diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Ashampoo [CRACKED] Free Burning Studio.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Ashampoo [CRACKED] Free Burning Studio.md deleted file mode 100644 index d17836a5271bdec1f100f0a336dfd7214488f4c4..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Ashampoo [CRACKED] Free Burning Studio.md +++ /dev/null @@ -1,14 +0,0 @@ - -

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If you are looking for a free and easy-to-use software for burning CDs, DVDs, and Blu-rays, you might want to check out Ashampoo Free Burning Studio. This software allows you to create and copy discs with various features and tools. You can also backup and restore your data, rip audio CDs, create covers and labels, and more.

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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/ARK Survival Evolved Full APK - Capture Train and Ride 80 Dinosaurs on Your Android.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/ARK Survival Evolved Full APK - Capture Train and Ride 80 Dinosaurs on Your Android.md deleted file mode 100644 index 8026f245c2e22b7932169d6b1ed5e3781d5f0fbc..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/ARK Survival Evolved Full APK - Capture Train and Ride 80 Dinosaurs on Your Android.md +++ /dev/null @@ -1,104 +0,0 @@ - -

ARK: Survival Evolved Full APK - How to Download and Play on Android

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If you are a fan of dinosaurs and survival games, you might have heard of ARK: Survival Evolved, a popular game that lets you explore a massive prehistoric world full of dangers and wonders. But did you know that you can also play this game on your Android device? In this article, we will show you how to download and install the full APK version of ARK: Survival Evolved on your Android phone or tablet, and what features and benefits it offers.

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What is ARK: Survival Evolved?

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ARK: Survival Evolved is a game that combines action, adventure, survival and sandbox elements. It was first released for PC and consoles in 2017 by Studio Wildcard, and later ported to mobile platforms in 2018. The game is set on a mysterious island called ARK, where you start out as a naked and unarmed human who has to survive in a harsh environment populated by over 80 different species of dinosaurs and other creatures. You can gather resources, craft tools and weapons, build shelters and bases, tame and ride dinosaurs, form tribes with other players, and fight against enemies and predators.

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What is the difference between the full APK and the Google Play version?

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The Google Play version of ARK: Survival Evolved is the official version that you can download from the Play Store for free. However, this version has some limitations and drawbacks, such as:

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The full APK version of ARK: Survival Evolved is an unofficial version that you can download from third-party websites such as FileHippo. This version has some advantages over the Google Play version, such as:

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How to download and install the full APK version of ARK: Survival Evolved?

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To download and install the full APK version of ARK: Survival Evolved on your Android device, you need to follow these steps:

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  1. Go to [FileHippo](^1^) or any other website that offers the full APK file of ARK: Survival Evolved. Make sure that the file size is around 2 GB and that it matches the latest version of the game (2.0.28 as of June 2023).
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  2. Download the APK file to your device's storage. You might need to enable the option to install apps from unknown sources in your device's settings.
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  3. Once the download is complete, locate the APK file in your file manager and tap on it to install it. You might need to grant some permissions for the installation process.
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  4. Wait for the installation to finish. You might need to restart your device for the changes to take effect.
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  5. Launch the game from your app drawer or home screen. You might need to allow some additional permissions for the game to run properly.
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What are the features and benefits of playing ARK: Survival Evolved on Android?

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Playing ARK: Survival Evolved on Android has many features and benefits that make it a fun and immersive experience. Some of them are:

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What are the drawbacks and risks of playing ARK: Survival Evolved on Android?

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Playing ARK: Survival Evolved on Android also has some drawbacks and risks that you should be aware of before downloading and installing the game. Some of them are:

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Conclusion

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ARK: Survival Evolved is an amazing game that lets you experience a prehistoric world full of dinosaurs and adventure on your Android device. However, if you want to play the full APK version of the game, you need to be careful about where you download it from, how you install it and what risks you might face. We hope this article has helped you understand how to download and play ARK: Survival Evolved full APK on Android, and what features and benefits it offers. If you have any questions or feedback, feel free to leave a comment below.

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FAQs

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Is ARK: Survival Evolved free on Android?

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The official Google Play version of ARK: Survival Evolved is free to download and play, but it has ads and in-app purchases. The full APK version of ARK: Survival Evolved is also free to download and play, but it has no ads or in-app purchases.

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How do I update ARK: Survival Evolved on Android?

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The Google Play version of ARK: Survival Evolved will update automatically when a new version is available. The full APK version of ARK: Survival Evolved will not update automatically, so you will need to download and install the latest APK file manually when a new version is released.

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Can I play ARK: Survival Evolved offline on Android?

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Yes, you can play ARK: Survival Evolved offline on Android by choosing the single-player mode. However, you will not be able to access some features such as online multiplayer, cloud saving or Primal Pass benefits.

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Can I transfer my progress from PC or console to Android?

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No, you cannot transfer your progress from PC or console to Android. The PC and console versions of ARK: Survival Evolved are different from the mobile version and use different servers and platforms. You will need to start a new game on Android.

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Can I play ARK: Survival Evolved with a controller on Android?

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Yes, you can play ARK: Survival Evolved with a controller on Android by connecting a compatible Bluetooth controller to your device. You can also customize the controller settings in the game options menu.

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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Download Asphalt 8 APK and Race with Luxury Cars and Motorbikes.md b/spaces/1phancelerku/anime-remove-background/Download Asphalt 8 APK and Race with Luxury Cars and Motorbikes.md deleted file mode 100644 index 5a05dd5f9a1d085eb332203301c8776390a881be..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Download Asphalt 8 APK and Race with Luxury Cars and Motorbikes.md +++ /dev/null @@ -1,74 +0,0 @@ - -

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If you are looking for a thrilling and adrenaline-pumping car racing game for your Android device, you should definitely check out Asphalt 8. This is one of the most popular and acclaimed games from Gameloft, a leading developer of mobile games. Asphalt 8 offers you an amazing racing experience with over 300 licensed cars and motorbikes, action-packed races, stunning graphics, realistic physics, and both online and offline modes. In this article, we will tell you everything you need to know about this game and how to download Asphalt 8 APK for Android.

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Licensed luxury cars and motorcycles

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One of the main attractions of Asphalt 8 is its impressive collection of vehicles that you can drive, drift, and put to the test on the asphalt. You can choose from over 300 high-performance cars and bikes from top licensed manufacturers, such as Lamborghini, Bugatti, Porsche, Ferrari, Ducati, BMW, and many more. You can also customize and design your own race cars and motorcycles with various options for colors, decals, rims, tires, etc. You can collect high-end vehicles, special edition cars, and rare models as you progress in the game. You can also explore different worlds and scenarios, from the Nevada Desert to Tokyo streets, as you race on more than 75 tracks.

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Get Airborne with Asphalt 8

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Asphalt 8 is not just about driving fast on the ground. It is also about taking off into the air and performing spectacular stunts and jumps that defy gravity. You can hit the ramps and launch your car or bike into the sky, performing barrel rolls, wild 360° jumps, flips, twists, and more. You can also maneuver through the air while pulling off stunts to maximize your speed and score. You can customize your controls to suit your preferences, whether you want to use tilt, touch, or tap controls. You can also rearrange your on-screen icons and adjust your sensitivity settings.

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Asphalt 8 offers you a lot of content to keep you entertained and challenged. You can play different seasons, each with its own set of races and difficulties. You can also participate in live events that are updated frequently with new themes and objectives. You can win various rewards, such as credits, tokens, cards, blueprints, etc., by completing these modes. You can also check out other modes for a fresh twist on racing, such as Infected, Gate Drift, Knockdown, Elimination, etc.

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Limited-Time Cups

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Another way to enjoy Asphalt 8 is to join the Limited-Time Cups that are available every day. These are special competitions that offer you exclusive access to some of the latest cars or motorbikes in the game. You can race against other players or against the clock to earn the chance to unlock or upgrade these vehicles. You can also get special rewards, such as fusion coins, boosters, pro kits, etc., by ranking high in these cups. You can check the schedule of the upcoming cups and plan your strategy accordingly.

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Multiplayer and single racing experience

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World Series

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If you want to test your skills against other players from around the world, you can join the World Series mode in Asphalt 8. This is the online multiplayer mode where you can race against up to 7 other players in real-time. You can choose from different leagues and divisions, depending on your rank and rating. You can also chat with other players, join or create clubs, and participate in club races and events. You can earn reputation points and rewards by winning races and completing missions.

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Racing Events

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Another way to enjoy multiplayer racing is to join the Racing Events mode. This is a limited-time mode where you can compete for points and prizes in different categories, such as speed, stunts, style, etc. You can also choose from different themes and rules, such as classic, slipstream, tag racing, etc. You can earn event coins and exchange them for exclusive items in the event shop. You can also check your progress and ranking on the event leaderboard.

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How to download Asphalt 8 APK for Android

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If you are interested in playing Asphalt 8 on your Android device, you have several options to download and install the game. Here are some of the ways you can do it:

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Whichever method you choose, you should make sure that you have enough storage space on your device and a stable internet connection. You should also check the compatibility of your device with the game requirements before downloading it.

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Conclusion

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Asphalt 8 is a fantastic car racing game that will keep you hooked for hours with its stunning graphics, realistic physics, amazing vehicles, exciting modes, and multiplayer features. If you are a fan of racing games, you should not miss this one. You can download Asphalt 8 APK for Android from various sources and enjoy the thrill of racing on your device. So what are you waiting for? Download Asphalt 8 now and get ready to burn some rubber!

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FAQs

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Here are some of the frequently asked questions and their answers about Asphalt 8:

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  1. How do I get more credits and tokens in Asphalt 8?
    -You can get more credits and tokens by winning races, completing missions, participating in events and cups, watching ads, etc. You can also buy them with real money if you want.
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  2. How do I upgrade my cars and bikes in Asphalt 8?
    -You can upgrade your vehicles by using credits, tokens, cards, blueprints, fusion coins, etc., depending on the type of vehicle. You can also use pro kits to improve their performance.
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  3. How do I unlock new cars and bikes in Asphalt 8?
    -You can unlock new vehicles by earning stars, completing seasons, participating in events and cups, collecting blueprints, etc. You can also buy them with credits or tokens if they are available.
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  4. How do I change my control settings in Asphalt 8?
    -You can change your control settings by going to Options > Controls. You can choose from tilt, touch, or tap controls. You can also adjust your sensitivity settings and rearrange your on-screen icons.
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  5. How do I play with my friends in Asphalt 8?br> -You can play with your friends in Asphalt 8 by inviting them to join your club or by creating a private room in the World Series mode. You can also chat with them and send them gifts.
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Download Haunted Dorm MOD APK v1.4.2 for Android and Experience a Thrilling Horror Adventure.md b/spaces/1phancelerku/anime-remove-background/Download Haunted Dorm MOD APK v1.4.2 for Android and Experience a Thrilling Horror Adventure.md deleted file mode 100644 index ed5ae34d86f1dff78fd2e314772f36943b6e8bc0..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Download Haunted Dorm MOD APK v1.4.2 for Android and Experience a Thrilling Horror Adventure.md +++ /dev/null @@ -1,88 +0,0 @@ - -

Haunted Dorm Mod APK v 1.4.2: A Spooky Strategy Game for Android

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Do you love spooky games that challenge your creativity and strategy skills? If so, you might want to check out Haunted Dorm Mod APK v 1.4.2, a fun and addictive game that lets you build and manage your own haunted dormitory.

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In this game, you can create your own unique haunted dorm with different rooms, decorations, and facilities. You can also collect and upgrade various types of ghosts, such as vampires, zombies, mummies, and witches. Each ghost has its own power, skill, and ability that you can use to scare away unwanted guests or raid other players' dorms.

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But be careful! Other players can also attack your dorm and steal your money and gems. You need to defend your dorm with traps, guards, and your own ghosts. You can also join a guild and cooperate with other players to become the most powerful haunted dorm in the world.

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If you are ready to experience this spooky strategy game on your Android device, read on to find out how to download and install Haunted Dorm Mod APK v 1.4.2.

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How to Download and Install Haunted Dorm Mod APK v 1.4.2

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Downloading and installing Haunted Dorm Mod APK v 1.4.2 is very easy and fast. All you need to do is follow these simple steps:

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One of the main features of Haunted Dorm Mod APK v 1.4.2 is that you can build your own haunted dormitory from scratch. You can choose from different types of rooms, such as bedrooms, bathrooms, kitchens, libraries, and more. You can also decorate your rooms with various items, such as furniture, paintings, candles, and skulls.

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But building your haunted dorm is not just for fun. It also has a strategic purpose. The more rooms and decorations you have, the more guests you can attract to your dorm. And the more guests you have, the more money you can earn from scaring them away.

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However, you also need to spend some money to maintain your dorm. You need to pay for electricity, water, and repairs. You also need to upgrade your dorm to unlock new rooms, decorations, and facilities. Upgrading your dorm also increases its value and reputation, which can help you attract more guests and earn more money.

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Collect and Upgrade Ghosts

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Another feature of Haunted Dorm Mod APK v 1.4.2 is that you can collect and upgrade various types of ghosts. There are four categories of ghosts: common, rare, epic, and legendary. Each category has different types of ghosts, such as vampires, zombies, mummies, witches, and more.

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You can collect ghosts by opening chests, completing missions, or buying them with money or gems. You can also upgrade your ghosts by using ghost cards and coins. Upgrading your ghosts increases their level, power, skill, and ability.

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Ghosts are not only your companions in the game. They are also your weapons. You can use your ghosts to scare away unwanted guests from your dorm or raid other players' dorms. Each ghost has its own skill and ability that you can activate during battles. For example, vampires can suck blood from enemies, zombies can infect enemies with a virus, mummies can wrap enemies with bandages, and witches can cast spells on enemies.

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Compete with Other Players

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The last feature of Haunted Dorm Mod APK v 1.4.2 is that you can compete with other players in different modes. There are two main modes: raid and defense.

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In raid mode, you can attack other players' dorms and try to steal their money and gems. You can choose which dorm to raid from a list of random players or search for a specific player by name or ID. You can also see the details of their dorms, such as their value, reputation, and defense level.

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In defense mode, you need to protect your own dorm from other players' attacks. You can set up traps, guards, and your own ghosts to defend your dorm. You can also see the details of the attackers, such as their name, ID, and attack level.

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Besides raid and defense modes, you can also join a guild and cooperate with other players. You can chat with your guild members, share tips and tricks, request or donate ghost cards and coins, and participate in guild wars. Guild wars are special events where you can team up with your guild members and fight against other guilds for rewards and glory.

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Now that you know how to download, install, and play Haunted Dorm Mod APK v 1.4.2, you might be wondering how to master the game and become the best haunted dorm manager in the world. Well, don't worry, because we have some useful tips and tricks for you:

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Conclusion

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Haunted Dorm Mod APK v 1.4.2 is a spooky strategy game for Android that lets you build and manage your own haunted dormitory. You can create your own unique haunted dorm with different rooms, decorations, and facilities. You can also collect and upgrade various types of ghosts, such as vampires, zombies, mummies, and witches. You can also compete with other players in different modes, such as raiding their dorms, defending your own dorm, and joining a guild.

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If you are looking for a fun and addictive game that combines creativity and strategy with a touch of horror, you should definitely try Haunted Dorm Mod APK v 1.4.2. You can download and install it easily and fast from this link. You can also follow our tips and tricks to master the game and become the best haunted dorm manager in the world.

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So what are you waiting for? Download Haunted Dorm Mod APK v 1.4.2 now and enjoy this spooky strategy game on your Android device!

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Here are some frequently asked questions about Haunted Dorm Mod APK v 1.4.2:

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    Haunted Dorm Mod APK v 1.4.2 is a modded version of Haunted Dorm, a spooky strategy game for Android that lets you build and manage your own haunted dormitory.

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    The features of Haunted Dorm Mod APK v 1.4.2 include unlimited money and gems, unlocked rooms and decorations, unlocked ghosts and upgrades, and more.

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    You can play Haunted Dorm Mod APK v 1.4.2 by following these simple steps: build your own haunted dorm with different rooms, decorations, and facilities; collect and upgrade various types of ghosts, such as vampires, zombies, mummies, and witches; compete with other players in different modes, such as raiding their dorms, defending your own dorm, and joining a guild; follow our tips and tricks to master the game and become the best haunted dorm manager in the world.

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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Enjoy the World of 3D Models with Pocket World 3D MOD APK on Android 1.md b/spaces/1phancelerku/anime-remove-background/Enjoy the World of 3D Models with Pocket World 3D MOD APK on Android 1.md deleted file mode 100644 index 67aaf03822e2a02c18acbe09666b21e9b664f755..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Enjoy the World of 3D Models with Pocket World 3D MOD APK on Android 1.md +++ /dev/null @@ -1,93 +0,0 @@ -
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Pocket World 3D is a mobile game developed by Minimonster Game Limited, a company that specializes in casual and puzzle games. Pocket World 3D is a game that combines puzzle-solving, creativity, and relaxation. In this game, you can build your own miniature models by assembling various pieces of materials. You can also collect and unlock different themes and models, such as famous landmarks, cultural icons, natural wonders, and more. You can enjoy the soothing music and sound effects as you create your pocket world in 3D. You can also share your creations with other players and see their works as well.

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Features of Pocket World 3D

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Build your own miniature models

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Pocket World 3D is a game that allows you to unleash your creativity and imagination. You can build your own miniature models by following the instructions or by using your own ideas. You can rotate, zoom, and move the pieces to fit them together. You can also customize the colors, textures, and details of your models. You can create anything from buildings, vehicles, animals, plants, and more. -

Collect and unlock various themes

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Pocket World 3D is a game that lets you explore different themes and cultures from around the world. You can collect and unlock various themes and models, such as famous landmarks, cultural icons, natural wonders, and more. You can travel to different countries and regions, such as China, Japan, Egypt, France, Italy, USA, and more. You can learn about the history, culture, and characteristics of each place as you build your models.

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Enjoy the relaxing music and sound effects

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Pocket World 3D is a game that helps you relax and unwind. You can enjoy the soothing music and sound effects as you create your pocket world in 3D. The music is composed by professional musicians who specialize in ambient and relaxing music. The sound effects are realistic and immersive, such as the sound of water, wind, birds, cars, etc. You can adjust the volume and mute the sound as you wish.

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Share your creations with other players

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Pocket World 3D is a game that allows you to share your creations with other players. You can upload your models to the online gallery and see what other players have made. You can also rate, comment, and like other players' works. You can also download other players' models and edit them as you like. You can also join the community and chat with other players who share your passion for building miniature models.

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What is Pocket World 3D Mod APK Android 1?

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Pocket World

Pocket World 3D Mod APK Android 1 is a modified version of the original game that gives you some extra benefits and features. Pocket World 3D Mod APK Android 1 is not available on the official Google Play Store, but you can download it from a trusted source online. Here are some of the benefits and features of Pocket World 3D Mod APK Android 1:

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Benefits of Pocket World 3D Mod APK Android 1

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Unlimited money and diamonds

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Pocket World 3D Mod APK Android 1 gives you unlimited money and diamonds, which are the main currencies in the game. You can use them to buy and unlock new themes, models, materials, and more. You can also use them to speed up the building process and skip the waiting time. You don't have to worry about running out of money or diamonds, as you can get as much as you want with Pocket World 3D Mod APK Android 1.

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All themes and models unlocked

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Pocket World 3D Mod APK Android 1 gives you access to all the themes and models in the game, without having to complete the levels or pay for them. You can explore and build any theme or model you want, such as famous landmarks, cultural icons, natural wonders, and more. You can also mix and match different themes and models to create your own unique pocket world in 3D.

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No ads and no root required

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Pocket World 3D Mod APK Android 1 removes all the ads from the game, so you can enjoy a smooth and uninterrupted gaming experience. You don't have to watch any annoying or intrusive ads that pop up on your screen or interrupt your gameplay. You also don't need to root your device to install or use Pocket World 3D Mod APK Android 1, as it works on any Android device without any problems.

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How to download and install Pocket World 3D Mod APK Android 1?

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If you want to download and install Pocket World 3D Mod APK Android 1 on your Android device, you need to follow these simple steps:

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Step-by-step guide for downloading and installing Pocket World 3D Mod APK Android 1

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Download the APK file from a trusted source

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The first step is to download the APK file of Pocket World 3D Mod APK Android 1 from a trusted source online. You can search for it on Google or use the link provided below. Make sure you download the latest version of the mod apk file, which is compatible with your device and has all the features and benefits mentioned above.

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Download Pocket World 3D Mod APK Android 1 here

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Enable unknown sources on your device settings

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The second step is to enable unknown sources on your device settings, so you can install apps from sources other than the Google Play Store. To do this, go to your device settings, then security, then unknown sources, and toggle it on. This will allow you to install Pocket World 3D Mod APK Android 1 on your device without any issues.

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Install the APK file and launch the game

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The third and final step is to install the APK file of Pocket World 3D Mod APK Android 1 on your device. To do this, locate the downloaded file on your device storage, tap on it, and follow the instructions on the screen. Once the installation is complete, launch the game and enjoy creating your own pocket world in 3D with unlimited money, diamonds, themes, models, and more.

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Conclusion

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Pocket World 3D is a relaxing and creative game that lets you build your own miniature models by assembling various pieces of materials. You can also collect and unlock different themes and models, such as famous landmarks, cultural icons, natural wonders, and more. You can enjoy the soothing music and sound effects as you create your pocket world in 3D. You can also share your creations with other players and see their works as well.

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Pocket World 3D Mod APK Android 1 is a modified version of the original game that gives you some extra benefits and features, such as unlimited money and diamonds, all themes and models unlocked, no ads and no root required. You can download and install Pocket World 3D Mod APK Android 1 on your Android device by following the simple steps mentioned above.

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If you are looking for a relaxing and creative game that lets you explore different themes and cultures from around the world, then you should try Pocket World 3D Mod APK Android 1 today.

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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Experience the 80s with GTA Vice City - Download for Windows 7 32 Bit.md b/spaces/1phancelerku/anime-remove-background/Experience the 80s with GTA Vice City - Download for Windows 7 32 Bit.md deleted file mode 100644 index 8435ef7045bddddfb372361fb998febd8ce8536d..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Experience the 80s with GTA Vice City - Download for Windows 7 32 Bit.md +++ /dev/null @@ -1,98 +0,0 @@ -
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GTA Vice City Download for Windows 7 32 Bit

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GTA Vice City is one of the most popular and iconic games in the Grand Theft Auto series. It is an action-adventure game that lets you explore the open world of Vice City, a fictional city based on Miami in the 1980s. You can play as Tommy Vercetti, a former mobster who is sent to Vice City by his boss to establish a criminal empire. You can complete various missions, drive different vehicles, use various weapons, interact with other characters, and enjoy the retro soundtrack and atmosphere.

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To avoid these problems, you should only download GTA Vice City from trusted and verified sources that have positive reviews and ratings from other users. One of these sources is Ocean of Games, a website that provides free and secure downloads of various games for PC. Ocean of Games has a direct download link for GTA Vice City that is easy and fast to use. You just need to click on the download button and follow the instructions to save the game setup file on your PC.

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How to Install GTA Vice City on Windows 7 32 Bit

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After downloading GTA Vice City from Ocean of Games, you will need to install it on your Windows 7 32 bit PC. The installation process is simple and straightforward, but you should follow these steps carefully to avoid any errors or issues:

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GTA Vice City is a fun and exciting game that offers a lot of features and options for players. However, it can also be challenging and frustrating at times, especially if you are new to the game or encounter some technical difficulties. Here are some tips and tricks that can help you improve your gaming experience and overcome some common problems:

