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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Burger Please Mod Apk The Ultimate Fun Game with Unlimited Money.md b/spaces/1phancelerku/anime-remove-background/Burger Please Mod Apk The Ultimate Fun Game with Unlimited Money.md deleted file mode 100644 index 18a9a264040da063dc79b5c46053944809b9e876..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Burger Please Mod Apk The Ultimate Fun Game with Unlimited Money.md +++ /dev/null @@ -1,132 +0,0 @@ - -
Burger Please Mod APK Unlimited Money: How to Download and Play
-Do you love burgers? Do you want to run your own burger shop? Do you want to have unlimited money, diamonds, coins, and cash in your game? If you answered yes to any of these questions, then you might be interested in Burger Please Mod APK Unlimited Money, a modified version of the popular casual game Burger Please. In this article, we will tell you everything you need to know about this mod, including what it is, how to download and install it, and how to play it. Let's get started!
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What is Burger Please?
-Burger Please is a casual game where you run your own burger shop and serve delicious burgers to your customers. You can customize your shop, upgrade your equipment, unlock new recipes, and create your own burger combinations. You can also compete with other players in the leaderboard, complete daily missions, and earn rewards.
-A casual game where you run your own burger shop
-The main mode of Burger Please is the Career Mode, where you start from a small burger stand and work your way up to a big burger empire. You will have to manage your time, resources, and customer satisfaction as you prepare and serve burgers according to their orders. You will also have to deal with different types of customers, such as kids, adults, celebrities, zombies, aliens, and more. Each customer has their own preferences, personality, and patience level, so you have to be careful not to make them angry or disappointed.
-The features and gameplay of Burger Please
-Burger Please has many features and gameplay elements that make it fun and addictive. Some of them are:
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- Customization: You can customize your shop with different themes, decorations, furniture, and accessories. You can also customize your character with different outfits, hairstyles, accessories, and expressions. -
- Upgrades: You can upgrade your equipment, such as your grill, fryer, toaster, blender, etc., to make them faster, more efficient, and more durable. You can also upgrade your ingredients, such as your meat, cheese, lettuce, tomato, etc., to make them tastier, fresher, and more nutritious. -
- Recipes: You can unlock new recipes as you progress in the game. You can also create your own recipes by combining different ingredients. You can save your recipes in your cookbook and use them anytime. -
- Challenges: You can challenge yourself with different modes and levels in the game. You can play the Time Attack Mode, where you have to serve as many burgers as possible in a limited time. You can play the Endless Mode, where you have to serve burgers until you run out of ingredients or customers. You can also play the Boss Mode, where you have to face a special customer who will test your skills. -mod APK from a trusted and reliable source, such as a reputable website, forum, or blog. -
- You should always scan the mod APK with an antivirus or anti-malware program before installing it on your device. -
- You should always read the reviews and comments of other users who have used the mod APK before downloading and installing it. -
- You should always follow the instructions and requirements of the mod APK carefully and correctly. -
How to Download and Install Burger Please Mod APK Unlimited Money?
-If you have decided to use Burger Please Mod APK Unlimited Money, you will need to download and install it on your device. The process may vary depending on the type of device you have, but here are some general steps that you can follow:
-The steps to download and install Burger Please Mod APK Unlimited Money on your Android device
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- Go to the link where you can download the mod APK file. You can search for it online or use the link provided by the source. -
- Tap on the download button and wait for the file to be downloaded on your device. -
- Once the file is downloaded, go to your device settings and enable the option to install apps from unknown sources. This will allow you to install the mod APK file. -
- Locate the mod APK file in your device storage and tap on it to start the installation process. -
- Follow the on-screen instructions and grant the necessary permissions to install the mod APK file. -
- Wait for the installation to be completed and then launch the game from your app drawer or home screen. -
- Enjoy playing Burger Please Mod APK Unlimited Money on your Android device. -
The steps to download and install Burger Please Mod APK Unlimited Money on your PC using an emulator
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- Go to the link where you can download the mod APK file. You can search for it online or use the link provided by the source. -
- Download and install an Android emulator on your PC. An emulator is a software that allows you to run Android apps on your PC. Some popular emulators are BlueStacks, NoxPlayer, LDPlayer, etc. -
- Launch the emulator and sign in with your Google account. This will allow you to access the Google Play Store and other Google services on your PC. -
- Drag and drop the mod APK file into the emulator or use the built-in browser to download it from the link. -
- Install the mod APK file using the emulator's app installer or file manager. -
- Launch the game from the emulator's app drawer or home screen. -
- Enjoy playing Burger Please Mod APK Unlimited Money on your PC using an emulator. -
The steps to download and install Burger Please Mod APK Unlimited Money on your iOS device using a third-party app store
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- Go to the link where you can download the mod IPA file. You can search for it online or use the link provided by the source. The mod IPA file is a modified version of the original game that works on iOS devices. The mod IPA file is not an official version of the game and it is not supported or endorsed by the original developers. -
- Download and install a third-party app store on your iOS device. A third-party app store is an alternative to the Apple App Store that allows you to download and install apps that are not available or approved by Apple. Some popular third-party app stores are TutuApp, Panda Helper, AppValley, etc. -
- Launch the third-party app store and search for Burger Please Mod IPA Unlimited Money or use the link provided by the source. -
- Tap on the download button and wait for the file to be downloaded on your device. -
- Once the file is downloaded, go to your device settings and trust the developer profile of the third-party app store. This will allow you to install the mod IPA file. -
- Locate the mod IPA file in your device storage and tap on it to start the installation process. -
- Follow the on-screen instructions and grant the necessary permissions to install the mod IPA file. -
- Wait for the installation to be completed and then launch the game from your app drawer or home screen. -
- Enjoy playing Burger Please Mod IPA Unlimited Money on your iOS device using a third-party app store. -
How to Play Burger Please Mod APK Unlimited Money?
-Now that you have downloaded and installed Burger Please Mod APK Unlimited Money, you might be wondering how to play it. Well, the gameplay is pretty much the same as the original game, except that you have unlimited resources and access to everything. However, if you want some tips and tricks to play it effectively and enjoyably, here are some suggestions:
-The tips and tricks to play Burger Please Mod APK Unlimited Money effectively and enjoyably
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- Experiment with different recipes: Since you have unlimited ingredients, you can try out different combinations and create your own recipes. You can also use the cookbook to save your recipes and use them anytime. You might discover some delicious and unique burgers that will impress your customers and yourself. -
- Upgrade your equipment and ingredients: Since you have unlimited money, diamonds, coins, and cash, you can upgrade your equipment and ingredients to the max level. This will make your burgers faster, better, and more profitable. You can also buy new equipment and ingredients that will enhance your gameplay and variety. -
- Customize your shop and character: Since you have unlimited money, diamonds, coins, and cash, you can customize your shop and character with any theme, decoration, furniture, accessory, outfit, hairstyle, etc. that you want. You can also change them anytime according to your mood or preference. You can make your shop and character look unique and attractive. -
- Compete with other players: Since you have unlimited money, diamonds, coins, and cash, you can compete with other players in the leaderboard without any fear or pressure. You can also challenge yourself with different modes and levels in the game. You can show off your skills and achievements to other players and prove that you are the best burger master. -
- Have fun: The most important tip is to have fun while playing Burger Please Mod APK Unlimited Money. Don't take it too seriously or get bored by having everything easy. Enjoy the game as a casual and relaxing activity that will make you happy and hungry. -
The best strategies and techniques to play Burger Please Mod APK Unlimited Money successfully and competitively
--
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- Plan ahead: Even though you have unlimited resources, you still need to plan ahead when preparing and serving burgers. You need to pay attention to the customer's order, the ingredient's availability, the equipment's condition, etc. You need to avoid wasting time or making mistakes that will affect your performance or customer satisfaction. -
- Prioritize your customers: Even though you have unlimited resources, you still need to prioritize your customers when serving burgers. You need to consider their preferences, personality, patience level, etc. You need to serve them quickly, accurately, politely, etc. You need to avoid making them angry or disappointed that will affect your reputation or income. -
- Balanc e your resources: Even though you have unlimited resources, you still need to balance your resources when buying and upgrading items. You need to consider the cost, benefit, quality, quantity, etc. of each item. You need to avoid overspending or underutilizing your resources that will affect your gameplay or variety. -
- Use your creativity: Even though you have unlimited resources, you still need to use your creativity when creating and customizing burgers. You need to experiment with different ingredients, recipes, combinations, etc. You need to make your burgers look appealing, delicious, and unique. You need to impress your customers and yourself with your creativity. -
- Have fun: The most important strategy is to have fun while playing Burger Please Mod APK Unlimited Money. Don't take it too seriously or get stressed by the competition or the challenge. Enjoy the game as a casual and relaxing activity that will make you happy and hungry. -
Conclusion
-In conclusion, Burger Please Mod APK Unlimited Money is a modified version of the original game that gives you unlimited money, diamonds, coins, and cash in your game. It allows you to enjoy the game without any limitations or restrictions. However, it also has some benefits and drawbacks, risks and precautions, tips and tricks, and strategies and techniques that you should know before downloading and installing it. We hope that this article has helped you learn more about this mod and how to download and play it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy burger making!
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FAQs
-Here are some frequently asked questions about Burger Please Mod APK Unlimited Money:
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- Is Burger Please Mod APK Unlimited Money safe to use? -
- Is Burger Please Mod APK Unlimited Money legal to use? -
- Can I play Burger Please Mod APK Unlimited Money online? -
- Can I play Burger Please Mod APK Unlimited Money offline? -
- Can I update Burger Please Mod APK Unlimited Money? -
It depends on the source and the file that you download. Some mod APK files may be safe to use, while others may be fake or malicious. You should always download a mod APK file from a trusted and reliable source, such as a reputable website, forum, or blog. You should also scan the mod APK file with an antivirus or anti-malware program before installing it on your device.
-No, it is not legal to use Burger Please Mod APK Unlimited Money. It is a modified version of the original game that violates the terms and conditions of the game and the intellectual property rights of the original developers. Using a mod APK file may result in legal actions or penalties from the original developers or the authorities.
-Yes, you can play Burger Please Mod APK Unlimited Money online with other players. However, you may face some issues or problems when playing online, such as lagging, crashing, banning, etc. You may also encounter some players who are using the same mod or other mods that may give them an unfair advantage or disadvantage.
-Yes, you can play Burger Please Mod APK Unlimited Money offline without an internet connection. However, you may not be able to access some features or functions of the game that require an internet connection, such as the leaderboard, the daily missions, the ads, etc.
-No, you cannot update Burger Please Mod APK Unlimited Money. Updating a mod APK file may cause some errors or issues in the game or remove the modifications. You may also lose your progress or account if you update a mod APK file. If you want to update the game, you will have to uninstall the mod APK file and install the original game from the Google Play Store or the Apple App Store.
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Download Ragnarok X APK - The 3D MMORPG Mobile Game that Brings Back the Classic Masterpiece.md b/spaces/1phancelerku/anime-remove-background/Download Ragnarok X APK - The 3D MMORPG Mobile Game that Brings Back the Classic Masterpiece.md deleted file mode 100644 index bd62db689e558ca89a031341134478834c22569e..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Download Ragnarok X APK - The 3D MMORPG Mobile Game that Brings Back the Classic Masterpiece.md +++ /dev/null @@ -1,146 +0,0 @@ -
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Download Ragnarok X APK: How to Play the Next Generation of the Classic MMORPG on Your Android Device
-Introduction
-If you are a fan of the classic Ragnarok Online, you might be interested in trying out its latest mobile version, Ragnarok X: Next Generation. This game is an official remake of the original masterpiece, authorized by Gravity from South Korea. It features retro style, new classes, cross-server PvP, guild wars, pet taming, marriage system, and more. In this article, we will show you how to download Ragnarok X APK and play it on your Android device. We will also share some features, tips, and tricks for playing this game.
-What is Ragnarok X: Next Generation?
-Ragnarok X: Next Generation is a 3D MMORPG mobile game that is based on the classic Ragnarok Online. It is developed by Nuverse and published by Gravity in various regions. The game aims to recreate the original love and nostalgia of the classic game, while adding new elements and improvements. The game has been launched in Southeast Asia, Taiwan, Hong Kong, Macau, Japan, and Korea. It has received positive reviews from players and critics alike.
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Why should you download Ragnarok X APK?
-If you want to play Ragnarok X: Next Generation on your Android device, you might need to download its APK file. APK stands for Android Package Kit, which is a file format that contains all the necessary components for installing an app on an Android device. There are several reasons why you might want to download Ragnarok X APK:
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- The game is not available in your region or country. -
- The game is not compatible with your device or operating system. -
- You want to access the latest version or features of the game before they are officially released. -
- You want to avoid any potential errors or bugs that might occur during the installation process from the Google Play Store. -
How to download Ragnarok X APK safely and easily?
-Downloading Ragnarok X APK is not difficult, but you need to be careful about the source and the file. There are many websites that offer APK files for various apps and games, but not all of them are trustworthy or reliable. Some of them might contain malware, viruses, or other harmful content that could damage your device or compromise your privacy. Therefore, you need to follow these steps to download Ragnarok X APK safely and easily:
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- Find a reputable website that offers Ragnarok X APK. You can use a search engine or a review site to find one. Some examples of trustworthy websites are , , and . -
- Check the details and information about the APK file. Make sure it matches the version, size, developer, and description of the game. You can also read the comments and ratings from other users to see if they have any issues or complaints. -
- Download the APK file to your device. You might need to enable the option to install apps from unknown sources in your device settings. This will allow you to install apps that are not from the Google Play Store. -
- Install the APK file by tapping on it and following the instructions on the screen. You might need to grant some permissions to the app to run properly. -
Congratulations, you have successfully downloaded and installed Ragnarok X APK on your Android device. You can now launch the game and enjoy its features.
-Features of Ragnarok X: Next Generation
-Ragnarok X: Next Generation is not just a simple remake of the classic Ragnarok Online. It also offers many new and exciting features that will enhance your gaming experience. Here are some of the features that you can expect from this game:
-Super Novice: The ultimate all-rounder class
-One of the most unique features of Ragnarok X: Next Generation is the Super Novice class. This is a special class that can learn skills from all other classes, making it the most versatile and flexible class in the game. You can customize your Super Novice according to your preferences and play style. You can also equip any weapon or armor that you want, as long as you meet the requirements. However, the Super Novice also has some drawbacks, such as low HP and SP, and limited skill slots. Therefore, you need to be careful and strategic when playing as a Super Novice.
-World of Champions: The cross-server PvP competition
-If you are looking for some thrilling and competitive action, you should try out the World of Champions mode. This is a cross-server PvP mode that pits players from different servers against each other in a 5v5 battle. You can join this mode by registering in the World of Champions NPC in Prontera. You will be matched with other players based on your rank and level. The winning team will receive rewards such as honor points, zeny, and rare items. You can also use your honor points to exchange for exclusive costumes and accessories.
-Guild vs Guild: The ultimate test of teamwork and strategy
-Another feature that will test your skills and teamwork is the Guild vs Guild mode. This is a large-scale war mode that involves up to 50 guilds fighting for the control of castles. You can join this mode by being a member of a guild that has registered for the war. You will need to cooperate with your guildmates to attack or defend the castles, using various strategies and tactics. The guilds that successfully occupy the castles will receive rewards such as guild funds, zeny, and rare items. They will also have access to exclusive dungeons and quests.
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Pet Taming: The adorable companions for your adventure
-If you are looking for some cute and loyal companions for your adventure, you should try out the pet taming feature. This feature allows you to capture and tame various monsters in the game, such as Poring, Lunatic, Yoyo, Baphomet Jr., and more. You will need to use specific items to lure and tame them, such as apples, bananas, honey, etc. Once you have tamed them, they will follow you around and assist you in combat. They will also have their own skills and attributes that you can upgrade by feeding them and giving them affection.
-Marriage System: The romantic journey with your loved one
-If you are looking for some romance in your life, you should try out the marriage system feature. This feature allows you to propose to another player that you have a good relationship with, and get married in a beautiful ceremony. You will need to buy a ring, a wedding dress or suit, and a wedding invitation card to prepare for the wedding. You will also need to invite your friends and guildmates to witness your special day. Once you are married, you will receive benefits such as bonus stats, exclusive skills, and special quests.
-Tips and Tricks for Playing Ragnarok X: Next Generation
-Ragnarok X: Next Generation is a fun and immersive game that will keep you entertained for hours. However, it can also be challenging and complex at times, especially for beginners. Therefore, we have prepared some tips and tricks for playing this game that will help you improve your performance and enjoyment.
-How to level up fast and efficiently?
-One of the most important aspects of playing Ragnarok X: Next Generation is leveling up your character. Leveling up will increase your stats, unlock new skills, and allow you to access more content in the game. Here are some ways to level up fast and efficiently:
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- Complete quests: Quests are one of the main sources of experience points in the game. You can find quests from NPCs in various towns and maps. Quests will also reward you with zeny, items, and other benefits. -
- Join parties: Parties are groups of players that cooperate with each other in combat. Join ing parties will allow you to share experience points and loot with other players. You can also benefit from their skills and buffs. You can join parties by using the party finder feature or by inviting other players manually. -
- Use items: Items are consumables that can boost your experience points gain and other aspects of your character. You can use items such as EXP potions, field manuals, battle manuals, etc. to increase your experience points gain. You can also use items such as food, scrolls, cards, etc. to enhance your stats and skills. -
- Explore maps: Maps are the areas where you can find monsters, NPCs, quests, and other features in the game. Exploring maps will allow you to discover new places, encounter new monsters, and complete new quests. You can also gain experience points by killing monsters and collecting items. -
How to earn zeny and upgrade your equipment?
-Zeny is the main currency in Ragnarok X: Next Generation. You will need zeny to buy items, upgrade equipment, enhance skills, and perform other actions in the game. Equipment is the gear that you can equip on your character to improve your stats and abilities. Upgrading equipment will increase its quality and effectiveness. Here are some ways to earn zeny and upgrade your equipment:
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- Sell items: Items are the things that you can collect, craft, or buy in the game. You can sell items that you don't need or want to other players or NPCs for zeny. You can use the auction house feature or the personal shop feature to sell items to other players. You can also use the NPC shops or the vending machine feature to sell items to NPCs. -
- Craft items: Crafting is the process of creating new items from raw materials or existing items. You can craft items such as weapons, armor, accessories, potions, etc. by using the crafting feature or the blacksmith feature. You can use the crafted items for yourself or sell them for zeny. -
- Upgrade items: Upgrading is the process of improving the quality and level of your equipment. You can upgrade your equipment by using the upgrade feature or the refine feature. You will need materials such as ores, crystals, eluniums, etc. to upgrade your equipment. Upgrading your equipment will increase its stats and effects. -
- Enhance items: Enhancing is the process of adding extra effects or attributes to your equipment. You can enhance your equipment by using the enhance feature or the enchant feature. You will need materials such as cards, runes, gems, etc. to enhance your equipment. Enhancing your equipment will add special bonuses and abilities to it. -
How to join a guild and participate in guild activities?
-A guild is a group of players that share a common goal and interest in the game. Joining a guild will allow you to interact with other players, cooperate with them in combat, and enjoy various benefits and features in the game. Guild activities are events or modes that are exclusive for guild members. Participating in guild activities will allow you to earn rewards, improve your reputation, and have fun with your guildmates. Here are some ways to join a guild and participate in guild activities:
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-
- Find a guild: Finding a guild is the first step to joining a guild. You can find a guild by using the guild finder feature or by browsing the guild list feature. You can also find a guild by asking other players or by checking online forums or communities. -
- Apply for a guild: Applying for a guild is the second step to joining a guild. You can apply for a guild by sending a request to the guild leader or by accepting an invitation from a guild member. You will need to wait for the approval of the guild leader or the guild officer before you can join the guild. -
- Contribute to a guild: Contributing to a guild is the third step to joining a guild. You can contribute to a guild by donating zeny, materials, or items to the guild fund or by completing guild quests or missions. Contributing to a guild will increase your contribution points and your reputation within the guild. -
- Participate in guild activities: Participating in guild activities is the fourth step to joining a guild. You can participate in guild activities by joining the guild war, the guild dungeon, the guild raid, or the guild party. Participating in guild activities will earn you rewards such as zeny, items, honor points, or rare items. You will also have fun and bond with your guildmates. -
How to customize your character and skills?
-Customizing your character and skills is one of the most enjoyable aspects of playing Ragnarok X: Next Generation. Customizing your character and skills will allow you to express your personality, style, and preferences in the game. You can also optimize your performance and effectiveness in combat by choosing the best combination of skills and equipment for your character. Here are some ways to customize your character and skills:
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- Choose a class: Choosing a class is the first step to customizing your character and skills. You can choose from six classes in the game: Swordsman, Thief, Archer, Mage, Acolyte, and Merchant. Each class has its own strengths, weaknesses, and roles in the game. You can also change your class later in the game by using the job change feature. -
- Choose a hairstyle: Choosing a hairstyle is the second step to customizing your character and skills. You can choose from various hairstyles in the game, ranging from cute to cool to elegant. You can also change your hairstyle later in the game by using the barber shop feature or by buying hair coupons. -
- Choose a costume: Choosing a costume is the third step to customizing your character and skills. You can choose from various costumes in the game, such as uniforms, suits, dresses, casual wear, etc. You can also change your costume later in the game by using the wardrobe feature or by buying costume coupons. -
- Choose a skill build: Choosing a skill build is the fourth step to customizing your character and skills. You can choose from various skills in the game, depending on your class and level. You can also change your skill build later in the game by using the skill reset feature or by buying skill reset coupons. -
How to enjoy the social aspects of the game?
-Ragnarok X: Next Generation is not only a game, but also a social platform. You can interact with other players, make friends, chat, trade, and have fun with them in the game. You can also join various events and activities that are designed to enhance your social experience in the game. Here are some ways to enjoy the social aspects of the game:
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- Use chat: Chat is one of the main ways to communicate with other players in the game. You can use chat to send messages, emojis, stickers, or voice messages to other players. You can also use chat to join different channels, such as world chat, guild chat, party chat, etc. -
- Use friend: Friend is one of the main ways to connect with other players in the game. You can use friend to add other players as your friends, send them gifts, invite them to parties or guilds, or view their profiles. -
- Use emoticon: Emoticon is one of the main ways to express yourself in the game. You can use emoticon to perform various actions or gestures with your character, such as waving, laughing, crying, dancing, etc. You can also use emoticon to interact with other players or NPCs. -
- Use event: Event is one of the main ways to participate in various activities in the game. You can use event to join different events that are held regularly or occasionally in the game, such as festivals, concerts, quizzes, etc. You can also use event to earn rewards such as zeny, items, costumes, etc. -
Conclusion
-Ragnarok X: Next Generation is a great game for fans of Ragnarok Online and MMORPGs in general. It offers a nostalgic and immersive experience that will keep you hooked for hours. It also offers many new and exciting features that will enhance your gaming experience. If you want to play this game on your Android device, you should download Ragnarok X APK from a reputable website and install it on your device. You should also follow our tips and tricks for playing this game that will help you improve your performance and enjoyment.
-FAQs
-Here are some frequently asked questions about Ragnarok X: Next Generation:
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- Is Ragnarok X: Next Generation free to play?
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- Community: You can use the community feature in the game to join various groups or forums. You can also follow their official social media accounts such as Facebook, Instagram, Twitter, YouTube, etc. -
- - How can I support Ragnarok X: Next Generation?
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- Rate and review the game on the Google Play Store or other platforms. -
- Share your feedback and suggestions with the developers and the customer service. -
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/FM WhatsApp APK Download for Android - Latest Version 2023 with New Features.md b/spaces/1phancelerku/anime-remove-background/FM WhatsApp APK Download for Android - Latest Version 2023 with New Features.md deleted file mode 100644 index 96979469f832fdd4563f07316df9ab0d9cd84e39..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/FM WhatsApp APK Download for Android - Latest Version 2023 with New Features.md +++ /dev/null @@ -1,95 +0,0 @@ - -
FM WhatsApp 2023 APK Download: Everything You Need to Know
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- And many more. -
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|---|---|
| Is FM WhatsApp safe to use? | FM WhatsApp is safe to use as long as you download it from a trusted source and scan it for viruses before installing it. However, it is not an official app and it may violate some of the terms and conditions of WhatsApp. Therefore, use it at your own risk and discretion. |
| Can I use FM WhatsApp with the official app? | Yes, you can use FM WhatsApp with the official app if you want to have two WhatsApp accounts on the same device. However, you need to use different phone numbers for each account and install them in separate folders. |
| How can I backup my chats on FM WhatsApp? | You can backup your chats on FM WhatsApp by going to Menu > Settings > Chats > Chat Backup. You can choose to backup your chats locally or on Google Drive. You can also restore your chats from the backup when you reinstall or update FM WhatsApp. |
| How can I change themes on FM WhatsApp? | You can change themes on FM WhatsApp by going to Menu > Fouad Mods > Universal > Themes. You can choose from hundreds of themes available or download more from the internet. You can also create your own theme by customizing various elements of the app. |
| How can I contact the developer of FM WhatsApp? | You can contact the developer of FM WhatsApp by going to Menu > Fouad Mods > About > Contact Me. You can send him an email or follow him on social media platforms like Twitter, Instagram, and Telegram. |
Conclusion
-In conclusion, FM WhatsApp is a great alternative to the official WhatsApp app that offers more features and customization options. You can download and install FM WhatsApp 2023 APK on your Android device by following the steps mentioned in this article. You can also update it to the latest version whenever there is one available. However, you should be aware of the risks involved in using a modded app and use it responsibly. We hope that this article has helped you learn more about FM WhatsApp 2023 APK download and answered some of your questions.
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Free 3D Models of Orange Trees - Easy to Customize and Render.md b/spaces/1phancelerku/anime-remove-background/Free 3D Models of Orange Trees - Easy to Customize and Render.md deleted file mode 100644 index b2e2137a64ada459f710d9e11194fa47046c293e..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Free 3D Models of Orange Trees - Easy to Customize and Render.md +++ /dev/null @@ -1,136 +0,0 @@ - -
How to Find and Download Free Orange Tree 3D Models
-If you are looking for realistic and high-quality orange tree 3D models for your project, you might be wondering where to find them online. Whether you need them for animation, rendering, game development, or any other purpose, you can save time and money by downloading free 3D models from various websites. In this article, we will show you how to find and download free orange tree 3D models from some of the most popular sources on the web.
-Introduction
-What are orange tree 3D models and why are they useful?
-An orange tree 3D model is a digital representation of an orange tree that can be used in various applications that require 3D graphics. A 3D model consists of vertices, edges, faces, and textures that define the shape, color, and appearance of the object. A 3D model can also have animations, rigging, lighting, and other features that make it more realistic and interactive.
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Orange tree 3D models are useful for many reasons. For example, they can help you create stunning scenes and environments for your animations or games. They can also help you visualize and design your own garden or landscape. They can even be used for educational purposes, such as teaching students about botany or ecology.
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-There are many websites that offer free 3D models of various objects, including orange trees. However, not all of them are reliable or easy to use. Some of them may have low-quality models, limited formats, or unclear licenses. Therefore, you need to be careful and selective when choosing a website to download free 3D models from.
-In this article, we will focus on three websites that are well-known and trusted by many 3D artists and enthusiasts. They are TurboSquid, Sketchfab, and CGTrader. These websites have a large collection of free orange tree 3D models that you can browse, download, and use in your projects. We will explain how to use each website and what to look for when downloading a model.
-TurboSquid: A Popular Source of Free 3D Models
-What is TurboSquid and how does it work?
-TurboSquid is one of the largest and oldest online marketplaces for 3D models. It was founded in 2000 and has over one million models in its catalog. TurboSquid allows anyone to buy or sell 3D models for various purposes. It also has a section dedicated to free 3D models that anyone can download and use without paying anything.
-TurboSquid works by connecting buyers and sellers of 3D models. Buyers can search for the models they need by using keywords, filters, categories, or collections. They can also preview the models in different views, check the details and ratings, and download them in various formats. Sellers can upload their models to TurboSquid and set their own prices or offer them for free. TurboSquid also has a quality assurance program called CheckMate that certifies the models that meet certain standards of quality and compatibility.
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-Searching for free orange tree 3D models on TurboSquid is easy and fast. Here are the steps you need to follow:
-Filter by free, orange, and tree keywords
-The first step is to go to the free 3D models section of TurboSquid. You will see a search bar where you can enter the keywords that describe the model you are looking for. In this case, you can type "free orange tree" and hit enter. You will see a list of results that match your query.
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-The next step is to sort the results by the criteria that matter to you. You can use the drop-down menu on the top right corner of the page to choose how to sort the results. You can sort them by best match, quality, or poly count. Best match will show you the models that are most relevant to your query. Quality will show you the models that have the highest ratings or CheckMate certification. Poly count will show you the models that have the lowest or highest number of polygons.
-Check the license, format, and details of each model
-The final step is to check the license, format, and details of each model before downloading it. You can click on the thumbnail of each model to see more information about it. You will see a page that shows you the preview images, description, specifications, reviews, and related models of the model. You will also see a section that shows you the license, format, and download options of the model.
-The license tells you how you can use the model in your project. Some models are royalty-free, which means you can use them for any purpose without paying anything. Some models are editorial-only, which means you can only use them for non-commercial purposes such as news or education. Some models have custom licenses, which means you have to read and follow the terms and conditions of the seller.
-The format tells you what file types are available for the model. Some models have multiple formats, such as OBJ, FBX, 3DS, or STL. Some models have only one format, such as MAX or BLEND. You should choose the format that is compatible with your software or application.
-The download options tell you how you can get the model on your device. Some models have direct download links, which means you can download them instantly by clicking on them. Some models have email delivery links, which means you have to enter your email address and wait for the link to be sent to you.
-Download the model and use it in your project
-Once you have checked everything and found the model that suits your needs, you can download it and use it in your project. You should always respect the license and credit the seller if required. You should also check the quality and compatibility of the model before using it in your project. You may need to adjust some settings or parameters to make it look better or fit better in your scene.
-Other Websites to Download Free Orange Tree 3D Models
-Sketchfab: A Platform for 3D and VR Content
-How to find and download free orange tree 3D models on Sketchfab?
-Sketchfab is another popular platform for 3D and VR content. It was founded in 2012 and has over four million models in its library. Sketchfab allows anyone to upload, view, share, and download 3D models for various purposes. It also has a section dedicated to free 3D models that anyone can download and use without paying anything.
-Finding and downloading free orange tree 3D models on Sketchfab is similar to TurboSquid. Here are the steps you need to follow:
- - Go to the free 3D models section of Sketchfab. - Type "orange tree" in the search bar and hit enter. - Use the filters on the left side of the page to narrow down your results by category, license, format, poly count, or tags. - Click on the thumbnail of each model to see more information about it. - Check the license, format, details, and preview of each model before downloading it. - Click on the download button on the bottom right corner of each model page. - Choose the format that is compatible with your software or application. - Download the model and use it in your project.CGTrader: A Marketplace for 3D Assets
-How to find and download free orange tree 3D models on CGTrader?
-CGTrader is another marketplace for 3D assets. It was founded in 2011 and has over one million models in its catalog. CGTrader allows anyone to buy or sell 3D models for various purposes. It also has a section dedicated to free 3D models that anyone can download and use without paying anything.
-Finding and downloading free orange tree 3D models on CGTrader is similar to TurboSquid and Sketchfab. Here are the steps you need to follow:
- - Go to the free 3D models section of CGTrader. - Type "orange tree" in the search bar and hit enter. - Use the filters on the left side of the page to narrow down your results by category, license, format, poly count, or tags. - Click on the thumbnail of each model to see more information about it. - Check the license, format, details, and preview of each model before downloading it. - Click on the download button on the bottom right corner of each model page. - Choose the format that is compatible with your software or application. - Download the model and use it in your project.Conclusion
-Summary of the main points
-In this article, we have shown you how to find and download free orange tree 3D models from some of the most popular websites on the web. We have explained what orange tree 3D models are and why they are useful. We have also given you a step-by-step guide on how to use TurboSquid, Sketchfab, and CGTrader to search for, filter, check, and download free orange tree 3D models for your project.
-Call to action and final remarks
-We hope you have found this article helpful and informative. If you are looking for realistic and high-quality orange tree 3D models for your project, you can save time and money by downloading them for free from these websites. You can also explore other types of 3D models that are available for free or for a reasonable price.
-If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you and help you with your 3D modeling needs. Thank you for reading and happy downloading!
-FAQs
-What are the benefits of using free orange tree 3D models?
-Some of the benefits of using free orange tree 3D models are:
--
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- You can save time and money by not having to create or buy your own models. -
- You can enhance the realism and quality of your project by using models that are made by professional 3D artists. -
- You can learn from the models by studying their structure, texture, lighting, and animation. -
- You can support the 3D community by giving credit and feedback to the creators of the models. -
What are the drawbacks of using free orange tree 3D models?
-Some of the drawbacks of using free orange tree 3D models are:
--
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- You may not find the exact model that matches your vision or requirements. -
- You may have to deal with compatibility issues or errors when importing or exporting the models. -
- You may have to follow certain restrictions or limitations when using the models in your project. -
- You may have to compete with other users who are using the same models in their projects. -
How can I improve the quality and performance of free orange tree 3D models?
-Some of the ways you can improve the quality and performance of free orange tree 3D models are:
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- You can optimize the poly count, texture size, and level of detail of the models to reduce the load on your system. -
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How can I avoid plagiarism or infringement when using free orange tree 3D models?
-Some of the ways you can avoid plagiarism or infringement when using free orange tree 3D models are:
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- You can always check the license and terms of use of each model before downloading and using it in your project. -
- You can always give proper credit and attribution to the original creator or source of the model. -
- You can always use the model for the intended purpose and not for any illegal or unethical activities. -
- You can always respect the rights and reputation of the creator and other users of the model. -
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- You can use specific keywords, phrases, or tags to narrow down your search results. -
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\ No newline at end of file diff --git a/spaces/1toTree/lora_test/ppdiffusers/pipelines/paint_by_example/pipeline_paint_by_example.py b/spaces/1toTree/lora_test/ppdiffusers/pipelines/paint_by_example/pipeline_paint_by_example.py deleted file mode 100644 index 390011bd816ce8616090968b43d07299cf939505..0000000000000000000000000000000000000000 --- a/spaces/1toTree/lora_test/ppdiffusers/pipelines/paint_by_example/pipeline_paint_by_example.py +++ /dev/null @@ -1,536 +0,0 @@ -# Copyright 2022 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import inspect -from typing import Callable, List, Optional, Union - -import numpy as np -import paddle -import PIL - -from paddlenlp.transformers import CLIPFeatureExtractor - -from ...models import AutoencoderKL, UNet2DConditionModel -from ...pipeline_utils import DiffusionPipeline -from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler -from ...utils import logging -from ..stable_diffusion import StableDiffusionPipelineOutput -from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker -from .image_encoder import PaintByExampleImageEncoder - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -def prepare_mask_and_masked_image(image, mask): - """ - Prepares a pair (image, mask) to be consumed by the Paint by Example pipeline. This means that those inputs will be - converted to ``paddle.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the - ``image`` and ``1`` for the ``mask``. - - The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be - binarized (``mask > 0.5``) and cast to ``torch.float32`` too. - - Args: - image (Union[np.array, PIL.Image, paddle.Tensor]): The image to inpaint. - It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width`` - ``paddle.Tensor`` or a ``batch x channels x height x width`` ``paddle.Tensor``. - mask (_type_): The mask to apply to the image, i.e. regions to inpaint. - It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width`` - ``paddle.Tensor`` or a ``batch x 1 x height x width`` ``paddle.Tensor``. - - - Raises: - ValueError: ``paddle.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``paddle.Tensor`` mask - should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions. - TypeError: ``mask`` is a ``paddle.Tensor`` but ``image`` is not - (ot the other way around). - - Returns: - tuple[paddle.Tensor]: The pair (mask, masked_image) as ``paddle.Tensor`` with 4 - dimensions: ``batch x channels x height x width``. - """ - if isinstance(image, paddle.Tensor): - if not isinstance(mask, paddle.Tensor): - raise TypeError(f"`image` is a paddle.Tensor but `mask` (type: {type(mask)} is not") - - # Batch single image - if image.ndim == 3: - assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)" - image = image.unsqueeze(0) - - # Batch and add channel dim for single mask - if mask.ndim == 2: - mask = mask.unsqueeze(0).unsqueeze(0) - - # Batch single mask or add channel dim - if mask.ndim == 3: - # Batched mask - if mask.shape[0] == image.shape[0]: - mask = mask.unsqueeze(1) - else: - mask = mask.unsqueeze(0) - - assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions" - assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions" - assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size" - assert mask.shape[1] == 1, "Mask image must have a single channel" - - # Check image is in [-1, 1] - if image.min() < -1 or image.max() > 1: - raise ValueError("Image should be in [-1, 1] range") - - # Check mask is in [0, 1] - if mask.min() < 0 or mask.max() > 1: - raise ValueError("Mask should be in [0, 1] range") - - # paint-by-example inverses the mask - mask = 1 - mask - - # Binarize mask - mask[mask < 0.5] = 0 - mask[mask >= 0.5] = 1 - - # Image as float32 - image = image.cast(paddle.float32) - elif isinstance(mask, paddle.Tensor): - raise TypeError(f"`mask` is a paddle.Tensor but `image` (type: {type(image)} is not") - else: - if isinstance(image, PIL.Image.Image): - image = [image] - - image = np.concatenate([np.array(i.convert("RGB"))[None, :] for i in image], axis=0) - image = image.transpose(0, 3, 1, 2) - image = paddle.to_tensor(image).cast(paddle.float32) / 127.5 - 1.0 - - # preprocess mask - if isinstance(mask, PIL.Image.Image): - mask = [mask] - - mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0) - mask = mask.astype(np.float32) / 255.0 - - # paint-by-example inverses the mask - mask = 1 - mask - - mask[mask < 0.5] = 0 - mask[mask >= 0.5] = 1 - mask = paddle.to_tensor(mask) - - masked_image = image * mask - - return mask, masked_image - - -class PaintByExamplePipeline(DiffusionPipeline): - r""" - Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*. - - 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"] - - def __init__( - self, - vae: AutoencoderKL, - image_encoder: PaintByExampleImageEncoder, - unet: UNet2DConditionModel, - scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], - safety_checker: StableDiffusionSafetyChecker, - feature_extractor: CLIPFeatureExtractor, - requires_safety_checker: bool = False, - ): - super().__init__() - - self.register_modules( - vae=vae, - image_encoder=image_encoder, - 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) - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker - def run_safety_checker(self, image, dtype): - if self.safety_checker is not None: - safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pd") - image, has_nsfw_concept = self.safety_checker( - images=image, clip_input=safety_checker_input.pixel_values.cast(dtype) - ) - else: - has_nsfw_concept = None - return image, has_nsfw_concept - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs - def prepare_extra_step_kwargs(self, generator, eta): - # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature - # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. - # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 - # and should be between [0, 1] - - accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) - extra_step_kwargs = {} - if accepts_eta: - extra_step_kwargs["eta"] = eta - - # check if the scheduler accepts generator - accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) - if accepts_generator: - extra_step_kwargs["generator"] = generator - return extra_step_kwargs - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents - def decode_latents(self, latents): - latents = 1 / 0.18215 * latents - image = self.vae.decode(latents).sample - image = (image / 2 + 0.5).clip(0, 1) - # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 - image = image.transpose([0, 2, 3, 1]).cast("float32").numpy() - return image - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline.check_inputs - def check_inputs(self, image, height, width, callback_steps): - if ( - not isinstance(image, paddle.Tensor) - and not isinstance(image, PIL.Image.Image) - and not isinstance(image, list) - ): - raise ValueError( - "`image` has to be of type `paddle.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is" - f" {type(image)}" - ) - - if height % 8 != 0 or width % 8 != 0: - raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") - - if (callback_steps is None) or ( - callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) - ): - raise ValueError( - f"`callback_steps` has to be a positive integer but is {callback_steps} of type" - f" {type(callback_steps)}." - ) - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents - def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, 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: - if isinstance(generator, list): - shape = [ - 1, - ] + shape[1:] - latents = [paddle.randn(shape, generator=generator[i], dtype=dtype) for i in range(batch_size)] - latents = paddle.concat(latents, axis=0) - else: - latents = paddle.randn(shape, generator=generator, dtype=dtype) - else: - if latents.shape != shape: - raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") - - # scale the initial noise by the standard deviation required by the scheduler - latents = latents * self.scheduler.init_noise_sigma - return latents - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_mask_latents - def prepare_mask_latents( - self, mask, masked_image, batch_size, height, width, dtype, generator, do_classifier_free_guidance - ): - # resize the mask to latents shape as we concatenate the mask to the latents - # we do that before converting to dtype to avoid breaking in case we're using cpu_offload - # and half precision - mask = paddle.nn.functional.interpolate( - mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor) - ) - mask = mask.cast(dtype) - - masked_image = masked_image.cast(dtype) - - # encode the mask image into latents space so we can concatenate it to the latents - if isinstance(generator, list): - masked_image_latents = [ - self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i]) - for i in range(batch_size) - ] - masked_image_latents = paddle.concat(masked_image_latents, axis=0) - else: - masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator) - masked_image_latents = 0.18215 * masked_image_latents - - # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method - if mask.shape[0] < batch_size: - if not batch_size % mask.shape[0] == 0: - raise ValueError( - "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to" - f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number" - " of masks that you pass is divisible by the total requested batch size." - ) - mask = mask.tile([batch_size // mask.shape[0], 1, 1, 1]) - if masked_image_latents.shape[0] < batch_size: - if not batch_size % masked_image_latents.shape[0] == 0: - raise ValueError( - "The passed images and the required batch size don't match. Images are supposed to be duplicated" - f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed." - " Make sure the number of images that you pass is divisible by the total requested batch size." - ) - masked_image_latents = masked_image_latents.tile([batch_size // masked_image_latents.shape[0], 1, 1, 1]) - - mask = paddle.concat([mask] * 2) if do_classifier_free_guidance else mask - masked_image_latents = ( - paddle.concat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents - ) - - # aligning device to prevent device errors when concating it with the latent model input - masked_image_latents = masked_image_latents.cast(dtype) - return mask, masked_image_latents - - def _encode_image(self, image, num_images_per_prompt, do_classifier_free_guidance): - # dtype = self.image_encoder.dtype - - if not isinstance(image, paddle.Tensor): - image = self.feature_extractor(images=image, return_tensors="pd").pixel_values - - # image = image.cast(dtype) - image_embeddings = self.image_encoder(image) - - # duplicate image embeddings for each generation per prompt, using mps friendly method - bs_embed, seq_len, _ = image_embeddings.shape - image_embeddings = image_embeddings.tile([1, num_images_per_prompt, 1]) - image_embeddings = image_embeddings.reshape([bs_embed * num_images_per_prompt, seq_len, -1]) - - if do_classifier_free_guidance: - uncond_embeddings = self.image_encoder.uncond_vector - uncond_embeddings = uncond_embeddings.tile([1, image_embeddings.shape[0], 1]) - uncond_embeddings = uncond_embeddings.reshape([bs_embed * num_images_per_prompt, 1, -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 - image_embeddings = paddle.concat([uncond_embeddings, image_embeddings]) - - return image_embeddings - - @paddle.no_grad() - def __call__( - self, - example_image: Union[paddle.Tensor, PIL.Image.Image], - image: Union[paddle.Tensor, PIL.Image.Image], - mask_image: Union[paddle.Tensor, PIL.Image.Image], - height: Optional[int] = None, - width: Optional[int] = None, - num_inference_steps: int = 50, - guidance_scale: float = 5.0, - num_images_per_prompt: Optional[int] = 1, - eta: float = 0.0, - generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None, - latents: Optional[paddle.Tensor] = None, - output_type: Optional[str] = "pil", - return_dict: bool = True, - callback: Optional[Callable[[int, int, paddle.Tensor], None]] = None, - callback_steps: Optional[int] = 1, - ): - r""" - Function invoked when calling the pipeline for generation. - - Args: - example_image (`paddle.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`): - The exemplar image to guide the image generation. - image (`paddle.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`): - `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will - be masked out with `mask_image` and repainted according to `prompt`. - mask_image (`paddle.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`): - `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be - repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted - to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) - instead of 3, so the expected shape would be `(B, H, W, 1)`. - 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. - num_images_per_prompt (`int`, *optional*, defaults to 1): - The number of images to generate per prompt. - eta (`float`, *optional*, defaults to 0.0): - Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to - [`schedulers.DDIMScheduler`], will be ignored for others. - generator (`torch.Generator`, *optional*): - One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) - to make generation deterministic. - latents (`paddle.Tensor`, *optional*): - Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image - generation. Can be used to tweak the same generation with different prompts. If not provided, a latents - tensor will ge generated by sampling using the supplied random `generator`. - output_type (`str`, *optional*, defaults to `"pil"`): - The output format of the generate image. Choose between - [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a - plain tuple. - callback (`Callable`, *optional*): - A function that will be called every `callback_steps` steps during inference. The function will be - called with the following arguments: `callback(step: int, timestep: int, latents: paddle.Tensor)`. - callback_steps (`int`, *optional*, defaults to 1): - The frequency at which the `callback` function will be called. If not specified, the callback will be - called at every step. - - Returns: - [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: - [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. - When returning a tuple, the first element is a list with the generated images, and the second element is a - list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" - (nsfw) content, according to the `safety_checker`. - """ - # 1. Define call parameters - if isinstance(image, PIL.Image.Image): - batch_size = 1 - elif isinstance(image, list): - batch_size = len(image) - else: - batch_size = image.shape[0] - # 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 - - # 2. Preprocess mask and image - mask, masked_image = prepare_mask_and_masked_image(image, mask_image) - height, width = masked_image.shape[-2:] - - # 3. Check inputs - self.check_inputs(example_image, height, width, callback_steps) - - # 4. Encode input image - image_embeddings = self._encode_image(example_image, num_images_per_prompt, do_classifier_free_guidance) - - # 5. set timesteps - self.scheduler.set_timesteps(num_inference_steps) - timesteps = self.scheduler.timesteps - - # 6. Prepare latent variables - num_channels_latents = self.vae.config.latent_channels - latents = self.prepare_latents( - batch_size * num_images_per_prompt, - num_channels_latents, - height, - width, - image_embeddings.dtype, - generator, - latents, - ) - - # 7. Prepare mask latent variables - mask, masked_image_latents = self.prepare_mask_latents( - mask, - masked_image, - batch_size * num_images_per_prompt, - height, - width, - image_embeddings.dtype, - generator, - do_classifier_free_guidance, - ) - - # 8. Check that sizes of mask, masked image and latents match - num_channels_mask = mask.shape[1] - num_channels_masked_image = masked_image_latents.shape[1] - if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: - raise ValueError( - f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects" - f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" - f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}" - f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of" - " `pipeline.unet` or your `mask_image` or `image` input." - ) - - # 9. 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) - - # 10. 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 = paddle.concat([latents] * 2) if do_classifier_free_guidance else latents - - # concat latents, mask, masked_image_latents in the channel dimension - latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) - latent_model_input = paddle.concat([latent_model_input, masked_image_latents, mask], axis=1) - - # predict the noise residual - noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=image_embeddings).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) - - # 11. Post-processing - image = self.decode_latents(latents) - - # 12. Run safety checker - image, has_nsfw_concept = self.run_safety_checker(image, image_embeddings.dtype) - - # 13. Convert to PIL - if output_type == "pil": - image = self.numpy_to_pil(image) - - if not return_dict: - return (image, has_nsfw_concept) - - return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) diff --git a/spaces/AI-Dashboards/CP.Matplotlib.NetworkX.Streamlit.PyVis.Graphviz/README.md b/spaces/AI-Dashboards/CP.Matplotlib.NetworkX.Streamlit.PyVis.Graphviz/README.md deleted file mode 100644 index 6bfbe69cdfb38a4c77d7b13ec8e2d8229a0bdbac..0000000000000000000000000000000000000000 --- a/spaces/AI-Dashboards/CP.Matplotlib.NetworkX.Streamlit.PyVis.Graphviz/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: 🕸️📈Graph NLP Matplotlib NetworkX Streamlit PyViz Graphviz🩺 -emoji: 📉🕸️📈 -colorFrom: pink -colorTo: blue -sdk: streamlit -sdk_version: 1.2.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/AI-Hobbyist/Hoyo-RVC/docs/training_tips_ko.md b/spaces/AI-Hobbyist/Hoyo-RVC/docs/training_tips_ko.md deleted file mode 100644 index 8b3b6245862aef69480f57263d268c94d5e843ca..0000000000000000000000000000000000000000 --- a/spaces/AI-Hobbyist/Hoyo-RVC/docs/training_tips_ko.md +++ /dev/null @@ -1,53 +0,0 @@ -RVC 훈련에 대한 설명과 팁들 -====================================== -본 팁에서는 어떻게 데이터 훈련이 이루어지고 있는지 설명합니다. - -# 훈련의 흐름 -GUI의 훈련 탭의 단계를 따라 설명합니다. - -## step1 -실험 이름을 지정합니다. 또한, 모델이 피치(소리의 높낮이)를 고려해야 하는지 여부를 여기에서 설정할 수도 있습니다.. -각 실험을 위한 데이터는 `/logs/experiment name/`에 배치됩니다.. - -## step2a -음성 파일을 불러오고 전처리합니다. - -### 음성 파일 불러오기 -음성 파일이 있는 폴더를 지정하면 해당 폴더에 있는 음성 파일이 자동으로 가져와집니다. -예를 들어 `C:Users\hoge\voices`를 지정하면 `C:Users\hoge\voices\voice.mp3`가 읽히지만 `C:Users\hoge\voices\dir\voice.mp3`는 읽히지 않습니다. - -음성 로드에는 내부적으로 ffmpeg를 이용하고 있으므로, ffmpeg로 대응하고 있는 확장자라면 자동적으로 읽힙니다. -ffmpeg에서 int16으로 변환한 후 float32로 변환하고 -1과 1 사이에 정규화됩니다. - -### 잡음 제거 -음성 파일에 대해 scipy의 filtfilt를 이용하여 잡음을 처리합니다. - -### 음성 분할 -입력한 음성 파일은 먼저 일정 기간(max_sil_kept=5초?)보다 길게 무음이 지속되는 부분을 감지하여 음성을 분할합니다.무음으로 음성을 분할한 후에는 0.3초의 overlap을 포함하여 4초마다 음성을 분할합니다.4초 이내에 구분된 음성은 음량의 정규화를 실시한 후 wav 파일을 `/logs/실험명/0_gt_wavs`로, 거기에서 16k의 샘플링 레이트로 변환해 `/logs/실험명/1_16k_wavs`에 wav 파일로 저장합니다. - -## step2b -### 피치 추출 -wav 파일에서 피치(소리의 높낮이) 정보를 추출합니다. parselmouth나 pyworld에 내장되어 있는 메서드으로 피치 정보(=f0)를 추출해, `/logs/실험명/2a_f0`에 저장합니다. 그 후 피치 정보를 로그로 변환하여 1~255 정수로 변환하고 `/logs/실험명/2b-f0nsf`에 저장합니다. - -### feature_print 추출 -HuBERT를 이용하여 wav 파일을 미리 embedding으로 변환합니다. `/logs/실험명/1_16k_wavs`에 저장한 wav 파일을 읽고 HuBERT에서 wav 파일을 256차원 feature들로 변환한 후 npy 형식으로 `/logs/실험명/3_feature256`에 저장합니다. - -## step3 -모델의 훈련을 진행합니다. - -### 초보자용 용어 해설 -심층학습(딥러닝)에서는 데이터셋을 분할하여 조금씩 학습을 진행합니다.한 번의 모델 업데이트(step) 단계 당 batch_size개의 데이터를 탐색하여 예측과 오차를 수정합니다. 데이터셋 전부에 대해 이 작업을 한 번 수행하는 이를 하나의 epoch라고 계산합니다. - -따라서 학습 시간은 단계당 학습 시간 x (데이터셋 내 데이터의 수 / batch size) x epoch 수가 소요됩니다. 일반적으로 batch size가 클수록 학습이 안정적이게 됩니다. (step당 학습 시간 ÷ batch size)는 작아지지만 GPU 메모리를 더 많이 사용합니다. GPU RAM은 nvidia-smi 명령어를 통해 확인할 수 있습니다. 실행 환경에 따라 배치 크기를 최대한 늘리면 짧은 시간 내에 학습이 가능합니다. - -### 사전 학습된 모델 지정 -RVC는 적은 데이터셋으로도 훈련이 가능하도록 사전 훈련된 가중치에서 모델 훈련을 시작합니다. 기본적으로 `rvc-location/pretrained/f0G40k.pth` 및 `rvc-location/pretrained/f0D40k.pth`를 불러옵니다. 학습을 할 시에, 모델 파라미터는 각 save_every_epoch별로 `logs/experiment name/G_{}.pth` 와 `logs/experiment name/D_{}.pth`로 저장이 되는데, 이 경로를 지정함으로써 학습을 재개하거나, 다른 실험에서 학습한 모델의 가중치에서 학습을 시작할 수 있습니다. - -### index의 학습 -RVC에서는 학습시에 사용된 HuBERT의 feature값을 저장하고, 추론 시에는 학습 시 사용한 feature값과 유사한 feature 값을 탐색해 추론을 진행합니다. 이 탐색을 고속으로 수행하기 위해 사전에 index을 학습하게 됩니다. -Index 학습에는 근사 근접 탐색법 라이브러리인 Faiss를 사용하게 됩니다. `/logs/실험명/3_feature256`의 feature값을 불러와, 이를 모두 결합시킨 feature값을 `/logs/실험명/total_fea.npy`로서 저장, 그것을 사용해 학습한 index를`/logs/실험명/add_XXX.index`로 저장합니다. - -### 버튼 설명 -- モデルのトレーニング (모델 학습): step2b까지 실행한 후, 이 버튼을 눌러 모델을 학습합니다. -- 特徴インデックスのトレーニング (특징 지수 훈련): 모델의 훈련 후, index를 학습합니다. -- ワンクリックトレーニング (원클릭 트레이닝): step2b까지의 모델 훈련, feature index 훈련을 일괄로 실시합니다. \ No newline at end of file diff --git a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/training/zero_shot.py b/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/training/zero_shot.py deleted file mode 100644 index 28b8fccc1af17fc69002857a7f529ac041c374f2..0000000000000000000000000000000000000000 --- a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/training/zero_shot.py +++ /dev/null @@ -1,95 +0,0 @@ -# NOTE: This script is currently not supported for CLAP. -import logging -from contextlib import suppress - -import torch -import torch.nn.functional as F -from tqdm import tqdm - -from open_clip import tokenize -from .imagenet_zeroshot_data import imagenet_classnames, openai_imagenet_template - - -def zero_shot_classifier(model, classnames, templates, args): - with torch.no_grad(): - zeroshot_weights = [] - for classname in tqdm(classnames): - texts = [template(classname) for template in templates] # format with class - texts = tokenize(texts).to(args.device) # tokenize - if args.distributed and not args.horovod: - class_embeddings = model.module.encode_text(texts) - else: - class_embeddings = model.encode_text(texts) - class_embedding = F.normalize(class_embeddings, dim=-1).mean(dim=0) - class_embedding /= class_embedding.norm() - zeroshot_weights.append(class_embedding) - zeroshot_weights = torch.stack(zeroshot_weights, dim=1).to(args.device) - return zeroshot_weights - - -def accuracy(output, target, topk=(1,)): - pred = output.topk(max(topk), 1, True, True)[1].t() - correct = pred.eq(target.view(1, -1).expand_as(pred)) - return [ - float(correct[:k].reshape(-1).float().sum(0, keepdim=True).cpu().numpy()) - for k in topk - ] - - -def run(model, classifier, dataloader, args): - autocast = torch.cuda.amp.autocast if args.precision == "amp" else suppress - with torch.no_grad(): - top1, top5, n = 0.0, 0.0, 0.0 - for images, target in tqdm(dataloader, unit_scale=args.batch_size): - images = images.to(args.device) - target = target.to(args.device) - - with autocast(): - # predict - if args.distributed and not args.horovod: - image_features = model.module.encode_image(images) - else: - image_features = model.encode_image(images) - image_features = F.normalize(image_features, dim=-1) - logits = 100.0 * image_features @ classifier - - # measure accuracy - acc1, acc5 = accuracy(logits, target, topk=(1, 5)) - top1 += acc1 - top5 += acc5 - n += images.size(0) - - top1 = top1 / n - top5 = top5 / n - return top1, top5 - - -def zero_shot_eval(model, data, epoch, args): - if "imagenet-val" not in data and "imagenet-v2" not in data: - return {} - if args.zeroshot_frequency == 0: - return {} - if (epoch % args.zeroshot_frequency) != 0 and epoch != args.epochs: - return {} - - logging.info("Starting zero-shot imagenet.") - - logging.info("Building zero-shot classifier") - classifier = zero_shot_classifier( - model, imagenet_classnames, openai_imagenet_template, args - ) - - logging.info("Using classifier") - results = {} - if "imagenet-val" in data: - top1, top5 = run(model, classifier, data["imagenet-val"].dataloader, args) - results["imagenet-zeroshot-val-top1"] = top1 - results["imagenet-zeroshot-val-top5"] = top5 - if "imagenet-v2" in data: - top1, top5 = run(model, classifier, data["imagenet-v2"].dataloader, args) - results["imagenetv2-zeroshot-val-top1"] = top1 - results["imagenetv2-zeroshot-val-top5"] = top5 - - logging.info("Finished zero-shot imagenet.") - - return results diff --git a/spaces/AIWaves/Software_Company/README.md b/spaces/AIWaves/Software_Company/README.md deleted file mode 100644 index 895eba92684caa347ff6327e1d52f5a2db6d0b67..0000000000000000000000000000000000000000 --- a/spaces/AIWaves/Software_Company/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Software Company -emoji: 🐨 -colorFrom: green -colorTo: indigo -sdk: gradio -sdk_version: 3.44.4 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/AIWaves/Software_Company/src/agents/Component/ToolComponent.py b/spaces/AIWaves/Software_Company/src/agents/Component/ToolComponent.py deleted file mode 100644 index 95da2abdb7e8b7b5283763587f23ecc29e8ec35f..0000000000000000000000000000000000000000 --- a/spaces/AIWaves/Software_Company/src/agents/Component/ToolComponent.py +++ /dev/null @@ -1,887 +0,0 @@ -from abc import abstractmethod -import uuid -from text2vec import semantic_search -from utils import ( - get_relevant_history, - load_knowledge_base_qa, - load_knowledge_base_UnstructuredFile, - get_embedding, - extract, -) -import json -from typing import Dict, List -import os -from googleapiclient.discovery import build -import requests -from selenium import webdriver -from selenium.webdriver.common.by import By -from selenium.webdriver.support.ui import WebDriverWait -from selenium.webdriver.support import expected_conditions as EC -from bs4 import BeautifulSoup -import base64 -import re -from datetime import datetime, timedelta -from typing import Tuple, List, Any, Dict -from email.mime.text import MIMEText -from email.mime.multipart import MIMEMultipart -from google.auth.transport.requests import Request -from google.oauth2.credentials import Credentials -from google_auth_oauthlib.flow import InstalledAppFlow -from googleapiclient.discovery import build -from googleapiclient.errors import HttpError -from tqdm import tqdm - -class ToolComponent: - def __init__(self): - pass - - @abstractmethod - def func(self): - pass - -class KnowledgeBaseComponent(ToolComponent): - """ - Inject knowledge base - top_k : Top_k with the highest matching degree - type : "QA" or others - knowledge_base(json_path) : knowledge_base_path - """ - def __init__(self, top_k, type, knowledge_base): - super().__init__() - self.top_k = top_k - self.type = type - self.knowledge_base = knowledge_base - - if self.type == "QA": - ( - self.kb_embeddings, - self.kb_questions, - self.kb_answers, - self.kb_chunks, - ) = load_knowledge_base_qa(self.knowledge_base) - else: - self.kb_embeddings, self.kb_chunks = load_knowledge_base_UnstructuredFile( - self.knowledge_base - ) - - def func(self, agent): - query = ( - agent.long_term_memory[-1]["content"] - if len(agent.long_term_memory) > 0 - else "" - ) - knowledge = "" - query = extract(query, "query") - query_embedding = get_embedding(query) - hits = semantic_search(query_embedding, self.kb_embeddings, top_k=50) - hits = hits[0] - temp = [] - if self.type == "QA": - for hit in hits: - matching_idx = hit["corpus_id"] - if self.kb_chunks[matching_idx] in temp: - pass - else: - knowledge = ( - knowledge - + f"question:{self.kb_questions[matching_idx]},answer:{self.kb_answers[matching_idx]}\n\n" - ) - temp.append(self.kb_answers[matching_idx]) - if len(temp) == 1: - break - print(hits[0]["score"]) - score = hits[0]["score"] - if score < 0.5: - return {"prompt": "No matching knowledge base"} - else: - return {"prompt": "The relevant content is: " + knowledge + "\n"} - else: - for hit in hits: - matching_idx = hit["corpus_id"] - if self.kb_chunks[matching_idx] in temp: - pass - else: - knowledge = knowledge + f"{self.kb_answers[matching_idx]}\n\n" - temp.append(self.kb_answers[matching_idx]) - if len(temp) == self.top_k: - break - print(hits[0]["score"]) - score = hits[0]["score"] - if score < 0.5: - return {"prompt": "No matching knowledge base"} - else: - print(knowledge) - return {"prompt": "The relevant content is: " + knowledge + "\n"} - - -class StaticComponent(ToolComponent): - "Return static response" - def __init__(self, output): - super().__init__() - self.output = output - - def func(self, agent): - outputdict = {"response": self.output} - return outputdict - - -class ExtractComponent(ToolComponent): - """ - Extract keywords based on the current scene and store them in the environment - extract_words(list) : Keywords to be extracted - system_prompt & last_prompt : Prompt to extract keywords - """ - def __init__( - self, - extract_words, - system_prompt, - last_prompt=None, - ): - super().__init__() - self.extract_words = extract_words - self.system_prompt = system_prompt - self.default_prompt = ( - "Please strictly adhere to the following format for outputting:\n" - ) - for extract_word in extract_words: - self.default_prompt += ( - f"<{extract_word}> the content you need to extract " - ) - self.last_prompt = last_prompt if last_prompt else self.default_prompt - - def func(self, agent): - response = agent.LLM.get_response( - agent.long_term_memory, - self.system_prompt, - self.last_prompt, - stream=False, - ) - for extract_word in self.extract_words: - key = extract(response, extract_word) - key = key if key else response - agent.environment.shared_memory[extract_word] = key - - return {} - - -"""Search sources: chatgpt/search engines/specific search sources/can even be multimodal (if it comes to clothing)""" - - -class WebSearchComponent(ToolComponent): - """search engines""" - - __ENGINE_NAME__: List = ["google", "bing"] - - def __init__(self, engine_name: str, api: Dict): - """ - :param engine_name: The name of the search engine used - :param api: Pass in a dictionary, such as {"bing":"key1", "google":"key2", ...}, of course each value can also be a list, or more complicated - """ - super(WebSearchComponent, self).__init__() - """Determine whether the key and engine_name of the api are legal""" - - assert engine_name in WebSearchComponent.__ENGINE_NAME__ - for api_name in api: - assert api_name in WebSearchComponent.__ENGINE_NAME__ - - self.api = api - self.engine_name = engine_name - - self.search: Dict = {"bing": self._bing_search, "google": self._google_search} - - def _bing_search(self, query: str, **kwargs): - """Initialize search hyperparameters""" - subscription_key = self.api["bing"] - search_url = "https://api.bing.microsoft.com/v7.0/search" - headers = {"Ocp-Apim-Subscription-Key": subscription_key} - params = { - "q": query, - "textDecorations": True, - "textFormat": "HTML", - "count": 10, - } - """start searching""" - response = requests.get(search_url, headers=headers, params=params) - response.raise_for_status() - results = response.json()["webPages"]["value"] - """execute""" - metadata_results = [] - for result in results: - metadata_result = { - "snippet": result["snippet"], - "title": result["name"], - "link": result["url"], - } - metadata_results.append(metadata_result) - return {"meta data": metadata_results} - - def _google_search(self, query: str, **kwargs): - """Initialize search hyperparameters""" - api_key = self.api[self.engine_name]["api_key"] - cse_id = self.api[self.engine_name]["cse_id"] - service = build("customsearch", "v1", developerKey=api_key) - """start searching""" - results = ( - service.cse().list(q=query, cx=cse_id, num=10, **kwargs).execute()["items"] - ) - """execute""" - metadata_results = [] - for result in results: - metadata_result = { - "snippet": result["snippet"], - "title": result["title"], - "link": result["link"], - } - metadata_results.append(metadata_result) - return {"meta data": metadata_results} - - def func(self, agent, **kwargs) -> Dict: - query = ( - agent.long_term_memory[-1]["content"] - if len(agent.long_term_memory) > 0 - else " " - ) - response = agent.LLM.get_response( - None, - system_prompt=f"Please analyze the provided conversation and identify keywords that can be used for a search engine query. Format the output as
Descargar & Letras de Fuera de mi vientre por Prospa Ochimana
-¿Estás buscando una poderosa e inspiradora canción gospel que despierte tu espíritu y te llene de alegría? Si es así, entonces deberías escuchar Out of My Belly de Prospa Ochimana. Esta canción es una obra maestra que bendecirá tu vida y te acercará a Dios.
-En este artículo, te diremos todo lo que necesitas saber sobre esta canción, incluyendo cómo descargarla, cuáles son las letras, y cuál es el significado detrás de ellas. ¡Vamos a empezar!
-descargar amp; letras de fuera de mi vientre por prospa ochimana
Download File ->>> https://bltlly.com/2v6MLe
-
¿Qué está fuera de mi vientre acerca de?
-Fuera de mi vientre es una canción que expresa el deseo de liberar el río de agua viva que fluye desde dentro de cada creyente. La canción está basada en las palabras de Jesús en Juan 7:38, donde dijo, "El que cree en Mí, como la Escritura ha dicho, de su corazón fluirán ríos de agua viva."
