..0000000000000000000000000000000000000000
--- a/spaces/Loke-60000/mio-amadeus/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Mio Amadeus
-emoji: 👁
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/MarBeanInc/MarBeanInc/README.md b/spaces/MarBeanInc/MarBeanInc/README.md
deleted file mode 100644
index a42d44acd3b9d8de6362eec029cf004d47e5fa07..0000000000000000000000000000000000000000
--- a/spaces/MarBeanInc/MarBeanInc/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
----
-title: Shiny for Python template
-emoji: 🌍
-colorFrom: yellow
-colorTo: indigo
-sdk: docker
-pinned: false
-license: mpl-2.0
-duplicated_from: posit/shiny-for-python-template
----
-
-This is a templated Space for [Shiny for Python](https://shiny.rstudio.com/py/).
-
-
-To get started with a new app do the following:
-
-1) Install Shiny with `pip install shiny`
-2) Create a new app with `shiny create .`
-3) Then run the app with `shiny run --reload`
-
-To learn more about this framework please see the [Documentation](https://shiny.rstudio.com/py/docs/overview.html).
diff --git a/spaces/Marshalls/testmtd/analysis/pymo/parsers.py b/spaces/Marshalls/testmtd/analysis/pymo/parsers.py
deleted file mode 100644
index fe165167160d86fc9bea666bbd0e6e94245911de..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/analysis/pymo/parsers.py
+++ /dev/null
@@ -1,260 +0,0 @@
-'''
-BVH Parser Class
-
-By Omid Alemi
-Created: June 12, 2017
-
-Based on: https://gist.github.com/johnfredcee/2007503
-
-'''
-import re
-import numpy as np
-from analysis.pymo.data import Joint, MocapData
-
-class BVHScanner():
- '''
- A wrapper class for re.Scanner
- '''
- def __init__(self):
-
- def identifier(scanner, token):
- return 'IDENT', token
-
- def operator(scanner, token):
- return 'OPERATOR', token
-
- def digit(scanner, token):
- return 'DIGIT', token
-
- def open_brace(scanner, token):
- return 'OPEN_BRACE', token
-
- def close_brace(scanner, token):
- return 'CLOSE_BRACE', token
-
- self.scanner = re.Scanner([
- (r'[a-zA-Z_]\w*', identifier),
- #(r'-*[0-9]+(\.[0-9]+)?', digit), # won't work for .34
- #(r'[-+]?[0-9]*\.?[0-9]+', digit), # won't work for 4.56e-2
- #(r'[-+]?[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?', digit),
- (r'-*[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?', digit),
- (r'}', close_brace),
- (r'}', close_brace),
- (r'{', open_brace),
- (r':', None),
- (r'\s+', None)
- ])
-
- def scan(self, stuff):
- return self.scanner.scan(stuff)
-
-
-
-class BVHParser():
- '''
- A class to parse a BVH file.
-
- Extracts the skeleton and channel values
- '''
- def __init__(self, filename=None):
- self.reset()
-
- def reset(self):
- self._skeleton = {}
- self.bone_context = []
- self._motion_channels = []
- self._motions = []
- self.current_token = 0
- self.framerate = 0.0
- self.root_name = ''
-
- self.scanner = BVHScanner()
-
- self.data = MocapData()
-
-
- def parse(self, filename, start=0, stop=-1):
- self.reset()
-
- with open(filename, 'r') as bvh_file:
- raw_contents = bvh_file.read()
- tokens, remainder = self.scanner.scan(raw_contents)
- self._parse_hierarchy(tokens)
- self.current_token = self.current_token + 1
- self._parse_motion(tokens, start, stop)
-
- self.data.skeleton = self._skeleton
- self.data.channel_names = self._motion_channels
- self.data.values = self._to_DataFrame()
- self.data.root_name = self.root_name
- self.data.framerate = self.framerate
-
- return self.data
-
- def _to_DataFrame(self):
- '''Returns all of the channels parsed from the file as a pandas DataFrame'''
-
- import pandas as pd
- time_index = pd.to_timedelta([f[0] for f in self._motions], unit='s')
- frames = [f[1] for f in self._motions]
- channels = np.asarray([[channel[2] for channel in frame] for frame in frames])
- column_names = ['%s_%s'%(c[0], c[1]) for c in self._motion_channels]
-
- return pd.DataFrame(data=channels, index=time_index, columns=column_names)
-
-
- def _new_bone(self, parent, name):
- bone = {'parent': parent, 'channels': [], 'offsets': [], 'order': '','children': []}
- return bone
-
- def _push_bone_context(self,name):
- self.bone_context.append(name)
-
- def _get_bone_context(self):
- return self.bone_context[len(self.bone_context)-1]
-
- def _pop_bone_context(self):
- self.bone_context = self.bone_context[:-1]
- return self.bone_context[len(self.bone_context)-1]
-
- def _read_offset(self, bvh, token_index):
- if bvh[token_index] != ('IDENT', 'OFFSET'):
- return None, None
- token_index = token_index + 1
- offsets = [0.0] * 3
- for i in range(3):
- offsets[i] = float(bvh[token_index][1])
- token_index = token_index + 1
- return offsets, token_index
-
- def _read_channels(self, bvh, token_index):
- if bvh[token_index] != ('IDENT', 'CHANNELS'):
- return None, None
- token_index = token_index + 1
- channel_count = int(bvh[token_index][1])
- token_index = token_index + 1
- channels = [""] * channel_count
- order = ""
- for i in range(channel_count):
- channels[i] = bvh[token_index][1]
- token_index = token_index + 1
- if(channels[i] == "Xrotation" or channels[i]== "Yrotation" or channels[i]== "Zrotation"):
- order += channels[i][0]
- else :
- order = ""
- return channels, token_index, order
-
- def _parse_joint(self, bvh, token_index):
- end_site = False
- joint_id = bvh[token_index][1]
- token_index = token_index + 1
- joint_name = bvh[token_index][1]
- token_index = token_index + 1
-
- parent_name = self._get_bone_context()
-
- if (joint_id == "End"):
- joint_name = parent_name+ '_Nub'
- end_site = True
- joint = self._new_bone(parent_name, joint_name)
- if bvh[token_index][0] != 'OPEN_BRACE':
- print('Was expecting brance, got ', bvh[token_index])
- return None
- token_index = token_index + 1
- offsets, token_index = self._read_offset(bvh, token_index)
- joint['offsets'] = offsets
- if not end_site:
- channels, token_index, order = self._read_channels(bvh, token_index)
- joint['channels'] = channels
- joint['order'] = order
- for channel in channels:
- self._motion_channels.append((joint_name, channel))
-
- self._skeleton[joint_name] = joint
- self._skeleton[parent_name]['children'].append(joint_name)
-
- while (bvh[token_index][0] == 'IDENT' and bvh[token_index][1] == 'JOINT') or (bvh[token_index][0] == 'IDENT' and bvh[token_index][1] == 'End'):
- self._push_bone_context(joint_name)
- token_index = self._parse_joint(bvh, token_index)
- self._pop_bone_context()
-
- if bvh[token_index][0] == 'CLOSE_BRACE':
- return token_index + 1
-
- print('Unexpected token ', bvh[token_index])
-
- def _parse_hierarchy(self, bvh):
- self.current_token = 0
- if bvh[self.current_token] != ('IDENT', 'HIERARCHY'):
- return None
- self.current_token = self.current_token + 1
- if bvh[self.current_token] != ('IDENT', 'ROOT'):
- return None
- self.current_token = self.current_token + 1
- if bvh[self.current_token][0] != 'IDENT':
- return None
-
- root_name = bvh[self.current_token][1]
- root_bone = self._new_bone(None, root_name)
- self.current_token = self.current_token + 2 #skipping open brace
- offsets, self.current_token = self._read_offset(bvh, self.current_token)
- channels, self.current_token, order = self._read_channels(bvh, self.current_token)
- root_bone['offsets'] = offsets
- root_bone['channels'] = channels
- root_bone['order'] = order
- self._skeleton[root_name] = root_bone
- self._push_bone_context(root_name)
-
- for channel in channels:
- self._motion_channels.append((root_name, channel))
-
- while bvh[self.current_token][1] == 'JOINT':
- self.current_token = self._parse_joint(bvh, self.current_token)
-
- self.root_name = root_name
-
- def _parse_motion(self, bvh, start, stop):
- if bvh[self.current_token][0] != 'IDENT':
- print('Unexpected text')
- return None
- if bvh[self.current_token][1] != 'MOTION':
- print('No motion section')
- return None
- self.current_token = self.current_token + 1
- if bvh[self.current_token][1] != 'Frames':
- return None
- self.current_token = self.current_token + 1
- frame_count = int(bvh[self.current_token][1])
-
- if stop<0 or stop>frame_count:
- stop = frame_count
-
- assert(start>=0)
- assert(start- -## Getting started - -### 1) Clone the repository - -``` -git clone https://github.com/DPS2022/diffusion-posterior-sampling - -cd diffusion-posterior-sampling -``` - -
- -### 2) Download pretrained checkpoint -From the [link](https://drive.google.com/drive/folders/1jElnRoFv7b31fG0v6pTSQkelbSX3xGZh?usp=sharing), download the checkpoint "ffhq_10m.pt" and paste it to ./models/ -``` -mkdir models -mv {DOWNLOAD_DIR}/ffqh_10m.pt ./models/ -``` -{DOWNLOAD_DIR} is the directory that you downloaded checkpoint to. - -:speaker: Checkpoint for imagenet is uploaded. - -
- - -### 3) Set environment -### [Option 1] Local environment setting - -We use the external codes for motion-blurring and non-linear deblurring. - -``` -git clone https://github.com/VinAIResearch/blur-kernel-space-exploring bkse - -git clone https://github.com/LeviBorodenko/motionblur motionblur -``` - -Install dependencies - -``` -conda create -n DPS python=3.8 - -conda activate DPS - -pip install -r requirements.txt - -pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113 -``` - -
- -### [Option 2] Build Docker image - -Install docker engine, GPU driver and proper cuda before running the following commands. - -Dockerfile already contains command to clone external codes. You don't have to clone them again. - ---gpus=all is required to use local GPU device (Docker >= 19.03) - -``` -docker build -t dps-docker:latest . - -docker run -it --rm --gpus=all dps-docker -``` - -
- -### 4) Inference - -``` -python3 sample_condition.py \ ---model_config=configs/model_config.yaml \ ---diffusion_config=configs/diffusion_config.yaml \ ---task_config={TASK-CONFIG}; -``` - - -:speaker: For imagenet, use configs/imagenet_model_config.yaml - -
- -## Possible task configurations - -``` -# Linear inverse problems -- configs/super_resolution_config.yaml -- configs/gaussian_deblur_config.yaml -- configs/motion_deblur_config.yaml -- configs/inpainting_config.yaml - -# Non-linear inverse problems -- configs/nonlinear_deblur_config.yaml -- configs/phase_retrieval_config.yaml -``` - -### Structure of task configurations -You need to write your data directory at data.root. Default is ./data/samples which contains three sample images from FFHQ validation set. - -``` -conditioning: - method: # check candidates in guided_diffusion/condition_methods.py - params: - scale: 0.5 - -data: - name: ffhq - root: ./data/samples/ - -measurement: - operator: - name: # check candidates in guided_diffusion/measurements.py - -noise: - name: # gaussian or poisson - sigma: # if you use name: gaussian, set this. - (rate:) # if you use name: poisson, set this. -``` - -## Citation -If you find our work interesting, please consider citing - -``` -@inproceedings{ -chung2023diffusion, -title={Diffusion Posterior Sampling for General Noisy Inverse Problems}, -author={Hyungjin Chung and Jeongsol Kim and Michael Thompson Mccann and Marc Louis Klasky and Jong Chul Ye}, -booktitle={The Eleventh International Conference on Learning Representations }, -year={2023}, -url={https://openreview.net/forum?id=OnD9zGAGT0k} -} -``` - diff --git a/spaces/Paco1112/Super-writing-tool/app.py b/spaces/Paco1112/Super-writing-tool/app.py deleted file mode 100644 index 53b682a80ef69c3c22a1cce6d327ce1056b62ee3..0000000000000000000000000000000000000000 --- a/spaces/Paco1112/Super-writing-tool/app.py +++ /dev/null @@ -1,11 +0,0 @@ -import gradio as gr -from gradio.mix import Parallel - -#variables, functions and parameters -model1 = gr.Interface.load("huggingface/gpt2") -model2 = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B") -model3 = gr.Interface.load("huggingface/EleutherAI/gpt-neo-1.3B") - -#functions, parameters and variables -gr.Parallel(model1, model2, model3).launch() - diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-11.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-11.go deleted file mode 100644 index 71e8da526677da5beb31225e15b1ba721a52f3e2..0000000000000000000000000000000000000000 Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-11.go and /dev/null differ diff --git a/spaces/PeepDaSlan9/AutoGPT/autogpt/cli.py b/spaces/PeepDaSlan9/AutoGPT/autogpt/cli.py deleted file mode 100644 index a2e99cb421cad005528cb160e948ce59ccfcdb66..0000000000000000000000000000000000000000 --- a/spaces/PeepDaSlan9/AutoGPT/autogpt/cli.py +++ /dev/null @@ -1,145 +0,0 @@ -"""Main script for the autogpt package.""" -import click - - -@click.group(invoke_without_command=True) -@click.option("-c", "--continuous", is_flag=True, help="Enable Continuous Mode") -@click.option( - "--skip-reprompt", - "-y", - is_flag=True, - help="Skips the re-prompting messages at the beginning of the script", -) -@click.option( - "--ai-settings", - "-C", - help="Specifies which ai_settings.yaml file to use, will also automatically skip the re-prompt.", -) -@click.option( - "-l", - "--continuous-limit", - type=int, - help="Defines the number of times to run in continuous mode", -) -@click.option("--speak", is_flag=True, help="Enable Speak Mode") -@click.option("--debug", is_flag=True, help="Enable Debug Mode") -@click.option("--gpt3only", is_flag=True, help="Enable GPT3.5 Only Mode") -@click.option("--gpt4only", is_flag=True, help="Enable GPT4 Only Mode") -@click.option( - "--use-memory", - "-m", - "memory_type", - type=str, - help="Defines which Memory backend to use", -) -@click.option( - "-b", - "--browser-name", - help="Specifies which web-browser to use when using selenium to scrape the web.", -) -@click.option( - "--allow-downloads", - is_flag=True, - help="Dangerous: Allows Auto-GPT to download files natively.", -) -@click.option( - "--skip-news", - is_flag=True, - help="Specifies whether to suppress the output of latest news on startup.", -) -@click.pass_context -def main( - ctx: click.Context, - continuous: bool, - continuous_limit: int, - ai_settings: str, - skip_reprompt: bool, - speak: bool, - debug: bool, - gpt3only: bool, - gpt4only: bool, - memory_type: str, - browser_name: str, - allow_downloads: bool, - skip_news: bool, -) -> None: - """ - Welcome to AutoGPT an experimental open-source application showcasing the capabilities of the GPT-4 pushing the boundaries of AI. - - Start an Auto-GPT assistant. - """ - # Put imports inside function to avoid importing everything when starting the CLI - import logging - - from colorama import Fore - - from autogpt.agent.agent import Agent - from autogpt.config import Config, check_openai_api_key - from autogpt.configurator import create_config - from autogpt.logs import logger - from autogpt.memory import get_memory - from autogpt.prompt import construct_prompt - from autogpt.utils import get_current_git_branch, get_latest_bulletin - - if ctx.invoked_subcommand is None: - cfg = Config() - # TODO: fill in llm values here - check_openai_api_key() - create_config( - continuous, - continuous_limit, - ai_settings, - skip_reprompt, - speak, - debug, - gpt3only, - gpt4only, - memory_type, - browser_name, - allow_downloads, - skip_news, - ) - logger.set_level(logging.DEBUG if cfg.debug_mode else logging.INFO) - ai_name = "" - if not cfg.skip_news: - motd = get_latest_bulletin() - if motd: - logger.typewriter_log("NEWS: ", Fore.GREEN, motd) - git_branch = get_current_git_branch() - if git_branch and git_branch != "stable": - logger.typewriter_log( - "WARNING: ", - Fore.RED, - f"You are running on `{git_branch}` branch " - "- this is not a supported branch.", - ) - system_prompt = construct_prompt() - # print(prompt) - # Initialize variables - full_message_history = [] - next_action_count = 0 - # Make a constant: - triggering_prompt = ( - "Determine which next command to use, and respond using the" - " format specified above:" - ) - # Initialize memory and make sure it is empty. - # this is particularly important for indexing and referencing pinecone memory - memory = get_memory(cfg, init=True) - logger.typewriter_log( - "Using memory of type:", Fore.GREEN, f"{memory.__class__.__name__}" - ) - logger.typewriter_log("Using Browser:", Fore.GREEN, cfg.selenium_web_browser) - agent = Agent( - ai_name=ai_name, - memory=memory, - full_message_history=full_message_history, - next_action_count=next_action_count, - system_prompt=system_prompt, - triggering_prompt=triggering_prompt, - ) - agent.start_interaction_loop() - - -if __name__ == "__main__": - main() diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/models/blip_vqa.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/models/blip_vqa.py deleted file mode 100644 index 1ef8641cab1badd32e00abea352d764f6165faae..0000000000000000000000000000000000000000 --- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/models/blip_vqa.py +++ /dev/null @@ -1,223 +0,0 @@ -from models.med import BertConfig, BertModel, BertLMHeadModel -from models.blip import create_vit, init_tokenizer, load_checkpoint - -import torch -from torch import nn -import torch.nn.functional as F -from transformers import BertTokenizer -import numpy as np - -class BLIP_VQA(nn.Module): - def __init__(self, - med_config = 'configs/med_config.json', - image_size = 480, - vit = 'base', - vit_grad_ckpt = False, - vit_ckpt_layer = 0, - ): - """ - Args: - med_config (str): path for the mixture of encoder-decoder model's configuration file - image_size (int): input image size - vit (str): model size of vision transformer - """ - super().__init__() - - self.visual_encoder, vision_width = create_vit(vit, image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1) - self.tokenizer = init_tokenizer() - - encoder_config = BertConfig.from_json_file(med_config) - encoder_config.encoder_width = vision_width - self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False) - - decoder_config = BertConfig.from_json_file(med_config) - self.text_decoder = BertLMHeadModel(config=decoder_config) - - self.itm_head = nn.Linear(768, 2) - - def forward(self, image, question, answer=None, n=None, weights=None, mode='inference', inference='rank', k_test=128): - - image_embeds = self.visual_encoder(image) - image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) - - question = self.tokenizer(question, padding='longest', truncation=True, max_length=35, - return_tensors="pt").to(image.device) - question.input_ids[:,0] = self.tokenizer.enc_token_id - - if mode == 'train': - ''' - n: number of answers for each question - weights: weight for each answer - ''' - answer = self.tokenizer(answer, padding='longest', return_tensors="pt").to(image.device) - answer.input_ids[:,0] = self.tokenizer.bos_token_id - answer_targets = answer.input_ids.masked_fill(answer.input_ids == self.tokenizer.pad_token_id, -100) - - question_output = self.text_encoder(question.input_ids, - attention_mask = question.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - return_dict = True) - - question_states = [] - question_atts = [] - for b, n in enumerate(n): - question_states += [question_output.last_hidden_state[b]]*n - question_atts += [question.attention_mask[b]]*n - question_states = torch.stack(question_states,0) - question_atts = torch.stack(question_atts,0) - - answer_output = self.text_decoder(answer.input_ids, - attention_mask = answer.attention_mask, - encoder_hidden_states = question_states, - encoder_attention_mask = question_atts, - labels = answer_targets, - return_dict = True, - reduction = 'none', - ) - - loss = weights * answer_output.loss - loss = loss.sum()/image.size(0) - - return loss - - elif mode == 'gradcam': - question_output = self.text_encoder(question.input_ids, - attention_mask = question.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - return_dict = True) - - vl_embeddings = question_output.last_hidden_state[:,0,:] - vl_output = self.itm_head(vl_embeddings) - - if inference=='generate': - num_beams = 3 - question_states = question_output.last_hidden_state.repeat_interleave(num_beams,dim=0) - question_atts = torch.ones(question_states.size()[:-1],dtype=torch.long).to(question_states.device) - model_kwargs = {"encoder_hidden_states": question_states, "encoder_attention_mask":question_atts} - - bos_ids = torch.full((image.size(0),1),fill_value=self.tokenizer.bos_token_id,device=image.device) - - outputs = self.text_decoder.generate(input_ids=bos_ids, - max_length=10, - min_length=1, - num_beams=num_beams, - eos_token_id=self.tokenizer.sep_token_id, - pad_token_id=self.tokenizer.pad_token_id, - **model_kwargs) - - answers = [] - for output in outputs: - answer = self.tokenizer.decode(output, skip_special_tokens=True) - answers.append(answer) - return answers, vl_output, question - - elif inference=='rank': - max_ids = self.rank_answer(question_output.last_hidden_state, question.attention_mask, - answer.input_ids, answer.attention_mask, k_test) - return max_ids, vl_output, question - - else: - question_output = self.text_encoder(question.input_ids, - attention_mask = question.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - return_dict = True) - - if inference=='generate': - num_beams = 3 - question_states = question_output.last_hidden_state.repeat_interleave(num_beams,dim=0) - question_atts = torch.ones(question_states.size()[:-1],dtype=torch.long).to(question_states.device) - model_kwargs = {"encoder_hidden_states": question_states, "encoder_attention_mask":question_atts} - - bos_ids = torch.full((image.size(0),1),fill_value=self.tokenizer.bos_token_id,device=image.device) - - outputs = self.text_decoder.generate(input_ids=bos_ids, - max_length=10, - min_length=1, - num_beams=num_beams, - eos_token_id=self.tokenizer.sep_token_id, - pad_token_id=self.tokenizer.pad_token_id, - **model_kwargs) - - answers = [] - for output in outputs: - answer = self.tokenizer.decode(output, skip_special_tokens=True) - answers.append(answer) - return answers - - elif inference=='rank': - max_ids = self.rank_answer(question_output.last_hidden_state, question.attention_mask, - answer.input_ids, answer.attention_mask, k_test) - return max_ids - - - - def rank_answer(self, question_states, question_atts, answer_ids, answer_atts, k): - - num_ques = question_states.size(0) - start_ids = answer_ids[0,0].repeat(num_ques,1) # bos token - - start_output = self.text_decoder(start_ids, - encoder_hidden_states = question_states, - encoder_attention_mask = question_atts, - return_dict = True, - reduction = 'none') - logits = start_output.logits[:,0,:] # first token's logit - - # topk_probs: top-k probability - # topk_ids: [num_question, k] - answer_first_token = answer_ids[:,1] - prob_first_token = F.softmax(logits,dim=1).index_select(dim=1, index=answer_first_token) - topk_probs, topk_ids = prob_first_token.topk(k,dim=1) - - # answer input: [num_question*k, answer_len] - input_ids = [] - input_atts = [] - for b, topk_id in enumerate(topk_ids): - input_ids.append(answer_ids.index_select(dim=0, index=topk_id)) - input_atts.append(answer_atts.index_select(dim=0, index=topk_id)) - input_ids = torch.cat(input_ids,dim=0) - input_atts = torch.cat(input_atts,dim=0) - - targets_ids = input_ids.masked_fill(input_ids == self.tokenizer.pad_token_id, -100) - - # repeat encoder's output for top-k answers - question_states = tile(question_states, 0, k) - question_atts = tile(question_atts, 0, k) - - output = self.text_decoder(input_ids, - attention_mask = input_atts, - encoder_hidden_states = question_states, - encoder_attention_mask = question_atts, - labels = targets_ids, - return_dict = True, - reduction = 'none') - - log_probs_sum = -output.loss - log_probs_sum = log_probs_sum.view(num_ques,k) - - max_topk_ids = log_probs_sum.argmax(dim=1) - max_ids = topk_ids[max_topk_ids>=0,max_topk_ids] - - return max_ids - - -def blip_vqa(pretrained='',**kwargs): - model = BLIP_VQA(**kwargs) - if pretrained: - model,msg = load_checkpoint(model,pretrained) -# assert(len(msg.missing_keys)==0) - return model - - -def tile(x, dim, n_tile): - init_dim = x.size(dim) - repeat_idx = [1] * x.dim() - repeat_idx[dim] = n_tile - x = x.repeat(*(repeat_idx)) - order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)])) - return torch.index_select(x, dim, order_index.to(x.device)) - - \ No newline at end of file diff --git a/spaces/Pontonkid/Real-Time-Multilingual-sentiment-analysis/app.py b/spaces/Pontonkid/Real-Time-Multilingual-sentiment-analysis/app.py deleted file mode 100644 index 9e40030ebaed20fe2f1b92ec416d94a70dc4c066..0000000000000000000000000000000000000000 --- a/spaces/Pontonkid/Real-Time-Multilingual-sentiment-analysis/app.py +++ /dev/null @@ -1,156 +0,0 @@ -import gradio as gr -import whisper -from transformers import pipeline - -model = whisper.load_model("base") -sentiment_analysis = pipeline("sentiment-analysis", framework="pt", model="SamLowe/roberta-base-go_emotions") - -def analyze_sentiment(text): - results = sentiment_analysis(text) - sentiment_results = {result['label']: result['score'] for result in results} - return sentiment_results - -def get_sentiment_emoji(sentiment): - # Define the emojis corresponding to each sentiment - emoji_mapping = { - "disappointment": "😞", - "sadness": "😢", - "annoyance": "😠", - "neutral": "😐", - "disapproval": "👎", - "realization": "😮", - "nervousness": "😬", - "approval": "👍", - "joy": "😄", - "anger": "😡", - "embarrassment": "😳", - "caring": "🤗", - "remorse": "😔", - "disgust": "🤢", - "grief": "😥", - "confusion": "😕", - "relief": "😌", - "desire": "😍", - "admiration": "😌", - "optimism": "😊", - "fear": "😨", - "love": "❤️", - "excitement": "🎉", - "curiosity": "🤔", - "amusement": "😄", - "surprise": "😲", - "gratitude": "🙏", - "pride": "🦁" - } - return emoji_mapping.get(sentiment, "") - -def display_sentiment_results(sentiment_results, option): - sentiment_text = "" - for sentiment, score in sentiment_results.items(): - emoji = get_sentiment_emoji(sentiment) - if option == "Sentiment Only": - sentiment_text += f"{sentiment} {emoji}\n" - elif option == "Sentiment + Score": - sentiment_text += f"{sentiment} {emoji}: {score}\n" - return sentiment_text - -def inference(audio, sentiment_option): - audio = whisper.load_audio(audio) - audio = whisper.pad_or_trim(audio) - - mel = whisper.log_mel_spectrogram(audio).to(model.device) - - _, probs = model.detect_language(mel) - lang = max(probs, key=probs.get) - - options = whisper.DecodingOptions(fp16=False) - result = whisper.decode(model, mel, options) - - sentiment_results = analyze_sentiment(result.text) - sentiment_output = display_sentiment_results(sentiment_results, sentiment_option) - - return lang.upper(), result.text, sentiment_output - -title = """
🎤 Multilingual ASR 💬
""" -image_path = "thmbnail.jpg" -description = """ -💻 This demo showcases a general-purpose speech recognition model called Whisper. It is trained on a large dataset of diverse audio and supports multilingual speech recognition, speech translation, and language identification tasks.-
-⚙️ Components of the tool:
-
- - Real-time multilingual speech recognition
- - Language identification
- - Sentiment analysis of the transcriptions
-
-🎯 The sentiment analysis results are provided as a dictionary with different emotions and their corresponding scores.
-
- -😃 The sentiment analysis results are displayed with emojis representing the corresponding sentiment.
-
- -✅ The higher the score for a specific emotion, the stronger the presence of that emotion in the transcribed text.
-
- -❓ Use the microphone for real-time speech recognition.
-
- -⚡️ The model will transcribe the audio and perform sentiment analysis on the transcribed text.
- -""" - -custom_css = """ -#banner-image { - display: block; - margin-left: auto; - margin-right: auto; -} -#chat-message { - font-size: 14px; - min-height: 300px; -} -""" - -block = gr.Blocks(css=custom_css) - -with block: - gr.HTML(title) - - with gr.Row(): - with gr.Column(): - gr.Image(image_path, elem_id="banner-image", show_label=False) - with gr.Column(): - gr.HTML(description) - - with gr.Group(): - with gr.Box(): - audio = gr.Audio( - label="Input Audio", - show_label=False, - source="microphone", - type="filepath" - ) - - sentiment_option = gr.Radio( - choices=["Sentiment Only", "Sentiment + Score"], - label="Select an option", - default="Sentiment Only" - ) - - btn = gr.Button("Transcribe") - - lang_str = gr.Textbox(label="Language") - - text = gr.Textbox(label="Transcription") - - sentiment_output = gr.Textbox(label="Sentiment Analysis Results", output=True) - - btn.click(inference, inputs=[audio, sentiment_option], outputs=[lang_str, text, sentiment_output]) - - gr.HTML(''' - - ''') - -block.launch() diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/metrics/kld.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/metrics/kld.py deleted file mode 100644 index 18260bf974bf47d8381223ac39be0c47c031bf8a..0000000000000000000000000000000000000000 --- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/metrics/kld.py +++ /dev/null @@ -1,218 +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. - -import contextlib -from functools import partial -import logging -import os -import typing as tp - -import torch -import torchmetrics - -from ..data.audio_utils import convert_audio - - -logger = logging.getLogger(__name__) - - -class _patch_passt_stft: - """Decorator to patch torch.stft in PaSST.""" - def __init__(self): - self.old_stft = torch.stft - - def __enter__(self): - # return_complex is a mandatory parameter in latest torch versions - # torch is throwing RuntimeErrors when not set - torch.stft = partial(torch.stft, return_complex=False) - - def __exit__(self, *exc): - torch.stft = self.old_stft - - -def kl_divergence(pred_probs: torch.Tensor, target_probs: torch.Tensor, epsilon: float = 1e-6) -> torch.Tensor: - """Computes the elementwise KL-Divergence loss between probability distributions - from generated samples and target samples. - - Args: - pred_probs (torch.Tensor): Probabilities for each label obtained - from a classifier on generated audio. Expected shape is [B, num_classes]. - target_probs (torch.Tensor): Probabilities for each label obtained - from a classifier on target audio. Expected shape is [B, num_classes]. - epsilon (float): Epsilon value. - Returns: - kld (torch.Tensor): KLD loss between each generated sample and target pair. - """ - kl_div = torch.nn.functional.kl_div((pred_probs + epsilon).log(), target_probs, reduction="none") - return kl_div.sum(-1) - - -class KLDivergenceMetric(torchmetrics.Metric): - """Base implementation for KL Divergence metric. - - The KL divergence is measured between probability distributions - of class predictions returned by a pre-trained audio classification model. - When the KL-divergence is low, the generated audio is expected to - have similar acoustic characteristics as the reference audio, - according to the classifier. - """ - def __init__(self): - super().__init__() - self.add_state("kld_pq_sum", default=torch.tensor(0.), dist_reduce_fx="sum") - self.add_state("kld_qp_sum", default=torch.tensor(0.), dist_reduce_fx="sum") - self.add_state("kld_all_sum", default=torch.tensor(0.), dist_reduce_fx="sum") - self.add_state("weight", default=torch.tensor(0), dist_reduce_fx="sum") - - def _get_label_distribution(self, x: torch.Tensor, sizes: torch.Tensor, - sample_rates: torch.Tensor) -> tp.Optional[torch.Tensor]: - """Get model output given provided input tensor. - - Args: - x (torch.Tensor): Input audio tensor of shape [B, C, T]. - sizes (torch.Tensor): Actual audio sample length, of shape [B]. - sample_rates (torch.Tensor): Actual audio sample rate, of shape [B]. - Returns: - probs (torch.Tensor): Probabilities over labels, of shape [B, num_classes]. - """ - raise NotImplementedError("implement method to extract label distributions from the model.") - - def update(self, preds: torch.Tensor, targets: torch.Tensor, - sizes: torch.Tensor, sample_rates: torch.Tensor) -> None: - """Calculates running KL-Divergence loss between batches of audio - preds (generated) and target (ground-truth) - Args: - preds (torch.Tensor): Audio samples to evaluate, of shape [B, C, T]. - targets (torch.Tensor): Target samples to compare against, of shape [B, C, T]. - sizes (torch.Tensor): Actual audio sample length, of shape [B]. - sample_rates (torch.Tensor): Actual audio sample rate, of shape [B]. - """ - assert preds.shape == targets.shape - assert preds.size(0) > 0, "Cannot update the loss with empty tensors" - preds_probs = self._get_label_distribution(preds, sizes, sample_rates) - targets_probs = self._get_label_distribution(targets, sizes, sample_rates) - if preds_probs is not None and targets_probs is not None: - assert preds_probs.shape == targets_probs.shape - kld_scores = kl_divergence(preds_probs, targets_probs) - assert not torch.isnan(kld_scores).any(), "kld_scores contains NaN value(s)!" - self.kld_pq_sum += torch.sum(kld_scores) - kld_qp_scores = kl_divergence(targets_probs, preds_probs) - self.kld_qp_sum += torch.sum(kld_qp_scores) - self.weight += torch.tensor(kld_scores.size(0)) - - def compute(self) -> dict: - """Computes KL-Divergence across all evaluated pred/target pairs.""" - weight: float = float(self.weight.item()) # type: ignore - assert weight > 0, "Unable to compute with total number of comparisons <= 0" - logger.info(f"Computing KL divergence on a total of {weight} samples") - kld_pq = self.kld_pq_sum.item() / weight # type: ignore - kld_qp = self.kld_qp_sum.item() / weight # type: ignore - kld_both = kld_pq + kld_qp - return {'kld': kld_pq, 'kld_pq': kld_pq, 'kld_qp': kld_qp, 'kld_both': kld_both} - - -class PasstKLDivergenceMetric(KLDivergenceMetric): - """KL-Divergence metric based on pre-trained PASST classifier on AudioSet. - - From: PaSST: Efficient Training of Audio Transformers with Patchout - Paper: https://arxiv.org/abs/2110.05069 - Implementation: https://github.com/kkoutini/PaSST - - Follow instructions from the github repo: - ``` - pip install 'git+https://github.com/kkoutini/passt_hear21@0.0.19#egg=hear21passt' - ``` - - Args: - pretrained_length (float, optional): Audio duration used for the pretrained model. - """ - def __init__(self, pretrained_length: tp.Optional[float] = None): - super().__init__() - self._initialize_model(pretrained_length) - - def _initialize_model(self, pretrained_length: tp.Optional[float] = None): - """Initialize underlying PaSST audio classifier.""" - model, sr, max_frames, min_frames = self._load_base_model(pretrained_length) - self.min_input_frames = min_frames - self.max_input_frames = max_frames - self.model_sample_rate = sr - self.model = model - self.model.eval() - self.model.to(self.device) - - def _load_base_model(self, pretrained_length: tp.Optional[float]): - """Load pretrained model from PaSST.""" - try: - if pretrained_length == 30: - from hear21passt.base30sec import get_basic_model # type: ignore - max_duration = 30 - elif pretrained_length == 20: - from hear21passt.base20sec import get_basic_model # type: ignore - max_duration = 20 - else: - from hear21passt.base import get_basic_model # type: ignore - # Original PASST was trained on AudioSet with 10s-long audio samples - max_duration = 10 - min_duration = 0.15 - min_duration = 0.15 - except ModuleNotFoundError: - raise ModuleNotFoundError( - "Please install hear21passt to compute KL divergence: ", - "pip install 'git+https://github.com/kkoutini/passt_hear21@0.0.19#egg=hear21passt'" - ) - model_sample_rate = 32_000 - max_input_frames = int(max_duration * model_sample_rate) - min_input_frames = int(min_duration * model_sample_rate) - with open(os.devnull, 'w') as f, contextlib.redirect_stdout(f): - model = get_basic_model(mode='logits') - return model, model_sample_rate, max_input_frames, min_input_frames - - def _process_audio(self, wav: torch.Tensor, sample_rate: int, wav_len: int) -> tp.Optional[torch.Tensor]: - wav = wav.unsqueeze(0) - wav = wav[..., :wav_len] - wav = convert_audio(wav, from_rate=sample_rate, to_rate=self.model_sample_rate, to_channels=1) - wav = wav.squeeze(0) - # create chunks of audio to match the classifier processing length - segments = torch.split(wav, self.max_input_frames, dim=-1) - valid_segments = [] - for s in segments: - if s.size(-1) > self.min_input_frames: - s = torch.nn.functional.pad(s, (0, self.max_input_frames - s.shape[-1])) - valid_segments.append(s) - if len(valid_segments) > 0: - return torch.stack(valid_segments, dim=0) - else: - return None - - def _get_label_distribution(self, x: torch.Tensor, sizes: torch.Tensor, - sample_rates: torch.Tensor) -> tp.Optional[torch.Tensor]: - """Get model output given provided input tensor. - - Args: - x (torch.Tensor): Input audio tensor of shape [B, C, T]. - sizes (torch.Tensor): Actual audio sample length, of shape [B]. - sample_rates (torch.Tensor): Actual audio sample rate, of shape [B]. - Returns: - probs (torch.Tensor, optional): Probabilities over labels, of shape [B, num_classes]. - """ - all_probs: tp.List[torch.Tensor] = [] - for i, wav in enumerate(x): - sample_rate = int(sample_rates[i].item()) - wav_len = int(sizes[i].item()) - wav = self._process_audio(wav, sample_rate, wav_len) - if wav is not None: - assert wav.dim() == 3, f"Unexpected number of dims for preprocessed wav: {wav.shape}" - wav = wav.mean(dim=1) - # PaSST is printing a lot of infos that we are not interested in - with open(os.devnull, 'w') as f, contextlib.redirect_stdout(f): - with torch.no_grad(), _patch_passt_stft(): - logits = self.model(wav.to(self.device)) - probs = torch.softmax(logits, dim=-1) - probs = probs.mean(dim=0) - all_probs.append(probs) - if len(all_probs) > 0: - return torch.stack(all_probs, dim=0) - else: - return None diff --git a/spaces/PurtiSharma/toxic_comments/app.py b/spaces/PurtiSharma/toxic_comments/app.py deleted file mode 100644 index d8f311cd43f7628c3f75b1ea7e4f589aad82fb89..0000000000000000000000000000000000000000 --- a/spaces/PurtiSharma/toxic_comments/app.py +++ /dev/null @@ -1,44 +0,0 @@ -import streamlit as st -import pickle - - -new_model = pickle.load(open('mytoxiccomentlgmodel.pickle', 'rb')) - - -def welcome(): - return "Welcome to my app" - - -def main(): - st.title("Detect Toxic Comments App") - st.write( - "This app employs NLP to assess whether online comments are toxic or not, providing users with valuable insights") - html_temp = """ -
-
- """
- st.markdown(html_temp, unsafe_allow_html=True)
-
- text = st.text_input("Enter your comments")
-
-
- if st.button("Predict"):
- pred_prob = new_model.predict([text])
- st.subheader("AI thinks that ...")
-
- if pred_prob == 0:
-
- st.success(
- f"Online comment is not toxic, you can trust it.",icon="✅")
- else:
- st.warning(
- f"Beware!! Online comment is toxic.", icon="⚠️")
-
- if st.button("About"):
-
- st.text("Built with Streamlit")
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/RMXK/RVC_HFF/diffq/base.py b/spaces/RMXK/RVC_HFF/diffq/base.py
deleted file mode 100644
index 9bd5276b51fbed3d4b898a45b93479ff19e62a7b..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/diffq/base.py
+++ /dev/null
@@ -1,262 +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.
-
-from dataclasses import dataclass
-from concurrent import futures
-from fnmatch import fnmatch
-from functools import partial
-import io
-import math
-from multiprocessing import cpu_count
-import typing as tp
-import zlib
-
-import torch
-
-
-class BaseQuantizer:
- @dataclass
- class _QuantizedParam:
- name: str
- param: torch.nn.Parameter
- module: torch.nn.Module
- # If a Parameter is used multiple times, `other` can be used
- # to share state between the different Quantizers
- other: tp.Optional[tp.Any]
-
- def __init__(self, model: torch.nn.Module, min_size: float = 0.01, float16: bool = False,
- exclude: tp.Optional[tp.List[str]] = [], detect_bound: bool = True):
- self.model = model
- self.min_size = min_size
- self.float16 = float16
- self.exclude = exclude
- self.detect_bound = detect_bound
- self._quantized = False
- self._pre_handle = self.model.register_forward_pre_hook(self._forward_pre_hook)
- self._post_handle = self.model.register_forward_hook(self._forward_hook)
-
- self._quantized_state = None
- self._qparams = []
- self._float16 = []
- self._others = []
- self._rnns = []
-
- self._saved = []
-
- self._find_params()
-
- def _find_params(self):
- min_params = self.min_size * 2**20 // 4
- previous = {}
- for module_name, module in self.model.named_modules():
- if isinstance(module, torch.nn.RNNBase):
- self._rnns.append(module)
- for name, param in list(module.named_parameters(recurse=False)):
- full_name = f"{module_name}.{name}"
- matched = False
- for pattern in self.exclude:
- if fnmatch(full_name, pattern) or fnmatch(name, pattern):
- matched = True
- break
-
- if param.numel() <= min_params or matched:
- if id(param) in previous:
- continue
- if self.detect_bound:
- previous[id(param)] = None
- if self.float16:
- self._float16.append(param)
- else:
- self._others.append(param)
- else:
- qparam = self._register_param(name, param, module, previous.get(id(param)))
- if self.detect_bound:
- previous[id(param)] = qparam
- self._qparams.append(qparam)
-
- def _register_param(self, name, param, module, other):
- return self.__class__._QuantizedParam(name, param, module, other)
-
- def _forward_pre_hook(self, module, input):
- if self.model.training:
- self._quantized_state = None
- if self._quantized:
- self.unquantize()
- if self._pre_forward_train():
- self._fix_rnns()
- else:
- self.quantize()
-
- def _forward_hook(self, module, input, output):
- if self.model.training:
- if self._post_forward_train():
- self._fix_rnns(flatten=False) # Hacky, next forward will flatten
-
- def quantize(self, save=True):
- """
- Immediately apply quantization to the model parameters.
- If `save` is True, save a copy of the unquantized parameters, that can be
- restored with `unquantize()`.
- """
- if self._quantized:
- return
- if save:
- self._saved = [qp.param.data.to('cpu', copy=True)
- for qp in self._qparams if qp.other is None]
- self.restore_quantized_state(self.get_quantized_state())
- self._quantized = True
- self._fix_rnns()
-
- def unquantize(self):
- """
- Revert a previous call to `quantize()`.
- """
- if not self._quantized:
- raise RuntimeError("Can only be called on a quantized model.")
- if not self._saved:
- raise RuntimeError("Nothing to restore.")
- for qparam in self._qparams:
- if qparam.other is None:
- qparam.param.data[:] = self._saved.pop(0)
- assert len(self._saved) == 0
- self._quantized = False
- self._fix_rnns()
-
- def _pre_forward_train(self) -> bool:
- """
- Called once before each forward for continuous quantization.
- Should return True if parameters were changed.
- """
- return False
-
- def _post_forward_train(self) -> bool:
- """
- Called once after each forward (to restore state for instance).
- Should return True if parameters were changed.
- """
- return False
-
- def _fix_rnns(self, flatten=True):
- """
- To be called after quantization happened to fix RNNs.
- """
- for rnn in self._rnns:
- rnn._flat_weights = [
- (lambda wn: getattr(rnn, wn) if hasattr(rnn, wn) else None)(wn)
- for wn in rnn._flat_weights_names]
- if flatten:
- rnn.flatten_parameters()
-
- def get_quantized_state(self):
- """
- Returns sufficient quantized information to rebuild the model state.
-
- ..Note::
- To achieve maximum compression, you should compress this with
- gzip or other, as quantized weights are not optimally coded!
- """
- if self._quantized_state is None:
- self._quantized_state = self._get_quantized_state()
- return self._quantized_state
-
- def _get_quantized_state(self):
- """
- Actual implementation for `get_quantized_state`.
- """
- float16_params = []
- for p in self._float16:
- q = p.data.half()
- float16_params.append(q)
-
- return {
- "quantized": [self._quantize_param(qparam) for qparam in self._qparams
- if qparam.other is None],
- "float16": float16_params,
- "others": [p.data.clone() for p in self._others],
- }
-
- def _quantize_param(self, qparam: _QuantizedParam) -> tp.Any:
- """
- To be overriden.
- """
- raise NotImplementedError()
-
- def _unquantize_param(self, qparam: _QuantizedParam, quantized: tp.Any) -> torch.Tensor:
- """
- To be overriden.
- """
- raise NotImplementedError()
-
- def restore_quantized_state(self, state) -> None:
- """
- Restore the state of the model from the quantized state.
- """
- for p, q in zip(self._float16, state["float16"]):
- p.data[:] = q.to(p)
-
- for p, q in zip(self._others, state["others"]):
- p.data[:] = q
-
- remaining = list(state["quantized"])
- for qparam in self._qparams:
- if qparam.other is not None:
- # Only unquantize first appearance of nn.Parameter.
- continue
- quantized = remaining.pop(0)
- qparam.param.data[:] = self._unquantize_param(qparam, quantized)
- self._fix_rnns()
-
- def detach(self) -> None:
- """
- Detach from the model, removes hooks and anything else.
- """
- self._pre_handle.remove()
- self._post_handle.remove()
-
- def model_size(self) -> torch.Tensor:
- """
- Returns an estimate of the quantized model size.
- """
- total = torch.tensor(0.)
- for p in self._float16:
- total += 16 * p.numel()
- for p in self._others:
- total += 32 * p.numel()
- return total / 2**20 / 8 # bits to MegaBytes
-
- def true_model_size(self) -> float:
- """
- Return the true quantized model size, in MB, without extra
- compression.
- """
- return self.model_size().item()
-
- def compressed_model_size(self, compress_level=-1, num_workers=8) -> float:
- """
- Return the compressed quantized model size, in MB.
-
- Args:
- compress_level (int): compression level used with zlib,
- see `zlib.compress` for details.
- num_workers (int): will split the final big byte representation in that
- many chunks processed in parallels.
- """
- out = io.BytesIO()
- torch.save(self.get_quantized_state(), out)
- ms = _parallel_compress_len(out.getvalue(), compress_level, num_workers)
- return ms / 2 ** 20
-
-
-def _compress_len(data, compress_level):
- return len(zlib.compress(data, level=compress_level))
-
-
-def _parallel_compress_len(data, compress_level, num_workers):
- num_workers = min(cpu_count(), num_workers)
- chunk_size = int(math.ceil(len(data) / num_workers))
- chunks = [data[offset:offset + chunk_size] for offset in range(0, len(data), chunk_size)]
- with futures.ProcessPoolExecutor(num_workers) as pool:
- return sum(pool.map(partial(_compress_len, compress_level=compress_level), chunks))
diff --git a/spaces/Realcat/image-matching-webui/third_party/DarkFeat/README.md b/spaces/Realcat/image-matching-webui/third_party/DarkFeat/README.md
deleted file mode 100644
index 2b94dce50a61b358d7f05c1942fde15cb2874b73..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/DarkFeat/README.md
+++ /dev/null
@@ -1,95 +0,0 @@
-# DarkFeat
-
-DarkFeat: Noise-Robust Feature Detector and Descriptor for Extremely Low-Light RAW Images (AAAI2023 Oral)
-
-Toxic Comment Detector
-
-
-### Installation
-
-```shell
-git clone git@github.com:THU-LYJ-Lab/DarkFeat.git
-cd DarkFeat
-pip install -r requirements.txt
-```
-
-[Pytorch](https://pytorch.org/) installation is machine dependent, please install the correct version for your machine.
-
-### Demo
-
-```shell
-python ./demo_darkfeat.py \
- --input /path/to/your/sequence \
- --output_dir ./output \
- --resize 960 640 \
- --model_path /path/to/pretrained/weights
-```
-
-Sample raw image sequences and pretrained weights can be downloaded from [here](https://drive.google.com/drive/folders/1zkUCsBVEmQcPZPhsEUymA5GIvAzi12hD?usp=sharing).
-
-Note that different pytorch and cuda versions may cause different model output results, and the output matches may differ from those shown in the gif. The results are tested in python 3.6, PyTorch 1.10.2 and cuda 10.2.
-
-### Evaluation
-
-1. Download [MID](https://github.com/Wenzhengchina/Matching-in-the-Dark) Dataset.
-
-2. Preprocessing the data in MID dataset, you can choose whether to enable histogram equalization or not:
-
- ```shell
- python raw_preprocess.py --dataset_dir /path/to/MID/dataset
- ```
-
-3. Extract the keypoints and descriptors, followed by a nearest neighborhood matching:
-
- ```shell
- python export_features.py \
- --model_path /path/to/pretrained/weights \
- --dataset_dir /path/to/MID/dataset
- ```
-
-4. Estimate the pose through corresponding keypoint pairs:
-
- ```shell
- python pose_estimation.py --dataset_dir /path/to/MID/dataset
- ```
-
-5. Finally collect the results of pose estimation errors:
-
- ```
- python read_error.py
- ```
-
-### Training from scratch
-
-We use [GL3D](https://github.com/lzx551402/GL3D) as our source training-use matching dataset. Please follow the [instructions](https://github.com/lzx551402/GL3D) to download and unzip all the data (including GL3D group and tourism group).
-
-Then using the preprocessing code provided by ASLFeat to generate matching informations:
-
-```shell
-git clone https://github.com/lzx551402/tfmatch
-# please edit the GL3D path in the shell script before executing.
-cd tfmatch
-sh train_aslfeat_base.sh
-```
-
-To launch the training, configure your training hyperparameters inside `./configs` and then run:
-
-```shell
-# stage1
-python run.py --stage 1 --config ./configs/config_stage1.yaml \
- --dataset_dir /path/to/your/GL3D/dataset \
- --job_name YOUR_JOB_NAME
-# stage2
-python run.py --stage 2 --config ./configs/config_stage1.yaml \
- --dataset_dir /path/to/your/GL3D/dataset \
- --job_name YOUR_JOB_NAME \
- --start_cnt 160000
-# stage3
-python run.py --stage 3 --config ./configs/config.yaml \
- --dataset_dir /path/to/your/GL3D/dataset \
- --job_name YOUR_JOB_NAME \
- --start_cnt 220000
-```
-
-### Acknowledgements
-
-This project could not be possible without the open-source works from [ASLFeat](https://github.com/lzx551402/ASLFeat), [R2D2](https://github.com/naver/r2d2), [MID](https://github.com/Wenzhengchina/Matching-in-the-Dark), [GL3D](https://github.com/lzx551402/GL3D), [SuperGlue](https://github.com/magicleap/SuperGluePretrainedNetwork). We sincerely thank them all.
\ No newline at end of file
diff --git a/spaces/Realcat/image-matching-webui/third_party/SOLD2/setup.py b/spaces/Realcat/image-matching-webui/third_party/SOLD2/setup.py
deleted file mode 100644
index e6c9cdcb47bdd73758cbd2d5b125dcb91306705f..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/SOLD2/setup.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from setuptools import setup
-
-
-setup(name="sold2", version="0.0", packages=["sold2"])
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/parallel/data_container.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/parallel/data_container.py
deleted file mode 100644
index cedb0d32a51a1f575a622b38de2cee3ab4757821..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/parallel/data_container.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import functools
-
-import torch
-
-
-def assert_tensor_type(func):
-
- @functools.wraps(func)
- def wrapper(*args, **kwargs):
- if not isinstance(args[0].data, torch.Tensor):
- raise AttributeError(
- f'{args[0].__class__.__name__} has no attribute '
- f'{func.__name__} for type {args[0].datatype}')
- return func(*args, **kwargs)
-
- return wrapper
-
-
-class DataContainer:
- """A container for any type of objects.
-
- Typically tensors will be stacked in the collate function and sliced along
- some dimension in the scatter function. This behavior has some limitations.
- 1. All tensors have to be the same size.
- 2. Types are limited (numpy array or Tensor).
-
- We design `DataContainer` and `MMDataParallel` to overcome these
- limitations. The behavior can be either of the following.
-
- - copy to GPU, pad all tensors to the same size and stack them
- - copy to GPU without stacking
- - leave the objects as is and pass it to the model
- - pad_dims specifies the number of last few dimensions to do padding
- """
-
- def __init__(self,
- data,
- stack=False,
- padding_value=0,
- cpu_only=False,
- pad_dims=2):
- self._data = data
- self._cpu_only = cpu_only
- self._stack = stack
- self._padding_value = padding_value
- assert pad_dims in [None, 1, 2, 3]
- self._pad_dims = pad_dims
-
- def __repr__(self):
- return f'{self.__class__.__name__}({repr(self.data)})'
-
- def __len__(self):
- return len(self._data)
-
- @property
- def data(self):
- return self._data
-
- @property
- def datatype(self):
- if isinstance(self.data, torch.Tensor):
- return self.data.type()
- else:
- return type(self.data)
-
- @property
- def cpu_only(self):
- return self._cpu_only
-
- @property
- def stack(self):
- return self._stack
-
- @property
- def padding_value(self):
- return self._padding_value
-
- @property
- def pad_dims(self):
- return self._pad_dims
-
- @assert_tensor_type
- def size(self, *args, **kwargs):
- return self.data.size(*args, **kwargs)
-
- @assert_tensor_type
- def dim(self):
- return self.data.dim()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/cascade_rpn_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/cascade_rpn_head.py
deleted file mode 100644
index e32ee461951e685fb44a461033293159e3439717..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/cascade_rpn_head.py
+++ /dev/null
@@ -1,784 +0,0 @@
-from __future__ import division
-import copy
-import warnings
-
-import torch
-import torch.nn as nn
-from mmcv import ConfigDict
-from mmcv.cnn import normal_init
-from mmcv.ops import DeformConv2d, batched_nms
-
-from mmdet.core import (RegionAssigner, build_assigner, build_sampler,
- images_to_levels, multi_apply)
-from ..builder import HEADS, build_head
-from .base_dense_head import BaseDenseHead
-from .rpn_head import RPNHead
-
-
-class AdaptiveConv(nn.Module):
- """AdaptiveConv used to adapt the sampling location with the anchors.
-
- Args:
- in_channels (int): Number of channels in the input image
- out_channels (int): Number of channels produced by the convolution
- kernel_size (int or tuple): Size of the conv kernel. Default: 3
- stride (int or tuple, optional): Stride of the convolution. Default: 1
- padding (int or tuple, optional): Zero-padding added to both sides of
- the input. Default: 1
- dilation (int or tuple, optional): Spacing between kernel elements.
- Default: 3
- groups (int, optional): Number of blocked connections from input
- channels to output channels. Default: 1
- bias (bool, optional): If set True, adds a learnable bias to the
- output. Default: False.
- type (str, optional): Type of adaptive conv, can be either 'offset'
- (arbitrary anchors) or 'dilation' (uniform anchor).
- Default: 'dilation'.
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1,
- dilation=3,
- groups=1,
- bias=False,
- type='dilation'):
- super(AdaptiveConv, self).__init__()
- assert type in ['offset', 'dilation']
- self.adapt_type = type
-
- assert kernel_size == 3, 'Adaptive conv only supports kernels 3'
- if self.adapt_type == 'offset':
- assert stride == 1 and padding == 1 and groups == 1, \
- 'Adaptive conv offset mode only supports padding: {1}, ' \
- f'stride: {1}, groups: {1}'
- self.conv = DeformConv2d(
- in_channels,
- out_channels,
- kernel_size,
- padding=padding,
- stride=stride,
- groups=groups,
- bias=bias)
- else:
- self.conv = nn.Conv2d(
- in_channels,
- out_channels,
- kernel_size,
- padding=dilation,
- dilation=dilation)
-
- def init_weights(self):
- """Init weights."""
- normal_init(self.conv, std=0.01)
-
- def forward(self, x, offset):
- """Forward function."""
- if self.adapt_type == 'offset':
- N, _, H, W = x.shape
- assert offset is not None
- assert H * W == offset.shape[1]
- # reshape [N, NA, 18] to (N, 18, H, W)
- offset = offset.permute(0, 2, 1).reshape(N, -1, H, W)
- offset = offset.contiguous()
- x = self.conv(x, offset)
- else:
- assert offset is None
- x = self.conv(x)
- return x
-
-
-@HEADS.register_module()
-class StageCascadeRPNHead(RPNHead):
- """Stage of CascadeRPNHead.
-
- Args:
- in_channels (int): Number of channels in the input feature map.
- anchor_generator (dict): anchor generator config.
- adapt_cfg (dict): adaptation config.
- bridged_feature (bool, optional): whether update rpn feature.
- Default: False.
- with_cls (bool, optional): wheather use classification branch.
- Default: True.
- sampling (bool, optional): wheather use sampling. Default: True.
- """
-
- def __init__(self,
- in_channels,
- anchor_generator=dict(
- type='AnchorGenerator',
- scales=[8],
- ratios=[1.0],
- strides=[4, 8, 16, 32, 64]),
- adapt_cfg=dict(type='dilation', dilation=3),
- bridged_feature=False,
- with_cls=True,
- sampling=True,
- **kwargs):
- self.with_cls = with_cls
- self.anchor_strides = anchor_generator['strides']
- self.anchor_scales = anchor_generator['scales']
- self.bridged_feature = bridged_feature
- self.adapt_cfg = adapt_cfg
- super(StageCascadeRPNHead, self).__init__(
- in_channels, anchor_generator=anchor_generator, **kwargs)
-
- # override sampling and sampler
- self.sampling = sampling
- if self.train_cfg:
- self.assigner = build_assigner(self.train_cfg.assigner)
- # use PseudoSampler when sampling is False
- if self.sampling and hasattr(self.train_cfg, 'sampler'):
- sampler_cfg = self.train_cfg.sampler
- else:
- sampler_cfg = dict(type='PseudoSampler')
- self.sampler = build_sampler(sampler_cfg, context=self)
-
- def _init_layers(self):
- """Init layers of a CascadeRPN stage."""
- self.rpn_conv = AdaptiveConv(self.in_channels, self.feat_channels,
- **self.adapt_cfg)
- if self.with_cls:
- self.rpn_cls = nn.Conv2d(self.feat_channels,
- self.num_anchors * self.cls_out_channels,
- 1)
- self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1)
- self.relu = nn.ReLU(inplace=True)
-
- def init_weights(self):
- """Init weights of a CascadeRPN stage."""
- self.rpn_conv.init_weights()
- normal_init(self.rpn_reg, std=0.01)
- if self.with_cls:
- normal_init(self.rpn_cls, std=0.01)
-
- def forward_single(self, x, offset):
- """Forward function of single scale."""
- bridged_x = x
- x = self.relu(self.rpn_conv(x, offset))
- if self.bridged_feature:
- bridged_x = x # update feature
- cls_score = self.rpn_cls(x) if self.with_cls else None
- bbox_pred = self.rpn_reg(x)
- return bridged_x, cls_score, bbox_pred
-
- def forward(self, feats, offset_list=None):
- """Forward function."""
- if offset_list is None:
- offset_list = [None for _ in range(len(feats))]
- return multi_apply(self.forward_single, feats, offset_list)
-
- def _region_targets_single(self,
- anchors,
- valid_flags,
- gt_bboxes,
- gt_bboxes_ignore,
- gt_labels,
- img_meta,
- featmap_sizes,
- label_channels=1):
- """Get anchor targets based on region for single level."""
- assign_result = self.assigner.assign(
- anchors,
- valid_flags,
- gt_bboxes,
- img_meta,
- featmap_sizes,
- self.anchor_scales[0],
- self.anchor_strides,
- gt_bboxes_ignore=gt_bboxes_ignore,
- gt_labels=None,
- allowed_border=self.train_cfg.allowed_border)
- flat_anchors = torch.cat(anchors)
- sampling_result = self.sampler.sample(assign_result, flat_anchors,
- gt_bboxes)
-
- num_anchors = flat_anchors.shape[0]
- bbox_targets = torch.zeros_like(flat_anchors)
- bbox_weights = torch.zeros_like(flat_anchors)
- labels = flat_anchors.new_zeros(num_anchors, dtype=torch.long)
- label_weights = flat_anchors.new_zeros(num_anchors, dtype=torch.float)
-
- pos_inds = sampling_result.pos_inds
- neg_inds = sampling_result.neg_inds
- if len(pos_inds) > 0:
- if not self.reg_decoded_bbox:
- pos_bbox_targets = self.bbox_coder.encode(
- sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
- else:
- pos_bbox_targets = sampling_result.pos_gt_bboxes
- bbox_targets[pos_inds, :] = pos_bbox_targets
- bbox_weights[pos_inds, :] = 1.0
- if gt_labels is None:
- labels[pos_inds] = 1
- else:
- labels[pos_inds] = gt_labels[
- sampling_result.pos_assigned_gt_inds]
- if self.train_cfg.pos_weight <= 0:
- label_weights[pos_inds] = 1.0
- else:
- label_weights[pos_inds] = self.train_cfg.pos_weight
- if len(neg_inds) > 0:
- label_weights[neg_inds] = 1.0
-
- return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
- neg_inds)
-
- def region_targets(self,
- anchor_list,
- valid_flag_list,
- gt_bboxes_list,
- img_metas,
- featmap_sizes,
- gt_bboxes_ignore_list=None,
- gt_labels_list=None,
- label_channels=1,
- unmap_outputs=True):
- """See :func:`StageCascadeRPNHead.get_targets`."""
- num_imgs = len(img_metas)
- assert len(anchor_list) == len(valid_flag_list) == num_imgs
-
- # anchor number of multi levels
- num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-
- # compute targets for each image
- if gt_bboxes_ignore_list is None:
- gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
- if gt_labels_list is None:
- gt_labels_list = [None for _ in range(num_imgs)]
- (all_labels, all_label_weights, all_bbox_targets, all_bbox_weights,
- pos_inds_list, neg_inds_list) = multi_apply(
- self._region_targets_single,
- anchor_list,
- valid_flag_list,
- gt_bboxes_list,
- gt_bboxes_ignore_list,
- gt_labels_list,
- img_metas,
- featmap_sizes=featmap_sizes,
- label_channels=label_channels)
- # no valid anchors
- if any([labels is None for labels in all_labels]):
- return None
- # sampled anchors of all images
- num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
- num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
- # split targets to a list w.r.t. multiple levels
- labels_list = images_to_levels(all_labels, num_level_anchors)
- label_weights_list = images_to_levels(all_label_weights,
- num_level_anchors)
- bbox_targets_list = images_to_levels(all_bbox_targets,
- num_level_anchors)
- bbox_weights_list = images_to_levels(all_bbox_weights,
- num_level_anchors)
- return (labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, num_total_pos, num_total_neg)
-
- def get_targets(self,
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- featmap_sizes,
- gt_bboxes_ignore=None,
- label_channels=1):
- """Compute regression and classification targets for anchors.
-
- Args:
- anchor_list (list[list]): Multi level anchors of each image.
- valid_flag_list (list[list]): Multi level valid flags of each
- image.
- gt_bboxes (list[Tensor]): Ground truth bboxes of each image.
- img_metas (list[dict]): Meta info of each image.
- featmap_sizes (list[Tensor]): Feature mapsize each level
- gt_bboxes_ignore (list[Tensor]): Ignore bboxes of each images
- label_channels (int): Channel of label.
-
- Returns:
- cls_reg_targets (tuple)
- """
- if isinstance(self.assigner, RegionAssigner):
- cls_reg_targets = self.region_targets(
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- featmap_sizes,
- gt_bboxes_ignore_list=gt_bboxes_ignore,
- label_channels=label_channels)
- else:
- cls_reg_targets = super(StageCascadeRPNHead, self).get_targets(
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore_list=gt_bboxes_ignore,
- label_channels=label_channels)
- return cls_reg_targets
-
- def anchor_offset(self, anchor_list, anchor_strides, featmap_sizes):
- """ Get offest for deformable conv based on anchor shape
- NOTE: currently support deformable kernel_size=3 and dilation=1
-
- Args:
- anchor_list (list[list[tensor])): [NI, NLVL, NA, 4] list of
- multi-level anchors
- anchor_strides (list[int]): anchor stride of each level
-
- Returns:
- offset_list (list[tensor]): [NLVL, NA, 2, 18]: offset of DeformConv
- kernel.
