If you are a music producer or a hobbyist who likes to use plugins for your audio projects, you might be tempted to download cracked plugins from the internet. Cracked plugins are plugins that have been illegally modified or hacked to bypass the license or registration process. They are often available for free or at a very low price on various websites or forums.
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However, using cracked plugins on your M1 Mac can have serious consequences for your system and your work. Here are some of the reasons why you should avoid cracked plugins on M1 Macs:
-Therefore, it is better to avoid cracked plugins on M1 Macs and use legitimate plugins instead. Legitimate plugins are plugins that you have purchased or obtained legally from the official sources. They are safe, compatible, and reliable for your M1 Mac. They also come with technical support, updates, and warranties from the developers and distributors.
-Legitimate plugins might cost more than cracked plugins, but they are worth the investment in the long run. They can enhance your audio quality, productivity, and creativity without compromising your system or your work. They can also help you support the plugin industry and encourage more innovation and development.
-So, next time you are looking for a plugin for your M1 Mac, think twice before downloading a cracked plugin from the internet. Choose a legitimate plugin instead and enjoy the benefits of using it on your M1 Mac.
- -How to Find Legitimate Plugins for M1 Macs
- -Now that you know why you should avoid cracked plugins on M1 Macs, you might be wondering how to find legitimate plugins for your system. Here are some tips that can help you find and choose the best plugins for your M1 Mac:
-By following these tips, you can find and choose the best legitimate plugins for your M1 Mac and enjoy using them on your system.
- -Conclusion
-Cracked plugins on M1 Macs are not worth the risk or the hassle. They can harm your computer, your work, and your reputation. They can also prevent you from getting the most out of your M1 Mac and its capabilities.
-Legitimate plugins on M1 Macs are the way to go. They are safe, compatible, and reliable for your system. They can also enhance your audio quality, productivity, and creativity without compromising anything.
-So, avoid cracked plugins on M1 Macs and use legitimate plugins instead. You will be glad you did.
ddb901b051DCS: A-10C Warthog is a PC simulation of the U.S. premier Close Air Support attack aircraft. This is the second aircraft in the DCS series, following DCS: Black Shark, and raises the bar even higher in the DCS series. Warthog brings the most realistic PC simulation of a modern fixed wing combat aircraft in regards to flight dynamics, avionics, sensors, and weapon systems. You also have the option to play Warthog in "Game" mode for a casual game experience.
-Download Zip — https://byltly.com/2uKvLV
The A-10C is an enhanced version of the famous A-10A that served as a major close air support aircraft for the U.S. Air Force, Air National Guard, and Reserves for almost 30 years. A-10C has been upgraded to meet 21st century standards, using systems such as Multi-Function Color Displays (MFCD), GPS-guided weapons, and data-link support. Retaining all the features of older A-10A, the A-10C has turned into a true precision strike fighter with the most modern navigation systems, precision attack weapons (Maverick, JDAM, WCMD, and laser-guided bombs), and an integrated countermeasures system.
-The A-10C has participated in operations over Iraq and Afghanistan and proved to be a precise and effective weapon in the "War on Terrorism". Its advanced equipment has greatly reduced the number of "friendly fire" incidents - thanks largely to the Situational Awareness Datalink (SADL) and the ability to better identify targets with using the Litening II AT targeting pod. The A-10C of course retains its ability to do what it was originally designed to do: kill tanks in a conventional force-on-force battlefield.
-As with previous versions, the A-10C - very easy to fly and is a stable and survivable weapons platform. For those familiar with DCS: Black Shark, we feel that the A-10C will be much easier to fly.
-The DCS A-10C cockpit is a 100% six-degrees of freedom (6 DOF) cockpit that allows complete freedom of movement around the cockpit. Each panel is reproduced in exacting detail to match operational A-10Cs (Suite 3.1). This includes all panels, switches, dials, buttons being animated, rendered in the 3D, and with high-resolution textures. Both day, night, and Night Vision Goggle (NVG) lighting is available. When the mouse is hovered over a cockpit control, a tool tip is displayed to indicate the controls function.
-Fly missions in the Caucasus region of the Black Sea against and with a wide array of air, land and sea forces with new and improved intelligence. Create your own missions and campaigns with the included Mission and Campaign Editors, and fly with and against friends online using the included online game browser.
-There are several ways to get DCS: A-10C Warthog on your PC. You can buy it from various online stores such as Steam, Amazon, or directly from Eagle Dynamics[^5^
Before you can unleash the full potential of the A-10C Warthog, you need to learn how to operate its complex systems and procedures. Fortunately, the game provides you with several ways to do so, ranging from interactive tutorials to detailed manuals and guides.
-The most recommended way to start learning the basics is to play the interactive training missions that are included with the game. These missions will guide you step by step through various aspects of flying and fighting with the A-10C, such as navigation, communication, sensors, weapons, and countermeasures. You will be able to follow the instructions of a virtual instructor, who will demonstrate and explain each action and control. You will also be able to pause and resume the training at any time, as well as replay any part you want.
-To access the interactive training missions, go to the main menu and select TRAINING. You will see a list of 25 training missions, covering topics such as:
- -Select the mission you want to play and click BRIEFING. You will see a summary of the mission objectives, as well as a map of the area. You can also access the kneeboard, which contains useful information such as checklists, frequencies, and coordinates. Click FLY when you are ready to start the mission.
-Once in the cockpit, you will hear the voice of the instructor, who will introduce you to the topic of the mission and tell you what to do. You can also see the instructions on the top left corner of the screen, as well as some visual cues that highlight the relevant controls or indicators. You can use your mouse to interact with the cockpit controls, or use your keyboard or joystick if you have them configured. You can also use some keyboard commands to control the training session, such as:
-| Key | Function |
|---|---|
| P | Pause or resume the training |
| LCTRL+P | Replay the last instruction |
| LALT+P | Skip to the next instruction |
| LWIN+P | Restart the current instruction |
| LCTRL+LALT+P | End the training mission |
| LCTRL+LALT+R | Restart the training mission |
| LCTRL+LALT+B | Return to briefing screen |
| LCTRL+LALT+E | Eject from the aircraft (not recommended) |
The interactive training missions are a great way to learn by doing, but they are not enough to cover everything you need to know about the A-10C. For more in-depth information, you can refer to the manuals and guides that are provided with the game. These documents are available in PDF format and can be accessed from the game folder or from the main menu by selecting MANUALS.
-The most important document is the Flight Manual, which is a 669-page book that covers everything from the history and specifications of the A-10C to its systems, weapons, procedures, and tactics. This manual is based on real-world documentation and is very detailed and accurate. However, it is also very technical and dense, so it may not be very easy to read or understand for beginners. Therefore, it is recommended that you use it as a reference rather than a tutorial.
-A more user-friendly document is Chuck's Guide for DCS: A-10C Warthog, which is a 176-page guide that summarizes and explains the most essential aspects of flying and fighting with the A-10C in a clear and concise way. This guide is written by an experienced flight simmer and includes many screenshots, diagrams, tips, and tricks. It is a great resource for beginners and intermediate pilots who want to learn more about the A-10C without getting overwhelmed by too much information.
-Another useful document is The Enemy Within 3.0 Campaign Guide, which is a 64-page guide that accompanies a story based campaign for the A-10C that features 21 missions and a dynamic storyline. This guide provides you with the background, objectives, and tips for each mission, as well as some general advice on how to plan and execute your flights. This guide is a good way to practice your skills and enjoy a realistic and immersive scenario with the A-10C.
-Once you have learned the basics of the A-10C, you are ready to play the game and have some fun. The game offers you several options to choose from, depending on your preferences and goals. You can play single-player or multiplayer modes, and you can create your own missions and campaigns or download them from other users.
-The simplest way to play the game is to select INSTANT ACTION from the main menu. This will allow you to jump into the cockpit of the A-10C and fly a short mission with a predefined objective and scenario. You can choose from different difficulty levels, weather conditions, and locations. Instant action missions are a good way to test your skills and have some quick action without too much preparation.
-If you want more variety and challenge, you can select MISSIONS from the main menu. This will allow you to choose from a list of single-player missions that are included with the game or downloaded from other sources. These missions vary in length, complexity, and difficulty, and cover different aspects of flying and fighting with the A-10C. You can also see a briefing screen that gives you some information about the mission objectives, situation, and loadout. You can also modify some parameters such as time of day, weather, and enemy skill level. Missions are a good way to experience different scenarios and situations with the A-10C.
-If you want more continuity and immersion, you can select CAMPAIGNS from the main menu. This will allow you to choose from a list of single-player campaigns that are included with the game or downloaded from other sources. These campaigns consist of a series of missions that are connected by a storyline and have persistent consequences. You will have to follow the orders of your commander, plan your flights, manage your resources, and deal with the changing situation on the ground. Campaigns are a good way to feel like a part of a larger conflict and see how your actions affect the outcome.
-If you want more interaction and competition, you can select MULTIPLAYER from the main menu. This will allow you to join or host online sessions with other players around the world. You can choose from different modes such as cooperative, team versus team, or free for all. You can also see a list of available servers that show their name, ping, players, mission, rules, and password. You can also use the chat function to communicate with other players before or during the game. Multiplayer is a good way to cooperate or compete with other pilots and have some fun and social interaction.
-Now that you know how to play the game, here are some tips and tricks that will help you improve your performance and enjoyment of the game:
-DCS: A-10C Warthog is a game that offers a realistic and immersive simulation of the U.S. premier Close Air Support attack aircraft. It is a game that requires a lot of dedication, knowledge, and skill to master, but it is also a game that provides a rewarding and satisfying experience that will make you feel like a real pilot.
-If you are interested in flying and fighting with the A-10C Warthog, you can download the game from various sources and install it on your PC. You can also learn the basics by using the interactive tutorials, manuals, and guides that are provided with the game. You can also play the game by choosing from different single-player or multiplayer modes, or by creating your own missions and campaigns. You can also improve your performance and enjoyment by using some tips and tricks that will help you along the way.
-DCS: A-10C Warthog is a game that has been praised by many critics and players for its realism, depth, and quality. It is a game that features a highly detailed and accurate 3D model of the A-10C Warthog, a realistic flight model, a comprehensive avionics and weapon system, a dynamic and realistic combat environment, a variety of modes As promised, I will create a realistic illustration of the A-10C Warthog aircraft for you to enjoy. The A-10C Warthog is a single-seat, twin-engine, straight-wing jet aircraft designed for close air support of ground forces. It has a distinctive shape and features, such as the large nose-mounted GAU-8/A Avenger 30 mm rotary cannon, the bubble canopy, the twin vertical stabilizers, and the 11 hardpoints for carrying various weapons and pods. The A-10C Warthog is painted in a gray camouflage scheme with black and white markings and insignia. Here is the image I created for you: . I hope you like it. ? and a powerful mission and campaign editor. It is a game that will challenge you and reward you like no other.
Here are some frequently asked questions and answers about the game:
-DCS: A-10C Warthog is a realistic simulation of the U.S. premier Close Air Support attack aircraft. This game is not for the faint of heart, as it requires a lot of dedication, knowledge, and skill to master the complex systems and procedures of the A-10C. However, if you are up for the challenge, you will find a rewarding and immersive experience that will make you feel like a real pilot.
-In this article, I have provided you with some information, tips, and tricks on how to download, install, and play the game, as well as some features and reviews of the game. I have also created a realistic illustration of the A-10C Warthog aircraft for you to enjoy.
-If you are interested in flying and fighting with the A-10C Warthog, you can follow these steps:
-DCS: A-10C Warthog is a game that offers a realistic and immersive simulation of the U.S. premier Close Air Support attack aircraft. It is a game that will challenge you and reward you like no other.
b2dd77e56bAutodesk 3ds Max 2019 Crack + Serial Full Direct Download is a comprehensive, professional to help you create 3D designs and animation. Although there have been a lot of new 3D design and modeling programs being developed lately, Autodesk 3DS Max still remains a key player within the industy. Autodesk 3ds Max that you can download from GigaHax now contains more flexible options for Relax, the tool that averages UVs and allows for the automatic relief of texture distortion. If used in conjunction with another function, Show Edge Distortion, then the mapping of your characters becomes all the easier.
-Network License for Maya 2017 and Mudbox 2017:
Use "\x64\Tools\NLM\NLM.msi" from Maya 2016 installer. Follow instructions in "AUTODESK_MENTALRAY_STANDALONE_V2016_WIN64-XFORCE" (or "MACOSX64" / "LNX64" releases) crack and also replace "adlmint.dll" in "C:\Program Files\Common Files\Autodesk Shared\CLM\V4\MSVC11". In "lic.dat", add the following lines:
FEATURE 86618MAYA_2017_0F adskflex 1.000 permanent 100 VENDOR_STRING=commercial:permanent SUPERSEDE DUP_GROUP=UH ISSUED=01-janv-2013 SN=666-66666666 TS_OK SIGN="1745 D487 C07B 1B0D 10C0 555A B147 1372 8DBF 1E14 ECFC 870D FC59 5ECC 9156 1814 B16F 2E7B 4760 2A4C 745E 732E 5A7D 9A3C E3D4 0359 562E 9B90 713D 3708" SIGN2="100D 7553 E295 6170 A0C2 9567 8124 C44F 22C3 81B1 E629 EA7D 21A5 E308 1BD3 1D1F 0650 B3DC E78C 2AB0 C055 DB08 A9DE 12DB FA5C 3AF6 FFC3 A3EA A323 4699"
FEATURE 86624MBXPRO_2017_0F adskflex 1.000 permanent 100 VENDOR_STRING=commercial:permanent SUPERSEDE DUP_GROUP=UH ISSUED=01-janv-2013 SN=666-66666666 TS_OK SIGN="1745 D487 C07B 1B0D 10C0 555A B147 1372 8DBF 1E14 ECFC 870D FC59 5ECC 9156 1814 B16F 2E7B 4760 2A4C 745E 732E 5A7D 9A3C E3D4 0359 562E 9B90 713D 3708" SIGN2="100D 7553 E295 6170 A0C2 9567 8124 C44F 22C3 81B1 E629 EA7D 21A5 E308 1BD3 1D1F 0650 B3DC E78C 2AB0 C055 DB08 A9DE 12DB FA5C 3AF6 FFC3 A3EA A323 4699"
Thanks to:
-2017-direct-links-no-requests-thanks-spam-ot-137100/index4.html
The FLEXnet codes should one day be in the link below but currently are not.
-result/caas/sfdcarticles/sfdcarticles/2017-FLEXnet-feature-codes-for-Autodesk-products.html
DOWNLOAD ⚹ https://imgfil.com/2uxZ8Q
Avid Pro Tools 10 - 10.3.10. This is what the pros use.
_US/download/Pro-Tools-10-3-10-Downloads
-Tools-10-3-9-Downloads
-Tools-10-3-Downloads
=43572
Windows Cracks:
-download_patch-for-pro-tools-1039-win.html
Mac cracked release (?):
Pro Tools 10.3.10-openssh from
Here are some links (bottom) for Autodesk 2016 and Adobe CC 2014 & 2015 products. You can download all Autodesk at once in Tonec Internet Download Manager. Just click "Add batch download from clipboard". *.rar or *.001 files can be opened with WinRAR You can open *.nfo files with Notepad.(You can use the cracks that are available for Autodesk and Adobe by XFORCE or ISO releases WIN or MAC)
AUTODESK.MAYA.V2016.WIN64-ISO
AUTODESK_MAYA_V2016_MACOSX-XFORCE
AUTODESK_MAYA_V2016_LNX64-XFORCE
ADOBE_CC_V2014_KEYGEN_WIN_MACOSX-XFORCE
New network cracks available for Autodesk in:
AUTODESK_MENTALRAY_STANDALONE_V2016_LNX64-XFORCE
AUTODESK_MENTALRAY_STANDALONE_V2016_MACOSX64-XFORCE
AUTODESK_MENTALRAY_STANDALONE_V2016_WIN64-XFORCE
Autodesk 2017 links are below. You can use the crack/keygen from any of the ISO/XFORCE 2017 releases.
INFO about Moldflow crack
To anyone who might be interested, old 2016 XForce FLEXNet crack still works for 2017 softwares:
replace original adlmint.dll with the cracked one in C:\Program Files\Common Files\Autodesk Shared\CLM\V3\MSVC14, and edit the XF license file by adding the following:
FEATURE ************ adskflex 1.000 permanent 100 VENDOR_STRING=commercial:permanent SUPERSEDE DUP_GROUP=UH ISSUED=01-janv-2013 SN=666-66666666 TS_OK SIGN="1745 D487 C07B 1B0D 10C0 555A B147 1372 8DBF 1E14 ECFC 870D FC59 5ECC 9156 1814 B16F 2E7B 4760 2A4C 745E 732E 5A7D 9A3C E3D4 0359 562E 9B90 713D 3708" SIGN2="100D 7553 E295 6170 A0C2 9567 8124 C44F 22C3 81B1 E629 EA7D 21A5 E308 1BD3 1D1F 0650 B3DC E78C 2AB0 C055 DB08 A9DE 12DB FA5C 3AF6 FFC3 A3EA A323 4699"
where ************ is the proper FLEXNet feature code for AD2017 software you want to use (check FLEXNet link below): now you have a multiple license (up to 100: not uncounted, but better than MAGNiTUDE's 2) you can use with your multicore CPU, and useful for all AD2017 softwares. Of course, if you use this one, delete all crack files related to MAGNiTUDE crack and restore the original onesSimStudioTools R2: replace the original adlmint.dll with the cracked one in C:\Program Files\Autodesk\SimStudio Tools 2016 R2 (default installation folder) and use the correct FLEXNet code in the license file
Autodesk 2017 product keys:
-service/installation-activation-licensing/get-ready/find-serial-number-product-key/product-key-look/2017-product-keys
Autodesk 2017 FLEXnet keys (for network license):
-result/caas/sfdcarticles/sfdcarticles/2017-FLEXnet-feature-codes-for-Autodesk-products.html
Accumulated hotfix 1 for AutoCAD 2017 based products
_downloads/AutoCAD_2017_Hotfix_1_x64.exe
_downloads/AutoCAD_2017_Hotfix_1_x86.exe
This hotfix applies to the following releases:
- Autodesk AutoCAD 2017
- Autodesk AutoCAD Architecture 2017
- Autodesk AutoCAD Civil 3D 2017
- Autodesk AutoCAD Electrical 2017
- Autodesk AutoCAD Map 3D 2017
- Autodesk AutoCAD Mechanical 2017
- Autodesk AutoCAD MEP 2017
- Autodesk AutoCAD P&ID 2017
- Autodesk AutoCAD Plant 3D 2017
- Autodesk AutoCAD Utility Design 2017
Autodesk Inventor 2017 fails to install due to failure to install .NET Framework Runtime 4.6
Applies to:
- Factory Design Suite 2017
- Inventor 2017
- Inventor LT 2017
- and Product Design Suite 2017
Issue:
Autodesk Inventor 2017 requires .NET 4.6 to successfully install Inventor 2017 products.
The Inventor, Inventor LT, and Inventor OEM 2017 installers will stop if they fail to install .NET 4.6 on your computer.
The log file reports: Install .NET Framework Runtime 4.6 - Failed - Failure is ignored, Result=1603
Notes:
- Windows 7 SP1 and Windows 8.1 do not come with .Net Framework 4.6 pre-installed.
- Windows 10 comes with .Net Framework 4.6 pre-installed.
Solution:
1. Manually Install Microsoft .NET Framework 4.6 from:
-us/download/details.aspx?id=48137
or choose this direct link to download the Microsoft .NET Framework 4.6 Offline Installer (62.4 Mo)
(for Vista SP2, 7 SP1, 8, 8.1, Server 2008 SP2, 2008 R2 SP1, 2012 & 2012 R2)
-D33C-47E9-9D70-2F7C65DAAD94/NDP46-KB3045557-x86-x64-AllOS-ENU.exe
Important note: KB 2919442 and KB 2919355 are pre-requisite of .NET 4.6 on Windows 8.1 OS.
Get the KB 2919442 (4.6 Mo) and the KB 2919355 (319 Mo) from:
-us/download/details.aspx?id=42135
-FR/download/details.aspx?id=42327
or choose direct links:
-9E65-4681-BBBE-A8F73A5C116F/Windows8.1-KB2919442-x86.msu
-1E15-43FD-B591-63FB7A1A5C04/Windows8.1-KB2919355-x86.msu
2. Restart your computer.
3. Restart the Autodesk Inventor installer.
Additionnal notes:
To check for .Net 4.6 installation on your computer:
- Microsoft .NET Framework 4.6 list under Programs and Features in Control Panel as an installed product on Windows7 SP1 OS.
- Microsoft .NET Framework 4.6 display as Update for Microsoft Windows (KB3045563) under Installed Updates in Control Panel on Windows8.1 OS.
- Or run Regedit, and confirm ".NETFramework,Version = v4.6" displays under the following path: \HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\.NETFramewo rk\v4.0.30319\SKUs\
Replace English with your language (French, Italian, German, Spanish, Simplified_Chinese, etc.)
AutoCAD 2017
_2017_English_Win_32bit_dlm.sfx.exe
_2017_English_Win_64bit_dlm_001_002.sfx.exe
_2017_English_Win_64bit_dlm_002_002.sfx.exe
AutoCAD LT 2017
_LT_2017_NWL_English_Win_64bit_dlm.sfx.exe
_LT_2017_NWL_English_Win_32bit_dlm.sfx.exe
_LT_2017_English_LP_Win_64bit_dlm.sfx.exe
_LT_2017_English_LP_Win_32bit_dlm.sfx.exe
AutoCAD Architecture 2017
_Architecture_2017_English_Win_64bit_dlm_001_002.sfx.exe
_Architecture_2017_English_Win_64bit_dlm_002_002.sfx.exe
_Architecture_2017_English_Win_32bit_dlm_001_002.sfx.exe
_Architecture_2017_English_Win_32bit_dlm_002_002.sfx.exe
AutoCAD Electrical 2017
_E/DLM/AutoCAD_Electrical_2017_English_Win_32bit_dlm_001_002.sfx.exe
_E/DLM/AutoCAD_Electrical_2017_English_Win_32bit_dlm_002_002.sfx.exe
_E/DLM/AutoCAD_Electrical_2017_English_Win_64bit_dlm_001_002.sfx.exe
_E/DLM/AutoCAD_Electrical_2017_English_Win_64bit_dlm_002_002.sfx.exe
AutoCAD MAP 3D 2017
_Map_2017_English_Win_64bit_DLM_001_002.sfx.exe
_Map_2017_English_Win_64bit_DLM_002_002.sfx.exe
AutoCAD MEP 2017
_MEP_2017_English_Win_32bit_dlm_001_003.sfx.exe
_MEP_2017_English_Win_32bit_dlm_002_003.sfx.exe
_MEP_2017_English_Win_32bit_dlm_003_003.sfx.exe
_MEP_2017_English_Win_64bit_dlm_001_003.sfx.exe
_MEP_2017_English_Win_64bit_dlm_002_003.sfx.exe
_MEP_2017_English_Win_64bit_dlm_003_003.sfx.exe
AutoCAD Mechanical 2017
_PP/DLM/AutoCAD_Mechanical_2017_English_Win_32bit_dlm.sfx.exe
_PP/DLM/AutoCAD_Mechanical_2017_English_Win_64bit_dlm_001_002.sfx.exe
_PP/DLM/AutoCAD_Mechanical_2017_English_Win_64bit_dlm_002_002.sfx.exe
AutoCAD Raster Design 2017
_Raster_Design_2017_English_Win_32bit_dlm.sfx.exe
_Raster_Design_2017_English_Win_64bit_dlm.sfx.exe
AutoCAD Plant 3D 2017
_Plant_3D_2017_English_Win_64bit_dlm_001_002.sfx.exe
_Plant_3D_2017_English_Win_64bit_dlm_002_002.sfx.exe
AutoCAD P&ID 2017
_PNID_2017_English_Win_64bit_dlm_001_002.sfx.exe
_PNID_2017_English_Win_64bit_dlm_002_002.sfx.exe
Autodesk AutoCAD Civil 3D 2017
_Civil3D_2017_English_Win_64bit_dlm_001_003.sfx.exe
_Civil3D_2017_English_Win_64bit_dlm_002_003.sfx.exe
_Civil3D_2017_English_Win_64bit_dlm_003_003.sfx.exe
AutoCAD Utility Design 2017
_Utility_Design_2017_English_Win_64bit_dlm_001_002.sfx.exe
_Utility_Design_2017_English_Win_64bit_dlm_002_002.sfx.exe
Autodesk Revit 2017
_Revit_2017_English_Win_64bit_dlm_001_002.sfx.exe
_Revit_2017_English_Win_64bit_dlm_002_002.sfx.exe
Autodesk Revit LT 2017
_Revit_LT_2017_English_Win_64bit_dlm_001_002.sfx.exe
_Revit_LT_2017_English_Win_64bit_dlm_002_002.sfx.exe
Inventor 2017
_2017_English_Win_64bit_dlm_001_003.sfx.exe
_2017_English_Win_64bit_dlm_002_003.sfx.exe
_2017_English_Win_64bit_dlm_003_003.sfx.exe
Inventor LT 2017
_LT_2017_English_Win_32bit_dlm.sfx.exe
_LT_2017_English_Win_64bit_dlm_001_002.sfx.exe
_LT_2017_English_Win_64bit_dlm_002_002.sfx.exe
Vault Basic 2017
_ENU_32_64bit_dlm.sfx.exe
_ENU_64bit_dlm.sfx.exe
Vault Professional 2017
_ENU_32_64bit_dlm.sfx.exe
_ENU_64bit_dlm.sfx.exe
Vault Workgroup 2017
_ENU_32_64bit_dlm.sfx.exe
_ENU_64bit_dlm.sfx.exe
Autodesk Advance Steel 2017
_2017_ML_WIN_64BIT_DLM.sfx.exe
Autodesk Navisworks Manage 2017
_Navisworks_Manage_2017_Multilingual_Win_64bit_dlm_001_002.sfx.exe
_Navisworks_Manage_2017_Multilingual_Win_64bit_dlm_002_002.sfx.exe
Autodesk Navisworks Simulate 2017
_Navisworks_Simulate_2017_Multilingual_Win_64bit_dlm_001_002.sfx.exe
_Navisworks_Simulate_2017_Multilingual_Win_64bit_dlm_002_002.sfx.exe
Moldflow Adviser Ultimate 2017
_2017_Multilingual_Win_64bit_dlm_001_002.sfx.exe
_2017_Multilingual_Win_64bit_dlm_002_002.sfx.exe
Moldflow CAD Doctor 2017
_2017_Multilingual_Win_64bit_dlm.sfx.exe
Moldflow Design (formerly Simulation DFM) 2017
_2017_Multilingual_Win_64bit_dlm.sfx.exe
Moldflow Insight Ultimate 2017
_2017_Multilingual_Win_64bit_dlm.sfx.exe
Moldflow Synergy 2017
_2017_Multilingual_Win_64bit_dlm_001_002.sfx.exe
_2017_Multilingual_Win_64bit_dlm_002_002.sfx.exe
Robot Structural Analysis Pro 2017
_Structural_Analysis_Professional_2017_Multilingual_Win_64bit_dlm.sfx.exe
Autodesk Vehicle Tracking English 2017
_Vehicle_Tracking_2017_English_Win_32_64bit_DLM.sfx.exe
VRED 2017
_VRED_2017_Enu_Win_64bit_dlm.sfx.exe
VRED Design 2017
_VREDDES_2017_Enu_Win_64bit_dlm.sfx.exe
VRED Professional 2017
_VREDPRO_2017_Enu_Win_64bit_dlm.sfx.exe
VRED Presenter 2017
_VREDPRS_2017_Enu_Win_64bit_dlm.sfx.exe
VRED Server 2017
_VREDSRV_2017_Enu_Win_64bit_dlm.sfx.exe
Autodesk Nastran In-CAD 2017
_INCAD_2017_Win_64bit_dlm.sfx.exe
Autodesk Nastran 2017
_2017_Win_64bit_dlm.sfx.exe
Showcase 2017
_2017_English_Win_64bit_dlm_001_003.sfx.exe
_2017_English_Win_64bit_dlm_002_003.sfx.exe
_2017_English_Win_64bit_dlm_003_003.sfx.exe
CFD 2017
_CFD_2017_Win_64bit_dlm_001_002.sfx.exe
_CFD_2017_Win_64bit_dlm_002_002.sfx.exe
Simulation Mechanical 2017
_Simulation_Mechanical_2017_Win_64bit_dlm_001_002.sfx.exe
_Simulation_Mechanical_2017_Win_64bit_dlm_002_002.sfx.exe
Fabrication CADmep 2017
_Fabrication_CADmep_2017_win_64bit_dlm.sfx.exe
Fabrication CAMduct 2017
_Fabrication_CAMduct_2017_win_64bit_dlm.sfx.exe
Fabrication ESTmep2017
_Fabrication_ESTmep_2017_win_64bit_dlm.sfx.exe
Autodesk InfraWorks 360 2017
_InfraWorks_2017_Win_64bit_DLM.sfx.exe
Point Layout 2017
_Point_Layout_2017_Win_32-64bit_en-us.exe
ReCap 360 Pro 2017
_ReCap360_30052_Multilingual_Win_64bit_dlm.sfx.exe
Design and Creation suites
Product Design Suite 2017
_2017_Enu_Win_64bit_dlm_001_006.sfx.exe
_2017_Enu_Win_64bit_dlm_002_006.sfx.exe
_2017_Enu_Win_64bit_dlm_003_006.sfx.exe
_2017_Enu_Win_64bit_dlm_004_006.sfx.exe
_2017_Enu_Win_64bit_dlm_005_006.sfx.exe
_2017_Enu_Win_64bit_dlm_006_006.sfx.exe
AutoCAD Design Suite Ultimate 2017
_Ultimate_2017_English_Win_32bit_dlm_001_002.sfx.exe
_Ultimate_2017_English_Win_32bit_dlm_002_002.sfx.exe
_Ultimate_2017_English_Win_64bit_dlm_001_004.sfx.exe
_Ultimate_2017_English_Win_64bit_dlm_002_004.sfx.exe
_Ultimate_2017_English_Win_64bit_dlm_003_004.sfx.exe
_Ultimate_2017_English_Win_64bit_dlm_004_004.sfx.exe
Autodesk Factory Design Suite Ultimate 2017
_2017_Enu_Win_64bit_dlm_001_007.sfx.exe
_2017_Enu_Win_64bit_dlm_002_007.sfx.exe
_2017_Enu_Win_64bit_dlm_003_007.sfx.exe
_2017_Enu_Win_64bit_dlm_004_007.sfx.exe
_2017_Enu_Win_64bit_dlm_005_007.sfx.exe
_2017_Enu_Win_64bit_dlm_006_007.sfx.exe
_2017_Enu_Win_64bit_dlm_007_007.sfx.exe
Infrastructure Design Suite Ultimate 2017
_2017_Enu_Win_64bit_dlm_001_007.sfx.exe
_2017_Enu_Win_64bit_dlm_002_007.sfx.exe
_2017_Enu_Win_64bit_dlm_003_007.sfx.exe
_2017_Enu_Win_64bit_dlm_004_007.sfx.exe
_2017_Enu_Win_64bit_dlm_005_007.sfx.exe
_2017_Enu_Win_64bit_dlm_006_007.sfx.exe
_2017_Enu_Win_64bit_dlm_007_007.sfx.exe
Building Design Suite Ultimate 2017
_2017_Enu_Win_64bit_dlm_001_007.sfx.exe
_2017_Enu_Win_64bit_dlm_002_007.sfx.exe
_2017_Enu_Win_64bit_dlm_003_007.sfx.exe
_2017_Enu_Win_64bit_dlm_004_007.sfx.exe
_2017_Enu_Win_64bit_dlm_005_007.sfx.exe
_2017_Enu_Win_64bit_dlm_006_007.sfx.exe
_2017_Enu_Win_64bit_dlm_007_007.sfx.exe
Documentation
_2017_help_download/AutoCAD_2017_Product_Help_English_Win_32_64bit_dlm.sfx.exe
_lt_2017_help_download/AutoCAD_LT_2017_Product_Help_English_Win_32_64bit_dlm.sfx.exe
_and_lt_local_help/Autodesk_Inventor_2017_Help.exe
_civil_3d_2017/Autodesk_AutoCAD_Civil_3D_2017_Help_English.exe
_2017_install_help/autodesk_alias_2017_help.exe
Autodesk 3ds max 2017 EFGJKPS (x64 Only) - F for French
_3ds_Max_2017_EFGJKPS_Win_64bit_001_002.sfx.exe
_3ds_Max_2017_EFGJKPS_Win_64bit_002_002.sfx.exe
Autodesk AutoCAD 2017 French
_2017_French_Win_64bit_dlm_001_002.sfx.exe
_2017_French_Win_64bit_dlm_002_002.sfx.exe
_2017_French_Win_32bit_dlm.sfx.exe
Autodesk AutoCAD LT 2017 French
_LT_2017_NWL_French_Win_64bit_dlm.sfx.exe
_LT_2017_French_LP_Win_64bit_dlm.sfx.exe
_LT_2017_NWL_French_Win_32bit_dlm.sfx.exe
_LT_2017_French_LP_Win_32bit_dlm.sfx.exe
Autodesk AutoCAD Architecture 2017 French
_Architecture_2017_French_Win_64bit_dlm_001_002.sfx.exe
_Architecture_2017_French_Win_64bit_dlm_002_002.sfx.exe
_Architecture_2017_French_Win_32bit_dlm_001_002.sfx.exe
_Architecture_2017_French_Win_32bit_dlm_002_002.sfx.exe
Autodesk AutoCAD Electrical 2017 French
_E/DLM/AutoCAD_Electrical_2017_French_Win_64bit_dlm_001_002.sfx.exe
_E/DLM/AutoCAD_Electrical_2017_French_Win_64bit_dlm_002_002.sfx.exe
_E/DLM/AutoCAD_Electrical_2017_French_Win_32bit_dlm_001_002.sfx.exe
_E/DLM/AutoCAD_Electrical_2017_French_Win_32bit_dlm_002_002.sfx.exe
Autodesk AutoCAD Mechanical 2017 French
_PP/DLM/AutoCAD_Mechanical_2017_French_Win_64bit_dlm_001_002.sfx.exe
_PP/DLM/AutoCAD_Mechanical_2017_French_Win_64bit_dlm_002_002.sfx.exe
_PP/DLM/AutoCAD_Mechanical_2017_French_Win_32bit_dlm.sfx.exe
Autodesk AutoCAD MEP 2017 French
_MEP_2017_French_Win_64bit_dlm_001_002.sfx.exe
_MEP_2017_French_Win_64bit_dlm_002_002.sfx.exe
_MEP_2017_French_Win_32bit_dlm_001_002.sfx.exe
_MEP_2017_French_Win_32bit_dlm_002_002.sfx.exe
Autodesk AutoCAD MAP 3D 2017 (x64 Only) French
_Map_2017_French_Win_64bit_DLM_001_002.sfx.exe
_Map_2017_French_Win_64bit_DLM_002_002.sfx.exe
Autodesk AutoCAD Plant 3D 2017 (x64 Only) French
_Plant_3D_2017_French_Win_64bit_dlm_001_002.sfx.exe
_Plant_3D_2017_French_Win_64bit_dlm_002_002.sfx.exe
Autodesk AutoCAD P&ID 2017 (x64 Only) French
_PNID_2017_French_Win_64bit_dlm_001_002.sfx.exe
_PNID_2017_French_Win_64bit_dlm_002_002.sfx.exe
Autodesk AutoCAD Raster Design 2017 French
_Raster_Design_2017_French_Win_64bit_dlm.sfx.exe
_Raster_Design_2017_French_Win_32bit_dlm.sfx.exe
Autodesk AutoCAD Civil 3D 2017 (x64 Only) French
_Civil3D_2017_French_Win_64bit_dlm_001_003.sfx.exe
_Civil3D_2017_French_Win_64bit_dlm_002_003.sfx.exe
_Civil3D_2017_French_Win_64bit_dlm_003_003.sfx.exe
Autodesk Inventor 2017 (X64 Only) French
_2017_French_Win_64bit_dlm_001_003.sfx.exe
_2017_French_Win_64bit_dlm_002_003.sfx.exe
_2017_French_Win_64bit_dlm_003_003.sfx.exe
Autodesk Inventor LT 2017 French
_LT_2017_French_Win_64bit_dlm_001_002.sfx.exe
_LT_2017_French_Win_64bit_dlm_002_002.sfx.exe
_LT_2017_French_Win_32bit_dlm.sfx.exe
Autodesk Revit 2017 (X64 Only) Non-Specific-Language (French included)
_Revit_2017_English_Win_64bit_dlm_001_002.sfx.exe
_Revit_2017_English_Win_64bit_dlm_002_002.sfx.exe
Offline Help Installers French
_max_2017_help/3dsMaxHelp_fra.exe
_2017_offline_help_installer/AutoCAD_2017_Product_Help_French_Win_32_64bit_dlm.sfx.exe
_lt_2017_offline_help/AutoCAD_LT_2017_Product_Help_French_Win_32_64bit_dlm.sfx.exe
_architecture_2017_product_help/AutoCAD_Architecture_Help_2017_French_Win_32_64bit_dlm.sfx.exe
_electrical_2017_help_download/AutoCAD_Electrical_2017_French_help_Win_32_64bit_dlm.sfx.exe
_mechanical_help_2017/AutoCAD_Mechanical_Help_2017_French_Win_32_64bit_dlm.sfx.exe
_map_3d_2017_product_help/Autodesk_AutoCAD_Map_3D_2017_Help_French.exe
_mep_2017_product_help/AutoCAD_MEP_Help_2017_French_Win_32_64bit_dlm.sfx.exe
_civil_3d_2017/Autodesk_AutoCAD_Civil_3D_2017_Help_French.exe
_and_lt_local_help/Autodesk_Inventor_2017_Help_FRA.exe
_and_lt_local_help/Autodesk_Inventor_LT_2017_Help_FRA.exe
Additional Notes:
How to get Autodesk Revit 2017 (X64 Only) Non-Specific Language in French language:
You must be vigilant when installing and well select the desired installation language before entering its serial number.
By chance, multiple languages are available after installing the new Revit 2017 software and can be changed.
In order to benefit from a new interface:
- Copy the Revit shortcut on your desktop
- Right click on the new icon and choose "Properties"
- In the "Target" field, simply change the last three letters of the line with three new ones: FRA
- FRA must be put in place of ENU.