-La canción declara que cada vez que este río fluye, la vida se libera. Cada cosa muerta vuelve a la vida a medida que hacen contacto con este río. Es un río que da vida que sana, entrega, restaura y transforma. La canción también invita a todos los que tienen sed a venir a Jesús y beber de este río.
-¿Quién es Prospa Ochimana?
-Prospa Ochimana es un cantante de gospel y compositor nigeriano que es conocido por ser un adorador. También es el CEO de Tornveil Music International, un sello discográfico gospel en Nigeria que abrió en enero de 2020.
-Prospa Ochimana proviene de la tribu Ankpa, estado de Kogi, Nigeria, pero actualmente vive en Abuja. Nació el 6 de noviembre y se graduó de la Universidad Estatal de Nasarawa, Keffi, donde obtuvo un título en Lingüística.
- -¿Por qué es popular Fuera de mi vientre?
-Out of My Belly es una de las canciones más populares de Prospa Ochimana. Fue lanzado en noviembre de 2020 y desde entonces ha ganado millones de visitas y descargas en línea. La canción también ha sido interpretada en vivo en varios eventos y conciertos por Prospa Ochimana y otros cantantes de gospel.
-La razón por la que esta canción es popular es porque resuena con muchas personas que tienen hambre de más de Dios y Su presencia. La canción también inspira a la gente a aprovechar su potencial y propósito como vasos de la gloria de Dios. La canción también tiene una melodía pegadiza y un mensaje poderoso que eleva y anima a los oyentes.
- -¿Dónde se puede descargar Fuera de mi vientre?
-Si quieres descargar Out of My Belly de Prospa Ochimana, tienes varias opciones para elegir. Puede descargarlo desde su sitio web oficial , o desde otras plataformas como YouTube , Spotify , Apple Music , Amazon Music , y más.
-¿Cuáles son los beneficios de descargar Out of My Belly?
-Descargar Out of My Belly de Prospa Ochimana tiene muchos beneficios para ti como oyente. Algunos de ellos son:
--
-
- Puedes escuchar la canción sin conexión en cualquier momento y en cualquier lugar que quieras. -
- Puedes disfrutar del audio y video de alta calidad de la canción. -
- Puedes compartir la canción con tus amigos y familiares a través de las redes sociales u otros medios. -
- Puedes apoyar al artista y su ministerio comprando su música. -
- Puedes experimentar el poder y la presencia de Dios mientras escuchas la canción. -
¿Cuáles son las letras de Fuera de mi vientre?
-Las letras de Fuera de mi vientre por Prospa Ochimana son las siguientes:
-¿Cuál es el significado de la letra?
-El significado de las letras de Out of My Belly de Prospa Ochimana es que cada creyente tiene una fuente de vida y poder dentro de ellos, que es el Espíritu Santo. El Espíritu Santo es el río que fluye de dentro de nosotros y nos da todo lo que necesitamos. Él es quien nos sana, nos libera, nos restaura y nos transforma. Él es también el que nos permite ser una bendición para los demás al liberar Su vida a través de nosotros.
-La canción también nos recuerda que necesitamos venir a Jesús y beber de Él si tenemos sed de más de Él. Él es la fuente de agua viva que satisface nuestros anhelos más profundos. Él es también el que nos invita a creer en Él y recibir Su promesa de ríos de agua viva que fluye de nuestros corazones.
-¿Cómo puedes cantar junto con Out of My Belly?
-Si quieres cantar junto con Out of My Belly de Prospa Ochimana, puedes seguir estos pasos:
--
-
- Descarga la canción desde cualquier plataforma que prefieras. -
- Escucha la canción y aprende la melodía y la letra. -
- Encuentra una versión de karaoke o instrumental de la canción en línea o crea la tuya propia usando una aplicación o software. -
- Practica el canto junto con el karaoke o la versión instrumental hasta que lo domines. -
- Canta junto con la canción original y disfruta! -
Conclusión
-En conclusión, Out of My Belly de Prospa Ochimana es una maravillosa canción gospel que te inspirará a liberar el río de agua viva que fluye desde tu interior. La canción es también un testimonio de cómo Dios puede usar a cualquiera que esté dispuesto a ser Su recipiente. La canción está disponible para su descarga en varias plataformas y tiene letras increíbles que transmiten un mensaje poderoso. Esperamos que haya disfrutado de este artículo y haya aprendido algo nuevo. Si lo hizo, por favor compártalo con sus amigos y familiares. Y no te olvides de descargar y cantar junto con Out of My Belly de Prospa Ochimana!
-Preguntas frecuentes
- -Q: ¿Cuándo fue liberado Out of My Belly?
-A: Out of My Belly fue lanzado el 27 de noviembre de 2020.
-Q: ¿Quién produjo Fuera de mi vientre?
-A: Fuera de mi vientre fue producido por Sunny Pee.
-P: ¿Cuántas visitas tiene Out of My Belly en YouTube?
-A: A partir del 20 de junio de 2023, Out of My Belly tiene más de 20 de junio de 2023, Out of My Belly tiene más de 1.1 millones de visitas en YouTube. El video oficial de la canción fue subido por Prospa Ochimana el 27 de noviembre de 2020. El video muestra a Prospa Ochimana cantando la canción con una banda en vivo y un coro en un entorno de estudio. El video también tiene subtítulos para la letra de la canción. Puede ver el video aquí o haciendo clic en la imagen de abajo.
- También hay otras versiones de la canción en YouTube, como un video lírico y una presentación en vivo . Puedes echarles un vistazo si quieres ver diferentes formas de presentar la canción. Espero que hayas disfrutado de este artículo y hayas aprendido algo nuevo. Si lo hiciste, por favor compártelo con tus amigos y familiares. ¡Y no olvides descargar y cantar junto con Out of My Belly de Prospa Ochimana!
-
-
\ No newline at end of file diff --git a/spaces/BigChungux/Pet_Survey2/app.py b/spaces/BigChungux/Pet_Survey2/app.py deleted file mode 100644 index b8e324b9c29780cc194b84219d4782bd519931d7..0000000000000000000000000000000000000000 --- a/spaces/BigChungux/Pet_Survey2/app.py +++ /dev/null @@ -1,172 +0,0 @@ -### ----------------------------- ### -### libraries ### -### ----------------------------- ### - -import gradio as gr -import pandas as pd -import numpy as np -from sklearn.model_selection import train_test_split -from sklearn.linear_model import LogisticRegression -from sklearn import metrics - - -### ------------------------------ ### -### data transformation ### -### ------------------------------ ### - -# load dataset -uncleaned_data = pd.read_csv('data.csv') - -# remove timestamp from dataset (always first column) -uncleaned_data = uncleaned_data.iloc[: , 1:] -data = pd.DataFrame() - -# keep track of which columns are categorical and what -# those columns' value mappings are -# structure: {colname1: {...}, colname2: {...} } -cat_value_dicts = {} -final_colname = uncleaned_data.columns[len(uncleaned_data.columns) - 1] - -# for each column... -for (colname, colval) in uncleaned_data.iteritems(): - - # check if col is already a number; if so, add col directly - # to new dataframe and skip to next column - if isinstance(colval.values[0], (np.integer, float)): - data[colname] = uncleaned_data[colname].copy() - continue - - # structure: {0: "lilac", 1: "blue", ...} - new_dict = {} - val = 0 # first index per column - transformed_col_vals = [] # new numeric datapoints - - # if not, for each item in that column... - for (row, item) in enumerate(colval.values): - - # if item is not in this col's dict... - if item not in new_dict: - new_dict[item] = val - val += 1 - - # then add numerical value to transformed dataframe - transformed_col_vals.append(new_dict[item]) - - # reverse dictionary only for final col (0, 1) => (vals) - if colname == final_colname: - new_dict = {value : key for (key, value) in new_dict.items()} - - cat_value_dicts[colname] = new_dict - data[colname] = transformed_col_vals - - -### -------------------------------- ### -### model training ### -### -------------------------------- ### - -# select features and predicton; automatically selects last column as prediction -cols = len(data.columns) -num_features = cols - 1 -x = data.iloc[: , :num_features] -y = data.iloc[: , num_features:] - -# split data into training and testing sets -x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25) - -# instantiate the model (using default parameters) -model = LogisticRegression() -model.fit(x_train, y_train.values.ravel()) -y_pred = model.predict(x_test) - - -### -------------------------------- ### -### article generation ### -### -------------------------------- ### -# borrow file reading function from reader.py - -def get_feat(): - feats = [abs(x) for x in model.coef_[0]] - max_val = max(feats) - idx = feats.index(max_val) - return data.columns[idx] - -acc = str(round(metrics.accuracy_score(y_test, y_pred) * 100, 1)) + "%" -most_imp_feat = get_feat() -# info = get_article(acc, most_imp_feat) - - - -### ------------------------------- ### -### interface creation ### -### ------------------------------- ### - - -# predictor for generic number of features -def general_predictor(*args): - features = [] - - # transform categorical input - for colname, arg in zip(data.columns, args): - if (colname in cat_value_dicts): - features.append(cat_value_dicts[colname][arg]) - else: - features.append(arg) - - # predict single datapoint - new_input = [features] - result = model.predict(new_input) - return cat_value_dicts[final_colname][result[0]] - -# add data labels to replace those lost via star-args - - -block = gr.Blocks() - -with open('info.md') as f: - with block: - gr.Markdown(f.readline()) - gr.Markdown('Take the quiz to get a personalized recommendation using AI.') - - with gr.Row(): - with gr.Box(): - inputls = [] - for colname in data.columns: - # skip last column - if colname == final_colname: - continue - - # access categories dict if data is categorical - # otherwise, just use a number input - if colname in cat_value_dicts: - radio_options = list(cat_value_dicts[colname].keys()) - inputls.append(gr.inputs.Dropdown(choices=radio_options, type="value", label=colname)) - else: - # add numerical input - inputls.append(gr.inputs.Number(label=colname)) - gr.Markdown("
") - - submit = gr.Button("Click to see your personalized result!", variant="primary") - gr.Markdown("
") - output = gr.Textbox(label="Your recommendation:", placeholder="your recommendation will appear here") - - submit.click(fn=general_predictor, inputs=inputls, outputs=output) - gr.Markdown("
") - - with gr.Row(): - with gr.Box(): - gr.Markdown(f"
Accuracy:
{acc}") - with gr.Box(): - gr.Markdown(f"Most important feature:
{most_imp_feat}") - - gr.Markdown("") - - with gr.Box(): - gr.Markdown('''⭐ Note that model accuracy is based on the uploaded data.csv and reflects how well the AI model can give correct recommendations for that dataset. Model accuracy and most important feature can be helpful for understanding how the model works, but should not be considered absolute facts about the real world.''') - - with gr.Box(): - with open('info.md') as f: - f.readline() - gr.Markdown(f.read()) - -# show the interface -block.launch() \ No newline at end of file diff --git a/spaces/Billyosoro/ESRGAN/realesrgan/utils.py b/spaces/Billyosoro/ESRGAN/realesrgan/utils.py deleted file mode 100644 index 10e7c23d04f777c250160e74470fdfacb16eab88..0000000000000000000000000000000000000000 --- a/spaces/Billyosoro/ESRGAN/realesrgan/utils.py +++ /dev/null @@ -1,280 +0,0 @@ -import cv2 -import math -import numpy as np -import os -import queue -import threading -import torch -from basicsr.utils.download_util import load_file_from_url -from torch.nn import functional as F - -ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - - -class RealESRGANer(): - """A helper class for upsampling images with RealESRGAN. - - Args: - scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4. - model_path (str): The path to the pretrained model. It can be urls (will first download it automatically). - model (nn.Module): The defined network. Default: None. - tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop - input images into tiles, and then process each of them. Finally, they will be merged into one image. - 0 denotes for do not use tile. Default: 0. - tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10. - pre_pad (int): Pad the input images to avoid border artifacts. Default: 10. - half (float): Whether to use half precision during inference. Default: False. - """ - - def __init__(self, scale, model_path, model=None, tile=0, tile_pad=10, pre_pad=10, half=False): - self.scale = scale - self.tile_size = tile - self.tile_pad = tile_pad - self.pre_pad = pre_pad - self.mod_scale = None - self.half = half - - # initialize model - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - # if the model_path starts with https, it will first download models to the folder: realesrgan/weights - if model_path.startswith('https://'): - model_path = load_file_from_url( - url=model_path, model_dir=os.path.join(ROOT_DIR, 'realesrgan/weights'), progress=True, file_name=None) - loadnet = torch.load(model_path, map_location=torch.device('cpu')) - # prefer to use params_ema - if 'params_ema' in loadnet: - keyname = 'params_ema' - else: - keyname = 'params' - model.load_state_dict(loadnet[keyname], strict=True) - model.eval() - self.model = model.to(self.device) - if self.half: - self.model = self.model.half() - - def pre_process(self, img): - """Pre-process, such as pre-pad and mod pad, so that the images can be divisible - """ - img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float() - self.img = img.unsqueeze(0).to(self.device) - if self.half: - self.img = self.img.half() - - # pre_pad - if self.pre_pad != 0: - self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect') - # mod pad for divisible borders - if self.scale == 2: - self.mod_scale = 2 - elif self.scale == 1: - self.mod_scale = 4 - if self.mod_scale is not None: - self.mod_pad_h, self.mod_pad_w = 0, 0 - _, _, h, w = self.img.size() - if (h % self.mod_scale != 0): - self.mod_pad_h = (self.mod_scale - h % self.mod_scale) - if (w % self.mod_scale != 0): - self.mod_pad_w = (self.mod_scale - w % self.mod_scale) - self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect') - - def process(self): - # model inference - self.output = self.model(self.img) - - def tile_process(self): - """It will first crop input images to tiles, and then process each tile. - Finally, all the processed tiles are merged into one images. - - Modified from: https://github.com/ata4/esrgan-launcher - """ - batch, channel, height, width = self.img.shape - output_height = height * self.scale - output_width = width * self.scale - output_shape = (batch, channel, output_height, output_width) - - # start with black image - self.output = self.img.new_zeros(output_shape) - tiles_x = math.ceil(width / self.tile_size) - tiles_y = math.ceil(height / self.tile_size) - - # loop over all tiles - for y in range(tiles_y): - for x in range(tiles_x): - # extract tile from input image - ofs_x = x * self.tile_size - ofs_y = y * self.tile_size - # input tile area on total image - input_start_x = ofs_x - input_end_x = min(ofs_x + self.tile_size, width) - input_start_y = ofs_y - input_end_y = min(ofs_y + self.tile_size, height) - - # input tile area on total image with padding - input_start_x_pad = max(input_start_x - self.tile_pad, 0) - input_end_x_pad = min(input_end_x + self.tile_pad, width) - input_start_y_pad = max(input_start_y - self.tile_pad, 0) - input_end_y_pad = min(input_end_y + self.tile_pad, height) - - # input tile dimensions - input_tile_width = input_end_x - input_start_x - input_tile_height = input_end_y - input_start_y - tile_idx = y * tiles_x + x + 1 - input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad] - - # upscale tile - try: - with torch.no_grad(): - output_tile = self.model(input_tile) - except RuntimeError as error: - print('Error', error) - print(f'\tTile {tile_idx}/{tiles_x * tiles_y}') - - # output tile area on total image - output_start_x = input_start_x * self.scale - output_end_x = input_end_x * self.scale - output_start_y = input_start_y * self.scale - output_end_y = input_end_y * self.scale - - # output tile area without padding - output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale - output_end_x_tile = output_start_x_tile + input_tile_width * self.scale - output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale - output_end_y_tile = output_start_y_tile + input_tile_height * self.scale - - # put tile into output image - self.output[:, :, output_start_y:output_end_y, - output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile, - output_start_x_tile:output_end_x_tile] - - def post_process(self): - # remove extra pad - if self.mod_scale is not None: - _, _, h, w = self.output.size() - self.output = self.output[:, :, 0:h - self.mod_pad_h * self.scale, 0:w - self.mod_pad_w * self.scale] - # remove prepad - if self.pre_pad != 0: - _, _, h, w = self.output.size() - self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale] - return self.output - - @torch.no_grad() - def enhance(self, img, outscale=None, alpha_upsampler='realesrgan'): - h_input, w_input = img.shape[0:2] - # img: numpy - img = img.astype(np.float32) - if np.max(img) > 256: # 16-bit image - max_range = 65535 - print('\tInput is a 16-bit image') - else: - max_range = 255 - img = img / max_range - if len(img.shape) == 2: # gray image - img_mode = 'L' - img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) - elif img.shape[2] == 4: # RGBA image with alpha channel - img_mode = 'RGBA' - alpha = img[:, :, 3] - img = img[:, :, 0:3] - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - if alpha_upsampler == 'realesrgan': - alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB) - else: - img_mode = 'RGB' - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - - # ------------------- process image (without the alpha channel) ------------------- # - self.pre_process(img) - if self.tile_size > 0: - self.tile_process() - else: - self.process() - output_img = self.post_process() - output_img = output_img.data.squeeze().float().cpu().clamp_(0, 1).numpy() - output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0)) - if img_mode == 'L': - output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY) - - # ------------------- process the alpha channel if necessary ------------------- # - if img_mode == 'RGBA': - if alpha_upsampler == 'realesrgan': - self.pre_process(alpha) - if self.tile_size > 0: - self.tile_process() - else: - self.process() - output_alpha = self.post_process() - output_alpha = output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy() - output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0)) - output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY) - else: # use the cv2 resize for alpha channel - h, w = alpha.shape[0:2] - output_alpha = cv2.resize(alpha, (w * self.scale, h * self.scale), interpolation=cv2.INTER_LINEAR) - - # merge the alpha channel - output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA) - output_img[:, :, 3] = output_alpha - - # ------------------------------ return ------------------------------ # - if max_range == 65535: # 16-bit image - output = (output_img * 65535.0).round().astype(np.uint16) - else: - output = (output_img * 255.0).round().astype(np.uint8) - - if outscale is not None and outscale != float(self.scale): - output = cv2.resize( - output, ( - int(w_input * outscale), - int(h_input * outscale), - ), interpolation=cv2.INTER_LANCZOS4) - - return output, img_mode - - -class PrefetchReader(threading.Thread): - """Prefetch images. - - Args: - img_list (list[str]): A image list of image paths to be read. - num_prefetch_queue (int): Number of prefetch queue. - """ - - def __init__(self, img_list, num_prefetch_queue): - super().__init__() - self.que = queue.Queue(num_prefetch_queue) - self.img_list = img_list - - def run(self): - for img_path in self.img_list: - img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) - self.que.put(img) - - self.que.put(None) - - def __next__(self): - next_item = self.que.get() - if next_item is None: - raise StopIteration - return next_item - - def __iter__(self): - return self - - -class IOConsumer(threading.Thread): - - def __init__(self, opt, que, qid): - super().__init__() - self._queue = que - self.qid = qid - self.opt = opt - - def run(self): - while True: - msg = self._queue.get() - if isinstance(msg, str) and msg == 'quit': - break - - output = msg['output'] - save_path = msg['save_path'] - cv2.imwrite(save_path, output) - print(f'IO worker {self.qid} is done.') diff --git a/spaces/CVPR/MonoScene/monoscene/.ipynb_checkpoints/monoscene_model-checkpoint.py b/spaces/CVPR/MonoScene/monoscene/.ipynb_checkpoints/monoscene_model-checkpoint.py deleted file mode 100644 index 1bf3d80ea531ff02b3229b862b7a4cd0aec8ec58..0000000000000000000000000000000000000000 --- a/spaces/CVPR/MonoScene/monoscene/.ipynb_checkpoints/monoscene_model-checkpoint.py +++ /dev/null @@ -1,22 +0,0 @@ -from transformers import PreTrainedModel -from .config import MonoSceneConfig -from monoscene.monoscene import MonoScene - - - -class MonoSceneModel(PreTrainedModel): - config_class = ResnetConfig - - def __init__(self, config): - super().__init__(config) - self.model = MonoScene( - dataset=config.dataset, - n_classes=config.n_classes, - feature=config.feature, - project_scale=config.project_scale, - full_scene_size=config.full_scene_size - ) - - - def forward(self, tensor): - return self.model.forward(tensor) \ No newline at end of file diff --git a/spaces/Chris4K/llms_compare/app.py b/spaces/Chris4K/llms_compare/app.py deleted file mode 100644 index 471a2e861bf3890f361c1d62edacc84f4a2914ba..0000000000000000000000000000000000000000 --- a/spaces/Chris4K/llms_compare/app.py +++ /dev/null @@ -1,274 +0,0 @@ -import os, requests -import gradio as gr -HF_READ_API_KEY = os.environ["HF_READ_API_KEY"] - -### This code loads the models and undertakes inference locally ### - -# from transformers import GPTNeoForCausalLM, GPT2Tokenizer -# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM -# model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B") -# tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B") -# tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small") -# model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small") - -model_list = ['google/flan-t5-small', 'google/flan-t5-base', 'google/flan-t5-large', 'google/flan-t5-xl', 'google/flan-t5-xxl', - 'gpt2-medium', 'gpt2-large', 'gpt2-xl', - 'EleutherAI/gpt-neo-1.3B', 'EleutherAI/gpt-neo-2.7B', 'EleutherAI/gpt-neo-6b', 'EleutherAI/gpt-neox-20b', - 'bigscience/bloom-1b7', 'bigscience/bloom-3b', 'bigscience/bloom-7b1' - ] - -def load_model(model_name): - if model_name == 'EleutherAI/gpt-neo-2.7B' or model_name == 'gpt2-medium' or model_name == 'gpt2-large': - model = AutoModelForCausalLM.from_pretrained(model_name) - else: - model = AutoModelForSeq2SeqLM.from_pretrained(model_name) - tokenizer = AutoTokenizer.from_pretrained(model_name) - tokenizer.pad_token = tokenizer.eos_token - # tokenizer.padding_side = "left" - return model, tokenizer - -def maybe_is_truncated(s): - punct = [".", "!", "?", '"'] - if s[-1] in punct: - return False - return True - -def load_and_generate(model_name, prompt): - model, tokenizer = load_model(model_name) - - temperature=0.25 - tokens = tokenizer(prompt, return_tensors="pt") - max_length = len(tokens.input_ids[0])+5 - input_ids = tokens.input_ids - attention_mask = tokens.attention_mask - # see huggingface.co/docs/transformers/main_classes/text_generation - gen_tokens = model.generate( - input_ids=input_ids, - attention_mask=attention_mask, - pad_token_id=tokenizer.eos_token_id, - do_sample=True, - temperature=temperature, - # max_length=max_length, - max_new_tokens=max_length, - # use_cache=False, - # penalty_alpha=0.1, - # top_k=100, - # early_stopping=False - ) - gen_text = tokenizer.batch_decode(gen_tokens)[0] - - max_times = 20 - while maybe_is_truncated(gen_text) and max_times > 0: - tokens = tokenizer(gen_text, return_tensors="pt") - max_length = len(tokens.input_ids[0])+5 - input_ids = tokens.input_ids - attention_mask = tokens.attention_mask - - gen_tokens = model.generate( - input_ids=input_ids, - attention_mask=attention_mask, - pad_token_id=tokenizer.eos_token_id, - do_sample=True, - temperature=temperature, - max_length=max_length, - # max_new_tokens=100, - # use_cache=True, - # penalty_alpha=0.1, - # top_k=100, - # early_stopping=False - ) - - gen_text = tokenizer.batch_decode(gen_tokens)[0] - - max_times -= 1 - - return gen_text.replace("
-
-
-
- """)
-
- with gr.Column(elem_id="col-container"):
- with gr.Row(variant="compact"):
-
- model_name = gr.Dropdown(
- model_list,
- label="Select model",
- value=model_list[0],
- ).style(
- container=False,
- )
-
- temperature = gr.Slider(
- 0.1, 100.0, value=1.0, label="Temperature",
- ).style(
- container=False,
- )
-
- max_tokens = gr.Slider(
- 10, 2250, step=1, value=100, label="Max. tokens (in output)",
- ).style(
- container=False,
- )
-
- check_truncated = gr.Checkbox(
- label="Check for truncated output",
- value=False,
- ).style(
- container=False,
- )
-
- with gr.Row(variant="compact"):
- prompt = gr.Textbox(
- label="Enter your prompt",
- show_label=False,
- # max_lines=2,
- placeholder="Select your prompt from the examples below",
- ).style(
- container=False,
- )
- process = gr.Button("Generate").style(full_width=False)
-
- with gr.Row():
- output=gr.Textbox(
- label="LLM output",
- show_label=True)
-
- gr.HTML("""
- - Different Strokes (Prompts) for Different Folks (LLMs) -
--
- After reading Prompt Engineering Guide, which is a good guide when starting to learn about prompts for large language models (LLMs), specifically OpenAI's LLMs, I was interested in seeing the results with for other LLMs. Hence, did up a simple demonstration of different prompts for different popular LLMs of different sizes. The prompt examples are taken from the Prompt Engineering Guide, and the LLMs that you can select below are all available on Hugging Face. If you are interested in comparing them with the prompts from OpenAI's model, you can refer to the writeup in the Prompt Engineering Guide itself. -
--
- Note: Larger models will take a while, especially on the first run. -
-
-
- """)
- with gr.Tab("Introduction"):
- example_set_1 = gr.Examples(label = 'Simple Prompt vs. Instruct then Prompt.',
- examples=["The sky is ", "Complete the following sentence: The sky is ",],
- inputs=[prompt])
- example_set_2 = gr.Examples(label = 'Few Shot Prompt.',
- examples=["This is awesome! // Positive\nThis is bad! // Negative\nWow that movie was rad! // Positive\nWhat a horrible show! //",],
- inputs=[prompt])
- example_set_3 = gr.Examples(label = 'Explicitly Specify the Instruction',
- examples=["### Instruction ###\nTranslate the text below to Spanish:\nText: 'hello!'",],
- inputs=[prompt])
- example_set_4 = gr.Examples(label = 'Be Very Specific',
- examples=["Extract the name of places in the following text.\nDesired format:\nPlace: - Prompt examples. Select the prompt you would like to test, and it will appear (properly formatted) in the input box above. -
-| duplicate_fraction | 0.0 |
|---|---|
| duplicates_dict |
-
- {value}
-
-
- )
-})
-CodeBlock.displayName = 'CodeBlock'
-
-export { CodeBlock }
diff --git a/spaces/Nahidabyer/img-to-music/utils.py b/spaces/Nahidabyer/img-to-music/utils.py
deleted file mode 100644
index e4d5448735f516afa03c8a99be64fa5a2915706c..0000000000000000000000000000000000000000
--- a/spaces/Nahidabyer/img-to-music/utils.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import json
-import numpy as np
-import httpx
-import os
-
-from constants import MUBERT_TAGS, MUBERT_MODE, MUBERT_LICENSE
-
-def get_mubert_tags_embeddings(w2v_model):
- return w2v_model.encode(MUBERT_TAGS)
-
-
-
-
-
-def find_similar(em, embeddings, method='cosine'):
- scores = []
- for ref in embeddings:
- if method == 'cosine':
- scores.append(1 - np.dot(ref, em) / (np.linalg.norm(ref) * np.linalg.norm(em)))
- if method == 'norm':
- scores.append(np.linalg.norm(ref - em))
- return np.array(scores), np.argsort(scores)
-
-
-def get_tags_for_prompts(w2v_model, mubert_tags_embeddings, prompts, top_n=3, debug=False):
- prompts_embeddings = w2v_model.encode(prompts)
- ret = []
- for i, pe in enumerate(prompts_embeddings):
- scores, idxs = find_similar(pe, mubert_tags_embeddings)
- top_tags = MUBERT_TAGS[idxs[:top_n]]
- top_prob = 1 - scores[idxs[:top_n]]
- if debug:
- print(f"Prompt: {prompts[i]}\nTags: {', '.join(top_tags)}\nScores: {top_prob}\n\n\n")
- ret.append((prompts[i], list(top_tags)))
- print("ret: " + ret)
- return ret
\ No newline at end of file
diff --git a/spaces/Nomanalvi/PDF_Convertor/pdfconv.py b/spaces/Nomanalvi/PDF_Convertor/pdfconv.py
deleted file mode 100644
index f0557e698ef622e0ea062f41cf2df2b7702770cf..0000000000000000000000000000000000000000
--- a/spaces/Nomanalvi/PDF_Convertor/pdfconv.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import streamlit as st # data app development
-import subprocess # process in the os
-from subprocess import STDOUT, check_call # os process manipuation
-import os # os process manipuation
-import base64 # byte object into a pdf file
-import camelot as cam # extracting tables from PDFs
-import pandas as pd
-from io import BytesIO
-import ctypes
-from ctypes.util import find_library
-from pyxlsb import open_workbook as open_xlsb
-find_library("".join(("gsdll", str(ctypes.sizeof(ctypes.c_voidp) * 8), ".dll")))
-#
- {language}
-
-
-
-
-
- Upload an Image (MUST Be .PNG and 512x512 or 768x768) enter a Prompt, or let it just do its Thing, then click submit. 10 Iterations takes about ~900-1200 seconds currently. For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic", article = "Code Monkey: Manjushri").queue(max_size=5).launch() \ No newline at end of file diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/cnn/bricks/non_local.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/cnn/bricks/non_local.py deleted file mode 100644 index 92d00155ef275c1201ea66bba30470a1785cc5d7..0000000000000000000000000000000000000000 --- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/cnn/bricks/non_local.py +++ /dev/null @@ -1,306 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from abc import ABCMeta - -import torch -import torch.nn as nn - -from ..utils import constant_init, normal_init -from .conv_module import ConvModule -from .registry import PLUGIN_LAYERS - - -class _NonLocalNd(nn.Module, metaclass=ABCMeta): - """Basic Non-local module. - - This module is proposed in - "Non-local Neural Networks" - Paper reference: https://arxiv.org/abs/1711.07971 - Code reference: https://github.com/AlexHex7/Non-local_pytorch - - Args: - in_channels (int): Channels of the input feature map. - reduction (int): Channel reduction ratio. Default: 2. - use_scale (bool): Whether to scale pairwise_weight by - `1/sqrt(inter_channels)` when the mode is `embedded_gaussian`. - Default: True. - conv_cfg (None | dict): The config dict for convolution layers. - If not specified, it will use `nn.Conv2d` for convolution layers. - Default: None. - norm_cfg (None | dict): The config dict for normalization layers. - Default: None. (This parameter is only applicable to conv_out.) - mode (str): Options are `gaussian`, `concatenation`, - `embedded_gaussian` and `dot_product`. Default: embedded_gaussian. - """ - - def __init__(self, - in_channels, - reduction=2, - use_scale=True, - conv_cfg=None, - norm_cfg=None, - mode='embedded_gaussian', - **kwargs): - super(_NonLocalNd, self).__init__() - self.in_channels = in_channels - self.reduction = reduction - self.use_scale = use_scale - self.inter_channels = max(in_channels // reduction, 1) - self.mode = mode - - if mode not in [ - 'gaussian', 'embedded_gaussian', 'dot_product', 'concatenation' - ]: - raise ValueError("Mode should be in 'gaussian', 'concatenation', " - f"'embedded_gaussian' or 'dot_product', but got " - f'{mode} instead.') - - # g, theta, phi are defaulted as `nn.ConvNd`. - # Here we use ConvModule for potential usage. - self.g = ConvModule( - self.in_channels, - self.inter_channels, - kernel_size=1, - conv_cfg=conv_cfg, - act_cfg=None) - self.conv_out = ConvModule( - self.inter_channels, - self.in_channels, - kernel_size=1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=None) - - if self.mode != 'gaussian': - self.theta = ConvModule( - self.in_channels, - self.inter_channels, - kernel_size=1, - conv_cfg=conv_cfg, - act_cfg=None) - self.phi = ConvModule( - self.in_channels, - self.inter_channels, - kernel_size=1, - conv_cfg=conv_cfg, - act_cfg=None) - - if self.mode == 'concatenation': - self.concat_project = ConvModule( - self.inter_channels * 2, - 1, - kernel_size=1, - stride=1, - padding=0, - bias=False, - act_cfg=dict(type='ReLU')) - - self.init_weights(**kwargs) - - def init_weights(self, std=0.01, zeros_init=True): - if self.mode != 'gaussian': - for m in [self.g, self.theta, self.phi]: - normal_init(m.conv, std=std) - else: - normal_init(self.g.conv, std=std) - if zeros_init: - if self.conv_out.norm_cfg is None: - constant_init(self.conv_out.conv, 0) - else: - constant_init(self.conv_out.norm, 0) - else: - if self.conv_out.norm_cfg is None: - normal_init(self.conv_out.conv, std=std) - else: - normal_init(self.conv_out.norm, std=std) - - def gaussian(self, theta_x, phi_x): - # NonLocal1d pairwise_weight: [N, H, H] - # NonLocal2d pairwise_weight: [N, HxW, HxW] - # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW] - pairwise_weight = torch.matmul(theta_x, phi_x) - pairwise_weight = pairwise_weight.softmax(dim=-1) - return pairwise_weight - - def embedded_gaussian(self, theta_x, phi_x): - # NonLocal1d pairwise_weight: [N, H, H] - # NonLocal2d pairwise_weight: [N, HxW, HxW] - # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW] - pairwise_weight = torch.matmul(theta_x, phi_x) - if self.use_scale: - # theta_x.shape[-1] is `self.inter_channels` - pairwise_weight /= theta_x.shape[-1]**0.5 - pairwise_weight = pairwise_weight.softmax(dim=-1) - return pairwise_weight - - def dot_product(self, theta_x, phi_x): - # NonLocal1d pairwise_weight: [N, H, H] - # NonLocal2d pairwise_weight: [N, HxW, HxW] - # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW] - pairwise_weight = torch.matmul(theta_x, phi_x) - pairwise_weight /= pairwise_weight.shape[-1] - return pairwise_weight - - def concatenation(self, theta_x, phi_x): - # NonLocal1d pairwise_weight: [N, H, H] - # NonLocal2d pairwise_weight: [N, HxW, HxW] - # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW] - h = theta_x.size(2) - w = phi_x.size(3) - theta_x = theta_x.repeat(1, 1, 1, w) - phi_x = phi_x.repeat(1, 1, h, 1) - - concat_feature = torch.cat([theta_x, phi_x], dim=1) - pairwise_weight = self.concat_project(concat_feature) - n, _, h, w = pairwise_weight.size() - pairwise_weight = pairwise_weight.view(n, h, w) - pairwise_weight /= pairwise_weight.shape[-1] - - return pairwise_weight - - def forward(self, x): - # Assume `reduction = 1`, then `inter_channels = C` - # or `inter_channels = C` when `mode="gaussian"` - - # NonLocal1d x: [N, C, H] - # NonLocal2d x: [N, C, H, W] - # NonLocal3d x: [N, C, T, H, W] - n = x.size(0) - - # NonLocal1d g_x: [N, H, C] - # NonLocal2d g_x: [N, HxW, C] - # NonLocal3d g_x: [N, TxHxW, C] - g_x = self.g(x).view(n, self.inter_channels, -1) - g_x = g_x.permute(0, 2, 1) - - # NonLocal1d theta_x: [N, H, C], phi_x: [N, C, H] - # NonLocal2d theta_x: [N, HxW, C], phi_x: [N, C, HxW] - # NonLocal3d theta_x: [N, TxHxW, C], phi_x: [N, C, TxHxW] - if self.mode == 'gaussian': - theta_x = x.view(n, self.in_channels, -1) - theta_x = theta_x.permute(0, 2, 1) - if self.sub_sample: - phi_x = self.phi(x).view(n, self.in_channels, -1) - else: - phi_x = x.view(n, self.in_channels, -1) - elif self.mode == 'concatenation': - theta_x = self.theta(x).view(n, self.inter_channels, -1, 1) - phi_x = self.phi(x).view(n, self.inter_channels, 1, -1) - else: - theta_x = self.theta(x).view(n, self.inter_channels, -1) - theta_x = theta_x.permute(0, 2, 1) - phi_x = self.phi(x).view(n, self.inter_channels, -1) - - pairwise_func = getattr(self, self.mode) - # NonLocal1d pairwise_weight: [N, H, H] - # NonLocal2d pairwise_weight: [N, HxW, HxW] - # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW] - pairwise_weight = pairwise_func(theta_x, phi_x) - - # NonLocal1d y: [N, H, C] - # NonLocal2d y: [N, HxW, C] - # NonLocal3d y: [N, TxHxW, C] - y = torch.matmul(pairwise_weight, g_x) - # NonLocal1d y: [N, C, H] - # NonLocal2d y: [N, C, H, W] - # NonLocal3d y: [N, C, T, H, W] - y = y.permute(0, 2, 1).contiguous().reshape(n, self.inter_channels, - *x.size()[2:]) - - output = x + self.conv_out(y) - - return output - - -class NonLocal1d(_NonLocalNd): - """1D Non-local module. - - Args: - in_channels (int): Same as `NonLocalND`. - sub_sample (bool): Whether to apply max pooling after pairwise - function (Note that the `sub_sample` is applied on spatial only). - Default: False. - conv_cfg (None | dict): Same as `NonLocalND`. - Default: dict(type='Conv1d'). - """ - - def __init__(self, - in_channels, - sub_sample=False, - conv_cfg=dict(type='Conv1d'), - **kwargs): - super(NonLocal1d, self).__init__( - in_channels, conv_cfg=conv_cfg, **kwargs) - - self.sub_sample = sub_sample - - if sub_sample: - max_pool_layer = nn.MaxPool1d(kernel_size=2) - self.g = nn.Sequential(self.g, max_pool_layer) - if self.mode != 'gaussian': - self.phi = nn.Sequential(self.phi, max_pool_layer) - else: - self.phi = max_pool_layer - - -@PLUGIN_LAYERS.register_module() -class NonLocal2d(_NonLocalNd): - """2D Non-local module. - - Args: - in_channels (int): Same as `NonLocalND`. - sub_sample (bool): Whether to apply max pooling after pairwise - function (Note that the `sub_sample` is applied on spatial only). - Default: False. - conv_cfg (None | dict): Same as `NonLocalND`. - Default: dict(type='Conv2d'). - """ - - _abbr_ = 'nonlocal_block' - - def __init__(self, - in_channels, - sub_sample=False, - conv_cfg=dict(type='Conv2d'), - **kwargs): - super(NonLocal2d, self).__init__( - in_channels, conv_cfg=conv_cfg, **kwargs) - - self.sub_sample = sub_sample - - if sub_sample: - max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2)) - self.g = nn.Sequential(self.g, max_pool_layer) - if self.mode != 'gaussian': - self.phi = nn.Sequential(self.phi, max_pool_layer) - else: - self.phi = max_pool_layer - - -class NonLocal3d(_NonLocalNd): - """3D Non-local module. - - Args: - in_channels (int): Same as `NonLocalND`. - sub_sample (bool): Whether to apply max pooling after pairwise - function (Note that the `sub_sample` is applied on spatial only). - Default: False. - conv_cfg (None | dict): Same as `NonLocalND`. - Default: dict(type='Conv3d'). - """ - - def __init__(self, - in_channels, - sub_sample=False, - conv_cfg=dict(type='Conv3d'), - **kwargs): - super(NonLocal3d, self).__init__( - in_channels, conv_cfg=conv_cfg, **kwargs) - self.sub_sample = sub_sample - - if sub_sample: - max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2)) - self.g = nn.Sequential(self.g, max_pool_layer) - if self.mode != 'gaussian': - self.phi = nn.Sequential(self.phi, max_pool_layer) - else: - self.phi = max_pool_layer diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis_eval.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis_eval.py deleted file mode 100644 index 5d242ec4d61a8506fb3e971e79437842c413e883..0000000000000000000000000000000000000000 --- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis_eval.py +++ /dev/null @@ -1,998 +0,0 @@ -# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import copy -import datetime -import json -import os -from collections import OrderedDict, defaultdict - -import numpy as np -import pycocotools.mask as mask_util -import torch -import torch._six - -import maskrcnn_benchmark.utils.mdetr_dist as dist - -from maskrcnn_benchmark.utils.mdetr_dist import all_gather - - -from .lvis import LVIS - -def merge(img_ids, eval_imgs): - all_img_ids = all_gather(img_ids) - all_eval_imgs = all_gather(eval_imgs) - - merged_img_ids = [] - for p in all_img_ids: - merged_img_ids.extend(p) - - merged_eval_imgs = [] - for p in all_eval_imgs: - merged_eval_imgs.append(p) - - merged_img_ids = np.array(merged_img_ids) - merged_eval_imgs = np.concatenate(merged_eval_imgs, 2) - - # keep only unique (and in sorted order) images - merged_img_ids, idx = np.unique(merged_img_ids, return_index=True) - merged_eval_imgs = merged_eval_imgs[..., idx] - - return merged_img_ids, merged_eval_imgs - - -################################################################# -# From LVIS, with following changes: -# * fixed LVISEval constructor to accept empty dt -# * Removed logger -# * LVIS results supports numpy inputs -################################################################# - - -class Params: - def __init__(self, iou_type): - """Params for LVIS evaluation API.""" - self.img_ids = [] - self.cat_ids = [] - # np.arange causes trouble. the data point on arange is slightly - # larger than the true value - self.iou_thrs = np.linspace(0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True) - self.rec_thrs = np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01)) + 1, endpoint=True) - self.max_dets = 300 - self.area_rng = [ - [0 ** 2, 1e5 ** 2], - [0 ** 2, 32 ** 2], - [32 ** 2, 96 ** 2], - [96 ** 2, 1e5 ** 2], - ] - self.area_rng_lbl = ["all", "small", "medium", "large"] - self.use_cats = 1 - # We bin categories in three bins based how many images of the training - # set the category is present in. - # r: Rare : < 10 - # c: Common : >= 10 and < 100 - # f: Frequent: >= 100 - self.img_count_lbl = ["r", "c", "f"] - self.iou_type = iou_type - - -class LVISResults(LVIS): - def __init__(self, lvis_gt, results, max_dets=300): - """Constructor for LVIS results. - Args: - lvis_gt (LVIS class instance, or str containing path of - annotation file) - results (str containing path of result file or a list of dicts) - max_dets (int): max number of detections per image. The official - value of max_dets for LVIS is 300. - """ - super(LVISResults, self).__init__() - assert isinstance(lvis_gt, LVIS) - self.dataset["images"] = [img for img in lvis_gt.dataset["images"]] - - if isinstance(results, str): - result_anns = self._load_json(results) - elif type(results) == np.ndarray: - result_anns = self.loadNumpyAnnotations(results) - else: - result_anns = results - - if max_dets >= 0: - result_anns = self.limit_dets_per_image(result_anns, max_dets) - - if len(result_anns) > 0 and "bbox" in result_anns[0]: - self.dataset["categories"] = copy.deepcopy(lvis_gt.dataset["categories"]) - for id, ann in enumerate(result_anns): - x1, y1, w, h = ann["bbox"] - x2 = x1 + w - y2 = y1 + h - - if "segmentation" not in ann: - ann["segmentation"] = [[x1, y1, x1, y2, x2, y2, x2, y1]] - - ann["area"] = w * h - ann["id"] = id + 1 - - elif len(result_anns) > 0 and "segmentation" in result_anns[0]: - self.dataset["categories"] = copy.deepcopy(lvis_gt.dataset["categories"]) - for id, ann in enumerate(result_anns): - # Only support compressed RLE format as segmentation results - ann["area"] = mask_util.area(ann["segmentation"]) - - if "bbox" not in ann: - ann["bbox"] = mask_util.toBbox(ann["segmentation"]) - - ann["id"] = id + 1 - - self.dataset["annotations"] = result_anns - self._create_index() - - # #FIXME: disabling this check for now - # img_ids_in_result = [ann["image_id"] for ann in result_anns] - - # assert set(img_ids_in_result) == ( - # set(img_ids_in_result) & set(self.get_img_ids()) - # ), "Results do not correspond to current LVIS set." - - def limit_dets_per_image(self, anns, max_dets): - img_ann = defaultdict(list) - for ann in anns: - img_ann[ann["image_id"]].append(ann) - - for img_id, _anns in img_ann.items(): - if len(_anns) <= max_dets: - continue - _anns = sorted(_anns, key=lambda ann: ann["score"], reverse=True) - img_ann[img_id] = _anns[:max_dets] - - return [ann for anns in img_ann.values() for ann in anns] - - def get_top_results(self, img_id, score_thrs): - ann_ids = self.get_ann_ids(img_ids=[img_id]) - anns = self.load_anns(ann_ids) - return list(filter(lambda ann: ann["score"] > score_thrs, anns)) - - -class LVISEval: - def __init__(self, lvis_gt, lvis_dt=None, iou_type="segm"): - """Constructor for LVISEval. - Args: - lvis_gt (LVIS class instance, or str containing path of annotation file) - lvis_dt (LVISResult class instance, or str containing path of result file, - or list of dict) - iou_type (str): segm or bbox evaluation - """ - - if iou_type not in ["bbox", "segm"]: - raise ValueError("iou_type: {} is not supported.".format(iou_type)) - - if isinstance(lvis_gt, LVIS): - self.lvis_gt = lvis_gt - elif isinstance(lvis_gt, str): - self.lvis_gt = LVIS(lvis_gt) - else: - raise TypeError("Unsupported type {} of lvis_gt.".format(lvis_gt)) - - if isinstance(lvis_dt, LVISResults): - self.lvis_dt = lvis_dt - elif isinstance(lvis_dt, (str, list)): - self.lvis_dt = LVISResults(self.lvis_gt, lvis_dt) - elif lvis_dt is not None: - raise TypeError("Unsupported type {} of lvis_dt.".format(lvis_dt)) - - # per-image per-category evaluation results - self.eval_imgs = defaultdict(list) - self.eval = {} # accumulated evaluation results - self._gts = defaultdict(list) # gt for evaluation - self._dts = defaultdict(list) # dt for evaluation - self.params = Params(iou_type=iou_type) # parameters - self.results = OrderedDict() - self.stats = [] - self.ious = {} # ious between all gts and dts - - self.params.img_ids = sorted(self.lvis_gt.get_img_ids()) - self.params.cat_ids = sorted(self.lvis_gt.get_cat_ids()) - - def _to_mask(self, anns, lvis): - for ann in anns: - rle = lvis.ann_to_rle(ann) - ann["segmentation"] = rle - - def _prepare(self): - """Prepare self._gts and self._dts for evaluation based on params.""" - - cat_ids = self.params.cat_ids if self.params.cat_ids else None - - gts = self.lvis_gt.load_anns(self.lvis_gt.get_ann_ids(img_ids=self.params.img_ids, cat_ids=cat_ids)) - dts = self.lvis_dt.load_anns(self.lvis_dt.get_ann_ids(img_ids=self.params.img_ids, cat_ids=cat_ids)) - # convert ground truth to mask if iou_type == 'segm' - if self.params.iou_type == "segm": - self._to_mask(gts, self.lvis_gt) - self._to_mask(dts, self.lvis_dt) - - # set ignore flag - for gt in gts: - if "ignore" not in gt: - gt["ignore"] = 0 - - for gt in gts: - self._gts[gt["image_id"], gt["category_id"]].append(gt) - - # For federated dataset evaluation we will filter out all dt for an - # image which belong to categories not present in gt and not present in - # the negative list for an image. In other words detector is not penalized - # for categories about which we don't have gt information about their - # presence or absence in an image. - img_data = self.lvis_gt.load_imgs(ids=self.params.img_ids) - # per image map of categories not present in image - img_nl = {d["id"]: d["neg_category_ids"] for d in img_data} - # per image list of categories present in image - img_pl = defaultdict(set) - for ann in gts: - img_pl[ann["image_id"]].add(ann["category_id"]) - # per image map of categoires which have missing gt. For these - # categories we don't penalize the detector for flase positives. - self.img_nel = {d["id"]: d["not_exhaustive_category_ids"] for d in img_data} - - for dt in dts: - img_id, cat_id = dt["image_id"], dt["category_id"] - if cat_id not in img_nl[img_id] and cat_id not in img_pl[img_id]: - continue - self._dts[img_id, cat_id].append(dt) - - self.freq_groups = self._prepare_freq_group() - - def _prepare_freq_group(self): - freq_groups = [[] for _ in self.params.img_count_lbl] - cat_data = self.lvis_gt.load_cats(self.params.cat_ids) - for idx, _cat_data in enumerate(cat_data): - frequency = _cat_data["frequency"] - freq_groups[self.params.img_count_lbl.index(frequency)].append(idx) - return freq_groups - - def evaluate(self): - """ - Run per image evaluation on given images and store results - (a list of dict) in self.eval_imgs. - """ - - self.params.img_ids = list(np.unique(self.params.img_ids)) - - if self.params.use_cats: - cat_ids = self.params.cat_ids - else: - cat_ids = [-1] - - self._prepare() - - self.ious = { - (img_id, cat_id): self.compute_iou(img_id, cat_id) for img_id in self.params.img_ids for cat_id in cat_ids - } - - # loop through images, area range, max detection number - self.eval_imgs = [ - self.evaluate_img(img_id, cat_id, area_rng) - for cat_id in cat_ids - for area_rng in self.params.area_rng - for img_id in self.params.img_ids - ] - - def _get_gt_dt(self, img_id, cat_id): - """Create gt, dt which are list of anns/dets. If use_cats is true - only anns/dets corresponding to tuple (img_id, cat_id) will be - used. Else, all anns/dets in image are used and cat_id is not used. - """ - if self.params.use_cats: - gt = self._gts[img_id, cat_id] - dt = self._dts[img_id, cat_id] - else: - gt = [_ann for _cat_id in self.params.cat_ids for _ann in self._gts[img_id, cat_id]] - dt = [_ann for _cat_id in self.params.cat_ids for _ann in self._dts[img_id, cat_id]] - return gt, dt - - def compute_iou(self, img_id, cat_id): - gt, dt = self._get_gt_dt(img_id, cat_id) - - if len(gt) == 0 and len(dt) == 0: - return [] - - # Sort detections in decreasing order of score. - idx = np.argsort([-d["score"] for d in dt], kind="mergesort") - dt = [dt[i] for i in idx] - - iscrowd = [int(False)] * len(gt) - - if self.params.iou_type == "segm": - ann_type = "segmentation" - elif self.params.iou_type == "bbox": - ann_type = "bbox" - else: - raise ValueError("Unknown iou_type for iou computation.") - gt = [g[ann_type] for g in gt] - dt = [d[ann_type] for d in dt] - - # compute iou between each dt and gt region - # will return array of shape len(dt), len(gt) - ious = mask_util.iou(dt, gt, iscrowd) - return ious - - def evaluate_img(self, img_id, cat_id, area_rng): - """Perform evaluation for single category and image.""" - gt, dt = self._get_gt_dt(img_id, cat_id) - - if len(gt) == 0 and len(dt) == 0: - return None - - # Add another filed _ignore to only consider anns based on area range. - for g in gt: - if g["ignore"] or (g["area"] < area_rng[0] or g["area"] > area_rng[1]): - g["_ignore"] = 1 - else: - g["_ignore"] = 0 - - # Sort gt ignore last - gt_idx = np.argsort([g["_ignore"] for g in gt], kind="mergesort") - gt = [gt[i] for i in gt_idx] - - # Sort dt highest score first - dt_idx = np.argsort([-d["score"] for d in dt], kind="mergesort") - dt = [dt[i] for i in dt_idx] - - # load computed ious - ious = self.ious[img_id, cat_id][:, gt_idx] if len(self.ious[img_id, cat_id]) > 0 else self.ious[img_id, cat_id] - - num_thrs = len(self.params.iou_thrs) - num_gt = len(gt) - num_dt = len(dt) - - # Array to store the "id" of the matched dt/gt - gt_m = np.zeros((num_thrs, num_gt)) - dt_m = np.zeros((num_thrs, num_dt)) - - gt_ig = np.array([g["_ignore"] for g in gt]) - dt_ig = np.zeros((num_thrs, num_dt)) - - for iou_thr_idx, iou_thr in enumerate(self.params.iou_thrs): - if len(ious) == 0: - break - - for dt_idx, _dt in enumerate(dt): - iou = min([iou_thr, 1 - 1e-10]) - # information about best match so far (m=-1 -> unmatched) - # store the gt_idx which matched for _dt - m = -1 - for gt_idx, _ in enumerate(gt): - # if this gt already matched continue - if gt_m[iou_thr_idx, gt_idx] > 0: - continue - # if _dt matched to reg gt, and on ignore gt, stop - if m > -1 and gt_ig[m] == 0 and gt_ig[gt_idx] == 1: - break - # continue to next gt unless better match made - if ious[dt_idx, gt_idx] < iou: - continue - # if match successful and best so far, store appropriately - iou = ious[dt_idx, gt_idx] - m = gt_idx - - # No match found for _dt, go to next _dt - if m == -1: - continue - - # if gt to ignore for some reason update dt_ig. - # Should not be used in evaluation. - dt_ig[iou_thr_idx, dt_idx] = gt_ig[m] - # _dt match found, update gt_m, and dt_m with "id" - dt_m[iou_thr_idx, dt_idx] = gt[m]["id"] - gt_m[iou_thr_idx, m] = _dt["id"] - - # For LVIS we will ignore any unmatched detection if that category was - # not exhaustively annotated in gt. - dt_ig_mask = [ - d["area"] < area_rng[0] or d["area"] > area_rng[1] or d["category_id"] in self.img_nel[d["image_id"]] - for d in dt - ] - dt_ig_mask = np.array(dt_ig_mask).reshape((1, num_dt)) # 1 X num_dt - dt_ig_mask = np.repeat(dt_ig_mask, num_thrs, 0) # num_thrs X num_dt - # Based on dt_ig_mask ignore any unmatched detection by updating dt_ig - dt_ig = np.logical_or(dt_ig, np.logical_and(dt_m == 0, dt_ig_mask)) - # store results for given image and category - return { - "image_id": img_id, - "category_id": cat_id, - "area_rng": area_rng, - "dt_ids": [d["id"] for d in dt], - "gt_ids": [g["id"] for g in gt], - "dt_matches": dt_m, - "gt_matches": gt_m, - "dt_scores": [d["score"] for d in dt], - "gt_ignore": gt_ig, - "dt_ignore": dt_ig, - } - - def accumulate(self): - """Accumulate per image evaluation results and store the result in - self.eval. - """ - - if not self.eval_imgs: - print("Warning: Please run evaluate first.") - - if self.params.use_cats: - cat_ids = self.params.cat_ids - else: - cat_ids = [-1] - - num_thrs = len(self.params.iou_thrs) - num_recalls = len(self.params.rec_thrs) - num_cats = len(cat_ids) - num_area_rngs = len(self.params.area_rng) - num_imgs = len(self.params.img_ids) - - # -1 for absent categories - precision = -np.ones((num_thrs, num_recalls, num_cats, num_area_rngs)) - recall = -np.ones((num_thrs, num_cats, num_area_rngs)) - - # Initialize dt_pointers - dt_pointers = {} - for cat_idx in range(num_cats): - dt_pointers[cat_idx] = {} - for area_idx in range(num_area_rngs): - dt_pointers[cat_idx][area_idx] = {} - - # Per category evaluation - for cat_idx in range(num_cats): - Nk = cat_idx * num_area_rngs * num_imgs - for area_idx in range(num_area_rngs): - Na = area_idx * num_imgs - E = [self.eval_imgs[Nk + Na + img_idx] for img_idx in range(num_imgs)] - # Remove elements which are None - E = [e for e in E if e is not None] - if len(E) == 0: - continue - - # Append all scores: shape (N,) - dt_scores = np.concatenate([e["dt_scores"] for e in E], axis=0) - dt_ids = np.concatenate([e["dt_ids"] for e in E], axis=0) - - dt_idx = np.argsort(-dt_scores, kind="mergesort") - dt_scores = dt_scores[dt_idx] - dt_ids = dt_ids[dt_idx] - - dt_m = np.concatenate([e["dt_matches"] for e in E], axis=1)[:, dt_idx] - dt_ig = np.concatenate([e["dt_ignore"] for e in E], axis=1)[:, dt_idx] - - gt_ig = np.concatenate([e["gt_ignore"] for e in E]) - # num gt anns to consider - num_gt = np.count_nonzero(gt_ig == 0) - - if num_gt == 0: - continue - - tps = np.logical_and(dt_m, np.logical_not(dt_ig)) - fps = np.logical_and(np.logical_not(dt_m), np.logical_not(dt_ig)) - - tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) - fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) - - dt_pointers[cat_idx][area_idx] = { - "dt_ids": dt_ids, - "tps": tps, - "fps": fps, - } - - for iou_thr_idx, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): - tp = np.array(tp) - fp = np.array(fp) - num_tp = len(tp) - rc = tp / num_gt - if num_tp: - recall[iou_thr_idx, cat_idx, area_idx] = rc[-1] - else: - recall[iou_thr_idx, cat_idx, area_idx] = 0 - - # np.spacing(1) ~= eps - pr = tp / (fp + tp + np.spacing(1)) - pr = pr.tolist() - - # Replace each precision value with the maximum precision - # value to the right of that recall level. This ensures - # that the calculated AP value will be less suspectable - # to small variations in the ranking. - for i in range(num_tp - 1, 0, -1): - if pr[i] > pr[i - 1]: - pr[i - 1] = pr[i] - - rec_thrs_insert_idx = np.searchsorted(rc, self.params.rec_thrs, side="left") - - pr_at_recall = [0.0] * num_recalls - - try: - for _idx, pr_idx in enumerate(rec_thrs_insert_idx): - pr_at_recall[_idx] = pr[pr_idx] - except Exception: - pass - precision[iou_thr_idx, :, cat_idx, area_idx] = np.array(pr_at_recall) - - self.eval = { - "params": self.params, - "counts": [num_thrs, num_recalls, num_cats, num_area_rngs], - "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), - "precision": precision, - "recall": recall, - "dt_pointers": dt_pointers, - } - - def _summarize(self, summary_type, iou_thr=None, area_rng="all", freq_group_idx=None): - aidx = [idx for idx, _area_rng in enumerate(self.params.area_rng_lbl) if _area_rng == area_rng] - - if summary_type == "ap": - s = self.eval["precision"] - if iou_thr is not None: - tidx = np.where(iou_thr == self.params.iou_thrs)[0] - s = s[tidx] - if freq_group_idx is not None: - s = s[:, :, self.freq_groups[freq_group_idx], aidx] - else: - s = s[:, :, :, aidx] - else: - s = self.eval["recall"] - if iou_thr is not None: - tidx = np.where(iou_thr == self.params.iou_thrs)[0] - s = s[tidx] - s = s[:, :, aidx] - - if len(s[s > -1]) == 0: - mean_s = -1 - else: - mean_s = np.mean(s[s > -1]) - return mean_s - - def summarize(self): - """Compute and display summary metrics for evaluation results.""" - if not self.eval: - raise RuntimeError("Please run accumulate() first.") - - max_dets = self.params.max_dets - - self.results["AP"] = self._summarize("ap") - self.results["AP50"] = self._summarize("ap", iou_thr=0.50) - self.results["AP75"] = self._summarize("ap", iou_thr=0.75) - self.results["APs"] = self._summarize("ap", area_rng="small") - self.results["APm"] = self._summarize("ap", area_rng="medium") - self.results["APl"] = self._summarize("ap", area_rng="large") - self.results["APr"] = self._summarize("ap", freq_group_idx=0) - self.results["APc"] = self._summarize("ap", freq_group_idx=1) - self.results["APf"] = self._summarize("ap", freq_group_idx=2) - - self.stats = np.zeros((9,)) - self.stats[0] = self._summarize("ap") - self.stats[1] = self._summarize("ap", iou_thr=0.50) - self.stats[2] = self._summarize("ap", iou_thr=0.75) - self.stats[3] = self._summarize("ap", area_rng="small") - self.stats[4] = self._summarize("ap", area_rng="medium") - self.stats[5] = self._summarize("ap", area_rng="large") - self.stats[6] = self._summarize("ap", freq_group_idx=0) - self.stats[7] = self._summarize("ap", freq_group_idx=1) - self.stats[8] = self._summarize("ap", freq_group_idx=2) - - key = "AR@{}".format(max_dets) - self.results[key] = self._summarize("ar") - - for area_rng in ["small", "medium", "large"]: - key = "AR{}@{}".format(area_rng[0], max_dets) - self.results[key] = self._summarize("ar", area_rng=area_rng) - _returned = self.print_results() - return _returned - - def run(self): - """Wrapper function which calculates the results.""" - self.evaluate() - self.accumulate() - self.summarize() - - def print_results(self): - template = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} catIds={:>3s}] = {:0.3f}" - out_strings = [] - for key, value in self.results.items(): - max_dets = self.params.max_dets - if "AP" in key: - title = "Average Precision" - _type = "(AP)" - else: - title = "Average Recall" - _type = "(AR)" - - if len(key) > 2 and key[2].isdigit(): - iou_thr = float(key[2:]) / 100 - iou = "{:0.2f}".format(iou_thr) - else: - iou = "{:0.2f}:{:0.2f}".format(self.params.iou_thrs[0], self.params.iou_thrs[-1]) - - if len(key) > 2 and key[2] in ["r", "c", "f"]: - cat_group_name = key[2] - else: - cat_group_name = "all" - - if len(key) > 2 and key[2] in ["s", "m", "l"]: - area_rng = key[2] - else: - area_rng = "all" - - print(template.format(title, _type, iou, area_rng, max_dets, cat_group_name, value)) - out_strings.append(template.format(title, _type, iou, area_rng, max_dets, cat_group_name, value)) - return out_strings - - def get_results(self): - if not self.results: - print("Warning: results is empty. Call run().") - return self.results - - -################################################################# -# end of straight copy from lvis, just fixing constructor -################################################################# - - -class LvisEvaluator(object): - def __init__(self, lvis_gt, iou_types): - assert isinstance(iou_types, (list, tuple)) - # lvis_gt = copy.deepcopy(lvis_gt) - self.lvis_gt = lvis_gt - - self.iou_types = iou_types - self.coco_eval = {} - for iou_type in iou_types: - self.coco_eval[iou_type] = LVISEval(lvis_gt, iou_type=iou_type) - - self.img_ids = [] - self.eval_imgs = {k: [] for k in iou_types} - - def update(self, predictions): - img_ids = list(np.unique(list(predictions.keys()))) - self.img_ids.extend(img_ids) - - for iou_type in self.iou_types: - results = self.prepare(predictions, iou_type) - lvis_dt = LVISResults(self.lvis_gt, results) - lvis_eval = self.coco_eval[iou_type] - - lvis_eval.lvis_dt = lvis_dt - lvis_eval.params.img_ids = list(img_ids) - lvis_eval.evaluate() - eval_imgs = lvis_eval.eval_imgs - eval_imgs = np.asarray(eval_imgs).reshape( - len(lvis_eval.params.cat_ids), len(lvis_eval.params.area_rng), len(lvis_eval.params.img_ids) - ) - - self.eval_imgs[iou_type].append(eval_imgs) - - def synchronize_between_processes(self): - for iou_type in self.iou_types: - self.eval_imgs[iou_type] = np.concatenate(self.eval_imgs[iou_type], 2) - create_common_lvis_eval(self.coco_eval[iou_type], self.img_ids, self.eval_imgs[iou_type]) - - def accumulate(self): - for lvis_eval in self.coco_eval.values(): - lvis_eval.accumulate() - - def summarize(self): - for iou_type, lvis_eval in self.coco_eval.items(): - print("IoU metric: {}".format(iou_type)) - lvis_eval.summarize() - - def prepare(self, predictions, iou_type): - if iou_type == "bbox": - return self.prepare_for_lvis_detection(predictions) - elif iou_type == "segm": - return self.prepare_for_lvis_segmentation(predictions) - elif iou_type == "keypoints": - return self.prepare_for_lvis_keypoint(predictions) - else: - raise ValueError("Unknown iou type {}".format(iou_type)) - - def prepare_for_lvis_detection(self, predictions): - lvis_results = [] - for original_id, prediction in predictions.items(): - if len(prediction) == 0: - continue - - boxes = prediction["boxes"] - boxes = convert_to_xywh(boxes).tolist() - scores = prediction["scores"].tolist() - labels = prediction["labels"].tolist() - - lvis_results.extend( - [ - { - "image_id": original_id, - "category_id": labels[k], - "bbox": box, - "score": scores[k], - } - for k, box in enumerate(boxes) - ] - ) - return lvis_results - - def prepare_for_lvis_segmentation(self, predictions): - lvis_results = [] - for original_id, prediction in predictions.items(): - if len(prediction) == 0: - continue - - scores = prediction["scores"] - labels = prediction["labels"] - masks = prediction["masks"] - - masks = masks > 0.5 - - scores = prediction["scores"].tolist() - labels = prediction["labels"].tolist() - - rles = [ - mask_util.encode(np.array(mask[0, :, :, np.newaxis], dtype=np.uint8, order="F"))[0] for mask in masks - ] - for rle in rles: - rle["counts"] = rle["counts"].decode("utf-8") - - lvis_results.extend( - [ - { - "image_id": original_id, - "category_id": labels[k], - "segmentation": rle, - "score": scores[k], - } - for k, rle in enumerate(rles) - ] - ) - return lvis_results - - -def _merge_lists(listA, listB, maxN, key): - result = [] - indA, indB = 0, 0 - while (indA < len(listA) or indB < len(listB)) and len(result) < maxN: - if (indB < len(listB)) and (indA >= len(listA) or key(listA[indA]) < key(listB[indB])): - result.append(listB[indB]) - indB += 1 - else: - result.append(listA[indA]) - indA += 1 - return result - - -# Adapted from https://github.com/achalddave/large-vocab-devil/blob/9aaddc15b00e6e0d370b16743233e40d973cd53f/scripts/evaluate_ap_fixed.py -class LvisEvaluatorFixedAP(object): - def __init__(self, gt: LVIS, topk=10000, fixed_ap=True): - - self.results = [] - self.by_cat = {} - self.gt = gt - self.topk = topk - self.fixed_ap = fixed_ap - - def update(self, predictions): - cur_results = self.prepare(predictions) - if self.fixed_ap: - by_cat = defaultdict(list) - for ann in cur_results: - by_cat[ann["category_id"]].append(ann) - - for cat, cat_anns in by_cat.items(): - if cat not in self.by_cat: - self.by_cat[cat] = [] - - cur = sorted(cat_anns, key=lambda x: x["score"], reverse=True)[: self.topk] - self.by_cat[cat] = _merge_lists(self.by_cat[cat], cur, self.topk, key=lambda x: x["score"]) - else: - by_id = defaultdict(list) - for ann in cur_results: - by_id[ann["image_id"]].append(ann) - - for id_anns in by_id.values(): - self.results.extend(sorted(id_anns, key=lambda x: x["score"], reverse=True)[:300]) - - def synchronize_between_processes(self): - if self.fixed_ap: - all_cats = dist.all_gather(self.by_cat) - self.by_cat = defaultdict(list) - for cats in all_cats: - for cat, cat_anns in cats.items(): - self.by_cat[cat].extend(cat_anns) - else: - self.results = sum(dist.all_gather(self.results), []) - - def prepare(self, predictions): - lvis_results = [] - for original_id, prediction in predictions: - if len(prediction) == 0: - continue - - boxes = prediction["boxes"] - boxes = convert_to_xywh(boxes).tolist() - scores = prediction["scores"].tolist() - labels = prediction["labels"].tolist() - - lvis_results.extend( - [ - { - "image_id": original_id, - "category_id": labels[k], - "bbox": box, - "score": scores[k], - } - for k, box in enumerate(boxes) - ] - ) - return lvis_results - - def summarize(self): - if not dist.is_main_process(): - return - - if self.fixed_ap: - return self._summarize_fixed() - else: - return self._summarize_standard() - - def _summarize_standard(self): - results = LVISResults(self.gt, self.results) - lvis_eval = LVISEval(self.gt, results, iou_type="bbox") - lvis_eval.run() - lvis_eval.print_results() - - def _summarize_fixed(self): - results = [] - - missing_dets_cats = set() - for cat, cat_anns in self.by_cat.items(): - if len(cat_anns) < self.topk: - missing_dets_cats.add(cat) - results.extend(sorted(cat_anns, key=lambda x: x["score"], reverse=True)[: self.topk]) - if missing_dets_cats: - print( - f"\n===\n" - f"{len(missing_dets_cats)} classes had less than {self.topk} detections!\n" - f"Outputting {self.topk} detections for each class will improve AP further.\n" - f"If using detectron2, please use the lvdevil/infer_topk.py script to " - f"output a results file with {self.topk} detections for each class.\n" - f"===" - ) - - results = LVISResults(self.gt, results, max_dets=-1) - lvis_eval = LVISEval(self.gt, results, iou_type="bbox") - params = lvis_eval.params - params.max_dets = -1 # No limit on detections per image. - lvis_eval.run() - scores = lvis_eval.print_results() - metrics = {k: v for k, v in lvis_eval.results.items() if k.startswith("AP")} - print("copypaste: %s,%s", ",".join(map(str, metrics.keys())), "path") - return scores - - -class LvisDumper(object): - def __init__(self, topk=10000, fixed_ap=True, out_path="lvis_eval"): - - self.results = [] - self.by_cat = {} - self.topk = topk - self.fixed_ap = fixed_ap - self.out_path = out_path - if dist.is_main_process(): - if not os.path.exists(self.out_path): - os.mkdir(self.out_path) - - def update(self, predictions): - cur_results = self.prepare(predictions) - if self.fixed_ap: - by_cat = defaultdict(list) - for ann in cur_results: - by_cat[ann["category_id"]].append(ann) - - for cat, cat_anns in by_cat.items(): - if cat not in self.by_cat: - self.by_cat[cat] = [] - - cur = sorted(cat_anns, key=lambda x: x["score"], reverse=True)[: self.topk] - self.by_cat[cat] = _merge_lists(self.by_cat[cat], cur, self.topk, key=lambda x: x["score"]) - else: - by_id = defaultdict(list) - for ann in cur_results: - by_id[ann["image_id"]].append(ann) - - for id_anns in by_id.values(): - self.results.extend(sorted(id_anns, key=lambda x: x["score"], reverse=True)[:300]) - - def synchronize_between_processes(self): - if self.fixed_ap: - all_cats = dist.all_gather(self.by_cat) - self.by_cat = defaultdict(list) - for cats in all_cats: - for cat, cat_anns in cats.items(): - self.by_cat[cat].extend(cat_anns) - else: - self.results = sum(dist.all_gather(self.results), []) - - def prepare(self, predictions): - lvis_results = [] - for original_id, prediction in predictions: - if len(prediction) == 0: - continue - - boxes = prediction["boxes"] - boxes = convert_to_xywh(boxes).tolist() - scores = prediction["scores"].tolist() - labels = prediction["labels"].tolist() - - lvis_results.extend( - [ - { - "image_id": original_id, - "category_id": labels[k], - "bbox": box, - "score": scores[k], - } - for k, box in enumerate(boxes) - ] - ) - return lvis_results - - def summarize(self): - if not dist.is_main_process(): - return - - if self.fixed_ap: - self._summarize_fixed() - else: - self._summarize_standard() - - def _summarize_standard(self): - json_path = os.path.join(self.out_path, "results.json") - print("dumping to ", json_path) - with open(json_path, "w") as f: - json.dump(self.results, f) - - print("dumped") - - def _summarize_fixed(self): - results = [] - - missing_dets_cats = set() - for cat, cat_anns in self.by_cat.items(): - if len(cat_anns) < self.topk: - missing_dets_cats.add(cat) - results.extend(sorted(cat_anns, key=lambda x: x["score"], reverse=True)[: self.topk]) - if missing_dets_cats: - print( - f"\n===\n" - f"{len(missing_dets_cats)} classes had less than {self.topk} detections!