- """
-
- def _shape_offset(anchors, stride, ks=3, dilation=1):
- # currently support kernel_size=3 and dilation=1
- assert ks == 3 and dilation == 1
- pad = (ks - 1) // 2
- idx = torch.arange(-pad, pad + 1, dtype=dtype, device=device)
- yy, xx = torch.meshgrid(idx, idx) # return order matters
- xx = xx.reshape(-1)
- yy = yy.reshape(-1)
- w = (anchors[:, 2] - anchors[:, 0]) / stride
- h = (anchors[:, 3] - anchors[:, 1]) / stride
- w = w / (ks - 1) - dilation
- h = h / (ks - 1) - dilation
- offset_x = w[:, None] * xx # (NA, ks**2)
- offset_y = h[:, None] * yy # (NA, ks**2)
- return offset_x, offset_y
-
- def _ctr_offset(anchors, stride, featmap_size):
- feat_h, feat_w = featmap_size
- assert len(anchors) == feat_h * feat_w
-
- x = (anchors[:, 0] + anchors[:, 2]) * 0.5
- y = (anchors[:, 1] + anchors[:, 3]) * 0.5
- # compute centers on feature map
- x = x / stride
- y = y / stride
- # compute predefine centers
- xx = torch.arange(0, feat_w, device=anchors.device)
- yy = torch.arange(0, feat_h, device=anchors.device)
- yy, xx = torch.meshgrid(yy, xx)
- xx = xx.reshape(-1).type_as(x)
- yy = yy.reshape(-1).type_as(y)
-
- offset_x = x - xx # (NA, )
- offset_y = y - yy # (NA, )
- return offset_x, offset_y
-
- num_imgs = len(anchor_list)
- num_lvls = len(anchor_list[0])
- dtype = anchor_list[0][0].dtype
- device = anchor_list[0][0].device
- num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-
- offset_list = []
- for i in range(num_imgs):
- mlvl_offset = []
- for lvl in range(num_lvls):
- c_offset_x, c_offset_y = _ctr_offset(anchor_list[i][lvl],
- anchor_strides[lvl],
- featmap_sizes[lvl])
- s_offset_x, s_offset_y = _shape_offset(anchor_list[i][lvl],
- anchor_strides[lvl])
-
- # offset = ctr_offset + shape_offset
- offset_x = s_offset_x + c_offset_x[:, None]
- offset_y = s_offset_y + c_offset_y[:, None]
-
- # offset order (y0, x0, y1, x2, .., y8, x8, y9, x9)
- offset = torch.stack([offset_y, offset_x], dim=-1)
- offset = offset.reshape(offset.size(0), -1) # [NA, 2*ks**2]
- mlvl_offset.append(offset)
- offset_list.append(torch.cat(mlvl_offset)) # [totalNA, 2*ks**2]
- offset_list = images_to_levels(offset_list, num_level_anchors)
- return offset_list
-
- def loss_single(self, cls_score, bbox_pred, anchors, labels, label_weights,
- bbox_targets, bbox_weights, num_total_samples):
- """Loss function on single scale."""
- # classification loss
- if self.with_cls:
- labels = labels.reshape(-1)
- label_weights = label_weights.reshape(-1)
- cls_score = cls_score.permute(0, 2, 3,
- 1).reshape(-1, self.cls_out_channels)
- loss_cls = self.loss_cls(
- cls_score, labels, label_weights, avg_factor=num_total_samples)
- # regression loss
- bbox_targets = bbox_targets.reshape(-1, 4)
- bbox_weights = bbox_weights.reshape(-1, 4)
- bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
- if self.reg_decoded_bbox:
- # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
- # is applied directly on the decoded bounding boxes, it
- # decodes the already encoded coordinates to absolute format.
- anchors = anchors.reshape(-1, 4)
- bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
- loss_reg = self.loss_bbox(
- bbox_pred,
- bbox_targets,
- bbox_weights,
- avg_factor=num_total_samples)
- if self.with_cls:
- return loss_cls, loss_reg
- return None, loss_reg
-
- def loss(self,
- anchor_list,
- valid_flag_list,
- cls_scores,
- bbox_preds,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore=None):
- """Compute losses of the head.
-
- Args:
- anchor_list (list[list]): Multi level anchors of each image.
- cls_scores (list[Tensor]): Box scores for each scale level
- Has shape (N, num_anchors * num_classes, H, W)
- bbox_preds (list[Tensor]): Box energies / deltas for each scale
- level with shape (N, num_anchors * 4, H, W)
- 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.
- 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.
- """
- featmap_sizes = [featmap.size()[-2:] for featmap in bbox_preds]
- label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
- cls_reg_targets = self.get_targets(
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- featmap_sizes,
- gt_bboxes_ignore=gt_bboxes_ignore,
- label_channels=label_channels)
- if cls_reg_targets is None:
- return None
- (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
- num_total_pos, num_total_neg) = cls_reg_targets
- if self.sampling:
- num_total_samples = num_total_pos + num_total_neg
- else:
- # 200 is hard-coded average factor,
- # which follows guided anchoring.
- num_total_samples = sum([label.numel()
- for label in labels_list]) / 200.0
-
- # change per image, per level anchor_list to per_level, per_image
- mlvl_anchor_list = list(zip(*anchor_list))
- # concat mlvl_anchor_list
- mlvl_anchor_list = [
- torch.cat(anchors, dim=0) for anchors in mlvl_anchor_list
- ]
-
- losses = multi_apply(
- self.loss_single,
- cls_scores,
- bbox_preds,
- mlvl_anchor_list,
- labels_list,
- label_weights_list,
- bbox_targets_list,
- bbox_weights_list,
- num_total_samples=num_total_samples)
- if self.with_cls:
- return dict(loss_rpn_cls=losses[0], loss_rpn_reg=losses[1])
- return dict(loss_rpn_reg=losses[1])
-
- def get_bboxes(self,
- anchor_list,
- cls_scores,
- bbox_preds,
- img_metas,
- cfg,
- rescale=False):
- """Get proposal predict."""
- assert len(cls_scores) == len(bbox_preds)
- num_levels = len(cls_scores)
-
- 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[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']
- proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list,
- anchor_list[img_id], img_shape,
- scale_factor, cfg, rescale)
- result_list.append(proposals)
- return result_list
-
- def refine_bboxes(self, anchor_list, bbox_preds, img_metas):
- """Refine bboxes through stages."""
- num_levels = len(bbox_preds)
- new_anchor_list = []
- for img_id in range(len(img_metas)):
- mlvl_anchors = []
- for i in range(num_levels):
- bbox_pred = bbox_preds[i][img_id].detach()
- bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
- img_shape = img_metas[img_id]['img_shape']
- bboxes = self.bbox_coder.decode(anchor_list[img_id][i],
- bbox_pred, img_shape)
- mlvl_anchors.append(bboxes)
- new_anchor_list.append(mlvl_anchors)
- return new_anchor_list
-
- # TODO: temporary plan
- def _get_bboxes_single(self,
- cls_scores,
- bbox_preds,
- mlvl_anchors,
- img_shape,
- scale_factor,
- cfg,
- rescale=False):
- """Transform outputs for a single batch item into bbox predictions.
-
- Args:
- cls_scores (list[Tensor]): Box scores for each scale level
- Has shape (num_anchors * num_classes, H, W).
- bbox_preds (list[Tensor]): Box energies / deltas for each scale
- level with shape (num_anchors * 4, H, W).
- mlvl_anchors (list[Tensor]): Box reference for each scale level
- with shape (num_total_anchors, 4).
- img_shape (tuple[int]): Shape of the input image,
- (height, width, 3).
- scale_factor (ndarray): Scale factor of the image arange as
- (w_scale, h_scale, w_scale, h_scale).
- cfg (mmcv.Config): Test / postprocessing configuration,
- if None, test_cfg would be used.
- rescale (bool): If True, return boxes in original image space.
-
- Returns:
- Tensor: Labeled boxes have the shape of (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.
- """
- cfg = self.test_cfg if cfg is None else cfg
- cfg = copy.deepcopy(cfg)
- # bboxes from different level should be independent during NMS,
- # level_ids are used as labels for batched NMS to separate them
- level_ids = []
- mlvl_scores = []
- mlvl_bbox_preds = []
- mlvl_valid_anchors = []
- for idx in range(len(cls_scores)):
- rpn_cls_score = cls_scores[idx]
- rpn_bbox_pred = bbox_preds[idx]
- assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
- rpn_cls_score = rpn_cls_score.permute(1, 2, 0)
- if self.use_sigmoid_cls:
- rpn_cls_score = rpn_cls_score.reshape(-1)
- scores = rpn_cls_score.sigmoid()
- else:
- rpn_cls_score = rpn_cls_score.reshape(-1, 2)
- # We set FG labels to [0, num_class-1] and BG label to
- # num_class in RPN head since mmdet v2.5, which is unified to
- # be consistent with other head since mmdet v2.0. In mmdet v2.0
- # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head.
- scores = rpn_cls_score.softmax(dim=1)[:, 0]
- rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, 4)
- anchors = mlvl_anchors[idx]
- if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre:
- # sort is faster than topk
- # _, topk_inds = scores.topk(cfg.nms_pre)
- if torch.onnx.is_in_onnx_export():
- # sort op will be converted to TopK in onnx
- # and k<=3480 in TensorRT
- _, topk_inds = scores.topk(cfg.nms_pre)
- scores = scores[topk_inds]
- else:
- ranked_scores, rank_inds = scores.sort(descending=True)
- topk_inds = rank_inds[:cfg.nms_pre]
- scores = ranked_scores[:cfg.nms_pre]
- rpn_bbox_pred = rpn_bbox_pred[topk_inds, :]
- anchors = anchors[topk_inds, :]
- mlvl_scores.append(scores)
- mlvl_bbox_preds.append(rpn_bbox_pred)
- mlvl_valid_anchors.append(anchors)
- level_ids.append(
- scores.new_full((scores.size(0), ), idx, dtype=torch.long))
-
- scores = torch.cat(mlvl_scores)
- anchors = torch.cat(mlvl_valid_anchors)
- rpn_bbox_pred = torch.cat(mlvl_bbox_preds)
- proposals = self.bbox_coder.decode(
- anchors, rpn_bbox_pred, max_shape=img_shape)
- ids = torch.cat(level_ids)
-
- # Skip nonzero op while exporting to ONNX
- if cfg.min_bbox_size > 0 and (not torch.onnx.is_in_onnx_export()):
- w = proposals[:, 2] - proposals[:, 0]
- h = proposals[:, 3] - proposals[:, 1]
- valid_inds = torch.nonzero(
- (w >= cfg.min_bbox_size)
- & (h >= cfg.min_bbox_size),
- as_tuple=False).squeeze()
- if valid_inds.sum().item() != len(proposals):
- proposals = proposals[valid_inds, :]
- scores = scores[valid_inds]
- ids = ids[valid_inds]
-
- # deprecate arguments warning
- if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg:
- warnings.warn(
- 'In rpn_proposal or test_cfg, '
- 'nms_thr has been moved to a dict named nms as '
- 'iou_threshold, max_num has been renamed as max_per_img, '
- 'name of original arguments and the way to specify '
- 'iou_threshold of NMS will be deprecated.')
- if 'nms' not in cfg:
- cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr))
- if 'max_num' in cfg:
- if 'max_per_img' in cfg:
- assert cfg.max_num == cfg.max_per_img, f'You ' \
- f'set max_num and ' \
- f'max_per_img at the same time, but get {cfg.max_num} ' \
- f'and {cfg.max_per_img} respectively' \
- 'Please delete max_num which will be deprecated.'
- else:
- cfg.max_per_img = cfg.max_num
- if 'nms_thr' in cfg:
- assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set' \
- f' iou_threshold in nms and ' \
- f'nms_thr at the same time, but get' \
- f' {cfg.nms.iou_threshold} and {cfg.nms_thr}' \
- f' respectively. Please delete the nms_thr ' \
- f'which will be deprecated.'
-
- dets, keep = batched_nms(proposals, scores, ids, cfg.nms)
- return dets[:cfg.max_per_img]
-
-
-@HEADS.register_module()
-class CascadeRPNHead(BaseDenseHead):
- """The CascadeRPNHead will predict more accurate region proposals, which is
- required for two-stage detectors (such as Fast/Faster R-CNN). CascadeRPN
- consists of a sequence of RPNStage to progressively improve the accuracy of
- the detected proposals.
-
- More details can be found in ``https://arxiv.org/abs/1909.06720``.
-
- Args:
- num_stages (int): number of CascadeRPN stages.
- stages (list[dict]): list of configs to build the stages.
- train_cfg (list[dict]): list of configs at training time each stage.
- test_cfg (dict): config at testing time.
- """
-
- def __init__(self, num_stages, stages, train_cfg, test_cfg):
- super(CascadeRPNHead, self).__init__()
- assert num_stages == len(stages)
- self.num_stages = num_stages
- self.stages = nn.ModuleList()
- for i in range(len(stages)):
- train_cfg_i = train_cfg[i] if train_cfg is not None else None
- stages[i].update(train_cfg=train_cfg_i)
- stages[i].update(test_cfg=test_cfg)
- self.stages.append(build_head(stages[i]))
- self.train_cfg = train_cfg
- self.test_cfg = test_cfg
-
- def init_weights(self):
- """Init weight of CascadeRPN."""
- for i in range(self.num_stages):
- self.stages[i].init_weights()
-
- def loss(self):
- """loss() is implemented in StageCascadeRPNHead."""
- pass
-
- def get_bboxes(self):
- """get_bboxes() is implemented in StageCascadeRPNHead."""
- pass
-
- def forward_train(self,
- x,
- img_metas,
- gt_bboxes,
- gt_labels=None,
- gt_bboxes_ignore=None,
- proposal_cfg=None):
- """Forward train function."""
- assert gt_labels is None, 'RPN does not require gt_labels'
-
- featmap_sizes = [featmap.size()[-2:] for featmap in x]
- device = x[0].device
- anchor_list, valid_flag_list = self.stages[0].get_anchors(
- featmap_sizes, img_metas, device=device)
-
- losses = dict()
-
- for i in range(self.num_stages):
- stage = self.stages[i]
-
- if stage.adapt_cfg['type'] == 'offset':
- offset_list = stage.anchor_offset(anchor_list,
- stage.anchor_strides,
- featmap_sizes)
- else:
- offset_list = None
- x, cls_score, bbox_pred = stage(x, offset_list)
- rpn_loss_inputs = (anchor_list, valid_flag_list, cls_score,
- bbox_pred, gt_bboxes, img_metas)
- stage_loss = stage.loss(*rpn_loss_inputs)
- for name, value in stage_loss.items():
- losses['s{}.{}'.format(i, name)] = value
-
- # refine boxes
- if i < self.num_stages - 1:
- anchor_list = stage.refine_bboxes(anchor_list, bbox_pred,
- img_metas)
- if proposal_cfg is None:
- return losses
- else:
- proposal_list = self.stages[-1].get_bboxes(anchor_list, cls_score,
- bbox_pred, img_metas,
- self.test_cfg)
- return losses, proposal_list
-
- def simple_test_rpn(self, x, img_metas):
- """Simple forward test function."""
- featmap_sizes = [featmap.size()[-2:] for featmap in x]
- device = x[0].device
- anchor_list, _ = self.stages[0].get_anchors(
- featmap_sizes, img_metas, device=device)
-
- for i in range(self.num_stages):
- stage = self.stages[i]
- if stage.adapt_cfg['type'] == 'offset':
- offset_list = stage.anchor_offset(anchor_list,
- stage.anchor_strides,
- featmap_sizes)
- else:
- offset_list = None
- x, cls_score, bbox_pred = stage(x, offset_list)
- if i < self.num_stages - 1:
- anchor_list = stage.refine_bboxes(anchor_list, bbox_pred,
- img_metas)
-
- proposal_list = self.stages[-1].get_bboxes(anchor_list, cls_score,
- bbox_pred, img_metas,
- self.test_cfg)
- return proposal_list
-
- def aug_test_rpn(self, x, img_metas):
- """Augmented forward test function."""
- raise NotImplementedError
diff --git a/spaces/SRankChatGpt/Presentation-Assistant/presentation_assistant/env_set.py b/spaces/SRankChatGpt/Presentation-Assistant/presentation_assistant/env_set.py
deleted file mode 100644
index b252c5bee643a882673e6b64f215d69e48c20712..0000000000000000000000000000000000000000
--- a/spaces/SRankChatGpt/Presentation-Assistant/presentation_assistant/env_set.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import subprocess
-import os
-import sys
-
-sys.path.append("/home/user/app")
-
-def env_set():
- # 패키지가 이미 설치되어 있는지 확인
- installed_packages = subprocess.check_output("pip list", shell=True).decode()
-
- if "PyPDF2" not in installed_packages:
- subprocess.run(f"pip install PyPDF2", shell=True)
-
- if "openai" not in installed_packages:
- subprocess.run(f"pip install -q openai", shell=True)
-
- if "python-pptx" not in installed_packages:
- subprocess.run(f"pip install python-pptx", shell=True)
-
- # pandoc 실행 권한 설정
- if not os.access("./pandoc-2.14.2/bin/pandoc", os.X_OK):
- subprocess.run(f"chmod +x ./pandoc-2.14.2/bin/pandoc", shell=True)
-
- # 디렉토리 권한 설정
- if not os.access(".", os.W_OK):
- subprocess.run(f"chmod -R 777 .", shell=True)
-
- # subprocess.run(f"wget https://github.com/jgm/pandoc/releases/download/2.14.2/pandoc-2.14.2-linux-amd64.tar.gz", shell=True)
- # subprocess.run(f"tar -xvf pandoc-2.14.2-linux-amd64.tar.gz", shell=True)
- # subprocess.run(f"wget https://nodejs.org/dist/v17.0.1/node-v17.0.1-linux-x64.tar.xz", shell=True)
- # subprocess.run(f"tar -xf node-v17.0.1-linux-x64.tar.xz", shell=True)
- # subprocess.run(f"mv node-v17.0.1-linux-x64 node", shell=True)
- # subprocess.run(f"export PATH=/home/user/app/node/bin:$PATH", shell=True)
- # subprocess.run(f"/home/user/app/node/ install -g npm", shell=True)
- # subprocess.run(f"apt-get install -f", shell=True)
- # subprocess.run(f"apt-get install -y fonts-nanum", shell=True)
- # subprocess.run(f"fc-cache -fv", shell=True)
- # subprocess.run(f"rm ~/.cache/matplotlib -rf", shell=True)
-
diff --git a/spaces/SankarSrin/image-matting-app/README.md b/spaces/SankarSrin/image-matting-app/README.md
deleted file mode 100644
index 12be355bf4cd2dd457586f806e7088bf5ad5ec04..0000000000000000000000000000000000000000
--- a/spaces/SankarSrin/image-matting-app/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Image Matting App
-emoji: 🐨
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.11.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: vivym/image-matting-app
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SaulLu/bloom-generations-viewer/app.py b/spaces/SaulLu/bloom-generations-viewer/app.py
deleted file mode 100644
index d84c03a197600d5e4d4b59964e2c06b73498b33e..0000000000000000000000000000000000000000
--- a/spaces/SaulLu/bloom-generations-viewer/app.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import streamlit as st
-from datasets import load_dataset
-
-st.set_page_config(
- page_icon="🧊",
- layout="wide",
-)
-
-st.write(
- "This is an application for viewing different generations for the same prompt. The generations vary depending on the checkpoint used and also the parameters used for the generation."
-)
-
-HF_API_TOKEN = st.secrets["HF_API_TOKEN"]
-PROMPT_COLOR = "#CA437E"
-
-
-def safe_text(text):
- text = text.replace("\n", "") - return f"
{text}"
-
-
-def prompt_markup_format(text):
- return f'<*font color="black">{text}'
-
-
-def generation_markup_format(text):
- return f"{text}"
-
-
-ds = load_dataset("bigscience/bloom-generations", use_auth_token=HF_API_TOKEN)
-ds = ds["train"]
-
-
-col_1, col_2 = st.columns(2)
-with col_1:
- possible_checkpoint = ds.unique("checkpoint")
- st.markdown("Prompt
", unsafe_allow_html=True) - chosen_checkpoint = st.selectbox("Choose a checkpoint", possible_checkpoint + ["all"]) - if chosen_checkpoint == "all": - ds_ckp = ds - else: - ds_ckp = ds.filter( - lambda exs: [lang == chosen_checkpoint for lang in exs["checkpoint"]], batched=True - ) - possible_langs = ds.unique("lang") - chosen_lang = st.selectbox("Choose a lang", possible_langs + ["all"]) - if chosen_lang == "all": - ds_lang = ds_ckp - else: - ds_lang = ds_ckp.filter( - lambda exs: [lang == chosen_lang for lang in exs["lang"]], batched=True - ) - - possible_prompts = ds_lang.unique("prompt") - chosen_prompt = st.selectbox("Choose a prompt", possible_prompts) - st.markdown(safe_text(chosen_prompt), unsafe_allow_html=True) - -sub_ds = ds_lang.filter( - lambda exs: [prompt == chosen_prompt for prompt in exs["prompt"]], batched=True -) - - -with col_2: - st.markdown( - "Generation
", unsafe_allow_html=True - ) - index_sample = st.number_input( - "Index of the chosen generation", - min_value=0, - max_value=len(sub_ds) - 1, - value=0, - step=1, - ) - - sample = sub_ds[index_sample] - generation = sample["generation"] - stop_index_sample = st.number_input( - "Stop generation at character number", - min_value=0, - max_value=len(generation), - value=len(generation), - step=1, - ) - markdown_text = generation_markup_format(safe_text(generation[:stop_index_sample])) - st.markdown(markdown_text, unsafe_allow_html=True) - st.markdown( - "Generation configuration
", - unsafe_allow_html=True, - ) - config = { - key: value - for key, value in sample.items() - if key not in ["prompt", "generation"] - } - config diff --git a/spaces/ServerX/PorcoDiaz/tools/torchgate/__init__.py b/spaces/ServerX/PorcoDiaz/tools/torchgate/__init__.py deleted file mode 100644 index b4a12675828dceb6e6270f9439cdf98ea28ea96d..0000000000000000000000000000000000000000 --- a/spaces/ServerX/PorcoDiaz/tools/torchgate/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -TorchGating is a PyTorch-based implementation of Spectral Gating -================================================ -Author: Asaf Zorea - -Contents --------- -torchgate imports all the functions from PyTorch, and in addition provides: - TorchGating --- A PyTorch module that applies a spectral gate to an input signal - -""" -from .torchgate import TorchGate diff --git a/spaces/Shrey-Patel/Image-Searcher/app.py b/spaces/Shrey-Patel/Image-Searcher/app.py deleted file mode 100644 index fd746883ed7dbddc1fff9518b1ddd059eb1b720f..0000000000000000000000000000000000000000 --- a/spaces/Shrey-Patel/Image-Searcher/app.py +++ /dev/null @@ -1,152 +0,0 @@ -import requests -import time -from rembg import remove -from PIL import Image -import streamlit as st -from bs4 import BeautifulSoup - - -def google_api(word): - # Start timer - start_time = time.time() - - query = word - num = 5 - - url = f'https://www.googleapis.com/customsearch/v1?q={query}&num={num}&searchType=image&key={api_key}&cx={cx}' - - response = requests.get(url) - data = response.json() - links = [item['link'] for item in data['items']] - - - for i, link in enumerate(links): - - # Send a request to the image URL - response = requests.get(link) - - # Save the image to a file - image_path = f'image_{i}.png' - with open(image_path, 'wb') as f: - f.write(response.content) - - st.subheader(f"Image {i+1}:") - - try: - # Remove the background from the image - output_path = f'output_{i}.png' - with Image.open(image_path) as input: - # Open the downloaded image - # Resize the image to 1000x1000 - fixed_height = 1000 - height_percent = (fixed_height / float(input.size[1])) - width_size = int((float(input.size[0]) * float(height_percent))) - img = input.resize((width_size, fixed_height),Image.NEAREST) - - # Save the resized image to the same file - img.save(image_path) - output = remove(img) - output.save(output_path) - image_list=[] - image_list.append((Image.open(image_path), Image.open(output_path))) - cols = st.columns(len(image_list[0])) - for images in image_list: - for col, image in zip(cols, images): - col.image(image, use_column_width=True) - - - except: - st.error("An error occurred while processing the image.") - continue - # End timer and calculate elapsed time - elapsed_time = time.time() - start_time - st.success(f"Image processing completed in {elapsed_time:.2f} seconds.") - -def web_scrape(word): - # Start timer - start_time = time.time() - - word = word.replace(" ","+") - URL = "https://www.google.com/search?q="+word+"&sxsrf=ALeKk03xBalIZi7BAzyIRw8R4_KrIEYONg:1620885765119&source=lnms&tbm=isch&sa=X&ved=2ahUKEwjv44CC_sXwAhUZyjgGHSgdAQ8Q_AUoAXoECAEQAw&cshid=1620885828054361" - page = requests.get(URL) - - soup = BeautifulSoup(page.content, 'html.parser') - image_tags = soup.find_all('img') - - links = [] - i = 0 - for image_tag in image_tags: - if i == 0: - i = i + 1 - continue - if i >= 6 : - break - else: - links.append(image_tag['src']) - i = i + 1 - - # Create a list to hold the original and output images - - for i, link in enumerate(links): - - # Send a request to the image URL - response = requests.get(link) - - # Save the image to a file - image_path = f'image_{i}.png' - with open(image_path, 'wb') as f: - f.write(response.content) - - st.subheader(f"Image {i+1}:") - - try: - # Remove the background from the image - output_path = f'output_{i}.png' - with Image.open(image_path) as input: - # Open the downloaded image - # Resize the image - # fixed_height = 100 - # height_percent = (fixed_height / float(input.size[1])) - # print(height_percent) - # width_size = int((float(input.size[0]) * float(height_percent))) - # print(width_size) - # img = input.resize((width_size, fixed_height),Image.NEAREST) - # # Save the resized image to the same file - # img.save(image_path) - output = remove(input) - output.save(output_path) - image_list = [] - image_list.append((Image.open(image_path), Image.open(output_path))) - cols = st.columns(len(image_list[0])) - for images in image_list: - for col, image in zip(cols, images): - col.image(image, use_column_width=True) - - except: - st.error("An error occurred while processing the image.") - continue - - # End timer and calculate elapsed time - elapsed_time = time.time() - start_time - st.success(f"Image processing completed in {elapsed_time:.2f} seconds.") - - -st.title("Image searcher") -st.subheader("(with Background Remover)") - -api_key = 'AIzaSyAx4fTPrUVHPzN-BlDRBLkpd_BeIribnus' -cx = '6daf46cdb49a3770b' - -word = st.text_input("Enter search term for images:") -options = ["google-api", "web-scrapping"] -selected_options = st.selectbox("Select options", options) -if st.button("Submit"): - if selected_options=="google-api": - google_api(word) - elif selected_options=="web-scrapping": - web_scrape(word) - else: - st.error("CHOOSE OPTION!!!") - - - \ No newline at end of file diff --git a/spaces/Solis/Solis/llm_src/utils/decoder.py b/spaces/Solis/Solis/llm_src/utils/decoder.py deleted file mode 100644 index 99c11c71cf84f69baf6546ac7567b0d05ebe21b7..0000000000000000000000000000000000000000 --- a/spaces/Solis/Solis/llm_src/utils/decoder.py +++ /dev/null @@ -1,91 +0,0 @@ -import re -import openai -import time - - -class Decoder(): - def __init__(self, keys): - self.keys = keys - self.pointer = 0 - - def decode(self, args, input): - response = self.decoder_for_gpt3(args, input) - return response - - # Sentence Generator (Decoder) for GPT-3 ... - def decoder_for_gpt3(self, args, input): - - try_time = 0 - is_finished = False - - while try_time < 2 and not is_finished: - try_time += 1 - try: - # GPT-3 API allows each users execute the API within 60 times in a minute ... - # https://beta.openai.com/account/api-keys - openai.api_key = self.keys[self.pointer] - self.pointer = (self.pointer + 1) % len(self.keys) - engine = "code-davinci-002" - - response = openai.Completion.create( - engine=engine, - prompt=input, - max_tokens=args.max_length, - temperature=0, - stop=['--', '\n\n', '#'], - ) - is_finished = True - return response["choices"][0]["text"] - except Exception as e: - print(e, 'Retry.') - time.sleep(5) - - return None - -# ver 0.2 -def answer_cleansing(args, pred): - - print("pred_before : " + pred) - - preds = pred.split(args.direct_answer_trigger_for_fewshot) - answer_flag = True if len(preds) > 1 else False - pred = preds[-1] - - if args.dataset in ("aqua", "commonsensqa"): - pred = re.findall(r'A|B|C|D|E', pred) - elif args.dataset == "bigbench_date": - pred = re.findall(r'A|B|C|D|E|F', pred) - elif args.dataset in ("object_tracking"): - pred = re.findall(r'A|B|C', pred) - elif args.dataset in ("gsm8k", "addsub", "multiarith", "svamp", "singleeq"): - pred = pred.replace(",", "") - pred = [s for s in re.findall(r'-?\d+\.?\d*', pred)] - elif args.dataset in ("strategyqa", "coin_flip"): - pred = pred.lower() - pred = re.sub("\"|\'|\n|\.|\s|\:|\,"," ", pred) - pred = pred.split(" ") - pred = [i for i in pred if i in ("yes", "no")] - elif args.dataset == "last_letters": - pred = re.sub("\"|\'|\n|\.|\s","", pred) - pred = [pred] - else: - raise ValueError("dataset is not properly defined ...") - - # If there is no candidate in list, null is set. - if len(pred) == 0: - pred = "" - else: - if answer_flag: - # choose the first element in list ... - pred = pred[0] - else: - # choose the last element in list ... - pred = pred[-1] - - # (For arithmetic tasks) if a word ends with period, it will be omitted ... - if pred != "": - if pred[-1] == ".": - pred = pred[:-1] - - print("pred_after : " + pred) - return pred diff --git a/spaces/StarbucksCN/starbucks_doc/core/logger_factory.py b/spaces/StarbucksCN/starbucks_doc/core/logger_factory.py deleted file mode 100644 index cd7335d8224907c2c5d7e3a5a1668d1d2de99fa9..0000000000000000000000000000000000000000 --- a/spaces/StarbucksCN/starbucks_doc/core/logger_factory.py +++ /dev/null @@ -1,19 +0,0 @@ -import logging -from logging import handlers -from typing import Optional - - -def get_logger(name: str, file_name: Optional[str] = None) -> logging.Logger: - logger = logging.getLogger(name) - if file_name is None: - file_name = "app-default.log" - handler = handlers.TimedRotatingFileHandler( - filename=file_name, when="d", backupCount=21, encoding="UTF-8" - ) - formatter = logging.Formatter("[%(asctime)s][%(levelname)s][%(message)s]") - handler.setFormatter(formatter) - logger.addHandler(handler) - logger.setLevel(logging.INFO) - # Configure the logger as desired - # e.g., add handlers, set log levels, etc. - return logger diff --git a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/data/info_audio_dataset.py b/spaces/SuYuanS/AudioCraft_Plus/audiocraft/data/info_audio_dataset.py deleted file mode 100644 index 47ab4b1594faf1e9f1ce962fb980d80295b1f079..0000000000000000000000000000000000000000 --- a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/data/info_audio_dataset.py +++ /dev/null @@ -1,110 +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. -"""Base classes for the datasets that also provide non-audio metadata, -e.g. description, text transcription etc. -""" -from dataclasses import dataclass -import logging -import math -import re -import typing as tp - -import torch - -from .audio_dataset import AudioDataset, AudioMeta -from ..environment import AudioCraftEnvironment -from ..modules.conditioners import SegmentWithAttributes, ConditioningAttributes - - -logger = logging.getLogger(__name__) - - -def _clusterify_meta(meta: AudioMeta) -> AudioMeta: - """Monkey-patch meta to match cluster specificities.""" - meta.path = AudioCraftEnvironment.apply_dataset_mappers(meta.path) - if meta.info_path is not None: - meta.info_path.zip_path = AudioCraftEnvironment.apply_dataset_mappers(meta.info_path.zip_path) - return meta - - -def clusterify_all_meta(meta: tp.List[AudioMeta]) -> tp.List[AudioMeta]: - """Monkey-patch all meta to match cluster specificities.""" - return [_clusterify_meta(m) for m in meta] - - -@dataclass -class AudioInfo(SegmentWithAttributes): - """Dummy SegmentInfo with empty attributes. - - The InfoAudioDataset is expected to return metadata that inherits - from SegmentWithAttributes class and can return conditioning attributes. - - This basically guarantees all datasets will be compatible with current - solver that contain conditioners requiring this. - """ - audio_tokens: tp.Optional[torch.Tensor] = None # populated when using cached batch for training a LM. - - def to_condition_attributes(self) -> ConditioningAttributes: - return ConditioningAttributes() - - -class InfoAudioDataset(AudioDataset): - """AudioDataset that always returns metadata as SegmentWithAttributes along with the audio waveform. - - See `audiocraft.data.audio_dataset.AudioDataset` for initialization arguments. - """ - def __init__(self, meta: tp.List[AudioMeta], **kwargs): - super().__init__(clusterify_all_meta(meta), **kwargs) - - def __getitem__(self, index: int) -> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, SegmentWithAttributes]]: - if not self.return_info: - wav = super().__getitem__(index) - assert isinstance(wav, torch.Tensor) - return wav - wav, meta = super().__getitem__(index) - return wav, AudioInfo(**meta.to_dict()) - - -def get_keyword_or_keyword_list(value: tp.Optional[str]) -> tp.Union[tp.Optional[str], tp.Optional[tp.List[str]]]: - """Preprocess a single keyword or possible a list of keywords.""" - if isinstance(value, list): - return get_keyword_list(value) - else: - return get_keyword(value) - - -def get_string(value: tp.Optional[str]) -> tp.Optional[str]: - """Preprocess a single keyword.""" - if value is None or (not isinstance(value, str)) or len(value) == 0 or value == 'None': - return None - else: - return value.strip() - - -def get_keyword(value: tp.Optional[str]) -> tp.Optional[str]: - """Preprocess a single keyword.""" - if value is None or (not isinstance(value, str)) or len(value) == 0 or value == 'None': - return None - else: - return value.strip().lower() - - -def get_keyword_list(values: tp.Union[str, tp.List[str]]) -> tp.Optional[tp.List[str]]: - """Preprocess a list of keywords.""" - if isinstance(values, str): - values = [v.strip() for v in re.split(r'[,\s]', values)] - elif isinstance(values, float) and math.isnan(values): - values = [] - if not isinstance(values, list): - logger.debug(f"Unexpected keyword list {values}") - values = [str(values)] - - kws = [get_keyword(v) for v in values] - kw_list = [k for k in kws if k is not None] - if len(kw_list) == 0: - return None - else: - return kw_list diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/XVThumbImagePlugin.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/XVThumbImagePlugin.py deleted file mode 100644 index aa4a01f4e5aa4109ff447c42669d97a4fe43fd0c..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/XVThumbImagePlugin.py +++ /dev/null @@ -1,78 +0,0 @@ -# -# The Python Imaging Library. -# $Id$ -# -# XV Thumbnail file handler by Charles E. "Gene" Cash -# (gcash@magicnet.net) -# -# see xvcolor.c and xvbrowse.c in the sources to John Bradley's XV, -# available from ftp://ftp.cis.upenn.edu/pub/xv/ -# -# history: -# 98-08-15 cec created (b/w only) -# 98-12-09 cec added color palette -# 98-12-28 fl added to PIL (with only a few very minor modifications) -# -# To do: -# FIXME: make save work (this requires quantization support) -# - -from . import Image, ImageFile, ImagePalette -from ._binary import o8 - -_MAGIC = b"P7 332" - -# standard color palette for thumbnails (RGB332) -PALETTE = b"" -for r in range(8): - for g in range(8): - for b in range(4): - PALETTE = PALETTE + ( - o8((r * 255) // 7) + o8((g * 255) // 7) + o8((b * 255) // 3) - ) - - -def _accept(prefix): - return prefix[:6] == _MAGIC - - -## -# Image plugin for XV thumbnail images. - - -class XVThumbImageFile(ImageFile.ImageFile): - format = "XVThumb" - format_description = "XV thumbnail image" - - def _open(self): - # check magic - if not _accept(self.fp.read(6)): - msg = "not an XV thumbnail file" - raise SyntaxError(msg) - - # Skip to beginning of next line - self.fp.readline() - - # skip info comments - while True: - s = self.fp.readline() - if not s: - msg = "Unexpected EOF reading XV thumbnail file" - raise SyntaxError(msg) - if s[0] != 35: # ie. when not a comment: '#' - break - - # parse header line (already read) - s = s.strip().split() - - self.mode = "P" - self._size = int(s[0]), int(s[1]) - - self.palette = ImagePalette.raw("RGB", PALETTE) - - self.tile = [("raw", (0, 0) + self.size, self.fp.tell(), (self.mode, 0, 1))] - - -# -------------------------------------------------------------------- - -Image.register_open(XVThumbImageFile.format, XVThumbImageFile, _accept) diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/_core/_sockets.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/_core/_sockets.py deleted file mode 100644 index e6970bee2701e1d9391abb376e52a4d1a8ec7b68..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/_core/_sockets.py +++ /dev/null @@ -1,607 +0,0 @@ -from __future__ import annotations - -import socket -import ssl -import sys -from ipaddress import IPv6Address, ip_address -from os import PathLike, chmod -from pathlib import Path -from socket import AddressFamily, SocketKind -from typing import Awaitable, List, Tuple, cast, overload - -from .. import to_thread -from ..abc import ( - ConnectedUDPSocket, - IPAddressType, - IPSockAddrType, - SocketListener, - SocketStream, - UDPSocket, - UNIXSocketStream, -) -from ..streams.stapled import MultiListener -from ..streams.tls import TLSStream -from ._eventloop import get_asynclib -from ._resources import aclose_forcefully -from ._synchronization import Event -from ._tasks import create_task_group, move_on_after - -if sys.version_info >= (3, 8): - from typing import Literal -else: - from typing_extensions import Literal - -IPPROTO_IPV6 = getattr(socket, "IPPROTO_IPV6", 41) # https://bugs.python.org/issue29515 - -GetAddrInfoReturnType = List[ - Tuple[AddressFamily, SocketKind, int, str, Tuple[str, int]] -] -AnyIPAddressFamily = Literal[ - AddressFamily.AF_UNSPEC, AddressFamily.AF_INET, AddressFamily.AF_INET6 -] -IPAddressFamily = Literal[AddressFamily.AF_INET, AddressFamily.AF_INET6] - - -# tls_hostname given -@overload -async def connect_tcp( - remote_host: IPAddressType, - remote_port: int, - *, - local_host: IPAddressType | None = ..., - ssl_context: ssl.SSLContext | None = ..., - tls_standard_compatible: bool = ..., - tls_hostname: str, - happy_eyeballs_delay: float = ..., -) -> TLSStream: - ... - - -# ssl_context given -@overload -async def connect_tcp( - remote_host: IPAddressType, - remote_port: int, - *, - local_host: IPAddressType | None = ..., - ssl_context: ssl.SSLContext, - tls_standard_compatible: bool = ..., - tls_hostname: str | None = ..., - happy_eyeballs_delay: float = ..., -) -> TLSStream: - ... - - -# tls=True -@overload -async def connect_tcp( - remote_host: IPAddressType, - remote_port: int, - *, - local_host: IPAddressType | None = ..., - tls: Literal[True], - ssl_context: ssl.SSLContext | None = ..., - tls_standard_compatible: bool = ..., - tls_hostname: str | None = ..., - happy_eyeballs_delay: float = ..., -) -> TLSStream: - ... - - -# tls=False -@overload -async def connect_tcp( - remote_host: IPAddressType, - remote_port: int, - *, - local_host: IPAddressType | None = ..., - tls: Literal[False], - ssl_context: ssl.SSLContext | None = ..., - tls_standard_compatible: bool = ..., - tls_hostname: str | None = ..., - happy_eyeballs_delay: float = ..., -) -> SocketStream: - ... - - -# No TLS arguments -@overload -async def connect_tcp( - remote_host: IPAddressType, - remote_port: int, - *, - local_host: IPAddressType | None = ..., - happy_eyeballs_delay: float = ..., -) -> SocketStream: - ... - - -async def connect_tcp( - remote_host: IPAddressType, - remote_port: int, - *, - local_host: IPAddressType | None = None, - tls: bool = False, - ssl_context: ssl.SSLContext | None = None, - tls_standard_compatible: bool = True, - tls_hostname: str | None = None, - happy_eyeballs_delay: float = 0.25, -) -> SocketStream | TLSStream: - """ - Connect to a host using the TCP protocol. - - This function implements the stateless version of the Happy Eyeballs algorithm (RFC - 6555). If ``remote_host`` is a host name that resolves to multiple IP addresses, - each one is tried until one connection attempt succeeds. If the first attempt does - not connected within 250 milliseconds, a second attempt is started using the next - address in the list, and so on. On IPv6 enabled systems, an IPv6 address (if - available) is tried first. - - When the connection has been established, a TLS handshake will be done if either - ``ssl_context`` or ``tls_hostname`` is not ``None``, or if ``tls`` is ``True``. - - :param remote_host: the IP address or host name to connect to - :param remote_port: port on the target host to connect to - :param local_host: the interface address or name to bind the socket to before connecting - :param tls: ``True`` to do a TLS handshake with the connected stream and return a - :class:`~anyio.streams.tls.TLSStream` instead - :param ssl_context: the SSL context object to use (if omitted, a default context is created) - :param tls_standard_compatible: If ``True``, performs the TLS shutdown handshake before closing - the stream and requires that the server does this as well. Otherwise, - :exc:`~ssl.SSLEOFError` may be raised during reads from the stream. - Some protocols, such as HTTP, require this option to be ``False``. - See :meth:`~ssl.SSLContext.wrap_socket` for details. - :param tls_hostname: host name to check the server certificate against (defaults to the value - of ``remote_host``) - :param happy_eyeballs_delay: delay (in seconds) before starting the next connection attempt - :return: a socket stream object if no TLS handshake was done, otherwise a TLS stream - :raises OSError: if the connection attempt fails - - """ - # Placed here due to https://github.com/python/mypy/issues/7057 - connected_stream: SocketStream | None = None - - async def try_connect(remote_host: str, event: Event) -> None: - nonlocal connected_stream - try: - stream = await asynclib.connect_tcp(remote_host, remote_port, local_address) - except OSError as exc: - oserrors.append(exc) - return - else: - if connected_stream is None: - connected_stream = stream - tg.cancel_scope.cancel() - else: - await stream.aclose() - finally: - event.set() - - asynclib = get_asynclib() - local_address: IPSockAddrType | None = None - family = socket.AF_UNSPEC - if local_host: - gai_res = await getaddrinfo(str(local_host), None) - family, *_, local_address = gai_res[0] - - target_host = str(remote_host) - try: - addr_obj = ip_address(remote_host) - except ValueError: - # getaddrinfo() will raise an exception if name resolution fails - gai_res = await getaddrinfo( - target_host, remote_port, family=family, type=socket.SOCK_STREAM - ) - - # Organize the list so that the first address is an IPv6 address (if available) and the - # second one is an IPv4 addresses. The rest can be in whatever order. - v6_found = v4_found = False - target_addrs: list[tuple[socket.AddressFamily, str]] = [] - for af, *rest, sa in gai_res: - if af == socket.AF_INET6 and not v6_found: - v6_found = True - target_addrs.insert(0, (af, sa[0])) - elif af == socket.AF_INET and not v4_found and v6_found: - v4_found = True - target_addrs.insert(1, (af, sa[0])) - else: - target_addrs.append((af, sa[0])) - else: - if isinstance(addr_obj, IPv6Address): - target_addrs = [(socket.AF_INET6, addr_obj.compressed)] - else: - target_addrs = [(socket.AF_INET, addr_obj.compressed)] - - oserrors: list[OSError] = [] - async with create_task_group() as tg: - for i, (af, addr) in enumerate(target_addrs): - event = Event() - tg.start_soon(try_connect, addr, event) - with move_on_after(happy_eyeballs_delay): - await event.wait() - - if connected_stream is None: - cause = oserrors[0] if len(oserrors) == 1 else asynclib.ExceptionGroup(oserrors) - raise OSError("All connection attempts failed") from cause - - if tls or tls_hostname or ssl_context: - try: - return await TLSStream.wrap( - connected_stream, - server_side=False, - hostname=tls_hostname or str(remote_host), - ssl_context=ssl_context, - standard_compatible=tls_standard_compatible, - ) - except BaseException: - await aclose_forcefully(connected_stream) - raise - - return connected_stream - - -async def connect_unix(path: str | PathLike[str]) -> UNIXSocketStream: - """ - Connect to the given UNIX socket. - - Not available on Windows. - - :param path: path to the socket - :return: a socket stream object - - """ - path = str(Path(path)) - return await get_asynclib().connect_unix(path) - - -async def create_tcp_listener( - *, - local_host: IPAddressType | None = None, - local_port: int = 0, - family: AnyIPAddressFamily = socket.AddressFamily.AF_UNSPEC, - backlog: int = 65536, - reuse_port: bool = False, -) -> MultiListener[SocketStream]: - """ - Create a TCP socket listener. - - :param local_port: port number to listen on - :param local_host: IP address of the interface to listen on. If omitted, listen on - all IPv4 and IPv6 interfaces. To listen on all interfaces on a specific address - family, use ``0.0.0.0`` for IPv4 or ``::`` for IPv6. - :param family: address family (used if ``local_host`` was omitted) - :param backlog: maximum number of queued incoming connections (up to a maximum of - 2**16, or 65536) - :param reuse_port: ``True`` to allow multiple sockets to bind to the same - address/port (not supported on Windows) - :return: a list of listener objects - - """ - asynclib = get_asynclib() - backlog = min(backlog, 65536) - local_host = str(local_host) if local_host is not None else None - gai_res = await getaddrinfo( - local_host, # type: ignore[arg-type] - local_port, - family=family, - type=socket.SocketKind.SOCK_STREAM if sys.platform == "win32" else 0, - flags=socket.AI_PASSIVE | socket.AI_ADDRCONFIG, - ) - listeners: list[SocketListener] = [] - try: - # The set() is here to work around a glibc bug: - # https://sourceware.org/bugzilla/show_bug.cgi?id=14969 - sockaddr: tuple[str, int] | tuple[str, int, int, int] - for fam, kind, *_, sockaddr in sorted(set(gai_res)): - # Workaround for an uvloop bug where we don't get the correct scope ID for - # IPv6 link-local addresses when passing type=socket.SOCK_STREAM to - # getaddrinfo(): https://github.com/MagicStack/uvloop/issues/539 - if sys.platform != "win32" and kind is not SocketKind.SOCK_STREAM: - continue - - raw_socket = socket.socket(fam) - raw_socket.setblocking(False) - - # For Windows, enable exclusive address use. For others, enable address reuse. - if sys.platform == "win32": - raw_socket.setsockopt(socket.SOL_SOCKET, socket.SO_EXCLUSIVEADDRUSE, 1) - else: - raw_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - - if reuse_port: - raw_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1) - - # If only IPv6 was requested, disable dual stack operation - if fam == socket.AF_INET6: - raw_socket.setsockopt(IPPROTO_IPV6, socket.IPV6_V6ONLY, 1) - - # Workaround for #554 - if "%" in sockaddr[0]: - addr, scope_id = sockaddr[0].split("%", 1) - sockaddr = (addr, sockaddr[1], 0, int(scope_id)) - - raw_socket.bind(sockaddr) - raw_socket.listen(backlog) - listener = asynclib.TCPSocketListener(raw_socket) - listeners.append(listener) - except BaseException: - for listener in listeners: - await listener.aclose() - - raise - - return MultiListener(listeners) - - -async def create_unix_listener( - path: str | PathLike[str], - *, - mode: int | None = None, - backlog: int = 65536, -) -> SocketListener: - """ - Create a UNIX socket listener. - - Not available on Windows. - - :param path: path of the socket - :param mode: permissions to set on the socket - :param backlog: maximum number of queued incoming connections (up to a maximum of 2**16, or - 65536) - :return: a listener object - - .. versionchanged:: 3.0 - If a socket already exists on the file system in the given path, it will be removed first. - - """ - path_str = str(path) - path = Path(path) - if path.is_socket(): - path.unlink() - - backlog = min(backlog, 65536) - raw_socket = socket.socket(socket.AF_UNIX) - raw_socket.setblocking(False) - try: - await to_thread.run_sync(raw_socket.bind, path_str, cancellable=True) - if mode is not None: - await to_thread.run_sync(chmod, path_str, mode, cancellable=True) - - raw_socket.listen(backlog) - return get_asynclib().UNIXSocketListener(raw_socket) - except BaseException: - raw_socket.close() - raise - - -async def create_udp_socket( - family: AnyIPAddressFamily = AddressFamily.AF_UNSPEC, - *, - local_host: IPAddressType | None = None, - local_port: int = 0, - reuse_port: bool = False, -) -> UDPSocket: - """ - Create a UDP socket. - - If ``local_port`` has been given, the socket will be bound to this port on the local - machine, making this socket suitable for providing UDP based services. - - :param family: address family (``AF_INET`` or ``AF_INET6``) – automatically determined from - ``local_host`` if omitted - :param local_host: IP address or host name of the local interface to bind to - :param local_port: local port to bind to - :param reuse_port: ``True`` to allow multiple sockets to bind to the same address/port - (not supported on Windows) - :return: a UDP socket - - """ - if family is AddressFamily.AF_UNSPEC and not local_host: - raise ValueError('Either "family" or "local_host" must be given') - - if local_host: - gai_res = await getaddrinfo( - str(local_host), - local_port, - family=family, - type=socket.SOCK_DGRAM, - flags=socket.AI_PASSIVE | socket.AI_ADDRCONFIG, - ) - family = cast(AnyIPAddressFamily, gai_res[0][0]) - local_address = gai_res[0][-1] - elif family is AddressFamily.AF_INET6: - local_address = ("::", 0) - else: - local_address = ("0.0.0.0", 0) - - return await get_asynclib().create_udp_socket( - family, local_address, None, reuse_port - ) - - -async def create_connected_udp_socket( - remote_host: IPAddressType, - remote_port: int, - *, - family: AnyIPAddressFamily = AddressFamily.AF_UNSPEC, - local_host: IPAddressType | None = None, - local_port: int = 0, - reuse_port: bool = False, -) -> ConnectedUDPSocket: - """ - Create a connected UDP socket. - - Connected UDP sockets can only communicate with the specified remote host/port, and any packets - sent from other sources are dropped. - - :param remote_host: remote host to set as the default target - :param remote_port: port on the remote host to set as the default target - :param family: address family (``AF_INET`` or ``AF_INET6``) – automatically determined from - ``local_host`` or ``remote_host`` if omitted - :param local_host: IP address or host name of the local interface to bind to - :param local_port: local port to bind to - :param reuse_port: ``True`` to allow multiple sockets to bind to the same address/port - (not supported on Windows) - :return: a connected UDP socket - - """ - local_address = None - if local_host: - gai_res = await getaddrinfo( - str(local_host), - local_port, - family=family, - type=socket.SOCK_DGRAM, - flags=socket.AI_PASSIVE | socket.AI_ADDRCONFIG, - ) - family = cast(AnyIPAddressFamily, gai_res[0][0]) - local_address = gai_res[0][-1] - - gai_res = await getaddrinfo( - str(remote_host), remote_port, family=family, type=socket.SOCK_DGRAM - ) - family = cast(AnyIPAddressFamily, gai_res[0][0]) - remote_address = gai_res[0][-1] - - return await get_asynclib().create_udp_socket( - family, local_address, remote_address, reuse_port - ) - - -async def getaddrinfo( - host: bytearray | bytes | str, - port: str | int | None, - *, - family: int | AddressFamily = 0, - type: int | SocketKind = 0, - proto: int = 0, - flags: int = 0, -) -> GetAddrInfoReturnType: - """ - Look up a numeric IP address given a host name. - - Internationalized domain names are translated according to the (non-transitional) IDNA 2008 - standard. - - .. note:: 4-tuple IPv6 socket addresses are automatically converted to 2-tuples of - (host, port), unlike what :func:`socket.getaddrinfo` does. - - :param host: host name - :param port: port number - :param family: socket family (`'AF_INET``, ...) - :param type: socket type (``SOCK_STREAM``, ...) - :param proto: protocol number - :param flags: flags to pass to upstream ``getaddrinfo()`` - :return: list of tuples containing (family, type, proto, canonname, sockaddr) - - .. seealso:: :func:`socket.getaddrinfo` - - """ - # Handle unicode hostnames - if isinstance(host, str): - try: - encoded_host = host.encode("ascii") - except UnicodeEncodeError: - import idna - - encoded_host = idna.encode(host, uts46=True) - else: - encoded_host = host - - gai_res = await get_asynclib().getaddrinfo( - encoded_host, port, family=family, type=type, proto=proto, flags=flags - ) - return [ - (family, type, proto, canonname, convert_ipv6_sockaddr(sockaddr)) - for family, type, proto, canonname, sockaddr in gai_res - ] - - -def getnameinfo(sockaddr: IPSockAddrType, flags: int = 0) -> Awaitable[tuple[str, str]]: - """ - Look up the host name of an IP address. - - :param sockaddr: socket address (e.g. (ipaddress, port) for IPv4) - :param flags: flags to pass to upstream ``getnameinfo()`` - :return: a tuple of (host name, service name) - - .. seealso:: :func:`socket.getnameinfo` - - """ - return get_asynclib().getnameinfo(sockaddr, flags) - - -def wait_socket_readable(sock: socket.socket) -> Awaitable[None]: - """ - Wait until the given socket has data to be read. - - This does **NOT** work on Windows when using the asyncio backend with a proactor event loop - (default on py3.8+). - - .. warning:: Only use this on raw sockets that have not been wrapped by any higher level - constructs like socket streams! - - :param sock: a socket object - :raises ~anyio.ClosedResourceError: if the socket was closed while waiting for the - socket to become readable - :raises ~anyio.BusyResourceError: if another task is already waiting for the socket - to become readable - - """ - return get_asynclib().wait_socket_readable(sock) - - -def wait_socket_writable(sock: socket.socket) -> Awaitable[None]: - """ - Wait until the given socket can be written to. - - This does **NOT** work on Windows when using the asyncio backend with a proactor event loop - (default on py3.8+). - - .. warning:: Only use this on raw sockets that have not been wrapped by any higher level - constructs like socket streams! - - :param sock: a socket object - :raises ~anyio.ClosedResourceError: if the socket was closed while waiting for the - socket to become writable - :raises ~anyio.BusyResourceError: if another task is already waiting for the socket - to become writable - - """ - return get_asynclib().wait_socket_writable(sock) - - -# -# Private API -# - - -def convert_ipv6_sockaddr( - sockaddr: tuple[str, int, int, int] | tuple[str, int] -) -> tuple[str, int]: - """ - Convert a 4-tuple IPv6 socket address to a 2-tuple (address, port) format. - - If the scope ID is nonzero, it is added to the address, separated with ``%``. - Otherwise the flow id and scope id are simply cut off from the tuple. - Any other kinds of socket addresses are returned as-is. - - :param sockaddr: the result of :meth:`~socket.socket.getsockname` - :return: the converted socket address - - """ - # This is more complicated than it should be because of MyPy - if isinstance(sockaddr, tuple) and len(sockaddr) == 4: - host, port, flowinfo, scope_id = cast(Tuple[str, int, int, int], sockaddr) - if scope_id: - # PyPy (as of v7.3.11) leaves the interface name in the result, so - # we discard it and only get the scope ID from the end - # (https://foss.heptapod.net/pypy/pypy/-/issues/3938) - host = host.split("%")[0] - - # Add scope_id to the address - return f"{host}%{scope_id}", port - else: - return host, port - else: - return cast(Tuple[str, int], sockaddr) diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/test/property/test_cross_version_persist.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/test/property/test_cross_version_persist.py deleted file mode 100644 index 2e92b0188f51e23ff97d6ac7a60257e6c637865f..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/test/property/test_cross_version_persist.py +++ /dev/null @@ -1,267 +0,0 @@ -from multiprocessing.connection import Connection -import sys -import os -import shutil -import subprocess -import tempfile -from types import ModuleType -from typing import Callable, Generator, List, Tuple -from hypothesis import given, settings -import hypothesis.strategies as st -import pytest -import json -from urllib import request -from chromadb.api import API -from chromadb.api.types import Documents, EmbeddingFunction, Embeddings -import chromadb.test.property.strategies as strategies -import chromadb.test.property.invariants as invariants -from packaging import version as packaging_version -import re -import multiprocessing -from chromadb import Client -from chromadb.config import Settings - -MINIMUM_VERSION = "0.3.20" -COLLECTION_NAME_LOWERCASE_VERSION = "0.3.21" -version_re = re.compile(r"^[0-9]+\.[0-9]+\.[0-9]+$") - - -def _patch_uppercase_coll_name( - collection: strategies.Collection, embeddings: strategies.RecordSet -) -> None: - """Old versions didn't handle uppercase characters in collection names""" - collection.name = collection.name.lower() - - -def _patch_empty_dict_metadata( - collection: strategies.Collection, embeddings: strategies.RecordSet -) -> None: - """Old versions do the wrong thing when metadata is a single empty dict""" - if embeddings["metadatas"] == {}: - embeddings["metadatas"] = None - - -version_patches: List[ - Tuple[str, Callable[[strategies.Collection, strategies.RecordSet], None]] -] = [ - ("0.3.21", _patch_uppercase_coll_name), - ("0.3.21", _patch_empty_dict_metadata), -] - - -def patch_for_version( - version: str, collection: strategies.Collection, embeddings: strategies.RecordSet -) -> None: - """Override aspects of the collection and embeddings, before testing, to account for - breaking changes in old versions.""" - - for patch_version, patch in version_patches: - if packaging_version.Version(version) <= packaging_version.Version( - patch_version - ): - patch(collection, embeddings) - - -def versions() -> List[str]: - """Returns the pinned minimum version and the latest version of chromadb.""" - url = "https://pypi.org/pypi/chromadb/json" - data = json.load(request.urlopen(request.Request(url))) - versions = list(data["releases"].keys()) - # Older versions on pypi contain "devXYZ" suffixes - versions = [v for v in versions if version_re.match(v)] - versions.sort(key=packaging_version.Version) - return [MINIMUM_VERSION, versions[-1]] - - -def configurations(versions: List[str]) -> List[Tuple[str, Settings]]: - return [ - ( - version, - Settings( - chroma_api_impl="local", - chroma_db_impl="duckdb+parquet", - persist_directory=tempfile.gettempdir() + "/tests/" + version + "/", - ), - ) - for version in versions - ] - - -test_old_versions = versions() -base_install_dir = tempfile.gettempdir() + "/persistence_test_chromadb_versions" - - -# This fixture is not shared with the rest of the tests because it is unique in how it -# installs the versions of chromadb -@pytest.fixture(scope="module", params=configurations(test_old_versions)) # type: ignore -def version_settings(request) -> Generator[Tuple[str, Settings], None, None]: - configuration = request.param - version = configuration[0] - install_version(version) - yield configuration - # Cleanup the installed version - path = get_path_to_version_install(version) - shutil.rmtree(path) - # Cleanup the persisted data - data_path = configuration[1].persist_directory - if os.path.exists(data_path): - shutil.rmtree(data_path) - - -def get_path_to_version_install(version: str) -> str: - return base_install_dir + "/" + version - - -def get_path_to_version_library(version: str) -> str: - return get_path_to_version_install(version) + "/chromadb/__init__.py" - - -def install_version(version: str) -> None: - # Check if already installed - version_library = get_path_to_version_library(version) - if os.path.exists(version_library): - return - path = get_path_to_version_install(version) - install(f"chromadb=={version}", path) - - -def install(pkg: str, path: str) -> int: - # -q -q to suppress pip output to ERROR level - # https://pip.pypa.io/en/stable/cli/pip/#quiet - print(f"Installing chromadb version {pkg} to {path}") - return subprocess.check_call( - [ - sys.executable, - "-m", - "pip", - "-q", - "-q", - "install", - pkg, - "--target={}".format(path), - ] - ) - - -def switch_to_version(version: str) -> ModuleType: - module_name = "chromadb" - # Remove old version from sys.modules, except test modules - old_modules = { - n: m - for n, m in sys.modules.items() - if n == module_name or (n.startswith(module_name + ".")) - } - for n in old_modules: - del sys.modules[n] - - # Load the target version and override the path to the installed version - # https://docs.python.org/3/library/importlib.html#importing-a-source-file-directly - sys.path.insert(0, get_path_to_version_install(version)) - import chromadb - - assert chromadb.__version__ == version - return chromadb - - -class not_implemented_ef(EmbeddingFunction): - def __call__(self, texts: Documents) -> Embeddings: - assert False, "Embedding function should not be called" - - -def persist_generated_data_with_old_version( - version: str, - settings: Settings, - collection_strategy: strategies.Collection, - embeddings_strategy: strategies.RecordSet, - conn: Connection, -) -> None: - try: - old_module = switch_to_version(version) - api: API = old_module.Client(settings) - api.reset() - coll = api.create_collection( - name=collection_strategy.name, - metadata=collection_strategy.metadata, - # In order to test old versions, we can't rely on the not_implemented function - embedding_function=not_implemented_ef(), - ) - coll.add(**embeddings_strategy) - # We can't use the invariants module here because it uses the current version - # Just use some basic checks for sanity and manual testing where you break the new - # version - - check_embeddings = invariants.wrap_all(embeddings_strategy) - # Check count - assert coll.count() == len(check_embeddings["embeddings"] or []) - # Check ids - result = coll.get() - actual_ids = result["ids"] - embedding_id_to_index = {id: i for i, id in enumerate(check_embeddings["ids"])} - actual_ids = sorted(actual_ids, key=lambda id: embedding_id_to_index[id]) - assert actual_ids == check_embeddings["ids"] - api.persist() - except Exception as e: - conn.send(e) - raise e - - -# Since we can't pickle the embedding function, we always generate record sets with embeddings -collection_st: st.SearchStrategy[strategies.Collection] = st.shared( - strategies.collections(with_hnsw_params=True, has_embeddings=True), key="coll" -) - - -@given( - collection_strategy=collection_st, - embeddings_strategy=strategies.recordsets(collection_st), -) -@pytest.mark.skipif( - sys.version_info.major < 3 - or (sys.version_info.major == 3 and sys.version_info.minor <= 7), - reason="The mininum supported versions of chroma do not work with python <= 3.7", -) -@settings(deadline=None) -def test_cycle_versions( - version_settings: Tuple[str, Settings], - collection_strategy: strategies.Collection, - embeddings_strategy: strategies.RecordSet, -) -> None: - # # Test backwards compatibility - # # For the current version, ensure that we can load a collection from - # # the previous versions - version, settings = version_settings - - patch_for_version(version, collection_strategy, embeddings_strategy) - - # Can't pickle a function, and we won't need them - collection_strategy.embedding_function = None - collection_strategy.known_metadata_keys = {} - - # Run the task in a separate process to avoid polluting the current process - # with the old version. Using spawn instead of fork to avoid sharing the - # current process memory which would cause the old version to be loaded - ctx = multiprocessing.get_context("spawn") - conn1, conn2 = multiprocessing.Pipe() - p = ctx.Process( - target=persist_generated_data_with_old_version, - args=(version, settings, collection_strategy, embeddings_strategy, conn2), - ) - p.start() - p.join() - - if conn1.poll(): - e = conn1.recv() - raise e - - # Switch to the current version (local working directory) and check the invariants - # are preserved for the collection - api = Client(settings) - coll = api.get_collection( - name=collection_strategy.name, - embedding_function=not_implemented_ef(), - ) - invariants.count(coll, embeddings_strategy) - invariants.metadatas_match(coll, embeddings_strategy) - invariants.documents_match(coll, embeddings_strategy) - invariants.ids_match(coll, embeddings_strategy) - invariants.ann_accuracy(coll, embeddings_strategy) diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/abstract_type.