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\ No newline at end of file diff --git a/spaces/AIConsultant/MusicGen/setup.py b/spaces/AIConsultant/MusicGen/setup.py deleted file mode 100644 index 64e7d6fcb1092748f8151f6d3ed1767d3be1b34b..0000000000000000000000000000000000000000 --- a/spaces/AIConsultant/MusicGen/setup.py +++ /dev/null @@ -1,62 +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 pathlib import Path - -from setuptools import setup, find_packages - - -NAME = 'audiocraft' -DESCRIPTION = 'Audio generation research library for PyTorch' - -URL = 'https://github.com/facebookresearch/audiocraft' -AUTHOR = 'FAIR Speech & Audio' -EMAIL = 'defossez@meta.com, jadecopet@meta.com' -REQUIRES_PYTHON = '>=3.8.0' - -for line in open('audiocraft/__init__.py'): - line = line.strip() - if '__version__' in line: - context = {} - exec(line, context) - VERSION = context['__version__'] - -HERE = Path(__file__).parent - -try: - with open(HERE / "README.md", encoding='utf-8') as f: - long_description = '\n' + f.read() -except FileNotFoundError: - long_description = DESCRIPTION - -REQUIRED = [i.strip() for i in open(HERE / 'requirements.txt') if not i.startswith('#')] - -setup( - name=NAME, - version=VERSION, - description=DESCRIPTION, - author_email=EMAIL, - long_description=long_description, - long_description_content_type='text/markdown', - author=AUTHOR, - url=URL, - python_requires=REQUIRES_PYTHON, - install_requires=REQUIRED, - extras_require={ - 'dev': ['coverage', 'flake8', 'mypy', 'pdoc3', 'pytest'], - }, - packages=find_packages(), - package_data={'audiocraft': ['py.typed']}, - include_package_data=True, - license='MIT License', - classifiers=[ - # Trove classifiers - # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers - 'License :: OSI Approved :: MIT License', - 'Topic :: Multimedia :: Sound/Audio', - 'Topic :: Scientific/Engineering :: Artificial Intelligence', - ], -) diff --git a/spaces/AIFILMS/StyleGANEX/datasets/images_dataset.py b/spaces/AIFILMS/StyleGANEX/datasets/images_dataset.py deleted file mode 100644 index 62bb3e3eb85f3841696bac02fa5fb217488a43cd..0000000000000000000000000000000000000000 --- a/spaces/AIFILMS/StyleGANEX/datasets/images_dataset.py +++ /dev/null @@ -1,33 +0,0 @@ -from torch.utils.data import Dataset -from PIL import Image -from utils import data_utils - - -class ImagesDataset(Dataset): - - def __init__(self, source_root, target_root, opts, target_transform=None, source_transform=None): - self.source_paths = sorted(data_utils.make_dataset(source_root)) - self.target_paths = sorted(data_utils.make_dataset(target_root)) - self.source_transform = source_transform - self.target_transform = target_transform - self.opts = opts - - def __len__(self): - return len(self.source_paths) - - def __getitem__(self, index): - from_path = self.source_paths[index] - from_im = Image.open(from_path) - from_im = from_im.convert('RGB') if self.opts.label_nc == 0 else from_im.convert('L') - - to_path = self.target_paths[index] - to_im = Image.open(to_path).convert('RGB') - if self.target_transform: - to_im = self.target_transform(to_im) - - if self.source_transform: - from_im = self.source_transform(from_im) - else: - from_im = to_im - - return from_im, to_im diff --git a/spaces/AIFILMS/generate_human_motion/VQ-Trans/models/encdec.py b/spaces/AIFILMS/generate_human_motion/VQ-Trans/models/encdec.py deleted file mode 100644 index ae72afaa5aa59ad67cadb38e0d83e420fc6edb09..0000000000000000000000000000000000000000 --- a/spaces/AIFILMS/generate_human_motion/VQ-Trans/models/encdec.py +++ /dev/null @@ -1,67 +0,0 @@ -import torch.nn as nn -from models.resnet import Resnet1D - -class Encoder(nn.Module): - def __init__(self, - input_emb_width = 3, - output_emb_width = 512, - down_t = 3, - stride_t = 2, - width = 512, - depth = 3, - dilation_growth_rate = 3, - activation='relu', - norm=None): - super().__init__() - - blocks = [] - filter_t, pad_t = stride_t * 2, stride_t // 2 - blocks.append(nn.Conv1d(input_emb_width, width, 3, 1, 1)) - blocks.append(nn.ReLU()) - - for i in range(down_t): - input_dim = width - block = nn.Sequential( - nn.Conv1d(input_dim, width, filter_t, stride_t, pad_t), - Resnet1D(width, depth, dilation_growth_rate, activation=activation, norm=norm), - ) - blocks.append(block) - blocks.append(nn.Conv1d(width, output_emb_width, 3, 1, 1)) - self.model = nn.Sequential(*blocks) - - def forward(self, x): - return self.model(x) - -class Decoder(nn.Module): - def __init__(self, - input_emb_width = 3, - output_emb_width = 512, - down_t = 3, - stride_t = 2, - width = 512, - depth = 3, - dilation_growth_rate = 3, - activation='relu', - norm=None): - super().__init__() - blocks = [] - - filter_t, pad_t = stride_t * 2, stride_t // 2 - blocks.append(nn.Conv1d(output_emb_width, width, 3, 1, 1)) - blocks.append(nn.ReLU()) - for i in range(down_t): - out_dim = width - block = nn.Sequential( - Resnet1D(width, depth, dilation_growth_rate, reverse_dilation=True, activation=activation, norm=norm), - nn.Upsample(scale_factor=2, mode='nearest'), - nn.Conv1d(width, out_dim, 3, 1, 1) - ) - blocks.append(block) - blocks.append(nn.Conv1d(width, width, 3, 1, 1)) - blocks.append(nn.ReLU()) - blocks.append(nn.Conv1d(width, input_emb_width, 3, 1, 1)) - self.model = nn.Sequential(*blocks) - - def forward(self, x): - return self.model(x) - diff --git a/spaces/AIGC-Audio/Make_An_Audio/vocoder/bigvgan/alias_free_torch/__init__.py b/spaces/AIGC-Audio/Make_An_Audio/vocoder/bigvgan/alias_free_torch/__init__.py deleted file mode 100644 index a2318b63198250856809c0cb46210a4147b829bc..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/Make_An_Audio/vocoder/bigvgan/alias_free_torch/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 -# LICENSE is in incl_licenses directory. - -from .filter import * -from .resample import * -from .act import * \ No newline at end of file diff --git a/spaces/AILab-CVC/SEED-Bench_Leaderboard/app.py b/spaces/AILab-CVC/SEED-Bench_Leaderboard/app.py deleted file mode 100644 index 156253cd86747674543e57744ab3835ae5cc1951..0000000000000000000000000000000000000000 --- a/spaces/AILab-CVC/SEED-Bench_Leaderboard/app.py +++ /dev/null @@ -1,313 +0,0 @@ - -__all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions'] - -import gradio as gr -import pandas as pd -import json -import pdb -import tempfile - -from constants import * -from src.auto_leaderboard.model_metadata_type import ModelType - -global data_component, filter_component - - -def upload_file(files): - file_paths = [file.name for file in files] - return file_paths - -def prediction_analyse(prediction_content): - predictions = prediction_content.split("\n") - - # 读取 ground_truth JSON 文件 - with open("./file/SEED-Bench.json", "r") as file: - ground_truth_data = json.load(file)["questions"] - - # 将 ground_truth 数据转换为以 question_id 为键的字典 - ground_truth = {item["question_id"]: item for item in ground_truth_data} - - # 初始化结果统计字典 - results = {i: {"correct": 0, "total": 0} for i in range(1, 13)} - - # 遍历 predictions，计算每个 question_type_id 的正确预测数和总预测数 - for prediction in predictions: - # pdb.set_trace() - prediction = prediction.strip() - if not prediction: - continue - try: - prediction = json.loads(prediction) - except json.JSONDecodeError: - print(f"Warning: Skipping invalid JSON data in line: {prediction}") - continue - question_id = prediction["question_id"] - gt_item = ground_truth[question_id] - question_type_id = gt_item["question_type_id"] - - if prediction["prediction"] == gt_item["answer"]: - results[question_type_id]["correct"] += 1 - - results[question_type_id]["total"] += 1 - - return results - -def add_new_eval( - input_file, - model_name_textbox: str, - revision_name_textbox: str, - model_type: str, - model_link: str, - LLM_type: str, - LLM_name_textbox: str, - Evaluation_dimension: str, -): - if input_file is None: - return "Error! Empty file!" - else: - content = input_file.decode("utf-8") - prediction = prediction_analyse(content) - csv_data = pd.read_csv(CSV_DIR) - - Start_dimension, End_dimension = 1, 13 - if Evaluation_dimension == 'Image': - End_dimension = 10 - elif Evaluation_dimension == 'Video': - Start_dimension = 10 - each_task_accuracy = {i: round(prediction[i]["correct"] / prediction[i]["total"] * 100, 1) if i >= Start_dimension and i < End_dimension else 0 for i in range(1, 13)} - - # count for average image\video\all - total_correct_image = sum(prediction[i]["correct"] for i in range(1, 10)) - total_correct_video = sum(prediction[i]["correct"] for i in range(10, 13)) - - total_image = sum(prediction[i]["total"] for i in range(1, 10)) - total_video = sum(prediction[i]["total"] for i in range(10, 13)) - - if Evaluation_dimension != 'Video': - average_accuracy_image = round(total_correct_image / total_image * 100, 1) - else: - average_accuracy_image = 0 - - if Evaluation_dimension != 'Image': - average_accuracy_video = round(total_correct_video / total_video * 100, 1) - else: - average_accuracy_video = 0 - - if Evaluation_dimension == 'All': - overall_accuracy = round((total_correct_image + total_correct_video) / (total_image + total_video) * 100, 1) - else: - overall_accuracy = 0 - - if LLM_type == 'Other': - LLM_name = LLM_name_textbox - else: - LLM_name = LLM_type - - if revision_name_textbox == '': - col = csv_data.shape[0] - model_name = model_name_textbox - else: - model_name = revision_name_textbox - model_name_list = csv_data['Model'] - name_list = [name.split(']')[0][1:] for name in model_name_list] - if revision_name_textbox not in name_list: - col = csv_data.shape[0] - else: - col = name_list.index(revision_name_textbox) - - if model_link == '': - model_name = model_name # no url - else: - model_name = '[' + model_name + '](' + model_link + ')' - - # add new data - new_data = [ - model_type, - model_name, - LLM_name, - overall_accuracy, - average_accuracy_image, - average_accuracy_video, - each_task_accuracy[1], - each_task_accuracy[2], - each_task_accuracy[3], - each_task_accuracy[4], - each_task_accuracy[5], - each_task_accuracy[6], - each_task_accuracy[7], - each_task_accuracy[8], - each_task_accuracy[9], - each_task_accuracy[10], - each_task_accuracy[11], - each_task_accuracy[12], - ] - csv_data.loc[col] = new_data - csv_data = csv_data.to_csv(CSV_DIR, index=False) - return 0 - -def get_baseline_df(): - # pdb.set_trace() - df = pd.read_csv(CSV_DIR) - df = df.sort_values(by="Avg. All", ascending=False) - present_columns = MODEL_INFO + checkbox_group.value - df = df[present_columns] - return df - -def get_all_df(): - df = pd.read_csv(CSV_DIR) - df = df.sort_values(by="Avg. All", ascending=False) - return df - -block = gr.Blocks() - - -with block: - gr.Markdown( - LEADERBORAD_INTRODUCTION - ) - with gr.Tabs(elem_classes="tab-buttons") as tabs: - with gr.TabItem("🏅 SEED Benchmark", elem_id="seed-benchmark-tab-table", id=0): - with gr.Row(): - with gr.Accordion("Citation", open=False): - citation_button = gr.Textbox( - value=CITATION_BUTTON_TEXT, - label=CITATION_BUTTON_LABEL, - elem_id="citation-button", - ).style(show_copy_button=True) - - gr.Markdown( - TABLE_INTRODUCTION - ) - - # selection for column part: - checkbox_group = gr.CheckboxGroup( - choices=TASK_INFO_v2, - value=AVG_INFO, - label="Select options", - interactive=True, - ) - - # 创建数据帧组件 - data_component = gr.components.Dataframe( - value=get_baseline_df, - headers=COLUMN_NAMES, - type="pandas", - datatype=DATA_TITILE_TYPE, - interactive=False, - visible=True, - ) - - def on_checkbox_group_change(selected_columns): - # pdb.set_trace() - selected_columns = [item for item in TASK_INFO_v2 if item in selected_columns] - present_columns = MODEL_INFO + selected_columns - updated_data = get_all_df()[present_columns] - updated_data = updated_data.sort_values(by=present_columns[3], ascending=False) - updated_headers = present_columns - update_datatype = [DATA_TITILE_TYPE[COLUMN_NAMES.index(x)] for x in updated_headers] - - filter_component = gr.components.Dataframe( - value=updated_data, - headers=updated_headers, - type="pandas", - datatype=update_datatype, - interactive=False, - visible=True, - ) - # pdb.set_trace() - - return filter_component.value - - # 将复选框组关联到处理函数 - checkbox_group.change(fn=on_checkbox_group_change, inputs=checkbox_group, outputs=data_component) - - # table 2 - with gr.TabItem("📝 About", elem_id="seed-benchmark-tab-table", id=2): - gr.Markdown(LEADERBORAD_INFO, elem_classes="markdown-text") - - # table 3 - with gr.TabItem("🚀 Submit here! ", elem_id="seed-benchmark-tab-table", id=3): - gr.Markdown(LEADERBORAD_INTRODUCTION, elem_classes="markdown-text") - - with gr.Row(): - gr.Markdown(SUBMIT_INTRODUCTION, elem_classes="markdown-text") - - with gr.Row(): - gr.Markdown("# ✉️✨ Submit your model evaluation json file here!", elem_classes="markdown-text") - - with gr.Row(): - with gr.Column(): - model_name_textbox = gr.Textbox( - label="Model name", placeholder="LLaMA-7B" - ) - revision_name_textbox = gr.Textbox( - label="Revision Model Name", placeholder="LLaMA-7B" - ) - model_type = gr.Dropdown( - choices=[ - "LLM", - "ImageLLM", - "VideoLLM", - "Other", - ], - label="Model type", - multiselect=False, - value="ImageLLM", - interactive=True, - ) - model_link = gr.Textbox( - label="Model Link", placeholder="https://huggingface.co/decapoda-research/llama-7b-hf" - ) - - with gr.Column(): - - LLM_type = gr.Dropdown( - choices=["Vicuna-7B", "Flan-T5-XL", "LLaMA-7B", "Other"], - label="LLM type", - multiselect=False, - value="LLaMA-7B", - interactive=True, - ) - LLM_name_textbox = gr.Textbox( - label="LLM model (for Other)", - placeholder="LLaMA-13B" - ) - Evaluation_dimension = gr.Dropdown( - choices=["All", "Image", "Video"], - label="Evaluation dimension", - multiselect=False, - value="All", - interactive=True, - ) - - with gr.Column(): - - input_file = gr.inputs.File(label = "Click to Upload a json File", file_count="single", type='binary') - submit_button = gr.Button("Submit Eval") - - submission_result = gr.Markdown() - submit_button.click( - add_new_eval, - inputs = [ - input_file, - model_name_textbox, - revision_name_textbox, - model_type, - model_link, - LLM_type, - LLM_name_textbox, - Evaluation_dimension, - ], - # outputs = submission_result, - ) - - - with gr.Row(): - data_run = gr.Button("Refresh") - data_run.click( - get_baseline_df, outputs=data_component - ) - - # block.load(get_baseline_df, outputs=data_title) - -block.launch() \ No newline at end of file diff --git a/spaces/AILab-CVC/SEED-LLaMA/models/seed_qformer/blip2.py b/spaces/AILab-CVC/SEED-LLaMA/models/seed_qformer/blip2.py deleted file mode 100644 index d9780f5d3135f6f7790bf7fdeedabc25503567da..0000000000000000000000000000000000000000 --- a/spaces/AILab-CVC/SEED-LLaMA/models/seed_qformer/blip2.py +++ /dev/null @@ -1,186 +0,0 @@ -""" - Copyright (c) 2023, salesforce.com, inc. - All rights reserved. - SPDX-License-Identifier: BSD-3-Clause - For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause -""" -import contextlib -import logging -import os -import time -import datetime - -import torch -import torch.nn as nn -import torch.distributed as dist -import torch.nn.functional as F - - -from .qformer_causual import BertConfig, BertLMHeadModel - -from .utils import download_cached_file, get_rank, get_dist_info, get_world_size, main_process, is_dist_avail_and_initialized, is_url -from .eva_vit import create_eva_vit_g -from .clip_vit import create_clip_vit_L -from transformers import BertTokenizer - - -# class Blip2Base(BaseModel): -class Blip2Base(nn.Module): - def __init__(self): - super().__init__() - - @property - def device(self): - return list(self.parameters())[0].device - - @classmethod - def init_tokenizer(cls, truncation_side="right"): - tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", truncation_side=truncation_side) - tokenizer.add_special_tokens({"bos_token": "[DEC]"}) - return tokenizer - - def maybe_autocast(self, dtype=torch.float16): - # if on cpu, don't use autocast - # if on gpu, use autocast with dtype if provided, otherwise use torch.float16 - enable_autocast = self.device != torch.device("cpu") - - if enable_autocast: - return torch.cuda.amp.autocast(dtype=dtype) - else: - return contextlib.nullcontext() - - @classmethod - def init_Qformer(cls, num_query_token, vision_width, cross_attention_freq=2): - encoder_config = BertConfig.from_pretrained("bert-base-uncased") - encoder_config.encoder_width = vision_width - # insert cross-attention layer every other block - encoder_config.add_cross_attention = True - encoder_config.cross_attention_freq = cross_attention_freq - encoder_config.query_length = num_query_token - Qformer = BertLMHeadModel.from_pretrained("bert-base-uncased", config=encoder_config) - query_tokens = nn.Parameter(torch.zeros(1, num_query_token, encoder_config.hidden_size)) - query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range) - return Qformer, query_tokens - - def init_vision_encoder(self, model_name, img_size, drop_path_rate, use_grad_checkpoint, precision): - assert model_name in [ - "eva_clip_g", - "eva2_clip_L", - "clip_L", - ], "vit model must be eva_clip_g, eva2_clip_L or clip_L" - if model_name == "eva_clip_g": - visual_encoder = create_eva_vit_g(img_size, drop_path_rate, use_grad_checkpoint, precision) - - elif model_name == "clip_L": - visual_encoder = create_clip_vit_L(img_size, use_grad_checkpoint, precision) - ln_vision = LayerNorm(visual_encoder.num_features) - self.vit_name = model_name - return visual_encoder, ln_vision - - def load_from_pretrained(self, url_or_filename): - if is_url(url_or_filename): - cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) - checkpoint = torch.load(cached_file, map_location="cpu") - elif os.path.isfile(url_or_filename): - checkpoint = torch.load(url_or_filename, map_location="cpu") - else: - raise RuntimeError("checkpoint url or path is invalid") - - state_dict = checkpoint["model"] - - msg = self.load_state_dict(state_dict, strict=False) - - # logging.info("Missing keys {}".format(msg.missing_keys)) - logging.info("load checkpoint from %s" % url_or_filename) - - return msg - - def get_optimizer_params(self, weight_decay, lr_scale=1): - if self.vit_name == "eva_clip_g": - vit_num_layers = self.visual_encoder.get_num_layer() - lr_scales = list(lr_scale**(vit_num_layers + 1 - i) for i in range(vit_num_layers + 2)) - - parameter_group_names = {} - parameter_group_vars = {} - - for name, param in self.named_parameters(): - if not param.requires_grad: - continue # frozen weights - if len(param.shape) == 1 or name.endswith(".bias"): - group_name = "no_decay" - this_weight_decay = 0. - else: - group_name = "decay" - this_weight_decay = weight_decay - if 'visual_encoder' in name: - layer_id = self.visual_encoder.get_num_layer(name.replace('visual_encoder.', '')) - group_name = "vit_layer_%d_%s" % (layer_id, group_name) - else: - layer_id = None - - if group_name not in parameter_group_names: - if layer_id is not None: - scale = lr_scales[layer_id] - else: - scale = 1 - parameter_group_names[group_name] = {"weight_decay": this_weight_decay, "params": [], "lr_scale": scale} - parameter_group_vars[group_name] = {"weight_decay": this_weight_decay, "params": [], "lr_scale": scale} - parameter_group_vars[group_name]["params"].append(param) - parameter_group_names[group_name]["params"].append(name) - # import json - # print("Param groups = %s" % json.dumps(parameter_group_names, indent=2)) - optim_params = list(parameter_group_vars.values()) - return optim_params - else: - return super().get_optimizer_params(weight_decay, lr_scale) - - def _lemmatize(self, answers): - def apply(answer): - doc = self.lemmatizer(answer) - - words = [] - for token in doc: - if token.pos_ in ["NOUN", "VERB"]: - words.append(token.lemma_) - else: - words.append(token.text) - answer = " ".join(words) - - return answer - - return [apply(answer) for answer in answers] - - @property - def lemmatizer(self): - if self._lemmatizer is None: - try: - import spacy - - self._lemmatizer = spacy.load("en_core_web_sm") - except ImportError: - logging.error(""" - Please install spacy and en_core_web_sm model to apply lemmatization. - python -m spacy download en_core_web_sm - OR - import spacy.cli - spacy.cli.download("en_core_web_sm") - """) - exit(1) - - return self._lemmatizer - - -def disabled_train(self, mode=True): - """Overwrite model.train with this function to make sure train/eval mode - does not change anymore.""" - return self - - -class LayerNorm(nn.LayerNorm): - """Subclass torch's LayerNorm to handle fp16.""" - def forward(self, x: torch.Tensor): - orig_type = x.dtype - ret = super().forward(x.type(torch.float32)) - return ret.type(orig_type) - - diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/work_dirs_1-x/td_hm_res50_4xb16-150e_deepfashion2_long_sleeved_dress_256x192/td_hm_res50_4xb16-150e_deepfashion2_long_sleeved_dress_256x192.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/work_dirs_1-x/td_hm_res50_4xb16-150e_deepfashion2_long_sleeved_dress_256x192/td_hm_res50_4xb16-150e_deepfashion2_long_sleeved_dress_256x192.py deleted file mode 100644 index c359fd78ad9873e36b67b1ae2b73a2c27104c195..0000000000000000000000000000000000000000 --- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/work_dirs_1-x/td_hm_res50_4xb16-150e_deepfashion2_long_sleeved_dress_256x192/td_hm_res50_4xb16-150e_deepfashion2_long_sleeved_dress_256x192.py +++ /dev/null @@ -1,2861 +0,0 @@ -default_scope = 'mmpose' -default_hooks = dict( - timer=dict(type='IterTimerHook'), - logger=dict(type='LoggerHook', interval=50), - param_scheduler=dict(type='ParamSchedulerHook'), - checkpoint=dict( - type='CheckpointHook', interval=10, save_best='PCK', rule='greater'), - sampler_seed=dict(type='DistSamplerSeedHook'), - visualization=dict(type='PoseVisualizationHook', enable=False)) -custom_hooks = [dict(type='SyncBuffersHook')] -env_cfg = dict( - cudnn_benchmark=False, - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), - dist_cfg=dict(backend='nccl')) -vis_backends = [dict(type='LocalVisBackend')] -visualizer = dict( - type='PoseLocalVisualizer', - vis_backends=[dict(type='LocalVisBackend'), - dict(type='WandbVisBackend')], - name='visualizer') -log_processor = dict( - type='LogProcessor', window_size=50, by_epoch=True, num_digits=6) -log_level = 'INFO' -load_from = None -resume = False -backend_args = dict(backend='local') -train_cfg = dict(by_epoch=True, max_epochs=150, val_interval=10) -val_cfg = dict() -test_cfg = dict() -colors = dict( - sss=[255, 128, 0], - lss=[255, 0, 128], - sso=[128, 0, 255], - lso=[0, 128, 255], - vest=[0, 128, 128], - sling=[0, 0, 128], - shorts=[128, 128, 128], - trousers=[128, 0, 128], - skirt=[64, 128, 128], - ssd=[64, 64, 128], - lsd=[128, 64, 0], - vd=[128, 64, 255], - sd=[128, 64, 0]) -dataset_info = dict( - dataset_name='deepfashion2', - paper_info=dict( - author= - 'Yuying Ge and Ruimao Zhang and Lingyun Wu and Xiaogang Wang and Xiaoou Tang and Ping Luo', - title= - 'DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images', - container= - 'Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)', - year='2019', - homepage='https://github.com/switchablenorms/DeepFashion2'), - keypoint_info=dict({ - 0: - dict(name='sss_kpt1', id=0, color=[255, 128, 0], type='', swap=''), - 1: - dict( - name='sss_kpt2', - id=1, - color=[255, 128, 0], - type='', - swap='sss_kpt6'), - 2: - dict( - name='sss_kpt3', - id=2, - color=[255, 128, 0], - type='', - swap='sss_kpt5'), - 3: - dict(name='sss_kpt4', id=3, color=[255, 128, 0], type='', swap=''), - 4: - dict( - name='sss_kpt5', - id=4, - color=[255, 128, 0], - type='', - swap='sss_kpt3'), - 5: - dict( - name='sss_kpt6', - id=5, - color=[255, 128, 0], - type='', - swap='sss_kpt2'), - 6: - dict( - name='sss_kpt7', - id=6, - color=[255, 128, 0], - type='', - swap='sss_kpt25'), - 7: - dict( - name='sss_kpt8', - id=7, - color=[255, 128, 0], - type='', - swap='sss_kpt24'), - 8: - dict( - name='sss_kpt9', - id=8, - color=[255, 128, 0], - type='', - swap='sss_kpt23'), - 9: - dict( - name='sss_kpt10', - id=9, - color=[255, 128, 0], - type='', - swap='sss_kpt22'), - 10: - dict( - name='sss_kpt11', - id=10, - color=[255, 128, 0], - type='', - swap='sss_kpt21'), - 11: - dict( - name='sss_kpt12', - id=11, - color=[255, 128, 0], - type='', - swap='sss_kpt20'), - 12: - dict( - name='sss_kpt13', - id=12, - color=[255, 128, 0], - type='', - swap='sss_kpt19'), - 13: - dict( - name='sss_kpt14', - id=13, - color=[255, 128, 0], - type='', - swap='sss_kpt18'), - 14: - dict( - name='sss_kpt15', - id=14, - color=[255, 128, 0], - type='', - swap='sss_kpt17'), - 15: - dict(name='sss_kpt16', id=15, color=[255, 128, 0], type='', swap=''), - 16: - dict( - name='sss_kpt17', - id=16, - color=[255, 128, 0], - type='', - swap='sss_kpt15'), - 17: - dict( - name='sss_kpt18', - id=17, - color=[255, 128, 0], - type='', - swap='sss_kpt14'), - 18: - dict( - name='sss_kpt19', - id=18, - color=[255, 128, 0], - type='', - swap='sss_kpt13'), - 19: - dict( - name='sss_kpt20', - id=19, - color=[255, 128, 0], - type='', - swap='sss_kpt12'), - 20: - dict( - name='sss_kpt21', - id=20, - color=[255, 128, 0], - type='', - swap='sss_kpt11'), - 21: - dict( - name='sss_kpt22', - id=21, - color=[255, 128, 0], - type='', - swap='sss_kpt10'), - 22: - dict( - name='sss_kpt23', - id=22, - color=[255, 128, 0], - type='', - swap='sss_kpt9'), - 23: - dict( - name='sss_kpt24', - id=23, - color=[255, 128, 0], - type='', - swap='sss_kpt8'), - 24: - dict( - name='sss_kpt25', - id=24, - color=[255, 128, 0], - type='', - swap='sss_kpt7'), - 25: - dict(name='lss_kpt1', id=25, color=[255, 0, 128], type='', swap=''), - 26: - dict( - name='lss_kpt2', - id=26, - color=[255, 0, 128], - type='', - swap='lss_kpt6'), - 27: - dict( - name='lss_kpt3', - id=27, - color=[255, 0, 128], - type='', - swap='lss_kpt5'), - 28: - dict(name='lss_kpt4', id=28, color=[255, 0, 128], type='', swap=''), - 29: - dict( - name='lss_kpt5', - id=29, - color=[255, 0, 128], - type='', - swap='lss_kpt3'), - 30: - dict( - name='lss_kpt6', - id=30, - color=[255, 0, 128], - type='', - swap='lss_kpt2'), - 31: - dict( - name='lss_kpt7', - id=31, - color=[255, 0, 128], - type='', - swap='lss_kpt33'), - 32: - dict( - name='lss_kpt8', - id=32, - color=[255, 0, 128], - type='', - swap='lss_kpt32'), - 33: - dict( - name='lss_kpt9', - id=33, - color=[255, 0, 128], - type='', - swap='lss_kpt31'), - 34: - dict( - name='lss_kpt10', - id=34, - color=[255, 0, 128], - type='', - swap='lss_kpt30'), - 35: - dict( - name='lss_kpt11', - id=35, - color=[255, 0, 128], - type='', - swap='lss_kpt29'), - 36: - dict( - name='lss_kpt12', - id=36, - color=[255, 0, 128], - type='', - swap='lss_kpt28'), - 37: - dict( - name='lss_kpt13', - id=37, - color=[255, 0, 128], - type='', - swap='lss_kpt27'), - 38: - dict( - name='lss_kpt14', - id=38, - color=[255, 0, 128], - type='', - swap='lss_kpt26'), - 39: - dict( - name='lss_kpt15', - id=39, - color=[255, 0, 128], - type='', - swap='lss_kpt25'), - 40: - dict( - name='lss_kpt16', - id=40, - color=[255, 0, 128], - type='', - swap='lss_kpt24'), - 41: - dict( - name='lss_kpt17', - id=41, - color=[255, 0, 128], - type='', - swap='lss_kpt23'), - 42: - dict( - name='lss_kpt18', - id=42, - color=[255, 0, 128], - type='', - swap='lss_kpt22'), - 43: - dict( - name='lss_kpt19', - id=43, - color=[255, 0, 128], - type='', - swap='lss_kpt21'), - 44: - dict(name='lss_kpt20', id=44, color=[255, 0, 128], type='', swap=''), - 45: - dict( - name='lss_kpt21', - id=45, - color=[255, 0, 128], - type='', - swap='lss_kpt19'), - 46: - dict( - name='lss_kpt22', - id=46, - color=[255, 0, 128], - type='', - swap='lss_kpt18'), - 47: - dict( - name='lss_kpt23', - id=47, - color=[255, 0, 128], - type='', - swap='lss_kpt17'), - 48: - dict( - name='lss_kpt24', - id=48, - color=[255, 0, 128], - type='', - swap='lss_kpt16'), - 49: - dict( - name='lss_kpt25', - id=49, - color=[255, 0, 128], - type='', - swap='lss_kpt15'), - 50: - dict( - name='lss_kpt26', - id=50, - color=[255, 0, 128], - type='', - swap='lss_kpt14'), - 51: - dict( - name='lss_kpt27', - id=51, - color=[255, 0, 128], - type='', - swap='lss_kpt13'), - 52: - dict( - name='lss_kpt28', - id=52, - color=[255, 0, 128], - type='', - swap='lss_kpt12'), - 53: - dict( - name='lss_kpt29', - id=53, - color=[255, 0, 128], - type='', - swap='lss_kpt11'), - 54: - dict( - name='lss_kpt30', - id=54, - color=[255, 0, 128], - type='', - swap='lss_kpt10'), - 55: - dict( - name='lss_kpt31', - id=55, - color=[255, 0, 128], - type='', - swap='lss_kpt9'), - 56: - dict( - name='lss_kpt32', - id=56, - color=[255, 0, 128], - type='', - swap='lss_kpt8'), - 57: - dict( - name='lss_kpt33', - id=57, - color=[255, 0, 128], - type='', - swap='lss_kpt7'), - 58: - dict(name='sso_kpt1', id=58, color=[128, 0, 255], type='', swap=''), - 59: - dict( - name='sso_kpt2', - id=59, - color=[128, 0, 255], - type='', - swap='sso_kpt26'), - 60: - dict( - name='sso_kpt3', - id=60, - color=[128, 0, 255], - type='', - swap='sso_kpt5'), - 61: - dict( - name='sso_kpt4', - id=61, - color=[128, 0, 255], - type='', - swap='sso_kpt6'), - 62: - dict( - name='sso_kpt5', - id=62, - color=[128, 0, 255], - type='', - swap='sso_kpt3'), - 63: - dict( - name='sso_kpt6', - id=63, - color=[128, 0, 255], - type='', - swap='sso_kpt4'), - 64: - dict( - name='sso_kpt7', - id=64, - color=[128, 0, 255], - type='', - swap='sso_kpt25'), - 65: - dict( - name='sso_kpt8', - id=65, - color=[128, 0, 255], - type='', - swap='sso_kpt24'), - 66: - dict( - name='sso_kpt9', - id=66, - color=[128, 0, 255], - type='', - swap='sso_kpt23'), - 67: - dict( - name='sso_kpt10', - id=67, - color=[128, 0, 255], - type='', - swap='sso_kpt22'), - 68: - dict( - name='sso_kpt11', - id=68, - color=[128, 0, 255], - type='', - swap='sso_kpt21'), - 69: - dict( - name='sso_kpt12', - id=69, - color=[128, 0, 255], - type='', - swap='sso_kpt20'), - 70: - dict( - name='sso_kpt13', - id=70, - color=[128, 0, 255], - type='', - swap='sso_kpt19'), - 71: - dict( - name='sso_kpt14', - id=71, - color=[128, 0, 255], - type='', - swap='sso_kpt18'), - 72: - dict( - name='sso_kpt15', - id=72, - color=[128, 0, 255], - type='', - swap='sso_kpt17'), - 73: - dict( - name='sso_kpt16', - id=73, - color=[128, 0, 255], - type='', - swap='sso_kpt29'), - 74: - dict( - name='sso_kpt17', - id=74, - color=[128, 0, 255], - type='', - swap='sso_kpt15'), - 75: - dict( - name='sso_kpt18', - id=75, - color=[128, 0, 255], - type='', - swap='sso_kpt14'), - 76: - dict( - name='sso_kpt19', - id=76, - color=[128, 0, 255], - type='', - swap='sso_kpt13'), - 77: - dict( - name='sso_kpt20', - id=77, - color=[128, 0, 255], - type='', - swap='sso_kpt12'), - 78: - dict( - name='sso_kpt21', - id=78, - color=[128, 0, 255], - type='', - swap='sso_kpt11'), - 79: - dict( - name='sso_kpt22', - id=79, - color=[128, 0, 255], - type='', - swap='sso_kpt10'), - 80: - dict( - name='sso_kpt23', - id=80, - color=[128, 0, 255], - type='', - swap='sso_kpt9'), - 81: - dict( - name='sso_kpt24', - id=81, - color=[128, 0, 255], - type='', - swap='sso_kpt8'), - 82: - dict( - name='sso_kpt25', - id=82, - color=[128, 0, 255], - type='', - swap='sso_kpt7'), - 83: - dict( - name='sso_kpt26', - id=83, - color=[128, 0, 255], - type='', - swap='sso_kpt2'), - 84: - dict( - name='sso_kpt27', - id=84, - color=[128, 0, 255], - type='', - swap='sso_kpt30'), - 85: - dict( - name='sso_kpt28', - id=85, - color=[128, 0, 255], - type='', - swap='sso_kpt31'), - 86: - dict( - name='sso_kpt29', - id=86, - color=[128, 0, 255], - type='', - swap='sso_kpt16'), - 87: - dict( - name='sso_kpt30', - id=87, - color=[128, 0, 255], - type='', - swap='sso_kpt27'), - 88: - dict( - name='sso_kpt31', - id=88, - color=[128, 0, 255], - type='', - swap='sso_kpt28'), - 89: - dict(name='lso_kpt1', id=89, color=[0, 128, 255], type='', swap=''), - 90: - dict( - name='lso_kpt2', - id=90, - color=[0, 128, 255], - type='', - swap='lso_kpt6'), - 91: - dict( - name='lso_kpt3', - id=91, - color=[0, 128, 255], - type='', - swap='lso_kpt5'), - 92: - dict( - name='lso_kpt4', - id=92, - color=[0, 128, 255], - type='', - swap='lso_kpt34'), - 93: - dict( - name='lso_kpt5', - id=93, - color=[0, 128, 255], - type='', - swap='lso_kpt3'), - 94: - dict( - name='lso_kpt6', - id=94, - color=[0, 128, 255], - type='', - swap='lso_kpt2'), - 95: - dict( - name='lso_kpt7', - id=95, - color=[0, 128, 255], - type='', - swap='lso_kpt33'), - 96: - dict( - name='lso_kpt8', - id=96, - color=[0, 128, 255], - type='', - swap='lso_kpt32'), - 97: - dict( - name='lso_kpt9', - id=97, - color=[0, 128, 255], - type='', - swap='lso_kpt31'), - 98: - dict( - name='lso_kpt10', - id=98, - color=[0, 128, 255], - type='', - swap='lso_kpt30'), - 99: - dict( - name='lso_kpt11', - id=99, - color=[0, 128, 255], - type='', - swap='lso_kpt29'), - 100: - dict( - name='lso_kpt12', - id=100, - color=[0, 128, 255], - type='', - swap='lso_kpt28'), - 101: - dict( - name='lso_kpt13', - id=101, - color=[0, 128, 255], - type='', - swap='lso_kpt27'), - 102: - dict( - name='lso_kpt14', - id=102, - color=[0, 128, 255], - type='', - swap='lso_kpt26'), - 103: - dict( - name='lso_kpt15', - id=103, - color=[0, 128, 255], - type='', - swap='lso_kpt25'), - 104: - dict( - name='lso_kpt16', - id=104, - color=[0, 128, 255], - type='', - swap='lso_kpt24'), - 105: - dict( - name='lso_kpt17', - id=105, - color=[0, 128, 255], - type='', - swap='lso_kpt23'), - 106: - dict( - name='lso_kpt18', - id=106, - color=[0, 128, 255], - type='', - swap='lso_kpt22'), - 107: - dict( - name='lso_kpt19', - id=107, - color=[0, 128, 255], - type='', - swap='lso_kpt21'), - 108: - dict( - name='lso_kpt20', - id=108, - color=[0, 128, 255], - type='', - swap='lso_kpt37'), - 109: - dict( - name='lso_kpt21', - id=109, - color=[0, 128, 255], - type='', - swap='lso_kpt19'), - 110: - dict( - name='lso_kpt22', - id=110, - color=[0, 128, 255], - type='', - swap='lso_kpt18'), - 111: - dict( - name='lso_kpt23', - id=111, - color=[0, 128, 255], - type='', - swap='lso_kpt17'), - 112: - dict( - name='lso_kpt24', - id=112, - color=[0, 128, 255], - type='', - swap='lso_kpt16'), - 113: - dict( - name='lso_kpt25', - id=113, - color=[0, 128, 255], - type='', - swap='lso_kpt15'), - 114: - dict( - name='lso_kpt26', - id=114, - color=[0, 128, 255], - type='', - swap='lso_kpt14'), - 115: - dict( - name='lso_kpt27', - id=115, - color=[0, 128, 255], - type='', - swap='lso_kpt13'), - 116: - dict( - name='lso_kpt28', - id=116, - color=[0, 128, 255], - type='', - swap='lso_kpt12'), - 117: - dict( - name='lso_kpt29', - id=117, - color=[0, 128, 255], - type='', - swap='lso_kpt11'), - 118: - dict( - name='lso_kpt30', - id=118, - color=[0, 128, 255], - type='', - swap='lso_kpt10'), - 119: - dict( - name='lso_kpt31', - id=119, - color=[0, 128, 255], - type='', - swap='lso_kpt9'), - 120: - dict( - name='lso_kpt32', - id=120, - color=[0, 128, 255], - type='', - swap='lso_kpt8'), - 121: - dict( - name='lso_kpt33', - id=121, - color=[0, 128, 255], - type='', - swap='lso_kpt7'), - 122: - dict( - name='lso_kpt34', - id=122, - color=[0, 128, 255], - type='', - swap='lso_kpt4'), - 123: - dict( - name='lso_kpt35', - id=123, - color=[0, 128, 255], - type='', - swap='lso_kpt38'), - 124: - dict( - name='lso_kpt36', - id=124, - color=[0, 128, 255], - type='', - swap='lso_kpt39'), - 125: - dict( - name='lso_kpt37', - id=125, - color=[0, 128, 255], - type='', - swap='lso_kpt20'), - 126: - dict( - name='lso_kpt38', - id=126, - color=[0, 128, 255], - type='', - swap='lso_kpt35'), - 127: - dict( - name='lso_kpt39', - id=127, - color=[0, 128, 255], - type='', - swap='lso_kpt36'), - 128: - dict(name='vest_kpt1', id=128, color=[0, 128, 128], type='', swap=''), - 129: - dict( - name='vest_kpt2', - id=129, - color=[0, 128, 128], - type='', - swap='vest_kpt6'), - 130: - dict( - name='vest_kpt3', - id=130, - color=[0, 128, 128], - type='', - swap='vest_kpt5'), - 131: - dict(name='vest_kpt4', id=131, color=[0, 128, 128], type='', swap=''), - 132: - dict( - name='vest_kpt5', - id=132, - color=[0, 128, 128], - type='', - swap='vest_kpt3'), - 133: - dict( - name='vest_kpt6', - id=133, - color=[0, 128, 128], - type='', - swap='vest_kpt2'), - 134: - dict( - name='vest_kpt7', - id=134, - color=[0, 128, 128], - type='', - swap='vest_kpt15'), - 135: - dict( - name='vest_kpt8', - id=135, - color=[0, 128, 128], - type='', - swap='vest_kpt14'), - 136: - dict( - name='vest_kpt9', - id=136, - color=[0, 128, 128], - type='', - swap='vest_kpt13'), - 137: - dict( - name='vest_kpt10', - id=137, - color=[0, 128, 128], - type='', - swap='vest_kpt12'), - 138: - dict(name='vest_kpt11', id=138, color=[0, 128, 128], type='', swap=''), - 139: - dict( - name='vest_kpt12', - id=139, - color=[0, 128, 128], - type='', - swap='vest_kpt10'), - 140: - dict(name='vest_kpt13', id=140, color=[0, 128, 128], type='', swap=''), - 141: - dict( - name='vest_kpt14', - id=141, - color=[0, 128, 128], - type='', - swap='vest_kpt8'), - 142: - dict( - name='vest_kpt15', - id=142, - color=[0, 