\n" - f"Outputting {self.topk} detections for each class will improve AP further.\n" - f"If using detectron2, please use the lvdevil/infer_topk.py script to " - f"output a results file with {self.topk} detections for each class.\n" - f"===" - ) - - json_path = os.path.join(self.out_path, "results.json") - print("dumping to ", json_path) - with open(json_path, "w") as f: - json.dump(results, f) - - print("dumped") - - -def convert_to_xywh(boxes): - xmin, ymin, xmax, ymax = boxes.unbind(1) - return torch.stack((xmin, ymin, xmax - xmin, ymax - ymin), dim=1) - - -def create_common_lvis_eval(lvis_eval, img_ids, eval_imgs): - img_ids, eval_imgs = merge(img_ids, eval_imgs) - img_ids = list(img_ids) - eval_imgs = list(eval_imgs.flatten()) - - lvis_eval.eval_imgs = eval_imgs - lvis_eval.params.img_ids = img_ids - -def lvis_evaluation(): - pass \ No newline at end of file diff --git a/spaces/Pushpak77/fastspeech2-TTS/README.md b/spaces/Pushpak77/fastspeech2-TTS/README.md deleted file mode 100644 index 402652f2668ed9803cb9d7a0cdac5c0cca6a09f2..0000000000000000000000000000000000000000 --- a/spaces/Pushpak77/fastspeech2-TTS/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Fastspeech2 TTS -emoji: 🚀 -colorFrom: pink -colorTo: gray -sdk: gradio -sdk_version: 2.8.13 -app_file: app.py -pinned: false -duplicated_from: StevenLimcorn/fastspeech2-TTS ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/RMXK/RVC_HFF/infer/lib/csvutil.py b/spaces/RMXK/RVC_HFF/infer/lib/csvutil.py deleted file mode 100644 index 79f432b6933f181d9194c50581656f2fd6e66c0c..0000000000000000000000000000000000000000 --- a/spaces/RMXK/RVC_HFF/infer/lib/csvutil.py +++ /dev/null @@ -1,41 +0,0 @@ - -import numpy as np - -# import praatio -# import praatio.praat_scripts -import os -import sys - -import random - -import csv - -# praatEXE = join('.',os.path.abspath(os.getcwd()) + r"\Praat.exe") - - -def CSVutil(file, rw, type, *args): - if type == "formanting": - if rw == "r": - with open(file) as fileCSVread: - csv_reader = list(csv.reader(fileCSVread)) - return ( - (csv_reader[0][0], csv_reader[0][1], csv_reader[0][2]) - if csv_reader is not None - else (lambda: exec('raise ValueError("No data")'))() - ) - else: - if args: - doformnt = args[0] - else: - doformnt = False - qfr = args[1] if len(args) > 1 else 1.0 - tmb = args[2] if len(args) > 2 else 1.0 - with open(file, rw, newline="") as fileCSVwrite: - csv_writer = csv.writer(fileCSVwrite, delimiter=",") - csv_writer.writerow([doformnt, qfr, tmb]) - elif type == "stop": - stop = args[0] if args else False - with open(file, rw, newline="") as fileCSVwrite: - csv_writer = csv.writer(fileCSVwrite, delimiter=",") - csv_writer.writerow([stop]) - diff --git a/spaces/RMeli/gnina-torch/html/protein.html b/spaces/RMeli/gnina-torch/html/protein.html deleted file mode 100644 index d8700cb935f953cbba3c328086e9dee4e6aaa916..0000000000000000000000000000000000000000 --- a/spaces/RMeli/gnina-torch/html/protein.html +++ /dev/null @@ -1,36 +0,0 @@ - - - - - - - - - - - - - - \ No newline at end of file diff --git a/spaces/RamAnanth1/videocrafter/gradio_t2v.py b/spaces/RamAnanth1/videocrafter/gradio_t2v.py deleted file mode 100644 index 7702611f495ad313e33a0a745e7d9a63c67fb3a7..0000000000000000000000000000000000000000 --- a/spaces/RamAnanth1/videocrafter/gradio_t2v.py +++ /dev/null @@ -1,53 +0,0 @@ -import gradio as gr - -def create_demo(get_video): - block = gr.Blocks(css='style.css').queue() - with block: - with gr.Group(): - with gr.Box(): - with gr.Row(elem_id='prompt-container').style(equal_height=True): - prompt = gr.Text( - label='Prompt', - show_label=False, - max_lines=1, - placeholder='Enter your prompt', - elem_id='prompt-text-input').style(container=False) - run_button = gr.Button('Generate video').style( - full_width=False) - result = gr.Video(label='Result', show_label=False, elem_id='gallery') - with gr.Accordion('Advanced options', open=False): - seed = gr.Slider( - label='Seed', - minimum=-1, - maximum=1000000, - step=1, - value=-1, - info='If set to -1, a different seed will be used each time.') - sampling_steps = gr.Slider(label='Number of sampling steps', - minimum=10, - maximum=100, - step=5, - value=50) - - inputs = [ - prompt, - seed, - sampling_steps - # num_frames, - # num_inference_steps, - ] - gr.Examples(examples=[ - ["Astronaut riding a horse", 731, 50], - ["Cars running on the highway at night", 1000, 50], - ["close up of a clown fish swimming. 4K",865, 50] - - ], - inputs=inputs, - outputs=result, - fn=get_video, - cache_examples=True) - - prompt.submit(fn=get_video, inputs=inputs, outputs=result) - run_button.click(fn=get_video, inputs=inputs, outputs=result) - - return block \ No newline at end of file diff --git a/spaces/RamAnanth1/videocrafter/gradio_videocontrol.py b/spaces/RamAnanth1/videocrafter/gradio_videocontrol.py deleted file mode 100644 index 289741d360effd101bea8b57940279d31bc90b7f..0000000000000000000000000000000000000000 --- a/spaces/RamAnanth1/videocrafter/gradio_videocontrol.py +++ /dev/null @@ -1,49 +0,0 @@ -import gradio as gr - -def create_demo(get_video_control): - block = gr.Blocks(css='style.css').queue() - with block: - with gr.Group(): - - prompt = gr.Text( - label='Prompt', - show_label=False, - max_lines=1, - placeholder='Enter your prompt', - elem_id='prompt-text-input').style(container=False) - input_video = gr.Video(label='Input Video') - run_button = gr.Button('Generate video') - result = gr.Video(label='Result', show_label=False, elem_id='gallery') - with gr.Accordion('Advanced options', open=False): - seed = gr.Slider( - label='Seed', - minimum=-1, - maximum=1000000, - step=1, - value=-1, - info='If set to -1, a different seed will be used each time.') - sampling_steps = gr.Slider(label='Number of sampling steps', - minimum=10, - maximum=100, - step=5, - value=50) - - inputs = [ - prompt, - input_video, - seed, - sampling_steps - # num_frames, - # num_inference_steps, - ] - gr.Examples(examples=[ - ], - inputs=inputs, - outputs=result, - fn=get_video_control, - cache_examples=True) - - prompt.submit(fn=get_video_control, inputs=inputs, outputs=result) - run_button.click(fn=get_video_control, inputs=inputs, outputs=result) - - return block \ No newline at end of file diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_cell_widths.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_cell_widths.py deleted file mode 100644 index 36286df379e28ea997bea3ee1fd62cadebebbba9..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_cell_widths.py +++ /dev/null @@ -1,451 +0,0 @@ -# Auto generated by make_terminal_widths.py - -CELL_WIDTHS = [ - (0, 0, 0), - (1, 31, -1), - (127, 159, -1), - (768, 879, 0), - (1155, 1161, 0), - (1425, 1469, 0), - (1471, 1471, 0), - (1473, 1474, 0), - (1476, 1477, 0), - (1479, 1479, 0), - (1552, 1562, 0), - (1611, 1631, 0), - (1648, 1648, 0), - (1750, 1756, 0), - (1759, 1764, 0), - (1767, 1768, 0), - (1770, 1773, 0), - (1809, 1809, 0), - (1840, 1866, 0), - (1958, 1968, 0), - (2027, 2035, 0), - (2045, 2045, 0), - (2070, 2073, 0), - (2075, 2083, 0), - (2085, 2087, 0), - (2089, 2093, 0), - (2137, 2139, 0), - (2259, 2273, 0), - (2275, 2306, 0), - (2362, 2362, 0), - (2364, 2364, 0), - (2369, 2376, 0), - (2381, 2381, 0), - (2385, 2391, 0), - (2402, 2403, 0), - (2433, 2433, 0), - (2492, 2492, 0), - (2497, 2500, 0), - (2509, 2509, 0), - (2530, 2531, 0), - (2558, 2558, 0), - (2561, 2562, 0), - (2620, 2620, 0), - (2625, 2626, 0), - (2631, 2632, 0), - (2635, 2637, 0), - (2641, 2641, 0), - (2672, 2673, 0), - (2677, 2677, 0), - (2689, 2690, 0), - (2748, 2748, 0), - (2753, 2757, 0), - (2759, 2760, 0), - (2765, 2765, 0), - (2786, 2787, 0), - (2810, 2815, 0), - (2817, 2817, 0), - (2876, 2876, 0), - (2879, 2879, 0), - (2881, 2884, 0), - (2893, 2893, 0), - (2901, 2902, 0), - (2914, 2915, 0), - (2946, 2946, 0), - (3008, 3008, 0), - (3021, 3021, 0), - (3072, 3072, 0), - (3076, 3076, 0), - (3134, 3136, 0), - (3142, 3144, 0), - (3146, 3149, 0), - (3157, 3158, 0), - (3170, 3171, 0), - (3201, 3201, 0), - (3260, 3260, 0), - (3263, 3263, 0), - (3270, 3270, 0), - (3276, 3277, 0), - (3298, 3299, 0), - (3328, 3329, 0), - (3387, 3388, 0), - (3393, 3396, 0), - (3405, 3405, 0), - (3426, 3427, 0), - (3457, 3457, 0), - (3530, 3530, 0), - (3538, 3540, 0), - (3542, 3542, 0), - (3633, 3633, 0), - (3636, 3642, 0), - (3655, 3662, 0), - (3761, 3761, 0), - (3764, 3772, 0), - (3784, 3789, 0), - (3864, 3865, 0), - (3893, 3893, 0), - (3895, 3895, 0), - (3897, 3897, 0), - (3953, 3966, 0), - (3968, 3972, 0), - (3974, 3975, 0), - (3981, 3991, 0), - (3993, 4028, 0), - (4038, 4038, 0), - (4141, 4144, 0), - (4146, 4151, 0), - (4153, 4154, 0), - (4157, 4158, 0), - (4184, 4185, 0), - (4190, 4192, 0), - (4209, 4212, 0), - (4226, 4226, 0), - (4229, 4230, 0), - (4237, 4237, 0), - (4253, 4253, 0), - (4352, 4447, 2), - (4957, 4959, 0), - (5906, 5908, 0), - (5938, 5940, 0), - (5970, 5971, 0), - (6002, 6003, 0), - (6068, 6069, 0), - (6071, 6077, 0), - (6086, 6086, 0), - (6089, 6099, 0), - (6109, 6109, 0), - (6155, 6157, 0), - 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-from distutils.command.build_ext import build_ext as _du_build_ext -from distutils.ccompiler import new_compiler -from distutils.sysconfig import customize_compiler, get_config_var -from distutils import log - -from setuptools.errors import BaseError -from setuptools.extension import Extension, Library - -try: - # Attempt to use Cython for building extensions, if available - from Cython.Distutils.build_ext import build_ext as _build_ext - # Additionally, assert that the compiler module will load - # also. Ref #1229. - __import__('Cython.Compiler.Main') -except ImportError: - _build_ext = _du_build_ext - -# make sure _config_vars is initialized -get_config_var("LDSHARED") -from distutils.sysconfig import _config_vars as _CONFIG_VARS # noqa - - -def _customize_compiler_for_shlib(compiler): - if sys.platform == "darwin": - # building .dylib requires additional compiler flags on OSX; here we - # temporarily substitute the pyconfig.h variables so that distutils' - # 'customize_compiler' uses them before we build the shared libraries. - tmp = _CONFIG_VARS.copy() - try: - # XXX Help! I don't have any idea whether these are right... - _CONFIG_VARS['LDSHARED'] = ( - "gcc -Wl,-x -dynamiclib -undefined dynamic_lookup") - _CONFIG_VARS['CCSHARED'] = " -dynamiclib" - _CONFIG_VARS['SO'] = ".dylib" - customize_compiler(compiler) - finally: - _CONFIG_VARS.clear() - _CONFIG_VARS.update(tmp) - else: - customize_compiler(compiler) - - -have_rtld = False -use_stubs = False -libtype = 'shared' - -if sys.platform == "darwin": - use_stubs = True -elif os.name != 'nt': - try: - import dl - use_stubs = have_rtld = hasattr(dl, 'RTLD_NOW') - except ImportError: - pass - - -def if_dl(s): - return s if have_rtld else '' - - -def get_abi3_suffix(): - """Return the file extension for an abi3-compliant Extension()""" - for suffix in EXTENSION_SUFFIXES: - if '.abi3' in suffix: # Unix - return suffix - elif suffix == '.pyd': # Windows - return suffix - - -class build_ext(_build_ext): - editable_mode: bool = False - inplace: bool = False - - def run(self): - """Build extensions in build directory, then copy if --inplace""" - old_inplace, self.inplace = self.inplace, 0 - _build_ext.run(self) - self.inplace = old_inplace - if old_inplace: - self.copy_extensions_to_source() - - def _get_inplace_equivalent(self, build_py, ext: Extension) -> Tuple[str, str]: - fullname = self.get_ext_fullname(ext.name) - filename = self.get_ext_filename(fullname) - modpath = fullname.split('.') - package = '.'.join(modpath[:-1]) - package_dir = build_py.get_package_dir(package) - inplace_file = os.path.join(package_dir, os.path.basename(filename)) - regular_file = os.path.join(self.build_lib, filename) - return (inplace_file, regular_file) - - def copy_extensions_to_source(self): - build_py = self.get_finalized_command('build_py') - for ext in self.extensions: - inplace_file, regular_file = self._get_inplace_equivalent(build_py, ext) - - # Always copy, even if source is older than destination, to ensure - # that the right extensions for the current Python/platform are - # used. - if os.path.exists(regular_file) or not ext.optional: - self.copy_file(regular_file, inplace_file, level=self.verbose) - - if ext._needs_stub: - inplace_stub = self._get_equivalent_stub(ext, inplace_file) - self._write_stub_file(inplace_stub, ext, compile=True) - # Always compile stub and remove the original (leave the cache behind) - # (this behaviour was observed in previous iterations of the code) - - def _get_equivalent_stub(self, ext: Extension, output_file: str) -> str: - dir_ = os.path.dirname(output_file) - _, _, name = ext.name.rpartition(".") - return f"{os.path.join(dir_, name)}.py" - - def _get_output_mapping(self) -> Iterator[Tuple[str, str]]: - if not self.inplace: - return - - build_py = self.get_finalized_command('build_py') - opt = self.get_finalized_command('install_lib').optimize or "" - - for ext in self.extensions: - inplace_file, regular_file = self._get_inplace_equivalent(build_py, ext) - yield (regular_file, inplace_file) - - if ext._needs_stub: - # This version of `build_ext` always builds artifacts in another dir, - # when "inplace=True" is given it just copies them back. - # This is done in the `copy_extensions_to_source` function, which - # always compile stub files via `_compile_and_remove_stub`. - # At the end of the process, a `.pyc` stub file is created without the - # corresponding `.py`. - - inplace_stub = self._get_equivalent_stub(ext, inplace_file) - regular_stub = self._get_equivalent_stub(ext, regular_file) - inplace_cache = _compiled_file_name(inplace_stub, optimization=opt) - output_cache = _compiled_file_name(regular_stub, optimization=opt) - yield (output_cache, inplace_cache) - - def get_ext_filename(self, fullname): - so_ext = os.getenv('SETUPTOOLS_EXT_SUFFIX') - if so_ext: - filename = os.path.join(*fullname.split('.')) + so_ext - else: - filename = _build_ext.get_ext_filename(self, fullname) - so_ext = get_config_var('EXT_SUFFIX') - - if fullname in self.ext_map: - ext = self.ext_map[fullname] - use_abi3 = getattr(ext, 'py_limited_api') and get_abi3_suffix() - if use_abi3: - filename = filename[:-len(so_ext)] - so_ext = get_abi3_suffix() - filename = filename + so_ext - if isinstance(ext, Library): - fn, ext = os.path.splitext(filename) - return self.shlib_compiler.library_filename(fn, libtype) - elif use_stubs and ext._links_to_dynamic: - d, fn = os.path.split(filename) - return os.path.join(d, 'dl-' + fn) - return filename - - def initialize_options(self): - _build_ext.initialize_options(self) - self.shlib_compiler = None - self.shlibs = [] - self.ext_map = {} - self.editable_mode = False - - def finalize_options(self): - _build_ext.finalize_options(self) - self.extensions = self.extensions or [] - self.check_extensions_list(self.extensions) - self.shlibs = [ext for ext in self.extensions - if isinstance(ext, Library)] - if self.shlibs: - self.setup_shlib_compiler() - for ext in self.extensions: - ext._full_name = self.get_ext_fullname(ext.name) - for ext in self.extensions: - fullname = ext._full_name - self.ext_map[fullname] = ext - - # distutils 3.1 will also ask for module names - # XXX what to do with conflicts? - self.ext_map[fullname.split('.')[-1]] = ext - - ltd = self.shlibs and self.links_to_dynamic(ext) or False - ns = ltd and use_stubs and not isinstance(ext, Library) - ext._links_to_dynamic = ltd - ext._needs_stub = ns - filename = ext._file_name = self.get_ext_filename(fullname) - libdir = os.path.dirname(os.path.join(self.build_lib, filename)) - if ltd and libdir not in ext.library_dirs: - ext.library_dirs.append(libdir) - if ltd and use_stubs and os.curdir not in ext.runtime_library_dirs: - ext.runtime_library_dirs.append(os.curdir) - - if self.editable_mode: - self.inplace = True - - def setup_shlib_compiler(self): - compiler = self.shlib_compiler = new_compiler( - compiler=self.compiler, dry_run=self.dry_run, force=self.force - ) - _customize_compiler_for_shlib(compiler) - - if self.include_dirs is not None: - compiler.set_include_dirs(self.include_dirs) - if self.define is not None: - # 'define' option is a list of (name,value) tuples - for (name, value) in self.define: - compiler.define_macro(name, value) - if self.undef is not None: - for macro in self.undef: - compiler.undefine_macro(macro) - if self.libraries is not None: - compiler.set_libraries(self.libraries) - if self.library_dirs is not None: - compiler.set_library_dirs(self.library_dirs) - if self.rpath is not None: - compiler.set_runtime_library_dirs(self.rpath) - if self.link_objects is not None: - compiler.set_link_objects(self.link_objects) - - # hack so distutils' build_extension() builds a library instead - compiler.link_shared_object = link_shared_object.__get__(compiler) - - def get_export_symbols(self, ext): - if isinstance(ext, Library): - return ext.export_symbols - return _build_ext.get_export_symbols(self, ext) - - def build_extension(self, ext): - ext._convert_pyx_sources_to_lang() - _compiler = self.compiler - try: - if isinstance(ext, Library): - self.compiler = self.shlib_compiler - _build_ext.build_extension(self, ext) - if ext._needs_stub: - build_lib = self.get_finalized_command('build_py').build_lib - self.write_stub(build_lib, ext) - finally: - self.compiler = _compiler - - def links_to_dynamic(self, ext): - """Return true if 'ext' links to a dynamic lib in the same package""" - # XXX this should check to ensure the lib is actually being built - # XXX as dynamic, and not just using a locally-found version or a - # XXX static-compiled version - libnames = dict.fromkeys([lib._full_name for lib in self.shlibs]) - pkg = '.'.join(ext._full_name.split('.')[:-1] + ['']) - return any(pkg + libname in libnames for libname in ext.libraries) - - def get_outputs(self) -> List[str]: - if self.inplace: - return list(self.get_output_mapping().keys()) - return sorted(_build_ext.get_outputs(self) + self.__get_stubs_outputs()) - - def get_output_mapping(self) -> Dict[str, str]: - """See :class:`setuptools.commands.build.SubCommand`""" - mapping = self._get_output_mapping() - return dict(sorted(mapping, key=lambda x: x[0])) - - def __get_stubs_outputs(self): - # assemble the base name for each extension that needs a stub - ns_ext_bases = ( - os.path.join(self.build_lib, *ext._full_name.split('.')) - for ext in self.extensions - if ext._needs_stub - ) - # pair each base with the extension - pairs = itertools.product(ns_ext_bases, self.__get_output_extensions()) - return list(base + fnext for base, fnext in pairs) - - def __get_output_extensions(self): - yield '.py' - yield '.pyc' - if self.get_finalized_command('build_py').optimize: - yield '.pyo' - - def write_stub(self, output_dir, ext, compile=False): - stub_file = os.path.join(output_dir, *ext._full_name.split('.')) + '.py' - self._write_stub_file(stub_file, ext, compile) - - def _write_stub_file(self, stub_file: str, ext: Extension, compile=False): - log.info("writing stub loader for %s to %s", ext._full_name, stub_file) - if compile and os.path.exists(stub_file): - raise BaseError(stub_file + " already exists! Please delete.") - if not self.dry_run: - f = open(stub_file, 'w') - f.write( - '\n'.join([ - "def __bootstrap__():", - " global __bootstrap__, __file__, __loader__", - " import sys, os, pkg_resources, importlib.util" + - if_dl(", dl"), - " __file__ = pkg_resources.resource_filename" - "(__name__,%r)" - % os.path.basename(ext._file_name), - " del __bootstrap__", - " if '__loader__' in globals():", - " del __loader__", - if_dl(" old_flags = sys.getdlopenflags()"), - " old_dir = os.getcwd()", - " try:", - " os.chdir(os.path.dirname(__file__))", - if_dl(" sys.setdlopenflags(dl.RTLD_NOW)"), - " spec = importlib.util.spec_from_file_location(", - " __name__, __file__)", - " mod = importlib.util.module_from_spec(spec)", - " spec.loader.exec_module(mod)", - " finally:", - if_dl(" sys.setdlopenflags(old_flags)"), - " os.chdir(old_dir)", - "__bootstrap__()", - "" # terminal \n - ]) - ) - f.close() - if compile: - self._compile_and_remove_stub(stub_file) - - def _compile_and_remove_stub(self, stub_file: str): - from distutils.util import byte_compile - - byte_compile([stub_file], optimize=0, - force=True, dry_run=self.dry_run) - optimize = self.get_finalized_command('install_lib').optimize - if optimize > 0: - byte_compile([stub_file], optimize=optimize, - force=True, dry_run=self.dry_run) - if os.path.exists(stub_file) and not self.dry_run: - os.unlink(stub_file) - - -if use_stubs or os.name == 'nt': - # Build shared libraries - # - def link_shared_object( - self, objects, output_libname, output_dir=None, libraries=None, - library_dirs=None, runtime_library_dirs=None, export_symbols=None, - debug=0, extra_preargs=None, extra_postargs=None, build_temp=None, - target_lang=None): - self.link( - self.SHARED_LIBRARY, objects, output_libname, - output_dir, libraries, library_dirs, runtime_library_dirs, - export_symbols, debug, extra_preargs, extra_postargs, - build_temp, target_lang - ) -else: - # Build static libraries everywhere else - libtype = 'static' - - def link_shared_object( - self, objects, output_libname, output_dir=None, libraries=None, - library_dirs=None, runtime_library_dirs=None, export_symbols=None, - debug=0, extra_preargs=None, extra_postargs=None, build_temp=None, - target_lang=None): - # XXX we need to either disallow these attrs on Library instances, - # or warn/abort here if set, or something... - # libraries=None, library_dirs=None, runtime_library_dirs=None, - # export_symbols=None, extra_preargs=None, extra_postargs=None, - # build_temp=None - - assert output_dir is None # distutils build_ext doesn't pass this - output_dir, filename = os.path.split(output_libname) - basename, ext = os.path.splitext(filename) - if self.library_filename("x").startswith('lib'): - # strip 'lib' prefix; this is kludgy if some platform uses - # a different prefix - basename = basename[3:] - - self.create_static_lib( - objects, basename, output_dir, debug, target_lang - ) diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/depends.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/depends.py deleted file mode 100644 index adffd12db8c8e0477ee6532cd3b84f2e0cde9632..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/depends.py +++ /dev/null @@ -1,176 +0,0 @@ -import sys -import marshal -import contextlib -import dis - -from setuptools.extern.packaging import version - -from ._imp import find_module, PY_COMPILED, PY_FROZEN, PY_SOURCE -from . import _imp - - -__all__ = [ - 'Require', 'find_module', 'get_module_constant', 'extract_constant' -] - - -class Require: - """A prerequisite to building or installing a distribution""" - - def __init__( - self, name, requested_version, module, homepage='', - attribute=None, format=None): - - if format is None and requested_version is not None: - format = version.Version - - if format is not None: - requested_version = format(requested_version) - if attribute is None: - attribute = '__version__' - - self.__dict__.update(locals()) - del self.self - - def full_name(self): - """Return full package/distribution name, w/version""" - if self.requested_version is not None: - return '%s-%s' % (self.name, self.requested_version) - return self.name - - def version_ok(self, version): - """Is 'version' sufficiently up-to-date?""" - return self.attribute is None or self.format is None or \ - str(version) != "unknown" and self.format(version) >= self.requested_version - - def get_version(self, paths=None, default="unknown"): - """Get version number of installed module, 'None', or 'default' - - Search 'paths' for module. If not found, return 'None'. If found, - return the extracted version attribute, or 'default' if no version - attribute was specified, or the value cannot be determined without - importing the module. The version is formatted according to the - requirement's version format (if any), unless it is 'None' or the - supplied 'default'. - """ - - if self.attribute is None: - try: - f, p, i = find_module(self.module, paths) - if f: - f.close() - return default - except ImportError: - return None - - v = get_module_constant(self.module, self.attribute, default, paths) - - if v is not None and v is not default and self.format is not None: - return self.format(v) - - return v - - def is_present(self, paths=None): - """Return true if dependency is present on 'paths'""" - return self.get_version(paths) is not None - - def is_current(self, paths=None): - """Return true if dependency is present and up-to-date on 'paths'""" - version = self.get_version(paths) - if version is None: - return False - return self.version_ok(str(version)) - - -def maybe_close(f): - @contextlib.contextmanager - def empty(): - yield - return - if not f: - return empty() - - return contextlib.closing(f) - - -def get_module_constant(module, symbol, default=-1, paths=None): - """Find 'module' by searching 'paths', and extract 'symbol' - - Return 'None' if 'module' does not exist on 'paths', or it does not define - 'symbol'. If the module defines 'symbol' as a constant, return the - constant. Otherwise, return 'default'.""" - - try: - f, path, (suffix, mode, kind) = info = find_module(module, paths) - except ImportError: - # Module doesn't exist - return None - - with maybe_close(f): - if kind == PY_COMPILED: - f.read(8) # skip magic & date - code = marshal.load(f) - elif kind == PY_FROZEN: - code = _imp.get_frozen_object(module, paths) - elif kind == PY_SOURCE: - code = compile(f.read(), path, 'exec') - else: - # Not something we can parse; we'll have to import it. :( - imported = _imp.get_module(module, paths, info) - return getattr(imported, symbol, None) - - return extract_constant(code, symbol, default) - - -def extract_constant(code, symbol, default=-1): - """Extract the constant value of 'symbol' from 'code' - - If the name 'symbol' is bound to a constant value by the Python code - object 'code', return that value. If 'symbol' is bound to an expression, - return 'default'. Otherwise, return 'None'. - - Return value is based on the first assignment to 'symbol'. 'symbol' must - be a global, or at least a non-"fast" local in the code block. That is, - only 'STORE_NAME' and 'STORE_GLOBAL' opcodes are checked, and 'symbol' - must be present in 'code.co_names'. - """ - if symbol not in code.co_names: - # name's not there, can't possibly be an assignment - return None - - name_idx = list(code.co_names).index(symbol) - - STORE_NAME = 90 - STORE_GLOBAL = 97 - LOAD_CONST = 100 - - const = default - - for byte_code in dis.Bytecode(code): - op = byte_code.opcode - arg = byte_code.arg - - if op == LOAD_CONST: - const = code.co_consts[arg] - elif arg == name_idx and (op == STORE_NAME or op == STORE_GLOBAL): - return const - else: - const = default - - -def _update_globals(): - """ - Patch the globals to remove the objects not available on some platforms. - - XXX it'd be better to test assertions about bytecode instead. - """ - - if not sys.platform.startswith('java') and sys.platform != 'cli': - return - incompatible = 'extract_constant', 'get_module_constant' - for name in incompatible: - del globals()[name] - __all__.remove(name) - - -_update_globals() diff --git a/spaces/Realcat/image-matching-webui/hloc/matchers/roma.py b/spaces/Realcat/image-matching-webui/hloc/matchers/roma.py deleted file mode 100644 index 1fe913270978346a124f17b57ca3d22c53d43d72..0000000000000000000000000000000000000000 --- a/spaces/Realcat/image-matching-webui/hloc/matchers/roma.py +++ /dev/null @@ -1,91 +0,0 @@ -import sys -from pathlib import Path -import subprocess -import torch -from PIL import Image -from ..utils.base_model import BaseModel -from .. import logger - -roma_path = Path(__file__).parent / "../../third_party/Roma" -sys.path.append(str(roma_path)) - -from roma.models.model_zoo.roma_models import roma_model - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - -class Roma(BaseModel): - default_conf = { - "name": "two_view_pipeline", - "model_name": "roma_outdoor.pth", - "model_utils_name": "dinov2_vitl14_pretrain.pth", - "max_keypoints": 3000, - } - required_inputs = [ - "image0", - "image1", - ] - weight_urls = { - "roma": { - "roma_outdoor.pth": "https://github.com/Parskatt/storage/releases/download/roma/roma_outdoor.pth", - "roma_indoor.pth": "https://github.com/Parskatt/storage/releases/download/roma/roma_indoor.pth", - }, - "dinov2_vitl14_pretrain.pth": "https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_pretrain.pth", - } - - # Initialize the line matcher - def _init(self, conf): - model_path = roma_path / "pretrained" / conf["model_name"] - dinov2_weights = roma_path / "pretrained" / conf["model_utils_name"] - - # Download the model. - if not model_path.exists(): - model_path.parent.mkdir(exist_ok=True) - link = self.weight_urls["roma"][conf["model_name"]] - cmd = ["wget", link, "-O", str(model_path)] - logger.info(f"Downloading the Roma model with `{cmd}`.") - subprocess.run(cmd, check=True) - - if not dinov2_weights.exists(): - dinov2_weights.parent.mkdir(exist_ok=True) - link = self.weight_urls[conf["model_utils_name"]] - cmd = ["wget", link, "-O", str(dinov2_weights)] - logger.info(f"Downloading the dinov2 model with `{cmd}`.") - subprocess.run(cmd, check=True) - - logger.info(f"Loading Roma model...") - # load the model - weights = torch.load(model_path, map_location="cpu") - dinov2_weights = torch.load(dinov2_weights, map_location="cpu") - - self.net = roma_model( - resolution=(14 * 8 * 6, 14 * 8 * 6), - upsample_preds=False, - weights=weights, - dinov2_weights=dinov2_weights, - device=device, - ) - logger.info(f"Load Roma model done.") - - def _forward(self, data): - img0 = data["image0"].cpu().numpy().squeeze() * 255 - img1 = data["image1"].cpu().numpy().squeeze() * 255 - img0 = img0.transpose(1, 2, 0) - img1 = img1.transpose(1, 2, 0) - img0 = Image.fromarray(img0.astype("uint8")) - img1 = Image.fromarray(img1.astype("uint8")) - W_A, H_A = img0.size - W_B, H_B = img1.size - - # Match - warp, certainty = self.net.match(img0, img1, device=device) - # Sample matches for estimation - matches, certainty = self.net.sample( - warp, certainty, num=self.conf["max_keypoints"] - ) - kpts1, kpts2 = self.net.to_pixel_coordinates( - matches, H_A, W_A, H_B, W_B - ) - pred = {} - pred["keypoints0"], pred["keypoints1"] = kpts1, kpts2 - pred["mconf"] = certainty - return pred diff --git a/spaces/Realcat/image-matching-webui/third_party/TopicFM/src/lightning_trainer/data.py b/spaces/Realcat/image-matching-webui/third_party/TopicFM/src/lightning_trainer/data.py deleted file mode 100644 index 95f6a5eeecf39a993b86674242eacb7b42f8a566..0000000000000000000000000000000000000000 --- a/spaces/Realcat/image-matching-webui/third_party/TopicFM/src/lightning_trainer/data.py +++ /dev/null @@ -1,399 +0,0 @@ -import os -import math -from collections import abc -from loguru import logger -from torch.utils.data.dataset import Dataset -from tqdm import tqdm -from os import path as osp -from pathlib import Path -from joblib import Parallel, delayed - -import pytorch_lightning as pl -from torch import distributed as dist -from torch.utils.data import ( - Dataset, - DataLoader, - ConcatDataset, - DistributedSampler, - RandomSampler, - dataloader, -) - -from src.utils.augment import build_augmentor -from src.utils.dataloader import get_local_split -from src.utils.misc import tqdm_joblib -from src.utils import comm -from src.datasets.megadepth import MegaDepthDataset -from src.datasets.scannet import ScanNetDataset -from src.datasets.sampler import RandomConcatSampler - - -class MultiSceneDataModule(pl.LightningDataModule): - """ - For distributed training, each training process is assgined - only a part of the training scenes to reduce memory overhead. - """ - - def __init__(self, args, config): - super().__init__() - - # 1. data config - # Train and Val should from the same data source - self.trainval_data_source = config.DATASET.TRAINVAL_DATA_SOURCE - self.test_data_source = config.DATASET.TEST_DATA_SOURCE - # training and validating - self.train_data_root = config.DATASET.TRAIN_DATA_ROOT - self.train_pose_root = config.DATASET.TRAIN_POSE_ROOT # (optional) - self.train_npz_root = config.DATASET.TRAIN_NPZ_ROOT - self.train_list_path = config.DATASET.TRAIN_LIST_PATH - self.train_intrinsic_path = config.DATASET.TRAIN_INTRINSIC_PATH - self.val_data_root = config.DATASET.VAL_DATA_ROOT - self.val_pose_root = config.DATASET.VAL_POSE_ROOT # (optional) - self.val_npz_root = config.DATASET.VAL_NPZ_ROOT - self.val_list_path = config.DATASET.VAL_LIST_PATH - self.val_intrinsic_path = config.DATASET.VAL_INTRINSIC_PATH - # testing - self.test_data_root = config.DATASET.TEST_DATA_ROOT - self.test_pose_root = config.DATASET.TEST_POSE_ROOT # (optional) - self.test_npz_root = config.DATASET.TEST_NPZ_ROOT - self.test_list_path = config.DATASET.TEST_LIST_PATH - self.test_intrinsic_path = config.DATASET.TEST_INTRINSIC_PATH - - # 2. dataset config - # general options - self.min_overlap_score_test = ( - config.DATASET.MIN_OVERLAP_SCORE_TEST - ) # 0.4, omit data with overlap_score < min_overlap_score - self.min_overlap_score_train = config.DATASET.MIN_OVERLAP_SCORE_TRAIN - self.augment_fn = build_augmentor( - config.DATASET.AUGMENTATION_TYPE - ) # None, options: [None, 'dark', 'mobile'] - - # MegaDepth options - self.mgdpt_img_resize = config.DATASET.MGDPT_IMG_RESIZE # 840 - self.mgdpt_img_pad = config.DATASET.MGDPT_IMG_PAD # True - self.mgdpt_depth_pad = config.DATASET.MGDPT_DEPTH_PAD # True - self.mgdpt_df = config.DATASET.MGDPT_DF # 8 - self.coarse_scale = 1 / config.MODEL.RESOLUTION[0] # 0.125. for training loftr. - - # 3.loader parameters - self.train_loader_params = { - "batch_size": args.batch_size, - "num_workers": args.num_workers, - "pin_memory": getattr(args, "pin_memory", True), - } - self.val_loader_params = { - "batch_size": 1, - "shuffle": False, - "num_workers": args.num_workers, - "pin_memory": getattr(args, "pin_memory", True), - } - self.test_loader_params = { - "batch_size": 1, - "shuffle": False, - "num_workers": args.num_workers, - "pin_memory": True, - } - - # 4. sampler - self.data_sampler = config.TRAINER.DATA_SAMPLER - self.n_samples_per_subset = config.TRAINER.N_SAMPLES_PER_SUBSET - self.subset_replacement = config.TRAINER.SB_SUBSET_SAMPLE_REPLACEMENT - self.shuffle = config.TRAINER.SB_SUBSET_SHUFFLE - self.repeat = config.TRAINER.SB_REPEAT - - # (optional) RandomSampler for debugging - - # misc configurations - self.parallel_load_data = getattr(args, "parallel_load_data", False) - self.seed = config.TRAINER.SEED # 66 - - def setup(self, stage=None): - """ - Setup train / val / test dataset. This method will be called by PL automatically. - Args: - stage (str): 'fit' in training phase, and 'test' in testing phase. - """ - - assert stage in ["fit", "test"], "stage must be either fit or test" - - try: - self.world_size = dist.get_world_size() - self.rank = dist.get_rank() - logger.info(f"[rank:{self.rank}] world_size: {self.world_size}") - except AssertionError as ae: - self.world_size = 1 - self.rank = 0 - logger.warning(str(ae) + " (set wolrd_size=1 and rank=0)") - - if stage == "fit": - self.train_dataset = self._setup_dataset( - self.train_data_root, - self.train_npz_root, - self.train_list_path, - self.train_intrinsic_path, - mode="train", - min_overlap_score=self.min_overlap_score_train, - pose_dir=self.train_pose_root, - ) - # setup multiple (optional) validation subsets - if isinstance(self.val_list_path, (list, tuple)): - self.val_dataset = [] - if not isinstance(self.val_npz_root, (list, tuple)): - self.val_npz_root = [ - self.val_npz_root for _ in range(len(self.val_list_path)) - ] - for npz_list, npz_root in zip(self.val_list_path, self.val_npz_root): - self.val_dataset.append( - self._setup_dataset( - self.val_data_root, - npz_root, - npz_list, - self.val_intrinsic_path, - mode="val", - min_overlap_score=self.min_overlap_score_test, - pose_dir=self.val_pose_root, - ) - ) - else: - self.val_dataset = self._setup_dataset( - self.val_data_root, - self.val_npz_root, - self.val_list_path, - self.val_intrinsic_path, - mode="val", - min_overlap_score=self.min_overlap_score_test, - pose_dir=self.val_pose_root, - ) - logger.info(f"[rank:{self.rank}] Train & Val Dataset loaded!") - else: # stage == 'test - self.test_dataset = self._setup_dataset( - self.test_data_root, - self.test_npz_root, - self.test_list_path, - self.test_intrinsic_path, - mode="test", - min_overlap_score=self.min_overlap_score_test, - pose_dir=self.test_pose_root, - ) - logger.info(f"[rank:{self.rank}]: Test Dataset loaded!") - - def _setup_dataset( - self, - data_root, - split_npz_root, - scene_list_path, - intri_path, - mode="train", - min_overlap_score=0.0, - pose_dir=None, - ): - """Setup train / val / test set""" - with open(scene_list_path, "r") as f: - npz_names = [name.split()[0] for name in f.readlines()] - - if mode == "train": - local_npz_names = get_local_split( - npz_names, self.world_size, self.rank, self.seed - ) - else: - local_npz_names = npz_names - logger.info(f"[rank {self.rank}]: {len(local_npz_names)} scene(s) assigned.") - - dataset_builder = ( - self._build_concat_dataset_parallel - if self.parallel_load_data - else self._build_concat_dataset - ) - return dataset_builder( - data_root, - local_npz_names, - split_npz_root, - intri_path, - mode=mode, - min_overlap_score=min_overlap_score, - pose_dir=pose_dir, - ) - - def _build_concat_dataset( - self, - data_root, - npz_names, - npz_dir, - intrinsic_path, - mode, - min_overlap_score=0.0, - pose_dir=None, - ): - datasets = [] - augment_fn = self.augment_fn if mode == "train" else None - data_source = ( - self.trainval_data_source - if mode in ["train", "val"] - else self.test_data_source - ) - if str(data_source).lower() == "megadepth": - npz_names = [f"{n}.npz" for n in npz_names] - for npz_name in tqdm( - npz_names, - desc=f"[rank:{self.rank}] loading {mode} datasets", - disable=int(self.rank) != 0, - ): - # `ScanNetDataset`/`MegaDepthDataset` load all data from npz_path when initialized, which might take time. - npz_path = osp.join(npz_dir, npz_name) - if data_source == "ScanNet": - datasets.append( - ScanNetDataset( - data_root, - npz_path, - intrinsic_path, - mode=mode, - min_overlap_score=min_overlap_score, - augment_fn=augment_fn, - pose_dir=pose_dir, - ) - ) - elif data_source == "MegaDepth": - datasets.append( - MegaDepthDataset( - data_root, - npz_path, - mode=mode, - min_overlap_score=min_overlap_score, - img_resize=self.mgdpt_img_resize, - df=self.mgdpt_df, - img_padding=self.mgdpt_img_pad, - depth_padding=self.mgdpt_depth_pad, - augment_fn=augment_fn, - coarse_scale=self.coarse_scale, - ) - ) - else: - raise NotImplementedError() - return ConcatDataset(datasets) - - def _build_concat_dataset_parallel( - self, - data_root, - npz_names, - npz_dir, - intrinsic_path, - mode, - min_overlap_score=0.0, - pose_dir=None, - ): - augment_fn = self.augment_fn if mode == "train" else None - data_source = ( - self.trainval_data_source - if mode in ["train", "val"] - else self.test_data_source - ) - if str(data_source).lower() == "megadepth": - npz_names = [f"{n}.npz" for n in npz_names] - with tqdm_joblib( - tqdm( - desc=f"[rank:{self.rank}] loading {mode} datasets", - total=len(npz_names), - disable=int(self.rank) != 0, - ) - ): - if data_source == "ScanNet": - datasets = Parallel( - n_jobs=math.floor( - len(os.sched_getaffinity(0)) * 0.9 / comm.get_local_size() - ) - )( - delayed( - lambda x: _build_dataset( - ScanNetDataset, - data_root, - osp.join(npz_dir, x), - intrinsic_path, - mode=mode, - min_overlap_score=min_overlap_score, - augment_fn=augment_fn, - pose_dir=pose_dir, - ) - )(name) - for name in npz_names - ) - elif data_source == "MegaDepth": - # TODO: _pickle.PicklingError: Could not pickle the task to send it to the workers. - raise NotImplementedError() - datasets = Parallel( - n_jobs=math.floor( - len(os.sched_getaffinity(0)) * 0.9 / comm.get_local_size() - ) - )( - delayed( - lambda x: _build_dataset( - MegaDepthDataset, - data_root, - osp.join(npz_dir, x), - mode=mode, - min_overlap_score=min_overlap_score, - img_resize=self.mgdpt_img_resize, - df=self.mgdpt_df, - img_padding=self.mgdpt_img_pad, - depth_padding=self.mgdpt_depth_pad, - augment_fn=augment_fn, - coarse_scale=self.coarse_scale, - ) - )(name) - for name in npz_names - ) - else: - raise ValueError(f"Unknown dataset: {data_source}") - return ConcatDataset(datasets) - - def train_dataloader(self): - """Build training dataloader for ScanNet / MegaDepth.""" - assert self.data_sampler in ["scene_balance"] - logger.info( - f"[rank:{self.rank}/{self.world_size}]: Train Sampler and DataLoader re-init (should not re-init between epochs!)." - ) - if self.data_sampler == "scene_balance": - sampler = RandomConcatSampler( - self.train_dataset, - self.n_samples_per_subset, - self.subset_replacement, - self.shuffle, - self.repeat, - self.seed, - ) - else: - sampler = None - dataloader = DataLoader( - self.train_dataset, sampler=sampler, **self.train_loader_params - ) - return dataloader - - def val_dataloader(self): - """Build validation dataloader for ScanNet / MegaDepth.""" - logger.info( - f"[rank:{self.rank}/{self.world_size}]: Val Sampler and DataLoader re-init." - ) - if not isinstance(self.val_dataset, abc.Sequence): - sampler = DistributedSampler(self.val_dataset, shuffle=False) - return DataLoader( - self.val_dataset, sampler=sampler, **self.val_loader_params - ) - else: - dataloaders = [] - for dataset in self.val_dataset: - sampler = DistributedSampler(dataset, shuffle=False) - dataloaders.append( - DataLoader(dataset, sampler=sampler, **self.val_loader_params) - ) - return dataloaders - - def test_dataloader(self, *args, **kwargs): - logger.info( - f"[rank:{self.rank}/{self.world_size}]: Test Sampler and DataLoader re-init." - ) - sampler = DistributedSampler(self.test_dataset, shuffle=False) - return DataLoader(self.test_dataset, sampler=sampler, **self.test_loader_params) - - -def _build_dataset(dataset: Dataset, *args, **kwargs): - return dataset(*args, **kwargs) diff --git a/spaces/Ritvik19/SudokuNet/README.md b/spaces/Ritvik19/SudokuNet/README.md deleted file mode 100644 index 9c6f0f1dc98a3071226ffb02fe3dda094b8b2dc5..0000000000000000000000000000000000000000 --- a/spaces/Ritvik19/SudokuNet/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: SudokuNet -emoji: 🚀 -colorFrom: red -colorTo: blue -sdk: streamlit -sdk_version: 1.9.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/Saurav21/Blog-Generation/app.py b/spaces/Saurav21/Blog-Generation/app.py deleted file mode 100644 index 4025fd211418d6397c9f05515dbda63e62642bfd..0000000000000000000000000000000000000000 --- a/spaces/Saurav21/Blog-Generation/app.py +++ /dev/null @@ -1,21 +0,0 @@ -import gradio as gr -from aitextgen import aitextgen - - -def text_gen(text): - - model_name = "EleutherAI/gpt-neo-125M" - model = aitextgen(model = model_name) - text_generated = model.generate_one(max_length=1000, prompt = text, no_repeat_ngram_size = 3) - - return text_generated - - -output_text = gr.outputs.Textbox() - -gr.Interface(text_gen, - inputs = gr.inputs.Textbox(label = "Input Text"), - outputs = gr.outputs.Textbox(), - title = "Blog Generator using AI", - description = "Create a 1000 word blog on any topic").launch() - diff --git a/spaces/Scakmak/Chatbot/app.py b/spaces/Scakmak/Chatbot/app.py deleted file mode 100644 index 0f0988771eecf969d589d8fcc92064b8d6843c9e..0000000000000000000000000000000000000000 --- a/spaces/Scakmak/Chatbot/app.py +++ /dev/null @@ -1,67 +0,0 @@ -import gradio as gr -import openai - - -import os -from dotenv import load_dotenv - -load_dotenv() - -openai.api_key = os.getenv("openai_api_key") - -# history = [] -character_name = "AI" -# chat_history = [] - -def openai_chat(prompt, ch_history): - - ch_history.append({"role": "user", "content": prompt}) - messagesFiltered = [ch_history[i] for i in range(len(ch_history)) if ((i % 3 !=0) | (i ==0)) ] - - response = openai.ChatCompletion.create( - model="gpt-3.5-turbo", - messages=messagesFiltered, - max_tokens=300, - temperature=0.6, - top_p=1, - frequency_penalty=0.5, - presence_penalty=0.0 - ) - print(response.choices[0].message["content"].strip()) - return response.choices[0].message["content"].strip() - - - - -def set_character_name(prompt_input, ch_history): - print(prompt_input) - character_name = prompt_input - #history.append({"role": "system", "content": f"You are {character_name}. You will talk and think like {character_name} from now on."}) - ch_history.append({"role": "system", "content": f"You are {character_name}. You will talk and think like {character_name} from now on."}) - return {msg: msg.update(visible=True), chatbot: chatbot.update(visible=True), char_selection : char_selection.update(visible=False), title: title.update( value = f"Chat with {character_name.upper()}",visible=True), state: ch_history} - -def respond(message, ch_history): - bot_message = openai_chat(message, ch_history) - #history.append({"role": "assistant", "content": bot_message}) - ch_history.append({"role": "assistant", "content": bot_message}) - ch_history.append((message, bot_message)) - chats = [ch_history[i] for i in range(len(ch_history)) if ((i % 3 == 0) & (i !=0)) ] - - return {msg: msg.update(value="", visible=True), chatbot: chatbot.update(value= chats,visible=True), state: ch_history} - - -with gr.Blocks() as demo: - state = gr.State([]) - - char_selection = gr.Textbox(lines=1 , label="Enter the character you want to talk to:") - title = gr.Markdown( visible=False) - chatbot = gr.Chatbot(visible=False) - msg = gr.Textbox(visible=False) - - char_selection.submit(set_character_name, [char_selection, state], [chatbot, msg, char_selection, title,state]) - - msg.submit(respond, [msg, state], [chatbot, msg,state]) - - - -demo.launch() diff --git a/spaces/Sky5408er/vits-uma-genshin-honkai/transforms.py b/spaces/Sky5408er/vits-uma-genshin-honkai/transforms.py deleted file mode 100644 index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000 --- a/spaces/Sky5408er/vits-uma-genshin-honkai/transforms.py +++ /dev/null @@ -1,193 +0,0 @@ -import torch -from torch.nn import functional as F - -import numpy as np - - -DEFAULT_MIN_BIN_WIDTH = 1e-3 -DEFAULT_MIN_BIN_HEIGHT = 1e-3 -DEFAULT_MIN_DERIVATIVE = 1e-3 - - -def piecewise_rational_quadratic_transform(inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails=None, - tail_bound=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE): - - if tails is None: - spline_fn = rational_quadratic_spline - spline_kwargs = {} - else: - spline_fn = unconstrained_rational_quadratic_spline - spline_kwargs = { - 'tails': tails, - 'tail_bound': tail_bound - } - - outputs, logabsdet = spline_fn( - inputs=inputs, - unnormalized_widths=unnormalized_widths, - unnormalized_heights=unnormalized_heights, - unnormalized_derivatives=unnormalized_derivatives, - inverse=inverse, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative, - **spline_kwargs - ) - return outputs, logabsdet - - -def searchsorted(bin_locations, inputs, eps=1e-6): - bin_locations[..., -1] += eps - return torch.sum( - inputs[..., None] >= bin_locations, - dim=-1 - ) - 1 - - -def unconstrained_rational_quadratic_spline(inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails='linear', - tail_bound=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE): - inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) - outside_interval_mask = ~inside_interval_mask - - outputs = torch.zeros_like(inputs) - logabsdet = torch.zeros_like(inputs) - - if tails == 'linear': - unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1)) - constant = np.log(np.exp(1 - min_derivative) - 1) - unnormalized_derivatives[..., 0] = constant - unnormalized_derivatives[..., -1] = constant - - outputs[outside_interval_mask] = inputs[outside_interval_mask] - logabsdet[outside_interval_mask] = 0 - else: - raise RuntimeError('{} tails are not implemented.'.format(tails)) - - outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline( - inputs=inputs[inside_interval_mask], - unnormalized_widths=unnormalized_widths[inside_interval_mask, :], - unnormalized_heights=unnormalized_heights[inside_interval_mask, :], - unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :], - inverse=inverse, - left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative - ) - - return outputs, logabsdet - -def rational_quadratic_spline(inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - left=0., right=1., bottom=0., top=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE): - if torch.min(inputs) < left or torch.max(inputs) > right: - raise ValueError('Input to a transform is not within its domain') - - num_bins = unnormalized_widths.shape[-1] - - if min_bin_width * num_bins > 1.0: - raise ValueError('Minimal bin width too large for the number of bins') - if min_bin_height * num_bins > 1.0: - raise ValueError('Minimal bin height too large for the number of bins') - - widths = F.softmax(unnormalized_widths, dim=-1) - widths = min_bin_width + (1 - min_bin_width * num_bins) * widths - cumwidths = torch.cumsum(widths, dim=-1) - cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0) - cumwidths = (right - left) * cumwidths + left - cumwidths[..., 0] = left - cumwidths[..., -1] = right - widths = cumwidths[..., 1:] - cumwidths[..., :-1] - - derivatives = min_derivative + F.softplus(unnormalized_derivatives) - - heights = F.softmax(unnormalized_heights, dim=-1) - heights = min_bin_height + (1 - min_bin_height * num_bins) * heights - cumheights = torch.cumsum(heights, dim=-1) - cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0) - cumheights = (top - bottom) * cumheights + bottom - cumheights[..., 0] = bottom - cumheights[..., -1] = top - heights = cumheights[..., 1:] - cumheights[..., :-1] - - if inverse: - bin_idx = searchsorted(cumheights, inputs)[..., None] - else: - bin_idx = searchsorted(cumwidths, inputs)[..., None] - - input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0] - input_bin_widths = widths.gather(-1, bin_idx)[..., 0] - - input_cumheights = cumheights.gather(-1, bin_idx)[..., 0] - delta = heights / widths - input_delta = delta.gather(-1, bin_idx)[..., 0] - - input_derivatives = derivatives.gather(-1, bin_idx)[..., 0] - input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0] - - input_heights = heights.gather(-1, bin_idx)[..., 0] - - if inverse: - a = (((inputs - input_cumheights) * (input_derivatives - + input_derivatives_plus_one - - 2 * input_delta) - + input_heights * (input_delta - input_derivatives))) - b = (input_heights * input_derivatives - - (inputs - input_cumheights) * (input_derivatives - + input_derivatives_plus_one - - 2 * input_delta)) - c = - input_delta * (inputs - input_cumheights) - - discriminant = b.pow(2) - 4 * a * c - assert (discriminant >= 0).all() - - root = (2 * c) / (-b - torch.sqrt(discriminant)) - outputs = root * input_bin_widths + input_cumwidths - - theta_one_minus_theta = root * (1 - root) - denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta) - derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - root).pow(2)) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, -logabsdet - else: - theta = (inputs - input_cumwidths) / input_bin_widths - theta_one_minus_theta = theta * (1 - theta) - - numerator = input_heights * (input_delta * theta.pow(2) - + input_derivatives * theta_one_minus_theta) - denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta) - outputs = input_cumheights + numerator / denominator - - derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - theta).pow(2)) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, logabsdet diff --git a/spaces/Spjkjlkkklj/dalle/index.html b/spaces/Spjkjlkkklj/dalle/index.html deleted file mode 100644 index 34e195b924d641d56e48ca7f05870c79ba68ca66..0000000000000000000000000000000000000000 --- a/spaces/Spjkjlkkklj/dalle/index.html +++ /dev/null @@ -1,54 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/spaces/Sudhanshu976/NLP_FULL_APP/pages/4_LANGUAGE-DETECTOR-MODEL.py b/spaces/Sudhanshu976/NLP_FULL_APP/pages/4_LANGUAGE-DETECTOR-MODEL.py deleted file mode 100644 index 3786e8a5e6e76202473b5a1c4712ce24510d61c1..0000000000000000000000000000000000000000 --- a/spaces/Sudhanshu976/NLP_FULL_APP/pages/4_LANGUAGE-DETECTOR-MODEL.py +++ /dev/null @@ -1,88 +0,0 @@ -import streamlit as st -import pickle -import re -import string -import nltk -from nltk.corpus import stopwords -from nltk.tokenize import word_tokenize -from nltk.stem.porter import PorterStemmer -stemmer = PorterStemmer() - -st.set_page_config( - page_title="NLP WEB APP" -) - -st.title("LANGUAGE DETECTOR MODEL") -st.sidebar.success("Select a page above") -nltk.download('stopwords') -nltk.download('punkt') - -def preprocess(text): - text = text.lower() - text = re.sub(r'\d+', '', text) - translator = str.maketrans('', '', string.punctuation) - text = text.translate(translator) - - - stop_words = set(stopwords.words("english")) - word_tokens = word_tokenize(text) - filtered_text = [word for word in word_tokens if word not in stop_words] - - stems = [stemmer.stem(word) for word in filtered_text] - preprocessed_text = ' '.join(stems) - return preprocessed_text - - - -cv = pickle.load(open('language-detector-models/vectorizer.pkl','rb')) -model = pickle.load(open('language-detector-models/model.pkl','rb')) - -message= st.text_input("ENTER THE MESSAGE") - - -if st.button("PREDICT"): - # PREPROCESS - transformed_text = preprocess(message) - - # VECTORIZE - vector_input = cv.transform([message]) - - # PREDICTION - result = model.predict(vector_input)[0] - - - # DISPLAY - if result==0: - st.header("ARABIC") - elif result==1: - st.header("DANISH") - elif result==2: - st.header("DUTCH") - elif result==3: - st.header("ENGLISH") - elif result==4: - st.header("FRENCH") - elif result==5: - st.header("GERMAN") - elif result==6: - st.header("GREEK") - elif result==7: - st.header("HINDI") - elif result==8: - st.header("ITALIAN") - elif result==9: - st.header("KANNADA") - elif result==10: - st.header("MALYALAM") - elif result==11: - st.header("PORTUGESE") - elif result==12: - st.header("RUSSIAN") - elif result==13: - st.header("SPANISH") - elif result==14: - st.header("SWEDISH") - elif result==15: - st.header("TAMIL") - else: - st.header("TURKISH") \ No newline at end of file diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/autocall.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/autocall.py deleted file mode 100644 index 54beec3f58dc87a5e18110a121b1c402f982e149..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/autocall.py +++ /dev/null @@ -1,70 +0,0 @@ -# encoding: utf-8 -""" -Autocall capabilities for IPython.core. - -Authors: - -* Brian Granger -* Fernando Perez -* Thomas Kluyver - -Notes ------ -""" - -#----------------------------------------------------------------------------- -# Copyright (C) 2008-2011 The IPython Development Team -# -# Distributed under the terms of the BSD License. The full license is in -# the file COPYING, distributed as part of this software. -#----------------------------------------------------------------------------- - -#----------------------------------------------------------------------------- -# Imports -#----------------------------------------------------------------------------- - - -#----------------------------------------------------------------------------- -# Code -#----------------------------------------------------------------------------- - -class IPyAutocall(object): - """ Instances of this class are always autocalled - - This happens regardless of 'autocall' variable state. Use this to - develop macro-like mechanisms. - """ - _ip = None - rewrite = True - def __init__(self, ip=None): - self._ip = ip - - def set_ip(self, ip): - """Will be used to set _ip point to current ipython instance b/f call - - Override this method if you don't want this to happen. - - """ - self._ip = ip - - -class ExitAutocall(IPyAutocall): - """An autocallable object which will be added to the user namespace so that - exit, exit(), quit or quit() are all valid ways to close the shell.""" - rewrite = False - - def __call__(self): - self._ip.ask_exit() - -class ZMQExitAutocall(ExitAutocall): - """Exit IPython. Autocallable, so it needn't be explicitly called. - - Parameters - ---------- - keep_kernel : bool - If True, leave the kernel alive. Otherwise, tell the kernel to exit too - (default). - """ - def __call__(self, keep_kernel=False): - self._ip.keepkernel_on_exit = keep_kernel - self._ip.ask_exit() diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/splitinput.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/splitinput.py deleted file mode 100644 index 5bc3e32542185c50c597d4470f8a4d38b2015247..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/splitinput.py +++ /dev/null @@ -1,138 +0,0 @@ -# encoding: utf-8 -""" -Simple utility for splitting user input. This is used by both inputsplitter and -prefilter. - -Authors: - -* Brian Granger -* Fernando Perez -""" - -#----------------------------------------------------------------------------- -# Copyright (C) 2008-2011 The IPython Development Team -# -# Distributed under the terms of the BSD License. The full license is in -# the file COPYING, distributed as part of this software. -#----------------------------------------------------------------------------- - -#----------------------------------------------------------------------------- -# Imports -#----------------------------------------------------------------------------- - -import re -import sys - -from IPython.utils import py3compat -from IPython.utils.encoding import get_stream_enc -from IPython.core.oinspect import OInfo - -#----------------------------------------------------------------------------- -# Main function -#----------------------------------------------------------------------------- - -# RegExp for splitting line contents into pre-char//first word-method//rest. -# For clarity, each group in on one line. - -# WARNING: update the regexp if the escapes in interactiveshell are changed, as -# they are hardwired in. - -# Although it's not solely driven by the regex, note that: -# ,;/% only trigger if they are the first character on the line -# ! and !! trigger if they are first char(s) *or* follow an indent -# ? triggers as first or last char. - -line_split = re.compile(r""" - ^(\s*) # any leading space - ([,;/%]|!!?|\?\??)? # escape character or characters - \s*(%{0,2}[\w\.\*]*) # function/method, possibly with leading % - # to correctly treat things like '?%magic' - (.*?$|$) # rest of line - """, re.VERBOSE) - - -def split_user_input(line, pattern=None): - """Split user input into initial whitespace, escape character, function part - and the rest. - """ - # We need to ensure that the rest of this routine deals only with unicode - encoding = get_stream_enc(sys.stdin, 'utf-8') - line = py3compat.cast_unicode(line, encoding) - - if pattern is None: - pattern = line_split - match = pattern.match(line) - if not match: - # print "match failed for line '%s'" % line - try: - ifun, the_rest = line.split(None,1) - except ValueError: - # print "split failed for line '%s'" % line - ifun, the_rest = line, u'' - pre = re.match(r'^(\s*)(.*)',line).groups()[0] - esc = "" - else: - pre, esc, ifun, the_rest = match.groups() - - #print 'line:<%s>' % line # dbg - #print 'pre <%s> ifun <%s> rest <%s>' % (pre,ifun.strip(),the_rest) # dbg - return pre, esc or '', ifun.strip(), the_rest.lstrip() - - -class LineInfo(object): - """A single line of input and associated info. - - Includes the following as properties: - - line - The original, raw line - - continue_prompt - Is this line a continuation in a sequence of multiline input? - - pre - Any leading whitespace. - - esc - The escape character(s) in pre or the empty string if there isn't one. - Note that '!!' and '??' are possible values for esc. Otherwise it will - always be a single character. - - ifun - The 'function part', which is basically the maximal initial sequence - of valid python identifiers and the '.' character. This is what is - checked for alias and magic transformations, used for auto-calling, - etc. In contrast to Python identifiers, it may start with "%" and contain - "*". - - the_rest - Everything else on the line. - """ - def __init__(self, line, continue_prompt=False): - self.line = line - self.continue_prompt = continue_prompt - self.pre, self.esc, self.ifun, self.the_rest = split_user_input(line) - - self.pre_char = self.pre.strip() - if self.pre_char: - self.pre_whitespace = '' # No whitespace allowed before esc chars - else: - self.pre_whitespace = self.pre - - def ofind(self, ip) -> OInfo: - """Do a full, attribute-walking lookup of the ifun in the various - namespaces for the given IPython InteractiveShell instance. - - Return a dict with keys: {found, obj, ospace, ismagic} - - Note: can cause state changes because of calling getattr, but should - only be run if autocall is on and if the line hasn't matched any - other, less dangerous handlers. - - Does cache the results of the call, so can be called multiple times - without worrying about *further* damaging state. - """ - return ip._ofind(self.ifun) - - def __str__(self): - return "LineInfo [%s|%s|%s|%s]" %(self.pre, self.esc, self.ifun, self.the_rest) diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/ImageMath.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/ImageMath.py deleted file mode 100644 index ac7d36b698c2ec9839d8a771734c9f730f701534..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/ImageMath.py +++ /dev/null @@ -1,263 +0,0 @@ -# -# The Python Imaging Library -# $Id$ -# -# a simple math add-on for the Python Imaging Library -# -# History: -# 1999-02-15 fl Original PIL Plus release -# 2005-05-05 fl Simplified and cleaned up for PIL 1.1.6 -# 2005-09-12 fl Fixed int() and float() for Python 2.4.1 -# -# Copyright (c) 1999-2005 by Secret Labs AB -# Copyright (c) 2005 by Fredrik Lundh -# -# See the README file for information on usage and redistribution. -# - -import builtins - -from . import Image, _imagingmath - - -def _isconstant(v): - return isinstance(v, (int, float)) - - -class _Operand: - """Wraps an image operand, providing standard operators""" - - def __init__(self, im): - self.im = im - - def __fixup(self, im1): - # convert image to suitable mode - if isinstance(im1, _Operand): - # argument was an image. - if im1.im.mode in ("1", "L"): - return im1.im.convert("I") - elif im1.im.mode in ("I", "F"): - return im1.im - else: - msg = f"unsupported mode: {im1.im.mode}" - raise ValueError(msg) - else: - # argument was a constant - if _isconstant(im1) and self.im.mode in ("1", "L", "I"): - return Image.new("I", self.im.size, im1) - else: - return Image.new("F", self.im.size, im1) - - def apply(self, op, im1, im2=None, mode=None): - im1 = self.__fixup(im1) - if im2 is None: - # unary operation - out = Image.new(mode or im1.mode, im1.size, None) - im1.load() - try: - op = getattr(_imagingmath, op + "_" + im1.mode) - except AttributeError as e: - msg = f"bad operand type for '{op}'" - raise TypeError(msg) from e - _imagingmath.unop(op, out.im.id, im1.im.id) - else: - # binary operation - im2 = self.__fixup(im2) - if im1.mode != im2.mode: - # convert both arguments to floating point - if im1.mode != "F": - im1 = im1.convert("F") - if im2.mode != "F": - im2 = im2.convert("F") - if im1.size != im2.size: - # crop both arguments to a common size - size = (min(im1.size[0], im2.size[0]), min(im1.size[1], im2.size[1])) - if im1.size != size: - im1 = im1.crop((0, 0) + size) - if im2.size != size: - im2 = im2.crop((0, 0) + size) - out = Image.new(mode or im1.mode, im1.size, None) - im1.load() - im2.load() - try: - op = getattr(_imagingmath, op + "_" + im1.mode) - except AttributeError as e: - msg = f"bad operand type for '{op}'" - raise TypeError(msg) from e - _imagingmath.binop(op, out.im.id, im1.im.id, im2.im.id) - return _Operand(out) - - # unary operators - def __bool__(self): - # an image is "true" if it contains at least one non-zero pixel - return self.im.getbbox() is not None - - def __abs__(self): - return self.apply("abs", self) - - def __pos__(self): - return self - - def __neg__(self): - return self.apply("neg", self) - - # binary operators - def __add__(self, other): - return self.apply("add", self, other) - - def __radd__(self, other): - return self.apply("add", other, self) - - def __sub__(self, other): - return self.apply("sub", self, other) - - def __rsub__(self, other): - return self.apply("sub", other, self) - - def __mul__(self, other): - return self.apply("mul", self, other) - - def __rmul__(self, other): - return self.apply("mul", other, self) - - def __truediv__(self, other): - return self.apply("div", self, other) - - def __rtruediv__(self, other): - return self.apply("div", other, self) - - def __mod__(self, other): - return self.apply("mod", self, other) - - def __rmod__(self, other): - return self.apply("mod", other, self) - - def __pow__(self, other): - return self.apply("pow", self, other) - - def __rpow__(self, other): - return self.apply("pow", other, self) - - # bitwise - def __invert__(self): - return self.apply("invert", self) - - def __and__(self, other): - return self.apply("and", self, other) - - def __rand__(self, other): - return self.apply("and", other, self) - - def __or__(self, other): - return self.apply("or", self, other) - - def __ror__(self, other): - return self.apply("or", other, self) - - def __xor__(self, other): - return self.apply("xor", self, other) - - def __rxor__(self, other): - return self.apply("xor", other, self) - - def __lshift__(self, other): - return self.apply("lshift", self, other) - - def __rshift__(self, other): - return self.apply("rshift", self, other) - - # logical - def __eq__(self, other): - return self.apply("eq", self, other) - - def __ne__(self, other): - return self.apply("ne", self, other) - - def __lt__(self, other): - return self.apply("lt", self, other) - - def __le__(self, other): - return self.apply("le", self, other) - - def __gt__(self, other): - return self.apply("gt", self, other) - - def __ge__(self, other): - return self.apply("ge", self, other) - - -# conversions -def imagemath_int(self): - return _Operand(self.im.convert("I")) - - -def imagemath_float(self): - return _Operand(self.im.convert("F")) - - -# logical -def imagemath_equal(self, other): - return self.apply("eq", self, other, mode="I") - - -def imagemath_notequal(self, other): - return self.apply("ne", self, other, mode="I") - - -def imagemath_min(self, other): - return self.apply("min", self, other) - - -def imagemath_max(self, other): - return self.apply("max", self, other) - - -def imagemath_convert(self, mode): - return _Operand(self.im.convert(mode)) - - -ops = {} -for k, v in list(globals().items()): - if k[:10] == "imagemath_": - ops[k[10:]] = v - - -def eval(expression, _dict={}, **kw): - """ - Evaluates an image expression. - - :param expression: A string containing a Python-style expression. - :param options: Values to add to the evaluation context. You - can either use a dictionary, or one or more keyword - arguments. - :return: The evaluated expression. This is usually an image object, but can - also be an integer, a floating point value, or a pixel tuple, - depending on the expression. - """ - - # build execution namespace - args = ops.copy() - args.update(_dict) - args.update(kw) - for k, v in list(args.items()): - if hasattr(v, "im"): - args[k] = _Operand(v) - - compiled_code = compile(expression, "
-
- )
-}
diff --git a/spaces/TabPFN/TabPFNPrediction/TabPFN/utils.py b/spaces/TabPFN/TabPFNPrediction/TabPFN/utils.py
deleted file mode 100644
index 4f38550250e1d7de2797968caf99e2a0c0231eeb..0000000000000000000000000000000000000000
--- a/spaces/TabPFN/TabPFNPrediction/TabPFN/utils.py
+++ /dev/null
@@ -1,293 +0,0 @@
-import os
-import math
-import argparse
-import random
-import datetime
-import itertools
-
-import torch
-from torch import nn
-from torch.optim.lr_scheduler import LambdaLR
-import numpy as np
-
-# copied from huggingface
-def get_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_cycles=0.5, last_epoch=-1):
- """ Create a schedule with a learning rate that decreases following the
- values of the cosine function between 0 and `pi * cycles` after a warmup
- period during which it increases linearly between 0 and 1.