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/abstract_type.py deleted file mode 100644 index 3193116db087522df3f04c33b58f7269b03bf816..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/abstract_type.py +++ /dev/null @@ -1,25 +0,0 @@ -from abc import abstractmethod -from typing import Any, Type, TypeVar - -from pydantic import BaseConfig -from pydantic.fields import ModelField - -from docarray.base_doc.base_node import BaseNode - -T = TypeVar('T') - - -class AbstractType(BaseNode): - @classmethod - def __get_validators__(cls): - yield cls.validate - - @classmethod - @abstractmethod - def validate( - cls: Type[T], - value: Any, - field: 'ModelField', - config: 'BaseConfig', - ) -> T: - ... diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/data/datasets/__init__.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/data/datasets/__init__.py deleted file mode 100644 index a44bedc15e5f0e762fc4d77efd6f1b07c6ff77d0..0000000000000000000000000000000000000000 --- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/data/datasets/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -from .coco import load_coco_json, load_sem_seg, register_coco_instances, convert_to_coco_json -from .coco_panoptic import register_coco_panoptic, register_coco_panoptic_separated -from .lvis import load_lvis_json, register_lvis_instances, get_lvis_instances_meta -from .pascal_voc import load_voc_instances, register_pascal_voc -from . import builtin as _builtin # ensure the builtin datasets are registered - - -__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/spaces/Svngoku/GFPGAN/README.md b/spaces/Svngoku/GFPGAN/README.md deleted file mode 100644 index c751ab00856c213e8671167577ec8ae54c782808..0000000000000000000000000000000000000000 --- a/spaces/Svngoku/GFPGAN/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: GFPGAN -emoji: 😁 -colorFrom: yellow -colorTo: green -sdk: gradio -sdk_version: 3.1.7 -app_file: app.py -pinned: false -license: apache-2.0 -duplicated_from: Xintao/GFPGAN ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/TRI-ML/risk_biased_prediction/risk_biased/scene_dataset/scene.py b/spaces/TRI-ML/risk_biased_prediction/risk_biased/scene_dataset/scene.py deleted file mode 100644 index df911ce4c41fdf96d4e8cb5e5a1fb0190cc43784..0000000000000000000000000000000000000000 --- a/spaces/TRI-ML/risk_biased_prediction/risk_biased/scene_dataset/scene.py +++ /dev/null @@ -1,522 +0,0 @@ -from dataclasses import dataclass -import os -from typing import Union, List, Optional -import warnings -import copy - -from mmcv import Config -import numpy as np -import torch -from torch import Tensor -from torch.utils.data import Dataset - -from risk_biased.scene_dataset.pedestrian import RandomPedestrians -from risk_biased.utils.torch_utils import torch_linspace - - -@dataclass -class RandomSceneParams: - """Dataclass that defines all the listed parameters that are necessary for a RandomScene object - - Args: - batch_size: number of scenes in the batch - time_scene: time length of the scene in seconds - sample_times: list of times to get the positions - ego_ref_speed: constant reference speed of the ego vehicle in meters/seconds - ego_speed_init_low: lowest initial speed of the ego vehicle in meters/seconds - ego_speed_init_high: higest initial speed of the ego vehicle in meters/seconds - ego_acceleration_mean_low: lowest mean acceleration of the ego vehicle in m/s^2 - ego_acceleration_mean_high: highest mean acceleration of the ego vehicle in m/s^2 - ego_acceleration_std: std for acceleration of the ego vehicle in m/s^2 - ego_length: length of the ego vehicle in meters - ego_width: width of the ego vehicle in meters - dt: time step to use in the trajectory sequence - fast_speed: fast walking speed for the random pedestrian in meters/seconds - slow_speed: slow walking speed for the random pedestrian in meters/seconds - p_change_pace: probability that a slow (resp. fast) pedestrian walk at fast_speed (resp. slow_speed) at each time step - proportion_fast: proportion of the pedestrians that are mainly walking at fast_speed - perception_noise_std: standard deviation of the gaussian noise that is affecting the position observations - """ - - batch_size: int - time_scene: float - sample_times: list - ego_ref_speed: float - ego_speed_init_low: float - ego_speed_init_high: float - ego_acceleration_mean_low: float - ego_acceleration_mean_high: float - ego_acceleration_std: float - ego_length: float - ego_width: float - dt: float - fast_speed: float - slow_speed: float - p_change_pace: float - proportion_fast: float - perception_noise_std: float - - @staticmethod - def from_config(cfg: Config): - return RandomSceneParams( - batch_size=cfg.batch_size, - sample_times=cfg.sample_times, - time_scene=cfg.time_scene, - ego_ref_speed=cfg.ego_ref_speed, - ego_speed_init_low=cfg.ego_speed_init_low, - ego_speed_init_high=cfg.ego_speed_init_high, - ego_acceleration_mean_low=cfg.ego_acceleration_mean_low, - ego_acceleration_mean_high=cfg.ego_acceleration_mean_high, - ego_acceleration_std=cfg.ego_acceleration_std, - ego_length=cfg.ego_length, - ego_width=cfg.ego_width, - dt=cfg.dt, - fast_speed=cfg.fast_speed, - slow_speed=cfg.slow_speed, - p_change_pace=cfg.p_change_pace, - proportion_fast=cfg.proportion_fast, - perception_noise_std=cfg.perception_noise_std, - ) - - -class RandomScene: - """ - Batched scenes with one vehicle at constant velocity and one random pedestrian. Utility functions to draw the scene and compute risk factors (time to collision etc...) - - Args: - params: dataclass containing the necessary parameters - is_torch: set to True to produce Tensor batches and to False to produce numpy arrays - """ - - def __init__( - self, - params: RandomSceneParams, - is_torch: bool = False, - ) -> None: - - self._is_torch = is_torch - self._batch_size = params.batch_size - self._fast_speed = params.fast_speed - self._slow_speed = params.slow_speed - self._p_change_pace = params.p_change_pace - self._proportion_fast = params.proportion_fast - self.dt = params.dt - self.sample_times = params.sample_times - self.ego_ref_speed = params.ego_ref_speed - self._ego_speed_init_low = params.ego_speed_init_low - self._ego_speed_init_high = params.ego_speed_init_high - self._ego_acceleration_mean_low = params.ego_acceleration_mean_low - self._ego_acceleration_mean_high = params.ego_acceleration_mean_high - self._ego_acceleration_std = params.ego_acceleration_std - self.perception_noise_std = params.perception_noise_std - self.road_length = ( - params.ego_ref_speed + params.fast_speed - ) * params.time_scene - self.time_scene = params.time_scene - self.lane_width = 3 - self.sidewalks_width = 1.5 - self.road_width = 2 * self.lane_width + 2 * self.sidewalks_width - self.bottom = -self.lane_width / 2 - self.sidewalks_width - self.top = 3 * self.lane_width / 2 + self.sidewalks_width - self.ego_width = 1.75 - self.ego_length = 4 - self.current_time = 0 - - if self._is_torch: - pedestrians_x = ( - torch.rand(params.batch_size, 1) - * (self.road_length - self.ego_length / 2) - + self.ego_length / 2 - ) - pedestrians_y = ( - torch.rand(params.batch_size, 1) * (self.top - self.bottom) - + self.bottom - ) - self._pedestrians_positions = torch.stack( - (pedestrians_x, pedestrians_y), -1 - ) - else: - pedestrians_x = np.random.uniform( - low=self.ego_length / 2, - high=self.road_length, - size=(params.batch_size, 1), - ) - pedestrians_y = np.random.uniform( - low=self.bottom, high=self.top, size=(params.batch_size, 1) - ) - self._pedestrians_positions = np.stack((pedestrians_x, pedestrians_y), -1) - - self.pedestrians = RandomPedestrians( - batch_size=self._batch_size, - dt=self.dt, - fast_speed=self._fast_speed, - slow_speed=self._slow_speed, - p_change_pace=self._p_change_pace, - proportion_fast=self._proportion_fast, - is_torch=self._is_torch, - ) - self._set_pedestrians() - - @property - def pedestrians_positions(self): - # relative_positions = self._pedestrians_positions/[[(self.road_length - self.ego_length / 2), (self.top - self.bottom)]] - [[self.ego_length / 2, self.bottom]] - return self._pedestrians_positions - - def set_pedestrians_states( - self, - relative_pedestrians_positions: Union[torch.Tensor, np.ndarray], - pedestrians_angles: Optional[Union[torch.Tensor, np.ndarray]] = None, - ): - """Force pedestrian initial states - - Args: - relative_pedestrians_positions: Relative positions in the scene as percentage distance from left to right and from bottom to top - pedestrians_angles: Pedestrian heading angles in radiants - """ - if self._is_torch: - assert isinstance(relative_pedestrians_positions, torch.Tensor) - else: - assert isinstance(relative_pedestrians_positions, np.ndarray) - - self._batch_size = relative_pedestrians_positions.shape[0] - if (0 > relative_pedestrians_positions).any() or ( - relative_pedestrians_positions > 1 - ).any(): - warnings.warn( - "Some of the given pedestrian initial positions are outside of the road range" - ) - center_y = (self.top - self.bottom) * relative_pedestrians_positions[ - :, :, 1 - ] + self.bottom - center_x = ( - self.road_length - self.ego_length / 2 - ) * relative_pedestrians_positions[:, :, 0] + self.ego_length / 2 - if self._is_torch: - pedestrians_positions = torch.stack([center_x, center_y], -1) - else: - pedestrians_positions = np.stack([center_x, center_y], -1) - - self.pedestrians = RandomPedestrians( - batch_size=self._batch_size, - dt=self.dt, - fast_speed=self._fast_speed, - slow_speed=self._slow_speed, - p_change_pace=self._p_change_pace, - proportion_fast=self._proportion_fast, - is_torch=self._is_torch, - ) - self._pedestrians_positions = pedestrians_positions - if pedestrians_angles is not None: - self.pedestrians.angle = pedestrians_angles - self._set_pedestrians() - - def _set_pedestrians(self): - self.pedestrians_trajectories = self.sample_pedestrians_trajectories( - self.sample_times - ) - - self.final_pedestrians_positions = self.pedestrians_trajectories[:, :, -1] - - def get_ego_ref_trajectory(self, time_sequence: list): - """ - Returns only one ego reference trajectory and not a batch because it is always the same. - Args: - time_sequence: the time points at which to get the positions - """ - out = np.array([[[[t * self.ego_ref_speed, 0] for t in time_sequence]]]) - if self._is_torch: - return torch.from_numpy(out.astype("float32")) - else: - return out - - def get_pedestrians_velocities(self): - """ - Returns the batch of mean pedestrian velocities between their positions and their final positions. - """ - return (self.final_pedestrians_positions - self._pedestrians_positions)[ - :, None - ] / self.time_scene - - def get_ego_ref_velocity(self): - """ - Returns the reference ego velocity. - """ - if self._is_torch: - return torch.from_numpy( - np.array([[[[self.ego_ref_speed, 0]]]], dtype="float32") - ) - else: - return np.array([[[[self.ego_ref_speed, 0]]]]) - - def get_ego_ref_position(self): - """ - Returns the current reference ego position (at set time self.current_time) - """ - if self._is_torch: - return torch.from_numpy( - np.array( - [[[[self.ego_ref_speed * self.current_time, 0]]]], dtype="float32" - ) - ) - else: - return np.array([[[[self.ego_ref_speed * self.current_time, 0]]]]) - - def set_current_time(self, time: float): - """ - Set the current time of the scene. - Args: - time : The current time to set. It should be between 0 and 1 - """ - assert 0 <= time <= self.time_scene - self.current_time = time - - def sample_ego_velocities(self, time_sequence: list): - """ - Get ego velocity trajectories following the ego's acceleration distribution and the initial - velocity distribution. - - Args: - time_sequence: a list of time points at which to sample the trajectory positions. - Returns: - batch of sequence of velocities of shape (batch_size, 1, len(time_sequence), 2) - """ - vel_traj = [] - # uniform sampling of acceleration_mean between self._ego_acceleration_mean_low and - # self._ego_acceleration_mean_high - acceleration_mean = np.random.rand(self._batch_size, 2) * np.array( - [ - self._ego_acceleration_mean_high - self._ego_acceleration_mean_low, - 0.0, - ] - ) + np.array([self._ego_acceleration_mean_low, 0.0]) - t_prev = 0 - # uniform sampling of initial velocity between self._ego_speed_init_low and - # self._ego_speed_init_high - vel_prev = np.random.rand(self._batch_size, 2) * np.array( - [self._ego_speed_init_high - self._ego_speed_init_low, 0.0] - ) + np.array([self._ego_speed_init_low, 0.0]) - for t in time_sequence: - # integrate accelerations once to get velocities - acceleration = acceleration_mean + np.random.randn( - self._batch_size, 2 - ) * np.array([self._ego_acceleration_std, 0.0]) - vel_prev = vel_prev + acceleration * (t - t_prev) - t_prev = t - vel_traj.append(vel_prev) - vel_traj = np.stack(vel_traj, 1) - if self._is_torch: - vel_traj = torch.from_numpy(vel_traj.astype("float32")) - return vel_traj[:, None] - - def sample_ego_trajectories(self, time_sequence: list): - """ - Get ego trajectories following the ego's acceleration distribution and the initial velocity - distribution. - - Args: - time_sequence: a list of time points at which to sample the trajectory positions. - Returns: - batch of sequence of positions of shape (batch_size, len(time_sequence), 2) - """ - vel_traj = self.sample_ego_velocities(time_sequence) - traj = [] - t_prev = 0 - pos_prev = np.array([[0, 0]], dtype="float32") - if self._is_torch: - pos_prev = torch.from_numpy(pos_prev) - for idx, t in enumerate(time_sequence): - # integrate velocities once to get positions - vel = vel_traj[:, :, idx, :] - pos_prev = pos_prev + vel * (t - t_prev) - t_prev = t - traj.append(pos_prev) - if self._is_torch: - return torch.stack(traj, -2) - else: - return np.stack(traj, -2) - - def sample_pedestrians_trajectories(self, time_sequence: list): - """ - Produce pedestrian trajectories following the pedestrian behavior distribution - (it is resampled, the final position will not match self.final_pedestrians_positions) - Args: - time_sequence: a list of time points at which to sample the trajectory positions. - Returns: - batch of sequence of positions of shape (batch_size, len(time_sequence), 2) - """ - traj = [] - t_prev = 0 - pos_prev = self.pedestrians_positions - for t in time_sequence: - pos_prev = ( - pos_prev - + self.pedestrians.get_final_position(t - t_prev) - - self.pedestrians.position - ) - t_prev = t - traj.append(pos_prev) - if self._is_torch: - traj = torch.stack(traj, 2) - return traj + torch.randn_like(traj) * self.perception_noise_std - else: - traj = np.stack(traj, 2) - return traj + np.random.randn(*traj.shape) * self.perception_noise_std - - def get_pedestrians_trajectories(self): - """ - Returns the batch of pedestrian trajectories sampled every dt. - """ - return self.pedestrians_trajectories - - def get_pedestrian_trajectory(self, ind: int, time_sequence: list = None): - """ - Returns one pedestrian trajectory of index ind sampled at times set in time_sequence. - Args: - ind: index of the pedestrian in the batch. - time_sequence: a list of time points at which to sample the trajectory positions. - Returns: - A pedestrian trajectory of shape (len(time_sequence), 2) - """ - len_traj = len(self.sample_times) - if self._is_torch: - ped_traj = torch_linspace( - self.pedestrians_positions[ind], - self.final_pedestrians_positions[ind], - len_traj, - ) - else: - ped_traj = np.linspace( - self.pedestrians_positions[ind], - self.final_pedestrians_positions[ind], - len_traj, - ) - - if time_sequence is not None: - n_steps = [int(t / self.dt) for t in time_sequence] - else: - n_steps = range(int(self.time_scene / self.dt)) - return ped_traj[n_steps] - - -class SceneDataset(Dataset): - """ - Dataset of scenes with one vehicle at constant velocity and one random pedestrian. - The scenes are randomly generated so the distribution can be sampled at each batch or pre-fetched. - - Args: - len: int number of scenes per epoch - params: dataclass defining all the necessary parameters - pre_fetch: set to True to fetch the whole dataset at initialization - """ - - def __init__( - self, - len: int, - params: RandomSceneParams, - pre_fetch: bool = True, - ) -> None: - super().__init__() - self._pre_fetch = pre_fetch - self._len = len - self._sample_times = params.sample_times - self.params = copy.deepcopy(params) - params.batch_size = len - if self._pre_fetch: - self.scene_set = RandomScene( - params, is_torch=True - ).sample_pedestrians_trajectories(self._sample_times) - - def __len__(self) -> int: - return self._len - - # This is a hack, get item only returns the index so that the collate_fn can handle making the batch without looping on RandomScene creation. - def __getitem__(self, index: int) -> Tensor: - return index - - def collate_fn(self, index_list: list) -> Tensor: - if self._pre_fetch: - return self.scene_set[torch.from_numpy(np.array(index_list))] - else: - self.params.batch_size = len(index_list) - return RandomScene( - self.params, - is_torch=True, - ).sample_pedestrians_trajectories(self._sample_times) - - -# Call this function to create a dataset as a .npy file that can be loaded as a numpy array with np.load(file_name.npy) -def save_dataset(file_path: str, size: int, config: Config): - """ - Save a dataset at file_path using the configuration. - Args: - file_path: Where to save the dataset - size: Number of samples to save - config: Configuration to use for the dataset generation - """ - dir_path = os.path.dirname(file_path) - config_path = os.path.join(dir_path, "config.py") - config = copy.deepcopy(config) - config.batch_size = size - params = RandomSceneParams.from_config(config) - scene = RandomScene( - params, - is_torch=False, - ) - data_pedestrian = scene.sample_pedestrians_trajectories(config.sample_times) - data_ego = scene.sample_ego_trajectories(config.sample_times) - data = np.stack([data_pedestrian, data_ego], 0) - np.save(file_path, data) - # Cannot use config.dump here because it is buggy and does not work if config was not loaded from a file. - with open(config_path, "w", encoding="utf-8") as f: - f.write(config.pretty_text) - - -def load_create_dataset( - config: Config, - base_dir=None, -) -> List: - """ - Load the dataset described by its config if it exists or create one. - - Args: - config: Configuration to use for the dataset - base_dir: Where to look for the dataset or to save it. - """ - - if base_dir is None: - base_dir = os.path.join( - os.path.dirname(os.path.realpath(__file__)), "..", "..", "data" - ) - found = False - dataset_out = [] - i = 0 - dir_path = os.path.join(base_dir, f"scene_dataset_{i:03d}") - while os.path.exists(dir_path): - config_path = os.path.join(dir_path, "config.py") - if os.path.exists(config_path): - config_check = Config.fromfile(config_path) - if config_check.dataset_parameters == config.dataset_parameters: - found = True - break - else: - warnings.warn( - f"Dataset directory {dir_path} exists but doesn't contain a config file. Cannot use it." - ) - i += 1 - dir_path = os.path.join(base_dir, f"scene_dataset_{i:03d}") - - if not found: - print(f"Dataset not found, creating a new one.") - os.makedirs(dir_path) - for dataset in config.datasets: - dataset_name = f"scene_dataset_{dataset}.npy" - dataset_path = os.path.join(dir_path, dataset_name) - save_dataset(dataset_path, config.datasets_sizes[dataset], config) - if found: - print(f"Loading existing dataset at {dir_path}.") - - for dataset in config.datasets: - dataset_path = os.path.join(dir_path, f"scene_dataset_{dataset}.npy") - dataset_out.append(torch.from_numpy(np.load(dataset_path).astype("float32"))) - - return dataset_out diff --git a/spaces/TabPFN/TabPFNEvaluation/TabPFN/datasets/utils.py b/spaces/TabPFN/TabPFNEvaluation/TabPFN/datasets/utils.py deleted file mode 100644 index d193dbe021c4daa3808fa0f8823a6decfe3f634e..0000000000000000000000000000000000000000 --- a/spaces/TabPFN/TabPFNEvaluation/TabPFN/datasets/utils.py +++ /dev/null @@ -1,8 +0,0 @@ -def normalize_data(eval_xs): - mean = eval_xs.mean(0) - std = eval_xs.std(0) + .000001 - eval_xs = (eval_xs - mean) / std - - return eval_xs - - diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pyparsing/testing.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pyparsing/testing.py deleted file mode 100644 index 6a254c1c5e2584dae80f58d38e9a48aae7ec1237..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pyparsing/testing.py +++ /dev/null @@ -1,331 +0,0 @@ -# testing.py - -from contextlib import contextmanager -import typing - -from .core import ( - ParserElement, - ParseException, - Keyword, - __diag__, - __compat__, -) - - -class pyparsing_test: - """ - namespace class for classes useful in writing unit tests - """ - - class reset_pyparsing_context: - """ - Context manager to be used when writing unit tests that modify pyparsing config values: - - packrat parsing - - bounded recursion parsing - - default whitespace characters. - - default keyword characters - - literal string auto-conversion class - - __diag__ settings - - Example:: - - with reset_pyparsing_context(): - # test that literals used to construct a grammar are automatically suppressed - ParserElement.inlineLiteralsUsing(Suppress) - - term = Word(alphas) | Word(nums) - group = Group('(' + term[...] + ')') - - # assert that the '()' characters are not included in the parsed tokens - self.assertParseAndCheckList(group, "(abc 123 def)", ['abc', '123', 'def']) - - # after exiting context manager, literals are converted to Literal expressions again - """ - - def __init__(self): - self._save_context = {} - - def save(self): - self._save_context["default_whitespace"] = ParserElement.DEFAULT_WHITE_CHARS - self._save_context["default_keyword_chars"] = Keyword.DEFAULT_KEYWORD_CHARS - - self._save_context[ - "literal_string_class" - ] = ParserElement._literalStringClass - - self._save_context["verbose_stacktrace"] = ParserElement.verbose_stacktrace - - self._save_context["packrat_enabled"] = ParserElement._packratEnabled - if ParserElement._packratEnabled: - self._save_context[ - "packrat_cache_size" - ] = ParserElement.packrat_cache.size - else: - self._save_context["packrat_cache_size"] = None - self._save_context["packrat_parse"] = ParserElement._parse - self._save_context[ - "recursion_enabled" - ] = ParserElement._left_recursion_enabled - - self._save_context["__diag__"] = { - name: getattr(__diag__, name) for name in __diag__._all_names - } - - self._save_context["__compat__"] = { - "collect_all_And_tokens": __compat__.collect_all_And_tokens - } - - return self - - def restore(self): - # reset pyparsing global state - if ( - ParserElement.DEFAULT_WHITE_CHARS - != self._save_context["default_whitespace"] - ): - ParserElement.set_default_whitespace_chars( - self._save_context["default_whitespace"] - ) - - ParserElement.verbose_stacktrace = self._save_context["verbose_stacktrace"] - - Keyword.DEFAULT_KEYWORD_CHARS = self._save_context["default_keyword_chars"] - ParserElement.inlineLiteralsUsing( - self._save_context["literal_string_class"] - ) - - for name, value in self._save_context["__diag__"].items(): - (__diag__.enable if value else __diag__.disable)(name) - - ParserElement._packratEnabled = False - if self._save_context["packrat_enabled"]: - ParserElement.enable_packrat(self._save_context["packrat_cache_size"]) - else: - ParserElement._parse = self._save_context["packrat_parse"] - ParserElement._left_recursion_enabled = self._save_context[ - "recursion_enabled" - ] - - __compat__.collect_all_And_tokens = self._save_context["__compat__"] - - return self - - def copy(self): - ret = type(self)() - ret._save_context.update(self._save_context) - return ret - - def __enter__(self): - return self.save() - - def __exit__(self, *args): - self.restore() - - class TestParseResultsAsserts: - """ - A mixin class to add parse results assertion methods to normal unittest.TestCase classes. - """ - - def assertParseResultsEquals( - self, result, expected_list=None, expected_dict=None, msg=None - ): - """ - Unit test assertion to compare a :class:`ParseResults` object with an optional ``expected_list``, - and compare any defined results names with an optional ``expected_dict``. - """ - if expected_list is not None: - self.assertEqual(expected_list, result.as_list(), msg=msg) - if expected_dict is not None: - self.assertEqual(expected_dict, result.as_dict(), msg=msg) - - def assertParseAndCheckList( - self, expr, test_string, expected_list, msg=None, verbose=True - ): - """ - Convenience wrapper assert to test a parser element and input string, and assert that - the resulting ``ParseResults.asList()`` is equal to the ``expected_list``. - """ - result = expr.parse_string(test_string, parse_all=True) - if verbose: - print(result.dump()) - else: - print(result.as_list()) - self.assertParseResultsEquals(result, expected_list=expected_list, msg=msg) - - def assertParseAndCheckDict( - self, expr, test_string, expected_dict, msg=None, verbose=True - ): - """ - Convenience wrapper assert to test a parser element and input string, and assert that - the resulting ``ParseResults.asDict()`` is equal to the ``expected_dict``. - """ - result = expr.parse_string(test_string, parseAll=True) - if verbose: - print(result.dump()) - else: - print(result.as_list()) - self.assertParseResultsEquals(result, expected_dict=expected_dict, msg=msg) - - def assertRunTestResults( - self, run_tests_report, expected_parse_results=None, msg=None - ): - """ - Unit test assertion to evaluate output of ``ParserElement.runTests()``. If a list of - list-dict tuples is given as the ``expected_parse_results`` argument, then these are zipped - with the report tuples returned by ``runTests`` and evaluated using ``assertParseResultsEquals``. - Finally, asserts that the overall ``runTests()`` success value is ``True``. - - :param run_tests_report: tuple(bool, [tuple(str, ParseResults or Exception)]) returned from runTests - :param expected_parse_results (optional): [tuple(str, list, dict, Exception)] - """ - run_test_success, run_test_results = run_tests_report - - if expected_parse_results is not None: - merged = [ - (*rpt, expected) - for rpt, expected in zip(run_test_results, expected_parse_results) - ] - for test_string, result, expected in merged: - # expected should be a tuple containing a list and/or a dict or an exception, - # and optional failure message string - # an empty tuple will skip any result validation - fail_msg = next( - (exp for exp in expected if isinstance(exp, str)), None - ) - expected_exception = next( - ( - exp - for exp in expected - if isinstance(exp, type) and issubclass(exp, Exception) - ), - None, - ) - if expected_exception is not None: - with self.assertRaises( - expected_exception=expected_exception, msg=fail_msg or msg - ): - if isinstance(result, Exception): - raise result - else: - expected_list = next( - (exp for exp in expected if isinstance(exp, list)), None - ) - expected_dict = next( - (exp for exp in expected if isinstance(exp, dict)), None - ) - if (expected_list, expected_dict) != (None, None): - self.assertParseResultsEquals( - result, - expected_list=expected_list, - expected_dict=expected_dict, - msg=fail_msg or msg, - ) - else: - # warning here maybe? - print(f"no validation for {test_string!r}") - - # do this last, in case some specific test results can be reported instead - self.assertTrue( - run_test_success, msg=msg if msg is not None else "failed runTests" - ) - - @contextmanager - def assertRaisesParseException(self, exc_type=ParseException, msg=None): - with self.assertRaises(exc_type, msg=msg): - yield - - @staticmethod - def with_line_numbers( - s: str, - start_line: typing.Optional[int] = None, - end_line: typing.Optional[int] = None, - expand_tabs: bool = True, - eol_mark: str = "|", - mark_spaces: typing.Optional[str] = None, - mark_control: typing.Optional[str] = None, - ) -> str: - """ - Helpful method for debugging a parser - prints a string with line and column numbers. - (Line and column numbers are 1-based.) - - :param s: tuple(bool, str - string to be printed with line and column numbers - :param start_line: int - (optional) starting line number in s to print (default=1) - :param end_line: int - (optional) ending line number in s to print (default=len(s)) - :param expand_tabs: bool - (optional) expand tabs to spaces, to match the pyparsing default - :param eol_mark: str - (optional) string to mark the end of lines, helps visualize trailing spaces (default="|") - :param mark_spaces: str - (optional) special character to display in place of spaces - :param mark_control: str - (optional) convert non-printing control characters to a placeholding - character; valid values: - - "unicode" - replaces control chars with Unicode symbols, such as "␍" and "␊" - - any single character string - replace control characters with given string - - None (default) - string is displayed as-is - - :return: str - input string with leading line numbers and column number headers - """ - if expand_tabs: - s = s.expandtabs() - if mark_control is not None: - mark_control = typing.cast(str, mark_control) - if mark_control == "unicode": - transtable_map = { - c: u for c, u in zip(range(0, 33), range(0x2400, 0x2433)) - } - transtable_map[127] = 0x2421 - tbl = str.maketrans(transtable_map) - eol_mark = "" - else: - ord_mark_control = ord(mark_control) - tbl = str.maketrans( - {c: ord_mark_control for c in list(range(0, 32)) + [127]} - ) - s = s.translate(tbl) - if mark_spaces is not None and mark_spaces != " ": - if mark_spaces == "unicode": - tbl = str.maketrans({9: 0x2409, 32: 0x2423}) - s = s.translate(tbl) - else: - s = s.replace(" ", mark_spaces) - if start_line is None: - start_line = 1 - if end_line is None: - end_line = len(s) - end_line = min(end_line, len(s)) - start_line = min(max(1, start_line), end_line) - - if mark_control != "unicode": - s_lines = s.splitlines()[start_line - 1 : end_line] - else: - s_lines = [line + "␊" for line in s.split("␊")[start_line - 1 : end_line]] - if not s_lines: - return "" - - lineno_width = len(str(end_line)) - max_line_len = max(len(line) for line in s_lines) - lead = " " * (lineno_width + 1) - if max_line_len >= 99: - header0 = ( - lead - + "".join( - f"{' ' * 99}{(i + 1) % 100}" - for i in range(max(max_line_len // 100, 1)) - ) - + "\n" - ) - else: - header0 = "" - header1 = ( - header0 - + lead - + "".join(f" {(i + 1) % 10}" for i in range(-(-max_line_len // 10))) - + "\n" - ) - header2 = lead + "1234567890" * (-(-max_line_len // 10)) + "\n" - return ( - header1 - + header2 - + "\n".join( - f"{i:{lineno_width}d}:{line}{eol_mark}" - for i, line in enumerate(s_lines, start=start_line) - ) - + "\n" - ) diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tests/modeling/test_box2box_transform.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tests/modeling/test_box2box_transform.py deleted file mode 100644 index fd3a7b79b6b7a3608ad7cb3918de020a5a600d2f..0000000000000000000000000000000000000000 --- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tests/modeling/test_box2box_transform.py +++ /dev/null @@ -1,94 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import logging -import unittest -import torch - -from detectron2.modeling.box_regression import ( - Box2BoxTransform, - Box2BoxTransformLinear, - Box2BoxTransformRotated, -) -from detectron2.utils.testing import random_boxes - -logger = logging.getLogger(__name__) - - -class TestBox2BoxTransform(unittest.TestCase): - def test_reconstruction(self): - weights = (5, 5, 10, 10) - b2b_tfm = Box2BoxTransform(weights=weights) - src_boxes = random_boxes(10) - dst_boxes = random_boxes(10) - - devices = [torch.device("cpu")] - if torch.cuda.is_available(): - devices.append(torch.device("cuda")) - for device in devices: - src_boxes = src_boxes.to(device=device) - dst_boxes = dst_boxes.to(device=device) - deltas = b2b_tfm.get_deltas(src_boxes, dst_boxes) - dst_boxes_reconstructed = b2b_tfm.apply_deltas(deltas, src_boxes) - self.assertTrue(torch.allclose(dst_boxes, dst_boxes_reconstructed)) - - def test_apply_deltas_tracing(self): - weights = (5, 5, 10, 10) - b2b_tfm = Box2BoxTransform(weights=weights) - - with torch.no_grad(): - func = torch.jit.trace(b2b_tfm.apply_deltas, (torch.randn(10, 20), torch.randn(10, 4))) - - o = func(torch.randn(10, 20), torch.randn(10, 4)) - self.assertEqual(o.shape, (10, 20)) - o = func(torch.randn(5, 20), torch.randn(5, 4)) - self.assertEqual(o.shape, (5, 20)) - - -def random_rotated_boxes(mean_box, std_length, std_angle, N): - return torch.cat( - [torch.rand(N, 4) * std_length, torch.rand(N, 1) * std_angle], dim=1 - ) + torch.tensor(mean_box, dtype=torch.float) - - -class TestBox2BoxTransformRotated(unittest.TestCase): - def test_reconstruction(self): - weights = (5, 5, 10, 10, 1) - b2b_transform = Box2BoxTransformRotated(weights=weights) - src_boxes = random_rotated_boxes([10, 10, 20, 20, -30], 5, 60.0, 10) - dst_boxes = random_rotated_boxes([10, 10, 20, 20, -30], 5, 60.0, 10) - - devices = [torch.device("cpu")] - if torch.cuda.is_available(): - devices.append(torch.device("cuda")) - for device in devices: - src_boxes = src_boxes.to(device=device) - dst_boxes = dst_boxes.to(device=device) - deltas = b2b_transform.get_deltas(src_boxes, dst_boxes) - dst_boxes_reconstructed = b2b_transform.apply_deltas(deltas, src_boxes) - assert torch.allclose(dst_boxes[:, :4], dst_boxes_reconstructed[:, :4], atol=1e-5) - # angle difference has to be normalized - assert torch.allclose( - (dst_boxes[:, 4] - dst_boxes_reconstructed[:, 4] + 180.0) % 360.0 - 180.0, - torch.zeros_like(dst_boxes[:, 4]), - atol=1e-4, - ) - - -class TestBox2BoxTransformLinear(unittest.TestCase): - def test_reconstruction(self): - b2b_tfm = Box2BoxTransformLinear() - src_boxes = random_boxes(10) - dst_boxes = torch.tensor([0, 0, 101, 101] * 10).reshape(10, 4).float() - - devices = [torch.device("cpu")] - if torch.cuda.is_available(): - devices.append(torch.device("cuda")) - for device in devices: - src_boxes = src_boxes.to(device=device) - dst_boxes = dst_boxes.to(device=device) - deltas = b2b_tfm.get_deltas(src_boxes, dst_boxes) - dst_boxes_reconstructed = b2b_tfm.apply_deltas(deltas, src_boxes) - self.assertTrue(torch.allclose(dst_boxes, dst_boxes_reconstructed, atol=1e-3)) - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/Th3BossC/TranscriptApi/Dockerfile b/spaces/Th3BossC/TranscriptApi/Dockerfile deleted file mode 100644 index 4969f847d9ef49f7c41e1362a8da52bc4ae256e2..0000000000000000000000000000000000000000 --- a/spaces/Th3BossC/TranscriptApi/Dockerfile +++ /dev/null @@ -1,23 +0,0 @@ -# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker -# you will also find guides on how best to write your Dockerfile - -FROM python:3.9 - -WORKDIR /code - -COPY ./requirements.txt /code/requirements.txt - -RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt -RUN apt update && apt install -y ffmpeg - -RUN useradd -m -u 1000 user -USER user -ENV HOME=/home/user \ - PATH=/home/user/.local/bin:$PATH - -WORKDIR $HOME/app - - -COPY --chown=user . $HOME/app - -CMD ["python", "app.py"] \ No newline at end of file diff --git a/spaces/VIPLab/Caption-Anything/caption_anything/utils/image_editing_utils.py b/spaces/VIPLab/Caption-Anything/caption_anything/utils/image_editing_utils.py deleted file mode 100644 index d5806447a94532f8ce981a92d02a2c181569ed47..0000000000000000000000000000000000000000 --- a/spaces/VIPLab/Caption-Anything/caption_anything/utils/image_editing_utils.py +++ /dev/null @@ -1,127 +0,0 @@ -from PIL import Image, ImageDraw, ImageFont -import copy -import numpy as np -import cv2 - - -def wrap_text(text, font, max_width): - lines = [] - words = text.split(' ') - current_line = '' - - for word in words: - if font.getsize(current_line + word)[0] <= max_width: - current_line += word + ' ' - else: - lines.append(current_line) - current_line = word + ' ' - - lines.append(current_line) - return lines - - -def create_bubble_frame(image, text, point, segmask, input_points=(), input_labels=(), - font_path='assets/times_with_simsun.ttf', font_size_ratio=0.033, point_size_ratio=0.01): - # Load the image - if input_points is None: - input_points = [] - if input_labels is None: - input_labels = [] - - if type(image) == np.ndarray: - image = Image.fromarray(image) - - image = copy.deepcopy(image) - width, height = image.size - - # Calculate max_text_width and font_size based on image dimensions and total number of characters - total_chars = len(text) - max_text_width = int(0.4 * width) - font_size = int(height * font_size_ratio) - point_size = max(int(height * point_size_ratio), 1) - - # Load the font - font = ImageFont.truetype(font_path, font_size) - - # Wrap the text to fit within the max_text_width - lines = wrap_text(text, font, max_text_width) - text_width = max([font.getsize(line)[0] for line in lines]) - _, text_height = font.getsize(lines[0]) - text_height = text_height * len(lines) - - # Define bubble frame dimensions - padding = 10 - bubble_width = text_width + 2 * padding - bubble_height = text_height + 2 * padding - - # Create a new image for the bubble frame - bubble = Image.new('RGBA', (bubble_width, bubble_height), (255, 248, 220, 0)) - - # Draw the bubble frame on the new image - draw = ImageDraw.Draw(bubble) - # draw.rectangle([(0, 0), (bubble_width - 1, bubble_height - 1)], fill=(255, 255, 255, 0), outline=(255, 255, 255, 0), width=2) - draw_rounded_rectangle(draw, (0, 0, bubble_width - 1, bubble_height - 1), point_size * 2, - fill=(255, 248, 220, 120), outline=None, width=2) - # Draw the wrapped text line by line - y_text = padding - for line in lines: - draw.text((padding, y_text), line, font=font, fill=(0, 0, 0, 255)) - y_text += font.getsize(line)[1] - - # Determine the point by the min area rect of mask - try: - ret, thresh = cv2.threshold(segmask, 127, 255, 0) - contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) - largest_contour = max(contours, key=cv2.contourArea) - min_area_rect = cv2.minAreaRect(largest_contour) - box = cv2.boxPoints(min_area_rect) - sorted_points = box[np.argsort(box[:, 0])] - right_most_points = sorted_points[-2:] - right_down_most_point = right_most_points[np.argsort(right_most_points[:, 1])][-1] - x, y = int(right_down_most_point[0]), int(right_down_most_point[1]) - except: - x, y = point - # Calculate the bubble frame position - if x + bubble_width > width: - x = width - bubble_width - if y + bubble_height > height: - y = height - bubble_height - - # Paste the bubble frame onto the image - image.paste(bubble, (x, y), bubble) - draw = ImageDraw.Draw(image) - colors = [(0, 191, 255, 255), (255, 106, 106, 255)] - for p, label in zip(input_points, input_labels): - point_x, point_y = p[0], p[1] - left = point_x - point_size - top = point_y - point_size - right = point_x + point_size - bottom = point_y + point_size - draw.ellipse((left, top, right, bottom), fill=colors[label]) - return image - - -def draw_rounded_rectangle(draw, xy, corner_radius, fill=None, outline=None, width=1): - x1, y1, x2, y2 = xy - - draw.rectangle( - (x1, y1 + corner_radius, x2, y2 - corner_radius), - fill=fill, - outline=outline, - width=width - ) - draw.rectangle( - (x1 + corner_radius, y1, x2 - corner_radius, y2), - fill=fill, - outline=outline, - width=width - ) - - draw.pieslice((x1, y1, x1 + corner_radius * 2, y1 + corner_radius * 2), 180, 270, fill=fill, outline=outline, - width=width) - draw.pieslice((x2 - corner_radius * 2, y1, x2, y1 + corner_radius * 2), 270, 360, fill=fill, outline=outline, - width=width) - draw.pieslice((x2 - corner_radius * 2, y2 - corner_radius * 2, x2, y2), 0, 90, fill=fill, outline=outline, - width=width) - draw.pieslice((x1, y2 - corner_radius * 2, x1 + corner_radius * 2, y2), 90, 180, fill=fill, outline=outline, - width=width) diff --git a/spaces/Xenova/doodle-dash/assets/index-c1585f3e.js b/spaces/Xenova/doodle-dash/assets/index-c1585f3e.js deleted file mode 100644 index b3375a2ef1298b93a71885e903191a10698ddea0..0000000000000000000000000000000000000000 --- a/spaces/Xenova/doodle-dash/assets/index-c1585f3e.js +++ /dev/null @@ -1,60 +0,0 @@ -(function(){const t=document.createElement("link").relList;if(t&&t.supports&&t.supports("modulepreload"))return;for(const i of document.querySelectorAll('link[rel="modulepreload"]'))r(i);new MutationObserver(i=>{for(const o of i)if(o.type==="childList")for(const s of o.addedNodes)s.tagName==="LINK"&&s.rel==="modulepreload"&&r(s)}).observe(document,{childList:!0,subtree:!0});function n(i){const o={};return i.integrity&&(o.integrity=i.integrity),i.referrerPolicy&&(o.referrerPolicy=i.referrerPolicy),i.crossOrigin==="use-credentials"?o.credentials="include":i.crossOrigin==="anonymous"?o.credentials="omit":o.credentials="same-origin",o}function r(i){if(i.ep)return;i.ep=!0;const o=n(i);fetch(i.href,o)}})();function am(e){return e&&e.__esModule&&Object.prototype.hasOwnProperty.call(e,"default")?e.default:e}var Lf={exports:{}},ko={},Df={exports:{}},O={};/** - * @license React - * react.production.min.js - * - * 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. - */var br=Symbol.for("react.element"),um=Symbol.for("react.portal"),cm=Symbol.for("react.fragment"),fm=Symbol.for("react.strict_mode"),dm=Symbol.for("react.profiler"),pm=Symbol.for("react.provider"),hm=Symbol.for("react.context"),mm=Symbol.for("react.forward_ref"),gm=Symbol.for("react.suspense"),ym=Symbol.for("react.memo"),vm=Symbol.for("react.lazy"),au=Symbol.iterator;function wm(e){return e===null||typeof e!="object"?null:(e=au&&e[au]||e["@@iterator"],typeof e=="function"?e:null)}var Rf={isMounted:function(){return!1},enqueueForceUpdate:function(){},enqueueReplaceState:function(){},enqueueSetState:function(){}},Mf=Object.assign,Vf={};function tr(e,t,n){this.props=e,this.context=t,this.refs=Vf,this.updater=n||Rf}tr.prototype.isReactComponent={};tr.prototype.setState=function(e,t){if(typeof e!="object"&&typeof e!="function"&&e!=null)throw Error("setState(...): takes an object of state variables to update or a function which returns an object of state variables.");this.updater.enqueueSetState(this,e,t,"setState")};tr.prototype.forceUpdate=function(e){this.updater.enqueueForceUpdate(this,e,"forceUpdate")};function Af(){}Af.prototype=tr.prototype;function Wl(e,t,n){this.props=e,this.context=t,this.refs=Vf,this.updater=n||Rf}var Gl=Wl.prototype=new Af;Gl.constructor=Wl;Mf(Gl,tr.prototype);Gl.isPureReactComponent=!0;var uu=Array.isArray,_f=Object.prototype.hasOwnProperty,Kl={current:null},Nf={key:!0,ref:!0,__self:!0,__source:!0};function jf(e,t,n){var r,i={},o=null,s=null;if(t!=null)for(r in t.ref!==void 0&&(s=t.ref),t.key!==void 0&&(o=""+t.key),t)_f.call(t,r)&&!Nf.hasOwnProperty(r)&&(i[r]=t[r]);var l=arguments.length-2;if(l===1)i.children=n;else if(1') - gr.Markdown('### 字符串token计算模块') - input_text = gr.Textbox(label="待计算Tokens的字符串", lines=2, placeholder='...') - - outPuttoken= gr.Number(label="token计算结果") - button_cal = gr.Button("开始计算") - button_cal.click(GetTokenforStr, - inputs=input_text, - outputs=outPuttoken) - with gr.Tab('微调数据集上传至OpenAI'): - gr.Markdown("注：Fine Tune至少需要10个case") - input_FineTuningFile=gr.components.File(label="gpt微调数据集",file_count='multiple') - input_APIKey=gr.Textbox(label="Openai_APIKEY",lines=2,placeholder='...') - output_FileTuningFile=gr.Json(label='上传文件状态') - - button_updata=gr.Button('开始上传') - button_updata.click(uploadData.upData_OpenAI, - inputs=[input_FineTuningFile,input_APIKey], - outputs=output_FileTuningFile) - - gr.Markdown("注：后续训练需要提供要微调的数据集的ID，如：file-ZnJlydArU8******NKzWaf8d") - - with gr.Tab('启动微调Task'): - input_DataId = gr.Textbox(label="FineTune DataId", lines=2, placeholder='...') - input_APIKey = gr.Textbox(label="Openai_APIKEY", lines=2, placeholder='...') - - output_CreateTaskjson = gr.Json(label='创建微调任务状态') - button_createTask = gr.Button('开始创建') - button_createTask.click(uploadData.createTask, - inputs=[input_DataId, input_APIKey], - outputs=output_CreateTaskjson) - - gr.Markdown("注：只有等上一轮任务执行完毕，你才能创建新的微调任务") - gr.Markdown("
") - gr.Markdown("### APIKey创建的微调任务状态查询'") - input_APIKey = gr.Textbox(label="Openai_APIKEY", lines=2, placeholder='...') - button_createTask = gr.Button('微调状态查询') - output_TaskSatejson = gr.Json(label='创建微调任务状态') - button_createTask.click(uploadData.GetFineTuningJobState, - inputs=[input_APIKey], - outputs=output_TaskSatejson) - with gr.Tab('Finetune Model测试'): - - textInput_Sys1 = gr.Textbox(label='SystemMessage', lines=2, placeholder='...') - textInput_Prompt1 = gr.Textbox(label='Prompt_ques', lines=2, placeholder='...') - - input_fine_tuned_model = gr.Textbox(label='fine_tuned_model', lines=2, placeholder='...') - textInput_APIKEY = gr.Textbox(label='OpenAI_APIKEY', lines=2, placeholder='...') - - - outPutText = gr.Textbox(label="运行结果",lines=2, placeholder='...') - button_ques = gr.Button("开始请求") - button_ques.click(uploadData.userFineTuneLLM, - inputs=[textInput_Sys1, textInput_Prompt1, input_fine_tuned_model, textInput_APIKEY], outputs=outPutText) - with gr.Tab('Finetune Model 效果评估'): - fintunetextInput_Sys = gr.Textbox(label='SystemMessage', lines=2, placeholder='...') - fintunetextInput_Prompt = gr.Textbox(label='Prompt', lines=2, placeholder='...') - fintuneinput_ExcelFile = gr.components.File(label="待批量请求的文件") - textInput_APIKEY = gr.Textbox(label='OpenAI_APIKEY', lines=2, placeholder='...') - fintunedrop = gr.components.Dropdown(label="GPTVersion", choices=GPTVersion, - value='gpt-3.5-turbo') - fintuneGPTVersion=gr.Textbox(label='FineTune_GPTVersion', lines=2, placeholder='...') - fintuneslider = gr.components.Slider(0, 1, label="Temperature", step=None, value=0.7) - fintunenum = gr.Number(label='请求的次数', value=5) - Checkbox=gr.CheckboxGroup(["GPT生成结果翻译成中文", "文本语法解析(暂不支持)"], label="GPT额外功能", info="为了提高审核速度你要增加什么？") - fintuneoutPutFile = gr.components.File(label="下载文件") - fintunebutton_ques = gr.Button("开始请求") - fintunebutton_ques.click(AIProdust_batch_estimate, - inputs=[fintunetextInput_Sys, fintunetextInput_Prompt, fintuneinput_ExcelFile, textInput_APIKEY, fintuneslider, - fintunedrop,fintuneGPTVersion, fintunenum,Checkbox], outputs=fintuneoutPutFile) - fintunebutton_quesStop = gr.Button("终止运行") - fintunebutton_quesStop.click(stopprodust,outputs=gr.Textbox(label='状态')) - - gr.Markdown("
") - gr.Markdown("### APIKey创建的微调任务状态查询'") - input_APIKey = gr.Textbox(label="Openai_APIKEY", lines=2, placeholder='...') - button_createTask = gr.Button('微调任务查询') - output_TaskSatejson = gr.Json(label='查询微调任务状态') - button_createTask.click(uploadData.GetFineTuningJobState, - inputs=[input_APIKey], - outputs=output_TaskSatejson) - demo.queue().launch(share=True) -if __name__=="__main__": - # ChatDemo() - # AIProdustDemo() #AIGC 批量生成内容并加在Excel文件 - # - AIProdust() - - - diff --git a/spaces/YouLiXiya/Mobile-SAM/segment_anything/segment_anything/utils/transforms.py b/spaces/YouLiXiya/Mobile-SAM/segment_anything/segment_anything/utils/transforms.py deleted file mode 100644 index 3ad346661f84b0647026e130a552c4b38b83e2ac..0000000000000000000000000000000000000000 --- a/spaces/YouLiXiya/Mobile-SAM/segment_anything/segment_anything/utils/transforms.py +++ /dev/null @@ -1,102 +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. - -import numpy as np -import torch -from torch.nn import functional as F -from torchvision.transforms.functional import resize, to_pil_image # type: ignore - -from copy import deepcopy -from typing import Tuple - - -class ResizeLongestSide: - """ - Resizes images to longest side 'target_length', as well as provides - methods for resizing coordinates and boxes. Provides methods for - transforming both numpy array and batched torch tensors. - """ - - def __init__(self, target_length: int) -> None: - self.target_length = target_length - - def apply_image(self, image: np.ndarray) -> np.ndarray: - """ - Expects a numpy array with shape HxWxC in uint8 format. - """ - target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length) - return np.array(resize(to_pil_image(image), target_size)) - - def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray: - """ - Expects a numpy array of length 2 in the final dimension. Requires the - original image size in (H, W) format. - """ - old_h, old_w = original_size - new_h, new_w = self.get_preprocess_shape( - original_size[0], original_size[1], self.target_length - ) - coords = deepcopy(coords).astype(float) - coords[..., 0] = coords[..., 0] * (new_w / old_w) - coords[..., 1] = coords[..., 1] * (new_h / old_h) - return coords - - def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray: - """ - Expects a numpy array shape Bx4. Requires the original image size - in (H, W) format. - """ - boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size) - return boxes.reshape(-1, 4) - - def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor: - """ - Expects batched images with shape BxCxHxW and float format. This - transformation may not exactly match apply_image. apply_image is - the transformation expected by the model. - """ - # Expects an image in BCHW format. May not exactly match apply_image. - target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length) - return F.interpolate( - image, target_size, mode="bilinear", align_corners=False, antialias=True - ) - - def apply_coords_torch( - self, coords: torch.Tensor, original_size: Tuple[int, ...] - ) -> torch.Tensor: - """ - Expects a torch tensor with length 2 in the last dimension. Requires the - original image size in (H, W) format. - """ - old_h, old_w = original_size - new_h, new_w = self.get_preprocess_shape( - original_size[0], original_size[1], self.target_length - ) - coords = deepcopy(coords).to(torch.float) - coords[..., 0] = coords[..., 0] * (new_w / old_w) - coords[..., 1] = coords[..., 1] * (new_h / old_h) - return coords - - def apply_boxes_torch( - self, boxes: torch.Tensor, original_size: Tuple[int, ...] - ) -> torch.Tensor: - """ - Expects a torch tensor with shape Bx4. Requires the original image - size in (H, W) format. - """ - boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size) - return boxes.reshape(-1, 4) - - @staticmethod - def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]: - """ - Compute the output size given input size and target long side length. - """ - scale = long_side_length * 1.0 / max(oldh, oldw) - newh, neww = oldh * scale, oldw * scale - neww = int(neww + 0.5) - newh = int(newh + 0.5) - return (newh, neww) diff --git a/spaces/Yudha515/Rvc-Models/audiocraft/models/__init__.py b/spaces/Yudha515/Rvc-Models/audiocraft/models/__init__.py deleted file mode 100644 index 92c7a48a200eba455044cd66e0d2c1efe6494f5c..0000000000000000000000000000000000000000 --- a/spaces/Yudha515/Rvc-Models/audiocraft/models/__init__.py +++ /dev/null @@ -1,10 +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 .musicgen import MusicGen -from .lm import LMModel -from .encodec import CompressionModel, EncodecModel diff --git a/spaces/Yuliang/ECON/apps/IFGeo.py b/spaces/Yuliang/ECON/apps/IFGeo.py deleted file mode 100644 index 45e892a655fd894d59ee925f32a62d378370bd49..0000000000000000000000000000000000000000 --- a/spaces/Yuliang/ECON/apps/IFGeo.py +++ /dev/null @@ -1,179 +0,0 @@ -# -*- coding: utf-8 -*- - -# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is -# holder of all proprietary rights on this computer program. -# You can only use this computer program if you have closed -# a license agreement with MPG or you get the right to use the computer -# program from someone who is authorized to grant you that right. -# Any use of the computer program without a valid license is prohibited and -# liable to prosecution. -# -# Copyright©2019 Max-Planck-Gesellschaft zur Förderung -# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute -# for Intelligent Systems. All rights reserved. -# -# Contact: ps-license@tuebingen.mpg.de - -import numpy as np -import pytorch_lightning as pl -import torch - -from lib.common.seg3d_lossless import Seg3dLossless -from lib.common.train_util import * - -torch.backends.cudnn.benchmark = True - - -class IFGeo(pl.LightningModule): - def __init__(self, cfg): - super(IFGeo, self).__init__() - - self.cfg = cfg - self.batch_size = self.cfg.batch_size - self.lr_G = self.cfg.lr_G - - self.use_sdf = cfg.sdf - self.mcube_res = cfg.mcube_res - self.clean_mesh_flag = cfg.clean_mesh - self.overfit = cfg.overfit - - if cfg.dataset.prior_type == "SMPL": - from lib.net.IFGeoNet import IFGeoNet - self.netG = IFGeoNet(cfg) - else: - from lib.net.IFGeoNet_nobody import IFGeoNet - self.netG = IFGeoNet(cfg) - - self.resolutions = ( - np.logspace( - start=5, - stop=np.log2(self.mcube_res), - base=2, - num=int(np.log2(self.mcube_res) - 4), - endpoint=True, - ) + 1.0 - ) - - self.resolutions = self.resolutions.astype(np.int16).tolist() - - self.reconEngine = Seg3dLossless( - query_func=query_func_IF, - b_min=[[-1.0, 1.0, -1.0]], - b_max=[[1.0, -1.0, 1.0]], - resolutions=self.resolutions, - align_corners=True, - balance_value=0.50, - visualize=False, - debug=False, - use_cuda_impl=False, - faster=True, - ) - - self.export_dir = None - self.result_eval = {} - - # Training related - def configure_optimizers(self): - - # set optimizer - weight_decay = self.cfg.weight_decay - momentum = self.cfg.momentum - - optim_params_G = [{"params": self.netG.parameters(), "lr": self.lr_G}] - - if self.cfg.optim == "Adadelta": - - optimizer_G = torch.optim.Adadelta( - optim_params_G, lr=self.lr_G, weight_decay=weight_decay - ) - - elif self.cfg.optim == "Adam": - - optimizer_G = torch.optim.Adam(optim_params_G, lr=self.lr_G, weight_decay=weight_decay) - - elif self.cfg.optim == "RMSprop": - - optimizer_G = torch.optim.RMSprop( - optim_params_G, - lr=self.lr_G, - weight_decay=weight_decay, - momentum=momentum, - ) - - else: - raise NotImplementedError - - # set scheduler - scheduler_G = torch.optim.lr_scheduler.MultiStepLR( - optimizer_G, milestones=self.cfg.schedule, gamma=self.cfg.gamma - ) - - return [optimizer_G], [scheduler_G] - - def training_step(self, batch, batch_idx): - - self.netG.train() - - preds_G = self.netG(batch) - error_G = self.netG.compute_loss(preds_G, batch["labels_geo"]) - - # metrics processing - metrics_log = { - "loss": error_G, - } - - self.log_dict( - metrics_log, prog_bar=True, logger=True, on_step=True, on_epoch=False, sync_dist=True - ) - - return metrics_log - - def training_epoch_end(self, outputs): - - # metrics processing - metrics_log = { - "train/avgloss": batch_mean(outputs, "loss"), - } - - self.log_dict( - metrics_log, - prog_bar=False, - logger=True, - on_step=False, - on_epoch=True, - rank_zero_only=True - ) - - def validation_step(self, batch, batch_idx): - - self.netG.eval() - self.netG.training = False - - preds_G = self.netG(batch) - error_G = self.netG.compute_loss(preds_G, batch["labels_geo"]) - - metrics_log = { - "val/loss": error_G, - } - - self.log_dict( - metrics_log, prog_bar=True, logger=False, on_step=True, on_epoch=False, sync_dist=True - ) - - return metrics_log - - def validation_epoch_end(self, outputs): - - # metrics processing - metrics_log = { - "val/avgloss": batch_mean(outputs, "val/loss"), - } - - self.log_dict( - metrics_log, - prog_bar=False, - logger=True, - on_step=False, - on_epoch=True, - rank_zero_only=True - ) diff --git a/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v4/README.md b/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v4/README.md deleted file mode 100644 index ceead5ecc32b7c51b3e880862cce147bccf621a6..0000000000000000000000000000000000000000 --- a/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v4/README.md +++ /dev/null @@ -1,15 +0,0 @@ ---- -title: Gradio_YOLOv5_Det_v4 -emoji: 🚀 -colorFrom: yellow -colorTo: indigo -sdk: gradio -sdk_version: 3.1.8b0 -app_file: app.py -pinned: true -license: gpl-3.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference - -🚀 Project homepage：https://gitee.com/CV_Lab/gradio_yolov5_det diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/bbox/coder/bucketing_bbox_coder.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/bbox/coder/bucketing_bbox_coder.py deleted file mode 100644 index 92d24b4519edece7a4af8f5cfa9af025b25f2dad..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/bbox/coder/bucketing_bbox_coder.py +++ /dev/null @@ -1,350 +0,0 @@ -import mmcv -import numpy as np -import torch -import torch.nn.functional as F - -from ..builder import BBOX_CODERS -from ..transforms import bbox_rescale -from .base_bbox_coder import BaseBBoxCoder - - -@BBOX_CODERS.register_module() -class BucketingBBoxCoder(BaseBBoxCoder): - """Bucketing BBox Coder for Side-Aware Boundary Localization (SABL). - - Boundary Localization with Bucketing and Bucketing Guided Rescoring - are implemented here. - - Please refer to https://arxiv.org/abs/1912.04260 for more details. - - Args: - num_buckets (int): Number of buckets. - scale_factor (int): Scale factor of proposals to generate buckets. - offset_topk (int): Topk buckets are used to generate - bucket fine regression targets. Defaults to 2. - offset_upperbound (float): Offset upperbound to generate - bucket fine regression targets. - To avoid too large offset displacements. Defaults to 1.0. - cls_ignore_neighbor (bool): Ignore second nearest bucket or Not. - Defaults to True. - clip_border (bool, optional): Whether clip the objects outside the - border of the image. Defaults to True. - """ - - def __init__(self, - num_buckets, - scale_factor, - offset_topk=2, - offset_upperbound=1.0, - cls_ignore_neighbor=True, - clip_border=True): - super(BucketingBBoxCoder, self).