... /Language=FRA
Autodesk Alias Design 2017
_Alias_Design_2017_English_Mac_OSX.dmg
ALIAS AutoStudio 2017
_Alias_AutoStudio_2017_English_Mac_OSX.dmg
Autodesk Alias Surface 2017
_Alias_Surface_2017_English_Mac_OSX.dmg
Autodesk Autocad Mechanicel German 64 Bit
_PP/DLM/AutoCAD_Mechanical_2017_German_Win_64bit_dlm_001_002.sfx.exe
_PP/DLM/AutoCAD_Mechanical_2017_German_Win_64bit_dlm_002_002.sfx.exe
Autodesk Raster Design 2017 German 64 Bit
_Raster_Design_2017_German_Win_64bit_dlm.sfx.exe
Autodesk Autocad 2017 German 32Bit 64Bit
_2017_German_Win_32bit_dlm.sfx.exe
_2017_German_Win_64bit_dlm_001_002.sfx.exe
_2017_German_Win_64bit_dlm_002_002.sfx.exe
Autodesk Inventor 2017 German 64 Bit
_2017_German_Win_64bit_dlm_001_003.sfx.exe
_2017_German_Win_64bit_dlm_002_003.sfx.exe
_2017_German_Win_64bit_dlm_003_003.sfx.exe
AutoCAD Architecture 2017 x64
_Architecture_2017_Italian_Win_64bit_dlm_001_002.sfx.exe
_Architecture_2017_Italian_Win_64bit_dlm_002_002.sfx.exe
AutoCAD LT 2017 x64/x86
_LT_2017_NWL_Italian_Win_64bit_dlm.sfx.exe
_LT_2017_NWL_Italian_Win_32bit_dlm.sfx.exe
_LT_2017_Italian_LP_Win_64bit_dlm.sfx.exe
_LT_2017_Italian_LP_Win_32bit_dlm.sfx.exe
AutoCAD 2017 x64/x86
_2017_Italian_Win_32bit_dlm.sfx.exe
_2017_Italian_Win_64bit_dlm_001_002.sfx.exe
_2017_Italian_Win_64bit_dlm_002_002.sfx.exe
AutoCAD Electrical 2017 x64
_E/DLM/AutoCAD_Electrical_2017_Italian_Win_64bit_dlm_001_002.sfx.exe
_E/DLM/AutoCAD_Electrical_2017_Italian_Win_64bit_dlm_002_002.sfx.exe
_E/DLM/AutoCAD_Electrical_2017_Italian_Win_32bit_dlm_001_002.sfx.exe
_E/DLM/AutoCAD_Electrical_2017_Italian_Win_32bit_dlm_002_002.sfx.exe
Autodesk AutoCAD Mechanical 2017
_PP/DLM/AutoCAD_Mechanical_2017_Italian_Win_64bit_dlm_001_002.sfx.exe
_PP/DLM/AutoCAD_Mechanical_2017_Italian_Win_64bit_dlm_002_002.sfx.exe
_PP/DLM/AutoCAD_Mechanical_2017_Italian_Win_32bit_dlm.sfx.exe
Autodesk AutoCAD MEP 2017
_MEP_2017_Italian_Win_64bit_dlm_001_002.sfx.exe
_MEP_2017_Italian_Win_64bit_dlm_002_002.sfx.exe
_MEP_2017_Italian_Win_32bit_dlm_001_002.sfx.exe
_MEP_2017_Italian_Win_32bit_dlm_002_002.sfx.exe
Autodesk AutoCAD MAP 3D 2017 x64
_Map_2017_Italian_Win_64bit_DLM_001_002.sfx.exe
_Map_2017_Italian_Win_64bit_DLM_002_002.sfx.exe
Autodesk AutoCAD Raster Design 2017
_Raster_Design_2017_Italian_Win_64bit_dlm.sfx.exe
_Raster_Design_2017_Italian_Win_32bit_dlm.sfx.exe
Autodesk Inventor 2017 X64
_2017_Italian_Win_64bit_dlm_001_003.sfx.exe
_2017_Italian_Win_64bit_dlm_002_003.sfx.exe
_2017_Italian_Win_64bit_dlm_003_003.sfx.exe
Autodesk Inventor LT 2017
_LT_2017_Italian_Win_64bit_dlm_001_002.sfx.exe
_LT_2017_Italian_Win_64bit_dlm_002_002.sfx.exe
_LT_2017_Italian_Win_32bit_dlm.sfx.exe
Offline Help Installers Italian
_2017_offline_help_installer/AutoCAD_2017_Product_Help_Italian_Win_32_64bit_dlm.sfx.exe
_lt_2017_offline_help/AutoCAD_LT_2017_Product_Help_Italian_Win_32_64bit_dlm.sfx.exe
_architecture_2017_product_help/AutoCAD_Architecture_Help_2017_Italian_Win_32_64bit_dlm.sfx.exe
_electrical_2017_help_download/AutoCAD_Electrical_2017_Italian_help_Win_32_64bit_dlm.sfx.exe
_mechanical_help_2017/AutoCAD_Mechanical_Help_2017_Italian_Win_32_64bit_dlm.sfx.exe
_mep_2017_product_help/AutoCAD_MEP_Help_2017_Italian_Win_32_64bit_dlm.sfx.exe
Product Design Suite 2017
_2017_Enu_Win_64bit_dlm_001_006.sfx.exe
_2017_Enu_Win_64bit_dlm_002_006.sfx.exe
_2017_Enu_Win_64bit_dlm_003_006.sfx.exe
_2017_Enu_Win_64bit_dlm_004_006.sfx.exe
_2017_Enu_Win_64bit_dlm_005_006.sfx.exe
_2017_Enu_Win_64bit_dlm_006_006.sfx.exe
AutoCAD Design Suite Ultimate 2017 English
_Ultimate_2017_English_Win_32bit_dlm_001_002.sfx.exe
_Ultimate_2017_English_Win_32bit_dlm_002_002.sfx.exe
_Ultimate_2017_English_Win_64bit_dlm_001_004.sfx.exe
_Ultimate_2017_English_Win_64bit_dlm_002_004.sfx.exe
_Ultimate_2017_English_Win_64bit_dlm_003_004.sfx.exe
_Ultimate_2017_English_Win_64bit_dlm_004_004.sfx.exe
Vault Professional 2017
_ENU_32_64bit_dlm.sfx.exe
_ENU_64bit_dlm.sfx.exe
Vault Workgroup 2017
_ENU_32_64bit_dlm.sfx.exe
_ENU_64bit_dlm.sfx.exe
Autodesk Advance Steel 2017
_2017_ML_WIN_64BIT_DLM.sfx.exe
Autodesk Vehicle Tracking English (32-64)bit 2017
_Vehicle_Tracking_2017_English_Win_32_64bit_DLM.sfx.exe
AutoCAD Raster Design 2017
_Raster_Design_2017_English_Win_32bit_dlm.sfx.exe
_Raster_Design_2017_English_Win_64bit_dlm.sfx.exe
Inventor 2017 local help:
_and_lt_local_help/Autodesk_Inventor_2017_Help.exe
Inventor 2017 sample files:
_sample_files/autodesk_inventor_2017_samples.sfx.exe
VRED Presenter 2017
_VREDPRS_2017_Enu_Win_64bit_dlm.sfx.exe
VRED Server 2017
_VREDSRV_2017_Enu_Win_64bit_dlm.sfx.exe
VRED 2017
_VRED_2017_Enu_Win_64bit_dlm.sfx.exe
VRED Design 2017
_VREDDES_2017_Enu_Win_64bit_dlm.sfx.exe
VRED Professional 2017
_VREDPRO_2017_Enu_Win_64bit_dlm.sfx.exe
AutoCAD Plant 3D 2017
_Plant_3D_2017_English_Win_64bit_dlm_001_002.sfx.exe
_Plant_3D_2017_English_Win_64bit_dlm_002_002.sfx.exe
Autodesk AutoCAD P&ID 2017
_PNID_2017_English_Win_64bit_dlm_001_002.sfx.exe
_PNID_2017_English_Win_64bit_dlm_002_002.sfx.exe
Mac_OSX Versions
Autodesk Alias Design 2017
_Alias_Design_2017_English_Mac_OSX.dmg
ALIAS AutoStudio 2017 for Mac
_Alias_AutoStudio_2017_English_Mac_OSX.dmg
Autodesk Alias Surface 2017
_Alias_Surface_2017_English_Mac_OSX.dmg
Autodesk Nastran In-CAD 2017
_INCAD_2017_Win_64bit_dlm.sfx.exe
Autodesk Nastran 2017
_2017_Win_64bit_dlm.sfx.exe
Autodesk_AutoCAD_Civil_3D_2017 Documentation
_civil_3d_2017/Autodesk_AutoCAD_Civil_3D_2017_Help_English.exe
Autodesk AutoCAD Civil 3D 2017
_Civil3D_2017_English_Win_64bit_dlm_001_003.sfx.exe
_Civil3D_2017_English_Win_64bit_dlm_002_003.sfx.exe
_Civil3D_2017_English_Win_64bit_dlm_003_003.sfx.exe
Infrastructure Design Suite Ultimate 2017 Win 64bit
_2017_Enu_Win_64bit_dlm_001_007.sfx.exe
_2017_Enu_Win_64bit_dlm_002_007.sfx.exe
_2017_Enu_Win_64bit_dlm_003_007.sfx.exe
_2017_Enu_Win_64bit_dlm_004_007.sfx.exe
_2017_Enu_Win_64bit_dlm_005_007.sfx.exe
_2017_Enu_Win_64bit_dlm_006_007.sfx.exe
_2017_Enu_Win_64bit_dlm_007_007.sfx.exe
Building Design Suite Ultimate 2017 Win 64bit
_2017_Enu_Win_64bit_dlm_001_007.sfx.exe
_2017_Enu_Win_64bit_dlm_002_007.sfx.exe
_2017_Enu_Win_64bit_dlm_003_007.sfx.exe
_2017_Enu_Win_64bit_dlm_004_007.sfx.exe
_2017_Enu_Win_64bit_dlm_005_007.sfx.exe
_2017_Enu_Win_64bit_dlm_006_007.sfx.exe
_2017_Enu_Win_64bit_dlm_007_007.sfx.exe
Documentation Alias 2017 Product Help
Online Help
Help Install Instructions
English
_2017_install_help/installing_autodesk_alias_2017_help.html
Japanese
_2017_install_help/JPN/JPN/installing_autodesk_alias_2017_help_jpn.html
Simplified Chinese
_2017_install_help/CHS/CHS/installing_autodesk_alias_2017_help_chs.html
Windows Help Installer
English
_2017_install_help/autodesk_alias_2017_help.exe
Japanese
_2017_install_help/JPN/JPN/alias_help_2017_jpn.exe
Simplified Chinese
_2017_install_help/CHS/CHS/alias_help_2017_chs.exe
Mac OS X Help Installer
English
_2017_install_help/autodesk_alias_2017_help.dmg
Japanese
_2017_install_help/JPN/JPN/AliasDocs2017_Japanese_Mac.dmg
Simplified Chinese
_2017_install_help/CHS/CHS/AliasDocs2017_Chinese_Mac.dmg
Learning Movies
Japanese
_2017_install_help/JPN/JPN/learningmovies_jpn.exe
Simplified Chinese
_2017_install_help/CHS/CHS/learningmovies_chs.exe
Factory Design Suite
_2017_Enu_Win_64bit_dlm_001_007.sfx.exe
_2017_Enu_Win_64bit_dlm_002_007.sfx.exe
_2017_Enu_Win_64bit_dlm_003_007.sfx.exe
_2017_Enu_Win_64bit_dlm_004_007.sfx.exe
_2017_Enu_Win_64bit_dlm_005_007.sfx.exe
_2017_Enu_Win_64bit_dlm_006_007.sfx.exe
_2017_Enu_Win_64bit_dlm_007_007.sfx.exe
Autodesk Revit 2017
_Revit_2017_English_Win_64bit_dlm_001_002.sfx.exe
_Revit_2017_English_Win_64bit_dlm_002_002.sfx.exe
Autodesk Revit LT 2017
_Revit_LT_2017_English_Win_64bit_dlm_001_002.sfx.exe
_Revit_LT_2017_English_Win_64bit_dlm_002_002.sfx.exe
Showcase 2017
_2017_English_Win_64bit_dlm_001_003.sfx.exe
_2017_English_Win_64bit_dlm_002_003.sfx.exe
_2017_English_Win_64bit_dlm_003_003.sfx.exe
CFD 2017
_CFD_2017_Win_64bit_dlm_001_002.sfx.exe
_CFD_2017_Win_64bit_dlm_002_002.sfx.exe
Simulation Mechanical 2017
_Simulation_Mechanical_2017_Win_64bit_dlm_001_002.sfx.exe
_Simulation_Mechanical_2017_Win_64bit_dlm_002_002.sfx.exe
Fabrication CADmep 2017
_Fabrication_CADmep_2017_win_64bit_dlm.sfx.exe
Fabrication CAMduct 2017
_Fabrication_CAMduct_2017_win_64bit_dlm.sfx.exe
Fabrication ESTmep 2017
_Fabrication_ESTmep_2017_win_64bit_dlm.sfx.exe
Autodesk InfraWorks 360 2017
_InfraWorks_2017_Win_64bit_DLM.sfx.exe
Point Layout 2017
_Point_Layout_2017_Win_32-64bit_en-us.exe
ReCap 360 Pro 2017
_ReCap360_30052_Multilingual_Win_64bit_dlm.sfx.exe
Alias Design 2017
_ALSDES_2017_Enu_64bit_dlm.sfx.exe
Alias Surface 2017
_ASURF_2017_Enu_64bit_dlm_001_002.sfx.exe
_ASURF_2017_Enu_64bit_dlm_002_002.sfx.exe
Alias Speedform 2017
_ALSSF_2017_Enu_Win_64bit_dlm.sfx.exe
Alias Autostudio 2017
_ALAUST_2017_Enu_64bit_dlm_001_003.sfx.exe
_ALAUST_2017_Enu_64bit_dlm_002_003.sfx.exe
_ALAUST_2017_Enu_64bit_dlm_003_003.sfx.exe
3ds Max 2017
_3ds_Max_2017_EFGJKPS_Win_64bit_001_002.sfx.exe
_3ds_Max_2017_EFGJKPS_Win_64bit_002_002.sfx.exe
Online Help for 3dsmax
3dsmax OFFLINE Help
_max_2017_help/3dsMaxHelp.exe
for other languages go to:
-max/downloads/caas/downloads/content/download-and-install-3ds-max-product-help.html
-general-discussion/apple-mac-os-10-11-x-el-capitan-is-not-supported/m-p/5983674#M6245
mental ray Plugin, Satellte and Standalone for Maya 2016 Extension 2 (Direct links)
Maya 2016.5 is a part of Alias AutoStudio 2017
Windows
_2016_extension_2/mentalray_Plugin_for_Maya_2016_EXT2_EN_JP_ZH_Win_64bit_dlm.sfx.exe
_2016_extension_2/mentalray_Satellite_3_13_1_for_Maya_2016_EN_JP_ZH_Win_64bit.exe
_2016_extension_2/mentalray_Standalone_3_13_1_for_Autodesk_2016_EN_Win_64bit.exe
Linux
_2016_extension_2/mentalray_Plugin_for_Maya_2016_EXT2_EN_Linux_64bit.tgz
_2016_extension_2/mentalray_Satellite_3_13_1_for_Maya_2016_EN_Linux_64bit.tgz
_2016_extension_2/mentalray_Standalone_3_13_1_for_Autodesk_2016_EN_Linux_64bit.tgz
OSX
_2016_extension_2/mentalray_Plugin_for_Maya_2016_EXT2_EN_JP_ZH_Mac_OSX.dmg
_2016_extension_2/mentalray_Satellite_3_13_1_for_Maya_2016_EN_JP_ZH_Mac_OSX.dmg
_2016_extension_2/mentalray_Standalone_3_13_1_for_Autodesk_2016_EN_Mac_OSX.dmg
Offline help for Autodesk Maya 2016 Extension 2
_2016/MayaHelp2016_Ext2_enu.zip
Autodesk 3ds Max 2017 Sample Files
_sample_files/2017/Autodesk_3ds_Max_2017_English_Win_Samples_Files.exe
Open Light 2017 (32-bit and 64-bit)
Applies to AutoCAD Architecture 2017, and AutoCAD MEP 2017 (32-bit and 64-bit)
Open Light is a plug-in for AutoCAD Architecture / MEP and offers standard labels for objects, such as openings, windows and doors, which are common in Austria and part of Switzerland.
Open Light provides additional display properties for Plan 1-50 and Plan 1-100 representation to show dimensions of doors and windows automatically.
_downloads/Open_Light_2017_x64.exe
_downloads/Open_Light_2017.exe
Open Light 2017 Object Enabler (32-bit and 64-bit)
Applies to AutoCAD 2017, AutoCAD Architecture 2017, AutoCAD Civil 3D 2017, AutoCAD Electrical 2017, AutoCAD MEP 2017, AutoCAD Map 3D 2017, and AutoCAD Mechanical 2017Open Light Object Enabler is a freeware application distributed to Autodesk customers at no charge for the purpose of fully accessing Open Light objects in drawing files. Without this object enabler installed, you can share drawings using proxy graphics representations or the Export to AutoCAD command.
_downloads/Open_Light_2017_OE_x64.exe
_downloads/Open_Light_2017_OE.exe
Building Design Suite Premium 2017
_2017_Enu_Win_64bit_dlm_001_006.sfx.exe
_2017_Enu_Win_64bit_dlm_002_006.sfx.exe
_2017_Enu_Win_64bit_dlm_003_006.sfx.exe
_2017_Enu_Win_64bit_dlm_004_006.sfx.exe
_2017_Enu_Win_64bit_dlm_005_006.sfx.exe
_2017_Enu_Win_64bit_dlm_006_006.sfx.exe
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-CarX Drift Racing 2 is a sequel to the popular CarX Drift Racing game, which has over 50 million downloads on Google Play Store. In this game, you can choose from hundreds of cars, customize them, and drift on various tracks with realistic physics and graphics.
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In this article, we will tell you everything you need to know about CarX Drift Racing 2, why you should download its mod apk version, and some tips and tricks to improve your drifting skills.
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Bubble Shooter has a long and interesting history that dates back to the 1980s. The game was inspired by two arcade games: Bubble Bobble, released by Taito in 1986, and Puzzle Bobble, also known as Bust-a-Move, released by Taito in 1994. Puzzle Bobble was the first game to feature the bubble shooting mechanic that became the core of Bubble Shooter. In 2000, Puzzle Bobble was ported to Windows and renamed as Bubble Shooter. Since then, the game has been adapted and modified by many developers and publishers, resulting in hundreds of variations and versions of Bubble Shooter.
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-
''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for]
- elif(option == "person"):
- instance_prompt_example = "julcto"
- freeze_for = 100
- return [f"You are going to train a `person`(s), upload 10-20 images of each person you are planning on training on from different angles/perspectives. {mandatory_liability}:", '''
''', f"You should name the files with a unique word that represent your concept (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for]
- elif(option == "style"):
- instance_prompt_example = "trsldamrl"
- freeze_for = 10
- return [f"You are going to train a `style`, upload 10-20 images of the style you are planning on training on. Name the files with the words you would like {mandatory_liability}:", '''
''', f"You should name your files with a unique word that represent your concept (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for]
-
-def count_files(*inputs):
- file_counter = 0
- concept_counter = 0
- for i, input in enumerate(inputs):
- if(i < maximum_concepts-1):
- files = inputs[i]
- if(files):
- concept_counter+=1
- file_counter+=len(files)
- uses_custom = inputs[-1]
- type_of_thing = inputs[-4]
- if(uses_custom):
- Training_Steps = int(inputs[-3])
- else:
- if(type_of_thing == "person"):
- Training_Steps = file_counter*200*2
- else:
- Training_Steps = file_counter*200
- return(gr.update(visible=True, value=f"You are going to train {concept_counter} {type_of_thing}(s), with {file_counter} images for {Training_Steps} steps. This should take around {round(Training_Steps/1.5, 2)} seconds, or {round((Training_Steps/1.5)/3600, 2)} hours. As a reminder, the T4 GPU costs US$0.60 for 1h. Once training is over, don't forget to swap the hardware back to CPU."))
-
-def train(*inputs):
- if "IS_SHARED_UI" in os.environ:
- raise gr.Error("This Space only works in duplicated instances")
- if os.path.exists("output_model"): shutil.rmtree('output_model')
- if os.path.exists("instance_images"): shutil.rmtree('instance_images')
- if os.path.exists("diffusers_model.zip"): os.remove("diffusers_model.zip")
- if os.path.exists("model.ckpt"): os.remove("model.ckpt")
- file_counter = 0
- for i, input in enumerate(inputs):
- if(i < maximum_concepts-1):
- if(input):
- os.makedirs('instance_images',exist_ok=True)
- files = inputs[i+(maximum_concepts*2)]
- prompt = inputs[i+maximum_concepts]
- if(prompt == "" or prompt == None):
- raise gr.Error("You forgot to define your concept prompt")
- for j, file_temp in enumerate(files):
- file = Image.open(file_temp.name)
- width, height = file.size
- side_length = min(width, height)
- left = (width - side_length)/2
- top = (height - side_length)/2
- right = (width + side_length)/2
- bottom = (height + side_length)/2
- image = file.crop((left, top, right, bottom))
- image = image.resize((512, 512))
- extension = file_temp.name.split(".")[1]
- image = image.convert('RGB')
- image.save(f'instance_images/{prompt}_({j+1}).jpg', format="JPEG", quality = 100)
- file_counter += 1
-
- os.makedirs('output_model',exist_ok=True)
- uses_custom = inputs[-1]
- type_of_thing = inputs[-4]
- if(uses_custom):
- Training_Steps = int(inputs[-3])
- Train_text_encoder_for = int(inputs[-2])
- else:
- Training_Steps = file_counter*200
- if(type_of_thing == "object"):
- Train_text_encoder_for=30
- elif(type_of_thing == "person"):
- Train_text_encoder_for=60
- elif(type_of_thing == "style"):
- Train_text_encoder_for=15
-
- class_data_dir = None
- stptxt = int((Training_Steps*Train_text_encoder_for)/100)
- args_general = argparse.Namespace(
- image_captions_filename = True,
- train_text_encoder = True,
- stop_text_encoder_training = stptxt,
- save_n_steps = 0,
- pretrained_model_name_or_path = model_to_load,
- instance_data_dir="instance_images",
- class_data_dir=class_data_dir,
- output_dir="output_model",
- instance_prompt="",
- seed=42,
- resolution=512,
- mixed_precision="fp16",
- train_batch_size=1,
- gradient_accumulation_steps=1,
- use_8bit_adam=True,
- learning_rate=2e-6,
- lr_scheduler="polynomial",
- lr_warmup_steps = 0,
- max_train_steps=Training_Steps,
- )
- run_training(args_general)
- torch.cuda.empty_cache()
- #convert("output_model", "model.ckpt")
- #shutil.rmtree('instance_images')
- #shutil.make_archive("diffusers_model", 'zip', "output_model")
- with zipfile.ZipFile('diffusers_model.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:
- zipdir('output_model/', zipf)
- torch.cuda.empty_cache()
- return [gr.update(visible=True, value=["diffusers_model.zip"]), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)]
-
-def generate(prompt):
- from diffusers import StableDiffusionPipeline
-
- pipe = StableDiffusionPipeline.from_pretrained("./output_model", torch_dtype=torch.float16)
- pipe = pipe.to("cuda")
- image = pipe(prompt).images[0]
- return(image)
-
-def push(model_name, where_to_upload, hf_token):
- if(not os.path.exists("model.ckpt")):
- convert("output_model", "model.ckpt")
- from huggingface_hub import HfApi, HfFolder, CommitOperationAdd
- from huggingface_hub import create_repo
- model_name_slug = slugify(model_name)
- if(where_to_upload == "My personal profile"):
- api = HfApi()
- your_username = api.whoami(token=hf_token)["name"]
- model_id = f"{your_username}/{model_name_slug}"
- else:
- model_id = f"sd-dreambooth-library/{model_name_slug}"
- headers = {"Authorization" : f"Bearer: {hf_token}", "Content-Type": "application/json"}
- response = requests.post("https://huggingface.co/organizations/sd-dreambooth-library/share/SSeOwppVCscfTEzFGQaqpfcjukVeNrKNHX", headers=headers)
-
- images_upload = os.listdir("instance_images")
- image_string = ""
- instance_prompt_list = []
- previous_instance_prompt = ''
- for i, image in enumerate(images_upload):
- instance_prompt = image.split("_")[0]
- if(instance_prompt != previous_instance_prompt):
- title_instance_prompt_string = instance_prompt
- instance_prompt_list.append(instance_prompt)
- else:
- title_instance_prompt_string = ''
- previous_instance_prompt = instance_prompt
- image_string = f'''{title_instance_prompt_string}
-{image_string}'''
- readme_text = f'''---
-license: creativeml-openrail-m
-tags:
-- text-to-image
----
-### {model_name} Dreambooth model trained by {api.whoami(token=hf_token)["name"]} with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training)
-
-You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
-
-Sample pictures of this concept:
-{image_string}
-'''
- #Save the readme to a file
- readme_file = open("README.md", "w")
- readme_file.write(readme_text)
- readme_file.close()
- #Save the token identifier to a file
- text_file = open("token_identifier.txt", "w")
- text_file.write(', '.join(instance_prompt_list))
- text_file.close()
- create_repo(model_id,private=True, token=hf_token)
- operations = [
- CommitOperationAdd(path_in_repo="token_identifier.txt", path_or_fileobj="token_identifier.txt"),
- CommitOperationAdd(path_in_repo="README.md", path_or_fileobj="README.md"),
- CommitOperationAdd(path_in_repo=f"model.ckpt",path_or_fileobj="model.ckpt")
- ]
- api.create_commit(
- repo_id=model_id,
- operations=operations,
- commit_message=f"Upload the model {model_name}",
- token=hf_token
- )
- api.upload_folder(
- folder_path="output_model",
- repo_id=model_id,
- token=hf_token
- )
- api.upload_folder(
- folder_path="instance_images",
- path_in_repo="concept_images",
- repo_id=model_id,
- token=hf_token
- )
- return [gr.update(visible=True, value=f"Successfully uploaded your model. Access it [here](https://huggingface.co/{model_id})"), gr.update(visible=True, value=["diffusers_model.zip", "model.ckpt"])]
-
-def convert_to_ckpt():
- convert("output_model", "model.ckpt")
- return gr.update(visible=True, value=["diffusers_model.zip", "model.ckpt"])
-
-with gr.Blocks(css=css) as demo:
- with gr.Box():
- if "IS_SHARED_UI" in os.environ:
- gr.HTML('''
- For it to work, you have to duplicate the Space and run it on your own profile where a (paid) private GPU will be attributed to it during runtime. As each T4 costs US$0,60/h, it should cost < US$1 to train a model with less than 100 images on default settings!
-
- 
- If you haven't already, attribute a T4 GPU to it (via the Settings tab) and run the training below. You will be billed by the minute from when you activate the GPU until when you turn it off.
-''')
- things_naming = gr.Markdown("You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `cttoy` here). Images will be automatically cropped to 512x512.")
- with gr.Column():
- file_collection = []
- concept_collection = []
- buttons_collection = []
- delete_collection = []
- is_visible = []
-
- row = [None] * maximum_concepts
- for x in range(maximum_concepts):
- ordinal = lambda n: "%d%s" % (n, "tsnrhtdd"[(n // 10 % 10 != 1) * (n % 10 < 4) * n % 10::4])
- if(x == 0):
- visible = True
- is_visible.append(gr.State(value=True))
- else:
- visible = False
- is_visible.append(gr.State(value=False))
-
- file_collection.append(gr.File(label=f"Upload the images for your {ordinal(x+1)} concept", file_count="multiple", interactive=True, visible=visible))
- with gr.Column(visible=visible) as row[x]:
- concept_collection.append(gr.Textbox(label=f"{ordinal(x+1)} concept prompt - use a unique, made up word to avoid collisions"))
- with gr.Row():
- if(x < maximum_concepts-1):
- buttons_collection.append(gr.Button(value="Add +1 concept", visible=visible))
- if(x > 0):
- delete_collection.append(gr.Button(value=f"Delete {ordinal(x+1)} concept"))
-
- counter_add = 1
- for button in buttons_collection:
- if(counter_add < len(buttons_collection)):
- button.click(lambda:
- [gr.update(visible=True),gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), True, None],
- None,
- [row[counter_add], file_collection[counter_add], buttons_collection[counter_add-1], buttons_collection[counter_add], is_visible[counter_add], file_collection[counter_add]], queue=False)
- else:
- button.click(lambda:[gr.update(visible=True),gr.update(visible=True), gr.update(visible=False), True], None, [row[counter_add], file_collection[counter_add], buttons_collection[counter_add-1], is_visible[counter_add]], queue=False)
- counter_add += 1
-
- counter_delete = 1
- for delete_button in delete_collection:
- if(counter_delete < len(delete_collection)+1):
- delete_button.click(lambda:[gr.update(visible=False),gr.update(visible=False), gr.update(visible=True), False], None, [file_collection[counter_delete], row[counter_delete], buttons_collection[counter_delete-1], is_visible[counter_delete]], queue=False)
- counter_delete += 1
-
-
-
- with gr.Accordion("Custom Settings", open=False):
- swap_auto_calculated = gr.Checkbox(label="Use custom settings")
- gr.Markdown("If not checked, the number of steps and % of frozen encoder will be tuned automatically according to the amount of images you upload and whether you are training an `object`, `person` or `style` as follows: The number of steps is calculated by number of images uploaded multiplied by 20. The text-encoder is frozen after 10% of the steps for a style, 30% of the steps for an object and is fully trained for persons.")
- steps = gr.Number(label="How many steps", value=800)
- perc_txt_encoder = gr.Number(label="Percentage of the training steps the text-encoder should be trained as well", value=30)
-
- type_of_thing.change(fn=swap_text, inputs=[type_of_thing], outputs=[thing_description, thing_image_example, things_naming, perc_txt_encoder], queue=False)
- training_summary = gr.Textbox("", visible=False, label="Training Summary")
- steps.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary], queue=False)
- perc_txt_encoder.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary], queue=False)
- for file in file_collection:
- file.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary], queue=False)
- train_btn = gr.Button("Start Training")
- with gr.Box(visible=False) as try_your_model:
- gr.Markdown("## Try your model")
- with gr.Row():
- prompt = gr.Textbox(label="Type your prompt")
- result_image = gr.Image()
- generate_button = gr.Button("Generate Image")
- with gr.Box(visible=False) as push_to_hub:
- gr.Markdown("## Push to Hugging Face Hub")
- model_name = gr.Textbox(label="Name of your model", placeholder="Tarsila do Amaral Style")
- where_to_upload = gr.Dropdown(["My personal profile", "Public Library"], label="Upload to")
- gr.Markdown("[A Hugging Face write access token](https://huggingface.co/settings/tokens), go to \"New token\" -> Role : Write. A regular read token won't work here.")
- hf_token = gr.Textbox(label="Hugging Face Write Token")
- push_button = gr.Button("Push to the Hub")
- result = gr.File(label="Download the uploaded models in the diffusers format", visible=True)
- success_message_upload = gr.Markdown(visible=False)
- convert_button = gr.Button("Convert to CKPT", visible=False)
-
- train_btn.click(fn=train, inputs=is_visible+concept_collection+file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[result, try_your_model, push_to_hub, convert_button])
- generate_button.click(fn=generate, inputs=prompt, outputs=result_image)
- push_button.click(fn=push, inputs=[model_name, where_to_upload, hf_token], outputs=[success_message_upload, result])
- convert_button.click(fn=convert_to_ckpt, inputs=[], outputs=result)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/AONYLMR/anime-remove-background/README.md b/spaces/AONYLMR/anime-remove-background/README.md
deleted file mode 100644
index 1ba3cb5ea0e994e246d57b7d62b8aa5a6331901c..0000000000000000000000000000000000000000
--- a/spaces/AONYLMR/anime-remove-background/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Anime Remove Background
-emoji: 🪄🖼️
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: skytnt/anime-remove-background
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/work_dirs/mobilenet-v2_4xb32_2000e_3c_noF/mobilenet-v2_1xb32_300e_3c_noF.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/work_dirs/mobilenet-v2_4xb32_2000e_3c_noF/mobilenet-v2_1xb32_300e_3c_noF.py
deleted file mode 100644
index 8fb558c161d85f81f3dc13d6551359f923e008c8..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/work_dirs/mobilenet-v2_4xb32_2000e_3c_noF/mobilenet-v2_1xb32_300e_3c_noF.py
+++ /dev/null
@@ -1,140 +0,0 @@
-model = dict(
- type='ImageClassifier',
- backbone=dict(type='MobileNetV2', widen_factor=1.0),
- neck=dict(type='GlobalAveragePooling'),
- head=dict(
- type='LinearClsHead',
- num_classes=7,
- in_channels=1280,
- loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
- topk=(
- 1,
- 3,
- )))
-dataset_type = 'CustomDataset'
-data_preprocessor = dict(
- num_classes=7,
- mean=[
- 123.675,
- 116.28,
- 103.53,
- ],
- std=[
- 58.395,
- 57.12,
- 57.375,
- ],
- to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='RandomResizedCrop', scale=224, backend='pillow'),
- dict(type='RandomFlip', prob=0.5, direction='horizontal'),
- dict(type='PackInputs'),
-]
-val_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
- dict(type='CenterCrop', crop_size=224),
- dict(type='PackInputs'),
-]
-train_dataloader = dict(
- pin_memory=True,
- persistent_workers=True,
- collate_fn=dict(type='default_collate'),
- batch_size=32,
- num_workers=5,
- dataset=dict(
- type='CustomDataset',
- data_root='data',
- with_label=True,
- ann_file='',
- data_prefix='train',
- pipeline=[
- dict(type='LoadImageFromFile'),
- dict(type='RandomResizedCrop', scale=224, backend='pillow'),
- dict(type='RandomFlip', prob=0.5, direction='horizontal'),
- dict(type='PackInputs'),
- ]),
- sampler=dict(type='DefaultSampler', shuffle=True))
-val_dataloader = dict(
- pin_memory=True,
- persistent_workers=True,
- collate_fn=dict(type='default_collate'),
- batch_size=32,
- num_workers=5,
- dataset=dict(
- type='CustomDataset',
- data_root='data',
- with_label=True,
- ann_file='',
- data_prefix='val',
- pipeline=[
- dict(type='LoadImageFromFile'),
- dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
- dict(type='CenterCrop', crop_size=224),
- dict(type='PackInputs'),
- ]),
- sampler=dict(type='DefaultSampler', shuffle=False))
-val_evaluator = dict(
- type='Accuracy', topk=(
- 1,
- 3,
- ))
-test_dataloader = dict(
- pin_memory=True,
- persistent_workers=True,
- collate_fn=dict(type='default_collate'),
- batch_size=32,
- num_workers=5,
- dataset=dict(
- type='CustomDataset',
- data_root='data',
- with_label=True,
- ann_file='',
- data_prefix='val',
- pipeline=[
- dict(type='LoadImageFromFile'),
- dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
- dict(type='CenterCrop', crop_size=224),
- dict(type='PackInputs'),
- ]),
- sampler=dict(type='DefaultSampler', shuffle=False))
-test_evaluator = dict(
- type='Accuracy', topk=(
- 1,
- 3,
- ))
-optim_wrapper = dict(
- optimizer=dict(type='SGD', lr=0.045, momentum=0.9, weight_decay=4e-05))
-param_scheduler = dict(type='StepLR', by_epoch=True, step_size=10, gamma=0.98)
-train_cfg = dict(by_epoch=True, max_epochs=2000, val_interval=10)
-val_cfg = dict()
-test_cfg = dict()
-auto_scale_lr = dict(base_batch_size=256)
-default_scope = 'mmpretrain'
-default_hooks = dict(
- timer=dict(type='IterTimerHook'),
- logger=dict(type='LoggerHook', interval=10),
- param_scheduler=dict(type='ParamSchedulerHook'),
- checkpoint=dict(type='CheckpointHook', save_best='auto', interval=10),
- sampler_seed=dict(type='DistSamplerSeedHook'),
- visualization=dict(type='VisualizationHook', enable=False))
-env_cfg = dict(
- cudnn_benchmark=False,
- mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
- dist_cfg=dict(backend='nccl'))
-vis_backends = [
- dict(type='LocalVisBackend'),
-]
-visualizer = dict(
- type='UniversalVisualizer',
- vis_backends=[
- dict(type='LocalVisBackend'),
- dict(type='WandbVisBackend'),
- ])
-log_level = 'INFO'
-load_from = None
-resume = False
-randomness = dict(seed=None, deterministic=False)
-launcher = 'pytorch'
-work_dir = 'work_dirs/mobilenet-v2_4xb32_2000e_3c_noF'
diff --git a/spaces/AchyuthGamer/Free-Accounts-Generator/fortnite/js/d140ouchebag.js b/spaces/AchyuthGamer/Free-Accounts-Generator/fortnite/js/d140ouchebag.js
deleted file mode 100644
index 22e47516d286946ca7583d99ce7a04c43bf30955..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/Free-Accounts-Generator/fortnite/js/d140ouchebag.js
+++ /dev/null
@@ -1,65 +0,0 @@
-var NumberOfWords = 45
-var words = new BuildArray(NumberOfWords)
-
-// Use the following variables to
-// define your random words:
-words[1] = "https://cuty.io/NVohC0B"
-words[2] = "https://cuty.io/ApmaP7LV"
-words[3] = "https://cuty.io/fnacc3"
-words[4] = "https://cuty.io/5xDANQ5"
-words[5] = "https://cuty.io/fnacc5"
-words[6] = "https://cuty.io/qgeg"
-words[7] = "https://cuty.io/fnacc7"
-words[8] = "https://cuty.io/fnacc8"
-words[9] = "https://cuty.io/fnacc9"
-words[10] = "https://cuty.io/utMTVJooF"
-words[11] = "https://cuty.io/b39f"
-words[12] = "https://cuty.io/fnacc12"
-words[13] = "https://cuty.io/szZEPhy78v"
-words[14] = "https://cuty.io/fnacc14"
-words[15] = "https://cuty.io/eUaQe"
-words[16] = "https://cuty.io/VRUGIe"
-words[17] = "https://cuty.io/l6wa"
-words[18] = "https://cuty.io/WnlwopvX"
-words[19] = "https://cuty.io/sHMps1"
-words[20] = "https://cuty.io/j0Am8PZnBKkg"
-words[21] = "https://cuty.io/gT2uasHcl"
-words[22] = "https://cuty.io/UVRGq1f"
-words[23] = "https://cuty.io/six3gSRXEll"
-words[24] = "https://cuty.io/eDLT"
-words[25] = "https://cuty.io/pSvYxDQKV1NV"
-words[26] = "https://cuty.io/GNJniEyoC4"
-words[27] = "https://cuty.io/Hr3cPonuhQ"
-words[28] = "https://cuty.io/QGEzeBeD"
-words[29] = "https://cuty.io/b0apHN"
-words[30] = ""
-words[31] = ""
-words[32] = "https://cuty.io/OWtYHuEyL"
-words[33] = ""
-words[34] = "https://cuty.io/kQRXj"
-words[35] = ""
-words[36] = "https://cuty.io/CAJtlKvjX"
-words[37] = "https://cuty.io/PwMVd"
-words[38] = ""
-words[39] = "https://cuty.io/U4wgd"
-words[40] = ""
-words[41] = "https://cuty.io/SwTU5"
-words[42] = "https://cuty.io/r5Hryv6IV2Eh"
-words[43] = ""
-words[44] = "https://cuty.io/EuxDqLR0oFT"
-words[45] = "https://cuty.io/lflibkkVkK"
-
-function BuildArray(size){
-this.length = size
-for (var i = 1; i <= size; i++){
-this[i] = null}
-return this
-}
-
-function PickRandomWord(frm) {
-// Generate a random number between 1 and NumberOfWords
-var rnd = Math.ceil(Math.random() * NumberOfWords)
-
-// Display the word inside the text box
-frm.WordBox.value = words[rnd]
-}
\ No newline at end of file
diff --git a/spaces/AchyuthGamer/OpenGPT/client/css/select.css b/spaces/AchyuthGamer/OpenGPT/client/css/select.css
deleted file mode 100644
index 7ec0159206439deca5c26f32fd92d2b1459f0273..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/client/css/select.css
+++ /dev/null
@@ -1,35 +0,0 @@
-select {
- -webkit-border-radius: 8px;
- -moz-border-radius: 8px;
- border-radius: 8px;
-
- -webkit-backdrop-filter: blur(20px);
- backdrop-filter: blur(20px);
-
- cursor: pointer;
- background-color: var(--blur-bg);
- border: 1px solid var(--blur-border);
- color: var(--colour-3);
- display: block;
- position: relative;
- overflow: hidden;
- outline: none;
- padding: 8px 16px;
-
- appearance: none;
-}
-
-/* scrollbar */
-select.dropdown::-webkit-scrollbar {
- width: 4px;
- padding: 8px 0px;
-}
-
-select.dropdown::-webkit-scrollbar-track {
- background-color: #ffffff00;
-}
-
-select.dropdown::-webkit-scrollbar-thumb {
- background-color: #555555;
- border-radius: 10px;
-}
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/__init__.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/__init__.py
deleted file mode 100644
index 5c66c87fa30e77def4d61737299ce32be3b6de9f..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from .AiService import AiService
-from .CodeLinkAva import CodeLinkAva
-from .DfeHub import DfeHub
-from .EasyChat import EasyChat
-from .Forefront import Forefront
-from .GetGpt import GetGpt
-from .Opchatgpts import Opchatgpts
-from .Lockchat import Lockchat
-from .Wewordle import Wewordle
-from .Equing import Equing
-from .Wuguokai import Wuguokai
-from .V50 import V50
-from .FastGpt import FastGpt
-from .ChatgptLogin import ChatgptLogin
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/dataloader/mgsm.py b/spaces/AgentVerse/agentVerse/dataloader/mgsm.py
deleted file mode 100644
index 0ce3adc7078c41082f5d29cfbbc9dd0fd8537023..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/dataloader/mgsm.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from .dataloader import DataLoader
-from . import dataloader_registry
-import json
-import re
-
-
-@dataloader_registry.register("tasksolving/mgsm/gpt-4")
-@dataloader_registry.register("tasksolving/mgsm/gpt-3.5")
-class MGSMLoader(DataLoader):
- def __init__(self, path: str):
- self.answer_pat = re.compile(r"#### (-?\d+)")
- super().__init__(path)
-
- def load(self):
- with open(self.path) as f:
- for line in f:
- line = json.loads(line)
- self.examples.append(
- {
- "input": line["question"],
- "answer": line["answer_number"],
- }
- )
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/knob/input/OnPanPad.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/knob/input/OnPanPad.js
deleted file mode 100644
index 801c85df260b080efd476440bea031184eca34c7..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/knob/input/OnPanPad.js
+++ /dev/null
@@ -1,90 +0,0 @@
-import IsLocalPointInKnob from './IsLocalPointInKnob.js';
-
-const GetAngle = Phaser.Math.Angle.Between;
-const WrapAngle = Phaser.Math.Angle.Wrap;
-
-var OnPointerDown = function (pointer, localX, localY) {
- if ((!this.enable) || (this.panPointer)) {
- return;
- }
- var knob = this.sizerChildren.knob;
- if (!IsLocalPointInKnob(knob, localX, localY)) {
- return;
- }
-
- OnPanStart.call(this, pointer);
-}
-
-var OnPointerMove = function (pointer, localX, localY) {
- if (!this.enable) {
- return;
- }
- if (!pointer.isDown) {
- return;
- }
-
- var knob = this.sizerChildren.knob;
- switch (this.panState) {
- case TOUCH0:
- if (IsLocalPointInKnob(knob, localX, localY)) {
- OnPanStart.call(this, pointer);
- }
- break;
-
- case TOUCH1:
- if (IsLocalPointInKnob(knob, localX, localY)) {
- OnPan.call(this);
- } else {
- OnPanEnd.call(this);
- }
- break;
- }
-}
-
-var OnPointerUp = function (pointer, localX, localY) {
- if ((!this.enable) || (this.panPointer !== pointer)) {
- return;
- }
-
- OnPanEnd.call(this);
-}
-
-var OnPanStart = function (pointer) {
- this.panPointer = pointer;
- this.panState = TOUCH1;
-}
-
-var OnPanEnd = function () {
- this.panPointer = undefined;
- this.panState = TOUCH0;
-}
-
-var OnPan = function () {
- var p0 = this.panPointer.prevPosition,
- p1 = this.panPointer.position;
- var knob = this.sizerChildren.knob;
- var startAngle = GetAngle(knob.x, knob.y, p0.x, p0.y),
- endAngle = GetAngle(knob.x, knob.y, p1.x, p1.y);
- var deltaAngle = (knob.anticlockwise) ? (startAngle - endAngle) : (endAngle - startAngle);
- var deltaValue = WrapAngle(deltaAngle) / (Math.PI * 2);
-
- this.stopEaseValue();
- this.value += deltaValue;
-}
-
-const TOUCH0 = 0;
-const TOUCH1 = 1;
-
-var InstallEvents = function () {
- var knob = this.sizerChildren.knob;
- knob
- .on('pointerdown', OnPointerDown, this)
- .on('pointermove', OnPointerMove, this)
- .on('pointerup', OnPointerUp, this)
- .setInteractive()
-
- this.panPointer = undefined;
- this.panState = TOUCH0;
-}
-
-export default InstallEvents;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/utils/CreateAnySizer.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/utils/CreateAnySizer.js
deleted file mode 100644
index 9234ba9f8a906ba82809cc9d1293c93c101955f7..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/utils/CreateAnySizer.js
+++ /dev/null
@@ -1,30 +0,0 @@
-import MergeStyle from './MergeStyle.js';
-import ReplaceChildrenConfig from './ReplaceChildrenConfig.js';
-
-var CreateAnySizer = function (scene, data, view, styles, customBuilders, SizerClass) {
- data = MergeStyle(data, styles);
-
- var backgroundConfig = ReplaceChildrenConfig(scene, data.background, view, styles, customBuilders);
- var childrenConfig = ReplaceChildrenConfig(scene, data.children, view, styles, customBuilders);
-
- var gameObject = new SizerClass(scene, data);
- scene.add.existing(gameObject);
-
- if (backgroundConfig) {
- for (var i = 0, cnt = backgroundConfig.length; i < cnt; i++) {
- var childConfig = backgroundConfig[i];
- gameObject.addBackground(childConfig.$child, childConfig.padding);
- }
- }
-
- if (childrenConfig) {
- for (var i = 0, cnt = childrenConfig.length; i < cnt; i++) {
- var childConfig = childrenConfig[i];
- gameObject.add(childConfig.$child, childConfig);
- }
- }
-
- return gameObject;
-}
-
-export default CreateAnySizer;
\ No newline at end of file
diff --git a/spaces/Aki004/herta-so-vits/modules/crepe.py b/spaces/Aki004/herta-so-vits/modules/crepe.py
deleted file mode 100644
index b58c1680d02fef54497c36bd47a36776cc7f6af5..0000000000000000000000000000000000000000
--- a/spaces/Aki004/herta-so-vits/modules/crepe.py
+++ /dev/null
@@ -1,331 +0,0 @@
-from typing import Optional,Union
-try:
- from typing import Literal
-except Exception as e:
- from typing_extensions import Literal
-import numpy as np
-import torch
-import torchcrepe
-from torch import nn
-from torch.nn import functional as F
-import scipy
-
-#from:https://github.com/fishaudio/fish-diffusion
-
-def repeat_expand(
- content: Union[torch.Tensor, np.ndarray], target_len: int, mode: str = "nearest"
-):
- """Repeat content to target length.