128, 128], - type='', - swap='vest_kpt7'), - 143: - dict(name='sling_kpt1', id=143, color=[0, 0, 128], type='', swap=''), - 144: - dict( - name='sling_kpt2', - id=144, - color=[0, 0, 128], - type='', - swap='sling_kpt6'), - 145: - dict( - name='sling_kpt3', - id=145, - color=[0, 0, 128], - type='', - swap='sling_kpt5'), - 146: - dict(name='sling_kpt4', id=146, color=[0, 0, 128], type='', swap=''), - 147: - dict( - name='sling_kpt5', - id=147, - color=[0, 0, 128], - type='', - swap='sling_kpt3'), - 148: - dict( - name='sling_kpt6', - id=148, - color=[0, 0, 128], - type='', - swap='sling_kpt2'), - 149: - dict( - name='sling_kpt7', - id=149, - color=[0, 0, 128], - type='', - swap='sling_kpt15'), - 150: - dict( - name='sling_kpt8', - id=150, - color=[0, 0, 128], - type='', - swap='sling_kpt14'), - 151: - dict( - name='sling_kpt9', - id=151, - color=[0, 0, 128], - type='', - swap='sling_kpt13'), - 152: - dict( - name='sling_kpt10', - id=152, - color=[0, 0, 128], - type='', - swap='sling_kpt12'), - 153: - dict(name='sling_kpt11', id=153, color=[0, 0, 128], type='', swap=''), - 154: - dict( - name='sling_kpt12', - id=154, - color=[0, 0, 128], - type='', - swap='sling_kpt10'), - 155: - dict( - name='sling_kpt13', - id=155, - color=[0, 0, 128], - type='', - swap='sling_kpt9'), - 156: - dict( - name='sling_kpt14', - id=156, - color=[0, 0, 128], - type='', - swap='sling_kpt8'), - 157: - dict( - name='sling_kpt15', - id=157, - color=[0, 0, 128], - type='', - swap='sling_kpt7'), - 158: - dict( - name='shorts_kpt1', - id=158, - color=[128, 128, 128], - type='', - swap='shorts_kpt3'), - 159: - dict( - name='shorts_kpt2', - id=159, - color=[128, 128, 128], - type='', - swap=''), - 160: - dict( - name='shorts_kpt3', - id=160, - color=[128, 128, 128], - type='', - swap='shorts_kpt1'), - 161: - dict( - name='shorts_kpt4', - id=161, - color=[128, 128, 128], - type='', - swap='shorts_kpt10'), - 162: - dict( - name='shorts_kpt5', - id=162, - color=[128, 128, 128], - type='', - swap='shorts_kpt9'), - 163: - dict( - name='shorts_kpt6', - id=163, - color=[128, 128, 128], - type='', - swap='shorts_kpt8'), - 164: - dict( - name='shorts_kpt7', - id=164, - color=[128, 128, 128], - type='', - swap=''), - 165: - dict( - name='shorts_kpt8', - id=165, - color=[128, 128, 128], - type='', - swap='shorts_kpt6'), - 166: - dict( - name='shorts_kpt9', - id=166, - color=[128, 128, 128], - type='', - swap='shorts_kpt5'), - 167: - dict( - name='shorts_kpt10', - id=167, - color=[128, 128, 128], - type='', - swap='shorts_kpt4'), - 168: - dict( - name='trousers_kpt1', - id=168, - color=[128, 0, 128], - type='', - swap='trousers_kpt3'), - 169: - dict( - name='trousers_kpt2', - id=169, - color=[128, 0, 128], - type='', - swap=''), - 170: - dict( - name='trousers_kpt3', - id=170, - color=[128, 0, 128], - type='', - swap='trousers_kpt1'), - 171: - dict( - name='trousers_kpt4', - id=171, - color=[128, 0, 128], - type='', - swap='trousers_kpt14'), - 172: - dict( - name='trousers_kpt5', - id=172, - color=[128, 0, 128], - type='', - swap='trousers_kpt13'), - 173: - dict( - name='trousers_kpt6', - id=173, - color=[128, 0, 128], - type='', - swap='trousers_kpt12'), - 174: - dict( - name='trousers_kpt7', - id=174, - color=[128, 0, 128], - type='', - swap='trousers_kpt11'), - 175: - dict( - name='trousers_kpt8', - id=175, - color=[128, 0, 128], - type='', - swap='trousers_kpt10'), - 176: - dict( - name='trousers_kpt9', - id=176, - color=[128, 0, 128], - type='', - swap=''), - 177: - dict( - name='trousers_kpt10', - id=177, - color=[128, 0, 128], - type='', - swap='trousers_kpt8'), - 178: - dict( - name='trousers_kpt11', - id=178, - color=[128, 0, 128], - type='', - swap='trousers_kpt7'), - 179: - dict( - name='trousers_kpt12', - id=179, - color=[128, 0, 128], - type='', - swap='trousers_kpt6'), - 180: - dict( - name='trousers_kpt13', - id=180, - color=[128, 0, 128], - type='', - swap='trousers_kpt5'), - 181: - dict( - name='trousers_kpt14', - id=181, - color=[128, 0, 128], - type='', - swap='trousers_kpt4'), - 182: - dict( - name='skirt_kpt1', - id=182, - color=[64, 128, 128], - type='', - swap='skirt_kpt3'), - 183: - dict( - name='skirt_kpt2', id=183, color=[64, 128, 128], type='', swap=''), - 184: - dict( - name='skirt_kpt3', - id=184, - color=[64, 128, 128], - type='', - swap='skirt_kpt1'), - 185: - dict( - name='skirt_kpt4', - id=185, - color=[64, 128, 128], - type='', - swap='skirt_kpt8'), - 186: - dict( - name='skirt_kpt5', - id=186, - color=[64, 128, 128], - type='', - swap='skirt_kpt7'), - 187: - dict( - name='skirt_kpt6', id=187, color=[64, 128, 128], type='', swap=''), - 188: - dict( - name='skirt_kpt7', - id=188, - color=[64, 128, 128], - type='', - swap='skirt_kpt5'), - 189: - dict( - name='skirt_kpt8', - id=189, - color=[64, 128, 128], - type='', - swap='skirt_kpt4'), - 190: - dict(name='ssd_kpt1', id=190, color=[64, 64, 128], type='', swap=''), - 191: - dict( - name='ssd_kpt2', - id=191, - color=[64, 64, 128], - type='', - swap='ssd_kpt6'), - 192: - dict( - name='ssd_kpt3', - id=192, - color=[64, 64, 128], - type='', - swap='ssd_kpt5'), - 193: - dict(name='ssd_kpt4', id=193, color=[64, 64, 128], type='', swap=''), - 194: - dict( - name='ssd_kpt5', - id=194, - color=[64, 64, 128], - type='', - swap='ssd_kpt3'), - 195: - dict( - name='ssd_kpt6', - id=195, - color=[64, 64, 128], - type='', - swap='ssd_kpt2'), - 196: - dict( - name='ssd_kpt7', - id=196, - color=[64, 64, 128], - type='', - swap='ssd_kpt29'), - 197: - dict( - name='ssd_kpt8', - id=197, - color=[64, 64, 128], - type='', - swap='ssd_kpt28'), - 198: - dict( - name='ssd_kpt9', - id=198, - color=[64, 64, 128], - type='', - swap='ssd_kpt27'), - 199: - dict( - name='ssd_kpt10', - id=199, - color=[64, 64, 128], - type='', - swap='ssd_kpt26'), - 200: - dict( - name='ssd_kpt11', - id=200, - color=[64, 64, 128], - type='', - swap='ssd_kpt25'), - 201: - dict( - name='ssd_kpt12', - id=201, - color=[64, 64, 128], - type='', - swap='ssd_kpt24'), - 202: - dict( - name='ssd_kpt13', - id=202, - color=[64, 64, 128], - type='', - swap='ssd_kpt23'), - 203: - dict( - name='ssd_kpt14', - id=203, - color=[64, 64, 128], - type='', - swap='ssd_kpt22'), - 204: - dict( - name='ssd_kpt15', - id=204, - color=[64, 64, 128], - type='', - swap='ssd_kpt21'), - 205: - dict( - name='ssd_kpt16', - id=205, - color=[64, 64, 128], - type='', - swap='ssd_kpt20'), - 206: - dict( - name='ssd_kpt17', - id=206, - color=[64, 64, 128], - type='', - swap='ssd_kpt19'), - 207: - dict(name='ssd_kpt18', id=207, color=[64, 64, 128], type='', swap=''), - 208: - dict( - name='ssd_kpt19', - id=208, - color=[64, 64, 128], - type='', - swap='ssd_kpt17'), - 209: - dict( - name='ssd_kpt20', - id=209, - color=[64, 64, 128], - type='', - swap='ssd_kpt16'), - 210: - dict( - name='ssd_kpt21', - id=210, - color=[64, 64, 128], - type='', - swap='ssd_kpt15'), - 211: - dict( - name='ssd_kpt22', - id=211, - color=[64, 64, 128], - type='', - swap='ssd_kpt14'), - 212: - dict( - name='ssd_kpt23', - id=212, - color=[64, 64, 128], - type='', - swap='ssd_kpt13'), - 213: - dict( - name='ssd_kpt24', - id=213, - color=[64, 64, 128], - type='', - swap='ssd_kpt12'), - 214: - dict( - name='ssd_kpt25', - id=214, - color=[64, 64, 128], - type='', - swap='ssd_kpt11'), - 215: - dict( - name='ssd_kpt26', - id=215, - color=[64, 64, 128], - type='', - swap='ssd_kpt10'), - 216: - dict( - name='ssd_kpt27', - id=216, - color=[64, 64, 128], - type='', - swap='ssd_kpt9'), - 217: - dict( - name='ssd_kpt28', - id=217, - color=[64, 64, 128], - type='', - swap='ssd_kpt8'), - 218: - dict( - name='ssd_kpt29', - id=218, - color=[64, 64, 128], - type='', - swap='ssd_kpt7'), - 219: - dict(name='lsd_kpt1', id=219, color=[128, 64, 0], type='', swap=''), - 220: - dict( - name='lsd_kpt2', - id=220, - color=[128, 64, 0], - type='', - swap='lsd_kpt6'), - 221: - dict( - name='lsd_kpt3', - id=221, - color=[128, 64, 0], - type='', - swap='lsd_kpt5'), - 222: - dict(name='lsd_kpt4', id=222, color=[128, 64, 0], type='', swap=''), - 223: - dict( - name='lsd_kpt5', - id=223, - color=[128, 64, 0], - type='', - swap='lsd_kpt3'), - 224: - dict( - name='lsd_kpt6', - id=224, - color=[128, 64, 0], - type='', - swap='lsd_kpt2'), - 225: - dict( - name='lsd_kpt7', - id=225, - color=[128, 64, 0], - type='', - swap='lsd_kpt37'), - 226: - dict( - name='lsd_kpt8', - id=226, - color=[128, 64, 0], - type='', - swap='lsd_kpt36'), - 227: - dict( - name='lsd_kpt9', - id=227, - color=[128, 64, 0], - type='', - swap='lsd_kpt35'), - 228: - dict( - name='lsd_kpt10', - id=228, - color=[128, 64, 0], - type='', - swap='lsd_kpt34'), - 229: - dict( - name='lsd_kpt11', - id=229, - color=[128, 64, 0], - type='', - swap='lsd_kpt33'), - 230: - dict( - name='lsd_kpt12', - id=230, - color=[128, 64, 0], - type='', - swap='lsd_kpt32'), - 231: - dict( - name='lsd_kpt13', - id=231, - color=[128, 64, 0], - type='', - swap='lsd_kpt31'), - 232: - dict( - name='lsd_kpt14', - id=232, - color=[128, 64, 0], - type='', - swap='lsd_kpt30'), - 233: - dict( - name='lsd_kpt15', - id=233, - color=[128, 64, 0], - type='', - swap='lsd_kpt29'), - 234: - dict( - name='lsd_kpt16', - id=234, - color=[128, 64, 0], - type='', - swap='lsd_kpt28'), - 235: - dict( - name='lsd_kpt17', - id=235, - color=[128, 64, 0], - type='', - swap='lsd_kpt27'), - 236: - dict( - name='lsd_kpt18', - id=236, - color=[128, 64, 0], - type='', - swap='lsd_kpt26'), - 237: - dict( - name='lsd_kpt19', - id=237, - color=[128, 64, 0], - type='', - swap='lsd_kpt25'), - 238: - dict( - name='lsd_kpt20', - id=238, - color=[128, 64, 0], - type='', - swap='lsd_kpt24'), - 239: - dict( - name='lsd_kpt21', - id=239, - color=[128, 64, 0], - type='', - swap='lsd_kpt23'), - 240: - dict(name='lsd_kpt22', id=240, color=[128, 64, 0], type='', swap=''), - 241: - dict( - name='lsd_kpt23', - id=241, - color=[128, 64, 0], - type='', - swap='lsd_kpt21'), - 242: - dict( - name='lsd_kpt24', - id=242, - color=[128, 64, 0], - type='', - swap='lsd_kpt20'), - 243: - dict( - name='lsd_kpt25', - id=243, - color=[128, 64, 0], - type='', - swap='lsd_kpt19'), - 244: - dict( - name='lsd_kpt26', - id=244, - color=[128, 64, 0], - type='', - swap='lsd_kpt18'), - 245: - dict( - name='lsd_kpt27', - id=245, - color=[128, 64, 0], - type='', - swap='lsd_kpt17'), - 246: - dict( - name='lsd_kpt28', - id=246, - color=[128, 64, 0], - type='', - swap='lsd_kpt16'), - 247: - dict( - name='lsd_kpt29', - id=247, - color=[128, 64, 0], - type='', - swap='lsd_kpt15'), - 248: - dict( - name='lsd_kpt30', - id=248, - color=[128, 64, 0], - type='', - swap='lsd_kpt14'), - 249: - dict( - name='lsd_kpt31', - id=249, - color=[128, 64, 0], - type='', - swap='lsd_kpt13'), - 250: - dict( - name='lsd_kpt32', - id=250, - color=[128, 64, 0], - type='', - swap='lsd_kpt12'), - 251: - dict( - name='lsd_kpt33', - id=251, - color=[128, 64, 0], - type='', - swap='lsd_kpt11'), - 252: - dict( - name='lsd_kpt34', - id=252, - color=[128, 64, 0], - type='', - swap='lsd_kpt10'), - 253: - dict( - name='lsd_kpt35', - id=253, - color=[128, 64, 0], - type='', - swap='lsd_kpt9'), - 254: - dict( - name='lsd_kpt36', - id=254, - color=[128, 64, 0], - type='', - swap='lsd_kpt8'), - 255: - dict( - name='lsd_kpt37', - id=255, - color=[128, 64, 0], - type='', - swap='lsd_kpt7'), - 256: - dict(name='vd_kpt1', id=256, color=[128, 64, 255], type='', swap=''), - 257: - dict( - name='vd_kpt2', - id=257, - color=[128, 64, 255], - type='', - swap='vd_kpt6'), - 258: - dict( - name='vd_kpt3', - id=258, - color=[128, 64, 255], - type='', - swap='vd_kpt5'), - 259: - dict(name='vd_kpt4', id=259, color=[128, 64, 255], type='', swap=''), - 260: - dict( - name='vd_kpt5', - id=260, - color=[128, 64, 255], - type='', - swap='vd_kpt3'), - 261: - dict( - name='vd_kpt6', - id=261, - color=[128, 64, 255], - type='', - swap='vd_kpt2'), - 262: - dict( - name='vd_kpt7', - id=262, - color=[128, 64, 255], - type='', - swap='vd_kpt19'), - 263: - dict( - name='vd_kpt8', - id=263, - color=[128, 64, 255], - type='', - swap='vd_kpt18'), - 264: - dict( - name='vd_kpt9', - id=264, - color=[128, 64, 255], - type='', - swap='vd_kpt17'), - 265: - dict( - name='vd_kpt10', - id=265, - color=[128, 64, 255], - type='', - swap='vd_kpt16'), - 266: - dict( - name='vd_kpt11', - id=266, - color=[128, 64, 255], - type='', - swap='vd_kpt15'), - 267: - dict( - name='vd_kpt12', - id=267, - color=[128, 64, 255], - type='', - swap='vd_kpt14'), - 268: - dict(name='vd_kpt13', id=268, color=[128, 64, 255], type='', swap=''), - 269: - dict( - name='vd_kpt14', - id=269, - color=[128, 64, 255], - type='', - swap='vd_kpt12'), - 270: - dict( - name='vd_kpt15', - id=270, - color=[128, 64, 255], - type='', - swap='vd_kpt11'), - 271: - dict( - name='vd_kpt16', - id=271, - color=[128, 64, 255], - type='', - swap='vd_kpt10'), - 272: - dict( - name='vd_kpt17', - id=272, - color=[128, 64, 255], - type='', - swap='vd_kpt9'), - 273: - dict( - name='vd_kpt18', - id=273, - color=[128, 64, 255], - type='', - swap='vd_kpt8'), - 274: - dict( - name='vd_kpt19', - id=274, - color=[128, 64, 255], - type='', - swap='vd_kpt7'), - 275: - dict(name='sd_kpt1', id=275, color=[128, 64, 0], type='', swap=''), - 276: - dict( - name='sd_kpt2', - id=276, - color=[128, 64, 0], - type='', - swap='sd_kpt6'), - 277: - dict( - name='sd_kpt3', - id=277, - color=[128, 64, 0], - type='', - swap='sd_kpt5'), - 278: - dict(name='sd_kpt4', id=278, color=[128, 64, 0], type='', swap=''), - 279: - dict( - name='sd_kpt5', - id=279, - color=[128, 64, 0], - type='', - swap='sd_kpt3'), - 280: - dict( - name='sd_kpt6', - id=280, - color=[128, 64, 0], - type='', - swap='sd_kpt2'), - 281: - dict( - name='sd_kpt7', - id=281, - color=[128, 64, 0], - type='', - swap='sd_kpt19'), - 282: - dict( - name='sd_kpt8', - id=282, - color=[128, 64, 0], - type='', - swap='sd_kpt18'), - 283: - dict( - name='sd_kpt9', - id=283, - color=[128, 64, 0], - type='', - swap='sd_kpt17'), - 284: - dict( - name='sd_kpt10', - id=284, - color=[128, 64, 0], - type='', - swap='sd_kpt16'), - 285: - dict( - name='sd_kpt11', - id=285, - color=[128, 64, 0], - type='', - swap='sd_kpt15'), - 286: - dict( - name='sd_kpt12', - id=286, - color=[128, 64, 0], - type='', - swap='sd_kpt14'), - 287: - dict(name='sd_kpt13', id=287, color=[128, 64, 0], type='', swap=''), - 288: - dict( - name='sd_kpt14', - id=288, - color=[128, 64, 0], - type='', - swap='sd_kpt12'), - 289: - dict( - name='sd_kpt15', - id=289, - color=[128, 64, 0], - type='', - swap='sd_kpt11'), - 290: - dict( - name='sd_kpt16', - id=290, - color=[128, 64, 0], - type='', - swap='sd_kpt10'), - 291: - dict( - name='sd_kpt17', - id=291, - color=[128, 64, 0], - type='', - swap='sd_kpt9'), - 292: - dict( - name='sd_kpt18', - id=292, - color=[128, 64, 0], - type='', - swap='sd_kpt8'), - 293: - dict( - name='sd_kpt19', - id=293, - color=[128, 64, 0], - type='', - swap='sd_kpt7') - }), - skeleton_info=dict({ - 0: - dict(link=('sss_kpt1', 'sss_kpt2'), id=0, color=[255, 128, 0]), - 1: - dict(link=('sss_kpt2', 'sss_kpt7'), id=1, color=[255, 128, 0]), - 2: - dict(link=('sss_kpt7', 'sss_kpt8'), id=2, color=[255, 128, 0]), - 3: - dict(link=('sss_kpt8', 'sss_kpt9'), id=3, color=[255, 128, 0]), - 4: - dict(link=('sss_kpt9', 'sss_kpt10'), id=4, color=[255, 128, 0]), - 5: - dict(link=('sss_kpt10', 'sss_kpt11'), id=5, color=[255, 128, 0]), - 6: - dict(link=('sss_kpt11', 'sss_kpt12'), id=6, color=[255, 128, 0]), - 7: - dict(link=('sss_kpt12', 'sss_kpt13'), id=7, color=[255, 128, 0]), - 8: - dict(link=('sss_kpt13', 'sss_kpt14'), id=8, color=[255, 128, 0]), - 9: - dict(link=('sss_kpt14', 'sss_kpt15'), id=9, color=[255, 128, 0]), - 10: - dict(link=('sss_kpt15', 'sss_kpt16'), id=10, color=[255, 128, 0]), - 11: - dict(link=('sss_kpt16', 'sss_kpt17'), id=11, color=[255, 128, 0]), - 12: - dict(link=('sss_kpt17', 'sss_kpt18'), id=12, color=[255, 128, 0]), - 13: - dict(link=('sss_kpt18', 'sss_kpt19'), id=13, color=[255, 128, 0]), - 14: - dict(link=('sss_kpt19', 'sss_kpt20'), id=14, color=[255, 128, 0]), - 15: - dict(link=('sss_kpt20', 'sss_kpt21'), id=15, color=[255, 128, 0]), - 16: - dict(link=('sss_kpt21', 'sss_kpt22'), id=16, color=[255, 128, 0]), - 17: - dict(link=('sss_kpt22', 'sss_kpt23'), id=17, color=[255, 128, 0]), - 18: - dict(link=('sss_kpt23', 'sss_kpt24'), id=18, color=[255, 128, 0]), - 19: - dict(link=('sss_kpt24', 'sss_kpt25'), id=19, color=[255, 128, 0]), - 20: - dict(link=('sss_kpt25', 'sss_kpt6'), id=20, color=[255, 128, 0]), - 21: - dict(link=('sss_kpt6', 'sss_kpt1'), id=21, color=[255, 128, 0]), - 22: - dict(link=('sss_kpt2', 'sss_kpt3'), id=22, color=[255, 128, 0]), - 23: - dict(link=('sss_kpt3', 'sss_kpt4'), id=23, color=[255, 128, 0]), - 24: - dict(link=('sss_kpt4', 'sss_kpt5'), id=24, color=[255, 128, 0]), - 25: - dict(link=('sss_kpt5', 'sss_kpt6'), id=25, color=[255, 128, 0]), - 26: - dict(link=('lss_kpt1', 'lss_kpt2'), id=26, color=[255, 0, 128]), - 27: - dict(link=('lss_kpt2', 'lss_kpt7'), id=27, color=[255, 0, 128]), - 28: - dict(link=('lss_kpt7', 'lss_kpt8'), id=28, color=[255, 0, 128]), - 29: - dict(link=('lss_kpt8', 'lss_kpt9'), id=29, color=[255, 0, 128]), - 30: - dict(link=('lss_kpt9', 'lss_kpt10'), id=30, color=[255, 0, 128]), - 31: - dict(link=('lss_kpt10', 'lss_kpt11'), id=31, color=[255, 0, 128]), - 32: - dict(link=('lss_kpt11', 'lss_kpt12'), id=32, color=[255, 0, 128]), - 33: - dict(link=('lss_kpt12', 'lss_kpt13'), id=33, color=[255, 0, 128]), - 34: - dict(link=('lss_kpt13', 'lss_kpt14'), id=34, color=[255, 0, 128]), - 35: - dict(link=('lss_kpt14', 'lss_kpt15'), id=35, color=[255, 0, 128]), - 36: - dict(link=('lss_kpt15', 'lss_kpt16'), id=36, color=[255, 0, 128]), - 37: - dict(link=('lss_kpt16', 'lss_kpt17'), id=37, color=[255, 0, 128]), - 38: - dict(link=('lss_kpt17', 'lss_kpt18'), id=38, color=[255, 0, 128]), - 39: - dict(link=('lss_kpt18', 'lss_kpt19'), id=39, color=[255, 0, 128]), - 40: - dict(link=('lss_kpt19', 'lss_kpt20'), id=40, color=[255, 0, 128]), - 41: - dict(link=('lss_kpt20', 'lss_kpt21'), id=41, color=[255, 0, 128]), - 42: - dict(link=('lss_kpt21', 'lss_kpt22'), id=42, color=[255, 0, 128]), - 43: - dict(link=('lss_kpt22', 'lss_kpt23'), id=43, color=[255, 0, 128]), - 44: - dict(link=('lss_kpt23', 'lss_kpt24'), id=44, color=[255, 0, 128]), - 45: - dict(link=('lss_kpt24', 'lss_kpt25'), id=45, color=[255, 0, 128]), - 46: - dict(link=('lss_kpt25', 'lss_kpt26'), id=46, color=[255, 0, 128]), - 47: - dict(link=('lss_kpt26', 'lss_kpt27'), id=47, color=[255, 0, 128]), - 48: - dict(link=('lss_kpt27', 'lss_kpt28'), id=48, color=[255, 0, 128]), - 49: - dict(link=('lss_kpt28', 'lss_kpt29'), id=49, color=[255, 0, 128]), - 50: - dict(link=('lss_kpt29', 'lss_kpt30'), id=50, color=[255, 0, 128]), - 51: - dict(link=('lss_kpt30', 'lss_kpt31'), id=51, color=[255, 0, 128]), - 52: - dict(link=('lss_kpt31', 'lss_kpt32'), id=52, color=[255, 0, 128]), - 53: - dict(link=('lss_kpt32', 'lss_kpt33'), id=53, color=[255, 0, 128]), - 54: - dict(link=('lss_kpt33', 'lss_kpt6'), id=54, color=[255, 0, 128]), - 55: - dict(link=('lss_kpt6', 'lss_kpt5'), id=55, color=[255, 0, 128]), - 56: - dict(link=('lss_kpt5', 'lss_kpt4'), id=56, color=[255, 0, 128]), - 57: - dict(link=('lss_kpt4', 'lss_kpt3'), id=57, color=[255, 0, 128]), - 58: - dict(link=('lss_kpt3', 'lss_kpt2'), id=58, color=[255, 0, 128]), - 59: - dict(link=('lss_kpt6', 'lss_kpt1'), id=59, color=[255, 0, 128]), - 60: - dict(link=('sso_kpt1', 'sso_kpt4'), id=60, color=[128, 0, 255]), - 61: - dict(link=('sso_kpt4', 'sso_kpt7'), id=61, color=[128, 0, 255]), - 62: - dict(link=('sso_kpt7', 'sso_kpt8'), id=62, color=[128, 0, 255]), - 63: - dict(link=('sso_kpt8', 'sso_kpt9'), id=63, color=[128, 0, 255]), - 64: - dict(link=('sso_kpt9', 'sso_kpt10'), id=64, color=[128, 0, 255]), - 65: - dict(link=('sso_kpt10', 'sso_kpt11'), id=65, color=[128, 0, 255]), - 66: - dict(link=('sso_kpt11', 'sso_kpt12'), id=66, color=[128, 0, 255]), - 67: - dict(link=('sso_kpt12', 'sso_kpt13'), id=67, color=[128, 0, 255]), - 68: - dict(link=('sso_kpt13', 'sso_kpt14'), id=68, color=[128, 0, 255]), - 69: - dict(link=('sso_kpt14', 'sso_kpt15'), id=69, color=[128, 0, 255]), - 70: - dict(link=('sso_kpt15', 'sso_kpt16'), id=70, color=[128, 0, 255]), - 71: - dict(link=('sso_kpt16', 'sso_kpt31'), id=71, color=[128, 0, 255]), - 72: - dict(link=('sso_kpt31', 'sso_kpt30'), id=72, color=[128, 0, 255]), - 73: - dict(link=('sso_kpt30', 'sso_kpt2'), id=73, color=[128, 0, 255]), - 74: - dict(link=('sso_kpt2', 'sso_kpt3'), id=74, color=[128, 0, 255]), - 75: - dict(link=('sso_kpt3', 'sso_kpt4'), id=75, color=[128, 0, 255]), - 76: - dict(link=('sso_kpt1', 'sso_kpt6'), id=76, color=[128, 0, 255]), - 77: - dict(link=('sso_kpt6', 'sso_kpt25'), id=77, color=[128, 0, 255]), - 78: - dict(link=('sso_kpt25', 'sso_kpt24'), id=78, color=[128, 0, 255]), - 79: - dict(link=('sso_kpt24', 'sso_kpt23'), id=79, color=[128, 0, 255]), - 80: - dict(link=('sso_kpt23', 'sso_kpt22'), id=80, color=[128, 0, 255]), - 81: - dict(link=('sso_kpt22', 'sso_kpt21'), id=81, color=[128, 0, 255]), - 82: - dict(link=('sso_kpt21', 'sso_kpt20'), id=82, color=[128, 0, 255]), - 83: - dict(link=('sso_kpt20', 'sso_kpt19'), id=83, color=[128, 0, 255]), - 84: - dict(link=('sso_kpt19', 'sso_kpt18'), id=84, color=[128, 0, 255]), - 85: - dict(link=('sso_kpt18', 'sso_kpt17'), id=85, color=[128, 0, 255]), - 86: - dict(link=('sso_kpt17', 'sso_kpt29'), id=86, color=[128, 0, 255]), - 87: - dict(link=('sso_kpt29', 'sso_kpt28'), id=87, color=[128, 0, 255]), - 88: - dict(link=('sso_kpt28', 'sso_kpt27'), id=88, color=[128, 0, 255]), - 89: - dict(link=('sso_kpt27', 'sso_kpt26'), id=89, color=[128, 0, 255]), - 90: - dict(link=('sso_kpt26', 'sso_kpt5'), id=90, color=[128, 0, 255]), - 91: - dict(link=('sso_kpt5', 'sso_kpt6'), id=91, color=[128, 0, 255]), - 92: - dict(link=('lso_kpt1', 'lso_kpt2'), id=92, color=[0, 128, 255]), - 93: - dict(link=('lso_kpt2', 'lso_kpt7'), id=93, color=[0, 128, 255]), - 94: - dict(link=('lso_kpt7', 'lso_kpt8'), id=94, color=[0, 128, 255]), - 95: - dict(link=('lso_kpt8', 'lso_kpt9'), id=95, color=[0, 128, 255]), - 96: - dict(link=('lso_kpt9', 'lso_kpt10'), id=96, color=[0, 128, 255]), - 97: - dict(link=('lso_kpt10', 'lso_kpt11'), id=97, color=[0, 128, 255]), - 98: - dict(link=('lso_kpt11', 'lso_kpt12'), id=98, color=[0, 128, 255]), - 99: - dict(link=('lso_kpt12', 'lso_kpt13'), id=99, color=[0, 128, 255]), - 100: - dict(link=('lso_kpt13', 'lso_kpt14'), id=100, color=[0, 128, 255]), - 101: - dict(link=('lso_kpt14', 'lso_kpt15'), id=101, color=[0, 128, 255]), - 102: - dict(link=('lso_kpt15', 'lso_kpt16'), id=102, color=[0, 128, 255]), - 103: - dict(link=('lso_kpt16', 'lso_kpt17'), id=103, color=[0, 128, 255]), - 104: - dict(link=('lso_kpt17', 'lso_kpt18'), id=104, color=[0, 128, 255]), - 105: - dict(link=('lso_kpt18', 'lso_kpt19'), id=105, color=[0, 128, 255]), - 106: - dict(link=('lso_kpt19', 'lso_kpt20'), id=106, color=[0, 128, 255]), - 107: - dict(link=('lso_kpt20', 'lso_kpt39'), id=107, color=[0, 128, 255]), - 108: - dict(link=('lso_kpt39', 'lso_kpt38'), id=108, color=[0, 128, 255]), - 109: - dict(link=('lso_kpt38', 'lso_kpt4'), id=109, color=[0, 128, 255]), - 110: - dict(link=('lso_kpt4', 'lso_kpt3'), id=110, color=[0, 128, 255]), - 111: - dict(link=('lso_kpt3', 'lso_kpt2'), id=111, color=[0, 128, 255]), - 112: - dict(link=('lso_kpt1', 'lso_kpt6'), id=112, color=[0, 128, 255]), - 113: - dict(link=('lso_kpt6', 'lso_kpt33'), id=113, color=[0, 128, 255]), - 114: - dict(link=('lso_kpt33', 'lso_kpt32'), id=114, color=[0, 128, 255]), - 115: - dict(link=('lso_kpt32', 'lso_kpt31'), id=115, color=[0, 128, 255]), - 116: - dict(link=('lso_kpt31', 'lso_kpt30'), id=116, color=[0, 128, 255]), - 117: - dict(link=('lso_kpt30', 'lso_kpt29'), id=117, color=[0, 128, 255]), - 118: - dict(link=('lso_kpt29', 'lso_kpt28'), id=118, color=[0, 128, 255]), - 119: - dict(link=('lso_kpt28', 'lso_kpt27'), id=119, color=[0, 128, 255]), - 120: - dict(link=('lso_kpt27', 'lso_kpt26'), id=120, color=[0, 128, 255]), - 121: - dict(link=('lso_kpt26', 'lso_kpt25'), id=121, color=[0, 128, 255]), - 122: - dict(link=('lso_kpt25', 'lso_kpt24'), id=122, color=[0, 128, 255]), - 123: - dict(link=('lso_kpt24', 'lso_kpt23'), id=123, color=[0, 128, 255]), - 124: - dict(link=('lso_kpt23', 'lso_kpt22'), id=124, color=[0, 128, 255]), - 125: - dict(link=('lso_kpt22', 'lso_kpt21'), id=125, color=[0, 128, 255]), - 126: - dict(link=('lso_kpt21', 'lso_kpt37'), id=126, color=[0, 128, 255]), - 127: - dict(link=('lso_kpt37', 'lso_kpt36'), id=127, color=[0, 128, 255]), - 128: - dict(link=('lso_kpt36', 'lso_kpt35'), id=128, color=[0, 128, 255]), - 129: - dict(link=('lso_kpt35', 'lso_kpt34'), id=129, color=[0, 128, 255]), - 130: - dict(link=('lso_kpt34', 'lso_kpt5'), id=130, color=[0, 128, 255]), - 131: - dict(link=('lso_kpt5', 'lso_kpt6'), id=131, color=[0, 128, 255]), - 132: - dict(link=('vest_kpt1', 'vest_kpt2'), id=132, color=[0, 128, 128]), - 133: - dict(link=('vest_kpt2', 'vest_kpt7'), id=133, color=[0, 128, 128]), - 134: - dict(link=('vest_kpt7', 'vest_kpt8'), id=134, color=[0, 128, 128]), - 135: - dict(link=('vest_kpt8', 'vest_kpt9'), id=135, color=[0, 128, 128]), - 136: - dict(link=('vest_kpt9', 'vest_kpt10'), id=136, color=[0, 128, 128]), - 137: - dict(link=('vest_kpt10', 'vest_kpt11'), id=137, color=[0, 128, 128]), - 138: - dict(link=('vest_kpt11', 'vest_kpt12'), id=138, color=[0, 128, 128]), - 139: - dict(link=('vest_kpt12', 'vest_kpt13'), id=139, color=[0, 128, 128]), - 140: - dict(link=('vest_kpt13', 'vest_kpt14'), id=140, color=[0, 128, 128]), - 141: - dict(link=('vest_kpt14', 'vest_kpt15'), id=141, color=[0, 128, 128]), - 142: - dict(link=('vest_kpt15', 'vest_kpt6'), id=142, color=[0, 128, 128]), - 143: - dict(link=('vest_kpt6', 'vest_kpt1'), id=143, color=[0, 128, 128]), - 144: - dict(link=('vest_kpt2', 'vest_kpt3'), id=144, color=[0, 128, 128]), - 145: - dict(link=('vest_kpt3', 'vest_kpt4'), id=145, color=[0, 128, 128]), - 146: - dict(link=('vest_kpt4', 'vest_kpt5'), id=146, color=[0, 128, 128]), - 147: - dict(link=('vest_kpt5', 'vest_kpt6'), id=147, color=[0, 128, 128]), - 148: - dict(link=('sling_kpt1', 'sling_kpt2'), id=148, color=[0, 0, 128]), - 149: - dict(link=('sling_kpt2', 'sling_kpt8'), id=149, color=[0, 0, 128]), - 150: - dict(link=('sling_kpt8', 'sling_kpt9'), id=150, color=[0, 0, 128]), - 151: - dict(link=('sling_kpt9', 'sling_kpt10'), id=151, color=[0, 0, 128]), - 152: - dict(link=('sling_kpt10', 'sling_kpt11'), id=152, color=[0, 0, 128]), - 153: - dict(link=('sling_kpt11', 'sling_kpt12'), id=153, color=[0, 0, 128]), - 154: - dict(link=('sling_kpt12', 'sling_kpt13'), id=154, color=[0, 0, 128]), - 155: - dict(link=('sling_kpt13', 'sling_kpt14'), id=155, color=[0, 0, 128]), - 156: - dict(link=('sling_kpt14', 'sling_kpt6'), id=156, color=[0, 0, 128]), - 157: - dict(link=('sling_kpt2', 'sling_kpt7'), id=157, color=[0, 0, 128]), - 158: - dict(link=('sling_kpt6', 'sling_kpt15'), id=158, color=[0, 0, 128]), - 159: - dict(link=('sling_kpt2', 'sling_kpt3'), id=159, color=[0, 0, 128]), - 160: - dict(link=('sling_kpt3', 'sling_kpt4'), id=160, color=[0, 0, 128]), - 161: - dict(link=('sling_kpt4', 'sling_kpt5'), id=161, color=[0, 0, 128]), - 162: - dict(link=('sling_kpt5', 'sling_kpt6'), id=162, color=[0, 0, 128]), - 163: - dict(link=('sling_kpt1', 'sling_kpt6'), id=163, color=[0, 0, 128]), - 164: - dict( - link=('shorts_kpt1', 'shorts_kpt4'), id=164, color=[128, 128, - 128]), - 165: - dict( - link=('shorts_kpt4', 'shorts_kpt5'), id=165, color=[128, 128, - 128]), - 166: - dict( - link=('shorts_kpt5', 'shorts_kpt6'), id=166, color=[128, 128, - 128]), - 167: - dict( - link=('shorts_kpt6', 'shorts_kpt7'), id=167, color=[128, 128, - 128]), - 168: - dict( - link=('shorts_kpt7', 'shorts_kpt8'), id=168, color=[128, 128, - 128]), - 169: - dict( - link=('shorts_kpt8', 'shorts_kpt9'), id=169, color=[128, 128, - 128]), - 170: - dict( - link=('shorts_kpt9', 'shorts_kpt10'), - id=170, - color=[128, 128, 128]), - 171: - dict( - link=('shorts_kpt10', 'shorts_kpt3'), - id=171, - color=[128, 128, 128]), - 172: - dict( - link=('shorts_kpt3', 'shorts_kpt2'), id=172, color=[128, 128, - 128]), - 173: - dict( - link=('shorts_kpt2', 'shorts_kpt1'), id=173, color=[128, 128, - 128]), - 174: - dict( - link=('trousers_kpt1', 'trousers_kpt4'), - id=174, - color=[128, 0, 128]), - 175: - dict( - link=('trousers_kpt4', 'trousers_kpt5'), - id=175, - color=[128, 0, 128]), - 176: - dict( - link=('trousers_kpt5', 'trousers_kpt6'), - id=176, - color=[128, 0, 128]), - 177: - dict( - link=('trousers_kpt6', 'trousers_kpt7'), - id=177, - color=[128, 0, 128]), - 178: - dict( - link=('trousers_kpt7', 'trousers_kpt8'), - id=178, - color=[128, 0, 128]), - 179: - dict( - link=('trousers_kpt8', 'trousers_kpt9'), - id=179, - color=[128, 0, 128]), - 180: - dict( - link=('trousers_kpt9', 'trousers_kpt10'), - id=180, - color=[128, 0, 128]), - 181: - dict( - link=('trousers_kpt10', 'trousers_kpt11'), - id=181, - color=[128, 0, 128]), - 182: - dict( - link=('trousers_kpt11', 'trousers_kpt12'), - id=182, - color=[128, 0, 128]), - 183: - dict( - link=('trousers_kpt12', 'trousers_kpt13'), - id=183, - color=[128, 0, 128]), - 184: - dict( - link=('trousers_kpt13', 'trousers_kpt14'), - id=184, - color=[128, 0, 128]), - 185: - dict( - link=('trousers_kpt14', 'trousers_kpt3'), - id=185, - color=[128, 0, 128]), - 186: - dict( - link=('trousers_kpt3', 'trousers_kpt2'), - id=186, - color=[128, 0, 128]), - 187: - dict( - link=('trousers_kpt2', 'trousers_kpt1'), - id=187, - color=[128, 0, 128]), - 188: - dict(link=('skirt_kpt1', 'skirt_kpt4'), id=188, color=[64, 128, 128]), - 189: - dict(link=('skirt_kpt4', 'skirt_kpt5'), id=189, color=[64, 128, 128]), - 190: - dict(link=('skirt_kpt5', 'skirt_kpt6'), id=190, color=[64, 128, 128]), - 191: - dict(link=('skirt_kpt6', 'skirt_kpt7'), id=191, color=[64, 128, 128]), - 192: - dict(link=('skirt_kpt7', 'skirt_kpt8'), id=192, color=[64, 128, 128]), - 193: - dict(link=('skirt_kpt8', 'skirt_kpt3'), id=193, color=[64, 128, 128]), - 194: - dict(link=('skirt_kpt3', 'skirt_kpt2'), id=194, color=[64, 128, 128]), - 195: - dict(link=('skirt_kpt2', 'skirt_kpt1'), id=195, color=[64, 128, 128]), - 196: - dict(link=('ssd_kpt1', 'ssd_kpt2'), id=196, color=[64, 64, 128]), - 197: - dict(link=('ssd_kpt2', 'ssd_kpt7'), id=197, color=[64, 64, 128]), - 198: - dict(link=('ssd_kpt7', 'ssd_kpt8'), id=198, color=[64, 64, 128]), - 199: - dict(link=('ssd_kpt8', 'ssd_kpt9'), id=199, color=[64, 64, 128]), - 200: - dict(link=('ssd_kpt9', 'ssd_kpt10'), id=200, color=[64, 64, 128]), - 201: - dict(link=('ssd_kpt10', 'ssd_kpt11'), id=201, color=[64, 64, 128]), - 202: - dict(link=('ssd_kpt11', 'ssd_kpt12'), id=202, color=[64, 64, 128]), - 203: - dict(link=('ssd_kpt12', 'ssd_kpt13'), id=203, color=[64, 64, 128]), - 204: - dict(link=('ssd_kpt13', 'ssd_kpt14'), id=204, color=[64, 64, 128]), - 205: - dict(link=('ssd_kpt14', 'ssd_kpt15'), id=205, color=[64, 64, 128]), - 206: - dict(link=('ssd_kpt15', 'ssd_kpt16'), id=206, color=[64, 64, 128]), - 207: - dict(link=('ssd_kpt16', 'ssd_kpt17'), id=207, color=[64, 64, 128]), - 208: - dict(link=('ssd_kpt17', 'ssd_kpt18'), id=208, color=[64, 64, 128]), - 209: - dict(link=('ssd_kpt18', 'ssd_kpt19'), id=209, color=[64, 64, 128]), - 210: - dict(link=('ssd_kpt19', 'ssd_kpt20'), id=210, color=[64, 64, 128]), - 211: - dict(link=('ssd_kpt20', 'ssd_kpt21'), id=211, color=[64, 64, 128]), - 212: - dict(link=('ssd_kpt21', 'ssd_kpt22'), id=212, color=[64, 64, 128]), - 213: - dict(link=('ssd_kpt22', 'ssd_kpt23'), id=213, color=[64, 64, 128]), - 214: - dict(link=('ssd_kpt23', 'ssd_kpt24'), id=214, color=[64, 64, 128]), - 215: - dict(link=('ssd_kpt24', 'ssd_kpt25'), id=215, color=[64, 64, 128]), - 216: - dict(link=('ssd_kpt25', 'ssd_kpt26'), id=216, color=[64, 64, 128]), - 217: - dict(link=('ssd_kpt26', 'ssd_kpt27'), id=217, color=[64, 64, 128]), - 218: - dict(link=('ssd_kpt27', 'ssd_kpt28'), id=218, color=[64, 64, 128]), - 219: - dict(link=('ssd_kpt28', 'ssd_kpt29'), id=219, color=[64, 64, 128]), - 220: - dict(link=('ssd_kpt29', 'ssd_kpt6'), id=220, color=[64, 64, 128]), - 221: - dict(link=('ssd_kpt6', 'ssd_kpt5'), id=221, color=[64, 64, 128]), - 222: - dict(link=('ssd_kpt5', 'ssd_kpt4'), id=222, color=[64, 64, 128]), - 223: - dict(link=('ssd_kpt4', 'ssd_kpt3'), id=223, color=[64, 64, 128]), - 224: - dict(link=('ssd_kpt3', 'ssd_kpt2'), id=224, color=[64, 64, 128]), - 225: - dict(link=('ssd_kpt6', 'ssd_kpt1'), id=225, color=[64, 64, 128]), - 226: - dict(link=('lsd_kpt1', 'lsd_kpt2'), id=226, color=[128, 64, 0]), - 227: - dict(link=('lsd_kpt2', 'lsd_kpt7'), id=228, color=[128, 64, 0]), - 228: - dict(link=('lsd_kpt7', 'lsd_kpt8'), id=228, color=[128, 64, 0]), - 229: - dict(link=('lsd_kpt8', 'lsd_kpt9'), id=229, color=[128, 64, 0]), - 230: - dict(link=('lsd_kpt9', 'lsd_kpt10'), id=230, color=[128, 64, 0]), - 231: - dict(link=('lsd_kpt10', 'lsd_kpt11'), id=231, color=[128, 64, 0]), - 232: - dict(link=('lsd_kpt11', 'lsd_kpt12'), id=232, color=[128, 64, 0]), - 233: - dict(link=('lsd_kpt12', 'lsd_kpt13'), id=233, color=[128, 64, 0]), - 234: - dict(link=('lsd_kpt13', 'lsd_kpt14'), id=234, color=[128, 64, 0]), - 235: - dict(link=('lsd_kpt14', 'lsd_kpt15'), id=235, color=[128, 64, 0]), - 236: - dict(link=('lsd_kpt15', 'lsd_kpt16'), id=236, color=[128, 64, 0]), - 237: - dict(link=('lsd_kpt16', 'lsd_kpt17'), id=237, color=[128, 64, 0]), - 238: - dict(link=('lsd_kpt17', 'lsd_kpt18'), id=238, color=[128, 64, 0]), - 239: - dict(link=('lsd_kpt18', 'lsd_kpt19'), id=239, color=[128, 64, 0]), - 240: - dict(link=('lsd_kpt19', 'lsd_kpt20'), id=240, color=[128, 64, 0]), - 241: - dict(link=('lsd_kpt20', 'lsd_kpt21'), id=241, color=[128, 64, 0]), - 242: - dict(link=('lsd_kpt21', 'lsd_kpt22'), id=242, color=[128, 64, 0]), - 243: - dict(link=('lsd_kpt22', 'lsd_kpt23'), id=243, color=[128, 64, 0]), - 244: - dict(link=('lsd_kpt23', 'lsd_kpt24'), id=244, color=[128, 64, 0]), - 245: - dict(link=('lsd_kpt24', 'lsd_kpt25'), id=245, color=[128, 64, 0]), - 246: - dict(link=('lsd_kpt25', 'lsd_kpt26'), id=246, color=[128, 64, 0]), - 247: - dict(link=('lsd_kpt26', 'lsd_kpt27'), id=247, color=[128, 64, 0]), - 248: - dict(link=('lsd_kpt27', 'lsd_kpt28'), id=248, color=[128, 64, 0]), - 249: - dict(link=('lsd_kpt28', 'lsd_kpt29'), id=249, color=[128, 64, 0]), - 250: - dict(link=('lsd_kpt29', 'lsd_kpt30'), id=250, color=[128, 64, 0]), - 251: - dict(link=('lsd_kpt30', 'lsd_kpt31'), id=251, color=[128, 64, 0]), - 252: - dict(link=('lsd_kpt31', 'lsd_kpt32'), id=252, color=[128, 64, 0]), - 253: - dict(link=('lsd_kpt32', 'lsd_kpt33'), id=253, color=[128, 64, 0]), - 254: - dict(link=('lsd_kpt33', 'lsd_kpt34'), id=254, color=[128, 64, 0]), - 255: - dict(link=('lsd_kpt34', 'lsd_kpt35'), id=255, color=[128, 64, 0]), - 256: - dict(link=('lsd_kpt35', 'lsd_kpt36'), id=256, color=[128, 64, 0]), - 257: - dict(link=('lsd_kpt36', 'lsd_kpt37'), id=257, color=[128, 64, 0]), - 258: - dict(link=('lsd_kpt37', 'lsd_kpt6'), id=258, color=[128, 64, 0]), - 259: - dict(link=('lsd_kpt6', 'lsd_kpt5'), id=259, color=[128, 64, 0]), - 260: - dict(link=('lsd_kpt5', 'lsd_kpt4'), id=260, color=[128, 64, 0]), - 261: - dict(link=('lsd_kpt4', 'lsd_kpt3'), id=261, color=[128, 64, 0]), - 262: - dict(link=('lsd_kpt3', 'lsd_kpt2'), id=262, color=[128, 64, 0]), - 263: - dict(link=('lsd_kpt6', 'lsd_kpt1'), id=263, color=[128, 64, 0]), - 264: - dict(link=('vd_kpt1', 'vd_kpt2'), id=264, color=[128, 64, 255]), - 265: - dict(link=('vd_kpt2', 'vd_kpt7'), id=265, color=[128, 64, 255]), - 266: - dict(link=('vd_kpt7', 'vd_kpt8'), id=266, color=[128, 64, 255]), - 267: - dict(link=('vd_kpt8', 'vd_kpt9'), id=267, color=[128, 64, 255]), - 268: - dict(link=('vd_kpt9', 'vd_kpt10'), id=268, color=[128, 64, 255]), - 269: - dict(link=('vd_kpt10', 'vd_kpt11'), id=269, color=[128, 64, 255]), - 270: - dict(link=('vd_kpt11', 'vd_kpt12'), id=270, color=[128, 64, 255]), - 271: - dict(link=('vd_kpt12', 'vd_kpt13'), id=271, color=[128, 64, 255]), - 272: - dict(link=('vd_kpt13', 'vd_kpt14'), id=272, color=[128, 64, 255]), - 273: - dict(link=('vd_kpt14', 'vd_kpt15'), id=273, color=[128, 64, 255]), - 274: - dict(link=('vd_kpt15', 'vd_kpt16'), id=274, color=[128, 64, 255]), - 275: - dict(link=('vd_kpt16', 'vd_kpt17'), id=275, color=[128, 64, 255]), - 276: - dict(link=('vd_kpt17', 'vd_kpt18'), id=276, color=[128, 64, 255]), - 277: - dict(link=('vd_kpt18', 'vd_kpt19'), id=277, color=[128, 64, 255]), - 278: - dict(link=('vd_kpt19', 'vd_kpt6'), id=278, color=[128, 64, 255]), - 279: - dict(link=('vd_kpt6', 'vd_kpt5'), id=279, color=[128, 64, 255]), - 280: - dict(link=('vd_kpt5', 'vd_kpt4'), id=280, color=[128, 64, 255]), - 281: - dict(link=('vd_kpt4', 'vd_kpt3'), id=281, color=[128, 64, 255]), - 282: - dict(link=('vd_kpt3', 'vd_kpt2'), id=282, color=[128, 64, 255]), - 283: - dict(link=('vd_kpt6', 'vd_kpt1'), id=283, color=[128, 64, 255]), - 284: - dict(link=('sd_kpt1', 'sd_kpt2'), id=284, color=[128, 64, 0]), - 285: - dict(link=('sd_kpt2', 'sd_kpt8'), id=285, color=[128, 64, 0]), - 286: - dict(link=('sd_kpt8', 'sd_kpt9'), id=286, color=[128, 64, 0]), - 