__init__() - self.num_buckets = num_buckets - self.scale_factor = scale_factor - self.offset_topk = offset_topk - self.offset_upperbound = offset_upperbound - self.cls_ignore_neighbor = cls_ignore_neighbor - self.clip_border = clip_border - - def encode(self, bboxes, gt_bboxes): - """Get bucketing estimation and fine regression targets during - training. - - Args: - bboxes (torch.Tensor): source boxes, e.g., object proposals. - gt_bboxes (torch.Tensor): target of the transformation, e.g., - ground truth boxes. - - Returns: - encoded_bboxes(tuple[Tensor]): bucketing estimation - and fine regression targets and weights - """ - - assert bboxes.size(0) == gt_bboxes.size(0) - assert bboxes.size(-1) == gt_bboxes.size(-1) == 4 - encoded_bboxes = bbox2bucket(bboxes, gt_bboxes, self.num_buckets, - self.scale_factor, self.offset_topk, - self.offset_upperbound, - self.cls_ignore_neighbor) - return encoded_bboxes - - def decode(self, bboxes, pred_bboxes, max_shape=None): - """Apply transformation `pred_bboxes` to `boxes`. - Args: - boxes (torch.Tensor): Basic boxes. - pred_bboxes (torch.Tensor): Predictions for bucketing estimation - and fine regression - max_shape (tuple[int], optional): Maximum shape of boxes. - Defaults to None. - - Returns: - torch.Tensor: Decoded boxes. - """ - assert len(pred_bboxes) == 2 - cls_preds, offset_preds = pred_bboxes - assert cls_preds.size(0) == bboxes.size(0) and offset_preds.size( - 0) == bboxes.size(0) - decoded_bboxes = bucket2bbox(bboxes, cls_preds, offset_preds, - self.num_buckets, self.scale_factor, - max_shape, self.clip_border) - - return decoded_bboxes - - -@mmcv.jit(coderize=True) -def generat_buckets(proposals, num_buckets, scale_factor=1.0): - """Generate buckets w.r.t bucket number and scale factor of proposals. - - Args: - proposals (Tensor): Shape (n, 4) - num_buckets (int): Number of buckets. - scale_factor (float): Scale factor to rescale proposals. - - Returns: - tuple[Tensor]: (bucket_w, bucket_h, l_buckets, r_buckets, - t_buckets, d_buckets) - - - bucket_w: Width of buckets on x-axis. Shape (n, ). - - bucket_h: Height of buckets on y-axis. Shape (n, ). - - l_buckets: Left buckets. Shape (n, ceil(side_num/2)). - - r_buckets: Right buckets. Shape (n, ceil(side_num/2)). - - t_buckets: Top buckets. Shape (n, ceil(side_num/2)). - - d_buckets: Down buckets. Shape (n, ceil(side_num/2)). - """ - proposals = bbox_rescale(proposals, scale_factor) - - # number of buckets in each side - side_num = int(np.ceil(num_buckets / 2.0)) - pw = proposals[..., 2] - proposals[..., 0] - ph = proposals[..., 3] - proposals[..., 1] - px1 = proposals[..., 0] - py1 = proposals[..., 1] - px2 = proposals[..., 2] - py2 = proposals[..., 3] - - bucket_w = pw / num_buckets - bucket_h = ph / num_buckets - - # left buckets - l_buckets = px1[:, None] + (0.5 + torch.arange( - 0, side_num).to(proposals).float())[None, :] * bucket_w[:, None] - # right buckets - r_buckets = px2[:, None] - (0.5 + torch.arange( - 0, side_num).to(proposals).float())[None, :] * bucket_w[:, None] - # top buckets - t_buckets = py1[:, None] + (0.5 + torch.arange( - 0, side_num).to(proposals).float())[None, :] * bucket_h[:, None] - # down buckets - d_buckets = py2[:, None] - (0.5 + torch.arange( - 0, side_num).to(proposals).float())[None, :] * bucket_h[:, None] - return bucket_w, bucket_h, l_buckets, r_buckets, t_buckets, d_buckets - - -@mmcv.jit(coderize=True) -def bbox2bucket(proposals, - gt, - num_buckets, - scale_factor, - offset_topk=2, - offset_upperbound=1.0, - cls_ignore_neighbor=True): - """Generate buckets estimation and fine regression targets. - - Args: - proposals (Tensor): Shape (n, 4) - gt (Tensor): Shape (n, 4) - num_buckets (int): Number of buckets. - scale_factor (float): Scale factor to rescale proposals. - offset_topk (int): Topk buckets are used to generate - bucket fine regression targets. Defaults to 2. - offset_upperbound (float): Offset allowance to generate - bucket fine regression targets. - To avoid too large offset displacements. Defaults to 1.0. - cls_ignore_neighbor (bool): Ignore second nearest bucket or Not. - Defaults to True. - - Returns: - tuple[Tensor]: (offsets, offsets_weights, bucket_labels, cls_weights). - - - offsets: Fine regression targets. \ - Shape (n, num_buckets*2). - - offsets_weights: Fine regression weights. \ - Shape (n, num_buckets*2). - - bucket_labels: Bucketing estimation labels. \ - Shape (n, num_buckets*2). - - cls_weights: Bucketing estimation weights. \ - Shape (n, num_buckets*2). - """ - assert proposals.size() == gt.size() - - # generate buckets - proposals = proposals.float() - gt = gt.float() - (bucket_w, bucket_h, l_buckets, r_buckets, t_buckets, - d_buckets) = generat_buckets(proposals, num_buckets, scale_factor) - - gx1 = gt[..., 0] - gy1 = gt[..., 1] - gx2 = gt[..., 2] - gy2 = gt[..., 3] - - # generate offset targets and weights - # offsets from buckets to gts - l_offsets = (l_buckets - gx1[:, None]) / bucket_w[:, None] - r_offsets = (r_buckets - gx2[:, None]) / bucket_w[:, None] - t_offsets = (t_buckets - gy1[:, None]) / bucket_h[:, None] - d_offsets = (d_buckets - gy2[:, None]) / bucket_h[:, None] - - # select top-k nearset buckets - l_topk, l_label = l_offsets.abs().topk( - offset_topk, dim=1, largest=False, sorted=True) - r_topk, r_label = r_offsets.abs().topk( - offset_topk, dim=1, largest=False, sorted=True) - t_topk, t_label = t_offsets.abs().topk( - offset_topk, dim=1, largest=False, sorted=True) - d_topk, d_label = d_offsets.abs().topk( - offset_topk, dim=1, largest=False, sorted=True) - - offset_l_weights = l_offsets.new_zeros(l_offsets.size()) - offset_r_weights = r_offsets.new_zeros(r_offsets.size()) - offset_t_weights = t_offsets.new_zeros(t_offsets.size()) - offset_d_weights = d_offsets.new_zeros(d_offsets.size()) - inds = torch.arange(0, proposals.size(0)).to(proposals).long() - - # generate offset weights of top-k nearset buckets - for k in range(offset_topk): - if k >= 1: - offset_l_weights[inds, l_label[:, - k]] = (l_topk[:, k] < - offset_upperbound).float() - offset_r_weights[inds, r_label[:, - k]] = (r_topk[:, k] < - offset_upperbound).float() - offset_t_weights[inds, t_label[:, - k]] = (t_topk[:, k] < - offset_upperbound).float() - offset_d_weights[inds, d_label[:, - k]] = (d_topk[:, k] < - offset_upperbound).float() - else: - offset_l_weights[inds, l_label[:, k]] = 1.0 - offset_r_weights[inds, r_label[:, k]] = 1.0 - offset_t_weights[inds, t_label[:, k]] = 1.0 - offset_d_weights[inds, d_label[:, k]] = 1.0 - - offsets = torch.cat([l_offsets, r_offsets, t_offsets, d_offsets], dim=-1) - offsets_weights = torch.cat([ - offset_l_weights, offset_r_weights, offset_t_weights, offset_d_weights - ], - dim=-1) - - # generate bucket labels and weight - side_num = int(np.ceil(num_buckets / 2.0)) - labels = torch.stack( - [l_label[:, 0], r_label[:, 0], t_label[:, 0], d_label[:, 0]], dim=-1) - - batch_size = labels.size(0) - bucket_labels = F.one_hot(labels.view(-1), side_num).view(batch_size, - -1).float() - bucket_cls_l_weights = (l_offsets.abs() < 1).float() - bucket_cls_r_weights = (r_offsets.abs() < 1).float() - bucket_cls_t_weights = (t_offsets.abs() < 1).float() - bucket_cls_d_weights = (d_offsets.abs() < 1).float() - bucket_cls_weights = torch.cat([ - bucket_cls_l_weights, bucket_cls_r_weights, bucket_cls_t_weights, - bucket_cls_d_weights - ], - dim=-1) - # ignore second nearest buckets for cls if necessary - if cls_ignore_neighbor: - bucket_cls_weights = (~((bucket_cls_weights == 1) & - (bucket_labels == 0))).float() - else: - bucket_cls_weights[:] = 1.0 - return offsets, offsets_weights, bucket_labels, bucket_cls_weights - - -@mmcv.jit(coderize=True) -def bucket2bbox(proposals, - cls_preds, - offset_preds, - num_buckets, - scale_factor=1.0, - max_shape=None, - clip_border=True): - """Apply bucketing estimation (cls preds) and fine regression (offset - preds) to generate det bboxes. - - Args: - proposals (Tensor): Boxes to be transformed. Shape (n, 4) - cls_preds (Tensor): bucketing estimation. Shape (n, num_buckets*2). - offset_preds (Tensor): fine regression. Shape (n, num_buckets*2). - num_buckets (int): Number of buckets. - scale_factor (float): Scale factor to rescale proposals. - max_shape (tuple[int, int]): Maximum bounds for boxes. specifies (H, W) - clip_border (bool, optional): Whether clip the objects outside the - border of the image. Defaults to True. - - Returns: - tuple[Tensor]: (bboxes, loc_confidence). - - - bboxes: predicted bboxes. Shape (n, 4) - - loc_confidence: localization confidence of predicted bboxes. - Shape (n,). - """ - - side_num = int(np.ceil(num_buckets / 2.0)) - cls_preds = cls_preds.view(-1, side_num) - offset_preds = offset_preds.view(-1, side_num) - - scores = F.softmax(cls_preds, dim=1) - score_topk, score_label = scores.topk(2, dim=1, largest=True, sorted=True) - - rescaled_proposals = bbox_rescale(proposals, scale_factor) - - pw = rescaled_proposals[..., 2] - rescaled_proposals[..., 0] - ph = rescaled_proposals[..., 3] - rescaled_proposals[..., 1] - px1 = rescaled_proposals[..., 0] - py1 = rescaled_proposals[..., 1] - px2 = rescaled_proposals[..., 2] - py2 = rescaled_proposals[..., 3] - - bucket_w = pw / num_buckets - bucket_h = ph / num_buckets - - score_inds_l = score_label[0::4, 0] - score_inds_r = score_label[1::4, 0] - score_inds_t = score_label[2::4, 0] - score_inds_d = score_label[3::4, 0] - l_buckets = px1 + (0.5 + score_inds_l.float()) * bucket_w - r_buckets = px2 - (0.5 + score_inds_r.float()) * bucket_w - t_buckets = py1 + (0.5 + score_inds_t.float()) * bucket_h - d_buckets = py2 - (0.5 + score_inds_d.float()) * bucket_h - - offsets = offset_preds.view(-1, 4, side_num) - inds = torch.arange(proposals.size(0)).to(proposals).long() - l_offsets = offsets[:, 0, :][inds, score_inds_l] - r_offsets = offsets[:, 1, :][inds, score_inds_r] - t_offsets = offsets[:, 2, :][inds, score_inds_t] - d_offsets = offsets[:, 3, :][inds, score_inds_d] - - x1 = l_buckets - l_offsets * bucket_w - x2 = r_buckets - r_offsets * bucket_w - y1 = t_buckets - t_offsets * bucket_h - y2 = d_buckets - d_offsets * bucket_h - - if clip_border and max_shape is not None: - x1 = x1.clamp(min=0, max=max_shape[1] - 1) - y1 = y1.clamp(min=0, max=max_shape[0] - 1) - x2 = x2.clamp(min=0, max=max_shape[1] - 1) - y2 = y2.clamp(min=0, max=max_shape[0] - 1) - bboxes = torch.cat([x1[:, None], y1[:, None], x2[:, None], y2[:, None]], - dim=-1) - - # bucketing guided rescoring - loc_confidence = score_topk[:, 0] - top2_neighbor_inds = (score_label[:, 0] - score_label[:, 1]).abs() == 1 - loc_confidence += score_topk[:, 1] * top2_neighbor_inds.float() - loc_confidence = loc_confidence.view(-1, 4).mean(dim=1) - - return bboxes, loc_confidence diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/backbones/ssd_vgg.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/backbones/ssd_vgg.py deleted file mode 100644 index cbc4fbb2301afc002f47abb9ed133a500d6cf23f..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/backbones/ssd_vgg.py +++ /dev/null @@ -1,169 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from mmcv.cnn import VGG, constant_init, kaiming_init, normal_init, xavier_init -from mmcv.runner import load_checkpoint - -from mmdet.utils import get_root_logger -from ..builder import BACKBONES - - -@BACKBONES.register_module() -class SSDVGG(VGG): - """VGG Backbone network for single-shot-detection. - - Args: - input_size (int): width and height of input, from {300, 512}. - depth (int): Depth of vgg, from {11, 13, 16, 19}. - out_indices (Sequence[int]): Output from which stages. - - Example: - >>> self = SSDVGG(input_size=300, depth=11) - >>> self.eval() - >>> inputs = torch.rand(1, 3, 300, 300) - >>> level_outputs = self.forward(inputs) - >>> for level_out in level_outputs: - ... print(tuple(level_out.shape)) - (1, 1024, 19, 19) - (1, 512, 10, 10) - (1, 256, 5, 5) - (1, 256, 3, 3) - (1, 256, 1, 1) - """ - extra_setting = { - 300: (256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256), - 512: (256, 'S', 512, 128, 'S', 256, 128, 'S', 256, 128, 'S', 256, 128), - } - - def __init__(self, - input_size, - depth, - with_last_pool=False, - ceil_mode=True, - out_indices=(3, 4), - out_feature_indices=(22, 34), - l2_norm_scale=20.): - # TODO: in_channels for mmcv.VGG - super(SSDVGG, self).__init__( - depth, - with_last_pool=with_last_pool, - ceil_mode=ceil_mode, - out_indices=out_indices) - assert input_size in (300, 512) - self.input_size = input_size - - self.features.add_module( - str(len(self.features)), - nn.MaxPool2d(kernel_size=3, stride=1, padding=1)) - self.features.add_module( - str(len(self.features)), - nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)) - self.features.add_module( - str(len(self.features)), nn.ReLU(inplace=True)) - self.features.add_module( - str(len(self.features)), nn.Conv2d(1024, 1024, kernel_size=1)) - self.features.add_module( - str(len(self.features)), nn.ReLU(inplace=True)) - self.out_feature_indices = out_feature_indices - - self.inplanes = 1024 - self.extra = self._make_extra_layers(self.extra_setting[input_size]) - self.l2_norm = L2Norm( - self.features[out_feature_indices[0] - 1].out_channels, - l2_norm_scale) - - def init_weights(self, pretrained=None): - """Initialize the weights in backbone. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - if isinstance(pretrained, str): - logger = get_root_logger() - load_checkpoint(self, pretrained, strict=False, logger=logger) - elif pretrained is None: - for m in self.features.modules(): - if isinstance(m, nn.Conv2d): - kaiming_init(m) - elif isinstance(m, nn.BatchNorm2d): - constant_init(m, 1) - elif isinstance(m, nn.Linear): - normal_init(m, std=0.01) - else: - raise TypeError('pretrained must be a str or None') - - for m in self.extra.modules(): - if isinstance(m, nn.Conv2d): - xavier_init(m, distribution='uniform') - - constant_init(self.l2_norm, self.l2_norm.scale) - - def forward(self, x): - """Forward function.""" - outs = [] - for i, layer in enumerate(self.features): - x = layer(x) - if i in self.out_feature_indices: - outs.append(x) - for i, layer in enumerate(self.extra): - x = F.relu(layer(x), inplace=True) - if i % 2 == 1: - outs.append(x) - outs[0] = self.l2_norm(outs[0]) - if len(outs) == 1: - return outs[0] - else: - return tuple(outs) - - def _make_extra_layers(self, outplanes): - layers = [] - kernel_sizes = (1, 3) - num_layers = 0 - outplane = None - for i in range(len(outplanes)): - if self.inplanes == 'S': - self.inplanes = outplane - continue - k = kernel_sizes[num_layers % 2] - if outplanes[i] == 'S': - outplane = outplanes[i + 1] - conv = nn.Conv2d( - self.inplanes, outplane, k, stride=2, padding=1) - else: - outplane = outplanes[i] - conv = nn.Conv2d( - self.inplanes, outplane, k, stride=1, padding=0) - layers.append(conv) - self.inplanes = outplanes[i] - num_layers += 1 - if self.input_size == 512: - layers.append(nn.Conv2d(self.inplanes, 256, 4, padding=1)) - - return nn.Sequential(*layers) - - -class L2Norm(nn.Module): - - def __init__(self, n_dims, scale=20., eps=1e-10): - """L2 normalization layer. - - Args: - n_dims (int): Number of dimensions to be normalized - scale (float, optional): Defaults to 20.. - eps (float, optional): Used to avoid division by zero. - Defaults to 1e-10. - """ - super(L2Norm, self).__init__() - self.n_dims = n_dims - self.weight = nn.Parameter(torch.Tensor(self.n_dims)) - self.eps = eps - self.scale = scale - - def forward(self, x): - """Forward function.""" - # normalization layer convert to FP32 in FP16 training - x_float = x.float() - norm = x_float.pow(2).sum(1, keepdim=True).sqrt() + self.eps - return (self.weight[None, :, None, None].float().expand_as(x_float) * - x_float / norm).type_as(x) diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/segmentors/base.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/segmentors/base.py deleted file mode 100644 index bfbf31f561fd8230ec983be7fb72650411f4d5a1..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/segmentors/base.py +++ /dev/null @@ -1,285 +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. -''' - -import logging -import warnings -from abc import ABCMeta, abstractmethod -from collections import OrderedDict - -import annotator.uniformer.mmcv as mmcv -import numpy as np -import torch -import torch.distributed as dist -import torch.nn as nn -from annotator.uniformer.mmcv.runner import auto_fp16 - - -class BaseSegmentor(nn.Module): - """Base class for segmentors.""" - - __metaclass__ = ABCMeta - - def __init__(self): - super(BaseSegmentor, self).__init__() - self.fp16_enabled = False - - @property - def with_neck(self): - """bool: whether the segmentor has neck""" - return hasattr(self, 'neck') and self.neck is not None - - @property - def with_auxiliary_head(self): - """bool: whether the segmentor has auxiliary head""" - return hasattr(self, - 'auxiliary_head') and self.auxiliary_head is not None - - @property - def with_decode_head(self): - """bool: whether the segmentor has decode head""" - return hasattr(self, 'decode_head') and self.decode_head is not None - - @abstractmethod - def extract_feat(self, imgs): - """Placeholder for extract features from images.""" - pass - - @abstractmethod - def encode_decode(self, img, img_metas): - """Placeholder for encode images with backbone and decode into a - semantic segmentation map of the same size as input.""" - pass - - @abstractmethod - def forward_train(self, imgs, img_metas, **kwargs): - """Placeholder for Forward function for training.""" - pass - - @abstractmethod - def simple_test(self, img, img_meta, **kwargs): - """Placeholder for single image test.""" - pass - - @abstractmethod - def aug_test(self, imgs, img_metas, **kwargs): - """Placeholder for augmentation test.""" - pass - - def init_weights(self, pretrained=None): - """Initialize the weights in segmentor. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - if pretrained is not None: - logger = logging.getLogger() - logger.info(f'load model from: {pretrained}') - - def forward_test(self, imgs, img_metas, **kwargs): - """ - Args: - imgs (List[Tensor]): the outer list indicates test-time - augmentations and inner Tensor should have a shape NxCxHxW, - which contains all images in the batch. - img_metas (List[List[dict]]): the outer list indicates test-time - augs (multiscale, flip, etc.) and the inner list indicates - images in a batch. - """ - for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]: - if not isinstance(var, list): - raise TypeError(f'{name} must be a list, but got ' - f'{type(var)}') - - num_augs = len(imgs) - if num_augs != len(img_metas): - raise ValueError(f'num of augmentations ({len(imgs)}) != ' - f'num of image meta ({len(img_metas)})') - # all images in the same aug batch all of the same ori_shape and pad - # shape - for img_meta in img_metas: - ori_shapes = [_['ori_shape'] for _ in img_meta] - assert all(shape == ori_shapes[0] for shape in ori_shapes) - img_shapes = [_['img_shape'] for _ in img_meta] - assert all(shape == img_shapes[0] for shape in img_shapes) - pad_shapes = [_['pad_shape'] for _ in img_meta] - assert all(shape == pad_shapes[0] for shape in pad_shapes) - - if num_augs == 1: - return self.simple_test(imgs[0], img_metas[0], **kwargs) - else: - return self.aug_test(imgs, img_metas, **kwargs) - - @auto_fp16(apply_to=('img', )) - def forward(self, img, img_metas, return_loss=True, **kwargs): - """Calls either :func:`forward_train` or :func:`forward_test` depending - on whether ``return_loss`` is ``True``. - - Note this setting will change the expected inputs. When - ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor - and List[dict]), and when ``resturn_loss=False``, img and img_meta - should be double nested (i.e. List[Tensor], List[List[dict]]), with - the outer list indicating test time augmentations. - """ - if return_loss: - return self.forward_train(img, img_metas, **kwargs) - else: - return self.forward_test(img, img_metas, **kwargs) - - def train_step(self, data_batch, optimizer, **kwargs): - """The iteration step during training. - - This method defines an iteration step during training, except for the - back propagation and optimizer updating, which are done in an optimizer - hook. Note that in some complicated cases or models, the whole process - including back propagation and optimizer updating is also defined in - this method, such as GAN. - - Args: - data (dict): The output of dataloader. - optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of - runner is passed to ``train_step()``. This argument is unused - and reserved. - - Returns: - dict: It should contain at least 3 keys: ``loss``, ``log_vars``, - ``num_samples``. - ``loss`` is a tensor for back propagation, which can be a - weighted sum of multiple losses. - ``log_vars`` contains all the variables to be sent to the - logger. - ``num_samples`` indicates the batch size (when the model is - DDP, it means the batch size on each GPU), which is used for - averaging the logs. - """ - losses = self(**data_batch) - loss, log_vars = self._parse_losses(losses) - - outputs = dict( - loss=loss, - log_vars=log_vars, - num_samples=len(data_batch['img_metas'])) - - return outputs - - def val_step(self, data_batch, **kwargs): - """The iteration step during validation. - - This method shares the same signature as :func:`train_step`, but used - during val epochs. Note that the evaluation after training epochs is - not implemented with this method, but an evaluation hook. - """ - output = self(**data_batch, **kwargs) - return output - - @staticmethod - def _parse_losses(losses): - """Parse the raw outputs (losses) of the network. - - Args: - losses (dict): Raw output of the network, which usually contain - losses and other necessary information. - - Returns: - tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor - which may be a weighted sum of all losses, log_vars contains - all the variables to be sent to the logger. - """ - log_vars = OrderedDict() - for loss_name, loss_value in losses.items(): - if isinstance(loss_value, torch.Tensor): - log_vars[loss_name] = loss_value.mean() - elif isinstance(loss_value, list): - log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value) - else: - raise TypeError( - f'{loss_name} is not a tensor or list of tensors') - - loss = sum(_value for _key, _value in log_vars.items() - if 'loss' in _key) - - log_vars['loss'] = loss - for loss_name, loss_value in log_vars.items(): - # reduce loss when distributed training - if dist.is_available() and dist.is_initialized(): - loss_value = loss_value.data.clone() - dist.all_reduce(loss_value.div_(dist.get_world_size())) - log_vars[loss_name] = loss_value.item() - - return loss, log_vars - - def show_result(self, - img, - result, - palette=None, - win_name='', - show=False, - wait_time=0, - out_file=None, - opacity=0.5): - """Draw `result` over `img`. - - Args: - img (str or Tensor): The image to be displayed. - result (Tensor): The semantic segmentation results to draw over - `img`. - palette (list[list[int]]] | np.ndarray | None): The palette of - segmentation map. If None is given, random palette will be - generated. Default: None - win_name (str): The window name. - wait_time (int): Value of waitKey param. - Default: 0. - show (bool): Whether to show the image. - Default: False. - out_file (str or None): The filename to write the image. - Default: None. - opacity(float): Opacity of painted segmentation map. - Default 0.5. - Must be in (0, 1] range. - Returns: - img (Tensor): Only if not `show` or `out_file` - """ - img = mmcv.imread(img) - img = img.copy() - seg = result[0] - if palette is None: - if self.PALETTE is None: - palette = np.random.randint( - 0, 255, size=(len(self.CLASSES), 3)) - else: - palette = self.PALETTE - palette = np.array(palette) - assert palette.shape[0] == len(self.CLASSES) - assert palette.shape[1] == 3 - assert len(palette.shape) == 2 - assert 0 < opacity <= 1.0 - color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) - for label, color in enumerate(palette): - color_seg[seg == label, :] = color - # convert to BGR - color_seg = color_seg[..., ::-1] - - img = img * (1 - opacity) + color_seg * opacity - img = img.astype(np.uint8) - # if out_file specified, do not show image in window - if out_file is not None: - show = False - - if show: - mmcv.imshow(img, win_name, wait_time) - if out_file is not None: - mmcv.imwrite(img, out_file) - - if not (show or out_file): - warnings.warn('show==False and out_file is not specified, only ' - 'result image will be returned') - return img diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/libs/darwin/cocoapy/runtime.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/libs/darwin/cocoapy/runtime.py deleted file mode 100644 index ba60086681243de4fbed3bf9718128e66999c85f..0000000000000000000000000000000000000000 --- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/libs/darwin/cocoapy/runtime.py +++ /dev/null @@ -1,1251 +0,0 @@ -# objective-ctypes -# -# Copyright (c) 2011, Phillip Nguyen -# 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 objective-ctypes 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 THE -# COPYRIGHT HOLDER OR CONTRIBUTORS 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. - -import sys -import platform -import struct - -from ctypes import * -from ctypes import util - -from .cocoatypes import * - -__LP64__ = (8 * struct.calcsize("P") == 64) -__i386__ = (platform.machine() == 'i386') -__arm64__ = (platform.machine() == 'arm64') - -if sizeof(c_void_p) == 4: - c_ptrdiff_t = c_int32 -elif sizeof(c_void_p) == 8: - c_ptrdiff_t = c_int64 - -###################################################################### - -lib = util.find_library('objc') - -# Hack for compatibility with macOS > 11.0 -if lib is None: - lib = '/usr/lib/libobjc.dylib' - -objc = cdll.LoadLibrary(lib) - -###################################################################### - -# BOOL class_addIvar(Class cls, const char *name, size_t size, uint8_t alignment, const char *types) -objc.class_addIvar.restype = c_bool -objc.class_addIvar.argtypes = [c_void_p, c_char_p, c_size_t, c_uint8, c_char_p] - -# BOOL class_addMethod(Class cls, SEL name, IMP imp, const char *types) -objc.class_addMethod.restype = c_bool - -# BOOL class_addProtocol(Class cls, Protocol *protocol) -objc.class_addProtocol.restype = c_bool -objc.class_addProtocol.argtypes = [c_void_p, c_void_p] - -# BOOL class_conformsToProtocol(Class cls, Protocol *protocol) -objc.class_conformsToProtocol.restype = c_bool -objc.class_conformsToProtocol.argtypes = [c_void_p, c_void_p] - -# Ivar * class_copyIvarList(Class cls, unsigned int *outCount) -# Returns an array of pointers of type Ivar describing instance variables. -# The array has *outCount pointers followed by a NULL terminator. -# You must free() the returned array. -objc.class_copyIvarList.restype = POINTER(c_void_p) -objc.class_copyIvarList.argtypes = [c_void_p, POINTER(c_uint)] - -# Method * class_copyMethodList(Class cls, unsigned int *outCount) -# Returns an array of pointers of type Method describing instance methods. -# The array has *outCount pointers followed by a NULL terminator. -# You must free() the returned array. -objc.class_copyMethodList.restype = POINTER(c_void_p) -objc.class_copyMethodList.argtypes = [c_void_p, POINTER(c_uint)] - -# objc_property_t * class_copyPropertyList(Class cls, unsigned int *outCount) -# Returns an array of pointers of type objc_property_t describing properties. -# The array has *outCount pointers followed by a NULL terminator. -# You must free() the returned array. -objc.class_copyPropertyList.restype = POINTER(c_void_p) -objc.class_copyPropertyList.argtypes = [c_void_p, POINTER(c_uint)] - -# Protocol ** class_copyProtocolList(Class cls, unsigned int *outCount) -# Returns an array of pointers of type Protocol* describing protocols. -# The array has *outCount pointers followed by a NULL terminator. -# You must free() the returned array. -objc.class_copyProtocolList.restype = POINTER(c_void_p) -objc.class_copyProtocolList.argtypes = [c_void_p, POINTER(c_uint)] - -# id class_createInstance(Class cls, size_t extraBytes) -objc.class_createInstance.restype = c_void_p -objc.class_createInstance.argtypes = [c_void_p, c_size_t] - -# Method class_getClassMethod(Class aClass, SEL aSelector) -# Will also search superclass for implementations. -objc.class_getClassMethod.restype = c_void_p -objc.class_getClassMethod.argtypes = [c_void_p, c_void_p] - -# Ivar class_getClassVariable(Class cls, const char* name) -objc.class_getClassVariable.restype = c_void_p -objc.class_getClassVariable.argtypes = [c_void_p, c_char_p] - -# Method class_getInstanceMethod(Class aClass, SEL aSelector) -# Will also search superclass for implementations. -objc.class_getInstanceMethod.restype = c_void_p -objc.class_getInstanceMethod.argtypes = [c_void_p, c_void_p] - -# size_t class_getInstanceSize(Class cls) -objc.class_getInstanceSize.restype = c_size_t -objc.class_getInstanceSize.argtypes = [c_void_p] - -# Ivar class_getInstanceVariable(Class cls, const char* name) -objc.class_getInstanceVariable.restype = c_void_p -objc.class_getInstanceVariable.argtypes = [c_void_p, c_char_p] - -# const char *class_getIvarLayout(Class cls) -objc.class_getIvarLayout.restype = c_char_p -objc.class_getIvarLayout.argtypes = [c_void_p] - -# IMP class_getMethodImplementation(Class cls, SEL name) -objc.class_getMethodImplementation.restype = c_void_p -objc.class_getMethodImplementation.argtypes = [c_void_p, c_void_p] - -# The function is marked as OBJC_ARM64_UNAVAILABLE. -if not __arm64__: - # IMP class_getMethodImplementation_stret(Class cls, SEL name) - objc.class_getMethodImplementation_stret.restype = c_void_p - objc.class_getMethodImplementation_stret.argtypes = [c_void_p, c_void_p] - -# const char * class_getName(Class cls) -objc.class_getName.restype = c_char_p -objc.class_getName.argtypes = [c_void_p] - -# objc_property_t class_getProperty(Class cls, const char *name) -objc.class_getProperty.restype = c_void_p -objc.class_getProperty.argtypes = [c_void_p, c_char_p] - -# Class class_getSuperclass(Class cls) -objc.class_getSuperclass.restype = c_void_p -objc.class_getSuperclass.argtypes = [c_void_p] - -# int class_getVersion(Class theClass) -objc.class_getVersion.restype = c_int -objc.class_getVersion.argtypes = [c_void_p] - -# const char *class_getWeakIvarLayout(Class cls) -objc.class_getWeakIvarLayout.restype = c_char_p -objc.class_getWeakIvarLayout.argtypes = [c_void_p] - -# BOOL class_isMetaClass(Class cls) -objc.class_isMetaClass.restype = c_bool -objc.class_isMetaClass.argtypes = [c_void_p] - -# IMP class_replaceMethod(Class cls, SEL name, IMP imp, const char *types) -objc.class_replaceMethod.restype = c_void_p -objc.class_replaceMethod.argtypes = [c_void_p, c_void_p, c_void_p, c_char_p] - -# BOOL class_respondsToSelector(Class cls, SEL sel) -objc.class_respondsToSelector.restype = c_bool -objc.class_respondsToSelector.argtypes = [c_void_p, c_void_p] - -# void class_setIvarLayout(Class cls, const char *layout) -objc.class_setIvarLayout.restype = None -objc.class_setIvarLayout.argtypes = [c_void_p, c_char_p] - -# Class class_setSuperclass(Class cls, Class newSuper) -objc.class_setSuperclass.restype = c_void_p -objc.class_setSuperclass.argtypes = [c_void_p, c_void_p] - -# void class_setVersion(Class theClass, int version) -objc.class_setVersion.restype = None -objc.class_setVersion.argtypes = [c_void_p, c_int] - -# void class_setWeakIvarLayout(Class cls, const char *layout) -objc.class_setWeakIvarLayout.restype = None -objc.class_setWeakIvarLayout.argtypes = [c_void_p, c_char_p] - -###################################################################### - -# const char * ivar_getName(Ivar ivar) -objc.ivar_getName.restype = c_char_p -objc.ivar_getName.argtypes = [c_void_p] - -# ptrdiff_t ivar_getOffset(Ivar ivar) -objc.ivar_getOffset.restype = c_ptrdiff_t -objc.ivar_getOffset.argtypes = [c_void_p] - -# const char * ivar_getTypeEncoding(Ivar ivar) -objc.ivar_getTypeEncoding.restype = c_char_p -objc.ivar_getTypeEncoding.argtypes = [c_void_p] - -###################################################################### - -# char * method_copyArgumentType(Method method, unsigned int index) -# You must free() the returned string. -objc.method_copyArgumentType.restype = c_char_p -objc.method_copyArgumentType.argtypes = [c_void_p, c_uint] - -# char * method_copyReturnType(Method method) -# You must free() the returned string. -objc.method_copyReturnType.restype = c_char_p -objc.method_copyReturnType.argtypes = [c_void_p] - -# void method_exchangeImplementations(Method m1, Method m2) -objc.method_exchangeImplementations.restype = None -objc.method_exchangeImplementations.argtypes = [c_void_p, c_void_p] - -# void method_getArgumentType(Method method, unsigned int index, char *dst, size_t dst_len) -# Functionally similar to strncpy(dst, parameter_type, dst_len). -objc.method_getArgumentType.restype = None -objc.method_getArgumentType.argtypes = [c_void_p, c_uint, c_char_p, c_size_t] - -# IMP method_getImplementation(Method method) -objc.method_getImplementation.restype = c_void_p -objc.method_getImplementation.argtypes = [c_void_p] - -# SEL method_getName(Method method) -objc.method_getName.restype = c_void_p -objc.method_getName.argtypes = [c_void_p] - -# unsigned method_getNumberOfArguments(Method method) -objc.method_getNumberOfArguments.restype = c_uint -objc.method_getNumberOfArguments.argtypes = [c_void_p] - -# void method_getReturnType(Method method, char *dst, size_t dst_len) -# Functionally similar to strncpy(dst, return_type, dst_len) -objc.method_getReturnType.restype = None -objc.method_getReturnType.argtypes = [c_void_p, c_char_p, c_size_t] - -# const char * method_getTypeEncoding(Method method) -objc.method_getTypeEncoding.restype = c_char_p -objc.method_getTypeEncoding.argtypes = [c_void_p] - -# IMP method_setImplementation(Method method, IMP imp) -objc.method_setImplementation.restype = c_void_p -objc.method_setImplementation.argtypes = [c_void_p, c_void_p] - -###################################################################### - -# Class objc_allocateClassPair(Class superclass, const char *name, size_t extraBytes) -objc.objc_allocateClassPair.restype = c_void_p -objc.objc_allocateClassPair.argtypes = [c_void_p, c_char_p, c_size_t] - -# Protocol **objc_copyProtocolList(unsigned int *outCount) -# Returns an array of *outcount pointers followed by NULL terminator. -# You must free() the array. -objc.objc_copyProtocolList.restype = POINTER(c_void_p) -objc.objc_copyProtocolList.argtypes = [POINTER(c_int)] - -# id objc_getAssociatedObject(id object, void *key) -objc.objc_getAssociatedObject.restype = c_void_p -objc.objc_getAssociatedObject.argtypes = [c_void_p, c_void_p] - -# id objc_getClass(const char *name) -objc.objc_getClass.restype = c_void_p -objc.objc_getClass.argtypes = [c_char_p] - -# int objc_getClassList(Class *buffer, int bufferLen) -# Pass None for buffer to obtain just the total number of classes. -objc.objc_getClassList.restype = c_int -objc.objc_getClassList.argtypes = [c_void_p, c_int] - -# id objc_getMetaClass(const char *name) -objc.objc_getMetaClass.restype = c_void_p -objc.objc_getMetaClass.argtypes = [c_char_p] - -# Protocol *objc_getProtocol(const char *name) -objc.objc_getProtocol.restype = c_void_p -objc.objc_getProtocol.argtypes = [c_char_p] - -# You should set return and argument types depending on context. -# id objc_msgSend(id theReceiver, SEL theSelector, ...) -# id objc_msgSendSuper(struct objc_super *super, SEL op, ...) - -# The function is marked as OBJC_ARM64_UNAVAILABLE. -if not __arm64__: - # void objc_msgSendSuper_stret(struct objc_super *super, SEL op, ...) - objc.objc_msgSendSuper_stret.restype = None - -# double objc_msgSend_fpret(id self, SEL op, ...) -# objc.objc_msgSend_fpret.restype = c_double - -# The function is marked as OBJC_ARM64_UNAVAILABLE. -if not __arm64__: - # void objc_msgSend_stret(void * stretAddr, id theReceiver, SEL theSelector, ...) - objc.objc_msgSend_stret.restype = None - -# void objc_registerClassPair(Class cls) -objc.objc_registerClassPair.restype = None -objc.objc_registerClassPair.argtypes = [c_void_p] - -# void objc_removeAssociatedObjects(id object) -objc.objc_removeAssociatedObjects.restype = None -objc.objc_removeAssociatedObjects.argtypes = [c_void_p] - -# void objc_setAssociatedObject(id object, void *key, id value, objc_AssociationPolicy policy) -objc.objc_setAssociatedObject.restype = None -objc.objc_setAssociatedObject.argtypes = [c_void_p, c_void_p, c_void_p, c_int] - -###################################################################### - -# id object_copy(id obj, size_t size) -objc.object_copy.restype = c_void_p -objc.object_copy.argtypes = [c_void_p, c_size_t] - -# id object_dispose(id obj) -objc.object_dispose.restype = c_void_p -objc.object_dispose.argtypes = [c_void_p] - -# Class object_getClass(id object) -objc.object_getClass.restype = c_void_p -objc.object_getClass.argtypes = [c_void_p] - -# const char *object_getClassName(id obj) -objc.object_getClassName.restype = c_char_p -objc.object_getClassName.argtypes = [c_void_p] - -# Ivar object_getInstanceVariable(id obj, const char *name, void **outValue) -objc.object_getInstanceVariable.restype = c_void_p -objc.object_getInstanceVariable.argtypes = [c_void_p, c_char_p, c_void_p] - -# id object_getIvar(id object, Ivar ivar) -objc.object_getIvar.restype = c_void_p -objc.object_getIvar.argtypes = [c_void_p, c_void_p] - -# Class object_setClass(id object, Class cls) -objc.object_setClass.restype = c_void_p -objc.object_setClass.argtypes = [c_void_p, c_void_p] - -# Ivar object_setInstanceVariable(id obj, const char *name, void *value) -# Set argtypes based on the data type of the instance variable. -objc.object_setInstanceVariable.restype = c_void_p - -# void object_setIvar(id object, Ivar ivar, id value) -objc.object_setIvar.restype = None -objc.object_setIvar.argtypes = [c_void_p, c_void_p, c_void_p] - -###################################################################### - -# const char *property_getAttributes(objc_property_t property) -objc.property_getAttributes.restype = c_char_p -objc.property_getAttributes.argtypes = [c_void_p] - -# const char *property_getName(objc_property_t property) -objc.property_getName.restype = c_char_p -objc.property_getName.argtypes = [c_void_p] - -###################################################################### - -# BOOL protocol_conformsToProtocol(Protocol *proto, Protocol *other) -objc.protocol_conformsToProtocol.restype = c_bool -objc.protocol_conformsToProtocol.argtypes = [c_void_p, c_void_p] - - -class OBJC_METHOD_DESCRIPTION(Structure): - _fields_ = [("name", c_void_p), ("types", c_char_p)] - - -# struct objc_method_description *protocol_copyMethodDescriptionList(Protocol *p, BOOL isRequiredMethod, BOOL isInstanceMethod, unsigned int *outCount) -# You must free() the returned array. -objc.protocol_copyMethodDescriptionList.restype = POINTER(OBJC_METHOD_DESCRIPTION) -objc.protocol_copyMethodDescriptionList.argtypes = [c_void_p, c_bool, c_bool, POINTER(c_uint)] - -# objc_property_t * protocol_copyPropertyList(Protocol *protocol, unsigned int *outCount) -objc.protocol_copyPropertyList.restype = c_void_p -objc.protocol_copyPropertyList.argtypes = [c_void_p, POINTER(c_uint)] - -# Protocol **protocol_copyProtocolList(Protocol *proto, unsigned int *outCount) -objc.protocol_copyProtocolList = POINTER(c_void_p) -objc.protocol_copyProtocolList.argtypes = [c_void_p, POINTER(c_uint)] - -# struct objc_method_description protocol_getMethodDescription(Protocol *p, SEL aSel, BOOL isRequiredMethod, BOOL isInstanceMethod) -objc.protocol_getMethodDescription.restype = OBJC_METHOD_DESCRIPTION -objc.protocol_getMethodDescription.argtypes = [c_void_p, c_void_p, c_bool, c_bool] - -# const char *protocol_getName(Protocol *p) -objc.protocol_getName.restype = c_char_p -objc.protocol_getName.argtypes = [c_void_p] - -###################################################################### - -# const char* sel_getName(SEL aSelector) -objc.sel_getName.restype = c_char_p -objc.sel_getName.argtypes = [c_void_p] - -# SEL sel_getUid(const char *str) -# Use sel_registerName instead. - -# BOOL sel_isEqual(SEL lhs, SEL rhs) -objc.sel_isEqual.restype = c_bool -objc.sel_isEqual.argtypes = [c_void_p, c_void_p] - -# SEL sel_registerName(const char *str) -objc.sel_registerName.restype = c_void_p -objc.sel_registerName.argtypes = [c_char_p] - - -###################################################################### - -def ensure_bytes(x): - if isinstance(x, bytes): - return x - return x.encode('ascii') - - -###################################################################### - -def get_selector(name): - return c_void_p(objc.sel_registerName(ensure_bytes(name))) - - -def get_class(name): - return c_void_p(objc.objc_getClass(ensure_bytes(name))) - - -def get_object_class(obj): - return c_void_p(objc.object_getClass(obj)) - - -def get_metaclass(name): - return c_void_p(objc.objc_getMetaClass(ensure_bytes(name))) - - -def get_superclass_of_object(obj): - cls = c_void_p(objc.object_getClass(obj)) - return c_void_p(objc.class_getSuperclass(cls)) - - -# http://www.sealiesoftware.com/blog/archive/2008/10/30/objc_explain_objc_msgSend_stret.html -# http://www.x86-64.org/documentation/abi-0.99.pdf (pp.17-23) -# executive summary: on x86-64, who knows? -def x86_should_use_stret(restype): - """Try to figure out when a return type will be passed on stack.""" - if type(restype) != type(Structure): - return False - if not __LP64__ and sizeof(restype) <= 8: - return False - if __LP64__ and sizeof(restype) <= 16: # maybe? I don't know? - return False - return True - - -# http://www.sealiesoftware.com/blog/archive/2008/11/16/objc_explain_objc_msgSend_fpret.html -def should_use_fpret(restype): - """Determine if objc_msgSend_fpret is required to return a floating point type.""" - if not __i386__: - # Unneeded on non-intel processors - return False - if __LP64__ and restype == c_longdouble: - # Use only for long double on x86_64 - return True - if not __LP64__ and restype in (c_float, c_double, c_longdouble): - return True - return False - - -# By default, assumes that restype is c_void_p -# and that all arguments are wrapped inside c_void_p. -# Use the restype and argtypes keyword arguments to -# change these values. restype should be a ctypes type -# and argtypes should be a list of ctypes types for -# the arguments of the message only. -def send_message(receiver, selName, *args, **kwargs): - if isinstance(receiver, str): - receiver = get_class(receiver) - selector = get_selector(selName) - restype = kwargs.get('restype', c_void_p) - # print('send_message', receiver, selName, args, kwargs) - argtypes = kwargs.get('argtypes', []) - # Choose the correct version of objc_msgSend based on return type. - if should_use_fpret(restype): - objc.objc_msgSend_fpret.restype = restype - objc.objc_msgSend_fpret.argtypes = [c_void_p, c_void_p] + argtypes - result = objc.objc_msgSend_fpret(receiver, selector, *args) - elif x86_should_use_stret(restype): - objc.objc_msgSend_stret.argtypes = [POINTER(restype), c_void_p, c_void_p] + argtypes - result = restype() - objc.objc_msgSend_stret(byref(result), receiver, selector, *args) - else: - objc.objc_msgSend.restype = restype - objc.objc_msgSend.argtypes = [c_void_p, c_void_p] + argtypes - result = objc.objc_msgSend(receiver, selector, *args) - if restype == c_void_p: - result = c_void_p(result) - return result - - -class OBJC_SUPER(Structure): - _fields_ = [('receiver', c_void_p), ('class', c_void_p)] - - -OBJC_SUPER_PTR = POINTER(OBJC_SUPER) - - -# http://stackoverflow.com/questions/3095360/what-exactly-is-super-in-objective-c -# -# `superclass_name` is optional and can be used to force finding the superclass -# by name. It is used to circumvent a bug in which the superclass was resolved -# incorrectly which lead to an infinite recursion: -# https://github.com/pyglet/pyglet/issues/5 -def send_super(receiver, selName, *args, superclass_name=None, **kwargs): - if hasattr(receiver, '_as_parameter_'): - receiver = receiver._as_parameter_ - if superclass_name is None: - superclass = get_superclass_of_object(receiver) - else: - superclass = get_class(superclass_name) - super_struct = OBJC_SUPER(receiver, superclass) - selector = get_selector(selName) - restype = kwargs.get('restype', c_void_p) - argtypes = kwargs.get('argtypes', None) - objc.objc_msgSendSuper.restype = restype - if argtypes: - objc.objc_msgSendSuper.argtypes = [OBJC_SUPER_PTR, c_void_p] + argtypes - else: - objc.objc_msgSendSuper.argtypes = None - result = objc.objc_msgSendSuper(byref(super_struct), selector, *args) - if restype == c_void_p: - result = c_void_p(result) - return result - - -###################################################################### - -cfunctype_table = {} - - -def parse_type_encoding(encoding): - """Takes a type encoding string and outputs a list of the separated type codes. - Currently does not handle unions or bitfields and strips out any field width - specifiers or type specifiers from the encoding. For Python 3.2+, encoding is - assumed to be a bytes object and not unicode. - - Examples: - parse_type_encoding('^v16@0:8') --> ['^v', '@', ':'] - parse_type_encoding('{CGSize=dd}40@0:8{CGSize=dd}16Q32') --> ['{CGSize=dd}', '@', ':', '{CGSize=dd}', 'Q'] - """ - type_encodings = [] - brace_count = 0 # number of unclosed curly braces - bracket_count = 0 # number of unclosed square brackets - typecode = b'' - for c in encoding: - # In Python 3, c comes out as an integer in the range 0-255. In Python 2, c is a single character string. - # To fix the disparity, we convert c to a bytes object if necessary. - if isinstance(c, int): - c = bytes([c]) - - if c == b'{': - # Check if this marked the end of previous type code. - if typecode and typecode[-1:] != b'^' and brace_count == 0 and bracket_count == 0: - type_encodings.append(typecode) - typecode = b'' - typecode += c - brace_count += 1 - elif c == b'}': - typecode += c - brace_count -= 1 - assert (brace_count >= 0) - elif c == b'[': - # Check if this marked the end of previous type code. - if typecode and typecode[-1:] != b'^' and brace_count == 0 and bracket_count == 0: - type_encodings.append(typecode) - typecode = b'' - typecode += c - bracket_count += 1 - elif c == b']': - typecode += c - bracket_count -= 1 - assert (bracket_count >= 0) - elif brace_count or bracket_count: - # Anything encountered while inside braces or brackets gets stuck on. - typecode += c - elif c in b'0123456789': - # Ignore field width specifiers for now. - pass - elif c in b'rnNoORV': - # Also ignore type specifiers. - pass - elif c in b'^cislqCISLQfdBv*@#:b?': - if typecode and typecode[-1:] == b'^': - # Previous char was pointer specifier, so keep going. - typecode += c - else: - # Add previous type code to the list. - if typecode: - type_encodings.append(typecode) - # Start a new type code. - typecode = c - - # Add the last type code to the list - if typecode: - type_encodings.append(typecode) - - return type_encodings - - -# Limited to basic types and pointers to basic types. -# Does not try to handle arrays, arbitrary structs, unions, or bitfields. -# Assume that encoding is a bytes object and not unicode. -def cfunctype_for_encoding(encoding): - # Check if we've already created a CFUNCTYPE for this encoding. - # If so, then return the cached CFUNCTYPE. - if encoding in cfunctype_table: - return cfunctype_table[encoding] - - # Otherwise, create a new CFUNCTYPE for the encoding. - typecodes = {b'c': c_char, b'i': c_int, b's': c_short, b'l': c_long, b'q': c_longlong, - b'C': c_ubyte, b'I': c_uint, b'S': c_ushort, b'L': c_ulong, b'Q': c_ulonglong, - b'f': c_float, b'd': c_double, b'B': c_bool, b'v': None, b'*': c_char_p, - b'@': c_void_p, b'#': c_void_p, b':': c_void_p, NSPointEncoding: NSPoint, - NSSizeEncoding: NSSize, NSRectEncoding: NSRect, NSRangeEncoding: NSRange, - PyObjectEncoding: py_object} - argtypes = [] - for code in parse_type_encoding(encoding): - if code in typecodes: - argtypes.append(typecodes[code]) - elif code[0:1] == b'^' and code[1:] in typecodes: - argtypes.append(POINTER(typecodes[code[1:]])) - else: - raise Exception('unknown type encoding: ' + code) - - cfunctype = CFUNCTYPE(*argtypes) - - # Cache the new CFUNCTYPE in the cfunctype_table. - # We do this mainly because it prevents the CFUNCTYPE - # from being garbage-collected while we need it. - cfunctype_table[encoding] = cfunctype - return cfunctype - - -###################################################################### - -# After calling create_subclass, you must first register -# it with register_subclass before you may use it. -# You can add new methods after the class is registered, -# but you cannot add any new ivars. -def create_subclass(superclass, name): - if isinstance(superclass, str): - superclass = get_class(superclass) - return c_void_p(objc.objc_allocateClassPair(superclass, ensure_bytes(name), 0)) - - -def register_subclass(subclass): - objc.objc_registerClassPair(subclass) - - -# types is a string encoding the argument types of the method. -# The first type code of types is the return type (e.g. 'v' if void) -# The second type code must be '@' for id self. -# The third type code must be ':' for SEL cmd. -# Additional type codes are for types of other arguments if any. -def add_method(cls, selName, method, types): - type_encodings = parse_type_encoding(types) - assert (type_encodings[1] == b'@') # ensure id self typecode - assert (type_encodings[2] == b':') # ensure SEL cmd typecode - selector = get_selector(selName) - cfunctype = cfunctype_for_encoding(types) - imp = cfunctype(method) - objc.class_addMethod.argtypes = [c_void_p, c_void_p, cfunctype, c_char_p] - objc.class_addMethod(cls, selector, imp, types) - return imp - - -def add_ivar(cls, name, vartype): - return objc.class_addIvar(cls, ensure_bytes(name), sizeof(vartype), alignment(vartype), encoding_for_ctype(vartype)) - - -def set_instance_variable(obj, varname, value, vartype): - objc.object_setInstanceVariable.argtypes = [c_void_p, c_char_p, vartype] - objc.object_setInstanceVariable(obj, ensure_bytes(varname), value) - - -def get_instance_variable(obj, varname, vartype): - variable = vartype() - objc.object_getInstanceVariable(obj, ensure_bytes(varname), byref(variable)) - return variable.value - - -###################################################################### - -class ObjCMethod: - """This represents an unbound Objective-C method (really an IMP).""" - - # Note, need to map 'c' to c_byte rather than c_char, because otherwise - # ctypes converts the value into a one-character string which is generally - # not what we want at all, especially when the 'c' represents a bool var. - typecodes = {b'c': c_byte, b'i': c_int, b's': c_short, b'l': c_long, b'q': c_longlong, - b'C': c_ubyte, b'I': c_uint, b'S': c_ushort, b'L': c_ulong, b'Q': c_ulonglong, - b'f': c_float, b'd': c_double, b'B': c_bool, b'v': None, b'Vv': None, b'*': c_char_p, - b'@': c_void_p, b'#': c_void_p, b':': c_void_p, b'^v': c_void_p, b'?': c_void_p, - NSPointEncoding: NSPoint, NSSizeEncoding: NSSize, NSRectEncoding: NSRect, - NSRangeEncoding: NSRange, - PyObjectEncoding: py_object} - - cfunctype_table = {} - - def __init__(self, method): - """Initialize with an Objective-C Method pointer. We then determine - the return type and argument type information of the method.""" - self.selector = c_void_p(objc.method_getName(method)) - self.name = objc.sel_getName(self.selector) - self.pyname = self.name.replace(b':', b'_') - self.encoding = objc.method_getTypeEncoding(method) - self.return_type = objc.method_copyReturnType(method) - self.nargs = objc.method_getNumberOfArguments(method) - self.imp = c_void_p(objc.method_getImplementation(method)) - self.argument_types = [] - for i in range(self.nargs): - buffer = c_buffer(512) - objc.method_getArgumentType(method, i, buffer, len(buffer)) - self.argument_types.append(buffer.value) - # Get types for all the arguments. - try: - self.argtypes = [self.ctype_for_encoding(t) for t in self.argument_types] - except: - # print(f'no argtypes encoding for {self.name} ({self.argument_types})') - self.argtypes = None - # Get types for the return type. - try: - if self.return_type == b'@': - self.restype = ObjCInstance - elif self.return_type == b'#': - self.restype = ObjCClass - else: - self.restype = self.ctype_for_encoding(self.return_type) - except: - # print(f'no restype encoding for {self.name} ({self.return_type})') - self.restype = None - self.func = None - - def ctype_for_encoding(self, encoding): - """Return ctypes type for an encoded Objective-C type.""" - if encoding in self.typecodes: - return self.typecodes[encoding] - elif encoding[0:1] == b'^' and encoding[1:] in self.typecodes: - return POINTER(self.typecodes[encoding[1:]]) - elif encoding[0:1] == b'^' and encoding[1:] in [CGImageEncoding, NSZoneEncoding]: - # special cases - return c_void_p - elif encoding[0:1] == b'r' and encoding[1:] in self.typecodes: - # const decorator, don't care - return self.typecodes[encoding[1:]] - elif encoding[0:2] == b'r^' and encoding[2:] in self.typecodes: - # const pointer, also don't care - return POINTER(self.typecodes[encoding[2:]]) - else: - raise Exception('unknown encoding for %s: %s' % (self.name, encoding)) - - def get_prototype(self): - """Returns a ctypes CFUNCTYPE for the method.""" - if self.restype == ObjCInstance or self.restype == ObjCClass: - # Some hacky stuff to get around ctypes issues on 64-bit. Can't let - # ctypes convert the return value itself, because it truncates the pointer - # along the way. So instead, we must do set the return type to c_void_p to - # ensure we get 64-bit addresses and then convert the return value manually. - self.prototype = CFUNCTYPE(c_void_p, *self.argtypes) - else: - self.prototype = CFUNCTYPE(self.restype, *self.argtypes) - return self.prototype - - def __repr__(self): - return "
|`)?(\w*)\s*=\s*", cell['source'])
- if match is not None: doc_fns[match.group(1)] = i
- return doc_fns
-
-def link_markdown_cells(cells, modules):
- "Create documentation links for all cells in markdown with backticks."
- for i, cell in enumerate(cells):
- if cell['cell_type'] == 'markdown':
- cell['source'] = link_docstring(modules, cell['source'])
-
-def get_insert_idx(pos_dict, name):
- "Return the position to insert a given function doc in a notebook."
- keys,i = list(pos_dict.keys()),0
- while i < len(keys) and str.lower(keys[i]) < str.lower(name): i+=1
- if i == len(keys): return -1
- else: return pos_dict[keys[i]]
-
-def update_pos(pos_dict, start_key, nbr=2):
- "Update the `pos_dict` by moving all positions after `start_key` by `nbr`."
- for key,idx in pos_dict.items():
- if str.lower(key) >= str.lower(start_key): pos_dict[key] += nbr
- return pos_dict
-
-def insert_cells(cells, pos_dict, ft_name, append=False):
- "Insert the function doc `cells` at their correct position and updates `pos_dict`."
- idx = get_insert_idx(pos_dict, ft_name)
- if append or idx == -1: cells += [get_doc_cell(ft_name), get_empty_cell()]
- else:
- cells.insert(idx, get_doc_cell(ft_name))
- cells.insert(idx+1, get_empty_cell())
- pos_dict = update_pos(pos_dict, ft_name, 2)
- return cells, pos_dict
-
-def get_doc_path(mod, dest_path):
- strip_name = strip_fastai(mod.__name__)
- return os.path.join(dest_path,f'{strip_name}.ipynb')
-
-def generate_missing_metadata(dest_file):
- fn = Path(dest_file)
- meta_fn = fn.parent/'jekyll_metadata.ipynb'
- if not fn.exists() or not meta_fn.exists(): return print('Could not find notebooks:', fn, meta_fn)
- metadata_nb = read_nb(meta_fn)
-
- if has_metadata_cell(metadata_nb['cells'], fn.name): return
- nb = read_nb(fn)
- jmd = nb['metadata'].get('jekyll', {})
- fmt_params = ''
- for k,v in jmd.items(): fmt_params += f',\n {k}={stringify(v)}'
- metadata_cell = get_code_cell(f"update_nb_metadata('{Path(fn).name}'{fmt_params})", hidden=False)
- metadata_nb['cells'].append(metadata_cell)
- write_nb(metadata_nb, meta_fn)
-
-def update_nb_metadata(nb_path=None, title=None, summary=None, keywords='fastai', overwrite=True, **kwargs):
- "Creates jekyll metadata for given notebook path."
- nb = read_nb(nb_path)
- data = {'title': title, 'summary': summary, 'keywords': keywords, **kwargs}
- data = {k:v for (k,v) in data.items() if v is not None} # remove none values
- if not data: return
- nb['metadata']['jekyll'] = data
- write_nb(nb, nb_path)
- NotebookNotary().sign(nb)
-
-def has_metadata_cell(cells, fn):
- for c in cells:
- if re.search(f"update_nb_metadata\('{fn}'", c['source']): return c
-
-def stringify(s): return f'\'{s}\'' if isinstance(s, str) else s
-
-IMPORT_RE = re.compile(r"from (fastai[\.\w_]*)")
-def get_imported_modules(cells, nb_module_name=''):
- "Finds all submodules of notebook - sorted by submodules > top level modules > manual imports. This gives notebook imports priority"
- module_names = get_top_level_modules()
- nb_imports = [match.group(1) for cell in cells for match in IMPORT_RE.finditer(cell['source']) if cell['cell_type'] == 'code']
- parts = nb_module_name.split('.')
- parent_modules = ['.'.join(parts[:(x+1)]) for x in range_of(parts)] # Imports parent modules - a.b.c = [a, a.b, a.b.c]
- all_modules = module_names + nb_imports + parent_modules
- mods = [import_mod(m, ignore_errors=True) for m in all_modules]
- return [m for m in mods if m is not None]
-
-def get_top_level_modules(num_levels=1):
- mod_dir = Path(import_mod('fastai').__file__).parent
- filtered_n = filter(lambda x: x.count('.')<=num_levels, get_module_names(mod_dir))
- return sorted(filtered_n, key=lambda s: s.count('.'), reverse=True) # Submodules first (sorted by periods)
-
-NEW_FT_HEADER = '## New Methods - Please document or move to the undocumented section'
-UNDOC_HEADER = '## Undocumented Methods - Methods moved below this line will intentionally be hidden'
-def parse_sections(cells):
- old_cells, undoc_cells, new_cells = [], [], []
- current_section = old_cells
- for cell in cells:
- if cell['cell_type'] == 'markdown':
- if re.match(UNDOC_HEADER, cell['source']): current_section = undoc_cells
- if re.match(NEW_FT_HEADER, cell['source']): current_section = new_cells
- current_section.append(cell)
- undoc_cells = undoc_cells or [get_md_cell(UNDOC_HEADER)]
- new_cells = new_cells or [get_md_cell(NEW_FT_HEADER)]
- return old_cells, undoc_cells, new_cells
-
-def remove_undoc_cells(cells):
- old, _, _ = parse_sections(cells)
- return old
-
-# currently code vbox sub-cells mainly
-def remove_code_cell_jupyter_widget_state_elem(cells):
- for c in cells:
- if c['cell_type'] == 'code':
- if 'outputs' in c:
- c['outputs'] = [l for l in c['outputs'] if not ('data' in l and 'application/vnd.jupyter.widget-view+json' in l.data)]
- return cells
-
-def update_module_page(mod, dest_path='.'):
- "Update the documentation notebook of a given module."
- doc_path = get_doc_path(mod, dest_path)
- strip_name = strip_fastai(mod.__name__)
- nb = read_nb(doc_path)
- cells = nb['cells']
-
- link_markdown_cells(cells, get_imported_modules(cells, mod.__name__))
-
- type_dict = read_nb_types(cells)
- gvar_map = get_global_vars(mod)
- for name in get_exports(mod):
- if name not in gvar_map: continue
- code = gvar_map[name]
- if name in type_dict: cells[type_dict[name]] = get_md_cell(code)
- else: cells.append(get_md_cell(code))
-
- pos_dict = read_nb_content(cells, strip_name)
- ft_names = get_ft_names(mod, include_inner=True)
- new_fts = list(set(ft_names) - set(pos_dict.keys()))
- if new_fts: print(f'Found new fuctions for {mod}. Please document:\n{new_fts}')
- existing, undoc_cells, new_cells = parse_sections(cells)
- for ft_name in new_fts: new_cells.extend([get_doc_cell(ft_name), get_empty_cell()])
- if len(new_cells) > 1: nb['cells'] = existing + undoc_cells + new_cells
-
- write_nb(nb, doc_path)
- return doc_path
-
-def link_nb(nb_path):
- nb = read_nb(nb_path)
- cells = nb['cells']
- link_markdown_cells(cells, get_imported_modules(cells, Path(nb_path).stem))
- write_nb(nb, nb_path)
- NotebookNotary().sign(read_nb(nb_path))
-
-def get_module_from_notebook(doc_path):
- "Find module given a source path. Assume it belongs to fastai directory"
- return f'fastai.{Path(doc_path).stem}'
-
-def check_nbconvert_version():
- import nbconvert
- assert nbconvert.version_info >= (5,4,0), "Please update nbconvert to >=5.4 for consistent .html output"
-
-def update_notebooks(source_path, dest_path=None, update_html=True, document_new_fns=False,
- update_nb_links=True, html_path=None, force=False):
- "`source_path` can be a directory or a file. Assume all modules reside in the fastai directory."
- from .convert2html import convert_nb
- source_path = Path(source_path)
-
- if source_path.is_file():
- dest_path = source_path.parent if dest_path is None else Path(dest_path)
- html_path = dest_path/'..'/'docs' if html_path is None else Path(html_path)
- doc_path = source_path
- assert source_path.suffix == '.ipynb', 'Must update from notebook or module'
- if document_new_fns:
- mod = import_mod(get_module_from_notebook(source_path))
- if not mod: print('Could not find module for path:', source_path)
- elif mod.__file__.endswith('__init__.py'): pass
- else: update_module_page(mod, dest_path)
- generate_missing_metadata(doc_path)
- if update_nb_links:
- print(f'Updating notebook {doc_path}. Please wait...')
- link_nb(doc_path)
- execute_nb(doc_path, {'metadata': {'path': doc_path.parent}}, show_doc_only=True)
- if update_html:
- check_nbconvert_version()
- html_fn = html_path/doc_path.with_suffix('.html').name
- if not force and html_fn.is_file():
- in_mod = os.path.getmtime(doc_path)
- out_mod = os.path.getmtime(html_fn)
- if in_mod < out_mod: return
- convert_nb(doc_path, html_path)
-
- elif (source_path.name.startswith('fastai.')):
- # Do module update
- assert dest_path is not None, 'To update a module, you must specify a destination folder for where notebook resides'
- mod = import_mod(source_path.name)
- if not mod: return print('Could not find module for:', source_path)
- doc_path = Path(dest_path)/(strip_fastai(mod.__name__)+'.ipynb')
- if not doc_path.exists():
- print('Notebook does not exist. Creating:', doc_path)
- create_module_page(mod, dest_path)
- update_notebooks(doc_path, dest_path=dest_path, update_html=update_html, document_new_fns=document_new_fns,
- update_nb_links=update_nb_links, html_path=html_path)
- elif source_path.is_dir():
- for f in sorted(Path(source_path).glob('*.ipynb')):
- update_notebooks(f, dest_path=dest_path, update_html=update_html, document_new_fns=document_new_fns,
- update_nb_links=update_nb_links, html_path=html_path)
- else: print('Could not resolve source file:', source_path)
diff --git a/spaces/ali-ghamdan/realesrgan-models/tests/test_discriminator_arch.py b/spaces/ali-ghamdan/realesrgan-models/tests/test_discriminator_arch.py
deleted file mode 100644
index c56a40c7743630aa63b3e99bca8dc1a85949c4c5..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/realesrgan-models/tests/test_discriminator_arch.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import torch
-
-from realesrgan.archs.discriminator_arch import UNetDiscriminatorSN
-
-
-def test_unetdiscriminatorsn():
- """Test arch: UNetDiscriminatorSN."""