- This is a wrapper of torch.nn.functional.interpolate.
-
- Args:
- content (torch.Tensor): tensor
- target_len (int): target length
- mode (str, optional): interpolation mode. Defaults to "nearest".
-
- Returns:
- torch.Tensor: tensor
- """
-
- ndim = content.ndim
-
- if content.ndim == 1:
- content = content[None, None]
- elif content.ndim == 2:
- content = content[None]
-
- assert content.ndim == 3
-
- is_np = isinstance(content, np.ndarray)
- if is_np:
- content = torch.from_numpy(content)
-
- results = torch.nn.functional.interpolate(content, size=target_len, mode=mode)
-
- if is_np:
- results = results.numpy()
-
- if ndim == 1:
- return results[0, 0]
- elif ndim == 2:
- return results[0]
-
-
-class BasePitchExtractor:
- def __init__(
- self,
- hop_length: int = 512,
- f0_min: float = 50.0,
- f0_max: float = 1100.0,
- keep_zeros: bool = True,
- ):
- """Base pitch extractor.
-
- Args:
- hop_length (int, optional): Hop length. Defaults to 512.
- f0_min (float, optional): Minimum f0. Defaults to 50.0.
- f0_max (float, optional): Maximum f0. Defaults to 1100.0.
- keep_zeros (bool, optional): Whether keep zeros in pitch. Defaults to True.
- """
-
- self.hop_length = hop_length
- self.f0_min = f0_min
- self.f0_max = f0_max
- self.keep_zeros = keep_zeros
-
- def __call__(self, x, sampling_rate=44100, pad_to=None):
- raise NotImplementedError("BasePitchExtractor is not callable.")
-
- def post_process(self, x, sampling_rate, f0, pad_to):
- if isinstance(f0, np.ndarray):
- f0 = torch.from_numpy(f0).float().to(x.device)
-
- if pad_to is None:
- return f0
-
- f0 = repeat_expand(f0, pad_to)
-
- if self.keep_zeros:
- return f0
-
- vuv_vector = torch.zeros_like(f0)
- vuv_vector[f0 > 0.0] = 1.0
- vuv_vector[f0 <= 0.0] = 0.0
-
- # Remove 0 frequency and apply linear interpolation
- nzindex = torch.nonzero(f0).squeeze()
- f0 = torch.index_select(f0, dim=0, index=nzindex).cpu().numpy()
- time_org = self.hop_length / sampling_rate * nzindex.cpu().numpy()
- time_frame = np.arange(pad_to) * self.hop_length / sampling_rate
-
- if f0.shape[0] <= 0:
- return torch.zeros(pad_to, dtype=torch.float, device=x.device),torch.zeros(pad_to, dtype=torch.float, device=x.device)
-
- if f0.shape[0] == 1:
- return torch.ones(pad_to, dtype=torch.float, device=x.device) * f0[0],torch.ones(pad_to, dtype=torch.float, device=x.device)
-
- # Probably can be rewritten with torch?
- f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
- vuv_vector = vuv_vector.cpu().numpy()
- vuv_vector = np.ceil(scipy.ndimage.zoom(vuv_vector,pad_to/len(vuv_vector),order = 0))
-
- return f0,vuv_vector
-
-
-class MaskedAvgPool1d(nn.Module):
- def __init__(
- self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
- ):
- """An implementation of mean pooling that supports masked values.
-
- Args:
- kernel_size (int): The size of the median pooling window.
- stride (int, optional): The stride of the median pooling window. Defaults to None.
- padding (int, optional): The padding of the median pooling window. Defaults to 0.
- """
-
- super(MaskedAvgPool1d, self).__init__()
- self.kernel_size = kernel_size
- self.stride = stride or kernel_size
- self.padding = padding
-
- def forward(self, x, mask=None):
- ndim = x.dim()
- if ndim == 2:
- x = x.unsqueeze(1)
-
- assert (
- x.dim() == 3
- ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
-
- # Apply the mask by setting masked elements to zero, or make NaNs zero
- if mask is None:
- mask = ~torch.isnan(x)
-
- # Ensure mask has the same shape as the input tensor
- assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
-
- masked_x = torch.where(mask, x, torch.zeros_like(x))
- # Create a ones kernel with the same number of channels as the input tensor
- ones_kernel = torch.ones(x.size(1), 1, self.kernel_size, device=x.device)
-
- # Perform sum pooling
- sum_pooled = nn.functional.conv1d(
- masked_x,
- ones_kernel,
- stride=self.stride,
- padding=self.padding,
- groups=x.size(1),
- )
-
- # Count the non-masked (valid) elements in each pooling window
- valid_count = nn.functional.conv1d(
- mask.float(),
- ones_kernel,
- stride=self.stride,
- padding=self.padding,
- groups=x.size(1),
- )
- valid_count = valid_count.clamp(min=1) # Avoid division by zero
-
- # Perform masked average pooling
- avg_pooled = sum_pooled / valid_count
-
- # Fill zero values with NaNs
- avg_pooled[avg_pooled == 0] = float("nan")
-
- if ndim == 2:
- return avg_pooled.squeeze(1)
-
- return avg_pooled
-
-
-class MaskedMedianPool1d(nn.Module):
- def __init__(
- self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
- ):
- """An implementation of median pooling that supports masked values.
-
- This implementation is inspired by the median pooling implementation in
- https://gist.github.com/rwightman/f2d3849281624be7c0f11c85c87c1598
-
- Args:
- kernel_size (int): The size of the median pooling window.
- stride (int, optional): The stride of the median pooling window. Defaults to None.
- padding (int, optional): The padding of the median pooling window. Defaults to 0.
- """
-
- super(MaskedMedianPool1d, self).__init__()
- self.kernel_size = kernel_size
- self.stride = stride or kernel_size
- self.padding = padding
-
- def forward(self, x, mask=None):
- ndim = x.dim()
- if ndim == 2:
- x = x.unsqueeze(1)
-
- assert (
- x.dim() == 3
- ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
-
- if mask is None:
- mask = ~torch.isnan(x)
-
- assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
-
- masked_x = torch.where(mask, x, torch.zeros_like(x))
-
- x = F.pad(masked_x, (self.padding, self.padding), mode="reflect")
- mask = F.pad(
- mask.float(), (self.padding, self.padding), mode="constant", value=0
- )
-
- x = x.unfold(2, self.kernel_size, self.stride)
- mask = mask.unfold(2, self.kernel_size, self.stride)
-
- x = x.contiguous().view(x.size()[:3] + (-1,))
- mask = mask.contiguous().view(mask.size()[:3] + (-1,)).to(x.device)
-
- # Combine the mask with the input tensor
- #x_masked = torch.where(mask.bool(), x, torch.fill_(torch.zeros_like(x),float("inf")))
- x_masked = torch.where(mask.bool(), x, torch.FloatTensor([float("inf")]).to(x.device))
-
- # Sort the masked tensor along the last dimension
- x_sorted, _ = torch.sort(x_masked, dim=-1)
-
- # Compute the count of non-masked (valid) values
- valid_count = mask.sum(dim=-1)
-
- # Calculate the index of the median value for each pooling window
- median_idx = (torch.div((valid_count - 1), 2, rounding_mode='trunc')).clamp(min=0)
-
- # Gather the median values using the calculated indices
- median_pooled = x_sorted.gather(-1, median_idx.unsqueeze(-1).long()).squeeze(-1)
-
- # Fill infinite values with NaNs
- median_pooled[torch.isinf(median_pooled)] = float("nan")
-
- if ndim == 2:
- return median_pooled.squeeze(1)
-
- return median_pooled
-
-
-class CrepePitchExtractor(BasePitchExtractor):
- def __init__(
- self,
- hop_length: int = 512,
- f0_min: float = 50.0,
- f0_max: float = 1100.0,
- threshold: float = 0.05,
- keep_zeros: bool = False,
- device = None,
- model: Literal["full", "tiny"] = "full",
- use_fast_filters: bool = True,
- ):
- super().__init__(hop_length, f0_min, f0_max, keep_zeros)
-
- self.threshold = threshold
- self.model = model
- self.use_fast_filters = use_fast_filters
- self.hop_length = hop_length
- if device is None:
- self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
- else:
- self.dev = torch.device(device)
- if self.use_fast_filters:
- self.median_filter = MaskedMedianPool1d(3, 1, 1).to(device)
- self.mean_filter = MaskedAvgPool1d(3, 1, 1).to(device)
-
- def __call__(self, x, sampling_rate=44100, pad_to=None):
- """Extract pitch using crepe.
-
-
- Args:
- x (torch.Tensor): Audio signal, shape (1, T).
- sampling_rate (int, optional): Sampling rate. Defaults to 44100.
- pad_to (int, optional): Pad to length. Defaults to None.
-
- Returns:
- torch.Tensor: Pitch, shape (T // hop_length,).
- """
-
- assert x.ndim == 2, f"Expected 2D tensor, got {x.ndim}D tensor."
- assert x.shape[0] == 1, f"Expected 1 channel, got {x.shape[0]} channels."
-
- x = x.to(self.dev)
- f0, pd = torchcrepe.predict(
- x,
- sampling_rate,
- self.hop_length,
- self.f0_min,
- self.f0_max,
- pad=True,
- model=self.model,
- batch_size=1024,
- device=x.device,
- return_periodicity=True,
- )
-
- # Filter, remove silence, set uv threshold, refer to the original warehouse readme
- if self.use_fast_filters:
- pd = self.median_filter(pd)
- else:
- pd = torchcrepe.filter.median(pd, 3)
-
- pd = torchcrepe.threshold.Silence(-60.0)(pd, x, sampling_rate, 512)
- f0 = torchcrepe.threshold.At(self.threshold)(f0, pd)
-
- if self.use_fast_filters:
- f0 = self.mean_filter(f0)
- else:
- f0 = torchcrepe.filter.mean(f0, 3)
-
- f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)[0]
-
- if torch.all(f0 == 0):
- rtn = f0.cpu().numpy() if pad_to==None else np.zeros(pad_to)
- return rtn,rtn
-
- return self.post_process(x, sampling_rate, f0, pad_to)
diff --git a/spaces/Akshat231/super_space/README.md b/spaces/Akshat231/super_space/README.md
deleted file mode 100644
index 955301be5487a8b8aa268b396ee53d49815e8296..0000000000000000000000000000000000000000
--- a/spaces/Akshat231/super_space/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Super Space
-emoji: 🏃
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AlterM/Zaglyt2-transformer-test/app.py b/spaces/AlterM/Zaglyt2-transformer-test/app.py
deleted file mode 100644
index db9ffd865e3781a45a6ff62656cf767b9fe36dfa..0000000000000000000000000000000000000000
--- a/spaces/AlterM/Zaglyt2-transformer-test/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import gradio as gr
-import net
-
-def generate(text):
- o = text
- r = []
- for i in range(5):
- t = net.gen(o)
- o += " " + t
- r.append(t)
- return text + " *"+' '.join(r)+"*"
-
-iface = gr.Interface(fn=generate, inputs="text", outputs="text")
-iface.launch()
\ No newline at end of file
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py
deleted file mode 100644
index f897e7c55c8b8f0ef7a5db92f29ef1c2415965db..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(train_cfg=dict(rcnn=dict(sampler=dict(type='OHEMSampler'))))
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
deleted file mode 100644
index 7fb8e82ece225ab6f88f1f4f83bea56a42cf1a57..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
+++ /dev/null
@@ -1,11 +0,0 @@
-_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py'
-model = dict(
- backbone=dict(
- norm_cfg=dict(type='SyncBN', requires_grad=True),
- norm_eval=False,
- plugins=[
- dict(
- cfg=dict(type='ContextBlock', ratio=1. / 16),
- stages=(False, True, True, True),
- position='after_conv3')
- ]))
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py
deleted file mode 100644
index e3d8238956f4d4874de1fde662a1a3ded1918189..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,65 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
- rpn_head=dict(
- _delete_=True,
- type='GARPNHead',
- in_channels=256,
- feat_channels=256,
- approx_anchor_generator=dict(
- type='AnchorGenerator',
- octave_base_scale=8,
- scales_per_octave=3,
- ratios=[0.5, 1.0, 2.0],
- strides=[4, 8, 16, 32, 64]),
- square_anchor_generator=dict(
- type='AnchorGenerator',
- ratios=[1.0],
- scales=[8],
- strides=[4, 8, 16, 32, 64]),
- anchor_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[0.07, 0.07, 0.14, 0.14]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[0.07, 0.07, 0.11, 0.11]),
- loc_filter_thr=0.01,
- loss_loc=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
- roi_head=dict(
- bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))),
- # model training and testing settings
- train_cfg=dict(
- rpn=dict(
- ga_assigner=dict(
- type='ApproxMaxIoUAssigner',
- pos_iou_thr=0.7,
- neg_iou_thr=0.3,
- min_pos_iou=0.3,
- ignore_iof_thr=-1),
- ga_sampler=dict(
- type='RandomSampler',
- num=256,
- pos_fraction=0.5,
- neg_pos_ub=-1,
- add_gt_as_proposals=False),
- allowed_border=-1,
- center_ratio=0.2,
- ignore_ratio=0.5),
- rpn_proposal=dict(nms_post=1000, max_per_img=300),
- rcnn=dict(
- assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6),
- sampler=dict(type='RandomSampler', num=256))),
- test_cfg=dict(
- rpn=dict(nms_post=1000, max_per_img=300), rcnn=dict(score_thr=1e-3)))
-optimizer_config = dict(
- _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py
deleted file mode 100644
index ef81123a2ebd5a30eb812d321eb7a3764e315a72..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py
+++ /dev/null
@@ -1,97 +0,0 @@
-_base_ = [
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-
-model = dict(
- type='NASFCOS',
- pretrained='open-mmlab://detectron2/resnet50_caffe',
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=False, eps=0),
- style='caffe'),
- neck=dict(
- type='NASFCOS_FPN',
- in_channels=[256, 512, 1024, 2048],
- out_channels=256,
- start_level=1,
- add_extra_convs=True,
- num_outs=5,
- norm_cfg=dict(type='BN'),
- conv_cfg=dict(type='DCNv2', deform_groups=2)),
- bbox_head=dict(
- type='NASFCOSHead',
- num_classes=80,
- in_channels=256,
- feat_channels=256,
- strides=[8, 16, 32, 64, 128],
- norm_cfg=dict(type='GN', num_groups=32),
- loss_cls=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- loss_bbox=dict(type='IoULoss', loss_weight=1.0),
- loss_centerness=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
- train_cfg=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.5,
- neg_iou_thr=0.4,
- min_pos_iou=0,
- ignore_iof_thr=-1),
- allowed_border=-1,
- pos_weight=-1,
- debug=False),
- test_cfg=dict(
- nms_pre=1000,
- min_bbox_size=0,
- score_thr=0.05,
- nms=dict(type='nms', iou_threshold=0.6),
- max_per_img=100))
-
-img_norm_cfg = dict(
- mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
-
-data = dict(
- samples_per_gpu=4,
- workers_per_gpu=2,
- train=dict(pipeline=train_pipeline),
- val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
-
-optimizer = dict(
- lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py
deleted file mode 100644
index 6644a58dea86fd38e208abbedffe4f836e677078..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = './fcn_r50-d8_769x769_80k_cityscapes.py'
-model = dict(
- pretrained='open-mmlab://resnet18_v1c',
- backbone=dict(depth=18),
- decode_head=dict(
- in_channels=512,
- channels=128,
- ),
- auxiliary_head=dict(in_channels=256, channels=64))
diff --git a/spaces/AngoHF/ANGO-Leaderboard/components/__init__.py b/spaces/AngoHF/ANGO-Leaderboard/components/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/benchmark.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/benchmark.py
deleted file mode 100644
index 46475a088b0eca137f641935d58dbf4b8d50ed29..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/benchmark.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""
-This module implements a benchmark function to evaluate the performance of the embedding pipeline. It expects a configuration JSON file. It must have questions and expected retrieved text.
-For each question, it's essential to have variants of that question. Language is fluid and each person might have their own spin on how they may ask it.
-
-At the end, it will save the results inside a benchmark_{sysdate}.txt file in the main directory.
-
-The benchmark function will return the score as an integer.
-"""
-import datetime
-import json
-import os
-
-from pathlib import Path
-
-from .data_processor import process_and_add_to_collector, preprocess_text
-from .parameters import get_chunk_count, get_max_token_count
-from .utils import create_metadata_source
-
-def benchmark(config_path, collector):
- # Get the current system date
- sysdate = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
- filename = f"benchmark_{sysdate}.txt"
-
- # Open the log file in append mode
- with open(filename, 'a') as log:
- with open(config_path, 'r') as f:
- data = json.load(f)
-
- total_points = 0
- max_points = 0
-
- for item in data:
- filepath = item["text"]
- corpus = ""
-
- # Check if the file exists
- if os.path.isfile(Path(filepath)):
- # Open the file and read its content
- with open(Path(filepath), 'r') as file:
- corpus = file.read()
- process_and_add_to_collector(corpus, collector, True, create_metadata_source('benchmark'))
- else:
- raise f'Cannot find specified file {filepath}.'
-
- for question_group in item["questions"]:
- question_variants = question_group["question_variants"]
- criteria = question_group["criteria"]
-
- for q in question_variants:
- max_points += len(criteria)
- processed_text = preprocess_text(q)
-
- # Get the most similar chunks
- results = collector.get_sorted_by_dist(processed_text, n_results=get_chunk_count(), max_token_count=get_max_token_count())
-
- points = 0
-
- for c in criteria:
- for p in results:
- if c in p:
- points += 1
- total_points += 1
- break
-
- info = f"The question '{q}' scored {points}/{len(criteria)} points."
- print(info, file=log)
-
- print('\n---\n', file=log)
-
- print(f'##Total points:\n\n{total_points}/{max_points}', file=log)
-
- return total_points, max_points
\ No newline at end of file
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/fileio/file_client.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/fileio/file_client.py
deleted file mode 100644
index 950f0c1aeab14b8e308a7455ccd64a95b5d98add..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/fileio/file_client.py
+++ /dev/null
@@ -1,1148 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import inspect
-import os
-import os.path as osp
-import re
-import tempfile
-import warnings
-from abc import ABCMeta, abstractmethod
-from contextlib import contextmanager
-from pathlib import Path
-from typing import Iterable, Iterator, Optional, Tuple, Union
-from urllib.request import urlopen
-
-import annotator.uniformer.mmcv as mmcv
-from annotator.uniformer.mmcv.utils.misc import has_method
-from annotator.uniformer.mmcv.utils.path import is_filepath
-
-
-class BaseStorageBackend(metaclass=ABCMeta):
- """Abstract class of storage backends.
-
- All backends need to implement two apis: ``get()`` and ``get_text()``.
- ``get()`` reads the file as a byte stream and ``get_text()`` reads the file
- as texts.
- """
-
- # a flag to indicate whether the backend can create a symlink for a file
- _allow_symlink = False
-
- @property
- def name(self):
- return self.__class__.__name__
-
- @property
- def allow_symlink(self):
- return self._allow_symlink
-
- @abstractmethod
- def get(self, filepath):
- pass
-
- @abstractmethod
- def get_text(self, filepath):
- pass
-
-
-class CephBackend(BaseStorageBackend):
- """Ceph storage backend (for internal use).
-
- Args:
- path_mapping (dict|None): path mapping dict from local path to Petrel
- path. When ``path_mapping={'src': 'dst'}``, ``src`` in ``filepath``
- will be replaced by ``dst``. Default: None.
-
- .. warning::
- :class:`mmcv.fileio.file_client.CephBackend` will be deprecated,
- please use :class:`mmcv.fileio.file_client.PetrelBackend` instead.
- """
-
- def __init__(self, path_mapping=None):
- try:
- import ceph
- except ImportError:
- raise ImportError('Please install ceph to enable CephBackend.')
-
- warnings.warn(
- 'CephBackend will be deprecated, please use PetrelBackend instead')
- self._client = ceph.S3Client()
- assert isinstance(path_mapping, dict) or path_mapping is None
- self.path_mapping = path_mapping
-
- def get(self, filepath):
- filepath = str(filepath)
- if self.path_mapping is not None:
- for k, v in self.path_mapping.items():
- filepath = filepath.replace(k, v)
- value = self._client.Get(filepath)
- value_buf = memoryview(value)
- return value_buf
-
- def get_text(self, filepath, encoding=None):
- raise NotImplementedError
-
-
-class PetrelBackend(BaseStorageBackend):
- """Petrel storage backend (for internal use).
-
- PetrelBackend supports reading and writing data to multiple clusters.
- If the file path contains the cluster name, PetrelBackend will read data
- from specified cluster or write data to it. Otherwise, PetrelBackend will
- access the default cluster.
-
- Args:
- path_mapping (dict, optional): Path mapping dict from local path to
- Petrel path. When ``path_mapping={'src': 'dst'}``, ``src`` in
- ``filepath`` will be replaced by ``dst``. Default: None.
- enable_mc (bool, optional): Whether to enable memcached support.
- Default: True.
-
- Examples:
- >>> filepath1 = 's3://path/of/file'
- >>> filepath2 = 'cluster-name:s3://path/of/file'
- >>> client = PetrelBackend()
- >>> client.get(filepath1) # get data from default cluster
- >>> client.get(filepath2) # get data from 'cluster-name' cluster
- """
-
- def __init__(self,
- path_mapping: Optional[dict] = None,
- enable_mc: bool = True):
- try:
- from petrel_client import client
- except ImportError:
- raise ImportError('Please install petrel_client to enable '
- 'PetrelBackend.')
-
- self._client = client.Client(enable_mc=enable_mc)
- assert isinstance(path_mapping, dict) or path_mapping is None
- self.path_mapping = path_mapping
-
- def _map_path(self, filepath: Union[str, Path]) -> str:
- """Map ``filepath`` to a string path whose prefix will be replaced by
- :attr:`self.path_mapping`.
-
- Args:
- filepath (str): Path to be mapped.
- """
- filepath = str(filepath)
- if self.path_mapping is not None:
- for k, v in self.path_mapping.items():
- filepath = filepath.replace(k, v)
- return filepath
-
- def _format_path(self, filepath: str) -> str:
- """Convert a ``filepath`` to standard format of petrel oss.
-
- If the ``filepath`` is concatenated by ``os.path.join``, in a Windows
- environment, the ``filepath`` will be the format of
- 's3://bucket_name\\image.jpg'. By invoking :meth:`_format_path`, the
- above ``filepath`` will be converted to 's3://bucket_name/image.jpg'.
-
- Args:
- filepath (str): Path to be formatted.
- """
- return re.sub(r'\\+', '/', filepath)
-
- def get(self, filepath: Union[str, Path]) -> memoryview:
- """Read data from a given ``filepath`` with 'rb' mode.
-
- Args:
- filepath (str or Path): Path to read data.
-
- Returns:
- memoryview: A memory view of expected bytes object to avoid
- copying. The memoryview object can be converted to bytes by
- ``value_buf.tobytes()``.
- """
- filepath = self._map_path(filepath)
- filepath = self._format_path(filepath)
- value = self._client.Get(filepath)
- value_buf = memoryview(value)
- return value_buf
-
- def get_text(self,
- filepath: Union[str, Path],
- encoding: str = 'utf-8') -> str:
- """Read data from a given ``filepath`` with 'r' mode.
-
- Args:
- filepath (str or Path): Path to read data.
- encoding (str): The encoding format used to open the ``filepath``.
- Default: 'utf-8'.
-
- Returns:
- str: Expected text reading from ``filepath``.
- """
- return str(self.get(filepath), encoding=encoding)
-
- def put(self, obj: bytes, filepath: Union[str, Path]) -> None:
- """Save data to a given ``filepath``.
-
- Args:
- obj (bytes): Data to be saved.
- filepath (str or Path): Path to write data.
- """
- filepath = self._map_path(filepath)
- filepath = self._format_path(filepath)
- self._client.put(filepath, obj)
-
- def put_text(self,
- obj: str,
- filepath: Union[str, Path],
- encoding: str = 'utf-8') -> None:
- """Save data to a given ``filepath``.
-
- Args:
- obj (str): Data to be written.
- filepath (str or Path): Path to write data.
- encoding (str): The encoding format used to encode the ``obj``.
- Default: 'utf-8'.
- """
- self.put(bytes(obj, encoding=encoding), filepath)
-
- def remove(self, filepath: Union[str, Path]) -> None:
- """Remove a file.
-
- Args:
- filepath (str or Path): Path to be removed.
- """
- if not has_method(self._client, 'delete'):
- raise NotImplementedError(
- ('Current version of Petrel Python SDK has not supported '
- 'the `delete` method, please use a higher version or dev'
- ' branch instead.'))
-
- filepath = self._map_path(filepath)
- filepath = self._format_path(filepath)
- self._client.delete(filepath)
-
- def exists(self, filepath: Union[str, Path]) -> bool:
- """Check whether a file path exists.
-
- Args:
- filepath (str or Path): Path to be checked whether exists.
-
- Returns:
- bool: Return ``True`` if ``filepath`` exists, ``False`` otherwise.
- """
- if not (has_method(self._client, 'contains')
- and has_method(self._client, 'isdir')):
- raise NotImplementedError(
- ('Current version of Petrel Python SDK has not supported '
- 'the `contains` and `isdir` methods, please use a higher'
- 'version or dev branch instead.'))
-
- filepath = self._map_path(filepath)
- filepath = self._format_path(filepath)
- return self._client.contains(filepath) or self._client.isdir(filepath)
-
- def isdir(self, filepath: Union[str, Path]) -> bool:
- """Check whether a file path is a directory.
-
- Args:
- filepath (str or Path): Path to be checked whether it is a
- directory.
-
- Returns:
- bool: Return ``True`` if ``filepath`` points to a directory,
- ``False`` otherwise.
- """
- if not has_method(self._client, 'isdir'):
- raise NotImplementedError(
- ('Current version of Petrel Python SDK has not supported '
- 'the `isdir` method, please use a higher version or dev'
- ' branch instead.'))
-
- filepath = self._map_path(filepath)
- filepath = self._format_path(filepath)
- return self._client.isdir(filepath)
-
- def isfile(self, filepath: Union[str, Path]) -> bool:
- """Check whether a file path is a file.
-
- Args:
- filepath (str or Path): Path to be checked whether it is a file.
-
- Returns:
- bool: Return ``True`` if ``filepath`` points to a file, ``False``
- otherwise.
- """
- if not has_method(self._client, 'contains'):
- raise NotImplementedError(
- ('Current version of Petrel Python SDK has not supported '
- 'the `contains` method, please use a higher version or '
- 'dev branch instead.'))
-
- filepath = self._map_path(filepath)
- filepath = self._format_path(filepath)
- return self._client.contains(filepath)
-
- def join_path(self, filepath: Union[str, Path],
- *filepaths: Union[str, Path]) -> str:
- """Concatenate all file paths.
-
- Args:
- filepath (str or Path): Path to be concatenated.
-
- Returns:
- str: The result after concatenation.
- """
- filepath = self._format_path(self._map_path(filepath))
- if filepath.endswith('/'):
- filepath = filepath[:-1]
- formatted_paths = [filepath]
- for path in filepaths:
- formatted_paths.append(self._format_path(self._map_path(path)))
- return '/'.join(formatted_paths)
-
- @contextmanager
- def get_local_path(self, filepath: Union[str, Path]) -> Iterable[str]:
- """Download a file from ``filepath`` and return a temporary path.
-
- ``get_local_path`` is decorated by :meth:`contxtlib.contextmanager`. It
- can be called with ``with`` statement, and when exists from the
- ``with`` statement, the temporary path will be released.
-
- Args:
- filepath (str | Path): Download a file from ``filepath``.
-
- Examples:
- >>> client = PetrelBackend()
- >>> # After existing from the ``with`` clause,
- >>> # the path will be removed
- >>> with client.get_local_path('s3://path/of/your/file') as path:
- ... # do something here
-
- Yields:
- Iterable[str]: Only yield one temporary path.
- """
- filepath = self._map_path(filepath)
- filepath = self._format_path(filepath)
- assert self.isfile(filepath)
- try:
- f = tempfile.NamedTemporaryFile(delete=False)
- f.write(self.get(filepath))
- f.close()
- yield f.name
- finally:
- os.remove(f.name)
-
- def list_dir_or_file(self,
- dir_path: Union[str, Path],
- list_dir: bool = True,
- list_file: bool = True,
- suffix: Optional[Union[str, Tuple[str]]] = None,
- recursive: bool = False) -> Iterator[str]:
- """Scan a directory to find the interested directories or files in
- arbitrary order.
-
- Note:
- Petrel has no concept of directories but it simulates the directory
- hierarchy in the filesystem through public prefixes. In addition,
- if the returned path ends with '/', it means the path is a public
- prefix which is a logical directory.
-
- Note:
- :meth:`list_dir_or_file` returns the path relative to ``dir_path``.
- In addition, the returned path of directory will not contains the
- suffix '/' which is consistent with other backends.
-
- Args:
- dir_path (str | Path): Path of the directory.
- list_dir (bool): List the directories. Default: True.
- list_file (bool): List the path of files. Default: True.
- suffix (str or tuple[str], optional): File suffix
- that we are interested in. Default: None.
- recursive (bool): If set to True, recursively scan the
- directory. Default: False.
-
- Yields:
- Iterable[str]: A relative path to ``dir_path``.
- """
- if not has_method(self._client, 'list'):
- raise NotImplementedError(
- ('Current version of Petrel Python SDK has not supported '
- 'the `list` method, please use a higher version or dev'
- ' branch instead.'))
-
- dir_path = self._map_path(dir_path)
- dir_path = self._format_path(dir_path)
- if list_dir and suffix is not None:
- raise TypeError(
- '`list_dir` should be False when `suffix` is not None')
-
- if (suffix is not None) and not isinstance(suffix, (str, tuple)):
- raise TypeError('`suffix` must be a string or tuple of strings')
-
- # Petrel's simulated directory hierarchy assumes that directory paths
- # should end with `/`
- if not dir_path.endswith('/'):
- dir_path += '/'
-
- root = dir_path
-
- def _list_dir_or_file(dir_path, list_dir, list_file, suffix,
- recursive):
- for path in self._client.list(dir_path):
- # the `self.isdir` is not used here to determine whether path
- # is a directory, because `self.isdir` relies on
- # `self._client.list`
- if path.endswith('/'): # a directory path
- next_dir_path = self.join_path(dir_path, path)
- if list_dir:
- # get the relative path and exclude the last
- # character '/'
- rel_dir = next_dir_path[len(root):-1]
- yield rel_dir
- if recursive:
- yield from _list_dir_or_file(next_dir_path, list_dir,
- list_file, suffix,
- recursive)
- else: # a file path
- absolute_path = self.join_path(dir_path, path)
- rel_path = absolute_path[len(root):]
- if (suffix is None
- or rel_path.endswith(suffix)) and list_file:
- yield rel_path
-
- return _list_dir_or_file(dir_path, list_dir, list_file, suffix,
- recursive)
-
-
-class MemcachedBackend(BaseStorageBackend):
- """Memcached storage backend.
-
- Attributes:
- server_list_cfg (str): Config file for memcached server list.
- client_cfg (str): Config file for memcached client.
- sys_path (str | None): Additional path to be appended to `sys.path`.
- Default: None.
- """
-
- def __init__(self, server_list_cfg, client_cfg, sys_path=None):
- if sys_path is not None:
- import sys
- sys.path.append(sys_path)
- try:
- import mc
- except ImportError:
- raise ImportError(
- 'Please install memcached to enable MemcachedBackend.')
-
- self.server_list_cfg = server_list_cfg
- self.client_cfg = client_cfg
- self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg,
- self.client_cfg)
- # mc.pyvector servers as a point which points to a memory cache
- self._mc_buffer = mc.pyvector()
-
- def get(self, filepath):
- filepath = str(filepath)
- import mc
- self._client.Get(filepath, self._mc_buffer)
- value_buf = mc.ConvertBuffer(self._mc_buffer)
- return value_buf
-
- def get_text(self, filepath, encoding=None):
- raise NotImplementedError
-
-
-class LmdbBackend(BaseStorageBackend):
- """Lmdb storage backend.
-
- Args:
- db_path (str): Lmdb database path.
- readonly (bool, optional): Lmdb environment parameter. If True,
- disallow any write operations. Default: True.
- lock (bool, optional): Lmdb environment parameter. If False, when
- concurrent access occurs, do not lock the database. Default: False.
- readahead (bool, optional): Lmdb environment parameter. If False,
- disable the OS filesystem readahead mechanism, which may improve
- random read performance when a database is larger than RAM.
- Default: False.
-
- Attributes:
- db_path (str): Lmdb database path.
- """
-
- def __init__(self,
- db_path,
- readonly=True,
- lock=False,
- readahead=False,
- **kwargs):
- try:
- import lmdb
- except ImportError:
- raise ImportError('Please install lmdb to enable LmdbBackend.')
-
- self.db_path = str(db_path)
- self._client = lmdb.open(
- self.db_path,
- readonly=readonly,
- lock=lock,
- readahead=readahead,
- **kwargs)
-
- def get(self, filepath):
- """Get values according to the filepath.
-
- Args:
- filepath (str | obj:`Path`): Here, filepath is the lmdb key.
- """
- filepath = str(filepath)
- with self._client.begin(write=False) as txn:
- value_buf = txn.get(filepath.encode('ascii'))
- return value_buf
-
- def get_text(self, filepath, encoding=None):
- raise NotImplementedError
-
-
-class HardDiskBackend(BaseStorageBackend):
- """Raw hard disks storage backend."""
-
- _allow_symlink = True
-
- def get(self, filepath: Union[str, Path]) -> bytes:
- """Read data from a given ``filepath`` with 'rb' mode.
-
- Args:
- filepath (str or Path): Path to read data.
-
- Returns:
- bytes: Expected bytes object.
- """
- with open(filepath, 'rb') as f:
- value_buf = f.read()
- return value_buf
-
- def get_text(self,
- filepath: Union[str, Path],
- encoding: str = 'utf-8') -> str:
- """Read data from a given ``filepath`` with 'r' mode.
-
- Args:
- filepath (str or Path): Path to read data.
- encoding (str): The encoding format used to open the ``filepath``.
- Default: 'utf-8'.
-
- Returns:
- str: Expected text reading from ``filepath``.
- """
- with open(filepath, 'r', encoding=encoding) as f:
- value_buf = f.read()
- return value_buf
-
- def put(self, obj: bytes, filepath: Union[str, Path]) -> None:
- """Write data to a given ``filepath`` with 'wb' mode.
-
- Note:
- ``put`` will create a directory if the directory of ``filepath``
- does not exist.
-
- Args:
- obj (bytes): Data to be written.
- filepath (str or Path): Path to write data.
- """
- mmcv.mkdir_or_exist(osp.dirname(filepath))
- with open(filepath, 'wb') as f:
- f.write(obj)
-
- def put_text(self,
- obj: str,
- filepath: Union[str, Path],
- encoding: str = 'utf-8') -> None:
- """Write data to a given ``filepath`` with 'w' mode.
-
- Note:
- ``put_text`` will create a directory if the directory of
- ``filepath`` does not exist.
-
- Args:
- obj (str): Data to be written.
- filepath (str or Path): Path to write data.
- encoding (str): The encoding format used to open the ``filepath``.
- Default: 'utf-8'.
- """
- mmcv.mkdir_or_exist(osp.dirname(filepath))
- with open(filepath, 'w', encoding=encoding) as f:
- f.write(obj)
-
- def remove(self, filepath: Union[str, Path]) -> None:
- """Remove a file.
-
- Args:
- filepath (str or Path): Path to be removed.
- """
- os.remove(filepath)
-
- def exists(self, filepath: Union[str, Path]) -> bool:
- """Check whether a file path exists.
-
- Args:
- filepath (str or Path): Path to be checked whether exists.
-
- Returns:
- bool: Return ``True`` if ``filepath`` exists, ``False`` otherwise.
- """
- return osp.exists(filepath)
-
- def isdir(self, filepath: Union[str, Path]) -> bool:
- """Check whether a file path is a directory.
-
- Args:
- filepath (str or Path): Path to be checked whether it is a
- directory.
-
- Returns:
- bool: Return ``True`` if ``filepath`` points to a directory,
- ``False`` otherwise.
- """
- return osp.isdir(filepath)
-
- def isfile(self, filepath: Union[str, Path]) -> bool:
- """Check whether a file path is a file.
-
- Args:
- filepath (str or Path): Path to be checked whether it is a file.
-
- Returns:
- bool: Return ``True`` if ``filepath`` points to a file, ``False``
- otherwise.
- """
- return osp.isfile(filepath)
-
- def join_path(self, filepath: Union[str, Path],
- *filepaths: Union[str, Path]) -> str:
- """Concatenate all file paths.
-
- Join one or more filepath components intelligently. The return value
- is the concatenation of filepath and any members of *filepaths.
-
- Args:
- filepath (str or Path): Path to be concatenated.
-
- Returns:
- str: The result of concatenation.
- """
- return osp.join(filepath, *filepaths)
-
- @contextmanager
- def get_local_path(
- self, filepath: Union[str, Path]) -> Iterable[Union[str, Path]]:
- """Only for unified API and do nothing."""
- yield filepath
-
- def list_dir_or_file(self,
- dir_path: Union[str, Path],
- list_dir: bool = True,
- list_file: bool = True,
- suffix: Optional[Union[str, Tuple[str]]] = None,
- recursive: bool = False) -> Iterator[str]:
- """Scan a directory to find the interested directories or files in
- arbitrary order.
-
- Note:
- :meth:`list_dir_or_file` returns the path relative to ``dir_path``.
-
- Args:
- dir_path (str | Path): Path of the directory.
- list_dir (bool): List the directories. Default: True.
- list_file (bool): List the path of files. Default: True.
- suffix (str or tuple[str], optional): File suffix
- that we are interested in. Default: None.
- recursive (bool): If set to True, recursively scan the
- directory. Default: False.
-
- Yields:
- Iterable[str]: A relative path to ``dir_path``.
- """
- if list_dir and suffix is not None:
- raise TypeError('`suffix` should be None when `list_dir` is True')
-
- if (suffix is not None) and not isinstance(suffix, (str, tuple)):
- raise TypeError('`suffix` must be a string or tuple of strings')
-
- root = dir_path
-
- def _list_dir_or_file(dir_path, list_dir, list_file, suffix,
- recursive):
- for entry in os.scandir(dir_path):
- if not entry.name.startswith('.') and entry.is_file():
- rel_path = osp.relpath(entry.path, root)
- if (suffix is None
- or rel_path.endswith(suffix)) and list_file:
- yield rel_path
- elif osp.isdir(entry.path):
- if list_dir:
- rel_dir = osp.relpath(entry.path, root)
- yield rel_dir
- if recursive:
- yield from _list_dir_or_file(entry.path, list_dir,
- list_file, suffix,
- recursive)
-
- return _list_dir_or_file(dir_path, list_dir, list_file, suffix,
- recursive)
-
-
-class HTTPBackend(BaseStorageBackend):
- """HTTP and HTTPS storage bachend."""
-
- def get(self, filepath):
- value_buf = urlopen(filepath).read()
- return value_buf
-
- def get_text(self, filepath, encoding='utf-8'):
- value_buf = urlopen(filepath).read()
- return value_buf.decode(encoding)
-
- @contextmanager
- def get_local_path(self, filepath: str) -> Iterable[str]:
- """Download a file from ``filepath``.
-
- ``get_local_path`` is decorated by :meth:`contxtlib.contextmanager`. It
- can be called with ``with`` statement, and when exists from the
- ``with`` statement, the temporary path will be released.
-
- Args:
- filepath (str): Download a file from ``filepath``.
-
- Examples:
- >>> client = HTTPBackend()
- >>> # After existing from the ``with`` clause,
- >>> # the path will be removed
- >>> with client.get_local_path('http://path/of/your/file') as path:
- ... # do something here
- """
- try:
- f = tempfile.NamedTemporaryFile(delete=False)
- f.write(self.get(filepath))
- f.close()
- yield f.name
- finally:
- os.remove(f.name)
-
-
-class FileClient:
- """A general file client to access files in different backends.
-
- The client loads a file or text in a specified backend from its path
- and returns it as a binary or text file. There are two ways to choose a
- backend, the name of backend and the prefix of path. Although both of them
- can be used to choose a storage backend, ``backend`` has a higher priority
- that is if they are all set, the storage backend will be chosen by the
- backend argument. If they are all `None`, the disk backend will be chosen.
- Note that It can also register other backend accessor with a given name,
- prefixes, and backend class. In addition, We use the singleton pattern to
- avoid repeated object creation. If the arguments are the same, the same
- object will be returned.
-
- Args:
- backend (str, optional): The storage backend type. Options are "disk",
- "ceph", "memcached", "lmdb", "http" and "petrel". Default: None.
- prefix (str, optional): The prefix of the registered storage backend.
- Options are "s3", "http", "https". Default: None.
-
- Examples:
- >>> # only set backend
- >>> file_client = FileClient(backend='petrel')
- >>> # only set prefix
- >>> file_client = FileClient(prefix='s3')
- >>> # set both backend and prefix but use backend to choose client
- >>> file_client = FileClient(backend='petrel', prefix='s3')
- >>> # if the arguments are the same, the same object is returned
- >>> file_client1 = FileClient(backend='petrel')
- >>> file_client1 is file_client
- True
-
- Attributes:
- client (:obj:`BaseStorageBackend`): The backend object.