287: - dict(link=('sd_kpt9', 'sd_kpt10'), id=287, color=[128, 64, 0]), - 288: - dict(link=('sd_kpt10', 'sd_kpt11'), id=288, color=[128, 64, 0]), - 289: - dict(link=('sd_kpt11', 'sd_kpt12'), id=289, color=[128, 64, 0]), - 290: - dict(link=('sd_kpt12', 'sd_kpt13'), id=290, color=[128, 64, 0]), - 291: - dict(link=('sd_kpt13', 'sd_kpt14'), id=291, color=[128, 64, 0]), - 292: - dict(link=('sd_kpt14', 'sd_kpt15'), id=292, color=[128, 64, 0]), - 293: - dict(link=('sd_kpt15', 'sd_kpt16'), id=293, color=[128, 64, 0]), - 294: - dict(link=('sd_kpt16', 'sd_kpt17'), id=294, color=[128, 64, 0]), - 295: - dict(link=('sd_kpt17', 'sd_kpt18'), id=295, color=[128, 64, 0]), - 296: - dict(link=('sd_kpt18', 'sd_kpt6'), id=296, color=[128, 64, 0]), - 297: - dict(link=('sd_kpt6', 'sd_kpt5'), id=297, color=[128, 64, 0]), - 298: - dict(link=('sd_kpt5', 'sd_kpt4'), id=298, color=[128, 64, 0]), - 299: - dict(link=('sd_kpt4', 'sd_kpt3'), id=299, color=[128, 64, 0]), - 300: - dict(link=('sd_kpt3', 'sd_kpt2'), id=300, color=[128, 64, 0]), - 301: - dict(link=('sd_kpt2', 'sd_kpt7'), id=301, color=[128, 64, 0]), - 302: - dict(link=('sd_kpt6', 'sd_kpt19'), id=302, color=[128, 64, 0]), - 303: - dict(link=('sd_kpt6', 'sd_kpt1'), id=303, color=[128, 64, 0]) - }), - joint_weights=[ - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 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1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 - ], - sigmas=[]) -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, by_epoch=False), - dict( - type='MultiStepLR', - begin=0, - end=150, - milestones=[100, 130], - gamma=0.1, - by_epoch=True) -] -optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005)) -auto_scale_lr = dict(base_batch_size=512) -dataset_type = 'DeepFashion2Dataset' -data_mode = 'topdown' -data_root = 'data/deepfashion2/' -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict( - type='RandomBBoxTransform', - shift_prob=0, - rotate_factor=60, - scale_factor=(0.75, 1.25)), - dict(type='TopdownAffine', input_size=(192, 256)), - dict( - type='GenerateTarget', - encoder=dict( - type='MSRAHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - sigma=2)), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=dict(backend='local')), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=(192, 256)), - dict(type='PackPoseInputs') -] -train_dataloader = dict( - batch_size=16, - num_workers=6, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type='DeepFashion2Dataset', - data_root='data/deepfashion2/', - data_mode='topdown', - ann_file='train/deepfashion2_long_sleeved_dress.json', - data_prefix=dict(img='train/image/'), - pipeline=[ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict( - type='RandomBBoxTransform', - shift_prob=0, - rotate_factor=60, - scale_factor=(0.75, 1.25)), - dict(type='TopdownAffine', input_size=(192, 256)), - dict( - type='GenerateTarget', - encoder=dict( - type='MSRAHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - sigma=2)), - dict(type='PackPoseInputs') - ])) -val_dataloader = dict( - batch_size=16, - num_workers=6, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False), - dataset=dict( - type='DeepFashion2Dataset', - data_root='data/deepfashion2/', - data_mode='topdown', - ann_file='validation/deepfashion2_long_sleeved_dress.json', - data_prefix=dict(img='validation/image/'), - test_mode=True, - pipeline=[ - dict(type='LoadImage', backend_args=dict(backend='local')), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=(192, 256)), - dict(type='PackPoseInputs') - ])) -test_dataloader = dict( - batch_size=16, - num_workers=6, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False), - dataset=dict( - type='DeepFashion2Dataset', - data_root='data/deepfashion2/', - data_mode='topdown', - ann_file='validation/deepfashion2_long_sleeved_dress.json', - data_prefix=dict(img='validation/image/'), - test_mode=True, - pipeline=[ - dict(type='LoadImage', backend_args=dict(backend='local')), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=(192, 256)), - dict(type='PackPoseInputs') - ])) -channel_cfg = dict( - num_output_channels=294, - dataset_joints=294, - dataset_channel=[[ - 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, - 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, - 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, - 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, - 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, - 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, - 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, - 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, - 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, - 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, - 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, - 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, - 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, - 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, - 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, - 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, - 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, - 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, - 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, - 290, 291, 292, 293 - ]], - inference_channel=[ - 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, - 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, - 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, - 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, - 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, - 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, - 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, - 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, - 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, - 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, - 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, - 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, - 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, - 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, - 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, - 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, - 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, - 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, - 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, - 290, 291, 292, 293 - ]) -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=294, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=dict( - type='MSRAHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - sigma=2)), - test_cfg=dict(flip_test=True, flip_mode='heatmap', shift_heatmap=True)) -val_evaluator = [ - dict(type='PCKAccuracy', thr=0.2), - dict(type='AUC'), - dict(type='EPE') -] -test_evaluator = [ - dict(type='PCKAccuracy', thr=0.2), - dict(type='AUC'), - dict(type='EPE') -] -launcher = 'pytorch' -work_dir = './work_dirs/td_hm_res50_4xb16-150e_deepfashion2_long_sleeved_dress_256x192' diff --git a/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/models/loaders.py b/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/models/loaders.py deleted file mode 100644 index 8da96666e84345906cd2b2a6d9297f90ffe62689..0000000000000000000000000000000000000000 --- a/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/models/loaders.py +++ /dev/null @@ -1,96 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -""" -Utility functions to load from the checkpoints. -Each checkpoint is a torch.saved dict with the following keys: -- 'xp.cfg': the hydra config as dumped during training. This should be used - to rebuild the object using the audiocraft.models.builders functions, -- 'model_best_state': a readily loadable best state for the model, including - the conditioner. The model obtained from `xp.cfg` should be compatible - with this state dict. In the case of a LM, the encodec model would not be - bundled along but instead provided separately. - -Those functions also support loading from a remote location with the Torch Hub API. -They also support overriding some parameters, in particular the device and dtype -of the returned model. -""" - -from pathlib import Path -from huggingface_hub import hf_hub_download -import typing as tp -import os - -from omegaconf import OmegaConf -import torch - -from . import builders - - -HF_MODEL_CHECKPOINTS_MAP = { - "small": "facebook/musicgen-small", - "medium": "facebook/musicgen-medium", - "large": "facebook/musicgen-large", - "melody": "facebook/musicgen-melody", -} - - -def _get_state_dict( - file_or_url_or_id: tp.Union[Path, str], - filename: tp.Optional[str] = None, - device='cpu', - cache_dir: tp.Optional[str] = None, -): - # Return the state dict either from a file or url - file_or_url_or_id = str(file_or_url_or_id) - assert isinstance(file_or_url_or_id, str) - - if os.path.isfile(file_or_url_or_id): - return torch.load(file_or_url_or_id, map_location=device) - - if os.path.isdir(file_or_url_or_id): - file = f"{file_or_url_or_id}/{filename}" - return torch.load(file, map_location=device) - - elif file_or_url_or_id.startswith('https://'): - return torch.hub.load_state_dict_from_url(file_or_url_or_id, map_location=device, check_hash=True) - - elif file_or_url_or_id in HF_MODEL_CHECKPOINTS_MAP: - assert filename is not None, "filename needs to be defined if using HF checkpoints" - - repo_id = HF_MODEL_CHECKPOINTS_MAP[file_or_url_or_id] - file = hf_hub_download(repo_id=repo_id, filename=filename, cache_dir=cache_dir) - return torch.load(file, map_location=device) - - else: - raise ValueError(f"{file_or_url_or_id} is not a valid name, path or link that can be loaded.") - - -def load_compression_model(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None): - pkg = _get_state_dict(file_or_url_or_id, filename="compression_state_dict.bin", cache_dir=cache_dir) - cfg = OmegaConf.create(pkg['xp.cfg']) - cfg.device = str(device) - model = builders.get_compression_model(cfg) - model.load_state_dict(pkg['best_state']) - model.eval() - return model - - -def load_lm_model(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None): - pkg = _get_state_dict(file_or_url_or_id, filename="state_dict.bin", cache_dir=cache_dir) - cfg = OmegaConf.create(pkg['xp.cfg']) - cfg.device = str(device) - if cfg.device == 'cpu': - cfg.transformer_lm.memory_efficient = False - cfg.transformer_lm.custom = True - cfg.dtype = 'float32' - else: - cfg.dtype = 'float16' - model = builders.get_lm_model(cfg) - model.load_state_dict(pkg['best_state']) - model.eval() - model.cfg = cfg - return model diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/npm/node_modules/crypto-js/README.md b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/npm/node_modules/crypto-js/README.md deleted file mode 100644 index 23795aa466e3199fa2582e4d47bceb76fc6093a2..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/npm/node_modules/crypto-js/README.md +++ /dev/null @@ -1,261 +0,0 @@ -# crypto-js [![Build Status](https://travis-ci.org/brix/crypto-js.svg?branch=develop)](https://travis-ci.org/brix/crypto-js) - -JavaScript library of crypto standards. - -## Node.js (Install) - -Requirements: - -- Node.js -- npm (Node.js package manager) - -```bash -npm install crypto-js -``` - -### Usage - -ES6 import for typical API call signing use case: - -```javascript -import sha256 from 'crypto-js/sha256'; -import hmacSHA512 from 'crypto-js/hmac-sha512'; -import Base64 from 'crypto-js/enc-base64'; - -const message, nonce, path, privateKey; // ... -const hashDigest = sha256(nonce + message); -const hmacDigest = Base64.stringify(hmacSHA512(path + hashDigest, privateKey)); -``` - -Modular include: - -```javascript -var AES = require("crypto-js/aes"); -var SHA256 = require("crypto-js/sha256"); -... -console.log(SHA256("Message")); -``` - -Including all libraries, for access to extra methods: - -```javascript -var CryptoJS = require("crypto-js"); -console.log(CryptoJS.HmacSHA1("Message", "Key")); -``` - -## Client (browser) - -Requirements: - -- Node.js -- Bower (package manager for frontend) - -```bash -bower install crypto-js -``` - -### Usage - -Modular include: - -```javascript -require.config({ - packages: [ - { - name: 'crypto-js', - location: 'path-to/bower_components/crypto-js', - main: 'index' - } - ] -}); - -require(["crypto-js/aes", "crypto-js/sha256"], function (AES, SHA256) { - console.log(SHA256("Message")); -}); -``` - -Including all libraries, for access to extra methods: - -```javascript -// Above-mentioned will work or use this simple form -require.config({ - paths: { - 'crypto-js': 'path-to/bower_components/crypto-js/crypto-js' - } -}); - -require(["crypto-js"], function (CryptoJS) { - console.log(CryptoJS.HmacSHA1("Message", "Key")); -}); -``` - -### Usage without RequireJS - -```html - - -``` - -## API - -See: https://cryptojs.gitbook.io/docs/ - -### AES Encryption - -#### Plain text encryption - -```javascript -var CryptoJS = require("crypto-js"); - -// Encrypt -var ciphertext = CryptoJS.AES.encrypt('my message', 'secret key 123').toString(); - -// Decrypt -var bytes = CryptoJS.AES.decrypt(ciphertext, 'secret key 123'); -var originalText = bytes.toString(CryptoJS.enc.Utf8); - -console.log(originalText); // 'my message' -``` - -#### Object encryption - -```javascript -var CryptoJS = require("crypto-js"); - -var data = [{id: 1}, {id: 2}] - -// Encrypt -var ciphertext = CryptoJS.AES.encrypt(JSON.stringify(data), 'secret key 123').toString(); - -// Decrypt -var bytes = CryptoJS.AES.decrypt(ciphertext, 'secret key 123'); -var decryptedData = JSON.parse(bytes.toString(CryptoJS.enc.Utf8)); - -console.log(decryptedData); // [{id: 1}, {id: 2}] -``` - -### List of modules - - -- ```crypto-js/core``` -- ```crypto-js/x64-core``` -- ```crypto-js/lib-typedarrays``` - ---- - -- ```crypto-js/md5``` -- ```crypto-js/sha1``` -- ```crypto-js/sha256``` -- ```crypto-js/sha224``` -- ```crypto-js/sha512``` -- ```crypto-js/sha384``` -- ```crypto-js/sha3``` -- ```crypto-js/ripemd160``` - ---- - -- ```crypto-js/hmac-md5``` -- ```crypto-js/hmac-sha1``` -- ```crypto-js/hmac-sha256``` -- ```crypto-js/hmac-sha224``` -- ```crypto-js/hmac-sha512``` -- ```crypto-js/hmac-sha384``` -- ```crypto-js/hmac-sha3``` -- ```crypto-js/hmac-ripemd160``` - ---- - -- ```crypto-js/pbkdf2``` - ---- - -- ```crypto-js/aes``` -- ```crypto-js/tripledes``` -- ```crypto-js/rc4``` -- ```crypto-js/rabbit``` -- ```crypto-js/rabbit-legacy``` -- ```crypto-js/evpkdf``` - ---- - -- ```crypto-js/format-openssl``` -- ```crypto-js/format-hex``` - ---- - -- ```crypto-js/enc-latin1``` -- ```crypto-js/enc-utf8``` -- ```crypto-js/enc-hex``` -- ```crypto-js/enc-utf16``` -- ```crypto-js/enc-base64``` - ---- - -- ```crypto-js/mode-cfb``` -- ```crypto-js/mode-ctr``` -- ```crypto-js/mode-ctr-gladman``` -- ```crypto-js/mode-ofb``` -- ```crypto-js/mode-ecb``` - ---- - -- ```crypto-js/pad-pkcs7``` -- ```crypto-js/pad-ansix923``` -- ```crypto-js/pad-iso10126``` -- ```crypto-js/pad-iso97971``` -- ```crypto-js/pad-zeropadding``` -- ```crypto-js/pad-nopadding``` - - -## Release notes - -### 4.1.1 - -Fix module order in bundled release. - -Include the browser field in the released package.json. - -### 4.1.0 - -Added url safe variant of base64 encoding. [357](https://github.com/brix/crypto-js/pull/357) - -Avoid webpack to add crypto-browser package. [364](https://github.com/brix/crypto-js/pull/364) - -### 4.0.0 - -This is an update including breaking changes for some environments. - -In this version `Math.random()` has been replaced by the random methods of the native crypto module. - -For this reason CryptoJS might not run in some JavaScript environments without native crypto module. Such as IE 10 or before or React Native. - -### 3.3.0 - -Rollback, `3.3.0` is the same as `3.1.9-1`. - -The move of using native secure crypto module will be shifted to a new `4.x.x` version. As it is a breaking change the impact is too big for a minor release. - -### 3.2.1 - -The usage of the native crypto module has been fixed. The import and access of the native crypto module has been improved. - -### 3.2.0 - -In this version `Math.random()` has been replaced by the random methods of the native crypto module. - -For this reason CryptoJS might does not run in some JavaScript environments without native crypto module. Such as IE 10 or before. - -If it's absolute required to run CryptoJS in such an environment, stay with `3.1.x` version. Encrypting and decrypting stays compatible. But keep in mind `3.1.x` versions still use `Math.random()` which is cryptographically not secure, as it's not random enough. - -This version came along with `CRITICAL` `BUG`. - -DO NOT USE THIS VERSION! Please, go for a newer version! - -### 3.1.x - -The `3.1.x` are based on the original CryptoJS, wrapped in CommonJS modules. - - diff --git a/spaces/Adapter/CoAdapter/ldm/models/diffusion/plms.py b/spaces/Adapter/CoAdapter/ldm/models/diffusion/plms.py deleted file mode 100644 index 273ffbebaf952ffc25f6b92506b7c91b4af4c3bf..0000000000000000000000000000000000000000 --- a/spaces/Adapter/CoAdapter/ldm/models/diffusion/plms.py +++ /dev/null @@ -1,243 +0,0 @@ -"""SAMPLING ONLY.""" - -import torch -import numpy as np -from tqdm import tqdm -from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like - - -class PLMSSampler(object): - def __init__(self, model, schedule="linear", **kwargs): - super().__init__() - self.model = model - self.ddpm_num_timesteps = model.num_timesteps - self.schedule = schedule - - def register_buffer(self, name, attr): - if type(attr) == torch.Tensor: - if attr.device != torch.device("cuda"): - attr = attr.to(torch.device("cuda")) - setattr(self, name, attr) - - def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): - if ddim_eta != 0: - raise ValueError('ddim_eta must be 0 for PLMS') - self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, - num_ddpm_timesteps=self.ddpm_num_timesteps, verbose=verbose) - alphas_cumprod = self.model.alphas_cumprod - assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' - to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) - - self.register_buffer('betas', to_torch(self.model.betas)) - self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) - self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) - - # calculations for diffusion q(x_t | x_{t-1}) and others - self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) - self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) - self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) - self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) - self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) - - # ddim sampling parameters - ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), - ddim_timesteps=self.ddim_timesteps, - eta=ddim_eta, verbose=verbose) - self.register_buffer('ddim_sigmas', ddim_sigmas) - self.register_buffer('ddim_alphas', ddim_alphas) - self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) - self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) - sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( - (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( - 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) - self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) - - @torch.no_grad() - def sample(self, - S, - batch_size, - shape, - conditioning=None, - callback=None, - normals_sequence=None, - img_callback=None, - quantize_x0=False, - eta=0., - mask=None, - x0=None, - temperature=1., - noise_dropout=0., - score_corrector=None, - corrector_kwargs=None, - verbose=True, - x_T=None, - log_every_t=100, - unconditional_guidance_scale=1., - unconditional_conditioning=None, - features_adapter=None, - cond_tau=0.4, - # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... - **kwargs - ): - # print('*'*20,x_T) - # exit(0) - if conditioning is not None: - if isinstance(conditioning, dict): - cbs = conditioning[list(conditioning.keys())[0]].shape[0] - if cbs != batch_size: - print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") - else: - if conditioning.shape[0] != batch_size: - print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") - - self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) - C, H, W = shape - size = (batch_size, C, H, W) - print(f'Data shape for PLMS sampling is {size}') - - samples, intermediates = self.plms_sampling(conditioning, size, - callback=callback, - img_callback=img_callback, - quantize_denoised=quantize_x0, - mask=mask, x0=x0, - ddim_use_original_steps=False, - noise_dropout=noise_dropout, - temperature=temperature, - score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, - x_T=x_T, - log_every_t=log_every_t, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - features_adapter=features_adapter, - cond_tau=cond_tau - ) - return samples, intermediates - - @torch.no_grad() - def plms_sampling(self, cond, shape, - x_T=None, ddim_use_original_steps=False, - callback=None, timesteps=None, quantize_denoised=False, - mask=None, x0=None, img_callback=None, log_every_t=100, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, features_adapter=None, - cond_tau=0.4): - device = self.model.betas.device - b = shape[0] - if x_T is None: - img = torch.randn(shape, device=device) - else: - img = x_T - if timesteps is None: - timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps - elif timesteps is not None and not ddim_use_original_steps: - subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 - timesteps = self.ddim_timesteps[:subset_end] - - intermediates = {'x_inter': [img], 'pred_x0': [img]} - time_range = list(reversed(range(0, timesteps))) if ddim_use_original_steps else np.flip(timesteps) - total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] - print(f"Running PLMS Sampling with {total_steps} timesteps") - - iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps) - old_eps = [] - - for i, step in enumerate(iterator): - index = total_steps - i - 1 - ts = torch.full((b,), step, device=device, dtype=torch.long) - ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long) - - if mask is not None: # and index>=10: - assert x0 is not None - img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? - img = img_orig * mask + (1. - mask) * img - - outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, - quantize_denoised=quantize_denoised, temperature=temperature, - noise_dropout=noise_dropout, score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - old_eps=old_eps, t_next=ts_next, - features_adapter=None if index < int( - (1 - cond_tau) * total_steps) else features_adapter) - - img, pred_x0, e_t = outs - old_eps.append(e_t) - if len(old_eps) >= 4: - old_eps.pop(0) - if callback: callback(i) - if img_callback: img_callback(pred_x0, i) - - if index % log_every_t == 0 or index == total_steps - 1: - intermediates['x_inter'].append(img) - intermediates['pred_x0'].append(pred_x0) - - return img, intermediates - - @torch.no_grad() - def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, - features_adapter=None): - b, *_, device = *x.shape, x.device - - def get_model_output(x, t): - if unconditional_conditioning is None or unconditional_guidance_scale == 1.: - e_t = self.model.apply_model(x, t, c, features_adapter=features_adapter) - else: - x_in = torch.cat([x] * 2) - t_in = torch.cat([t] * 2) - c_in = torch.cat([unconditional_conditioning, c]) - e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in, features_adapter=features_adapter).chunk(2) - e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) - - if score_corrector is not None: - assert self.model.parameterization == "eps" - e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) - - return e_t - - alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas - alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev - sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas - sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas - - def get_x_prev_and_pred_x0(e_t, index): - # select parameters corresponding to the currently considered timestep - a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) - a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) - sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) - sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device) - - # current prediction for x_0 - pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() - if quantize_denoised: - pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) - # direction pointing to x_t - dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t - noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature - if noise_dropout > 0.: - noise = torch.nn.functional.dropout(noise, p=noise_dropout) - x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise - return x_prev, pred_x0 - - e_t = get_model_output(x, t) - if len(old_eps) == 0: - # Pseudo Improved Euler (2nd order) - x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) - e_t_next = get_model_output(x_prev, t_next) - e_t_prime = (e_t + e_t_next) / 2 - elif len(old_eps) == 1: - # 2nd order Pseudo Linear Multistep (Adams-Bashforth) - e_t_prime = (3 * e_t - old_eps[-1]) / 2 - elif len(old_eps) == 2: - # 3nd order Pseudo Linear Multistep (Adams-Bashforth) - e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 - elif len(old_eps) >= 3: - # 4nd order Pseudo Linear Multistep (Adams-Bashforth) - e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 - - x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) - - return x_prev, pred_x0, e_t diff --git a/spaces/Aditya9790/yolo7-object-tracking/utils/general.py b/spaces/Aditya9790/yolo7-object-tracking/utils/general.py deleted file mode 100644 index decdcc64ecd72927bc6c185683977854e593711d..0000000000000000000000000000000000000000 --- a/spaces/Aditya9790/yolo7-object-tracking/utils/general.py +++ /dev/null @@ -1,892 +0,0 @@ -# YOLOR general utils - -import glob -import logging -import math -import os -import platform -import random -import re -import subprocess -import time -from pathlib import Path - -import cv2 -import numpy as np -import pandas as pd -import torch -import torchvision -import yaml - -from utils.google_utils import gsutil_getsize -from utils.metrics import fitness -from utils.torch_utils import init_torch_seeds - -# Settings -torch.set_printoptions(linewidth=320, precision=5, profile='long') -np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 -pd.options.display.max_columns = 10 -cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) -os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8)) # NumExpr max threads - - -def set_logging(rank=-1): - logging.basicConfig( - format="%(message)s", - level=logging.INFO if rank in [-1, 0] else logging.WARN) - - -def init_seeds(seed=0): - # Initialize random number generator (RNG) seeds - random.seed(seed) - np.random.seed(seed) - init_torch_seeds(seed) - - -def get_latest_run(search_dir='.'): - # Return path to most recent 'last.pt' in /runs (i.e. to --resume from) - last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True) - return max(last_list, key=os.path.getctime) if last_list else '' - - -def isdocker(): - # Is environment a Docker container - return Path('/workspace').exists() # or Path('/.dockerenv').exists() - - -def emojis(str=''): - # Return platform-dependent emoji-safe version of string - return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str - - -def check_online(): - # Check internet connectivity - import socket - try: - socket.create_connection(("1.1.1.1", 443), 5) # check host accesability - return True - except OSError: - return False - - -def check_git_status(): - # Recommend 'git pull' if code is out of date - print(colorstr('github: '), end='') - try: - assert Path('.git').exists(), 'skipping check (not a git repository)' - assert not isdocker(), 'skipping check (Docker image)' - assert check_online(), 'skipping check (offline)' - - cmd = 'git fetch && git config --get remote.origin.url' - url = subprocess.check_output(cmd, shell=True).decode().strip().rstrip('.git') # github repo url - branch = subprocess.check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out - n = int(subprocess.check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind - if n > 0: - s = f"⚠️ WARNING: code is out of date by {n} commit{'s' * (n > 1)}. " \ - f"Use 'git pull' to update or 'git clone {url}' to download latest." - else: - s = f'up to date with {url} ✅' - print(emojis(s)) # emoji-safe - except Exception as e: - print(e) - - -def check_requirements(requirements='requirements.txt', exclude=()): - # Check installed dependencies meet requirements (pass *.txt file or list of packages) - import pkg_resources as pkg - prefix = colorstr('red', 'bold', 'requirements:') - if isinstance(requirements, (str, Path)): # requirements.txt file - file = Path(requirements) - if not file.exists(): - print(f"{prefix} {file.resolve()} not found, check failed.") - return - requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(file.open()) if x.name not in exclude] - else: # list or tuple of packages - requirements = [x for x in requirements if x not in exclude] - - n = 0 # number of packages updates - for r in requirements: - try: - pkg.require(r) - except Exception as e: # DistributionNotFound or VersionConflict if requirements not met - n += 1 - print(f"{prefix} {e.req} not found and is required by YOLOR, attempting auto-update...") - print(subprocess.check_output(f"pip install '{e.req}'", shell=True).decode()) - - if n: # if packages updated - source = file.resolve() if 'file' in locals() else requirements - s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \ - f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n" - print(emojis(s)) # emoji-safe - - -def check_img_size(img_size, s=32): - # Verify img_size is a multiple of stride s - new_size = make_divisible(img_size, int(s)) # ceil gs-multiple - if new_size != img_size: - print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size)) - return new_size - - -def check_imshow(): - # Check if environment supports image displays - try: - assert not isdocker(), 'cv2.imshow() is disabled in Docker environments' - cv2.imshow('test', np.zeros((1, 1, 3))) - cv2.waitKey(1) - cv2.destroyAllWindows() - cv2.waitKey(1) - return True - except Exception as e: - print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}') - return False - - -def check_file(file): - # Search for file if not found - if Path(file).is_file() or file == '': - return file - else: - files = glob.glob('./**/' + file, recursive=True) # find file - assert len(files), f'File Not Found: {file}' # assert file was found - assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique - return files[0] # return file - - -def check_dataset(dict): - # Download dataset if not found locally - val, s = dict.get('val'), dict.get('download') - if val and len(val): - val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path - if not all(x.exists() for x in val): - print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()]) - if s and len(s): # download script - print('Downloading %s ...' % s) - if s.startswith('http') and s.endswith('.zip'): # URL - f = Path(s).name # filename - torch.hub.download_url_to_file(s, f) - r = os.system('unzip -q %s -d ../ && rm %s' % (f, f)) # unzip - else: # bash script - r = os.system(s) - print('Dataset autodownload %s\n' % ('success' if r == 0 else 'failure')) # analyze return value - else: - raise Exception('Dataset not found.') - - -def make_divisible(x, divisor): - # Returns x evenly divisible by divisor - return math.ceil(x / divisor) * divisor - - -def clean_str(s): - # Cleans a string by replacing special characters with underscore _ - return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s) - - -def one_cycle(y1=0.0, y2=1.0, steps=100): - # lambda function for sinusoidal ramp from y1 to y2 - return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 - - -def colorstr(*input): - # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world') - *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string - colors = {'black': '\033[30m', # basic colors - 'red': '\033[31m', - 'green': '\033[32m', - 'yellow': '\033[33m', - 'blue': '\033[34m', - 'magenta': '\033[35m', - 'cyan': '\033[36m', - 'white': '\033[37m', - 'bright_black': '\033[90m', # bright colors - 'bright_red': '\033[91m', - 'bright_green': '\033[92m', - 'bright_yellow': '\033[93m', - 'bright_blue': '\033[94m', - 'bright_magenta': '\033[95m', - 'bright_cyan': '\033[96m', - 'bright_white': '\033[97m', - 'end': '\033[0m', # misc - 'bold': '\033[1m', - 'underline': '\033[4m'} - return ''.join(colors[x] for x in args) + f'{string}' + colors['end'] - - -def labels_to_class_weights(labels, nc=80): - # Get class weights (inverse frequency) from training labels - if labels[0] is None: # no labels loaded - return torch.Tensor() - - labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO - classes = labels[:, 0].astype(np.int32) # labels = [class xywh] - weights = np.bincount(classes, minlength=nc) # occurrences per class - - # Prepend gridpoint count (for uCE training) - # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image - # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start - - weights[weights == 0] = 1 # replace empty bins with 1 - weights = 1 / weights # number of targets per class - weights /= weights.sum() # normalize - return torch.from_numpy(weights) - - -def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): - # Produces image weights based on class_weights and image contents - class_counts = np.array([np.bincount(x[:, 0].astype(np.int32), minlength=nc) for x in labels]) - image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1) - # index = random.choices(range(n), weights=image_weights, k=1) # weight image sample - return image_weights - - -def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) - # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ - # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') - # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') - # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco - # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet - x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, - 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, - 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] - return x - - -def xyxy2xywh(x): - # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center - y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center - y[:, 2] = x[:, 2] - x[:, 0] # width - y[:, 3] = x[:, 3] - x[:, 1] # height - return y - - -def xywh2xyxy(x): - # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x - y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y - y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x - y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y - return y - - -def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): - # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x - y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y - y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x - y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y - return y - - -def xyn2xy(x, w=640, h=640, padw=0, padh=0): - # Convert normalized segments into pixel segments, shape (n,2) - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = w * x[:, 0] + padw # top left x - y[:, 1] = h * x[:, 1] + padh # top left y - return y - - -def segment2box(segment, width=640, height=640): - # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy) - x, y = segment.T # segment xy - inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) - x, y, = x[inside], y[inside] - return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy - - -def segments2boxes(segments): - # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh) - boxes = [] - for s in segments: - x, y = s.T # segment xy - boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy - return xyxy2xywh(np.array(boxes)) # cls, xywh - - -def resample_segments(segments, n=1000): - # Up-sample an (n,2) segment - for i, s in enumerate(segments): - s = np.concatenate((s, s[0:1, :]), axis=0) - x = np.linspace(0, len(s) - 1, n) - xp = np.arange(len(s)) - segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy - return segments - - -def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): - # Rescale coords (xyxy) from img1_shape to img0_shape - if ratio_pad is None: # calculate from img0_shape - gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new - pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding - else: - gain = ratio_pad[0][0] - pad = ratio_pad[1] - - coords[:, [0, 2]] -= pad[0] # x padding - coords[:, [1, 3]] -= pad[1] # y padding - coords[:, :4] /= gain - clip_coords(coords, img0_shape) - return coords - - -def clip_coords(boxes, img_shape): - # Clip bounding xyxy bounding boxes to image shape (height, width) - boxes[:, 0].clamp_(0, img_shape[1]) # x1 - boxes[:, 1].clamp_(0, img_shape[0]) # y1 - boxes[:, 2].clamp_(0, img_shape[1]) # x2 - boxes[:, 3].clamp_(0, img_shape[0]) # y2 - - -def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): - # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 - box2 = box2.T - - # Get the coordinates of bounding boxes - if x1y1x2y2: # x1, y1, x2, y2 = box1 - b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] - b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] - else: # transform from xywh to xyxy - b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 - b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 - b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 - b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 - - # Intersection area - inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \ - (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) - - # Union Area - w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps - w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps - union = w1 * h1 + w2 * h2 - inter + eps - - iou = inter / union - - if GIoU or DIoU or CIoU: - cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width - ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height - if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 - c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared - rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + - (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared - if DIoU: - return iou - rho2 / c2 # DIoU - elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 - v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / (h2 + eps)) - torch.atan(w1 / (h1 + eps)), 2) - with torch.no_grad(): - alpha = v / (v - iou + (1 + eps)) - return iou - (rho2 / c2 + v * alpha) # CIoU - else: # GIoU https://arxiv.org/pdf/1902.09630.pdf - c_area = cw * ch + eps # convex area - return iou - (c_area - union) / c_area # GIoU - else: - return iou # IoU - - - - -def bbox_alpha_iou(box1, box2, x1y1x2y2=False, GIoU=False, DIoU=False, CIoU=False, alpha=2, eps=1e-9): - # Returns tsqrt_he IoU of box1 to box2. box1 is 4, box2 is nx4 - box2 = box2.T - - # Get the coordinates of bounding boxes - if x1y1x2y2: # x1, y1, x2, y2 = box1 - b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] - b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] - else: # transform from xywh to xyxy - b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 - b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 - b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 - b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 - - # Intersection area - inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \ - (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) - - # Union Area - w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps - w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps - union = w1 * h1 + w2 * h2 - inter + eps - - # change iou into pow(iou+eps) - # iou = inter / union - iou = torch.pow(inter/union + eps, alpha) - # beta = 2 * alpha - if GIoU or DIoU or CIoU: - cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width - ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height - if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 - c2 = (cw ** 2 + ch ** 2) ** alpha + eps # convex diagonal - rho_x = torch.abs(b2_x1 + b2_x2 - b1_x1 - b1_x2) - rho_y = torch.abs(b2_y1 + b2_y2 - b1_y1 - b1_y2) - rho2 = ((rho_x ** 2 + rho_y ** 2) / 4) ** alpha # center distance - if DIoU: - return iou - rho2 / c2 # DIoU - elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 - v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) - with torch.no_grad(): - alpha_ciou = v / ((1 + eps) - inter / union + v) - # return iou - (rho2 / c2 + v * alpha_ciou) # CIoU - return iou - (rho2 / c2 + torch.pow(v * alpha_ciou + eps, alpha)) # CIoU - else: # GIoU https://arxiv.org/pdf/1902.09630.pdf - # c_area = cw * ch + eps # convex area - # return iou - (c_area - union) / c_area # GIoU - c_area = torch.max(cw * ch + eps, union) # convex area - return iou - torch.pow((c_area - union) / c_area + eps, alpha) # GIoU - else: - return iou # torch.log(iou+eps) or iou - - -def box_iou(box1, box2): - # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py - """ - Return intersection-over-union (Jaccard index) of boxes. - Both sets of boxes are expected to be in (x1, y1, x2, y2) format. - Arguments: - box1 (Tensor[N, 4]) - box2 (Tensor[M, 4]) - Returns: - iou (Tensor[N, M]): the NxM matrix containing the pairwise - IoU values for every element in boxes1 and boxes2 - """ - - def box_area(box): - # box = 4xn - return (box[2] - box[0]) * (box[3] - box[1]) - - area1 = box_area(box1.T) - area2 = box_area(box2.T) - - # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) - inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) - return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter) - - -def wh_iou(wh1, wh2): - # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2 - wh1 = wh1[:, None] # [N,1,2] - wh2 = wh2[None] # [1,M,2] - inter = torch.min(wh1, wh2).prod(2) # [N,M] - return inter / (wh1.prod(2) + wh2.prod(2) - inter) # iou = inter / (area1 + area2 - inter) - - -def box_giou(box1, box2): - """ - Return generalized intersection-over-union (Jaccard index) between two sets of boxes. - Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with - ``0 <= x1 < x2`` and ``0 <= y1 < y2``. - Args: - boxes1 (Tensor[N, 4]): first set of boxes - boxes2 (Tensor[M, 4]): second set of boxes - Returns: - Tensor[N, M]: the NxM matrix containing the pairwise generalized IoU values - for every element in boxes1 and boxes2 - """ - - def box_area(box): - # box = 4xn - return (box[2] - box[0]) * (box[3] - box[1]) - - area1 = box_area(box1.T) - area2 = box_area(box2.T) - - inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) - union = (area1[:, None] + area2 - inter) - - iou = inter / union - - lti = torch.min(box1[:, None, :2], box2[:, :2]) - rbi = torch.max(box1[:, None, 2:], box2[:, 2:]) - - whi = (rbi - lti).clamp(min=0) # [N,M,2] - areai = whi[:, :, 0] * whi[:, :, 1] - - return iou - (areai - union) / areai - - -def box_ciou(box1, box2, eps: float = 1e-7): - """ - Return complete intersection-over-union (Jaccard index) between two sets of boxes. - Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with - ``0 <= x1 < x2`` and ``0 <= y1 < y2``. - Args: - boxes1 (Tensor[N, 4]): first set of boxes - boxes2 (Tensor[M, 4]): second set of boxes - eps (float, optional): small number to prevent division by zero. Default: 1e-7 - Returns: - Tensor[N, M]: the NxM matrix containing the pairwise complete IoU values - for every element in boxes1 and boxes2 - """ - - def box_area(box): - # box = 4xn - return (box[2] - box[0]) * (box[3] - box[1]) - - area1 = box_area(box1.T) - area2 = box_area(box2.T) - - inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) - union = (area1[:, None] + area2 - inter) - - iou = inter / union - - lti = torch.min(box1[:, None, :2], box2[:, :2]) - rbi = torch.max(box1[:, None, 2:], box2[:, 2:]) - - whi = (rbi - lti).clamp(min=0) # [N,M,2] - diagonal_distance_squared = (whi[:, :, 0] ** 2) + (whi[:, :, 1] ** 2) + eps - - # centers of boxes - x_p = (box1[:, None, 0] + box1[:, None, 2]) / 2 - y_p = (box1[:, None, 1] + box1[:, None, 3]) / 2 - x_g = (box2[:, 0] + box2[:, 2]) / 2 - y_g = (box2[:, 1] + box2[:, 3]) / 2 - # The distance between boxes' centers squared. - centers_distance_squared = (x_p - x_g) ** 2 + (y_p - y_g) ** 2 - - w_pred = box1[:, None, 2] - box1[:, None, 0] - h_pred = box1[:, None, 3] - box1[:, None, 1] - - w_gt = box2[:, 2] - box2[:, 0] - h_gt = box2[:, 3] - box2[:, 1] - - v = (4 / (torch.pi ** 2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2) - with torch.no_grad(): - alpha = v / (1 - iou + v + eps) - return iou - (centers_distance_squared / diagonal_distance_squared) - alpha * v - - -def box_diou(box1, box2, eps: float = 1e-7): - """ - Return distance intersection-over-union (Jaccard index) between two sets of boxes. - Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with - ``0 <= x1 < x2`` and ``0 <= y1 < y2``. - Args: - boxes1 (Tensor[N, 4]): first set of boxes - boxes2 (Tensor[M, 4]): second set of boxes - eps (float, optional): small number to prevent division by zero. Default: 1e-7 - Returns: - Tensor[N, M]: the NxM matrix containing the pairwise distance IoU values - for every element in boxes1 and boxes2 - """ - - def box_area(box): - # box = 4xn - return (box[2] - box[0]) * (box[3] - box[1]) - - area1 = box_area(box1.T) - area2 = box_area(box2.T) - - inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) - union = (area1[:, None] + area2 - inter) - - iou = inter / union - - lti = torch.min(box1[:, None, :2], box2[:, :2]) - rbi = torch.max(box1[:, None, 2:], box2[:, 2:]) - - whi = (rbi - lti).clamp(min=0) # [N,M,2] - diagonal_distance_squared = (whi[:, :, 0] ** 2) + (whi[:, :, 1] ** 2) + eps - - # centers of boxes - x_p = (box1[:, None, 0] + box1[:, None, 2]) / 2 - y_p = (box1[:, None, 1] + box1[:, None, 3]) / 2 - x_g = (box2[:, 0] + box2[:, 2]) / 2 - y_g = (box2[:, 1] + box2[:, 3]) / 2 - # The distance between boxes' centers squared. - centers_distance_squared = (x_p - x_g) ** 2 + (y_p - y_g) ** 2 - - # The distance IoU is the IoU penalized by a normalized - # distance between boxes' centers squared. - return iou - (centers_distance_squared / diagonal_distance_squared) - - -def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, - labels=()): - """Runs Non-Maximum Suppression (NMS) on inference results - - Returns: - list of detections, on (n,6) tensor per image [xyxy, conf, cls] - """ - - nc = prediction.shape[2] - 5 # number of classes - xc = prediction[..., 4] > conf_thres # candidates - - # Settings - min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height - max_det = 300 # maximum number of detections per image - max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() - time_limit = 10.0 # seconds to quit after - redundant = True # require redundant detections - multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) - merge = False # use merge-NMS - - t = time.time() - output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0] - for xi, x in enumerate(prediction): # image index, image inference - # Apply constraints - # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height - x = x[xc[xi]] # confidence - - # Cat apriori labels if autolabelling - if labels and len(labels[xi]): - l = labels[xi] - v = torch.zeros((len(l), nc + 5), device=x.device) - v[:, :4] = l[:, 1:5] # box - v[:, 4] = 1.0 # conf - v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls - x = torch.cat((x, v), 0) - - # If none remain process next image - if not x.shape[0]: - continue - - # Compute conf - if nc == 1: - x[:, 5:] = x[:, 4:5] # for models with one class, cls_loss is 0 and cls_conf is always 0.5, - # so there is no need to multiplicate. - else: - x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf - - # Box (center x, center y, width, height) to (x1, y1, x2, y2) - box = xywh2xyxy(x[:, :4]) - - # Detections matrix nx6 (xyxy, conf, cls) - if multi_label: - i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T - x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) - else: # best class only - conf, j = x[:, 5:].max(1, keepdim=True) - x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] - - # Filter by class - if classes is not None: - x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] - - # Apply finite constraint - # if not torch.isfinite(x).all(): - # x = x[torch.isfinite(x).all(1)] - - # Check shape - n = x.shape[0] # number of boxes - if not n: # no boxes - continue - elif n > max_nms: # excess boxes - x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence - - # Batched NMS - c = x[:, 5:6] * (0 if agnostic else max_wh) # classes - boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores - i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS - if i.shape[0] > max_det: # limit detections - i = i[:max_det] - if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) - # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) - iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix - weights = iou * scores[None] # box weights - x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes - if redundant: - i = i[iou.sum(1) > 1] # require redundancy - - output[xi] = x[i] - if (time.time() - t) > time_limit: - print(f'WARNING: NMS time limit {time_limit}s exceeded') - break # time limit exceeded - - return output - - -def non_max_suppression_kpt(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, - labels=(), kpt_label=False, nc=None, nkpt=None): - """Runs Non-Maximum Suppression (NMS) on inference results - - Returns: - list of detections, on (n,6) tensor per image [xyxy, conf, cls] - """ - if nc is None: - nc = prediction.shape[2] - 5 if not kpt_label else prediction.shape[2] - 56 # number of classes - xc = prediction[..., 4] > conf_thres # candidates - - # Settings - min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height - max_det = 300 # maximum number of detections per image - max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() - time_limit = 10.0 # seconds to quit after - redundant = True # require redundant detections - multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) - merge = False # use merge-NMS - - t = time.time() - output = [torch.zeros((0,6), device=prediction.device)] * prediction.shape[0] - for xi, x in enumerate(prediction): # image index, image inference - # Apply constraints - # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height - x = x[xc[xi]] # confidence - - # Cat apriori labels if autolabelling - if labels and len(labels[xi]): - l = labels[xi] - v = torch.zeros((len(l), nc + 5), device=x.device) - v[:, :4] = l[:, 1:5] # box - v[:, 4] = 1.0 # conf - v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls - x = torch.cat((x, v), 0) - - # If none remain process next image - if not x.shape[0]: - continue - - # Compute conf - x[:, 5:5+nc] *= x[:, 4:5] # conf = obj_conf * cls_conf - - # Box (center x, center y, width, height) to (x1, y1, x2, y2) - box = xywh2xyxy(x[:, :4]) - - # Detections matrix nx6 (xyxy, conf, cls) - if multi_label: - i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T - x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) - else: # best class only - if not kpt_label: - conf, j = x[:, 5:].max(1, keepdim=True) - x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] - else: - kpts = x[:, 6:] - conf, j = x[:, 5:6].max(1, keepdim=True) - x = torch.cat((box, conf, j.float(), kpts), 1)[conf.view(-1) > conf_thres] - - - # Filter by class - if classes is not None: - x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] - - # Apply finite constraint - # if not torch.isfinite(x).all(): - # x = x[torch.isfinite(x).all(1)] - - # Check shape - n = x.shape[0] # number of boxes - if not n: # no boxes - continue - elif n > max_nms: # excess boxes - x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence - - # Batched NMS - c = x[:, 5:6] * (0 if agnostic else max_wh) # classes - boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores - i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS - if i.shape[0] > max_det: # limit detections - i = i[:max_det] - if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) - # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) - iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix - weights = iou * scores[None] # box weights - x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes - if redundant: - i = i[iou.sum(1) > 1] # require redundancy - - output[xi] = x[i] - if (time.time() - t) > time_limit: - print(f'WARNING: NMS time limit {time_limit}s exceeded') - break # time limit exceeded - - return output - - -def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer() - # Strip optimizer from 'f' to finalize training, optionally save as 's' - x = torch.load(f, map_location=torch.device('cpu')) - if x.get('ema'): - x['model'] = x['ema'] # replace model with ema - for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates': # keys - x[k] = None - x['epoch'] = -1 - x['model'].half() # to FP16 - for p in x['model'].parameters(): - p.requires_grad = False - torch.save(x, s or f) - mb = os.path.getsize(s or f) / 1E6 # filesize - print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB") - - -def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''): - # Print mutation results to evolve.txt (for use with train.py --evolve) - a = '%10s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys - b = '%10.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values - c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) - print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c)) - - if bucket: - url = 'gs://%s/evolve.txt' % bucket - if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0): - os.system('gsutil cp %s .' % url) # download evolve.txt if larger than local - - with open('evolve.txt', 'a') as f: # append result - f.write(c + b + '\n') - x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows - x = x[np.argsort(-fitness(x))] # sort - np.savetxt('evolve.txt', x, '%10.3g') # save sort by fitness - - # Save yaml - for i, k in enumerate(hyp.keys()): - hyp[k] = float(x[0, i + 7]) - with open(yaml_file, 'w') as f: - results = tuple(x[0, :7]) - c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) - f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n') - yaml.dump(hyp, f, sort_keys=False) - - if bucket: - os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket)) # upload - - -def apply_classifier(x, model, img, im0): - # applies a second stage classifier to yolo outputs - im0 = [im0] if isinstance(im0, np.ndarray) else im0 - for i, d in enumerate(x): # per image - if d is not None and len(d): - d = d.clone() - - # Reshape and pad cutouts - b = xyxy2xywh(d[:, :4]) # boxes - b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square - b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad - d[:, :4] = xywh2xyxy(b).long() - - # Rescale boxes from img_size to im0 size - scale_coords(img.shape[2:], d[:, :4], im0[i].shape) - - # Classes - pred_cls1 = d[:, 5].long() - ims = [] - for j, a in enumerate(d): # per item - cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])] - im = cv2.resize(cutout, (224, 224)) # BGR - # cv2.imwrite('test%i.jpg' % j, cutout) - - im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 - im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32 - im /= 255.0 # 0 - 255 to 0.0 - 1.0 - ims.append(im) - - pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction - x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections - - return x - - -def increment_path(path, exist_ok=True, sep=''): - # Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc. - path = Path(path) # os-agnostic - if (path.exists() and exist_ok) or (not path.exists()): - return str(path) - else: - dirs = glob.glob(f"{path}{sep}*") # similar paths - matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs] - i = [int(m.groups()[0]) for m in matches if m] # indices - n = max(i) + 1 if i else 2 # increment number - return f"{path}{sep}{n}" # update path diff --git a/spaces/AgentVerse/agentVerse/ui/dist/index.html b/spaces/AgentVerse/agentVerse/ui/dist/index.html deleted file mode 100644 index d8f3f9c1be0bec56da47b4c27462b64bf2c7cb4e..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/dist/index.html +++ /dev/null @@ -1,20 +0,0 @@ - - - - - - - - - - -
- - - diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pages/methods/Methods.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pages/methods/Methods.js deleted file mode 100644 index 8755ebc70a338a0e5590200934b9a592460d86ff..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pages/methods/Methods.js +++ /dev/null @@ -1,17 +0,0 @@ -import AddChildMethods from './AddChildMethods.js'; -import GetPage from './GetPage.js'; -import SwapPage from './SwapPage.js'; -import HasPage from './HasPage.js'; - -var methods = { - getPage: GetPage, - swapPage: SwapPage, - hasPage: HasPage, -} - -Object.assign( - methods, - AddChildMethods, -); - -export default methods; \ No newline at end of file diff --git a/spaces/AkshayKollimarala/MygenAI/README.md b/spaces/AkshayKollimarala/MygenAI/README.md deleted file mode 100644 index 31a94a58da8bda13bf97149f92039d90e3682c57..0000000000000000000000000000000000000000 --- a/spaces/AkshayKollimarala/MygenAI/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: MygenAI -emoji: 🏃 -colorFrom: purple -colorTo: pink -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/utils/utils_callbacks.py b/spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/utils/utils_callbacks.py deleted file mode 100644 index bd2f56cba47c57de102710ff56eaac591e59f4da..0000000000000000000000000000000000000000 --- a/spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/utils/utils_callbacks.py +++ /dev/null @@ -1,117 +0,0 @@ -import logging -import os -import time -from typing import List - -import torch - -from eval import verification -from utils.utils_logging import AverageMeter - - -class CallBackVerification(object): - def __init__(self, frequent, rank, val_targets, rec_prefix, image_size=(112, 112)): - self.frequent: int = frequent - self.rank: int = rank - self.highest_acc: float = 0.0 - self.highest_acc_list: List[float] = [0.0] * len(val_targets) - self.ver_list: List[object] = [] - self.ver_name_list: List[str] = [] - if self.rank is 0: - self.init_dataset(val_targets=val_targets, data_dir=rec_prefix, image_size=image_size) - - def ver_test(self, backbone: torch.nn.Module, global_step: int): - results = [] - for i in range(len(self.ver_list)): - acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test( - self.ver_list[i], backbone, 10, 10) - logging.info('[%s][%d]XNorm: %f' % (self.ver_name_list[i], global_step, xnorm)) - logging.info('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (self.ver_name_list[i], global_step, acc2, std2)) - if acc2 > self.highest_acc_list[i]: - self.highest_acc_list[i] = acc2 - logging.info( - '[%s][%d]Accuracy-Highest: %1.5f' % (self.ver_name_list[i], global_step, self.highest_acc_list[i])) - results.append(acc2) - - def init_dataset(self, val_targets, data_dir, image_size): - for name in val_targets: - path = os.path.join(data_dir, name + ".bin") - if os.path.exists(path): - data_set = verification.load_bin(path, image_size) - self.ver_list.append(data_set) - self.ver_name_list.append(name) - - def __call__(self, num_update, backbone: torch.nn.Module): - if self.rank is 0 and num_update > 0 and num_update % self.frequent == 0: - backbone.eval() - self.ver_test(backbone, num_update) - backbone.train() - - -class CallBackLogging(object): - def __init__(self, frequent, rank, total_step, batch_size, world_size, writer=None): - self.frequent: int = frequent - self.rank: int = rank - self.time_start = time.time() - self.total_step: int = total_step - self.batch_size: int = batch_size - self.world_size: int = world_size - self.writer = writer - - self.init = False - self.tic = 0 - - def __call__(self, - global_step: int, - loss: AverageMeter, - epoch: int, - fp16: bool, - learning_rate: float, - grad_scaler: torch.cuda.amp.GradScaler): - if self.rank == 0 and global_step > 0 and global_step % self.frequent == 0: - if self.init: - try: - speed: float = self.frequent * self.batch_size / (time.time() - self.tic) - speed_total = speed * self.world_size - except ZeroDivisionError: - speed_total = float('inf') - - time_now = (time.time() - self.time_start) / 3600 - time_total = time_now / ((global_step + 1) / self.total_step) - time_for_end = time_total - time_now - if self.writer is not None: - self.writer.add_scalar('time_for_end', time_for_end, global_step) - self.writer.add_scalar('learning_rate', learning_rate, global_step) - self.writer.add_scalar('loss', loss.avg, global_step) - if fp16: - msg = "Speed %.2f samples/sec Loss %.4f LearningRate %.4f Epoch: %d Global Step: %d " \ - "Fp16 Grad Scale: %2.f Required: %1.f hours" % ( - speed_total, loss.avg, learning_rate, epoch, global_step, - grad_scaler.get_scale(), time_for_end - ) - else: - msg = "Speed %.2f samples/sec Loss %.4f LearningRate %.4f Epoch: %d Global Step: %d " \ - "Required: %1.f hours" % ( - speed_total, loss.avg, learning_rate, epoch, global_step, time_for_end - ) - logging.info(msg) - loss.reset() - self.tic = time.time() - else: - self.init = True - self.tic = time.time() - - -class CallBackModelCheckpoint(object): - def __init__(self, rank, output="./"): - self.rank: int = rank - self.output: str = output - - def __call__(self, global_step, backbone, partial_fc, ): - if global_step > 100 and self.rank == 0: - path_module = os.path.join(self.output, "backbone.pth") - torch.save(backbone.module.state_dict(), path_module) - logging.info("Pytorch Model Saved in '{}'".format(path_module)) - - if global_step > 100 and partial_fc is not None: - partial_fc.save_params() diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/stable_diffusion_ipex.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/stable_diffusion_ipex.py deleted file mode 100644 index 9abe16d56f100980ccc4da5153ad4789169457ca..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/stable_diffusion_ipex.py +++ /dev/null @@ -1,848 +0,0 @@ -# Copyright 2023 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 Any, Callable, Dict, List, Optional, Union - -import intel_extension_for_pytorch as ipex -import torch -from packaging import version -from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer - -from diffusers.configuration_utils import FrozenDict -from diffusers.models import AutoencoderKL, UNet2DConditionModel -from diffusers.pipeline_utils import DiffusionPipeline -from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput -from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker -from diffusers.schedulers import KarrasDiffusionSchedulers -from diffusers.utils import ( - deprecate, - is_accelerate_available, - is_accelerate_version, - logging, - randn_tensor, - replace_example_docstring, -) - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - -EXAMPLE_DOC_STRING = """ - Examples: - ```py - >>> import torch - >>> from diffusers import StableDiffusionPipeline - - >>> pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_ipex") - - >>> # For Float32 - >>> pipe.prepare_for_ipex(prompt, dtype=torch.float32, height=512, width=512) #value of image height/width should be consistent with the pipeline inference - >>> # For BFloat16 - >>> pipe.prepare_for_ipex(prompt, dtype=torch.bfloat16, height=512, width=512) #value of image height/width should be consistent with the pipeline inference - - >>> prompt = "a photo of an astronaut riding a horse on mars" - >>> # For Float32 - >>> image = pipe(prompt, num_inference_steps=num_inference_steps, height=512, width=512).images[0] #value of image height/width should be consistent with 'prepare_for_ipex()' - >>> # For BFloat16 - >>> with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16): - >>> image = pipe(prompt, num_inference_steps=num_inference_steps, height=512, width=512).images[0] #value of image height/width should be consistent with 'prepare_for_ipex()' - ``` -""" - - -class StableDiffusionIPEXPipeline(DiffusionPipeline): - r""" - Pipeline for text-to-image generation using Stable Diffusion on IPEX. - - This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the - library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) - - Args: - vae ([`AutoencoderKL`]): - Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. - text_encoder ([`CLIPTextModel`]): - Frozen text-encoder. Stable Diffusion uses the text portion of - [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically - the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. - tokenizer (`CLIPTokenizer`): - Tokenizer of class - [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). - unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. - scheduler ([`SchedulerMixin`]): - A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of - [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. - safety_checker ([`StableDiffusionSafetyChecker`]): - Classification module that estimates whether generated images could be considered offensive or harmful. - Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. - feature_extractor ([`CLIPFeatureExtractor`]): - Model that extracts features from generated images to be used as inputs for the `safety_checker`. - """ - _optional_components = ["safety_checker", "feature_extractor"] - - def __init__( - self, - vae: AutoencoderKL, - text_encoder: CLIPTextModel, - tokenizer: CLIPTokenizer, - unet: UNet2DConditionModel, - scheduler: KarrasDiffusionSchedulers, - safety_checker: StableDiffusionSafetyChecker, - 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." - ) - - is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( - version.parse(unet.config._diffusers_version).base_version - ) < version.parse("0.9.0.dev0") - is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 - if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: - deprecation_message = ( - "The configuration file of the unet has set the default `sample_size` to smaller than" - " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" - " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" - " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" - " in the config might lead to incorrect results in future versions. If you have downloaded this" - " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" - " the `unet/config.json` file" - ) - deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) - new_config = dict(unet.config) - new_config["sample_size"] = 64 - unet._internal_dict = FrozenDict(new_config) - - self.register_modules( - vae=vae, - text_encoder=text_encoder, - tokenizer=tokenizer, - unet=unet, - scheduler=scheduler, - safety_checker=safety_checker, - feature_extractor=feature_extractor, - ) - self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) - self.register_to_config(requires_safety_checker=requires_safety_checker) - - def get_input_example(self, prompt, height=None, width=None, guidance_scale=7.5, num_images_per_prompt=1): - prompt_embeds = None - negative_prompt_embeds = None - negative_prompt = None - callback_steps = 1 - generator = None - latents = None - - # 0. Default height and width to unet - height = height or self.unet.config.sample_size * self.vae_scale_factor - width = width or self.unet.config.sample_size * self.vae_scale_factor - - # 1. Check inputs. Raise error if not correct - self.check_inputs( - prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds - ) - - # 2. Define call parameters - if prompt is not None and isinstance(prompt, str): - batch_size = 1 - elif prompt is not None and isinstance(prompt, list): - batch_size = len(prompt) - - device = "cpu" - # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) - # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` - # corresponds to doing no classifier free guidance. - do_classifier_free_guidance = guidance_scale > 1.0 - - # 3. Encode input prompt - prompt_embeds = self._encode_prompt( - prompt, - device, - num_images_per_prompt, - do_classifier_free_guidance, - negative_prompt, - prompt_embeds=prompt_embeds, - negative_prompt_embeds=negative_prompt_embeds, - ) - - # 5. Prepare latent variables - latents = self.prepare_latents( - batch_size * num_images_per_prompt, - self.unet.in_channels, - height, - width, - prompt_embeds.dtype, - device, - generator, - latents, - ) - dummy = torch.ones(1, dtype=torch.int32) - latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - latent_model_input = self.scheduler.scale_model_input(latent_model_input, dummy) - - unet_input_example = (latent_model_input, dummy, prompt_embeds) - vae_decoder_input_example = latents - - return unet_input_example, vae_decoder_input_example - - def prepare_for_ipex(self, promt, dtype=torch.float32, height=None, width=None, guidance_scale=7.5): - self.unet = self.unet.to(memory_format=torch.channels_last) - self.vae.decoder = self.vae.decoder.to(memory_format=torch.channels_last) - self.text_encoder = self.text_encoder.to(memory_format=torch.channels_last) - if self.safety_checker is not None: - self.safety_checker = self.safety_checker.to(memory_format=torch.channels_last) - - unet_input_example, vae_decoder_input_example = self.get_input_example(promt, height, width, guidance_scale) - - # optimize with ipex - if dtype == torch.bfloat16: - self.unet = ipex.optimize( - self.unet.eval(), dtype=torch.bfloat16, inplace=True, sample_input=unet_input_example - ) - self.vae.decoder = ipex.optimize(self.vae.decoder.eval(), dtype=torch.bfloat16, inplace=True) - self.text_encoder = ipex.optimize(self.text_encoder.eval(), dtype=torch.bfloat16, inplace=True) - if self.safety_checker is not None: - self.safety_checker = ipex.optimize(self.safety_checker.eval(), dtype=torch.bfloat16, inplace=True) - elif dtype == torch.float32: - self.unet = ipex.optimize( - self.unet.eval(), - dtype=torch.float32, - inplace=True, - sample_input=unet_input_example, - level="O1", - weights_prepack=True, - auto_kernel_selection=False, - ) - self.vae.decoder = ipex.optimize( - self.vae.decoder.eval(), - dtype=torch.float32, - inplace=True, - level="O1", - weights_prepack=True, - auto_kernel_selection=False, - ) - self.text_encoder = ipex.optimize( - self.text_encoder.eval(), - dtype=torch.float32, - inplace=True, - level="O1", - weights_prepack=True, - auto_kernel_selection=False, - ) - if self.safety_checker is not None: - self.safety_checker = ipex.optimize( - self.safety_checker.eval(), - dtype=torch.float32, - inplace=True, - level="O1", - weights_prepack=True, - auto_kernel_selection=False, - ) - else: - raise ValueError(" The value of 'dtype' should be 'torch.bfloat16' or 'torch.float32' !") - - # trace unet model to get better performance on IPEX - with torch.cpu.amp.autocast(enabled=dtype == torch.bfloat16), torch.no_grad(): - unet_trace_model = torch.jit.trace(self.unet, unet_input_example, check_trace=False, strict=False) - unet_trace_model = torch.jit.freeze(unet_trace_model) - self.unet.forward = unet_trace_model.forward - - # trace vae.decoder model to get better performance on IPEX - with torch.cpu.amp.autocast(enabled=dtype == torch.bfloat16), torch.no_grad(): - ave_decoder_trace_model = torch.jit.trace( - self.vae.decoder, vae_decoder_input_example, check_trace=False, strict=False - ) - ave_decoder_trace_model = torch.jit.freeze(ave_decoder_trace_model) - self.vae.decoder.forward = ave_decoder_trace_model.forward - - def enable_vae_slicing(self): - r""" - Enable sliced VAE decoding. - - When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several - steps. This is useful to save some memory and allow larger batch sizes. - """ - self.vae.enable_slicing() - - def disable_vae_slicing(self): - r""" - Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to - computing decoding in one step. - """ - self.vae.disable_slicing() - - def enable_vae_tiling(self): - r""" - Enable tiled VAE decoding. - - When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in - several steps. This is useful to save a large amount of memory and to allow the processing of larger images. - """ - self.vae.enable_tiling() - - def disable_vae_tiling(self): - r""" - Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to - computing decoding in one step. - """ - self.vae.disable_tiling() - - def enable_sequential_cpu_offload(self, gpu_id=0): - r""" - Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, - text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a - `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. - Note that offloading happens on a submodule basis. Memory savings are higher than with - `enable_model_cpu_offload`, but performance is lower. - """ - if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): - from accelerate import cpu_offload - else: - raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher") - - device = torch.device(f"cuda:{gpu_id}") - - if self.device.type != "cpu": - self.to("cpu", silence_dtype_warnings=True) - torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) - - for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: - cpu_offload(cpu_offloaded_model, device) - - if self.safety_checker is not None: - cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) - - def enable_model_cpu_offload(self, gpu_id=0): - r""" - Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared - to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` - method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with - `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. - """ - if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): - from accelerate import