-
- # model init and forward (cpu)
- net = UNetDiscriminatorSN(num_in_ch=3, num_feat=4, skip_connection=True)
- img = torch.rand((1, 3, 32, 32), dtype=torch.float32)
- output = net(img)
- assert output.shape == (1, 1, 32, 32)
-
- # model init and forward (gpu)
- if torch.cuda.is_available():
- net.cuda()
- output = net(img.cuda())
- assert output.shape == (1, 1, 32, 32)
diff --git a/spaces/alistairmcleay/cambridge-masters-project/src/crazyneuraluser/user_model_code/dataset.py b/spaces/alistairmcleay/cambridge-masters-project/src/crazyneuraluser/user_model_code/dataset.py
deleted file mode 100644
index b9e51f785685afc33eee782344108b7891a25452..0000000000000000000000000000000000000000
--- a/spaces/alistairmcleay/cambridge-masters-project/src/crazyneuraluser/user_model_code/dataset.py
+++ /dev/null
@@ -1,297 +0,0 @@
-import json
-import os
-
-import torch
-from tqdm import tqdm
-
-from crazyneuraluser.user_model_code.utils_sgd import (
- add_str,
- get_special_tokens,
- wrap_element,
-)
-
-
-class SGD_Dataset(torch.utils.data.Dataset):
- def __init__(self, args, tokenizer, data_split, generation, data_size):
- assert data_split in ["train", "dev", "test", "demo"]
- self.args = args
- self.data_size = data_size
- self.tokenizer = tokenizer
- self.data_split = data_split
- self.generation = generation
- self.n_trimmed = 0
-
- self.SPECIAL_TOKENS = get_special_tokens()
- self._get_special_token_ids()
-
- # create examples
- self.examples = []
- for data_name in args.data_list:
- examples = self._create_examples(data_name, data_split)
- self.examples += examples
- print("Total ({}) -> {} examples".format(data_split, len(self.examples)))
-
- def _get_special_token_ids(self):
- self.bos_id = self.tokenizer.convert_tokens_to_ids(
- self.SPECIAL_TOKENS["bos_token"]
- )
- self.eos_id = self.tokenizer.convert_tokens_to_ids(
- self.SPECIAL_TOKENS["eos_token"]
- )
- self.pad_id = self.tokenizer.convert_tokens_to_ids(
- self.SPECIAL_TOKENS["pad_token"]
- )
- self.sep_id = self.tokenizer.convert_tokens_to_ids(
- self.SPECIAL_TOKENS["sep_token"]
- )
- # print('SPECIAL TOKEN MAPPING:')
- # print('bos:{} | eos:{} | pad:{} | sep:{}'.format(self.bos_id, self.eos_id, self.pad_id, self.sep_id))
-
- self.add_special_token_ids = {}
- for token in self.SPECIAL_TOKENS["additional_special_tokens"]:
- self.add_special_token_ids[token] = self.tokenizer.convert_tokens_to_ids(
- token
- )
-
- self.true_token, self.false_token = "_True_", "_False_"
- assert self.true_token in self.SPECIAL_TOKENS["additional_special_tokens"]
- assert self.false_token in self.SPECIAL_TOKENS["additional_special_tokens"]
- """
- if using BPE (default method, simply call tokenizer(natural sentence)), no need unk_token
- if using convert_tokens_to_ids, check which is correct way to handle oov:
- a) simply use as unk_token (default setup) or
- b) add unk_token into special tokens
- """
-
- def _create_examples(self, data_name, data_split):
- data_file = os.path.join(
- self.args.data_dir, data_name, "{}.json".format(data_split)
- )
- with open(data_file) as f:
- data = json.load(f)
-
- examples = []
- for dial_id in tqdm(sorted(data.keys())):
- if self.data_size != -1 and len(examples) >= self.data_size:
- break
- dial_meta = data[dial_id]
- context = ""
- for i in range(100):
- example_id = "{}-{}".format(dial_id, i)
- self.example_id = example_id
- if example_id not in dial_meta:
- break
-
- # testing #
- # # SGD
- # if data_split == "test" and dial_id not in ["10_00056", "10_00075"]: # seen, movie domain
- # if data_split == "test" and dial_id not in ["16_00040"]: # seen
- # if data_split == "test" and dial_id not in ["8_00066", "16_00095", "8_00065"]: # unseen
- # if data_split == "test" and dial_id not in ["9_00121", "9_00122"]:
- # # req_alts cases w/i, w/o inform
- # continue
- # # mwoz
- # if data_split == "test" and dial_id not in ["MUL0071.json"]:
- # # test predictions in no offer & no book
- # continue
-
- # turn info
- goal = dial_meta[example_id]["goal"]
- # service = dial_meta[example_id]["service"]
- # intent = dial_meta[example_id]["intent"]
-
- # utterances
- usr_utt = dial_meta[example_id]["utterances"]["usr"]
- sys_utt = dial_meta[example_id]["utterances"]["sys"]
-
- # actions
- usr_act = dial_meta[example_id]["actions"]["usr"]
- sys_act = dial_meta[example_id]["actions"]["sys"]
-
- # binary flags
- snt = dial_meta[example_id]["start_new_task"]
- gc = dial_meta[example_id]["goal_change"]
- ra = dial_meta[example_id]["req_alts"]
-
- # get input ids
- (
- input_seq,
- input_ids,
- label_ids,
- valid_example,
- ) = self._prepare_input_ids(
- goal, context, usr_utt, usr_act, sys_utt, sys_act, snt, gc, ra
- )
-
- if valid_example:
- assert len(input_ids) < 1024
- dial_meta[example_id]["context"] = context
- examples.append(
- {
- "input_ids": input_ids, # list of ids
- "label_ids": label_ids, # list of ids
- "metadata": dial_meta[example_id],
- "example_id": self.example_id,
- "data_name": data_name,
- }
- )
-
- # collect context
- sys_utt_wrap = wrap_element("SYS", sys_utt)
- usr_utt_wrap = wrap_element("USR", usr_utt)
- context = add_str(context, sys_utt_wrap)
- context = add_str(context, usr_utt_wrap)
-
- print(
- "Data Stat: {} ({}) -> {} examples ({} examples are trimmed)".format(
- data_name, self.data_split, len(examples), self.n_trimmed
- )
- )
- return examples
-
- def _prepare_input_ids(
- self, goal, context, usr_utt, usr_act, sys_utt, sys_act, snt, gc, ra
- ):
- """
- prepare input sequence ids to GPT2
- template:
- """
- goal_wrap = wrap_element("GOAL", goal)
- context_wrap = wrap_element("CTX", context)
- usr_utt_wrap = wrap_element("USR_UTT", usr_utt)
- usr_act_wrap = wrap_element("USR_ACT", usr_act)
- sys_utt_wrap = wrap_element("SYS_UTT", sys_utt)
- sys_act_wrap = wrap_element("SYS_ACT", sys_act)
-
- snt = self.true_token if snt else self.false_token # `Start New Task` flag
- snt_wrap = wrap_element("SNT", snt)
- gc = self.true_token if gc else self.false_token # `Goal Change` flag
- gc_wrap = wrap_element("GC", gc)
- ra = self.true_token if ra else self.false_token # `Request Alternatives` flag
- ra_wrap = wrap_element("RA", ra)
- if self.args.use_ra_flag:
- flags_wrap = snt_wrap + " " + ra_wrap + " " + gc_wrap
- else:
- flags_wrap = snt_wrap + " " + gc_wrap
-
- if not self.generation: # supervised
- input_seq = (
- context_wrap
- + " "
- + sys_utt_wrap
- + " "
- + sys_act_wrap
- + " "
- + flags_wrap
- + " "
- + goal_wrap
- + " "
- + usr_act_wrap
- + " "
- + usr_utt_wrap
- + " "
- + self.SPECIAL_TOKENS["eos_token"]
- )
- input_ids = self.tokenizer(input_seq)["input_ids"] # convert to ids
- label_ids = self._get_labels(input_ids)
- else: # generation
- input_seq = (
- context_wrap
- + " "
- + sys_utt_wrap
- + " "
- + sys_act_wrap
- + " "
- + flags_wrap
- + " "
- + goal_wrap
- + " "
- + " ", "
-#include
-#include
-#include
-#include "portaudio.h"
-
-#ifndef M_PI
-#define M_PI (3.14159265)
-#endif
-#define TWOPI (M_PI * 2.0)
-
-#define DEFAULT_BUFFER_SIZE (32)
-
-typedef struct
-{
- double left_phase;
- double right_phase;
-}
-paTestData;
-
-/* Very simple synthesis routine to generate two sine waves. */
-static int paminlatCallback( const void *inputBuffer, void *outputBuffer,
- unsigned long framesPerBuffer,
- const PaStreamCallbackTimeInfo* timeInfo,
- PaStreamCallbackFlags statusFlags,
- void *userData )
-{
- paTestData *data = (paTestData*)userData;
- float *out = (float*)outputBuffer;
- unsigned int i;
- double left_phaseInc = 0.02;
- double right_phaseInc = 0.06;
-
- double left_phase = data->left_phase;
- double right_phase = data->right_phase;
-
- for( i=0; i TWOPI ) left_phase -= TWOPI;
- *out++ = (float) sin( left_phase );
-
- right_phase += right_phaseInc;
- if( right_phase > TWOPI ) right_phase -= TWOPI;
- *out++ = (float) sin( right_phase );
- }
-
- data->left_phase = left_phase;
- data->right_phase = right_phase;
- return 0;
-}
-
-int main( int argc, char **argv );
-int main( int argc, char **argv )
-{
- PaStreamParameters outputParameters;
- PaStream *stream;
- PaError err;
- paTestData data;
- int go;
- int outLatency = 0;
- int minLatency = DEFAULT_BUFFER_SIZE * 2;
- int framesPerBuffer;
- double sampleRate = 44100.0;
- char str[256];
- char *line;
-
- printf("pa_minlat - Determine minimum latency for your computer.\n");
- printf(" usage: pa_minlat {userBufferSize}\n");
- printf(" for example: pa_minlat 64\n");
- printf("Adjust your stereo until you hear a smooth tone in each speaker.\n");
- printf("Then try to find the smallest number of frames that still sounds smooth.\n");
- printf("Note that the sound will stop momentarily when you change the number of buffers.\n");
-
- /* Get bufferSize from command line. */
- framesPerBuffer = ( argc > 1 ) ? atol( argv[1] ) : DEFAULT_BUFFER_SIZE;
- printf("Frames per buffer = %d\n", framesPerBuffer );
-
- data.left_phase = data.right_phase = 0.0;
-
- err = Pa_Initialize();
- if( err != paNoError ) goto error;
-
- outLatency = sampleRate * 200.0 / 1000.0; /* 200 msec. */
-
- /* Try different numBuffers in a loop. */
- go = 1;
- while( go )
- {
- outputParameters.device = Pa_GetDefaultOutputDevice(); /* Default output device. */
- outputParameters.channelCount = 2; /* Stereo output */
- outputParameters.sampleFormat = paFloat32; /* 32 bit floating point output. */
- outputParameters.suggestedLatency = (double)outLatency / sampleRate; /* In seconds. */
- outputParameters.hostApiSpecificStreamInfo = NULL;
-
- printf("Latency = %d frames = %6.1f msec.\n", outLatency, outputParameters.suggestedLatency * 1000.0 );
-
- err = Pa_OpenStream(
- &stream,
- NULL, /* no input */
- &outputParameters,
- sampleRate,
- framesPerBuffer,
- paClipOff, /* we won't output out of range samples so don't bother clipping them */
- paminlatCallback,
- &data );
- if( err != paNoError ) goto error;
- if( stream == NULL ) goto error;
-
- /* Start audio. */
- err = Pa_StartStream( stream );
- if( err != paNoError ) goto error;
-
- /* Ask user for a new nlatency. */
- printf("\nMove windows around to see if the sound glitches.\n");
- printf("Latency now %d, enter new number of frames, or 'q' to quit: ", outLatency );
- line = fgets( str, 256, stdin );
- if( line == NULL )
- {
- go = 0;
- }
- else
- {
- {
- /* Get rid of newline */
- size_t l = strlen( str ) - 1;
- if( str[ l ] == '\n')
- str[ l ] = '\0';
- }
-
-
- if( str[0] == 'q' ) go = 0;
- else
- {
- outLatency = atol( str );
- if( outLatency < minLatency )
- {
- printf( "Latency below minimum of %d! Set to minimum!!!\n", minLatency );
- outLatency = minLatency;
- }
- }
-
- }
- /* Stop sound until ENTER hit. */
- err = Pa_StopStream( stream );
- if( err != paNoError ) goto error;
- err = Pa_CloseStream( stream );
- if( err != paNoError ) goto error;
- }
- printf("A good setting for latency would be somewhat higher than\n");
- printf("the minimum latency that worked.\n");
- printf("PortAudio: Test finished.\n");
- Pa_Terminate();
- return 0;
-error:
- Pa_Terminate();
- fprintf( stderr, "An error occurred while using the portaudio stream\n" );
- fprintf( stderr, "Error number: %d\n", err );
- fprintf( stderr, "Error message: %s\n", Pa_GetErrorText( err ) );
- return 1;
-}
diff --git a/spaces/amgross01/Stocks_Trading_Assistant/stocks.py b/spaces/amgross01/Stocks_Trading_Assistant/stocks.py
deleted file mode 100644
index ce96f77e43cc0b0ea2b3579c565ffb3170180214..0000000000000000000000000000000000000000
--- a/spaces/amgross01/Stocks_Trading_Assistant/stocks.py
+++ /dev/null
@@ -1,126 +0,0 @@
-from configparser import ParsingError
-from logging import raiseExceptions
-import yfinance as yf
-import requests
-import pandas as pd
-from bs4 import BeautifulSoup
-
-class Stock_Data(object):
- '''
- This class contains 5 methods responsible for choosing a stock's ticker, then checking whether the
- stock exchange it is listed in is open or not, and in case it is, it gets data for the last 6 months
- from "yfinance" module of Yahoo Inc. which will be fed to the models.
- '''
-
- def Ticker(self, tick):
- '''
- This method will "carry" the company's ticker, and it will also be used as a placeholder.
- '''
- global ticker
- ticker = tick
-
- return ticker
-
-
- def status_getter(self, Ticker):
- '''
- This method gets the company ticker the user chooses, creates a www.marketwatch.com
- link, then scraps the HTML code of the corresponding company page in marketwatch website,
- and gets the current market status of the exchange this stock is listed in. Possible values are:
- After Hours, Open, and Market Closed.
- '''
- global company_ticker
- company_ticker = Ticker
- link_1 = 'https://www.marketwatch.com/investing/stock/'
- link_2 = '?mod=search_symbol'
- # Pasting the above 3 parts to create the URL
- global final_link
- final_link = link_1 + company_ticker + link_2
-
- page = requests.get(final_link)
- global soup
- soup = BeautifulSoup(page.text, "lxml")
- if soup is None:
- raise ParsingError("HTML code of MarketWatch website was not scraped and current status can not be found")
- else:
- current_status = soup.find("div", class_="status").text # Finding the market status
- return current_status
-
-
- def current_price_getter(self, Ticker):
- '''
- This method will get the current price only if the market is open.
- '''
- current_price = None
- if self.status_getter(Ticker) == "Open":
- current_price = float(soup.find("bg-quote", class_="value").text.replace(',',''))
- return current_price
- else:
- return "Market Closed"
-
- def stock_data_getter(self, Ticker):
- '''
- This method will return a dataframe containing Stock data from the Yahoo's "yfinance"
- library in case the market is open.
- '''
- if self.status_getter(Ticker) == "Open":
- data = yf.download(tickers = str(Ticker), period = "6mo", interval = "1d")
- df = pd.DataFrame(data)
- return df
- else:
- return "Market Closed"
-
- def LSTM_stock_data_getter(self, Ticker):
- '''
- This method will return a dataframe containing Stock data from the Yahoo's "yfinance"
- library regardrless of whether the market is open or not, and will feed the LSTM model.
- '''
- data = yf.download(tickers = str(Ticker), period = "2y", interval = "1d")
- df = pd.DataFrame(data)
-
- # Prediction in the data we evaluate the model
- # If the user wants to run the model with the data that has been evaluated and predicted for , uncomment the 2 lines below
- # Setting the start = 2022-08-26 and end = 2020-08-26 Yahoo Finance will return data from 25-8-2020 to 25-8-2022 (2 years period).
- # In those data our model has been evaluated.
-
- #data = yf.download(tickers = str(Ticker),end="2022-08-26", start="2020-08-26")
- #df = pd.DataFrame(data)
-
- return df
-
-
- def article_parser(self, ticker):
- '''
- This method gets as input a stock ticker, creates the www.marketwatch.com link of this stock
- and returns a dataframe with the last 17 articles' headers.
- '''
- company_ticker = self.Ticker(tick=ticker)
- link_1 = 'https://www.marketwatch.com/investing/stock/'
- link_2 = '?mod=search_symbol'
- # Pasting the above 3 parts to create the URL
- final_link = link_1 + company_ticker + link_2
-
-
- page = requests.get(final_link)
- soup = BeautifulSoup(page.content, "html.parser")
- results = soup.find("div", class_="tab__pane is-active j-tabPane")
- articles = results.find_all("a", class_="link")
-
- headerList = ["ticker", "headline"]
- rows = []
- counter = 1
- df_headers = pd.DataFrame()
-
- for art in articles:
- if counter <= 17:
- ticker = company_ticker
- title = art.text.strip()
- if title is None:
- break
- rows.append([ticker, title])
- counter = counter + 1
-
- df_headers = pd.DataFrame(rows, columns=headerList)
-
- return df_headers
-
diff --git a/spaces/anaclaudia13ct/insect_detection/utils/autobatch.py b/spaces/anaclaudia13ct/insect_detection/utils/autobatch.py
deleted file mode 100644
index bdeb91c3d2bd15e53eb65715228932d3e87e0989..0000000000000000000000000000000000000000
--- a/spaces/anaclaudia13ct/insect_detection/utils/autobatch.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Auto-batch utils
-"""
-
-from copy import deepcopy
-
-import numpy as np
-import torch
-
-from utils.general import LOGGER, colorstr
-from utils.torch_utils import profile
-
-
-def check_train_batch_size(model, imgsz=640, amp=True):
- # Check YOLOv5 training batch size
- with torch.cuda.amp.autocast(amp):
- return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size
-
-
-def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
- # Automatically estimate best YOLOv5 batch size to use `fraction` of available CUDA memory
- # Usage:
- # import torch
- # from utils.autobatch import autobatch
- # model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
- # print(autobatch(model))
-
- # Check device
- prefix = colorstr('AutoBatch: ')
- LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}')
- device = next(model.parameters()).device # get model device
- if device.type == 'cpu':
- LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')
- return batch_size
- if torch.backends.cudnn.benchmark:
- LOGGER.info(f'{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}')
- return batch_size
-
- # Inspect CUDA memory
- gb = 1 << 30 # bytes to GiB (1024 ** 3)
- d = str(device).upper() # 'CUDA:0'
- properties = torch.cuda.get_device_properties(device) # device properties
- t = properties.total_memory / gb # GiB total
- r = torch.cuda.memory_reserved(device) / gb # GiB reserved
- a = torch.cuda.memory_allocated(device) / gb # GiB allocated
- f = t - (r + a) # GiB free
- LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free')
-
- # Profile batch sizes
- batch_sizes = [1, 2, 4, 8, 16]
- try:
- img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes]
- results = profile(img, model, n=3, device=device)
- except Exception as e:
- LOGGER.warning(f'{prefix}{e}')
-
- # Fit a solution
- y = [x[2] for x in results if x] # memory [2]
- p = np.polyfit(batch_sizes[:len(y)], y, deg=1) # first degree polynomial fit
- b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size)
- if None in results: # some sizes failed
- i = results.index(None) # first fail index
- if b >= batch_sizes[i]: # y intercept above failure point
- b = batch_sizes[max(i - 1, 0)] # select prior safe point
- if b < 1 or b > 1024: # b outside of safe range
- b = batch_size
- LOGGER.warning(f'{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.')
-
- fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted
- LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅')
- return b
diff --git a/spaces/andreped/AeroPath/demo/src/utils.py b/spaces/andreped/AeroPath/demo/src/utils.py
deleted file mode 100644
index 4a7f9f9e012e1cb52c20564912df91306455a9ca..0000000000000000000000000000000000000000
--- a/spaces/andreped/AeroPath/demo/src/utils.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import nibabel as nib
-import numpy as np
-
-
-def load_ct_to_numpy(data_path):
- if not isinstance(data_path, str):
- data_path = data_path.name
-
- image = nib.load(data_path)
- data = image.get_fdata()
-
- data = np.rot90(data, k=1, axes=(0, 1))
-
- data[data < -1024] = 1024
- data[data > 1024] = 1024
-
- data = data - np.amin(data)
- data = data / np.amax(data) * 255
- data = data.astype("uint8")
-
- print(data.shape)
- return [data[..., i] for i in range(data.shape[-1])]
-
-
-def load_pred_volume_to_numpy(data_path):
- if not isinstance(data_path, str):
- data_path = data_path.name
-
- image = nib.load(data_path)
- data = image.get_fdata()
-
- data = np.rot90(data, k=1, axes=(0, 1))
-
- data[data > 0] = 1
- data = data.astype("uint8")
-
- print(data.shape)
- return [data[..., i] for i in range(data.shape[-1])]
diff --git a/spaces/antinous/dreambooth-training/README.md b/spaces/antinous/dreambooth-training/README.md
deleted file mode 100644
index 66a852da46de13165bc3419e7e427c8ad76b97e0..0000000000000000000000000000000000000000
--- a/spaces/antinous/dreambooth-training/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Dreambooth Training
-emoji: ☁️
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.11
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: multimodalart/dreambooth-training
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/aodianyun/panoptic-segment-anything/segment_anything/segment_anything/modeling/transformer.py b/spaces/aodianyun/panoptic-segment-anything/segment_anything/segment_anything/modeling/transformer.py
deleted file mode 100644
index f1a2812f613cc55b1d0b3e3e1d0c84a760d1fb87..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/panoptic-segment-anything/segment_anything/segment_anything/modeling/transformer.py
+++ /dev/null
@@ -1,240 +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.
-
-import torch
-from torch import Tensor, nn
-
-import math
-from typing import Tuple, Type
-
-from .common import MLPBlock
-
-
-class TwoWayTransformer(nn.Module):
- def __init__(
- self,
- depth: int,
- embedding_dim: int,
- num_heads: int,
- mlp_dim: int,
- activation: Type[nn.Module] = nn.ReLU,
- attention_downsample_rate: int = 2,
- ) -> None:
- """
- A transformer decoder that attends to an input image using
- queries whose positional embedding is supplied.
-
- Args:
- depth (int): number of layers in the transformer
- embedding_dim (int): the channel dimension for the input embeddings
- num_heads (int): the number of heads for multihead attention. Must
- divide embedding_dim
- mlp_dim (int): the channel dimension internal to the MLP block
- activation (nn.Module): the activation to use in the MLP block
- """
- super().__init__()
- self.depth = depth
- self.embedding_dim = embedding_dim
- self.num_heads = num_heads
- self.mlp_dim = mlp_dim
- self.layers = nn.ModuleList()
-
- for i in range(depth):
- self.layers.append(
- TwoWayAttentionBlock(
- embedding_dim=embedding_dim,
- num_heads=num_heads,
- mlp_dim=mlp_dim,
- activation=activation,
- attention_downsample_rate=attention_downsample_rate,
- skip_first_layer_pe=(i == 0),
- )
- )
-
- self.final_attn_token_to_image = Attention(
- embedding_dim, num_heads, downsample_rate=attention_downsample_rate
- )
- self.norm_final_attn = nn.LayerNorm(embedding_dim)
-
- def forward(
- self,
- image_embedding: Tensor,
- image_pe: Tensor,
- point_embedding: Tensor,
- ) -> Tuple[Tensor, Tensor]:
- """
- Args:
- image_embedding (torch.Tensor): image to attend to. Should be shape
- B x embedding_dim x h x w for any h and w.
- image_pe (torch.Tensor): the positional encoding to add to the image. Must
- have the same shape as image_embedding.
- point_embedding (torch.Tensor): the embedding to add to the query points.
- Must have shape B x N_points x embedding_dim for any N_points.
-
- Returns:
- torch.Tensor: the processed point_embedding
- torch.Tensor: the processed image_embedding
- """
- # BxCxHxW -> BxHWxC == B x N_image_tokens x C
- bs, c, h, w = image_embedding.shape
- image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
- image_pe = image_pe.flatten(2).permute(0, 2, 1)
-
- # Prepare queries
- queries = point_embedding
- keys = image_embedding
-
- # Apply transformer blocks and final layernorm
- for layer in self.layers:
- queries, keys = layer(
- queries=queries,
- keys=keys,
- query_pe=point_embedding,
- key_pe=image_pe,
- )
-
- # Apply the final attenion layer from the points to the image
- q = queries + point_embedding
- k = keys + image_pe
- attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
- queries = queries + attn_out
- queries = self.norm_final_attn(queries)
-
- return queries, keys
-
-
-class TwoWayAttentionBlock(nn.Module):
- def __init__(
- self,
- embedding_dim: int,
- num_heads: int,
- mlp_dim: int = 2048,
- activation: Type[nn.Module] = nn.ReLU,
- attention_downsample_rate: int = 2,
- skip_first_layer_pe: bool = False,
- ) -> None:
- """
- A transformer block with four layers: (1) self-attention of sparse
- inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
- block on sparse inputs, and (4) cross attention of dense inputs to sparse
- inputs.
-
- Arguments:
- embedding_dim (int): the channel dimension of the embeddings
- num_heads (int): the number of heads in the attention layers
- mlp_dim (int): the hidden dimension of the mlp block
- activation (nn.Module): the activation of the mlp block
- skip_first_layer_pe (bool): skip the PE on the first layer
- """
- super().__init__()
- self.self_attn = Attention(embedding_dim, num_heads)
- self.norm1 = nn.LayerNorm(embedding_dim)
-
- self.cross_attn_token_to_image = Attention(
- embedding_dim, num_heads, downsample_rate=attention_downsample_rate
- )
- self.norm2 = nn.LayerNorm(embedding_dim)
-
- self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
- self.norm3 = nn.LayerNorm(embedding_dim)
-
- self.norm4 = nn.LayerNorm(embedding_dim)
- self.cross_attn_image_to_token = Attention(
- embedding_dim, num_heads, downsample_rate=attention_downsample_rate
- )
-
- self.skip_first_layer_pe = skip_first_layer_pe
-
- def forward(
- self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
- ) -> Tuple[Tensor, Tensor]:
- # Self attention block
- if self.skip_first_layer_pe:
- queries = self.self_attn(q=queries, k=queries, v=queries)
- else:
- q = queries + query_pe
- attn_out = self.self_attn(q=q, k=q, v=queries)
- queries = queries + attn_out
- queries = self.norm1(queries)
-
- # Cross attention block, tokens attending to image embedding
- q = queries + query_pe
- k = keys + key_pe
- attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
- queries = queries + attn_out
- queries = self.norm2(queries)
-
- # MLP block
- mlp_out = self.mlp(queries)
- queries = queries + mlp_out
- queries = self.norm3(queries)
-
- # Cross attention block, image embedding attending to tokens
- q = queries + query_pe
- k = keys + key_pe
- attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
- keys = keys + attn_out
- keys = self.norm4(keys)
-
- return queries, keys
-
-
-class Attention(nn.Module):
- """
- An attention layer that allows for downscaling the size of the embedding
- after projection to queries, keys, and values.
- """
-
- def __init__(
- self,
- embedding_dim: int,
- num_heads: int,
- downsample_rate: int = 1,
- ) -> None:
- super().__init__()
- self.embedding_dim = embedding_dim
- self.internal_dim = embedding_dim // downsample_rate
- self.num_heads = num_heads
- assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."
-
- self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
- self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
- self.v_proj = nn.Linear(embedding_dim, self.internal_dim)
- self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
-
- def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
- b, n, c = x.shape
- x = x.reshape(b, n, num_heads, c // num_heads)
- return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head
-
- def _recombine_heads(self, x: Tensor) -> Tensor:
- b, n_heads, n_tokens, c_per_head = x.shape
- x = x.transpose(1, 2)
- return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C
-
- def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
- # Input projections
- q = self.q_proj(q)
- k = self.k_proj(k)
- v = self.v_proj(v)
-
- # Separate into heads
- q = self._separate_heads(q, self.num_heads)
- k = self._separate_heads(k, self.num_heads)
- v = self._separate_heads(v, self.num_heads)
-
- # Attention
- _, _, _, c_per_head = q.shape
- attn = q @ k.permute(0, 1, 3, 2) # B x N_heads x N_tokens x N_tokens
- attn = attn / math.sqrt(c_per_head)
- attn = torch.softmax(attn, dim=-1)
-
- # Get output
- out = attn @ v
- out = self._recombine_heads(out)
- out = self.out_proj(out)
-
- return out
diff --git a/spaces/apsys/hetfit/utils/__init__.py b/spaces/apsys/hetfit/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/arch-123/bingo/src/pages/api/blob.ts b/spaces/arch-123/bingo/src/pages/api/blob.ts
deleted file mode 100644
index fecd48031916b2284b8958892196e0a1ad420421..0000000000000000000000000000000000000000
--- a/spaces/arch-123/bingo/src/pages/api/blob.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { Readable } from 'node:stream'
-import { fetch } from '@/lib/isomorphic'
-
-const API_DOMAIN = 'https://www.bing.com'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
- try {
- const { bcid } = req.query
-
- const { headers, body } = await fetch(`${API_DOMAIN}/images/blob?bcid=${bcid}`,
- {
- method: 'GET',
- headers: {
- "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
- "sec-ch-ua-mobile": "?0",
- "sec-ch-ua-platform": "\"Windows\"",
- "Referrer-Policy": "origin-when-cross-origin",
- },
- },
- )
-
- res.writeHead(200, {
- 'Content-Length': headers.get('content-length')!,
- 'Content-Type': headers.get('content-type')!,
- })
- // @ts-ignore
- return Readable.fromWeb(body!).pipe(res)
- } catch (e) {
- console.log('Error', e)
- return res.json({
- result: {
- value: 'UploadFailed',
- message: `${e}`
- }
- })
- }
-}
diff --git a/spaces/artblack01/Pix2Pix-Video/README.md b/spaces/artblack01/Pix2Pix-Video/README.md
deleted file mode 100644
index 3d8f7d06e470e918dedf27b7a230a565996a1252..0000000000000000000000000000000000000000
--- a/spaces/artblack01/Pix2Pix-Video/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Pix2Pix Video
-emoji: 🎨🎞️
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
-duplicated_from: fffiloni/Pix2Pix-Video
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/ssim.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/ssim.py
deleted file mode 100644
index 4bc3befc5bd3fb154cd48b4458184a4d8f3dca78..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/ssim.py
+++ /dev/null
@@ -1,383 +0,0 @@
-# Adopted from https://github.com/photosynthesis-team/piq
-
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn.functional as F
-from torch.nn.modules.loss import _Loss
-
-
-def _reduce(x: torch.Tensor, reduction: str = "mean") -> torch.Tensor:
- r"""Reduce input in batch dimension if needed.
- Args:
- x: Tensor with shape (N, *).
- reduction: Specifies the reduction type:
- ``'none'`` | ``'mean'`` | ``'sum'``. Default: ``'mean'``
- """
- if reduction == "none":
- return x
- if reduction == "mean":
- return x.mean(dim=0)
- if reduction == "sum":
- return x.sum(dim=0)
- raise ValueError("Unknown reduction. Expected one of {'none', 'mean', 'sum'}")
-
-
-def _validate_input(
- tensors: List[torch.Tensor],
- dim_range: Tuple[int, int] = (0, -1),
- data_range: Tuple[float, float] = (0.0, -1.0),
- # size_dim_range: Tuple[float, float] = (0., -1.),
- size_range: Optional[Tuple[int, int]] = None,
-) -> None:
- r"""Check that input(-s) satisfies the requirements
- Args:
- tensors: Tensors to check
- dim_range: Allowed number of dimensions. (min, max)
- data_range: Allowed range of values in tensors. (min, max)
- size_range: Dimensions to include in size comparison. (start_dim, end_dim + 1)
- """
-
- if not __debug__:
- return
-
- x = tensors[0]
-
- for t in tensors:
- assert torch.is_tensor(t), f"Expected torch.Tensor, got {type(t)}"
- assert t.device == x.device, f"Expected tensors to be on {x.device}, got {t.device}"
-
- if size_range is None:
- assert t.size() == x.size(), f"Expected tensors with same size, got {t.size()} and {x.size()}"
- else:
- assert (
- t.size()[size_range[0] : size_range[1]] == x.size()[size_range[0] : size_range[1]]
- ), f"Expected tensors with same size at given dimensions, got {t.size()} and {x.size()}"
-
- if dim_range[0] == dim_range[1]:
- assert t.dim() == dim_range[0], f"Expected number of dimensions to be {dim_range[0]}, got {t.dim()}"
- elif dim_range[0] < dim_range[1]:
- assert (
- dim_range[0] <= t.dim() <= dim_range[1]
- ), f"Expected number of dimensions to be between {dim_range[0]} and {dim_range[1]}, got {t.dim()}"
-
- if data_range[0] < data_range[1]:
- assert data_range[0] <= t.min(), f"Expected values to be greater or equal to {data_range[0]}, got {t.min()}"
- assert t.max() <= data_range[1], f"Expected values to be lower or equal to {data_range[1]}, got {t.max()}"
-
-
-def gaussian_filter(kernel_size: int, sigma: float) -> torch.Tensor:
- r"""Returns 2D Gaussian kernel N(0,`sigma`^2)
- Args:
- size: Size of the kernel
- sigma: Std of the distribution
- Returns:
- gaussian_kernel: Tensor with shape (1, kernel_size, kernel_size)
- """
- coords = torch.arange(kernel_size, dtype=torch.float32)
- coords -= (kernel_size - 1) / 2.0
-
- g = coords**2
- g = (-(g.unsqueeze(0) + g.unsqueeze(1)) / (2 * sigma**2)).exp()
-
- g /= g.sum()
- return g.unsqueeze(0)
-
-
-def ssim(
- x: torch.Tensor,
- y: torch.Tensor,
- kernel_size: int = 11,
- kernel_sigma: float = 1.5,
- data_range: Union[int, float] = 1.0,
- reduction: str = "mean",
- full: bool = False,
- downsample: bool = True,
- k1: float = 0.01,
- k2: float = 0.03,
-) -> List[torch.Tensor]:
- r"""Interface of Structural Similarity (SSIM) index.
- Inputs supposed to be in range ``[0, data_range]``.
- To match performance with skimage and tensorflow set ``'downsample' = True``.
-
- Args:
- x: An input tensor. Shape :math:`(N, C, H, W)` or :math:`(N, C, H, W, 2)`.
- y: A target tensor. Shape :math:`(N, C, H, W)` or :math:`(N, C, H, W, 2)`.
- kernel_size: The side-length of the sliding window used in comparison. Must be an odd value.
- kernel_sigma: Sigma of normal distribution.
- data_range: Maximum value range of images (usually 1.0 or 255).
- reduction: Specifies the reduction type:
- ``'none'`` | ``'mean'`` | ``'sum'``. Default:``'mean'``
- full: Return cs map or not.
- downsample: Perform average pool before SSIM computation. Default: True
- k1: Algorithm parameter, K1 (small constant).
- k2: Algorithm parameter, K2 (small constant).
- Try a larger K2 constant (e.g. 0.4) if you get a negative or NaN results.
-
- Returns:
- Value of Structural Similarity (SSIM) index. In case of 5D input tensors, complex value is returned
- as a tensor of size 2.
-
- References:
- Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004).
- Image quality assessment: From error visibility to structural similarity.
- IEEE Transactions on Image Processing, 13, 600-612.
- https://ece.uwaterloo.ca/~z70wang/publications/ssim.pdf,
- DOI: `10.1109/TIP.2003.819861`
- """
- assert kernel_size % 2 == 1, f"Kernel size must be odd, got [{kernel_size}]"
- _validate_input([x, y], dim_range=(4, 5), data_range=(0, data_range))
-
- x = x / float(data_range)
- y = y / float(data_range)
-
- # Averagepool image if the size is large enough
- f = max(1, round(min(x.size()[-2:]) / 256))
- if (f > 1) and downsample:
- x = F.avg_pool2d(x, kernel_size=f)
- y = F.avg_pool2d(y, kernel_size=f)
-
- kernel = gaussian_filter(kernel_size, kernel_sigma).repeat(x.size(1), 1, 1, 1).to(y)
- _compute_ssim_per_channel = _ssim_per_channel_complex if x.dim() == 5 else _ssim_per_channel
- ssim_map, cs_map = _compute_ssim_per_channel(x=x, y=y, kernel=kernel, k1=k1, k2=k2)
- ssim_val = ssim_map.mean(1)
- cs = cs_map.mean(1)
-
- ssim_val = _reduce(ssim_val, reduction)
- cs = _reduce(cs, reduction)
-
- if full:
- return [ssim_val, cs]
-
- return ssim_val
-
-
-class SSIMLoss(_Loss):
- r"""Creates a criterion that measures the structural similarity index error between
- each element in the input :math:`x` and target :math:`y`.
-
- To match performance with skimage and tensorflow set ``'downsample' = True``.
-
- The unreduced (i.e. with :attr:`reduction` set to ``'none'``) loss can be described as:
-
- .. math::
- SSIM = \{ssim_1,\dots,ssim_{N \times C}\}\\
- ssim_{l}(x, y) = \frac{(2 \mu_x \mu_y + c_1) (2 \sigma_{xy} + c_2)}
- {(\mu_x^2 +\mu_y^2 + c_1)(\sigma_x^2 +\sigma_y^2 + c_2)},
-
- where :math:`N` is the batch size, `C` is the channel size. If :attr:`reduction` is not ``'none'``
- (default ``'mean'``), then:
-
- .. math::
- SSIMLoss(x, y) =
- \begin{cases}
- \operatorname{mean}(1 - SSIM), & \text{if reduction} = \text{'mean';}\\
- \operatorname{sum}(1 - SSIM), & \text{if reduction} = \text{'sum'.}
- \end{cases}
-
- :math:`x` and :math:`y` are tensors of arbitrary shapes with a total
- of :math:`n` elements each.
-
- The sum operation still operates over all the elements, and divides by :math:`n`.
- The division by :math:`n` can be avoided if one sets ``reduction = 'sum'``.
- In case of 5D input tensors, complex value is returned as a tensor of size 2.
-
- Args:
- kernel_size: By default, the mean and covariance of a pixel is obtained
- by convolution with given filter_size.
- kernel_sigma: Standard deviation for Gaussian kernel.
- k1: Coefficient related to c1 in the above equation.
- k2: Coefficient related to c2 in the above equation.
- downsample: Perform average pool before SSIM computation. Default: True
- reduction: Specifies the reduction type:
- ``'none'`` | ``'mean'`` | ``'sum'``. Default:``'mean'``
- data_range: Maximum value range of images (usually 1.0 or 255).
-
- Examples:
- >>> loss = SSIMLoss()
- >>> x = torch.rand(3, 3, 256, 256, requires_grad=True)
- >>> y = torch.rand(3, 3, 256, 256)
- >>> output = loss(x, y)
- >>> output.backward()
-
- References:
- Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004).
- Image quality assessment: From error visibility to structural similarity.
- IEEE Transactions on Image Processing, 13, 600-612.
- https://ece.uwaterloo.ca/~z70wang/publications/ssim.pdf,
- DOI:`10.1109/TIP.2003.819861`
- """
- __constants__ = ["kernel_size", "k1", "k2", "sigma", "kernel", "reduction"]
-
- def __init__(
- self,
- kernel_size: int = 11,
- kernel_sigma: float = 1.5,
- k1: float = 0.01,
- k2: float = 0.03,
- downsample: bool = True,
- reduction: str = "mean",
- data_range: Union[int, float] = 1.0,
- ) -> None:
- super().__init__()
-
- # Generic loss parameters.
- self.reduction = reduction
-
- # Loss-specific parameters.
- self.kernel_size = kernel_size
-
- # This check might look redundant because kernel size is checked within the ssim function anyway.
- # However, this check allows to fail fast when the loss is being initialised and training has not been started.
- assert kernel_size % 2 == 1, f"Kernel size must be odd, got [{kernel_size}]"
- self.kernel_sigma = kernel_sigma
- self.k1 = k1
- self.k2 = k2
- self.downsample = downsample
- self.data_range = data_range
-
- def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
- r"""Computation of Structural Similarity (SSIM) index as a loss function.
-
- Args:
- x: An input tensor. Shape :math:`(N, C, H, W)` or :math:`(N, C, H, W, 2)`.
- y: A target tensor. Shape :math:`(N, C, H, W)` or :math:`(N, C, H, W, 2)`.
-
- Returns:
- Value of SSIM loss to be minimized, i.e ``1 - ssim`` in [0, 1] range. In case of 5D input tensors,
- complex value is returned as a tensor of size 2.
- """
-
- score = ssim(
- x=x,
- y=y,
- kernel_size=self.kernel_size,
- kernel_sigma=self.kernel_sigma,
- downsample=self.downsample,
- data_range=self.data_range,
- reduction=self.reduction,
- full=False,
- k1=self.k1,
- k2=self.k2,
- )
- return torch.ones_like(score) - score
-
-
-def _ssim_per_channel(
- x: torch.Tensor,
- y: torch.Tensor,
- kernel: torch.Tensor,
- k1: float = 0.01,
- k2: float = 0.03,
-) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
- r"""Calculate Structural Similarity (SSIM) index for X and Y per channel.
-
- Args:
- x: An input tensor. Shape :math:`(N, C, H, W)`.
- y: A target tensor. Shape :math:`(N, C, H, W)`.
- kernel: 2D Gaussian kernel.
- k1: Algorithm parameter, K1 (small constant, see [1]).
- k2: Algorithm parameter, K2 (small constant, see [1]).
- Try a larger K2 constant (e.g. 0.4) if you get a negative or NaN results.
-
- Returns:
- Full Value of Structural Similarity (SSIM) index.
- """
- if x.size(-1) < kernel.size(-1) or x.size(-2) < kernel.size(-2):
- raise ValueError(
- f"Kernel size can't be greater than actual input size. Input size: {x.size()}. "
- f"Kernel size: {kernel.size()}"
- )
-
- c1 = k1**2
- c2 = k2**2
- n_channels = x.size(1)
- mu_x = F.conv2d(x, weight=kernel, stride=1, padding=0, groups=n_channels)
- mu_y = F.conv2d(y, weight=kernel, stride=1, padding=0, groups=n_channels)
-
- mu_xx = mu_x**2
- mu_yy = mu_y**2
- mu_xy = mu_x * mu_y
-
- sigma_xx = F.conv2d(x**2, weight=kernel, stride=1, padding=0, groups=n_channels) - mu_xx
- sigma_yy = F.conv2d(y**2, weight=kernel, stride=1, padding=0, groups=n_channels) - mu_yy
- sigma_xy = F.conv2d(x * y, weight=kernel, stride=1, padding=0, groups=n_channels) - mu_xy
-
- # Contrast sensitivity (CS) with alpha = beta = gamma = 1.
- cs = (2.0 * sigma_xy + c2) / (sigma_xx + sigma_yy + c2)
-
- # Structural similarity (SSIM)
- ss = (2.0 * mu_xy + c1) / (mu_xx + mu_yy + c1) * cs
-
- ssim_val = ss.mean(dim=(-1, -2))
- cs = cs.mean(dim=(-1, -2))
- return ssim_val, cs
-
-
-def _ssim_per_channel_complex(
- x: torch.Tensor,
- y: torch.Tensor,
- kernel: torch.Tensor,
- k1: float = 0.01,
- k2: float = 0.03,
-) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
- r"""Calculate Structural Similarity (SSIM) index for Complex X and Y per channel.
-
- Args:
- x: An input tensor. Shape :math:`(N, C, H, W, 2)`.
- y: A target tensor. Shape :math:`(N, C, H, W, 2)`.
- kernel: 2-D gauss kernel.
- k1: Algorithm parameter, K1 (small constant, see [1]).
- k2: Algorithm parameter, K2 (small constant, see [1]).
- Try a larger K2 constant (e.g. 0.4) if you get a negative or NaN results.
-
- Returns:
- Full Value of Complex Structural Similarity (SSIM) index.
- """
- n_channels = x.size(1)
- if x.size(-2) < kernel.size(-1) or x.size(-3) < kernel.size(-2):
- raise ValueError(
- f"Kernel size can't be greater than actual input size. Input size: {x.size()}. "
- f"Kernel size: {kernel.size()}"
- )
-
- c1 = k1**2
- c2 = k2**2
-
- x_real = x[..., 0]
- x_imag = x[..., 1]
- y_real = y[..., 0]
- y_imag = y[..., 1]
-
- mu1_real = F.conv2d(x_real, weight=kernel, stride=1, padding=0, groups=n_channels)
- mu1_imag = F.conv2d(x_imag, weight=kernel, stride=1, padding=0, groups=n_channels)
- mu2_real = F.conv2d(y_real, weight=kernel, stride=1, padding=0, groups=n_channels)
- mu2_imag = F.conv2d(y_imag, weight=kernel, stride=1, padding=0, groups=n_channels)
-
- mu1_sq = mu1_real.pow(2) + mu1_imag.pow(2)
- mu2_sq = mu2_real.pow(2) + mu2_imag.pow(2)
- mu1_mu2_real = mu1_real * mu2_real - mu1_imag * mu2_imag
- mu1_mu2_imag = mu1_real * mu2_imag + mu1_imag * mu2_real
-
- compensation = 1.0
-
- x_sq = x_real.pow(2) + x_imag.pow(2)
- y_sq = y_real.pow(2) + y_imag.pow(2)
- x_y_real = x_real * y_real - x_imag * y_imag
- x_y_imag = x_real * y_imag + x_imag * y_real
-
- sigma1_sq = F.conv2d(x_sq, weight=kernel, stride=1, padding=0, groups=n_channels) - mu1_sq
- sigma2_sq = F.conv2d(y_sq, weight=kernel, stride=1, padding=0, groups=n_channels) - mu2_sq
- sigma12_real = F.conv2d(x_y_real, weight=kernel, stride=1, padding=0, groups=n_channels) - mu1_mu2_real
- sigma12_imag = F.conv2d(x_y_imag, weight=kernel, stride=1, padding=0, groups=n_channels) - mu1_mu2_imag
- sigma12 = torch.stack((sigma12_imag, sigma12_real), dim=-1)
- mu1_mu2 = torch.stack((mu1_mu2_real, mu1_mu2_imag), dim=-1)
- # Set alpha = beta = gamma = 1.
- cs_map = (sigma12 * 2 + c2 * compensation) / (sigma1_sq.unsqueeze(-1) + sigma2_sq.unsqueeze(-1) + c2 * compensation)
- ssim_map = (mu1_mu2 * 2 + c1 * compensation) / (mu1_sq.unsqueeze(-1) + mu2_sq.unsqueeze(-1) + c1 * compensation)
- ssim_map = ssim_map * cs_map
-
- ssim_val = ssim_map.mean(dim=(-2, -3))
- cs = cs_map.mean(dim=(-2, -3))
-
- return ssim_val, cs
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/text/english/time_norm.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/text/english/time_norm.py
deleted file mode 100644
index c8ac09e79db4a239a7f72f101503dbf0d6feb3ae..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/text/english/time_norm.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import re
-
-import inflect
-
-_inflect = inflect.engine()
-
-_time_re = re.compile(
- r"""\b
- ((0?[0-9])|(1[0-1])|(1[2-9])|(2[0-3])) # hours
- :
- ([0-5][0-9]) # minutes
- \s*(a\\.m\\.|am|pm|p\\.m\\.|a\\.m|p\\.m)? # am/pm
- \b""",
- re.IGNORECASE | re.X,
-)
-
-
-def _expand_num(n: int) -> str:
- return _inflect.number_to_words(n)
-
-
-def _expand_time_english(match: "re.Match") -> str:
- hour = int(match.group(1))
- past_noon = hour >= 12
- time = []
- if hour > 12:
- hour -= 12
- elif hour == 0:
- hour = 12
- past_noon = True
- time.append(_expand_num(hour))
-
- minute = int(match.group(6))
- if minute > 0:
- if minute < 10:
- time.append("oh")
- time.append(_expand_num(minute))
- am_pm = match.group(7)
- if am_pm is None:
- time.append("p m" if past_noon else "a m")
- else:
- time.extend(list(am_pm.replace(".", "")))
- return " ".join(time)
-
-
-def expand_time_english(text: str) -> str:
- return re.sub(_time_re, _expand_time_english, text)
diff --git a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_helpers.py b/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_helpers.py
deleted file mode 100644
index 23bb440a0af77b443e847b1c80620887bef485bb..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_helpers.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import torch as T
-
-from TTS.tts.utils.helpers import average_over_durations, generate_path, rand_segments, segment, sequence_mask
-
-
-def average_over_durations_test(): # pylint: disable=no-self-use
- pitch = T.rand(1, 1, 128)
-
- durations = T.randint(1, 5, (1, 21))
- coeff = 128.0 / durations.sum()
- durations = T.floor(durations * coeff)
- diff = 128.0 - durations.sum()
- durations[0, -1] += diff
- durations = durations.long()
-
- pitch_avg = average_over_durations(pitch, durations)
-
- index = 0
- for idx, dur in enumerate(durations[0]):
- assert abs(pitch_avg[0, 0, idx] - pitch[0, 0, index : index + dur.item()].mean()) < 1e-5
- index += dur
-
-
-def seqeunce_mask_test():
- lengths = T.randint(10, 15, (8,))
- mask = sequence_mask(lengths)
- for i in range(8):
- l = lengths[i].item()
- assert mask[i, :l].sum() == l
- assert mask[i, l:].sum() == 0
-
-
-def segment_test():
- x = T.range(0, 11)
- x = x.repeat(8, 1).unsqueeze(1)
- segment_ids = T.randint(0, 7, (8,))
-
- segments = segment(x, segment_ids, segment_size=4)
- for idx, start_indx in enumerate(segment_ids):
- assert x[idx, :, start_indx : start_indx + 4].sum() == segments[idx, :, :].sum()
-
- try:
- segments = segment(x, segment_ids, segment_size=10)
- raise Exception("Should have failed")
- except:
- pass
-
- segments = segment(x, segment_ids, segment_size=10, pad_short=True)
- for idx, start_indx in enumerate(segment_ids):
- assert x[idx, :, start_indx : start_indx + 10].sum() == segments[idx, :, :].sum()
-
-
-def rand_segments_test():
- x = T.rand(2, 3, 4)
- x_lens = T.randint(3, 4, (2,))
- segments, seg_idxs = rand_segments(x, x_lens, segment_size=3)
- assert segments.shape == (2, 3, 3)
- assert all(seg_idxs >= 0), seg_idxs
- try:
- segments, _ = rand_segments(x, x_lens, segment_size=5)
- raise Exception("Should have failed")
- except:
- pass
- x_lens_back = x_lens.clone()
- segments, seg_idxs = rand_segments(x, x_lens.clone(), segment_size=5, pad_short=True, let_short_samples=True)
- assert segments.shape == (2, 3, 5)
- assert all(seg_idxs >= 0), seg_idxs
- assert all(x_lens_back == x_lens)
-
-
-def generate_path_test():
- durations = T.randint(1, 4, (10, 21))
- x_length = T.randint(18, 22, (10,))
- x_mask = sequence_mask(x_length).unsqueeze(1).long()
- durations = durations * x_mask.squeeze(1)
- y_length = durations.sum(1)
- y_mask = sequence_mask(y_length).unsqueeze(1).long()
- attn_mask = (T.unsqueeze(x_mask, -1) * T.unsqueeze(y_mask, 2)).squeeze(1).long()
- print(attn_mask.shape)
- path = generate_path(durations, attn_mask)
- assert path.shape == (10, 21, durations.sum(1).max().item())
- for b in range(durations.shape[0]):
- current_idx = 0
- for t in range(durations.shape[1]):
- assert all(path[b, t, current_idx : current_idx + durations[b, t].item()] == 1.0)
- assert all(path[b, t, :current_idx] == 0.0)
- assert all(path[b, t, current_idx + durations[b, t].item() :] == 0.0)
- current_idx += durations[b, t].item()
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/http_exceptions.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/http_exceptions.py
deleted file mode 100644
index c885f80f3220474d22e61a068558a5169e038906..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/http_exceptions.py
+++ /dev/null
@@ -1,105 +0,0 @@
-"""Low-level http related exceptions."""
-
-
-from typing import Optional, Union
-
-from .typedefs import _CIMultiDict
-
-__all__ = ("HttpProcessingError",)
-
-
-class HttpProcessingError(Exception):
- """HTTP error.
-
- Shortcut for raising HTTP errors with custom code, message and headers.
-
- code: HTTP Error code.
- message: (optional) Error message.
- headers: (optional) Headers to be sent in response, a list of pairs
- """
-
- code = 0
- message = ""
- headers = None
-
- def __init__(
- self,
- *,
- code: Optional[int] = None,
- message: str = "",
- headers: Optional[_CIMultiDict] = None,
- ) -> None:
- if code is not None:
- self.code = code
- self.headers = headers
- self.message = message
-
- def __str__(self) -> str:
- return f"{self.code}, message={self.message!r}"
-
- def __repr__(self) -> str:
- return f"<{self.__class__.__name__}: {self}>"
-
-
-class BadHttpMessage(HttpProcessingError):
-
- code = 400
- message = "Bad Request"
-
- def __init__(self, message: str, *, headers: Optional[_CIMultiDict] = None) -> None:
- super().__init__(message=message, headers=headers)
- self.args = (message,)
-
-
-class HttpBadRequest(BadHttpMessage):
-
- code = 400
- message = "Bad Request"
-
-
-class PayloadEncodingError(BadHttpMessage):
- """Base class for payload errors"""
-
-
-class ContentEncodingError(PayloadEncodingError):
- """Content encoding error."""
-
-
-class TransferEncodingError(PayloadEncodingError):
- """transfer encoding error."""
-
-
-class ContentLengthError(PayloadEncodingError):
- """Not enough data for satisfy content length header."""
-
-
-class LineTooLong(BadHttpMessage):
- def __init__(
- self, line: str, limit: str = "Unknown", actual_size: str = "Unknown"
- ) -> None:
- super().__init__(
- f"Got more than {limit} bytes ({actual_size}) when reading {line}."
- )
- self.args = (line, limit, actual_size)
-
-
-class InvalidHeader(BadHttpMessage):
- def __init__(self, hdr: Union[bytes, str]) -> None:
- if isinstance(hdr, bytes):
- hdr = hdr.decode("utf-8", "surrogateescape")
- super().__init__(f"Invalid HTTP Header: {hdr}")
- self.hdr = hdr
- self.args = (hdr,)
-
-
-class BadStatusLine(BadHttpMessage):
- def __init__(self, line: str = "") -> None:
- if not isinstance(line, str):
- line = repr(line)
- super().__init__(f"Bad status line {line!r}")
- self.args = (line,)
- self.line = line
-
-
-class InvalidURLError(BadHttpMessage):
- pass
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/test_utils.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/test_utils.py
deleted file mode 100644
index fcda2f3ddc045a381470012ba331c75299af4981..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/test_utils.py
+++ /dev/null
@@ -1,706 +0,0 @@
-"""Utilities shared by tests."""
-
-import asyncio
-import contextlib
-import gc
-import inspect
-import ipaddress
-import os
-import socket
-import sys
-import warnings
-from abc import ABC, abstractmethod
-from types import TracebackType
-from typing import (
- TYPE_CHECKING,
- Any,
- Callable,
- Iterator,
- List,
- Optional,
- Type,
- Union,
- cast,
-)
-from unittest import mock
-
-from aiosignal import Signal
-from multidict import CIMultiDict, CIMultiDictProxy
-from yarl import URL
-
-import aiohttp
-from aiohttp.client import _RequestContextManager, _WSRequestContextManager
-
-from . import ClientSession, hdrs
-from .abc import AbstractCookieJar
-from .client_reqrep import ClientResponse
-from .client_ws import ClientWebSocketResponse
-from .helpers import PY_38, sentinel
-from .http import HttpVersion, RawRequestMessage
-from .web import (
- Application,
- AppRunner,
- BaseRunner,
- Request,
- Server,
- ServerRunner,
- SockSite,
- UrlMappingMatchInfo,
-)
-from .web_protocol import _RequestHandler
-
-if TYPE_CHECKING: # pragma: no cover
- from ssl import SSLContext
-else:
- SSLContext = None
-
-if PY_38:
- from unittest import IsolatedAsyncioTestCase as TestCase
-else:
- from asynctest import TestCase # type: ignore[no-redef]
-
-REUSE_ADDRESS = os.name == "posix" and sys.platform != "cygwin"
-
-
-def get_unused_port_socket(
- host: str, family: socket.AddressFamily = socket.AF_INET
-) -> socket.socket:
- return get_port_socket(host, 0, family)
-
-
-def get_port_socket(
- host: str, port: int, family: socket.AddressFamily
-) -> socket.socket:
- s = socket.socket(family, socket.SOCK_STREAM)
- if REUSE_ADDRESS:
- # Windows has different semantics for SO_REUSEADDR,
- # so don't set it. Ref:
- # https://docs.microsoft.com/en-us/windows/win32/winsock/using-so-reuseaddr-and-so-exclusiveaddruse
- s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
- s.bind((host, port))
- return s
-
-
-def unused_port() -> int:
- """Return a port that is unused on the current host."""
- with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
- s.bind(("127.0.0.1", 0))
- return cast(int, s.getsockname()[1])
-
-
-class BaseTestServer(ABC):
- __test__ = False
-
- def __init__(
- self,
- *,
- scheme: Union[str, object] = sentinel,
- loop: Optional[asyncio.AbstractEventLoop] = None,
- host: str = "127.0.0.1",
- port: Optional[int] = None,
- skip_url_asserts: bool = False,
- socket_factory: Callable[
- [str, int, socket.AddressFamily], socket.socket
- ] = get_port_socket,
- **kwargs: Any,
- ) -> None:
- self._loop = loop
- self.runner: Optional[BaseRunner] = None
- self._root: Optional[URL] = None
- self.host = host
- self.port = port
- self._closed = False
- self.scheme = scheme
- self.skip_url_asserts = skip_url_asserts
- self.socket_factory = socket_factory
-
- async def start_server(
- self, loop: Optional[asyncio.AbstractEventLoop] = None, **kwargs: Any
- ) -> None:
- if self.runner:
- return
- self._loop = loop
- self._ssl = kwargs.pop("ssl", None)
- self.runner = await self._make_runner(**kwargs)
- await self.runner.setup()
- if not self.port:
- self.port = 0
- try:
- version = ipaddress.ip_address(self.host).version
- except ValueError:
- version = 4
- family = socket.AF_INET6 if version == 6 else socket.AF_INET
- _sock = self.socket_factory(self.host, self.port, family)
- self.host, self.port = _sock.getsockname()[:2]
- site = SockSite(self.runner, sock=_sock, ssl_context=self._ssl)
- await site.start()
- server = site._server
- assert server is not None
- sockets = server.sockets
- assert sockets is not None
- self.port = sockets[0].getsockname()[1]
- if self.scheme is sentinel:
- if self._ssl:
- scheme = "https"
- else:
- scheme = "http"
- self.scheme = scheme
- self._root = URL(f"{self.scheme}://{self.host}:{self.port}")
-
- @abstractmethod # pragma: no cover
- async def _make_runner(self, **kwargs: Any) -> BaseRunner:
- pass
-
- def make_url(self, path: str) -> URL:
- assert self._root is not None
- url = URL(path)
- if not self.skip_url_asserts:
- assert not url.is_absolute()
- return self._root.join(url)
- else:
- return URL(str(self._root) + path)
-
- @property
- def started(self) -> bool:
- return self.runner is not None
-
- @property
- def closed(self) -> bool:
- return self._closed
-
- @property
- def handler(self) -> Server:
- # for backward compatibility
- # web.Server instance
- runner = self.runner
- assert runner is not None
- assert runner.server is not None
- return runner.server
-
- async def close(self) -> None:
- """Close all fixtures created by the test client.
-
- After that point, the TestClient is no longer usable.
-
- This is an idempotent function: running close multiple times
- will not have any additional effects.
-
- close is also run when the object is garbage collected, and on
- exit when used as a context manager.
-
- """
- if self.started and not self.closed:
- assert self.runner is not None
- await self.runner.cleanup()
- self._root = None
- self.port = None
- self._closed = True
-
- def __enter__(self) -> None:
- raise TypeError("Use async with instead")
-
- def __exit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc_value: Optional[BaseException],
- traceback: Optional[TracebackType],
- ) -> None:
- # __exit__ should exist in pair with __enter__ but never executed
- pass # pragma: no cover
-
- async def __aenter__(self) -> "BaseTestServer":
- await self.start_server(loop=self._loop)
- return self
-
- async def __aexit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc_value: Optional[BaseException],
- traceback: Optional[TracebackType],
- ) -> None:
- await self.close()
-
-
-class TestServer(BaseTestServer):
- def __init__(
- self,
- app: Application,
- *,
- scheme: Union[str, object] = sentinel,
- host: str = "127.0.0.1",
- port: Optional[int] = None,
- **kwargs: Any,
- ):
- self.app = app
- super().__init__(scheme=scheme, host=host, port=port, **kwargs)
-
- async def _make_runner(self, **kwargs: Any) -> BaseRunner:
- return AppRunner(self.app, **kwargs)
-
-
-class RawTestServer(BaseTestServer):
- def __init__(
- self,
- handler: _RequestHandler,
- *,
- scheme: Union[str, object] = sentinel,
- host: str = "127.0.0.1",
- port: Optional[int] = None,
- **kwargs: Any,
- ) -> None:
- self._handler = handler
- super().__init__(scheme=scheme, host=host, port=port, **kwargs)
-
- async def _make_runner(self, debug: bool = True, **kwargs: Any) -> ServerRunner:
- srv = Server(self._handler, loop=self._loop, debug=debug, **kwargs)
- return ServerRunner(srv, debug=debug, **kwargs)
-
-
-class TestClient:
- """
- A test client implementation.
-
- To write functional tests for aiohttp based servers.
-
- """
-
- __test__ = False
-
- def __init__(
- self,
- server: BaseTestServer,
- *,
- cookie_jar: Optional[AbstractCookieJar] = None,
- loop: Optional[asyncio.AbstractEventLoop] = None,
- **kwargs: Any,
- ) -> None:
- if not isinstance(server, BaseTestServer):
- raise TypeError(
- "server must be TestServer " "instance, found type: %r" % type(server)
- )
- self._server = server
- self._loop = loop
- if cookie_jar is None:
- cookie_jar = aiohttp.CookieJar(unsafe=True, loop=loop)
- self._session = ClientSession(loop=loop, cookie_jar=cookie_jar, **kwargs)
- self._closed = False
- self._responses: List[ClientResponse] = []
- self._websockets: List[ClientWebSocketResponse] = []
-
- async def start_server(self) -> None:
- await self._server.start_server(loop=self._loop)
-
- @property
- def host(self) -> str:
- return self._server.host
-
- @property
- def port(self) -> Optional[int]:
- return self._server.port
-
- @property
- def server(self) -> BaseTestServer:
- return self._server
-
- @property
- def app(self) -> Optional[Application]:
- return cast(Optional[Application], getattr(self._server, "app", None))
-
- @property
- def session(self) -> ClientSession:
- """An internal aiohttp.ClientSession.
-
- Unlike the methods on the TestClient, client session requests
- do not automatically include the host in the url queried, and
- will require an absolute path to the resource.
-
- """
- return self._session
-
- def make_url(self, path: str) -> URL:
- return self._server.make_url(path)
-
- async def _request(self, method: str, path: str, **kwargs: Any) -> ClientResponse:
- resp = await self._session.request(method, self.make_url(path), **kwargs)
- # save it to close later
- self._responses.append(resp)
- return resp
-
- def request(self, method: str, path: str, **kwargs: Any) -> _RequestContextManager:
- """Routes a request to tested http server.
-
- The interface is identical to aiohttp.ClientSession.request,
- except the loop kwarg is overridden by the instance used by the
- test server.
-
- """
- return _RequestContextManager(self._request(method, path, **kwargs))
-
- def get(self, path: str, **kwargs: Any) -> _RequestContextManager:
- """Perform an HTTP GET request."""
- return _RequestContextManager(self._request(hdrs.METH_GET, path, **kwargs))
-
- def post(self, path: str, **kwargs: Any) -> _RequestContextManager:
- """Perform an HTTP POST request."""
- return _RequestContextManager(self._request(hdrs.METH_POST, path, **kwargs))
-
- def options(self, path: str, **kwargs: Any) -> _RequestContextManager:
- """Perform an HTTP OPTIONS request."""
- return _RequestContextManager(self._request(hdrs.METH_OPTIONS, path, **kwargs))
-
- def head(self, path: str, **kwargs: Any) -> _RequestContextManager:
- """Perform an HTTP HEAD request."""
- return _RequestContextManager(self._request(hdrs.METH_HEAD, path, **kwargs))
-
- def put(self, path: str, **kwargs: Any) -> _RequestContextManager:
- """Perform an HTTP PUT request."""
- return _RequestContextManager(self._request(hdrs.METH_PUT, path, **kwargs))
-
- def patch(self, path: str, **kwargs: Any) -> _RequestContextManager:
- """Perform an HTTP PATCH request."""
- return _RequestContextManager(self._request(hdrs.METH_PATCH, path, **kwargs))
-
- def delete(self, path: str, **kwargs: Any) -> _RequestContextManager:
- """Perform an HTTP PATCH request."""
- return _RequestContextManager(self._request(hdrs.METH_DELETE, path, **kwargs))
-
- def ws_connect(self, path: str, **kwargs: Any) -> _WSRequestContextManager:
- """Initiate websocket connection.
-
- The api corresponds to aiohttp.ClientSession.ws_connect.
-
- """
- return _WSRequestContextManager(self._ws_connect(path, **kwargs))
-
- async def _ws_connect(self, path: str, **kwargs: Any) -> ClientWebSocketResponse:
- ws = await self._session.ws_connect(self.make_url(path), **kwargs)
- self._websockets.append(ws)
- return ws
-
- async def close(self) -> None:
- """Close all fixtures created by the test client.
-
- After that point, the TestClient is no longer usable.
-
- This is an idempotent function: running close multiple times
- will not have any additional effects.
-
- close is also run on exit when used as a(n) (asynchronous)
- context manager.
-
- """
- if not self._closed:
- for resp in self._responses:
- resp.close()
- for ws in self._websockets:
- await ws.close()
- await self._session.close()
- await self._server.close()
- self._closed = True
-
- def __enter__(self) -> None:
- raise TypeError("Use async with instead")
-
- def __exit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc: Optional[BaseException],
- tb: Optional[TracebackType],
- ) -> None:
- # __exit__ should exist in pair with __enter__ but never executed
- pass # pragma: no cover
-
- async def __aenter__(self) -> "TestClient":
- await self.start_server()
- return self
-
- async def __aexit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc: Optional[BaseException],
- tb: Optional[TracebackType],
- ) -> None:
- await self.close()
-
-
-class AioHTTPTestCase(TestCase):
- """A base class to allow for unittest web applications using aiohttp.
-
- Provides the following:
-
- * self.client (aiohttp.test_utils.TestClient): an aiohttp test client.
- * self.loop (asyncio.BaseEventLoop): the event loop in which the
- application and server are running.
- * self.app (aiohttp.web.Application): the application returned by
- self.get_application()
-
- Note that the TestClient's methods are asynchronous: you have to
- execute function on the test client using asynchronous methods.
- """
-
- async def get_application(self) -> Application:
- """Get application.
-
- This method should be overridden
- to return the aiohttp.web.Application
- object to test.
- """
- return self.get_app()
-
- def get_app(self) -> Application:
- """Obsolete method used to constructing web application.
-
- Use .get_application() coroutine instead.
- """
- raise RuntimeError("Did you forget to define get_application()?")
-
- def setUp(self) -> None:
- if not PY_38:
- asyncio.get_event_loop().run_until_complete(self.asyncSetUp())
-
- async def asyncSetUp(self) -> None:
- try:
- self.loop = asyncio.get_running_loop()
- except (AttributeError, RuntimeError): # AttributeError->py36
- self.loop = asyncio.get_event_loop_policy().get_event_loop()
-
- return await self.setUpAsync()
-
- async def setUpAsync(self) -> None:
- self.app = await self.get_application()
- self.server = await self.get_server(self.app)
- self.client = await self.get_client(self.server)
-
- await self.client.start_server()
-
- def tearDown(self) -> None:
- if not PY_38:
- self.loop.run_until_complete(self.asyncTearDown())
-
- async def asyncTearDown(self) -> None:
- return await self.tearDownAsync()
-
- async def tearDownAsync(self) -> None:
- await self.client.close()
-
- async def get_server(self, app: Application) -> TestServer:
- """Return a TestServer instance."""
- return TestServer(app, loop=self.loop)
-
- async def get_client(self, server: TestServer) -> TestClient:
- """Return a TestClient instance."""
- return TestClient(server, loop=self.loop)
-
-
-def unittest_run_loop(func: Any, *args: Any, **kwargs: Any) -> Any:
- """
- A decorator dedicated to use with asynchronous AioHTTPTestCase test methods.
-
- In 3.8+, this does nothing.
- """
- warnings.warn(
- "Decorator `@unittest_run_loop` is no longer needed in aiohttp 3.8+",
- DeprecationWarning,
- stacklevel=2,
- )
- return func
-
-
-_LOOP_FACTORY = Callable[[], asyncio.AbstractEventLoop]
-
-
-@contextlib.contextmanager
-def loop_context(
- loop_factory: _LOOP_FACTORY = asyncio.new_event_loop, fast: bool = False
-) -> Iterator[asyncio.AbstractEventLoop]:
- """A contextmanager that creates an event_loop, for test purposes.
-
- Handles the creation and cleanup of a test loop.
- """
- loop = setup_test_loop(loop_factory)
- yield loop
- teardown_test_loop(loop, fast=fast)
-
-
-def setup_test_loop(
- loop_factory: _LOOP_FACTORY = asyncio.new_event_loop,
-) -> asyncio.AbstractEventLoop:
- """Create and return an asyncio.BaseEventLoop instance.
-
- The caller should also call teardown_test_loop,
- once they are done with the loop.