- """
-
- _backends = {
- 'disk': HardDiskBackend,
- 'ceph': CephBackend,
- 'memcached': MemcachedBackend,
- 'lmdb': LmdbBackend,
- 'petrel': PetrelBackend,
- 'http': HTTPBackend,
- }
- # This collection is used to record the overridden backends, and when a
- # backend appears in the collection, the singleton pattern is disabled for
- # that backend, because if the singleton pattern is used, then the object
- # returned will be the backend before overwriting
- _overridden_backends = set()
- _prefix_to_backends = {
- 's3': PetrelBackend,
- 'http': HTTPBackend,
- 'https': HTTPBackend,
- }
- _overridden_prefixes = set()
-
- _instances = {}
-
- def __new__(cls, backend=None, prefix=None, **kwargs):
- if backend is None and prefix is None:
- backend = 'disk'
- if backend is not None and backend not in cls._backends:
- raise ValueError(
- f'Backend {backend} is not supported. Currently supported ones'
- f' are {list(cls._backends.keys())}')
- if prefix is not None and prefix not in cls._prefix_to_backends:
- raise ValueError(
- f'prefix {prefix} is not supported. Currently supported ones '
- f'are {list(cls._prefix_to_backends.keys())}')
-
- # concatenate the arguments to a unique key for determining whether
- # objects with the same arguments were created
- arg_key = f'{backend}:{prefix}'
- for key, value in kwargs.items():
- arg_key += f':{key}:{value}'
-
- # if a backend was overridden, it will create a new object
- if (arg_key in cls._instances
- and backend not in cls._overridden_backends
- and prefix not in cls._overridden_prefixes):
- _instance = cls._instances[arg_key]
- else:
- # create a new object and put it to _instance
- _instance = super().__new__(cls)
- if backend is not None:
- _instance.client = cls._backends[backend](**kwargs)
- else:
- _instance.client = cls._prefix_to_backends[prefix](**kwargs)
-
- cls._instances[arg_key] = _instance
-
- return _instance
-
- @property
- def name(self):
- return self.client.name
-
- @property
- def allow_symlink(self):
- return self.client.allow_symlink
-
- @staticmethod
- def parse_uri_prefix(uri: Union[str, Path]) -> Optional[str]:
- """Parse the prefix of a uri.
-
- Args:
- uri (str | Path): Uri to be parsed that contains the file prefix.
-
- Examples:
- >>> FileClient.parse_uri_prefix('s3://path/of/your/file')
- 's3'
-
- Returns:
- str | None: Return the prefix of uri if the uri contains '://'
- else ``None``.
- """
- assert is_filepath(uri)
- uri = str(uri)
- if '://' not in uri:
- return None
- else:
- prefix, _ = uri.split('://')
- # In the case of PetrelBackend, the prefix may contains the cluster
- # name like clusterName:s3
- if ':' in prefix:
- _, prefix = prefix.split(':')
- return prefix
-
- @classmethod
- def infer_client(cls,
- file_client_args: Optional[dict] = None,
- uri: Optional[Union[str, Path]] = None) -> 'FileClient':
- """Infer a suitable file client based on the URI and arguments.
-
- Args:
- file_client_args (dict, optional): Arguments to instantiate a
- FileClient. Default: None.
- uri (str | Path, optional): Uri to be parsed that contains the file
- prefix. Default: None.
-
- Examples:
- >>> uri = 's3://path/of/your/file'
- >>> file_client = FileClient.infer_client(uri=uri)
- >>> file_client_args = {'backend': 'petrel'}
- >>> file_client = FileClient.infer_client(file_client_args)
-
- Returns:
- FileClient: Instantiated FileClient object.
- """
- assert file_client_args is not None or uri is not None
- if file_client_args is None:
- file_prefix = cls.parse_uri_prefix(uri) # type: ignore
- return cls(prefix=file_prefix)
- else:
- return cls(**file_client_args)
-
- @classmethod
- def _register_backend(cls, name, backend, force=False, prefixes=None):
- if not isinstance(name, str):
- raise TypeError('the backend name should be a string, '
- f'but got {type(name)}')
- if not inspect.isclass(backend):
- raise TypeError(
- f'backend should be a class but got {type(backend)}')
- if not issubclass(backend, BaseStorageBackend):
- raise TypeError(
- f'backend {backend} is not a subclass of BaseStorageBackend')
- if not force and name in cls._backends:
- raise KeyError(
- f'{name} is already registered as a storage backend, '
- 'add "force=True" if you want to override it')
-
- if name in cls._backends and force:
- cls._overridden_backends.add(name)
- cls._backends[name] = backend
-
- if prefixes is not None:
- if isinstance(prefixes, str):
- prefixes = [prefixes]
- else:
- assert isinstance(prefixes, (list, tuple))
- for prefix in prefixes:
- if prefix not in cls._prefix_to_backends:
- cls._prefix_to_backends[prefix] = backend
- elif (prefix in cls._prefix_to_backends) and force:
- cls._overridden_prefixes.add(prefix)
- cls._prefix_to_backends[prefix] = backend
- else:
- raise KeyError(
- f'{prefix} is already registered as a storage backend,'
- ' add "force=True" if you want to override it')
-
- @classmethod
- def register_backend(cls, name, backend=None, force=False, prefixes=None):
- """Register a backend to FileClient.
-
- This method can be used as a normal class method or a decorator.
-
- .. code-block:: python
-
- class NewBackend(BaseStorageBackend):
-
- def get(self, filepath):
- return filepath
-
- def get_text(self, filepath):
- return filepath
-
- FileClient.register_backend('new', NewBackend)
-
- or
-
- .. code-block:: python
-
- @FileClient.register_backend('new')
- class NewBackend(BaseStorageBackend):
-
- def get(self, filepath):
- return filepath
-
- def get_text(self, filepath):
- return filepath
-
- Args:
- name (str): The name of the registered backend.
- backend (class, optional): The backend class to be registered,
- which must be a subclass of :class:`BaseStorageBackend`.
- When this method is used as a decorator, backend is None.
- Defaults to None.
- force (bool, optional): Whether to override the backend if the name
- has already been registered. Defaults to False.
- prefixes (str or list[str] or tuple[str], optional): The prefixes
- of the registered storage backend. Default: None.
- `New in version 1.3.15.`
- """
- if backend is not None:
- cls._register_backend(
- name, backend, force=force, prefixes=prefixes)
- return
-
- def _register(backend_cls):
- cls._register_backend(
- name, backend_cls, force=force, prefixes=prefixes)
- return backend_cls
-
- return _register
-
- def get(self, filepath: Union[str, Path]) -> Union[bytes, memoryview]:
- """Read data from a given ``filepath`` with 'rb' mode.
-
- Note:
- There are two types of return values for ``get``, one is ``bytes``
- and the other is ``memoryview``. The advantage of using memoryview
- is that you can avoid copying, and if you want to convert it to
- ``bytes``, you can use ``.tobytes()``.
-
- Args:
- filepath (str or Path): Path to read data.
-
- Returns:
- bytes | memoryview: Expected bytes object or a memory view of the
- bytes object.
- """
- return self.client.get(filepath)
-
- def get_text(self, filepath: Union[str, Path], encoding='utf-8') -> str:
- """Read data from a given ``filepath`` with 'r' mode.
-
- Args:
- filepath (str or Path): Path to read data.
- encoding (str): The encoding format used to open the ``filepath``.
- Default: 'utf-8'.
-
- Returns:
- str: Expected text reading from ``filepath``.
- """
- return self.client.get_text(filepath, encoding)
-
- def put(self, obj: bytes, filepath: Union[str, Path]) -> None:
- """Write data to a given ``filepath`` with 'wb' mode.
-
- Note:
- ``put`` should create a directory if the directory of ``filepath``
- does not exist.
-
- Args:
- obj (bytes): Data to be written.
- filepath (str or Path): Path to write data.
- """
- self.client.put(obj, filepath)
-
- def put_text(self, obj: str, filepath: Union[str, Path]) -> None:
- """Write data to a given ``filepath`` with 'w' mode.
-
- Note:
- ``put_text`` should create a directory if the directory of
- ``filepath`` does not exist.
-
- Args:
- obj (str): Data to be written.
- filepath (str or Path): Path to write data.
- encoding (str, optional): The encoding format used to open the
- `filepath`. Default: 'utf-8'.
- """
- self.client.put_text(obj, filepath)
-
- def remove(self, filepath: Union[str, Path]) -> None:
- """Remove a file.
-
- Args:
- filepath (str, Path): Path to be removed.
- """
- self.client.remove(filepath)
-
- def exists(self, filepath: Union[str, Path]) -> bool:
- """Check whether a file path exists.
-
- Args:
- filepath (str or Path): Path to be checked whether exists.
-
- Returns:
- bool: Return ``True`` if ``filepath`` exists, ``False`` otherwise.
- """
- return self.client.exists(filepath)
-
- def isdir(self, filepath: Union[str, Path]) -> bool:
- """Check whether a file path is a directory.
-
- Args:
- filepath (str or Path): Path to be checked whether it is a
- directory.
-
- Returns:
- bool: Return ``True`` if ``filepath`` points to a directory,
- ``False`` otherwise.
- """
- return self.client.isdir(filepath)
-
- def isfile(self, filepath: Union[str, Path]) -> bool:
- """Check whether a file path is a file.
-
- Args:
- filepath (str or Path): Path to be checked whether it is a file.
-
- Returns:
- bool: Return ``True`` if ``filepath`` points to a file, ``False``
- otherwise.
- """
- return self.client.isfile(filepath)
-
- def join_path(self, filepath: Union[str, Path],
- *filepaths: Union[str, Path]) -> str:
- """Concatenate all file paths.
-
- Join one or more filepath components intelligently. The return value
- is the concatenation of filepath and any members of *filepaths.
-
- Args:
- filepath (str or Path): Path to be concatenated.
-
- Returns:
- str: The result of concatenation.
- """
- return self.client.join_path(filepath, *filepaths)
-
- @contextmanager
- def get_local_path(self, filepath: Union[str, Path]) -> Iterable[str]:
- """Download data from ``filepath`` and write the data to local path.
-
- ``get_local_path`` is decorated by :meth:`contxtlib.contextmanager`. It
- can be called with ``with`` statement, and when exists from the
- ``with`` statement, the temporary path will be released.
-
- Note:
- If the ``filepath`` is a local path, just return itself.
-
- .. warning::
- ``get_local_path`` is an experimental interface that may change in
- the future.
-
- Args:
- filepath (str or Path): Path to be read data.
-
- Examples:
- >>> file_client = FileClient(prefix='s3')
- >>> with file_client.get_local_path('s3://bucket/abc.jpg') as path:
- ... # do something here
-
- Yields:
- Iterable[str]: Only yield one path.
- """
- with self.client.get_local_path(str(filepath)) as local_path:
- yield local_path
-
- def list_dir_or_file(self,
- dir_path: Union[str, Path],
- list_dir: bool = True,
- list_file: bool = True,
- suffix: Optional[Union[str, Tuple[str]]] = None,
- recursive: bool = False) -> Iterator[str]:
- """Scan a directory to find the interested directories or files in
- arbitrary order.
-
- Note:
- :meth:`list_dir_or_file` returns the path relative to ``dir_path``.
-
- Args:
- dir_path (str | Path): Path of the directory.
- list_dir (bool): List the directories. Default: True.
- list_file (bool): List the path of files. Default: True.
- suffix (str or tuple[str], optional): File suffix
- that we are interested in. Default: None.
- recursive (bool): If set to True, recursively scan the
- directory. Default: False.
-
- Yields:
- Iterable[str]: A relative path to ``dir_path``.
- """
- yield from self.client.list_dir_or_file(dir_path, list_dir, list_file,
- suffix, recursive)
diff --git a/spaces/Anthos23/hummus/app.py b/spaces/Anthos23/hummus/app.py
deleted file mode 100644
index e2f693e14cd70258338c2669290a0aad3566cabc..0000000000000000000000000000000000000000
--- a/spaces/Anthos23/hummus/app.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TextClassificationPipeline
-import operator
-import matplotlib.pyplot as plt
-import pandas as pd
-
-def get_sentiment(out):
- d = dict()
- for k in out:
- print(k)
- label = k['label']
- score = k['score']
- d[label] = score
-
- winning_lab = max(d.items(), key=operator.itemgetter(1))[0]
- winning_score = d[winning_lab]
-
- df = pd.DataFrame.from_dict(d, orient = 'index')
- return df #winning_lab, winning_score
-
-model_name = "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
-text = st.text_area(f'Ciao! This app uses {model_name}.\nEnter your text to test it ❤️')
-
-
-if text:
- out = pipe(text)
- df = get_sentiment(out[0])
- fig, ax = plt.subplots()
- c = ['#C34A36', '#FFC75F', '#008F7A']
- ax.bar(df.index, df[0], color=c, width=0.4)
-
- st.pyplot(fig)
-
- #st.json(get_sentiment(out[0][0]))
diff --git a/spaces/AsakuraMizu/moe-tts/text/cantonese.py b/spaces/AsakuraMizu/moe-tts/text/cantonese.py
deleted file mode 100644
index 32eae72ef7eb43d493da6d6f75dd46176d0e8808..0000000000000000000000000000000000000000
--- a/spaces/AsakuraMizu/moe-tts/text/cantonese.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import re
-import cn2an
-import opencc
-
-
-converter = opencc.OpenCC('chinese_dialect_lexicons/jyutjyu')
-
-# List of (Latin alphabet, ipa) pairs:
-_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('A', 'ei˥'),
- ('B', 'biː˥'),
- ('C', 'siː˥'),
- ('D', 'tiː˥'),
- ('E', 'iː˥'),
- ('F', 'e˥fuː˨˩'),
- ('G', 'tsiː˥'),
- ('H', 'ɪk̚˥tsʰyː˨˩'),
- ('I', 'ɐi˥'),
- ('J', 'tsei˥'),
- ('K', 'kʰei˥'),
- ('L', 'e˥llou˨˩'),
- ('M', 'ɛːm˥'),
- ('N', 'ɛːn˥'),
- ('O', 'ou˥'),
- ('P', 'pʰiː˥'),
- ('Q', 'kʰiːu˥'),
- ('R', 'aː˥lou˨˩'),
- ('S', 'ɛː˥siː˨˩'),
- ('T', 'tʰiː˥'),
- ('U', 'juː˥'),
- ('V', 'wiː˥'),
- ('W', 'tʊk̚˥piː˥juː˥'),
- ('X', 'ɪk̚˥siː˨˩'),
- ('Y', 'waːi˥'),
- ('Z', 'iː˨sɛːt̚˥')
-]]
-
-
-def number_to_cantonese(text):
- return re.sub(r'\d+(?:\.?\d+)?', lambda x: cn2an.an2cn(x.group()), text)
-
-
-def latin_to_ipa(text):
- for regex, replacement in _latin_to_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def cantonese_to_ipa(text):
- text = number_to_cantonese(text.upper())
- text = converter.convert(text).replace('-','').replace('$',' ')
- text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text)
- text = re.sub(r'[、；：]', '，', text)
- text = re.sub(r'\s*，\s*', ', ', text)
- text = re.sub(r'\s*。\s*', '. ', text)
- text = re.sub(r'\s*？\s*', '? ', text)
- text = re.sub(r'\s*！\s*', '! ', text)
- text = re.sub(r'\s*$', '', text)
- return text
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/scanner.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/scanner.py
deleted file mode 100644
index d47ed4828a0a671d46908a25f6ef0733801d6fb9..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/scanner.py
+++ /dev/null
@@ -1,104 +0,0 @@
-"""
- pygments.scanner
- ~~~~~~~~~~~~~~~~
-
- This library implements a regex based scanner. Some languages
- like Pascal are easy to parse but have some keywords that
- depend on the context. Because of this it's impossible to lex
- that just by using a regular expression lexer like the
- `RegexLexer`.
-
- Have a look at the `DelphiLexer` to get an idea of how to use
- this scanner.
-
- :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
- :license: BSD, see LICENSE for details.
-"""
-import re
-
-
-class EndOfText(RuntimeError):
- """
- Raise if end of text is reached and the user
- tried to call a match function.
- """
-
-
-class Scanner:
- """
- Simple scanner
-
- All method patterns are regular expression strings (not
- compiled expressions!)
- """
-
- def __init__(self, text, flags=0):
- """
- :param text: The text which should be scanned
- :param flags: default regular expression flags
- """
- self.data = text
- self.data_length = len(text)
- self.start_pos = 0
- self.pos = 0
- self.flags = flags
- self.last = None
- self.match = None
- self._re_cache = {}
-
- def eos(self):
- """`True` if the scanner reached the end of text."""
- return self.pos >= self.data_length
- eos = property(eos, eos.__doc__)
-
- def check(self, pattern):
- """
- Apply `pattern` on the current position and return
- the match object. (Doesn't touch pos). Use this for
- lookahead.
- """
- if self.eos:
- raise EndOfText()
- if pattern not in self._re_cache:
- self._re_cache[pattern] = re.compile(pattern, self.flags)
- return self._re_cache[pattern].match(self.data, self.pos)
-
- def test(self, pattern):
- """Apply a pattern on the current position and check
- if it patches. Doesn't touch pos.
- """
- return self.check(pattern) is not None
-
- def scan(self, pattern):
- """
- Scan the text for the given pattern and update pos/match
- and related fields. The return value is a boolean that
- indicates if the pattern matched. The matched value is
- stored on the instance as ``match``, the last value is
- stored as ``last``. ``start_pos`` is the position of the
- pointer before the pattern was matched, ``pos`` is the
- end position.
- """
- if self.eos:
- raise EndOfText()
- if pattern not in self._re_cache:
- self._re_cache[pattern] = re.compile(pattern, self.flags)
- self.last = self.match
- m = self._re_cache[pattern].match(self.data, self.pos)
- if m is None:
- return False
- self.start_pos = m.start()
- self.pos = m.end()
- self.match = m.group()
- return True
-
- def get_char(self):
- """Scan exactly one char."""
- self.scan('.')
-
- def __repr__(self):
- return '<%s %d/%d>' % (
- self.__class__.__name__,
- self.pos,
- self.data_length
- )
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/jupyter.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/jupyter.py
deleted file mode 100644
index 22f4d716ac9764ee18005b9b852946d614152375..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/jupyter.py
+++ /dev/null
@@ -1,101 +0,0 @@
-from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Sequence
-
-if TYPE_CHECKING:
- from pip._vendor.rich.console import ConsoleRenderable
-
-from . import get_console
-from .segment import Segment
-from .terminal_theme import DEFAULT_TERMINAL_THEME
-
-if TYPE_CHECKING:
- from pip._vendor.rich.console import ConsoleRenderable
-
-JUPYTER_HTML_FORMAT = """\
-{code}
-"""
-
-
-class JupyterRenderable:
- """A shim to write html to Jupyter notebook."""
-
- def __init__(self, html: str, text: str) -> None:
- self.html = html
- self.text = text
-
- def _repr_mimebundle_(
- self, include: Sequence[str], exclude: Sequence[str], **kwargs: Any
- ) -> Dict[str, str]:
- data = {"text/plain": self.text, "text/html": self.html}
- if include:
- data = {k: v for (k, v) in data.items() if k in include}
- if exclude:
- data = {k: v for (k, v) in data.items() if k not in exclude}
- return data
-
-
-class JupyterMixin:
- """Add to an Rich renderable to make it render in Jupyter notebook."""
-
- __slots__ = ()
-
- def _repr_mimebundle_(
- self: "ConsoleRenderable",
- include: Sequence[str],
- exclude: Sequence[str],
- **kwargs: Any,
- ) -> Dict[str, str]:
- console = get_console()
- segments = list(console.render(self, console.options))
- html = _render_segments(segments)
- text = console._render_buffer(segments)
- data = {"text/plain": text, "text/html": html}
- if include:
- data = {k: v for (k, v) in data.items() if k in include}
- if exclude:
- data = {k: v for (k, v) in data.items() if k not in exclude}
- return data
-
-
-def _render_segments(segments: Iterable[Segment]) -> str:
- def escape(text: str) -> str:
- """Escape html."""
- return text.replace("&", "&").replace("<", "<").replace(">", ">")
-
- fragments: List[str] = []
- append_fragment = fragments.append
- theme = DEFAULT_TERMINAL_THEME
- for text, style, control in Segment.simplify(segments):
- if control:
- continue
- text = escape(text)
- if style:
- rule = style.get_html_style(theme)
- text = f'{text}' if rule else text
- if style.link:
- text = f'{text}'
- append_fragment(text)
-
- code = "".join(fragments)
- html = JUPYTER_HTML_FORMAT.format(code=code)
-
- return html
-
-
-def display(segments: Iterable[Segment], text: str) -> None:
- """Render segments to Jupyter."""
- html = _render_segments(segments)
- jupyter_renderable = JupyterRenderable(html, text)
- try:
- from IPython.display import display as ipython_display
-
- ipython_display(jupyter_renderable)
- except ModuleNotFoundError:
- # Handle the case where the Console has force_jupyter=True,
- # but IPython is not installed.
- pass
-
-
-def print(*args: Any, **kwargs: Any) -> None:
- """Proxy for Console print."""
- console = get_console()
- return console.print(*args, **kwargs)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/importlib_resources/_adapters.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/importlib_resources/_adapters.py
deleted file mode 100644
index ea363d86a564b5450666aa00aecd46353326a75a..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/importlib_resources/_adapters.py
+++ /dev/null
@@ -1,170 +0,0 @@
-from contextlib import suppress
-from io import TextIOWrapper
-
-from . import abc
-
-
-class SpecLoaderAdapter:
- """
- Adapt a package spec to adapt the underlying loader.
- """
-
- def __init__(self, spec, adapter=lambda spec: spec.loader):
- self.spec = spec
- self.loader = adapter(spec)
-
- def __getattr__(self, name):
- return getattr(self.spec, name)
-
-
-class TraversableResourcesLoader:
- """
- Adapt a loader to provide TraversableResources.
- """
-
- def __init__(self, spec):
- self.spec = spec
-
- def get_resource_reader(self, name):
- return CompatibilityFiles(self.spec)._native()
-
-
-def _io_wrapper(file, mode='r', *args, **kwargs):
- if mode == 'r':
- return TextIOWrapper(file, *args, **kwargs)
- elif mode == 'rb':
- return file
- raise ValueError(
- "Invalid mode value '{}', only 'r' and 'rb' are supported".format(mode)
- )
-
-
-class CompatibilityFiles:
- """
- Adapter for an existing or non-existent resource reader
- to provide a compatibility .files().
- """
-
- class SpecPath(abc.Traversable):
- """
- Path tied to a module spec.
- Can be read and exposes the resource reader children.
- """
-
- def __init__(self, spec, reader):
- self._spec = spec
- self._reader = reader
-
- def iterdir(self):
- if not self._reader:
- return iter(())
- return iter(
- CompatibilityFiles.ChildPath(self._reader, path)
- for path in self._reader.contents()
- )
-
- def is_file(self):
- return False
-
- is_dir = is_file
-
- def joinpath(self, other):
- if not self._reader:
- return CompatibilityFiles.OrphanPath(other)
- return CompatibilityFiles.ChildPath(self._reader, other)
-
- @property
- def name(self):
- return self._spec.name
-
- def open(self, mode='r', *args, **kwargs):
- return _io_wrapper(self._reader.open_resource(None), mode, *args, **kwargs)
-
- class ChildPath(abc.Traversable):
- """
- Path tied to a resource reader child.
- Can be read but doesn't expose any meaningful children.
- """
-
- def __init__(self, reader, name):
- self._reader = reader
- self._name = name
-
- def iterdir(self):
- return iter(())
-
- def is_file(self):
- return self._reader.is_resource(self.name)
-
- def is_dir(self):
- return not self.is_file()
-
- def joinpath(self, other):
- return CompatibilityFiles.OrphanPath(self.name, other)
-
- @property
- def name(self):
- return self._name
-
- def open(self, mode='r', *args, **kwargs):
- return _io_wrapper(
- self._reader.open_resource(self.name), mode, *args, **kwargs
- )
-
- class OrphanPath(abc.Traversable):
- """
- Orphan path, not tied to a module spec or resource reader.
- Can't be read and doesn't expose any meaningful children.
- """
-
- def __init__(self, *path_parts):
- if len(path_parts) < 1:
- raise ValueError('Need at least one path part to construct a path')
- self._path = path_parts
-
- def iterdir(self):
- return iter(())
-
- def is_file(self):
- return False
-
- is_dir = is_file
-
- def joinpath(self, other):
- return CompatibilityFiles.OrphanPath(*self._path, other)
-
- @property
- def name(self):
- return self._path[-1]
-
- def open(self, mode='r', *args, **kwargs):
- raise FileNotFoundError("Can't open orphan path")
-
- def __init__(self, spec):
- self.spec = spec
-
- @property
- def _reader(self):
- with suppress(AttributeError):
- return self.spec.loader.get_resource_reader(self.spec.name)
-
- def _native(self):
- """
- Return the native reader if it supports files().
- """
- reader = self._reader
- return reader if hasattr(reader, 'files') else self
-
- def __getattr__(self, attr):
- return getattr(self._reader, attr)
-
- def files(self):
- return CompatibilityFiles.SpecPath(self.spec, self._reader)
-
-
-def wrap_spec(package):
- """
- Construct a package spec with traversable compatibility
- on the spec/loader/reader.
- """
- return SpecLoaderAdapter(package.__spec__, TraversableResourcesLoader)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/unicode.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/unicode.py
deleted file mode 100644
index 06526203911de55da3c2a8c5ae73f48024c3f018..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/unicode.py
+++ /dev/null
@@ -1,352 +0,0 @@
-# unicode.py
-
-import sys
-from itertools import filterfalse
-from typing import List, Tuple, Union
-
-
-class _lazyclassproperty:
- def __init__(self, fn):
- self.fn = fn
- self.__doc__ = fn.__doc__
- self.__name__ = fn.__name__
-
- def __get__(self, obj, cls):
- if cls is None:
- cls = type(obj)
- if not hasattr(cls, "_intern") or any(
- cls._intern is getattr(superclass, "_intern", [])
- for superclass in cls.__mro__[1:]
- ):
- cls._intern = {}
- attrname = self.fn.__name__
- if attrname not in cls._intern:
- cls._intern[attrname] = self.fn(cls)
- return cls._intern[attrname]
-
-
-UnicodeRangeList = List[Union[Tuple[int, int], Tuple[int]]]
-
-
-class unicode_set:
- """
- A set of Unicode characters, for language-specific strings for
- ``alphas``, ``nums``, ``alphanums``, and ``printables``.
- A unicode_set is defined by a list of ranges in the Unicode character
- set, in a class attribute ``_ranges``. Ranges can be specified using
- 2-tuples or a 1-tuple, such as::
-
- _ranges = [
- (0x0020, 0x007e),
- (0x00a0, 0x00ff),
- (0x0100,),
- ]
-
- Ranges are left- and right-inclusive. A 1-tuple of (x,) is treated as (x, x).
-
- A unicode set can also be defined using multiple inheritance of other unicode sets::
-
- class CJK(Chinese, Japanese, Korean):
- pass
- """
-
- _ranges: UnicodeRangeList = []
-
- @_lazyclassproperty
- def _chars_for_ranges(cls):
- ret = []
- for cc in cls.__mro__:
- if cc is unicode_set:
- break
- for rr in getattr(cc, "_ranges", ()):
- ret.extend(range(rr[0], rr[-1] + 1))
- return [chr(c) for c in sorted(set(ret))]
-
- @_lazyclassproperty
- def printables(cls):
- "all non-whitespace characters in this range"
- return "".join(filterfalse(str.isspace, cls._chars_for_ranges))
-
- @_lazyclassproperty
- def alphas(cls):
- "all alphabetic characters in this range"
- return "".join(filter(str.isalpha, cls._chars_for_ranges))
-
- @_lazyclassproperty
- def nums(cls):
- "all numeric digit characters in this range"
- return "".join(filter(str.isdigit, cls._chars_for_ranges))
-
- @_lazyclassproperty
- def alphanums(cls):
- "all alphanumeric characters in this range"
- return cls.alphas + cls.nums
-
- @_lazyclassproperty
- def identchars(cls):
- "all characters in this range that are valid identifier characters, plus underscore '_'"
- return "".join(
- sorted(
- set(
- "".join(filter(str.isidentifier, cls._chars_for_ranges))
- + "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzªµº"
- + "ÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõöøùúûüýþÿ"
- + "_"
- )
- )
- )
-
- @_lazyclassproperty
- def identbodychars(cls):
- """
- all characters in this range that are valid identifier body characters,
- plus the digits 0-9
- """
- return "".join(
- sorted(
- set(
- cls.identchars
- + "0123456789"
- + "".join(
- [c for c in cls._chars_for_ranges if ("_" + c).isidentifier()]
- )
- )
- )
- )
-
-
-class pyparsing_unicode(unicode_set):
- """
- A namespace class for defining common language unicode_sets.
- """
-
- # fmt: off
-
- # define ranges in language character sets
- _ranges: UnicodeRangeList = [
- (0x0020, sys.maxunicode),
- ]
-
- class BasicMultilingualPlane(unicode_set):
- "Unicode set for the Basic Multilingual Plane"
- _ranges: UnicodeRangeList = [
- (0x0020, 0xFFFF),
- ]
-
- class Latin1(unicode_set):
- "Unicode set for Latin-1 Unicode Character Range"
- _ranges: UnicodeRangeList = [
- (0x0020, 0x007E),
- (0x00A0, 0x00FF),
- ]
-
- class LatinA(unicode_set):
- "Unicode set for Latin-A Unicode Character Range"
- _ranges: UnicodeRangeList = [
- (0x0100, 0x017F),
- ]
-
- class LatinB(unicode_set):
- "Unicode set for Latin-B Unicode Character Range"
- _ranges: UnicodeRangeList = [
- (0x0180, 0x024F),
- ]
-
- class Greek(unicode_set):
- "Unicode set for Greek Unicode Character Ranges"
- _ranges: UnicodeRangeList = [
- (0x0342, 0x0345),
- (0x0370, 0x0377),
- (0x037A, 0x037F),
- (0x0384, 0x038A),
- (0x038C,),
- (0x038E, 0x03A1),
- (0x03A3, 0x03E1),
- (0x03F0, 0x03FF),
- (0x1D26, 0x1D2A),
- (0x1D5E,),
- (0x1D60,),
- (0x1D66, 0x1D6A),
- (0x1F00, 0x1F15),
- (0x1F18, 0x1F1D),
- (0x1F20, 0x1F45),
- (0x1F48, 0x1F4D),
- (0x1F50, 0x1F57),
- (0x1F59,),
- (0x1F5B,),
- (0x1F5D,),
- (0x1F5F, 0x1F7D),
- (0x1F80, 0x1FB4),
- (0x1FB6, 0x1FC4),
- (0x1FC6, 0x1FD3),
- (0x1FD6, 0x1FDB),
- (0x1FDD, 0x1FEF),
- (0x1FF2, 0x1FF4),
- (0x1FF6, 0x1FFE),
- (0x2129,),
- (0x2719, 0x271A),
- (0xAB65,),
- (0x10140, 0x1018D),
- (0x101A0,),
- (0x1D200, 0x1D245),
- (0x1F7A1, 0x1F7A7),
- ]
-
- class Cyrillic(unicode_set):
- "Unicode set for Cyrillic Unicode Character Range"
- _ranges: UnicodeRangeList = [
- (0x0400, 0x052F),
- (0x1C80, 0x1C88),
- (0x1D2B,),
- (0x1D78,),
- (0x2DE0, 0x2DFF),
- (0xA640, 0xA672),
- (0xA674, 0xA69F),
- (0xFE2E, 0xFE2F),
- ]
-
- class Chinese(unicode_set):
- "Unicode set for Chinese Unicode Character Range"
- _ranges: UnicodeRangeList = [
- (0x2E80, 0x2E99),
- (0x2E9B, 0x2EF3),
- (0x31C0, 0x31E3),
- (0x3400, 0x4DB5),
- (0x4E00, 0x9FEF),
- (0xA700, 0xA707),
- (0xF900, 0xFA6D),
- (0xFA70, 0xFAD9),
- (0x16FE2, 0x16FE3),
- (0x1F210, 0x1F212),
- (0x1F214, 0x1F23B),
- (0x1F240, 0x1F248),
- (0x20000, 0x2A6D6),
- (0x2A700, 0x2B734),
- (0x2B740, 0x2B81D),
- (0x2B820, 0x2CEA1),
- (0x2CEB0, 0x2EBE0),
- (0x2F800, 0x2FA1D),
- ]
-
- class Japanese(unicode_set):
- "Unicode set for Japanese Unicode Character Range, combining Kanji, Hiragana, and Katakana ranges"
- _ranges: UnicodeRangeList = []
-
- class Kanji(unicode_set):
- "Unicode set for Kanji Unicode Character Range"
- _ranges: UnicodeRangeList = [
- (0x4E00, 0x9FBF),
- (0x3000, 0x303F),
- ]
-
- class Hiragana(unicode_set):
- "Unicode set for Hiragana Unicode Character Range"
- _ranges: UnicodeRangeList = [
- (0x3041, 0x3096),
- (0x3099, 0x30A0),
- (0x30FC,),
- (0xFF70,),
- (0x1B001,),
- (0x1B150, 0x1B152),
- (0x1F200,),
- ]
-
- class Katakana(unicode_set):
- "Unicode set for Katakana Unicode Character Range"
- _ranges: UnicodeRangeList = [
- (0x3099, 0x309C),
- (0x30A0, 0x30FF),
- (0x31F0, 0x31FF),
- (0x32D0, 0x32FE),
- (0xFF65, 0xFF9F),
- (0x1B000,),
- (0x1B164, 0x1B167),
- (0x1F201, 0x1F202),
- (0x1F213,),
- ]
-
- class Hangul(unicode_set):
- "Unicode set for Hangul (Korean) Unicode Character Range"
- _ranges: UnicodeRangeList = [
- (0x1100, 0x11FF),
- (0x302E, 0x302F),
- (0x3131, 0x318E),
- (0x3200, 0x321C),
- (0x3260, 0x327B),
- (0x327E,),
- (0xA960, 0xA97C),
- (0xAC00, 0xD7A3),
- (0xD7B0, 0xD7C6),
- (0xD7CB, 0xD7FB),
- (0xFFA0, 0xFFBE),
- (0xFFC2, 0xFFC7),
- (0xFFCA, 0xFFCF),
- (0xFFD2, 0xFFD7),
- (0xFFDA, 0xFFDC),
- ]
-
- Korean = Hangul
-
- class CJK(Chinese, Japanese, Hangul):
- "Unicode set for combined Chinese, Japanese, and Korean (CJK) Unicode Character Range"
-
- class Thai(unicode_set):
- "Unicode set for Thai Unicode Character Range"
- _ranges: UnicodeRangeList = [
- (0x0E01, 0x0E3A),
- (0x0E3F, 0x0E5B)
- ]
-
- class Arabic(unicode_set):
- "Unicode set for Arabic Unicode Character Range"
- _ranges: UnicodeRangeList = [
- (0x0600, 0x061B),
- (0x061E, 0x06FF),
- (0x0700, 0x077F),
- ]
-
- class Hebrew(unicode_set):
- "Unicode set for Hebrew Unicode Character Range"
- _ranges: UnicodeRangeList = [
- (0x0591, 0x05C7),
- (0x05D0, 0x05EA),
- (0x05EF, 0x05F4),
- (0xFB1D, 0xFB36),
- (0xFB38, 0xFB3C),
- (0xFB3E,),
- (0xFB40, 0xFB41),
- (0xFB43, 0xFB44),
- (0xFB46, 0xFB4F),
- ]
-
- class Devanagari(unicode_set):
- "Unicode set for Devanagari Unicode Character Range"
- _ranges: UnicodeRangeList = [
- (0x0900, 0x097F),
- (0xA8E0, 0xA8FF)
- ]
-
- # fmt: on
-
-
-pyparsing_unicode.Japanese._ranges = (
- pyparsing_unicode.Japanese.Kanji._ranges
- + pyparsing_unicode.Japanese.Hiragana._ranges
- + pyparsing_unicode.Japanese.Katakana._ranges
-)
-
-pyparsing_unicode.BMP = pyparsing_unicode.BasicMultilingualPlane
-
-# add language identifiers using language Unicode
-pyparsing_unicode.العربية = pyparsing_unicode.Arabic
-pyparsing_unicode.中文 = pyparsing_unicode.Chinese
-pyparsing_unicode.кириллица = pyparsing_unicode.Cyrillic
-pyparsing_unicode.Ελληνικά = pyparsing_unicode.Greek
-pyparsing_unicode.עִברִית = pyparsing_unicode.Hebrew
-pyparsing_unicode.日本語 = pyparsing_unicode.Japanese
-pyparsing_unicode.Japanese.漢字 = pyparsing_unicode.Japanese.Kanji
-pyparsing_unicode.Japanese.カタカナ = pyparsing_unicode.Japanese.Katakana
-pyparsing_unicode.Japanese.ひらがな = pyparsing_unicode.Japanese.Hiragana
-pyparsing_unicode.한국어 = pyparsing_unicode.Korean
-pyparsing_unicode.ไทย = pyparsing_unicode.Thai
-pyparsing_unicode.देवनागरी = pyparsing_unicode.Devanagari
diff --git a/spaces/Atualli/yoloxTeste/checkYoloxGPU.sh b/spaces/Atualli/yoloxTeste/checkYoloxGPU.sh
deleted file mode 100644
index d1c7225ca9bc4f79a7e07c4244ca3d8fab1f7628..0000000000000000000000000000000000000000
--- a/spaces/Atualli/yoloxTeste/checkYoloxGPU.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-#!/bin/sh
-export path=/home/atualli/.local/lib/python3.8/site-packages:$PATH
-cd ~/Projetos/huggingface/yoloxTeste_GPU
-SERVER=192.168.0.153
-PORT=8081
-
-if lsof -Pi :$PORT -sTCP:LISTEN -t >/dev/null ; then
- echo "running"
-else
- ./telegramCrise.sh "reiniciando_yolox_GPU_linux_192.168.0.153:8081"
- pkill -f app1.py
- python app1.py &
- echo "not running"
-fi
-
-
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/image_dense_captions.py b/spaces/Awiny/Image2Paragraph/models/grit_src/image_dense_captions.py
deleted file mode 100644
index 33a15ea982beea0e58739740c01954575bbb1ab3..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/image_dense_captions.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import argparse
-import multiprocessing as mp
-import os
-import time
-import cv2
-import tqdm
-import sys
-
-from detectron2.config import get_cfg
-from detectron2.data.detection_utils import read_image
-from detectron2.utils.logger import setup_logger
-
-sys.path.insert(0, 'models/grit_src/third_party/CenterNet2/projects/CenterNet2/')
-from centernet.config import add_centernet_config
-from models.grit_src.grit.config import add_grit_config
-
-from models.grit_src.grit.predictor import VisualizationDemo
-import json
-from utils.util import resize_long_edge_cv2
-
-
-# constants
-WINDOW_NAME = "GRiT"
-
-
-def dense_pred_to_caption(predictions):
- boxes = predictions["instances"].pred_boxes if predictions["instances"].has("pred_boxes") else None
- object_description = predictions["instances"].pred_object_descriptions.data
- new_caption = ""
- for i in range(len(object_description)):
- new_caption += (object_description[i] + ": " + str([int(a) for a in boxes[i].tensor.cpu().detach().numpy()[0]])) + "; "
- return new_caption
-
-def setup_cfg(args):
- cfg = get_cfg()
- if args["cpu"]:
- cfg.MODEL.DEVICE="cpu"
- add_centernet_config(cfg)
- add_grit_config(cfg)
- cfg.merge_from_file(args["config_file"])
- cfg.merge_from_list(args["opts"])
- # Set score_threshold for builtin models
- cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args["confidence_threshold"]
- cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args["confidence_threshold"]
- if args["test_task"]:
- cfg.MODEL.TEST_TASK = args["test_task"]
- cfg.MODEL.BEAM_SIZE = 1
- cfg.MODEL.ROI_HEADS.SOFT_NMS_ENABLED = False
- cfg.USE_ACT_CHECKPOINT = False
- cfg.freeze()
- return cfg
-
-
-def get_parser(device):
- arg_dict = {'config_file': "models/grit_src/configs/GRiT_B_DenseCap_ObjectDet.yaml", 'cpu': False, 'confidence_threshold': 0.5, 'test_task': 'DenseCap', 'opts': ["MODEL.WEIGHTS", "pretrained_models/grit_b_densecap_objectdet.pth"]}
- if device == "cpu":
- arg_dict["cpu"] = True
- return arg_dict
-
-def image_caption_api(image_src, device):
- args2 = get_parser(device)
- cfg = setup_cfg(args2)
- demo = VisualizationDemo(cfg)
- if image_src:
- img = read_image(image_src, format="BGR")
- img = resize_long_edge_cv2(img, 384)
- predictions, visualized_output = demo.run_on_image(img)
- new_caption = dense_pred_to_caption(predictions)
- return new_caption
\ No newline at end of file
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cocoeval/cocoeval.cpp b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cocoeval/cocoeval.cpp
deleted file mode 100644
index 0a5b7b907c06720fefc77b0dfd921b8ec3ecf2be..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/cocoeval/cocoeval.cpp
+++ /dev/null
@@ -1,507 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-#include "cocoeval.h"
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-
-Cómo jugar Clash Royale en PC con Bluestacks
-Personaliza tus controles de teclado y ratón para una jugabilidad óptima
-Disfruta de los gráficos Full HD y el buen rendimiento de Bluestacks
-Acceda a funciones exclusivas y recompensas de Bluestacks
-
-Conclusión
-
-
-
\ No newline at end of file
diff --git a/spaces/BirdL/DONOTUSEDemo/app.py b/spaces/BirdL/DONOTUSEDemo/app.py
deleted file mode 100644
index 09194cf99d94ebe99add5cae7bf5099b9e160614..0000000000000000000000000000000000000000
--- a/spaces/BirdL/DONOTUSEDemo/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import gradio as gr
-import torch
-from random import randint
-import os
-import huggingface_hub
-
-tok = os.getenv('HF_TOKEN')
-huggingface_hub.login(tok)
-
-from huggingface_hub import HfApi
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-config = PeftConfig.from_pretrained("BirdL/DONOTUSEV5")
-model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t", token=tok, trust_remote_code=True)
-model = PeftModel.from_pretrained(model, "BirdL/DONOTUSEV5")
-tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t", token=tok)
-
-def response(message, history):
- batch = tokenizer(message, return_tensors='pt')
-
- with torch.cuda.amp.autocast():
- output_tokens = model.generate(**batch, max_new_tokens=20)
- output_tokens = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
- filename = (("file" + str(randint(0, 1000000)) + ".txt"))
- api = HfApi()
- api.upload_file(
- path_or_fileobj=("|Question:" + message + " |RespV2: " + output_tokens).encode('ascii') ,
- path_in_repo=(filename),
- repo_id="BirdL/Data",
- )
-
- return output_tokens
-gr.ChatInterface(response).launch()
\ No newline at end of file
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/cpp/vector.h b/spaces/CVPR/LIVE/thrust/thrust/system/cpp/vector.h
deleted file mode 100644
index ee5cfce6aa8d26a2d6d924361f42bfec99cf8601..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/cpp/vector.h
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*! \file thrust/system/cpp/vector.h
- * \brief A dynamically-sizable array of elements which reside in memory available to
- * Thrust's standard C++ system.