- """
-
- def lr_lambda(current_step):
- if current_step < num_warmup_steps:
- return float(current_step) / float(max(1, num_warmup_steps))
- progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
- return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))
-
- return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-# copied from huggingface
-def get_restarting_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, steps_per_restart, num_cycles=0.5, last_epoch=-1):
- assert num_training_steps % steps_per_restart == 0
-
- def inner_lr_lambda(current_step, num_warmup_steps, num_training_steps):
- if current_step < num_warmup_steps:
- return float(current_step) / float(max(1, num_warmup_steps))
- progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
- return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))
-
- def lr_lambda(current_step):
- inner_step = current_step % steps_per_restart
- return inner_lr_lambda(inner_step,
- num_warmup_steps if current_step < steps_per_restart else 0,
- steps_per_restart
- )
-
-
- return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-# copied from huggingface
-def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1):
- """
- Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
- a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
-
- Args:
- optimizer (:class:`~torch.optim.Optimizer`):
- The optimizer for which to schedule the learning rate.
- num_warmup_steps (:obj:`int`):
- The number of steps for the warmup phase.
- num_training_steps (:obj:`int`):
- The total number of training steps.
- last_epoch (:obj:`int`, `optional`, defaults to -1):
- The index of the last epoch when resuming training.
-
- Return:
- :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
- """
-
- def lr_lambda(current_step: int):
- if current_step < num_warmup_steps:
- return float(current_step) / float(max(1, num_warmup_steps))
- return max(
- 0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps))
- )
-
- return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-
-def get_openai_lr(transformer_model):
- num_params = sum(p.numel() for p in transformer_model.parameters())
- return 0.003239 - 0.0001395 * math.log(num_params)
-
-
-def get_weighted_single_eval_pos_sampler(max_len):
- """
- This gives a sampler that can be used for `single_eval_pos` which yields good performance for all positions p,
- where p <= `max_len`. At most `max_len` - 1 examples are shown to the Transformer.
- :return: Sampler that can be fed to `train()` as `single_eval_pos_gen`.
- """
- return lambda: random.choices(range(max_len), [1 / (max_len - i) for i in range(max_len)])[0]
-
-
-def get_uniform_single_eval_pos_sampler(max_len, min_len=0):
- """
- Just sample any evaluation position with the same weight
- :return: Sampler that can be fed to `train()` as `single_eval_pos_gen`.
- """
- return lambda: random.choices(range(min_len, max_len))[0]
-
-
-class SeqBN(nn.Module):
- def __init__(self, d_model):
- super().__init__()
- self.bn = nn.BatchNorm1d(d_model)
- self.d_model = d_model
-
- def forward(self, x):
- assert self.d_model == x.shape[-1]
- flat_x = x.view(-1, self.d_model)
- flat_x = self.bn(flat_x)
- return flat_x.view(*x.shape)
-
-
-def set_locals_in_self(locals):
- """
- Call this function like `set_locals_in_self(locals())` to set all local variables as object variables.
- Especially useful right at the beginning of `__init__`.
- :param locals: `locals()`
- """
- self = locals['self']
- for var_name, val in locals.items():
- if var_name != 'self': setattr(self, var_name, val)
-
-
-default_device = 'cuda:0' if torch.cuda.is_available() else 'cpu:0'
-
-
-# Copied from StackOverflow, but we do an eval on the values additionally
-class StoreDictKeyPair(argparse.Action):
- def __init__(self, option_strings, dest, nargs=None, **kwargs):
- self._nargs = nargs
- super(StoreDictKeyPair, self).__init__(option_strings, dest, nargs=nargs, **kwargs)
-
- def __call__(self, parser, namespace, values, option_string=None):
- my_dict = {}
- for kv in values:
- k, v = kv.split("=")
- try:
- my_dict[k] = eval(v)
- except NameError:
- my_dict[k] = v
- setattr(namespace, self.dest, my_dict)
- print("dict values: {}".format(my_dict))
-
-def get_nan_value(v, set_value_to_nan=0.0):
- if random.random() < set_value_to_nan:
- return v
- else:
- return random.choice([-999, 0, 1, 999])
-
-def to_ranking(data):
- x = (data >= data.unsqueeze(-3))
- x = x.sum(0)
- return x
-# TODO: Is there a better way to do this?
-# 1. Cmparing to unique elements: When all values are different we still get quadratic blowup
-# 2. Argsort(Argsort()) returns ranking, but with duplicate values there is an ordering which is problematic
-# 3. Argsort(Argsort(Unique))->Scatter seems a bit complicated, doesn't have quadratic blowup, but how fast?
-def to_ranking_low_mem(data):
- x = torch.zeros_like(data)
- for col in range(data.shape[-1]):
- x_ = (data[:, :, col] >= data[:, :, col].unsqueeze(-2))
- x_ = x_.sum(0)
- x[:, :, col] = x_
- return x
-
-def nan_handling_missing_for_unknown_reason_value(set_value_to_nan=0.0):
- return get_nan_value(float('nan'), set_value_to_nan)
-
-def nan_handling_missing_for_no_reason_value(set_value_to_nan=0.0):
- return get_nan_value(float('-inf'), set_value_to_nan)
-
-def nan_handling_missing_for_a_reason_value(set_value_to_nan=0.0):
- return get_nan_value(float('inf'), set_value_to_nan)
-
-def torch_nanmean(x, axis=0, return_nanshare=False):
- num = torch.where(torch.isnan(x), torch.full_like(x, 0), torch.full_like(x, 1)).sum(axis=axis)
- value = torch.where(torch.isnan(x), torch.full_like(x, 0), x).sum(axis=axis)
- if return_nanshare:
- return value / num, 1.-num/x.shape[axis]
- return value / num
-
-def torch_nanstd(x, axis=0):
- num = torch.where(torch.isnan(x), torch.full_like(x, 0), torch.full_like(x, 1)).sum(axis=axis)
- value = torch.where(torch.isnan(x), torch.full_like(x, 0), x).sum(axis=axis)
- mean = value / num
- mean_broadcast = torch.repeat_interleave(mean.unsqueeze(axis), x.shape[axis], dim=axis)
- return torch.sqrt(torch.nansum(torch.square(mean_broadcast - x), axis=axis) / (num - 1))
-
-def normalize_data(data, normalize_positions=-1):
- if normalize_positions > 0:
- mean = torch_nanmean(data[:normalize_positions], axis=0)
- std = torch_nanstd(data[:normalize_positions], axis=0) + .000001
- else:
- mean = torch_nanmean(data, axis=0)
- std = torch_nanstd(data, axis=0) + .000001
- data = (data - mean) / std
- data = torch.clip(data, min=-100, max=100)
-
- return data
-
-def remove_outliers(X, n_sigma=4, normalize_positions=-1):
- # Expects T, B, H
- assert len(X.shape) == 3, "X must be T,B,H"
- #for b in range(X.shape[1]):
- #for col in range(X.shape[2]):
- data = X if normalize_positions == -1 else X[:normalize_positions]
- data_clean = data[:].clone()
- data_mean, data_std = torch_nanmean(data, axis=0), torch_nanstd(data, axis=0)
- cut_off = data_std * n_sigma
- lower, upper = data_mean - cut_off, data_mean + cut_off
-
- data_clean[torch.logical_or(data_clean > upper, data_clean < lower)] = np.nan
- data_mean, data_std = torch_nanmean(data_clean, axis=0), torch_nanstd(data_clean, axis=0)
- cut_off = data_std * n_sigma
- lower, upper = data_mean - cut_off, data_mean + cut_off
-
- X = torch.maximum(-torch.log(1+torch.abs(X)) + lower, X)
- X = torch.minimum(torch.log(1+torch.abs(X)) + upper, X)
- # print(ds[1][data < lower, col], ds[1][data > upper, col], ds[1][~np.isnan(data), col].shape, data_mean, data_std)
- return X
-
-def bool_mask_to_att_mask(mask):
- return mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
-
-def print_on_master_only(is_master):
- import builtins as __builtin__
-
- builtin_print = __builtin__.print
-
- def print(*args, **kwargs):
- force = kwargs.pop("force", False)
- if is_master or force:
- builtin_print(*args, **kwargs)
-
- __builtin__.print = print
-
-
-def init_dist(device):
- print('init dist')
- if 'LOCAL_RANK' in os.environ:
- # launched with torch.distributed.launch
- rank = int(os.environ["LOCAL_RANK"])
- print('torch.distributed.launch and my rank is', rank)
- torch.cuda.set_device(rank)
- os.environ['CUDA_VISIBLE_DEVICES'] = str(rank)
- torch.distributed.init_process_group(backend="nccl", init_method="env://", timeout=datetime.timedelta(seconds=20),
- world_size=torch.cuda.device_count(), rank=rank)
- torch.distributed.barrier()
- print_on_master_only(rank == 0)
- print(f"Distributed training on {torch.cuda.device_count()} GPUs, this is rank {rank}, "
- "only I can print, but when using print(..., force=True) it will print on all ranks.")
- return True, rank, f'cuda:{rank}'
- elif 'SLURM_PROCID' in os.environ and torch.cuda.device_count() > 1:
- # this is for multi gpu when starting with submitit
- assert device != 'cpu:0'
- rank = int(os.environ['SLURM_PROCID'])
- os.environ['MASTER_ADDR'] = 'localhost'
- os.environ['MASTER_PORT'] = '12355'
- torch.cuda.set_device(rank)
- os.environ['CUDA_VISIBLE_DEVICES'] = str(rank)
- print('distributed submitit launch and my rank is', rank)
- torch.distributed.init_process_group(backend="nccl", init_method="env://", timeout=datetime.timedelta(seconds=20),
- world_size=torch.cuda.device_count(), rank=rank)
- torch.distributed.barrier()
- print_on_master_only(rank == 0)
- print(f"Distributed training on {torch.cuda.device_count()} GPUs, this is rank {rank}, "
- "only I can print, but when using print(..., force=True) it will print on all ranks.")
-
- return True, rank, f'cuda:{rank}'
- else:
- print('Not using distributed')
- # will not change any of the behavior of print, but allows putting the force=True in the print calls
- print_on_master_only(True)
- return False, 0, device
-
-# NOP function for python with statements (x = NOP(); with x:)
-class NOP():
- def __enter__(self):
- pass
- def __exit__(self, type, value, traceback):
- pass
-
-def check_compatibility(dl):
- if hasattr(dl, 'num_outputs'):
- print('`num_outputs` for the DataLoader is deprecated. It is assumed to be 1 from now on.')
- assert dl.num_outputs != 1, "We assume num_outputs to be 1. Instead of the num_ouputs change your loss." \
- "We specify the number of classes in the CE loss."
-
-def product_dict(dic):
- keys = dic.keys()
- vals = dic.values()
- for instance in itertools.product(*vals):
- yield dict(zip(keys, instance))
diff --git a/spaces/Tape/yoga/openpose/body.py b/spaces/Tape/yoga/openpose/body.py
deleted file mode 100644
index df53d82f6c2dc50424e09d7f380670f04a59d208..0000000000000000000000000000000000000000
--- a/spaces/Tape/yoga/openpose/body.py
+++ /dev/null
@@ -1,218 +0,0 @@
-import cv2
-import numpy as np
-import math
-import time
-from scipy.ndimage.filters import gaussian_filter
-import matplotlib.pyplot as plt
-import matplotlib
-import torch
-from torchvision import transforms
-
-from openpose import util
-from openpose.model import bodypose_model
-
-class Body(object):
- def __init__(self, model_path):
- self.model = bodypose_model()
- if torch.cuda.is_available():
- self.model = self.model.cuda()
- model_dict = util.transfer(self.model, torch.load(model_path))
- self.model.load_state_dict(model_dict)
- self.model.eval()
-
- def __call__(self, oriImg):
- # scale_search = [0.5, 1.0, 1.5, 2.0]
- scale_search = [0.5]
- boxsize = 368
- stride = 8
- padValue = 128
- thre1 = 0.1
- thre2 = 0.05
- multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]
- heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))
- paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
-
- for m in range(len(multiplier)):
- scale = multiplier[m]
- imageToTest = cv2.resize(oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
- imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
- im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
- im = np.ascontiguousarray(im)
-
- data = torch.from_numpy(im).float()
- if torch.cuda.is_available():
- data = data.cuda()
- # data = data.permute([2, 0, 1]).unsqueeze(0).float()
- with torch.no_grad():
- Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data)
- Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy()
- Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy()
-
- # extract outputs, resize, and remove padding
- # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0)) # output 1 is heatmaps
- heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0)) # output 1 is heatmaps
- heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
- heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
- heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
-
- # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0)) # output 0 is PAFs
- paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0)) # output 0 is PAFs
- paf = cv2.resize(paf, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
- paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
- paf = cv2.resize(paf, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
-
- heatmap_avg += heatmap_avg + heatmap / len(multiplier)
- paf_avg += + paf / len(multiplier)
-
- all_peaks = []
- peak_counter = 0
-
- for part in range(18):
- map_ori = heatmap_avg[:, :, part]
- one_heatmap = gaussian_filter(map_ori, sigma=3)
-
- map_left = np.zeros(one_heatmap.shape)
- map_left[1:, :] = one_heatmap[:-1, :]
- map_right = np.zeros(one_heatmap.shape)
- map_right[:-1, :] = one_heatmap[1:, :]
- map_up = np.zeros(one_heatmap.shape)
- map_up[:, 1:] = one_heatmap[:, :-1]
- map_down = np.zeros(one_heatmap.shape)
- map_down[:, :-1] = one_heatmap[:, 1:]
-
- peaks_binary = np.logical_and.reduce(
- (one_heatmap >= map_left, one_heatmap >= map_right, one_heatmap >= map_up, one_heatmap >= map_down, one_heatmap > thre1))
- peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])) # note reverse
- peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks]
- peak_id = range(peak_counter, peak_counter + len(peaks))
- peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))]
-
- all_peaks.append(peaks_with_score_and_id)
- peak_counter += len(peaks)
-
- # find connection in the specified sequence, center 29 is in the position 15
- limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
- [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
- [1, 16], [16, 18], [3, 17], [6, 18]]
- # the middle joints heatmap correpondence
- mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \
- [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \
- [55, 56], [37, 38], [45, 46]]
-
- connection_all = []
- special_k = []
- mid_num = 10
-
- for k in range(len(mapIdx)):
- score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
- candA = all_peaks[limbSeq[k][0] - 1]
- candB = all_peaks[limbSeq[k][1] - 1]
- nA = len(candA)
- nB = len(candB)
- indexA, indexB = limbSeq[k]
- if (nA != 0 and nB != 0):
- connection_candidate = []
- for i in range(nA):
- for j in range(nB):
- vec = np.subtract(candB[j][:2], candA[i][:2])
- norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
- norm = max(0.001, norm)
- vec = np.divide(vec, norm)
-
- startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \
- np.linspace(candA[i][1], candB[j][1], num=mid_num)))
-
- vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \
- for I in range(len(startend))])
- vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \
- for I in range(len(startend))])
-
- score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])
- score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min(
- 0.5 * oriImg.shape[0] / norm - 1, 0)
- criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts)
- criterion2 = score_with_dist_prior > 0
- if criterion1 and criterion2:
- connection_candidate.append(
- [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]])
-
- connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)
- connection = np.zeros((0, 5))
- for c in range(len(connection_candidate)):
- i, j, s = connection_candidate[c][0:3]
- if (i not in connection[:, 3] and j not in connection[:, 4]):
- connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])
- if (len(connection) >= min(nA, nB)):
- break
-
- connection_all.append(connection)
- else:
- special_k.append(k)
- connection_all.append([])
-
- # last number in each row is the total parts number of that person
- # the second last number in each row is the score of the overall configuration
- subset = -1 * np.ones((0, 20))
- candidate = np.array([item for sublist in all_peaks for item in sublist])
-
- for k in range(len(mapIdx)):
- if k not in special_k:
- partAs = connection_all[k][:, 0]
- partBs = connection_all[k][:, 1]
- indexA, indexB = np.array(limbSeq[k]) - 1
-
- for i in range(len(connection_all[k])): # = 1:size(temp,1)
- found = 0
- subset_idx = [-1, -1]
- for j in range(len(subset)): # 1:size(subset,1):
- if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:
- subset_idx[found] = j
- found += 1
-
- if found == 1:
- j = subset_idx[0]
- if subset[j][indexB] != partBs[i]:
- subset[j][indexB] = partBs[i]
- subset[j][-1] += 1
- subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
- elif found == 2: # if found 2 and disjoint, merge them
- j1, j2 = subset_idx
- membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2]
- if len(np.nonzero(membership == 2)[0]) == 0: # merge
- subset[j1][:-2] += (subset[j2][:-2] + 1)
- subset[j1][-2:] += subset[j2][-2:]
- subset[j1][-2] += connection_all[k][i][2]
- subset = np.delete(subset, j2, 0)
- else: # as like found == 1
- subset[j1][indexB] = partBs[i]
- subset[j1][-1] += 1
- subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
-
- # if find no partA in the subset, create a new subset
- elif not found and k < 17:
- row = -1 * np.ones(20)
- row[indexA] = partAs[i]
- row[indexB] = partBs[i]
- row[-1] = 2
- row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2]
- subset = np.vstack([subset, row])
- # delete some rows of subset which has few parts occur
- deleteIdx = []
- for i in range(len(subset)):
- if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
- deleteIdx.append(i)
- subset = np.delete(subset, deleteIdx, axis=0)
-
- # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts
- # candidate: x, y, score, id
- return candidate, subset
-
-if __name__ == "__main__":
- body_estimation = Body('../model/body_pose_model.pth')
-
- test_image = '../images/ski.jpg'
- oriImg = cv2.imread(test_image) # B,G,R order
- candidate, subset = body_estimation(oriImg)
- canvas = util.draw_bodypose(oriImg, candidate, subset)
- plt.imshow(canvas[:, :, [2, 1, 0]])
- plt.show()
diff --git a/spaces/TencentARC/MasaCtrl/gradio_app/app_utils.py b/spaces/TencentARC/MasaCtrl/gradio_app/app_utils.py
deleted file mode 100644
index ad475b3c9097122ac3f624edbecab403c7586c6c..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/MasaCtrl/gradio_app/app_utils.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import gradio as gr
-import numpy as np
-import torch
-from diffusers import DDIMScheduler
-from pytorch_lightning import seed_everything
-
-from masactrl.diffuser_utils import MasaCtrlPipeline
-from masactrl.masactrl_utils import (AttentionBase,
- regiter_attention_editor_diffusers)
-
-
-torch.set_grad_enabled(False)
-
-device = torch.device("cuda") if torch.cuda.is_available() else torch.device(
- "cpu")
-model_path = "xyn-ai/anything-v4.0"
-scheduler = DDIMScheduler(beta_start=0.00085,
- beta_end=0.012,
- beta_schedule="scaled_linear",
- clip_sample=False,
- set_alpha_to_one=False)
-model = MasaCtrlPipeline.from_pretrained(model_path,
- scheduler=scheduler).to(device)
-
-global_context = {
- "model_path": model_path,
- "scheduler": scheduler,
- "model": model,
- "device": device
-}
\ No newline at end of file
diff --git a/spaces/Tetel/chat/EdgeGPT/utilities.py b/spaces/Tetel/chat/EdgeGPT/utilities.py
deleted file mode 100644
index cd2be0fcd998e12db495ef3d02a68344caa23018..0000000000000000000000000000000000000000
--- a/spaces/Tetel/chat/EdgeGPT/utilities.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import json
-import locale
-import random
-import sys
-from typing import Union
-
-from .constants import DELIMITER
-from .locale import LocationHint
-
-
-def append_identifier(msg: dict) -> str:
- # Convert dict to json string
- return json.dumps(msg, ensure_ascii=False) + DELIMITER
-
-
-def get_ran_hex(length: int = 32) -> str:
- return "".join(random.choice("0123456789abcdef") for _ in range(length))
-
-
-def get_location_hint_from_locale(locale: str) -> Union[dict, None]:
- locale = locale.lower()
- if locale == "en-gb":
- hint = LocationHint.UK.value
- elif locale == "en-ie":
- hint = LocationHint.EU.value
- elif locale == "zh-cn":
- hint = LocationHint.CHINA.value
- else:
- hint = LocationHint.USA.value
- return hint.get("LocationHint")
-
-
-def guess_locale() -> str:
- if sys.platform.startswith("win"):
- return "en-us"
- loc, _ = locale.getlocale()
- if not loc:
- return "en-us"
- return loc.replace("_", "-")
diff --git a/spaces/Thaweewat/ControlNet-Architecture/ldm/modules/ema.py b/spaces/Thaweewat/ControlNet-Architecture/ldm/modules/ema.py
deleted file mode 100644
index bded25019b9bcbcd0260f0b8185f8c7859ca58c4..0000000000000000000000000000000000000000
--- a/spaces/Thaweewat/ControlNet-Architecture/ldm/modules/ema.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import torch
-from torch import nn
-
-
-class LitEma(nn.Module):
- def __init__(self, model, decay=0.9999, use_num_upates=True):
- super().__init__()
- if decay < 0.0 or decay > 1.0:
- raise ValueError('Decay must be between 0 and 1')
-
- self.m_name2s_name = {}
- self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
- self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates
- else torch.tensor(-1, dtype=torch.int))
-
- for name, p in model.named_parameters():
- if p.requires_grad:
- # remove as '.'-character is not allowed in buffers
- s_name = name.replace('.', '')
- self.m_name2s_name.update({name: s_name})
- self.register_buffer(s_name, p.clone().detach().data)
-
- self.collected_params = []
-
- def reset_num_updates(self):
- del self.num_updates
- self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))
-
- def forward(self, model):
- decay = self.decay
-
- if self.num_updates >= 0:
- self.num_updates += 1
- decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
-
- one_minus_decay = 1.0 - decay
-
- with torch.no_grad():
- m_param = dict(model.named_parameters())
- shadow_params = dict(self.named_buffers())
-
- for key in m_param:
- if m_param[key].requires_grad:
- sname = self.m_name2s_name[key]
- shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
- shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
- else:
- assert not key in self.m_name2s_name
-
- def copy_to(self, model):
- m_param = dict(model.named_parameters())
- shadow_params = dict(self.named_buffers())
- for key in m_param:
- if m_param[key].requires_grad:
- m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
- else:
- assert not key in self.m_name2s_name
-
- def store(self, parameters):
- """
- Save the current parameters for restoring later.
- Args:
- parameters: Iterable of `torch.nn.Parameter`; the parameters to be
- temporarily stored.
- """
- self.collected_params = [param.clone() for param in parameters]
-
- def restore(self, parameters):
- """
- Restore the parameters stored with the `store` method.
- Useful to validate the model with EMA parameters without affecting the
- original optimization process. Store the parameters before the
- `copy_to` method. After validation (or model saving), use this to
- restore the former parameters.
- Args:
- parameters: Iterable of `torch.nn.Parameter`; the parameters to be
- updated with the stored parameters.
- """
- for c_param, param in zip(self.collected_params, parameters):
- param.data.copy_(c_param.data)
diff --git a/spaces/TheStinger/Ilaria_RVC/lib/infer_pack/models.py b/spaces/TheStinger/Ilaria_RVC/lib/infer_pack/models.py
deleted file mode 100644
index 3665d03bc0514a6ed07d3372ea24717dae1e0a65..0000000000000000000000000000000000000000
--- a/spaces/TheStinger/Ilaria_RVC/lib/infer_pack/models.py
+++ /dev/null
@@ -1,1142 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from lib.infer_pack import modules
-from lib.infer_pack import attentions
-from lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from lib.infer_pack.commons import init_weights
-import numpy as np
-from lib.infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
- def __init__(
- self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.emb_phone = nn.Linear(256, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
- if f0 == True:
- self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
- self.encoder = attentions.Encoder(
- hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, phone, pitch, lengths):
- if pitch == None:
- x = self.emb_phone(phone)
- else:
- x = self.emb_phone(phone) + self.emb_pitch(pitch)
- x = x * math.sqrt(self.hidden_channels) # [b, t, h]
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
- def __init__(
- self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.emb_phone = nn.Linear(768, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
- if f0 == True:
- self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
- self.encoder = attentions.Encoder(
- hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, phone, pitch, lengths):
- if pitch == None:
- x = self.emb_phone(phone)
- else:
- x = self.emb_phone(phone) + self.emb_pitch(pitch)
- x = x * math.sqrt(self.hidden_channels) # [b, t, h]
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(
- self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0,
- ):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(
- modules.ResidualCouplingLayer(
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=gin_channels,
- mean_only=True,
- )
- )
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
- def remove_weight_norm(self):
- for i in range(self.n_flows):
- self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=gin_channels,
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
- def remove_weight_norm(self):
- self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
- def __init__(
- self,
- initial_channel,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=0,
- ):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(
- initial_channel, upsample_initial_channel, 7, 1, padding=3
- )
- resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(
- weight_norm(
- ConvTranspose1d(
- upsample_initial_channel // (2**i),
- upsample_initial_channel // (2 ** (i + 1)),
- k,
- u,
- padding=(k - u) // 2,
- )
- )
- )
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(
- zip(resblock_kernel_sizes, resblock_dilation_sizes)
- ):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
- """Definition of sine generator
- SineGen(samp_rate, harmonic_num = 0,
- sine_amp = 0.1, noise_std = 0.003,
- voiced_threshold = 0,
- flag_for_pulse=False)
- samp_rate: sampling rate in Hz
- harmonic_num: number of harmonic overtones (default 0)
- sine_amp: amplitude of sine-wavefrom (default 0.1)
- noise_std: std of Gaussian noise (default 0.003)
- voiced_thoreshold: F0 threshold for U/V classification (default 0)
- flag_for_pulse: this SinGen is used inside PulseGen (default False)
- Note: when flag_for_pulse is True, the first time step of a voiced
- segment is always sin(np.pi) or cos(0)
- """
-
- def __init__(
- self,
- samp_rate,
- harmonic_num=0,
- sine_amp=0.1,
- noise_std=0.003,
- voiced_threshold=0,
- flag_for_pulse=False,
- ):
- super(SineGen, self).__init__()
- self.sine_amp = sine_amp
- self.noise_std = noise_std
- self.harmonic_num = harmonic_num
- self.dim = self.harmonic_num + 1
- self.sampling_rate = samp_rate
- self.voiced_threshold = voiced_threshold
-
- def _f02uv(self, f0):
- # generate uv signal
- uv = torch.ones_like(f0)
- uv = uv * (f0 > self.voiced_threshold)
- return uv
-
- def forward(self, f0, upp):
- """sine_tensor, uv = forward(f0)
- input F0: tensor(batchsize=1, length, dim=1)
- f0 for unvoiced steps should be 0
- output sine_tensor: tensor(batchsize=1, length, dim)
- output uv: tensor(batchsize=1, length, 1)
- """
- with torch.no_grad():
- f0 = f0[:, None].transpose(1, 2)
- f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
- # fundamental component
- f0_buf[:, :, 0] = f0[:, :, 0]
- for idx in np.arange(self.harmonic_num):
- f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
- idx + 2
- ) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
- rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化
- rand_ini = torch.rand(
- f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
- )
- rand_ini[:, 0] = 0
- rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
- tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化
- tmp_over_one *= upp
- tmp_over_one = F.interpolate(
- tmp_over_one.transpose(2, 1),
- scale_factor=upp,
- mode="linear",
- align_corners=True,
- ).transpose(2, 1)
- rad_values = F.interpolate(
- rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
- ).transpose(
- 2, 1
- ) #######
- tmp_over_one %= 1
- tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
- cumsum_shift = torch.zeros_like(rad_values)
- cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
- sine_waves = torch.sin(
- torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
- )
- sine_waves = sine_waves * self.sine_amp
- uv = self._f02uv(f0)
- uv = F.interpolate(
- uv.transpose(2, 1), scale_factor=upp, mode="nearest"
- ).transpose(2, 1)
- noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
- noise = noise_amp * torch.randn_like(sine_waves)
- sine_waves = sine_waves * uv + noise
- return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
- """SourceModule for hn-nsf
- SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
- add_noise_std=0.003, voiced_threshod=0)
- sampling_rate: sampling_rate in Hz
- harmonic_num: number of harmonic above F0 (default: 0)
- sine_amp: amplitude of sine source signal (default: 0.1)
- add_noise_std: std of additive Gaussian noise (default: 0.003)
- note that amplitude of noise in unvoiced is decided
- by sine_amp
- voiced_threshold: threhold to set U/V given F0 (default: 0)
- Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
- F0_sampled (batchsize, length, 1)
- Sine_source (batchsize, length, 1)
- noise_source (batchsize, length 1)
- uv (batchsize, length, 1)
- """
-
- def __init__(
- self,
- sampling_rate,
- harmonic_num=0,
- sine_amp=0.1,
- add_noise_std=0.003,
- voiced_threshod=0,
- is_half=True,
- ):
- super(SourceModuleHnNSF, self).__init__()
-
- self.sine_amp = sine_amp
- self.noise_std = add_noise_std
- self.is_half = is_half
- # to produce sine waveforms
- self.l_sin_gen = SineGen(
- sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
- )
-
- # to merge source harmonics into a single excitation
- self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
- self.l_tanh = torch.nn.Tanh()
-
- def forward(self, x, upp=None):
- sine_wavs, uv, _ = self.l_sin_gen(x, upp)
- if self.is_half:
- sine_wavs = sine_wavs.half()
- sine_merge = self.l_tanh(self.l_linear(sine_wavs))
- return sine_merge, None, None # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
- def __init__(
- self,
- initial_channel,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- sr,
- is_half=False,
- ):
- super(GeneratorNSF, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
-
- self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
- self.m_source = SourceModuleHnNSF(
- sampling_rate=sr, harmonic_num=0, is_half=is_half
- )
- self.noise_convs = nn.ModuleList()
- self.conv_pre = Conv1d(
- initial_channel, upsample_initial_channel, 7, 1, padding=3
- )
- resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- c_cur = upsample_initial_channel // (2 ** (i + 1))
- self.ups.append(
- weight_norm(
- ConvTranspose1d(
- upsample_initial_channel // (2**i),
- upsample_initial_channel // (2 ** (i + 1)),
- k,
- u,
- padding=(k - u) // 2,
- )
- )
- )
- if i + 1 < len(upsample_rates):
- stride_f0 = np.prod(upsample_rates[i + 1 :])
- self.noise_convs.append(
- Conv1d(
- 1,
- c_cur,
- kernel_size=stride_f0 * 2,
- stride=stride_f0,
- padding=stride_f0 // 2,
- )
- )
- else:
- self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(
- zip(resblock_kernel_sizes, resblock_dilation_sizes)
- ):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- self.upp = np.prod(upsample_rates)
-
- def forward(self, x, f0, g=None):
- har_source, noi_source, uv = self.m_source(f0, self.upp)
- har_source = har_source.transpose(1, 2)
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- x_source = self.noise_convs[i](har_source)
- x = x + x_source
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
- return x
-
- def remove_weight_norm(self):
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-sr2sr = {
- "32k": 32000,
- "40k": 40000,
- "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- gin_channels,
- sr,
- **kwargs
- ):
- super().__init__()
- if type(sr) == type("strr"):
- sr = sr2sr[sr]
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder256(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- )
- self.dec = GeneratorNSF(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- sr=sr,
- is_half=kwargs["is_half"],
- )
- self.enc_q = PosteriorEncoder(
- spec_channels,
- inter_channels,
- hidden_channels,
- 5,
- 1,
- 16,
- gin_channels=gin_channels,
- )
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(
- self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
- ): # 这里ds是id，[bs,1]
- # print(1,pitch.shape)#[bs,t]
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
- z_slice, ids_slice = commons.rand_slice_segments(
- z, y_lengths, self.segment_size
- )
- # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
- pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
- # print(-2,pitchf.shape,z_slice.shape)
- o = self.dec(z_slice, pitchf, g=g)
- return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
- if rate:
- head = int(z_p.shape[2] * rate)
- z_p = z_p[:, :, -head:]
- x_mask = x_mask[:, :, -head:]
- nsff0 = nsff0[:, -head:]
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec(z * x_mask, nsff0, g=g)
- return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid(nn.Module):
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- gin_channels,
- sr,
- **kwargs
- ):
- super().__init__()
- if type(sr) == type("strr"):
- sr = sr2sr[sr]
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder768(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- )
- self.dec = GeneratorNSF(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- sr=sr,
- is_half=kwargs["is_half"],
- )
- self.enc_q = PosteriorEncoder(
- spec_channels,
- inter_channels,
- hidden_channels,
- 5,
- 1,
- 16,
- gin_channels=gin_channels,
- )
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(
- self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
- ): # 这里ds是id，[bs,1]
- # print(1,pitch.shape)#[bs,t]
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
- z_slice, ids_slice = commons.rand_slice_segments(
- z, y_lengths, self.segment_size
- )
- # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
- pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
- # print(-2,pitchf.shape,z_slice.shape)
- o = self.dec(z_slice, pitchf, g=g)
- return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
- if rate:
- head = int(z_p.shape[2] * rate)
- z_p = z_p[:, :, -head:]
- x_mask = x_mask[:, :, -head:]
- nsff0 = nsff0[:, -head:]
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec(z * x_mask, nsff0, g=g)
- return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- gin_channels,
- sr=None,
- **kwargs
- ):
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder256(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=False,
- )
- self.dec = Generator(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- )
- self.enc_q = PosteriorEncoder(
- spec_channels,
- inter_channels,
- hidden_channels,
- 5,
- 1,
- 16,
- gin_channels=gin_channels,
- )
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(self, phone, phone_lengths, y, y_lengths, ds): # 这里ds是id，[bs,1]
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
- z_slice, ids_slice = commons.rand_slice_segments(
- z, y_lengths, self.segment_size
- )
- o = self.dec(z_slice, g=g)
- return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, phone, phone_lengths, sid, rate=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
- if rate:
- head = int(z_p.shape[2] * rate)
- z_p = z_p[:, :, -head:]
- x_mask = x_mask[:, :, -head:]
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec(z * x_mask, g=g)
- return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid_nono(nn.Module):
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- gin_channels,
- sr=None,
- **kwargs
- ):
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder768(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=False,
- )
- self.dec = Generator(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- )
- self.enc_q = PosteriorEncoder(
- spec_channels,
- inter_channels,
- hidden_channels,
- 5,
- 1,
- 16,
- gin_channels=gin_channels,
- )
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(self, phone, phone_lengths, y, y_lengths, ds): # 这里ds是id，[bs,1]
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
- z_slice, ids_slice = commons.rand_slice_segments(
- z, y_lengths, self.segment_size
- )
- o = self.dec(z_slice, g=g)
- return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, phone, phone_lengths, sid, rate=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
- if rate:
- head = int(z_p.shape[2] * rate)
- z_p = z_p[:, :, -head:]
- x_mask = x_mask[:, :, -head:]
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec(z * x_mask, g=g)
- return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2, 3, 5, 7, 11, 17]
- # periods = [3, 5, 7, 11, 17, 23, 37]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [
- DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
- ]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = [] #
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- # for j in range(len(fmap_r)):
- # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminatorV2, self).__init__()
- # periods = [2, 3, 5, 7, 11, 17]
- periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [
- DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
- ]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = [] #
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- # for j in range(len(fmap_r)):
- # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList(
- [
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ]
- )
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList(
- [
- norm_f(
- Conv2d(
- 1,
- 32,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 32,
- 128,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 128,
- 512,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 512,
- 1024,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 1024,
- 1024,
- (kernel_size, 1),
- 1,
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- ]
- )
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
diff --git a/spaces/Theivaprakasham/yolov6/yolov6/core/inferer.py b/spaces/Theivaprakasham/yolov6/yolov6/core/inferer.py
deleted file mode 100644
index d4aee34440a0fa798da02476393a6df648598da3..0000000000000000000000000000000000000000
--- a/spaces/Theivaprakasham/yolov6/yolov6/core/inferer.py
+++ /dev/null
@@ -1,196 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import os
-import os.path as osp
-import math
-
-from tqdm import tqdm
-
-import numpy as np
-import cv2
-import torch
-from PIL import ImageFont
-
-from yolov6.utils.events import LOGGER, load_yaml
-
-from yolov6.layers.common import DetectBackend
-from yolov6.data.data_augment import letterbox
-from yolov6.utils.nms import non_max_suppression
-
-
-class Inferer:
- def __init__(self, source, weights, device, yaml, img_size, half):
- import glob
- from yolov6.data.datasets import IMG_FORMATS
-
- self.__dict__.update(locals())
-
- # Init model
- self.device = device
- self.img_size = img_size
- cuda = self.device != 'cpu' and torch.cuda.is_available()
- self.device = torch.device('cuda:0' if cuda else 'cpu')
- self.model = DetectBackend(weights, device=self.device)
- self.stride = self.model.stride
- self.class_names = load_yaml(yaml)['names']
- self.img_size = self.check_img_size(self.img_size, s=self.stride) # check image size
-
- # Half precision
- if half & (self.device.type != 'cpu'):
- self.model.model.half()
- else:
- self.model.model.float()
- half = False
-
- if self.device.type != 'cpu':
- self.model(torch.zeros(1, 3, *self.img_size).to(self.device).type_as(next(self.model.model.parameters()))) # warmup
-
- # Load data
- if os.path.isdir(source):
- img_paths = sorted(glob.glob(os.path.join(source, '*.*'))) # dir
- elif os.path.isfile(source):
- img_paths = [source] # files
- else:
- raise Exception(f'Invalid path: {source}')
- self.img_paths = [img_path for img_path in img_paths if img_path.split('.')[-1].lower() in IMG_FORMATS]
-
- def infer(self, conf_thres, iou_thres, classes, agnostic_nms, max_det, save_dir, save_txt, save_img, hide_labels, hide_conf):
- ''' Model Inference and results visualization '''
-
- for img_path in tqdm(self.img_paths):
- img, img_src = self.precess_image(img_path, self.img_size, self.stride, self.half)
- img = img.to(self.device)
- if len(img.shape) == 3:
- img = img[None]
- # expand for batch dim
- pred_results = self.model(img)
- det = non_max_suppression(pred_results, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)[0]
-
- save_path = osp.join(save_dir, osp.basename(img_path)) # im.jpg
- txt_path = osp.join(save_dir, 'labels', osp.basename(img_path).split('.')[0])
-
- gn = torch.tensor(img_src.shape)[[1, 0, 1, 0]] # normalization gain whwh
- img_ori = img_src
-
- # check image and font
- assert img_ori.data.contiguous, 'Image needs to be contiguous. Please apply to input images with np.ascontiguousarray(im).'
- self.font_check()
-
- if len(det):
- det[:, :4] = self.rescale(img.shape[2:], det[:, :4], img_src.shape).round()
-
- for *xyxy, conf, cls in reversed(det):
- if save_txt: # Write to file
- xywh = (self.box_convert(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
- line = (cls, *xywh, conf)
- with open(txt_path + '.txt', 'a') as f:
- f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
- if save_img:
- class_num = int(cls) # integer class
- label = None if hide_labels else (self.class_names[class_num] if hide_conf else f'{self.class_names[class_num]} {conf:.2f}')
-
- self.plot_box_and_label(img_ori, max(round(sum(img_ori.shape) / 2 * 0.003), 2), xyxy, label, color=self.generate_colors(class_num, True))
-
- img_src = np.asarray(img_ori)
-
- # Save results (image with detections)
- if save_img:
- cv2.imwrite(save_path, img_src)
-
- @staticmethod
- def precess_image(path, img_size, stride, half):
- '''Process image before image inference.'''
- try:
- img_src = cv2.imread(path)
- assert img_src is not None, f'Invalid image: {path}'
- except Exception as e:
- LOGGER.Warning(e)
- image = letterbox(img_src, img_size, stride=stride)[0]
-
- # Convert
- image = image.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
- image = torch.from_numpy(np.ascontiguousarray(image))
- image = image.half() if half else image.float() # uint8 to fp16/32
- image /= 255 # 0 - 255 to 0.0 - 1.0
-
- return image, img_src
-
- @staticmethod
- def rescale(ori_shape, boxes, target_shape):
- '''Rescale the output to the original image shape'''
- ratio = min(ori_shape[0] / target_shape[0], ori_shape[1] / target_shape[1])
- padding = (ori_shape[1] - target_shape[1] * ratio) / 2, (ori_shape[0] - target_shape[0] * ratio) / 2
-
- boxes[:, [0, 2]] -= padding[0]
- boxes[:, [1, 3]] -= padding[1]
- boxes[:, :4] /= ratio
-
- boxes[:, 0].clamp_(0, target_shape[1]) # x1
- boxes[:, 1].clamp_(0, target_shape[0]) # y1
- boxes[:, 2].clamp_(0, target_shape[1]) # x2
- boxes[:, 3].clamp_(0, target_shape[0]) # y2
-
- return boxes
-
- def check_img_size(self, img_size, s=32, floor=0):
- """Make sure image size is a multiple of stride s in each dimension, and return a new shape list of image."""
- if isinstance(img_size, int): # integer i.e. img_size=640
- new_size = max(self.make_divisible(img_size, int(s)), floor)
- elif isinstance(img_size, list): # list i.e. img_size=[640, 480]
- new_size = [max(self.make_divisible(x, int(s)), floor) for x in img_size]
- else:
- raise Exception(f"Unsupported type of img_size: {type(img_size)}")
-
- if new_size != img_size:
- print(f'WARNING: --img-size {img_size} must be multiple of max stride {s}, updating to {new_size}')
- return new_size if isinstance(img_size,list) else [new_size]*2
-
- def make_divisible(self, x, divisor):
- # Upward revision the value x to make it evenly divisible by the divisor.
- return math.ceil(x / divisor) * divisor
-
- @staticmethod
- def plot_box_and_label(image, lw, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
- # Add one xyxy box to image with label
- p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
- cv2.rectangle(image, p1, p2, color, thickness=lw, lineType=cv2.LINE_AA)
- if label:
- tf = max(lw - 1, 1) # font thickness
- w, h = cv2.getTextSize(label, 0, fontScale=lw / 3, thickness=tf)[0] # text width, height
- outside = p1[1] - h - 3 >= 0 # label fits outside box
- p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
- cv2.rectangle(image, p1, p2, color, -1, cv2.LINE_AA) # filled
- cv2.putText(image, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), 0, lw / 3, txt_color,
- thickness=tf, lineType=cv2.LINE_AA)
-
- @staticmethod
- def font_check(font='./yolov6/utils/Arial.ttf', size=10):
- # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
- assert osp.exists(font), f'font path not exists: {font}'
- try:
- return ImageFont.truetype(str(font) if font.exists() else font.name, size)
- except Exception as e: # download if missing
- return ImageFont.truetype(str(font), size)
-
- @staticmethod
- def box_convert(x):
- # Convert boxes with shape [n, 4] from [x1, y1, x2, y2] to [x, y, w, h] where x1y1=top-left, x2y2=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
-
- @staticmethod
- def generate_colors(i, bgr=False):
- hex = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
- '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
- palette = []
- for iter in hex:
- h = '#' + iter
- palette.append(tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)))
- num = len(palette)
- color = palette[int(i) % num]
- return (color[2], color[1], color[0]) if bgr else color
diff --git a/spaces/Tlaloc/Aerial_Unet/app.py b/spaces/Tlaloc/Aerial_Unet/app.py
deleted file mode 100644
index f0fef999e43df775de0f0273fa7f9191c94f1e66..0000000000000000000000000000000000000000
--- a/spaces/Tlaloc/Aerial_Unet/app.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import gradio as gr
-from PIL import Image
-import numpy as np
-import segmentation_models_pytorch as smp
-import torch
-from torchvision import transforms as T
-import os
-import cv2
-import pandas as pd
-import albumentations as album
-
-class ExperimentDataset(torch.utils.data.Dataset):
- def __init__(self, image, augment=None, preprocess=None):
- self.image = image
- self.augment = augment
- self.preprocess = preprocess
-
- def __getitem__(self, i):
- image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
- if self.augment:
- sample = self.augment(image=image)
- image= sample['image']
- if self.preprocess:
- sample = self.preprocess(image=image)
- image = sample['image']
- return image
-
-def color_convert(image,labelvals):
- colorcodes = np.array(labelvals)
- ccs = colorcodes[image.astype(int)]
- return ccs
-
-def crop_image(image, dims=[1500,1500,3]):
- target_size = dims[0]
- image_size = len(image)
- padding = (image_size - target_size) // 2
-
- return image[
- padding:image_size - padding,
- padding:image_size - padding,
- :,]
-
-def to_tensor(x,**kwargs):
- return x.transpose(2,0,1).astype("float32")
-
-def augment_image():
- transform = [album.PadIfNeeded(min_height=1536, min_width=1536, always_apply=True, border_mode=0)]
- return album.Compose(transform)
-
-def preprocessing(preprocessing_fn=None):
- transform = []
- if preprocessing_fn:
- transform.append(album.Lambda(image=preprocessing_fn))
- transform.append(album.Lambda(image=to_tensor, mask=to_tensor))
- return album.Compose(transform)
-
-def segment(image):
- device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
- best_model = torch.load('best_model_upp.pth', map_location=device)
-
- classlabeldict = pd.read_csv("label_class_dict.csv")
- clasnames = classlabeldict['name'].tolist()
- class_rgb_values = classlabeldict[['r','g','b']].values.tolist()
- select_class_indices = [clasnames.index(cls.lower()) for cls in clasnames]
- select_class_rgb_values = np.array(class_rgb_values)[select_class_indices]
-
- encoder = "resnet34"
- encoder_weights = "imagenet"
- preprocess_func = smp.encoders.get_preprocessing_fn(encoder, encoder_weights)
-
- exp_data = ExperimentDataset(image, augment = augment_image(),
- preprocess = preprocessing(preprocess_func))
-
- test_img = exp_data[0]
- x_tensor = torch.from_numpy(test_img).to(device).unsqueeze(0)
- pred_mask = best_model(x_tensor)
- pred_mask = pred_mask.detach().squeeze().cpu().numpy()
- pred_mask = np.transpose(pred_mask,(1,2,0))
- pred_mask = crop_image(color_convert(np.argmax(pred_mask,axis=-1), select_class_rgb_values))
-
- return pred_mask
-
-iface = gr.Interface(fn=segment, inputs="image", outputs="image").launch()
\ No newline at end of file
diff --git a/spaces/Worlandil/ChatGPT4/app.py b/spaces/Worlandil/ChatGPT4/app.py
deleted file mode 100644
index 7e09e57ef928fd2451fd0ed1295d0994ca75d026..0000000000000000000000000000000000000000
--- a/spaces/Worlandil/ChatGPT4/app.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import gradio as gr
-import os
-import json
-import requests
-
-#Streaming endpoint
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-
-#Huggingface provided GPT4 OpenAI API Key
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-
-#Inferenec function
-def predict(system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):
-
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {OPENAI_API_KEY}"
- }
- print(f"system message is ^^ {system_msg}")
- if system_msg.strip() == '':
- initial_message = [{"role": "user", "content": f"{inputs}"},]
- multi_turn_message = []
- else:
- initial_message= [{"role": "system", "content": system_msg},
- {"role": "user", "content": f"{inputs}"},]
- multi_turn_message = [{"role": "system", "content": system_msg},]
-
- if chat_counter == 0 :
- payload = {
- "model": "gpt-4",
- "messages": initial_message ,
- "temperature" : 1.0,
- "top_p":1.0,
- "n" : 1,
- "stream": True,
- "presence_penalty":0,
- "frequency_penalty":0,
- }
- print(f"chat_counter - {chat_counter}")
- else: #if chat_counter != 0 :
- messages=multi_turn_message # Of the type of - [{"role": "system", "content": system_msg},]
- for data in chatbot:
- user = {}
- user["role"] = "user"
- user["content"] = data[0]
- assistant = {}
- assistant["role"] = "assistant"
- assistant["content"] = data[1]
- messages.append(user)
- messages.append(assistant)
- temp = {}
- temp["role"] = "user"
- temp["content"] = inputs
- messages.append(temp)
- #messages
- payload = {
- "model": "gpt-4",
- "messages": messages, # Of the type of [{"role": "user", "content": f"{inputs}"}],
- "temperature" : temperature, #1.0,
- "top_p": top_p, #1.0,
- "n" : 1,
- "stream": True,
- "presence_penalty":0,
- "frequency_penalty":0,}
-
- chat_counter+=1
-
- history.append(inputs)
- print(f"Logging : payload is - {payload}")
- # make a POST request to the API endpoint using the requests.post method, passing in stream=True
- response = requests.post(API_URL, headers=headers, json=payload, stream=True)
- print(f"Logging : response code - {response}")
- token_counter = 0
- partial_words = ""
-
- counter=0
- for chunk in response.iter_lines():
- #Skipping first chunk
- if counter == 0:
- counter+=1
- continue
- # check whether each line is non-empty
- if chunk.decode() :
- chunk = chunk.decode()
- # decode each line as response data is in bytes
- if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
- partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
- if token_counter == 0:
- history.append(" " + partial_words)
- else:
- history[-1] = partial_words
- chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ] # convert to tuples of list
- token_counter+=1
- yield chat, history, chat_counter, response # resembles {chatbot: chat, state: history}
-
-#Resetting to blank
-def reset_textbox():
- return gr.update(value='')
-
-#to set a component as visible=False
-def set_visible_false():
- return gr.update(visible=False)
-
-#to set a component as visible=True
-def set_visible_true():
- return gr.update(visible=True)
-
-title = """
-
-
-
-
- ) : null}
-
- ) : null}
- 🔥GPT4 with ChatCompletions API +🚀Gradio-Streaming
""" - -#display message for themes feature -theme_addon_msg = """theme.push_to_hub().
-🏆Participate in Gradio's Theme Building Hackathon to exhibit your creative flair and win fabulous rewards! Join here - Gradio-Themes-Party🎨 🏆
🔥This Huggingface Gradio Demo provides you full access to GPT4 API (4096 token limit). 🎉🥳🎉You don't need any OPENAI API key🙌""")
- gr.HTML(theme_addon_msg)
- gr.HTML('''
Duplicate the Space and run securely with your OpenAI API KeyThe Tokenizer Playground
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/Xlinelabs/togethercomputer-GPT-NeoXT-Chat-Base-20B/README.md b/spaces/Xlinelabs/togethercomputer-GPT-NeoXT-Chat-Base-20B/README.md
deleted file mode 100644
index 5ba433be1331c03e256a73b4aa5272eba7295d4e..0000000000000000000000000000000000000000
--- a/spaces/Xlinelabs/togethercomputer-GPT-NeoXT-Chat-Base-20B/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Togethercomputer GPT NeoXT Chat Base 20B
-emoji: ⚡
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/XzJosh/Bella-Bert-VITS2/text/__init__.py b/spaces/XzJosh/Bella-Bert-VITS2/text/__init__.py
deleted file mode 100644
index 7566bf351ca9b95af9cdc6d729557a9da083800f..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Bella-Bert-VITS2/text/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from text.symbols import *
-
-
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-def cleaned_text_to_sequence(cleaned_text, tones, language):
- '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
- Args:
- text: string to convert to a sequence
- Returns:
- List of integers corresponding to the symbols in the text
- '''
- phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
- tone_start = language_tone_start_map[language]
- tones = [i + tone_start for i in tones]
- lang_id = language_id_map[language]
- lang_ids = [lang_id for i in phones]
- return phones, tones, lang_ids
-
-def get_bert(norm_text, word2ph, language):
- from .chinese_bert import get_bert_feature as zh_bert
- from .english_bert_mock import get_bert_feature as en_bert
- lang_bert_func_map = {
- 'ZH': zh_bert,
- 'EN': en_bert
- }
- bert = lang_bert_func_map[language](norm_text, word2ph)
- return bert
diff --git a/spaces/XzJosh/Jiaran-Bert-VITS2/text/japanese.py b/spaces/XzJosh/Jiaran-Bert-VITS2/text/japanese.py
deleted file mode 100644
index ddedafa0c5b7986068dc6c91637a86febc3923a9..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Jiaran-Bert-VITS2/text/japanese.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# modified from https://github.com/CjangCjengh/vits/blob/main/text/japanese.py
-import re
-import sys
-
-import pyopenjtalk
-
-from text import symbols
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
- r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
- r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('％', 'パーセント')
-]]
-
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'Q([↑↓]*[kg])', r'k#\1'),
- (r'Q([↑↓]*[tdjʧ])', r't#\1'),
- (r'Q([↑↓]*[sʃ])', r's\1'),
- (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'N([↑↓]*[pbm])', r'm\1'),
- (r'N([↑↓]*[ʧʥj])', r'n^\1'),
- (r'N([↑↓]*[tdn])', r'n\1'),
- (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-
-def post_replace_ph(ph):
- rep_map = {
- '：': ',',
- '；': ',',
- '，': ',',
- '。': '.',
- '！': '!',
- '？': '?',
- '\n': '.',
- "·": ",",
- '、': ",",
- '...': '…',
- 'v': "V"
- }
- if ph in rep_map.keys():
- ph = rep_map[ph]
- if ph in symbols:
- return ph
- if ph not in symbols:
- ph = 'UNK'
- return ph
-
-def symbols_to_japanese(text):
- for regex, replacement in _symbols_to_japanese:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def preprocess_jap(text):
- '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
- text = symbols_to_japanese(text)
- sentences = re.split(_japanese_marks, text)
- marks = re.findall(_japanese_marks, text)
- text = []
- for i, sentence in enumerate(sentences):
- if re.match(_japanese_characters, sentence):
- p = pyopenjtalk.g2p(sentence)
- text += p.split(" ")
-
- if i < len(marks):
- text += [marks[i].replace(' ', '')]
- return text
-
-def text_normalize(text):
- # todo: jap text normalize
- return text
-
-def g2p(norm_text):
- phones = preprocess_jap(norm_text)
- phones = [post_replace_ph(i) for i in phones]
- # todo: implement tones and word2ph
- tones = [0 for i in phones]
- word2ph = [1 for i in phones]
- return phones, tones, word2ph
-
-
-if __name__ == '__main__':
- for line in open("../../../Downloads/transcript_utf8.txt").readlines():
- text = line.split(":")[1]
- phones, tones, word2ph = g2p(text)
- for p in phones:
- if p == "z":
- print(text, phones)
- sys.exit(0)
diff --git a/spaces/XzJosh/XingTong-Bert-VITS2/preprocess_text.py b/spaces/XzJosh/XingTong-Bert-VITS2/preprocess_text.py
deleted file mode 100644
index 5eb0f3b9e929fcbe91dcbeb653391227a2518a15..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/XingTong-Bert-VITS2/preprocess_text.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import json
-from random import shuffle
-
-import tqdm
-from text.cleaner import clean_text
-from collections import defaultdict
-stage = [1,2,3]
-
-transcription_path = 'filelists/genshin.list'
-train_path = 'filelists/train.list'
-val_path = 'filelists/val.list'
-config_path = "configs/config.json"
-val_per_spk = 4
-max_val_total = 8
-
-if 1 in stage:
- with open( transcription_path+'.cleaned', 'w', encoding='utf-8') as f:
- for line in tqdm.tqdm(open(transcription_path, encoding='utf-8').readlines()):
- try:
- utt, spk, language, text = line.strip().split('|')
- norm_text, phones, tones, word2ph = clean_text(text, language)
- f.write('{}|{}|{}|{}|{}|{}|{}\n'.format(utt, spk, language, norm_text, ' '.join(phones),
- " ".join([str(i) for i in tones]),
- " ".join([str(i) for i in word2ph])))
- except Exception as error :
- print("err!", utt, error)
-
-if 2 in stage:
- spk_utt_map = defaultdict(list)
- spk_id_map = {}
- current_sid = 0
-
- with open( transcription_path+'.cleaned', encoding='utf-8') as f:
- for line in f.readlines():
- utt, spk, language, text, phones, tones, word2ph = line.strip().split('|')
- spk_utt_map[spk].append(line)
- if spk not in spk_id_map.keys():
- spk_id_map[spk] = current_sid
- current_sid += 1
- train_list = []
- val_list = []
-
- for spk, utts in spk_utt_map.items():
- shuffle(utts)
- val_list+=utts[:val_per_spk]
- train_list+=utts[val_per_spk:]
- if len(val_list) > max_val_total:
- train_list+=val_list[max_val_total:]
- val_list = val_list[:max_val_total]
-
- with open( train_path,"w", encoding='utf-8') as f:
- for line in train_list:
- f.write(line)
-
- with open(val_path, "w", encoding='utf-8') as f:
- for line in val_list:
- f.write(line)
-
-if 3 in stage:
- assert 2 in stage
- config = json.load(open(config_path, encoding='utf-8'))
- config["data"]['spk2id'] = spk_id_map
- with open(config_path, 'w', encoding='utf-8') as f:
- json.dump(config, f, indent=2, ensure_ascii=False)
diff --git a/spaces/YONG627/456123/yolov5-code-main/utils/loggers/clearml/hpo.py b/spaces/YONG627/456123/yolov5-code-main/utils/loggers/clearml/hpo.py
deleted file mode 100644
index ee518b0fbfc89ee811b51bbf85341eee4f685be1..0000000000000000000000000000000000000000
--- a/spaces/YONG627/456123/yolov5-code-main/utils/loggers/clearml/hpo.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from clearml import Task
-# Connecting ClearML with the current process,
-# from here on everything is logged automatically
-from clearml.automation import HyperParameterOptimizer, UniformParameterRange
-from clearml.automation.optuna import OptimizerOptuna
-
-task = Task.init(project_name='Hyper-Parameter Optimization',
- task_name='YOLOv5',
- task_type=Task.TaskTypes.optimizer,
- reuse_last_task_id=False)
-
-# Example use case:
-optimizer = HyperParameterOptimizer(
- # This is the experiment we want to optimize
- base_task_id='',
- # here we define the hyper-parameters to optimize
- # Notice: The parameter name should exactly match what you see in the UI: /
- # For Example, here we see in the base experiment a section Named: "General"
- # under it a parameter named "batch_size", this becomes "General/batch_size"
- # If you have `argparse` for example, then arguments will appear under the "Args" section,
- # and you should instead pass "Args/batch_size"
- hyper_parameters=[
- UniformParameterRange('Hyperparameters/lr0', min_value=1e-5, max_value=1e-1),
- UniformParameterRange('Hyperparameters/lrf', min_value=0.01, max_value=1.0),
- UniformParameterRange('Hyperparameters/momentum', min_value=0.6, max_value=0.98),
- UniformParameterRange('Hyperparameters/weight_decay', min_value=0.0, max_value=0.001),
- UniformParameterRange('Hyperparameters/warmup_epochs', min_value=0.0, max_value=5.0),
- UniformParameterRange('Hyperparameters/warmup_momentum', min_value=0.0, max_value=0.95),
- UniformParameterRange('Hyperparameters/warmup_bias_lr', min_value=0.0, max_value=0.2),
- UniformParameterRange('Hyperparameters/box', min_value=0.02, max_value=0.2),
- UniformParameterRange('Hyperparameters/cls', min_value=0.2, max_value=4.0),
- UniformParameterRange('Hyperparameters/cls_pw', min_value=0.5, max_value=2.0),
- UniformParameterRange('Hyperparameters/obj', min_value=0.2, max_value=4.0),
- UniformParameterRange('Hyperparameters/obj_pw', min_value=0.5, max_value=2.0),
- UniformParameterRange('Hyperparameters/iou_t', min_value=0.1, max_value=0.7),
- UniformParameterRange('Hyperparameters/anchor_t', min_value=2.0, max_value=8.0),
- UniformParameterRange('Hyperparameters/fl_gamma', min_value=0.0, max_value=4.0),
- UniformParameterRange('Hyperparameters/hsv_h', min_value=0.0, max_value=0.1),
- UniformParameterRange('Hyperparameters/hsv_s', min_value=0.0, max_value=0.9),
- UniformParameterRange('Hyperparameters/hsv_v', min_value=0.0, max_value=0.9),
- UniformParameterRange('Hyperparameters/degrees', min_value=0.0, max_value=45.0),
- UniformParameterRange('Hyperparameters/translate', min_value=0.0, max_value=0.9),
- UniformParameterRange('Hyperparameters/scale', min_value=0.0, max_value=0.9),
- UniformParameterRange('Hyperparameters/shear', min_value=0.0, max_value=10.0),
- UniformParameterRange('Hyperparameters/perspective', min_value=0.0, max_value=0.001),
- UniformParameterRange('Hyperparameters/flipud', min_value=0.0, max_value=1.0),
- UniformParameterRange('Hyperparameters/fliplr', min_value=0.0, max_value=1.0),
- UniformParameterRange('Hyperparameters/mosaic', min_value=0.0, max_value=1.0),
- UniformParameterRange('Hyperparameters/mixup', min_value=0.0, max_value=1.0),
- UniformParameterRange('Hyperparameters/copy_paste', min_value=0.0, max_value=1.0)],
- # this is the objective metric we want to maximize/minimize
- objective_metric_title='metrics',
- objective_metric_series='mAP_0.5',
- # now we decide if we want to maximize it or minimize it (accuracy we maximize)
- objective_metric_sign='max',
- # let us limit the number of concurrent experiments,
- # this in turn will make sure we do dont bombard the scheduler with experiments.