cpu_offload_with_hook - else: - raise ImportError("`enable_model_offload` requires `accelerate v0.17.0` or higher.") - - device = torch.device(f"cuda:{gpu_id}") - - if self.device.type != "cpu": - self.to("cpu", silence_dtype_warnings=True) - torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) - - hook = None - for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: - _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) - - if self.safety_checker is not None: - _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook) - - # We'll offload the last model manually. - self.final_offload_hook = hook - - @property - def _execution_device(self): - r""" - Returns the device on which the pipeline's models will be executed. After calling - `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module - hooks. - """ - if not hasattr(self.unet, "_hf_hook"): - return self.device - for module in self.unet.modules(): - if ( - hasattr(module, "_hf_hook") - and hasattr(module._hf_hook, "execution_device") - and module._hf_hook.execution_device is not None - ): - return torch.device(module._hf_hook.execution_device) - return self.device - - def _encode_prompt( - self, - prompt, - device, - num_images_per_prompt, - do_classifier_free_guidance, - negative_prompt=None, - prompt_embeds: Optional[torch.FloatTensor] = None, - negative_prompt_embeds: Optional[torch.FloatTensor] = None, - ): - r""" - Encodes the prompt into text encoder hidden states. - - Args: - prompt (`str` or `List[str]`, *optional*): - prompt to be encoded - device: (`torch.device`): - torch device - num_images_per_prompt (`int`): - number of images that should be generated per prompt - do_classifier_free_guidance (`bool`): - whether to use classifier free guidance or not - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation. If not defined, one has to pass - `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. - Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). - prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not - provided, text embeddings will be generated from `prompt` input argument. - negative_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input - argument. - """ - if prompt is not None and isinstance(prompt, str): - batch_size = 1 - elif prompt is not None and isinstance(prompt, list): - batch_size = len(prompt) - else: - batch_size = prompt_embeds.shape[0] - - if prompt_embeds is None: - text_inputs = self.tokenizer( - prompt, - padding="max_length", - max_length=self.tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - text_input_ids = text_inputs.input_ids - untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids - - if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( - text_input_ids, untruncated_ids - ): - removed_text = self.tokenizer.batch_decode( - untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] - ) - logger.warning( - "The following part of your input was truncated because CLIP can only handle sequences up to" - f" {self.tokenizer.model_max_length} tokens: {removed_text}" - ) - - if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: - attention_mask = text_inputs.attention_mask.to(device) - else: - attention_mask = None - - prompt_embeds = self.text_encoder( - text_input_ids.to(device), - attention_mask=attention_mask, - ) - prompt_embeds = prompt_embeds[0] - - prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) - - bs_embed, seq_len, _ = prompt_embeds.shape - # duplicate text embeddings for each generation per prompt, using mps friendly method - prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) - prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) - - # get unconditional embeddings for classifier free guidance - if do_classifier_free_guidance and negative_prompt_embeds is None: - uncond_tokens: List[str] - if negative_prompt is None: - uncond_tokens = [""] * batch_size - elif type(prompt) is not type(negative_prompt): - raise TypeError( - f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" - f" {type(prompt)}." - ) - elif isinstance(negative_prompt, str): - uncond_tokens = [negative_prompt] - elif batch_size != len(negative_prompt): - raise ValueError( - f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" - f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" - " the batch size of `prompt`." - ) - else: - uncond_tokens = negative_prompt - - max_length = prompt_embeds.shape[1] - uncond_input = self.tokenizer( - uncond_tokens, - padding="max_length", - max_length=max_length, - truncation=True, - return_tensors="pt", - ) - - if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: - attention_mask = uncond_input.attention_mask.to(device) - else: - attention_mask = None - - negative_prompt_embeds = self.text_encoder( - uncond_input.input_ids.to(device), - attention_mask=attention_mask, - ) - negative_prompt_embeds = negative_prompt_embeds[0] - - if do_classifier_free_guidance: - # duplicate unconditional embeddings for each generation per prompt, using mps friendly method - seq_len = negative_prompt_embeds.shape[1] - - negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) - - negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) - negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) - - # 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 - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) - - return prompt_embeds - - def run_safety_checker(self, image, device, dtype): - if self.safety_checker is not None: - safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) - image, has_nsfw_concept = self.safety_checker( - images=image, clip_input=safety_checker_input.pixel_values.to(dtype) - ) - else: - has_nsfw_concept = None - return image, has_nsfw_concept - - def decode_latents(self, latents): - latents = 1 / self.vae.config.scaling_factor * latents - image = self.vae.decode(latents).sample - image = (image / 2 + 0.5).clamp(0, 1) - # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 - image = image.cpu().permute(0, 2, 3, 1).float().numpy() - return image - - def prepare_extra_step_kwargs(self, generator, eta): - # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature - # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. - # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 - # and should be between [0, 1] - - accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) - extra_step_kwargs = {} - if accepts_eta: - extra_step_kwargs["eta"] = eta - - # check if the scheduler accepts generator - accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) - if accepts_generator: - extra_step_kwargs["generator"] = generator - return extra_step_kwargs - - def check_inputs( - self, - prompt, - height, - width, - callback_steps, - negative_prompt=None, - prompt_embeds=None, - negative_prompt_embeds=None, - ): - if height % 8 != 0 or width % 8 != 0: - raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") - - if (callback_steps is None) or ( - callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) - ): - raise ValueError( - f"`callback_steps` has to be a positive integer but is {callback_steps} of type" - f" {type(callback_steps)}." - ) - - if prompt is not None and prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" - " only forward one of the two." - ) - elif prompt is None and prompt_embeds is None: - raise ValueError( - "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." - ) - elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): - raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") - - if negative_prompt is not None and negative_prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" - f" {negative_prompt_embeds}. Please make sure to only forward one of the two." - ) - - if prompt_embeds is not None and negative_prompt_embeds is not None: - if prompt_embeds.shape != negative_prompt_embeds.shape: - raise ValueError( - "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" - f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" - f" {negative_prompt_embeds.shape}." - ) - - def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): - shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) - if isinstance(generator, list) and len(generator) != batch_size: - raise ValueError( - f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" - f" size of {batch_size}. Make sure the batch size matches the length of the generators." - ) - - if latents is None: - latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) - else: - latents = latents.to(device) - - # scale the initial noise by the standard deviation required by the scheduler - latents = latents * self.scheduler.init_noise_sigma - return latents - - @torch.no_grad() - @replace_example_docstring(EXAMPLE_DOC_STRING) - def __call__( - self, - prompt: Union[str, List[str]] = None, - height: Optional[int] = None, - width: Optional[int] = None, - num_inference_steps: int = 50, - guidance_scale: float = 7.5, - negative_prompt: Optional[Union[str, List[str]]] = None, - num_images_per_prompt: Optional[int] = 1, - eta: float = 0.0, - generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, - latents: Optional[torch.FloatTensor] = None, - prompt_embeds: Optional[torch.FloatTensor] = None, - negative_prompt_embeds: Optional[torch.FloatTensor] = None, - output_type: Optional[str] = "pil", - return_dict: bool = True, - callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, - callback_steps: int = 1, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - ): - r""" - Function invoked when calling the pipeline for generation. - - Args: - prompt (`str` or `List[str]`, *optional*): - The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. - instead. - height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): - The height in pixels of the generated image. - width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): - The width in pixels of the generated image. - num_inference_steps (`int`, *optional*, defaults to 50): - The number of denoising steps. More denoising steps usually lead to a higher quality image at the - expense of slower inference. - guidance_scale (`float`, *optional*, defaults to 7.5): - Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). - `guidance_scale` is defined as `w` of equation 2. of [Imagen - Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > - 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, - usually at the expense of lower image quality. - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation. If not defined, one has to pass - `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. - Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). - num_images_per_prompt (`int`, *optional*, defaults to 1): - The number of images to generate per prompt. - eta (`float`, *optional*, defaults to 0.0): - Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to - [`schedulers.DDIMScheduler`], will be ignored for others. - generator (`torch.Generator` or `List[torch.Generator]`, *optional*): - One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) - to make generation deterministic. - latents (`torch.FloatTensor`, *optional*): - Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image - generation. Can be used to tweak the same generation with different prompts. If not provided, a latents - tensor will ge generated by sampling using the supplied random `generator`. - prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not - provided, text embeddings will be generated from `prompt` input argument. - negative_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input - argument. - output_type (`str`, *optional*, defaults to `"pil"`): - The output format of the generate image. Choose between - [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a - plain tuple. - callback (`Callable`, *optional*): - A function that will be called every `callback_steps` steps during inference. The function will be - called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. - callback_steps (`int`, *optional*, defaults to 1): - The frequency at which the `callback` function will be called. If not specified, the callback will be - called at every step. - cross_attention_kwargs (`dict`, *optional*): - A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under - `self.processor` in - [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). - - Examples: - - Returns: - [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: - [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. - When returning a tuple, the first element is a list with the generated images, and the second element is a - list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" - (nsfw) content, according to the `safety_checker`. - """ - # 0. Default height and width to unet - height = height or self.unet.config.sample_size * self.vae_scale_factor - width = width or self.unet.config.sample_size * self.vae_scale_factor - - # 1. Check inputs. Raise error if not correct - self.check_inputs( - prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds - ) - - # 2. Define call parameters - if prompt is not None and isinstance(prompt, str): - batch_size = 1 - elif prompt is not None and isinstance(prompt, list): - batch_size = len(prompt) - else: - batch_size = prompt_embeds.shape[0] - - device = self._execution_device - # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) - # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` - # corresponds to doing no classifier free guidance. - do_classifier_free_guidance = guidance_scale > 1.0 - - # 3. Encode input prompt - prompt_embeds = self._encode_prompt( - prompt, - device, - num_images_per_prompt, - do_classifier_free_guidance, - negative_prompt, - prompt_embeds=prompt_embeds, - negative_prompt_embeds=negative_prompt_embeds, - ) - - # 4. Prepare timesteps - self.scheduler.set_timesteps(num_inference_steps, device=device) - timesteps = self.scheduler.timesteps - - # 5. Prepare latent variables - num_channels_latents = self.unet.in_channels - latents = self.prepare_latents( - batch_size * num_images_per_prompt, - num_channels_latents, - height, - width, - prompt_embeds.dtype, - device, - generator, - latents, - ) - - # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline - extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) - - # 7. Denoising loop - num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order - with self.progress_bar(total=num_inference_steps) as progress_bar: - for i, t in enumerate(timesteps): - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) - - # predict the noise residual - noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds)["sample"] - - # perform guidance - if do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) - - # compute the previous noisy sample x_t -> x_t-1 - latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample - - # call the callback, if provided - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): - progress_bar.update() - if callback is not None and i % callback_steps == 0: - callback(i, t, latents) - - if output_type == "latent": - image = latents - has_nsfw_concept = None - elif output_type == "pil": - # 8. Post-processing - image = self.decode_latents(latents) - - # 9. Run safety checker - image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) - - # 10. Convert to PIL - image = self.numpy_to_pil(image) - else: - # 8. Post-processing - image = self.decode_latents(latents) - - # 9. Run safety checker - image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) - - # Offload last model to CPU - if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: - self.final_offload_hook.offload() - - if not return_dict: - return (image, has_nsfw_concept) - - return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/__init__.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py deleted file mode 100644 index ccbdae09dc08ab0ed4ac05ec5e317159be91d1af..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py +++ /dev/null @@ -1,294 +0,0 @@ -# Copyright 2023 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. - -from typing import Callable, List, Optional, Union - -import torch - -from ...models import UNet2DConditionModel, VQModel -from ...schedulers import DDPMScheduler -from ...utils import ( - is_accelerate_available, - is_accelerate_version, - logging, - randn_tensor, - replace_example_docstring, -) -from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - -EXAMPLE_DOC_STRING = """ - Examples: - ```py - >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline - >>> import torch - - >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior") - >>> pipe_prior.to("cuda") - >>> prompt = "red cat, 4k photo" - >>> out = pipe_prior(prompt) - >>> image_emb = out.image_embeds - >>> zero_image_emb = out.negative_image_embeds - >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder") - >>> pipe.to("cuda") - >>> image = pipe( - ... image_embeds=image_emb, - ... negative_image_embeds=zero_image_emb, - ... height=768, - ... width=768, - ... num_inference_steps=50, - ... ).images - >>> image[0].save("cat.png") - ``` -""" - - -def downscale_height_and_width(height, width, scale_factor=8): - new_height = height // scale_factor**2 - if height % scale_factor**2 != 0: - new_height += 1 - new_width = width // scale_factor**2 - if width % scale_factor**2 != 0: - new_width += 1 - return new_height * scale_factor, new_width * scale_factor - - -class KandinskyV22Pipeline(DiffusionPipeline): - """ - Pipeline for text-to-image generation using Kandinsky - - 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: - scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]): - A scheduler to be used in combination with `unet` to generate image latents. - unet ([`UNet2DConditionModel`]): - Conditional U-Net architecture to denoise the image embedding. - movq ([`VQModel`]): - MoVQ Decoder to generate the image from the latents. - """ - - def __init__( - self, - unet: UNet2DConditionModel, - scheduler: DDPMScheduler, - movq: VQModel, - ): - super().__init__() - - self.register_modules( - unet=unet, - scheduler=scheduler, - movq=movq, - ) - self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1) - - # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents - def prepare_latents(self, shape, dtype, device, generator, latents, scheduler): - if latents is None: - latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) - else: - if latents.shape != shape: - raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") - latents = latents.to(device) - - latents = latents * scheduler.init_noise_sigma - return latents - - def enable_model_cpu_offload(self, gpu_id=0): - r""" - Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared - to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` - method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with - `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. - """ - if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): - from accelerate import cpu_offload_with_hook - else: - raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") - - device = torch.device(f"cuda:{gpu_id}") - - if self.device.type != "cpu": - self.to("cpu", silence_dtype_warnings=True) - torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) - - hook = None - for cpu_offloaded_model in [self.unet, self.movq]: - _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) - - # We'll offload the last model manually. - self.final_offload_hook = hook - - @torch.no_grad() - @replace_example_docstring(EXAMPLE_DOC_STRING) - def __call__( - self, - image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]], - negative_image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]], - height: int = 512, - width: int = 512, - num_inference_steps: int = 100, - guidance_scale: float = 4.0, - num_images_per_prompt: int = 1, - generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, - latents: Optional[torch.FloatTensor] = None, - output_type: Optional[str] = "pil", - callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, - callback_steps: int = 1, - return_dict: bool = True, - ): - """ - Function invoked when calling the pipeline for generation. - - Args: - image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`): - The clip image embeddings for text prompt, that will be used to condition the image generation. - negative_image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`): - The clip image embeddings for negative text prompt, will be used to condition the image generation. - height (`int`, *optional*, defaults to 512): - The height in pixels of the generated image. - width (`int`, *optional*, defaults to 512): - The width in pixels of the generated image. - num_inference_steps (`int`, *optional*, defaults to 100): - The number of denoising steps. More denoising steps usually lead to a higher quality image at the - expense of slower inference. - guidance_scale (`float`, *optional*, defaults to 4.0): - 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. - num_images_per_prompt (`int`, *optional*, defaults to 1): - The number of images to generate per prompt. - generator (`torch.Generator` or `List[torch.Generator]`, *optional*): - One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) - to make generation deterministic. - latents (`torch.FloatTensor`, *optional*): - Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image - generation. Can be used to tweak the same generation with different prompts. If not provided, a latents - tensor will ge generated by sampling using the supplied random `generator`. - output_type (`str`, *optional*, defaults to `"pil"`): - The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"` - (`np.array`) or `"pt"` (`torch.Tensor`). - callback (`Callable`, *optional*): - A function that calls every `callback_steps` steps during inference. The function is called with the - following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. - callback_steps (`int`, *optional*, defaults to 1): - The frequency at which the `callback` function is called. If not specified, the callback is called at - every step. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. - - Examples: - - Returns: - [`~pipelines.ImagePipelineOutput`] or `tuple` - """ - device = self._execution_device - - do_classifier_free_guidance = guidance_scale > 1.0 - - if isinstance(image_embeds, list): - image_embeds = torch.cat(image_embeds, dim=0) - batch_size = image_embeds.shape[0] * num_images_per_prompt - if isinstance(negative_image_embeds, list): - negative_image_embeds = torch.cat(negative_image_embeds, dim=0) - - if do_classifier_free_guidance: - image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) - negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0) - - image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to( - dtype=self.unet.dtype, device=device - ) - - self.scheduler.set_timesteps(num_inference_steps, device=device) - timesteps_tensor = self.scheduler.timesteps - - num_channels_latents = self.unet.config.in_channels - - height, width = downscale_height_and_width(height, width, self.movq_scale_factor) - - # create initial latent - latents = self.prepare_latents( - (batch_size, num_channels_latents, height, width), - image_embeds.dtype, - device, - generator, - latents, - self.scheduler, - ) - - for i, t in enumerate(self.progress_bar(timesteps_tensor)): - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - - added_cond_kwargs = {"image_embeds": image_embeds} - noise_pred = self.unet( - sample=latent_model_input, - timestep=t, - encoder_hidden_states=None, - added_cond_kwargs=added_cond_kwargs, - return_dict=False, - )[0] - - if do_classifier_free_guidance: - noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1) - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - _, variance_pred_text = variance_pred.chunk(2) - noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) - noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1) - - if not ( - hasattr(self.scheduler.config, "variance_type") - and self.scheduler.config.variance_type in ["learned", "learned_range"] - ): - noise_pred, _ = noise_pred.split(latents.shape[1], dim=1) - - # compute the previous noisy sample x_t -> x_t-1 - latents = self.scheduler.step( - noise_pred, - t, - latents, - generator=generator, - )[0] - - if callback is not None and i % callback_steps == 0: - callback(i, t, latents) - # post-processing - image = self.movq.decode(latents, force_not_quantize=True)["sample"] - - # Offload last model to CPU - if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: - self.final_offload_hook.offload() - - if output_type not in ["pt", "np", "pil"]: - raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}") - - if output_type in ["np", "pil"]: - image = image * 0.5 + 0.5 - image = image.clamp(0, 1) - image = image.cpu().permute(0, 2, 3, 1).float().numpy() - - if output_type == "pil": - image = self.numpy_to_pil(image) - - if not return_dict: - return (image,) - - return ImagePipelineOutput(images=image) diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/assigners/max_iou_assigner.py b/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/assigners/max_iou_assigner.py deleted file mode 100644 index 5cf4c4b4b450f87dfb99c3d33d8ed83d3e5cfcb3..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/assigners/max_iou_assigner.py +++ /dev/null @@ -1,212 +0,0 @@ -import torch - -from ..builder import BBOX_ASSIGNERS -from ..iou_calculators import build_iou_calculator -from .assign_result import AssignResult -from .base_assigner import BaseAssigner - - -@BBOX_ASSIGNERS.register_module() -class MaxIoUAssigner(BaseAssigner): - """Assign a corresponding gt bbox or background to each bbox. - - Each proposals will be assigned with `-1`, or a semi-positive integer - indicating the ground truth index. - - - -1: negative sample, no assigned gt - - semi-positive integer: positive sample, index (0-based) of assigned gt - - Args: - pos_iou_thr (float): IoU threshold for positive bboxes. - neg_iou_thr (float or tuple): IoU threshold for negative bboxes. - min_pos_iou (float): Minimum iou for a bbox to be considered as a - positive bbox. Positive samples can have smaller IoU than - pos_iou_thr due to the 4th step (assign max IoU sample to each gt). - gt_max_assign_all (bool): Whether to assign all bboxes with the same - highest overlap with some gt to that gt. - ignore_iof_thr (float): IoF threshold for ignoring bboxes (if - `gt_bboxes_ignore` is specified). Negative values mean not - ignoring any bboxes. - ignore_wrt_candidates (bool): Whether to compute the iof between - `bboxes` and `gt_bboxes_ignore`, or the contrary. - match_low_quality (bool): Whether to allow low quality matches. This is - usually allowed for RPN and single stage detectors, but not allowed - in the second stage. Details are demonstrated in Step 4. - gpu_assign_thr (int): The upper bound of the number of GT for GPU - assign. When the number of gt is above this threshold, will assign - on CPU device. Negative values mean not assign on CPU. - """ - - def __init__(self, - pos_iou_thr, - neg_iou_thr, - min_pos_iou=.0, - gt_max_assign_all=True, - ignore_iof_thr=-1, - ignore_wrt_candidates=True, - match_low_quality=True, - gpu_assign_thr=-1, - iou_calculator=dict(type='BboxOverlaps2D')): - self.pos_iou_thr = pos_iou_thr - self.neg_iou_thr = neg_iou_thr - self.min_pos_iou = min_pos_iou - self.gt_max_assign_all = gt_max_assign_all - self.ignore_iof_thr = ignore_iof_thr - self.ignore_wrt_candidates = ignore_wrt_candidates - self.gpu_assign_thr = gpu_assign_thr - self.match_low_quality = match_low_quality - self.iou_calculator = build_iou_calculator(iou_calculator) - - def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): - """Assign gt to bboxes. - - This method assign a gt bbox to every bbox (proposal/anchor), each bbox - will be assigned with -1, or a semi-positive number. -1 means negative - sample, semi-positive number is the index (0-based) of assigned gt. - The assignment is done in following steps, the order matters. - - 1. assign every bbox to the background - 2. assign proposals whose iou with all gts < neg_iou_thr to 0 - 3. for each bbox, if the iou with its nearest gt >= pos_iou_thr, - assign it to that bbox - 4. for each gt bbox, assign its nearest proposals (may be more than - one) to itself - - Args: - bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4). - gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). - gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are - labelled as `ignored`, e.g., crowd boxes in COCO. - gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ). - - Returns: - :obj:`AssignResult`: The assign result. - - Example: - >>> self = MaxIoUAssigner(0.5, 0.5) - >>> bboxes = torch.Tensor([[0, 0, 10, 10], [10, 10, 20, 20]]) - >>> gt_bboxes = torch.Tensor([[0, 0, 10, 9]]) - >>> assign_result = self.assign(bboxes, gt_bboxes) - >>> expected_gt_inds = torch.LongTensor([1, 0]) - >>> assert torch.all(assign_result.gt_inds == expected_gt_inds) - """ - assign_on_cpu = True if (self.gpu_assign_thr > 0) and ( - gt_bboxes.shape[0] > self.gpu_assign_thr) else False - # compute overlap and assign gt on CPU when number of GT is large - if assign_on_cpu: - device = bboxes.device - bboxes = bboxes.cpu() - gt_bboxes = gt_bboxes.cpu() - if gt_bboxes_ignore is not None: - gt_bboxes_ignore = gt_bboxes_ignore.cpu() - if gt_labels is not None: - gt_labels = gt_labels.cpu() - - overlaps = self.iou_calculator(gt_bboxes, bboxes) - - if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None - and gt_bboxes_ignore.numel() > 0 and bboxes.numel() > 0): - if self.ignore_wrt_candidates: - ignore_overlaps = self.iou_calculator( - bboxes, gt_bboxes_ignore, mode='iof') - ignore_max_overlaps, _ = ignore_overlaps.max(dim=1) - else: - ignore_overlaps = self.iou_calculator( - gt_bboxes_ignore, bboxes, mode='iof') - ignore_max_overlaps, _ = ignore_overlaps.max(dim=0) - overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1 - - assign_result = self.assign_wrt_overlaps(overlaps, gt_labels) - if assign_on_cpu: - assign_result.gt_inds = assign_result.gt_inds.to(device) - assign_result.max_overlaps = assign_result.max_overlaps.to(device) - if assign_result.labels is not None: - assign_result.labels = assign_result.labels.to(device) - return assign_result - - def assign_wrt_overlaps(self, overlaps, gt_labels=None): - """Assign w.r.t. the overlaps of bboxes with gts. - - Args: - overlaps (Tensor): Overlaps between k gt_bboxes and n bboxes, - shape(k, n). - gt_labels (Tensor, optional): Labels of k gt_bboxes, shape (k, ). - - Returns: - :obj:`AssignResult`: The assign result. - """ - num_gts, num_bboxes = overlaps.size(0), overlaps.size(1) - - # 1. assign -1 by default - assigned_gt_inds = overlaps.new_full((num_bboxes, ), - -1, - dtype=torch.long) - - if num_gts == 0 or num_bboxes == 0: - # No ground truth or boxes, return empty assignment - max_overlaps = overlaps.new_zeros((num_bboxes, )) - if num_gts == 0: - # No truth, assign everything to background - assigned_gt_inds[:] = 0 - if gt_labels is None: - assigned_labels = None - else: - assigned_labels = overlaps.new_full((num_bboxes, ), - -1, - dtype=torch.long) - return AssignResult( - num_gts, - assigned_gt_inds, - max_overlaps, - labels=assigned_labels) - - # for each anchor, which gt best overlaps with it - # for each anchor, the max iou of all gts - max_overlaps, argmax_overlaps = overlaps.max(dim=0) - # for each gt, which anchor best overlaps with it - # for each gt, the max iou of all proposals - gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1) - - # 2. assign negative: below - # the negative inds are set to be 0 - if isinstance(self.neg_iou_thr, float): - assigned_gt_inds[(max_overlaps >= 0) - & (max_overlaps < self.neg_iou_thr)] = 0 - elif isinstance(self.neg_iou_thr, tuple): - assert len(self.neg_iou_thr) == 2 - assigned_gt_inds[(max_overlaps >= self.neg_iou_thr[0]) - & (max_overlaps < self.neg_iou_thr[1])] = 0 - - # 3. assign positive: above positive IoU threshold - pos_inds = max_overlaps >= self.pos_iou_thr - assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1 - - if self.match_low_quality: - # Low-quality matching will overwrite the assigned_gt_inds assigned - # in Step 3. Thus, the assigned gt might not be the best one for - # prediction. - # For example, if bbox A has 0.9 and 0.8 iou with GT bbox 1 & 2, - # bbox 1 will be assigned as the best target for bbox A in step 3. - # However, if GT bbox 2's gt_argmax_overlaps = A, bbox A's - # assigned_gt_inds will be overwritten to be bbox B. - # This might be the reason that it is not used in ROI Heads. - for i in range(num_gts): - if gt_max_overlaps[i] >= self.min_pos_iou: - if self.gt_max_assign_all: - max_iou_inds = overlaps[i, :] == gt_max_overlaps[i] - assigned_gt_inds[max_iou_inds] = i + 1 - else: - assigned_gt_inds[gt_argmax_overlaps[i]] = i + 1 - - if gt_labels is not None: - assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1) - pos_inds = torch.nonzero( - assigned_gt_inds > 0, as_tuple=False).squeeze() - if pos_inds.numel() > 0: - assigned_labels[pos_inds] = gt_labels[ - assigned_gt_inds[pos_inds] - 1] - else: - assigned_labels = None - - return AssignResult( - num_gts, assigned_gt_inds, max_overlaps, labels=assigned_labels) diff --git a/spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/quarto-html/quarto-syntax-highlighting.css b/spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/quarto-html/quarto-syntax-highlighting.css deleted file mode 100644 index d9fd98f040973821b431026876f51351960f58e0..0000000000000000000000000000000000000000 --- a/spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/quarto-html/quarto-syntax-highlighting.css +++ /dev/null @@ -1,203 +0,0 @@ -/* quarto syntax highlight colors */ -:root { - --quarto-hl-ot-color: #003B4F; - --quarto-hl-at-color: #657422; - --quarto-hl-ss-color: #20794D; - --quarto-hl-an-color: #5E5E5E; - --quarto-hl-fu-color: #4758AB; - --quarto-hl-st-color: #20794D; - --quarto-hl-cf-color: #003B4F; - --quarto-hl-op-color: #5E5E5E; - --quarto-hl-er-color: #AD0000; - --quarto-hl-bn-color: #AD0000; - --quarto-hl-al-color: #AD0000; - --quarto-hl-va-color: #111111; - --quarto-hl-bu-color: inherit; - --quarto-hl-ex-color: inherit; - --quarto-hl-pp-color: #AD0000; - --quarto-hl-in-color: #5E5E5E; - --quarto-hl-vs-color: #20794D; - --quarto-hl-wa-color: #5E5E5E; - --quarto-hl-do-color: #5E5E5E; - --quarto-hl-im-color: #00769E; - --quarto-hl-ch-color: #20794D; - --quarto-hl-dt-color: #AD0000; - --quarto-hl-fl-color: #AD0000; - --quarto-hl-co-color: #5E5E5E; - --quarto-hl-cv-color: #5E5E5E; - --quarto-hl-cn-color: #8f5902; - --quarto-hl-sc-color: #5E5E5E; - --quarto-hl-dv-color: #AD0000; - --quarto-hl-kw-color: #003B4F; -} - -/* other quarto variables */ -:root { - --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; -} - -pre > code.sourceCode > span { - color: #003B4F; -} - -code span { - color: #003B4F; -} - -code.sourceCode > span { - color: #003B4F; -} - -div.sourceCode, -div.sourceCode pre.sourceCode { - color: #003B4F; -} - -code span.ot { - color: #003B4F; - font-style: inherit; -} - -code span.at { - color: #657422; - font-style: inherit; -} - -code span.ss { - color: #20794D; - font-style: inherit; -} - -code span.an { - color: #5E5E5E; - font-style: inherit; -} - -code span.fu { - color: #4758AB; - font-style: inherit; -} - -code span.st { - color: #20794D; - font-style: inherit; -} - -code span.cf { - color: #003B4F; - font-style: inherit; -} - -code span.op { - color: #5E5E5E; - font-style: inherit; -} - -code span.er { - color: #AD0000; - font-style: inherit; -} - -code span.bn { - color: #AD0000; - font-style: inherit; -} - -code span.al { - color: #AD0000; - font-style: inherit; -} - -code span.va { - color: #111111; - font-style: inherit; -} - -code span.bu { - font-style: inherit; -} - -code span.ex { - font-style: inherit; -} - -code span.pp { - color: #AD0000; - font-style: inherit; -} - -code span.in { - color: #5E5E5E; - font-style: inherit; -} - -code span.vs { - color: #20794D; - font-style: inherit; -} - -code span.wa { - color: #5E5E5E; - font-style: italic; -} - -code span.do { - color: #5E5E5E; - font-style: italic; -} - -code span.im { - color: #00769E; - font-style: inherit; -} - -code span.ch { - color: #20794D; - font-style: inherit; -} - -code span.dt { - color: #AD0000; - font-style: inherit; -} - -code span.fl { - color: #AD0000; - font-style: inherit; -} - -code span.co { - color: #5E5E5E; - font-style: inherit; -} - -code span.cv { - color: #5E5E5E; - font-style: italic; -} - -code span.cn { - color: #8f5902; - font-style: inherit; -} - -code span.sc { - color: #5E5E5E; - font-style: inherit; -} - -code span.dv { - color: #AD0000; - font-style: inherit; -} - -code span.kw { - color: #003B4F; - font-style: inherit; -} - -.prevent-inlining { - content: " {document.body.innerHTML=\'