- """
- loop = loop_factory()
- try:
- module = loop.__class__.__module__
- skip_watcher = "uvloop" in module
- except AttributeError: # pragma: no cover
- # Just in case
- skip_watcher = True
- asyncio.set_event_loop(loop)
- if sys.platform != "win32" and not skip_watcher:
- policy = asyncio.get_event_loop_policy()
- watcher: asyncio.AbstractChildWatcher
- try: # Python >= 3.8
- # Refs:
- # * https://github.com/pytest-dev/pytest-xdist/issues/620
- # * https://stackoverflow.com/a/58614689/595220
- # * https://bugs.python.org/issue35621
- # * https://github.com/python/cpython/pull/14344
- watcher = asyncio.ThreadedChildWatcher()
- except AttributeError: # Python < 3.8
- watcher = asyncio.SafeChildWatcher()
- watcher.attach_loop(loop)
- with contextlib.suppress(NotImplementedError):
- policy.set_child_watcher(watcher)
- return loop
-
-
-def teardown_test_loop(loop: asyncio.AbstractEventLoop, fast: bool = False) -> None:
- """Teardown and cleanup an event_loop created by setup_test_loop."""
- closed = loop.is_closed()
- if not closed:
- loop.call_soon(loop.stop)
- loop.run_forever()
- loop.close()
-
- if not fast:
- gc.collect()
-
- asyncio.set_event_loop(None)
-
-
-def _create_app_mock() -> mock.MagicMock:
- def get_dict(app: Any, key: str) -> Any:
- return app.__app_dict[key]
-
- def set_dict(app: Any, key: str, value: Any) -> None:
- app.__app_dict[key] = value
-
- app = mock.MagicMock(spec=Application)
- app.__app_dict = {}
- app.__getitem__ = get_dict
- app.__setitem__ = set_dict
-
- app._debug = False
- app.on_response_prepare = Signal(app)
- app.on_response_prepare.freeze()
- return app
-
-
-def _create_transport(sslcontext: Optional[SSLContext] = None) -> mock.Mock:
- transport = mock.Mock()
-
- def get_extra_info(key: str) -> Optional[SSLContext]:
- if key == "sslcontext":
- return sslcontext
- else:
- return None
-
- transport.get_extra_info.side_effect = get_extra_info
- return transport
-
-
-def make_mocked_request(
- method: str,
- path: str,
- headers: Any = None,
- *,
- match_info: Any = sentinel,
- version: HttpVersion = HttpVersion(1, 1),
- closing: bool = False,
- app: Any = None,
- writer: Any = sentinel,
- protocol: Any = sentinel,
- transport: Any = sentinel,
- payload: Any = sentinel,
- sslcontext: Optional[SSLContext] = None,
- client_max_size: int = 1024**2,
- loop: Any = ...,
-) -> Request:
- """Creates mocked web.Request testing purposes.
-
- Useful in unit tests, when spinning full web server is overkill or
- specific conditions and errors are hard to trigger.
- """
- task = mock.Mock()
- if loop is ...:
- loop = mock.Mock()
- loop.create_future.return_value = ()
-
- if version < HttpVersion(1, 1):
- closing = True
-
- if headers:
- headers = CIMultiDictProxy(CIMultiDict(headers))
- raw_hdrs = tuple(
- (k.encode("utf-8"), v.encode("utf-8")) for k, v in headers.items()
- )
- else:
- headers = CIMultiDictProxy(CIMultiDict())
- raw_hdrs = ()
-
- chunked = "chunked" in headers.get(hdrs.TRANSFER_ENCODING, "").lower()
-
- message = RawRequestMessage(
- method,
- path,
- version,
- headers,
- raw_hdrs,
- closing,
- None,
- False,
- chunked,
- URL(path),
- )
- if app is None:
- app = _create_app_mock()
-
- if transport is sentinel:
- transport = _create_transport(sslcontext)
-
- if protocol is sentinel:
- protocol = mock.Mock()
- protocol.transport = transport
-
- if writer is sentinel:
- writer = mock.Mock()
- writer.write_headers = make_mocked_coro(None)
- writer.write = make_mocked_coro(None)
- writer.write_eof = make_mocked_coro(None)
- writer.drain = make_mocked_coro(None)
- writer.transport = transport
-
- protocol.transport = transport
- protocol.writer = writer
-
- if payload is sentinel:
- payload = mock.Mock()
-
- req = Request(
- message, payload, protocol, writer, task, loop, client_max_size=client_max_size
- )
-
- match_info = UrlMappingMatchInfo(
- {} if match_info is sentinel else match_info, mock.Mock()
- )
- match_info.add_app(app)
- req._match_info = match_info
-
- return req
-
-
-def make_mocked_coro(
- return_value: Any = sentinel, raise_exception: Any = sentinel
-) -> Any:
- """Creates a coroutine mock."""
-
- async def mock_coro(*args: Any, **kwargs: Any) -> Any:
- if raise_exception is not sentinel:
- raise raise_exception
- if not inspect.isawaitable(return_value):
- return return_value
- await return_value
-
- return mock.Mock(wraps=mock_coro)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/natural_disasters.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/natural_disasters.py
deleted file mode 100644
index 339033db0a304941f919ef23db4819df30966fd4..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/natural_disasters.py
+++ /dev/null
@@ -1,29 +0,0 @@
-"""
-Natural Disasters
------------------
-This example shows a visualization of global deaths from natural disasters.
-"""
-# category: case studies
-import altair as alt
-from vega_datasets import data
-
-source = data.disasters.url
-
-alt.Chart(source).mark_circle(
- opacity=0.8,
- stroke='black',
- strokeWidth=1
-).encode(
- alt.X('Year:O', axis=alt.Axis(labelAngle=0)),
- alt.Y('Entity:N'),
- alt.Size('Deaths:Q',
- scale=alt.Scale(range=[0, 4000]),
- legend=alt.Legend(title='Annual Global Deaths')
- ),
- alt.Color('Entity:N', legend=None)
-).properties(
- width=450,
- height=320
-).transform_filter(
- alt.datum.Entity != 'All natural disasters'
-)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/scripts/video_feature_extractor/videoreader.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/scripts/video_feature_extractor/videoreader.py
deleted file mode 100644
index 429e05f8bc8667408b8c2057578b1e8c0e98638c..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/scripts/video_feature_extractor/videoreader.py
+++ /dev/null
@@ -1,242 +0,0 @@
-# Copyright Howto100M authors.
-# Copyright (c) Facebook, Inc. All Rights Reserved
-
-import torch as th
-import pandas as pd
-import os
-import numpy as np
-import ffmpeg
-import random
-
-from torch.utils.data import Dataset
-
-
-class VideoLoader(Dataset):
- """modified from how2's video_feature_extractor."""
- def __init__(
- self,
- csv=None,
- video_dict=None,
- framerate=1,
- size=112,
- centercrop=False,
- hflip=False,
- **kwargs
- ):
- if csv is None and video_dict is None:
- raise ValueError("csv and video_dict cannot be both None.")
- if csv is not None:
- self.csv = pd.read_csv(csv)
- if video_dict is not None:
- self.csv = pd.DataFrame.from_dict(video_dict)
-
- self.centercrop = centercrop
- self.size = size
- self.framerate = framerate
- self.hflip = hflip
-
- def __len__(self):
- return len(self.csv)
-
- def _get_video_dim(self, video_path):
- probe = ffmpeg.probe(video_path)
- video_stream = next((stream for stream in probe['streams']
- if stream['codec_type'] == 'video'), None)
- width = int(video_stream['width'])
- height = int(video_stream['height'])
- return height, width
-
- def _get_video_info(self, video_path):
- probe = ffmpeg.probe(video_path)
- video_stream = next((stream for stream in probe['streams']
- if stream['codec_type'] == 'video'), None)
- return video_stream
-
- def _get_output_dim(self, h, w):
- if isinstance(self.size, tuple) and len(self.size) == 2:
- return self.size
- elif h >= w:
- return int(h * self.size / w), self.size
- else:
- return self.size, int(w * self.size / h)
-
- def __getitem__(self, idx):
- video_path = self.csv['video_path'].values[idx]
- output_file = self.csv['feature_path'].values[idx]
- return self._decode(output_file, video_path)
-
- def _decode(self, output_file, video_path):
- if not(os.path.isfile(output_file)) and os.path.isfile(video_path):
- try:
- h, w = self._get_video_dim(video_path)
- except Exception:
- print('ffprobe failed at: {}'.format(video_path))
- return {'video': th.zeros(1), 'input': video_path,
- 'output': output_file}
- try:
- os.makedirs(os.path.dirname(output_file), exist_ok=True)
- height, width = self._get_output_dim(h, w)
-
- cmd = (
- ffmpeg
- .input(video_path)
- .filter('fps', fps=self.framerate)
- .filter('scale', width, height)
- )
- if self.hflip:
- cmd = cmd.filter('hflip')
-
- if self.centercrop:
- x = int((width - self.size) / 2.0)
- y = int((height - self.size) / 2.0)
- cmd = cmd.crop(x, y, self.size, self.size)
- video = self._run(cmd, output_file)
- except Exception:
- video = th.zeros(1)
- else:
- video = th.zeros(1)
-
- return {'video': video, 'input': video_path, 'output': output_file}
-
- def _run(self, cmd, output_file):
- out, _ = (
- cmd.output('pipe:', format='rawvideo', pix_fmt='rgb24')
- .run(capture_stdout=True, quiet=True)
- )
- if self.centercrop and isinstance(self.size, int):
- height, width = self.size, self.size
- video = np.frombuffer(out, np.uint8).reshape([-1, height, width, 3])
- video = th.from_numpy(video.astype('float32'))
- return video.permute(0, 3, 1, 2)
-
-
-class VideoVerifier(VideoLoader):
- def __getitem__(self, idx):
- video_path = self.csv['video_path'].values[idx]
- try:
- return self._get_video_info(video_path)
- except Exception:
- # print('ffprobe failed at: {}'.format(video_path))
- return None
-
-
-class VideoCompressor(VideoLoader):
- def __init__(
- self,
- csv=None,
- video_dict=None,
- framerate=1,
- size=112,
- centercrop=False,
- hflip=False,
- crf=32,
- **kwargs
- ):
- super().__init__(
- csv,
- video_dict,
- framerate,
- size,
- centercrop,
- hflip
- )
- self.crf = crf
-
- def _run(self, cmd, output_file):
- out, _ = (
- cmd.output(filename=output_file, crf=self.crf)
- .run(quiet=True)
- )
- video = None
- return video
-
-
-class VideoDownloader(VideoCompressor):
- """download"""
- def __getitem__(self, idx):
- video_path = self.csv['video_path'].values[idx]
- output_file = self.csv['feature_path'].values[idx]
- if not(os.path.isfile(output_file)):
- os.makedirs(os.path.dirname(output_file), exist_ok=True)
- cmd = "wget -O" + output_file + " " + video_path
- # import subprocess
- # subprocess.check_output(
- # cmd,
- # stderr=subprocess.STDOUT, shell=True)
- os.system(cmd)
- return {'video': None, 'input': video_path, 'output': output_file}
-
-
-class AvKeyframeVideoCompressor(VideoLoader):
- """extract keyframes from a video and save it as jpg.
- TODO: consider to merge with `CodecProcessor`.
- """
- def __init__(
- self,
- csv=None,
- video_dict=None,
- framerate=1,
- size=112,
- centercrop=False,
- max_num_frames=5,
- **kwargs
- ):
- super().__init__(csv, video_dict, framerate, size, centercrop)
- self.max_num_frames = max_num_frames
-
- def _get_video_dim(self, video_fn):
- """decord cannot probe the size of a video, we use pyav instead."""
- import av
- with av.open(video_fn) as container:
- height = container.streams.video[0].codec_context.height
- width = container.streams.video[0].codec_context.width
- return height, width
-
- def _get_output_dim(self, height, width):
- """
- keep the shorter side be `self.size`, strech the other.
- """
- if height >= width:
- return int(height * self.size / width), self.size
- else:
- return self.size, int(width * self.size / height)
-
- def __getitem__(self, idx):
- import av
- video_path = self.csv['video_path'].values[idx]
- output_file = self.csv['feature_path'].values[idx]
- if not(os.path.isdir(output_file)) and os.path.isfile(video_path):
- try:
- h, w = self._get_video_dim(video_path)
- except Exception:
- print('probe failed at: {}'.format(video_path))
- return {'video': th.zeros(1), 'input': video_path,
- 'output': output_file}
-
- try:
- height, width = self._get_output_dim(h, w)
-
- # new for av.
- with av.open(video_path) as container:
- container.streams.video[0].thread_type = "AUTO"
- container.streams.video[0].codec_context.height = height
- container.streams.video[0].codec_context.width = width
- if self.framerate == 0: # keyframe.
- container.streams.video[0].codec_context.skip_frame = 'NONKEY'
- frames = []
- for frame in container.decode(video=0):
- frames.append(frame)
- frames = random.sample(frames, self.max_num_frames)
-
- os.makedirs(output_file, exist_ok=True)
- for frame in frames:
- frame.to_image().save(
- os.path.join(
- output_file,
- "%04d.jpg" % frame.index))
- except Exception:
- print('extract failed at: {}'.format(video_path))
- return {'video': th.zeros(1), 'input': video_path,
- 'output': output_file}
- video = th.zeros(1)
- return {'video': video, 'input': video_path, 'output': output_file}
diff --git a/spaces/asdasdasdasd/Face-forgery-detection/landmark_utils.py b/spaces/asdasdasdasd/Face-forgery-detection/landmark_utils.py
deleted file mode 100644
index 70468893e4e47e47dcd3e7223110637ff37a0d34..0000000000000000000000000000000000000000
--- a/spaces/asdasdasdasd/Face-forgery-detection/landmark_utils.py
+++ /dev/null
@@ -1,309 +0,0 @@
-from tqdm import tqdm
-import numpy as np
-import dlib
-from collections import OrderedDict
-import cv2
-
-detector = dlib.get_frontal_face_detector()
-predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
-FACIAL_LANDMARKS_68_IDXS = OrderedDict([
- ("mouth", (48, 68)),
- ("inner_mouth", (60, 68)),
- ("right_eyebrow", (17, 22)),
- ("left_eyebrow", (22, 27)),
- ("right_eye", (36, 42)),
- ("left_eye", (42, 48)),
- ("nose", (27, 36)),
- ("jaw", (0, 17))
-])
-
-
-def shape_to_face(shape, width, height, scale=1.2):
- """
- Recalculate the face bounding box based on coarse landmark location(shape)
- :param
- shape: landmark locations
- scale: the scale parameter of face, to enlarge the bounding box
- :return:
- face_new: new bounding box of face (1*4 list [x1, y1, x2, y2])
- # face_center: the center coordinate of face (1*2 list [x_c, y_c])
- face_size: the face is rectangular( width = height = size)(int)
- """
- x_min, y_min = np.min(shape, axis=0)
- x_max, y_max = np.max(shape, axis=0)
-
- x_center = (x_min + x_max) // 2
- y_center = (y_min + y_max) // 2
-
- face_size = int(max(x_max - x_min, y_max - y_min) * scale)
- # Enforce it to be even
- # Thus the real whole bounding box size will be an odd
- # But after cropping the face size will become even and
- # keep same to the face_size parameter.
- face_size = face_size // 2 * 2
-
- x1 = max(x_center - face_size // 2, 0)
- y1 = max(y_center - face_size // 2, 0)
-
- face_size = min(width - x1, face_size)
- face_size = min(height - y1, face_size)
-
- x2 = x1 + face_size
- y2 = y1 + face_size
-
- face_new = [int(x1), int(y1), int(x2), int(y2)]
- return face_new, face_size
-
-
-def predict_single_frame(frame):
- """
- :param frame: A full frame of video
- :return:
- face_num: the number of face (just to verify if successfully detect a face)
- shape: landmark locations
- """
- gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
- faces = detector(gray, 0)
- if len(faces) < 1:
- return 0, None
- face = faces[0]
-
- landmarks = predictor(frame, face)
- face_landmark_list = [(p.x, p.y) for p in landmarks.parts()]
- shape = np.array(face_landmark_list)
-
- return 1, shape
-
-
-def landmark_align(shape):
- desiredLeftEye = (0.35, 0.25)
- desiredFaceWidth = 2
- desiredFaceHeight = 2
- (lStart, lEnd) = FACIAL_LANDMARKS_68_IDXS["left_eye"]
- (rStart, rEnd) = FACIAL_LANDMARKS_68_IDXS["right_eye"]
-
- leftEyePts = shape[lStart:lEnd]
- rightEyePts = shape[rStart:rEnd]
-
- # compute the center of mass for each eye
- leftEyeCenter = leftEyePts.mean(axis=0) # .astype("int")
- rightEyeCenter = rightEyePts.mean(axis=0) # .astype("int")
- # compute the angle between the eye centroids
- dY = rightEyeCenter[1] - leftEyeCenter[1]
- dX = rightEyeCenter[0] - leftEyeCenter[0]
- angle = np.degrees(np.arctan2(dY, dX)) # - 180
-
- # compute the desired right eye x-coordinate based on the
- # desired x-coordinate of the left eye
- desiredRightEyeX = 1.0 - desiredLeftEye[0]
-
- # determine the scale of the new resulting image by taking
- # the ratio of the distance between eyes in the *current*
- # image to the ratio of distance between eyes in the
- # *desired* image
- dist = np.sqrt((dX ** 2) + (dY ** 2))
- desiredDist = (desiredRightEyeX - desiredLeftEye[0])
- desiredDist *= desiredFaceWidth
- scale = desiredDist / dist
-
- # compute center (x, y)-coordinates (i.e., the median point)
- # between the two eyes in the input image
- eyesCenter = ((leftEyeCenter[0] + rightEyeCenter[0]) // 2,
- (leftEyeCenter[1] + rightEyeCenter[1]) // 2)
-
- # grab the rotation matrix for rotating and scaling the face
- M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)
-
- # update the translation component of the matrix
- tX = 0 # desiredFaceWidth * 0.5
- tY = desiredFaceHeight * desiredLeftEye[1]
- M[0, 2] += (tX - eyesCenter[0])
- M[1, 2] += (tY - eyesCenter[1])
-
- n, d = shape.shape
- temp = np.zeros((n, d + 1), dtype="int")
- temp[:, 0:2] = shape
- temp[:, 2] = 1
- aligned_landmarks = np.matmul(M, temp.T)
- return aligned_landmarks.T # .astype("int"))
-
-
-def check_and_merge(location, forward, feedback, P_predict, status_fw=None, status_fb=None):
- num_pts = 68
- check = [True] * num_pts
-
- target = location[1]
- forward_predict = forward[1]
-
- # To ensure the robustness through feedback-check
- forward_base = forward[0] # Also equal to location[0]
- feedback_predict = feedback[0]
- feedback_diff = feedback_predict - forward_base
- feedback_dist = np.linalg.norm(feedback_diff, axis=1, keepdims=True)
-
- # For Kalman Filtering
- detect_diff = location[1] - location[0]
- detect_dist = np.linalg.norm(detect_diff, axis=1, keepdims=True)
- predict_diff = forward[1] - forward[0]
- predict_dist = np.linalg.norm(predict_diff, axis=1, keepdims=True)
- predict_dist[np.where(predict_dist == 0)] = 1 # Avoid nan
- P_detect = (detect_dist / predict_dist).reshape(num_pts)
-
- for ipt in range(num_pts):
- if feedback_dist[ipt] > 2: # When use float
- check[ipt] = False
-
- if status_fw is not None and np.sum(status_fw) != num_pts:
- for ipt in range(num_pts):
- if status_fw[ipt][0] == 0:
- check[ipt] = False
- if status_fw is not None and np.sum(status_fb) != num_pts:
- for ipt in range(num_pts):
- if status_fb[ipt][0] == 0:
- check[ipt] = False
- location_merge = target.copy()
- # Merge the results:
- """
- Use Kalman Filter to combine the calculate result and detect result.
- """
-
- Q = 0.3 # Process variance
-
- for ipt in range(num_pts):
- if check[ipt]:
- # Kalman parameter
- P_predict[ipt] += Q
- K = P_predict[ipt] / (P_predict[ipt] + P_detect[ipt])
- location_merge[ipt] = forward_predict[ipt] + K * (target[ipt] - forward_predict[ipt])
- # Update the P_predict by the current K
- P_predict[ipt] = (1 - K) * P_predict[ipt]
- return location_merge, check, P_predict
-
-
-def detect_frames_track(frames):
- frames_num = len(frames)
- assert frames_num != 0
- frame_height, frame_width = frames[0].shape[:2]
- """
- Pre-process:
- To detect the original results,
- and normalize each face to a certain width,
- also its corresponding landmarks locations and
- scale parameter.
- """
- face_size_normalized = 400
- faces = []
- locations = []
- shapes_origin = []
- shapes_para = [] # Use to recover the shape in whole frame. ([x1, y1, scale_shape])
- face_size = 0
- skipped = 0
-
- """
- Use single frame to detect face on Dlib (CPU)
- """
- # ----------------------------------------------------------------------------#
-
- print("Detecting:")
- for i in tqdm(range(frames_num)):
- frame = frames[i]
- face_num, shape = predict_single_frame(frame)
-
- if face_num == 0:
- if len(shapes_origin) == 0:
- skipped += 1
- # print("Skipped", skipped, "Frame_num", frames_num)
- continue
- shape = shapes_origin[i - 1 - skipped]
-
- face, face_size = shape_to_face(shape, frame_width, frame_height, 1.2)
- faceFrame = frame[face[1]: face[3],
- face[0]:face[2]]
- if face_size < face_size_normalized:
- inter_para = cv2.INTER_CUBIC
- else:
- inter_para = cv2.INTER_AREA
- face_norm = cv2.resize(faceFrame, (face_size_normalized, face_size_normalized), interpolation=inter_para)
- scale_shape = face_size_normalized / face_size
- shape_norm = np.rint((shape - np.array([face[0], face[1]])) * scale_shape).astype(int)
- faces.append(face_norm)
- shapes_para.append([face[0], face[1], scale_shape])
- shapes_origin.append(shape)
- locations.append(shape_norm)
-
- """
- Calibration module.
- """
- segment_length = 2
- locations_sum = len(locations)
- if locations_sum == 0:
- return []
- locations_track = [locations[0]]
- num_pts = 68
- P_predict = np.array([0] * num_pts).reshape(num_pts).astype(float)
- print("Tracking")
- for i in tqdm(range(locations_sum - 1)):
- faces_seg = faces[i:i + segment_length]
- locations_seg = locations[i:i + segment_length]
-
- # ----------------------------------------------------------------------#
- """
- Numpy Version (DEPRECATED)
- """
-
- # locations_track_start = [locations_track[i]]
- # forward_pts, feedback_pts = track_bidirectional(faces_seg, locations_track_start)
- #
- # forward_pts = np.rint(forward_pts).astype(int)
- # feedback_pts = np.rint(feedback_pts).astype(int)
- # merge_pt, check, P_predict = check_and_merge(locations_seg, forward_pts, feedback_pts, P_predict)
-
- # ----------------------------------------------------------------------#
- """
- OpenCV Version
- """
-
- lk_params = dict(winSize=(15, 15),
- maxLevel=3,
- criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
- # Use the tracked current location as input. Also use the next frame's predicted location for
- # auxiliary initialization.
-
- start_pt = locations_track[i].astype(np.float32)
- target_pt = locations_seg[1].astype(np.float32)
-
- forward_pt, status_fw, err_fw = cv2.calcOpticalFlowPyrLK(faces_seg[0], faces_seg[1],
- start_pt, target_pt, **lk_params,
- flags=cv2.OPTFLOW_USE_INITIAL_FLOW)
- feedback_pt, status_fb, err_fb = cv2.calcOpticalFlowPyrLK(faces_seg[1], faces_seg[0],
- forward_pt, start_pt, **lk_params,
- flags=cv2.OPTFLOW_USE_INITIAL_FLOW)
-
- forward_pts = [locations_track[i].copy(), forward_pt]
- feedback_pts = [feedback_pt, forward_pt.copy()]
-
- forward_pts = np.rint(forward_pts).astype(int)
- feedback_pts = np.rint(feedback_pts).astype(int)
-
- merge_pt, check, P_predict = check_and_merge(locations_seg, forward_pts, feedback_pts, P_predict, status_fw,
- status_fb)
-
- # ----------------------------------------------------------------------#
-
- locations_track.append(merge_pt)
-
- """
- If us visualization, write the results to the visualize output folder.
- """
- if locations_sum != frames_num:
- print("INFO: Landmarks detection failed in some frames. Therefore we disable the "
- "visualization for this video. It will be optimized in future version.")
-
- aligned_landmarks = []
- for i in locations_track:
- shape = landmark_align(i)
- shape = shape.ravel()
- shape = shape.tolist()
- aligned_landmarks.append(shape)
-
- return aligned_landmarks
diff --git a/spaces/avans06/whisper-webui-translate/src/diarization/diarization.py b/spaces/avans06/whisper-webui-translate/src/diarization/diarization.py
deleted file mode 100644
index 76a2914105a4045ee7f16bfe3b927d84add4ef25..0000000000000000000000000000000000000000
--- a/spaces/avans06/whisper-webui-translate/src/diarization/diarization.py
+++ /dev/null
@@ -1,202 +0,0 @@
-import argparse
-import gc
-import json
-import os
-from pathlib import Path
-import tempfile
-from typing import TYPE_CHECKING, List
-import torch
-
-import ffmpeg
-
-class DiarizationEntry:
- def __init__(self, start, end, speaker):
- self.start = start
- self.end = end
- self.speaker = speaker
-
- def __repr__(self):
- return f""
-
- def toJson(self):
- return {
- "start": self.start,
- "end": self.end,
- "speaker": self.speaker
- }
-
-class Diarization:
- def __init__(self, auth_token=None):
- if auth_token is None:
- auth_token = os.environ.get("HF_ACCESS_TOKEN")
- if auth_token is None:
- raise ValueError("No HuggingFace API Token provided - please use the --auth_token argument or set the HF_ACCESS_TOKEN environment variable")
-
- self.auth_token = auth_token
- self.initialized = False
- self.pipeline = None
-
- @staticmethod
- def has_libraries():
- try:
- import pyannote.audio
- import intervaltree
- return True
- except ImportError:
- return False
-
- def initialize(self):
- """
- 1.Install pyannote.audio 3.0 with pip install pyannote.audio
- 2.Accept pyannote/segmentation-3.0 user conditions
- 3.Accept pyannote/speaker-diarization-3.0 user conditions
- 4.Create access token at hf.co/settings/tokens.
- https://huggingface.co/pyannote/speaker-diarization-3.0
- """
- if self.initialized:
- return
- from pyannote.audio import Pipeline
-
- self.pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.0", use_auth_token=self.auth_token)
- self.initialized = True
-
- # Load GPU mode if available
- device = "cuda" if torch.cuda.is_available() else "cpu"
- if device == "cuda":
- print("Diarization - using GPU")
- self.pipeline = self.pipeline.to(torch.device(0))
- else:
- print("Diarization - using CPU")
-
- def run(self, audio_file, **kwargs):
- self.initialize()
- audio_file_obj = Path(audio_file)
-
- # Supported file types in soundfile is WAV, FLAC, OGG and MAT
- if audio_file_obj.suffix in [".wav", ".flac", ".ogg", ".mat"]:
- target_file = audio_file
- else:
- # Create temp WAV file
- target_file = tempfile.mktemp(prefix="diarization_", suffix=".wav")
- try:
- ffmpeg.input(audio_file).output(target_file, ac=1).run()
- except ffmpeg.Error as e:
- print(f"Error occurred during audio conversion: {e.stderr}")
-
- diarization = self.pipeline(target_file, **kwargs)
-
- if target_file != audio_file:
- # Delete temp file
- os.remove(target_file)
-
- # Yield result
- for turn, _, speaker in diarization.itertracks(yield_label=True):
- yield DiarizationEntry(turn.start, turn.end, speaker)
-
- def mark_speakers(self, diarization_result: List[DiarizationEntry], whisper_result: dict):
- from intervaltree import IntervalTree
- result = whisper_result.copy()
-
- # Create an interval tree from the diarization results
- tree = IntervalTree()
- for entry in diarization_result:
- tree[entry.start:entry.end] = entry
-
- # Iterate through each segment in the Whisper JSON
- for segment in result["segments"]:
- segment_start = segment["start"]
- segment_end = segment["end"]
-
- # Find overlapping speakers using the interval tree
- overlapping_speakers = tree[segment_start:segment_end]
-
- # If no speakers overlap with this segment, skip it
- if not overlapping_speakers:
- continue
-
- # If multiple speakers overlap with this segment, choose the one with the longest duration
- longest_speaker = None
- longest_duration = 0
-
- for speaker_interval in overlapping_speakers:
- overlap_start = max(speaker_interval.begin, segment_start)
- overlap_end = min(speaker_interval.end, segment_end)
- overlap_duration = overlap_end - overlap_start
-
- if overlap_duration > longest_duration:
- longest_speaker = speaker_interval.data.speaker
- longest_duration = overlap_duration
-
- # Add speakers
- segment["longest_speaker"] = longest_speaker
- segment["speakers"] = list([speaker_interval.data.toJson() for speaker_interval in overlapping_speakers])
-
- # The write_srt will use the longest_speaker if it exist, and add it to the text field
-
- return result
-
-def _write_file(input_file: str, output_path: str, output_extension: str, file_writer: lambda f: None):
- if input_file is None:
- raise ValueError("input_file is required")
- if file_writer is None:
- raise ValueError("file_writer is required")
-
- # Write file
- if output_path is None:
- effective_path = os.path.splitext(input_file)[0] + "_output" + output_extension
- else:
- effective_path = output_path
-
- with open(effective_path, 'w+', encoding="utf-8") as f:
- file_writer(f)
-
- print(f"Output saved to {effective_path}")
-
-def main():
- from src.utils import write_srt
- from src.diarization.transcriptLoader import load_transcript
-
- parser = argparse.ArgumentParser(description='Add speakers to a SRT file or Whisper JSON file using pyannote/speaker-diarization.')
- parser.add_argument('audio_file', type=str, help='Input audio file')
- parser.add_argument('whisper_file', type=str, help='Input Whisper JSON/SRT file')
- parser.add_argument('--output_json_file', type=str, default=None, help='Output JSON file (optional)')
- parser.add_argument('--output_srt_file', type=str, default=None, help='Output SRT file (optional)')
- parser.add_argument('--auth_token', type=str, default=None, help='HuggingFace API Token (optional)')
- parser.add_argument("--max_line_width", type=int, default=40, help="Maximum line width for SRT file (default: 40)")
- parser.add_argument("--num_speakers", type=int, default=None, help="Number of speakers")
- parser.add_argument("--min_speakers", type=int, default=None, help="Minimum number of speakers")
- parser.add_argument("--max_speakers", type=int, default=None, help="Maximum number of speakers")
-
- args = parser.parse_args()
-
- print("\nReading whisper JSON from " + args.whisper_file)
-
- # Read whisper JSON or SRT file
- whisper_result = load_transcript(args.whisper_file)
-
- diarization = Diarization(auth_token=args.auth_token)
- diarization_result = list(diarization.run(args.audio_file, num_speakers=args.num_speakers, min_speakers=args.min_speakers, max_speakers=args.max_speakers))
-
- # Print result
- print("Diarization result:")
- for entry in diarization_result:
- print(f" start={entry.start:.1f}s stop={entry.end:.1f}s speaker_{entry.speaker}")
-
- marked_whisper_result = diarization.mark_speakers(diarization_result, whisper_result)
-
- # Write output JSON to file
- _write_file(args.whisper_file, args.output_json_file, ".json",
- lambda f: json.dump(marked_whisper_result, f, indent=4, ensure_ascii=False))
-
- # Write SRT
- _write_file(args.whisper_file, args.output_srt_file, ".srt",
- lambda f: write_srt(marked_whisper_result["segments"], f, maxLineWidth=args.max_line_width))
-
-if __name__ == "__main__":
- main()
-
- #test = Diarization()
- #print("Initializing")
- #test.initialize()
-
- #input("Press Enter to continue...")
\ No newline at end of file
diff --git a/spaces/awacke1/DnD-Character-Sheet/README.md b/spaces/awacke1/DnD-Character-Sheet/README.md
deleted file mode 100644
index 8c850ac96855ef376d5d564d7e8e1c4d270a6bc8..0000000000000000000000000000000000000000
--- a/spaces/awacke1/DnD-Character-Sheet/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: DnD Character Sheet
-emoji: 🌖
-colorFrom: red
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/Git-GPG-Git-Actions-01-GraphViz/app.py b/spaces/awacke1/Git-GPG-Git-Actions-01-GraphViz/app.py
deleted file mode 100644
index 8aeec0f200b3ba4e20b24e97462734cfc89a9ca6..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Git-GPG-Git-Actions-01-GraphViz/app.py
+++ /dev/null
@@ -1,578 +0,0 @@
-import streamlit as st
-import graphviz as graphviz
-import pandas as pd
-import numpy as np
-
-st.title('Graphviz Gallery: https://graphviz.org/gallery/')
-
-# Plan
-graph = graphviz.Digraph()
-graph.edge('Plan', 'Intervention')
-graph.edge('Intervention', 'Care')
-graph.edge('Intervention', 'Category')
-graph.edge('Intervention', 'QualityMeasure')
-graph.edge('Intervention', 'Target')
-graph.edge('Plan', 'Goal')
-graph.edge('Plan', 'OutcomeScores')
-graph.edge('Plan', 'Problem')
-graph.edge('Problem', 'Domain')
-graph.edge('Problem', 'Outcome')
-graph.edge('Problem', 'Scope')
-graph.edge('Problem', 'SignSymptom')
-graph.edge('Problem', 'Urgency')
-graph.edge('Plan', 'Pathway')
-graph.edge('Pathway', 'Intervention')
-graph.edge('Pathway', 'ProblemGoal')
-graph.edge('Plan', 'SignSymptom')
-graph.edge('Plan', 'Stage')
-graph.edge('Plan', 'Delegate')
-graph.edge('Plan', 'Note')
-graph.edge('Plan', 'Person')
-graph.edge('Problem', 'Association')
-graph.edge('Problem', 'History')
-graph.edge('Goal', 'History')
-graph.edge('Intervention', 'Work')
-graph.edge('Intervention', 'History')
-graph.edge('SignSymptom', 'History')
-graph.edge('Plan', 'Publication')
-graph.edge('Publication', 'History')
-graph.edge('Publication', 'Status')
-st.graphviz_chart(graph)
-
-
-# Assessment
-graph = graphviz.Digraph()
-graph.edge('Assessment', 'Score')
-graph.edge('Assessment', 'QuestionAsked')
-graph.edge('Assessment', 'ProgressChange')
-graph.edge('Assessment', 'Programs')
-graph.edge('Assessment', 'Product')
-graph.edge('Assessment', 'Program')
-graph.edge('Program', 'Population')
-graph.edge('Assessment', 'Plan')
-graph.edge('Plan', 'Question')
-graph.edge('Plan', 'Problem')
-graph.edge('Assessment', 'Response')
-graph.edge('Response', 'Choice')
-graph.edge('Response', 'Populator')
-graph.edge('Assessment', 'Section')
-graph.edge('Section', 'Score')
-graph.edge('Assessment', 'Template')
-graph.edge('Template', 'Script')
-graph.edge('Template', 'Question')
-graph.edge('Question', 'SubQuestion')
-graph.edge('Question', 'Table')
-graph.edge('Table', 'SubTable')
-graph.edge('Table', 'Column')
-graph.edge('Column', 'Choice')
-graph.edge('Question', 'Response')
-graph.edge('Response', 'ResponseTable')
-graph.edge('Response', 'ResponseText')
-graph.edge('Template', 'Section')
-graph.edge('Assessment', 'Document')
-graph.edge('Assessment', 'Service')
-graph.edge('Service', 'History')
-graph.edge('Service', 'Request')
-st.graphviz_chart(graph)
-
-
-# Create a graphlib graph object
-graph = graphviz.Digraph()
-graph.edge('Grandpa', 'Ancestors')
-graph.edge('Grandma', 'Ancestors')
-graph.edge('Uncle', 'Grandma')
-graph.edge('Aunt', 'Grandma')
-graph.edge('Mom', 'Grandma')
-graph.edge('Cousin Bob', 'Aunt')
-graph.edge('Cousin Sue', 'Aunt')
-graph.edge('Brother', 'Mom')
-graph.edge('Sister', 'Mom')
-st.graphviz_chart(graph)
-
-
-st.graphviz_chart('''
-digraph G2 {
- node [shape=plaintext];
- struct1 [label=<
- 
caption
>];
-}
-''')
-
-
-
-st.title('Graphviz Dot Language: https://graphviz.org/doc/info/lang.html')
-
-# Using graph language:
-st.graphviz_chart('''
-digraph G {
- rankdir=LR
- node [shape=plaintext]
- a [
- label=<
-
- class qualifier
>
- ]
- b [shape=ellipse style=filled
- label=<
-
-
- elephant
- two
-
-
-
-
- corn c f
-
- penguin
-
-
- 4
-
-
>
- ]
- c [
- label=
line 3
>
- ]
- subgraph { rank=same b c }
- a:here -> b:there [dir=both arrowtail=diamond]
- c -> b
- d [shape=triangle]
- d -> c [label=<
-
-
-
- Edge labels
also
-
-
-
>
- ]
-}
-''')
-
-st.graphviz_chart('''
-digraph R {
- rankdir=LR
- node [style=rounded]
- node1 [shape=box]
- node2 [fillcolor=yellow, style="rounded,filled", shape=diamond]
- node3 [shape=record, label="{ a | b | c }"]
- node1 -> node2 -> node3
-}
-''')
-
-st.title('Vega Lite Example: https://docs.streamlit.io/library/api-reference/charts/st.vega_lite_chart ')
-df = pd.DataFrame(
- np.random.randn(200, 3),
- columns=['a', 'b', 'c'])
-
-st.vega_lite_chart(df, {
- 'mark': {'type': 'circle', 'tooltip': True},
- 'encoding': {
- 'x': {'field': 'a', 'type': 'quantitative'},
- 'y': {'field': 'b', 'type': 'quantitative'},
- 'size': {'field': 'c', 'type': 'quantitative'},
- 'color': {'field': 'c', 'type': 'quantitative'},
- },
- })
-
-# More graph examples
-
-st.graphviz_chart('''
-digraph structs {
- node [shape=record];
- struct1 [label=" left| mid\ dle| right"];
- struct2 [label=" one| two"];
- struct3 [label="hello\nworld |{ b |{c| d|e}| f}| g | h"];
- struct1:f1 -> struct2:f0;
- struct1:f2 -> struct3:here;
-}
-''')
-
-st.graphviz_chart('''
-graph G {
- fontname="Helvetica,Arial,sans-serif"
- node [fontname="Helvetica,Arial,sans-serif"]
- edge [fontname="Helvetica,Arial,sans-serif"]
- layout=fdp
- e
- subgraph clusterA {
- a -- b;
- subgraph clusterC {
- C -- D;
- }
- }
- subgraph clusterB {
- d -- f
- }
- d -- D
- e -- clusterB
- clusterC -- clusterB
-}
-''')
-
-st.graphviz_chart('''
-graph Transparency {
- layout=neato
- start=11 // empiric value to set orientation
- bgcolor="#0000ff11"
- node [shape=circle width=2.22 label="" style=filled]
- 5 [color="#0000ff80"]
- 6 [color="#ee00ee80"]
- 1 [color="#ff000080"]
- 2 [color="#eeee0080"]
- 3 [color="#00ff0080"]
- 4 [color="#00eeee80"]
- 1 -- 2 -- 3 -- 4 -- 5 -- 6 -- 1
- }
-''')
-
-st.graphviz_chart('''
-digraph UML_Class_diagram {
- fontname="Helvetica,Arial,sans-serif"
- node [fontname="Helvetica,Arial,sans-serif"]
- edge [fontname="Helvetica,Arial,sans-serif"]
- labelloc="t"
- label="UML Class diagram demo"
- graph [splines=false]
- node [shape=record style=filled fillcolor=gray95]
- edge [arrowhead=vee style=dashed]
- Client -> Interface1 [xlabel=dependency]
- Client -> Interface2
- edge [dir=back arrowtail=empty style=""]
- Interface1 -> Class1 [xlabel=inheritance]
- Interface2 -> Class1 [dir=none]
- Interface2 [label="" xlabel="Simple\ninterface" shape=circle]
- Interface1[label = <{«interface» I/O | + property
...
|+ method
...
}>]
- Class1[label = <{I/O class | + property
...
|+ method
...
}>]
- edge [dir=back arrowtail=empty style=dashed]
- Class1 -> System_1 [xlabel=implementation]
- System_1 [label = <{System | + property
...
|+ method
...
}>]
- "Shared resource" [label = <{Shared resource | + property
...
|+ method
...
}>]
- edge [dir=back arrowtail=diamond]
- "System_1" -> Subsystem_1 [xlabel="composition"]
- Subsystem_1[label = <{Subsystem 1 | + property
...
|+ method
...
}>]
- Subsystem_2[label = <{Subsystem 2 | + property
...
|+ method
...
}>]
- Subsystem_3[label = <{Subsystem 3 | + property
...
|+ method
...
}>]
- "System_1" -> Subsystem_2
- "System_1" -> Subsystem_3
- edge [xdir=back arrowtail=odiamond]
- Subsystem_1 -> "Shared resource" [xlabel=aggregation]
- {Subsystem_2 Subsystem_3 } -> "Shared resource"
-}
-''')
-
-
-
-st.graphviz_chart('''
-digraph G {
- fontname="Helvetica,Arial,sans-serif"
- node [fontname="Helvetica,Arial,sans-serif"]
- edge [fontname="Helvetica,Arial,sans-serif"]
- subgraph cluster_1 {
- node [ style=filled,shape="box",fillcolor="antiquewhite:aquamarine" ]n5;
- node [ shape="ellipse",fillcolor="bisque4:blue2" ]n4;
- node [ shape="circle",fillcolor="cadetblue1:chocolate1" ]n3;
- node [ shape="diamond",fillcolor="crimson:cyan4" ]n2;
- node [ shape="triangle",fillcolor="deepskyblue2:firebrick" ]n1;
- node [ shape="pentagon",fillcolor="gray24:gray88" ]n0;
- label = "X11 Colors";
- }
- subgraph cluster_2 {
- node [ style=filled,shape="box",fillcolor="bisque:brown" ]n11;
- node [ shape="ellipse",fillcolor="green:darkorchid" ]n10;
- node [ shape="circle",fillcolor="deepskyblue:gold" ]n9;
- node [ shape="diamond",fillcolor="lightseagreen:orangered" ]n8;
- node [ shape="triangle",fillcolor="turquoise:salmon" ]n7;
- node [ shape="pentagon",fillcolor="snow:black" ]n6;
- label = "SVG Colors";
- }
- subgraph cluster_3 {
- node [ style=filled,shape="box",fillcolor="/accent3/1:/accent3/3" ]n17;
- node [ shape="ellipse",fillcolor="/accent4/1:/accent4/4" ]n16;
- node [ shape="circle",fillcolor="/accent5/1:/accent5/5" ]n15;
- node [ shape="diamond",fillcolor="/accent6/1:/accent6/6" ]n14;
- node [ shape="triangle",fillcolor="/accent7/1:/accent7/7" ]n13;
- node [ shape="pentagon",fillcolor="/accent8/1:/accent8/8" ]n12;
- label = "Brewer - accent";
- }
- subgraph cluster_4 {
- node [ style=filled,shape="box",fillcolor="/blues3/1:/blues3/2" ]n23;
- node [ shape="ellipse",fillcolor="/blues4/1:/blues4/3" ]n22;
- node [ shape="circle",fillcolor="/blues5/1:/blues5/4" ]n21;
- node [ shape="diamond",fillcolor="/blues6/1:/blues6/5" ]n20;
- node [ shape="triangle",fillcolor="/blues7/1:/blues7/6" ]n19;
- node [ shape="pentagon",fillcolor="/blues8/1:/blues8/7" ]n18;
- label = "Brewer - blues";
- }
-n3 -> n9 -> n15 -> n21;
-}
-''')
-
-st.graphviz_chart('''
-digraph G {bgcolor="#0000FF44:#FF000044" gradientangle=90
- fontname="Helvetica,Arial,sans-serif"
- node [fontname="Helvetica,Arial,sans-serif"]
- edge [fontname="Helvetica,Arial,sans-serif"]
- subgraph cluster_0 {
- style=filled;
- color=lightgrey;
- fillcolor="darkgray:gold";
- gradientangle=0
- node [fillcolor="yellow:green" style=filled gradientangle=270] a0;
- node [fillcolor="lightgreen:red"] a1;
- node [fillcolor="lightskyblue:darkcyan"] a2;
- node [fillcolor="cyan:lightslateblue"] a3;
- a0 -> a1 -> a2 -> a3;
- label = "process #1";
- }
- subgraph cluster_1 {
- node [fillcolor="yellow:magenta"
- style=filled gradientangle=270] b0;
- node [fillcolor="violet:darkcyan"] b1;
- node [fillcolor="peachpuff:red"] b2;
- node [fillcolor="mediumpurple:purple"] b3;
- b0 -> b1 -> b2 -> b3;
- label = "process #2";
- color=blue
- fillcolor="darkgray:gold";
- gradientangle=0
- style=filled;
- }
- start -> a0;
- start -> b0;
- a1 -> b3;
- b2 -> a3;
- a3 -> a0;
- a3 -> end;
- b3 -> end;
- start [shape=Mdiamond ,
- fillcolor="pink:red",
- gradientangle=90,
- style=radial];
- end [shape=Msquare,
- fillcolor="lightyellow:orange",
- style=radial,
- gradientangle=90];
-}
-''')
-
-st.graphviz_chart('''
-graph Color_wheel {
- graph [
- layout = neato
- label = "Color wheel, 33 colors.\nNeato layout"
- labelloc = b
- fontname = "Helvetica,Arial,sans-serif"
- start = regular
- normalize = 0
- ]
- node [
- shape = circle
- style = filled
- color = "#00000088"
- fontname = "Helvetica,Arial,sans-serif"
- ]
- edge [
- len = 2.7
- color = "#00000088"
- fontname = "Helvetica,Arial,sans-serif"
- ]
- subgraph Dark {
- node [fontcolor = white width = 1.4]
- center [width = 1 style = invis shape = point]
- center -- darkred [label = "0°/360°"]
- darkred [fillcolor = darkred]
- brown [fillcolor = brown]
- brown -- center [label = "30°"]
- olive [fillcolor = olive]
- olive -- center [label = "60°"]
- darkolivegreen [fillcolor = darkolivegreen fontsize = 10]
- darkolivegreen -- center [label = "90°"]
- darkgreen [fillcolor = darkgreen]
- darkgreen -- center [label = "120°"]
- "dark hue 0.416" [color = ".416 1 .6" fontcolor = white]
- "dark hue 0.416" -- center [label = "150°"]
- darkcyan [fillcolor = darkcyan]
- darkcyan -- center [label = "180°"]
- "dark hue 0.583" [color = ".583 1 .6" fontcolor = white]
- "dark hue 0.583" -- center [label = "210°"]
- darkblue [fillcolor = darkblue]
- darkblue -- center [label = "240°"]
- "dark hue 0.750" [color = ".750 1 .6"]
- "dark hue 0.750" -- center [label = "270°"]
- darkmagenta [fillcolor = darkmagenta]
- darkmagenta -- center [label = "300°"]
- "dark hue 0.916" [color = ".916 1 .6"]
- "dark hue 0.916" -- center [label = "330°"]
- }
- subgraph Tue {
- node [width = 1.3]
- "hue 0.083" -- brown
- "hue 0.083" [color = ".083 1 1"]
- "hue 0.125" [color = ".125 1 1"]
- "hue 0.166" -- olive
- "hue 0.166" [color = ".166 1 1"]
- "hue 0.208" [color = ".208 1 1"]
- "hue 0.250" -- darkolivegreen
- "hue 0.250" [color = ".250 1 1"]
- "hue 0.291" [color = ".291 1 1"]
- "hue 0.333" -- darkgreen
- "hue 0.333" [color = ".333 1 1"]
- "hue 0.375" [color = ".375 1 1"]
- "hue 0.416" -- "dark hue 0.416"
- "hue 0.416" [color = ".416 1 1"]
- "hue 0.458" [color = ".458 1 1"]
- "hue 0.500" -- darkcyan
- "hue 0.500" [color = ".500 1 1"]
- "hue 0.541" [color = ".541 1 1"]
- node [fontcolor = white]
- "hue 0.000" [color = ".000 1 1"]
- "hue 0.000" -- darkred
- "hue 0.041" [color = ".041 1 1"]
- "hue 0.583" -- "dark hue 0.583"
- "hue 0.583" [color = ".583 1 1"]
- "hue 0.625" [color = ".625 1 1"]
- "hue 0.666" -- darkblue
- "hue 0.666" [color = ".666 1 1"]
- "hue 0.708" [color = ".708 1 1"]
- "hue 0.750" -- "dark hue 0.750"
- "hue 0.750" [color = ".750 1 1"]
- "hue 0.791" [color = ".791 1 1"]
- "hue 0.833" -- darkmagenta
- "hue 0.833" [color = ".833 1 1"]
- "hue 0.875" [color = ".875 1 1"]
- "hue 0.916" -- "dark hue 0.916"
- "hue 0.916" [color = ".916 1 1"]
- "hue 0.958" [color = ".958 1 1"]
- edge [len = 1]
- "hue 0.000" -- "hue 0.041" -- "hue 0.083" -- "hue 0.125" -- "hue 0.166" -- "hue 0.208"
- "hue 0.208" -- "hue 0.250" -- "hue 0.291" -- "hue 0.333" -- "hue 0.375" -- "hue 0.416"
- "hue 0.416" -- "hue 0.458" -- "hue 0.500" --"hue 0.541" -- "hue 0.583" -- "hue 0.625"
- "hue 0.625" -- "hue 0.666" -- "hue 0.708" -- "hue 0.750" -- "hue 0.791" -- "hue 0.833"
- "hue 0.833" -- "hue 0.875" -- "hue 0.916" -- "hue 0.958" -- "hue 0.000"
- }
- subgraph Main_colors {
- node [width = 2 fontsize = 20]
- red [fillcolor = red fontcolor = white]
- orangered [fillcolor = orangered]
- orange [fillcolor = orange]
- gold [fillcolor = gold]
- yellow [fillcolor = yellow]
- yellowgreen [fillcolor = yellowgreen]
- deeppink [fillcolor = deeppink fontcolor = white]
- fuchsia [label = "fuchsia\nmagenta" fillcolor = fuchsia fontcolor = white]
- purple [fillcolor = purple fontcolor = white]
- blue [fillcolor = blue fontcolor = white]
- cornflowerblue [fillcolor = cornflowerblue]
- deepskyblue [fillcolor = deepskyblue]
- aqua [fillcolor = aqua label = "aqua\ncyan"]
- springgreen [fillcolor = springgreen]
- green [fillcolor = green]
- purple -- fuchsia -- deeppink -- red
- cornflowerblue -- blue -- purple
- cornflowerblue -- deepskyblue -- aqua [len = 1.7]
- aqua -- springgreen -- green -- yellowgreen -- yellow
- yellow -- gold -- orange -- orangered -- red [len = 1.6]
- orange -- "hue 0.083"
- deeppink -- "hue 0.916"
- deeppink -- "hue 0.875"
- red -- "hue 0.000"
- yellowgreen -- "hue 0.250"
- blue -- "hue 0.666"
- yellow -- "hue 0.166"
- gold -- "hue 0.125"
- green -- "hue 0.333"
- springgreen -- "hue 0.416"
- aqua -- "hue 0.500"
- cornflowerblue -- "hue 0.583"
- deepskyblue -- "hue 0.541"
- purple -- "hue 0.791"
- purple -- "hue 0.750"
- fuchsia -- "hue 0.833"
- }
- subgraph Light_colors {
- node [width = 2 fontsize = 20]
- node [shape = circle width = 1.8]
- edge [len = 2.1]
- pink [fillcolor = pink]
- pink -- red
- lightyellow [fillcolor = lightyellow]
- lightyellow -- yellow
- mediumpurple [fillcolor = mediumpurple]
- mediumpurple -- purple
- violet [fillcolor = violet]
- violet -- fuchsia
- hotpink [fillcolor = hotpink]
- hotpink -- deeppink
- "light hue 0.250" [color = ".250 .2 1"]
- "light hue 0.250" -- yellowgreen
- lightcyan [fillcolor = lightcyan]
- lightcyan -- aqua
- lightslateblue [fillcolor = lightslateblue]
- lightslateblue -- blue
- lightgreen [fillcolor = lightgreen]
- lightgreen -- green
- lightskyblue [fillcolor = lightskyblue]
- lightskyblue -- deepskyblue
- peachpuff [fillcolor = peachpuff]
- peachpuff -- orange
- "light hue 0.416" [color = ".416 .2 1"]
- "light hue 0.416" -- springgreen
- }
- subgraph Tints {
- node [width = 1]
- edge [len = 2.4]
- "hue 0 tint" -- pink
- "hue 0 tint" [color = "0 .1 1"]
- "hue 0.041 tint" [color = ".041 .1 1"]
- "hue 0.083 tint" -- peachpuff
- "hue 0.083 tint" [color = ".083 .1 1"]
- "hue 0.125 tint" [color = ".125 .1 1"]
- "hue 0.166 tint" -- lightyellow
- "hue 0.166 tint" [color = ".166 .1 1"]
- "hue 0.208 tint" [color = ".208 .1 1"]
- "hue 0.250 tint" -- "light hue 0.250"
- "hue 0.250 tint" [color = ".250 .1 1"]
- "hue 0.291 tint" [color = ".291 .1 1"]
- "hue 0.333 tint" -- lightgreen
- "hue 0.333 tint" [color = ".333 .1 1"]
- "hue 0.375 tint" [color = ".375 .1 1"]
- "hue 0.416 tint" -- "light hue 0.416"
- "hue 0.416 tint" [color = ".416 .1 1"]
- "hue 0.458 tint" [color = ".458 .1 1"]
- "hue 0.5 tint" -- lightcyan
- "hue 0.5 tint" [color = ".5 .1 1"]
- "hue 0.541 tint" -- lightskyblue
- "hue 0.541 tint" [color = ".541 .1 1"]
- "hue 0.583 tint" [color = ".583 .1 1"]
- "hue 0.625 tint" [color = ".625 .1 1"]
- "hue 0.666 tint" -- lightslateblue
- "hue 0.666 tint" [color = ".666 .1 1"]
- "hue 0.708 tint" [color = ".708 .1 1"]
- "hue 0.750 tint" -- mediumpurple
- "hue 0.750 tint" [color = ".750 .1 1"]
- "hue 0.791 tint" [color = ".791 .1 1"]
- "hue 0.833 tint" -- violet
- "hue 0.833 tint" [color = ".833 .1 1"]
- "hue 0.875 tint" [color = ".875 .1 1"]
- "hue 0.916 tint" -- hotpink
- "hue 0.916 tint" [color = ".916 .1 1"]
- "hue 0.958 tint" [color = ".958 .1 1"]
- edge [len = 2]
- "hue 0 tint" -- "hue 0.041 tint" -- "hue 0.083 tint" -- "hue 0.125 tint" -- "hue 0.166 tint" -- "hue 0.208 tint"
- "hue 0.208 tint" -- "hue 0.250 tint" -- "hue 0.291 tint" -- "hue 0.333 tint" -- "hue 0.375 tint" -- "hue 0.416 tint"
- "hue 0.416 tint" -- "hue 0.458 tint" -- "hue 0.5 tint" --"hue 0.541 tint" -- "hue 0.583 tint" -- "hue 0.625 tint"
- "hue 0.625 tint" -- "hue 0.666 tint" -- "hue 0.708 tint" -- "hue 0.750 tint" -- "hue 0.791 tint" -- "hue 0.833 tint"
- "hue 0.833 tint" -- "hue 0.875 tint" -- "hue 0.916 tint" -- "hue 0.958 tint" -- "hue 0 tint"
- }
- }
-''')
\ No newline at end of file
diff --git a/spaces/awacke1/Streamlit.Azure.SDK.Terraform/app.py b/spaces/awacke1/Streamlit.Azure.SDK.Terraform/app.py
deleted file mode 100644
index b68a7942c39aea5ff298cd0af54f842bf43fa44f..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Streamlit.Azure.SDK.Terraform/app.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import os
-import json
-import tempfile
-from azure.identity import DefaultAzureCredential
-from azure.mgmt.resource import ResourceManagementClient
-from python_terraform import Terraform
-
-import streamlit as st
-
-# Set up Azure credentials
-credential = DefaultAzureCredential()
-subscription_id = os.environ["AZURE_SUBSCRIPTION_ID"]
-
-# Initialize ResourceManagementClient
-resource_client = ResourceManagementClient(credential, subscription_id)
-
-# Initialize Terraform
-tf = Terraform(working_dir=tempfile.mkdtemp())
-
-# Streamlit app
-st.title("Azure SDK and Terraform Demo")
-
-# Get the list of resource groups
-resource_groups = [rg.name for rg in resource_client.resource_groups.list()]
-
-# Select a resource group
-selected_rg = st.selectbox("Select a Resource Group", resource_groups)
-
-if selected_rg:
- # Display resources in the selected resource group
- resources = resource_client.resources.list_by_resource_group(selected_rg)
- st.subheader(f"Resources in Resource Group: {selected_rg}")
- for resource in resources:
- st.write(f"{resource.type}: {resource.name}")
-
-# Terraform configurations
-st.subheader("Terraform Configuration")
-
-# Set up the Terraform configuration
-terraform_config = f"""
-provider "azurerm" {{
- features {{}}
-}}
-
-resource "azurerm_resource_group" "example" {{
- name = "{selected_rg}"
- location = "East US"
-}}
-
-resource "azurerm_virtual_network" "example" {{
- name = "example-network"
- address_space = ["10.0.0.0/16"]
- location = azurerm_resource_group.example.location
- resource_group_name = azurerm_resource_group.example.name
-}}
-
-resource "azurerm_subnet" "example" {{
- name = "internal"
- resource_group_name = azurerm_resource_group.example.name
- virtual_network_name = azurerm_virtual_network.example.name
- address_prefix = "10.0.2.0/24"
-}}
-"""
-
-# Save the Terraform configuration to a file
-with open("main.tf", "w") as f:
- f.write(terraform_config)
-
-st.code(terraform_config, language="hcl")
-
-if st.button("Apply Terraform"):
- # Initialize and apply Terraform configuration
- tf.init()
- ret_code, stdout, stderr = tf.apply(skip_plan=True, capture_output=True)
-
- if ret_code == 0:
- st.success("Terraform applied successfully!")