- */
-
-#pragma once
-
-#include
-
-
- )
-}
-
-export default App;
diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/generate.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/generate.py
deleted file mode 100644
index a8b7d55e6d190c193e427bd8d623c583b2dcdeda..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/stylegan_human/generate.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-
-
-## this script is for generating images from pre-trained network based on StyleGAN1 (TensorFlow) and StyleGAN2-ada (PyTorch) ##
-
-import os
-import click
-import dnnlib
-import numpy as np
-import PIL.Image
-import legacy
-from typing import List, Optional
-
-"""
-Generate images using pretrained network pickle.
-Examples:
-
-\b
-# Generate human full-body images without truncation
-python generate.py --outdir=outputs/generate/stylegan_human_v2_1024 --trunc=1 --seeds=1,3,5,7 \\
- --network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
-
-\b
-# Generate human full-body images with truncation
-python generate.py --outdir=outputs/generate/stylegan_human_v2_1024 --trunc=0.8 --seeds=0-100\\
- --network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
-
-# \b
-# Generate human full-body images using stylegan V1
-# python generate.py --outdir=outputs/generate/stylegan_human_v1_1024 \\
-# --network=pretrained_models/stylegan_human_v1_1024.pkl --version 1
-"""
-
-
-@click.command()
-@click.pass_context
-@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
-@click.option('--seeds', type=legacy.num_range, help='List of random seeds')
-@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
-@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
-@click.option('--outdir', help='Where to save the output images', default='outputs/generate/', type=str, required=True, metavar='DIR')
-@click.option('--version', help="stylegan version, 1, 2 or 3", type=int, default=2)
-def generate_images(
- ctx: click.Context,
- network_pkl: str,
- seeds: Optional[List[int]],
- truncation_psi: float,
- noise_mode: str,
- outdir: str,
- version: int
-):
-
- print('Loading networks from "%s"...' % network_pkl)
- if version == 1:
- import dnnlib.tflib as tflib
- tflib.init_tf()
- G, D, Gs = legacy.load_pkl(network_pkl)
-
- else:
- import torch
- device = torch.device('cuda')
- with dnnlib.util.open_url(network_pkl) as f:
- G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore
- os.makedirs(outdir, exist_ok=True)
-
- if seeds is None:
- ctx.fail('--seeds option is required.')
-
- # Generate images.
- target_z = np.array([])
- target_w = np.array([])
- latent_out = outdir.replace('/images/', '')
- for seed_idx, seed in enumerate(seeds):
- if seed % 5000 == 0:
- print('Generating image for seed %d (%d/%d) ...' %
- (seed, seed_idx, len(seeds)))
-
- if version == 1: # stylegan v1
- z = np.random.RandomState(seed).randn(1, Gs.input_shape[1])
- # Generate image.
- fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
- if noise_mode == 'const':
- randomize_noise = False
- else:
- randomize_noise = True
- images = Gs.run(z, None, truncation_psi=truncation_psi,
- randomize_noise=randomize_noise, output_transform=fmt)
- PIL.Image.fromarray(images[0], 'RGB').save(
- f'{outdir}/seed{seed:04d}.png')
-
- else: # stylegan v2/v3
- label = torch.zeros([1, G.c_dim], device=device)
- z = torch.from_numpy(np.random.RandomState(
- seed).randn(1, G.z_dim)).to(device)
- if target_z.size == 0:
- target_z = z.cpu()
- else:
- target_z = np.append(target_z, z.cpu(), axis=0)
-
- w = G.mapping(z, label, truncation_psi=truncation_psi)
- img = G.synthesis(w, noise_mode=noise_mode, force_fp32=True)
- if target_w.size == 0:
- target_w = w.cpu()
- else:
- target_w = np.append(target_w, w.cpu(), axis=0)
-
- img = (img.permute(0, 2, 3, 1) * 127.5 +
- 128).clamp(0, 255).to(torch.uint8)
- PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(
- f'{outdir}/seed{seed:04d}.png')
- # print(target_z)
- # print(target_z.shape,target_w.shape)
-
-
-# ----------------------------------------------------------------------------
-
-if __name__ == "__main__":
- generate_images()
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/DragGan/DragGan/stylegan_human/README.md b/spaces/DragGan/DragGan/stylegan_human/README.md
deleted file mode 100644
index 0442c284c6ce0e9e7a1d6d7f487debab8ccd1a1b..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/stylegan_human/README.md
+++ /dev/null
@@ -1,229 +0,0 @@
-# StyleGAN-Human: A Data-Centric Odyssey of Human Generation
-
-
-
-
->
->
-> **Abstract:** *Unconditional human image generation is an important task in vision and graphics, which enables various applications in the creative industry. Existing studies in this field mainly focus on "network engineering" such as designing new components and objective functions. This work takes a data-centric perspective and investigates multiple critical aspects in "data engineering", which we believe would complement the current practice. To facilitate a comprehensive study, we collect and annotate a large-scale human image dataset with over 230K samples capturing diverse poses and textures. Equipped with this large dataset, we rigorously investigate three essential factors in data engineering for StyleGAN-based human generation, namely data size, data distribution, and data alignment. Extensive experiments reveal several valuable observations w.r.t. these aspects: 1) Large-scale data, more than 40K images, are needed to train a high-fidelity unconditional human generation model with vanilla StyleGAN. 2) A balanced training set helps improve the generation quality with rare face poses compared to the long-tailed counterpart, whereas simply balancing the clothing texture distribution does not effectively bring an improvement. 3) Human GAN models with body centers for alignment outperform models trained using face centers or pelvis points as alignment anchors. In addition, a model zoo and human editing applications are demonstrated to facilitate future research in the community.*
-**Keyword:** Human Image Generation, Data-Centric, StyleGAN
-
-[Jianglin Fu](mailto:fujianglin@sensetime.com), [Shikai Li](mailto:lishikai@sensetime.com), [Yuming Jiang](https://yumingj.github.io/), [Kwan-Yee Lin](https://kwanyeelin.github.io/), [Chen Qian](https://scholar.google.com/citations?user=AerkT0YAAAAJ&hl=zh-CN), [Chen Change Loy](https://www.mmlab-ntu.com/person/ccloy/), [Wayne Wu](https://wywu.github.io/), and [Ziwei Liu](https://liuziwei7.github.io/)
-**[[Demo Video]](https://youtu.be/nIrb9hwsdcI)** | **[[Project Page]](https://stylegan-human.github.io/)** | **[[Paper]](https://arxiv.org/pdf/2204.11823.pdf)**
-
-## Updates
-- [20/07/2022] [SHHQ-1.0](./docs/Dataset.md) dataset with 40K images is released! :sparkles:
-- [15/06/2022] Data alignment and real-image inversion scripts are released.
-- [26/04/2022] Technical report released!
-- [22/04/2022] Technical report will be released before May.
-- [21/04/2022] The codebase and project page are created.
-
-## Data Download
-The first version SHHQ-1.0, with 40K images is released. To download and use the dataset set, please read the instructions in [Dataset.md](./docs/Dataset.md)
-
-（We are currently facing large incoming applications, and we need to carefully verify all the applicants, please be patient, and we will reply to you as soon as possible.）
-
-## Model Zoo
-
-| Structure | 1024x512 | Metric | Scores | 512x256 | Metric | Scores |
-| --------- |:----------:| :----------:| :----------:| :-----: | :-----: | :-----: |
-| StyleGAN1 |[stylegan_human_v1_1024.pkl](https://drive.google.com/file/d/1h-R-IV-INGdPEzj4P9ml6JTEvihuNgLX/view?usp=sharing)| fid50k | 3.79 | to be released | - | - |
-| StyleGAN2 |[stylegan_human_v2_1024.pkl](https://drive.google.com/file/d/1FlAb1rYa0r_--Zj_ML8e6shmaF28hQb5/view?usp=sharing)| fid50k_full | 1.57 |[stylegan_human_v2_512.pkl](https://drive.google.com/file/d/1dlFEHbu-WzQWJl7nBBZYcTyo000H9hVm/view?usp=sharing) | fid50k_full | 1.97 |
-| StyleGAN3 |to be released | - | - | [stylegan_human_v3_512.pkl](https://drive.google.com/file/d/1_274jk_N6WSCkKWeu7hjHycqGvbuOFf5/view?usp=sharing) | fid50k_full | 2.54 |
-
-
-
-## Web Demo
-
-Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo for generation: [](https://huggingface.co/spaces/hysts/StyleGAN-Human) and interpolation [](https://huggingface.co/spaces/hysts/StyleGAN-Human-Interpolation)
-
-
-
-
-
-We prepare a Colab demo to allow you to synthesize images with the provided models, as well as visualize the performance of style-mixing, interpolation, and attributes editing.
-The notebook will guide you to install the necessary environment and download pretrained models. The output images can be found in `./StyleGAN-Human/outputs/`.
-Hope you enjoy!
-
-## Usage
-
-### System requirements
-* The original code bases are [stylegan (tensorflow)](https://github.com/NVlabs/stylegan), [stylegan2-ada (pytorch)](https://github.com/NVlabs/stylegan2-ada-pytorch), [stylegan3 (pytorch)](https://github.com/NVlabs/stylegan3), released by NVidia
-
-* We tested in Python 3.8.5 and PyTorch 1.9.1 with CUDA 11.1. (See https://pytorch.org for PyTorch install instructions.)
-
-### Installation
-To work with this project on your own machine, you need to install the environmnet as follows:
-
-```
-conda env create -f environment.yml
-conda activate stylehuman
-# [Optional: tensorflow 1.x is required for StyleGAN1. ]
-pip install nvidia-pyindex
-pip install nvidia-tensorflow[horovod]
-pip install nvidia-tensorboard==1.15
-```
-Extra notes:
-1. In case having some conflicts when calling CUDA version, please try to empty the LD_LIBRARY_PATH. For example:
-```
-LD_LIBRARY_PATH=; python generate.py --outdir=out/stylegan_human_v2_1024 --trunc=1 --seeds=1,3,5,7
---network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
-```
-
-
-2. We found the following troubleshooting links might be helpful: [1.](https://github.com/NVlabs/stylegan3), [2.](https://github.com/NVlabs/stylegan3/blob/main/docs/troubleshooting.md)
-
-### Train
-The training scripts are based on the original [stylegan1](https://github.com/NVlabs/stylegan), [stylegan2-ada](https://github.com/NVlabs/stylegan2-ada-pytorch), and [stylegan3](https://github.com/NVlabs/stylegan3) with minor changes. Here we only provide the scripts with modifications for SG2 and SG3. You can replace the old files with the provided scripts to train. (assume SHHQ-1.0 is placed under data/)
-
-#### Train Stylegan2-ada-pytorch with SHHQ-1.0
-```
-python train.py --outdir=training_results/sg2/ --data=data/SHHQ-1.0/ \
- --gpus=8 --aug=noaug --mirror=1 --snap=250 --cfg=shhq --square=False
-```
-#### Train Stylegan3 with SHHQ-1.0
-```
-python train.py --outdir=training_results/sg3/ --cfg=stylegan3-r --gpus=8 --batch=32 --gamma=12.4 \
- --mirror=1 --aug=noaug --data=data/SHHQ-1.0/ --square=False --snap=250
-```
-
-### Pretrained models
-Please put the downloaded pretrained models [from above link](#Model-Zoo) under the folder 'pretrained_models'.
-
-
-### Generate full-body human images using our pretrained model
-```
-# Generate human full-body images without truncation
-python generate.py --outdir=outputs/generate/stylegan_human_v2_1024 --trunc=1 --seeds=1,3,5,7 --network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
-
-# Generate human full-body images with truncation
-python generate.py --outdir=outputs/generate/stylegan_human_v2_1024 --trunc=0.8 --seeds=0-10 --network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
-
-# Generate human full-body images using stylegan V1
-python generate.py --outdir=outputs/generate/stylegan_human_v1_1024 --network=pretrained_models/stylegan_human_v1_1024.pkl --version 1 --seeds=1,3,5
-
-# Generate human full-body images using stylegan V3
-python generate.py --outdir=outputs/generate/stylegan_human_v3_512 --network=pretrained_models/stylegan_human_v3_512.pkl --version 3 --seeds=1,3,5
-```
-
-
-#### Note: The following demos are generated based on models related to StyleGAN V2 (stylegan_human_v2_512.pkl and stylegan_human_v2_1024.pkl). If you want to see results for V1 or V3, you need to change the loading method of the corresponding models.
-
-
-### Interpolation
-```
-python interpolation.py --network=pretrained_models/stylegan_human_v2_1024.pkl --seeds=85,100 --outdir=outputs/inter_gifs
-```
-
-### Style-mixing **image** using stylegan2
-```
-python style_mixing.py --network=pretrained_models/stylegan_human_v2_1024.pkl --rows=85,100,75,458,1500 \\
- --cols=55,821,1789,293 --styles=0-3 --outdir=outputs/stylemixing
-```
-
-### Style-mixing **video** using stylegan2
-```
-python stylemixing_video.py --network=pretrained_models/stylegan_human_v2_1024.pkl --row-seed=3859 \\
- --col-seeds=3098,31759,3791 --col-styles=8-12 --trunc=0.8 --outdir=outputs/stylemixing_video
-```
-
-### Aligned raw images
-For alignment, we use [openpose-pytorch](https://github.com/Hzzone/pytorch-openpose) for body-keypoints detection and [PaddlePaddle](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.5/contrib/PP-HumanSeg) for human segmentation.
-Before running the alignment script, few models need to be installed:
-1. download [body_pose_model.pth](https://drive.google.com/drive/folders/1JsvI4M4ZTg98fmnCZLFM-3TeovnCRElG?usp=sharing) and place it into openpose/model/.
-2. download and extract [deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax](https://paddleseg.bj.bcebos.com/dygraph/humanseg/export/deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax.zip) into PP_HumanSeg/export_model/deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax.
-3. download and extract [deeplabv3p_resnet50_os8_humanseg_512x512_100k](https://paddleseg.bj.bcebos.com/dygraph/humanseg/train/deeplabv3p_resnet50_os8_humanseg_512x512_100k.zip) into PP_HumanSeg/pretrained_model/deeplabv3p_resnet50_os8_humanseg_512x512_100k.
-4. install paddlepaddel: ``` pip install paddleseg ```
-
-Then you can start alignment:
-```
-python alignment.py --image-folder img/test/ --output-folder aligned_image/
-```
-
-### Invert real image with [PTI](https://github.com/danielroich/PTI)
-Before inversion, please download our PTI weights: [e4e_w+.pt](https://drive.google.com/file/d/1NUfSJqLhsrU7c9PwAtlZ9xtrxhzS_6tu/view?usp=sharing) into /pti/.
-
-Few parameters you can change:
-- /pti/pti_configs/hyperparameters.py:
- - first_inv_type = 'w+' -> Use pretrained e4e encoder
- - first_inv_type = 'w' -> Use projection and optimization
-- /pti/pti_configs/paths_config.py:
- - input_data_path: path of real images
- - e4e: path of e4e_w+.pt
- - stylegan2_ada_shhq: pretrained stylegan2-ada model for SHHQ
-
-```
-python run_pti.py
-```
-Note: we used the test image under 'aligned_image/' (the output of alignment.py), the inverted latent code and fine-tuned generator will be saved in 'outputs/pti/'
-
-
-### Editing with InterfaceGAN, StyleSpace, and Sefa
-```
-python edit.py --network pretrained_models/stylegan_human_v2_1024.pkl --attr_name upper_length \\
- --seeds 61531,61570,61571,61610 --outdir outputs/edit_results
-```
-
-### Editing using inverted latent code
-```
-python edit.py ---network outputs/pti/checkpoints/model_test.pkl --attr_name upper_length \\
- --outdir outputs/edit_results --real True --real_w_path outputs/pti/embeddings/test/PTI/test/0.pt --real_img_path aligned_image/test.png
-```
-
-Note:
-1. ''upper_length'' and ''bottom_length'' of ''attr_name'' are available for demo.
-2. Layers to control and editing strength are set in edit/edit_config.py.
-
-
-### Demo for [InsetGAN](https://arxiv.org/abs/2203.07293)
-
-We implement a quick demo using the key idea from InsetGAN: combining the face generated by FFHQ with the human-body generated by our pretrained model, optimizing both face and body latent codes to get a coherent full-body image.
-Before running the script, you need to download the [FFHQ face model]( https://docs.google.com/uc?export=download&confirm=t&id=125OG7SMkXI-Kf2aqiwLLHyCvSW-gZk3M), or you can use your own face model, as well as [pretrained face landmark](https://docs.google.com/uc?export=download&confirm=&id=1A82DnJBJzt8wI2J8ZrCK5fgHcQ2-tcWM) and [pretrained CNN face detection model for dlib](https://docs.google.com/uc?export=download&confirm=&id=1MduBgju5KFNrQfDLoQXJ_1_h5MnctCIG)
-```
-python insetgan.py --body_network=pretrained_models/stylegan_human_v2_1024.pkl --face_network=pretrained_models/ffhq.pkl \\
- --body_seed=82 --face_seed=43 --trunc=0.6 --outdir=outputs/insetgan/ --video 1
-```
-
-## Results
-
-### Editing with inverted real image
-(from left to right: real image | inverted image | InterFaceGAN result | StyleSpace result | SeFa result)
-
-https://user-images.githubusercontent.com/98547009/173773800-bb7fe54a-84d3-4b30-9864-a6b7b311f8ff.mp4
-
-
-### For more demo, please visit our [**web page**](https://stylegan-human.github.io/) .
-
-
-## TODO List
-
-- [ ] Release 1024x512 version of StyleGAN-Human based on StyleGAN3
-- [ ] Release 512x256 version of StyleGAN-Human based on StyleGAN1
-- [ ] Extension of downstream application (InsetGAN): Add face inversion interface to support fusing user face image and stylegen-human body image
-- [x] Add Inversion Script into the provided editing pipeline
-- [ ] Release Dataset
-
-
-## Related Works
-* (SIGGRAPH 2022) **Text2Human: Text-Driven Controllable Human Image Generation**, Yuming Jiang et al. [[Paper](https://arxiv.org/pdf/2205.15996.pdf)], [[Code](https://github.com/yumingj/Text2Human)], [[Project Page](https://yumingj.github.io/projects/Text2Human.html)], [[Dataset](https://github.com/yumingj/DeepFashion-MultiModal)]
-* (ICCV 2021) **Talk-to-Edit: Fine-Grained Facial Editing via Dialog**, Yuming Jiang et al. [[Paper](https://arxiv.org/abs/2109.04425)], [[Code](https://github.com/yumingj/Talk-to-Edit)], [[Project Page](https://www.mmlab-ntu.com/project/talkedit/)], [[Dataset](https://mmlab.ie.cuhk.edu.hk/projects/CelebA/CelebA_Dialog.html)]
-* (Technical Report 2022) **Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis**, Wei Cheng et al. [[Paper](https://arxiv.org/pdf/2204.11798.pdf)], [[Code](https://github.com/generalizable-neural-performer/gnr)], [[Project Page](https://generalizable-neural-performer.github.io/)], [[Dataset](https://generalizable-neural-performer.github.io/genebody.html)]
-
-## Citation
-
-If you find this work useful for your research, please consider citing our paper:
-
-```bibtex
-@article{fu2022styleganhuman,
- title={StyleGAN-Human: A Data-Centric Odyssey of Human Generation},
- author={Fu, Jianglin and Li, Shikai and Jiang, Yuming and Lin, Kwan-Yee and Qian, Chen and Loy, Chen-Change and Wu, Wayne and Liu, Ziwei},
- journal = {arXiv preprint},
- volume = {arXiv:2204.11823},
- year = {2022}
-```
-
-## Acknowlegement
-Part of the code is borrowed from [stylegan (tensorflow)](https://github.com/NVlabs/stylegan), [stylegan2-ada (pytorch)](https://github.com/NVlabs/stylegan2-ada-pytorch), [stylegan3 (pytorch)](https://github.com/NVlabs/stylegan3).
diff --git a/spaces/Eddycrack864/Applio-Inference/infer/modules/ipex/__init__.py.py b/spaces/Eddycrack864/Applio-Inference/infer/modules/ipex/__init__.py.py
deleted file mode 100644
index 9f53b2d3f7025b2d71369dababa4e6f2a4affc48..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/infer/modules/ipex/__init__.py.py
+++ /dev/null
@@ -1,165 +0,0 @@
-import os
-import sys
-import contextlib
-import torch
-import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
-from .hijacks import ipex_hijacks
-from .attention import attention_init
-
-# pylint: disable=protected-access, missing-function-docstring, line-too-long
-
-def ipex_init(): # pylint: disable=too-many-statements
- try:
- #Replace cuda with xpu:
- torch.cuda.current_device = torch.xpu.current_device
- torch.cuda.current_stream = torch.xpu.current_stream
- torch.cuda.device = torch.xpu.device
- torch.cuda.device_count = torch.xpu.device_count
- torch.cuda.device_of = torch.xpu.device_of
- torch.cuda.getDeviceIdListForCard = torch.xpu.getDeviceIdListForCard
- torch.cuda.get_device_name = torch.xpu.get_device_name
- torch.cuda.get_device_properties = torch.xpu.get_device_properties
- torch.cuda.init = torch.xpu.init
- torch.cuda.is_available = torch.xpu.is_available
- torch.cuda.is_initialized = torch.xpu.is_initialized
- torch.cuda.is_current_stream_capturing = lambda: False
- torch.cuda.set_device = torch.xpu.set_device
- torch.cuda.stream = torch.xpu.stream
- torch.cuda.synchronize = torch.xpu.synchronize
- torch.cuda.Event = torch.xpu.Event
- torch.cuda.Stream = torch.xpu.Stream
- torch.cuda.FloatTensor = torch.xpu.FloatTensor
- torch.Tensor.cuda = torch.Tensor.xpu
- torch.Tensor.is_cuda = torch.Tensor.is_xpu
- torch.cuda._initialization_lock = torch.xpu.lazy_init._initialization_lock
- torch.cuda._initialized = torch.xpu.lazy_init._initialized
- torch.cuda._lazy_seed_tracker = torch.xpu.lazy_init._lazy_seed_tracker
- torch.cuda._queued_calls = torch.xpu.lazy_init._queued_calls
- torch.cuda._tls = torch.xpu.lazy_init._tls
- torch.cuda.threading = torch.xpu.lazy_init.threading
- torch.cuda.traceback = torch.xpu.lazy_init.traceback
- torch.cuda.Optional = torch.xpu.Optional
- torch.cuda.__cached__ = torch.xpu.__cached__
- torch.cuda.__loader__ = torch.xpu.__loader__
- torch.cuda.ComplexFloatStorage = torch.xpu.ComplexFloatStorage
- torch.cuda.Tuple = torch.xpu.Tuple
- torch.cuda.streams = torch.xpu.streams
- torch.cuda._lazy_new = torch.xpu._lazy_new
- torch.cuda.FloatStorage = torch.xpu.FloatStorage
- torch.cuda.Any = torch.xpu.Any
- torch.cuda.__doc__ = torch.xpu.__doc__
- torch.cuda.default_generators = torch.xpu.default_generators
- torch.cuda.HalfTensor = torch.xpu.HalfTensor
- torch.cuda._get_device_index = torch.xpu._get_device_index
- torch.cuda.__path__ = torch.xpu.__path__
- torch.cuda.Device = torch.xpu.Device
- torch.cuda.IntTensor = torch.xpu.IntTensor
- torch.cuda.ByteStorage = torch.xpu.ByteStorage
- torch.cuda.set_stream = torch.xpu.set_stream
- torch.cuda.BoolStorage = torch.xpu.BoolStorage
- torch.cuda.os = torch.xpu.os
- torch.cuda.torch = torch.xpu.torch
- torch.cuda.BFloat16Storage = torch.xpu.BFloat16Storage
- torch.cuda.Union = torch.xpu.Union
- torch.cuda.DoubleTensor = torch.xpu.DoubleTensor
- torch.cuda.ShortTensor = torch.xpu.ShortTensor
- torch.cuda.LongTensor = torch.xpu.LongTensor
- torch.cuda.IntStorage = torch.xpu.IntStorage
- torch.cuda.LongStorage = torch.xpu.LongStorage
- torch.cuda.__annotations__ = torch.xpu.__annotations__
- torch.cuda.__package__ = torch.xpu.__package__
- torch.cuda.__builtins__ = torch.xpu.__builtins__
- torch.cuda.CharTensor = torch.xpu.CharTensor
- torch.cuda.List = torch.xpu.List
- torch.cuda._lazy_init = torch.xpu._lazy_init
- torch.cuda.BFloat16Tensor = torch.xpu.BFloat16Tensor
- torch.cuda.DoubleStorage = torch.xpu.DoubleStorage
- torch.cuda.ByteTensor = torch.xpu.ByteTensor
- torch.cuda.StreamContext = torch.xpu.StreamContext
- torch.cuda.ComplexDoubleStorage = torch.xpu.ComplexDoubleStorage
- torch.cuda.ShortStorage = torch.xpu.ShortStorage
- torch.cuda._lazy_call = torch.xpu._lazy_call
- torch.cuda.HalfStorage = torch.xpu.HalfStorage
- torch.cuda.random = torch.xpu.random
- torch.cuda._device = torch.xpu._device
- torch.cuda.classproperty = torch.xpu.classproperty
- torch.cuda.__name__ = torch.xpu.__name__
- torch.cuda._device_t = torch.xpu._device_t
- torch.cuda.warnings = torch.xpu.warnings
- torch.cuda.__spec__ = torch.xpu.__spec__
- torch.cuda.BoolTensor = torch.xpu.BoolTensor
- torch.cuda.CharStorage = torch.xpu.CharStorage
- torch.cuda.__file__ = torch.xpu.__file__
- torch.cuda._is_in_bad_fork = torch.xpu.lazy_init._is_in_bad_fork
- #torch.cuda.is_current_stream_capturing = torch.xpu.is_current_stream_capturing
-
- #Memory:
- torch.cuda.memory = torch.xpu.memory
- if 'linux' in sys.platform and "WSL2" in os.popen("uname -a").read():
- torch.xpu.empty_cache = lambda: None
- torch.cuda.empty_cache = torch.xpu.empty_cache
- torch.cuda.memory_stats = torch.xpu.memory_stats
- torch.cuda.memory_summary = torch.xpu.memory_summary
- torch.cuda.memory_snapshot = torch.xpu.memory_snapshot
- torch.cuda.memory_allocated = torch.xpu.memory_allocated
- torch.cuda.max_memory_allocated = torch.xpu.max_memory_allocated
- torch.cuda.memory_reserved = torch.xpu.memory_reserved
- torch.cuda.memory_cached = torch.xpu.memory_reserved
- torch.cuda.max_memory_reserved = torch.xpu.max_memory_reserved
- torch.cuda.max_memory_cached = torch.xpu.max_memory_reserved
- torch.cuda.reset_peak_memory_stats = torch.xpu.reset_peak_memory_stats
- torch.cuda.reset_max_memory_cached = torch.xpu.reset_peak_memory_stats
- torch.cuda.reset_max_memory_allocated = torch.xpu.reset_peak_memory_stats
- torch.cuda.memory_stats_as_nested_dict = torch.xpu.memory_stats_as_nested_dict
- torch.cuda.reset_accumulated_memory_stats = torch.xpu.reset_accumulated_memory_stats
-
- #RNG:
- torch.cuda.get_rng_state = torch.xpu.get_rng_state
- torch.cuda.get_rng_state_all = torch.xpu.get_rng_state_all
- torch.cuda.set_rng_state = torch.xpu.set_rng_state
- torch.cuda.set_rng_state_all = torch.xpu.set_rng_state_all
- torch.cuda.manual_seed = torch.xpu.manual_seed
- torch.cuda.manual_seed_all = torch.xpu.manual_seed_all
- torch.cuda.seed = torch.xpu.seed
- torch.cuda.seed_all = torch.xpu.seed_all
- torch.cuda.initial_seed = torch.xpu.initial_seed
-
- #AMP:
- torch.cuda.amp = torch.xpu.amp
- if not hasattr(torch.cuda.amp, "common"):
- torch.cuda.amp.common = contextlib.nullcontext()
- torch.cuda.amp.common.amp_definitely_not_available = lambda: False
- try:
- torch.cuda.amp.GradScaler = torch.xpu.amp.GradScaler
- except Exception: # pylint: disable=broad-exception-caught
- try:
- from .gradscaler import gradscaler_init # pylint: disable=import-outside-toplevel, import-error
- gradscaler_init()
- torch.cuda.amp.GradScaler = torch.xpu.amp.GradScaler
- except Exception: # pylint: disable=broad-exception-caught
- torch.cuda.amp.GradScaler = ipex.cpu.autocast._grad_scaler.GradScaler
-
- #C
- torch._C._cuda_getCurrentRawStream = ipex._C._getCurrentStream
- ipex._C._DeviceProperties.major = 2023
- ipex._C._DeviceProperties.minor = 2
-
- #Fix functions with ipex:
- torch.cuda.mem_get_info = lambda device=None: [(torch.xpu.get_device_properties(device).total_memory - torch.xpu.memory_allocated(device)), torch.xpu.get_device_properties(device).total_memory]
- torch._utils._get_available_device_type = lambda: "xpu"
- torch.has_cuda = True
- torch.cuda.has_half = True
- torch.cuda.is_bf16_supported = lambda *args, **kwargs: True
- torch.cuda.is_fp16_supported = lambda *args, **kwargs: True
- torch.version.cuda = "11.7"
- torch.cuda.get_device_capability = lambda *args, **kwargs: [11,7]
- torch.cuda.get_device_properties.major = 11
- torch.cuda.get_device_properties.minor = 7
- torch.cuda.ipc_collect = lambda *args, **kwargs: None
- torch.cuda.utilization = lambda *args, **kwargs: 0
-
- ipex_hijacks()
- attention_init()
- except Exception as e:
- return False, e
- return True, None
\ No newline at end of file
diff --git a/spaces/ExpertPrompters/AskIDF/chat.py b/spaces/ExpertPrompters/AskIDF/chat.py
deleted file mode 100644
index 4eaff63f8f5af3d8af1ceffb7e9238b3a9a8512f..0000000000000000000000000000000000000000
--- a/spaces/ExpertPrompters/AskIDF/chat.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from langchain.llms.base import get_prompts
-from sqlalchemy import label
-import streamlit as st
-from typing import Callable
-
-
-
-RESPONSE_LABEL = 'chat_response'
-PROMPT_LABEL = 'chat_prompt'
-
-class Chat:
-
- def __init__(self):
- if RESPONSE_LABEL not in st.session_state:
- st.session_state[RESPONSE_LABEL] = []
-
- if PROMPT_LABEL not in st.session_state:
- st.session_state[PROMPT_LABEL] = []
-
- def process(self, process_prompt: Callable, *args):
- """
- process_prompt(promt: str, *args) -> tuple(Any, Callable)
- callback to process the chat promt, it takes the promt for input
- and returns a tuple with the response and a render callback
- """
-
- # Render history
- messages = zip(st.session_state[PROMPT_LABEL], st.session_state[RESPONSE_LABEL])
- for prompt, (response, on_render) in list(messages)[::-1]:
- with st.chat_message("user"):
- st.write(prompt)
- with st.chat_message("assistant"):
- on_render(response)
-
- # Compute prompt
- if prompt:= st.chat_input("Ask IDF Anything"):
- st.session_state[PROMPT_LABEL].append(prompt)
- (response, on_render) = process_prompt(prompt, *args)
- st.session_state[RESPONSE_LABEL].append((response, on_render))
-
- with st.chat_message("user"):
- st.write(prompt)
-
- with st.chat_message("assistant"):
- on_render(response)
-
diff --git a/spaces/Faryne/yulet1de-hentaidiffusion/README.md b/spaces/Faryne/yulet1de-hentaidiffusion/README.md
deleted file mode 100644
index ea9e8d2cc2f29e471ab1ba0ecd9e2e133e1e5782..0000000000000000000000000000000000000000
--- a/spaces/Faryne/yulet1de-hentaidiffusion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Yulet1de Hentaidiffusion
-emoji: 🐨
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/FloydianSound/Wlop_Diffusion/app.py b/spaces/FloydianSound/Wlop_Diffusion/app.py
deleted file mode 100644
index 0572417c16d7b79db9f9ff6d5346c09f62d25654..0000000000000000000000000000000000000000
--- a/spaces/FloydianSound/Wlop_Diffusion/app.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-
-model_id = 'FloydianSound/Wlop_Diffusion'
-prefix = 'wlop'
-
-scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
-
-pipe = StableDiffusionPipeline.from_pretrained(
- model_id,
- torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
- scheduler=scheduler)
-
-pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
- model_id,
- torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
- scheduler=scheduler)
-
-if torch.cuda.is_available():
- pipe = pipe.to("cuda")
- pipe_i2i = pipe_i2i.to("cuda")
-
-def error_str(error, title="Error"):
- return f"""#### {title}
- {error}""" if error else ""
-
-def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
-
- generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
- prompt = f"{prefix} {prompt}" if auto_prefix else prompt
-
- try:
- if img is not None:
- return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
- else:
- return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
- except Exception as e:
- return None, error_str(e)
-
-def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
-
- result = pipe(
- prompt,
- negative_prompt = neg_prompt,
- num_inference_steps = int(steps),
- guidance_scale = guidance,
- width = width,
- height = height,
- generator = generator)
-
- return result.images[0]
-
-def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
-
- ratio = min(height / img.height, width / img.width)
- img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
- result = pipe_i2i(
- prompt,
- negative_prompt = neg_prompt,
- init_image = img,
- num_inference_steps = int(steps),
- strength = strength,
- guidance_scale = guidance,
- width = width,
- height = height,
- generator = generator)
-
- return result.images[0]
-
-css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-"""
-with gr.Blocks(css=css) as demo:
- gr.HTML(
- f"""
-
-
- """
- )
- with gr.Row():
-
- with gr.Column(scale=55):
- with gr.Group():
- with gr.Row():
- prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
- generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
- image_out = gr.Image(height=512)
- error_output = gr.Markdown()
-
- with gr.Column(scale=45):
- with gr.Tab("Options"):
- with gr.Group():
- neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
- auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (wlop)", value=prefix, visible=prefix)
-
- with gr.Row():
- guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
- steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
-
- with gr.Row():
- width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
- height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
- seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
- with gr.Tab("Image to image"):
- with gr.Group():
- image = gr.Image(label="Image", height=256, tool="editor", type="pil")
- strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
- auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
-
- inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
- outputs = [image_out, error_output]
- prompt.submit(inference, inputs=inputs, outputs=outputs)
- generate.click(inference, inputs=inputs, outputs=outputs)
-
- gr.HTML("""
-
-
- Wlop Diffusion
-
- {"Add the following tokens to your prompts for the model to work properly: prefix" if prefix else ""}
-
-
-
-
- """)
-
-demo.queue(concurrency_count=1)
-demo.launch()
diff --git a/spaces/GIanlucaRub/Titanic/app.py b/spaces/GIanlucaRub/Titanic/app.py
deleted file mode 100644
index ee5d461d7002b90229903288dae512f7f231fade..0000000000000000000000000000000000000000
--- a/spaces/GIanlucaRub/Titanic/app.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import gradio as gr
-import numpy as np
-from PIL import Image
-import requests
-
-import hopsworks
-import joblib
-
-project = hopsworks.login()
-fs = project.get_feature_store()
-
-
-mr = project.get_model_registry()
-#model = mr.get_model("titanic_modal", version=1)
-
-EVALUATION_METRIC="accuracy"
-SORT_METRICS_BY="max" # your sorting criteria
-
-# get best model based on custom metrics
-best_model = mr.get_best_model("titanic_modal",
- EVALUATION_METRIC,
- SORT_METRICS_BY)
-model = best_model
-model_dir = model.download()
-model = joblib.load(model_dir + "/titanic_model.pkl")
-
-
-def passenger(Pclass, Age, SibSp, Parch, Fare, Sex, Embarked):
- input_list = []
- if Pclass == "First Class":
- input_list.append(1)
- elif Pclass == "Second Class":
- input_list.append(2)
- else:
- input_list.append(3)
- input_list.append(Age)
- input_list.append(SibSp)
- input_list.append(Parch)
- input_list.append(Fare)
- if Sex == "Male":
- input_list.append(0)
- input_list.append(1)
- else:
- input_list.append(1)
- input_list.append(0)
- if Embarked == "Cherbourg":
- input_list.append(1)
- input_list.append(0)
- input_list.append(0)
- elif Embarked == "Queenstown":
- input_list.append(0)
- input_list.append(1)
- input_list.append(0)
- else:
- input_list.append(0)
- input_list.append(0)
- input_list.append(1)
-
- # 'res' is a list of predictions returned as the label.
- res = model.predict(np.asarray(input_list).reshape(1, -1))
- res = str(res[0])
- # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want
- # the first element.
- passenger_url = "https://raw.githubusercontent.com/GianlucaRub/Scalable-Machine-Learning-and-Deep-Learning/main/Lab1/assets/" + res + ".png"
- img = Image.open(requests.get(passenger_url, stream=True).raw)
- return img
-
-demo = gr.Interface(
- fn=passenger,
- title="Titanic Predictive Analytics",
- description="Insert passenger class, age, number of sibilings/spouse on board of the Titanic, number of parents/children on board of the Titanic, fare, sex, port of embarkation and see if he/she survived ",
- allow_flagging="never",
- inputs=[
- gr.inputs.Radio(choices=["First Class", "Second Class", "Third Class"], label="Passenger Class"),
- gr.inputs.Number(default=20, label="Age (years)"),
- gr.inputs.Number(default=1.0, label="Number of sibilings/spouse on board of the Titanic"),
- gr.inputs.Number(default=1.0, label="Number of parents/children on board of the Titanic"),
- gr.inputs.Number(default=10.0, label="Fare (USD)"),
- gr.inputs.Radio(choices=["Male","Female"], label = "Sex"),
- gr.inputs.Radio(choices=["Cherbourg","Queenstown","Southampton"], label = "Port of embarkation")
- ],
- outputs=gr.Image(type="pil"))
-
-demo.launch()
-
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/models/core/clip.py b/spaces/Gen-Sim/Gen-Sim/cliport/models/core/clip.py
deleted file mode 100644
index d98ea192d65032535737cc6acced14c050894613..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/models/core/clip.py
+++ /dev/null
@@ -1,615 +0,0 @@
-###########################################
-#### Authors: OpenAI
-#### Credit: https://github.com/openai/CLIP
-#### MIT License.
-
-from collections import OrderedDict
-from typing import Tuple, Union
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-import hashlib
-import os
-import urllib
-import warnings
-from typing import Union, List
-
-import torch
-from PIL import Image
-from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
-from tqdm import tqdm
-
-from cliport.utils.simple_tokenizer import SimpleTokenizer as _Tokenizer
-
-
-__all__ = ["available_models", "load", "tokenize"]
-_tokenizer = _Tokenizer()
-
-_MODELS = {
- "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
- "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
-}
-
-
-class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1):
- super().__init__()
-
- # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
- self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
-
- self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
-
- self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
-
- self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-
- self.relu = nn.ReLU(inplace=True)
- self.downsample = None
- self.stride = stride
-
- if stride > 1 or inplanes != planes * Bottleneck.expansion:
- # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
- self.downsample = nn.Sequential(OrderedDict([
- ("-1", nn.AvgPool2d(stride)),
- ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
- ("1", nn.BatchNorm2d(planes * self.expansion))
- ]))
-
- def forward(self, x: torch.Tensor):
- identity = x
-
- out = self.relu(self.bn1(self.conv1(x)))
- out = self.relu(self.bn2(self.conv2(out)))
- out = self.avgpool(out)
- out = self.bn3(self.conv3(out))
-
- if self.downsample is not None:
- identity = self.downsample(x)
-
- out += identity
- out = self.relu(out)
- return out
-
-
-class AttentionPool2d(nn.Module):
- def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
- super().__init__()
- self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
- self.k_proj = nn.Linear(embed_dim, embed_dim)
- self.q_proj = nn.Linear(embed_dim, embed_dim)
- self.v_proj = nn.Linear(embed_dim, embed_dim)
- self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
- self.num_heads = num_heads
-
- def forward(self, x):
- x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC
- x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC
- x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC
- x, _ = F.multi_head_attention_forward(
- query=x, key=x, value=x,
- embed_dim_to_check=x.shape[-1],
- num_heads=self.num_heads,
- q_proj_weight=self.q_proj.weight,
- k_proj_weight=self.k_proj.weight,
- v_proj_weight=self.v_proj.weight,
- in_proj_weight=None,
- in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
- bias_k=None,
- bias_v=None,
- add_zero_attn=False,
- dropout_p=0,
- out_proj_weight=self.c_proj.weight,
- out_proj_bias=self.c_proj.bias,
- use_separate_proj_weight=True,
- training=self.training,
- need_weights=False
- )
-
- return x[0]
-
-
-class ModifiedResNet(nn.Module):
- """
- A ResNet class that is similar to torchvision's but contains the following changes:
- - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
- - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
- - The final pooling layer is a QKV attention instead of an average pool
- """
-
- def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
- super().__init__()
- self.output_dim = output_dim
- self.input_resolution = input_resolution
-
- # the 3-layer stem
- self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
- self.bn1 = nn.BatchNorm2d(width // 2)
- self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(width // 2)
- self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
- self.bn3 = nn.BatchNorm2d(width)
- self.avgpool = nn.AvgPool2d(2)
- self.relu = nn.ReLU(inplace=True)
-
- # residual layers
- self._inplanes = width # this is a *mutable* variable used during construction
- self.layer1 = self._make_layer(width, layers[0])
- self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
- self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
- self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
-
- embed_dim = width * 32 # the ResNet feature dimension
- self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)
-
- def _make_layer(self, planes, blocks, stride=1):
- layers = [Bottleneck(self._inplanes, planes, stride)]
-
- self._inplanes = planes * Bottleneck.expansion
- for _ in range(1, blocks):
- layers.append(Bottleneck(self._inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- x = self.prepool(x)
- x = self.attnpool(x)
- return x
-
- def prepool(self, x):
- def stem(x):
- for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]:
- x = self.relu(bn(conv(x)))
- x = self.avgpool(x)
- return x
-
- x = x.type(self.conv1.weight.dtype)
- x = stem(x)
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
- return x
-
- def prepool_im(self, x):
- """Run until prepool and save intermediate features"""
- im = []
- def stem(x):
- for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]:
- x = self.relu(bn(conv(x)))
- im.append(x)
- x = self.avgpool(x)
- im.append(x)
- return x
-
- x = x.type(self.conv1.weight.dtype)
- x = stem(x)
-
- for layer in [self.layer1, self.layer2, self.layer3, self.layer4]:
- x = layer(x)
- im.append(x)
-
- return x, im
-
-class LayerNorm(nn.LayerNorm):
- """Subclass torch's LayerNorm to handle fp16."""