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\'; setTimeout(function(){location.reload()},2500); return []}') - - shared.gradio['toggle_dark_mode'].click(lambda: None, None, None, _js='() => {document.getElementsByTagName("body")[0].classList.toggle("dark")}') - shared.gradio['save_settings'].click( - ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then( - ui.save_settings, gradio('interface_state', 'preset_menu', 'instruction_template', 'extensions_menu', 'show_controls'), gradio('save_contents')).then( - lambda: './', None, gradio('save_root')).then( - lambda: 'settings.yaml', None, gradio('save_filename')).then( - lambda: gr.update(visible=True), None, gradio('file_saver')) - - -def set_interface_arguments(extensions, bool_active): - shared.args.extensions = extensions - - bool_list = get_boolean_arguments() - - for k in bool_list: - setattr(shared.args, k, False) - for k in bool_active: - setattr(shared.args, k, True) - - shared.need_restart = True - - -def get_boolean_arguments(active=False): - exclude = ["default", "notebook", "chat"] - - cmd_list = vars(shared.args) - bool_list = sorted([k for k in cmd_list if type(cmd_list[k]) is bool and k not in exclude + ui.list_model_elements()]) - bool_active = [k for k in bool_list if vars(shared.args)[k]] - - if active: - return bool_active - else: - return bool_list diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/configs/_base_/datasets/stare.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/configs/_base_/datasets/stare.py deleted file mode 100644 index 3f71b25488cc11a6b4d582ac52b5a24e1ad1cf8e..0000000000000000000000000000000000000000 --- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/configs/_base_/datasets/stare.py +++ /dev/null @@ -1,59 +0,0 @@ -# dataset settings -dataset_type = 'STAREDataset' -data_root = 'data/STARE' -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) -img_scale = (605, 700) -crop_size = (128, 128) -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/Aphrodite/stable-diffusion-2/README.md b/spaces/Aphrodite/stable-diffusion-2/README.md deleted file mode 100644 index 3a96046df97c8f00eee2ef6a5b0a595e87bd68db..0000000000000000000000000000000000000000 --- a/spaces/Aphrodite/stable-diffusion-2/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Stable Diffusion 2 -emoji: 🐠 -colorFrom: purple -colorTo: green -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/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/coco.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/coco.py deleted file mode 100644 index ed4f7ccb20efa3b54c719783e279c381ca5d8587..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/coco.py +++ /dev/null @@ -1,539 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import contextlib -import datetime -import io -import json -import logging -import numpy as np -import os -import shutil -import pycocotools.mask as mask_util -from fvcore.common.timer import Timer -from iopath.common.file_io import file_lock -from PIL import Image - -from detectron2.structures import Boxes, BoxMode, PolygonMasks, RotatedBoxes -from detectron2.utils.file_io import PathManager - -from .. import DatasetCatalog, MetadataCatalog - -""" -This file contains functions to parse COCO-format annotations into dicts in "Detectron2 format". -""" - - -logger = logging.getLogger(__name__) - -__all__ = ["load_coco_json", "load_sem_seg", "convert_to_coco_json", "register_coco_instances"] - - -def load_coco_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): - """ - Load a json file with COCO's instances annotation format. - Currently supports instance detection, instance segmentation, - and person keypoints annotations. - - Args: - json_file (str): full path to the json file in COCO instances annotation format. - image_root (str or path-like): the directory where the images in this json file exists. - dataset_name (str or None): the name of the dataset (e.g., coco_2017_train). - When provided, this function will also do the following: - - * Put "thing_classes" into the metadata associated with this dataset. - * Map the category ids into a contiguous range (needed by standard dataset format), - and add "thing_dataset_id_to_contiguous_id" to the metadata associated - with this dataset. - - This option should usually be provided, unless users need to load - the original json content and apply more processing manually. - extra_annotation_keys (list[str]): list of per-annotation keys that should also be - loaded into the dataset dict (besides "iscrowd", "bbox", "keypoints", - "category_id", "segmentation"). The values for these keys will be returned as-is. - For example, the densepose annotations are loaded in this way. - - Returns: - list[dict]: a list of dicts in Detectron2 standard dataset dicts format (See - `Using Custom Datasets `_ ) when `dataset_name` is not None. - If `dataset_name` is None, the returned `category_ids` may be - incontiguous and may not conform to the Detectron2 standard format. - - Notes: - 1. This function does not read the image files. - The results do not have the "image" field. - """ - from pycocotools.coco import COCO - - timer = Timer() - json_file = PathManager.get_local_path(json_file) - with contextlib.redirect_stdout(io.StringIO()): - coco_api = COCO(json_file) - if timer.seconds() > 1: - logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) - - id_map = None - if dataset_name is not None: - meta = MetadataCatalog.get(dataset_name) - cat_ids = sorted(coco_api.getCatIds()) - cats = coco_api.loadCats(cat_ids) - # The categories in a custom json file may not be sorted. - thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])] - meta.thing_classes = thing_classes - - # In COCO, certain category ids are artificially removed, - # and by convention they are always ignored. - # We deal with COCO's id issue and translate - # the category ids to contiguous ids in [0, 80). - - # It works by looking at the "categories" field in the json, therefore - # if users' own json also have incontiguous ids, we'll - # apply this mapping as well but print a warning. - if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): - if "coco" not in dataset_name: - logger.warning( - """ -Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. -""" - ) - id_map = {v: i for i, v in enumerate(cat_ids)} - meta.thing_dataset_id_to_contiguous_id = id_map - - # sort indices for reproducible results - img_ids = sorted(coco_api.imgs.keys()) - # imgs is a list of dicts, each looks something like: - # {'license': 4, - # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', - # 'file_name': 'COCO_val2014_000000001268.jpg', - # 'height': 427, - # 'width': 640, - # 'date_captured': '2013-11-17 05:57:24', - # 'id': 1268} - imgs = coco_api.loadImgs(img_ids) - # anns is a list[list[dict]], where each dict is an annotation - # record for an object. The inner list enumerates the objects in an image - # and the outer list enumerates over images. Example of anns[0]: - # [{'segmentation': [[192.81, - # 247.09, - # ... - # 219.03, - # 249.06]], - # 'area': 1035.749, - # 'iscrowd': 0, - # 'image_id': 1268, - # 'bbox': [192.81, 224.8, 74.73, 33.43], - # 'category_id': 16, - # 'id': 42986}, - # ...] - anns = [coco_api.imgToAnns[img_id] for img_id in img_ids] - total_num_valid_anns = sum([len(x) for x in anns]) - total_num_anns = len(coco_api.anns) - if total_num_valid_anns < total_num_anns: - logger.warning( - f"{json_file} contains {total_num_anns} annotations, but only " - f"{total_num_valid_anns} of them match to images in the file." - ) - - if "minival" not in json_file: - # The popular valminusminival & minival annotations for COCO2014 contain this bug. - # However the ratio of buggy annotations there is tiny and does not affect accuracy. - # Therefore we explicitly white-list them. - ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] - assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format( - json_file - ) - - imgs_anns = list(zip(imgs, anns)) - logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file)) - - dataset_dicts = [] - - ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"] + (extra_annotation_keys or []) - - num_instances_without_valid_segmentation = 0 - - for (img_dict, anno_dict_list) in imgs_anns: - record = {} - record["file_name"] = os.path.join(image_root, img_dict["file_name"]) - record["height"] = img_dict["height"] - record["width"] = img_dict["width"] - image_id = record["image_id"] = img_dict["id"] - - objs = [] - for anno in anno_dict_list: - # Check that the image_id in this annotation is the same as - # the image_id we're looking at. - # This fails only when the data parsing logic or the annotation file is buggy. - - # The original COCO valminusminival2014 & minival2014 annotation files - # actually contains bugs that, together with certain ways of using COCO API, - # can trigger this assertion. - assert anno["image_id"] == image_id - - assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.' - - obj = {key: anno[key] for key in ann_keys if key in anno} - if "bbox" in obj and len(obj["bbox"]) == 0: - raise ValueError( - f"One annotation of image {image_id} contains empty 'bbox' value! " - "This json does not have valid COCO format." - ) - - segm = anno.get("segmentation", None) - if segm: # either list[list[float]] or dict(RLE) - if isinstance(segm, dict): - if isinstance(segm["counts"], list): - # convert to compressed RLE - segm = mask_util.frPyObjects(segm, *segm["size"]) - else: - # filter out invalid polygons (< 3 points) - segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] - if len(segm) == 0: - num_instances_without_valid_segmentation += 1 - continue # ignore this instance - obj["segmentation"] = segm - - keypts = anno.get("keypoints", None) - if keypts: # list[int] - for idx, v in enumerate(keypts): - if idx % 3 != 2: - # COCO's segmentation coordinates are floating points in [0, H or W], - # but keypoint coordinates are integers in [0, H-1 or W-1] - # Therefore we assume the coordinates are "pixel indices" and - # add 0.5 to convert to floating point coordinates. - keypts[idx] = v + 0.5 - obj["keypoints"] = keypts - - obj["bbox_mode"] = BoxMode.XYWH_ABS - if id_map: - annotation_category_id = obj["category_id"] - try: - obj["category_id"] = id_map[annotation_category_id] - except KeyError as e: - raise KeyError( - f"Encountered category_id={annotation_category_id} " - "but this id does not exist in 'categories' of the json file." - ) from e - objs.append(obj) - record["annotations"] = objs - dataset_dicts.append(record) - - if num_instances_without_valid_segmentation > 0: - logger.warning( - "Filtered out {} instances without valid segmentation. ".format( - num_instances_without_valid_segmentation - ) - + "There might be issues in your dataset generation process. Please " - "check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully" - ) - return dataset_dicts - - -def load_sem_seg(gt_root, image_root, gt_ext="png", image_ext="jpg"): - """ - Load semantic segmentation datasets. All files under "gt_root" with "gt_ext" extension are - treated as ground truth annotations and all files under "image_root" with "image_ext" extension - as input images. Ground truth and input images are matched using file paths relative to - "gt_root" and "image_root" respectively without taking into account file extensions. - This works for COCO as well as some other datasets. - - Args: - gt_root (str): full path to ground truth semantic segmentation files. Semantic segmentation - annotations are stored as images with integer values in pixels that represent - corresponding semantic labels. - image_root (str): the directory where the input images are. - gt_ext (str): file extension for ground truth annotations. - image_ext (str): file extension for input images. - - Returns: - list[dict]: - a list of dicts in detectron2 standard format without instance-level - annotation. - - Notes: - 1. This function does not read the image and ground truth files. - The results do not have the "image" and "sem_seg" fields. - """ - - # We match input images with ground truth based on their relative filepaths (without file - # extensions) starting from 'image_root' and 'gt_root' respectively. - def file2id(folder_path, file_path): - # extract relative path starting from `folder_path` - image_id = os.path.normpath(os.path.relpath(file_path, start=folder_path)) - # remove file extension - image_id = os.path.splitext(image_id)[0] - return image_id - - input_files = sorted( - (os.path.join(image_root, f) for f in PathManager.ls(image_root) if f.endswith(image_ext)), - key=lambda file_path: file2id(image_root, file_path), - ) - gt_files = sorted( - (os.path.join(gt_root, f) for f in PathManager.ls(gt_root) if f.endswith(gt_ext)), - key=lambda file_path: file2id(gt_root, file_path), - ) - - assert len(gt_files) > 0, "No annotations found in {}.".format(gt_root) - - # Use the intersection, so that val2017_100 annotations can run smoothly with val2017 images - if len(input_files) != len(gt_files): - logger.warn( - "Directory {} and {} has {} and {} files, respectively.".format( - image_root, gt_root, len(input_files), len(gt_files) - ) - ) - input_basenames = [os.path.basename(f)[: -len(image_ext)] for f in input_files] - gt_basenames = [os.path.basename(f)[: -len(gt_ext)] for f in gt_files] - intersect = list(set(input_basenames) & set(gt_basenames)) - # sort, otherwise each worker may obtain a list[dict] in different order - intersect = sorted(intersect) - logger.warn("Will use their intersection of {} files.".format(len(intersect))) - input_files = [os.path.join(image_root, f + image_ext) for f in intersect] - gt_files = [os.path.join(gt_root, f + gt_ext) for f in intersect] - - logger.info( - "Loaded {} images with semantic segmentation from {}".format(len(input_files), image_root) - ) - - dataset_dicts = [] - for (img_path, gt_path) in zip(input_files, gt_files): - record = {} - record["file_name"] = img_path - record["sem_seg_file_name"] = gt_path - dataset_dicts.append(record) - - return dataset_dicts - - -def convert_to_coco_dict(dataset_name): - """ - Convert an instance detection/segmentation or keypoint detection dataset - in detectron2's standard format into COCO json format. - - Generic dataset description can be found here: - https://detectron2.readthedocs.io/tutorials/datasets.html#register-a-dataset - - COCO data format description can be found here: - http://cocodataset.org/#format-data - - Args: - dataset_name (str): - name of the source dataset - Must be registered in DatastCatalog and in detectron2's standard format. - Must have corresponding metadata "thing_classes" - Returns: - coco_dict: serializable dict in COCO json format - """ - - dataset_dicts = DatasetCatalog.get(dataset_name) - metadata = MetadataCatalog.get(dataset_name) - - # unmap the category mapping ids for COCO - if hasattr(metadata, "thing_dataset_id_to_contiguous_id"): - reverse_id_mapping = {v: k for k, v in metadata.thing_dataset_id_to_contiguous_id.items()} - reverse_id_mapper = lambda contiguous_id: reverse_id_mapping[contiguous_id] # noqa - else: - reverse_id_mapper = lambda contiguous_id: contiguous_id # noqa - - categories = [ - {"id": reverse_id_mapper(id), "name": name} - for id, name in enumerate(metadata.thing_classes) - ] - - logger.info("Converting dataset dicts into COCO format") - coco_images = [] - coco_annotations = [] - - for image_id, image_dict in enumerate(dataset_dicts): - coco_image = { - "id": image_dict.get("image_id", image_id), - "width": int(image_dict["width"]), - "height": int(image_dict["height"]), - "file_name": str(image_dict["file_name"]), - } - coco_images.append(coco_image) - - anns_per_image = image_dict.get("annotations", []) - for annotation in anns_per_image: - # create a new dict with only COCO fields - coco_annotation = {} - - # COCO requirement: XYWH box format for axis-align and XYWHA for rotated - bbox = annotation["bbox"] - if isinstance(bbox, np.ndarray): - if bbox.ndim != 1: - raise ValueError(f"bbox has to be 1-dimensional. Got shape={bbox.shape}.") - bbox = bbox.tolist() - if len(bbox) not in [4, 5]: - raise ValueError(f"bbox has to has length 4 or 5. Got {bbox}.") - from_bbox_mode = annotation["bbox_mode"] - to_bbox_mode = BoxMode.XYWH_ABS if len(bbox) == 4 else BoxMode.XYWHA_ABS - bbox = BoxMode.convert(bbox, from_bbox_mode, to_bbox_mode) - - # COCO requirement: instance area - if "segmentation" in annotation: - # Computing areas for instances by counting the pixels - segmentation = annotation["segmentation"] - # TODO: check segmentation type: RLE, BinaryMask or Polygon - if isinstance(segmentation, list): - polygons = PolygonMasks([segmentation]) - area = polygons.area()[0].item() - elif isinstance(segmentation, dict): # RLE - area = mask_util.area(segmentation).item() - else: - raise TypeError(f"Unknown segmentation type {type(segmentation)}!") - else: - # Computing areas using bounding boxes - if to_bbox_mode == BoxMode.XYWH_ABS: - bbox_xy = BoxMode.convert(bbox, to_bbox_mode, BoxMode.XYXY_ABS) - area = Boxes([bbox_xy]).area()[0].item() - else: - area = RotatedBoxes([bbox]).area()[0].item() - - if "keypoints" in annotation: - keypoints = annotation["keypoints"] # list[int] - for idx, v in enumerate(keypoints): - if idx % 3 != 2: - # COCO's segmentation coordinates are floating points in [0, H or W], - # but keypoint coordinates are integers in [0, H-1 or W-1] - # For COCO format consistency we substract 0.5 - # https://github.com/facebookresearch/detectron2/pull/175#issuecomment-551202163 - keypoints[idx] = v - 0.5 - if "num_keypoints" in annotation: - num_keypoints = annotation["num_keypoints"] - else: - num_keypoints = sum(kp > 0 for kp in keypoints[2::3]) - - # COCO requirement: - # linking annotations to images - # "id" field must start with 1 - coco_annotation["id"] = len(coco_annotations) + 1 - coco_annotation["image_id"] = coco_image["id"] - coco_annotation["bbox"] = [round(float(x), 3) for x in bbox] - coco_annotation["area"] = float(area) - coco_annotation["iscrowd"] = int(annotation.get("iscrowd", 0)) - coco_annotation["category_id"] = int(reverse_id_mapper(annotation["category_id"])) - - # Add optional fields - if "keypoints" in annotation: - coco_annotation["keypoints"] = keypoints - coco_annotation["num_keypoints"] = num_keypoints - - if "segmentation" in annotation: - seg = coco_annotation["segmentation"] = annotation["segmentation"] - if isinstance(seg, dict): # RLE - counts = seg["counts"] - if not isinstance(counts, str): - # make it json-serializable - seg["counts"] = counts.decode("ascii") - - coco_annotations.append(coco_annotation) - - logger.info( - "Conversion finished, " - f"#images: {len(coco_images)}, #annotations: {len(coco_annotations)}" - ) - - info = { - "date_created": str(datetime.datetime.now()), - "description": "Automatically generated COCO json file for Detectron2.", - } - coco_dict = {"info": info, "images": coco_images, "categories": categories, "licenses": None} - if len(coco_annotations) > 0: - coco_dict["annotations"] = coco_annotations - return coco_dict - - -def convert_to_coco_json(dataset_name, output_file, allow_cached=True): - """ - Converts dataset into COCO format and saves it to a json file. - dataset_name must be registered in DatasetCatalog and in detectron2's standard format. - - Args: - dataset_name: - reference from the config file to the catalogs - must be registered in DatasetCatalog and in detectron2's standard format - output_file: path of json file that will be saved to - allow_cached: if json file is already present then skip conversion - """ - - # TODO: The dataset or the conversion script *may* change, - # a checksum would be useful for validating the cached data - - PathManager.mkdirs(os.path.dirname(output_file)) - with file_lock(output_file): - if PathManager.exists(output_file) and allow_cached: - logger.warning( - f"Using previously cached COCO format annotations at '{output_file}'. " - "You need to clear the cache file if your dataset has been modified." - ) - else: - logger.info(f"Converting annotations of dataset '{dataset_name}' to COCO format ...)") - coco_dict = convert_to_coco_dict(dataset_name) - - logger.info(f"Caching COCO format annotations at '{output_file}' ...") - tmp_file = output_file + ".tmp" - with PathManager.open(tmp_file, "w") as f: - json.dump(coco_dict, f) - shutil.move(tmp_file, output_file) - - -def register_coco_instances(name, metadata, json_file, image_root): - """ - Register a dataset in COCO's json annotation format for - instance detection, instance segmentation and keypoint detection. - (i.e., Type 1 and 2 in http://cocodataset.org/#format-data. - `instances*.json` and `person_keypoints*.json` in the dataset). - - This is an example of how to register a new dataset. - You can do something similar to this function, to register new datasets. - - Args: - name (str): the name that identifies a dataset, e.g. "coco_2014_train". - metadata (dict): extra metadata associated with this dataset. You can - leave it as an empty dict. - json_file (str): path to the json instance annotation file. - image_root (str or path-like): directory which contains all the images. - """ - assert isinstance(name, str), name - assert isinstance(json_file, (str, os.PathLike)), json_file - assert isinstance(image_root, (str, os.PathLike)), image_root - # 1. register a function which returns dicts - DatasetCatalog.register(name, lambda: load_coco_json(json_file, image_root, name)) - - # 2. Optionally, add metadata about this dataset, - # since they might be useful in evaluation, visualization or logging - MetadataCatalog.get(name).set( - json_file=json_file, image_root=image_root, evaluator_type="coco", **metadata - ) - - -if __name__ == "__main__": - """ - Test the COCO json dataset loader. - - Usage: - python -m detectron2.data.datasets.coco \ - path/to/json path/to/image_root dataset_name - - "dataset_name" can be "coco_2014_minival_100", or other - pre-registered ones - """ - from detectron2.utils.logger import setup_logger - from detectron2.utils.visualizer import Visualizer - import detectron2.data.datasets # noqa # add pre-defined metadata - import sys - - logger = setup_logger(name=__name__) - assert sys.argv[3] in DatasetCatalog.list() - meta = MetadataCatalog.get(sys.argv[3]) - - dicts = load_coco_json(sys.argv[1], sys.argv[2], sys.argv[3]) - logger.info("Done loading {} samples.".format(len(dicts))) - - dirname = "coco-data-vis" - os.makedirs(dirname, exist_ok=True) - for d in dicts: - img = np.array(Image.open(d["file_name"])) - visualizer = Visualizer(img, metadata=meta) - vis = visualizer.draw_dataset_dict(d) - fpath = os.path.join(dirname, os.path.basename(d["file_name"])) - vis.save(fpath) diff --git a/spaces/Bart92/RVC_HF/demucs/separate.py b/spaces/Bart92/RVC_HF/demucs/separate.py deleted file mode 100644 index 3fc7af9e711978b3e21398aa6f1deb9ae87dd370..0000000000000000000000000000000000000000 --- a/spaces/Bart92/RVC_HF/demucs/separate.py +++ /dev/null @@ -1,185 +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. - -import argparse -import sys -from pathlib import Path -import subprocess - -import julius -import torch as th -import torchaudio as ta - -from .audio import AudioFile, convert_audio_channels -from .pretrained import is_pretrained, load_pretrained -from .utils import apply_model, load_model - - -def load_track(track, device, audio_channels, samplerate): - errors = {} - wav = None - - try: - wav = AudioFile(track).read( - streams=0, - samplerate=samplerate, - channels=audio_channels).to(device) - except FileNotFoundError: - errors['ffmpeg'] = 'Ffmpeg is not installed.' - except subprocess.CalledProcessError: - errors['ffmpeg'] = 'FFmpeg could not read the file.' - - if wav is None: - try: - wav, sr = ta.load(str(track)) - except RuntimeError as err: - errors['torchaudio'] = err.args[0] - else: - wav = convert_audio_channels(wav, audio_channels) - wav = wav.to(device) - wav = julius.resample_frac(wav, sr, samplerate) - - if wav is None: - print(f"Could not load file {track}. " - "Maybe it is not a supported file format? ") - for backend, error in errors.items(): - print(f"When trying to load using {backend}, got the following error: {error}") - sys.exit(1) - return wav - - -def encode_mp3(wav, path, bitrate=320, samplerate=44100, channels=2, verbose=False): - try: - import lameenc - except ImportError: - print("Failed to call lame encoder. Maybe it is not installed? " - "On windows, run `python.exe -m pip install -U lameenc`, " - "on OSX/Linux, run `python3 -m pip install -U lameenc`, " - "then try again.", file=sys.stderr) - sys.exit(1) - encoder = lameenc.Encoder() - encoder.set_bit_rate(bitrate) - encoder.set_in_sample_rate(samplerate) - encoder.set_channels(channels) - encoder.set_quality(2) # 2-highest, 7-fastest - if not verbose: - encoder.silence() - wav = wav.transpose(0, 1).numpy() - mp3_data = encoder.encode(wav.tobytes()) - mp3_data += encoder.flush() - with open(path, "wb") as f: - f.write(mp3_data) - - -def main(): - parser = argparse.ArgumentParser("demucs.separate", - description="Separate the sources for the given tracks") - parser.add_argument("tracks", nargs='+', type=Path, default=[], help='Path to tracks') - parser.add_argument("-n", - "--name", - default="demucs_quantized", - help="Model name. See README.md for the list of pretrained models. " - "Default is demucs_quantized.") - parser.add_argument("-v", "--verbose", action="store_true") - parser.add_argument("-o", - "--out", - type=Path, - default=Path("separated"), - help="Folder where to put extracted tracks. A subfolder " - "with the model name will be created.") - parser.add_argument("--models", - type=Path, - default=Path("models"), - help="Path to trained models. " - "Also used to store downloaded pretrained models") - parser.add_argument("-d", - "--device", - default="cuda" if th.cuda.is_available() else "cpu", - help="Device to use, default is cuda if available else cpu") - parser.add_argument("--shifts", - default=0, - type=int, - help="Number of random shifts for equivariant stabilization." - "Increase separation time but improves quality for Demucs. 10 was used " - "in the original paper.") - parser.add_argument("--overlap", - default=0.25, - type=float, - help="Overlap between the splits.") - parser.add_argument("--no-split", - action="store_false", - dest="split", - default=True, - help="Doesn't split audio in chunks. This can use large amounts of memory.") - parser.add_argument("--float32", - action="store_true", - help="Convert the output wavefile to use pcm f32 format instead of s16. " - "This should not make a difference if you just plan on listening to the " - "audio but might be needed to compute exactly metrics like SDR etc.") - parser.add_argument("--int16", - action="store_false", - dest="float32", - help="Opposite of --float32, here for compatibility.") - parser.add_argument("--mp3", action="store_true", - help="Convert the output wavs to mp3.") - parser.add_argument("--mp3-bitrate", - default=320, - type=int, - help="Bitrate of converted mp3.") - - args = parser.parse_args() - name = args.name + ".th" - model_path = args.models / name - if model_path.is_file(): - model = load_model(model_path) - else: - if is_pretrained(args.name): - model = load_pretrained(args.name) - else: - print(f"No pre-trained model {args.name}", file=sys.stderr) - sys.exit(1) - model.to(args.device) - - out = args.out / args.name - out.mkdir(parents=True, exist_ok=True) - print(f"Separated tracks will be stored in {out.resolve()}") - for track in args.tracks: - if not track.exists(): - print( - f"File {track} does not exist. If the path contains spaces, " - "please try again after surrounding the entire path with quotes \"\".", - file=sys.stderr) - continue - print(f"Separating track {track}") - wav = load_track(track, args.device, model.audio_channels, model.samplerate) - - ref = wav.mean(0) - wav = (wav - ref.mean()) / ref.std() - sources = apply_model(model, wav, shifts=args.shifts, split=args.split, - overlap=args.overlap, progress=True) - sources = sources * ref.std() + ref.mean() - - track_folder = out / track.name.rsplit(".", 1)[0] - track_folder.mkdir(exist_ok=True) - for source, name in zip(sources, model.sources): - source = source / max(1.01 * source.abs().max(), 1) - if args.mp3 or not args.float32: - source = (source * 2**15).clamp_(-2**15, 2**15 - 1).short() - source = source.cpu() - stem = str(track_folder / name) - if args.mp3: - encode_mp3(source, stem + ".mp3", - bitrate=args.mp3_bitrate, - samplerate=model.samplerate, - channels=model.audio_channels, - verbose=args.verbose) - else: - wavname = str(track_folder / f"{name}.wav") - ta.save(wavname, source, sample_rate=model.samplerate) - - -if __name__ == "__main__": - main() diff --git a/spaces/Benson/text-generation/Examples/Camin Simulador ltimo Mod Apk Android Oyun Club.md b/spaces/Benson/text-generation/Examples/Camin Simulador ltimo Mod Apk Android Oyun Club.md deleted file mode 100644 index 2a8e81c2289c133d0e7632e1fee479dbb4c122b4..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Camin Simulador ltimo Mod Apk Android Oyun Club.md +++ /dev/null @@ -1,124 +0,0 @@ - -