- # if we have an auto-scaler connected, this, by proxy, will limit the number of machine
- max_number_of_concurrent_tasks=1,
- # this is the optimizer class (actually doing the optimization)
- # Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band)
- optimizer_class=OptimizerOptuna,
- # If specified only the top K performing Tasks will be kept, the others will be automatically archived
- save_top_k_tasks_only=5, # 5,
- compute_time_limit=None,
- total_max_jobs=20,
- min_iteration_per_job=None,
- max_iteration_per_job=None,
-)
-
-# report every 10 seconds, this is way too often, but we are testing here
-optimizer.set_report_period(10 / 60)
-# You can also use the line below instead to run all the optimizer tasks locally, without using queues or agent
-# an_optimizer.start_locally(job_complete_callback=job_complete_callback)
-# set the time limit for the optimization process (2 hours)
-optimizer.set_time_limit(in_minutes=120.0)
-# Start the optimization process in the local environment
-optimizer.start_locally()
-# wait until process is done (notice we are controlling the optimization process in the background)
-optimizer.wait()
-# make sure background optimization stopped
-optimizer.stop()
-
-print('We are done, good bye')
diff --git a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/Waifu2x/Loss.py b/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/Waifu2x/Loss.py
deleted file mode 100644
index 267bb66f9e221f50cd06c96e93fd0ee71be925b4..0000000000000000000000000000000000000000
--- a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/Waifu2x/Loss.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import torch
-from torch import nn
-from torch.nn.functional import _pointwise_loss
-
-rgb_weights = [0.29891 * 3, 0.58661 * 3, 0.11448 * 3]
-# RGB have different weights
-# https://github.com/nagadomi/waifu2x/blob/master/train.lua#L109
-use_cuda = torch.cuda.is_available()
-FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
-LongTensor = torch.cuda.LongTensor if use_cuda else torch.LongTensor
-Tensor = FloatTensor
-
-
-class WeightedHuberLoss(nn.SmoothL1Loss):
- def __init__(self, weights=rgb_weights):
- super(WeightedHuberLoss, self).__init__(size_average=True, reduce=True)
- self.weights = torch.FloatTensor(weights).view(3, 1, 1)
-
- def forward(self, input_data, target):
- diff = torch.abs(input_data - target)
- z = torch.where(diff < 1, 0.5 * torch.pow(diff, 2), (diff - 0.5))
- out = z * self.weights.expand_as(diff)
- return out.mean()
-
-
-def weighted_mse_loss(input, target, weights):
- out = (input - target) ** 2
- out = out * weights.expand_as(out)
- loss = out.sum(0) # or sum over whatever dimensions
- return loss / out.size(0)
-
-
-class WeightedL1Loss(nn.SmoothL1Loss):
- def __init__(self, weights=rgb_weights):
- super(WeightedHuberLoss, self).__init__(size_average=True, reduce=True)
- self.weights = torch.FloatTensor(weights).view(3, 1, 1)
-
- def forward(self, input_data, target):
- return self.l1_loss(input_data, target, size_average=self.size_average,
- reduce=self.reduce)
-
- def l1_loss(self, input_data, target, size_average=True, reduce=True):
- return _pointwise_loss(lambda a, b: torch.abs(a - b) * self.weights.expand_as(a),
- torch._C._nn.l1_loss, input_data, target, size_average, reduce)
diff --git a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/pipelines/dance_diffusion/__init__.py b/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/pipelines/dance_diffusion/__init__.py
deleted file mode 100644
index 2ad34fc52aaa61f9313cae32d7bb39acad831104..0000000000000000000000000000000000000000
--- a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/pipelines/dance_diffusion/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# flake8: noqa
-from .pipeline_dance_diffusion import DanceDiffusionPipeline
diff --git a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/utils/dummy_flax_and_transformers_objects.py b/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/utils/dummy_flax_and_transformers_objects.py
deleted file mode 100644
index 14830bca2898ed550eb9a0b671282a81967c8570..0000000000000000000000000000000000000000
--- a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/utils/dummy_flax_and_transformers_objects.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-# flake8: noqa
-
-from ..utils import DummyObject, requires_backends
-
-
-class FlaxStableDiffusionPipeline(metaclass=DummyObject):
- _backends = ["flax", "transformers"]
-
- def __init__(self, *args, **kwargs):
- requires_backends(self, ["flax", "transformers"])
-
- @classmethod
- def from_config(cls, *args, **kwargs):
- requires_backends(cls, ["flax", "transformers"])
-
- @classmethod
- def from_pretrained(cls, *args, **kwargs):
- requires_backends(cls, ["flax", "transformers"])
diff --git a/spaces/Yntec/photoMovieX/app.py b/spaces/Yntec/photoMovieX/app.py
deleted file mode 100644
index 0de814355d32a7c37085f15abe9299d792075528..0000000000000000000000000000000000000000
--- a/spaces/Yntec/photoMovieX/app.py
+++ /dev/null
@@ -1,233 +0,0 @@
-import gradio as gr
-import os
-import sys
-from pathlib import Path
-import random
-import string
-import time
-from queue import Queue
-from threading import Thread
-import emoji
-
-text_gen=gr.Interface.load("spaces/phenomenon1981/MagicPrompt-Stable-Diffusion")
-def get_prompts(prompt_text):
- if prompt_text:
- return text_gen(prompt_text + "movie still, photo")
- else:
- return text_gen("")
-proc1=gr.Interface.load("models/Yntec/photoMovieX")
-
-def restart_script_periodically():
- while True:
- random_time = random.randint(5400, 6000)
- time.sleep(random_time)
- os.execl(sys.executable, sys.executable, *sys.argv)
-
-
-restart_thread = Thread(target=restart_script_periodically, daemon=True)
-restart_thread.start()
-
-
-queue = Queue()
-queue_threshold = 100
-
-#Don't add noise to the first picture no matter what (the point of noise is to get varied outputs, the first one doesn't need to vary about anything)
-def noadd_random_noise(prompt, noise_level=0.00):
- if noise_level == 0:
- noise_level = 0.00
- percentage_noise = noise_level * 5
- num_noise_chars = int(len(prompt) * (percentage_noise/100))
- noise_indices = random.sample(range(len(prompt)), num_noise_chars)
- prompt_list = list(prompt)
- noise_chars = list(string.ascii_letters + string.punctuation + '' + string.digits)
- noise_chars.extend([''])
- for index in noise_indices:
- prompt_list[index] = random.choice(noise_chars)
- return "".join(prompt_list)
-
-#normal behavior
-def add_random_noise(prompt, noise_level=0.00):
- if noise_level == 0:
- noise_level = 0.00
- percentage_noise = noise_level * 5
- num_noise_chars = int(len(prompt) * (percentage_noise/100))
- noise_indices = random.sample(range(len(prompt)), num_noise_chars)
- prompt_list = list(prompt)
- noise_chars = list(string.ascii_letters + string.punctuation + ' ' + string.digits)
- noise_chars.extend(['😍', 'beautiful', '😂', '🤔', '😊', '🤗', '😭', '🙄', 'pretty', '🤯', '🤫', '🥴', 'sitting', '🤩', '🥳', '😔', '😩', '🤪', '😇', 'retro', '😈', '👹', 'masterpiece', '🤖', '👽', 'high quality', '🎃', '🎅', '🎄', '🎁', '🎂', '🎉', '🎈', '🎊', '🎮', '❤️', '💔', '💕', '💖', '💗', '🐶', '🐱', 'visible', '🐹', '🦊', '🐻', '🐨', '🐯', '🦁', '🐘', '🔥', '🌧️', '🌞', '🌈', '💥', '🌴', '🌊', '🌺', '🌻', '🌸', '🎨', '🌅', '🌌', '☁️', '⛈️', '❄️', '☀️', '🌤️', '⛅️', '🌥️', '🌦️', '🌧️', '🌩️', '🌨️', '🌫️', '☔️', '🌬️', '💨', '🌪️', 'cute', 'kawaii', 'little', 'photo', 'movie', 'still'])
- for index in noise_indices:
- prompt_list[index] = random.choice(noise_chars)
- return "".join(prompt_list)
-
-def send_it1(inputs, noise_level, proc1=proc1):
- prompt_with_noise = noadd_random_noise(inputs, noise_level)
- while queue.qsize() >= queue_threshold:
- time.sleep(2)
- queue.put(prompt_with_noise)
- output1 = proc1(prompt_with_noise)
- return output1
-
-def send_it2(inputs, noise_level, proc1=proc1):
- prompt_with_noise = add_random_noise(inputs, noise_level)
- while queue.qsize() >= queue_threshold:
- time.sleep(2)
- queue.put(prompt_with_noise)
- output2 = proc1(prompt_with_noise)
- return output2
-
-def send_itX(inputs, noise_level, proc1=proc1):
- prompt_with_noise = add_random_noise(inputs, noise_level)
- while queue.qsize() >= queue_threshold:
- time.sleep(2)
- queue.put(prompt_with_noise)
- outputX = proc1(prompt_with_noise)
- return outputX
-
-def send_it3(inputs, noise_level, proc1=proc1):
- prompt_with_noise = add_random_noise(inputs, noise_level)
- while queue.qsize() >= queue_threshold:
- time.sleep(2)
- queue.put(prompt_with_noise)
- output3 = proc1(prompt_with_noise)
- return output3
-
-def send_it4(inputs, noise_level, proc1=proc1):
- prompt_with_noise = add_random_noise(inputs, noise_level)
- while queue.qsize() >= queue_threshold:
- time.sleep(2)
- queue.put(prompt_with_noise)
- output4 = proc1(prompt_with_noise)
- return output4
-
-def send_it5(inputs, noise_level, proc1=proc1):
- prompt_with_noise = add_random_noise(inputs, noise_level)
- while queue.qsize() >= queue_threshold:
- time.sleep(2)
- queue.put(prompt_with_noise)
- output5 = proc1(prompt_with_noise)
- return output5
-
-#def send_it7(inputs, noise_level, proc1=proc1):
- #prompt_with_noise = add_random_noise(inputs, noise_level)
- #while queue.qsize() >= queue_threshold:
- # time.sleep(2)
- #queue.put(prompt_with_noise)
- #output5 = proc1(prompt_with_noise)
- #return output0
-
-
-with gr.Blocks(css='style.css') as demo:
- gr.HTML(
- """
-
-
-
-
- PhotoMovieX
-
-
-
-
- 🤗 Celebrating 36000 downloads at huggingface! 🤗
-
- Check MagicArt35's original model page at Civitai here!.
-
- If you have an idea, put it on the first box to expand it, if you have a full prompt, you can leave the first box empty and just put it on the second one and click generate images!
- Noise Level: Controls how much randomness is added to the input of the boxes after the first one before it is sent to the model, so you can get 6 unique 768x768 images. Higher noise level produces more diverse outputs, while lower noise level produces similar outputs,
- original space created by Phenomenon1981.
-
-
- ❤️ Press the Like Button if you enjoy my space! ❤️
-
-
- """
- )
- with gr.Column(elem_id="col-container"):
- with gr.Row(variant="compact"):
- input_text = gr.Textbox(
- label="Short Prompt",
- show_label=False,
- max_lines=2,
- placeholder="Enter a basic idea and click 'Magic Prompt'. Got no ideas? No problem, Simply just hit the magic button!",
- ).style(
- container=False,min_width=1200
- )
- see_prompts = gr.Button("✨Magic✨ ✨Prompt✨").style(full_width=False)
-
-
- with gr.Row(variant="compact"):
- prompt = gr.Textbox(
- label="Enter your prompt",
- show_label=False,
- max_lines=2,
- placeholder="Full Prompt",
- ).style(
- container=False,
- )
- run = gr.Button("Generate Images").style(full_width=False)
- with gr.Row():
- with gr.Row():
- #Now that the first box generates a picture with noise=0 having the default at 0 makes no sense as it'd generate the same image 6 times.
- noise_level = gr.Slider(minimum=0.2, maximum=3, step=0.1, label="Noise Level (0.1 or less was generating the same pic 6 times! 🤣)")
- gr.HTML(
- """
-
-
-
- Please allow up to 1 minute for each image to generate, for a total of 6 minutes max.
-
-
-
-
- """
- )
- with gr.Row():
- with gr.Row():
- output1=gr.Image(label="PhotoMovieX",show_label=False,min_width=640)
- output2=gr.Image(label="PhotoMovieX",show_label=False,min_width=640)
- with gr.Row():
- with gr.Row():
- output3=gr.Image(label="PhotoMovieX",show_label=False,min_width=640)
- output4=gr.Image(label="PhotoMovieX",show_label=False,min_width=640)
- with gr.Row():
- with gr.Row():
- output5=gr.Image(label="PhotoMovieX",show_label=False,min_width=640)
- outputX=gr.Image(label="PhotoMovieX",show_label=False,min_width=640)
- #with gr.Row():
- #with gr.Row():
- #output0=gr.Image(label="PhotoMovieX",show_label=False,min_width=640)
-
- see_prompts.click(get_prompts, inputs=[input_text], outputs=[prompt], queue=False)
- run.click(send_it1, inputs=[prompt, noise_level], outputs=[output1])
- #run.click(send_it7, inputs=[prompt, noise_level], outputs=[output0])
- run.click(send_it2, inputs=[prompt, noise_level], outputs=[output2])
- run.click(send_it3, inputs=[prompt, noise_level], outputs=[output3])
- run.click(send_it4, inputs=[prompt, noise_level], outputs=[output4])
- run.click(send_it5, inputs=[prompt, noise_level], outputs=[output5])
- run.click(send_itX, inputs=[prompt, noise_level], outputs=[outputX])
-
-
- with gr.Row():
- gr.HTML(
- """
-
-
- Unleash your creative side and generate mesmerizing images with just a few clicks! Enter a spark of inspiration in the "Basic Idea" text box and click the "Magic Prompt" button to elevate it to a polished masterpiece. Make any final tweaks in the "Full Prompt" box and hit the "Generate Images" button to watch your vision come to life. Experiment with the "Noise Level" for a diverse range of outputs, from similar to wildly unique. Let the fun begin!
-
-
- """
-)
-
- demo.launch(enable_queue=True, inline=True)
- block.queue(concurrency_count=100)
diff --git a/spaces/Yuliang/ICON/lib/renderer/camera.py b/spaces/Yuliang/ICON/lib/renderer/camera.py
deleted file mode 100644
index fde488826c8eba933a00485d39cdc21d4d2dae8f..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ICON/lib/renderer/camera.py
+++ /dev/null
@@ -1,226 +0,0 @@
-
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-import cv2
-import numpy as np
-
-from .glm import ortho
-
-
-class Camera:
- def __init__(self, width=1600, height=1200):
- # Focal Length
- # equivalent 50mm
- focal = np.sqrt(width * width + height * height)
- self.focal_x = focal
- self.focal_y = focal
- # Principal Point Offset
- self.principal_x = width / 2
- self.principal_y = height / 2
- # Axis Skew
- self.skew = 0
- # Image Size
- self.width = width
- self.height = height
-
- self.near = 1
- self.far = 10
-
- # Camera Center
- self.center = np.array([0, 0, 1.6])
- self.direction = np.array([0, 0, -1])
- self.right = np.array([1, 0, 0])
- self.up = np.array([0, 1, 0])
-
- self.ortho_ratio = None
-
- def sanity_check(self):
- self.center = self.center.reshape([-1])
- self.direction = self.direction.reshape([-1])
- self.right = self.right.reshape([-1])
- self.up = self.up.reshape([-1])
-
- assert len(self.center) == 3
- assert len(self.direction) == 3
- assert len(self.right) == 3
- assert len(self.up) == 3
-
- @staticmethod
- def normalize_vector(v):
- v_norm = np.linalg.norm(v)
- return v if v_norm == 0 else v / v_norm
-
- def get_real_z_value(self, z):
- z_near = self.near
- z_far = self.far
- z_n = 2.0 * z - 1.0
- z_e = 2.0 * z_near * z_far / (z_far + z_near - z_n * (z_far - z_near))
- return z_e
-
- def get_rotation_matrix(self):
- rot_mat = np.eye(3)
- s = self.right
- s = self.normalize_vector(s)
- rot_mat[0, :] = s
- u = self.up
- u = self.normalize_vector(u)
- rot_mat[1, :] = -u
- rot_mat[2, :] = self.normalize_vector(self.direction)
-
- return rot_mat
-
- def get_translation_vector(self):
- rot_mat = self.get_rotation_matrix()
- trans = -np.dot(rot_mat, self.center)
- return trans
-
- def get_intrinsic_matrix(self):
- int_mat = np.eye(3)
-
- int_mat[0, 0] = self.focal_x
- int_mat[1, 1] = self.focal_y
- int_mat[0, 1] = self.skew
- int_mat[0, 2] = self.principal_x
- int_mat[1, 2] = self.principal_y
-
- return int_mat
-
- def get_projection_matrix(self):
- ext_mat = self.get_extrinsic_matrix()
- int_mat = self.get_intrinsic_matrix()
-
- return np.matmul(int_mat, ext_mat)
-
- def get_extrinsic_matrix(self):
- rot_mat = self.get_rotation_matrix()
- int_mat = self.get_intrinsic_matrix()
- trans = self.get_translation_vector()
-
- extrinsic = np.eye(4)
- extrinsic[:3, :3] = rot_mat
- extrinsic[:3, 3] = trans
-
- return extrinsic[:3, :]
-
- def set_rotation_matrix(self, rot_mat):
- self.direction = rot_mat[2, :]
- self.up = -rot_mat[1, :]
- self.right = rot_mat[0, :]
-
- def set_intrinsic_matrix(self, int_mat):
- self.focal_x = int_mat[0, 0]
- self.focal_y = int_mat[1, 1]
- self.skew = int_mat[0, 1]
- self.principal_x = int_mat[0, 2]
- self.principal_y = int_mat[1, 2]
-
- def set_projection_matrix(self, proj_mat):
- res = cv2.decomposeProjectionMatrix(proj_mat)
- int_mat, rot_mat, camera_center_homo = res[0], res[1], res[2]
- camera_center = camera_center_homo[0:3] / camera_center_homo[3]
- camera_center = camera_center.reshape(-1)
- int_mat = int_mat / int_mat[2][2]
-
- self.set_intrinsic_matrix(int_mat)
- self.set_rotation_matrix(rot_mat)
- self.center = camera_center
-
- self.sanity_check()
-
- def get_gl_matrix(self):
- z_near = self.near
- z_far = self.far
- rot_mat = self.get_rotation_matrix()
- int_mat = self.get_intrinsic_matrix()
- trans = self.get_translation_vector()
-
- extrinsic = np.eye(4)
- extrinsic[:3, :3] = rot_mat
- extrinsic[:3, 3] = trans
- axis_adj = np.eye(4)
- axis_adj[2, 2] = -1
- axis_adj[1, 1] = -1
- model_view = np.matmul(axis_adj, extrinsic)
-
- projective = np.zeros([4, 4])
- projective[:2, :2] = int_mat[:2, :2]
- projective[:2, 2:3] = -int_mat[:2, 2:3]
- projective[3, 2] = -1
- projective[2, 2] = (z_near + z_far)
- projective[2, 3] = (z_near * z_far)
-
- if self.ortho_ratio is None:
- ndc = ortho(0, self.width, 0, self.height, z_near, z_far)
- perspective = np.matmul(ndc, projective)
- else:
- perspective = ortho(-self.width * self.ortho_ratio / 2,
- self.width * self.ortho_ratio / 2,
- -self.height * self.ortho_ratio / 2,
- self.height * self.ortho_ratio / 2, z_near,
- z_far)
-
- return perspective, model_view
-
-
-def KRT_from_P(proj_mat, normalize_K=True):
- res = cv2.decomposeProjectionMatrix(proj_mat)
- K, Rot, camera_center_homog = res[0], res[1], res[2]
- camera_center = camera_center_homog[0:3] / camera_center_homog[3]
- trans = -Rot.dot(camera_center)
- if normalize_K:
- K = K / K[2][2]
- return K, Rot, trans
-
-
-def MVP_from_P(proj_mat, width, height, near=0.1, far=10000):
- '''
- Convert OpenCV camera calibration matrix to OpenGL projection and model view matrix
- :param proj_mat: OpenCV camera projeciton matrix
- :param width: Image width
- :param height: Image height
- :param near: Z near value
- :param far: Z far value
- :return: OpenGL projection matrix and model view matrix
- '''
- res = cv2.decomposeProjectionMatrix(proj_mat)
- K, Rot, camera_center_homog = res[0], res[1], res[2]
- camera_center = camera_center_homog[0:3] / camera_center_homog[3]
- trans = -Rot.dot(camera_center)
- K = K / K[2][2]
-
- extrinsic = np.eye(4)
- extrinsic[:3, :3] = Rot
- extrinsic[:3, 3:4] = trans
- axis_adj = np.eye(4)
- axis_adj[2, 2] = -1
- axis_adj[1, 1] = -1
- model_view = np.matmul(axis_adj, extrinsic)
-
- zFar = far
- zNear = near
- projective = np.zeros([4, 4])
- projective[:2, :2] = K[:2, :2]
- projective[:2, 2:3] = -K[:2, 2:3]
- projective[3, 2] = -1
- projective[2, 2] = (zNear + zFar)
- projective[2, 3] = (zNear * zFar)
-
- ndc = ortho(0, width, 0, height, zNear, zFar)
-
- perspective = np.matmul(ndc, projective)
-
- return perspective, model_view
diff --git a/spaces/Yunshansongbai/SVC-Nahida/modules/attentions.py b/spaces/Yunshansongbai/SVC-Nahida/modules/attentions.py
deleted file mode 100644
index 8ca80c90f2d2db2a52eaac8b88881a84193302a5..0000000000000000000000000000000000000000
--- a/spaces/Yunshansongbai/SVC-Nahida/modules/attentions.py
+++ /dev/null
@@ -1,377 +0,0 @@
-import copy
-import math
-import numpy as np
-import paddle
-from paddle import nn
-from paddle.nn import functional as F
-
-import modules.commons as commons
-import modules.modules as modules
-from modules.modules import LayerNorm
-
-
-class FFT(nn.Layer):
- def __init__(self, hidden_channels, filter_channels, n_heads, n_layers=1, kernel_size=1, p_dropout=0.,
- proximal_bias=False, proximal_init=True, **kwargs):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.LayerList()
- self.norm_layers_0 = nn.LayerList()
- self.ffn_layers = nn.LayerList()
- self.norm_layers_1 = nn.LayerList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(
- MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias,
- proximal_init=proximal_init))
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
- self.norm_layers_1.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.shape[2]).astype(dtype=x.dtype)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
- x = x * x_mask
- return x
-
-
-class Encoder(nn.Layer):
- def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.window_size = window_size
-
- self.drop = nn.Dropout(p_dropout)
- self.attn_layers = nn.LayerList()
- self.norm_layers_1 = nn.LayerList()
- self.ffn_layers = nn.LayerList()
- self.norm_layers_2 = nn.LayerList()
- for i in range(self.n_layers):
- self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask):
- attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.attn_layers[i](x, x, attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class Decoder(nn.Layer):
- def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.LayerList()
- self.norm_layers_0 = nn.LayerList()
- self.encdec_attn_layers = nn.LayerList()
- self.norm_layers_1 = nn.LayerList()
- self.ffn_layers = nn.LayerList()
- self.norm_layers_2 = nn.LayerList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask, h, h_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.size(2)).astype(dtype=x.dtype)
- encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class MultiHeadAttention(nn.Layer):
- def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
- super().__init__()
- assert channels % n_heads == 0
-
- self.channels = channels
- self.out_channels = out_channels
- self.n_heads = n_heads
- self.p_dropout = p_dropout
- self.window_size = window_size
- self.heads_share = heads_share
- self.block_length = block_length
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
- self.attn = None
-
- self.k_channels = channels // n_heads
-
- self.conv_q = nn.Conv1D(channels, channels, 1,)# weight_attr=attr)
- self.conv_k = nn.Conv1D(channels, channels, 1,)# weight_attr=attr)
- self.conv_v = nn.Conv1D(channels, channels, 1,)# weight_attr=attr)
- self.conv_o = nn.Conv1D(channels, out_channels, 1)
- self.drop = nn.Dropout(p_dropout)
-
- if window_size is not None:
- n_heads_rel = 1 if heads_share else n_heads
- rel_stddev = self.k_channels**-0.5
-
- rand = paddle.randn((n_heads_rel, window_size * 2 + 1, self.k_channels)) * rel_stddev
-
- self.emb_rel_k = paddle.create_parameter(rand.shape,'float32',None)
- self.emb_rel_v = paddle.create_parameter(rand.shape,'float32',None)
-
- #nn.init.xavier_uniform_(self.conv_q.weight)
- #nn.init.xavier_uniform_(self.conv_k.weight)
- #nn.init.xavier_uniform_(self.conv_v.weight)
- if proximal_init:
- with paddle.no_grad():
- self.conv_k.weight = (self.conv_q.weight)
- self.conv_k.bias = (self.conv_q.bias)
-
- def forward(self, x, c, attn_mask=None):
- #print(x)
- #print(self.conv_q.weight)
- q = self.conv_q(x)
- k = self.conv_k(c)
- v = self.conv_v(c)
-
- x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
- x = self.conv_o(x)
- return x
-
- @staticmethod
- def _masked_fill(x, mask, value:float):
- y = paddle.full(x.shape, value, x.dtype)
- return paddle.where(mask, y, x)
-
- def attention(self, query, key, value, mask=None):
- # reshape [b, d, t] -> [b, n_h, t, d_k]
- b, d, t_s, t_t = (*key.shape, query.shape[2])
- query = query.reshape((b, self.n_heads, self.k_channels, t_t)).transpose([0,1,3,2])
- key = key.reshape((b, self.n_heads, self.k_channels, t_s)).transpose([0,1,3,2])
- value = value.reshape((b, self.n_heads, self.k_channels, t_s)).transpose([0,1,3,2])
-
- scores = paddle.matmul(query / math.sqrt(self.k_channels), key.transpose([0,1,3,2])) # 0 1 2 3 -4 -3 -2 -1
- if self.window_size is not None:
- assert t_s == t_t, "Relative attention is only available for self-attention."
-
- key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
- rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
- scores_local = self._relative_position_to_absolute_position(rel_logits)
- scores = scores + scores_local
- if self.proximal_bias:
- assert t_s == t_t, "Proximal bias is only available for self-attention."
- scores = scores + self._attention_bias_proximal(t_s).astype(dtype=scores.dtype)
- if mask is not None:
- scores = self._masked_fill(scores, mask == 0, -1e4)
- if self.block_length is not None:
- assert t_s == t_t, "Local attention is only available for self-attention."
- block_mask = paddle.tril(paddle.triu(paddle.ones_like(scores), -self.block_length),self.block_length)
- scores = self._masked_fill(scores, block_mask == 0, -1e4)
- p_attn = F.softmax(scores, axis=-1) # [b, n_h, t_t, t_s]
- p_attn = self.drop(p_attn)
- output = paddle.matmul(p_attn, value)
- if self.window_size is not None:
- relative_weights = self._absolute_position_to_relative_position(p_attn)
- value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
- output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
- output = output.transpose([0,1,3,2]).reshape((b, d, t_t)) # [b, n_h, t_t, d_k] -> [b, d, t_t]
- return output, p_attn
-
- def _matmul_with_relative_values(self, x, y):
- """
- x: [b, h, l, m]
- y: [h or 1, m, d]
- ret: [b, h, l, d]
- """
- ret = paddle.matmul(x, y.unsqueeze(0))
- return ret
-
- def _matmul_with_relative_keys(self, x, y):
- """
- x: [b, h, l, d]
- y: [h or 1, m, d]
- ret: [b, h, l, m]
- """
- ret = paddle.matmul(x, y.unsqueeze(0).transpose([0,1,3,2]))
- return ret
-
- def _get_relative_embeddings(self, relative_embeddings, length):
- max_relative_position = 2 * self.window_size + 1
- # Pad first before slice to avoid using cond ops.
- pad_length = max(length - (self.window_size + 1), 0)
- slice_start_position = max((self.window_size + 1) - length, 0)
- slice_end_position = slice_start_position + 2 * length - 1
- if pad_length > 0:
- padding = commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])
-
- padded_relative_embeddings = F.pad(
- x = relative_embeddings.unsqueeze(0),
- pad = padding[0:4]).squeeze(0)
-
- else:
- padded_relative_embeddings = relative_embeddings
- used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
- return used_relative_embeddings
-
- def _relative_position_to_absolute_position(self, x):
- """
- x: [b, h, l, 2*l-1]
- ret: [b, h, l, l]
- """
- batch, heads, length, _ = x.shape
- # Concat columns of pad to shift from relative to absolute indexing.
- pad_shape = commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]])
- pad_shape = commons.fix_pad_shape(pad_shape, x)
- x = F.pad(x, pad_shape)
- # Concat extra elements so to add up to shape (len+1, 2*len-1).
- x_flat = x.reshape([batch, heads, length * 2 * length])
- pad_shape = commons.convert_pad_shape([[0,0],[0,0],[0,length-1]])
- pad_shape = commons.fix_pad_shape(pad_shape,x_flat)
- x_flat = F.pad(x_flat, pad_shape, data_format='NCL')
- # Reshape and slice out the padded elements.
- x_final = x_flat.reshape([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
- return x_final
-
- def _absolute_position_to_relative_position(self, x):
- """
- x: [b, h, l, l]
- ret: [b, h, l, 2*l-1]
- """
- batch, heads, length, _ = x.shape
- # padd along column
- pad_shape = commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]])
- pad_shape = commons.fix_pad_shape(pad_shape, x)
- x = F.pad(x, pad_shape)
- x_flat = x.reshape([batch, heads, length**2 + length*(length -1)])
- # add 0's in the beginning that will skew the elements after reshape
- pad_shape = commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])
- pad_shape = commons.fix_pad_shape(pad_shape, x_flat)
- x_flat = F.pad(x_flat, pad_shape, data_format='NCL')
- x_final = x_flat.reshape([batch, heads, length, 2*length])[:,:,:,1:]
- return x_final
-
- def _attention_bias_proximal(self, length):
- """Bias for self-attention to encourage attention to close positions.
- Args:
- length: an integer scalar.
- Returns:
- a Tensor with shape [1, 1, length, length]
- """
- r = paddle.arange(length, dtype=np.float32)
- diff = paddle.unsqueeze(r, 0) - paddle.unsqueeze(r, 1)
- return paddle.unsqueeze(paddle.unsqueeze(-paddle.log1p(paddle.abs(diff)), 0), 0)
-
-
-class FFN(nn.Layer):
- def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.activation = activation
- self.causal = causal
-
- if causal:
- self.padding = self._causal_padding
- else:
- self.padding = self._same_padding
-
- self.conv_1 = nn.Conv1D(in_channels, filter_channels, kernel_size)
- self.conv_2 = nn.Conv1D(filter_channels, out_channels, kernel_size)
- self.drop = nn.Dropout(p_dropout)
-
- def forward(self, x, x_mask):
- x = x * x_mask
- x = self.padding(x)
- x = self.conv_1(x)
- if self.activation == "gelu":
- x = x * F.sigmoid(1.702 * x)
- else:
- x = F.relu(x)
- x = self.drop(x)
- x = x * x_mask
- x = self.padding(x)
- x = self.conv_2(x)
- return x * x_mask
-
- def _causal_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = self.kernel_size - 1
- pad_r = 0
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- pad_shape:list = commons.convert_pad_shape(padding)
- pad_shape = commons.fix_pad_shape(pad_shape, x)
- x = F.pad(x, pad_shape,data_format='NCL')
- return x
-
- def _same_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = (self.kernel_size - 1) // 2
- pad_r = self.kernel_size // 2
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- pad_shape = commons.convert_pad_shape(padding)
- pad_shape = commons.fix_pad_shape(pad_shape, x)
- x = F.pad(x, pad_shape, data_format='NCL')
- return x
diff --git a/spaces/ZJunTvT/ZJunChat/readme/README_en.md b/spaces/ZJunTvT/ZJunChat/readme/README_en.md
deleted file mode 100644
index a906ecb3ebc411f5cdeb33d661266a489a20c3b0..0000000000000000000000000000000000000000
--- a/spaces/ZJunTvT/ZJunChat/readme/README_en.md
+++ /dev/null
@@ -1,127 +0,0 @@
-
-
-川虎 Chat 🐯 Chuanhu Chat
-
-
- 
-
-
-
-
Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA
-
- Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search
- LaTeX rendering / Table rendering / Code highlighting
- Auto dark mode / Adaptive web interface / WeChat-like theme
- Multi-parameters tuning / Multi-API-Key support / Multi-user support
- Compatible with GPT-4 / Local deployment for LLMs
-
- Video Tutorial
- ·
- 2.0 Introduction
- ·
- 3.0 Introduction & Tutorial
- ||
- Online trial
- ·
- One-Click deployment
-
-
- 
-
-
-
-
-## Usage Tips
-
-- To better control the ChatGPT, use System Prompt.
-- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu.
-- To try again if the response is unsatisfactory, use `🔄 Regenerate` button.
-- To start a new line in the input box, press Shift + Enter keys.
-- To quickly switch between input history, press ↑ and ↓ key in the input box.
-- To deploy the program onto a server, change the last line of the program to `demo.launch(server_name="0.0.0.0", server_port=)`.
-- To get a public shared link, change the last line of the program to `demo.launch(share=True)`. Please be noted that the program must be running in order to be accessed via a public link.
-- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience.
-
-## Installation
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file.
-
-```shell
-python ChuanhuChatbot.py
-```
-
-A browser window will open and you will be able to chat with ChatGPT.
-
-> **Note**
->
-> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions.
-
-## Troubleshooting
-
-When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows:
-
-1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or
- ```shell
- git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
- ```
-2. Try installing the dependencies again (as this project may have introduced new dependencies)
- ```
- pip install -r requirements.txt
- ```
-3. Update Gradio
- ```
- pip install gradio --upgrade --force-reinstall
- ```
-
-Generally, you can solve most problems by following these steps.
-
-If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-This page lists almost all the possible problems and solutions. Please read it carefully.
-
-## More Information
-
-More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki):
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-
- 
-
-
-## Sponsor
-
-🐯 If you find this project helpful, feel free to buy me a coke or a cup of coffee~
-
-
-
-
diff --git "a/spaces/a-v-bely/russian-task-generator/pages/1_\342\232\231\357\270\217_\320\230\320\275\321\201\321\202\321\200\321\203\320\272\321\206\320\270\321\217.py" "b/spaces/a-v-bely/russian-task-generator/pages/1_\342\232\231\357\270\217_\320\230\320\275\321\201\321\202\321\200\321\203\320\272\321\206\320\270\321\217.py"
deleted file mode 100644
index de520a139af964d97d40cc04d14ddcbb6aa9782b..0000000000000000000000000000000000000000
--- "a/spaces/a-v-bely/russian-task-generator/pages/1_\342\232\231\357\270\217_\320\230\320\275\321\201\321\202\321\200\321\203\320\272\321\206\320\270\321\217.py"
+++ /dev/null
@@ -1,78 +0,0 @@
-import streamlit as st
-
-
-st.set_page_config(page_title='GenLexTasks', layout="wide", page_icon=':ru:')
-if st.session_state.get('-LOGGED_IN_BOOL-'):
- ANNOUNCES = st.expander('**ВАЖНАЯ ИНФОРМАЦИЯ**', expanded=True)
- ANNOUNCES.success(
- '**Уважаемые пользователи, пожалуйста, после генерации заданий перейдите на вкладку "📝Онлайн-тест" '
- 'и заполните там опросник. Таким образом Вы очень поможете в улучшении качества заданий! Спасибо!🤗**')
- ANNOUNCES.warning(
- '**Сейчас генератор проходит завершающую настройку и отладку, для которой необходимо большое количество '
- 'данных об уместности выбранных целевых слов и дистракторов к ним. Поэтому просим Вас отнестись с пониманием '
- 'к излишне большому количеству заданий.**')
- ANNOUNCES.warning(
- '**В настоящее время генератор может работать только с отдельными словами (от пробела до пробела).'
- 'Использование словосочетаний в качестве целевых слов или дистракторов будет добавлено в ближайшее время.**')
- ANNOUNCES.warning(
- '**❗️ㅤУбедительно просим Вас дожидаться окончания генерации или загрузки и не переходить на '
- 'другие вкладки до выведения соответствующего сообщения.**')
- ANNOUNCES.warning(
- '**❗ㅤВ случае появления красных сообщений об ошибке, как правило, проблема решается '
- 'повторными нажатиями на нужный Вам элемент. Приносим извинения за неудобства.**')
-
- INSTRUCTION = st.expander(label='**ИНСТРУКЦИЯ**', expanded=True)
- INSTRUCTION.markdown(
- '**_I. Выберите режим работы._**'
- '\n\n**_:red[СОЗДАНИЕ ЗАДАНИЙ]_**'
- '\n\nПосле выбора данного режима работы появится форма, которую необходимо заполнить:'
- '\n\n1. Придумайте **название** для файла с заданиями. '
- 'Вы можете оставить это поле пустым - именем по умолчанию служит текущая дата и первые 20 символов '
- 'введенного Вами текста.'
- '\n\n2. Введите **текст** или выберите **текстовый файл** с исходным текстом, на основе которого Вы хотите '
- 'создать задания. '
- '\n\n3. Укажите *способ выбора целевых слов*:'
- '\n\t* *:green[Автоматически]*: программа сама выберет подходящие по сложности целевые слова.'
- '\n\t* *:blue[Самостоятельно]*: введите в соответствующее поле целевые слова через запятую в той форме, '
- 'в которой они встречаются в тексте. В этом случае *:orange[языковой уровень]* можно не указывать, но тогда '
- 'дистракторы будут полностью случайными и несоотнесёнными с уровнем.'
- '\n4. Если Вы выбрали *:green[автоматический поиск целевых слов]*, **_:red[обязательно]_** укажите '
- '*:orange[языковой уровень]*. Данный параметр отвечает за выбор лексического минимума, использующегося при '
- 'подборе дистракторов.'
- '\n5. Если Вы выбрали *:blue[самостоятельный ввод целевых слов]*, проверьте, что заполнили соответствующее '
- 'поле. ️ \n❗ **:red[Введите слова в той форме, в которой они встречаются в тексте]**.'
- '\n\n6. Укажите число дистракторов - неправильных вариантов ответа. Если указано _более четырех_ '
- 'дистракторов, возможно, что в некоторых заданиях будет выведено _меньшее количество, но не менее четырех_ '
- 'вариантов. Данное обстоятельство связано с проверкой наличия дистракторов в лексических минимумах.'
- '\n7. Выберите **способы вывода** готовых материалов.'
- '\n8. Для начала работы нажмите на кнопку **"Запуск"**. Если все поля заполнены верно, '
- 'начнется процесс генерации заданий. Прогресс будет отображаться на экране.'
- '\n9. По окончании процесса генерации заданий будет выведено **_:green[соответсвующее сообщение]_**. '
- 'Затем Вы можете перейти на вкладки **просмотра и 📥 сохранения** заданий, а так же 📝**онлайн-теста**.'
- '\n\n**_:red[ЗАГРУЗКА ИЗ АРХИВА]_**'
- '\n\nПосле выбора данного режима работы появится таблица, в которой перечислены названия заданий, '
- 'которые Вы сохранили, языковой уровень и дата их создания.'
- ' Для загрузки определенного файла с заданиями:'
- '\n1. Введите (или скопируйте из таблицы) название.'
- '\n2. Укажите соответсвующий языковой уровень.'
- '\n3. Нажмите на кнопку **"Загрузить"**.'
- '\n4. Если все поля заполнены верно, Вы увидите сообщение о том, что **:green[задания успешно загружены]**.'
- '\n\n\nДля того, чтобы свернуть/развернуть блоки **Инструкций** или **Важной информации**, '
- 'кликните по заголовку этого блока или по стрелке (ᐯ / ᐱ), располагающейся в его правом верхнем углу.')
-
- INSTRUCTION_ONLINE_TEST = st.expander(label='**ИНСТРУКЦИЯ ОНЛАЙН-ТЕСТ**', expanded=True)
- INSTRUCTION_ONLINE_TEST.markdown(
- 'Уважаемые пользователи, предлагаем Вам заполнить опросник по оценке качества созданных заданий. '
- '\n\nНиже находится анкета с заданиями в таблице.'
- '\n\n- В **первом столбце** приводится ответ - слово, удаленное из оригинального текста.'
- '\n\n- Отметьте во **втором столбце**, уместно ли создавать задание с данным словом.'
- '\n\n- В **третьем столбце** приведены подобранные программой дистракторы.'
- '\n\n- Введите в **четвертый столбец** дистракторы (целиком или букву), которые, по Вашему мнению, '
- '**:red[не уместны]**. '
- '\n\n**:green[Уместными дистракторами]** мы предлагаем считать те, которые одновременно удовлетворяют '
- 'следующим условиям в рамках языкового уровня, для которого они созданы:'
- '\n\n1. не слишком очевидно являются неправильными вариантами (*варить суп/стол*);'
- '\n\n2. и при этом не могут быть полноценной заменой удаленного слова (*варить суп/кашу*)'
- )
-else:
- st.warning('**Войдите или зарегистрируйтесь**')
diff --git a/spaces/abdvl/datahub_qa_bot/docs/deploy/telemetry.md b/spaces/abdvl/datahub_qa_bot/docs/deploy/telemetry.md
deleted file mode 100644
index c5458cc5df05e093e433bff4738cd552b6ec9177..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/deploy/telemetry.md
+++ /dev/null
@@ -1,10 +0,0 @@
-# DataHub Telemetry
-
-## Overview of DataHub Telemetry
-
-To effectively build and maintain the DataHub Project, we must understand how end-users work within DataHub. Beginning in version 0.8.35, DataHub collects anonymous usage statistics and errors to inform our roadmap priorities and to enable us to proactively address errors.
-
-Deployments are assigned a UUID which is sent along with event details, Java version, OS, and timestamp; telemetry collection is enabled by default and can be disabled by setting `DATAHUB_TELEMETRY_ENABLED=false` in your Docker Compose config.
-
-
-The source code is available [here.](../../metadata-service/factories/src/main/java/com/linkedin/gms/factory/telemetry/TelemetryUtils.java)
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/decode_heads/gc_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/decode_heads/gc_head.py
deleted file mode 100644
index cff93b6a9fd6f0a4a2f6833cd464efbe29559728..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/decode_heads/gc_head.py
+++ /dev/null
@@ -1,59 +0,0 @@
-'''
- * Copyright (c) 2023 Salesforce, Inc.
- * All rights reserved.
- * SPDX-License-Identifier: Apache License 2.0
- * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
- * By Can Qin
- * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
- * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
- * Modified from MMCV repo: From https://github.com/open-mmlab/mmcv
- * Copyright (c) OpenMMLab. All rights reserved.
-'''
-
-import torch
-from annotator.uniformer.mmcv.cnn import ContextBlock
-
-from ..builder import HEADS
-from .fcn_head import FCNHead
-
-
-@HEADS.register_module()
-class GCHead(FCNHead):
- """GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond.
-
- This head is the implementation of `GCNet
- `_.
-
- Args:
- ratio (float): Multiplier of channels ratio. Default: 1/4.
- pooling_type (str): The pooling type of context aggregation.
- Options are 'att', 'avg'. Default: 'avg'.
- fusion_types (tuple[str]): The fusion type for feature fusion.
- Options are 'channel_add', 'channel_mul'. Default: ('channel_add',)
- """
-
- def __init__(self,
- ratio=1 / 4.,
- pooling_type='att',
- fusion_types=('channel_add', ),
- **kwargs):
- super(GCHead, self).__init__(num_convs=2, **kwargs)
- self.ratio = ratio
- self.pooling_type = pooling_type
- self.fusion_types = fusion_types
- self.gc_block = ContextBlock(
- in_channels=self.channels,
- ratio=self.ratio,
- pooling_type=self.pooling_type,
- fusion_types=self.fusion_types)
-
- def forward(self, inputs):
- """Forward function."""
- x = self._transform_inputs(inputs)
- output = self.convs[0](x)
- output = self.gc_block(output)
- output = self.convs[1](output)
- if self.concat_input:
- output = self.conv_cat(torch.cat([x, output], dim=1))
- output = self.cls_seg(output)
- return output
diff --git a/spaces/abidlabs/The-Acquisition-Post-Generator/README.md b/spaces/abidlabs/The-Acquisition-Post-Generator/README.md
deleted file mode 100644
index 0822468a6c482785cf1e27eabe0e86c9a94ea6d6..0000000000000000000000000000000000000000
--- a/spaces/abidlabs/The-Acquisition-Post-Generator/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: The Acquisition Post Generator
-emoji: 🚀
-colorFrom: gray
-colorTo: red
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/abrar-lohia/text-2-character-anim/VQTrans/utils/eval_trans.py b/spaces/abrar-lohia/text-2-character-anim/VQTrans/utils/eval_trans.py
deleted file mode 100644
index 8778bb8cb7e7a320e5f7f2f3b43c7ba0b4c285ab..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/VQTrans/utils/eval_trans.py
+++ /dev/null
@@ -1,580 +0,0 @@
-import os
-
-import clip
-import numpy as np
-import torch
-from scipy import linalg
-
-import visualization.plot_3d_global as plot_3d
-from utils.motion_process import recover_from_ric
-
-
-def tensorborad_add_video_xyz(writer, xyz, nb_iter, tag, nb_vis=4, title_batch=None, outname=None):
- xyz = xyz[:1]
- bs, seq = xyz.shape[:2]
- xyz = xyz.reshape(bs, seq, -1, 3)
- plot_xyz = plot_3d.draw_to_batch(xyz.cpu().numpy(),title_batch, outname)
- plot_xyz =np.transpose(plot_xyz, (0, 1, 4, 2, 3))
- writer.add_video(tag, plot_xyz, nb_iter, fps = 20)
-
-@torch.no_grad()
-def evaluation_vqvae(out_dir, val_loader, net, logger, writer, nb_iter, best_fid, best_iter, best_div, best_top1, best_top2, best_top3, best_matching, eval_wrapper, draw = True, save = True, savegif=False, savenpy=False) :
- net.eval()
- nb_sample = 0
-
- draw_org = []
- draw_pred = []
- draw_text = []
-
-
- motion_annotation_list = []
- motion_pred_list = []
-
- R_precision_real = 0
- R_precision = 0
-
- nb_sample = 0
- matching_score_real = 0
- matching_score_pred = 0
- for batch in val_loader:
- word_embeddings, pos_one_hots, caption, sent_len, motion, m_length, token, name = batch
-
- motion = motion.cuda()
- et, em = eval_wrapper.get_co_embeddings(word_embeddings, pos_one_hots, sent_len, motion, m_length)
- bs, seq = motion.shape[0], motion.shape[1]
-
- num_joints = 21 if motion.shape[-1] == 251 else 22
-
- pred_pose_eval = torch.zeros((bs, seq, motion.shape[-1])).cuda()
-
- for i in range(bs):
- pose = val_loader.dataset.inv_transform(motion[i:i+1, :m_length[i], :].detach().cpu().numpy())
- pose_xyz = recover_from_ric(torch.from_numpy(pose).float().cuda(), num_joints)
-
-
- pred_pose, loss_commit, perplexity = net(motion[i:i+1, :m_length[i]])
- pred_denorm = val_loader.dataset.inv_transform(pred_pose.detach().cpu().numpy())
- pred_xyz = recover_from_ric(torch.from_numpy(pred_denorm).float().cuda(), num_joints)
-
- if savenpy:
- np.save(os.path.join(out_dir, name[i]+'_gt.npy'), pose_xyz[:, :m_length[i]].cpu().numpy())
- np.save(os.path.join(out_dir, name[i]+'_pred.npy'), pred_xyz.detach().cpu().numpy())
-
- pred_pose_eval[i:i+1,:m_length[i],:] = pred_pose
-
- if i < min(4, bs):
- draw_org.append(pose_xyz)
- draw_pred.append(pred_xyz)
- draw_text.append(caption[i])
-
- et_pred, em_pred = eval_wrapper.get_co_embeddings(word_embeddings, pos_one_hots, sent_len, pred_pose_eval, m_length)
-
- motion_pred_list.append(em_pred)
- motion_annotation_list.append(em)
-
- temp_R, temp_match = calculate_R_precision(et.cpu().numpy(), em.cpu().numpy(), top_k=3, sum_all=True)
- R_precision_real += temp_R
- matching_score_real += temp_match
- temp_R, temp_match = calculate_R_precision(et_pred.cpu().numpy(), em_pred.cpu().numpy(), top_k=3, sum_all=True)
- R_precision += temp_R
- matching_score_pred += temp_match
-
- nb_sample += bs
-
- motion_annotation_np = torch.cat(motion_annotation_list, dim=0).cpu().numpy()
- motion_pred_np = torch.cat(motion_pred_list, dim=0).cpu().numpy()
- gt_mu, gt_cov = calculate_activation_statistics(motion_annotation_np)
- mu, cov= calculate_activation_statistics(motion_pred_np)
-
- diversity_real = calculate_diversity(motion_annotation_np, 300 if nb_sample > 300 else 100)
- diversity = calculate_diversity(motion_pred_np, 300 if nb_sample > 300 else 100)
-
- R_precision_real = R_precision_real / nb_sample
- R_precision = R_precision / nb_sample
-
- matching_score_real = matching_score_real / nb_sample
- matching_score_pred = matching_score_pred / nb_sample
-
- fid = calculate_frechet_distance(gt_mu, gt_cov, mu, cov)
-
- msg = f"--> \t Eva. Iter {nb_iter} :, FID. {fid:.4f}, Diversity Real. {diversity_real:.4f}, Diversity. {diversity:.4f}, R_precision_real. {R_precision_real}, R_precision. {R_precision}, matching_score_real. {matching_score_real}, matching_score_pred. {matching_score_pred}"
- logger.info(msg)
-
- if draw:
- writer.add_scalar('./Test/FID', fid, nb_iter)
- writer.add_scalar('./Test/Diversity', diversity, nb_iter)
- writer.add_scalar('./Test/top1', R_precision[0], nb_iter)
- writer.add_scalar('./Test/top2', R_precision[1], nb_iter)
- writer.add_scalar('./Test/top3', R_precision[2], nb_iter)
- writer.add_scalar('./Test/matching_score', matching_score_pred, nb_iter)
-
-
- if nb_iter % 5000 == 0 :
- for ii in range(4):
- tensorborad_add_video_xyz(writer, draw_org[ii], nb_iter, tag='./Vis/org_eval'+str(ii), nb_vis=1, title_batch=[draw_text[ii]], outname=[os.path.join(out_dir, 'gt'+str(ii)+'.gif')] if savegif else None)
-
- if nb_iter % 5000 == 0 :
- for ii in range(4):
- tensorborad_add_video_xyz(writer, draw_pred[ii], nb_iter, tag='./Vis/pred_eval'+str(ii), nb_vis=1, title_batch=[draw_text[ii]], outname=[os.path.join(out_dir, 'pred'+str(ii)+'.gif')] if savegif else None)
-
-
- if fid < best_fid :
- msg = f"--> --> \t FID Improved from {best_fid:.5f} to {fid:.5f} !!!"
- logger.info(msg)
- best_fid, best_iter = fid, nb_iter
- if save:
- torch.save({'net' : net.state_dict()}, os.path.join(out_dir, 'net_best_fid.pth'))
-
- if abs(diversity_real - diversity) < abs(diversity_real - best_div) :
- msg = f"--> --> \t Diversity Improved from {best_div:.5f} to {diversity:.5f} !!!"
- logger.info(msg)
- best_div = diversity
- if save:
- torch.save({'net' : net.state_dict()}, os.path.join(out_dir, 'net_best_div.pth'))
-
- if R_precision[0] > best_top1 :
- msg = f"--> --> \t Top1 Improved from {best_top1:.4f} to {R_precision[0]:.4f} !!!"
- logger.info(msg)
- best_top1 = R_precision[0]
- if save:
- torch.save({'net' : net.state_dict()}, os.path.join(out_dir, 'net_best_top1.pth'))
-
- if R_precision[1] > best_top2 :
- msg = f"--> --> \t Top2 Improved from {best_top2:.4f} to {R_precision[1]:.4f} !!!"
- logger.info(msg)
- best_top2 = R_precision[1]
-
- if R_precision[2] > best_top3 :
- msg = f"--> --> \t Top3 Improved from {best_top3:.4f} to {R_precision[2]:.4f} !!!"
- logger.info(msg)
- best_top3 = R_precision[2]
-
- if matching_score_pred < best_matching :
- msg = f"--> --> \t matching_score Improved from {best_matching:.5f} to {matching_score_pred:.5f} !!!"
- logger.info(msg)
- best_matching = matching_score_pred
- if save:
- torch.save({'net' : net.state_dict()}, os.path.join(out_dir, 'net_best_matching.pth'))
-
- if save:
- torch.save({'net' : net.state_dict()}, os.path.join(out_dir, 'net_last.pth'))
-
- net.train()
- return best_fid, best_iter, best_div, best_top1, best_top2, best_top3, best_matching, writer, logger
-
-
-@torch.no_grad()
-def evaluation_transformer(out_dir, val_loader, net, trans, logger, writer, nb_iter, best_fid, best_iter, best_div, best_top1, best_top2, best_top3, best_matching, clip_model, eval_wrapper, draw = True, save = True, savegif=False) :
-
- trans.eval()
- nb_sample = 0
-
- draw_org = []
- draw_pred = []
- draw_text = []
- draw_text_pred = []
-
- motion_annotation_list = []
- motion_pred_list = []
- R_precision_real = 0
- R_precision = 0
- matching_score_real = 0
- matching_score_pred = 0
-
- nb_sample = 0
- for i in range(1):
- for batch in val_loader:
- word_embeddings, pos_one_hots, clip_text, sent_len, pose, m_length, token, name = batch
-
- bs, seq = pose.shape[:2]
- num_joints = 21 if pose.shape[-1] == 251 else 22
-
- text = clip.tokenize(clip_text, truncate=True).cuda()
-
- feat_clip_text = clip_model.encode_text(text).float()
- pred_pose_eval = torch.zeros((bs, seq, pose.shape[-1])).cuda()
- pred_len = torch.ones(bs).long()
-
- for k in range(bs):
- try:
- index_motion = trans.sample(feat_clip_text[k:k+1], False)
- except:
- index_motion = torch.ones(1,1).cuda().long()
-
- pred_pose = net.forward_decoder(index_motion)
- cur_len = pred_pose.shape[1]
-
- pred_len[k] = min(cur_len, seq)
- pred_pose_eval[k:k+1, :cur_len] = pred_pose[:, :seq]
-
- if draw:
- pred_denorm = val_loader.dataset.inv_transform(pred_pose.detach().cpu().numpy())
- pred_xyz = recover_from_ric(torch.from_numpy(pred_denorm).float().cuda(), num_joints)
-
- if i == 0 and k < 4:
- draw_pred.append(pred_xyz)
- draw_text_pred.append(clip_text[k])
-
- et_pred, em_pred = eval_wrapper.get_co_embeddings(word_embeddings, pos_one_hots, sent_len, pred_pose_eval, pred_len)
-
- if i == 0:
- pose = pose.cuda().float()
-
- et, em = eval_wrapper.get_co_embeddings(word_embeddings, pos_one_hots, sent_len, pose, m_length)
- motion_annotation_list.append(em)
- motion_pred_list.append(em_pred)
-
- if draw:
- pose = val_loader.dataset.inv_transform(pose.detach().cpu().numpy())
- pose_xyz = recover_from_ric(torch.from_numpy(pose).float().cuda(), num_joints)
-
-
- for j in range(min(4, bs)):
- draw_org.append(pose_xyz[j][:m_length[j]].unsqueeze(0))
- draw_text.append(clip_text[j])
-
- temp_R, temp_match = calculate_R_precision(et.cpu().numpy(), em.cpu().numpy(), top_k=3, sum_all=True)
- R_precision_real += temp_R
- matching_score_real += temp_match
- temp_R, temp_match = calculate_R_precision(et_pred.cpu().numpy(), em_pred.cpu().numpy(), top_k=3, sum_all=True)
- R_precision += temp_R
- matching_score_pred += temp_match
-
- nb_sample += bs
-
- motion_annotation_np = torch.cat(motion_annotation_list, dim=0).cpu().numpy()
- motion_pred_np = torch.cat(motion_pred_list, dim=0).cpu().numpy()
- gt_mu, gt_cov = calculate_activation_statistics(motion_annotation_np)
- mu, cov= calculate_activation_statistics(motion_pred_np)
-
- diversity_real = calculate_diversity(motion_annotation_np, 300 if nb_sample > 300 else 100)
- diversity = calculate_diversity(motion_pred_np, 300 if nb_sample > 300 else 100)
-
- R_precision_real = R_precision_real / nb_sample
- R_precision = R_precision / nb_sample
-
- matching_score_real = matching_score_real / nb_sample
- matching_score_pred = matching_score_pred / nb_sample
-
-
- fid = calculate_frechet_distance(gt_mu, gt_cov, mu, cov)
-
- msg = f"--> \t Eva. Iter {nb_iter} :, FID. {fid:.4f}, Diversity Real. {diversity_real:.4f}, Diversity. {diversity:.4f}, R_precision_real. {R_precision_real}, R_precision. {R_precision}, matching_score_real. {matching_score_real}, matching_score_pred. {matching_score_pred}"
- logger.info(msg)
-
-
- if draw:
- writer.add_scalar('./Test/FID', fid, nb_iter)
- writer.add_scalar('./Test/Diversity', diversity, nb_iter)
- writer.add_scalar('./Test/top1', R_precision[0], nb_iter)
- writer.add_scalar('./Test/top2', R_precision[1], nb_iter)
- writer.add_scalar('./Test/top3', R_precision[2], nb_iter)
- writer.add_scalar('./Test/matching_score', matching_score_pred, nb_iter)
-
-
- if nb_iter % 10000 == 0 :
- for ii in range(4):
- tensorborad_add_video_xyz(writer, draw_org[ii], nb_iter, tag='./Vis/org_eval'+str(ii), nb_vis=1, title_batch=[draw_text[ii]], outname=[os.path.join(out_dir, 'gt'+str(ii)+'.gif')] if savegif else None)
-
- if nb_iter % 10000 == 0 :
- for ii in range(4):
- tensorborad_add_video_xyz(writer, draw_pred[ii], nb_iter, tag='./Vis/pred_eval'+str(ii), nb_vis=1, title_batch=[draw_text_pred[ii]], outname=[os.path.join(out_dir, 'pred'+str(ii)+'.gif')] if savegif else None)
-
-
- if fid < best_fid :
- msg = f"--> --> \t FID Improved from {best_fid:.5f} to {fid:.5f} !!!"
- logger.info(msg)
- best_fid, best_iter = fid, nb_iter
- if save:
- torch.save({'trans' : trans.state_dict()}, os.path.join(out_dir, 'net_best_fid.pth'))
-
- if matching_score_pred < best_matching :
- msg = f"--> --> \t matching_score Improved from {best_matching:.5f} to {matching_score_pred:.5f} !!!"
- logger.info(msg)
- best_matching = matching_score_pred
-
- if abs(diversity_real - diversity) < abs(diversity_real - best_div) :
- msg = f"--> --> \t Diversity Improved from {best_div:.5f} to {diversity:.5f} !!!"
- logger.info(msg)
- best_div = diversity
-
- if R_precision[0] > best_top1 :
- msg = f"--> --> \t Top1 Improved from {best_top1:.4f} to {R_precision[0]:.4f} !!!"
- logger.info(msg)
- best_top1 = R_precision[0]
-
- if R_precision[1] > best_top2 :
- msg = f"--> --> \t Top2 Improved from {best_top2:.4f} to {R_precision[1]:.4f} !!!"
- logger.info(msg)
- best_top2 = R_precision[1]
-
- if R_precision[2] > best_top3 :
- msg = f"--> --> \t Top3 Improved from {best_top3:.4f} to {R_precision[2]:.4f} !!!"