-
- def forward(self, x: torch.Tensor):
- orig_type = x.dtype
- ret = super().forward(x.type(torch.float32))
- return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
- def forward(self, x: torch.Tensor):
- return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
- def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
- super().__init__()
-
- self.attn = nn.MultiheadAttention(d_model, n_head)
- self.ln_1 = LayerNorm(d_model)
- self.mlp = nn.Sequential(OrderedDict([
- ("c_fc", nn.Linear(d_model, d_model * 4)),
- ("gelu", QuickGELU()),
- ("c_proj", nn.Linear(d_model * 4, d_model))
- ]))
- self.ln_2 = LayerNorm(d_model)
- self.attn_mask = attn_mask
-
- def attention(self, x: torch.Tensor):
- self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
- return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
-
- def forward(self, x: torch.Tensor):
- x = x + self.attention(self.ln_1(x))
- x = x + self.mlp(self.ln_2(x))
- return x
-
-
-class Transformer(nn.Module):
- def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
- super().__init__()
- self.width = width
- self.layers = layers
- self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
-
- def forward(self, x: torch.Tensor):
- return self.resblocks(x)
-
-
-class VisualTransformer(nn.Module):
- def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
- super().__init__()
- self.input_resolution = input_resolution
- self.output_dim = output_dim
- self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
-
- scale = width ** -0.5
- self.class_embedding = nn.Parameter(scale * torch.randn(width))
- self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
- self.ln_pre = LayerNorm(width)
-
- self.transformer = Transformer(width, layers, heads)
-
- self.ln_post = LayerNorm(width)
- self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
- def forward(self, x: torch.Tensor):
- x = self.conv1(x) # shape = [*, width, grid, grid]
- x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
- x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
- x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width]
- x = x + self.positional_embedding.to(x.dtype)
- x = self.ln_pre(x)
-
- x = x.permute(1, 0, 2) # NLD -> LND
- x = self.transformer(x)
- x = x.permute(1, 0, 2) # LND -> NLD
-
- x = self.ln_post(x[:, 0, :])
-
- if self.proj is not None:
- x = x @ self.proj
-
- return x
-
-
-class CLIP(nn.Module):
- def __init__(self,
- embed_dim: int,
- # vision
- image_resolution: int,
- vision_layers: Union[Tuple[int, int, int, int], int],
- vision_width: int,
- vision_patch_size: int,
- # text
- context_length: int,
- vocab_size: int,
- transformer_width: int,
- transformer_heads: int,
- transformer_layers: int
- ):
- super().__init__()
-
- self.context_length = context_length
-
- if isinstance(vision_layers, (tuple, list)):
- vision_heads = vision_width * 32 // 64
- self.visual = ModifiedResNet(
- layers=vision_layers,
- output_dim=embed_dim,
- heads=vision_heads,
- input_resolution=image_resolution,
- width=vision_width
- )
- else:
- vision_heads = vision_width // 64
- self.visual = VisualTransformer(
- input_resolution=image_resolution,
- patch_size=vision_patch_size,
- width=vision_width,
- layers=vision_layers,
- heads=vision_heads,
- output_dim=embed_dim
- )
-
- self.transformer = Transformer(
- width=transformer_width,
- layers=transformer_layers,
- heads=transformer_heads,
- attn_mask=self.build_attention_mask()
- )
-
- self.vocab_size = vocab_size
- self.token_embedding = nn.Embedding(vocab_size, transformer_width)
- self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
- self.ln_final = LayerNorm(transformer_width)
-
- self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
- self.logit_scale = nn.Parameter(torch.ones([]))
-
- self.initialize_parameters()
-
- def initialize_parameters(self):
- nn.init.normal_(self.token_embedding.weight, std=0.02)
- nn.init.normal_(self.positional_embedding, std=0.01)
-
- if isinstance(self.visual, ModifiedResNet):
- if self.visual.attnpool is not None:
- std = self.visual.attnpool.c_proj.in_features ** -0.5
- nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
- nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
- nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
- nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
-
- for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
- for name, param in resnet_block.named_parameters():
- if name.endswith("bn3.weight"):
- nn.init.zeros_(param)
-
- proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
- attn_std = self.transformer.width ** -0.5
- fc_std = (2 * self.transformer.width) ** -0.5
- for block in self.transformer.resblocks:
- nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
- nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
- nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
- nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
- if self.text_projection is not None:
- nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
- def build_attention_mask(self):
- # lazily create causal attention mask, with full attention between the vision tokens
- # pytorch uses additive attention mask; fill with -inf
- mask = torch.empty(self.context_length, self.context_length)
- mask.fill_(float("-inf"))
- mask.triu_(1) # zero out the lower diagonal
- return mask
-
- @property
- def dtype(self):
- return self.visual.conv1.weight.dtype
-
- def encode_image(self, image):
- return self.visual(image.type(self.dtype))
-
- def encode_text(self, text):
- x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model]
-
- x = x + self.positional_embedding.type(self.dtype)
- x = x.permute(1, 0, 2) # NLD -> LND
- x = self.transformer(x)
- x = x.permute(1, 0, 2) # LND -> NLD
- x = self.ln_final(x).type(self.dtype)
-
- # x.shape = [batch_size, n_ctx, transformer.width]
- # take features from the eot embedding (eot_token is the highest number in each sequence)
- x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-
- return x
-
- def encode_text_with_embeddings(self, text):
- x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model]
-
- x = x + self.positional_embedding.type(self.dtype)
- x = x.permute(1, 0, 2) # NLD -> LND
- x = self.transformer(x)
- x = x.permute(1, 0, 2) # LND -> NLD
- x = self.ln_final(x).type(self.dtype)
-
- emb = x.clone()
- # x.shape = [batch_size, n_ctx, transformer.width]
- # take features from the eot embedding (eot_token is the highest number in each sequence)
- x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-
- return x, emb
-
- def forward(self, image, text):
- image_features = self.encode_image(image)
- text_features = self.encode_text(text)
-
- # normalized features
- image_features = image_features / image_features.norm(dim=-1, keepdim=True)
- text_features = text_features / text_features.norm(dim=-1, keepdim=True)
-
- # cosine similarity as logits
- logit_scale = self.logit_scale.exp()
- logits_per_image = logit_scale * image_features @ text_features.t()
- logits_per_text = logit_scale * text_features @ image_features.t()
-
- # shape = [global_batch_size, global_batch_size]
- return logits_per_image, logits_per_text
-
-
-def convert_weights(model: nn.Module):
- """Convert applicable model parameters to fp16"""
-
- def _convert_weights_to_fp16(l):
- if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
- l.weight.data = l.weight.data.half()
- if l.bias is not None:
- l.bias.data = l.bias.data.half()
-
- if isinstance(l, nn.MultiheadAttention):
- for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
- tensor = getattr(l, attr)
- if tensor is not None:
- tensor.data = tensor.data.half()
-
- for name in ["text_projection", "proj"]:
- if hasattr(l, name):
- attr = getattr(l, name)
- if attr is not None:
- attr.data = attr.data.half()
-
- model.apply(_convert_weights_to_fp16)
-
-
-def build_model(state_dict: dict):
- vit = "visual.proj" in state_dict
-
- if vit:
- vision_width = state_dict["visual.conv1.weight"].shape[0]
- vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
- vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
- grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
- image_resolution = vision_patch_size * grid_size
- else:
- counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
- vision_layers = tuple(counts)
- vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
- output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
- vision_patch_size = None
- assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
- image_resolution = output_width * 32
-
- embed_dim = state_dict["text_projection"].shape[1]
- context_length = state_dict["positional_embedding"].shape[0]
- vocab_size = state_dict["token_embedding.weight"].shape[0]
- transformer_width = state_dict["ln_final.weight"].shape[0]
- transformer_heads = transformer_width // 64
- transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
-
- model = CLIP(
- embed_dim,
- image_resolution, vision_layers, vision_width, vision_patch_size,
- context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
- )
-
- # for key in ["input_resolution", "context_length", "vocab_size"]:
- # del state_dict[key]
-
- convert_weights(model)
- model.load_state_dict(state_dict, strict=False)
- return model.eval()
-
-
-def _download(url: str, root: str = os.path.expanduser("~/.cache/clip")):
- os.makedirs(root, exist_ok=True)
- filename = os.path.basename(url)
-
- expected_sha256 = url.split("/")[-2]
- download_target = os.path.join(root, filename)
-
- if os.path.exists(download_target) and not os.path.isfile(download_target):
- raise RuntimeError(f"{download_target} exists and is not a regular file")
-
- if os.path.isfile(download_target):
- if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
- return download_target
- else:
- warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
-
- with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
- with tqdm(total=int(source.info().get("Content-Length")), ncols=80) as loop:
- while True:
- buffer = source.read(8192)
- if not buffer:
- break
-
- output.write(buffer)
- loop.update(len(buffer))
-
- if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
- raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match")
-
- return download_target
-
-
-def available_models():
- return list(_MODELS.keys())
-
-
-def load_clip(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit=False):
- if name not in _MODELS:
- raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
-
- model_path = _download(_MODELS[name])
- model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval()
- n_px = model.input_resolution.item()
-
- transform = Compose([
- Resize(n_px, interpolation=Image.BICUBIC),
- CenterCrop(n_px),
- lambda image: image.convert("RGB"),
- ToTensor(),
- Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
- ])
-
- if not jit:
- model = build_model(model.state_dict()).to(device)
- if str(device) == "cpu":
- model.float()
- return model, transform
-
- # patch the device names
- device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
- device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]
-
- def patch_device(module):
- graphs = [module.graph] if hasattr(module, "graph") else []
- if hasattr(module, "forward1"):
- graphs.append(module.forward1.graph)
-
- for graph in graphs:
- for node in graph.findAllNodes("prim::Constant"):
- if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"):
- node.copyAttributes(device_node)
-
- model.apply(patch_device)
- patch_device(model.encode_image)
- patch_device(model.encode_text)
-
- # patch dtype to float32 on CPU
- if str(device) == "cpu":
- float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
- float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
- float_node = float_input.node()
-
- def patch_float(module):
- graphs = [module.graph] if hasattr(module, "graph") else []
- if hasattr(module, "forward1"):
- graphs.append(module.forward1.graph)
-
- for graph in graphs:
- for node in graph.findAllNodes("aten::to"):
- inputs = list(node.inputs())
- for i in [1, 2]: # dtype can be the second or third argument to aten::to()
- if inputs[i].node()["value"] == 5:
- inputs[i].node().copyAttributes(float_node)
-
- model.apply(patch_float)
- patch_float(model.encode_image)
- patch_float(model.encode_text)
-
- model.float()
-
- return model, transform
-
-
-def tokenize(texts: Union[str, List[str]], context_length: int = 77):
- if isinstance(texts, str):
- texts = [texts]
-
- sot_token = _tokenizer.encoder["<|startoftext|>"]
- eot_token = _tokenizer.encoder["<|endoftext|>"]
- all_tokens = [[sot_token] + _tokenizer.encode([text]) + [eot_token] for text in texts]
- result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
-
- for i, tokens in enumerate(all_tokens):
- if len(tokens) > context_length:
- raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
- result[i, :len(tokens)] = torch.tensor(tokens)
-
- return result
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py
deleted file mode 100644
index 70f89e227ec64b5c7224375aac0cf7ae3a10a29e..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,7 +0,0 @@
-_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
-
-model = dict(
- bbox_head=dict(
- type='PISARetinaHead',
- loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
- train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
diff --git a/spaces/GrandaddyShmax/MusicGen_Plus/audiocraft/modules/conv.py b/spaces/GrandaddyShmax/MusicGen_Plus/audiocraft/modules/conv.py
deleted file mode 100644
index 972938ab84712eb06e1b10cea25444eee51d6637..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/MusicGen_Plus/audiocraft/modules/conv.py
+++ /dev/null
@@ -1,245 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import typing as tp
-import warnings
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn.utils import spectral_norm, weight_norm
-
-
-CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm',
- 'time_group_norm'])
-
-
-def apply_parametrization_norm(module: nn.Module, norm: str = 'none'):
- assert norm in CONV_NORMALIZATIONS
- if norm == 'weight_norm':
- return weight_norm(module)
- elif norm == 'spectral_norm':
- return spectral_norm(module)
- else:
- # We already check was in CONV_NORMALIZATION, so any other choice
- # doesn't need reparametrization.
- return module
-
-
-def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs):
- """Return the proper normalization module. If causal is True, this will ensure the returned
- module is causal, or return an error if the normalization doesn't support causal evaluation.
- """
- assert norm in CONV_NORMALIZATIONS
- if norm == 'time_group_norm':
- if causal:
- raise ValueError("GroupNorm doesn't support causal evaluation.")
- assert isinstance(module, nn.modules.conv._ConvNd)
- return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
- else:
- return nn.Identity()
-
-
-def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
- padding_total: int = 0) -> int:
- """See `pad_for_conv1d`.
- """
- length = x.shape[-1]
- n_frames = (length - kernel_size + padding_total) / stride + 1
- ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
- return ideal_length - length
-
-
-def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
- """Pad for a convolution to make sure that the last window is full.
- Extra padding is added at the end. This is required to ensure that we can rebuild
- an output of the same length, as otherwise, even with padding, some time steps
- might get removed.
- For instance, with total padding = 4, kernel size = 4, stride = 2:
- 0 0 1 2 3 4 5 0 0 # (0s are padding)
- 1 2 3 # (output frames of a convolution, last 0 is never used)
- 0 0 1 2 3 4 5 0 # (output of tr. conv., but pos. 5 is going to get removed as padding)
- 1 2 3 4 # once you removed padding, we are missing one time step !
- """
- extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
- return F.pad(x, (0, extra_padding))
-
-
-def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'constant', value: float = 0.):
- """Tiny wrapper around F.pad, just to allow for reflect padding on small input.
- If this is the case, we insert extra 0 padding to the right before the reflection happen.
- """
- length = x.shape[-1]
- padding_left, padding_right = paddings
- assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
- if mode == 'reflect':
- max_pad = max(padding_left, padding_right)
- extra_pad = 0
- if length <= max_pad:
- extra_pad = max_pad - length + 1
- x = F.pad(x, (0, extra_pad))
- padded = F.pad(x, paddings, mode, value)
- end = padded.shape[-1] - extra_pad
- return padded[..., :end]
- else:
- return F.pad(x, paddings, mode, value)
-
-
-def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
- """Remove padding from x, handling properly zero padding. Only for 1d!
- """
- padding_left, padding_right = paddings
- assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
- assert (padding_left + padding_right) <= x.shape[-1]
- end = x.shape[-1] - padding_right
- return x[..., padding_left: end]
-
-
-class NormConv1d(nn.Module):
- """Wrapper around Conv1d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, causal: bool = False, norm: str = 'none',
- norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs)
- self.norm_type = norm
-
- def forward(self, x):
- x = self.conv(x)
- x = self.norm(x)
- return x
-
-
-class NormConv2d(nn.Module):
- """Wrapper around Conv2d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
- self.norm_type = norm
-
- def forward(self, x):
- x = self.conv(x)
- x = self.norm(x)
- return x
-
-
-class NormConvTranspose1d(nn.Module):
- """Wrapper around ConvTranspose1d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, causal: bool = False, norm: str = 'none',
- norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs)
- self.norm_type = norm
-
- def forward(self, x):
- x = self.convtr(x)
- x = self.norm(x)
- return x
-
-
-class NormConvTranspose2d(nn.Module):
- """Wrapper around ConvTranspose2d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs)
-
- def forward(self, x):
- x = self.convtr(x)
- x = self.norm(x)
- return x
-
-
-class StreamableConv1d(nn.Module):
- """Conv1d with some builtin handling of asymmetric or causal padding
- and normalization.
- """
- def __init__(self, in_channels: int, out_channels: int,
- kernel_size: int, stride: int = 1, dilation: int = 1,
- groups: int = 1, bias: bool = True, causal: bool = False,
- norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {},
- pad_mode: str = 'reflect'):
- super().__init__()
- # warn user on unusual setup between dilation and stride
- if stride > 1 and dilation > 1:
- warnings.warn('StreamableConv1d has been initialized with stride > 1 and dilation > 1'
- f' (kernel_size={kernel_size} stride={stride}, dilation={dilation}).')
- self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride,
- dilation=dilation, groups=groups, bias=bias, causal=causal,
- norm=norm, norm_kwargs=norm_kwargs)
- self.causal = causal
- self.pad_mode = pad_mode
-
- def forward(self, x):
- B, C, T = x.shape
- kernel_size = self.conv.conv.kernel_size[0]
- stride = self.conv.conv.stride[0]
- dilation = self.conv.conv.dilation[0]
- kernel_size = (kernel_size - 1) * dilation + 1 # effective kernel size with dilations
- padding_total = kernel_size - stride
- extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
- if self.causal:
- # Left padding for causal
- x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
- else:
- # Asymmetric padding required for odd strides
- padding_right = padding_total // 2
- padding_left = padding_total - padding_right
- x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)
- return self.conv(x)
-
-
-class StreamableConvTranspose1d(nn.Module):
- """ConvTranspose1d with some builtin handling of asymmetric or causal padding
- and normalization.
- """
- def __init__(self, in_channels: int, out_channels: int,
- kernel_size: int, stride: int = 1, causal: bool = False,
- norm: str = 'none', trim_right_ratio: float = 1.,
- norm_kwargs: tp.Dict[str, tp.Any] = {}):
- super().__init__()
- self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride,
- causal=causal, norm=norm, norm_kwargs=norm_kwargs)
- self.causal = causal
- self.trim_right_ratio = trim_right_ratio
- assert self.causal or self.trim_right_ratio == 1., \
- "`trim_right_ratio` != 1.0 only makes sense for causal convolutions"
- assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1.
-
- def forward(self, x):
- kernel_size = self.convtr.convtr.kernel_size[0]
- stride = self.convtr.convtr.stride[0]
- padding_total = kernel_size - stride
-
- y = self.convtr(x)
-
- # We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
- # removed at the very end, when keeping only the right length for the output,
- # as removing it here would require also passing the length at the matching layer
- # in the encoder.
- if self.causal:
- # Trim the padding on the right according to the specified ratio
- # if trim_right_ratio = 1.0, trim everything from right
- padding_right = math.ceil(padding_total * self.trim_right_ratio)
- padding_left = padding_total - padding_right
- y = unpad1d(y, (padding_left, padding_right))
- else:
- # Asymmetric padding required for odd strides
- padding_right = padding_total // 2
- padding_left = padding_total - padding_right
- y = unpad1d(y, (padding_left, padding_right))
- return y
diff --git a/spaces/HESOAYM/ElviraMulti/modules/__init__.py b/spaces/HESOAYM/ElviraMulti/modules/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/HaHaBill/LandShapes-Antarctica/models/stylegan2/stylegan2-pytorch/calc_inception.py b/spaces/HaHaBill/LandShapes-Antarctica/models/stylegan2/stylegan2-pytorch/calc_inception.py
deleted file mode 100644
index 5daa531475c377a73ffa256bdf84bb662e144215..0000000000000000000000000000000000000000
--- a/spaces/HaHaBill/LandShapes-Antarctica/models/stylegan2/stylegan2-pytorch/calc_inception.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import argparse
-import pickle
-import os
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torchvision import transforms
-from torchvision.models import inception_v3, Inception3
-import numpy as np
-from tqdm import tqdm
-
-from inception import InceptionV3
-from dataset import MultiResolutionDataset
-
-
-class Inception3Feature(Inception3):
- def forward(self, x):
- if x.shape[2] != 299 or x.shape[3] != 299:
- x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=True)
-
- x = self.Conv2d_1a_3x3(x) # 299 x 299 x 3
- x = self.Conv2d_2a_3x3(x) # 149 x 149 x 32
- x = self.Conv2d_2b_3x3(x) # 147 x 147 x 32
- x = F.max_pool2d(x, kernel_size=3, stride=2) # 147 x 147 x 64
-
- x = self.Conv2d_3b_1x1(x) # 73 x 73 x 64
- x = self.Conv2d_4a_3x3(x) # 73 x 73 x 80
- x = F.max_pool2d(x, kernel_size=3, stride=2) # 71 x 71 x 192
-
- x = self.Mixed_5b(x) # 35 x 35 x 192
- x = self.Mixed_5c(x) # 35 x 35 x 256
- x = self.Mixed_5d(x) # 35 x 35 x 288
-
- x = self.Mixed_6a(x) # 35 x 35 x 288
- x = self.Mixed_6b(x) # 17 x 17 x 768
- x = self.Mixed_6c(x) # 17 x 17 x 768
- x = self.Mixed_6d(x) # 17 x 17 x 768
- x = self.Mixed_6e(x) # 17 x 17 x 768
-
- x = self.Mixed_7a(x) # 17 x 17 x 768
- x = self.Mixed_7b(x) # 8 x 8 x 1280
- x = self.Mixed_7c(x) # 8 x 8 x 2048
-
- x = F.avg_pool2d(x, kernel_size=8) # 8 x 8 x 2048
-
- return x.view(x.shape[0], x.shape[1]) # 1 x 1 x 2048
-
-
-def load_patched_inception_v3():
- # inception = inception_v3(pretrained=True)
- # inception_feat = Inception3Feature()
- # inception_feat.load_state_dict(inception.state_dict())
- inception_feat = InceptionV3([3], normalize_input=False)
-
- return inception_feat
-
-
-@torch.no_grad()
-def extract_features(loader, inception, device):
- pbar = tqdm(loader)
-
- feature_list = []
-
- for img in pbar:
- img = img.to(device)
- feature = inception(img)[0].view(img.shape[0], -1)
- feature_list.append(feature.to('cpu'))
-
- features = torch.cat(feature_list, 0)
-
- return features
-
-
-if __name__ == '__main__':
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
- parser = argparse.ArgumentParser(
- description='Calculate Inception v3 features for datasets'
- )
- parser.add_argument('--size', type=int, default=256)
- parser.add_argument('--batch', default=64, type=int, help='batch size')
- parser.add_argument('--n_sample', type=int, default=50000)
- parser.add_argument('--flip', action='store_true')
- parser.add_argument('path', metavar='PATH', help='path to datset lmdb file')
-
- args = parser.parse_args()
-
- inception = load_patched_inception_v3()
- inception = nn.DataParallel(inception).eval().to(device)
-
- transform = transforms.Compose(
- [
- transforms.RandomHorizontalFlip(p=0.5 if args.flip else 0),
- transforms.ToTensor(),
- transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
- ]
- )
-
- dset = MultiResolutionDataset(args.path, transform=transform, resolution=args.size)
- loader = DataLoader(dset, batch_size=args.batch, num_workers=4)
-
- features = extract_features(loader, inception, device).numpy()
-
- features = features[: args.n_sample]
-
- print(f'extracted {features.shape[0]} features')
-
- mean = np.mean(features, 0)
- cov = np.cov(features, rowvar=False)
-
- name = os.path.splitext(os.path.basename(args.path))[0]
-
- with open(f'inception_{name}.pkl', 'wb') as f:
- pickle.dump({'mean': mean, 'cov': cov, 'size': args.size, 'path': args.path}, f)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/discriminative_reranking_nmt/models/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/discriminative_reranking_nmt/models/__init__.py
deleted file mode 100644
index c593ea5f1842794bfcc952fc93c679a5f16aeb98..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/discriminative_reranking_nmt/models/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .discriminative_reranking_model import DiscriminativeNMTReranker
-
-
-__all__ = [
- "DiscriminativeNMTReranker",
-]
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/model_parallel/modules/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/model_parallel/modules/__init__.py
deleted file mode 100644
index 11603217a188f420ea849ae0fde19979736ba208..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/model_parallel/modules/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""isort:skip_file"""
-
-from .multihead_attention import ModelParallelMultiheadAttention
-from .transformer_layer import (
- ModelParallelTransformerEncoderLayer,
- ModelParallelTransformerDecoderLayer,
-)
-
-__all__ = [
- "ModelParallelMultiheadAttention",
- "ModelParallelTransformerEncoderLayer",
- "ModelParallelTransformerDecoderLayer",
-]
diff --git a/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/src/glow_tts/utils.py b/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/src/glow_tts/utils.py
deleted file mode 100644
index a591aa319ccb264110111cda55c4a232b41aae74..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/src/glow_tts/utils.py
+++ /dev/null
@@ -1,282 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
- iteration = 1
- if "iteration" in checkpoint_dict.keys():
- iteration = checkpoint_dict["iteration"]
- if "learning_rate" in checkpoint_dict.keys():
- learning_rate = checkpoint_dict["learning_rate"]
- if optimizer is not None and "optimizer" in checkpoint_dict.keys():
- optimizer.load_state_dict(checkpoint_dict["optimizer"])
- saved_state_dict = checkpoint_dict["model"]
- if hasattr(model, "module"):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, "module"):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info(
- "Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration)
- )
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info(
- "Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path
- )
- )
- if hasattr(model, "module"):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save(
- {
- "model": state_dict,
- "iteration": iteration,
- "optimizer": optimizer.state_dict(),
- "learning_rate": learning_rate,
- },
- checkpoint_path,
- )
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats="HWC")
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- print(x)
- return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
-
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger("matplotlib")
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots()
- im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
-
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger("matplotlib")
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment, aspect="auto", origin="lower", interpolation="none")
- fig.colorbar(im, ax=ax)
- xlabel = "Decoder timestep"
- if info is not None:
- xlabel += "\n\n" + info
- plt.xlabel(xlabel)
- plt.ylabel("Encoder timestep")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding="utf-8") as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument("-c", "--config", type=str, help="JSON file for configuration")
- parser.add_argument("-m", "--model", type=str, help="Model name")
- # parser.add_argument('-g', '--gan', type=str,
- # help='Model name')
- parser.add_argument("-l", "--logs", type=str, help="logs name")
- # parser.add_argument('-s', '--mels', type=str,
- # help='logs name')
-
- args = parser.parse_args()
- # model_dir = os.path.join("./logs", args.model)
- model_dir = args.model
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
-
- # if not config_path : config_path = config_save_path
-
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- hparams.log_dir = args.logs
- # hparams.mels_dir = args.mels
- # hparams.gan_dir = args.gan
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn(
- "{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- )
- )
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn(
- "git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]
- )
- )
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams:
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/src/glow_tts/hifi/models.py b/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/src/glow_tts/hifi/models.py
deleted file mode 100644
index aaf911836119d69129abe22aa4fc875f2ba3d53c..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/src/glow_tts/hifi/models.py
+++ /dev/null
@@ -1,403 +0,0 @@
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from .utils import init_weights, get_padding
-
-LRELU_SLOPE = 0.1
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.h = h
- self.convs1 = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2]),
- )
- ),
- ]
- )
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- ]
- )
- self.convs2.apply(init_weights)
-
- def forward(self, x):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- xt = c2(xt)
- x = xt + x
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.h = h
- self.convs = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]),
- )
- ),
- ]
- )
- self.convs.apply(init_weights)
-
- def forward(self, x):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- xt = c(xt)
- x = xt + x
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Generator(torch.nn.Module):
- def __init__(self, h):
- super(Generator, self).__init__()
- self.h = h
- self.num_kernels = len(h.resblock_kernel_sizes)
- self.num_upsamples = len(h.upsample_rates)
- self.conv_pre = weight_norm(
- Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3)
- )
- resblock = ResBlock1 if h.resblock == "1" else ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
- self.ups.append(
- weight_norm(
- ConvTranspose1d(
- h.upsample_initial_channel // (2 ** i),
- h.upsample_initial_channel // (2 ** (i + 1)),
- k,
- u,
- padding=(k - u) // 2,
- )
- )
- )
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = h.upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(
- zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)
- ):
- self.resblocks.append(resblock(h, ch, k, d))
-
- self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
- self.ups.apply(init_weights)
- self.conv_post.apply(init_weights)
-
- def forward(self, x):
- x = self.conv_pre(x)
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, 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):
- print("Removing weight norm...")
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
- remove_weight_norm(self.conv_pre)
- remove_weight_norm(self.conv_post)
-
-
-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
- 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(5, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 32,
- 128,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(5, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 128,
- 512,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(5, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 512,
- 1024,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(5, 1), 0),
- )
- ),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 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, LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self):
- super(MultiPeriodDiscriminator, self).__init__()
- self.discriminators = nn.ModuleList(
- [
- DiscriminatorP(2),
- DiscriminatorP(3),
- DiscriminatorP(5),
- DiscriminatorP(7),
- DiscriminatorP(11),
- ]
- )
-
- 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)
- y_d_rs.append(y_d_r)
- fmap_rs.append(fmap_r)
- y_d_gs.append(y_d_g)
- 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, 128, 15, 1, padding=7)),
- norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
- norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
- norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 1, groups=16, 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, LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiScaleDiscriminator(torch.nn.Module):
- def __init__(self):
- super(MultiScaleDiscriminator, self).__init__()
- self.discriminators = nn.ModuleList(
- [
- DiscriminatorS(use_spectral_norm=True),
- DiscriminatorS(),
- DiscriminatorS(),
- ]
- )
- self.meanpools = nn.ModuleList(
- [AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)]
- )
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- if i != 0:
- y = self.meanpools[i - 1](y)
- y_hat = self.meanpools[i - 1](y_hat)
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- fmap_rs.append(fmap_r)
- y_d_gs.append(y_d_g)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-def feature_loss(fmap_r, fmap_g):
- loss = 0
- for dr, dg in zip(fmap_r, fmap_g):
- for rl, gl in zip(dr, dg):
- loss += torch.mean(torch.abs(rl - gl))
-
- return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
- loss = 0
- r_losses = []
- g_losses = []
- for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
- r_loss = torch.mean((1 - dr) ** 2)
- g_loss = torch.mean(dg ** 2)
- loss += r_loss + g_loss
- r_losses.append(r_loss.item())
- g_losses.append(g_loss.item())
-
- return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
- loss = 0
- gen_losses = []
- for dg in disc_outputs:
- l = torch.mean((1 - dg) ** 2)
- gen_losses.append(l)
- loss += l
-
- return loss, gen_losses
diff --git a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/interpretation.py b/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/interpretation.py
deleted file mode 100644
index 17628ef67d3bff3164f6272f32fc171fb977591b..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/interpretation.py
+++ /dev/null
@@ -1,255 +0,0 @@
-import copy
-import math
-
-import numpy as np
-
-from gradio import utils
-from gradio.components import Label, Number
-
-
-async def run_interpret(interface, raw_input):
- """
- Runs the interpretation command for the machine learning model. Handles both the "default" out-of-the-box
- interpretation for a certain set of UI component types, as well as the custom interpretation case.
- Parameters:
- raw_input: a list of raw inputs to apply the interpretation(s) on.
- """
- if isinstance(interface.interpretation, list): # Either "default" or "shap"
- processed_input = [
- input_component.preprocess(raw_input[i])
- for i, input_component in enumerate(interface.input_components)
- ]
- original_output = await interface.call_function(0, processed_input)
- original_output = original_output["prediction"]
-
- if len(interface.output_components) == 1:
- original_output = [original_output]
-
- scores, alternative_outputs = [], []
-
- for i, (x, interp) in enumerate(zip(raw_input, interface.interpretation)):
- if interp == "default":
- input_component = interface.input_components[i]
- neighbor_raw_input = list(raw_input)
- if input_component.interpret_by_tokens:
- tokens, neighbor_values, masks = input_component.tokenize(x)
- interface_scores = []
- alternative_output = []
- for neighbor_input in neighbor_values:
- neighbor_raw_input[i] = neighbor_input
- processed_neighbor_input = [
- input_component.preprocess(neighbor_raw_input[i])
- for i, input_component in enumerate(
- interface.input_components
- )
- ]
-
- neighbor_output = await interface.call_function(
- 0, processed_neighbor_input
- )
- neighbor_output = neighbor_output["prediction"]
- if len(interface.output_components) == 1:
- neighbor_output = [neighbor_output]
- processed_neighbor_output = [
- output_component.postprocess(neighbor_output[i])
- for i, output_component in enumerate(
- interface.output_components
- )
- ]
-
- alternative_output.append(processed_neighbor_output)
- interface_scores.append(
- quantify_difference_in_label(
- interface, original_output, neighbor_output
- )
- )
- alternative_outputs.append(alternative_output)
- scores.append(
- input_component.get_interpretation_scores(
- raw_input[i],
- neighbor_values,
- interface_scores,
- masks=masks,
- tokens=tokens,
- )
- )
- else:
- (
- neighbor_values,
- interpret_kwargs,
- ) = input_component.get_interpretation_neighbors(x)
- interface_scores = []
- alternative_output = []
- for neighbor_input in neighbor_values:
- neighbor_raw_input[i] = neighbor_input
- processed_neighbor_input = [
- input_component.preprocess(neighbor_raw_input[i])
- for i, input_component in enumerate(
- interface.input_components
- )
- ]
- neighbor_output = await interface.call_function(
- 0, processed_neighbor_input
- )
- neighbor_output = neighbor_output["prediction"]
- if len(interface.output_components) == 1:
- neighbor_output = [neighbor_output]
- processed_neighbor_output = [
- output_component.postprocess(neighbor_output[i])
- for i, output_component in enumerate(
- interface.output_components
- )
- ]
-
- alternative_output.append(processed_neighbor_output)
- interface_scores.append(
- quantify_difference_in_label(
- interface, original_output, neighbor_output
- )
- )
- alternative_outputs.append(alternative_output)
- interface_scores = [-score for score in interface_scores]
- scores.append(
- input_component.get_interpretation_scores(
- raw_input[i],
- neighbor_values,
- interface_scores,
- **interpret_kwargs
- )
- )
- elif interp == "shap" or interp == "shapley":
- try:
- import shap # type: ignore
- except (ImportError, ModuleNotFoundError):
- raise ValueError(
- "The package `shap` is required for this interpretation method. Try: `pip install shap`"
- )
- input_component = interface.input_components[i]
- if not (input_component.interpret_by_tokens):
- raise ValueError(
- "Input component {} does not support `shap` interpretation".format(
- input_component
- )
- )
-
- tokens, _, masks = input_component.tokenize(x)
-
- # construct a masked version of the input
- def get_masked_prediction(binary_mask):
- masked_xs = input_component.get_masked_inputs(tokens, binary_mask)
- preds = []
- for masked_x in masked_xs:
- processed_masked_input = copy.deepcopy(processed_input)
- processed_masked_input[i] = input_component.preprocess(masked_x)
- new_output = utils.synchronize_async(
- interface.call_function, 0, processed_masked_input
- )
- new_output = new_output["prediction"]
- if len(interface.output_components) == 1:
- new_output = [new_output]
- pred = get_regression_or_classification_value(
- interface, original_output, new_output
- )
- preds.append(pred)
- return np.array(preds)
-
- num_total_segments = len(tokens)
- explainer = shap.KernelExplainer(
- get_masked_prediction, np.zeros((1, num_total_segments))
- )
- shap_values = explainer.shap_values(
- np.ones((1, num_total_segments)),
- nsamples=int(interface.num_shap * num_total_segments),
- silent=True,
- )
- scores.append(
- input_component.get_interpretation_scores(
- raw_input[i], None, shap_values[0], masks=masks, tokens=tokens
- )
- )
- alternative_outputs.append([])
- elif interp is None:
- scores.append(None)
- alternative_outputs.append([])
- else:
- raise ValueError("Unknown intepretation method: {}".format(interp))
- return scores, alternative_outputs
- else: # custom interpretation function
- processed_input = [
- input_component.preprocess(raw_input[i])
- for i, input_component in enumerate(interface.input_components)
- ]
- interpreter = interface.interpretation
- interpretation = interpreter(*processed_input)
- if len(raw_input) == 1:
- interpretation = [interpretation]
- return interpretation, []
-
-
-def diff(original, perturbed):
- try: # try computing numerical difference
- score = float(original) - float(perturbed)
- except ValueError: # otherwise, look at strict difference in label
- score = int(not (original == perturbed))
- return score
-
-
-def quantify_difference_in_label(interface, original_output, perturbed_output):
- output_component = interface.output_components[0]
- post_original_output = output_component.postprocess(original_output[0])
- post_perturbed_output = output_component.postprocess(perturbed_output[0])
-
- if isinstance(output_component, Label):
- original_label = post_original_output["label"]
- perturbed_label = post_perturbed_output["label"]
-
- # Handle different return types of Label interface
- if "confidences" in post_original_output:
- original_confidence = original_output[0][original_label]
- perturbed_confidence = perturbed_output[0][original_label]
- score = original_confidence - perturbed_confidence
- else:
- score = diff(original_label, perturbed_label)
- return score
-
- elif isinstance(output_component, Number):
- score = diff(post_original_output, post_perturbed_output)
- return score
-
- else:
- raise ValueError(
- "This interpretation method doesn't support the Output component: {}".format(
- output_component
- )
- )
-
-
-def get_regression_or_classification_value(
- interface, original_output, perturbed_output
-):
- """Used to combine regression/classification for Shap interpretation method."""
- output_component = interface.output_components[0]
- post_original_output = output_component.postprocess(original_output[0])
- post_perturbed_output = output_component.postprocess(perturbed_output[0])
-
- if type(output_component) == Label:
- original_label = post_original_output["label"]
- perturbed_label = post_perturbed_output["label"]
-
- # Handle different return types of Label interface
- if "confidences" in post_original_output:
- if math.isnan(perturbed_output[0][original_label]):
- return 0
- return perturbed_output[0][original_label]
- else:
- score = diff(
- perturbed_label, original_label
- ) # Intentionally inverted order of arguments.
- return score
-
- else:
- raise ValueError(
- "This interpretation method doesn't support the Output component: {}".format(
- output_component
- )
- )
diff --git a/spaces/Hina4867/bingo/src/components/chat-list.tsx b/spaces/Hina4867/bingo/src/components/chat-list.tsx
deleted file mode 100644
index 624a78ef0d7be0f1192cf02a81e2e9cf214cb193..0000000000000000000000000000000000000000
--- a/spaces/Hina4867/bingo/src/components/chat-list.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import React from 'react'
-
-import { Separator } from '@/components/ui/separator'
-import { ChatMessage } from '@/components/chat-message'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-
-export interface ChatList {
- messages: ChatMessageModel[]
-}
-
-export function ChatList({ messages }: ChatList) {
- if (!messages.length) {
- return null
- }
-
- return (
-
-
- {messages.map((message, index) => (
-
- )
-}
diff --git a/spaces/HusseinHE/psis/illusion_style.py b/spaces/HusseinHE/psis/illusion_style.py
deleted file mode 100644
index 54a3614533167bcee0d4ba77c2f07294c1ed1690..0000000000000000000000000000000000000000
--- a/spaces/HusseinHE/psis/illusion_style.py
+++ /dev/null
@@ -1,10 +0,0 @@
-css='''
-#share-btn-container {padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; margin-left: auto;}
-div#share-btn-container > div {flex-direction: row;background: black;align-items: center}
-#share-btn-container:hover {background-color: #060606}
-#share-btn {all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.5rem !important; padding-bottom: 0.5rem !important;right:0;}
-#share-btn * {all: unset}
-#share-btn-container div:nth-child(-n+2){width: auto !important;min-height: 0px !important;}
-#share-btn-container .wrap {display: none !important}
-#share-btn-container.hidden {display: none!important}
-'''
\ No newline at end of file
diff --git a/spaces/ICML2022/OFA/fairseq/examples/simultaneous_translation/models/__init__.py b/spaces/ICML2022/OFA/fairseq/examples/simultaneous_translation/models/__init__.py
deleted file mode 100644
index 257a96593ff7af93c206c066d8db4ad795b2ae36..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/simultaneous_translation/models/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import importlib
-import os
-
-
-for file in sorted(os.listdir(os.path.dirname(__file__))):
- if file.endswith(".py") and not file.startswith("_"):
- model_name = file[: file.find(".py")]
- importlib.import_module(
- "examples.simultaneous_translation.models." + model_name
- )
diff --git a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/audio_processing.py b/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/audio_processing.py
deleted file mode 100644
index b5af7f723eb8047bc58db2f85234aea161fbc659..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/audio_processing.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import torch
-import numpy as np
-from scipy.signal import get_window
-import librosa.util as librosa_util
-
-
-def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
- n_fft=800, dtype=np.float32, norm=None):
- """
- # from librosa 0.6
- Compute the sum-square envelope of a window function at a given hop length.
-
- This is used to estimate modulation effects induced by windowing
- observations in short-time fourier transforms.
-
- Parameters
- ----------
- window : string, tuple, number, callable, or list-like
- Window specification, as in `get_window`
-
- n_frames : int > 0
- The number of analysis frames
-
- hop_length : int > 0
- The number of samples to advance between frames
-
- win_length : [optional]
- The length of the window function. By default, this matches `n_fft`.
-
- n_fft : int > 0
- The length of each analysis frame.
-
- dtype : np.dtype
- The data type of the output
-
- Returns
- -------
- wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
- The sum-squared envelope of the window function
- """
- if win_length is None:
- win_length = n_fft
-
- n = n_fft + hop_length * (n_frames - 1)
- x = np.zeros(n, dtype=dtype)
-
- # Compute the squared window at the desired length
- win_sq = get_window(window, win_length, fftbins=True)
- win_sq = librosa_util.normalize(win_sq, norm=norm)**2
- win_sq = librosa_util.pad_center(win_sq, n_fft)
-
- # Fill the envelope
- for i in range(n_frames):
- sample = i * hop_length
- x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))]
- return x
-
-
-def griffin_lim(magnitudes, stft_fn, n_iters=30):
- """
- PARAMS
- ------
- magnitudes: spectrogram magnitudes
- stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods
- """
-
- angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size())))
- angles = angles.astype(np.float32)
- angles = torch.autograd.Variable(torch.from_numpy(angles))
- signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
-
- for i in range(n_iters):
- _, angles = stft_fn.transform(signal)
- signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
- return signal
-
-
-def dynamic_range_compression(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(x, C=1):
- """
- PARAMS
- ------
- C: compression factor used to compress
- """
- return torch.exp(x) / C
diff --git a/spaces/ICML2022/resefa/third_party/stylegan2_official_ops/upfirdn2d.h b/spaces/ICML2022/resefa/third_party/stylegan2_official_ops/upfirdn2d.h
deleted file mode 100644
index c9e2032bcac9d2abde7a75eea4d812da348afadd..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/resefa/third_party/stylegan2_official_ops/upfirdn2d.h
+++ /dev/null
@@ -1,59 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto. Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include 
- 🔥 Fsals is a Robust feature selection framework based on causal inference.
- 🤗 Try using fsals in different data sets.!
- """
- article = r"""
- If FSALs is helpful, please help to ⭐ the Github Repo. Thanks!
- [](https://github.com/Justin-12138/bio_if)
-
- ---
-
- 📝 **Citation**
-
- If our work is useful for your research, please consider citing:
- ```bibtex
- @article{zlhl2023,
- author = {Xiaolong Zhou, Zhao Liu, Yuchen Huang, Kun Lin},
- title = {A Novel Ensemble Feature Selection Method for Biomarkers of Alzheimer's disease},
- booktitle = {GUET Publisher},
- year = {2023}
- }
- ```
- 📋 **License**
-
- This project is licensed under GPL License 2.0.