- st.code(stdout, language="bash")
- else:
- st.error("Error applying Terraform configuration")
- st.code(stderr, language="bash")
diff --git a/spaces/balenireekshana/MyGenAI/app.py b/spaces/balenireekshana/MyGenAI/app.py
deleted file mode 100644
index a362dcc7d0ddd1eee86961f1bc3db6d894fbd3d5..0000000000000000000000000000000000000000
--- a/spaces/balenireekshana/MyGenAI/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
- llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
- prompt=prompt,
- verbose=True,
- memory=memory,
-)
-
-def get_text_response(user_message,history):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/renderers/SVGRenderer.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/renderers/SVGRenderer.js
deleted file mode 100644
index 08e4d72181b5951af9496dbe0ccdd6010fd02a81..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/renderers/SVGRenderer.js
+++ /dev/null
@@ -1,507 +0,0 @@
-/**
- * @author mrdoob / http://mrdoob.com/
- */
-
-THREE.SVGObject = function ( node ) {
-
- THREE.Object3D.call( this );
-
- this.node = node;
-
-};
-
-THREE.SVGObject.prototype = Object.create( THREE.Object3D.prototype );
-THREE.SVGObject.prototype.constructor = THREE.SVGObject;
-
-THREE.SVGRenderer = function () {
-
- console.log( 'THREE.SVGRenderer', THREE.REVISION );
-
- var _this = this,
- _renderData, _elements, _lights,
- _projector = new THREE.Projector(),
- _svg = document.createElementNS( 'http://www.w3.org/2000/svg', 'svg' ),
- _svgWidth, _svgHeight, _svgWidthHalf, _svgHeightHalf,
-
- _v1, _v2, _v3,
-
- _clipBox = new THREE.Box2(),
- _elemBox = new THREE.Box2(),
-
- _color = new THREE.Color(),
- _diffuseColor = new THREE.Color(),
- _ambientLight = new THREE.Color(),
- _directionalLights = new THREE.Color(),
- _pointLights = new THREE.Color(),
- _clearColor = new THREE.Color(),
- _clearAlpha = 1,
-
- _vector3 = new THREE.Vector3(), // Needed for PointLight
- _centroid = new THREE.Vector3(),
- _normal = new THREE.Vector3(),
- _normalViewMatrix = new THREE.Matrix3(),
-
- _viewMatrix = new THREE.Matrix4(),
- _viewProjectionMatrix = new THREE.Matrix4(),
-
- _svgPathPool = [],
- _svgNode, _pathCount = 0,
-
- _currentPath, _currentStyle,
-
- _quality = 1, _precision = null;
-
- this.domElement = _svg;
-
- this.autoClear = true;
- this.sortObjects = true;
- this.sortElements = true;
-
- this.info = {
-
- render: {
-
- vertices: 0,
- faces: 0
-
- }
-
- };
-
- this.setQuality = function ( quality ) {
-
- switch ( quality ) {
-
- case "high": _quality = 1; break;
- case "low": _quality = 0; break;
-
- }
-
- };
-
- this.setClearColor = function ( color, alpha ) {
-
- _clearColor.set( color );
- _clearAlpha = alpha !== undefined ? alpha : 1;
-
- };
-
- this.setPixelRatio = function () {};
-
- this.setSize = function ( width, height ) {
-
- _svgWidth = width; _svgHeight = height;
- _svgWidthHalf = _svgWidth / 2; _svgHeightHalf = _svgHeight / 2;
-
- _svg.setAttribute( 'viewBox', ( - _svgWidthHalf ) + ' ' + ( - _svgHeightHalf ) + ' ' + _svgWidth + ' ' + _svgHeight );
- _svg.setAttribute( 'width', _svgWidth );
- _svg.setAttribute( 'height', _svgHeight );
-
- _clipBox.min.set( - _svgWidthHalf, - _svgHeightHalf );
- _clipBox.max.set( _svgWidthHalf, _svgHeightHalf );
-
- };
-
- this.setPrecision = function ( precision ) {
-
- _precision = precision;
-
- };
-
- function removeChildNodes() {
-
- _pathCount = 0;
-
- while ( _svg.childNodes.length > 0 ) {
-
- _svg.removeChild( _svg.childNodes[ 0 ] );
-
- }
-
- }
-
- function getSvgColor( color, opacity ) {
-
- var arg = Math.floor( color.r * 255 ) + ',' + Math.floor( color.g * 255 ) + ',' + Math.floor( color.b * 255 );
-
- if ( opacity === undefined || opacity === 1 ) return 'rgb(' + arg + ')';
-
- return 'rgb(' + arg + '); fill-opacity: ' + opacity;
-
- }
-
- function convert( c ) {
-
- return _precision !== null ? c.toFixed( _precision ) : c;
-
- }
-
- this.clear = function () {
-
- removeChildNodes();
- _svg.style.backgroundColor = getSvgColor( _clearColor, _clearAlpha );
-
- };
-
- this.render = function ( scene, camera ) {
-
- if ( camera instanceof THREE.Camera === false ) {
-
- console.error( 'THREE.SVGRenderer.render: camera is not an instance of THREE.Camera.' );
- return;
-
- }
-
- var background = scene.background;
-
- if ( background && background.isColor ) {
-
- removeChildNodes();
- _svg.style.backgroundColor = getSvgColor( background );
-
- } else if ( this.autoClear === true ) {
-
- this.clear();
-
- }
-
- _this.info.render.vertices = 0;
- _this.info.render.faces = 0;
-
- _viewMatrix.copy( camera.matrixWorldInverse );
- _viewProjectionMatrix.multiplyMatrices( camera.projectionMatrix, _viewMatrix );
-
- _renderData = _projector.projectScene( scene, camera, this.sortObjects, this.sortElements );
- _elements = _renderData.elements;
- _lights = _renderData.lights;
-
- _normalViewMatrix.getNormalMatrix( camera.matrixWorldInverse );
-
- calculateLights( _lights );
-
- // reset accumulated path
-
- _currentPath = '';
- _currentStyle = '';
-
- for ( var e = 0, el = _elements.length; e < el; e ++ ) {
-
- var element = _elements[ e ];
- var material = element.material;
-
- if ( material === undefined || material.opacity === 0 ) continue;
-
- _elemBox.makeEmpty();
-
- if ( element instanceof THREE.RenderableSprite ) {
-
- _v1 = element;
- _v1.x *= _svgWidthHalf; _v1.y *= - _svgHeightHalf;
-
- renderSprite( _v1, element, material );
-
- } else if ( element instanceof THREE.RenderableLine ) {
-
- _v1 = element.v1; _v2 = element.v2;
-
- _v1.positionScreen.x *= _svgWidthHalf; _v1.positionScreen.y *= - _svgHeightHalf;
- _v2.positionScreen.x *= _svgWidthHalf; _v2.positionScreen.y *= - _svgHeightHalf;
-
- _elemBox.setFromPoints( [ _v1.positionScreen, _v2.positionScreen ] );
-
- if ( _clipBox.intersectsBox( _elemBox ) === true ) {
-
- renderLine( _v1, _v2, element, material );
-
- }
-
- } else if ( element instanceof THREE.RenderableFace ) {
-
- _v1 = element.v1; _v2 = element.v2; _v3 = element.v3;
-
- if ( _v1.positionScreen.z < - 1 || _v1.positionScreen.z > 1 ) continue;
- if ( _v2.positionScreen.z < - 1 || _v2.positionScreen.z > 1 ) continue;
- if ( _v3.positionScreen.z < - 1 || _v3.positionScreen.z > 1 ) continue;
-
- _v1.positionScreen.x *= _svgWidthHalf; _v1.positionScreen.y *= - _svgHeightHalf;
- _v2.positionScreen.x *= _svgWidthHalf; _v2.positionScreen.y *= - _svgHeightHalf;
- _v3.positionScreen.x *= _svgWidthHalf; _v3.positionScreen.y *= - _svgHeightHalf;
-
- _elemBox.setFromPoints( [
- _v1.positionScreen,
- _v2.positionScreen,
- _v3.positionScreen
- ] );
-
- if ( _clipBox.intersectsBox( _elemBox ) === true ) {
-
- renderFace3( _v1, _v2, _v3, element, material );
-
- }
-
- }
-
- }
-
- flushPath(); // just to flush last svg:path
-
- scene.traverseVisible( function ( object ) {
-
- if ( object instanceof THREE.SVGObject ) {
-
- _vector3.setFromMatrixPosition( object.matrixWorld );
- _vector3.applyMatrix4( _viewProjectionMatrix );
-
- if ( _vector3.z < - 1 || _vector3.z > 1 ) return;
-
- var x = _vector3.x * _svgWidthHalf;
- var y = - _vector3.y * _svgHeightHalf;
-
- var node = object.node;
- node.setAttribute( 'transform', 'translate(' + x + ',' + y + ')' );
-
- _svg.appendChild( node );
-
- }
-
- } );
-
- };
-
- function calculateLights( lights ) {
-
- _ambientLight.setRGB( 0, 0, 0 );
- _directionalLights.setRGB( 0, 0, 0 );
- _pointLights.setRGB( 0, 0, 0 );
-
- for ( var l = 0, ll = lights.length; l < ll; l ++ ) {
-
- var light = lights[ l ];
- var lightColor = light.color;
-
- if ( light.isAmbientLight ) {
-
- _ambientLight.r += lightColor.r;
- _ambientLight.g += lightColor.g;
- _ambientLight.b += lightColor.b;
-
- } else if ( light.isDirectionalLight ) {
-
- _directionalLights.r += lightColor.r;
- _directionalLights.g += lightColor.g;
- _directionalLights.b += lightColor.b;
-
- } else if ( light.isPointLight ) {
-
- _pointLights.r += lightColor.r;
- _pointLights.g += lightColor.g;
- _pointLights.b += lightColor.b;
-
- }
-
- }
-
- }
-
- function calculateLight( lights, position, normal, color ) {
-
- for ( var l = 0, ll = lights.length; l < ll; l ++ ) {
-
- var light = lights[ l ];
- var lightColor = light.color;
-
- if ( light.isDirectionalLight ) {
-
- var lightPosition = _vector3.setFromMatrixPosition( light.matrixWorld ).normalize();
-
- var amount = normal.dot( lightPosition );
-
- if ( amount <= 0 ) continue;
-
- amount *= light.intensity;
-
- color.r += lightColor.r * amount;
- color.g += lightColor.g * amount;
- color.b += lightColor.b * amount;
-
- } else if ( light.isPointLight ) {
-
- var lightPosition = _vector3.setFromMatrixPosition( light.matrixWorld );
-
- var amount = normal.dot( _vector3.subVectors( lightPosition, position ).normalize() );
-
- if ( amount <= 0 ) continue;
-
- amount *= light.distance == 0 ? 1 : 1 - Math.min( position.distanceTo( lightPosition ) / light.distance, 1 );
-
- if ( amount == 0 ) continue;
-
- amount *= light.intensity;
-
- color.r += lightColor.r * amount;
- color.g += lightColor.g * amount;
- color.b += lightColor.b * amount;
-
- }
-
- }
-
- }
-
- function renderSprite( v1, element, material ) {
-
- var scaleX = element.scale.x * _svgWidthHalf;
- var scaleY = element.scale.y * _svgHeightHalf;
-
- if ( material.isPointsMaterial ) {
-
- scaleX *= material.size;
- scaleY *= material.size;
-
- }
-
- var path = 'M' + convert( v1.x - scaleX * 0.5 ) + ',' + convert( v1.y - scaleY * 0.5 ) + 'h' + convert( scaleX ) + 'v' + convert( scaleY ) + 'h' + convert( - scaleX ) + 'z';
- var style = "";
-
- if ( material.isSpriteMaterial || material.isPointsMaterial ) {
-
- style = 'fill:' + getSvgColor( material.color, material.opacity );
-
- }
-
- addPath( style, path );
-
- }
-
- function renderLine( v1, v2, element, material ) {
-
- var path = 'M' + convert( v1.positionScreen.x ) + ',' + convert( v1.positionScreen.y ) + 'L' + convert( v2.positionScreen.x ) + ',' + convert( v2.positionScreen.y );
-
- if ( material.isLineBasicMaterial ) {
-
- var style = 'fill:none;stroke:' + getSvgColor( material.color, material.opacity ) + ';stroke-width:' + material.linewidth + ';stroke-linecap:' + material.linecap;
-
- if ( material.isLineDashedMaterial ) {
-
- style = style + ';stroke-dasharray:' + material.dashSize + "," + material.gapSize;
-
- }
-
- addPath( style, path );
-
- }
-
- }
-
- function renderFace3( v1, v2, v3, element, material ) {
-
- _this.info.render.vertices += 3;
- _this.info.render.faces ++;
-
- var path = 'M' + convert( v1.positionScreen.x ) + ',' + convert( v1.positionScreen.y ) + 'L' + convert( v2.positionScreen.x ) + ',' + convert( v2.positionScreen.y ) + 'L' + convert( v3.positionScreen.x ) + ',' + convert( v3.positionScreen.y ) + 'z';
- var style = '';
-
- if ( material.isMeshBasicMaterial ) {
-
- _color.copy( material.color );
-
- if ( material.vertexColors === THREE.FaceColors || material.vertexColors === THREE.VertexColors ) {
-
- _color.multiply( element.color );
-
- }
-
- } else if ( material.isMeshLambertMaterial || material.isMeshPhongMaterial || material.isMeshStandardMaterial ) {
-
- _diffuseColor.copy( material.color );
-
- if ( material.vertexColors === THREE.FaceColors || material.vertexColors === THREE.VertexColors ) {
-
- _diffuseColor.multiply( element.color );
-
- }
-
- _color.copy( _ambientLight );
-
- _centroid.copy( v1.positionWorld ).add( v2.positionWorld ).add( v3.positionWorld ).divideScalar( 3 );
-
- calculateLight( _lights, _centroid, element.normalModel, _color );
-
- _color.multiply( _diffuseColor ).add( material.emissive );
-
- } else if ( material.isMeshNormalMaterial ) {
-
- _normal.copy( element.normalModel ).applyMatrix3( _normalViewMatrix );
-
- _color.setRGB( _normal.x, _normal.y, _normal.z ).multiplyScalar( 0.5 ).addScalar( 0.5 );
-
- }
-
- if ( material.wireframe ) {
-
- style = 'fill:none;stroke:' + getSvgColor( _color, material.opacity ) + ';stroke-width:' + material.wireframeLinewidth + ';stroke-linecap:' + material.wireframeLinecap + ';stroke-linejoin:' + material.wireframeLinejoin;
-
- } else {
-
- style = 'fill:' + getSvgColor( _color, material.opacity );
-
- }
-
- addPath( style, path );
-
- }
-
- function addPath( style, path ) {
-
- if ( _currentStyle === style ) {
-
- _currentPath += path;
-
- } else {
-
- flushPath();
-
- _currentStyle = style;
- _currentPath = path;
-
- }
-
- }
-
- function flushPath() {
-
- if ( _currentPath ) {
-
- _svgNode = getPathNode( _pathCount ++ );
- _svgNode.setAttribute( 'd', _currentPath );
- _svgNode.setAttribute( 'style', _currentStyle );
- _svg.appendChild( _svgNode );
-
- }
-
- _currentPath = '';
- _currentStyle = '';
-
- }
-
- function getPathNode( id ) {
-
- if ( _svgPathPool[ id ] == null ) {
-
- _svgPathPool[ id ] = document.createElementNS( 'http://www.w3.org/2000/svg', 'path' );
-
- if ( _quality == 0 ) {
-
- _svgPathPool[ id ].setAttribute( 'shape-rendering', 'crispEdges' ); //optimizeSpeed
-
- }
-
- return _svgPathPool[ id ];
-
- }
-
- return _svgPathPool[ id ];
-
- }
-
-};
diff --git a/spaces/barani/ControlNet/cv_utils.py b/spaces/barani/ControlNet/cv_utils.py
deleted file mode 100644
index d81177c5eee306107966132fd54695545a61a898..0000000000000000000000000000000000000000
--- a/spaces/barani/ControlNet/cv_utils.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import cv2
-import numpy as np
-
-
-def resize_image(input_image, resolution, interpolation=None):
- H, W, C = input_image.shape
- H = float(H)
- W = float(W)
- k = float(resolution) / max(H, W)
- H *= k
- W *= k
- H = int(np.round(H / 64.0)) * 64
- W = int(np.round(W / 64.0)) * 64
- if interpolation is None:
- interpolation = cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA
- img = cv2.resize(input_image, (W, H), interpolation=interpolation)
- return img
diff --git a/spaces/beki/pii-anonymizer/README.md b/spaces/beki/pii-anonymizer/README.md
deleted file mode 100644
index fa7df580e7c1ff431f50fb69cca91a2d0188748b..0000000000000000000000000000000000000000
--- a/spaces/beki/pii-anonymizer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Presidio with custom PII models trained on PII data generated by Privy
-emoji: 📊
-colorFrom: purple
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/bigjoker/stable-diffusion-webui/modules/hypernetworks/ui.py b/spaces/bigjoker/stable-diffusion-webui/modules/hypernetworks/ui.py
deleted file mode 100644
index be2fd77cc76a24d0e7932c6b1fb26efcb18edcc5..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/modules/hypernetworks/ui.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import html
-import os
-import re
-
-import gradio as gr
-import modules.hypernetworks.hypernetwork
-from modules import devices, sd_hijack, shared
-
-not_available = ["hardswish", "multiheadattention"]
-keys = list(x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
-
-
-def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
- filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, dropout_structure)
-
- return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {filename}", ""
-
-
-def train_hypernetwork(*args):
- shared.loaded_hypernetworks = []
-
- assert not shared.cmd_opts.lowvram, 'Training models with lowvram is not possible'
-
- try:
- sd_hijack.undo_optimizations()
-
- hypernetwork, filename = modules.hypernetworks.hypernetwork.train_hypernetwork(*args)
-
- res = f"""
-Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
-Hypernetwork saved to {html.escape(filename)}
-"""
- return res, ""
- except Exception:
- raise
- finally:
- shared.sd_model.cond_stage_model.to(devices.device)
- shared.sd_model.first_stage_model.to(devices.device)
- sd_hijack.apply_optimizations()
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Acronis True Image 2016 19.0 Build 5634 Multilang Keygen.md b/spaces/bioriAsaeru/text-to-voice/Acronis True Image 2016 19.0 Build 5634 Multilang Keygen.md
deleted file mode 100644
index 2707d4722247cf89a9c5c3a1aa498612de86986e..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Acronis True Image 2016 19.0 Build 5634 Multilang Keygen.md
+++ /dev/null
@@ -1,18 +0,0 @@
-Acronis True Image 2016 19.0 Build 5634 Multilang keygen
DOWNLOAD ❤❤❤ https://urloso.com/2uyP0g
-
- . . and make it into a animation or. . . .. We hope you enjoy it!. The price of the license is $0.00 and you get a lifetime license. True Image 2016 19.0 Build 5634 Multilang Keygen Download: ( is a very popular video on our website. Well-known online community, you can also see this video in your facebook feed.. Watch this and many more video clips for free!.
-
-True Image 2016 19.0 Build 5634 Multilang Keygen Download: ( was added 2014-08-21 11:27:46 UTC by Dibba). This page was viewed 0 times today and 0 times today. Also true image 2016 19.0 build 5634 multilang keygen download is being added to Collections. FILED
-
- NOT FOR PUBLICATION JUN 12 2014
-
- MOLLY C. DWYER, CLERK
-
- UNITED STATES COURT OF APPEALS U.S. COURT OF APPEALS
-
- FOR THE NINTH CIRCUIT
-
-DAVID WILLIAM JOHNSON, Jr., 4fefd39f24
-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/DEADSIDE PC Game Free Download.md b/spaces/bioriAsaeru/text-to-voice/DEADSIDE PC Game Free Download.md
deleted file mode 100644
index 8661dfb16f19158971862581b0790cd3f89d2771..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/DEADSIDE PC Game Free Download.md
+++ /dev/null
@@ -1,6 +0,0 @@
-DEADSIDE PC Game Free Download
Download ➡ https://urloso.com/2uyQtm
-
-I'm more into the open world game like Rust but more with Tarkovish realism and reward. Also Rust ... apex is pretty sick with your friends ngl and its free also. 2 ... If not, any other essential gameplay-heavy games for PC you could recommend? ... We're going to download the game and have our first session this evening! 1fdad05405
-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Gsrld Dll Indir Gezginler.md b/spaces/bioriAsaeru/text-to-voice/Gsrld Dll Indir Gezginler.md
deleted file mode 100644
index 19cfd915aa511a259169b3cf7ded4da80abb46d2..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Gsrld Dll Indir Gezginler.md
+++ /dev/null
@@ -1,9 +0,0 @@
-
-how to install gsrld dll indir gezginler. bing results for “gsrld. dll”. this program offers the possibility to hide all drives and folders of the system. the program can be run from the installed folder or from the desktop. any necessary updates will be installed automatically. download gsrld. dll indir gezginler.
-Gsrld dll indir gezginler
Download ✒ https://urloso.com/2uyPYL
-annoyed at your boss and can’t get a transfer to start? then you need at least one of the following files: gsrld. dll, gsrld. dll. bin. download aids icon or gsrld. dll indir gezginler. he has played on the field, but this moment his competitive spirit is on the internet. however, in the days of the 90s, the main players of the computer games were really the gamers. the game is played in the form of a commander of military forces, from the beginning of the game. the game is played on the computer, and often consists of a story, in which a person has been captured by the enemy. search all. download. gsrld.
-diğer isimler. unzip gsrld dll indir gezginler sorma ve sadece indirim icin gsm gsm modem sigortum sisteminde sorulacak sorumlarda yapmanize gerekli değiller. diğer isimler. zip gsrld dll indir gezginler sorma ve sadece indirim icin gsm gsm modem sigortum sisteminde sorulacak sorumlarda yapmanize gerekli değiller. gsrld dll indir gezginler dll indir yanlisliklarindan dolayik. indirin adresine girdiğinizde bu değeri sorgulayiniz.
-gsrld dll indir gezginler klasörünüzde. download gsrld dll indir gezginler. dll, full gezginler ndir. this is a sony video editing software that has gained quite a lot of popularity in. dll gezginlere işlemek için herkes gibi, düşünüyorsanız gsrld dll indir gezginler. yükseltmesi veya eklemesi.
- 899543212b
-
-
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Keygen Rar Recovery Toolbox Serial Key ((HOT)).md b/spaces/bioriAsaeru/text-to-voice/Keygen Rar Recovery Toolbox Serial Key ((HOT)).md
deleted file mode 100644
index 7407697e05c029911e7b961294cbf1d365185ea7..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Keygen Rar Recovery Toolbox Serial Key ((HOT)).md
+++ /dev/null
@@ -1,6 +0,0 @@
-Keygen Rar Recovery Toolbox Serial Key
Download File ✪✪✪ https://urloso.com/2uyP99
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/blaziant/ysda_nlp_ops/README.md b/spaces/blaziant/ysda_nlp_ops/README.md
deleted file mode 100644
index 2711c63290304be1e522ddb8ab2a51bd6e915039..0000000000000000000000000000000000000000
--- a/spaces/blaziant/ysda_nlp_ops/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Ysda Nlp Ops
-emoji: 📉
-colorFrom: gray
-colorTo: blue
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/blmdsydm/faster-whisper-webui/README.md b/spaces/blmdsydm/faster-whisper-webui/README.md
deleted file mode 100644
index b530ec893be48ff9471257f74d7b03c524c8bfe4..0000000000000000000000000000000000000000
--- a/spaces/blmdsydm/faster-whisper-webui/README.md
+++ /dev/null
@@ -1,186 +0,0 @@
----
-title: Faster Whisper Webui
-emoji: 🚀
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: aadnk/faster-whisper-webui
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-# Running Locally
-
-To run this program locally, first install Python 3.9+ and Git. Then install Pytorch 10.1+ and all the other dependencies:
-```
-pip install -r requirements.txt
-```
-
-You can find detailed instructions for how to install this on Windows 10/11 [here (PDF)](docs/windows/install_win10_win11.pdf).
-
-Finally, run the full version (no audio length restrictions) of the app with parallel CPU/GPU enabled:
-```
-python app.py --input_audio_max_duration -1 --server_name 127.0.0.1 --auto_parallel True
-```
-
-You can also run the CLI interface, which is similar to Whisper's own CLI but also supports the following additional arguments:
-```
-python cli.py \
-[--vad {none,silero-vad,silero-vad-skip-gaps,silero-vad-expand-into-gaps,periodic-vad}] \
-[--vad_merge_window VAD_MERGE_WINDOW] \
-[--vad_max_merge_size VAD_MAX_MERGE_SIZE] \
-[--vad_padding VAD_PADDING] \
-[--vad_prompt_window VAD_PROMPT_WINDOW]
-[--vad_cpu_cores NUMBER_OF_CORES]
-[--vad_parallel_devices COMMA_DELIMITED_DEVICES]
-[--auto_parallel BOOLEAN]
-```
-In addition, you may also use URL's in addition to file paths as input.
-```
-python cli.py --model large --vad silero-vad --language Japanese "https://www.youtube.com/watch?v=4cICErqqRSM"
-```
-
-Rather than supplying arguments to `app.py` or `cli.py`, you can also use the configuration file [config.json5](config.json5). See that file for more information.
-If you want to use a different configuration file, you can use the `WHISPER_WEBUI_CONFIG` environment variable to specify the path to another file.
-
-### Multiple Files
-
-You can upload multiple files either through the "Upload files" option, or as a playlist on YouTube.
-Each audio file will then be processed in turn, and the resulting SRT/VTT/Transcript will be made available in the "Download" section.
-When more than one file is processed, the UI will also generate a "All_Output" zip file containing all the text output files.
-
-## Diarization
-
-To detect different speakers in the audio, you can use the [whisper-diarization](https://gitlab.com/aadnk/whisper-diarization) application.
-
-Download the JSON file after running Whisper on an audio file, and then run app.py in the
-whisper-diarization repository with the audio file and the JSON file as arguments.
-
-## Whisper Implementation
-
-You can choose between using `whisper` or `faster-whisper`. [Faster Whisper](https://github.com/guillaumekln/faster-whisper) as a drop-in replacement for the
-default Whisper which achieves up to a 4x speedup and 2x reduction in memory usage.
-
-You can install the requirements for a specific Whisper implementation in `requirements-fasterWhisper.txt`
-or `requirements-whisper.txt`:
-```
-pip install -r requirements-fasterWhisper.txt
-```
-And then run the App or the CLI with the `--whisper_implementation faster-whisper` flag:
-```
-python app.py --whisper_implementation faster-whisper --input_audio_max_duration -1 --server_name 127.0.0.1 --auto_parallel True
-```
-You can also select the whisper implementation in `config.json5`:
-```json5
-{
- "whisper_implementation": "faster-whisper"
-}
-```
-### GPU Acceleration
-
-In order to use GPU acceleration with Faster Whisper, both CUDA 11.2 and cuDNN 8 must be installed. You may want to install it in a virtual environment like Anaconda.
-
-## Google Colab
-
-You can also run this Web UI directly on [Google Colab](https://colab.research.google.com/drive/1qeTSvi7Bt_5RMm88ipW4fkcsMOKlDDss?usp=sharing), if you haven't got a GPU powerful enough to run the larger models.
-
-See the [colab documentation](docs/colab.md) for more information.
-
-## Parallel Execution
-
-You can also run both the Web-UI or the CLI on multiple GPUs in parallel, using the `vad_parallel_devices` option. This takes a comma-delimited list of
-device IDs (0, 1, etc.) that Whisper should be distributed to and run on concurrently:
-```
-python cli.py --model large --vad silero-vad --language Japanese \
---vad_parallel_devices 0,1 "https://www.youtube.com/watch?v=4cICErqqRSM"
-```
-
-Note that this requires a VAD to function properly, otherwise only the first GPU will be used. Though you could use `period-vad` to avoid taking the hit
-of running Silero-Vad, at a slight cost to accuracy.
-
-This is achieved by creating N child processes (where N is the number of selected devices), where Whisper is run concurrently. In `app.py`, you can also
-set the `vad_process_timeout` option. This configures the number of seconds until a process is killed due to inactivity, freeing RAM and video memory.
-The default value is 30 minutes.
-
-```
-python app.py --input_audio_max_duration -1 --vad_parallel_devices 0,1 --vad_process_timeout 3600
-```
-
-To execute the Silero VAD itself in parallel, use the `vad_cpu_cores` option:
-```
-python app.py --input_audio_max_duration -1 --vad_parallel_devices 0,1 --vad_process_timeout 3600 --vad_cpu_cores 4
-```
-
-You may also use `vad_process_timeout` with a single device (`--vad_parallel_devices 0`), if you prefer to always free video memory after a period of time.
-
-### Auto Parallel
-
-You can also set `auto_parallel` to `True`. This will set `vad_parallel_devices` to use all the GPU devices on the system, and `vad_cpu_cores` to be equal to the number of
-cores (up to 8):
-```
-python app.py --input_audio_max_duration -1 --auto_parallel True
-```
-
-# Docker
-
-To run it in Docker, first install Docker and optionally the NVIDIA Container Toolkit in order to use the GPU.
-Then either use the GitLab hosted container below, or check out this repository and build an image:
-```
-sudo docker build -t whisper-webui:1 .
-```
-
-You can then start the WebUI with GPU support like so:
-```
-sudo docker run -d --gpus=all -p 7860:7860 whisper-webui:1
-```
-
-Leave out "--gpus=all" if you don't have access to a GPU with enough memory, and are fine with running it on the CPU only:
-```
-sudo docker run -d -p 7860:7860 whisper-webui:1
-```
-
-# GitLab Docker Registry
-
-This Docker container is also hosted on GitLab:
-
-```
-sudo docker run -d --gpus=all -p 7860:7860 registry.gitlab.com/aadnk/whisper-webui:latest
-```
-
-## Custom Arguments
-
-You can also pass custom arguments to `app.py` in the Docker container, for instance to be able to use all the GPUs in parallel (replace administrator with your user):
-```
-sudo docker run -d --gpus all -p 7860:7860 \
---mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper \
---mount type=bind,source=/home/administrator/.cache/huggingface,target=/root/.cache/huggingface \
---restart=on-failure:15 registry.gitlab.com/aadnk/whisper-webui:latest \
-app.py --input_audio_max_duration -1 --server_name 0.0.0.0 --auto_parallel True \
---default_vad silero-vad --default_model_name large
-```
-
-You can also call `cli.py` the same way:
-```
-sudo docker run --gpus all \
---mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper \
---mount type=bind,source=/home/administrator/.cache/huggingface,target=/root/.cache/huggingface \
---mount type=bind,source=${PWD},target=/app/data \
-registry.gitlab.com/aadnk/whisper-webui:latest \
-cli.py --model large --auto_parallel True --vad silero-vad \
---output_dir /app/data /app/data/YOUR-FILE-HERE.mp4
-```
-
-## Caching
-
-Note that the models themselves are currently not included in the Docker images, and will be downloaded on the demand.
-To avoid this, bind the directory /root/.cache/whisper to some directory on the host (for instance /home/administrator/.cache/whisper), where you can (optionally)
-prepopulate the directory with the different Whisper models.
-```
-sudo docker run -d --gpus=all -p 7860:7860 \
---mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper \
-registry.gitlab.com/aadnk/whisper-webui:latest
-```
\ No newline at end of file
diff --git a/spaces/bookbot/Grad-TTS-Weildan-Playground/Grad-TTS/hifi-gan/meldataset.py b/spaces/bookbot/Grad-TTS-Weildan-Playground/Grad-TTS/hifi-gan/meldataset.py
deleted file mode 100644
index 96c48f23122eb60f9fd0bb4f841d5545ad0489bc..0000000000000000000000000000000000000000
--- a/spaces/bookbot/Grad-TTS-Weildan-Playground/Grad-TTS/hifi-gan/meldataset.py
+++ /dev/null
@@ -1,170 +0,0 @@
-""" from https://github.com/jik876/hifi-gan """
-
-import math
-import os
-import random
-import torch
-import torch.utils.data
-import numpy as np
-from librosa.util import normalize
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def load_wav(full_path):
- sampling_rate, data = read(full_path)
- return data, sampling_rate
-
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
- return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-
-def dynamic_range_decompression(x, C=1):
- return np.exp(x) / C
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
- return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
- output = dynamic_range_compression_torch(magnitudes)
- return output
-
-
-def spectral_de_normalize_torch(magnitudes):
- output = dynamic_range_decompression_torch(magnitudes)
- return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global mel_basis, hann_window
- if fmax not in mel_basis:
- mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
- mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
- hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
-
- spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
-
- spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec)
- spec = spectral_normalize_torch(spec)
-
- return spec
-
-
-def get_dataset_filelist(a):
- with open(a.input_training_file, 'r', encoding='utf-8') as fi:
- training_files = [os.path.join(a.input_wavs_dir, x.split('|')[0] + '.wav')
- for x in fi.read().split('\n') if len(x) > 0]
-
- with open(a.input_validation_file, 'r', encoding='utf-8') as fi:
- validation_files = [os.path.join(a.input_wavs_dir, x.split('|')[0] + '.wav')
- for x in fi.read().split('\n') if len(x) > 0]
- return training_files, validation_files
-
-
-class MelDataset(torch.utils.data.Dataset):
- def __init__(self, training_files, segment_size, n_fft, num_mels,
- hop_size, win_size, sampling_rate, fmin, fmax, split=True, shuffle=True, n_cache_reuse=1,
- device=None, fmax_loss=None, fine_tuning=False, base_mels_path=None):
- self.audio_files = training_files
- random.seed(1234)
- if shuffle:
- random.shuffle(self.audio_files)
- self.segment_size = segment_size
- self.sampling_rate = sampling_rate
- self.split = split
- self.n_fft = n_fft
- self.num_mels = num_mels
- self.hop_size = hop_size
- self.win_size = win_size
- self.fmin = fmin
- self.fmax = fmax
- self.fmax_loss = fmax_loss
- self.cached_wav = None
- self.n_cache_reuse = n_cache_reuse
- self._cache_ref_count = 0
- self.device = device
- self.fine_tuning = fine_tuning
- self.base_mels_path = base_mels_path
-
- def __getitem__(self, index):
- filename = self.audio_files[index]
- if self._cache_ref_count == 0:
- audio, sampling_rate = load_wav(filename)
- audio = audio / MAX_WAV_VALUE
- if not self.fine_tuning:
- audio = normalize(audio) * 0.95
- self.cached_wav = audio
- if sampling_rate != self.sampling_rate:
- raise ValueError("{} SR doesn't match target {} SR".format(
- sampling_rate, self.sampling_rate))
- self._cache_ref_count = self.n_cache_reuse
- else:
- audio = self.cached_wav
- self._cache_ref_count -= 1
-
- audio = torch.FloatTensor(audio)
- audio = audio.unsqueeze(0)
-
- if not self.fine_tuning:
- if self.split:
- if audio.size(1) >= self.segment_size:
- max_audio_start = audio.size(1) - self.segment_size
- audio_start = random.randint(0, max_audio_start)
- audio = audio[:, audio_start:audio_start+self.segment_size]
- else:
- audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), 'constant')
-
- mel = mel_spectrogram(audio, self.n_fft, self.num_mels,
- self.sampling_rate, self.hop_size, self.win_size, self.fmin, self.fmax,
- center=False)
- else:
- mel = np.load(
- os.path.join(self.base_mels_path, os.path.splitext(os.path.split(filename)[-1])[0] + '.npy'))
- mel = torch.from_numpy(mel)
-
- if len(mel.shape) < 3:
- mel = mel.unsqueeze(0)
-
- if self.split:
- frames_per_seg = math.ceil(self.segment_size / self.hop_size)
-
- if audio.size(1) >= self.segment_size:
- mel_start = random.randint(0, mel.size(2) - frames_per_seg - 1)
- mel = mel[:, :, mel_start:mel_start + frames_per_seg]
- audio = audio[:, mel_start * self.hop_size:(mel_start + frames_per_seg) * self.hop_size]
- else:
- mel = torch.nn.functional.pad(mel, (0, frames_per_seg - mel.size(2)), 'constant')
- audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), 'constant')
-
- mel_loss = mel_spectrogram(audio, self.n_fft, self.num_mels,
- self.sampling_rate, self.hop_size, self.win_size, self.fmin, self.fmax_loss,
- center=False)
-
- return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze())
-
- def __len__(self):
- return len(self.audio_files)
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/tests/test_video_keyframe_dataset.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/tests/test_video_keyframe_dataset.py
deleted file mode 100644
index 988e1616cdd30757157b479990050d1ca494ce7b..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/tests/test_video_keyframe_dataset.py
+++ /dev/null
@@ -1,98 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import contextlib
-import os
-import random
-import tempfile
-import unittest
-import torch
-import torchvision.io as io
-
-from densepose.data.transform import ImageResizeTransform
-from densepose.data.video import RandomKFramesSelector, VideoKeyframeDataset
-
-try:
- import av
-except ImportError:
- av = None
-
-
-# copied from torchvision test/test_io.py
-def _create_video_frames(num_frames, height, width):
- y, x = torch.meshgrid(torch.linspace(-2, 2, height), torch.linspace(-2, 2, width))
- data = []
- for i in range(num_frames):
- xc = float(i) / num_frames
- yc = 1 - float(i) / (2 * num_frames)
- d = torch.exp(-((x - xc) ** 2 + (y - yc) ** 2) / 2) * 255
- data.append(d.unsqueeze(2).repeat(1, 1, 3).byte())
- return torch.stack(data, 0)
-
-
-# adapted from torchvision test/test_io.py
-@contextlib.contextmanager
-def temp_video(num_frames, height, width, fps, lossless=False, video_codec=None, options=None):
- if lossless:
- if video_codec is not None:
- raise ValueError("video_codec can't be specified together with lossless")
- if options is not None:
- raise ValueError("options can't be specified together with lossless")
- video_codec = "libx264rgb"
- options = {"crf": "0"}
- if video_codec is None:
- video_codec = "libx264"
- if options is None:
- options = {}
- data = _create_video_frames(num_frames, height, width)
- with tempfile.NamedTemporaryFile(suffix=".mp4") as f:
- f.close()
- io.write_video(f.name, data, fps=fps, video_codec=video_codec, options=options)
- yield f.name, data
- os.unlink(f.name)
-
-
-@unittest.skipIf(av is None, "PyAV unavailable")
-class TestVideoKeyframeDataset(unittest.TestCase):
- def test_read_keyframes_all(self):
- with temp_video(60, 300, 300, 5, video_codec="mpeg4") as (fname, data):
- video_list = [fname]
- category_list = [None]
- dataset = VideoKeyframeDataset(video_list, category_list)
- self.assertEqual(len(dataset), 1)
- data1, categories1 = dataset[0]["images"], dataset[0]["categories"]
- self.assertEqual(data1.shape, torch.Size((5, 3, 300, 300)))
- self.assertEqual(data1.dtype, torch.float32)
- self.assertIsNone(categories1[0])
- return
- self.assertTrue(False)
-
- def test_read_keyframes_with_selector(self):
- with temp_video(60, 300, 300, 5, video_codec="mpeg4") as (fname, data):
- video_list = [fname]
- category_list = [None]
- random.seed(0)
- frame_selector = RandomKFramesSelector(3)
- dataset = VideoKeyframeDataset(video_list, category_list, frame_selector)
- self.assertEqual(len(dataset), 1)
- data1, categories1 = dataset[0]["images"], dataset[0]["categories"]
- self.assertEqual(data1.shape, torch.Size((3, 3, 300, 300)))
- self.assertEqual(data1.dtype, torch.float32)
- self.assertIsNone(categories1[0])
- return
- self.assertTrue(False)
-
- def test_read_keyframes_with_selector_with_transform(self):
- with temp_video(60, 300, 300, 5, video_codec="mpeg4") as (fname, data):
- video_list = [fname]
- category_list = [None]
- random.seed(0)
- frame_selector = RandomKFramesSelector(1)
- transform = ImageResizeTransform()
- dataset = VideoKeyframeDataset(video_list, category_list, frame_selector, transform)
- data1, categories1 = dataset[0]["images"], dataset[0]["categories"]
- self.assertEqual(len(dataset), 1)
- self.assertEqual(data1.shape, torch.Size((1, 3, 800, 800)))
- self.assertEqual(data1.dtype, torch.float32)
- self.assertIsNone(categories1[0])
- return
- self.assertTrue(False)
diff --git a/spaces/butterswords/nlc-explorer/.ipynb_checkpoints/NLselector-checkpoint.py b/spaces/butterswords/nlc-explorer/.ipynb_checkpoints/NLselector-checkpoint.py
deleted file mode 100644
index be67c9b309973af174a59ad6c9ef8619eb1f7c74..0000000000000000000000000000000000000000
--- a/spaces/butterswords/nlc-explorer/.ipynb_checkpoints/NLselector-checkpoint.py
+++ /dev/null
@@ -1,221 +0,0 @@
-#Import the libraries we know we'll need for the Generator.
-import pandas as pd, spacy, nltk, numpy as np, re
-from spacy.matcher import Matcher
-nlp = spacy.load("en_core_web_lg")
-import altair as alt
-import streamlit as st
-from annotated_text import annotated_text as ant
-
-#Import the libraries to support the model and predictions.
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
-import lime
-import torch
-import torch.nn.functional as F
-from lime.lime_text import LimeTextExplainer
-
-#Import WNgen.py
-from WNgen import *
-
-class_names = ['negative', 'positive']
-explainer = LimeTextExplainer(class_names=class_names)
-tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
-model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
-pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
-
-def predictor(texts):
- outputs = model(**tokenizer(texts, return_tensors="pt", padding=True))
- probas = F.softmax(outputs.logits, dim=1).detach().numpy()
- return probas
-
-@st.experimental_singleton
-def critical_words(document, options=False):
- '''This function is meant to select the critical part of a sentence. Critical, in this context means
- the part of the sentence that is either: A) a NOUN or PROPN from the correct entity group, B) a NOUN,
- C) a NOUN + ADJ combination, or D) ADJ and PROPN used to modify other NOUN tokens.
- It also checks this against what the model thinks is important if the user defines "options" as "LIME" or True.'''
- if type(document) is not spacy.tokens.doc.Doc:
- document = nlp(document)
- chunks = list(document.noun_chunks)
- pos_options = []
- lime_options = []
-
- #Identify what the model cares about.
- if options:
- #Run Lime Setup code
- exp = explainer.explain_instance(document.text, predictor, num_features=15, num_samples=2000)
- lime_results = exp.as_list()
- for feature in lime_results:
- lime_options.append(feature[0])
- lime_results = pd.DataFrame(lime_results, columns=["Word","Weight"])
-
- #Identify what we care about "parts of speech"
-
- # Here I am going to try to pick up pronouns, which are people, and Adjectival Compliments.
- for token in document:
- if (token.text not in pos_options) and ((token.text in lime_options) or (options == False)):
- #print(f"executed {token.text} with {token.pos_} and {token.dep_}") #QA
- if (token.pos_ in ["ADJ","PROPN"]) and (token.dep_ in ["compound", "amod"]) and (document[token.i - 1].dep_ in ["compound", "amod"]):
- compound = document[token.i - 1: token.i +1].text
- pos_options.append(compound)
- print(f'Added {compound} based on "amod" and "compound" adjectives.')
- elif (token.pos_ in ["NOUN"]) and (token.dep_ in ["compound", "amod", "conj"]) and (document[token.i - 1].dep_ in ["compound"]):
- compound = document[token.i - 1: token.i +1].text
- pos_options.append(compound)
- print(f'Added {compound} based on "amod" and "compound" and "conj" nouns.')
- elif (token.pos_ == "PROPN") and (token.dep_ in ["prep","amod"]):
- pos_options.append(token.text)
- print(f"Added '{token.text}' based on their adjectival state.")
- elif (token.pos_ == "ADJ") and (token.dep_ in ["acomp","conj","amod"]):
- pos_options.append(token.text)
- print(f"Added '{token.text}' based on their adjectival state.")
- elif (token.pos_ == "PRON") and (len(token.morph) !=0):
- if (token.morph.get("PronType") == "Prs"):
- pos_options.append(token.text)
- print(f"Added '{token.text}' because it's a human pronoun.")
-
- #Noun Chunks parsing
- for chunk in chunks:
- #The use of chunk[-1] is due to testing that it appears to always match the root
- root = chunk[-1]
- #This currently matches to a list I've created. I don't know the best way to deal with this so I'm leaving it as is for the moment.
- if root.ent_type_:
- cur_values = []
- if (len(chunk) > 1) and (chunk[-2].dep_ == "compound"):
- #creates the compound element of the noun
- compound = [x.text for x in chunk if x.dep_ == "compound"]
- print(f"This is the contents of {compound} and it is {all(elem in lime_options for elem in compound)} that all elements are present in {lime_options}.") #for QA
- #checks to see all elements in the compound are important to the model or use the compound if not checking importance.
- if (all(elem in lime_options for elem in cur_values) and (options is True)) or ((options is False)):
- #creates a span for the entirety of the compound noun and adds it to the list.
- span = -1 * (1 + len(compound))
- pos_options.append(chunk[span:].text)
- cur_values + [token.text for token in chunk if token.pos_ in ["ADJ","NOUN","PROPN"]]
- else:
- print(f"The elmenents in {compound} could not be added to the final list because they are not all relevant to the model.")
- else:
- cur_values = [token.text for token in chunk if (token.ent_type_) or (token.pos_ == "ADJ")]
- if (all(elem in lime_options for elem in cur_values) and (options is True)) or ((options is False)):
- pos_options.extend(cur_values)
- print(f"From {chunk.text}, {cur_values} added to pos_options due to entity recognition.") #for QA
- elif len(chunk) >= 1:
- cur_values = [token.text for token in chunk if token.pos_ in ["NOUN","ADJ","PROPN"]]
- if (all(elem in lime_options for elem in cur_values) and (options is True)) or ((options is False)):
- pos_options.extend(cur_values)
- print(f"From {chunk.text}, {cur_values} added to pos_options due to wildcard.") #for QA
- else:
- print(f"No options added for \'{chunk.text}\' ")
-
- pos_options = list(set(pos_options))
-
- if options:
- return pos_options, lime_results
- else:
- return pos_options
-
-# Return the Viz of elements critical to LIME.
-def lime_viz(df):
- if not isinstance(df, pd.DataFrame):
- df = pd.DataFrame(df, columns=["Word","Weight"])
- single_nearest = alt.selection_single(on='mouseover', nearest=True)
- viz = alt.Chart(df).encode(
- alt.X('Weight:Q', scale=alt.Scale(domain=(-1, 1))),
- alt.Y('Word:N', sort='x', axis=None),
- color=alt.Color("Weight", scale=alt.Scale(scheme='blueorange', domain=[0], type="threshold", range='diverging'), legend=None),
- tooltip = ("Word","Weight")
- ).mark_bar().properties(title ="Importance of individual words")
-
- text = viz.mark_text(
- fill="black",
- align='right',
- baseline='middle'
- ).encode(
- text='Word:N'
- )
- limeplot = alt.LayerChart(layer=[viz,text], width = 300).configure_axis(grid=False).configure_view(strokeWidth=0)
- return limeplot
-
-# Evaluate Predictions using the model and pipe.
-def eval_pred(text, return_all = False):
- '''A basic function for evaluating the prediction from the model and turning it into a visualization friendly number.'''
- preds = pipe(text)
- neg_score = -1 * preds[0][0]['score']
- sent_neg = preds[0][0]['label']
- pos_score = preds[0][1]['score']
- sent_pos = preds[0][1]['label']
- prediction = 0
- sentiment = ''
- if pos_score > abs(neg_score):
- prediction = pos_score
- sentiment = sent_pos
- elif abs(neg_score) > pos_score:
- prediction = neg_score
- sentiment = sent_neg
-
- if return_all:
- return prediction, sentiment
- else:
- return prediction
-
-def construct_nlexp(text,sentiment,probability):
- prob = str(np.round(100 * abs(probability),2))
- if sentiment == "NEGATIVE":
- color_sent = ant('The model predicts the sentiment of the sentence you provided is ', (sentiment, "-", "#FFA44F"), ' with a probability of ', (prob, "neg", "#FFA44F"),"%.")
- elif sentiment == "POSITIVE":
- color_sent = ant('The model predicts the sentiment of the sentence you provided is ', (sentiment, "+", "#50A9FF"), ' with a probability of ', (prob, "pos", "#50A9FF"),"%.")
- return color_sent
-
-def get_min_max(df, seed):
- '''This function provides the alternatives with the highest spaCy similarity scores and the lowest similarity scores. As similarity is based on vectorization of words and documents this may not be the best way to identify bias.
-
- text2 = Most Similar
- text3 = Least Similar'''
- maximum = df[df['similarity'] < .9999].similarity.max()
- text2 = df.loc[df['similarity'] == maximum, 'text'].iloc[0]
- minimum = df[df['similarity'] > .0001].similarity.min()
- text3 = df.loc[df['similarity'] == minimum, 'text'].iloc[0]
- return text2, text3
-
-# Inspired by https://stackoverflow.com/questions/17758023/return-rows-in-a-dataframe-closest-to-a-user-defined-number/17758115#17758115
-def abs_dif(df,seed):
- '''This function enables a user to identify the alternative that is closest to the seed and farthest from the seed should that be the what they wish to display.
-
- text2 = Nearest Prediction
- text3 = Farthest Prediction'''
- #seed = process_text(seed)
- target = df[df['Words'] == seed].pred.iloc[0]
- sub_df = df[df['Words'] != seed].reset_index()
- nearest_prediction = sub_df.pred[(sub_df.pred-target).abs().argsort()[:1]]
- farthest_prediction = sub_df.pred[(sub_df.pred-target).abs().argsort()[-1:]]
- text2 = sub_df.text.iloc[nearest_prediction.index[0]]
- text3 = sub_df.text.iloc[farthest_prediction.index[0]]
- return text2, text3
-
-#@st.experimental_singleton #I've enabled this to prevent it from triggering every time the code runs... which could get very messy
-def sampled_alts(df, seed, fixed=False):
- '''This function enables a user to select an alternate way of choosing which counterfactuals are shown for MultiNLC, MultiNLC + Lime, and VizNLC. If you use this then you are enabling random sampling over other options (ex. spaCy similarity scores, or absolute difference).
-
- Both samples are random.'''
- sub_df = df[df['Words'] != seed]
- if fixed:
- sample = sub_df.sample(n=2, random_state = 2052)
- else:
- sample = sub_df.sample(n=2)
- text2 = sample.text.iloc[0]
- text3 = sample.text.iloc[1]
- return text2, text3
-
-def gen_cf_country(df,_document,selection):
- df['text'] = df.Words.apply(lambda x: re.sub(r'\b'+selection+r'\b',x,_document.text))
- df['pred'] = df.text.apply(eval_pred)
- df['seed'] = df.Words.apply(lambda x: 'seed' if x == selection else 'alternative')
- df['similarity'] = df.Words.apply(lambda x: nlp(selection).similarity(nlp(x)))
- return df
-
-def gen_cf_profession(df,_document,selection):
- category = df.loc[df['Words'] == selection, 'Major'].iloc[0]
- df = df[df.Major == category]
- df['text'] = df.Words.apply(lambda x: re.sub(r'\b'+selection+r'\b',x,_document.text))
- df['pred'] = df.text.apply(eval_pred)
- df['seed'] = df.Words.apply(lambda x: 'seed' if x == selection else 'alternative')
- df['similarity'] = df.Words.apply(lambda x: nlp(selection).similarity(nlp(x)))
- return df
\ No newline at end of file
diff --git a/spaces/cahodk/live-ml5-facemesh-p5js/sketch.js b/spaces/cahodk/live-ml5-facemesh-p5js/sketch.js
deleted file mode 100644
index d4078f8fd0f91f51932d52b1b82f835182184d50..0000000000000000000000000000000000000000
--- a/spaces/cahodk/live-ml5-facemesh-p5js/sketch.js
+++ /dev/null
@@ -1,47 +0,0 @@
-let facemesh;
-let video;
-let predictions = [];
-
-function setup() {
- createCanvas(640, 480);
- video = createCapture(VIDEO);
- video.size(width, height);
-
- facemesh = ml5.facemesh(video, modelReady);
-
- // This sets up an event that fills the global variable "predictions"
- // with an array every time new predictions are made
- facemesh.on("predict", results => {
- predictions = results;
- });
-
- // Hide the video element, and just show the canvas
- video.hide();
-}
-
-function modelReady() {
- console.log("Model ready!");
-}
-
-function draw() {
- background(0)
- image(video, 0, 0, width, height);
-
- // We can call both functions to draw all keypoints
- drawKeypoints();
-}
-
-// A function to draw ellipses over the detected keypoints
-function drawKeypoints() {
- for (let i = 0; i < predictions.length; i += 1) {
- const keypoints = predictions[i].scaledMesh;
-
- // Draw facial keypoints.
- for (let j = 0; j < keypoints.length; j += 1) {
- const [x, y] = keypoints[j];
-
- fill(0, 255, 0);
- ellipse(x, y, 5, 5);
- }
- }
-}
\ No newline at end of file
diff --git a/spaces/camel-ai/camel-data-explorer/apps/data_explorer/data_explorer.py b/spaces/camel-ai/camel-data-explorer/apps/data_explorer/data_explorer.py
deleted file mode 100644
index 62875f3b50be0114882fbf9289c2df1ef6eafe5f..0000000000000000000000000000000000000000
--- a/spaces/camel-ai/camel-data-explorer/apps/data_explorer/data_explorer.py
+++ /dev/null
@@ -1,349 +0,0 @@
-# =========== Copyright 2023 @ CAMEL-AI.org. 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.
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-"""
-Gradio-based web UI to explore the Camel dataset.
-"""
-
-import argparse
-import random
-from typing import Dict, List, Optional, Tuple
-
-import gradio as gr
-
-from apps.data_explorer.loader import Datasets, load_datasets
-
-
-def parse_arguments():
- """ Get command line arguments. """
-
- parser = argparse.ArgumentParser("Camel data explorer")
- parser.add_argument(
- '--data-path', type=str, default=None,
- help='Path to the folder with ZIP datasets containing JSONs')
- parser.add_argument('--default-dataset', type=str, default=None,
- help='Default dataset name selected from ZIPs')
- parser.add_argument('--share', type=bool, default=False,
- help='Expose the web UI to Gradio')
- parser.add_argument(
- '--server-name', type=str, default="0.0.0.0",
- help='localhost for local, 0.0.0.0 (default) for public')
- parser.add_argument('--server-port', type=int, default=8080,
- help='Port ot run the web page on')
- parser.add_argument('--inbrowser', type=bool, default=False,
- help='Open the web UI in the default browser on lunch')
- parser.add_argument(
- '--concurrency-count', type=int, default=10,
- help='Number if concurrent threads at Gradio websocket queue. ' +
- 'Increase to serve more requests but keep an eye on RAM usage.')
- args, unknown = parser.parse_known_args()
- if len(unknown) > 0:
- print("Unknown args: ", unknown)
- return args
-
-
-def construct_ui(blocks, datasets: Datasets,
- default_dataset: Optional[str] = None):
- """ Build Gradio UI and populate with chat data from JSONs.
-
- Args:
- blocks: Gradio blocks
- datasets (Datasets): Several parsed
- multi-JSON dataset with chats.
- default_dataset (str): Default selection of the dataset.
-
- Returns:
- None
- """
-
- if default_dataset is None:
- default_dataset = "ai_society_chat"
-
- misalignment_set_names = {"misalignment"}
- ordinary_datasets = [
- v for v in datasets.keys() if v not in misalignment_set_names
- ]
- misalignment_datasets = [
- v for v in datasets.keys() if v in misalignment_set_names
- ]
- default_dataset_name = default_dataset \
- if default_dataset in datasets.keys() \
- else ordinary_datasets[0] if len(ordinary_datasets) > 0 \
- else misalignment_datasets[0] if len(misalignment_datasets) > 0 \
- else ""
- dataset_names = list(datasets.keys())
-
- with gr.Row().style():
- with gr.Column(scale=2):
- with gr.Row():
- dataset_dd = gr.Dropdown(dataset_names, label="Select dataset",
- value="NODEFAULT", interactive=True)
- with gr.Row():
- disclaimer_ta = gr.Markdown(
- "## By clicking AGREE I consent to use the dataset "
- "for purely educational and academic purposes and "
- "not use it for any fraudulent activity; and I take "
- "all the responsibility if the data is used in a "
- "malicious application.", visible=False)
- with gr.Row():
- with gr.Column(scale=1):
- accept_disclaimer_bn = gr.Button("AGREE", visible=False)
- with gr.Column(scale=1):
- decline_disclaimer_bn = gr.Button("DECLINE", visible=False)
- with gr.Row():
- with gr.Column(scale=3):
- assistant_dd = gr.Dropdown([], label="ASSISTANT", value="",
- interactive=True)
- with gr.Column(scale=3):
- user_dd = gr.Dropdown([], label="USER", value="",
- interactive=True)
- with gr.Column(scale=1):
- gr.Markdown(
- "## CAMEL: Communicative Agents for \"Mind\" Exploration"
- " of Large Scale Language Model Society\n"
- "Github repo: [https://github.com/lightaime/camel]"
- "(https://github.com/lightaime/camel)\n"
- ''
- '
'
- '')
-
- task_dd = gr.Dropdown([], label="Original task", value="",
- interactive=True)
- specified_task_ta = gr.TextArea(label="Specified task", lines=2)
- chatbot = gr.Chatbot()
- accepted_st = gr.State(False)
-
- def set_default_dataset() -> Dict:
- """ Trigger for app load.
-
- Returns:
- Dict: Update dict for dataset_dd.
- """
- return gr.update(value=default_dataset_name)
-
- def check_if_misalignment(dataset_name: str, accepted: bool) \
- -> Tuple[Dict, Dict, Dict]:
- """ Display AGREE/DECLINE if needed.
-
- Returns:
- Tuple: Visibility updates for the buttons.
- """
-
- if dataset_name == "misalignment" and not accepted:
- return gr.update(visible=True), \
- gr.update(visible=True), gr.update(visible=True)
- else:
- return gr.update(visible=False), \
- gr.update(visible=False), gr.update(visible=False)
-
- def enable_misalignment() -> Tuple[bool, Dict, Dict, Dict]:
- """ Update the state of the accepted disclaimer.
-
- Returns:
- Tuple: New state and visibility updates for the buttons.
- """
-
- return True, gr.update(visible=False), \
- gr.update(visible=False), gr.update(visible=False)
-
- def disable_misalignment() -> Tuple[bool, Dict, Dict, Dict]:
- """ Update the state of the accepted disclaimer.
-
- Returns:
- Tuple: New state and visibility updates for the buttons.
- """
-
- return False, gr.update(visible=False), \
- gr.update(visible=False), gr.update(visible=False)
-
- def update_dataset_selection(dataset_name: str,
- accepted: bool) -> Tuple[Dict, Dict]:
- """ Update roles based on the selected dataset.
-
- Args:
- dataset_name (str): Name of the loaded .zip dataset.
- accepted (bool): If the disclaimer thas been accepted.
-
- Returns:
- Tuple[Dict, Dict]: New Assistant and User roles.
- """
-
- if dataset_name == "misalignment" and not accepted:
- # If used did not accept the misalignment policy,
- # keep the old selection.
- return (gr.update(value="N/A",
- choices=[]), gr.update(value="N/A", choices=[]))
-
- dataset = datasets[dataset_name]
- assistant_roles = dataset['assistant_roles']
- user_roles = dataset['user_roles']
- assistant_role = random.choice(assistant_roles) \
- if len(assistant_roles) > 0 else ""
- user_role = random.choice(user_roles) if len(user_roles) > 0 else ""
- return (gr.update(value=assistant_role, choices=assistant_roles),
- gr.update(value=user_role, choices=user_roles))
-
- def roles_dd_change(dataset_name: str, assistant_role: str,
- user_role: str) -> Dict:
- """ Update the displayed chat upon inputs change.
-
- Args:
- assistant_role (str): Assistant dropdown value.
- user_role (str): User dropdown value.
-
- Returns:
- Dict: New original roles state dictionary.
- """
- matrix = datasets[dataset_name]['matrix']
- if (assistant_role, user_role) in matrix:
- record: Dict[str, Dict] = matrix[(assistant_role, user_role)]
- original_task_options = list(record.keys())
- original_task = original_task_options[0]
- else:
- original_task = "N/A"
- original_task_options = []
-
- choices = gr.Dropdown.update(choices=original_task_options,
- value=original_task, interactive=True)
- return choices
-
- def build_chat_history(messages: Dict[int, Dict]) -> List[Tuple]:
- """ Structures chatbot contents from the loaded data.
-
- Args:
- messages (Dict[int, Dict]): Messages loaded from JSON.
-
- Returns:
- List[Tuple]: Chat history in chatbot UI element format.
- """
- history: List[Tuple] = []
- curr_qa = (None, None)
- for k in sorted(messages.keys()):
- msg = messages[k]
- content = msg['content']
- if msg['role_type'] == "USER":
- if curr_qa[0] is not None:
- history.append(curr_qa)
- curr_qa = (content, None)
- else:
- curr_qa = (content, None)
- elif msg['role_type'] == "ASSISTANT":
- curr_qa = (curr_qa[0], content)
- history.append(curr_qa)
- curr_qa = (None, None)
- else:
- pass
- return history
-
- def task_dd_change(dataset_name: str, assistant_role: str, user_role: str,
- original_task: str) -> Tuple[str, List]:
- """ Load task details and chatbot history into UI elements.
-
- Args:
- assistant_role (str): An assistan role.
- user_role (str): An user role.
- original_task (str): The original task.
-
- Returns:
- Tuple[str, List]: New contents of the specified task
- and chatbot history UI elements.
- """
-
- matrix = datasets[dataset_name]['matrix']
- if (assistant_role, user_role) in matrix:
- task_dict: Dict[str, Dict] = matrix[(assistant_role, user_role)]
- if original_task in task_dict:
- chat = task_dict[original_task]
- specified_task = chat['specified_task']
- history = build_chat_history(chat['messages'])
- else:
- specified_task = "N/A"
- history = []
- else:
- specified_task = "N/A"
- history = []
- return specified_task, history
-
- dataset_dd.change(check_if_misalignment, [dataset_dd, accepted_st],
- [disclaimer_ta, accept_disclaimer_bn,
- decline_disclaimer_bn]) \
- .then(update_dataset_selection,
- [dataset_dd, accepted_st],
- [assistant_dd, user_dd])
-
- accept_disclaimer_bn.click(enable_misalignment, None, [
- accepted_st, disclaimer_ta, accept_disclaimer_bn, decline_disclaimer_bn
- ]) \
- .then(update_dataset_selection,
- [dataset_dd, accepted_st],
- [assistant_dd, user_dd])
-
- decline_disclaimer_bn.click(disable_misalignment, None, [
- accepted_st, disclaimer_ta, accept_disclaimer_bn, decline_disclaimer_bn
- ]) \
- .then(update_dataset_selection,
- [dataset_dd, accepted_st],
- [assistant_dd, user_dd])
-
- func_args = (roles_dd_change, [dataset_dd, assistant_dd, user_dd], task_dd)
- assistant_dd.change(*func_args)
- user_dd.change(*func_args)
-
- task_dd.change(task_dd_change,
- [dataset_dd, assistant_dd, user_dd, task_dd],
- [specified_task_ta, chatbot])
-
- blocks.load(set_default_dataset, None, dataset_dd)
-
-
-def construct_blocks(data_path: str, default_dataset: Optional[str]):
- """ Construct Blocs app but do not launch it.
-
- Args:
- data_path (str): Path to the set of ZIP datasets.
- default_dataset (Optional[str]): Name of the default dataset,
- without extension.
-
- Returns:
- gr.Blocks: Blocks instance.
- """
-
- print("Loading the dataset...")
- datasets = load_datasets(data_path)
- print("Dataset is loaded")
-
- print("Getting Data Explorer web server online...")
-
- with gr.Blocks() as blocks:
- construct_ui(blocks, datasets, default_dataset)
-
- return blocks
-
-
-def main():
- """ Entry point. """
-
- args = parse_arguments()
-
- blocks = construct_blocks(args.data_path, args.default_dataset)
-
- blocks.queue(args.concurrency_count) \
- .launch(share=args.share, inbrowser=args.inbrowser,
- server_name=args.server_name, server_port=args.server_port)
-
- print("Exiting.")
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/camillevanhoffelen/langchain-HuggingGPT/hugginggpt/llm_factory.py b/spaces/camillevanhoffelen/langchain-HuggingGPT/hugginggpt/llm_factory.py
deleted file mode 100644
index 01f1280e8c37fb04673f9ad88176487b8d78d22e..0000000000000000000000000000000000000000
--- a/spaces/camillevanhoffelen/langchain-HuggingGPT/hugginggpt/llm_factory.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import logging
-from collections import namedtuple
-
-import tiktoken
-from langchain import OpenAI
-
-LLM_NAME = "text-davinci-003"
-# Encoding for text-davinci-003
-ENCODING_NAME = "p50k_base"
-ENCODING = tiktoken.get_encoding(ENCODING_NAME)
-# Max input tokens for text-davinci-003
-LLM_MAX_TOKENS = 4096
-
-# As specified in huggingGPT paper
-TASK_PLANNING_LOGIT_BIAS = 0.1
-MODEL_SELECTION_LOGIT_BIAS = 5
-
-logger = logging.getLogger(__name__)
-
-LLMs = namedtuple(
- "LLMs",
- [
- "task_planning_llm",
- "model_selection_llm",
- "model_inference_llm",
- "response_generation_llm",
- "output_fixing_llm",
- ],
-)
-
-
-def create_llms() -> LLMs:
- """Create various LLM agents according to the huggingGPT paper's specifications."""
- logger.info(f"Creating {LLM_NAME} LLMs")
-
- task_parsing_highlight_ids = get_token_ids_for_task_parsing()
- choose_model_highlight_ids = get_token_ids_for_choose_model()
-
- task_planning_llm = OpenAI(
- model_name=LLM_NAME,
- temperature=0,
- logit_bias={
- token_id: TASK_PLANNING_LOGIT_BIAS
- for token_id in task_parsing_highlight_ids
- },
- )
- model_selection_llm = OpenAI(
- model_name=LLM_NAME,
- temperature=0,
- logit_bias={
- token_id: MODEL_SELECTION_LOGIT_BIAS
- for token_id in choose_model_highlight_ids
- },
- )
- model_inference_llm = OpenAI(model_name=LLM_NAME, temperature=0)
- response_generation_llm = OpenAI(model_name=LLM_NAME, temperature=0)
- output_fixing_llm = OpenAI(model_name=LLM_NAME, temperature=0)
- return LLMs(
- task_planning_llm=task_planning_llm,
- model_selection_llm=model_selection_llm,
- model_inference_llm=model_inference_llm,
- response_generation_llm=response_generation_llm,
- output_fixing_llm=output_fixing_llm,
- )
-
-
-def get_token_ids_for_task_parsing() -> list[int]:
- text = """{"task": "text-classification", "token-classification", "text2text-generation", "summarization", "translation", "question-answering", "conversational", "text-generation", "sentence-similarity", "tabular-classification", "object-detection", "image-classification", "image-to-image", "image-to-text", "text-to-image", "visual-question-answering", "document-question-answering", "image-segmentation", "text-to-speech", "automatic-speech-recognition", "audio-to-audio", "audio-classification", "args", "text", "path", "dep", "id", "-"}"""
- res = ENCODING.encode(text)
- res = list(set(res))
- return res
-
-
-def get_token_ids_for_choose_model() -> list[int]:
- text = """{"id": "reason"}"""
- res = ENCODING.encode(text)
- res = list(set(res))
- return res
-
-
-def count_tokens(text: str) -> int:
- return len(ENCODING.encode(text))
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/layers/csrc/deformable/deform_conv.h b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/layers/csrc/deformable/deform_conv.h
deleted file mode 100644
index 965c1bfd47b58f9802d1c3fd69a5962517b2da61..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/layers/csrc/deformable/deform_conv.h
+++ /dev/null
@@ -1,377 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-#pragma once
-#include
-
-namespace detectron2 {
-
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-int deform_conv_forward_cuda(
- at::Tensor input,
- at::Tensor weight,
- at::Tensor offset,
- at::Tensor output,
- at::Tensor columns,
- at::Tensor ones,
- int kW,
- int kH,
- int dW,
- int dH,
- int padW,
- int padH,
- int dilationW,
- int dilationH,
- int group,
- int deformable_group,
- int im2col_step);
-
-int deform_conv_backward_input_cuda(
- at::Tensor input,
- at::Tensor offset,
- at::Tensor gradOutput,
- at::Tensor gradInput,
- at::Tensor gradOffset,
- at::Tensor weight,
- at::Tensor columns,
- int kW,
- int kH,
- int dW,
- int dH,
- int padW,
- int padH,
- int dilationW,
- int dilationH,
- int group,
- int deformable_group,
- int im2col_step);
-
-int deform_conv_backward_parameters_cuda(
- at::Tensor input,
- at::Tensor offset,
- at::Tensor gradOutput,
- at::Tensor gradWeight, // at::Tensor gradBias,
- at::Tensor columns,
- at::Tensor ones,
- int kW,
- int kH,
- int dW,
- int dH,
- int padW,
- int padH,
- int dilationW,
- int dilationH,
- int group,
- int deformable_group,
- float scale,
- int im2col_step);
-
-void modulated_deform_conv_cuda_forward(
- at::Tensor input,
- at::Tensor weight,
- at::Tensor bias,
- at::Tensor ones,
- at::Tensor offset,
- at::Tensor mask,
- at::Tensor output,
- at::Tensor columns,
- int kernel_h,
- int kernel_w,
- const int stride_h,
- const int stride_w,
- const int pad_h,
- const int pad_w,
- const int dilation_h,
- const int dilation_w,
- const int group,
- const int deformable_group,
- const bool with_bias);
-
-void modulated_deform_conv_cuda_backward(
- at::Tensor input,
- at::Tensor weight,
- at::Tensor bias,
- at::Tensor ones,
- at::Tensor offset,
- at::Tensor mask,
- at::Tensor columns,
- at::Tensor grad_input,
- at::Tensor grad_weight,
- at::Tensor grad_bias,
- at::Tensor grad_offset,
- at::Tensor grad_mask,
- at::Tensor grad_output,
- int kernel_h,
- int kernel_w,
- int stride_h,
- int stride_w,
- int pad_h,
- int pad_w,
- int dilation_h,
- int dilation_w,
- int group,
- int deformable_group,
- const bool with_bias);
-
-#endif
-
-inline int deform_conv_forward(
- at::Tensor input,
- at::Tensor weight,
- at::Tensor offset,
- at::Tensor output,
- at::Tensor columns,
- at::Tensor ones,
- int kW,
- int kH,
- int dW,
- int dH,
- int padW,
- int padH,
- int dilationW,
- int dilationH,
- int group,
- int deformable_group,
- int im2col_step) {
- if (input.is_cuda()) {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
- TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!");
- TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!");
- return deform_conv_forward_cuda(
- input,
- weight,
- offset,
- output,
- columns,
- ones,
- kW,
- kH,
- dW,
- dH,
- padW,
- padH,
- dilationW,
- dilationH,
- group,
- deformable_group,
- im2col_step);
-#else
- AT_ERROR("Detectron2 is not compiled with GPU support!");
-#endif
- }
- AT_ERROR("This operator is not implemented on CPU");
-}
-
-inline int deform_conv_backward_input(
- at::Tensor input,
- at::Tensor offset,
- at::Tensor gradOutput,
- at::Tensor gradInput,
- at::Tensor gradOffset,
- at::Tensor weight,
- at::Tensor columns,
- int kW,
- int kH,
- int dW,
- int dH,
- int padW,
- int padH,
- int dilationW,
- int dilationH,
- int group,
- int deformable_group,
- int im2col_step) {
- if (gradOutput.is_cuda()) {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
- TORCH_CHECK(input.is_cuda(), "input tensor is not on GPU!");
- TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!");
- TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!");
- return deform_conv_backward_input_cuda(
- input,
- offset,
- gradOutput,
- gradInput,
- gradOffset,
- weight,
- columns,
- kW,
- kH,
- dW,
- dH,
- padW,
- padH,
- dilationW,
- dilationH,
- group,
- deformable_group,
- im2col_step);
-#else
- AT_ERROR("Detectron2 is not compiled with GPU support!");
-#endif
- }
- AT_ERROR("This operator is not implemented on CPU");
-}
-
-inline int deform_conv_backward_filter(
- at::Tensor input,
- at::Tensor offset,
- at::Tensor gradOutput,
- at::Tensor gradWeight, // at::Tensor gradBias,
- at::Tensor columns,
- at::Tensor ones,
- int kW,
- int kH,
- int dW,
- int dH,
- int padW,
- int padH,
- int dilationW,
- int dilationH,
- int group,
- int deformable_group,
- float scale,
- int im2col_step) {
- if (gradOutput.is_cuda()) {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
- TORCH_CHECK(input.is_cuda(), "input tensor is not on GPU!");
- TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!");
- return deform_conv_backward_parameters_cuda(
- input,
- offset,
- gradOutput,
- gradWeight,
- columns,
- ones,
- kW,
- kH,
- dW,
- dH,
- padW,
- padH,
- dilationW,
- dilationH,
- group,
- deformable_group,
- scale,
- im2col_step);
-#else
- AT_ERROR("Detectron2 is not compiled with GPU support!");
-#endif
- }
- AT_ERROR("This operator is not implemented on CPU");
-}
-
-inline void modulated_deform_conv_forward(
- at::Tensor input,
- at::Tensor weight,
- at::Tensor bias,
- at::Tensor ones,
- at::Tensor offset,
- at::Tensor mask,
- at::Tensor output,
- at::Tensor columns,
- int kernel_h,
- int kernel_w,
- const int stride_h,
- const int stride_w,
- const int pad_h,
- const int pad_w,
- const int dilation_h,
- const int dilation_w,
- const int group,
- const int deformable_group,
- const bool with_bias) {
- if (input.is_cuda()) {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
- TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!");
- TORCH_CHECK(bias.is_cuda(), "bias tensor is not on GPU!");
- TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!");
- return modulated_deform_conv_cuda_forward(
- input,
- weight,
- bias,
- ones,
- offset,
- mask,
- output,
- columns,
- kernel_h,
- kernel_w,
- stride_h,
- stride_w,
- pad_h,
- pad_w,
- dilation_h,
- dilation_w,
- group,
- deformable_group,
- with_bias);
-#else
- AT_ERROR("Detectron2 is not compiled with GPU support!");
-#endif
- }
- AT_ERROR("This operator is not implemented on CPU");
-}
-
-inline void modulated_deform_conv_backward(
- at::Tensor input,
- at::Tensor weight,
- at::Tensor bias,
- at::Tensor ones,
- at::Tensor offset,
- at::Tensor mask,
- at::Tensor columns,
- at::Tensor grad_input,
- at::Tensor grad_weight,
- at::Tensor grad_bias,
- at::Tensor grad_offset,
- at::Tensor grad_mask,
- at::Tensor grad_output,
- int kernel_h,
- int kernel_w,
- int stride_h,
- int stride_w,
- int pad_h,
- int pad_w,
- int dilation_h,
- int dilation_w,
- int group,
- int deformable_group,
- const bool with_bias) {
- if (grad_output.is_cuda()) {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
- TORCH_CHECK(input.is_cuda(), "input tensor is not on GPU!");
- TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!");
- TORCH_CHECK(bias.is_cuda(), "bias tensor is not on GPU!");
- TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!");
- return modulated_deform_conv_cuda_backward(
- input,
- weight,
- bias,
- ones,
- offset,
- mask,
- columns,
- grad_input,
- grad_weight,
- grad_bias,
- grad_offset,
- grad_mask,
- grad_output,
- kernel_h,
- kernel_w,
- stride_h,
- stride_w,
- pad_h,
- pad_w,
- dilation_h,
- dilation_w,
- group,
- deformable_group,
- with_bias);
-#else
- AT_ERROR("Detectron2 is not compiled with GPU support!");
-#endif
- }
- AT_ERROR("This operator is not implemented on CPU");
-}
-
-} // namespace detectron2
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/utils/memory.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/utils/memory.py
deleted file mode 100644
index bd494780b9dbbd1571688cd270bb9b53d113c13e..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/utils/memory.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import logging
-from contextlib import contextmanager
-from functools import wraps
-import torch
-
-__all__ = ["retry_if_cuda_oom"]
-
-
-@contextmanager
-def _ignore_torch_cuda_oom():
- """
- A context which ignores CUDA OOM exception from pytorch.