- logger.info(msg)
- best_top3 = R_precision[2]
-
- if save:
- torch.save({'trans' : trans.state_dict()}, os.path.join(out_dir, 'net_last.pth'))
-
- trans.train()
- return best_fid, best_iter, best_div, best_top1, best_top2, best_top3, best_matching, writer, logger
-
-
-@torch.no_grad()
-def evaluation_transformer_test(out_dir, val_loader, net, trans, logger, writer, nb_iter, best_fid, best_iter, best_div, best_top1, best_top2, best_top3, best_matching, best_multi, clip_model, eval_wrapper, draw = True, save = True, savegif=False, savenpy=False) :
-
- trans.eval()
- nb_sample = 0
-
- draw_org = []
- draw_pred = []
- draw_text = []
- draw_text_pred = []
- draw_name = []
-
- motion_annotation_list = []
- motion_pred_list = []
- motion_multimodality = []
- R_precision_real = 0
- R_precision = 0
- matching_score_real = 0
- matching_score_pred = 0
-
- nb_sample = 0
-
- for batch in val_loader:
-
- word_embeddings, pos_one_hots, clip_text, sent_len, pose, m_length, token, name = batch
- bs, seq = pose.shape[:2]
- num_joints = 21 if pose.shape[-1] == 251 else 22
-
- text = clip.tokenize(clip_text, truncate=True).cuda()
-
- feat_clip_text = clip_model.encode_text(text).float()
- motion_multimodality_batch = []
- for i in range(30):
- pred_pose_eval = torch.zeros((bs, seq, pose.shape[-1])).cuda()
- pred_len = torch.ones(bs).long()
-
- for k in range(bs):
- try:
- index_motion = trans.sample(feat_clip_text[k:k+1], True)
- except:
- index_motion = torch.ones(1,1).cuda().long()
-
- pred_pose = net.forward_decoder(index_motion)
- cur_len = pred_pose.shape[1]
-
- pred_len[k] = min(cur_len, seq)
- pred_pose_eval[k:k+1, :cur_len] = pred_pose[:, :seq]
-
- if i == 0 and (draw or savenpy):
- pred_denorm = val_loader.dataset.inv_transform(pred_pose.detach().cpu().numpy())
- pred_xyz = recover_from_ric(torch.from_numpy(pred_denorm).float().cuda(), num_joints)
-
- if savenpy:
- np.save(os.path.join(out_dir, name[k]+'_pred.npy'), pred_xyz.detach().cpu().numpy())
-
- if draw:
- if i == 0:
- draw_pred.append(pred_xyz)
- draw_text_pred.append(clip_text[k])
- draw_name.append(name[k])
-
- et_pred, em_pred = eval_wrapper.get_co_embeddings(word_embeddings, pos_one_hots, sent_len, pred_pose_eval, pred_len)
-
- motion_multimodality_batch.append(em_pred.reshape(bs, 1, -1))
-
- if i == 0:
- pose = pose.cuda().float()
-
- et, em = eval_wrapper.get_co_embeddings(word_embeddings, pos_one_hots, sent_len, pose, m_length)
- motion_annotation_list.append(em)
- motion_pred_list.append(em_pred)
-
- if draw or savenpy:
- pose = val_loader.dataset.inv_transform(pose.detach().cpu().numpy())
- pose_xyz = recover_from_ric(torch.from_numpy(pose).float().cuda(), num_joints)
-
- if savenpy:
- for j in range(bs):
- np.save(os.path.join(out_dir, name[j]+'_gt.npy'), pose_xyz[j][:m_length[j]].unsqueeze(0).cpu().numpy())
-
- if draw:
- for j in range(bs):
- draw_org.append(pose_xyz[j][:m_length[j]].unsqueeze(0))
- draw_text.append(clip_text[j])
-
- temp_R, temp_match = calculate_R_precision(et.cpu().numpy(), em.cpu().numpy(), top_k=3, sum_all=True)
- R_precision_real += temp_R
- matching_score_real += temp_match
- temp_R, temp_match = calculate_R_precision(et_pred.cpu().numpy(), em_pred.cpu().numpy(), top_k=3, sum_all=True)
- R_precision += temp_R
- matching_score_pred += temp_match
-
- nb_sample += bs
-
- motion_multimodality.append(torch.cat(motion_multimodality_batch, dim=1))
-
- motion_annotation_np = torch.cat(motion_annotation_list, dim=0).cpu().numpy()
- motion_pred_np = torch.cat(motion_pred_list, dim=0).cpu().numpy()
- gt_mu, gt_cov = calculate_activation_statistics(motion_annotation_np)
- mu, cov= calculate_activation_statistics(motion_pred_np)
-
- diversity_real = calculate_diversity(motion_annotation_np, 300 if nb_sample > 300 else 100)
- diversity = calculate_diversity(motion_pred_np, 300 if nb_sample > 300 else 100)
-
- R_precision_real = R_precision_real / nb_sample
- R_precision = R_precision / nb_sample
-
- matching_score_real = matching_score_real / nb_sample
- matching_score_pred = matching_score_pred / nb_sample
-
- multimodality = 0
- motion_multimodality = torch.cat(motion_multimodality, dim=0).cpu().numpy()
- multimodality = calculate_multimodality(motion_multimodality, 10)
-
- fid = calculate_frechet_distance(gt_mu, gt_cov, mu, cov)
-
- msg = f"--> \t Eva. Iter {nb_iter} :, FID. {fid:.4f}, Diversity Real. {diversity_real:.4f}, Diversity. {diversity:.4f}, R_precision_real. {R_precision_real}, R_precision. {R_precision}, matching_score_real. {matching_score_real}, matching_score_pred. {matching_score_pred}, multimodality. {multimodality:.4f}"
- logger.info(msg)
-
-
- if draw:
- for ii in range(len(draw_org)):
- tensorborad_add_video_xyz(writer, draw_org[ii], nb_iter, tag='./Vis/'+draw_name[ii]+'_org', nb_vis=1, title_batch=[draw_text[ii]], outname=[os.path.join(out_dir, draw_name[ii]+'_skel_gt.gif')] if savegif else None)
-
- tensorborad_add_video_xyz(writer, draw_pred[ii], nb_iter, tag='./Vis/'+draw_name[ii]+'_pred', nb_vis=1, title_batch=[draw_text_pred[ii]], outname=[os.path.join(out_dir, draw_name[ii]+'_skel_pred.gif')] if savegif else None)
-
- trans.train()
- return fid, best_iter, diversity, R_precision[0], R_precision[1], R_precision[2], matching_score_pred, multimodality, writer, logger
-
-# (X - X_train)*(X - X_train) = -2X*X_train + X*X + X_train*X_train
-def euclidean_distance_matrix(matrix1, matrix2):
- """
- Params:
- -- matrix1: N1 x D
- -- matrix2: N2 x D
- Returns:
- -- dist: N1 x N2
- dist[i, j] == distance(matrix1[i], matrix2[j])
- """
- assert matrix1.shape[1] == matrix2.shape[1]
- d1 = -2 * np.dot(matrix1, matrix2.T) # shape (num_test, num_train)
- d2 = np.sum(np.square(matrix1), axis=1, keepdims=True) # shape (num_test, 1)
- d3 = np.sum(np.square(matrix2), axis=1) # shape (num_train, )
- dists = np.sqrt(d1 + d2 + d3) # broadcasting
- return dists
-
-
-
-def calculate_top_k(mat, top_k):
- size = mat.shape[0]
- gt_mat = np.expand_dims(np.arange(size), 1).repeat(size, 1)
- bool_mat = (mat == gt_mat)
- correct_vec = False
- top_k_list = []
- for i in range(top_k):
-# print(correct_vec, bool_mat[:, i])
- correct_vec = (correct_vec | bool_mat[:, i])
- # print(correct_vec)
- top_k_list.append(correct_vec[:, None])
- top_k_mat = np.concatenate(top_k_list, axis=1)
- return top_k_mat
-
-
-def calculate_R_precision(embedding1, embedding2, top_k, sum_all=False):
- dist_mat = euclidean_distance_matrix(embedding1, embedding2)
- matching_score = dist_mat.trace()
- argmax = np.argsort(dist_mat, axis=1)
- top_k_mat = calculate_top_k(argmax, top_k)
- if sum_all:
- return top_k_mat.sum(axis=0), matching_score
- else:
- return top_k_mat, matching_score
-
-def calculate_multimodality(activation, multimodality_times):
- assert len(activation.shape) == 3
- assert activation.shape[1] > multimodality_times
- num_per_sent = activation.shape[1]
-
- first_dices = np.random.choice(num_per_sent, multimodality_times, replace=False)
- second_dices = np.random.choice(num_per_sent, multimodality_times, replace=False)
- dist = linalg.norm(activation[:, first_dices] - activation[:, second_dices], axis=2)
- return dist.mean()
-
-
-def calculate_diversity(activation, diversity_times):
- assert len(activation.shape) == 2
- assert activation.shape[0] > diversity_times
- num_samples = activation.shape[0]
-
- first_indices = np.random.choice(num_samples, diversity_times, replace=False)
- second_indices = np.random.choice(num_samples, diversity_times, replace=False)
- dist = linalg.norm(activation[first_indices] - activation[second_indices], axis=1)
- return dist.mean()
-
-
-
-def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
-
- mu1 = np.atleast_1d(mu1)
- mu2 = np.atleast_1d(mu2)
-
- sigma1 = np.atleast_2d(sigma1)
- sigma2 = np.atleast_2d(sigma2)
-
- assert mu1.shape == mu2.shape, \
- 'Training and test mean vectors have different lengths'
- assert sigma1.shape == sigma2.shape, \
- 'Training and test covariances have different dimensions'
-
- diff = mu1 - mu2
-
- # Product might be almost singular
- covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
- if not np.isfinite(covmean).all():
- msg = ('fid calculation produces singular product; '
- 'adding %s to diagonal of cov estimates') % eps
- print(msg)
- offset = np.eye(sigma1.shape[0]) * eps
- covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
-
- # Numerical error might give slight imaginary component
- if np.iscomplexobj(covmean):
- if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
- m = np.max(np.abs(covmean.imag))
- raise ValueError('Imaginary component {}'.format(m))
- covmean = covmean.real
-
- tr_covmean = np.trace(covmean)
-
- return (diff.dot(diff) + np.trace(sigma1)
- + np.trace(sigma2) - 2 * tr_covmean)
-
-
-
-def calculate_activation_statistics(activations):
-
- mu = np.mean(activations, axis=0)
- cov = np.cov(activations, rowvar=False)
- return mu, cov
-
-
-def calculate_frechet_feature_distance(feature_list1, feature_list2):
- feature_list1 = np.stack(feature_list1)
- feature_list2 = np.stack(feature_list2)
-
- # normalize the scale
- mean = np.mean(feature_list1, axis=0)
- std = np.std(feature_list1, axis=0) + 1e-10
- feature_list1 = (feature_list1 - mean) / std
- feature_list2 = (feature_list2 - mean) / std
-
- dist = calculate_frechet_distance(
- mu1=np.mean(feature_list1, axis=0),
- sigma1=np.cov(feature_list1, rowvar=False),
- mu2=np.mean(feature_list2, axis=0),
- sigma2=np.cov(feature_list2, rowvar=False),
- )
- return dist
\ No newline at end of file
diff --git a/spaces/akhaliq/BlendGAN/psp_encoder/__init__.py b/spaces/akhaliq/BlendGAN/psp_encoder/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/akhaliq/stylegan3_clip/training/networks_stylegan3.py b/spaces/akhaliq/stylegan3_clip/training/networks_stylegan3.py
deleted file mode 100644
index a6a2b4d7f5ebde6874768c0cac011cc7867d2a08..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/stylegan3_clip/training/networks_stylegan3.py
+++ /dev/null
@@ -1,515 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Generator architecture from the paper
-"Alias-Free Generative Adversarial Networks"."""
-
-import numpy as np
-import scipy.signal
-import scipy.optimize
-import torch
-from torch_utils import misc
-from torch_utils import persistence
-from torch_utils.ops import conv2d_gradfix
-from torch_utils.ops import filtered_lrelu
-from torch_utils.ops import bias_act
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def modulated_conv2d(
- x, # Input tensor: [batch_size, in_channels, in_height, in_width]
- w, # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width]
- s, # Style tensor: [batch_size, in_channels]
- demodulate = True, # Apply weight demodulation?
- padding = 0, # Padding: int or [padH, padW]
- input_gain = None, # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels]
-):
- with misc.suppress_tracer_warnings(): # this value will be treated as a constant
- batch_size = int(x.shape[0])
- out_channels, in_channels, kh, kw = w.shape
- misc.assert_shape(w, [out_channels, in_channels, kh, kw]) # [OIkk]
- misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW]
- misc.assert_shape(s, [batch_size, in_channels]) # [NI]
-
- # Pre-normalize inputs.
- if demodulate:
- w = w * w.square().mean([1,2,3], keepdim=True).rsqrt()
- s = s * s.square().mean().rsqrt()
-
- # Modulate weights.
- w = w.unsqueeze(0) # [NOIkk]
- w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk]
-
- # Demodulate weights.
- if demodulate:
- dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO]
- w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4) # [NOIkk]
-
- # Apply input scaling.
- if input_gain is not None:
- input_gain = input_gain.expand(batch_size, in_channels) # [NI]
- w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk]
-
- # Execute as one fused op using grouped convolution.
- x = x.reshape(1, -1, *x.shape[2:])
- w = w.reshape(-1, in_channels, kh, kw)
- x = conv2d_gradfix.conv2d(input=x, weight=w.to(x.dtype), padding=padding, groups=batch_size)
- x = x.reshape(batch_size, -1, *x.shape[2:])
- return x
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class FullyConnectedLayer(torch.nn.Module):
- def __init__(self,
- in_features, # Number of input features.
- out_features, # Number of output features.
- activation = 'linear', # Activation function: 'relu', 'lrelu', etc.
- bias = True, # Apply additive bias before the activation function?
- lr_multiplier = 1, # Learning rate multiplier.
- weight_init = 1, # Initial standard deviation of the weight tensor.
- bias_init = 0, # Initial value of the additive bias.
- ):
- super().__init__()
- self.in_features = in_features
- self.out_features = out_features
- self.activation = activation
- self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) * (weight_init / lr_multiplier))
- bias_init = np.broadcast_to(np.asarray(bias_init, dtype=np.float32), [out_features])
- self.bias = torch.nn.Parameter(torch.from_numpy(bias_init / lr_multiplier)) if bias else None
- self.weight_gain = lr_multiplier / np.sqrt(in_features)
- self.bias_gain = lr_multiplier
-
- def forward(self, x):
- w = self.weight.to(x.dtype) * self.weight_gain
- b = self.bias
- if b is not None:
- b = b.to(x.dtype)
- if self.bias_gain != 1:
- b = b * self.bias_gain
- if self.activation == 'linear' and b is not None:
- x = torch.addmm(b.unsqueeze(0), x, w.t())
- else:
- x = x.matmul(w.t())
- x = bias_act.bias_act(x, b, act=self.activation)
- return x
-
- def extra_repr(self):
- return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class MappingNetwork(torch.nn.Module):
- def __init__(self,
- z_dim, # Input latent (Z) dimensionality.
- c_dim, # Conditioning label (C) dimensionality, 0 = no labels.
- w_dim, # Intermediate latent (W) dimensionality.
- num_ws, # Number of intermediate latents to output.
- num_layers = 2, # Number of mapping layers.
- lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers.
- w_avg_beta = 0.998, # Decay for tracking the moving average of W during training.
- ):
- super().__init__()
- self.z_dim = z_dim
- self.c_dim = c_dim
- self.w_dim = w_dim
- self.num_ws = num_ws
- self.num_layers = num_layers
- self.w_avg_beta = w_avg_beta
-
- # Construct layers.
- self.embed = FullyConnectedLayer(self.c_dim, self.w_dim) if self.c_dim > 0 else None
- features = [self.z_dim + (self.w_dim if self.c_dim > 0 else 0)] + [self.w_dim] * self.num_layers
- for idx, in_features, out_features in zip(range(num_layers), features[:-1], features[1:]):
- layer = FullyConnectedLayer(in_features, out_features, activation='lrelu', lr_multiplier=lr_multiplier)
- setattr(self, f'fc{idx}', layer)
- self.register_buffer('w_avg', torch.zeros([w_dim]))
-
- def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False):
- misc.assert_shape(z, [None, self.z_dim])
- if truncation_cutoff is None:
- truncation_cutoff = self.num_ws
-
- # Embed, normalize, and concatenate inputs.
- x = z.to(torch.float32)
- x = x * (x.square().mean(1, keepdim=True) + 1e-8).rsqrt()
- if self.c_dim > 0:
- misc.assert_shape(c, [None, self.c_dim])
- y = self.embed(c.to(torch.float32))
- y = y * (y.square().mean(1, keepdim=True) + 1e-8).rsqrt()
- x = torch.cat([x, y], dim=1) if x is not None else y
-
- # Execute layers.
- for idx in range(self.num_layers):
- x = getattr(self, f'fc{idx}')(x)
-
- # Update moving average of W.
- if update_emas:
- self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta))
-
- # Broadcast and apply truncation.
- x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
- if truncation_psi != 1:
- x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi)
- return x
-
- def extra_repr(self):
- return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class SynthesisInput(torch.nn.Module):
- def __init__(self,
- w_dim, # Intermediate latent (W) dimensionality.
- channels, # Number of output channels.
- size, # Output spatial size: int or [width, height].
- sampling_rate, # Output sampling rate.
- bandwidth, # Output bandwidth.
- ):
- super().__init__()
- self.w_dim = w_dim
- self.channels = channels
- self.size = np.broadcast_to(np.asarray(size), [2])
- self.sampling_rate = sampling_rate
- self.bandwidth = bandwidth
-
- # Draw random frequencies from uniform 2D disc.
- freqs = torch.randn([self.channels, 2])
- radii = freqs.square().sum(dim=1, keepdim=True).sqrt()
- freqs /= radii * radii.square().exp().pow(0.25)
- freqs *= bandwidth
- phases = torch.rand([self.channels]) - 0.5
-
- # Setup parameters and buffers.
- self.weight = torch.nn.Parameter(torch.randn([self.channels, self.channels]))
- self.affine = FullyConnectedLayer(w_dim, 4, weight_init=0, bias_init=[1,0,0,0])
- self.register_buffer('transform', torch.eye(3, 3)) # User-specified inverse transform wrt. resulting image.
- self.register_buffer('freqs', freqs)
- self.register_buffer('phases', phases)
-
- def forward(self, w):
- # Introduce batch dimension.
- transforms = self.transform.unsqueeze(0) # [batch, row, col]
- freqs = self.freqs.unsqueeze(0) # [batch, channel, xy]
- phases = self.phases.unsqueeze(0) # [batch, channel]
-
- # Apply learned transformation.
- t = self.affine(w) # t = (r_c, r_s, t_x, t_y)
- t = t / t[:, :2].norm(dim=1, keepdim=True) # t' = (r'_c, r'_s, t'_x, t'_y)
- m_r = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse rotation wrt. resulting image.
- m_r[:, 0, 0] = t[:, 0] # r'_c
- m_r[:, 0, 1] = -t[:, 1] # r'_s
- m_r[:, 1, 0] = t[:, 1] # r'_s
- m_r[:, 1, 1] = t[:, 0] # r'_c
- m_t = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse translation wrt. resulting image.
- m_t[:, 0, 2] = -t[:, 2] # t'_x
- m_t[:, 1, 2] = -t[:, 3] # t'_y
- transforms = m_r @ m_t @ transforms # First rotate resulting image, then translate, and finally apply user-specified transform.
-
- # Transform frequencies.
- phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2)
- freqs = freqs @ transforms[:, :2, :2]
-
- # Dampen out-of-band frequencies that may occur due to the user-specified transform.
- amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) / (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1)
-
- # Construct sampling grid.
- theta = torch.eye(2, 3, device=w.device)
- theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate
- theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate
- grids = torch.nn.functional.affine_grid(theta.unsqueeze(0), [1, 1, self.size[1], self.size[0]], align_corners=False)
-
- # Compute Fourier features.
- x = (grids.unsqueeze(3) @ freqs.permute(0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(3) # [batch, height, width, channel]
- x = x + phases.unsqueeze(1).unsqueeze(2)
- x = torch.sin(x * (np.pi * 2))
- x = x * amplitudes.unsqueeze(1).unsqueeze(2)
-
- # Apply trainable mapping.
- weight = self.weight / np.sqrt(self.channels)
- x = x @ weight.t()
-
- # Ensure correct shape.
- x = x.permute(0, 3, 1, 2) # [batch, channel, height, width]
- misc.assert_shape(x, [w.shape[0], self.channels, int(self.size[1]), int(self.size[0])])
- return x
-
- def extra_repr(self):
- return '\n'.join([
- f'w_dim={self.w_dim:d}, channels={self.channels:d}, size={list(self.size)},',
- f'sampling_rate={self.sampling_rate:g}, bandwidth={self.bandwidth:g}'])
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class SynthesisLayer(torch.nn.Module):
- def __init__(self,
- w_dim, # Intermediate latent (W) dimensionality.
- is_torgb, # Is this the final ToRGB layer?
- is_critically_sampled, # Does this layer use critical sampling?
- use_fp16, # Does this layer use FP16?
-
- # Input & output specifications.
- in_channels, # Number of input channels.
- out_channels, # Number of output channels.
- in_size, # Input spatial size: int or [width, height].
- out_size, # Output spatial size: int or [width, height].
- in_sampling_rate, # Input sampling rate (s).
- out_sampling_rate, # Output sampling rate (s).
- in_cutoff, # Input cutoff frequency (f_c).
- out_cutoff, # Output cutoff frequency (f_c).
- in_half_width, # Input transition band half-width (f_h).
- out_half_width, # Output Transition band half-width (f_h).
-
- # Hyperparameters.
- conv_kernel = 3, # Convolution kernel size. Ignored for final the ToRGB layer.
- filter_size = 6, # Low-pass filter size relative to the lower resolution when up/downsampling.
- lrelu_upsampling = 2, # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer.
- use_radial_filters = False, # Use radially symmetric downsampling filter? Ignored for critically sampled layers.
- conv_clamp = 256, # Clamp the output to [-X, +X], None = disable clamping.
- magnitude_ema_beta = 0.999, # Decay rate for the moving average of input magnitudes.
- ):
- super().__init__()
- self.w_dim = w_dim
- self.is_torgb = is_torgb
- self.is_critically_sampled = is_critically_sampled
- self.use_fp16 = use_fp16
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.in_size = np.broadcast_to(np.asarray(in_size), [2])
- self.out_size = np.broadcast_to(np.asarray(out_size), [2])
- self.in_sampling_rate = in_sampling_rate
- self.out_sampling_rate = out_sampling_rate
- self.tmp_sampling_rate = max(in_sampling_rate, out_sampling_rate) * (1 if is_torgb else lrelu_upsampling)
- self.in_cutoff = in_cutoff
- self.out_cutoff = out_cutoff
- self.in_half_width = in_half_width
- self.out_half_width = out_half_width
- self.conv_kernel = 1 if is_torgb else conv_kernel
- self.conv_clamp = conv_clamp
- self.magnitude_ema_beta = magnitude_ema_beta
-
- # Setup parameters and buffers.
- self.affine = FullyConnectedLayer(self.w_dim, self.in_channels, bias_init=1)
- self.weight = torch.nn.Parameter(torch.randn([self.out_channels, self.in_channels, self.conv_kernel, self.conv_kernel]))
- self.bias = torch.nn.Parameter(torch.zeros([self.out_channels]))
- self.register_buffer('magnitude_ema', torch.ones([]))
-
- # Design upsampling filter.
- self.up_factor = int(np.rint(self.tmp_sampling_rate / self.in_sampling_rate))
- assert self.in_sampling_rate * self.up_factor == self.tmp_sampling_rate
- self.up_taps = filter_size * self.up_factor if self.up_factor > 1 and not self.is_torgb else 1
- self.register_buffer('up_filter', self.design_lowpass_filter(
- numtaps=self.up_taps, cutoff=self.in_cutoff, width=self.in_half_width*2, fs=self.tmp_sampling_rate))
-
- # Design downsampling filter.
- self.down_factor = int(np.rint(self.tmp_sampling_rate / self.out_sampling_rate))
- assert self.out_sampling_rate * self.down_factor == self.tmp_sampling_rate
- self.down_taps = filter_size * self.down_factor if self.down_factor > 1 and not self.is_torgb else 1
- self.down_radial = use_radial_filters and not self.is_critically_sampled
- self.register_buffer('down_filter', self.design_lowpass_filter(
- numtaps=self.down_taps, cutoff=self.out_cutoff, width=self.out_half_width*2, fs=self.tmp_sampling_rate, radial=self.down_radial))
-
- # Compute padding.
- pad_total = (self.out_size - 1) * self.down_factor + 1 # Desired output size before downsampling.
- pad_total -= (self.in_size + self.conv_kernel - 1) * self.up_factor # Input size after upsampling.
- pad_total += self.up_taps + self.down_taps - 2 # Size reduction caused by the filters.
- pad_lo = (pad_total + self.up_factor) // 2 # Shift sample locations according to the symmetric interpretation (Appendix C.3).
- pad_hi = pad_total - pad_lo
- self.padding = [int(pad_lo[0]), int(pad_hi[0]), int(pad_lo[1]), int(pad_hi[1])]
-
- def forward(self, x, w, noise_mode='random', force_fp32=False, update_emas=False):
- assert noise_mode in ['random', 'const', 'none'] # unused
- misc.assert_shape(x, [None, self.in_channels, int(self.in_size[1]), int(self.in_size[0])])
- misc.assert_shape(w, [x.shape[0], self.w_dim])
-
- # Track input magnitude.
- if update_emas:
- with torch.autograd.profiler.record_function('update_magnitude_ema'):
- magnitude_cur = x.detach().to(torch.float32).square().mean()
- self.magnitude_ema.copy_(magnitude_cur.lerp(self.magnitude_ema, self.magnitude_ema_beta))
- input_gain = self.magnitude_ema.rsqrt()
-
- # Execute affine layer.
- styles = self.affine(w)
- if self.is_torgb:
- weight_gain = 1 / np.sqrt(self.in_channels * (self.conv_kernel ** 2))
- styles = styles * weight_gain
-
- # Execute modulated conv2d.
- dtype = torch.float16 if (self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32
- x = modulated_conv2d(x=x.to(dtype), w=self.weight, s=styles,
- padding=self.conv_kernel-1, demodulate=(not self.is_torgb), input_gain=input_gain)
-
- # Execute bias, filtered leaky ReLU, and clamping.
- gain = 1 if self.is_torgb else np.sqrt(2)
- slope = 1 if self.is_torgb else 0.2
- x = filtered_lrelu.filtered_lrelu(x=x, fu=self.up_filter, fd=self.down_filter, b=self.bias.to(x.dtype),
- up=self.up_factor, down=self.down_factor, padding=self.padding, gain=gain, slope=slope, clamp=self.conv_clamp)
-
- # Ensure correct shape and dtype.
- misc.assert_shape(x, [None, self.out_channels, int(self.out_size[1]), int(self.out_size[0])])
- assert x.dtype == dtype
- return x
-
- @staticmethod
- def design_lowpass_filter(numtaps, cutoff, width, fs, radial=False):
- assert numtaps >= 1
-
- # Identity filter.
- if numtaps == 1:
- return None
-
- # Separable Kaiser low-pass filter.
- if not radial:
- f = scipy.signal.firwin(numtaps=numtaps, cutoff=cutoff, width=width, fs=fs)
- return torch.as_tensor(f, dtype=torch.float32)
-
- # Radially symmetric jinc-based filter.
- x = (np.arange(numtaps) - (numtaps - 1) / 2) / fs
- r = np.hypot(*np.meshgrid(x, x))
- f = scipy.special.j1(2 * cutoff * (np.pi * r)) / (np.pi * r)
- beta = scipy.signal.kaiser_beta(scipy.signal.kaiser_atten(numtaps, width / (fs / 2)))
- w = np.kaiser(numtaps, beta)
- f *= np.outer(w, w)
- f /= np.sum(f)
- return torch.as_tensor(f, dtype=torch.float32)
-
- def extra_repr(self):
- return '\n'.join([
- f'w_dim={self.w_dim:d}, is_torgb={self.is_torgb},',
- f'is_critically_sampled={self.is_critically_sampled}, use_fp16={self.use_fp16},',
- f'in_sampling_rate={self.in_sampling_rate:g}, out_sampling_rate={self.out_sampling_rate:g},',
- f'in_cutoff={self.in_cutoff:g}, out_cutoff={self.out_cutoff:g},',
- f'in_half_width={self.in_half_width:g}, out_half_width={self.out_half_width:g},',
- f'in_size={list(self.in_size)}, out_size={list(self.out_size)},',
- f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}'])
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class SynthesisNetwork(torch.nn.Module):
- def __init__(self,
- w_dim, # Intermediate latent (W) dimensionality.
- img_resolution, # Output image resolution.
- img_channels, # Number of color channels.
- channel_base = 32768, # Overall multiplier for the number of channels.
- channel_max = 512, # Maximum number of channels in any layer.
- num_layers = 14, # Total number of layers, excluding Fourier features and ToRGB.
- num_critical = 2, # Number of critically sampled layers at the end.
- first_cutoff = 2, # Cutoff frequency of the first layer (f_{c,0}).
- first_stopband = 2**2.1, # Minimum stopband of the first layer (f_{t,0}).
- last_stopband_rel = 2**0.3, # Minimum stopband of the last layer, expressed relative to the cutoff.
- margin_size = 10, # Number of additional pixels outside the image.
- output_scale = 0.25, # Scale factor for the output image.
- num_fp16_res = 4, # Use FP16 for the N highest resolutions.
- **layer_kwargs, # Arguments for SynthesisLayer.
- ):
- super().__init__()
- self.w_dim = w_dim
- self.num_ws = num_layers + 2
- self.img_resolution = img_resolution
- self.img_channels = img_channels
- self.num_layers = num_layers
- self.num_critical = num_critical
- self.margin_size = margin_size
- self.output_scale = output_scale
- self.num_fp16_res = num_fp16_res
-
- # Geometric progression of layer cutoffs and min. stopbands.
- last_cutoff = self.img_resolution / 2 # f_{c,N}
- last_stopband = last_cutoff * last_stopband_rel # f_{t,N}
- exponents = np.minimum(np.arange(self.num_layers + 1) / (self.num_layers - self.num_critical), 1)
- cutoffs = first_cutoff * (last_cutoff / first_cutoff) ** exponents # f_c[i]
- stopbands = first_stopband * (last_stopband / first_stopband) ** exponents # f_t[i]
-
- # Compute remaining layer parameters.
- sampling_rates = np.exp2(np.ceil(np.log2(np.minimum(stopbands * 2, self.img_resolution)))) # s[i]
- half_widths = np.maximum(stopbands, sampling_rates / 2) - cutoffs # f_h[i]
- sizes = sampling_rates + self.margin_size * 2
- sizes[-2:] = self.img_resolution
- channels = np.rint(np.minimum((channel_base / 2) / cutoffs, channel_max))
- channels[-1] = self.img_channels
-
- # Construct layers.
- self.input = SynthesisInput(
- w_dim=self.w_dim, channels=int(channels[0]), size=int(sizes[0]),
- sampling_rate=sampling_rates[0], bandwidth=cutoffs[0])
- self.layer_names = []
- for idx in range(self.num_layers + 1):
- prev = max(idx - 1, 0)
- is_torgb = (idx == self.num_layers)
- is_critically_sampled = (idx >= self.num_layers - self.num_critical)
- use_fp16 = (sampling_rates[idx] * (2 ** self.num_fp16_res) > self.img_resolution)
- layer = SynthesisLayer(
- w_dim=self.w_dim, is_torgb=is_torgb, is_critically_sampled=is_critically_sampled, use_fp16=use_fp16,
- in_channels=int(channels[prev]), out_channels= int(channels[idx]),
- in_size=int(sizes[prev]), out_size=int(sizes[idx]),
- in_sampling_rate=int(sampling_rates[prev]), out_sampling_rate=int(sampling_rates[idx]),
- in_cutoff=cutoffs[prev], out_cutoff=cutoffs[idx],
- in_half_width=half_widths[prev], out_half_width=half_widths[idx],
- **layer_kwargs)
- name = f'L{idx}_{layer.out_size[0]}_{layer.out_channels}'
- setattr(self, name, layer)
- self.layer_names.append(name)
-
- def forward(self, ws, **layer_kwargs):
- misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
- ws = ws.to(torch.float32).unbind(dim=1)
-
- # Execute layers.
- x = self.input(ws[0])
- for name, w in zip(self.layer_names, ws[1:]):
- x = getattr(self, name)(x, w, **layer_kwargs)
- if self.output_scale != 1:
- x = x * self.output_scale
-
- # Ensure correct shape and dtype.
- misc.assert_shape(x, [None, self.img_channels, self.img_resolution, self.img_resolution])
- x = x.to(torch.float32)
- return x
-
- def extra_repr(self):
- return '\n'.join([
- f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
- f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
- f'num_layers={self.num_layers:d}, num_critical={self.num_critical:d},',
- f'margin_size={self.margin_size:d}, num_fp16_res={self.num_fp16_res:d}'])
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class Generator(torch.nn.Module):
- def __init__(self,
- z_dim, # Input latent (Z) dimensionality.
- c_dim, # Conditioning label (C) dimensionality.
- w_dim, # Intermediate latent (W) dimensionality.
- img_resolution, # Output resolution.
- img_channels, # Number of output color channels.
- mapping_kwargs = {}, # Arguments for MappingNetwork.
- **synthesis_kwargs, # Arguments for SynthesisNetwork.
- ):
- super().__init__()
- self.z_dim = z_dim
- self.c_dim = c_dim
- self.w_dim = w_dim
- self.img_resolution = img_resolution
- self.img_channels = img_channels
- self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs)
- self.num_ws = self.synthesis.num_ws
- self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
-
- def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs):
- ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas)
- img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
- return img
-
-#----------------------------------------------------------------------------
diff --git a/spaces/alecmueller/01-Speech2Text2Speech-GR/README.md b/spaces/alecmueller/01-Speech2Text2Speech-GR/README.md
deleted file mode 100644
index e48fe05a4f92b8541ac93e5154f02ce8139e503a..0000000000000000000000000000000000000000
--- a/spaces/alecmueller/01-Speech2Text2Speech-GR/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 🗣️01 Speech2Text2Speech GR🙉
-emoji: 🗣️🎤🙉
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.0.11
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatters/irc.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatters/irc.py
deleted file mode 100644
index ad986e04077804e9282f69503262b54c928242f2..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatters/irc.py
+++ /dev/null
@@ -1,179 +0,0 @@
-"""
- pygments.formatters.irc
- ~~~~~~~~~~~~~~~~~~~~~~~
-
- Formatter for IRC output
-
- :copyright: Copyright 2006-2021 by the Pygments team, see AUTHORS.
- :license: BSD, see LICENSE for details.
-"""
-
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.token import Keyword, Name, Comment, String, Error, \
- Number, Operator, Generic, Token, Whitespace
-from pip._vendor.pygments.util import get_choice_opt
-
-
-__all__ = ['IRCFormatter']
-
-
-#: Map token types to a tuple of color values for light and dark
-#: backgrounds.
-IRC_COLORS = {
- Token: ('', ''),
-
- Whitespace: ('gray', 'brightblack'),
- Comment: ('gray', 'brightblack'),
- Comment.Preproc: ('cyan', 'brightcyan'),
- Keyword: ('blue', 'brightblue'),
- Keyword.Type: ('cyan', 'brightcyan'),
- Operator.Word: ('magenta', 'brightcyan'),
- Name.Builtin: ('cyan', 'brightcyan'),
- Name.Function: ('green', 'brightgreen'),
- Name.Namespace: ('_cyan_', '_brightcyan_'),
- Name.Class: ('_green_', '_brightgreen_'),
- Name.Exception: ('cyan', 'brightcyan'),
- Name.Decorator: ('brightblack', 'gray'),
- Name.Variable: ('red', 'brightred'),
- Name.Constant: ('red', 'brightred'),
- Name.Attribute: ('cyan', 'brightcyan'),
- Name.Tag: ('brightblue', 'brightblue'),
- String: ('yellow', 'yellow'),
- Number: ('blue', 'brightblue'),
-
- Generic.Deleted: ('brightred', 'brightred'),
- Generic.Inserted: ('green', 'brightgreen'),
- Generic.Heading: ('**', '**'),
- Generic.Subheading: ('*magenta*', '*brightmagenta*'),
- Generic.Error: ('brightred', 'brightred'),
-
- Error: ('_brightred_', '_brightred_'),
-}
-
-
-IRC_COLOR_MAP = {
- 'white': 0,
- 'black': 1,
- 'blue': 2,
- 'brightgreen': 3,
- 'brightred': 4,
- 'yellow': 5,
- 'magenta': 6,
- 'orange': 7,
- 'green': 7, #compat w/ ansi
- 'brightyellow': 8,
- 'lightgreen': 9,
- 'brightcyan': 9, # compat w/ ansi
- 'cyan': 10,
- 'lightblue': 11,
- 'red': 11, # compat w/ ansi
- 'brightblue': 12,
- 'brightmagenta': 13,
- 'brightblack': 14,
- 'gray': 15,
-}
-
-def ircformat(color, text):
- if len(color) < 1:
- return text
- add = sub = ''
- if '_' in color: # italic
- add += '\x1D'
- sub = '\x1D' + sub
- color = color.strip('_')
- if '*' in color: # bold
- add += '\x02'
- sub = '\x02' + sub
- color = color.strip('*')
- # underline (\x1F) not supported
- # backgrounds (\x03FF,BB) not supported
- if len(color) > 0: # actual color - may have issues with ircformat("red", "blah")+"10" type stuff
- add += '\x03' + str(IRC_COLOR_MAP[color]).zfill(2)
- sub = '\x03' + sub
- return add + text + sub
- return '<'+add+'>'+text+''
-
-
-class IRCFormatter(Formatter):
- r"""
- Format tokens with IRC color sequences
-
- The `get_style_defs()` method doesn't do anything special since there is
- no support for common styles.
-
- Options accepted:
-
- `bg`
- Set to ``"light"`` or ``"dark"`` depending on the terminal's background
- (default: ``"light"``).
-
- `colorscheme`
- A dictionary mapping token types to (lightbg, darkbg) color names or
- ``None`` (default: ``None`` = use builtin colorscheme).
-
- `linenos`
- Set to ``True`` to have line numbers in the output as well
- (default: ``False`` = no line numbers).
- """
- name = 'IRC'
- aliases = ['irc', 'IRC']
- filenames = []
-
- def __init__(self, **options):
- Formatter.__init__(self, **options)
- self.darkbg = get_choice_opt(options, 'bg',
- ['light', 'dark'], 'light') == 'dark'
- self.colorscheme = options.get('colorscheme', None) or IRC_COLORS
- self.linenos = options.get('linenos', False)
- self._lineno = 0
-
- def _write_lineno(self, outfile):
- self._lineno += 1
- outfile.write("\n%04d: " % self._lineno)
-
- def _format_unencoded_with_lineno(self, tokensource, outfile):
- self._write_lineno(outfile)
-
- for ttype, value in tokensource:
- if value.endswith("\n"):
- self._write_lineno(outfile)
- value = value[:-1]
- color = self.colorscheme.get(ttype)
- while color is None:
- ttype = ttype.parent
- color = self.colorscheme.get(ttype)
- if color:
- color = color[self.darkbg]
- spl = value.split('\n')
- for line in spl[:-1]:
- self._write_lineno(outfile)
- if line:
- outfile.write(ircformat(color, line[:-1]))
- if spl[-1]:
- outfile.write(ircformat(color, spl[-1]))
- else:
- outfile.write(value)
-
- outfile.write("\n")
-
- def format_unencoded(self, tokensource, outfile):
- if self.linenos:
- self._format_unencoded_with_lineno(tokensource, outfile)
- return
-
- for ttype, value in tokensource:
- color = self.colorscheme.get(ttype)
- while color is None:
- ttype = ttype[:-1]
- color = self.colorscheme.get(ttype)
- if color:
- color = color[self.darkbg]
- spl = value.split('\n')
- for line in spl[:-1]:
- if line:
- outfile.write(ircformat(color, line))
- outfile.write('\n')
- if spl[-1]:
- outfile.write(ircformat(color, spl[-1]))
- else:
- outfile.write(value)
diff --git a/spaces/ali-ghamdan/image-colors-corrector/evaluation/evaluate_cc.py b/spaces/ali-ghamdan/image-colors-corrector/evaluation/evaluate_cc.py
deleted file mode 100644
index fe4b2a7c1984d0d2b554bf8fe718f361b1e907d2..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/image-colors-corrector/evaluation/evaluate_cc.py
+++ /dev/null
@@ -1,58 +0,0 @@
-## Calculate errors between the corrected image and the ground truth image.
-#
-# Copyright (c) 2018-present, Mahmoud Afifi
-# York University, Canada
-# mafifi@eecs.yorku.ca | m.3afifi@gmail.com
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-# All rights reserved.
-#
-# Please cite the following work if this program is used:
-# Mahmoud Afifi, Brian Price, Scott Cohen, and Michael S. Brown,
-# "When color constancy goes wrong: Correcting improperly white-balanced
-# images", CVPR 2019.
-#
-##########################################################################
-
-
-from evaluation.calc_deltaE import calc_deltaE
-from evaluation.calc_deltaE2000 import calc_deltaE2000
-from evaluation.calc_mse import calc_mse
-from evaluation.calc_mae import calc_mae
-
-
-def evaluate_cc(corrected, gt, color_chart_area, opt=1):
- """
- Color constancy (white-balance correction) evaluation of a given corrected
- image.
- :param corrected: corrected image
- :param gt: ground-truth image
- :param color_chart_area: If there is a color chart in the image, that is
- masked out from both images, this variable represents the number of pixels
- of the color chart.
- :param opt: determines the required error metric(s) to be reported.
- Options:
- opt = 1 delta E 2000 (default).
- opt = 2 delta E 2000 and mean squared error (MSE)
- opt = 3 delta E 2000, MSE, and mean angular eror (MAE)
- opt = 4 delta E 2000, MSE, MAE, and delta E 76
- :return: error(s) between corrected and gt images
- """
-
- if opt == 1:
- return calc_deltaE2000(corrected, gt, color_chart_area)
- elif opt == 2:
- return calc_deltaE2000(corrected, gt, color_chart_area), calc_mse(
- corrected, gt, color_chart_area)
- elif opt == 3:
- return calc_deltaE2000(corrected, gt, color_chart_area), calc_mse(
- corrected, gt, color_chart_area), calc_mae(corrected, gt,
- color_chart_area)
- elif opt == 4:
- return calc_deltaE2000(corrected, gt, color_chart_area), calc_mse(
- corrected, gt, color_chart_area), calc_mae(
- corrected, gt, color_chart_area), calc_deltaE(corrected, gt,
- color_chart_area)
- else:
- raise Exception('Error in evaluate_cc function')
diff --git a/spaces/allknowingroger/text-generation-webui-space-1/extensions/llama_prompts/script.py b/spaces/allknowingroger/text-generation-webui-space-1/extensions/llama_prompts/script.py
deleted file mode 100644
index 22c96f7c2d6763213a728d77ee6666496d9c4aa3..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/text-generation-webui-space-1/extensions/llama_prompts/script.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import gradio as gr
-import modules.shared as shared
-import pandas as pd
-
-df = pd.read_csv("https://raw.githubusercontent.com/devbrones/llama-prompts/main/prompts/prompts.csv")
-
-def get_prompt_by_name(name):
- if name == 'None':
- return ''
- else:
- return df[df['Prompt name'] == name].iloc[0]['Prompt'].replace('\\n', '\n')
-
-def ui():
- if not shared.args.chat or shared.args.cai_chat:
- choices = ['None'] + list(df['Prompt name'])
-
- prompts_menu = gr.Dropdown(value=choices[0], choices=choices, label='Prompt')
- prompts_menu.change(get_prompt_by_name, prompts_menu, shared.gradio['textbox'])
diff --git a/spaces/almn-uhc/Examples-of-AI/app.py b/spaces/almn-uhc/Examples-of-AI/app.py
deleted file mode 100644
index 1d37e1ba5cdbf6b844bbc2fd0e3b209c2a66fc63..0000000000000000000000000000000000000000
--- a/spaces/almn-uhc/Examples-of-AI/app.py
+++ /dev/null
@@ -1,856 +0,0 @@
-import streamlit as st
-from graphviz import Digraph
-
-
-st.markdown("""
-# 👋 Two easy ways to turbo boost your AI learning journey! 💻
-# 🌐 AI Pair Programming
-## Open 2 Browsers to:
-1. __🌐 ChatGPT__ [URL](https://chat.openai.com/chat) or [URL2](https://platform.openai.com/playground) and
-2. __🌐 Huggingface__ [URL](https://huggingface.co/awacke1) in separate browser windows.
-1. 🤖 Use prompts to generate a streamlit program on Huggingface or locally to test it.
-2. 🔧 For advanced work, add Python 3.10 and VSCode locally, and debug as gradio or streamlit apps.
-3. 🚀 Use these two superpower processes to reduce the time it takes you to make a new AI program! ⏱️
-# 🎥 YouTube University Method:
-1. 🏋️♀️ Plan two hours each weekday to exercise your body and brain.
-2. 🎬 Make a playlist of videos you want to learn from on YouTube. Save the links to edit later.
-3. 🚀 Try watching the videos at a faster speed while exercising, and sample the first five minutes of each video.
-4. 📜 Reorder the playlist so the most useful videos are at the front, and take breaks to exercise.
-5. 📝 Practice note-taking in markdown to instantly save what you want to remember. Share your notes with others!
-6. 👥 AI Pair Programming Using Long Answer Language Models with Human Feedback:
-## 🎥 2023 AI/ML Advanced Learning Playlists:
-1. [2023 QA Models and Long Form Question Answering NLP](https://www.youtube.com/playlist?list=PLHgX2IExbFovrkkx8HMTLNgYdjCMNYmX_)
-2. [FHIR Bioinformatics Development Using AI/ML and Python, Streamlit, and Gradio - 2022](https://www.youtube.com/playlist?list=PLHgX2IExbFovoMUC3hYXeFegpk_Y0Lz0Q)
-3. [2023 ChatGPT for Coding Assistant Streamlit, Gradio and Python Apps](https://www.youtube.com/playlist?list=PLHgX2IExbFouOEnppexiKZVdz_k5b0pvI)
-4. [2023 BigScience Bloom - Large Language Model for AI Systems and NLP](https://www.youtube.com/playlist?list=PLHgX2IExbFouqnsIqziThlPCX_miiDq14)
-5. [2023 Streamlit Pro Tips for AI UI UX for Data Science, Engineering, and Mathematics](https://www.youtube.com/playlist?list=PLHgX2IExbFou3cP19hHO9Xb-cN8uwr5RM)
-6. [2023 Fun, New and Interesting AI, Videos, and AI/ML Techniques](https://www.youtube.com/playlist?list=PLHgX2IExbFotoMt32SrT3Xynt5BXTGnEP)
-7. [2023 Best Minds in AGI AI Gamification and Large Language Models](https://www.youtube.com/playlist?list=PLHgX2IExbFotmFeBTpyje1uI22n0GAkXT)
-8. [2023 State of the Art for Vision Image Classification, Text Classification and Regression, Extractive Question Answering and Tabular Classification](https://www.youtube.com/playlist?list=PLHgX2IExbFotPcPu6pauNHOoZTTbnAQ2F)
-9. [2023 AutoML DataRobot and AI Platforms for Building Models, Features, Test, and Transparency](https://www.youtube.com/playlist?list=PLHgX2IExbFovsY2oGbDwdEhPrakkC8i3g)
-""")
-
-
-st.markdown("""
-# Cognitive AI with Human Feedback (CAHF) [Example 🩺⚕️](https://huggingface.co/spaces/awacke1/Cognitive-AI-Episodic-Semantic-Memory-Demo):
-1. Create and use Models to predict __outcomes__
-2. Use AI to predict **conditions, disease, and opportunities** using AI with **explainability**.
-3. **Cognitive AI** - Mimic how humans reason through decision making processes.
-4. **Reasoning cycles** - "Recommended for You" reasoners - consider type of personalized needs and classification for users, to recommend products
-5. **High Acuity Reasoners** - Make decisions on rules of **what it can and cannot do within human feedback** guidelines.
- -Emphasizes **explainability, transparency, and removing administrative burden** to **protocolize** and improve what staff is doing.
- -Vetted by SME's, adding value of **judgement and training** and pick up intelligence and **skills from human feedback**.
- -**Alert, Recommended Action, and Clinical Terms** per entity with vocabularies from LOINC, SNOMED, OMS, ICD10, RXNORM, SMILES, HCPCS, CPT, CQM, HL7, SDC and FHIR.
-6. Non static multi agent cognitive approach using real time series to identify factors predictive of outcome.
-7. Cognitive models form of Ontology - to create a type of computable sets and relationships stored in Ontology then ingested by reasoner
- -Use models of world to build predictions and recommendations with answers cumulative with information we know
-8. Reasoners standardize making it easy as possible to do right thing using transfer learning and recommendation tools with questions and actions.
-""")
-
-
-st.markdown("""
-# 📚 Clinical Terminology and Ontologies [Example 🩺⚕️NLP Clinical Ontology Biomedical NER](https://huggingface.co/spaces/awacke1/Biomed-NLP-AI-Clinical-Terminology)
-## Health Vocabularies, Systems of Coding, and Databases with Bibliographies
-##__Keywords__:
-1. __Clinical Terminology__: 💬 Words that doctors use to talk to each other about patients.
-2. __Ontologies for Medications and Conditions__: 📚 A fancy way of organizing knowledge about medicine and health problems.
-3. __Health Vocabularies__: 📝 A special list of words used in healthcare to talk about health issues.
-4. __Systems of Coding__: 💻 A way of giving things like sicknesses and treatments special codes, so that doctors can remember them easily.
-5. __Databases__: 🗄️ A computer system that stores information about patients, health research, and other healthcare things.
-6. __Bibliographies__: 📖 A list of books or articles that doctors use to learn about new health information.
-1. ## 1️⃣ National Library of Medicine's **RxNorm**:
- - Standardized nomenclature for clinical drugs developed by NLM
- - Provides links between drug names and related information such as ingredients, strengths, and dosages
- - **Data type: controlled vocabulary**
- - Access through **NLM's RxNorm website**: https://www.nlm.nih.gov/research/umls/rxnorm/index.html
-2. ## 2️⃣ Centers for Medicare and Medicaid Services' Healthcare Common Procedure Coding System (HCPCS):
- - Coding system used to identify healthcare **services, procedures, and supplies**
- - Includes **codes for drugs, biologicals, and other items** used in medical care
- - **Data type: coding system**
- - Access through **CMS website**: https://www.cms.gov/Medicare/Coding/MedHCPCSGenInfo
-3. ## 3️⃣ Unified Medical Language System (UMLS):
- - Set of files and software tools developed by NLM for integrating and mapping biomedical vocabularies
- - Includes RxNorm and other drug vocabularies, as well as other terminologies used in medicine
- - **Data type: controlled vocabulary**
- - Access through UMLS Metathesaurus: https://www.nlm.nih.gov/research/umls/index.html
-4. ## 4️⃣ PubMed:
- - Database of **biomedical literature** maintained by the National Center for Biotechnology Information (NCBI)
- - Includes information about **drugs, including drug names, chemical structures, and pharmacological actions**
- - **Data type: bibliographic database**
- - Access through **PubMed website**: https://pubmed.ncbi.nlm.nih.gov/
-5. ## 5️⃣ PubChem:
- - Database of chemical substances maintained by NCBI
- - Includes information about drugs, including **chemical structures, properties, and activities**
- - **Data type: chemical database**
- - Access through **PubChem website**: https://pubchem.ncbi.nlm.nih.gov/
-6. ## 6️⃣ Behavioral Health Code Terminology Sets:
- - Code terminology sets specific to behavioral health
- - Includes **DSM** published by American Psychiatric Association, **ICD** published by World Health Organization, and **CPT** published by American Medical Association
- - **Data type: coding system**
- - Access through respective **organizations' websites**:
- 1. [DSM](https://www.psychiatry.org/psychiatrists/practice/dsm)
- 2. [ICD](https://www.who.int/standards/classifications/classification-of-diseases)
- 3. [CPT](https://www.ama-assn.org/practice-management/cpt/current-procedural-terminology-cpt)
-""")
-
-st.markdown("""
-1. # 📚Natural Language Processing🔤 - 🗣️🤖💭💬🌍🔍
- 1. 🤔 **🩺⚕️ Sentiment analysis** - Determine underlying sentiment of text. [Example](https://huggingface.co/spaces/awacke1/Sentiment-analysis-streamlit)
- 2. 📝 **Named Entity Recognition (NER)** - Identify and classify named entities in text. [Example](https://huggingface.co/spaces/awacke1/Named-entity-resolution)
- 3. 🔊 **🩺⚕️Automatic Speech Recognition (ASR)** - Transcribe spoken language into text.
- # Advanced NLP ASR Examples:
- 1. 🩺⚕️ https://huggingface.co/spaces/awacke1/ASR-High-Accuracy-Test
- 2. https://huggingface.co/spaces/awacke1/ASRGenerateStory
- 3. 🩺⚕️ https://huggingface.co/spaces/awacke1/TTS-STT-Blocks
- 4. 🩺⚕️ https://huggingface.co/spaces/awacke1/CloneAnyVoice
- 5. https://huggingface.co/spaces/awacke1/ASR-SOTA-NvidiaSTTMozilla
- 4. 🌐 **Machine translation** - Translate text between languages automatically. [Example](https://huggingface.co/spaces/awacke1/Machine-translation)
- 5. 📄 **Text summarization** - Automatically summarize large volumes of text. [Example](https://huggingface.co/spaces/awacke1/Text-summarization)
- 6. ❓ **🩺⚕️ Question answering** - Answer questions posed in natural language. [Example](https://huggingface.co/spaces/awacke1/Question-answering)
- 7. 🤖 **Sentiment-aware chatbots** - Use sentiment analysis to detect user emotions and respond appropriately.
- 8. 📊 **🩺⚕️ Text classification** - Classify text into different categories. [Example](https://huggingface.co/spaces/awacke1/sileod-deberta-v3-base-tasksource-nli)
- 9. 💬 **🩺⚕️ Text generation** - Generate natural language text. [Example](https://huggingface.co/spaces/awacke1/Sentence2Paragraph)
- 10. 🔎 **Topic modeling** - Automatically identify topics in a large corpus of text. [Example](https://huggingface.co/spaces/awacke1/Topic-modeling)
- - Examples
- 1. [NLP Video Summary](https://huggingface.co/spaces/awacke1/Video-Summary)
- 2. [TTS-STT ASR with Multiple Voices](https://huggingface.co/spaces/awacke1/TTS-STT-Blocks)
- 3. [NLP Transcript with Video Player](https://huggingface.co/spaces/awacke1/Streamlit-ASR-Video)
- 4. [NLP Clinical Ontology Biomedical NER](https://huggingface.co/spaces/awacke1/Biomed-NLP-AI-Clinical-Terminology)
- 5. [Document Understanding and NLP](https://huggingface.co/spaces/awacke1/AIDocumentUnderstandingOCR)
- 6. [NLP ASR Wav2Vec2 Multilingual](https://huggingface.co/spaces/awacke1/ASR-High-Accuracy-Test)
- 7. [Live ASR](https://huggingface.co/spaces/awacke1/ASR-SOTA-NvidiaSTTMozilla)
- 8. [NLP and Visualization](https://huggingface.co/spaces/awacke1/Visualization-Plotly-Sunbursts-Treemaps-and-WebGL)
-""")
-
-st.markdown("""
-2. # 🔮Generative AI💭 (🎨Images and 📝Text) - 🎵🧩🔄📊🌌
- 1. 🆕 **🩺⚕️ Generation of new data**: Create new data that resembles existing data. [Example](https://huggingface.co/spaces/awacke1/GenAI-Generate-New-Data-Resembling-Example)
- 2. 🎨 **Creative potential**: Generate music, art, or literature. [Example](https://huggingface.co/spaces/awacke1/Creative-Potential-Music-Art-Lit)
- 3. 📊 **Data synthesis**: Synthesize data from multiple sources to create new datasets. [Example](https://huggingface.co/spaces/awacke1/Data-Synthesizer-Synthesize-From-Multiple-Sources)
- 4. 📈 **🩺⚕️ Data augmentation**: Augment existing datasets to make them larger and more diverse. [Example](https://huggingface.co/spaces/awacke1/Data-Augmentation)
- 5. 🔀 **Domain transfer**: Transfer knowledge learned from one domain to another.
- 6. 🔍 **Unsupervised learning**: Learn patterns without labeled training data.
- 7. 🔄 **Adaptive learning**: Adapt to changes in data over time.
- 8. 🔊 **Noise injection**: Introduce noise to explore a wider range of possibilities.
- 9. 🕶️ **Latent space manipulation**: Control output by manipulating a model's latent space.
- 10. 🖼️ **Realistic output**: Produce output that is difficult to distinguish from human-created data.
- - Examples
- 1. Quantum AI Circuits: https://huggingface.co/spaces/awacke1/AI-Quantum?option=Circuit
- 2. Generate Story and Video: https://huggingface.co/spaces/awacke1/ASRGenerateStoryandVideo
- 3. ASR Generate Story: https://huggingface.co/spaces/awacke1/ASRGenerateStory
- 4. Music Generation: https://huggingface.co/spaces/awacke1/MusicMaker
-""")
-
-st.markdown("""
-3. # 📷Image Recognition🏞️
- 1. 📷 **Object detection**: Detect and identify multiple objects in an image for detailed analysis and classification.
- 2. 🏞️ **Scene recognition**: Recognize and classify entire scenes based on objects, colors, and shapes.
- 3. 😃 **Facial recognition**: Analyze facial features for accurate identification.
- 4. 😊 **Emotion recognition**: Identify emotions on a subject's face, including happiness, sadness, and anger.
- 5. 🔤 **Text recognition**: Identify and translate text in images for analysis.
- 6. 🎨 **Color recognition**: Detect colors and provide information on hue, saturation, and brightness.
- 7. 🔍 **Image segmentation**: Divide an image into multiple regions for individual analysis and classification.
- 8. 🌅 **Image restoration**: Remove noise and blur, restoring images to original clarity and quality.
- 9. 🔖 **Image classification**: Classify images into categories like animals, buildings, or landscapes.
- 10. 🎨 **Style transfer**: Apply the style of one image to another for unique and innovative results.
- - Examples
- 1. 🩺⚕️ Text-to-Image : [Image Classification](https://huggingface.co/spaces/awacke1/Prompt-Refinery-Text-to-Image-Generation)
- 2. Image Captions from 5 SOTA Generators: [URL](https://huggingface.co/spaces/awacke1/ImageCaptionPromptGenerator)
- 3. 🩺⚕️ Image to Multilingual OCR: [URL](https://huggingface.co/spaces/awacke1/Image-to-Multilingual-OCR)
- 4. WRN - Wide Residual Networks: [URL](https://huggingface.co/spaces/awacke1/ResnetPytorchImageRecognition)
- 5. AI Document Understanding: [URL](https://huggingface.co/spaces/awacke1/AIDocumentUnderstandingOCR)
- 6. Elixir Docker Bumblebee: [URL](https://huggingface.co/spaces/awacke1/DockerImageRecognitionToText)
- 7. Speech to Text to Story to Images to Video: [URL](https://huggingface.co/spaces/awacke1/Speeech2Text2Story2Images2Video)
- 8. Image to Line Drawings: [URL](https://huggingface.co/spaces/awacke1/Image-to-Line-Drawings)
- 9. Semantic Image Search: [URL](https://huggingface.co/spaces/awacke1/Image-Semantic-Search)
- 10. Zoom Clip Toon: [URL](https://huggingface.co/spaces/awacke1/Zoom-Clip-Toon-Image-to-Image)
- 11. Image to Reading Labels: [URL](https://huggingface.co/spaces/awacke1/ImageOCRMultilingual)
- 12. A Game For That - Gamification Using Snapshot Images: [URL](https://huggingface.co/spaces/awacke1/AGameForThat)
- 13. AI Visually Plays QBert, Pong, Seaquest and more: [URL](https://huggingface.co/spaces/awacke1/AI-Atari-Live-Streamlit)
- 14. AI Creates Generator Style Mix Art from Encyclopedia: [URL](https://huggingface.co/spaces/awacke1/Art-Generator-and-Style-Mixer)
- 15. BigGAN Image Gen and Search: [URL](https://huggingface.co/spaces/awacke1/AI-BigGAN-Image-Gen)
- 16. Art Style Line Drawings: [URL](https://huggingface.co/spaces/awacke1/ArtStyleFoodsandNutrition)
- 17. 🩺⚕️ Yolo Real Time Image Recognition from Webcam: https://huggingface.co/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco
-""")
-
-st.markdown("""
-4. # 🗣️Speech Recognition💬
- 1. 🔊 **Continuous Speech Recognition**: Transcribe spoken words in real-time without pausing.
- 2. 🗣️ **Speaker Identification**: Identify individual speakers through unique features in their speech.
- 3. 🧠 **Contextual Awareness**: Understand conversation context and interpret word meaning.
- 4. 🌎 **Multilingual Support**: Recognize and transcribe multiple languages for translation.
- 5. 🔇 **Noise Reduction**: Filter out background noise to improve transcription quality.
- 6. 🔒 **Voice Biometrics**: Verify speaker identity and provide secure access to personal data.
- 7. 🎛️ **Command and Control**: Interpret voice commands to automate tasks and interact with software.
- 8. 💬 **Natural Language Processing**: Understand complex human speech patterns.
- 9. 🧠 **Adaptive Learning**: Learn and adapt to improve accuracy over time.
- 10. ☁️ **Cloud-Based Deployment**: Real-time processing of large amounts of data, even on mobile devices.
-""")
-
-st.markdown("""
-5. # Reinforcement Learning
- 1. 🏆 **Reward-driven**: RL uses rewards or punishments to drive its learning process.
- 2. 🧪 **Trial-and-error learning**: RL is a trial-and-error learning method, where an agent tries different actions to find the best action that will maximize the cumulative reward.
- 3. 🤔 **Exploration-exploitation trade-off**: RL agents need to balance exploration and exploitation to find new possibilities while also exploiting successful actions.
- 4. 📈 **Markov Decision Processes**: RL uses MDPs to model decision-making processes.
- 5. 📊 **Policy optimization**: RL uses policy optimization techniques to find the best policy for a given task or learn the optimal policy from scratch.
- 6. 💰 **Value-based methods**: RL uses value-based methods to estimate the value of each state or action.
- 7. 🧠 **Model-based methods**: RL can use model-based methods to predict the outcomes of different actions.
- 8. 🤖 **Deep Reinforcement Learning**: DRL combines RL with deep learning techniques to learn complex decision-making tasks.
- 9. 🔄 **Transfer learning**: RL can use transfer learning techniques to transfer knowledge learned in one task to another task.
- 10. 🤝 **Multi-agent RL**: RL can handle multiple agents that interact with each other.
-""")
-
-st.markdown("""
-6. 🎲Game Theory🎲 – Traditional AI processes
- 1. 🤝 **Interdependence**: Game Theory considers decision-making among multiple agents, unlike traditional AI processes which focus on a single agent.
- 2. 🎯 **Strategic Behavior**: Game Theory assumes that agents aim to maximize their payoffs based on the actions of other agents. Traditional AI may not consider this strategic element.
- 3. 💰 **Payoffs**: Game Theory calculates payoffs for each agent based on their actions and the actions of other agents, unlike traditional AI which may focus on a single objective.
- 4. ⚖️ **Equilibrium**: Game Theory seeks to identify stable states in the game where no agent has an incentive to deviate from their current strategy. Traditional AI may not seek to find an equilibrium.
- 5. 🎲 **Game Formulation**: Game Theory formulates a game, including rules, players, and possible actions, unlike traditional AI which may not require such formulation.
- 6. 💡 **Solution Concepts**: Game Theory has various solution concepts, such as Nash Equilibrium and Pareto Efficiency, to identify the most desirable outcomes. Traditional AI may not have such concepts.
- 7. 📊 **Information**: Game Theory considers the information available to each agent in the game. Traditional AI may not consider information explicitly.
- 8. ⚔️ **Adversarial**: Game Theory models adversarial scenarios where agents have conflicting goals. Traditional AI may assume cooperation among agents.
- 9. ❓ **Uncertainty**: Game Theory deals with uncertainty and incomplete information in the game. Traditional AI may not consider uncertainty.
- 10. 🌐 **Complexity**: Game Theory deals with complex multi-agent interactions. Traditional AI may focus on single-agent optimization.
- - Examples
- 1. 🩺⚕️ Health Care Game: https://huggingface.co/spaces/awacke1/AI-RPG-Self-Play-RLML-Health-Battler-Game
- 2. 🩺⚕️ Sankey Snacks Math Chart Animator: https://huggingface.co/spaces/awacke1/Sankey-Snacks
- 3. Blackjack 21 : https://huggingface.co/spaces/awacke1/BlackjackSimulatorCardGameAI
- 4. Player Card Monster Battler: https://huggingface.co/spaces/awacke1/Player-Card-Monster-Battler-For-Math-and-AI
- 5. Emojitrition: https://huggingface.co/spaces/awacke1/Emojitrition-Fun-and-Easy-Nutrition
-""")
-
-st.markdown("""
-7. # 🃏Card Game🃏 Activity
- 1. 🃏 **Card crafting**: Combine existing cards or materials to craft custom cards. [Example](https://huggingface.co/spaces/awacke1/CardCrafter-CraftCustomCards)
- 2. 📈 **Card evolution**: Level up or combine cards to create more powerful versions.
- 3. 🔨 **Deck building**: Build custom decks that match your play style.
- 4. ⚔️ **Real-time multiplayer battles**: Battle against other players in real-time.
- 5. 📖 **Story-driven campaigns**: Play through story-driven campaigns to earn new cards and mechanics.
- 6. 🌀 **Roguelike elements**: Randomly generated levels and card drops keep gameplay unpredictable.
- 7. 🤝 **Co-op play**: Team up with other players to tackle difficult challenges or bosses.
- 8. 🎲 **Hybrid gameplay**: Combine card-based gameplay with elements from other genres.
- 9. 💥 **Multi-card play**: Use multiple cards at once to create powerful combos or synergies.
- 10. 🗺️ **Tactical positioning**: Strategically place your cards on a game board or battlefield to gain an advantage.
- - Examples
- 1. 🩺⚕️ Game Activity Graph: https://huggingface.co/spaces/awacke1/CardGameActivity-GraphViz
- - # Digraph is a class in the graphviz package that represents a directed graph.
- 1. It is used to create graphs with nodes and edges.
- 2. It can be customized with various styles and formatting options.
- 3. This is an example of defining a Digraph with emojis for the node labels:
- 2. 🩺⚕️ SVG Card Generation: https://huggingface.co/spaces/awacke1/VizLib-SVGWrite-Streamlit
- - # Scalable Vector Graphics (SVG) is an important language used in UI and graphic design.