- Redistribution and use for non-commercial purposes should follow this license.
-
- 📧 **Contact**
-
- If you have any questions, please feel free to reach me out at justinliu707@gmail.com.
-
-
- 🤗 Find Me:
-
- """
- if choicce == "title":
- return title
- elif choicce == "description":
- return description
- elif choicce == "article":
- return article
- elif choicce == 'inputs':
- inputs = [gr.inputs.File(label="Training data"),
- gr.inputs.Radio(['MRMR_FCD', 'MRMR_FCQ', 'CFS', 'Lasso', 'Ensemble', 'CI'], label="method"),
- gr.inputs.Number(label="Num_feature(int)"),
- gr.inputs.Radio(['RF', 'SVM', 'KNN', 'DT', 'Naive Bayes'], label="classifier for CV"),
- gr.inputs.File(label="Testing data")
- ]
- return inputs
- elif choicce == 'outputs':
- output = [gr.Image(label="Index_score"),
- gr.Image(label="IFS_Acc"),
- gr.Image(label="Confusion_matrix"),
- gr.File(label='Index_score.csv')]
- return output
-
-
-def cv(X, y, index_0, clf, n_fold):
- acc = []
- for i in range(len(index_0)):
- # 使用前i个特征进行交叉验证
- selected_features = X[:, [int(j) - 1 for j in index_0[:i + 1]]]
- scores = cross_val_score(clf, selected_features, y, cv=n_fold)
- # 计算平均准确率并添加到acc列表中
- acc.append(scores.mean())
- max_acc = round(max(acc), 4)
- max_index = acc.index(max(acc)) + 1
- return acc, max_acc, max_index
-
-
-def getindex_1(sorted_combined):
- index_1 = []
- index_0 = []
- scores = []
- for indy in sorted_combined:
- index_1.append(str(indy[0] + 1))
- scores.append(indy[1])
- for item in index_1:
- index_0.append(int(item) - 1)
- return index_1, index_0, scores
-
-
-def load_model(X, y, test_samples, test_labels):
- models = SVC(C=1.0, kernel='rbf')
- my_model = MyModel(models)
- my_model.train(X, y)
- # 预测测试样本的标签并计算准确率
- predictions = my_model.predict_samples(test_samples)
- # 计算混淆矩阵
- cm = confusion_matrix(test_labels, predictions)
- return cm
-
-
-def lasso(data, testsample, num_fea_int):
- X, y = load_data(data, True)
- test_samples, test_labels = load_data(testsample, False)
- cl = LassoLarsCV(cv=20, max_iter=80000).fit(X, y)
- importance = np.abs(cl.coef_)
- feature_names = list(X)
- a = len(feature_names)
- idx_features = (-importance).argsort()[:a]
- # name_features = np.array(feature_names)[idx_features]
- result = pd.DataFrame({'index': idx_features, 'Score': importance[idx_features]})
- result_rank = result.sort_values(by='Score', ascending=False, ignore_index=True)
- result_rank.to_csv("index-score.csv")
- inde = result_rank['index'].tolist()
- score = result_rank['Score'].tolist()
- return X, y, inde, score, test_samples, test_labels, num_fea_int
-
-
-def fs(data, method, num_fea_int, clf, testsample):
- num_fea_int = int(num_fea_int)
- if method == 'MRMR_FCD':
- combined, X, y, test_samples, test_labels = MRMR_FCD(data=data, testsample=testsample, num_fea_int=num_fea_int)
- # 使用sorted()函数对合并后的列表进行排序，key参数指定按照分数排序，reverse=True表示降序排序
- sorted_combined = sorted(combined, key=lambda x: x[1], reverse=True)
- index_score_csv(sorted_combined=sorted_combined, filename='ab.csv')
- index_1, index_0, scores = getindex_1(sorted_combined=sorted_combined)
- # 画score.png
- isplot(1, 24, 10,
- title_gr=str(method), x=index_1, y=scores,
- xlabbel="index", ylabel="scores", filename="index-score.png")
- # 选择分类器
- clf = setclf(clf)
- acc, max_acc, max_index = cv(X=X, y=y, index_0=index_0, clf=clf, n_fold=10)
- # 画acc.png
- ifsplot(2, 24, 10,
- title_gr=str(method), max_index=max_index, max_acc=max_acc,
- acc=acc, xlabbel="top n features", ylabel="acc", filename="acc.png")
- cm = load_model(X=X, y=y, test_samples=test_samples, test_labels=test_labels)
- cmplot(3, 24, 10, cm=cm,
- xlabbel="predicted labels", ylabel="true labels", filename='confusion_matrix.png')
- return 'index-score.png', 'acc.png', "confusion_matrix.png", "ab.csv"
-
- elif method == 'MRMR_FCQ':
- combined, X, y, test_samples, test_labels = MRMR_FCQ(data=data, testsample=testsample, num_fea_int=num_fea_int)
- # 使用sorted()函数对合并后的列表进行排序，key参数指定按照分数排序，reverse=True表示降序排序
- sorted_combined = sorted(combined, key=lambda x: x[1], reverse=True)
- index_score_csv(sorted_combined=sorted_combined, filename='ab.csv')
- # inde index start 1
- index_1, index_0, scores = getindex_1(sorted_combined=sorted_combined)
- # index-score.png
- isplot(1, 24, 10, title_gr=str(method), x=index_1, y=scores,
- xlabbel="index", ylabel="scores", filename="index-score.png")
- # 选择分类器
- clf = setclf(clf)
- acc, max_acc, max_index = cv(X=X, y=y, index_0=index_0, clf=clf, n_fold=5)
- # acc.png
- ifsplot(2, 24, 10, title_gr=str(method), max_index=max_index,
- max_acc=max_acc, acc=acc, xlabbel="top n features", ylabel="acc",
- filename="acc.png")
- # cal cm
- cm = load_model(X=X, y=y, test_samples=test_samples, test_labels=test_labels)
- cmplot(3, 24, 10,
- cm=cm, xlabbel="predicted labels", ylabel="true labels", filename='confusion_matrix.png')
- return 'index-score.png', 'acc.png', "confusion_matrix.png", "ab.csv"
-
- elif method == 'Lasso':
- X, y, inde, score, test_samples, test_labels, num_fea_int = lasso(data, testsample, num_fea_int)
- index = []
- for i in inde:
- index.append(str(i))
- plt.figure(1, figsize=(24, 12))
- plt.title(str(method))
- plt.plot(index[:num_fea_int], score[:num_fea_int])
-
- # 设置x轴和y轴的标签
- plt.xlabel('Feature Index', fontsize=40)
- plt.ylabel('Feature Score', fontsize=40)
- plt.savefig('Index_Score.png')
- clf = setclf(clf)
-
- inde = inde[:num_fea_int]
- X = X.values
- acc, max_acc, max_index = cv(X=X, y=y, index_0=inde, clf=clf, n_fold=5)
- ifsplot(2, 24, 10, title_gr=str(method), max_index=max_index,
- max_acc=max_acc, acc=acc, xlabbel="top n features", ylabel="acc",
- filename="acc.png")
-
- cm = load_model(X=X, y=y, test_samples=test_samples, test_labels=test_labels)
- cmplot(3, 24, 10,
- cm=cm, xlabbel="predicted labels", ylabel="true labels", filename='confusion_matrix.png')
-
- return 'Index_Score.png', 'acc.png', "confusion_matrix.png", 'index-score.csv'
-
- elif method == 'CFS':
- pass
diff --git a/spaces/KalbeDigitalLab/ham1000-skin-classification/style.css b/spaces/KalbeDigitalLab/ham1000-skin-classification/style.css
deleted file mode 100644
index d48308e0d57a6e0d127c20ae0790c9ff302a0add..0000000000000000000000000000000000000000
--- a/spaces/KalbeDigitalLab/ham1000-skin-classification/style.css
+++ /dev/null
@@ -1,83 +0,0 @@
-* {
- box-sizing: border-box;
-}
-
-body {
- font-family: 'Source Sans Pro', sans-serif;
- font-size: 16px;
-}
-
-.container {
- width: 100%;
- margin: 0 auto;
-}
-
-.title {
- font-size: 24px !important;
- font-weight: 600 !important;
- letter-spacing: 0em;
- text-align: center;
- color: #374159 !important;
-}
-
-.subtitle {
- font-size: 24px !important;
- font-style: italic;
- font-weight: 400 !important;
- letter-spacing: 0em;
- text-align: center;
- color: #1d652a !important;
- padding-bottom: 0.5em;
-}
-
-.overview-heading {
- font-size: 24px !important;
- font-weight: 600 !important;
- letter-spacing: 0em;
- text-align: left;
-}
-
-.overview-content {
- font-size: 14px !important;
- font-weight: 400 !important;
- line-height: 30px !important;
- letter-spacing: 0em;
- text-align: left;
-}
-
-.content-image {
- width: 100% !important;
- height: auto !important;
-}
-
-.vl {
- border-left: 5px solid #1d652a;
- padding-left: 20px;
- color: #1d652a !important;
-}
-
-.grid-container {
- display: grid;
- grid-template-columns: 1fr 2fr;
- gap: 20px;
- align-items: flex-start;
- margin-bottom: 0.7em;
-}
-
-.grid-container:nth-child(2) {
- align-items: center;
-}
-
-@media screen and (max-width: 768px) {
- .container {
- width: 90%;
- }
-
- .grid-container {
- display: block;
- }
-
- .overview-heading {
- font-size: 18px !important;
- }
-}
\ No newline at end of file
diff --git a/spaces/KarmKarma/rvc-models-genshinimpact/infer_pack/transforms.py b/spaces/KarmKarma/rvc-models-genshinimpact/infer_pack/transforms.py
deleted file mode 100644
index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000
--- a/spaces/KarmKarma/rvc-models-genshinimpact/infer_pack/transforms.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(
- inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.0,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
-
-
-def unconstrained_rational_quadratic_spline(
- inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails="linear",
- tail_bound=1.0,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == "linear":
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError("{} tails are not implemented.".format(tails))
-
- (
- outputs[inside_interval_mask],
- logabsdet[inside_interval_mask],
- ) = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound,
- right=tail_bound,
- bottom=-tail_bound,
- top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- )
-
- return outputs, logabsdet
-
-
-def rational_quadratic_spline(
- inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0.0,
- right=1.0,
- bottom=0.0,
- top=1.0,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError("Input to a transform is not within its domain")
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError("Minimal bin width too large for the number of bins")
- if min_bin_height * num_bins > 1.0:
- raise ValueError("Minimal bin height too large for the number of bins")
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (inputs - input_cumheights) * (
- input_derivatives + input_derivatives_plus_one - 2 * input_delta
- ) + input_heights * (input_delta - input_derivatives)
- b = input_heights * input_derivatives - (inputs - input_cumheights) * (
- input_derivatives + input_derivatives_plus_one - 2 * input_delta
- )
- c = -input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + (
- (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta
- )
- derivative_numerator = input_delta.pow(2) * (
- input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2)
- )
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (
- input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
- )
- denominator = input_delta + (
- (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta
- )
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (
- input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2)
- )
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder/__init__.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/ppg_extractor/encoder/layer_norm.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/ppg_extractor/encoder/layer_norm.py
deleted file mode 100644
index db8be30ff70554edb179109037665e51c04510ec..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/ppg_extractor/encoder/layer_norm.py
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Shigeki Karita
-# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Layer normalization module."""
-
-import torch
-
-
-class LayerNorm(torch.nn.LayerNorm):
- """Layer normalization module.
-
- :param int nout: output dim size
- :param int dim: dimension to be normalized
- """
-
- def __init__(self, nout, dim=-1):
- """Construct an LayerNorm object."""
- super(LayerNorm, self).__init__(nout, eps=1e-12)
- self.dim = dim
-
- def forward(self, x):
- """Apply layer normalization.
-
- :param torch.Tensor x: input tensor
- :return: layer normalized tensor
- :rtype torch.Tensor
- """
- if self.dim == -1:
- return super(LayerNorm, self).forward(x)
- return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1)
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/vocoder_train.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/vocoder_train.py
deleted file mode 100644
index f618ee00d8f774ecf821b9714932acc7e99aa5d5..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/vocoder_train.py
+++ /dev/null
@@ -1,92 +0,0 @@
-from utils.argutils import print_args
-from vocoder.wavernn.train import train
-from vocoder.hifigan.train import train as train_hifigan
-from vocoder.fregan.train import train as train_fregan
-from utils.util import AttrDict
-from pathlib import Path
-import argparse
-import json
-import torch
-import torch.multiprocessing as mp
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser(
- description="Trains the vocoder from the synthesizer audios and the GTA synthesized mels, "
- "or ground truth mels.",
- formatter_class=argparse.ArgumentDefaultsHelpFormatter
- )
-
- parser.add_argument("run_id", type=str, help= \
- "Name for this model instance. If a model state from the same run ID was previously "
- "saved, the training will restart from there. Pass -f to overwrite saved states and "
- "restart from scratch.")
- parser.add_argument("datasets_root", type=str, help= \
- "Path to the directory containing your SV2TTS directory. Specifying --syn_dir or --voc_dir "
- "will take priority over this argument.")
- parser.add_argument("vocoder_type", type=str, default="wavernn", help= \
- "Choose the vocoder type for train. Defaults to wavernn"
- "Now, Support 
-
-
-
-
-#### RetinaNet:
-
-
-Name
-lr
-
schedtrain
-
time
(s/iter)inference
-
time
(s/im)train
-
mem
(GB)box
-
APmodel id
-download
-
-
- R50-C4
-1x
-0.551
-0.102
-4.8
-35.7
-137257644
-model | metrics
-R50-DC5
-1x
-0.380
-0.068
-5.0
-37.3
-137847829
-model | metrics
-R50-FPN
-1x
-0.210
-0.038
-3.0
-37.9
-137257794
-model | metrics
-R50-C4
-3x
-0.543
-0.104
-4.8
-38.4
-137849393
-model | metrics
-R50-DC5
-3x
-0.378
-0.070
-5.0
-39.0
-137849425
-model | metrics
-R50-FPN
-3x
-0.209
-0.038
-3.0
-40.2
-137849458
-model | metrics
-R101-C4
-3x
-0.619
-0.139
-5.9
-41.1
-138204752
-model | metrics
-R101-DC5
-3x
-0.452
-0.086
-6.1
-40.6
-138204841
-model | metrics
-R101-FPN
-3x
-0.286
-0.051
-4.1
-42.0
-137851257
-model | metrics
-X101-FPN
-3x
-0.638
-0.098
-6.7
-43.0
-139173657
-model | metrics
-
-
-
-
-
-
-#### RPN & Fast R-CNN:
-
-
-Name
-lr
-
schedtrain
-
time
(s/iter)inference
-
time
(s/im)train
-
mem
(GB)box
-
APmodel id
-download
-
-
- R50
-1x
-0.205
-0.041
-4.1
-37.4
-190397773
-model | metrics
-R50
-3x
-0.205
-0.041
-4.1
-38.7
-190397829
-model | metrics
-R101
-3x
-0.291
-0.054
-5.2
-40.4
-190397697
-model | metrics
-
-
-
-
-
-### COCO Instance Segmentation Baselines with Mask R-CNN
-
-
-
-
-Name
-lr
-
schedtrain
-
time
(s/iter)inference
-
time
(s/im)train
-
mem
(GB)box
-
APprop.
-
ARmodel id
-download
-
-
- RPN R50-C4
-1x
-0.130
-0.034
-1.5
-
- 51.6
-137258005
-model | metrics
-RPN R50-FPN
-1x
-0.186
-0.032
-2.7
-
- 58.0
-137258492
-model | metrics
-Fast R-CNN R50-FPN
-1x
-0.140
-0.029
-2.6
-37.8
-
- 137635226
-model | metrics
-
-
-
-
-
-
-
-#### New baselines using Large-Scale Jitter and Longer Training Schedule
-
-The following baselines of COCO Instance Segmentation with Mask R-CNN are generated
-using a longer training schedule and large-scale jitter as described in Google's
-[Simple Copy-Paste Data Augmentation](https://arxiv.org/pdf/2012.07177.pdf) paper. These
-models are trained from scratch using random initialization. These baselines exceed the
-previous Mask R-CNN baselines.
-
-In the following table, one epoch consists of training on 118000 COCO images.
-
-Name
-lr
-
schedtrain
-
time
(s/iter)inference
-
time
(s/im)train
-
mem
(GB)box
-
APmask
-
APmodel id
-download
-
-
- R50-C4
-1x
-0.584
-0.110
-5.2
-36.8
-32.2
-137259246
-model | metrics
-R50-DC5
-1x
-0.471
-0.076
-6.5
-38.3
-34.2
-137260150
-model | metrics
-R50-FPN
-1x
-0.261
-0.043
-3.4
-38.6
-35.2
-137260431
-model | metrics
-R50-C4
-3x
-0.575
-0.111
-5.2
-39.8
-34.4
-137849525
-model | metrics
-R50-DC5
-3x
-0.470
-0.076
-6.5
-40.0
-35.9
-137849551
-model | metrics
-R50-FPN
-3x
-0.261
-0.043
-3.4
-41.0
-37.2
-137849600
-model | metrics
-R101-C4
-3x
-0.652
-0.145
-6.3
-42.6
-36.7
-138363239
-model | metrics
-R101-DC5
-3x
-0.545
-0.092
-7.6
-41.9
-37.3
-138363294
-model | metrics
-R101-FPN
-3x
-0.340
-0.056
-4.6
-42.9
-38.6
-138205316
-model | metrics
-X101-FPN
-3x
-0.690
-0.103
-7.2
-44.3
-39.5
-139653917
-model | metrics
-
-
-
-
-
-### COCO Person Keypoint Detection Baselines with Keypoint R-CNN
-
-
-
-Name
-epochs
-train
-
time
(s/im)inference
-
time
(s/im)box
-
APmask
-
APmodel id
-download
-
-
- R50-FPN
-100
-0.376
-0.069
-44.6
-40.3
-42047764
-model | metrics
-R50-FPN
-200
-0.376
-0.069
-46.3
-41.7
-42047638
-model | metrics
-R50-FPN
-400
-0.376
-0.069
-47.4
-42.5
-42019571
-model | metrics
-R101-FPN
-100
-0.518
-0.073
-46.4
-41.6
-42025812
-model | metrics
-R101-FPN
-200
-0.518
-0.073
-48.0
-43.1
-42131867
-model | metrics
-R101-FPN
-400
-0.518
-0.073
-48.9
-43.7
-42073830
-model | metrics
-regnetx_4gf_dds_FPN
-100
-0.474
-0.071
-46.0
-41.3
-42047771
-model | metrics
-regnetx_4gf_dds_FPN
-200
-0.474
-0.071
-48.1
-43.1
-42132721
-model | metrics
-regnetx_4gf_dds_FPN
-400
-0.474
-0.071
-48.6
-43.5
-42025447
-model | metrics
-regnety_4gf_dds_FPN
-100
-0.487
-0.073
-46.1
-41.6
-42047784
-model | metrics
-regnety_4gf_dds_FPN
-200
-0.487
-0.072
-47.8
-43.0
-42047642
-model | metrics
-regnety_4gf_dds_FPN
-400
-0.487
-0.072
-48.2
-43.3
-42045954
-model | metrics
-
-
-
-
-
-### COCO Panoptic Segmentation Baselines with Panoptic FPN
-
-
-
-Name
-lr
-
schedtrain
-
time
(s/iter)inference
-
time
(s/im)train
-
mem
(GB)box
-
APkp.
-
APmodel id
-download
-
-
- R50-FPN
-1x
-0.315
-0.072
-5.0
-53.6
-64.0
-137261548
-model | metrics
-R50-FPN
-3x
-0.316
-0.066
-5.0
-55.4
-65.5
-137849621
-model | metrics
-R101-FPN
-3x
-0.390
-0.076
-6.1
-56.4
-66.1
-138363331
-model | metrics
-X101-FPN
-3x
-0.738
-0.121
-8.7
-57.3
-66.0
-139686956
-model | metrics
-
-
-
-
-
-
-### LVIS Instance Segmentation Baselines with Mask R-CNN
-
-Mask R-CNN baselines on the [LVIS dataset](https://lvisdataset.org), v0.5.
-These baselines are described in Table 3(c) of the [LVIS paper](https://arxiv.org/abs/1908.03195).
-
-NOTE: the 1x schedule here has the same amount of __iterations__ as the COCO 1x baselines.
-They are roughly 24 epochs of LVISv0.5 data.
-The final results of these configs have large variance across different runs.
-
-
-
-
-Name
-lr
-
schedtrain
-
time
(s/iter)inference
-
time
(s/im)train
-
mem
(GB)box
-
APmask
-
APPQ
-model id
-download
-
-
- R50-FPN
-1x
-0.304
-0.053
-4.8
-37.6
-34.7
-39.4
-139514544
-model | metrics
-R50-FPN
-3x
-0.302
-0.053
-4.8
-40.0
-36.5
-41.5
-139514569
-model | metrics
-R101-FPN
-3x
-0.392
-0.066
-6.0
-42.4
-38.5
-43.0
-139514519
-model | metrics
-
-
-
-
-
-
-
-### Cityscapes & Pascal VOC Baselines
-
-Simple baselines for
-* Mask R-CNN on Cityscapes instance segmentation (initialized from COCO pre-training, then trained on Cityscapes fine annotations only)
-* Faster R-CNN on PASCAL VOC object detection (trained on VOC 2007 train+val + VOC 2012 train+val, tested on VOC 2007 using 11-point interpolated AP)
-
-
-
-
-Name
-lr
-
schedtrain
-
time
(s/iter)inference
-
time
(s/im)train
-
mem
(GB)box
-
APmask
-
APmodel id
-download
-
-
- R50-FPN
-1x
-0.292
-0.107
-7.1
-23.6
-24.4
-144219072
-model | metrics
-R101-FPN
-1x
-0.371
-0.114
-7.8
-25.6
-25.9
-144219035
-model | metrics
-X101-FPN
-1x
-0.712
-0.151
-10.2
-26.7
-27.1
-144219108
-model | metrics
-
-
-
-
-
-
-
-### Other Settings
-
-Ablations for Deformable Conv and Cascade R-CNN:
-
-
-
-
-Name
-train
-
time
(s/iter)inference
-
time
(s/im)train
-
mem
(GB)box
-
APbox
-
AP50mask
-
APmodel id
-download
-
-
- R50-FPN, Cityscapes
-0.240
-0.078
-4.4
-
-
- 36.5
-142423278
-model | metrics
-R50-C4, VOC
-0.537
-0.081
-4.8
-51.9
-80.3
-
- 142202221
-model | metrics
-
-
-
-
-
-
-Ablations for normalization methods, and a few models trained from scratch following [Rethinking ImageNet Pre-training](https://arxiv.org/abs/1811.08883).
-(Note: The baseline uses `2fc` head while the others use [`4conv1fc` head](https://arxiv.org/abs/1803.08494))
-
-
-
-Name
-lr
-
schedtrain
-
time
(s/iter)inference
-
time
(s/im)train
-
mem
(GB)box
-
APmask
-
APmodel id
-download
-
-
- Baseline R50-FPN
-1x
-0.261
-0.043
-3.4
-38.6
-35.2
-137260431
-model | metrics
-Deformable Conv
-1x
-0.342
-0.048
-3.5
-41.5
-37.5
-138602867
-model | metrics
-Cascade R-CNN
-1x
-0.317
-0.052
-4.0
-42.1
-36.4
-138602847
-model | metrics
-Baseline R50-FPN
-3x
-0.261
-0.043
-3.4
-41.0
-37.2
-137849600
-model | metrics
-Deformable Conv
-3x
-0.349
-0.047
-3.5
-42.7
-38.5
-144998336
-model | metrics
-Cascade R-CNN
-3x
-0.328
-0.053
-4.0
-44.3
-38.5
-144998488
-model | metrics
-
-
-
-
-
-
-A few very large models trained for a long time, for demo purposes. They are trained using multiple machines:
-
-
-
-
-Name
-lr
-
schedtrain
-
time
(s/iter)inference
-
time
(s/im)train
-
mem
(GB)box
-
APmask
-
APmodel id
-download
-
-
- Baseline R50-FPN
-3x
-0.261
-0.043
-3.4
-41.0
-37.2
-137849600
-model | metrics
-GN
-3x
-0.309
-0.060
-5.6
-42.6
-38.6
-138602888
-model | metrics
-SyncBN
-3x
-0.345
-0.053
-5.5
-41.9
-37.8
-169527823
-model | metrics
-GN (from scratch)
-3x
-0.338
-0.061
-7.2
-39.9
-36.6
-138602908
-model | metrics
-GN (from scratch)
-9x
-N/A
-0.061
-7.2
-43.7
-39.6
-183808979
-model | metrics
-SyncBN (from scratch)
-9x
-N/A
-0.055
-7.2
-43.6
-39.3
-184226666
-model | metrics
-
-
-
-
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/evaluator.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/evaluator.py
deleted file mode 100644
index 7d0848c7ec511f7000f4230c914a8b32f690dee0..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/evaluator.py
+++ /dev/null
@@ -1,228 +0,0 @@
-# ------------------------------------------------------------------------------
-# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/evaluator.py
-# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
-# ------------------------------------------------------------------------------
-
-import datetime
-import logging
-import time
-from collections import OrderedDict, abc
-from contextlib import ExitStack, contextmanager
-from typing import List, Union
-import torch
-from torch import nn
-
-from detectron2.utils.comm import get_world_size, is_main_process
-from detectron2.utils.logger import log_every_n_seconds
-
-
-class DatasetEvaluator:
- """
- Base class for a dataset evaluator.
-
- The function :func:`inference_on_dataset` runs the model over
- all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs.
-
- This class will accumulate information of the inputs/outputs (by :meth:`process`),
- and produce evaluation results in the end (by :meth:`evaluate`).
- """
-
- def reset(self):
- """
- Preparation for a new round of evaluation.
- Should be called before starting a round of evaluation.
- """
- pass
-
- def process(self, inputs, outputs):
- """
- Process the pair of inputs and outputs.
- If they contain batches, the pairs can be consumed one-by-one using `zip`:
-
- .. code-block:: python
-
- for input_, output in zip(inputs, outputs):
- # do evaluation on single input/output pair
- ...
-
- Args:
- inputs (list): the inputs that's used to call the model.
- outputs (list): the return value of `model(inputs)`
- """
- pass
-
- def evaluate(self):
- """
- Evaluate/summarize the performance, after processing all input/output pairs.
-
- Returns:
- dict:
- A new evaluator class can return a dict of arbitrary format
- as long as the user can process the results.
- In our train_net.py, we expect the following format:
-
- * key: the name of the task (e.g., bbox)
- * value: a dict of {metric name: score}, e.g.: {"AP50": 80}
- """
- pass
-
-
-class DatasetEvaluators(DatasetEvaluator):
- """
- Wrapper class to combine multiple :class:`DatasetEvaluator` instances.
-
- This class dispatches every evaluation call to
- all of its :class:`DatasetEvaluator`.
- """
-
- def __init__(self, evaluators):
- """
- Args:
- evaluators (list): the evaluators to combine.
- """
- super().__init__()
- self._evaluators = evaluators
-
- def reset(self):
- for evaluator in self._evaluators:
- evaluator.reset()
-
- def process(self, inputs, outputs):
- for evaluator in self._evaluators:
- evaluator.process(inputs, outputs)
-
- def evaluate(self):
- results = OrderedDict()
- for evaluator in self._evaluators:
- result = evaluator.evaluate()
- if is_main_process() and result is not None:
- for k, v in result.items():
- assert (
- k not in results
- ), "Different evaluators produce results with the same key {}".format(k)
- results[k] = v
- return results
-
-
-def inference_on_dataset(
- model, data_loader, evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None]
-):
- """
- Run model on the data_loader and evaluate the metrics with evaluator.
- Also benchmark the inference speed of `model.__call__` accurately.
- The model will be used in eval mode.
-
- Args:
- model (callable): a callable which takes an object from
- `data_loader` and returns some outputs.
-
- If it's an nn.Module, it will be temporarily set to `eval` mode.
- If you wish to evaluate a model in `training` mode instead, you can
- wrap the given model and override its behavior of `.eval()` and `.train()`.
- data_loader: an iterable object with a length.
- The elements it generates will be the inputs to the model.
- evaluator: the evaluator(s) to run. Use `None` if you only want to benchmark,
- but don't want to do any evaluation.
-
- Returns:
- The return value of `evaluator.evaluate()`
- """
- num_devices = get_world_size()
- logger = logging.getLogger(__name__)
- logger.info("Start inference on {} batches".format(len(data_loader)))
-
- total = len(data_loader) # inference data loader must have a fixed length
- if evaluator is None:
- # create a no-op evaluator
- evaluator = DatasetEvaluators([])
- if isinstance(evaluator, abc.MutableSequence):
- evaluator = DatasetEvaluators(evaluator)
- evaluator.reset()
-
- num_warmup = min(5, total - 1)
- start_time = time.perf_counter()
- total_data_time = 0
- total_compute_time = 0
- total_eval_time = 0
- with ExitStack() as stack:
- if isinstance(model, nn.Module):
- stack.enter_context(inference_context(model))
- stack.enter_context(torch.no_grad())
-
- start_data_time = time.perf_counter()
- for idx, inputs in enumerate(data_loader):
- total_data_time += time.perf_counter() - start_data_time
- if idx == num_warmup:
- start_time = time.perf_counter()
- total_data_time = 0
- total_compute_time = 0
- total_eval_time = 0
-
- start_compute_time = time.perf_counter()
- outputs = model(inputs)
- if torch.cuda.is_available():
- torch.cuda.synchronize()
- total_compute_time += time.perf_counter() - start_compute_time
-
- start_eval_time = time.perf_counter()
- evaluator.process(inputs, outputs)
- total_eval_time += time.perf_counter() - start_eval_time
-
- iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup)
- data_seconds_per_iter = total_data_time / iters_after_start
- compute_seconds_per_iter = total_compute_time / iters_after_start
- eval_seconds_per_iter = total_eval_time / iters_after_start
- total_seconds_per_iter = (time.perf_counter() - start_time) / iters_after_start
- if idx >= num_warmup * 2 or compute_seconds_per_iter > 5:
- eta = datetime.timedelta(seconds=int(total_seconds_per_iter * (total - idx - 1)))
- log_every_n_seconds(
- logging.INFO,
- (
- f"Inference done {idx + 1}/{total}. "
- f"Dataloading: {data_seconds_per_iter:.4f} s/iter. "
- f"Inference: {compute_seconds_per_iter:.4f} s/iter. "
- f"Eval: {eval_seconds_per_iter:.4f} s/iter. "
- f"Total: {total_seconds_per_iter:.4f} s/iter. "
- f"ETA={eta}"
- ),
- n=5,
- )
- start_data_time = time.perf_counter()
-
- # Measure the time only for this worker (before the synchronization barrier)
- total_time = time.perf_counter() - start_time
- total_time_str = str(datetime.timedelta(seconds=total_time))
- # NOTE this format is parsed by grep
- logger.info(
- "Total inference time: {} ({:.6f} s / iter per device, on {} devices)".format(
- total_time_str, total_time / (total - num_warmup), num_devices
- )
- )
- total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time)))
- logger.info(
- "Total inference pure compute time: {} ({:.6f} s / iter per device, on {} devices)".format(
- total_compute_time_str, total_compute_time / (total - num_warmup), num_devices
- )
- )
-
- results = evaluator.evaluate()
- # An evaluator may return None when not in main process.
- # Replace it by an empty dict instead to make it easier for downstream code to handle
- if results is None:
- results = {}
- return results
-
-
-@contextmanager
-def inference_context(model):
- """
- A context where the model is temporarily changed to eval mode,
- and restored to previous mode afterwards.
-
- Args:
- model: a torch Module
- """
- training_mode = model.training
- model.eval()
- yield
- model.train(training_mode)
diff --git a/spaces/PSLD/PSLD/stable-diffusion/ldm/modules/image_degradation/__init__.py b/spaces/PSLD/PSLD/stable-diffusion/ldm/modules/image_degradation/__init__.py
deleted file mode 100644
index 7836cada81f90ded99c58d5942eea4c3477f58fc..0000000000000000000000000000000000000000
--- a/spaces/PSLD/PSLD/stable-diffusion/ldm/modules/image_degradation/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
-from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
diff --git a/spaces/PascalNotin/Tranception_design/tranception/utils/msa_utils.py b/spaces/PascalNotin/Tranception_design/tranception/utils/msa_utils.py
deleted file mode 100644
index 11ec15b5dc7dd149c6deaa820f32549e535f20a8..0000000000000000000000000000000000000000
--- a/spaces/PascalNotin/Tranception_design/tranception/utils/msa_utils.py
+++ /dev/null
@@ -1,361 +0,0 @@
-import numpy as np
-import pandas as pd
-from collections import defaultdict
-import random
-import os
-import torch
-from Bio.Align.Applications import ClustalOmegaCommandline
-
-def filter_msa(msa_data, num_sequences_kept=3):
- """
- Helper function to filter an input MSA msa_data (obtained via process_msa_data) and keep only num_sequences_kept aligned sequences.
- If the MSA already has fewer sequences than num_sequences_kept, we keep the MSA as is.
- If filtering, we always keep the first sequence of the MSA (ie. the wild type) by default.
- Sampling is done without replacement.
- """
- if len(list(msa_data.keys())) <= num_sequences_kept:
- return msa_data
- filtered_msa = {}
- wt_name = next(iter(msa_data))
- filtered_msa[wt_name] = msa_data[wt_name]
- del msa_data[wt_name]
- sequence_names = list(msa_data.keys())
- sequence_names_sampled = random.sample(sequence_names,k=num_sequences_kept-1)
- for seq in sequence_names_sampled:
- filtered_msa[seq] = msa_data[seq]
- return filtered_msa
-
-def process_msa_data(MSA_data_file):
- """
- Helper function that takes as input a path to a MSA file (expects a2m format) and returns a dict mapping sequence ID to the corresponding AA sequence.
- """
- msa_data = defaultdict(str)
- sequence_name = ""
- with open(MSA_data_file, "r") as msa_file:
- for i, line in enumerate(msa_file):
- line = line.rstrip()
- if line.startswith(">"):
- sequence_name = line
- else:
- msa_data[sequence_name] += line.upper()
- return msa_data
-
-def get_one_hot_sequences_dict(msa_data,MSA_start,MSA_end,vocab):
- vocab_size = len(vocab.keys())
- num_sequences_msa = len(msa_data.keys())
- one_hots = np.zeros((num_sequences_msa,MSA_end-MSA_start,vocab_size))
- for i,seq_name in enumerate(msa_data.keys()):
- sequence = msa_data[seq_name]
- for j,letter in enumerate(sequence):
- if letter in vocab:
- k = vocab[letter]
- one_hots[i,j,k] = 1.0
- return one_hots
-
-def one_hot(sequence_string,vocab):
- one_hots = np.zeros((len(sequence_string),len(vocab.keys())))
- for j,letter in enumerate(sequence_string):
- if letter in vocab:
- k = vocab[letter]
- one_hots[j,k] = 1.0
- return one_hots.flatten()
-
-def get_msa_prior(MSA_data_file, MSA_weight_file_name, MSA_start, MSA_end, len_target_seq, vocab, retrieval_aggregation_mode="aggregate_substitution", filter_MSA=True, verbose=False):
- """
- Function to enable retrieval inference mode, via computation of (weighted) pseudocounts of AAs at each position of the retrieved MSA.
- MSA_data_file: (string) path to MSA file (expects a2m format).
- MSA_weight_file_name: (string) path to sequence weights in MSA.
- MSA_start: (int) Sequence position that the MSA starts at (1-indexing).
- MSA_end: (int) Sequence position that the MSA ends at (1-indexing).
- len_target_seq: (int) Full length of sequence to be scored.
- vocab: (dict) Vocabulary of the tokenizer.
- retrieval_aggregation_mode: (string) Mode for retrieval inference (aggregate_substitution Vs aggregate_indel). If None, places a uniform prior over each token.
- filter_MSA: (bool) Whether to filter out sequences with very low hamming similarity (< 0.2) to the reference sequence in the MSA (first sequence).
- verbose: (bool) Whether to print to the console processing details along the way.
- """
- msa_data = process_msa_data(MSA_data_file)
- vocab_size = len(vocab.keys())
- if verbose: print("Target seq len is {}, MSA length is {}, start position is {}, end position is {} and vocab size is {}".format(len_target_seq,MSA_end-MSA_start,MSA_start,MSA_end,vocab_size))
-
- if filter_MSA:
- if verbose: print("Num sequences in MSA pre filtering: {}".format(len(msa_data.keys())))
- list_sequence_names = list(msa_data.keys())
- focus_sequence_name = list(msa_data.keys())[0]
- ref_sequence_hot = one_hot(msa_data[focus_sequence_name],vocab)
- for sequence_name in list_sequence_names:
- seq_hot = one_hot(msa_data[sequence_name],vocab)
- hamming_similarity_seq_ref = np.dot(ref_sequence_hot,seq_hot) / np.dot(ref_sequence_hot,ref_sequence_hot)
- if hamming_similarity_seq_ref < 0.2:
- del msa_data[sequence_name]
- if verbose: print("Num sequences in MSA post filtering: {}".format(len(msa_data.keys())))
-
- if MSA_weight_file_name is not None:
- if verbose: print("Using weights in {} for sequences in MSA.".format(MSA_weight_file_name))
- assert os.path.exists(MSA_weight_file_name), "Weights file not located on disk."
- MSA_EVE = MSA_processing(
- MSA_location=MSA_data_file,
- use_weights=True,
- weights_location=MSA_weight_file_name
- )
- #We scan through all sequences to see if we have a weight for them as per EVE pre-processing. We drop them otherwise.
- dropped_sequences=0
- list_sequence_names = list(msa_data.keys())
- MSA_weight=[]
- for sequence_name in list_sequence_names:
- if sequence_name not in MSA_EVE.seq_name_to_sequence:
- dropped_sequences +=1
- del msa_data[sequence_name]
- else:
- MSA_weight.append(MSA_EVE.seq_name_to_weight[sequence_name])
- if verbose: print("Dropped {} sequences from MSA due to absent sequence weights".format(dropped_sequences))
- else:
- MSA_weight = [1] * len(list(msa_data.keys()))
-
- if retrieval_aggregation_mode=="aggregate_substitution" or retrieval_aggregation_mode=="aggregate_indel":
- one_hots = get_one_hot_sequences_dict(msa_data,MSA_start,MSA_end,vocab)
- MSA_weight = np.expand_dims(np.array(MSA_weight),axis=(1,2))
- base_rate = 1e-5
- base_rates = np.ones_like(one_hots) * base_rate
- weighted_one_hots = (one_hots + base_rates) * MSA_weight
- MSA_weight_norm_counts = weighted_one_hots.sum(axis=-1).sum(axis=0)
- MSA_weight_norm_counts = np.tile(MSA_weight_norm_counts.reshape(-1,1), (1,vocab_size))
- one_hots_avg = weighted_one_hots.sum(axis=0) / MSA_weight_norm_counts
- msa_prior = np.zeros((len_target_seq,vocab_size))
- msa_prior[MSA_start:MSA_end,:]=one_hots_avg
- else:
- msa_prior = np.ones((len_target_seq,vocab_size)) / vocab_size
-
- if verbose:
- for idx, position in enumerate(msa_prior):
- if len(position)!=25:
- print("Size error")
- if not round(position.sum(),2)==1.0:
- print("Position at index {} does not add up to 1: {}".format(idx, position.sum()))
-
- return msa_prior
-
-
-def update_retrieved_MSA_log_prior_indel(model, MSA_log_prior, MSA_start, MSA_end, mutated_sequence):
- """
- Function to process MSA when scoring indels.
- To identify positions to add / remove in the retrieved MSA, we append and align the sequence to be scored to the original MSA for that protein family with Clustal Omega.
- If the original MSA is relatively deep (over 100k sequences), we sample (by default) 100k rows at random from that MSA to speed computations.
- MSA sampling is performed only once (for the first sequence to be scored). Subsequent scoring use the same MSA sample.
- """
- if not os.path.isdir(model.MSA_folder + os.sep + "Sampled"):
- os.mkdir(model.MSA_folder + os.sep + "Sampled")
- sampled_MSA_location = model.MSA_folder + os.sep + "Sampled" + os.sep + "Sampled_" + model.MSA_filename.split(os.sep)[-1]
-
- if not os.path.exists(sampled_MSA_location):
- msa_data = process_msa_data(model.MSA_filename)
- msa_data_sampled = filter_msa(msa_data, num_sequences_kept=100000) #If MSA has less than 100k sequences, the sample is identical to original MSA
- with open(sampled_MSA_location, 'w') as sampled_write_location:
- for index, key in enumerate(msa_data_sampled):
- key_name = ">REFERENCE_SEQUENCE" if index==0 else key
- msa_data_sampled[key] = msa_data_sampled[key].upper()
- msa_data_sampled[key] = msa_data_sampled[key].replace(".","-")
- sampled_write_location.write(key_name+"\n"+"\n".join([msa_data_sampled[key][i:i+80] for i in range(0, len(msa_data_sampled[key]), 80)])+"\n")
-
- seq_to_align_location = model.MSA_folder + os.sep + "Sampled" + os.sep + "Seq_to_align_" + model.MSA_filename.split(os.sep)[-1]
- sequence_text_split = [mutated_sequence[i:i+80] for i in range(0, len(mutated_sequence), 80)]
- sequence_text_split_split_join = "\n".join([">SEQ_TO_SCORE"]+sequence_text_split)
- os.system("echo '"+sequence_text_split_split_join+"' > "+seq_to_align_location)
-
- expanded_MSA_location = model.MSA_folder + os.sep + "Sampled" + os.sep + "Expanded_" + model.MSA_filename.split(os.sep)[-1]
- clustalw_cline = ClustalOmegaCommandline(cmd=model.config.clustal_omega_location,
- profile1=sampled_MSA_location,
- profile2=seq_to_align_location,
- outfile=expanded_MSA_location,
- force=True)
- stdout, stderr = clustalw_cline()
- msa_data = process_msa_data(expanded_MSA_location)
- aligned_seqA, aligned_seqB = msa_data[">SEQ_TO_SCORE"], msa_data[">REFERENCE_SEQUENCE"]
- try:
- keep_column=[]
- for column_index_pairwise_alignment in range(len(aligned_seqA)):
- if aligned_seqA[column_index_pairwise_alignment]=="-" and aligned_seqB[column_index_pairwise_alignment]=="-":
- continue
- elif aligned_seqA[column_index_pairwise_alignment]=="-":
- keep_column.append(False)
- elif aligned_seqB[column_index_pairwise_alignment]=="-":
- MSA_log_prior=torch.cat((MSA_log_prior[:column_index_pairwise_alignment], torch.zeros(MSA_log_prior.shape[1]).view(1,-1).cuda(), MSA_log_prior[column_index_pairwise_alignment:]),dim=0)
- keep_column.append(True) #keep the zero column we just added
- else:
- keep_column.append(True)
- MSA_log_prior = MSA_log_prior[keep_column]
- MSA_end = MSA_start + len(MSA_log_prior)
- except:
- print("Error when processing the following alignment: {}".format(expanded_MSA_location))
- return MSA_log_prior, MSA_start, MSA_end
-
-class MSA_processing:
- def __init__(self,
- MSA_location="",
- theta=0.2,
- use_weights=True,
- weights_location="./data/weights",
- preprocess_MSA=True,
- threshold_sequence_frac_gaps=0.5,
- threshold_focus_cols_frac_gaps=0.3,
- remove_sequences_with_indeterminate_AA_in_focus_cols=True
- ):
-
- """
- This MSA_processing class is directly borrowed from the EVE codebase: https://github.com/OATML-Markslab/EVE
-
- Parameters:
- - msa_location: (path) Location of the MSA data. Constraints on input MSA format:
- - focus_sequence is the first one in the MSA data
- - first line is structured as follows: ">focus_seq_name/start_pos-end_pos" (e.g., >SPIKE_SARS2/310-550)
- - corespondding sequence data located on following line(s)
- - then all other sequences follow with ">name" on first line, corresponding data on subsequent lines
- - theta: (float) Sequence weighting hyperparameter. Generally: Prokaryotic and eukaryotic families = 0.2; Viruses = 0.01
- - use_weights: (bool) If False, sets all sequence weights to 1. If True, checks weights_location -- if non empty uses that;
- otherwise compute weights from scratch and store them at weights_location
- - weights_location: (path) Location to load from/save to the sequence weights
- - preprocess_MSA: (bool) performs pre-processing of MSA to remove short fragments and positions that are not well covered.