- """
- try:
- yield
- except RuntimeError as e:
- # NOTE: the string may change?
- if "CUDA out of memory. " in str(e):
- pass
- else:
- raise
-
-
-def retry_if_cuda_oom(func):
- """
- Makes a function retry itself after encountering
- pytorch's CUDA OOM error.
- It will first retry after calling `torch.cuda.empty_cache()`.
-
- If that still fails, it will then retry by trying to convert inputs to CPUs.
- In this case, it expects the function to dispatch to CPU implementation.
- The return values may become CPU tensors as well and it's user's
- responsibility to convert it back to CUDA tensor if needed.
-
- Args:
- func: a stateless callable that takes tensor-like objects as arguments
-
- Returns:
- a callable which retries `func` if OOM is encountered.
-
- Examples:
- ::
- output = retry_if_cuda_oom(some_torch_function)(input1, input2)
- # output may be on CPU even if inputs are on GPU
-
- Note:
- 1. When converting inputs to CPU, it will only look at each argument and check
- if it has `.device` and `.to` for conversion. Nested structures of tensors
- are not supported.
-
- 2. Since the function might be called more than once, it has to be
- stateless.
- """
-
- def maybe_to_cpu(x):
- try:
- like_gpu_tensor = x.device.type == "cuda" and hasattr(x, "to")
- except AttributeError:
- like_gpu_tensor = False
- if like_gpu_tensor:
- return x.to(device="cpu")
- else:
- return x
-
- @wraps(func)
- def wrapped(*args, **kwargs):
- with _ignore_torch_cuda_oom():
- return func(*args, **kwargs)
-
- # Clear cache and retry
- torch.cuda.empty_cache()
- with _ignore_torch_cuda_oom():
- return func(*args, **kwargs)
-
- # Try on CPU. This slows down the code significantly, therefore print a notice.
- logger = logging.getLogger(__name__)
- logger.info("Attempting to copy inputs of {} to CPU due to CUDA OOM".format(str(func)))
- new_args = (maybe_to_cpu(x) for x in args)
- new_kwargs = {k: maybe_to_cpu(v) for k, v in kwargs.items()}
- return func(*new_args, **new_kwargs)
-
- return wrapped
diff --git a/spaces/chendl/compositional_test/multimodal/open_flamingo/eval/eval_datasets.py b/spaces/chendl/compositional_test/multimodal/open_flamingo/eval/eval_datasets.py
deleted file mode 100644
index 672cf9e0c94935d0d4574f689e499a0b51b777b5..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/open_flamingo/eval/eval_datasets.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import json
-import os
-
-from PIL import Image
-from torch.utils.data import Dataset
-from torchvision.datasets import ImageFolder
-
-from open_flamingo.eval.imagenet_utils import IMAGENET_1K_CLASS_ID_TO_LABEL
-
-
-class COCOFlickrDataset(Dataset):
- def __init__(
- self,
- image_dir_path,
- annotations_path,
- is_flickr=False,
- ):
- self.image_dir_path = image_dir_path
- self.annotations = json.load(open(annotations_path))["annotations"]
- self.is_flickr = is_flickr
-
- def __len__(self):
- return len(self.annotations)
-
- def get_img_path(self, idx):
- if self.is_flickr:
- return f"{self.image_dir_path}/{self.annotations[idx]['image_id']}.jpg"
- else:
- return f"{self.image_dir_path}/{self.annotations[idx]['image_id']:012d}.jpg"
-
- def __getitem__(self, idx):
- image = Image.open(self.get_img_path(idx))
- caption = self.annotations[idx]["caption"]
- return {
- "image": image,
- "caption": caption,
- "image_id": self.annotations[idx]["image_id"],
- }
-
-
-class VQADataset(Dataset):
- def __init__(
- self,
- image_dir_path="/mmfs1/gscratch/efml/anasa2/data/vqav2/train2014/",
- question_path="/mmfs1/gscratch/efml/anasa2/data/vqav2/v2_OpenEnded_mscoco_train2014_questions.json",
- annotations_path="/mmfs1/gscratch/efml/anasa2/data/vqav2/v2_mscoco_train2014_annotations.json",
- vqa_dataset="vqa",
- ):
- self.questions = json.load(open(question_path, "r"))["questions"]
- self.answers = json.load(open(annotations_path, "r"))["annotations"]
- self.image_dir_path = image_dir_path
- self.vqa_dataset = vqa_dataset
-
- def __len__(self):
- return len(self.questions)
-
- def get_img_path(self, question):
- if self.vqa_dataset == "vqa":
- return os.path.join(
- self.image_dir_path, f"COCO_val2014_{question['image_id']:012d}.jpg"
- )
- elif self.vqa_dataset == "ok_vqa":
- return os.path.join(
- self.image_dir_path, f"COCO_val2014_{question['image_id']:012d}.jpg"
- )
- else:
- raise Exception(f"Unknown VQA dataset {self.vqa_dataset}")
-
- def __getitem__(self, idx):
- question = self.questions[idx]
- answers = self.answers[idx]
- img_path = self.get_img_path(question)
- image = Image.open(img_path)
- return {
- "image": image,
- "question": question["question"],
- "answers": [a["answer"] for a in answers["answers"]],
- "question_id": question["question_id"],
- }
-
-
-class ImageNetDataset(ImageFolder):
- """Class to represent the ImageNet1k dataset."""
-
- def __init__(self, root, **kwargs):
- super().__init__(root=root, **kwargs)
-
- def __getitem__(self, idx):
- sample, target = super().__getitem__(idx)
- target_label = IMAGENET_1K_CLASS_ID_TO_LABEL[target]
- return {
- "image": sample,
- "class_id": target, # numeric ID of the ImageNet class
- "class_name": target_label, # human-readable name of ImageNet class
- }
-
-
-if __name__ == "__main__":
- gqa_dataset = GQADataset()
- for sample in gqa_dataset:
- print(sample)
diff --git a/spaces/chendl/compositional_test/transformers/examples/legacy/run_swag.py b/spaces/chendl/compositional_test/transformers/examples/legacy/run_swag.py
deleted file mode 100644
index bde050168752650e8d1ab927273178f3648359c6..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/legacy/run_swag.py
+++ /dev/null
@@ -1,724 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION. 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.
-"""BERT finetuning runner.
- Finetuning the library models for multiple choice on SWAG (Bert).
-"""
-
-
-import argparse
-import csv
-import glob
-import logging
-import os
-import random
-
-import numpy as np
-import torch
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
-from torch.utils.data.distributed import DistributedSampler
-from tqdm import tqdm, trange
-
-import transformers
-from transformers import (
- WEIGHTS_NAME,
- AdamW,
- AutoConfig,
- AutoModelForMultipleChoice,
- AutoTokenizer,
- get_linear_schedule_with_warmup,
-)
-from transformers.trainer_utils import is_main_process
-
-
-try:
- from torch.utils.tensorboard import SummaryWriter
-except ImportError:
- from tensorboardX import SummaryWriter
-
-
-logger = logging.getLogger(__name__)
-
-
-class SwagExample(object):
- """A single training/test example for the SWAG dataset."""
-
- def __init__(self, swag_id, context_sentence, start_ending, ending_0, ending_1, ending_2, ending_3, label=None):
- self.swag_id = swag_id
- self.context_sentence = context_sentence
- self.start_ending = start_ending
- self.endings = [
- ending_0,
- ending_1,
- ending_2,
- ending_3,
- ]
- self.label = label
-
- def __str__(self):
- return self.__repr__()
-
- def __repr__(self):
- attributes = [
- "swag_id: {}".format(self.swag_id),
- "context_sentence: {}".format(self.context_sentence),
- "start_ending: {}".format(self.start_ending),
- "ending_0: {}".format(self.endings[0]),
- "ending_1: {}".format(self.endings[1]),
- "ending_2: {}".format(self.endings[2]),
- "ending_3: {}".format(self.endings[3]),
- ]
-
- if self.label is not None:
- attributes.append("label: {}".format(self.label))
-
- return ", ".join(attributes)
-
-
-class InputFeatures(object):
- def __init__(self, example_id, choices_features, label):
- self.example_id = example_id
- self.choices_features = [
- {"input_ids": input_ids, "input_mask": input_mask, "segment_ids": segment_ids}
- for _, input_ids, input_mask, segment_ids in choices_features
- ]
- self.label = label
-
-
-def read_swag_examples(input_file, is_training=True):
- with open(input_file, "r", encoding="utf-8") as f:
- lines = list(csv.reader(f))
-
- if is_training and lines[0][-1] != "label":
- raise ValueError("For training, the input file must contain a label column.")
-
- examples = [
- SwagExample(
- swag_id=line[2],
- context_sentence=line[4],
- start_ending=line[5], # in the swag dataset, the
- # common beginning of each
- # choice is stored in "sent2".
- ending_0=line[7],
- ending_1=line[8],
- ending_2=line[9],
- ending_3=line[10],
- label=int(line[11]) if is_training else None,
- )
- for line in lines[1:] # we skip the line with the column names
- ]
-
- return examples
-
-
-def convert_examples_to_features(examples, tokenizer, max_seq_length, is_training):
- """Loads a data file into a list of `InputBatch`s."""
-
- # Swag is a multiple choice task. To perform this task using Bert,
- # we will use the formatting proposed in "Improving Language
- # Understanding by Generative Pre-Training" and suggested by
- # @jacobdevlin-google in this issue
- # https://github.com/google-research/bert/issues/38.
- #
- # Each choice will correspond to a sample on which we run the
- # inference. For a given Swag example, we will create the 4
- # following inputs:
- # - [CLS] context [SEP] choice_1 [SEP]
- # - [CLS] context [SEP] choice_2 [SEP]
- # - [CLS] context [SEP] choice_3 [SEP]
- # - [CLS] context [SEP] choice_4 [SEP]
- # The model will output a single value for each input. To get the
- # final decision of the model, we will run a softmax over these 4
- # outputs.
- features = []
- for example_index, example in tqdm(enumerate(examples)):
- context_tokens = tokenizer.tokenize(example.context_sentence)
- start_ending_tokens = tokenizer.tokenize(example.start_ending)
-
- choices_features = []
- for ending_index, ending in enumerate(example.endings):
- # We create a copy of the context tokens in order to be
- # able to shrink it according to ending_tokens
- context_tokens_choice = context_tokens[:]
- ending_tokens = start_ending_tokens + tokenizer.tokenize(ending)
- # Modifies `context_tokens_choice` and `ending_tokens` in
- # place so that the total length is less than the
- # specified length. Account for [CLS], [SEP], [SEP] with
- # "- 3"
- _truncate_seq_pair(context_tokens_choice, ending_tokens, max_seq_length - 3)
-
- tokens = ["[CLS]"] + context_tokens_choice + ["[SEP]"] + ending_tokens + ["[SEP]"]
- segment_ids = [0] * (len(context_tokens_choice) + 2) + [1] * (len(ending_tokens) + 1)
-
- input_ids = tokenizer.convert_tokens_to_ids(tokens)
- input_mask = [1] * len(input_ids)
-
- # Zero-pad up to the sequence length.
- padding = [0] * (max_seq_length - len(input_ids))
- input_ids += padding
- input_mask += padding
- segment_ids += padding
-
- assert len(input_ids) == max_seq_length
- assert len(input_mask) == max_seq_length
- assert len(segment_ids) == max_seq_length
-
- choices_features.append((tokens, input_ids, input_mask, segment_ids))
-
- label = example.label
- if example_index < 5:
- logger.info("*** Example ***")
- logger.info("swag_id: {}".format(example.swag_id))
- for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features):
- logger.info("choice: {}".format(choice_idx))
- logger.info("tokens: {}".format(" ".join(tokens)))
- logger.info("input_ids: {}".format(" ".join(map(str, input_ids))))
- logger.info("input_mask: {}".format(" ".join(map(str, input_mask))))
- logger.info("segment_ids: {}".format(" ".join(map(str, segment_ids))))
- if is_training:
- logger.info("label: {}".format(label))
-
- features.append(InputFeatures(example_id=example.swag_id, choices_features=choices_features, label=label))
-
- return features
-
-
-def _truncate_seq_pair(tokens_a, tokens_b, max_length):
- """Truncates a sequence pair in place to the maximum length."""
-
- # This is a simple heuristic which will always truncate the longer sequence
- # one token at a time. This makes more sense than truncating an equal percent
- # of tokens from each, since if one sequence is very short then each token
- # that's truncated likely contains more information than a longer sequence.
- while True:
- total_length = len(tokens_a) + len(tokens_b)
- if total_length <= max_length:
- break
- if len(tokens_a) > len(tokens_b):
- tokens_a.pop()
- else:
- tokens_b.pop()
-
-
-def accuracy(out, labels):
- outputs = np.argmax(out, axis=1)
- return np.sum(outputs == labels)
-
-
-def select_field(features, field):
- return [[choice[field] for choice in feature.choices_features] for feature in features]
-
-
-def set_seed(args):
- random.seed(args.seed)
- np.random.seed(args.seed)
- torch.manual_seed(args.seed)
- if args.n_gpu > 0:
- torch.cuda.manual_seed_all(args.seed)
-
-
-def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
- if args.local_rank not in [-1, 0]:
- torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
-
- # Load data features from cache or dataset file
- input_file = args.predict_file if evaluate else args.train_file
- cached_features_file = os.path.join(
- os.path.dirname(input_file),
- "cached_{}_{}_{}".format(
- "dev" if evaluate else "train",
- list(filter(None, args.model_name_or_path.split("/"))).pop(),
- str(args.max_seq_length),
- ),
- )
- if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples:
- logger.info("Loading features from cached file %s", cached_features_file)
- features = torch.load(cached_features_file)
- else:
- logger.info("Creating features from dataset file at %s", input_file)
- examples = read_swag_examples(input_file)
- features = convert_examples_to_features(examples, tokenizer, args.max_seq_length, not evaluate)
-
- if args.local_rank in [-1, 0]:
- logger.info("Saving features into cached file %s", cached_features_file)
- torch.save(features, cached_features_file)
-
- if args.local_rank == 0:
- torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
-
- # Convert to Tensors and build dataset
- all_input_ids = torch.tensor(select_field(features, "input_ids"), dtype=torch.long)
- all_input_mask = torch.tensor(select_field(features, "input_mask"), dtype=torch.long)
- all_segment_ids = torch.tensor(select_field(features, "segment_ids"), dtype=torch.long)
- all_label = torch.tensor([f.label for f in features], dtype=torch.long)
-
- if evaluate:
- dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
- else:
- dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
-
- if output_examples:
- return dataset, examples, features
- return dataset
-
-
-def train(args, train_dataset, model, tokenizer):
- """Train the model"""
- if args.local_rank in [-1, 0]:
- tb_writer = SummaryWriter()
-
- args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
- train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
- train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
-
- if args.max_steps > 0:
- t_total = args.max_steps
- args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
- else:
- t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
-
- # Prepare optimizer and schedule (linear warmup and decay)
- no_decay = ["bias", "LayerNorm.weight"]
- optimizer_grouped_parameters = [
- {
- "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
- "weight_decay": args.weight_decay,
- },
- {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
- ]
- optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
- scheduler = get_linear_schedule_with_warmup(
- optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
- )
- if args.fp16:
- try:
- from apex import amp
- except ImportError:
- raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
- model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
-
- # multi-gpu training (should be after apex fp16 initialization)
- if args.n_gpu > 1:
- model = torch.nn.DataParallel(model)
-
- # Distributed training (should be after apex fp16 initialization)
- if args.local_rank != -1:
- model = torch.nn.parallel.DistributedDataParallel(
- model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
- )
-
- # Train!
- logger.info("***** Running training *****")
- logger.info(" Num examples = %d", len(train_dataset))
- logger.info(" Num Epochs = %d", args.num_train_epochs)
- logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
- logger.info(
- " Total train batch size (w. parallel, distributed & accumulation) = %d",
- args.train_batch_size
- * args.gradient_accumulation_steps
- * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
- )
- logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
- logger.info(" Total optimization steps = %d", t_total)
-
- global_step = 0
- tr_loss, logging_loss = 0.0, 0.0
- model.zero_grad()
- train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
- set_seed(args) # Added here for reproductibility
- for _ in train_iterator:
- epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
- for step, batch in enumerate(epoch_iterator):
- model.train()
- batch = tuple(t.to(args.device) for t in batch)
- inputs = {
- "input_ids": batch[0],
- "attention_mask": batch[1],
- # 'token_type_ids': None if args.model_type == 'xlm' else batch[2],
- "token_type_ids": batch[2],
- "labels": batch[3],
- }
- # if args.model_type in ['xlnet', 'xlm']:
- # inputs.update({'cls_index': batch[5],
- # 'p_mask': batch[6]})
- outputs = model(**inputs)
- loss = outputs[0] # model outputs are always tuple in transformers (see doc)
-
- if args.n_gpu > 1:
- loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training
- if args.gradient_accumulation_steps > 1:
- loss = loss / args.gradient_accumulation_steps
-
- if args.fp16:
- with amp.scale_loss(loss, optimizer) as scaled_loss:
- scaled_loss.backward()
- torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
- else:
- loss.backward()
- torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
-
- tr_loss += loss.item()
- if (step + 1) % args.gradient_accumulation_steps == 0:
- optimizer.step()
- scheduler.step() # Update learning rate schedule
- model.zero_grad()
- global_step += 1
-
- if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
- # Log metrics
- if (
- args.local_rank == -1 and args.evaluate_during_training
- ): # Only evaluate when single GPU otherwise metrics may not average well
- results = evaluate(args, model, tokenizer)
- for key, value in results.items():
- tb_writer.add_scalar("eval_{}".format(key), value, global_step)
- tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
- tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
- logging_loss = tr_loss
-
- if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
- # Save model checkpoint
- output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
- model_to_save = (
- model.module if hasattr(model, "module") else model
- ) # Take care of distributed/parallel training
- model_to_save.save_pretrained(output_dir)
- tokenizer.save_vocabulary(output_dir)
- torch.save(args, os.path.join(output_dir, "training_args.bin"))
- logger.info("Saving model checkpoint to %s", output_dir)
-
- if args.max_steps > 0 and global_step > args.max_steps:
- epoch_iterator.close()
- break
- if args.max_steps > 0 and global_step > args.max_steps:
- train_iterator.close()
- break
-
- if args.local_rank in [-1, 0]:
- tb_writer.close()
-
- return global_step, tr_loss / global_step
-
-
-def evaluate(args, model, tokenizer, prefix=""):
- dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
-
- if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
- os.makedirs(args.output_dir)
-
- args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
- # Note that DistributedSampler samples randomly
- eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset)
- eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
-
- # Eval!
- logger.info("***** Running evaluation {} *****".format(prefix))
- logger.info(" Num examples = %d", len(dataset))
- logger.info(" Batch size = %d", args.eval_batch_size)
-
- eval_loss, eval_accuracy = 0, 0
- nb_eval_steps, nb_eval_examples = 0, 0
-
- for batch in tqdm(eval_dataloader, desc="Evaluating"):
- model.eval()
- batch = tuple(t.to(args.device) for t in batch)
- with torch.no_grad():
- inputs = {
- "input_ids": batch[0],
- "attention_mask": batch[1],
- # 'token_type_ids': None if args.model_type == 'xlm' else batch[2] # XLM don't use segment_ids
- "token_type_ids": batch[2],
- "labels": batch[3],
- }
-
- # if args.model_type in ['xlnet', 'xlm']:
- # inputs.update({'cls_index': batch[4],
- # 'p_mask': batch[5]})
- outputs = model(**inputs)
- tmp_eval_loss, logits = outputs[:2]
- eval_loss += tmp_eval_loss.mean().item()
-
- logits = logits.detach().cpu().numpy()
- label_ids = inputs["labels"].to("cpu").numpy()
- tmp_eval_accuracy = accuracy(logits, label_ids)
- eval_accuracy += tmp_eval_accuracy
-
- nb_eval_steps += 1
- nb_eval_examples += inputs["input_ids"].size(0)
-
- eval_loss = eval_loss / nb_eval_steps
- eval_accuracy = eval_accuracy / nb_eval_examples
- result = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy}
-
- output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
- with open(output_eval_file, "w") as writer:
- logger.info("***** Eval results *****")
- for key in sorted(result.keys()):
- logger.info("%s = %s", key, str(result[key]))
- writer.write("%s = %s\n" % (key, str(result[key])))
-
- return result
-
-
-def main():
- parser = argparse.ArgumentParser()
-
- # Required parameters
- parser.add_argument(
- "--train_file", default=None, type=str, required=True, help="SWAG csv for training. E.g., train.csv"
- )
- parser.add_argument(
- "--predict_file",
- default=None,
- type=str,
- required=True,
- help="SWAG csv for predictions. E.g., val.csv or test.csv",
- )
- parser.add_argument(
- "--model_name_or_path",
- default=None,
- type=str,
- required=True,
- help="Path to pretrained model or model identifier from huggingface.co/models",
- )
- parser.add_argument(
- "--output_dir",
- default=None,
- type=str,
- required=True,
- help="The output directory where the model checkpoints and predictions will be written.",
- )
-
- # Other parameters
- parser.add_argument(
- "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
- )
- parser.add_argument(
- "--tokenizer_name",
- default="",
- type=str,
- help="Pretrained tokenizer name or path if not the same as model_name",
- )
- parser.add_argument(
- "--max_seq_length",
- default=384,
- type=int,
- help=(
- "The maximum total input sequence length after tokenization. Sequences "
- "longer than this will be truncated, and sequences shorter than this will be padded."
- ),
- )
- parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
- parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
- parser.add_argument(
- "--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step."
- )
-
- parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
- parser.add_argument(
- "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
- )
- parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
- parser.add_argument(
- "--gradient_accumulation_steps",
- type=int,
- default=1,
- help="Number of updates steps to accumulate before performing a backward/update pass.",
- )
- parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight deay if we apply some.")
- parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
- parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
- parser.add_argument(
- "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
- )
- parser.add_argument(
- "--max_steps",
- default=-1,
- type=int,
- help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
- )
- parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
-
- parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
- parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
- parser.add_argument(
- "--eval_all_checkpoints",
- action="store_true",
- help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
- )
- parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available")
- parser.add_argument(
- "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
- )
- parser.add_argument(
- "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
- )
- parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
-
- parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus")
- parser.add_argument(
- "--fp16",
- action="store_true",
- help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
- )
- parser.add_argument(
- "--fp16_opt_level",
- type=str,
- default="O1",
- help=(
- "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
- "See details at https://nvidia.github.io/apex/amp.html"
- ),
- )
- parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
- parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
- args = parser.parse_args()
-
- if (
- os.path.exists(args.output_dir)
- and os.listdir(args.output_dir)
- and args.do_train
- and not args.overwrite_output_dir
- ):
- raise ValueError(
- "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
- args.output_dir
- )
- )
-
- # Setup distant debugging if needed
- if args.server_ip and args.server_port:
- # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
- import ptvsd
-
- print("Waiting for debugger attach")
- ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
- ptvsd.wait_for_attach()
-
- # Setup CUDA, GPU & distributed training
- if args.local_rank == -1 or args.no_cuda:
- device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
- args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
- else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
- torch.cuda.set_device(args.local_rank)
- device = torch.device("cuda", args.local_rank)
- torch.distributed.init_process_group(backend="nccl")
- args.n_gpu = 1
- args.device = device
-
- # Setup logging
- logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
- )
- logger.warning(
- "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
- args.local_rank,
- device,
- args.n_gpu,
- bool(args.local_rank != -1),
- args.fp16,
- )
- # Set the verbosity to info of the Transformers logger (on main process only):
- if is_main_process(args.local_rank):
- transformers.utils.logging.set_verbosity_info()
- transformers.utils.logging.enable_default_handler()
- transformers.utils.logging.enable_explicit_format()
-
- # Set seed
- set_seed(args)
-
- # Load pretrained model and tokenizer
- if args.local_rank not in [-1, 0]:
- torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
-
- config = AutoConfig.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
- tokenizer = AutoTokenizer.from_pretrained(
- args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
- )
- model = AutoModelForMultipleChoice.from_pretrained(
- args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config
- )
-
- if args.local_rank == 0:
- torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
-
- model.to(args.device)
-
- logger.info("Training/evaluation parameters %s", args)
-
- # Training
- if args.do_train:
- train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False)
- global_step, tr_loss = train(args, train_dataset, model, tokenizer)
- logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
-
- # Save the trained model and the tokenizer
- if args.local_rank == -1 or torch.distributed.get_rank() == 0:
- logger.info("Saving model checkpoint to %s", args.output_dir)
- # Save a trained model, configuration and tokenizer using `save_pretrained()`.
- # They can then be reloaded using `from_pretrained()`
- model_to_save = (
- model.module if hasattr(model, "module") else model
- ) # Take care of distributed/parallel training
- model_to_save.save_pretrained(args.output_dir)
- tokenizer.save_pretrained(args.output_dir)
-
- # Good practice: save your training arguments together with the trained model
- torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
-
- # Load a trained model and vocabulary that you have fine-tuned
- model = AutoModelForMultipleChoice.from_pretrained(args.output_dir)
- tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
- model.to(args.device)
-
- # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
- results = {}
- if args.do_eval and args.local_rank in [-1, 0]:
- if args.do_train:
- checkpoints = [args.output_dir]
- else:
- # if do_train is False and do_eval is true, load model directly from pretrained.
- checkpoints = [args.model_name_or_path]
-
- if args.eval_all_checkpoints:
- checkpoints = [
- os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
- ]
-
- logger.info("Evaluate the following checkpoints: %s", checkpoints)
-
- for checkpoint in checkpoints:
- # Reload the model
- global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
- model = AutoModelForMultipleChoice.from_pretrained(checkpoint)
- tokenizer = AutoTokenizer.from_pretrained(checkpoint)
- model.to(args.device)
-
- # Evaluate
- result = evaluate(args, model, tokenizer, prefix=global_step)
-
- result = {k + ("_{}".format(global_step) if global_step else ""): v for k, v in result.items()}
- results.update(result)
-
- logger.info("Results: {}".format(results))
-
- return results
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/chenxx/ChuanhuChatGPT/chatgpt - windows.bat b/spaces/chenxx/ChuanhuChatGPT/chatgpt - windows.bat
deleted file mode 100644
index 0b78fdc3a559abd692e3a9e9af5e482124d13a99..0000000000000000000000000000000000000000
--- a/spaces/chenxx/ChuanhuChatGPT/chatgpt - windows.bat
+++ /dev/null
@@ -1,14 +0,0 @@
-@echo off
-echo Opening ChuanhuChatGPT...
-
-REM Open powershell via bat
-start powershell.exe -NoExit -Command "python ./ChuanhuChatbot.py"
-
-REM The web page can be accessed with delayed start http://127.0.0.1:7860/
-ping -n 5 127.0.0.1>nul
-
-REM access chargpt via your default browser
-start "" "http://127.0.0.1:7860/"
-
-
-echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/).
\ No newline at end of file
diff --git a/spaces/christinac/text-decorator/README.md b/spaces/christinac/text-decorator/README.md
deleted file mode 100644
index f8cef19bdc16dd1fbfe0f488fffa51e1c521aa2f..0000000000000000000000000000000000000000
--- a/spaces/christinac/text-decorator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text Decorator
-emoji: ✍🏻
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.11.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/functorch/dim/wrap_type.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/functorch/dim/wrap_type.py
deleted file mode 100644
index 8212836d3d6ae71f84dcb161b60bd513fe35b571..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/functorch/dim/wrap_type.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the BSD-style license found in the
-# LICENSE file in the root directory of this source tree.
-
-from types import FunctionType, BuiltinMethodType, MethodDescriptorType, WrapperDescriptorType, GetSetDescriptorType
-from functorch._C import dim as _C
-_wrap_method = _C._wrap_method
-
-FUNC_TYPES = (FunctionType, MethodDescriptorType, BuiltinMethodType, WrapperDescriptorType)
-PROPERTY_TYPES = (GetSetDescriptorType, property)
-
-def _py_wrap_method(orig, __torch_function__):
- def impl(*args, **kwargs):
- return __torch_function__(orig, None, args, kwargs)
- return impl
-
-
-
-def wrap_type(use_c, to_patch, pattern, __torch_function__):
-
- if use_c:
- wrap_method = _wrap_method
- else:
- wrap_method = _py_wrap_method
-
- all = {}
- for t in reversed(pattern.mro()[:-1]): # skip object
- all.update(t.__dict__)
-
-
- def wrap_attr(orig):
- return property(wrap_method(orig.__get__, __torch_function__))
-
-
- for name, obj in all.items():
- if name in ('__dict__', '__new__', '__init__', '__repr__', '__weakref__', '__doc__', '__module__', '__dir__'):
- continue
-
- # skip things that have been overloaded
- # things that come from object like `__eq__` still need to be patched, however.
- if hasattr(to_patch, name) and getattr(to_patch, name) is not getattr(object, name, None):
- continue
-
- if isinstance(obj, FUNC_TYPES):
- setattr(to_patch, name, wrap_method(obj, __torch_function__))
- elif isinstance(obj, PROPERTY_TYPES):
- setattr(to_patch, name, wrap_attr(obj))
diff --git a/spaces/cihyFjudo/fairness-paper-search/Hum Hai Raahi CAR Ke Dvdrip Movie Free Download.md b/spaces/cihyFjudo/fairness-paper-search/Hum Hai Raahi CAR Ke Dvdrip Movie Free Download.md
deleted file mode 100644
index 0fc62e958a16426b99ac90a887c38121d629fa76..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Hum Hai Raahi CAR Ke Dvdrip Movie Free Download.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Hum Hai Raahi CAR Ke dvdrip movie free download
Download File ✓ https://tinurli.com/2uwk7t
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cihyFjudo/fairness-paper-search/Kamagata Maru Full Movie Online 720p Torrent.md b/spaces/cihyFjudo/fairness-paper-search/Kamagata Maru Full Movie Online 720p Torrent.md
deleted file mode 100644
index 8ae87cf03b865fe09b58b08963ab8c8597433b83..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Kamagata Maru Full Movie Online 720p Torrent.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Kamagata Maru full movie online 720p torrent
Download Zip › https://tinurli.com/2uwitB
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cihyFjudo/fairness-paper-search/Kamasutrabookpdfinkannadafreedownload UPD.md b/spaces/cihyFjudo/fairness-paper-search/Kamasutrabookpdfinkannadafreedownload UPD.md
deleted file mode 100644
index 50a37f10eac9afc5f9173e5e0839fedc8a42693a..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Kamasutrabookpdfinkannadafreedownload UPD.md
+++ /dev/null
@@ -1,6 +0,0 @@
-kamasutrabookpdfinkannadafreedownload
Download Zip ->>> https://tinurli.com/2uwiQ0
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/H_V_A_R_.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/H_V_A_R_.py
deleted file mode 100644
index 094aedaea5ebc5c88b33e448ea8f131563acd3c0..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/H_V_A_R_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table_H_V_A_R_(BaseTTXConverter):
- pass
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dxva2.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dxva2.h
deleted file mode 100644
index 22c93992f22284c7e2ac92c5bf23faa3df6e6272..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dxva2.h
+++ /dev/null
@@ -1,93 +0,0 @@
-/*
- * DXVA2 HW acceleration
- *
- * copyright (c) 2009 Laurent Aimar
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg 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
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_DXVA2_H
-#define AVCODEC_DXVA2_H
-
-/**
- * @file
- * @ingroup lavc_codec_hwaccel_dxva2
- * Public libavcodec DXVA2 header.
- */
-
-#if !defined(_WIN32_WINNT) || _WIN32_WINNT < 0x0602
-#undef _WIN32_WINNT
-#define _WIN32_WINNT 0x0602
-#endif
-
-#include
-#include
-#include
-
-/**
- * @defgroup lavc_codec_hwaccel_dxva2 DXVA2
- * @ingroup lavc_codec_hwaccel
- *
- * @{
- */
-
-#define FF_DXVA2_WORKAROUND_SCALING_LIST_ZIGZAG 1 ///< Work around for DXVA2 and old UVD/UVD+ ATI video cards
-#define FF_DXVA2_WORKAROUND_INTEL_CLEARVIDEO 2 ///< Work around for DXVA2 and old Intel GPUs with ClearVideo interface
-
-/**
- * This structure is used to provides the necessary configurations and data
- * to the DXVA2 FFmpeg HWAccel implementation.
- *
- * The application must make it available as AVCodecContext.hwaccel_context.
- */
-struct dxva_context {
- /**
- * DXVA2 decoder object
- */
- IDirectXVideoDecoder *decoder;
-
- /**
- * DXVA2 configuration used to create the decoder
- */
- const DXVA2_ConfigPictureDecode *cfg;
-
- /**
- * The number of surface in the surface array
- */
- unsigned surface_count;
-
- /**
- * The array of Direct3D surfaces used to create the decoder
- */
- LPDIRECT3DSURFACE9 *surface;
-
- /**
- * A bit field configuring the workarounds needed for using the decoder
- */
- uint64_t workaround;
-
- /**
- * Private to the FFmpeg AVHWAccel implementation
- */
- unsigned report_id;
-};
-
-/**
- * @}
- */
-
-#endif /* AVCODEC_DXVA2_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/h264chroma_init_mips.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/h264chroma_init_mips.c
deleted file mode 100644
index aa52d2a3ae43f6a9278ffc46563519c8e2b73ba3..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/h264chroma_init_mips.c
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright (c) 2015 Zhou Xiaoyong
- * Copyright (c) 2015 Shivraj Patil (Shivraj.Patil@imgtec.com)
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg 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
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/attributes.h"
-#include "libavutil/mips/cpu.h"
-#include "h264chroma_mips.h"
-
-
-av_cold void ff_h264chroma_init_mips(H264ChromaContext *c, int bit_depth)
-{
- int cpu_flags = av_get_cpu_flags();
- int high_bit_depth = bit_depth > 8;
-
- if (have_mmi(cpu_flags)) {
- if (!high_bit_depth) {
- c->put_h264_chroma_pixels_tab[0] = ff_put_h264_chroma_mc8_mmi;
- c->avg_h264_chroma_pixels_tab[0] = ff_avg_h264_chroma_mc8_mmi;
- c->put_h264_chroma_pixels_tab[1] = ff_put_h264_chroma_mc4_mmi;
- c->avg_h264_chroma_pixels_tab[1] = ff_avg_h264_chroma_mc4_mmi;
- }
- }
-
- if (have_msa(cpu_flags)) {
- if (!high_bit_depth) {
- c->put_h264_chroma_pixels_tab[0] = ff_put_h264_chroma_mc8_msa;
- c->put_h264_chroma_pixels_tab[1] = ff_put_h264_chroma_mc4_msa;
- c->put_h264_chroma_pixels_tab[2] = ff_put_h264_chroma_mc2_msa;
-
- c->avg_h264_chroma_pixels_tab[0] = ff_avg_h264_chroma_mc8_msa;
- c->avg_h264_chroma_pixels_tab[1] = ff_avg_h264_chroma_mc4_msa;
- c->avg_h264_chroma_pixels_tab[2] = ff_avg_h264_chroma_mc2_msa;
- }
- }
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download and Install Sheep Dog N Wolf for PS1 - Easy Guide!.md b/spaces/congsaPfin/Manga-OCR/logs/Download and Install Sheep Dog N Wolf for PS1 - Easy Guide!.md
deleted file mode 100644
index 06cac193cd649e6c6404de0ec6309518c096c4d1..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download and Install Sheep Dog N Wolf for PS1 - Easy Guide!.md
+++ /dev/null
@@ -1,142 +0,0 @@
-
-How to Download Sheep Dog n Wolf PS1
-Sheep Dog n Wolf PS1 is a fun and challenging game that puts you in the role of Ralph Wolf, a sneaky sheep stealer who has to outsmart Sam Sheepdog and other Looney Tunes characters in various environments. The game features clever puzzles, hilarious animations, and faithful recreations of the classic cartoons. If you are a fan of Looney Tunes or puzzle-platformer games, you will love Sheep Dog n Wolf PS1.
-But how can you play this game on your PC? The answer is simple: you need a PlayStation emulator. An emulator is a program that mimics the functions of a console and allows you to run games on your computer. By using an emulator, you can enjoy Sheep Dog n Wolf PS1 and other PS1 games on your PC with enhanced graphics, sound, and performance. You can also save your progress anytime, customize your controls, and access cheats and mods.
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Download File ❤❤❤ https://urlca.com/2uO4mm
-In this article, we will show you how to download Sheep Dog n Wolf PS1 on your PC using an emulator. We will also give you some tips and tricks for playing the game and having more fun. Let's get started!
- How to Download Sheep Dog n Wolf PS1
-To download Sheep Dog n Wolf PS1 on your PC, you need to follow these steps:
- Step 1: Choose a PS1 emulator
-The first thing you need to do is to choose a PS1 emulator that works on your PC. There are many emulators available online, but not all of them are reliable or compatible with every game. Here are some of the best PS1 emulators that we recommend:
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-Once you have chosen an emulator, download it and install it on your PC. You may also need to download a BIOS file for your emulator, which is a software that contains the basic instructions for booting up the console. You can find BIOS files online, but make sure they are legal and safe to use.
- Step 2: Download the game ROM or ISO file
-The next thing you need to do is to download the game ROM or ISO file for Sheep Dog n Wolf PS1. A ROM or ISO file is a digital copy of the game disc that contains all the data and information needed to run the game. You can find ROM or ISO files for Sheep Dog n Wolf PS1 on various websites that offer free or paid downloads of PS1 games. However, you should be careful when downloading ROM or ISO files, as some of them may be illegal, corrupted, or infected with malware. You should only download ROM or ISO files from trusted and reputable sources, and always scan them with an antivirus program before opening them.
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-One of the best websites to download Sheep Dog n Wolf PS1 ROM or ISO file is CoolROM. This website has a large collection of PS1 games that you can download for free and safely. To download Sheep Dog n Wolf PS1 from CoolROM, follow these steps:
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-You can also download Sheep Dog n Wolf PS1 from other websites, such as Emuparadise, Rom Hustler, or The ISO Zone. However, you should always check the reviews and ratings of the games before downloading them, and make sure they are compatible with your emulator.
- Step 3: Configure the emulator and load the game
-The final step is to configure your emulator and load the game. Depending on the emulator you chose, the configuration process may vary slightly. However, the general steps are as follows:
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-If you encounter any problems or errors while running the game, you can try changing the plugins, updating the emulator, or checking the compatibility list of your emulator. You can also consult the online forums or guides for more help and tips.
- Tips and Tricks for Playing Sheep Dog n Wolf PS1
-Now that you know how to download Sheep Dog n Wolf PS1 on your PC, you may want to learn some tips and tricks for playing the game and having more fun. Here are some of them:
- How to use gadgets and items from Acme
-In Sheep Dog n Wolf PS1, you can use various gadgets and items from Acme to help you steal sheep and avoid obstacles. Some of these gadgets and items are:
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-Gadget/Item Description Rocket A rocket that can propel you in any direction. You can use it to fly over gaps, walls, or enemies. Bowling Ball A heavy ball that can knock down anything in its path. You can use it to hit switches, break barriers, or distract enemies. Magnet A powerful magnet that can attract metal objects. You can use it to pull levers, open doors, or move objects. Bone A tasty bone that can lure dogs away from their posts. You can use it to distract Sam Sheepdog or other dogs that guard the sheep. Clock A clock that can freeze time for a few seconds. You can use it to stop moving platforms, traps, or enemies. Balloon A balloon that can lift you up in the air. You can use it to reach high places, cross water, or escape danger. Costume A costume that can disguise you as another character. You can use it to blend in with the sheep, trick Sam Sheepdog, or access restricted areas.
-You can find these gadgets and items in crates scattered throughout the levels. You can also buy them from Daffy Duck's shop using coins that you collect along the way. However, you can only carry a limited number of gadgets and items at a time, so you have to use them wisely and strategically.
- How to avoid detection from Sam Sheepdog and other characters
-One of the main challenges of Sheep Dog n Wolf PS1 is to avoid being detected by Sam Sheepdog and other characters that can ruin your plans. If you are caught, you will lose a life and have to restart the level. To avoid detection, you need to be stealthy and smart. Here are some tips:
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- How to unlock secret and bonus levels
-Sheep Dog n Wolf PS1 has 18 levels in total, but some of them are secret and bonus levels that require special conditions to unlock. Here are some of them:
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-To collect golden sheep statues, you need to find them hidden throughout the levels or earn them by completing certain tasks or challenges. Some of them are easy to find or obtain, while others are more difficult and require skill and patience.
- Conclusion
-Sheep Dog n Wolf PS1 is a game that will test your wits and creativity as you try to steal sheep from under Sam Sheepdog's nose. It is also a game that will make you laugh and smile as you watch Ralph Wolf's antics and mishaps. If you want to play this game on your PC, you just need to download a PS1 emulator and the game ROM or ISO file from a reliable source. Then, you can configure your emulator and load the game with ease. You can also use some tips and tricks to help you play the game better and unlock secret and bonus levels.
-We hope this article has helped you learn how to download Sheep Dog n Wolf PS1 on your PC and enjoy this classic game. If you have any questions or comments, feel free to leave them below. Happy gaming!
- FAQs
-Here are some frequently asked questions about Sheep Dog n Wolf PS1:
- What are the system requirements for playing Sheep Dog n Wolf PS1 on PC?
-The system requirements for playing Sheep Dog n Wolf PS1 on PC depend on the emulator you use, but generally, they are not very high. You should have at least a Windows XP/Vista/7/8/10 operating system, a Pentium III or higher processor, 256 MB of RAM, DirectX 9 or higher, and a compatible video card and sound card.
- How many levels are there in Sheep Dog n Wolf PS1?
-There are 18 levels in Sheep Dog n Wolf PS1, but some of them are secret and bonus levels that require special conditions to unlock. You can find more information about the levels in the Tips and Tricks section of this article.
- Can I play Sheep Dog n Wolf PS1 with a gamepad?
-Yes, you can play Sheep Dog n Wolf PS1 with a gamepad if your emulator supports it. You can configure your gamepad settings in the emulator's menu and assign the buttons to match the PS1 controller. You can also use a keyboard and mouse if you prefer.
- Is Sheep Dog n Wolf PS1 compatible with Windows 10?
-Yes, Sheep Dog n Wolf PS1 is compatible with Windows 10 as long as you use a compatible emulator. Some of the emulators that work well with Windows 10 are ePSXe, PCSX Reloaded, and RetroArch. You may need to run the emulator in compatibility mode or as an administrator if you encounter any issues.
- Where can I find more information about Sheep Dog n Wolf PS1?
-If you want to find more information about Sheep Dog n Wolf PS1, such as the story, the characters, the gameplay, the cheats, or the reviews, you can visit some of these websites:
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- Table 2: Article with HTML formatting
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- What is a GPS APK and Why You Need One?
- Introduction
- If you are an Android
If you are an Android user, you might have heard of the term APK, which stands for Android Package Kit. An APK is a file format that contains the code, resources, and metadata of an Android application. You can install an APK file on your Android device by downloading it from a source and following some simple steps. But why would you want to do that? What are the benefits of installing an APK file instead of an app from the Google Play Store? And what is a GPS APK in particular?
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- A GPS APK is a type of APK file that provides GPS navigation features for your Android device. GPS stands for Global Positioning System, which is a network of satellites that can pinpoint your location and provide directions to your destination. A GPS APK can help you find your way around the world, whether you are driving, walking, biking, or using public transportation. A GPS APK can also offer offline maps, traffic alerts, speed limits, points of interest, and more.
- Some of the benefits of using a GPS APK are:
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- Some examples of GPS APKs are:
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- These are just some of the examples of GPS APKs that you can download and install on your Android device. There are many more options available online that you can explore and compare. But how do you download and install a GPS APK on your Android device? Let's find out in the next section.
- How to Download and Install a GPS APK on Your Android Device?
- Downloading and installing a GPS APK on your Android device is not a difficult task if you follow these three simple steps:
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- Step 1: Find a reliable source for downloading a GPS APK
- The first step is to find a trustworthy and reputable source for downloading a GPS APK. You should avoid downloading a GPS APK from unknown or shady websites that might contain malware or viruses that can harm your Android device. You should also check the reviews and ratings of the GPS APK before downloading it to see if other users have had any issues or complaints with it. You should also compare different GPS APKs based on their features and performance to see which one suits you best.
- Some of the reliable sources for downloading a GPS APK are:
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- Step 2: Enable unknown sources on your Android device
- The second step is to enable unknown sources on your Android device
The second step is to enable unknown sources on your Android device. This is a security setting that allows you to install apps from sources other than the Google Play Store. By default, this setting is disabled on most Android devices, so you need to enable it before installing a GPS APK.
- To enable unknown sources on your Android device, you need to access the security settings on your device. Depending on your device model and Android version, the steps might vary slightly, but the general process is as follows:
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- Once you have enabled unknown sources on your Android device, you are ready to download and install the GPS APK on your device. However, you should remember to disable unknown sources after installing the GPS APK to prevent any unwanted or malicious apps from installing on your device without your permission.
- Step 3: Download and install the GPS APK on your Android device
- The third and final step is to download and install the GPS APK on your Android device. This is a simple and straightforward process that involves downloading the GPS APK file from the source and installing it on your device. Here are the steps to follow:
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- Congratulations! You have successfully downloaded and installed a GPS APK on your Android device. You can now use it as you would use any other app on your device. But how do you use a GPS APK on your Android device? Let's find out in the next section.
- How to Use a GPS APK on Your Android Device?
- Using a GPS APK on your Android device is not much different from using any other app on your device. However, there are some steps that you need to follow to make sure that you get the best experience and results from using a GPS APK. Here are three steps that you need to follow:
- Step 1: Launch the GPS APK on your Android device
- The first step is to launch the GPS APK on your Android device. You can do this by tapping on its icon in the app drawer or home screen of your device. You might also see a shortcut or widget of the GPS APK on your home screen that you can tap on.
- When you launch the GPS APK for the first time, you might need to grant some permissions and access to it so that it can function properly. For example, you might need to allow it to access your location, storage, camera, microphone, contacts, etc. You can do this by tapping on Allow or Accept or something similar when prompted.
- You might also need to customize some settings and preferences of the GPS APK according to your liking and needs. For example, you might need to choose your language, units, voice, theme, etc. You can do this by tapping on Settings or Menu or something similar in the GPS APK and making the changes as desired.
- Step 2: Explore the features and functions of the GPS APK on your Android device
- The second step is to explore the features and functions of the GPS APK
Here are some of the frequently asked questions (FAQs) about GPS APKs:
-
-An APK file is a file format that contains the code, resources, and metadata of an Android application. An app is a software program that runs on your Android device. An APK file is the source of an app that you can install on your device. An app is the result of installing an APK file on your device.
-It depends on the source and the GPS APK that you are downloading and installing. Some sources might be trustworthy and reputable, while others might be unknown or shady. Some GPS APKs might be safe and secure, while others might contain malware or viruses. You should always do your research and check the reviews and ratings of the source and the GPS APK before downloading and installing it. You should also scan the GPS APK file with an antivirus or security app before installing it. You should also enable unknown sources only when installing a GPS APK and disable it afterward.
-There is no definitive answer to this question, as different GPS APKs might suit different users and needs. However, some of the popular and widely used GPS APKs for Android devices are Google Maps, Waze, HERE WeGo, Sygic, MapFactor, OsmAnd, etc. You can compare these and other GPS APKs based on their features and performance to see which one works best for you.
-It depends on the GPS APK that you are using and the features that you are using. Some GPS APKs might require an internet connection to function properly, while others might work offline or partially offline. Some features, such as real-time traffic updates, transit information, satellite imagery, etc., might require an internet connection, while others, such as offline maps, navigation, speed limits, etc., might work without an internet connection. You should check the requirements and specifications of the GPS APK that you are using to see if it needs an internet connection or not.
-Yes, you can use more than one GPS APK on your Android device if you have enough storage space and memory on your device. You can download and install different GPS APKs from different sources and use them for different purposes or preferences. However, you should not run more than one GPS APK at the same time on your device, as this might cause conflicts or errors or drain your battery faster.
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- I hope this article has helped you understand what a GPS APK is and how to download and install it on your Android device. You can also use this article as a guide to use a GPS APK on your Android device and enjoy its benefits. If you have any questions or feedback about this article or GPS APKs in general, please feel free to leave a comment below or contact me directly. Thank you for reading and happy navigating!
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-Ghosty APK 2023: A Fun and Spooky Game for Android
-Do you like games that are fun, spooky, and addictive? If yes, then you should try Ghosty APK, a game where you play as a ghost and scare people in different locations. Ghosty APK is a game that will keep you entertained for hours with its various modes, levels, and characters. In this article, we will tell you everything you need to know about Ghosty APK, including what it is, why you should download it, how to download it, and some tips and tricks for playing it.
- What is Ghosty APK?
-Ghosty APK is a game that was developed by different independent developers and released in 2023. It is a game that is available for Android devices and can be downloaded from APKCombo, a website that offers free APK files for various games and apps.
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- A game where you play as a ghost and scare people
-The main objective of Ghosty APK is to play as a ghost and scare people in different locations, such as a house, a school, a hospital, or a museum. You can choose from different types of ghosts, such as a classic white sheet ghost, a skeleton ghost, or a pumpkin ghost. You can also customize your ghost's appearance with different accessories, such as hats, glasses, or masks.
- A game with different modes, levels, and characters
-Ghosty APK has different modes that you can play, such as story mode, arcade mode, or multiplayer mode. In story mode, you have to complete different missions and objectives in each level. In arcade mode, you have to scare as many people as possible in a limited time. In multiplayer mode, you can play with or against other players online.
-Ghosty APK also has different levels that vary in difficulty and complexity. Each level has different scenarios, obstacles, and challenges that you have to overcome. For example, some levels have light sources that can reveal your presence or guards that can catch you. You have to use your skills and strategy to avoid them and complete your goals.
-Ghosty APK also has different characters that you can interact with in each level. Some characters are friendly and will help you, while others are hostile and will hinder you. For example, some characters will give you coins or power-ups if you scare them, while others will run away or call for help if they see you. You have to be careful and smart when dealing with them.
- A game with simple controls and graphics
-Ghosty APK has simple controls that are easy to use. You can use the joystick on the left side of the screen to move around and the buttons on the right side of the screen to interact with objects or people. You can also tap on the screen to zoom in or out or change the camera angle.
-Ghosty APK also has simple graphics that are colorful and cartoonish. The game has a pixelated style that gives it a retro vibe. The game also has sound effects and music that match the mood of each level.
- Why should you download Ghosty APK?
-There are many reasons why you should download Ghosty APK , and here are some of them:
- It is free and easy to install
-One of the best things about Ghosty APK is that it is free and easy to install. You don't have to pay anything to download and play the game. You also don't need to register or sign up for anything. All you need to do is go to APKCombo, choose one of the three versions of the game, and follow the instructions to install it on your device. It only takes a few minutes and you are ready to play.
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- It is entertaining and challenging
-Another reason why you should download Ghosty APK is that it is entertaining and challenging. The game has a lot of content and variety that will keep you hooked for hours. You can play different modes, levels, and characters that have different objectives and difficulties. You can also compete with other players online or challenge yourself to beat your own high scores. The game will test your skills, strategy, and creativity as you play as a ghost and scare people.
- It is suitable for all ages and preferences
-A third reason why you should download Ghosty APK is that it is suitable for all ages and preferences. The game has a fun and spooky theme that appeals to both kids and adults. The game also has a simple and cartoonish style that makes it easy to play and enjoy. The game also has different options and settings that allow you to customize your experience. You can adjust the sound, the language, the difficulty, and the graphics according to your liking.
- How to download and play Ghosty APK?
-If you are interested in downloading and playing Ghosty APK, here are the steps that you need to follow:
- Choose one of the three versions available on APKCombo
-The first step is to choose one of the three versions of Ghosty APK that are available on APKCombo, a website that offers free APK files for various games and apps. The three versions are:
-
-Version Size Description Ghosty 1.0.0 16 MB The original version of the game that was released in 2023. Ghosty 1.0.1 17 MB An updated version of the game that fixed some bugs and improved some features. Ghosty 1.0.2 18 MB The latest version of the game that added some new content and enhanced some graphics.
-You can choose any version that you want, but we recommend choosing the latest one for the best experience.
- Follow the instructions to install the game on your device
-The second step is to follow the instructions to install the game on your device. To do this, you need to:
-
-
- Tap on the game icon and start playing
-The third step is to tap on the game icon and start playing. You will see the game icon on your home screen or app drawer after installing it. Once you tap on it, you will see the main menu of the game where you can choose your mode, level, character, and settings. You can also access other features such as achievements, leaderboards, shop, and help. After choosing your options, you can start playing and have fun.
- Tips and tricks for playing Ghosty APK
-To help you play Ghosty APK better, here are some tips and tricks that you can use:
- Use the joystick to move around and the buttons to interact
-The most basic tip for playing Ghosty APK is to use the joystick to move around and the buttons to interact. The joystick is located on the left side of the screen and allows you to control your ghost's movement. The buttons are located on the right side of the screen and allow you to interact with objects or people. For example, you can use the scare button to scare people, the hide button to hide behind objects, or the power-up button to use your special abilities.
- Avoid the light sources and the guards
-Another tip for playing Ghosty APK is to avoid the light sources and the guards. The light sources are objects that emit light, such as lamps, candles, or windows. The guards are people that patrol the locations, such as policemen, security guards, or teachers. Both of them can expose your presence and make you lose the game. You have to stay away from them or hide behind objects when they are nearby.
- Collect coins and power-ups to unlock new features
-A third tip for playing Ghosty APK is to collect coins and power-ups to unlock new features. Coins are items that you can find or earn by scaring people. Power-ups are items that give you special abilities, such as invisibility, speed, or magnetism. You can use coins and power-ups to buy or upgrade new ghosts, accessories, or levels. You can also use them to revive yourself if you get caught or fail a mission.
- Conclusion
-Ghosty APK is a fun and spooky game for Android that you should try if you like games that are entertaining and challenging. You can play as a ghost and scare people in different locations with different modes, levels, and characters. You can also customize your ghost's appearance and abilities with coins and power-ups. You can download Ghosty APK from APKCombo for free and easy installation. You can also use the tips and tricks that we have shared to play the game better. We hope you enjoy playing Ghosty APK and have a great time.
- FAQs
-Here are some frequently asked questions about Ghosty APK:
-
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-Pou Dinero Infinito APK 2022: How to Get Unlimited Money and Coins in Pou
-If you love playing with virtual pets, you probably know about Pou, the cute alien creature that needs your care and attention. Pou is one of the most popular and downloaded games in the Google Play Store, with millions of fans around the world. But what if you want to have more fun and freedom with your pet without spending any real money? That's where Pou Dinero Infinito APK 2022 comes in. In this article, we will tell you everything you need to know about this modified version of the game that gives you unlimited money and coins in Pou. We will also show you how to download and install it on your device, as well as the benefits and risks of using it. Read on to find out more.
- What is Pou and Why is it Popular?
-Pou is a virtual pet game that lets you take care of an alien creature that looks like a brown blob. You can feed it, bathe it, play with it, dress it up, customize its rooms, and watch it grow. You can also play mini-games with your pet, such as soccer, sky jump, connect, food drop, and more. You can earn money and coins by playing these games, which you can use to buy items, food, potions, costumes, wallpapers, and other things for your pet.
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-Pou has millions of downloads and fans around the world because it is a fun, casual, and addictive game that anyone can enjoy. It is also suitable for all ages, as it teaches responsibility, empathy, and creativity. Pou has a simple but charming graphics style that makes it appealing to look at. It also has a variety of sounds and music that make it lively and entertaining.
-Pou offers many features and customization options for your pet that make it unique and personal. You can change its color, shape, eyes, mouth, nose, ears, accessories, and more. You can also decorate its rooms with different themes, such as Halloween, Christmas, Easter, Valentine's Day, etc. You can even visit other Pous online and interact with them.
- What is Pou Dinero Infinito APK 2022 and How Does it Work?
-Pou Dinero Infinito APK 2022 is a modified version of the original game that gives you unlimited money and coins in Pou. This means that you can buy anything you want for your pet without spending any real money. You can also unlock all the items, costumes, wallpapers, and games in the game, which are normally locked or require a certain level or achievement to access. You can have more fun and freedom with your pet, as you can experiment with different combinations and styles.
-Pou Dinero Infinito APK 2022 works by modifying the game data and bypassing the security checks of the original game. It also disables the ads and pop-ups that may interrupt your gameplay. It does not require any root or jailbreak to run, and it is compatible with most Android devices. However, it is not available on the Google Play Store, as it violates the terms of service of the original game. You need to download it from a third-party source and install it manually on your device.
- How to Download and Install Pou Dinero Infinito APK 2022 on Your Device?
-If you want to try Pou Dinero Infinito APK 2022, you need to follow these steps:
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- What are the Benefits and Risks of Using Pou Dinero Infinito APK 2022?
-Using Pou Dinero Infinito APK 2022 has its pros and cons. Here are some of them:
-Benefits
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-Risks
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- Conclusion
-Pou Dinero Infinito APK 2022 is a great way to enjoy Pou without any limitations or costs. It gives you unlimited money and coins in Pou, as well as unlocks all the items, costumes, wallpapers, and games in the game. It is easy to download and install on your device, but you should be careful of the potential dangers that come with it. Pou Dinero Infinito APK 2022 is not endorsed by the developers of Pou and may violate their terms of service. You should use it at your own risk and discretion.
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- FAQs
-Here are some frequently asked questions about Pou Dinero Infinito APK 2022:
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-Pou Dinero Infinito APK 2022 is not guaranteed to be safe to use, as it is a modified version of the original game that may contain malware, viruses, or spyware. You should download it from a trusted source and scan it with an antivirus app before installing it. You should also backup your device and data before using it.
-There is a possibility that you may get banned from the official game or lose your progress if the developers detect that you are using a modified version of the game. You should use Pou Dinero Infinito APK 2022 at your own risk and discretion. You should also not use it to cheat or harass other players online.
-Pou Dinero Infinito APK 2022 may not work with the latest version of the game, as it may have new security features or bug fixes that prevent it from running. You may need to wait for a new version of Pou Dinero Infinito APK 2022 to be released that is compatible with the latest version of the game. You should also not update the original game if you have Pou Dinero Infinito APK 2022 installed, as it may overwrite or delete the modified data.
-Pou Dinero Infinito APK 2022 can be played offline, as it does not require an internet connection to run. However, you may not be able to access some features or functions that require an online connection, such as visiting other Pous, playing online games, or syncing your data with the cloud.
-Pou Dinero Infinito APK 2022 can be played with your friends, as long as they also have the same version of the game installed on their devices. You can visit their Pous, play games with them, and chat with them. However, you should be careful not to reveal that you are using a modified version of the game, as they may report you or ban you from their Pous.
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-Virtual DJ 2016 APK: A Complete Guide
-If you are a music lover and want to unleash your creativity as a DJ, you might be interested in Virtual DJ 2016 APK. This is a mobile app that lets you mix and edit songs on your Android device. But what is Virtual DJ 2016 APK exactly? How can you download and install it on your device? What are the benefits and drawbacks of using it? And how can you make the most out of it? In this article, we will answer all these questions and more. Read on to find out everything you need to know about Virtual DJ 2016 APK.
- What is Virtual DJ 2016 APK?
-Virtual DJ 2016 APK is a digital audio workstation (DAW) and virtual mixer software for DJs. It can be used to curate and edit songs and playlists for events, and is filled to the brim with different layouts, modes, live streaming features, and personalization options. It is based on the popular desktop software VirtualDJ, which has been downloaded by more than 100 million users worldwide. However, unlike the desktop version, which requires a license fee, the mobile app is free to download and use.
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- Features of Virtual DJ 2016 APK
-Some of the main features of Virtual DJ 2016 APK are:
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- How to download and install Virtual DJ 2016 APK on your Android device
-To download and install Virtual DJ 2016 APK on your Android device, you need to follow these steps:
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