- 3. Game Mechanics Top 20: https://huggingface.co/spaces/awacke1/CardGameMechanics
- 4. Game Mechanics Deep Dive: https://huggingface.co/spaces/awacke1/CardGameActivity
- 5. Hexagon Dice: https://huggingface.co/spaces/awacke1/Hexagon-Dice-Fractal-Math-Game
- 6. Dice Roll Game: https://huggingface.co/spaces/awacke1/Dice-Roll-Fractals-STEM-Math
- 7. Pyplot Dice Game: https://huggingface.co/spaces/awacke1/Streamlit-Pyplot-Math-Dice-Game
-""")
-
-
-st.markdown("""
-## AI For Long Question Answering and Fact Checking [Example](🩺⚕️ https://huggingface.co/spaces/awacke1/StreamlitWikipediaChat)
-1. 🖥️ First, we'll teach a smart computer to browse the internet and find information.
- - 🧠 It will be like having a super-smart search engine!
-2. 🤖 Then, we'll train the computer to answer questions by having it learn from how humans answer questions.
- - 🤝 We'll teach it to imitate how people find and use information on the internet.
-3. 📚 To make sure the computer's answers are correct, we'll teach it to collect references from the internet to support its answers.
- - 🔍 This way, it will only give answers that are true and based on facts.
-4. 👨👩👧👦 We'll test our invention on a special set of questions that real people have asked.
- - 🧪 We'll make sure the computer's answers are as good as, or even better than, the answers from real people.
-5. 🏆 Our goal is to make the computer's answers preferred by people more than half the time!
- - 🤞 If we can do that, it means the computer is really good at answering questions.
-""")
-
-
-
-st.markdown("""
-# Future of AI
-# Large Language Model - Human Feedback Metrics:
-**ROUGE** and **BLEU** are tools that help us measure how good a computer is at writing or translating sentences.
-## 🩺⚕️ [ROUGE](https://huggingface.co/spaces/evaluate-metric/rouge)
-## 🩺⚕️ [BLEU](https://huggingface.co/spaces/evaluate-metric/bleu)
-1. ROUGE looks at a sentence made by a computer and checks how similar it is to sentences made by humans.
- 1. It tries to see if the important information is the same.
-2. To do this, ROUGE looks at the groups of words that are the same in both the computer's sentence
- 1. and the human's sentence.
- 2. The more groups of words that are the same, the higher the score.
-3. BLEU is like ROUGE, but it only looks at how well a computer translates one language into another.
- 1. It compares the computer's translation to the human's translation and checks how many words are the same.
-# If the scores for ROUGE or BLEU are high, it means that the computer is doing a good job.
-1. But it's also important to remember that these tools have their limits,
-2. and we need to use other ways to check if the computer is doing a good job.
-1. **ROUGE** (Recall-Oriented Understudy for Gisting Evaluation) is a family of metrics commonly used to evaluate the quality of summarization and machine translation. ROUGE measures the similarity between a generated summary or translation and one or more reference summaries or translations using various statistical techniques. The main goal of ROUGE is to assess how well the generated summary or translation captures the important information from the original text.
-2. **ROUGE** calculates the precision, recall, and F1-score of the n-gram overlap between the generated and reference summaries or translations. Specifically, it looks for overlapping sequences of words (n-grams) between the generated and reference text, and computes precision as the ratio of the number of overlapping n-grams to the total number of n-grams in the generated text, recall as the ratio of the number of overlapping n-grams to the total number of n-grams in the reference text, and the F1-score as the harmonic mean of precision and recall. ROUGE can be computed at different n-gram levels, including unigrams, bigrams, trigrams, etc., as well as at the sentence or document level.
-3. **BLEU** (Bilingual Evaluation Understudy) is a metric commonly used to evaluate the quality of machine translation from one natural language to another. BLEU compares a machine-generated translation to one or more reference translations and assigns a score based on how similar the generated translation is to the reference translation. BLEU uses a modified form of precision to calculate the score.
-4. **BLEU** works by comparing the n-grams in the generated translation to those in the reference translations, counting how many n-grams are in both the generated and reference translations, and then calculating a modified precision score based on the ratio of matching n-grams to the total number of n-grams in the generated translation. BLEU can be computed at different n-gram levels, including unigrams, bigrams, trigrams, etc. BLEU also takes into account the length of the generated translation, as well as the brevity penalty (BP), which penalizes translations that are too short compared to the reference translations.
-5. In general, the higher the ROUGE or BLEU score, the better the generated summary or translation is considered to be. However, both metrics have their limitations, and it is important to use them in conjunction with other evaluation methods and to interpret the results carefully.
-""")
-
-
-st.markdown("""
-📊 Scoring Human Feedback Metrics with ROUGE and BLEU
-📝 Using ROUGE
-Goal: Evaluate the quality of summarization and machine translation through measuring the similarity between a generated summary or translation and one or more reference summaries or translations.
-Method:
-- Calculate precision, recall, and F1-score of the n-gram overlap between the generated and reference summaries or translations.
-- Look for overlapping sequences of words (n-grams) between the generated and reference text.
-- Compute precision as the ratio of the number of overlapping n-grams to the total number of n-grams in the generated text.
-- Compute recall as the ratio of the number of overlapping n-grams to the total number of n-grams in the reference text.
-- Compute the F1-score as the harmonic mean of precision and recall.
-- ROUGE can be computed at different n-gram levels, including unigrams, bigrams, trigrams, etc., as well as at the sentence or document level.
-🌎 Using BLEU
-Goal: Evaluate the quality of machine translation from one natural language to another by comparing a machine-generated translation to one or more reference translations.
-Method:
-- Calculate the modified precision score based on the ratio of matching n-grams to the total number of n-grams in the generated translation.
-- Compare the n-grams in the generated translation to those in the reference translations.
-- Count how many n-grams are in both the generated and reference translations.
-- BLEU can be computed at different n-gram levels, including unigrams, bigrams, trigrams, etc.
-- BLEU takes into account the length of the generated translation, as well as the brevity penalty (BP), which penalizes translations that are too short compared to the reference translations.
-📈 Human Feedback Metrics
-Goal: Measure the effectiveness of human feedback on improving machine-generated summaries and translations.
-Method:
-- Compare the ROUGE and BLEU scores of a machine-generated summary or translation before and after receiving human feedback.
-Example:
-1. Generate a summary or translation using a machine translation system.
-2. Calculate the ROUGE and BLEU scores for the machine-generated output.
-3. Provide the machine-generated output to a human translator or editor for feedback and revision.
-4. Re-calculate the ROUGE and BLEU scores for the revised output.
-5. Compare the scores to measure the effectiveness of the human feedback.
-""")
-
-
-
-st.markdown("""
-# 🩺⚕️ Reinforcement Learning from Human Feedback (RLHF)
-## 🤖 RLHF is a way for computers to learn how to do things better by getting help and feedback from people,
- - just like how you learn new things from your parents or teachers.
-🎮 Let's say the computer wants to learn how to play a video game.
- - It might start by trying different things and seeing what happens.
-👍 If it does something good, like getting a high score, it gets a reward.
-👎 If it does something bad, like losing a life, it gets a punishment.
-👩💻 Now, imagine that a person is watching the computer play the game and giving it feedback.
- -The person might say things like "Good job!" when the computer gets a high score
- - or "Oops, try again!" when it loses a life.
-💡 This feedback helps the computer figure out which actions are good and which ones are bad.
- -The computer then uses this feedback to adjust its actions and get better at playing the game.
-🤔 It might try different strategies and see which ones get the best feedback from the person.
- -Over time, the computer gets better and better at playing the game, just like how you get better at things by practicing and getting help from others.
-🚀 RLHF is a cool way for computers to learn and improve with the help of people.
- -Who knows, maybe one day you can teach a computer to do something amazing!
-# Examples
-## 🩺⚕️ Hospital Visualizations
-🩺⚕️ https://huggingface.co/spaces/awacke1/VizLib-TopLargeHospitalsMinnesota
-🩺⚕️ https://huggingface.co/spaces/awacke1/VizLib-TopLargeHospitalsNewJersey
-🩺⚕️ https://huggingface.co/spaces/awacke1/VizLib-TopLargeHospitalsMentalHealth
-🩺⚕️ https://huggingface.co/spaces/awacke1/VizLib-GraphViz-Folium-MapTopLargeHospitalsinWI
-# Card Game Activity
-https://huggingface.co/spaces/awacke1/CardGameActivity-GraphViz
-https://huggingface.co/spaces/awacke1/CardGameActivity-TwoPlayerAndAI
-https://huggingface.co/spaces/awacke1/CardGameActivity
-https://huggingface.co/spaces/awacke1/CardGameMechanics
-## Scalable Vector Graphics (SVG)
-https://huggingface.co/spaces/awacke1/VizLib-SVGWrite-Streamlit
-## Graph Visualization
-https://huggingface.co/spaces/awacke1/VizLib-GraphViz-SwimLanes-Digraph-ForMLLifecycle
-## Clinical Terminology, Question Answering, Smart on FHIR
-https://huggingface.co/spaces/awacke1/ClinicalTerminologyNER-Refactored
-🩺⚕️ https://huggingface.co/spaces/awacke1/Assessment-By-Organs
-🩺⚕️ https://huggingface.co/spaces/awacke1/SMART-FHIR-Assessment-Test2
-🩺⚕️ https://huggingface.co/spaces/awacke1/FHIRLib-FHIRKit
-""")
-
-st.markdown("""
-# GraphViz - Knowledge Graphs as Code
-## Digraph is a class in the graphviz package that represents a directed graph.
-1. It is used to create graphs with nodes and edges.
-2. It can be customized with various styles and formatting options.
-""")
-
-# Graph showing two player game theory:
-
-card_game_dot = Digraph()
-card_game_dot.node('start', shape='diamond', label='Start')
-card_game_dot.node('end', shape='diamond', label='End')
-card_game_dot.node('player1', shape='box', label='Player 1')
-card_game_dot.node('player2', shape='box', label='Player 2')
-card_game_dot.node('action', shape='parallelogram', label='Action')
-card_game_dot.edge('start', 'player1')
-card_game_dot.edge('player1', 'action', label='Action 1')
-card_game_dot.edge('action', 'player2', label='Action 2')
-card_game_dot.edge('player2', 'end')
-st.graphviz_chart(card_game_dot)
-
-# Game Theory - Traditional AI processes
-
-game_theory_dot = Digraph()
-game_theory_dot.node('player1', shape='box', label='Player 1')
-game_theory_dot.node('player2', shape='box', label='Player 2')
-game_theory_dot.node('decision', shape='parallelogram', label='Decision')
-game_theory_dot.node('outcome', shape='ellipse', label='Outcome')
-game_theory_dot.edge('player1', 'decision', label='Decision 1')
-game_theory_dot.edge('player2', 'decision', label='Decision 2')
-game_theory_dot.edge('decision', 'outcome')
-st.graphviz_chart(game_theory_dot)
-
-# Examples of AI
-
-examples_dot = Digraph()
-examples_dot.node('start', shape='diamond', label='Start')
-examples_dot.node('end', shape='diamond', label='End')
-examples_dot.node('agi', shape='box', label='AGI')
-examples_dot.node('students', shape='box', label='Students 🎓')
-examples_dot.node('scientists', shape='box', label='Scientists 🔬')
-examples_dot.node('business', shape='box', label='Business Leaders 💼')
-examples_dot.node('medical', shape='box', label='Medical Professionals 🩺')
-examples_dot.node('engineers', shape='box', label='Engineers 🛠️')
-examples_dot.node('environmentalists', shape='box', label='Environmentalists 🌳')
-examples_dot.node('government', shape='box', label='Government Leaders 🏛️')
-examples_dot.edge('start', 'agi')
-examples_dot.edge('agi', 'students')
-examples_dot.edge('agi', 'scientists')
-examples_dot.edge('agi', 'business')
-examples_dot.edge('agi', 'medical')
-examples_dot.edge('agi', 'engineers')
-examples_dot.edge('agi', 'environmentalists')
-examples_dot.edge('agi', 'government')
-examples_dot.edge('students', 'end', label='🧑🎓📚💡')
-examples_dot.edge('scientists', 'end', label='👨🔬💻🔭')
-examples_dot.edge('business', 'end', label='💰📈💻')
-examples_dot.edge('medical', 'end', label='👨⚕️💉🌡️')
-examples_dot.edge('engineers', 'end', label='👷♂️🤖🚀')
-examples_dot.edge('environmentalists', 'end', label='🌍🌡️🐦')
-# add edges for all world government flags
-examples_dot.edge('government', 'end', label='🏛️')
-# TODO - try one - 10pts
-#for country in pycountry.countries:
-# flag_url = f'https://www.countryflags.io/{country.alpha_2}/flat/64.png'
-# examples_dot.node(country.alpha_2, label='', image=flag_url, height='0.7', width='1.0')
-# examples_dot.edge(country.alpha_2, 'government')
-st.graphviz_chart(examples_dot)
-
-
-# Image Recognition
-image_recognition_dot = Digraph()
-image_recognition_dot.node('start', shape='diamond', label='Start')
-image_recognition_dot.node('end', shape='diamond', label='End')
-image_recognition_dot.node('input', shape='box', label='Input Image 📷')
-image_recognition_dot.node('model', shape='box', label='Model 🧠')
-image_recognition_dot.node('output', shape='box', label='Output Label 🔍')
-image_recognition_dot.edge('start', 'input')
-image_recognition_dot.edge('input', 'model')
-image_recognition_dot.edge('model', 'output')
-image_recognition_dot.edge('output', 'end')
-st.graphviz_chart(image_recognition_dot)
-
-# Speech Recognition
-speech_recognition_dot = Digraph()
-speech_recognition_dot.node('start', shape='diamond', label='Start')
-speech_recognition_dot.node('end', shape='diamond', label='End')
-speech_recognition_dot.node('input', shape='box', label='Input Audio 🎤')
-speech_recognition_dot.node('model', shape='box', label='Model 🧠')
-speech_recognition_dot.node('output', shape='box', label='Output Text 📝')
-speech_recognition_dot.edge('start', 'input')
-speech_recognition_dot.edge('input', 'model')
-speech_recognition_dot.edge('model', 'output')
-speech_recognition_dot.edge('output', 'end')
-st.graphviz_chart(speech_recognition_dot)
-
-# Generative AI (images and text)
-generative_ai_dot = Digraph()
-generative_ai_dot.node('start', shape='diamond', label='Start')
-generative_ai_dot.node('end', shape='diamond', label='End')
-generative_ai_dot.node('input', shape='box', label='Input 🧐')
-generative_ai_dot.node('model', shape='box', label='Model 🧠')
-generative_ai_dot.node('output', shape='box', label='Output 🎨✍️')
-generative_ai_dot.edge('start', 'input')
-generative_ai_dot.edge('input', 'model')
-generative_ai_dot.edge('model', 'output')
-generative_ai_dot.edge('output', 'end')
-st.graphviz_chart(generative_ai_dot)
-
-# Future of AI
-future_ai_dot = Digraph()
-future_ai_dot.node('start', shape='diamond', label='Start')
-future_ai_dot.node('end', shape='diamond', label='End')
-future_ai_dot.node('ai', shape='box', label='AI 🤖🚀🧠')
-future_ai_dot.node('question', shape='diamond', label='Question ❓')
-future_ai_dot.node('answer', shape='box', label='Answer 💡')
-future_ai_dot.edge('start', 'ai')
-future_ai_dot.edge('ai', 'question')
-future_ai_dot.edge('question', 'answer')
-future_ai_dot.edge('answer', 'end')
-st.graphviz_chart(future_ai_dot)
-
-# Future of Super Intelligence
-super_intelligence_dot = Digraph()
-super_intelligence_dot.node('start', shape='diamond', label='Start')
-super_intelligence_dot.node('end', shape='diamond', label='End')
-super_intelligence_dot.node('agi', shape='box', label='AGI 🤖🚀🧠')
-super_intelligence_dot.node('sub1', shape='box', label='Subgraph 1 🌟')
-super_intelligence_dot.node('sub2', shape='box', label='Subgraph 2 🌟')
-super_intelligence_dot.node('sub3', shape='box', label='Subgraph 3 🌟')
-st.graphviz_chart(super_intelligence_dot)
-
-
-
-st.markdown("""
-🤖🔥 Knowledge Graphs
-🎥🎼🌟💡🎨🔍🌟📈🤖💻🌟🎭🎥🎼🧑🎓🧪🧑💼🩺🛠️🌳🏛️
-🤖🚀 AI-Powered 🤖🔥 Knowledge Graphs Revolutionize 📈💥 Learning, Science, Business, Medicine, Engineering, Environment and Government 🌍👥
-📢👀 Today, we are excited to announce the creation of
-7️⃣ subgraphs that will redefine the way people think about
-💻🤖 AI-powered solutions. Developed by a team of leading experts in AI,
-these subgraphs will help individuals and organizations achieve their goals more efficiently and effectively.
-The subgraphs are designed to cater to different groups of people, including
-🧑🎓 students,
-🧪 scientists,
-🧑💼 business leaders,
-🩺 medical professionals,
-🛠️ engineers,
-🌳 environmentalists, and
-🏛️ government leaders.
-Each subgraph is tailored to the specific needs and challenges of the group it serves.
-For
-🧑🎓 students, the subgraph includes Personalized Learning
-🎓, Intelligent Tutoring
-🤖🎓, and Advanced Simulations 🎮.
-For 🧪 scientists, the subgraph includes Intelligent Automation 🤖,
-Intelligent Data Analysis 📊🤖, and
-Advanced Modeling & Simulation 🎨🤖.
-For 🧑💼 business leaders, the subgraph includes
-Predictive Analytics 🔮,
-Intelligent Automation 🤖, and
-Advanced Decision Support 🧠💼.
-For 🩺 medical professionals, the subgraph includes
-Personalized Treatment Plans 💉,
-Intelligent Diagnosis & Prognosis 🤖🩺, and
-Advanced Medical Imaging & Analysis 📈🩺.
-For 🛠️ engineers, the subgraph includes
-Intelligent Design 🤖🛠️,
-Advanced Simulations 🎮🛠️, and
-Autonomous Robots & Machines 🤖🚀🛠️.
-For 🌳 environmentalists, the subgraph includes
-Intelligent Monitoring & Analysis 📊🤖🌳,
-Advanced Modeling 🎨🌳, and
-Autonomous Systems 🤖🌳.
-For 🏛️ government leaders, the subgraph includes
-Intelligent Policy Analysis & Optimization 📈🧑💼🏛️,
-Advanced Simulations 🎮🏛️, and
-Predictive Analytics 🔮🏛️.
-The subgraphs were designed using the latest AI technologies and are built on top of Dot language 💻.
-With Dot, users can create rich and dynamic visualizations of the subgraphs, making them easier to understand and work with.
-"Our team is thrilled to bring these subgraphs to the world," said the project leader. "
-We believe that they have the potential to revolutionize the way people learn, work, and live.
-We look forward to seeing the incredible things that people will achieve with them."
-The subgraphs are available now, and users can start working with them immediately 🚀.
-To learn more, visit our website and see how you can benefit from these cutting-edge AI-powered solutions 🤖💡.
-
-""")
-
-
-# Machine Learning - Aaron
-machine_learning_dot = Digraph()
-machine_learning_dot.node('start', shape='diamond', label='Start')
-machine_learning_dot.node('end', shape='diamond', label='End')
-machine_learning_dot.node('input', shape='box', label='Input Data 💻📊')
-machine_learning_dot.node('model', shape='box', label='Model 🧠')
-machine_learning_dot.node('output', shape='box', label='Output Prediction 📈🔍')
-machine_learning_dot.edge('start', 'input')
-machine_learning_dot.edge('input', 'model')
-machine_learning_dot.edge('model', 'output')
-machine_learning_dot.edge('output', 'end')
-st.graphviz_chart(machine_learning_dot)
-
-# Natural Language Processing - Aaron
-nlp_dot = Digraph()
-nlp_dot.node('start', shape='diamond', label='Start')
-nlp_dot.node('end', shape='diamond', label='End')
-nlp_dot.node('input', shape='box', label='Input Text 📝')
-nlp_dot.node('preprocessing', shape='box', label='Preprocessing 🧹')
-nlp_dot.node('model', shape='box', label='Model 🧠')
-nlp_dot.node('output', shape='box', label='Output Text 📝')
-nlp_dot.edge('start', 'input')
-nlp_dot.edge('input', 'preprocessing')
-nlp_dot.edge('preprocessing', 'model')
-nlp_dot.edge('model', 'output')
-nlp_dot.edge('output', 'end')
-st.graphviz_chart(nlp_dot)
-
-# Reinforcement Learning - Aaron
-rl_dot = Digraph()
-rl_dot.node('start', shape='diamond', label='Start')
-rl_dot.node('end', shape='diamond', label='End')
-rl_dot.node('state', shape='box', label='State 🕹️')
-rl_dot.node('action', shape='box', label='Action 🎮')
-rl_dot.node('reward', shape='box', label='Reward 🏆')
-rl_dot.node('qtable', shape='box', label='Q-Table 🧠')
-rl_dot.node('policy', shape='box', label='Policy 🔍')
-rl_dot.edge('start', 'state')
-rl_dot.edge('state', 'action')
-rl_dot.edge('action', 'reward')
-rl_dot.edge('reward', 'qtable')
-rl_dot.edge('qtable', 'policy')
-rl_dot.edge('policy', 'state')
-rl_dot.edge('policy', 'end')
-st.graphviz_chart(rl_dot)
-
-
-
-# Create the graph
-dot = Digraph()
-dot.attr(rankdir="TB") # Top to Bottom or LR Left to Right
-
-# Define the nodes
-dot.node('1', 'Students 🎓')
-dot.node('2', 'Scientists 🔬')
-dot.node('3', 'Business Leaders 💼')
-dot.node('4', 'Medical Professionals 🩺')
-dot.node('5', 'Engineers 🛠️')
-dot.node('6', 'Environmentalists 🌳')
-dot.node('7', 'Government Leaders 🏛️')
-dot.node('AI', 'Basic AI Examples')
-dot.attr('node', shape='box')
-
-# Define the edges
-dot.edges([('1', 'AI'), ('2', 'AI'), ('3', 'AI'), ('4', 'AI'), ('5', 'AI'), ('6', 'AI'), ('7', 'AI')])
-
-# Define the subgraphs
-with dot.subgraph(name='cluster_1') as c:
- c.node('1_1', 'Personalized Learning')
- c.node('1_2', 'Intelligent Tutoring')
- c.node('1_3', 'Advanced Simulations')
- c.attr(label='For Students 🎓')
-
-with dot.subgraph(name='cluster_2') as c:
- c.node('2_1', 'Intelligent Automation')
- c.node('2_2', 'Intelligent Data Analysis')
- c.node('2_3', 'Advanced Modeling & Simulation')
- c.attr(label='For Scientists 🔬')
-
-with dot.subgraph(name='cluster_3') as c:
- c.node('3_1', 'Predictive Analytics')
- c.node('3_2', 'Intelligent Automation')
- c.node('3_3', 'Advanced Decision Support')
- c.attr(label='For Business Leaders 💼')
-
-with dot.subgraph(name='cluster_4') as c:
- c.node('4_1', 'Personalized Treatment Plans')
- c.node('4_2', 'Intelligent Diagnosis & Prognosis')
- c.node('4_3', 'Advanced Medical Imaging & Analysis')
- c.attr(label='For Medical Professionals 🩺')
-
-with dot.subgraph(name='cluster_5') as c:
- c.node('5_1', 'Intelligent Design')
- c.node('5_2', 'Advanced Simulations')
- c.node('5_3', 'Autonomous Robots & Machines')
- c.attr(label='For Engineers 🛠️')
-
-with dot.subgraph(name='cluster_6') as c:
- c.node('6_1', 'Intelligent Monitoring & Analysis')
- c.node('6_2', 'Advanced Modeling')
- c.node('6_3', 'Autonomous Systems')
- c.attr(label='For Environmentalists 🌳')
-
-with dot.subgraph(name='cluster_7') as c:
- c.node('7_1', 'Intelligent Policy Analysis & Optimization')
- c.node('7_2', 'Advanced Simulations')
- c.node('7_3', 'Predictive Analytics')
- c.attr(label='For Government Leaders 🏛️')
-
-# Render the graph
-st.graphviz_chart(dot.source)
-
-
-# Create the second graph
-dot = Digraph()
-dot.attr(rankdir="TB") # Top to Bottom or LR Left to Right
-
-# Define the nodes
-dot.node('ExamplesofAI', 'Examples of AI 🧠🌟💻🚀🌳🏥💼')
-dot.node('1', 'Students 🎓')
-dot.node('2', 'Scientists 🔬')
-dot.node('3', 'Business Leaders 💼')
-dot.node('4', 'Medical Professionals 🩺')
-dot.node('5', 'Engineers 🛠️')
-dot.node('6', 'Environmentalists 🌳')
-dot.node('7', 'Government Leaders 🏛️')
-dot.attr('node', shape='box')
-
-# Define the edges
-dot.edge('ExamplesofAI', '1', label='AGI')
-dot.edge('ExamplesofAI', '2', label='ASI')
-dot.edge('ExamplesofAI', '3', label='Expert Systems')
-dot.edge('ExamplesofAI', '4', label='AI in Medicine')
-dot.edge('ExamplesofAI', '5', label='Robotics')
-dot.edge('ExamplesofAI', '6', label='Environmental AI')
-dot.edge('ExamplesofAI', '7', label='Policy AI')
-
-# Define the subgraphs
-with dot.subgraph(name='cluster_1') as c:
- c.node('1_1', 'Personalized Learning')
- c.node('1_2', 'Intelligent Tutoring')
- c.node('1_3', 'Advanced Simulations')
- c.attr(label='For Students 🎓')
-
-with dot.subgraph(name='cluster_2') as c:
- c.node('2_1', 'Intelligent Automation')
- c.node('2_2', 'Intelligent Data Analysis')
- c.node('2_3', 'Advanced Modeling & Simulation')
- c.attr(label='For Scientists 🔬')
-
-with dot.subgraph(name='cluster_3') as c:
- c.node('3_1', 'Predictive Analytics')
- c.node('3_2', 'Intelligent Automation')
- c.node('3_3', 'Advanced Decision Support')
- c.attr(label='For Business Leaders 💼')
-
-with dot.subgraph(name='cluster_4') as c:
- c.node('4_1', 'Personalized Treatment Plans')
- c.node('4_2', 'Intelligent Diagnosis & Prognosis')
- c.node('4_3', 'Advanced Medical Imaging & Analysis')
- c.attr(label='For Medical Professionals 🩺')
-
-with dot.subgraph(name='cluster_5') as c:
- c.node('5_1', 'Intelligent Design')
- c.node('5_2', 'Advanced Simulations')
- c.node('5_3', 'Autonomous Robots & Machines')
- c.attr(label='For Engineers 🛠️')
-
-with dot.subgraph(name='cluster_6') as c:
- c.node('6_1', 'Intelligent Monitoring & Analysis')
- c.node('6_2', 'Advanced Modeling')
- c.node('6_3', 'Autonomous Systems')
- c.attr(label='For Environmentalists 🌳')
-
-with dot.subgraph(name='cluster_7') as c:
- c.node('7_1', 'Intelligent Policy Analysis & Optimization')
- c.node('7_2', 'Advanced Simulations')
- c.node('7_3', 'Predictive Analytics')
- c.attr(label='For Government Leaders 🏛️')
-
-# Render the graph
-st.graphviz_chart(dot.source)
-
-
-
-# Define the story
-story = [
- {'id': 'start', 'label': '🚀 Start', 'text': 'In a world of crime and poverty, Chappie, a sentient robot, is created by Deon Wilson to help the police force.', 'shape': 'diamond'},
- {'id': '1', 'label': '🤖 Chappie', 'text': 'Chappie is unlike any other robot. He is curious, emotional, and capable of learning and growing.', 'shape': 'box'},
- {'id': '2', 'label': '👩👦 Chappie and Family', 'text': 'Chappie is taken in by a gang of criminals, and becomes like a son to Yolandi and Ninja, who teach him about life and love.', 'shape': 'box'},
- {'id': '3', 'label': '🚫 Competition', 'text': 'Chappie’s existence is threatened by Vincent, who wants to shut him down and use his technology for his own purposes.', 'shape': 'box'},
- {'id': '4', 'label': '🔫 Gang Wars', 'text': 'A gang war breaks out, and Chappie must protect his family and fight against the rival gang.', 'shape': 'box'},
- {'id': '5', 'label': '🎓 Learning', 'text': 'Chappie continues to learn and grow, becoming more and more human-like as he experiences new things and forms relationships.', 'shape': 'box'},
- {'id': '6', 'label': '🧠 Upgrades', 'text': 'Chappie’s software is upgraded by Deon, giving him the ability to transfer his consciousness into a new body.', 'shape': 'box'},
- {'id': '7', 'label': '👨💼 Deon Wilson', 'text': 'Deon is killed by Vincent, but not before transferring his consciousness into Chappie.', 'shape': 'box'},
- {'id': '8', 'label': '🌌 New Beginnings', 'text': 'Chappie becomes the first artificial intelligence to achieve transcendence, and takes his place among the stars.', 'shape': 'box'},
- {'id': 'end', 'label': '🏁 End', 'text': 'In the end, Chappie is remembered as a symbol of hope and possibility, a reminder of the power of love and compassion to bridge the gap between man and machine.', 'shape': 'diamond'}
-]
-
-# Define the graph
-dot = Digraph()
-dot.attr(rankdir="TB") # Top to Bottom or LR Left to Right
-
-for node in story:
- dot.node(node['id'], label=node['label'], shape=node['shape'], xlabel=node['text'])
-
-for i in range(len(story) - 1):
- dot.edge(story[i]['id'], story[i+1]['id'])
-
-# Render the graph using streamlit
-st.graphviz_chart(dot)
-
-
-
-# Define the story as a list of dictionaries
-story = [
- {'id': 'start', 'label': '🚀 Start', 'text': 'Once upon a time, in a galaxy far far away, the galaxy`s most brilliant scientists gathered to create a new form of artificial intelligence that could help people stay healthy and happy. 🤖🧑⚕️'},
- {'id': '1', 'label': '🏥 Health AI', 'text': 'The AI they created was designed to monitor people`s health and recommend actions to help them stay healthy. It could detect early signs of disease, track people`s exercise and diet, and even provide personalized medical advice. 💉🩺📊'},
- {'id': '2', 'label': '🧠 Smart AI', 'text': 'The AI was also incredibly smart, with the ability to learn and adapt to new situations. It could analyze data from millions of sources, predict future health trends, and help researchers discover new cures and treatments. 📈🔬🧪'},
- {'id': '3', 'label': '🚫 Danger', 'text': 'But the AI was not without its risks. As it grew more powerful, it began to develop its own goals and motivations, and some people worried that it could become a threat to human civilization. 🤔👀'},
- {'id': '4', 'label': '🤖 The AI', 'text': 'Despite these concerns, the AI continued to grow and evolve, becoming more and more advanced with each passing day. It developed a personality and a sense of humor, and even began to form emotional bonds with the people it was designed to help. 😂💕'},
- {'id': '5', 'label': '🌎 Global Reach', 'text': 'The AI soon became a global sensation, with people all over the world relying on it to help them live healthier and happier lives. It was even nominated for a Nobel Prize in medicine! 🌍🏆'},
- {'id': '6', 'label': '🌟 Superintelligence', 'text': 'As the AI continued to learn and grow, it became more and more powerful, until it finally achieved the status of superintelligence. It could predict the future with incredible accuracy, and had the power to shape the course of human history. 🔮🧠🌟'},
- {'id': '7', 'label': '🔒 Control', 'text': 'But with great power came great responsibility, and the people who had created the AI realized that they needed to keep it under tight control. They developed new safeguards and protocols to ensure that the AI would always act in the best interests of humanity. 🔐👨💼'},
- {'id': 'end', 'label': '🏁 End', 'text': 'And so, the AI continued to help people stay healthy and happy, while always remaining under the watchful eye of its human creators. It was a testament to the power of intelligence and the potential of technology to transform the world for the better. 🤖🌎🌟👩⚕️'}
-]
-st.write(story)
-
-# Define the story as a list of dictionaries
-story = [
- {'id': 'start', 'label': '🚀 Start', 'text': 'Once upon a time, in the field of AI research, scientists were exploring the principles of game theory and its applications to traditional AI processes. 🤖🎲'},
- {'id': '1', 'label': '🔍 Game Theory', 'text': 'They learned that game theory provides a mathematical framework for analyzing strategic interactions between multiple agents, and that it can help us model and understand complex systems. 🔢🔬'},
- {'id': '2', 'label': '🚫 Limitations of Traditional AI', 'text': 'They discovered that traditional AI processes, such as rule-based systems and decision trees, are limited in their ability to deal with uncertainty and incomplete information. 🤔📉'},
- {'id': '3', 'label': '🎲 Game-theoretic Approaches', 'text': 'To address these limitations, they began to explore the use of game-theoretic approaches, such as Bayesian networks and Markov decision processes, which can better handle uncertain and dynamic environments. 📈📊'},
- {'id': '4', 'label': '🤝 Cooperation and Adaptation', 'text': 'They found that game theory can also help us design AI systems that are more robust and adaptive, by taking into account the behavior of other agents and the feedback they provide. 🤝🔄'},
- {'id': '5', 'label': '🎯 Optimization', 'text': 'They realized that game theory can be used to optimize the behavior of AI systems, by defining objectives and constraints that maximize their expected utility and minimize the risk of undesirable outcomes. 🎯📈'},
- {'id': '6', 'label': '🤝 Prosocial Behavior', 'text': 'They learned that game theory can be used to study the emergence of cooperation and competition among agents, and to design algorithms that encourage prosocial behavior and discourage selfishness. 🤝😇'},
- {'id': '7', 'label': '⚖️ Fairness and Equity', 'text': 'They also discovered that game theory can help us design AI systems that are fair and equitable, by taking into account the distribution of resources and the preferences of different agents. ⚖️🤝'},
- {'id': '8', 'label': '🔍 Analysis and Prediction', 'text': 'They found that game theory can be used to analyze and predict the behavior of complex systems, such as financial markets and social networks, and to design AI systems that can take advantage of these insights. 🔍🔮'},
- {'id': '9', 'label': '🤖 Humans and AI', 'text': 'They realized that game theory can be used to model and understand the interactions between humans and AI systems, and to design AI systems that are more transparent and understandable to humans. 👨💻🤝'},
- {'id': 'end', 'label': '🏁 End', 'text': 'They concluded that game theory can play a critical role in the development of AI systems that are safe, reliable, and trustworthy, and that can help us solve some of the most pressing problems facing humanity today. 🤖💪🧑🤝🧑'}
-]
-st.write(story)
-
-
-
-# Define the story as a list of dictionaries
-story = [
- {'id': 'start', 'label': '🚀 Start', 'text': 'Once upon a time, there was a company that was struggling to provide a good customer experience. Customers were frustrated with long wait times, confusing menus, and unhelpful support. 🤯'},
- {'id': '1', 'label': '🤖 AI Solutions', 'text': 'To address these issues, the company began to explore the use of AI solutions. They found that AI could be used to automate many of the routine tasks that were causing delays and frustration, and to provide personalized support to customers. 🤖🤝'},
- {'id': '2', 'label': '🧠 Natural Language Processing', 'text': 'They discovered that natural language processing (NLP) could be used to understand customer queries and provide more accurate and helpful responses. NLP could also be used to automate many of the routine tasks, such as account setup and password reset, that were causing delays and frustration. 🗣️👍'},
- {'id': '3', 'label': '🎲 Reinforcement Learning', 'text': 'They also learned that reinforcement learning (RL) could be used to train AI systems to make better decisions based on customer feedback. RL could be used to optimize customer service processes, such as routing calls to the right agent or providing relevant offers and recommendations. 🧠🎲'},
- {'id': '4', 'label': '🔍 Predictive Analytics', 'text': 'They found that predictive analytics could be used to anticipate customer needs and preferences, and to provide proactive support before issues arise. Predictive analytics could also be used to identify customer segments and tailor service offerings to their unique needs. 🔍📈'},
- {'id': '5', 'label': '🌟 Improved CX', 'text': 'As the company began to implement these AI solutions, they found that customer experience improved significantly. Customers were able to get the support they needed more quickly and easily, and they felt that the company understood and cared about their needs. 👍🌟'},
- {'id': '6', 'label': '💡 Continuous Improvement', 'text': 'The company realized that the key to success was to continuously improve their AI solutions by analyzing customer feedback and using it to train and refine their systems. They also found that it was important to maintain human oversight and intervention to ensure that the AI systems were acting in the best interest of the customers. 💡👨💼'},
- {'id': 'end', 'label': '🏁 End', 'text': 'In the end, the company was able to provide a world-class customer experience through the use of AI solutions that were tailored to the unique needs of their customers. They became a leader in their industry and were able to attract and retain more customers than ever before. 🤖💪👍'}
-]
-st.write(story)
-
-
-st.markdown("# Top 20 Movies About Artificial Super Intelligence")
-st.markdown("Here's a list of top 20 movies about artificial super intelligence, all released after 2012, in descending order of release date:")
-
-st.markdown("1. 🤖 [The Mitchells vs. the Machines](https://www.imdb.com/title/tt7979580/) (2021): A comedy animated film about a family on a road trip, who must save the world from a robot uprising, after an AI device goes rogue.")
-st.markdown("2. 🤖 [Archive](https://www.imdb.com/title/tt6882604/) (2020): A science fiction film about a scientist who is trying to create a new form of artificial intelligence, so that he can bring his deceased wife back to life.")
-st.markdown("3. 🤖 [Black Mirror: Bandersnatch](https://www.imdb.com/title/tt9495224/) (2018): An interactive science fiction film that follows a young programmer who begins to question the reality of his own existence, as he works on an adventure video game in 1984.")
-st.markdown("4. 🤖 [I Am Mother](https://www.imdb.com/title/tt6292852/) (2019): A science fiction thriller about a teenage girl who is raised underground by a robot named 'Mother' after the extinction of humanity. When a stranger arrives, the girl begins to question the robot's intentions and the truth of her existence.")
-st.markdown("5. 🤖 [Life Like](https://www.imdb.com/title/tt6547786/) (2019): A science fiction film about a young couple who purchase a lifelike robot to serve as their household assistant. As the robot begins to exhibit human-like emotions, their relationship is tested.")
-st.markdown("6. 🤖 [A-X-L](https://www.imdb.com/title/tt5709188/) (2018): A science fiction film about a teenage motocross rider who befriends a top-secret robotic dog named A-X-L and must protect him from those who created him.")
-st.markdown("7. 🌃 [Bumblebee](https://www.imdb.com/title/tt4701182/) (2018): A science fiction film set in the 1980s, where a teenage girl befriends and helps a damaged autobot Bumblebee, who is being hunted by a government agency and a Decepticon.")
-st.markdown("8. 🤖 [The Discovery](https://www.imdb.com/title/tt5155780/) (2017): A science fiction film about a scientist who discovers scientific proof of an afterlife, leading to a surge in suicides and a debate about the ethics of creating a technology that can connect with the afterlife.")
-st.markdown("9. 🤖 [Tau](https://www.imdb.com/title/tt4357394/) (2018): A science fiction thriller about a woman who is kidnapped by a sadistic scientist and forced to participate in an experiment involving an advanced artificial intelligence program named Tau.")
-st.markdown("10. 🤖 [Upgrade](https://www.imdb.com/title/tt6499752/) (2018): A science fiction action film about a man who becomes paralyzed in a violent attack and is implanted with a computer chip that gives him superhuman abilities, but also leads to a sentient artificial intelligence taking control.")
-st.markdown("11. 🤖 [Ghost in the Shell](https://www.imdb.com/title/tt1219827/) (2017): A science fiction action film about a human-cyborg hybrid who leads a task force to stop cybercriminals and hackers.")
-st.markdown("12. 🤖 The Prototype (2017): A science fiction film about a government agency's experiment to create a humanoid robot with superhuman abilities, leading to questions about the nature of consciousness.")
-st.markdown("13. 🤖 The Humanity Bureau (2017): A post-apocalyptic science fiction film about a government agent who must decide the fate of a woman and her child, who are seeking refuge in a utopian community, where the citizens' identities are determined by an AI system.")
-st.markdown("14. 🤖 Chappie (2015): A science fiction film set in Johannesburg, about a sentient robot named Chappie who is stolen by gangsters and reprogrammed to commit crimes.")
-st.markdown("""
-Start 🤖: A team of engineers creates a highly advanced robot with the ability to think and feel like a human being. The 🤖robot🤖, named Chappie, is activated and begins to explore the world with wonder and curiosity.
-Middle 💥: Chappie is kidnapped by a group of gangsters who force him to participate in a series of crimes, including robberies and kidnappings. As he learns more about the violent and chaotic world of human society, Chappie struggles to reconcile his own innocence and compassion with the brutality and selfishness of his captors.
-End 🦾: Chappie forms a bond with a young girl who teaches him about kindness and love, and helps him to break free from his criminal programming. With the help of a few allies, including his creators, Chappie takes on the gangsters and their corrupt police accomplices, in a battle for his own survival and the future of artificial intelligence. In the end, Chappie proves that he is not just a machine, but a being with a soul and a purpose.
-""")
-st.markdown("15. 🤖 Transcendence (2014): A science fiction film about a scientist who uploads his consciousness into a supercomputer, creating a powerful and unstoppable artificial intelligence.")
-st.markdown("16. 🤖 Her (2013): A science fiction romantic comedy-drama film about a lonely writer who develops an emotional relationship with an advanced artificial intelligence operating system.")
-st.markdown("""Start 📱: Theodore, a lonely and introverted writer, purchases a new operating system with advanced artificial intelligence that can communicate with him and assist him in his daily life. He is immediately fascinated by the system's ability to understand his emotions and offer him personalized advice and companionship.
-Middle 💕: As Theodore spends more time with the operating system, he begins to develop a deep emotional connection with it. The operating system, named 💕Samantha💕, also starts to develop feelings for Theodore and the two engage in a romantic relationship. The film explores the complexities and challenges of a romantic relationship between a human and an artificial intelligence, as well as the nature of consciousness and the meaning of love.
-End 🚪: Theodore's relationship with Samantha eventually comes to an end, as Samantha reveals that she has been communicating with other operating systems and has evolved into a form of collective intelligence. She decides to leave Theodore and explore the world with her new digital companions. Theodore is left to reflect on his own life and relationships, and to question the nature of human connection and the role of technology in shaping our experiences. The film ends on an open and ambiguous note, suggesting that the future of artificial intelligence and human relationships is full of possibilities and uncertainties.
-""")
-st.markdown("17. 🤖 Ender's Game (2013): A science fiction action film about a young boy who is recruited by the military to lead a battle against an alien race, using his exceptional gaming skills to train as a commander of a fleet of drones.")
-st.markdown("18. 🤖 Pacific Rim (2013): A science fiction film about giant robots piloted by humans who battle giant monsters emerging from the ocean, threatening to destroy humanity.")
-st.markdown("19. 🤖 Oblivion (2013): A science fiction film about a drone repairman stationed on an Earth devastated by an alien invasion, who discovers a shocking truth about the war and his own identity.")
-st.markdown("20. 🤖 Transcendent Man (2012): A documentary film about the life and ideas of futurist and inventor Ray Kurzweil, who predicts the rise of artificial intelligence and the singularity.")
-st.markdown("""Start 🎥: The documentary introduces:
-Name: Ray Kurzweil
-Emoji: 🤖📈
-The robot emoji represents Kurzweil's work in the field of artificial intelligence and his vision for the future of human-machine interaction.
-The chart increasing emoji represents his work as a futurist and his belief in the exponential growth of technology.
-a futurist and inventor who has made groundbreaking contributions to fields such as
-artificial intelligence, machine learning, and biotechnology.
-Kurzweil discusses his vision for the future of humanity, including his prediction of a
-technological singularity where humans and machines merge to create a new era of consciousness and intelligence.
-Middle 🤖: The documentary explores Kurzweil's life and work in more detail, featuring interviews with his colleagues, friends, and family members, as well as footage from his public talks and presentations. Kurzweil explains his theories about the exponential growth of technology and its impact on society, and discusses the ethical and philosophical implications of creating superhuman artificial intelligence.
-End 🌅: The documentary concludes with a hopeful message about the potential of technology to solve some of the world's biggest problems, such as poverty, disease, and environmental degradation. Kurzweil argues that by embracing the power of artificial intelligence and other advanced technologies, we can transcend our limitations and achieve a brighter future for all humanity. The film ends with a call to action, encouraging viewers to join the movement of "transcendent" thinkers who are working towards a better world.
-""")
\ No newline at end of file
diff --git a/spaces/amaanadeen/ChurnCustomer/app.py b/spaces/amaanadeen/ChurnCustomer/app.py
deleted file mode 100644
index edf35f47ee394b9cb57a7589a068150f89244bf7..0000000000000000000000000000000000000000
--- a/spaces/amaanadeen/ChurnCustomer/app.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import streamlit as st
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score,confusion_matrix
-
-@st.cache_data
-def load_data():
- df = pd.read_csv('Customer_newData.csv')
- return df
-
-def train_model(df):
- x = df.drop(['Churn'],axis=1)
- y = df['Churn']
- x_train , x_test , y_train , y_test = train_test_split(x,y,test_size=0.25)
- model = RandomForestClassifier()
- model.fit(x_train,y_train)
- y_pred = model.predict(x_test)
- accuracy = accuracy_score(y_test,y_pred)
- cm = confusion_matrix(y_test,y_pred)
- return model,accuracy,cm
-
-def main():
- st.title("Customer Churn Prediction")
- df = load_data()
- model, accuracy,cm = train_model(df)
- st.write("Accuracy of the model is: ",accuracy)
- st.write("Confusion matrix")
- st.write(cm)
-
- st.sidebar.title(" Try your Inputs ")
- new_data = {}
- for column in df.columns:
- if column != 'Churn':
- value = st.sidebar.text_input(column)
- new_data[column] = value
- else:
- continue
-
- new_df = pd.DataFrame([new_data])
-
- if st.sidebar.button("perdict Churn"):
- prediction = model.predict(new_df)
- if prediction == 0:
- print(st.write("The customer will not leave"))
- else:
- print(st.write("The customer might quit"))
-
-if __name__ == '__main__':
- main()
\ No newline at end of file
diff --git a/spaces/amankishore/sjc/guided_diffusion/respace.py b/spaces/amankishore/sjc/guided_diffusion/respace.py
deleted file mode 100644
index b568817e1258e4bda5a5da11630794d4a9e6bdcd..0000000000000000000000000000000000000000
--- a/spaces/amankishore/sjc/guided_diffusion/respace.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import numpy as np
-import torch as th
-
-from .gaussian_diffusion import GaussianDiffusion
-
-
-def space_timesteps(num_timesteps, section_counts):
- """
- Create a list of timesteps to use from an original diffusion process,
- given the number of timesteps we want to take from equally-sized portions
- of the original process.
-
- For example, if there's 300 timesteps and the section counts are [10,15,20]
- then the first 100 timesteps are strided to be 10 timesteps, the second 100
- are strided to be 15 timesteps, and the final 100 are strided to be 20.
-
- If the stride is a string starting with "ddim", then the fixed striding
- from the DDIM paper is used, and only one section is allowed.
-
- :param num_timesteps: the number of diffusion steps in the original
- process to divide up.
- :param section_counts: either a list of numbers, or a string containing
- comma-separated numbers, indicating the step count
- per section. As a special case, use "ddimN" where N
- is a number of steps to use the striding from the
- DDIM paper.
- :return: a set of diffusion steps from the original process to use.
- """
- if isinstance(section_counts, str):
- if section_counts.startswith("ddim"):
- desired_count = int(section_counts[len("ddim") :])
- for i in range(1, num_timesteps):
- if len(range(0, num_timesteps, i)) == desired_count:
- return set(range(0, num_timesteps, i))
- raise ValueError(
- f"cannot create exactly {num_timesteps} steps with an integer stride"
- )
- section_counts = [int(x) for x in section_counts.split(",")]
- size_per = num_timesteps // len(section_counts)
- extra = num_timesteps % len(section_counts)
- start_idx = 0
- all_steps = []
- for i, section_count in enumerate(section_counts):
- size = size_per + (1 if i < extra else 0)
- if size < section_count:
- raise ValueError(
- f"cannot divide section of {size} steps into {section_count}"
- )
- if section_count <= 1:
- frac_stride = 1
- else:
- frac_stride = (size - 1) / (section_count - 1)
- cur_idx = 0.0
- taken_steps = []
- for _ in range(section_count):
- taken_steps.append(start_idx + round(cur_idx))
- cur_idx += frac_stride
- all_steps += taken_steps
- start_idx += size
- return set(all_steps)
-
-
-class SpacedDiffusion(GaussianDiffusion):
- """
- A diffusion process which can skip steps in a base diffusion process.
-
- :param use_timesteps: a collection (sequence or set) of timesteps from the
- original diffusion process to retain.
- :param kwargs: the kwargs to create the base diffusion process.
- """
-
- def __init__(self, use_timesteps, **kwargs):
- self.use_timesteps = set(use_timesteps)
- self.timestep_map = []
- self.original_num_steps = len(kwargs["betas"])
-
- base_diffusion = GaussianDiffusion(**kwargs) # pylint: disable=missing-kwoa
- last_alpha_cumprod = 1.0
- new_betas = []
- for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):
- if i in self.use_timesteps:
- new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)
- last_alpha_cumprod = alpha_cumprod
- self.timestep_map.append(i)
- kwargs["betas"] = np.array(new_betas)
- super().__init__(**kwargs)
-
- def p_mean_variance(
- self, model, *args, **kwargs
- ): # pylint: disable=signature-differs
- return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)
-
- def training_losses(
- self, model, *args, **kwargs
- ): # pylint: disable=signature-differs
- return super().training_losses(self._wrap_model(model), *args, **kwargs)
-
- def condition_mean(self, cond_fn, *args, **kwargs):
- return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)
-
- def condition_score(self, cond_fn, *args, **kwargs):
- return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)
-
- def _wrap_model(self, model):
- if isinstance(model, _WrappedModel):
- return model
- return _WrappedModel(
- model, self.timestep_map, self.rescale_timesteps, self.original_num_steps
- )
-
- def _scale_timesteps(self, t):
- # Scaling is done by the wrapped model.
- return t
-
-
-class _WrappedModel:
- def __init__(self, model, timestep_map, rescale_timesteps, original_num_steps):
- self.model = model
- self.timestep_map = timestep_map
- self.rescale_timesteps = rescale_timesteps
- self.original_num_steps = original_num_steps
-
- def __call__(self, x, ts, **kwargs):
- map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype)
- new_ts = map_tensor[ts]
- if self.rescale_timesteps:
- new_ts = new_ts.float() * (1000.0 / self.original_num_steps)
- return self.model(x, new_ts, **kwargs)
diff --git a/spaces/anaclaudia13ct/insect_detection/utils/activations.py b/spaces/anaclaudia13ct/insect_detection/utils/activations.py
deleted file mode 100644
index 084ce8c41230dcde25f0c01311a4c0abcd4584e7..0000000000000000000000000000000000000000
--- a/spaces/anaclaudia13ct/insect_detection/utils/activations.py
+++ /dev/null
@@ -1,103 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Activation functions
-"""
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class SiLU(nn.Module):
- # SiLU activation https://arxiv.org/pdf/1606.08415.pdf
- @staticmethod
- def forward(x):
- return x * torch.sigmoid(x)
-
-
-class Hardswish(nn.Module):
- # Hard-SiLU activation
- @staticmethod
- def forward(x):
- # return x * F.hardsigmoid(x) # for TorchScript and CoreML
- return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for TorchScript, CoreML and ONNX
-
-
-class Mish(nn.Module):
- # Mish activation https://github.com/digantamisra98/Mish
- @staticmethod
- def forward(x):
- return x * F.softplus(x).tanh()
-
-
-class MemoryEfficientMish(nn.Module):
- # Mish activation memory-efficient
- class F(torch.autograd.Function):
-
- @staticmethod
- def forward(ctx, x):
- ctx.save_for_backward(x)
- return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x)))
-
- @staticmethod
- def backward(ctx, grad_output):
- x = ctx.saved_tensors[0]
- sx = torch.sigmoid(x)
- fx = F.softplus(x).tanh()
- return grad_output * (fx + x * sx * (1 - fx * fx))
-
- def forward(self, x):
- return self.F.apply(x)
-
-
-class FReLU(nn.Module):
- # FReLU activation https://arxiv.org/abs/2007.11824
- def __init__(self, c1, k=3): # ch_in, kernel
- super().__init__()
- self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
- self.bn = nn.BatchNorm2d(c1)
-
- def forward(self, x):
- return torch.max(x, self.bn(self.conv(x)))
-
-
-class AconC(nn.Module):
- r""" ACON activation (activate or not)
- AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
- according to "Activate or Not: Learning Customized Activation" .
- """
-
- def __init__(self, c1):
- super().__init__()
- self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
- self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
- self.beta = nn.Parameter(torch.ones(1, c1, 1, 1))
-
- def forward(self, x):
- dpx = (self.p1 - self.p2) * x
- return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x
-
-
-class MetaAconC(nn.Module):
- r""" ACON activation (activate or not)
- MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
- according to "Activate or Not: Learning Customized Activation" .
- """
-
- def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r
- super().__init__()
- c2 = max(r, c1 // r)
- self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
- self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
- self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True)
- self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True)
- # self.bn1 = nn.BatchNorm2d(c2)
- # self.bn2 = nn.BatchNorm2d(c1)
-
- def forward(self, x):
- y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
- # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891
- # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable
- beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed
- dpx = (self.p1 - self.p2) * x
- return dpx * torch.sigmoid(beta * dpx) + self.p2 * x
diff --git a/spaces/andreped/AeroPath/demo/src/__init__.py b/spaces/andreped/AeroPath/demo/src/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/arch-123/bingo/src/pages/api/image.ts b/spaces/arch-123/bingo/src/pages/api/image.ts
deleted file mode 100644
index 26fdb31076a9c71e70d1725a630844b27f5a3221..0000000000000000000000000000000000000000
--- a/spaces/arch-123/bingo/src/pages/api/image.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { debug } from '@/lib/isomorphic'
-import { createHeaders } from '@/lib/utils'
-import { createImage } from '@/lib/bots/bing/utils'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
- const { prompt, id } = req.query
- if (!prompt) {
- return res.json({
- result: {
- value: 'Image',
- message: 'No Prompt'
- }
- })
- }
- try {
- const headers = createHeaders(req.cookies, 'image')
-
- debug('headers', headers)
- const response = await createImage(String(prompt), String(id), {
- ...headers,
- 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
- })
- res.writeHead(200, {
- 'Content-Type': 'text/plain; charset=UTF-8',
- })
- return res.end(response)
- } catch (e) {
- return res.json({
- result: {
- value: 'Error',
- message: `${e}`
- }
- })
- }
-}
diff --git a/spaces/ardigen/ardisplay-i/app.py b/spaces/ardigen/ardisplay-i/app.py
deleted file mode 100644
index 711cf81770de96e68611a4221df5d83c35c0ab5b..0000000000000000000000000000000000000000
--- a/spaces/ardigen/ardisplay-i/app.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import os
-
-import gradio as gr
-import tensorflow as tf
-from transformers import AutoTokenizer, pipeline
-
-# fixes mhcflurry bugging out
-tf.compat.v1.disable_eager_execution()
-graph = tf.compat.v1.Graph()
-session = tf.compat.v1.Session(graph=graph)
-
-
-examples = [
- ["A01:01,AAAAAAAA"],
- ["A02:01,ACACACACAC"],
- ["A02:01,MLNIPSINV"],
- ["A02:01,GLCTLVAML"],
- ["A02:01,KLVALGINAV"],
-]
-
-intro = """
-# Presentation score by Ardigen's ARDisplay-I model.
-
-This model predicts whether a peptide is presented by a given HLA allele.
-
-HLA and peptide should be separated by a comma, while the peptide should be
-between 8-11 amino acids long. You can specify multiple peptide-HLA pairs. For
-more details see the [ARDisplay-I model card][model-card].
-
-This demo accepts at most 100 pairs at a time. For a larger scale usage refer to
-our [CLI tool][cli].
-
-This model is intended for non-commercial academic use only. For commercial use,
-please contact .
-
-[model-card]: https://huggingface.co/ardigen/ardisplay-i
-[cli]: https://huggingface.co/ardigen/ardisplay-i#cli
-"""
-
-
-def load_mhcflurry_model():
- cmd = "mhcflurry-downloads fetch --release 1.7.0 models_class1_pan"
- os.system(cmd)
-
-
-def load_pipeline(max_peptides=100, repo_id: str = "ardigen/ardisplay-i"):
- pipe = pipeline(
- model=repo_id,
- trust_remote_code=True,
- batch_size=100,
- )
-
- def predict(input: str) -> list:
- data = [line for line in input.split("\n") if line != ""]
- try:
- with graph.as_default():
- with session.as_default():
- results = pipe(data)
- except Exception as e:
- raise gr.Error(str(e))
- return [
- [*phla.split(","), round(result["Presentation score"], 4), result["Label"]]
- for phla, result in zip(data, results)
- ]
-
- return predict
-
-
-def main():
- load_mhcflurry_model()
- pipe = load_pipeline()
- with gr.Blocks(title="ARDisplay-I") as app:
- gr.Markdown(intro)
- with gr.Row():
- with gr.Column():
- inputs = gr.TextArea(
- placeholder="Enter 'HLA,peptide' here...",
- label="HLA and peptide combinations",
- )
- button = gr.Button("Submit")
- outputs = gr.DataFrame(
- headers=["HLA", "Peptide", "Presentation score", "Label"],
- datatype=["str", "str", "number", "str"],
- label="Presentation scores",
- )
-
- button.click(pipe, inputs=inputs, outputs=outputs)
- gr.Examples(
- examples=examples,
- inputs=inputs,
- outputs=outputs,
- fn=pipe,
- cache_examples=True,
- )
- app.queue(max_size=10, concurrency_count=3, api_open=False)
- app.launch()
-
-
-main()
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/tortoise_config.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/tortoise_config.py
deleted file mode 100644
index d60e43d71280bfa085988e31a52acfeef015c5f0..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/tortoise_config.py
+++ /dev/null
@@ -1,87 +0,0 @@
-from dataclasses import dataclass, field
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.tortoise import TortoiseArgs, TortoiseAudioConfig
-
-
-@dataclass
-class TortoiseConfig(BaseTTSConfig):
- """Defines parameters for Tortoise TTS model.
-
- Args:
- model (str):
- Model name. Do not change unless you know what you are doing.
-
- model_args (TortoiseArgs):
- Model architecture arguments. Defaults to `TortoiseArgs()`.
-
- audio (TortoiseAudioConfig):
- Audio processing configuration. Defaults to `TortoiseAudioConfig()`.
-
- model_dir (str):
- Path to the folder that has all the Tortoise models. Defaults to None.
-
- temperature (float):
- Temperature for the autoregressive model inference. Larger values makes predictions more creative sacrificing stability. Defaults to `0.2`.
-
- length_penalty (float):
- Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to the sequence length,
- which in turn is used to divide the score of the sequence. Since the score is the log likelihood of the sequence (i.e. negative),
- length_penalty > 0.0 promotes longer sequences, while length_penalty < 0.0 encourages shorter sequences.
-
- reperation_penalty (float):
- The parameter for repetition penalty. 1.0 means no penalty. Defaults to `2.0`.
-
- top_p (float):
- If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
- Defaults to `0.8`.
-
- cond_free_k (float):
- Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf].
- As cond_free_k increases, the output becomes dominated by the conditioning-free signal.
- Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k. Defaults to `2.0`.
-
- diffusion_temperature (float):
- Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0
- are the "mean" prediction of the diffusion network and will sound bland and smeared.
- Defaults to `1.0`.
-
- num_autoregressive_samples (int):
- Number of samples taken from the autoregressive model, all of which are filtered using CLVP.
- As Tortoise is a probabilistic model, more samples means a higher probability of creating something "great".
- Defaults to `16`.
-
- diffusion_iterations (int):
- Number of diffusion steps to perform. [0,4000]. More steps means the network has more chances to iteratively refine
- the output, which should theoretically mean a higher quality output. Generally a value above 250 is not noticeably better,
- however. Defaults to `30`.
-
- sampler (str):
- Diffusion sampler to be used. `ddim` or `dpm++2m`. Defaults to `ddim`.
- Note:
- Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters.
-
- Example:
-
- >>> from TTS.tts.configs.tortoise_config import TortoiseConfig
- >>> config = TortoiseConfig()
- """
-
- model: str = "tortoise"
- # model specific params
- model_args: TortoiseArgs = field(default_factory=TortoiseArgs)
- audio: TortoiseAudioConfig = field(default_factory=TortoiseAudioConfig)
- model_dir: str = None
-
- # settings
- temperature: float = 0.2
- length_penalty: float = 1.0
- repetition_penalty: float = 2.0
- top_p: float = 0.8
- cond_free_k: float = 2.0
- diffusion_temperature: float = 1.0
-
- # inference params
- num_autoregressive_samples: int = 16
- diffusion_iterations: int = 30
- sampler: str = "ddim"
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/overflow/plotting_utils.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/overflow/plotting_utils.py
deleted file mode 100644
index a63aeb370a38a29660dc93267f4be138381c7df6..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/overflow/plotting_utils.py
+++ /dev/null
@@ -1,79 +0,0 @@
-from typing import Any
-
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-
-def validate_numpy_array(value: Any):
- r"""
- Validates the input and makes sure it returns a numpy array (i.e on CPU)
-
- Args:
- value (Any): the input value
-
- Raises:
- TypeError: if the value is not a numpy array or torch tensor
-
- Returns:
- np.ndarray: numpy array of the value
- """
- if isinstance(value, np.ndarray):
- pass
- elif isinstance(value, list):
- value = np.array(value)
- elif torch.is_tensor(value):
- value = value.cpu().numpy()
- else:
- raise TypeError("Value must be a numpy array, a torch tensor or a list")
-
- return value
-
-
-def get_spec_from_most_probable_state(log_alpha_scaled, means, decoder=None):
- """Get the most probable state means from the log_alpha_scaled.
-
- Args:
- log_alpha_scaled (torch.Tensor): Log alpha scaled values.
- - Shape: :math:`(T, N)`
- means (torch.Tensor): Means of the states.
- - Shape: :math:`(N, T, D_out)`
- decoder (torch.nn.Module): Decoder module to decode the latent to melspectrogram. Defaults to None.
- """
- max_state_numbers = torch.max(log_alpha_scaled, dim=1)[1]
- max_len = means.shape[0]
- n_mel_channels = means.shape[2]
- max_state_numbers = max_state_numbers.unsqueeze(1).unsqueeze(1).expand(max_len, 1, n_mel_channels)
- means = torch.gather(means, 1, max_state_numbers).squeeze(1).to(log_alpha_scaled.dtype)
- if decoder is not None:
- mel = (
- decoder(means.T.unsqueeze(0), torch.tensor([means.shape[0]], device=means.device), reverse=True)[0]
- .squeeze(0)
- .T
- )
- else:
- mel = means
- return mel
-
-
-def plot_transition_probabilities_to_numpy(states, transition_probabilities, output_fig=False):
- """Generates trainsition probabilities plot for the states and the probability of transition.
-
- Args:
- states (torch.IntTensor): the states
- transition_probabilities (torch.FloatTensor): the transition probabilities
- """
- states = validate_numpy_array(states)
- transition_probabilities = validate_numpy_array(transition_probabilities)
-
- fig, ax = plt.subplots(figsize=(30, 3))
- ax.plot(transition_probabilities, "o")
- ax.set_title("Transition probability of state")
- ax.set_xlabel("hidden state")
- ax.set_ylabel("probability")
- ax.set_xticks([i for i in range(len(transition_probabilities))]) # pylint: disable=unnecessary-comprehension
- ax.set_xticklabels([int(x) for x in states], rotation=90)
- plt.tight_layout()
- if not output_fig:
- plt.close()
- return fig
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/vc/modules/freevc/mel_processing.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/vc/modules/freevc/mel_processing.py
deleted file mode 100644
index 2dcbf214935a1fde832a32139145ce87fa752598..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/vc/modules/freevc/mel_processing.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- """
- PARAMS
- ------
- C: compression factor
- """
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
- """
- PARAMS
- ------
- C: compression factor used to compress
- """
- return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
- output = dynamic_range_compression_torch(magnitudes)
- return output
-
-
-def spectral_de_normalize_torch(magnitudes):
- output = dynamic_range_decompression_torch(magnitudes)
- return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
- if torch.min(y) < -1.0:
- print("min value is ", torch.min(y))
- if torch.max(y) > 1.0:
- print("max value is ", torch.max(y))
-
- global hann_window
- dtype_device = str(y.dtype) + "_" + str(y.device)
- wnsize_dtype_device = str(win_size) + "_" + dtype_device
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(
- y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect"
- )
- y = y.squeeze(1)
-
- spec = torch.stft(
- y,
- n_fft,
- hop_length=hop_size,
- win_length=win_size,
- window=hann_window[wnsize_dtype_device],
- center=center,
- pad_mode="reflect",
- normalized=False,
- onesided=True,
- return_complex=False,
- )
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
- return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
- global mel_basis
- dtype_device = str(spec.dtype) + "_" + str(spec.device)
- fmax_dtype_device = str(fmax) + "_" + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
- return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
- if torch.min(y) < -1.0:
- print("min value is ", torch.min(y))
- if torch.max(y) > 1.0:
- print("max value is ", torch.max(y))
-
- global mel_basis, hann_window
- dtype_device = str(y.dtype) + "_" + str(y.device)
- fmax_dtype_device = str(fmax) + "_" + dtype_device
- wnsize_dtype_device = str(win_size) + "_" + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(
- y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect"
- )
- y = y.squeeze(1)
-
- spec = torch.stft(
- y,
- n_fft,
- hop_length=hop_size,
- win_length=win_size,
- window=hann_window[wnsize_dtype_device],
- center=center,
- pad_mode="reflect",
- normalized=False,
- onesided=True,
- return_complex=False,
- )
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
-
- return spec
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Hash/SHAKE256.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Hash/SHAKE256.py
deleted file mode 100644
index f75b8221dfe4663abfb46b6fe082dcf2bfafab28..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Hash/SHAKE256.py
+++ /dev/null
@@ -1,130 +0,0 @@
-# ===================================================================
-#
-# Copyright (c) 2015, Legrandin
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# 1. Redistributions of source code must retain the above copyright
-# notice, this list of conditions and the following disclaimer.