Camión simulador final temporada de invierno Mod Apk: Un deber-Tener para los fans de simulación de camiones

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Si eres un fan de los juegos de simulación de camiones, es posible que hayas oído hablar de Truck Simulator Ultimate, un juego desarrollado por Zuuks Games que ofrece una experiencia de transporte realista e inmersiva. Pero ¿sabías que hay un apk mod que añade una temporada de invierno para el juego, por lo que es aún más difícil y divertido? En este artículo, le diremos todo lo que necesita saber sobre el camión simulador final invierno mod apk temporada, incluyendo sus características, juego, revisión y calificación.

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¿Qué es Camión Simulador Ultimate temporada de invierno Mod Apk?

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Camión Simulador Ultimate Winter Season Mod Apk es una versión modificada del juego original Truck Simulator Ultimate que añade una temporada de invierno para el juego. Esto significa que tendrá que lidiar con nieve, hielo, niebla, lluvia y otras condiciones climáticas duras mientras conduce su camión por Europa. También tendrá que adaptarse a carreteras resbaladizas, visibilidad reducida y aumento de los accidentes de tráfico. El apk mod también añade algunas nuevas características y mejoras al juego, tales como nuevos camiones, nuevos trabajos, nuevos mapas, y más.

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¿Por qué es popular entre los fans de simulación de camiones?

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El camión simulador final invierno temporada mod apk es popular entre los fans de la simulación de camiones, ya que añade un nuevo nivel de realismo y desafío para el juego. La temporada de invierno hace que el juego sea más dinámico e impredecible, ya que nunca se sabe qué tipo de clima o condición de la carretera se encontrará. El mod apk también mejora los gráficos y efectos de sonido del juego, por lo que es más atractivo visual y envolvente. El mod apk también ofrece más variedad y contenido para el juego, ya que se puede elegir entre diferentes camiones, cargas, rutas y misiones.

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Características de Camión Simulador Ultimate temporada de invierno Mod Apk

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Cómo descargar e instalar el mod apk

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Para descargar e instalar el simulador de camiones última temporada de invierno mod apk, es necesario seguir estos pasos:

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  1. Descargar el archivo apk mod de una fuente de confianza. Puede encontrar el enlace a la última versión del apk mod al final de este artículo.
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  2. Habilita la instalación de aplicaciones de fuentes desconocidas en tu dispositivo. Puede hacer esto yendo a Configuración > Seguridad > Fuentes desconocidas y activando.
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  3. Busque el archivo apk mod descargado en su dispositivo y toque en él para iniciar el proceso de instalación.
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  4. Iniciar el juego y disfrutar de la temporada de invierno.
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Cómo iniciar y ejecutar una empresa de transporte por carretera

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Una vez que haya instalado el apk mod, puede iniciar su propia empresa de transporte en el juego. Aquí hay algunos consejos sobre cómo ejecutar una empresa de transporte exitoso:

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Cómo conducir y entregar carga en toda Europa

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Conducir y entregar carga en toda Europa no es una tarea fácil, especialmente en temporada de invierno. Usted tendrá que hacer frente a diversos desafíos y riesgos en el camino. Aquí hay algunos consejos y trucos sobre cómo conducir y entregar carga de forma segura y eficiente:

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