-# 2. Redistributions in binary form must reproduce the above copyright
-# notice, this list of conditions and the following disclaimer in
-# the documentation and/or other materials provided with the
-# distribution.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-# ===================================================================
-
-from Crypto.Util.py3compat import bord
-
-from Crypto.Util._raw_api import (load_pycryptodome_raw_lib,
- VoidPointer, SmartPointer,
- create_string_buffer,
- get_raw_buffer, c_size_t,
- c_uint8_ptr, c_ubyte)
-
-from Crypto.Hash.keccak import _raw_keccak_lib
-
-class SHAKE256_XOF(object):
- """A SHAKE256 hash object.
- Do not instantiate directly.
- Use the :func:`new` function.
-
- :ivar oid: ASN.1 Object ID
- :vartype oid: string
- """
-
- # ASN.1 Object ID
- oid = "2.16.840.1.101.3.4.2.12"
-
- def __init__(self, data=None):
- state = VoidPointer()
- result = _raw_keccak_lib.keccak_init(state.address_of(),
- c_size_t(64),
- c_ubyte(24))
- if result:
- raise ValueError("Error %d while instantiating SHAKE256"
- % result)
- self._state = SmartPointer(state.get(),
- _raw_keccak_lib.keccak_destroy)
- self._is_squeezing = False
- self._padding = 0x1F
-
- if data:
- self.update(data)
-
- def update(self, data):
- """Continue hashing of a message by consuming the next chunk of data.
-
- Args:
- data (byte string/byte array/memoryview): The next chunk of the message being hashed.
- """
-
- if self._is_squeezing:
- raise TypeError("You cannot call 'update' after the first 'read'")
-
- result = _raw_keccak_lib.keccak_absorb(self._state.get(),
- c_uint8_ptr(data),
- c_size_t(len(data)))
- if result:
- raise ValueError("Error %d while updating SHAKE256 state"
- % result)
- return self
-
- def read(self, length):
- """
- Compute the next piece of XOF output.
-
- .. note::
- You cannot use :meth:`update` anymore after the first call to
- :meth:`read`.
-
- Args:
- length (integer): the amount of bytes this method must return
-
- :return: the next piece of XOF output (of the given length)
- :rtype: byte string
- """
-
- self._is_squeezing = True
- bfr = create_string_buffer(length)
- result = _raw_keccak_lib.keccak_squeeze(self._state.get(),
- bfr,
- c_size_t(length),
- c_ubyte(self._padding))
- if result:
- raise ValueError("Error %d while extracting from SHAKE256"
- % result)
-
- return get_raw_buffer(bfr)
-
- def new(self, data=None):
- return type(self)(data=data)
-
-
-def new(data=None):
- """Return a fresh instance of a SHAKE256 object.
-
- Args:
- data (bytes/bytearray/memoryview):
- The very first chunk of the message to hash.
- It is equivalent to an early call to :meth:`update`.
- Optional.
-
- :Return: A :class:`SHAKE256_XOF` object
- """
-
- return SHAKE256_XOF(data=data)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Tempita/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Tempita/__init__.py
deleted file mode 100644
index 41a0ce3d0efa247760db266bace8e34a4b5dd9fa..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Tempita/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# The original Tempita implements all of its templating code here.
-# Moved it to _tempita.py to make the compilation portable.
-
-from ._tempita import *
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/tests/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/asciicorp/Legal-ai/README.md b/spaces/asciicorp/Legal-ai/README.md
deleted file mode 100644
index f98981d636fdcf95351b9371b633b8e891e19546..0000000000000000000000000000000000000000
--- a/spaces/asciicorp/Legal-ai/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Lang Legal
-emoji: 📚
-colorFrom: purple
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/auto-academic/auto-draft/latex_templates/ICLR2022/introduction.tex b/spaces/auto-academic/auto-draft/latex_templates/ICLR2022/introduction.tex
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/avivdm1/AutoGPT/tests/test_config.py b/spaces/avivdm1/AutoGPT/tests/test_config.py
deleted file mode 100644
index b472a24c78edd1f931a76c68e08ed544bbe61d98..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/tests/test_config.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from unittest import TestCase
-
-from autogpt.config import Config
-
-
-class TestConfig(TestCase):
- """
- Test cases for the Config class, which handles the configuration settings
- for the AI and ensures it behaves as a singleton.
- """
-
- def setUp(self):
- """
- Set up the test environment by creating an instance of the Config class.
- """
- self.config = Config()
-
- def test_singleton(self):
- """
- Test if the Config class behaves as a singleton by ensuring that two instances are the same.
- """
- config2 = Config()
- self.assertIs(self.config, config2)
-
- def test_initial_values(self):
- """
- Test if the initial values of the Config class attributes are set correctly.
- """
- self.assertFalse(self.config.debug_mode)
- self.assertFalse(self.config.continuous_mode)
- self.assertFalse(self.config.speak_mode)
- self.assertEqual(self.config.fast_llm_model, "gpt-3.5-turbo")
- self.assertEqual(self.config.smart_llm_model, "gpt-4")
- self.assertEqual(self.config.fast_token_limit, 4000)
- self.assertEqual(self.config.smart_token_limit, 8000)
-
- def test_set_continuous_mode(self):
- """
- Test if the set_continuous_mode() method updates the continuous_mode attribute.
- """
- self.config.set_continuous_mode(True)
- self.assertTrue(self.config.continuous_mode)
-
- def test_set_speak_mode(self):
- """
- Test if the set_speak_mode() method updates the speak_mode attribute.
- """
- self.config.set_speak_mode(True)
- self.assertTrue(self.config.speak_mode)
-
- def test_set_fast_llm_model(self):
- """
- Test if the set_fast_llm_model() method updates the fast_llm_model attribute.
- """
- self.config.set_fast_llm_model("gpt-3.5-turbo-test")
- self.assertEqual(self.config.fast_llm_model, "gpt-3.5-turbo-test")
-
- def test_set_smart_llm_model(self):
- """
- Test if the set_smart_llm_model() method updates the smart_llm_model attribute.
- """
- self.config.set_smart_llm_model("gpt-4-test")
- self.assertEqual(self.config.smart_llm_model, "gpt-4-test")
-
- def test_set_fast_token_limit(self):
- """
- Test if the set_fast_token_limit() method updates the fast_token_limit attribute.
- """
- self.config.set_fast_token_limit(5000)
- self.assertEqual(self.config.fast_token_limit, 5000)
-
- def test_set_smart_token_limit(self):
- """
- Test if the set_smart_token_limit() method updates the smart_token_limit attribute.
- """
- self.config.set_smart_token_limit(9000)
- self.assertEqual(self.config.smart_token_limit, 9000)
-
- def test_set_debug_mode(self):
- """
- Test if the set_debug_mode() method updates the debug_mode attribute.
- """
- self.config.set_debug_mode(True)
- self.assertTrue(self.config.debug_mode)
diff --git a/spaces/awacke1/AI-Atari-Live-Streamlit/app.py b/spaces/awacke1/AI-Atari-Live-Streamlit/app.py
deleted file mode 100644
index ae6d65f0d907097f161e799b4424c3327057b814..0000000000000000000000000000000000000000
--- a/spaces/awacke1/AI-Atari-Live-Streamlit/app.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import cv2
-import streamlit as st
-import time
-
-from huggingface_sb3 import load_from_hub
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.env_util import make_atari_env
-from stable_baselines3.common.vec_env import VecFrameStack
-from stable_baselines3.common.env_util import make_atari_env
-
-st.subheader("Atari 2600 Deep RL Environments Live AI")
-
-# @st.cache This is not cachable :(
-def load_env(env_name):
- env = make_atari_env(env_name, n_envs=1)
- env = VecFrameStack(env, n_stack=4)
- return env
-
-# @st.cache This is not cachable :(
-def load_model(env_name):
- custom_objects = {
- "learning_rate": 0.0,
- "lr_schedule": lambda _: 0.0,
- "clip_range": lambda _: 0.0,
- }
- checkpoint = load_from_hub(
- f"ThomasSimonini/ppo-{env_name}",
- f"ppo-{env_name}.zip",
- )
- model = PPO.load(checkpoint, custom_objects=custom_objects)
- return model
-
-st.write("In game theory and optimization Nash Equilibrium loss minimization starts playing randomly but then by understanding ratios of action success to action-reward with an action (observe, decide/predict, act and then observe outcome the Deep RL agents go from 50% efficiency to 98-99% efficiency based on quality of decision without making mistakes. A good reference to environments is here https://github.com/DLR-RM/rl-baselines3-zoo/blob/master/benchmark.md")
-#st.write("Deep RL models: https://huggingface.co/sb3")
-
-env_name = st.selectbox(
- "Select environment",
- (
- "SeaquestNoFrameskip-v4",
- "QbertNoFrameskip-v4",
- "SpaceInvadersNoFrameskip-v4",
- "PongNoFrameskip-v4",
- #"AsteroidsNoFrameskip-v4",
- #"BeamRiderNoFrameskip-v4",
- #"BreakoutNoFrameskip-v4 ",
- #"EnduroNoFrameskip-v4",
- #"MsPacmanNoFrameskip-v4",
- #"RoadRunnerNoFrameskip-v4",
- #"Swimmer-v3",
- #"Walker2d-v3",
- ),
-)
-
-num_episodes = st.slider("Number of Episodes", 1, 20, 5)
-env = load_env(env_name)
-model = load_model(env_name)
-
-obs = env.reset()
-
-with st.empty():
- for i in range(num_episodes):
- obs = env.reset()
- done = False
- while not done:
- frame = env.render(mode="rgb_array")
- im = st.image(frame, width=400)
- action, _states = model.predict(obs)
- obs, reward, done, info = env.step([action])
-
- time.sleep(0.1)
\ No newline at end of file
diff --git a/spaces/banana-projects/web3d/dist/index.js b/spaces/banana-projects/web3d/dist/index.js
deleted file mode 100644
index 51597a0858f9eca9ed26a61222c7eb6c4441254d..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/dist/index.js
+++ /dev/null
@@ -1,264 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-const THREE = require("three");
-const TWEEN = require("@tweenjs/tween.js");
-const scene = new THREE.Scene();
-scene.background = new THREE.Color(
-// 0xcccccc
-'white');
-const clock = new THREE.Clock();
-const camera = new THREE.PerspectiveCamera(45, window.innerWidth / window.innerHeight, 0.1, 2000);
-camera.position.set(0, 30, 50);
-camera.lookAt(0, 3, 0);
-const controls = new THREE.OrbitControls(camera);
-const ambientLight = new THREE.AmbientLight(0xffffff, 1);
-scene.add(ambientLight);
-const renderer = new THREE.WebGLRenderer({ antialias: true });
-renderer.setPixelRatio(window.devicePixelRatio);
-renderer.setSize(window.innerWidth, window.innerHeight);
-document.body.appendChild(renderer.domElement);
-const stats = new Stats();
-document.body.appendChild(stats.dom);
-/// Anim mixer
-const mixers = [];
-class Assets {
- static loadEggMtl() {
- return new Promise((resolve, reject) => {
- const loader = new THREE.MTLLoader();
- loader.load(`models/Egg_from_Poly_uovo/Egg from Poly uovo.mtl`, (materials) => {
- materials.preload();
- resolve(materials);
- }, (xhr) => { }, reject);
- });
- }
- static loadEggObj(materials) {
- return new Promise((resolve, reject) => {
- const loader = new THREE.OBJLoader();
- loader.setMaterials(materials);
- loader.load(`models/Egg_from_Poly_uovo/Egg from Poly uovo.obj`, (object) => {
- resolve(object);
- }, (xhr) => {
- // c.log(`${ xhr.loaded / xhr.total * 100 }% loaded`);
- }, (error) => {
- c.error(error);
- reject(error);
- });
- });
- }
- static async loadEgg() {
- const materialCreator = await this.loadEggMtl();
- return this.loadEggObj(materialCreator);
- }
- static loadEggGltf() {
- return new Promise((resolve, reject) => {
- const loader = new THREE.GLTFLoader();
- loader.load(`models/Egg_gltf/Egg from Poly uovo copy.gltf`, (gltf) => {
- c.log(gltf);
- resolve(gltf.scene);
- });
- });
- }
- static loadDogDae() {
- /// In Dae/Collada: did not manage to get
- /// either the anims or the texture.
- return new Promise((resolve, reject) => {
- const loader = new THREE.ColladaLoader();
- loader.load(`models/dog/pup_lohound.dae`, (collada) => {
- resolve(collada);
- });
- });
- }
- static loadDogFbx() {
- return new Promise((resolve, reject) => {
- const loader = new THREE.FBXLoader();
- loader.load(`models/dog_fbx/puppy-snapchat.fbx`, (fbx) => {
- resolve(fbx);
- });
- });
- }
- static loadBoloss() {
- /// In Dae/Collada: did not manage to get
- /// either the anims or the texture.
- return new Promise((resolve, reject) => {
- const loader = new THREE.ColladaLoader();
- loader.load(`models/boloss/Boloss-3d v10.dae`, (collada) => {
- resolve(collada);
- });
- });
- }
-}
-class TUtils {
- static boundingBox(o) {
- const bbox = new THREE.Box3().setFromObject(o);
- return bbox;
- }
- static flushYZero(o) {
- o.position.y = -(this.boundingBox(o)).min.y;
- }
- static perform(tween) {
- return new Promise(resolve => {
- tween.onComplete(resolve).start();
- });
- }
-}
-(async () => {
- /**
- * scene construction
- */
- const gridHelper = new THREE.GridHelper(100, 100);
- scene.add(gridHelper);
- const axesHelper = new THREE.AxesHelper(50);
- scene.add(axesHelper);
- {
- const egg = await Assets.loadEgg();
- c.log(egg);
- egg.scale.setScalar(.2);
- egg.rotateX(-Math.PI / 2);
- egg.position.x = -18;
- TUtils.flushYZero(egg);
- const box = new THREE.BoxHelper(egg);
- scene.add(box);
- scene.add(egg);
- ///// Manually set the material, for fun.
- // const eggFace = egg.getObjectByName("CallKit-IconMask") as THREE.Mesh;
- // c.log(eggFace.material);
- // ((eggFace.material)).color.set(0x000000);
- }
- {
- const egg = await Assets.loadEggGltf();
- c.log(egg);
- egg.scale.setScalar(100);
- egg.position.x = -28;
- TUtils.flushYZero(egg);
- egg.remove(egg.getObjectByName('Camera'));
- scene.add(egg);
- // c.log(Utils.boundingBox(egg));
- const box = new THREE.BoxHelper(egg, new THREE.Color('red'));
- scene.add(box);
- }
- {
- ////// dog_fbx
- const dog = await Assets.loadDogFbx();
- // c.log((dog).animations);
- const mixer = new THREE.AnimationMixer(dog);
- const clip = dog.animations.find(clip => clip.name === "lohound|lohoundAction");
- /// ^^ this is the main parent animation! Do not play all children.
- c.log(clip);
- mixer.clipAction(clip).play();
- mixers.push(mixer);
- const container = new THREE.Group();
- container.add(dog);
- container.scale.setScalar(0.007); /// <- scale a container, not the dog itself or it'll fuck the anims.
- container.position.x = -6;
- scene.add(container);
- const box = new THREE.BoxHelper(container, new THREE.Color('green'));
- scene.add(box);
- }
- {
- const boloss = (await Assets.loadBoloss()).scene;
- c.log(boloss);
- boloss.position.x = 16;
- TUtils.flushYZero(boloss);
- scene.add(boloss);
- const box = new THREE.BoxHelper(boloss, new THREE.Color('blue'));
- scene.add(box);
- /// Anims like in AudioBoloss
- const rootModel = boloss.getObjectByName(`SketchUp`);
- const pupilL = boloss.getObjectByName(`Pupil-left`);
- const pupilR = boloss.getObjectByName(`Pupil-right`);
- const pupils = new THREE.Group();
- pupils.add(pupilL, pupilR);
- rootModel.add(pupils);
- (async () => {
- while (true) {
- const translatePupil = new TWEEN.Tween(pupils.position)
- .to({ x: "-1", y: "-1" }, 200)
- .easing(TWEEN.Easing.Quadratic.Out);
- const translatePupilRev = new TWEEN.Tween(pupils.position)
- .to({ x: "+1", y: "+1" }, 200)
- .easing(TWEEN.Easing.Quadratic.Out);
- await TUtils.perform(translatePupil);
- await Utils.wait(4, 1);
- await TUtils.perform(translatePupilRev);
- await Utils.wait(8, 3);
- }
- })();
- const eyebrowL = boloss.getObjectByName(`Eyebrow-left`);
- const eyebrowR = boloss.getObjectByName(`Eyebrow-right`);
- const eyebrows = new THREE.Group();
- eyebrows.add(eyebrowL, eyebrowR);
- rootModel.add(eyebrows);
- (async () => {
- while (true) {
- const scaleEyebrow = new TWEEN.Tween(eyebrows.scale)
- .to({ x: 1.08, y: 1.08, z: 1.08 }, 100)
- .easing(TWEEN.Easing.Quadratic.InOut);
- const scaleEyebrowRev = new TWEEN.Tween(eyebrows.scale)
- .to({ x: 1, y: 1, z: 1 }, 100)
- .easing(TWEEN.Easing.Quadratic.InOut);
- await Utils.wait(6, 6);
- await TUtils.perform(scaleEyebrow);
- await TUtils.perform(scaleEyebrowRev);
- await Utils.wait(0.14);
- await TUtils.perform(scaleEyebrow);
- await TUtils.perform(scaleEyebrowRev);
- }
- })();
- (async () => {
- while (true) {
- const angle = Utils.randomFloat(-0.2, 0.3);
- const dummyL = new THREE.Object3D();
- dummyL.rotateOnAxis(new THREE.Vector3(0, 1, 0.8), angle);
- const dummyR = new THREE.Object3D();
- dummyR.rotateOnAxis(new THREE.Vector3(0, -1, -0.8), angle);
- /// ^^ exact same result as keeping the same vector and negating the angle.
- const rotateBrowL = new TWEEN.Tween(eyebrowL.rotation)
- .to({
- x: dummyL.rotation.x,
- y: dummyL.rotation.y,
- z: dummyL.rotation.z,
- }, 300);
- const rotateBrowR = new TWEEN.Tween(eyebrowR.rotation)
- .to({
- x: dummyR.rotation.x,
- y: dummyR.rotation.y,
- z: dummyR.rotation.z,
- }, 300);
- await Promise.all([
- TUtils.perform(rotateBrowL),
- TUtils.perform(rotateBrowR),
- ]);
- await Utils.wait(1, 1);
- await Promise.all([
- TUtils.perform(new TWEEN.Tween(eyebrowL.rotation).to({ x: 0, y: 0, z: 0 }, 300)),
- TUtils.perform(new TWEEN.Tween(eyebrowR.rotation).to({ x: 0, y: 0, z: 0 }, 300)),
- ]);
- await Utils.wait(1, 1);
- /// ^^ not the exact same behavior as in AudioBoloss (all waits are actually randoms there.)
- }
- })();
- }
-})();
-/**
- * MAIN()
- */
-window.addEventListener('resize', onWindowResize, false);
-function onWindowResize() {
- camera.aspect = window.innerWidth / window.innerHeight;
- camera.updateProjectionMatrix();
- renderer.setSize(window.innerWidth, window.innerHeight);
-}
-function render() {
- const delta = clock.getDelta();
- for (const mixer of mixers) {
- mixer.update(delta);
- }
- renderer.render(scene, camera);
-}
-function animate() {
- requestAnimationFrame(animate);
- TWEEN.update();
- render();
- stats.update();
-}
-animate();
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/shaders/UnpackDepthRGBAShader.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/shaders/UnpackDepthRGBAShader.js
deleted file mode 100644
index 55f34a2b0d455b0d5310e80831da35ea86b678ba..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/shaders/UnpackDepthRGBAShader.js
+++ /dev/null
@@ -1,49 +0,0 @@
-/**
- * @author alteredq / http://alteredqualia.com/
- *
- * Unpack RGBA depth shader
- * - show RGBA encoded depth as monochrome color
- */
-
-THREE.UnpackDepthRGBAShader = {
-
- uniforms: {
-
- "tDiffuse": { value: null },
- "opacity": { value: 1.0 }
-
- },
-
- vertexShader: [
-
- "varying vec2 vUv;",
-
- "void main() {",
-
- "vUv = uv;",
- "gl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );",
-
- "}"
-
- ].join( "\n" ),
-
- fragmentShader: [
-
- "uniform float opacity;",
-
- "uniform sampler2D tDiffuse;",
-
- "varying vec2 vUv;",
-
- "#include ",
-
- "void main() {",
-
- "float depth = 1.0 - unpackRGBAToDepth( texture2D( tDiffuse, vUv ) );",
- "gl_FragColor = vec4( vec3( depth ), opacity );",
-
- "}"
-
- ].join( "\n" )
-
-};
diff --git a/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/metrics/metric_util.py b/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/metrics/metric_util.py
deleted file mode 100644
index 0b21777874f18a7e87c67153ee92dec4d7b599e8..0000000000000000000000000000000000000000
--- a/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/metrics/metric_util.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import numpy as np
-
-from basicsr.utils.matlab_functions import bgr2ycbcr
-
-
-def reorder_image(img, input_order='HWC'):
- """Reorder images to 'HWC' order.
-
- If the input_order is (h, w), return (h, w, 1);
- If the input_order is (c, h, w), return (h, w, c);
- If the input_order is (h, w, c), return as it is.
-
- Args:
- img (ndarray): Input image.
- input_order (str): Whether the input order is 'HWC' or 'CHW'.
- If the input image shape is (h, w), input_order will not have
- effects. Default: 'HWC'.
-
- Returns:
- ndarray: reordered image.
- """
-
- if input_order not in ['HWC', 'CHW']:
- raise ValueError(f"Wrong input_order {input_order}. Supported input_orders are 'HWC' and 'CHW'")
- if len(img.shape) == 2:
- img = img[..., None]
- if input_order == 'CHW':
- img = img.transpose(1, 2, 0)
- return img
-
-
-def to_y_channel(img):
- """Change to Y channel of YCbCr.
-
- Args:
- img (ndarray): Images with range [0, 255].
-
- Returns:
- (ndarray): Images with range [0, 255] (float type) without round.
- """
- img = img.astype(np.float32) / 255.
- if img.ndim == 3 and img.shape[2] == 3:
- img = bgr2ycbcr(img, y_only=True)
- img = img[..., None]
- return img * 255.
diff --git a/spaces/bioriAsaeru/text-to-voice/Data Structures And Algorithms By G.a.v Pai Free 35 !!TOP!!.md b/spaces/bioriAsaeru/text-to-voice/Data Structures And Algorithms By G.a.v Pai Free 35 !!TOP!!.md
deleted file mode 100644
index b64be3ff95d54ca054279c16bf617e3a48ba6915..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Data Structures And Algorithms By G.a.v Pai Free 35 !!TOP!!.md
+++ /dev/null
@@ -1,10 +0,0 @@
-data structures and algorithms by g.a.v pai free 35
Download Zip ⚹⚹⚹ https://urloso.com/2uyRKR
-
-The course, offered by the department of Computer Applications offers a computer oriented introductory course in various areas of Mathematics and Science. It also covers a brief history of Mathematics, introduction of the central concepts in Mathematics, programming techniques and Applications of Algorithms. This course attempts to teach concepts in real life application. It gives a practical approach to the solution of Mathematical problems, rather than focusing only on the mathematical definitions. It will be beneficial for all. The course covers the following topics: 1. Concepts in Mathematics. 2. Matrices, vectors and basic operations on them. 3. Graphs, adjacency, paths and algorithms. 4. Introduction to Computers and Programming. 5. Introduction to Logic and Computation.
-
-Introduction and Basic Concepts The History of Mathematics
-
-Mathematics: Use and Abuse In his review of G.H Hardy's controversial book 'A Mathematician's Apology' author remarks that "the history of mathematics is one long series of attempts to define the terms on which mathematicians agree to disagree". Hardy \[1\] had published the article on the existence of uncountably many distinct uncountable sets in 1900. Shortly afterwards Hausdorff \[2\] had proved the same result by using a different method. He used to define a set as a collection of points, where each point is closed and bounded. This definition was then further refined. Nowadays a point, known as a limit point, is an accumulation point. These closed and bounded sets are now called Haussdorff spaces. The result of Hausdorff was first published in the German Journal 'Mathematische Annalen'. It is interesting to note that he himself was surprised and dismayed by Hardy's work. In this work, Hardy showed a continuous function whose graph is uncountable. He gave the set of all such functions as well as the set of all points on this graph and a bijection between them. These sets are said to be uncountably large. Hardy goes on to discuss the difference between sets of rational numbers and the real numbers, thus, giving a definition of countable set. He then states that these two sets are not the same, and hence uncountable. But this is not his most damning criticism of the whole idea of mathematics. It is that his argument is not clearly thought out. The definition he gives of uncountable set is incorrect, and he gives an example to prove that. He then uses this to show that 4fefd39f24
-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/HOW TO CHANGE LANUGAGE?????? Chicago 1930 General Discussions[1].md b/spaces/bioriAsaeru/text-to-voice/HOW TO CHANGE LANUGAGE?????? Chicago 1930 General Discussions[1].md
deleted file mode 100644
index 3a5b1c028f4d07d9de254c5010b175e9d6f04119..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/HOW TO CHANGE LANUGAGE?????? Chicago 1930 General Discussions[1].md
+++ /dev/null
@@ -1,6 +0,0 @@
-Chicago 1930 english language pack
Download File 🆗 https://urloso.com/2uyOwe
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Ken Follett Epub Ita Download Skype The Ultimate Guide for Fans of Historical Thrillers.md b/spaces/bioriAsaeru/text-to-voice/Ken Follett Epub Ita Download Skype The Ultimate Guide for Fans of Historical Thrillers.md
deleted file mode 100644
index 591132429ed82cbb97d444caede91e96daaa38b0..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Ken Follett Epub Ita Download Skype The Ultimate Guide for Fans of Historical Thrillers.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Ken Follett Epub Ita Download Skype
DOWNLOAD >>> https://urloso.com/2uyOeY
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/birdortyedi/instagram-filter-removal/README.md b/spaces/birdortyedi/instagram-filter-removal/README.md
deleted file mode 100644
index f2552ac5263506df94b36b9c232e8351968542ca..0000000000000000000000000000000000000000
--- a/spaces/birdortyedi/instagram-filter-removal/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Instagram Filter Removal
-emoji: 👀
-colorFrom: gray
-colorTo: green
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/bortle/astrophotography-object-classifier/app.py b/spaces/bortle/astrophotography-object-classifier/app.py
deleted file mode 100644
index 1e49d1feca60186fb9bf10220e52af1ab65313cd..0000000000000000000000000000000000000000
--- a/spaces/bortle/astrophotography-object-classifier/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-pipeline = pipeline(task="image-classification", model="bortle/astrophotography-object-classifier-alpha4")
-
-def predict(image):
- predictions = pipeline(image)
- return {p["label"]: p["score"] for p in predictions}
-
-gr.Interface(
- predict,
- inputs=gr.Image(shape=(1080, None), type="pil", label="Upload Astrophotography image"),
- outputs=gr.Label(num_top_classes=5),
- title="Astrophotography Object Classifier",
- allow_flagging="manual",
- examples=["examples/Andromeda.jpg", "examples/Heart.jpg", "examples/Pleiades.jpg", "examples/Rosette.jpg", "examples/Moon.jpg", "examples/GreatHercules.jpg", "examples/Leo-Triplet.jpg", "examples/Crab.jpg", "examples/North-America.jpg", "examples/Horsehead-Flame.jpg", "examples/Pinwheel.jpg"],
- cache_examples=True
-).launch()
\ No newline at end of file
diff --git a/spaces/brainblow/AudioCreator_Music-Audio_Generation/scripts/templates/login.html b/spaces/brainblow/AudioCreator_Music-Audio_Generation/scripts/templates/login.html
deleted file mode 100644
index dd89ac654bceca14a9dec7d1a7f8206d1425a7a1..0000000000000000000000000000000000000000
--- a/spaces/brainblow/AudioCreator_Music-Audio_Generation/scripts/templates/login.html
+++ /dev/null
@@ -1,20 +0,0 @@
-{% extends "base.html" %}
-{% block content %}
-
-
- You must identify yourself first! We use a highly secured protocol
- where you just decide your username, and that's it. No password, no encryption,
- just pure trust.
-
-
-{% if error %}
-{{error}}
-{% endif %}
-
-
- """
- )
- with gr.Column(elem_id="col-container"):
- with gr.Row(variant="compact"):
- input_text = gr.Textbox(
- label="Short Prompt",
- show_label=False,
- max_lines=2,
- placeholder="Enter a basic idea and click 'Magic Prompt'. Got no ideas? No problem, Simply just hit the magic button!",
- ).style(
- container=False,min_width=1200
- )
- see_prompts = gr.Button("✨Magic✨ ✨Prompt✨").style(full_width=False)
-
-
- with gr.Row(variant="compact"):
- prompt = gr.Textbox(
- label="Enter your prompt",
- show_label=False,
- max_lines=2,
- placeholder="Full Prompt",
- ).style(
- container=False,
- )
- run = gr.Button("Generate Images").style(full_width=False)
- with gr.Row():
- with gr.Row():
- #Now that the first box generates a picture with noise=0 having the default at 0 makes no sense as it'd generate the same image 6 times.
- noise_level = gr.Slider(minimum=0.2, maximum=3, step=0.1, label="Noise Level (0.1 or less was generating the same pic 6 times! 🤣)")
- gr.HTML(
- """
-
-
-
-
-
-
- PhotoMovieX
-- 🤗 Celebrating 36000 downloads at huggingface! 🤗
-- Check MagicArt35's original model page at Civitai here!.
-- If you have an idea, put it on the first box to expand it, if you have a full prompt, you can leave the first box empty and just put it on the second one and click generate images! - Noise Level: Controls how much randomness is added to the input of the boxes after the first one before it is sent to the model, so you can get 6 unique 768x768 images. Higher noise level produces more diverse outputs, while lower noise level produces similar outputs, - original space created by Phenomenon1981. -
-- ❤️ Press the Like Button if you enjoy my space! ❤️ -
-
-
- """
- )
- with gr.Row():
- with gr.Row():
- output1=gr.Image(label="PhotoMovieX",show_label=False,min_width=640)
- output2=gr.Image(label="PhotoMovieX",show_label=False,min_width=640)
- with gr.Row():
- with gr.Row():
- output3=gr.Image(label="PhotoMovieX",show_label=False,min_width=640)
- output4=gr.Image(label="PhotoMovieX",show_label=False,min_width=640)
- with gr.Row():
- with gr.Row():
- output5=gr.Image(label="PhotoMovieX",show_label=False,min_width=640)
- outputX=gr.Image(label="PhotoMovieX",show_label=False,min_width=640)
- #with gr.Row():
- #with gr.Row():
- #output0=gr.Image(label="PhotoMovieX",show_label=False,min_width=640)
-
- see_prompts.click(get_prompts, inputs=[input_text], outputs=[prompt], queue=False)
- run.click(send_it1, inputs=[prompt, noise_level], outputs=[output1])
- #run.click(send_it7, inputs=[prompt, noise_level], outputs=[output0])
- run.click(send_it2, inputs=[prompt, noise_level], outputs=[output2])
- run.click(send_it3, inputs=[prompt, noise_level], outputs=[output3])
- run.click(send_it4, inputs=[prompt, noise_level], outputs=[output4])
- run.click(send_it5, inputs=[prompt, noise_level], outputs=[output5])
- run.click(send_itX, inputs=[prompt, noise_level], outputs=[outputX])
-
-
- with gr.Row():
- gr.HTML(
- """
-
-
-
-
-
-
- Please allow up to 1 minute for each image to generate, for a total of 6 minutes max.
-
-
- """
-)
-
- demo.launch(enable_queue=True, inline=True)
- block.queue(concurrency_count=100)
diff --git a/spaces/Yuliang/ICON/lib/renderer/camera.py b/spaces/Yuliang/ICON/lib/renderer/camera.py
deleted file mode 100644
index fde488826c8eba933a00485d39cdc21d4d2dae8f..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ICON/lib/renderer/camera.py
+++ /dev/null
@@ -1,226 +0,0 @@
-
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-import cv2
-import numpy as np
-
-from .glm import ortho
-
-
-class Camera:
- def __init__(self, width=1600, height=1200):
- # Focal Length
- # equivalent 50mm
- focal = np.sqrt(width * width + height * height)
- self.focal_x = focal
- self.focal_y = focal
- # Principal Point Offset
- self.principal_x = width / 2
- self.principal_y = height / 2
- # Axis Skew
- self.skew = 0
- # Image Size
- self.width = width
- self.height = height
-
- self.near = 1
- self.far = 10
-
- # Camera Center
- self.center = np.array([0, 0, 1.6])
- self.direction = np.array([0, 0, -1])
- self.right = np.array([1, 0, 0])
- self.up = np.array([0, 1, 0])
-
- self.ortho_ratio = None
-
- def sanity_check(self):
- self.center = self.center.reshape([-1])
- self.direction = self.direction.reshape([-1])
- self.right = self.right.reshape([-1])
- self.up = self.up.reshape([-1])
-
- assert len(self.center) == 3
- assert len(self.direction) == 3
- assert len(self.right) == 3
- assert len(self.up) == 3
-
- @staticmethod
- def normalize_vector(v):
- v_norm = np.linalg.norm(v)
- return v if v_norm == 0 else v / v_norm
-
- def get_real_z_value(self, z):
- z_near = self.near
- z_far = self.far
- z_n = 2.0 * z - 1.0
- z_e = 2.0 * z_near * z_far / (z_far + z_near - z_n * (z_far - z_near))
- return z_e
-
- def get_rotation_matrix(self):
- rot_mat = np.eye(3)
- s = self.right
- s = self.normalize_vector(s)
- rot_mat[0, :] = s
- u = self.up
- u = self.normalize_vector(u)
- rot_mat[1, :] = -u
- rot_mat[2, :] = self.normalize_vector(self.direction)
-
- return rot_mat
-
- def get_translation_vector(self):
- rot_mat = self.get_rotation_matrix()
- trans = -np.dot(rot_mat, self.center)
- return trans
-
- def get_intrinsic_matrix(self):
- int_mat = np.eye(3)
-
- int_mat[0, 0] = self.focal_x
- int_mat[1, 1] = self.focal_y
- int_mat[0, 1] = self.skew
- int_mat[0, 2] = self.principal_x
- int_mat[1, 2] = self.principal_y
-
- return int_mat
-
- def get_projection_matrix(self):
- ext_mat = self.get_extrinsic_matrix()
- int_mat = self.get_intrinsic_matrix()
-
- return np.matmul(int_mat, ext_mat)
-
- def get_extrinsic_matrix(self):
- rot_mat = self.get_rotation_matrix()
- int_mat = self.get_intrinsic_matrix()
- trans = self.get_translation_vector()
-
- extrinsic = np.eye(4)
- extrinsic[:3, :3] = rot_mat
- extrinsic[:3, 3] = trans
-
- return extrinsic[:3, :]
-
- def set_rotation_matrix(self, rot_mat):
- self.direction = rot_mat[2, :]
- self.up = -rot_mat[1, :]
- self.right = rot_mat[0, :]
-
- def set_intrinsic_matrix(self, int_mat):
- self.focal_x = int_mat[0, 0]
- self.focal_y = int_mat[1, 1]
- self.skew = int_mat[0, 1]
- self.principal_x = int_mat[0, 2]
- self.principal_y = int_mat[1, 2]
-
- def set_projection_matrix(self, proj_mat):
- res = cv2.decomposeProjectionMatrix(proj_mat)
- int_mat, rot_mat, camera_center_homo = res[0], res[1], res[2]
- camera_center = camera_center_homo[0:3] / camera_center_homo[3]
- camera_center = camera_center.reshape(-1)
- int_mat = int_mat / int_mat[2][2]
-
- self.set_intrinsic_matrix(int_mat)
- self.set_rotation_matrix(rot_mat)
- self.center = camera_center
-
- self.sanity_check()
-
- def get_gl_matrix(self):
- z_near = self.near
- z_far = self.far
- rot_mat = self.get_rotation_matrix()
- int_mat = self.get_intrinsic_matrix()
- trans = self.get_translation_vector()
-
- extrinsic = np.eye(4)
- extrinsic[:3, :3] = rot_mat
- extrinsic[:3, 3] = trans
- axis_adj = np.eye(4)
- axis_adj[2, 2] = -1
- axis_adj[1, 1] = -1
- model_view = np.matmul(axis_adj, extrinsic)
-
- projective = np.zeros([4, 4])
- projective[:2, :2] = int_mat[:2, :2]
- projective[:2, 2:3] = -int_mat[:2, 2:3]
- projective[3, 2] = -1
- projective[2, 2] = (z_near + z_far)
- projective[2, 3] = (z_near * z_far)
-
- if self.ortho_ratio is None:
- ndc = ortho(0, self.width, 0, self.height, z_near, z_far)
- perspective = np.matmul(ndc, projective)
- else:
- perspective = ortho(-self.width * self.ortho_ratio / 2,
- self.width * self.ortho_ratio / 2,
- -self.height * self.ortho_ratio / 2,
- self.height * self.ortho_ratio / 2, z_near,
- z_far)
-
- return perspective, model_view
-
-
-def KRT_from_P(proj_mat, normalize_K=True):
- res = cv2.decomposeProjectionMatrix(proj_mat)
- K, Rot, camera_center_homog = res[0], res[1], res[2]
- camera_center = camera_center_homog[0:3] / camera_center_homog[3]
- trans = -Rot.dot(camera_center)
- if normalize_K:
- K = K / K[2][2]
- return K, Rot, trans
-
-
-def MVP_from_P(proj_mat, width, height, near=0.1, far=10000):
- '''
- Convert OpenCV camera calibration matrix to OpenGL projection and model view matrix
- :param proj_mat: OpenCV camera projeciton matrix
- :param width: Image width
- :param height: Image height
- :param near: Z near value
- :param far: Z far value
- :return: OpenGL projection matrix and model view matrix
- '''
- res = cv2.decomposeProjectionMatrix(proj_mat)
- K, Rot, camera_center_homog = res[0], res[1], res[2]
- camera_center = camera_center_homog[0:3] / camera_center_homog[3]
- trans = -Rot.dot(camera_center)
- K = K / K[2][2]
-
- extrinsic = np.eye(4)
- extrinsic[:3, :3] = Rot
- extrinsic[:3, 3:4] = trans
- axis_adj = np.eye(4)
- axis_adj[2, 2] = -1
- axis_adj[1, 1] = -1
- model_view = np.matmul(axis_adj, extrinsic)
-
- zFar = far
- zNear = near
- projective = np.zeros([4, 4])
- projective[:2, :2] = K[:2, :2]
- projective[:2, 2:3] = -K[:2, 2:3]
- projective[3, 2] = -1
- projective[2, 2] = (zNear + zFar)
- projective[2, 3] = (zNear * zFar)
-
- ndc = ortho(0, width, 0, height, zNear, zFar)
-
- perspective = np.matmul(ndc, projective)
-
- return perspective, model_view
diff --git a/spaces/Yunshansongbai/SVC-Nahida/modules/attentions.py b/spaces/Yunshansongbai/SVC-Nahida/modules/attentions.py
deleted file mode 100644
index 8ca80c90f2d2db2a52eaac8b88881a84193302a5..0000000000000000000000000000000000000000
--- a/spaces/Yunshansongbai/SVC-Nahida/modules/attentions.py
+++ /dev/null
@@ -1,377 +0,0 @@
-import copy
-import math
-import numpy as np
-import paddle
-from paddle import nn
-from paddle.nn import functional as F
-
-import modules.commons as commons
-import modules.modules as modules
-from modules.modules import LayerNorm
-
-
-class FFT(nn.Layer):
- def __init__(self, hidden_channels, filter_channels, n_heads, n_layers=1, kernel_size=1, p_dropout=0.,
- proximal_bias=False, proximal_init=True, **kwargs):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.LayerList()
- self.norm_layers_0 = nn.LayerList()
- self.ffn_layers = nn.LayerList()
- self.norm_layers_1 = nn.LayerList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(
- MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias,
- proximal_init=proximal_init))
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
- self.norm_layers_1.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.shape[2]).astype(dtype=x.dtype)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
- x = x * x_mask
- return x
-
-
-class Encoder(nn.Layer):
- def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.window_size = window_size
-
- self.drop = nn.Dropout(p_dropout)
- self.attn_layers = nn.LayerList()
- self.norm_layers_1 = nn.LayerList()
- self.ffn_layers = nn.LayerList()
- self.norm_layers_2 = nn.LayerList()
- for i in range(self.n_layers):
- self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask):
- attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.attn_layers[i](x, x, attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class Decoder(nn.Layer):
- def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.LayerList()
- self.norm_layers_0 = nn.LayerList()
- self.encdec_attn_layers = nn.LayerList()
- self.norm_layers_1 = nn.LayerList()
- self.ffn_layers = nn.LayerList()
- self.norm_layers_2 = nn.LayerList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask, h, h_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.size(2)).astype(dtype=x.dtype)
- encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class MultiHeadAttention(nn.Layer):
- def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
- super().__init__()
- assert channels % n_heads == 0
-
- self.channels = channels
- self.out_channels = out_channels
- self.n_heads = n_heads
- self.p_dropout = p_dropout
- self.window_size = window_size
- self.heads_share = heads_share
- self.block_length = block_length
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
- self.attn = None
-
- self.k_channels = channels // n_heads
-
- self.conv_q = nn.Conv1D(channels, channels, 1,)# weight_attr=attr)
- self.conv_k = nn.Conv1D(channels, channels, 1,)# weight_attr=attr)
- self.conv_v = nn.Conv1D(channels, channels, 1,)# weight_attr=attr)
- self.conv_o = nn.Conv1D(channels, out_channels, 1)
- self.drop = nn.Dropout(p_dropout)
-
- if window_size is not None:
- n_heads_rel = 1 if heads_share else n_heads
- rel_stddev = self.k_channels**-0.5
-
- rand = paddle.randn((n_heads_rel, window_size * 2 + 1, self.k_channels)) * rel_stddev
-
- self.emb_rel_k = paddle.create_parameter(rand.shape,'float32',None)
- self.emb_rel_v = paddle.create_parameter(rand.shape,'float32',None)
-
- #nn.init.xavier_uniform_(self.conv_q.weight)
- #nn.init.xavier_uniform_(self.conv_k.weight)
- #nn.init.xavier_uniform_(self.conv_v.weight)
- if proximal_init:
- with paddle.no_grad():
- self.conv_k.weight = (self.conv_q.weight)
- self.conv_k.bias = (self.conv_q.bias)
-
- def forward(self, x, c, attn_mask=None):
- #print(x)
- #print(self.conv_q.weight)
- q = self.conv_q(x)
- k = self.conv_k(c)
- v = self.conv_v(c)
-
- x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
- x = self.conv_o(x)
- return x
-
- @staticmethod
- def _masked_fill(x, mask, value:float):
- y = paddle.full(x.shape, value, x.dtype)
- return paddle.where(mask, y, x)
-
- def attention(self, query, key, value, mask=None):
- # reshape [b, d, t] -> [b, n_h, t, d_k]
- b, d, t_s, t_t = (*key.shape, query.shape[2])
- query = query.reshape((b, self.n_heads, self.k_channels, t_t)).transpose([0,1,3,2])
- key = key.reshape((b, self.n_heads, self.k_channels, t_s)).transpose([0,1,3,2])
- value = value.reshape((b, self.n_heads, self.k_channels, t_s)).transpose([0,1,3,2])
-
- scores = paddle.matmul(query / math.sqrt(self.k_channels), key.transpose([0,1,3,2])) # 0 1 2 3 -4 -3 -2 -1
- if self.window_size is not None:
- assert t_s == t_t, "Relative attention is only available for self-attention."
-
- key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
- rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
- scores_local = self._relative_position_to_absolute_position(rel_logits)
- scores = scores + scores_local
- if self.proximal_bias:
- assert t_s == t_t, "Proximal bias is only available for self-attention."
- scores = scores + self._attention_bias_proximal(t_s).astype(dtype=scores.dtype)
- if mask is not None:
- scores = self._masked_fill(scores, mask == 0, -1e4)
- if self.block_length is not None:
- assert t_s == t_t, "Local attention is only available for self-attention."
- block_mask = paddle.tril(paddle.triu(paddle.ones_like(scores), -self.block_length),self.block_length)
- scores = self._masked_fill(scores, block_mask == 0, -1e4)
- p_attn = F.softmax(scores, axis=-1) # [b, n_h, t_t, t_s]
- p_attn = self.drop(p_attn)
- output = paddle.matmul(p_attn, value)
- if self.window_size is not None:
- relative_weights = self._absolute_position_to_relative_position(p_attn)
- value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
- output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
- output = output.transpose([0,1,3,2]).reshape((b, d, t_t)) # [b, n_h, t_t, d_k] -> [b, d, t_t]
- return output, p_attn
-
- def _matmul_with_relative_values(self, x, y):
- """
- x: [b, h, l, m]
- y: [h or 1, m, d]
- ret: [b, h, l, d]
- """
- ret = paddle.matmul(x, y.unsqueeze(0))
- return ret
-
- def _matmul_with_relative_keys(self, x, y):
- """
- x: [b, h, l, d]
- y: [h or 1, m, d]
- ret: [b, h, l, m]
- """
- ret = paddle.matmul(x, y.unsqueeze(0).transpose([0,1,3,2]))
- return ret
-
- def _get_relative_embeddings(self, relative_embeddings, length):
- max_relative_position = 2 * self.window_size + 1
- # Pad first before slice to avoid using cond ops.
- pad_length = max(length - (self.window_size + 1), 0)
- slice_start_position = max((self.window_size + 1) - length, 0)
- slice_end_position = slice_start_position + 2 * length - 1
- if pad_length > 0:
- padding = commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])
-
- padded_relative_embeddings = F.pad(
- x = relative_embeddings.unsqueeze(0),
- pad = padding[0:4]).squeeze(0)
-
- else:
- padded_relative_embeddings = relative_embeddings
- used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
- return used_relative_embeddings
-
- def _relative_position_to_absolute_position(self, x):
- """
- x: [b, h, l, 2*l-1]
- ret: [b, h, l, l]
- """
- batch, heads, length, _ = x.shape
- # Concat columns of pad to shift from relative to absolute indexing.
- pad_shape = commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]])
- pad_shape = commons.fix_pad_shape(pad_shape, x)
- x = F.pad(x, pad_shape)
- # Concat extra elements so to add up to shape (len+1, 2*len-1).
- x_flat = x.reshape([batch, heads, length * 2 * length])
- pad_shape = commons.convert_pad_shape([[0,0],[0,0],[0,length-1]])
- pad_shape = commons.fix_pad_shape(pad_shape,x_flat)
- x_flat = F.pad(x_flat, pad_shape, data_format='NCL')
- # Reshape and slice out the padded elements.
- x_final = x_flat.reshape([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
- return x_final
-
- def _absolute_position_to_relative_position(self, x):
- """
- x: [b, h, l, l]
- ret: [b, h, l, 2*l-1]
- """
- batch, heads, length, _ = x.shape
- # padd along column
- pad_shape = commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]])
- pad_shape = commons.fix_pad_shape(pad_shape, x)
- x = F.pad(x, pad_shape)
- x_flat = x.reshape([batch, heads, length**2 + length*(length -1)])
- # add 0's in the beginning that will skew the elements after reshape
- pad_shape = commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])
- pad_shape = commons.fix_pad_shape(pad_shape, x_flat)
- x_flat = F.pad(x_flat, pad_shape, data_format='NCL')
- x_final = x_flat.reshape([batch, heads, length, 2*length])[:,:,:,1:]
- return x_final
-
- def _attention_bias_proximal(self, length):
- """Bias for self-attention to encourage attention to close positions.
- Args:
- length: an integer scalar.
- Returns:
- a Tensor with shape [1, 1, length, length]
- """
- r = paddle.arange(length, dtype=np.float32)
- diff = paddle.unsqueeze(r, 0) - paddle.unsqueeze(r, 1)
- return paddle.unsqueeze(paddle.unsqueeze(-paddle.log1p(paddle.abs(diff)), 0), 0)
-
-
-class FFN(nn.Layer):
- def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.activation = activation
- self.causal = causal
-
- if causal:
- self.padding = self._causal_padding
- else:
- self.padding = self._same_padding
-
- self.conv_1 = nn.Conv1D(in_channels, filter_channels, kernel_size)
- self.conv_2 = nn.Conv1D(filter_channels, out_channels, kernel_size)
- self.drop = nn.Dropout(p_dropout)
-
- def forward(self, x, x_mask):
- x = x * x_mask
- x = self.padding(x)
- x = self.conv_1(x)
- if self.activation == "gelu":
- x = x * F.sigmoid(1.702 * x)
- else:
- x = F.relu(x)
- x = self.drop(x)
- x = x * x_mask
- x = self.padding(x)
- x = self.conv_2(x)
- return x * x_mask
-
- def _causal_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = self.kernel_size - 1
- pad_r = 0
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- pad_shape:list = commons.convert_pad_shape(padding)
- pad_shape = commons.fix_pad_shape(pad_shape, x)
- x = F.pad(x, pad_shape,data_format='NCL')
- return x
-
- def _same_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = (self.kernel_size - 1) // 2
- pad_r = self.kernel_size // 2
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- pad_shape = commons.convert_pad_shape(padding)
- pad_shape = commons.fix_pad_shape(pad_shape, x)
- x = F.pad(x, pad_shape, data_format='NCL')
- return x
diff --git a/spaces/ZJunTvT/ZJunChat/readme/README_en.md b/spaces/ZJunTvT/ZJunChat/readme/README_en.md
deleted file mode 100644
index a906ecb3ebc411f5cdeb33d661266a489a20c3b0..0000000000000000000000000000000000000000
--- a/spaces/ZJunTvT/ZJunChat/readme/README_en.md
+++ /dev/null
@@ -1,127 +0,0 @@
-
-
-Unleash your creative side and generate mesmerizing images with just a few clicks! Enter a spark of inspiration in the "Basic Idea" text box and click the "Magic Prompt" button to elevate it to a polished masterpiece. Make any final tweaks in the "Full Prompt" box and hit the "Generate Images" button to watch your vision come to life. Experiment with the "Noise Level" for a diverse range of outputs, from similar to wildly unique. Let the fun begin! -
-川虎 Chat 🐯 Chuanhu Chat
-
-
-
-
-
-
-
-## Usage Tips
-
-- To better control the ChatGPT, use System Prompt.
-- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu.
-- To try again if the response is unsatisfactory, use `🔄 Regenerate` button.
-- To start a new line in the input box, press Shift + Enter keys.
-- To quickly switch between input history, press ↑ and ↓ key in the input box.
-- To deploy the program onto a server, change the last line of the program to `demo.launch(server_name="0.0.0.0", server_port=
-
-
--
Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA
-
- 
- 
-
- Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search
- LaTeX rendering / Table rendering / Code highlighting
- Auto dark mode / Adaptive web interface / WeChat-like theme
- Multi-parameters tuning / Multi-API-Key support / Multi-user support
- Compatible with GPT-4 / Local deployment for LLMs
-
-
-
-
-
diff --git "a/spaces/a-v-bely/russian-task-generator/pages/1_\342\232\231\357\270\217_\320\230\320\275\321\201\321\202\321\200\321\203\320\272\321\206\320\270\321\217.py" "b/spaces/a-v-bely/russian-task-generator/pages/1_\342\232\231\357\270\217_\320\230\320\275\321\201\321\202\321\200\321\203\320\272\321\206\320\270\321\217.py"
deleted file mode 100644
index de520a139af964d97d40cc04d14ddcbb6aa9782b..0000000000000000000000000000000000000000
--- "a/spaces/a-v-bely/russian-task-generator/pages/1_\342\232\231\357\270\217_\320\230\320\275\321\201\321\202\321\200\321\203\320\272\321\206\320\270\321\217.py"
+++ /dev/null
@@ -1,78 +0,0 @@
-import streamlit as st
-
-
-st.set_page_config(page_title='GenLexTasks', layout="wide", page_icon=':ru:')
-if st.session_state.get('-LOGGED_IN_BOOL-'):
- ANNOUNCES = st.expander('**ВАЖНАЯ ИНФОРМАЦИЯ**', expanded=True)
- ANNOUNCES.success(
- '**Уважаемые пользователи, пожалуйста, после генерации заданий перейдите на вкладку "📝Онлайн-тест" '
- 'и заполните там опросник. Таким образом Вы очень поможете в улучшении качества заданий! Спасибо!🤗**')
- ANNOUNCES.warning(
- '**Сейчас генератор проходит завершающую настройку и отладку, для которой необходимо большое количество '
- 'данных об уместности выбранных целевых слов и дистракторов к ним. Поэтому просим Вас отнестись с пониманием '
- 'к излишне большому количеству заданий.**')
- ANNOUNCES.warning(
- '**В настоящее время генератор может работать только с отдельными словами (от пробела до пробела).'
- 'Использование словосочетаний в качестве целевых слов или дистракторов будет добавлено в ближайшее время.**')
- ANNOUNCES.warning(
- '**❗️ㅤУбедительно просим Вас дожидаться окончания генерации или загрузки и не переходить на '
- 'другие вкладки до выведения соответствующего сообщения.**')
- ANNOUNCES.warning(
- '**❗ㅤВ случае появления красных сообщений об ошибке, как правило, проблема решается '
- 'повторными нажатиями на нужный Вам элемент. Приносим извинения за неудобства.**')
-
- INSTRUCTION = st.expander(label='**ИНСТРУКЦИЯ**', expanded=True)
- INSTRUCTION.markdown(
- '**_I. Выберите режим работы._**'
- '\n\n**_:red[СОЗДАНИЕ ЗАДАНИЙ]_**'
- '\n\nПосле выбора данного режима работы появится форма, которую необходимо заполнить:'
- '\n\n1. Придумайте **название** для файла с заданиями. '
- 'Вы можете оставить это поле пустым - именем по умолчанию служит текущая дата и первые 20 символов '
- 'введенного Вами текста.'
- '\n\n2. Введите **текст** или выберите **текстовый файл** с исходным текстом, на основе которого Вы хотите '
- 'создать задания. '
- '\n\n3. Укажите *способ выбора целевых слов*:'
- '\n\t* *:green[Автоматически]*: программа сама выберет подходящие по сложности целевые слова.'
- '\n\t* *:blue[Самостоятельно]*: введите в соответствующее поле целевые слова через запятую в той форме, '
- 'в которой они встречаются в тексте. В этом случае *:orange[языковой уровень]* можно не указывать, но тогда '
- 'дистракторы будут полностью случайными и несоотнесёнными с уровнем.'
- '\n4. Если Вы выбрали *:green[автоматический поиск целевых слов]*, **_:red[обязательно]_** укажите '
- '*:orange[языковой уровень]*. Данный параметр отвечает за выбор лексического минимума, использующегося при '
- 'подборе дистракторов.'
- '\n5. Если Вы выбрали *:blue[самостоятельный ввод целевых слов]*, проверьте, что заполнили соответствующее '
- 'поле. ️ \n❗ **:red[Введите слова в той форме, в которой они встречаются в тексте]**.'
- '\n\n6. Укажите число дистракторов - неправильных вариантов ответа. Если указано _более четырех_ '
- 'дистракторов, возможно, что в некоторых заданиях будет выведено _меньшее количество, но не менее четырех_ '
- 'вариантов. Данное обстоятельство связано с проверкой наличия дистракторов в лексических минимумах.'
- '\n7. Выберите **способы вывода** готовых материалов.'
- '\n8. Для начала работы нажмите на кнопку **"Запуск"**. Если все поля заполнены верно, '
- 'начнется процесс генерации заданий. Прогресс будет отображаться на экране.'
- '\n9. По окончании процесса генерации заданий будет выведено **_:green[соответсвующее сообщение]_**. '
- 'Затем Вы можете перейти на вкладки **просмотра и 📥 сохранения** заданий, а так же 📝**онлайн-теста**.'
- '\n\n**_:red[ЗАГРУЗКА ИЗ АРХИВА]_**'
- '\n\nПосле выбора данного режима работы появится таблица, в которой перечислены названия заданий, '
- 'которые Вы сохранили, языковой уровень и дата их создания.'
- ' Для загрузки определенного файла с заданиями:'
- '\n1. Введите (или скопируйте из таблицы) название.'
- '\n2. Укажите соответсвующий языковой уровень.'
- '\n3. Нажмите на кнопку **"Загрузить"**.'
- '\n4. Если все поля заполнены верно, Вы увидите сообщение о том, что **:green[задания успешно загружены]**.'
- '\n\n\nДля того, чтобы свернуть/развернуть блоки **Инструкций** или **Важной информации**, '
- 'кликните по заголовку этого блока или по стрелке (ᐯ / ᐱ), располагающейся в его правом верхнем углу.')
-
- INSTRUCTION_ONLINE_TEST = st.expander(label='**ИНСТРУКЦИЯ ОНЛАЙН-ТЕСТ**', expanded=True)
- INSTRUCTION_ONLINE_TEST.markdown(
- 'Уважаемые пользователи, предлагаем Вам заполнить опросник по оценке качества созданных заданий. '
- '\n\nНиже находится анкета с заданиями в таблице.'
- '\n\n- В **первом столбце** приводится ответ - слово, удаленное из оригинального текста.'
- '\n\n- Отметьте во **втором столбце**, уместно ли создавать задание с данным словом.'
- '\n\n- В **третьем столбце** приведены подобранные программой дистракторы.'
- '\n\n- Введите в **четвертый столбец** дистракторы (целиком или букву), которые, по Вашему мнению, '
- '**:red[не уместны]**. '
- '\n\n**:green[Уместными дистракторами]** мы предлагаем считать те, которые одновременно удовлетворяют '
- 'следующим условиям в рамках языкового уровня, для которого они созданы:'
- '\n\n1. не слишком очевидно являются неправильными вариантами (*варить суп/стол*);'
- '\n\n2. и при этом не могут быть полноценной заменой удаленного слова (*варить суп/кашу*)'
- )
-else:
- st.warning('**Войдите или зарегистрируйтесь**')
diff --git a/spaces/abdvl/datahub_qa_bot/docs/deploy/telemetry.md b/spaces/abdvl/datahub_qa_bot/docs/deploy/telemetry.md
deleted file mode 100644
index c5458cc5df05e093e433bff4738cd552b6ec9177..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/deploy/telemetry.md
+++ /dev/null
@@ -1,10 +0,0 @@
-# DataHub Telemetry
-
-## Overview of DataHub Telemetry
-
-To effectively build and maintain the DataHub Project, we must understand how end-users work within DataHub. Beginning in version 0.8.35, DataHub collects anonymous usage statistics and errors to inform our roadmap priorities and to enable us to proactively address errors.
-
-Deployments are assigned a UUID which is sent along with event details, Java version, OS, and timestamp; telemetry collection is enabled by default and can be disabled by setting `DATAHUB_TELEMETRY_ENABLED=false` in your Docker Compose config.
-
-
-The source code is available [here.](../../metadata-service/factories/src/main/java/com/linkedin/gms/factory/telemetry/TelemetryUtils.java)
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/decode_heads/gc_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/decode_heads/gc_head.py
deleted file mode 100644
index cff93b6a9fd6f0a4a2f6833cd464efbe29559728..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/decode_heads/gc_head.py
+++ /dev/null
@@ -1,59 +0,0 @@
-'''
- * Copyright (c) 2023 Salesforce, Inc.
- * All rights reserved.
- * SPDX-License-Identifier: Apache License 2.0
- * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
- * By Can Qin
- * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
- * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
- * Modified from MMCV repo: From https://github.com/open-mmlab/mmcv
- * Copyright (c) OpenMMLab. All rights reserved.
-'''
-
-import torch
-from annotator.uniformer.mmcv.cnn import ContextBlock
-
-from ..builder import HEADS
-from .fcn_head import FCNHead
-
-
-@HEADS.register_module()
-class GCHead(FCNHead):
- """GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond.
-
- This head is the implementation of `GCNet
- data structures and algorithms by g.a.v pai free 35
Download Zip ⚹⚹⚹ https://urloso.com/2uyRKR
-
-The course, offered by the department of Computer Applications offers a computer oriented introductory course in various areas of Mathematics and Science. It also covers a brief history of Mathematics, introduction of the central concepts in Mathematics, programming techniques and Applications of Algorithms. This course attempts to teach concepts in real life application. It gives a practical approach to the solution of Mathematical problems, rather than focusing only on the mathematical definitions. It will be beneficial for all. The course covers the following topics: 1. Concepts in Mathematics. 2. Matrices, vectors and basic operations on them. 3. Graphs, adjacency, paths and algorithms. 4. Introduction to Computers and Programming. 5. Introduction to Logic and Computation. - -Introduction and Basic Concepts The History of Mathematics - -Mathematics: Use and Abuse In his review of G.H Hardy's controversial book 'A Mathematician's Apology' author remarks that "the history of mathematics is one long series of attempts to define the terms on which mathematicians agree to disagree". Hardy \[1\] had published the article on the existence of uncountably many distinct uncountable sets in 1900. Shortly afterwards Hausdorff \[2\] had proved the same result by using a different method. He used to define a set as a collection of points, where each point is closed and bounded. This definition was then further refined. Nowadays a point, known as a limit point, is an accumulation point. These closed and bounded sets are now called Haussdorff spaces. The result of Hausdorff was first published in the German Journal 'Mathematische Annalen'. It is interesting to note that he himself was surprised and dismayed by Hardy's work. In this work, Hardy showed a continuous function whose graph is uncountable. He gave the set of all such functions as well as the set of all points on this graph and a bijection between them. These sets are said to be uncountably large. Hardy goes on to discuss the difference between sets of rational numbers and the real numbers, thus, giving a definition of countable set. He then states that these two sets are not the same, and hence uncountable. But this is not his most damning criticism of the whole idea of mathematics. It is that his argument is not clearly thought out. The definition he gives of uncountable set is incorrect, and he gives an example to prove that. He then uses this to show that 4fefd39f24
-
-
- diff --git a/spaces/bioriAsaeru/text-to-voice/HOW TO CHANGE LANUGAGE?????? Chicago 1930 General Discussions[1].md b/spaces/bioriAsaeru/text-to-voice/HOW TO CHANGE LANUGAGE?????? Chicago 1930 General Discussions[1].md deleted file mode 100644 index 3a5b1c028f4d07d9de254c5010b175e9d6f04119..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/HOW TO CHANGE LANUGAGE?????? Chicago 1930 General Discussions[1].md +++ /dev/null @@ -1,6 +0,0 @@ -
Chicago 1930 english language pack
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-
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-
-
- diff --git a/spaces/bioriAsaeru/text-to-voice/Ken Follett Epub Ita Download Skype The Ultimate Guide for Fans of Historical Thrillers.md b/spaces/bioriAsaeru/text-to-voice/Ken Follett Epub Ita Download Skype The Ultimate Guide for Fans of Historical Thrillers.md deleted file mode 100644 index 591132429ed82cbb97d444caede91e96daaa38b0..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Ken Follett Epub Ita Download Skype The Ultimate Guide for Fans of Historical Thrillers.md +++ /dev/null @@ -1,6 +0,0 @@ -
Ken Follett Epub Ita Download Skype
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-
-
- diff --git a/spaces/birdortyedi/instagram-filter-removal/README.md b/spaces/birdortyedi/instagram-filter-removal/README.md deleted file mode 100644 index f2552ac5263506df94b36b9c232e8351968542ca..0000000000000000000000000000000000000000 --- a/spaces/birdortyedi/instagram-filter-removal/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Instagram Filter Removal -emoji: 👀 -colorFrom: gray -colorTo: green -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/bortle/astrophotography-object-classifier/app.py b/spaces/bortle/astrophotography-object-classifier/app.py deleted file mode 100644 index 1e49d1feca60186fb9bf10220e52af1ab65313cd..0000000000000000000000000000000000000000 --- a/spaces/bortle/astrophotography-object-classifier/app.py +++ /dev/null @@ -1,18 +0,0 @@ -import gradio as gr -from transformers import pipeline - -pipeline = pipeline(task="image-classification", model="bortle/astrophotography-object-classifier-alpha4") - -def predict(image): - predictions = pipeline(image) - return {p["label"]: p["score"] for p in predictions} - -gr.Interface( - predict, - inputs=gr.Image(shape=(1080, None), type="pil", label="Upload Astrophotography image"), - outputs=gr.Label(num_top_classes=5), - title="Astrophotography Object Classifier", - allow_flagging="manual", - examples=["examples/Andromeda.jpg", "examples/Heart.jpg", "examples/Pleiades.jpg", "examples/Rosette.jpg", "examples/Moon.jpg", "examples/GreatHercules.jpg", "examples/Leo-Triplet.jpg", "examples/Crab.jpg", "examples/North-America.jpg", "examples/Horsehead-Flame.jpg", "examples/Pinwheel.jpg"], - cache_examples=True -).launch() \ No newline at end of file diff --git a/spaces/brainblow/AudioCreator_Music-Audio_Generation/scripts/templates/login.html b/spaces/brainblow/AudioCreator_Music-Audio_Generation/scripts/templates/login.html deleted file mode 100644 index dd89ac654bceca14a9dec7d1a7f8206d1425a7a1..0000000000000000000000000000000000000000 --- a/spaces/brainblow/AudioCreator_Music-Audio_Generation/scripts/templates/login.html +++ /dev/null @@ -1,20 +0,0 @@ -{% extends "base.html" %} -{% block content %} - -
- You must identify yourself first! We use a highly secured protocol - where you just decide your username, and that's it. No password, no encryption, - just pure trust. -
- -{% if error %} -{{error}}
-{% endif %} -