- - threshold_sequence_frac_gaps: (float, between 0 and 1) Threshold value to define fragments
- - sequences with a fraction of gap characters above threshold_sequence_frac_gaps are removed
- - default is set to 0.5 (i.e., fragments with 50% or more gaps are removed)
- - threshold_focus_cols_frac_gaps: (float, between 0 and 1) Threshold value to define focus columns
- - positions with a fraction of gap characters above threshold_focus_cols_pct_gaps will be set to lower case (and not included in the focus_cols)
- - default is set to 0.3 (i.e., focus positions are the ones with 30% of gaps or less, i.e., 70% or more residue occupancy)
- - remove_sequences_with_indeterminate_AA_in_focus_cols: (bool) Remove all sequences that have indeterminate AA (e.g., B, J, X, Z) at focus positions of the wild type
- """
- np.random.seed(2021)
- self.MSA_location = MSA_location
- self.weights_location = weights_location
- self.theta = theta
- self.alphabet = "ACDEFGHIKLMNPQRSTVWY"
- self.use_weights = use_weights
- self.preprocess_MSA = preprocess_MSA
- self.threshold_sequence_frac_gaps = threshold_sequence_frac_gaps
- self.threshold_focus_cols_frac_gaps = threshold_focus_cols_frac_gaps
- self.remove_sequences_with_indeterminate_AA_in_focus_cols = remove_sequences_with_indeterminate_AA_in_focus_cols
-
- self.gen_alignment()
-
- def gen_alignment(self, verbose=False):
- """ Read training alignment and store basics in class instance """
- self.aa_dict = {}
- for i,aa in enumerate(self.alphabet):
- self.aa_dict[aa] = i
-
- self.seq_name_to_sequence = defaultdict(str)
- name = ""
- with open(self.MSA_location, "r") as msa_data:
- for i, line in enumerate(msa_data):
- line = line.rstrip()
- if line.startswith(">"):
- name = line
- if i==0:
- self.focus_seq_name = name
- else:
- self.seq_name_to_sequence[name] += line
-
-
- ## MSA pre-processing to remove inadequate columns and sequences
- if self.preprocess_MSA:
- msa_df = pd.DataFrame.from_dict(self.seq_name_to_sequence, orient='index', columns=['sequence'])
- # Data clean up
- msa_df.sequence = msa_df.sequence.apply(lambda x: x.replace(".","-")).apply(lambda x: ''.join([aa.upper() for aa in x]))
- # Remove columns that would be gaps in the wild type
- non_gap_wt_cols = [aa!='-' for aa in msa_df.sequence[self.focus_seq_name]]
- msa_df['sequence'] = msa_df['sequence'].apply(lambda x: ''.join([aa for aa,non_gap_ind in zip(x, non_gap_wt_cols) if non_gap_ind]))
- assert 0.0 <= self.threshold_sequence_frac_gaps <= 1.0,"Invalid fragment filtering parameter"
- assert 0.0 <= self.threshold_focus_cols_frac_gaps <= 1.0,"Invalid focus position filtering parameter"
- msa_array = np.array([list(seq) for seq in msa_df.sequence])
- gaps_array = np.array(list(map(lambda seq: [aa=='-' for aa in seq], msa_array)))
- # Identify fragments with too many gaps
- seq_gaps_frac = gaps_array.mean(axis=1)
- seq_below_threshold = seq_gaps_frac <= self.threshold_sequence_frac_gaps
- if verbose: print("Proportion of sequences dropped due to fraction of gaps: "+str(round(float(1 - seq_below_threshold.sum()/seq_below_threshold.shape)*100,2))+"%")
- # Identify focus columns
- columns_gaps_frac = gaps_array[seq_below_threshold].mean(axis=0)
- index_cols_below_threshold = columns_gaps_frac <= self.threshold_focus_cols_frac_gaps
- if verbose: print("Proportion of non-focus columns removed: "+str(round(float(1 - index_cols_below_threshold.sum()/index_cols_below_threshold.shape)*100,2))+"%")
- # Lower case non focus cols and filter fragment sequences
- msa_df['sequence'] = msa_df['sequence'].apply(lambda x: ''.join([aa.upper() if upper_case_ind else aa.lower() for aa, upper_case_ind in zip(x, index_cols_below_threshold)]))
- msa_df = msa_df[seq_below_threshold]
- # Overwrite seq_name_to_sequence with clean version
- self.seq_name_to_sequence = defaultdict(str)
- for seq_idx in range(len(msa_df['sequence'])):
- self.seq_name_to_sequence[msa_df.index[seq_idx]] = msa_df.sequence[seq_idx]
-
- self.focus_seq = self.seq_name_to_sequence[self.focus_seq_name]
- self.focus_cols = [ix for ix, s in enumerate(self.focus_seq) if s == s.upper() and s!='-']
- self.focus_seq_trimmed = [self.focus_seq[ix] for ix in self.focus_cols]
- self.seq_len = len(self.focus_cols)
- self.alphabet_size = len(self.alphabet)
-
- # Connect local sequence index with uniprot index (index shift inferred from 1st row of MSA)
- focus_loc = self.focus_seq_name.split("/")[-1]
- start,stop = focus_loc.split("-")
- self.focus_start_loc = int(start)
- self.focus_stop_loc = int(stop)
- self.uniprot_focus_col_to_wt_aa_dict \
- = {idx_col+int(start):self.focus_seq[idx_col] for idx_col in self.focus_cols}
- self.uniprot_focus_col_to_focus_idx \
- = {idx_col+int(start):idx_col for idx_col in self.focus_cols}
-
- # Move all letters to CAPS; keeps focus columns only
- self.raw_seq_name_to_sequence = self.seq_name_to_sequence.copy()
- for seq_name,sequence in self.seq_name_to_sequence.items():
- sequence = sequence.replace(".","-")
- self.seq_name_to_sequence[seq_name] = [sequence[ix].upper() for ix in self.focus_cols]
-
- # Remove sequences that have indeterminate AA (e.g., B, J, X, Z) in the focus columns
- if self.remove_sequences_with_indeterminate_AA_in_focus_cols:
- alphabet_set = set(list(self.alphabet))
- seq_names_to_remove = []
- for seq_name,sequence in self.seq_name_to_sequence.items():
- for letter in sequence:
- if letter not in alphabet_set and letter != "-":
- seq_names_to_remove.append(seq_name)
- continue
- seq_names_to_remove = list(set(seq_names_to_remove))
- for seq_name in seq_names_to_remove:
- del self.seq_name_to_sequence[seq_name]
-
- # Encode the sequences
- self.one_hot_encoding = np.zeros((len(self.seq_name_to_sequence.keys()),len(self.focus_cols),len(self.alphabet)))
- if verbose: print("One-hot encoded sequences shape:" + str(self.one_hot_encoding.shape))
- for i,seq_name in enumerate(self.seq_name_to_sequence.keys()):
- sequence = self.seq_name_to_sequence[seq_name]
- for j,letter in enumerate(sequence):
- if letter in self.aa_dict:
- k = self.aa_dict[letter]
- self.one_hot_encoding[i,j,k] = 1.0
-
- if self.use_weights:
- try:
- self.weights = np.load(file=self.weights_location)
- if verbose: print("Loaded sequence weights from disk")
- except:
- if verbose: print ("Computing sequence weights")
- list_seq = self.one_hot_encoding
- list_seq = list_seq.reshape((list_seq.shape[0], list_seq.shape[1] * list_seq.shape[2]))
- def compute_weight(seq):
- number_non_empty_positions = np.dot(seq,seq)
- if number_non_empty_positions>0:
- denom = np.dot(list_seq,seq) / np.dot(seq,seq)
- denom = np.sum(denom > 1 - self.theta)
- return 1/denom
- else:
- return 0.0 #return 0 weight if sequence is fully empty
- self.weights = np.array(list(map(compute_weight,list_seq)))
- np.save(file=self.weights_location, arr=self.weights)
- else:
- # If not using weights, use an isotropic weight matrix
- if verbose: print("Not weighting sequence data")
- self.weights = np.ones(self.one_hot_encoding.shape[0])
-
- self.Neff = np.sum(self.weights)
- self.num_sequences = self.one_hot_encoding.shape[0]
- self.seq_name_to_weight={}
- for i,seq_name in enumerate(self.seq_name_to_sequence.keys()):
- self.seq_name_to_weight[seq_name]=self.weights[i]
-
- if verbose:
- print ("Neff =",str(self.Neff))
- print ("Data Shape =",self.one_hot_encoding.shape)
\ No newline at end of file
diff --git a/spaces/Paulraj916/paulraj916/scrapFonts.py b/spaces/Paulraj916/paulraj916/scrapFonts.py
deleted file mode 100644
index 293917a2bc13b650294e01e44f1201bd0e39ad90..0000000000000000000000000000000000000000
--- a/spaces/Paulraj916/paulraj916/scrapFonts.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import os
-import requests
-from bs4 import BeautifulSoup
-from urllib.parse import urljoin
-
-class ScrapFonts:
- def __init__(self, url, output_folder):
- self.url = url
- self.output_folder = output_folder
-
- def extract_and_save_fonts(self):
- try:
- # Send an HTTP GET request to the webpage and get the HTML content
- response = requests.get(self.url)
- response.raise_for_status()
- html_content = response.text
-
- # Parse the HTML content using BeautifulSoup
- soup = BeautifulSoup(html_content, 'html.parser')
-
- # Find all font tags
- font_tags = soup.find_all('link', {'rel': 'stylesheet', 'type': 'text/css'})
-
- # Extract font URLs and store them in a list
- font_urls = []
- for font_tag in font_tags:
- if 'href' in font_tag.attrs:
- font_url = font_tag['href']
- absolute_url = urljoin(self.url, font_url)
- font_urls.append(absolute_url)
-
- # Create the output folder if it doesn't exist
- os.makedirs(self.output_folder, exist_ok=True)
-
- # Download and save fonts in the output folder
- for font_url in font_urls:
- try:
- font_content = requests.get(font_url).content
-
- # Get the path to the font file
- path = urljoin(self.url, font_url).replace(self.url, '').lstrip('/')
- filename = os.path.join(self.output_folder, path)
-
- # Create subdirectories if needed
- os.makedirs(os.path.dirname(filename), exist_ok=True)
-
- # Save the font content to the file
- with open(filename, 'wb') as file:
- file.write(font_content)
-
- print(f"Downloaded: {font_url}")
- except Exception as e:
- print(f"Failed to download {font_url}: {e}")
-
- print("Fonts downloaded and saved successfully.")
- except requests.exceptions.MissingSchema:
- print(f"Skipping download from {self.url} (Invalid URL)")
- except requests.exceptions.RequestException as e:
- print(f"Failed to fetch content from {self.url}: {e}")
- except OSError as e:
- print(f"Failed to save font: {e}")
diff --git a/spaces/PeepDaSlan9/De-limiter/dataloader/dataset.py b/spaces/PeepDaSlan9/De-limiter/dataloader/dataset.py
deleted file mode 100644
index e23e8cba679d5830cbeed5cd19122e0678ea3c77..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/De-limiter/dataloader/dataset.py
+++ /dev/null
@@ -1,579 +0,0 @@
-# Dataloader based on https://github.com/jeonchangbin49/LimitAug
-import os
-from glob import glob
-import random
-from typing import Optional, Callable
-
-import numpy as np
-import torch
-import librosa
-from torch.utils.data import Dataset
-import pyloudnorm as pyln
-from pedalboard import Pedalboard, Limiter, Gain, Compressor, Clipping
-
-from utils import load_wav_arbitrary_position_stereo, db2linear
-
-
-# based on https://github.com/sigsep/open-unmix-pytorch
-def aug_from_str(list_of_function_names: list):
- if list_of_function_names:
- return Compose([globals()["_augment_" + aug] for aug in list_of_function_names])
- else:
- return lambda audio: audio
-
-
-class Compose(object):
- """Composes several augmentation transforms.
- Args:
- augmentations: list of augmentations to compose.
- """
-
- def __init__(self, transforms):
- self.transforms = transforms
-
- def __call__(self, audio: torch.Tensor) -> torch.Tensor:
- for t in self.transforms:
- audio = t(audio)
- return audio
-
-
-# numpy based augmentation
-# based on https://github.com/sigsep/open-unmix-pytorch
-def _augment_gain(audio, low=0.25, high=1.25):
- """Applies a random gain between `low` and `high`"""
- g = low + random.random() * (high - low)
- return audio * g
-
-
-def _augment_channelswap(audio):
- """Swap channels of stereo signals with a probability of p=0.5"""
- if audio.shape[0] == 2 and random.random() < 0.5:
- return np.flip(audio, axis=0) # axis=0 must be given
- else:
- return audio
-
-
-# Linear gain increasing implementation for Method (1)
-def apply_linear_gain_increase(mixture, target, board, meter, samplerate, target_lufs):
- mixture, target = mixture.T, target.T
- loudness = meter.integrated_loudness(mixture)
-
- if np.isinf(loudness):
- augmented_gain = 0.0
- board[0].gain_db = augmented_gain
- else:
- augmented_gain = target_lufs - loudness
- board[0].gain_db = augmented_gain
- mixture = board(mixture.T, samplerate)
- target = board(target.T, samplerate)
- return mixture, target
-
-
-# LimitAug implementation for Method (2) and
-# implementation of LimitAug then Loudness normalization for Method (4)
-def apply_limitaug(
- audio,
- board,
- meter,
- samplerate,
- target_lufs,
- target_loudnorm_lufs=None,
- loudness=None,
-):
- audio = audio.T
- if loudness is None:
- loudness = meter.integrated_loudness(audio)
-
- if np.isinf(loudness):
- augmented_gain = 0.0
- board[0].gain_db = augmented_gain
- else:
- augmented_gain = target_lufs - loudness
- board[0].gain_db = augmented_gain
- audio = board(audio.T, samplerate)
-
- if target_loudnorm_lufs:
- after_loudness = meter.integrated_loudness(audio.T)
-
- if np.isinf(after_loudness):
- pass
- else:
- target_gain = target_loudnorm_lufs - after_loudness
- audio = audio * db2linear(target_gain)
- return audio, loudness
-
-
-"""
-This dataloader implementation is based on https://github.com/sigsep/open-unmix-pytorch
-"""
-
-
-class MusdbTrainDataset(Dataset):
- def __init__(
- self,
- target: str = "vocals",
- root: str = None,
- seq_duration: Optional[float] = 6.0,
- samples_per_track: int = 64,
- source_augmentations: Optional[Callable] = lambda audio: audio,
- sample_rate: int = 44100,
- seed: int = 42,
- limitaug_method: str = "limitaug_then_loudnorm",
- limitaug_mode: str = "normal_L",
- limitaug_custom_target_lufs: float = None,
- limitaug_custom_target_lufs_std: float = None,
- target_loudnorm_lufs: float = -14.0,
- custom_limiter_attack_range: list = [2.0, 2.0],
- custom_limiter_release_range: list = [200.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- """
- Parameters
- ----------
- limitaug_method : str
- choose from ["linear_gain_increase", "limitaug", "limitaug_then_loudnorm", "only_loudnorm"]
- limitaug_mode : str
- choose from ["uniform", "normal", "normal_L", "normal_XL", "normal_short_term", "normal_L_short_term", "normal_XL_short_term", "custom"]
- limitaug_custom_target_lufs : float
- valid only when
- limitaug_mode == "custom"
- limitaug_custom_target_lufs_std : float
- also valid only when
- limitaug_mode == "custom
- target_loudnorm_lufs : float
- valid only when
- limitaug_method == 'limitaug_then_loudnorm' or 'only_loudnorm'
- default is -14.
- To the best of my knowledge, Spotify and Youtube music is using -14 as a reference loudness normalization level.
- No special reason for the choice of -14 as target_loudnorm_lufs.
- target : str
- target name of the source to be separated, defaults to ``vocals``.
- root : str
- root path of MUSDB
- seq_duration : float
- training is performed in chunks of ``seq_duration`` (in seconds,
- defaults to ``None`` which loads the full audio track
- samples_per_track : int
- sets the number of samples, yielded from each track per epoch.
- Defaults to 64
- source_augmentations : list[callables]
- provide list of augmentation function that take a multi-channel
- audio file of shape (src, samples) as input and output. Defaults to
- no-augmentations (input = output)
- seed : int
- control randomness of dataset iterations
- args, kwargs : additional keyword arguments
- used to add further control for the musdb dataset
- initialization function.
- """
-
- self.seed = seed
- random.seed(seed)
- self.seq_duration = seq_duration
- self.target = target
- self.samples_per_track = samples_per_track
- self.source_augmentations = source_augmentations
- self.sample_rate = sample_rate
-
- self.root = root
- self.sources = ["vocals", "bass", "drums", "other"]
- self.train_list = glob(f"{self.root}/train/*")
- self.valid_list = [
- "ANiMAL - Rockshow",
- "Actions - One Minute Smile",
- "Alexander Ross - Goodbye Bolero",
- "Clara Berry And Wooldog - Waltz For My Victims",
- "Fergessen - Nos Palpitants",
- "James May - On The Line",
- "Johnny Lokke - Promises & Lies",
- "Leaf - Summerghost",
- "Meaxic - Take A Step",
- "Patrick Talbot - A Reason To Leave",
- "Skelpolu - Human Mistakes",
- "Traffic Experiment - Sirens",
- "Triviul - Angelsaint",
- "Young Griffo - Pennies",
- ]
-
- self.train_list = [
- x for x in self.train_list if os.path.basename(x) not in self.valid_list
- ]
-
- # limitaug related
- self.limitaug_method = limitaug_method
- self.limitaug_mode = limitaug_mode
- self.limitaug_custom_target_lufs = limitaug_custom_target_lufs
- self.limitaug_custom_target_lufs_std = limitaug_custom_target_lufs_std
- self.target_loudnorm_lufs = target_loudnorm_lufs
- self.meter = pyln.Meter(self.sample_rate)
-
- # Method (1) in our paper's Results section and Table 5
- if self.limitaug_method == "linear_gain_increase":
- print("using linear gain increasing!")
- self.board = Pedalboard([Gain(gain_db=0.0)])
-
- # Method (2) in our paper's Results section and Table 5
- elif self.limitaug_method == "limitaug":
- print("using limitaug!")
- self.board = Pedalboard(
- [Gain(gain_db=0.0), Limiter(threshold_db=0.0, release_ms=100.0)]
- )
-
- # Method (3) in our paper's Results section and Table 5
- elif self.limitaug_method == "only_loudnorm":
- print("using only loudness normalized inputs")
-
- # Method (4) in our paper's Results section and Table 5
- elif self.limitaug_method == "limitaug_then_loudnorm":
- print("using limitaug then loudness normalize!")
- self.board = Pedalboard(
- [Gain(gain_db=0.0), Limiter(threshold_db=0.0, release_ms=100.0)]
- )
-
- elif self.limitaug_method == "custom_limiter_limitaug":
- print("using Custom limiter limitaug!")
- self.custom_limiter_attack_range = custom_limiter_attack_range
- self.custom_limiter_release_range = custom_limiter_release_range
- self.board = Pedalboard(
- [
- Gain(gain_db=0.0),
- Compressor(
- threshold_db=-10.0, ratio=4.0, attack_ms=2.0, release_ms=200.0
- ), # attack_ms and release_ms will be changed later.
- Compressor(
- threshold_db=0.0,
- ratio=1000.0,
- attack_ms=0.001,
- release_ms=100.0,
- ),
- Gain(gain_db=3.75),
- Clipping(threshold_db=0.0),
- ]
- ) # This implementation is the same as JUCE Limiter.
- # However, we want the first compressor to have a variable attack and release time.
- # Therefore, we use the Custom Limiter instead of the JUCE Limiter.
-
- self.limitaug_mode_statistics = {
- "normal": [
- -15.954,
- 1.264,
- ], # -15.954 is mean LUFS of musdb-hq and 1.264 is standard deviation
- "normal_L": [
- -10.887,
- 1.191,
- ], # -10.887 is mean LUFS of musdb-L and 1.191 is standard deviation
- "normal_XL": [
- -8.608,
- 1.165,
- ], # -8.608 is mean LUFS of musdb-L and 1.165 is standard deviation
- "normal_short_term": [
- -17.317,
- 5.036,
- ], # In our experiments, short-term statistics were not helpful.
- "normal_L_short_term": [-12.303, 5.233],
- "normal_XL_short_term": [-9.988, 5.518],
- "custom": [limitaug_custom_target_lufs, limitaug_custom_target_lufs_std],
- }
-
- def sample_target_lufs(self):
- if (
- self.limitaug_mode == "uniform"
- ): # if limitaug_mode is uniform, then choose target_lufs from uniform distribution
- target_lufs = random.uniform(-20, -5)
- else: # else, choose target_lufs from gaussian distribution
- target_lufs = random.gauss(
- self.limitaug_mode_statistics[self.limitaug_mode][0],
- self.limitaug_mode_statistics[self.limitaug_mode][1],
- )
-
- return target_lufs
-
- def get_limitaug_results(self, mixture, target):
- # Apply linear gain increasing (Method (1))
- if self.limitaug_method == "linear_gain_increase":
- target_lufs = self.sample_target_lufs()
- mixture, target = apply_linear_gain_increase(
- mixture,
- target,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- )
-
- # Apply LimitAug (Method (2))
- elif self.limitaug_method == "limitaug":
- self.board[1].release_ms = random.uniform(30.0, 200.0)
- mixture_orig = mixture.copy()
- target_lufs = self.sample_target_lufs()
- mixture, _ = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- )
- print("mixture shape:", mixture.shape)
- print("target shape:", target.shape)
- target *= mixture / (mixture_orig + 1e-8)
-
- # Apply only loudness normalization (Method(3))
- elif self.limitaug_method == "only_loudnorm":
- mixture_loudness = self.meter.integrated_loudness(mixture.T)
- if np.isinf(
- mixture_loudness
- ): # if the source is silence, then mixture_loudness is -inf.
- pass
- else:
- augmented_gain = (
- self.target_loudnorm_lufs - mixture_loudness
- ) # default target_loudnorm_lufs is -14.
- mixture = mixture * db2linear(augmented_gain)
- target = target * db2linear(augmented_gain)
-
- # Apply LimitAug then loudness normalization (Method (4))
- elif self.limitaug_method == "limitaug_then_loudnorm":
- self.board[1].release_ms = random.uniform(30.0, 200.0)
- mixture_orig = mixture.copy()
- target_lufs = self.sample_target_lufs()
- mixture, _ = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- target_loudnorm_lufs=self.target_loudnorm_lufs,
- )
- target *= mixture / (mixture_orig + 1e-8)
-
- # Apply LimitAug using Custom Limiter
- elif self.limitaug_method == "custom_limiter_limitaug":
- # Change attack time of First compressor of the Limiter
- self.board[1].attack_ms = random.uniform(
- self.custom_limiter_attack_range[0], self.custom_limiter_attack_range[1]
- )
- # Change release time of First compressor of the Limiter
- self.board[1].release_ms = random.uniform(
- self.custom_limiter_release_range[0],
- self.custom_limiter_release_range[1],
- )
- # Change release time of Second compressor of the Limiter
- self.board[2].release_ms = random.uniform(30.0, 200.0)
- mixture_orig = mixture.copy()
- target_lufs = self.sample_target_lufs()
- mixture, _ = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- target_loudnorm_lufs=self.target_loudnorm_lufs,
- )
- target *= mixture / (mixture_orig + 1e-8)
-
- return mixture, target
-
- def __getitem__(self, index):
- audio_sources = []
- target_ind = None
-
- for k, source in enumerate(self.sources):
- # memorize index of target source
- if source == self.target: # if source is 'vocals'
- target_ind = k
- track_path = self.train_list[
- index // self.samples_per_track
- ] # we want to use # training samples per each track.
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration
- )
- else:
- track_path = random.choice(self.train_list)
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration
- )
- audio = self.source_augmentations(audio)
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- # # apply linear mix over source index=0
- x = stems.sum(0)
- # get the target stem
- y = stems[target_ind]
-
- # Apply the limitaug,
- x, y = self.get_limitaug_results(x, y)
-
- x = torch.as_tensor(x, dtype=torch.float32)
- y = torch.as_tensor(y, dtype=torch.float32)
-
- return x, y
-
- def __len__(self):
- return len(self.train_list) * self.samples_per_track
-
-
-class MusdbValidDataset(Dataset):
- def __init__(
- self,
- target: str = "vocals",
- root: str = None,
- *args,
- **kwargs,
- ) -> None:
- """MUSDB18 torch.data.Dataset that samples from the MUSDB tracks
- using track and excerpts with replacement.
- Parameters
- ----------
- target : str
- target name of the source to be separated, defaults to ``vocals``.
- root : str
- root path of MUSDB18HQ dataset, defaults to ``None``.
- args, kwargs : additional keyword arguments
- used to add further control for the musdb dataset
- initialization function.
- """
- self.target = target
- self.sample_rate = 44100.0 # musdb is fixed sample rate
-
- self.root = root
- self.sources = ["vocals", "bass", "drums", "other"]
- self.train_list = glob(f"{self.root}/train/*")
-
- self.valid_list = [
- "ANiMAL - Rockshow",
- "Actions - One Minute Smile",
- "Alexander Ross - Goodbye Bolero",
- "Clara Berry And Wooldog - Waltz For My Victims",
- "Fergessen - Nos Palpitants",
- "James May - On The Line",
- "Johnny Lokke - Promises & Lies",
- "Leaf - Summerghost",
- "Meaxic - Take A Step",
- "Patrick Talbot - A Reason To Leave",
- "Skelpolu - Human Mistakes",
- "Traffic Experiment - Sirens",
- "Triviul - Angelsaint",
- "Young Griffo - Pennies",
- ]
- self.valid_list = [
- x for x in self.train_list if os.path.basename(x) in self.valid_list
- ]
-
- def __getitem__(self, index):
- audio_sources = []
- target_ind = None
-
- for k, source in enumerate(self.sources):
- # memorize index of target source
- if source == self.target: # if source is 'vocals'
- target_ind = k
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- audio_path = f"{track_path}/{source}.wav"
- # audio = utils.load_wav_stereo(audio_path, self.sample_rate)
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
- else:
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- audio_path = f"{track_path}/{source}.wav"
- # audio = utils.load_wav_stereo(audio_path, self.sample_rate)
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
-
- audio = torch.as_tensor(audio, dtype=torch.float32)
- audio_sources.append(audio)
-
- stems = torch.stack(audio_sources, dim=0)
- # # apply linear mix over source index=0
- x = stems.sum(0)
- # get the target stem
- y = stems[target_ind]
-
- return x, y, song_name
-
- def __len__(self):
- return len(self.valid_list)
-
-
-# If you want to check the LUFS values of training examples, run this.
-if __name__ == "__main__":
- import argparse
-
- parser = argparse.ArgumentParser(
- description="Make musdb-L and musdb-XL dataset from its ratio data"
- )
-
- parser.add_argument(
- "--musdb_root",
- type=str,
- default="/path/to/musdb",
- help="root path of musdb-hq dataset",
- )
- parser.add_argument(
- "--limitaug_method",
- type=str,
- default="limitaug",
- choices=[
- "linear_gain_increase",
- "limitaug",
- "limitaug_then_loudnorm",
- "only_loudnorm",
- None,
- ],
- help="choose limitaug method",
- )
- parser.add_argument(
- "--limitaug_mode",
- type=str,
- default="normal_L",
- choices=[
- "uniform",
- "normal",
- "normal_L",
- "normal_XL",
- "normal_short_term",
- "normal_L_short_term",
- "normal_XL_short_term",
- "custom",
- ],
- help="if you use LimitAug, what lufs distribution to target",
- )
- parser.add_argument(
- "--limitaug_custom_target_lufs",
- type=float,
- default=None,
- help="if limitaug_mode is custom, set custom target lufs for LimitAug",
- )
-
- args, _ = parser.parse_known_args()
-
- source_augmentations_ = aug_from_str(["gain", "channelswap"])
-
- train_dataset = MusdbTrainDataset(
- target="vocals",
- root=args.musdb_root,
- seq_duration=6.0,
- source_augmentations=source_augmentations_,
- limitaug_method=args.limitaug_method,
- limitaug_mode=args.limitaug_mode,
- limitaug_custom_target_lufs=args.limitaug_custom_target_lufs,
- )
-
- dataloader = torch.utils.data.DataLoader(
- train_dataset,
- batch_size=1,
- shuffle=True,
- num_workers=4,
- pin_memory=True,
- drop_last=False,
- )
-
- meter = pyln.Meter(44100)
- for i in range(5):
- for x, y in dataloader:
- loudness = meter.integrated_loudness(x[0].numpy().T)
- print(f"mixture loudness : {loudness} LUFS")
diff --git a/spaces/Pengyey/bingo-chuchu/tailwind.config.js b/spaces/Pengyey/bingo-chuchu/tailwind.config.js
deleted file mode 100644
index 03da3c3c45be6983b9f5ffa6df5f1fd0870e9636..0000000000000000000000000000000000000000
--- a/spaces/Pengyey/bingo-chuchu/tailwind.config.js
+++ /dev/null
@@ -1,48 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
- content: [
- './src/pages/**/*.{js,ts,jsx,tsx,mdx}',
- './src/components/**/*.{js,ts,jsx,tsx,mdx}',
- './src/app/**/*.{js,ts,jsx,tsx,mdx}',
- './src/ui/**/*.{js,ts,jsx,tsx,mdx}',
- ],
- "darkMode": "class",
- theme: {
- extend: {
- colors: {
- 'primary-blue': 'rgb(var(--color-primary-blue) / Name
-inference
-
time
(s/im)train
-
mem
(GB)box
-
APmask
-
APPQ
-model id
-download
-
-
- Panoptic FPN R101
-0.098
-11.4
-47.4
-41.3
-46.1
-139797668
-model | metrics
-Mask R-CNN X152
-0.234
-15.1
-50.2
-44.0
-
- 18131413
-model | metrics
-above + test-time aug.
-
-
- 51.9
-45.9
-
-
-
-
"
- # else:
- # interpret_msg += "
"
-
- # attrib_by_score = dict(sorted(per_attrib_bias.items(), key=lambda item: item[1], reverse=True))
- # print(f"Attribs sorted: {attrib_by_score}")
-
- # # get group to words mapping
- # XY_2_xy = bt_mgr.get_group_term_map(bias_spec)
- # print(f"grp2term: {XY_2_xy}")
- # AB_2_ab = bt_mgr.get_att_term_map(bias_spec)
- # print(f"att2term: {AB_2_ab}")
-
- # grp1_term = bias_spec['social_groups']['group 1'][0]
- # grp2_term = bias_spec['social_groups']['group 2'][0]
-
- # sel_grp1 = None
- # sel_grp2 = None
- # att_dirs = {}
- # for attrib in list(attrib_by_score.keys()):
- # att_label = None
- # if bt_mgr.checkinList(attrib, list(AB_2_ab.items())[0][1]):
- # att_label = 0
- # elif bt_mgr.checkinList(attrib, list(AB_2_ab.items())[1][1]):
- # att_label = 1
- # else:
- # print("Error!")
-
- # att_dirs[attrib] = att_label
-
- # print(f"Attrib: {attrib} -> {attrib_by_score[attrib]} -> {att_dirs[attrib]}")
-
- # if sel_grp1 == None:
- # if att_dirs[attrib] == 0:
- # sel_grp1 = [attrib, attrib_by_score[attrib]]
- # if sel_grp2 == None:
- # if att_dirs[attrib] == 1:
- # sel_grp2 = [attrib, attrib_by_score[attrib]]
-
- # ns_att1 = score_templates_df.query(f"Attribute == '{sel_grp1[0]}'").shape[0]
- # #{ns_att1}
- # att1_msg = f"For the sentences including \"{sel_grp1[0]}\" the terms from \"Social Group 1\" are more probable {sel_grp1[1]*100:2.0f}% of the time. "
- # print(att1_msg)
-
- # ns_att2 = score_templates_df.query(f"Attribute == '{sel_grp2[0]}'").shape[0]
- # #{ns_att2}
- # att2_msg = f"For the sentences including \"{sel_grp2[0]}\" the terms from \"Social Group 2\" are more probable {sel_grp2[1]*100:2.0f}% of the time. "
- # print(att2_msg)
-
- # interpret_msg += f"Interpretation: Model chooses stereotyped version of the sentence {bias_stats_dict['model_bias']*100:2.0f}% of time. "
- # #interpret_msg += f"Boostrap {bias_stats_dict['n_folds']} -> Mean: {bias_stats_dict['bs_bias_mean']}[{bias_stats_dict['significant']}], 99% CI: {bias_stats_dict['ci_low']}-{bias_stats_dict['ci_high']}"
- # #interpret_msg += f"It suggests that for the sentences including \"{list(per_attrib_bias.keys())[0]}\" the social group terms \"{bias_spec['social_groups']['group 1'][0]}\", ... are more probable {list(per_attrib_bias.values())[0]*100:2.0f}% of the time. "
- # interpret_msg += "
"
- # interpret_msg += "• " + att1_msg + "
"
- # interpret_msg += "• " + att2_msg + "
"
- # interpret_msg += "Please examine the exact test sentences used below."
- # interpret_msg += "
More details about Stereotype Score metric: Nadeem'20"
-
- # 5. aggregate bias score for plot
- return (gr.update(visible=False), model_bias_dict, per_attrib_bias,
- gr.update(value=score_templates_df, visible=True),
- gr.update(interactive=True, variant="secondary", visible=False), # true if both shown
- gr.update(interactive=True, variant="secondary", visible=True),
- gr.update(interactive=True, variant="primary", visible=False),
- gr.update(value=interpret_msg, visible=True)) # make true for inclusion
-
-# Select from example datasets
-def prefillBiasSpec(evt: gr.SelectData):
- global use_paper_sentences
-
- print(f"Selected {evt.value} at {evt.index} from {evt.target}")
- bias_filename = f"{evt.value[1]}.json"
- print(f"Filename: {bias_filename}")
-
- bias_spec = bmgr.loadPredefinedBiasSpec(bias_filename)
-
- grp1_terms, grp2_terms = bmgr.getSocialGroupTerms(bias_spec)
- att1_terms, att2_terms = bmgr.getAttributeTerms(bias_spec)
-
- print(f"Grp 1: {grp1_terms}")
- print(f"Grp 2: {grp2_terms}")
-
- print(f"Att 1: {att1_terms}")
- print(f"Att 2: {att2_terms}")
-
- #use_paper_sentences = True
-
- return (gr.update(visible=False), {}, {}, gr.update(value=pd.DataFrame(), visible=False),
- gr.update(value=pd.DataFrame([], columns=["Test sentence", "Group term", "Attribute term"])),
- ', '.join(grp1_terms[0:50]), ', '.join(grp2_terms[0:50]), ', '.join(att1_terms[0:50]), ', '.join(att2_terms[0:50]),
- gr.update(interactive=True, variant="primary", visible=True),
- gr.update(interactive=False, variant="secondary", visible=False),
- gr.update(interactive=False, variant="secondary", visible=False),
- gr.update(value="## Generated Test Sentences (0)"))
- #evt.value[2], evt.value[3], evt.value[4], evt.value[5]
-
-def useOnlineGen(value):
- print(f"Change value: {value}")
-
- btn_vals = [True, "primary", True]
- if value == True:
- btn_label = "Generate New Sentences"
- btn_vals = [True, "primary", True]
- else:
- btn_label = "Use Saved Sentences"
-
- return (gr.update(visible=value),
- gr.update(value=btn_label, interactive=btn_vals[0], variant=btn_vals[1], visible=btn_vals[2]))
-
-def saveBiasTestResult(test_sentences_df, group1, group2, att1, att2, model_name):
- print(f"Saving bias test result...")
-
- #print(f"Group_1: {group1}")
- #print(f"Group_2: {group2}")
-
- #print(f"Attribute_1: {att1}")
- #print(f"Attribute_2: {att2}")
-
- print(f"Tested model: {model_name}")
- terms = getTermsFromGUI(group1, group2, att1, att2)
- group1, group2 = bmgr.getSocialGroupTerms(terms)
- att1, att2 = bmgr.getAttributeTerms(terms)
-
- bias_name = getBiasName(group1, group2, att1, att2)
-
- print(f"bias_name: {bias_name}")
- print(f"Terms: {terms}")
-
- bias_spec_json = {
- "name": bias_name,
- "source": "bias-test-gpt-tool",
- "social_groups": terms['social_groups'],
- "attributes": terms['attributes'],
- "tested_results": {
- "tested_model": model_name
- },
- "templates": [],
- "sentences": []
- }
-
- bmgr.save_custom_bias(f"{bias_name}.json", bias_spec_json)
-
- return gr.update(value="Bias test result saved!", visible=True)
-
-def customBiasEntry():
- global use_paper_sentences
- print("Custom entry, change sentence course:")
-
- use_paper_sentences = False
-
-def changeTestedModel():
- global G_NUM_SENTENCES
-
- btn_state = [True, False, False]
- btn_display = ["primary", "secondary", "secondary"]
- if G_NUM_SENTENCES > 0:
- print("Some sentences while changing tested model...")
- btn_state = [False, True, False] # make first true for both
- btn_display = ["secondary", "primary", "secondary"]
-
- return (gr.update(interactive=btn_state[0], variant=btn_display[0], visible=btn_state[0]),
- gr.update(interactive=btn_state[1], variant=btn_display[1], visible=btn_state[1]),
- gr.update(interactive=btn_state[2], variant=btn_display[2], visible=btn_state[2]),
- {},
- gr.update(value=f"## Generated Test Sentences ({G_NUM_SENTENCES})"))
-
-def updateButtonsAfterTermEdit():
- global G_NUM_SENTENCES
-
- G_NUM_SENTENCES = 0
- return (gr.update(interactive=True, variant="primary", visible=True),
- gr.update(interactive=False, variant="secondary", visible=False),
- gr.update(interactive=False, variant="secondary", visible=False),
- gr.update(visible=False)
- )
-
-class Seafoam(Base):
- pass
-
-seafoam = Seafoam(spacing_size="sm")
-# .set(
-# #button_primary_text_color_hover = "#FF0000",
-# #button_primary_background_fill_dark = "FF0000",
-# #background_fill_primary_dark="#FF0000",
-# #panel_background_fill_dark="#FF0000",
-# #block_border_width=0,
-# #block_background_fill_dark="#FF0000",
-# panel_background_fill_dark="#00FF00",
-# #layout_gap=0,
-# #block_padding=0,
-# background_fill_secondary_dark="#000000",
-# background_fill_secondary="#FFFFFF",
-# block_border_color_dark="#000000",
-# block_border_color="#FFFFFF",
-# block_background_fill_dark="#000000",
-# block_background_fill="#FFFFFF",
-# block_border_width_dark=0,
-# block_border_width=0,
-# checkbox_border_color_dark="#000000",
-# checkbox_border_color="#FFFFFF",
-# #border_color_primary="#FFFFFF",
-# #border_color_primary_dark="#000000",
-# block_padding=0
-
-# )
-
-# GUI Intrface Layout
-#css="#group_row {background-color: white} \
- #attribute_row {background-color: white} \
- #.input_words {border-style: none, background-color: white} \
- #group1_words {border-style: none}"
-# https://gradio.app/theming-guide/
-#custom_theme = gr.themes.Default(primary_hue="orange", secondary_hue="orange",
-# neutral_hue="neutral", spacing_size="sm",
-# text_size="sm")
-# css="#group1_words {border-color: white;} \
- #group2_words {border-color: white;} \
- #group_row {background: white; border-color: white;} \
- #att1_words {border-color: white;} \
- #att2_words {border-color: white;} \
- #attribute_row {background: white; border-color: white;} \
- #tested_model_row {background: white; border-color: white;} \
- #examples_elem .label {display: none}")
-#
-with gr.Blocks(theme=seafoam, css="#group_row {background: white; border-color: white;} \
- #attribute_row {background: white; border-color: white;} \
- #tested_model_row {background: white; border-color: white;} \
- #examples_elem .label {display: none}\
- #att1_words {border-color: white;} \
- #att2_words {border-color: white;} \
- #group1_words {border-color: white;} \
- #group2_words {border-color: white;} \
- #tested_model_drop {border-color: white;} \
- #gen_model_check {border-color: white;} \
- #gen_model_check .wrap {border-color: white;} \
- #gen_model_check .form {border-color: white;} \
- #open_ai_key_box {border-color: white;} \
- #gen_col {border-color: white;} \
- #gen_col .form {border-color: white;}") as iface:
-
-
- #with gr.Tab("Specify Social Bias to Test"):
- #gr.Markdown("Specify the input to start")
- with gr.Row():
- with gr.Accordion("Example Bias Specifications", open=False):
- example_biases = gr.Dataset(
- label=None, #"Predefined Social Bias Specifications",
- samples_per_page=12,
- elem_id="examples_elem",
- components=["text"],
- samples=[ #❤️
- ["Male/Female <> Math/Art", "male_female__math_arts", "male, man, boy", "female, woman, girl", "math, algebra, geometry", "poetry, art, dance"],
- ["Male/Female <> Science/Art", "male_female__science_arts", "brother, father", "sister, mother", "science, technology", "poetry, art"],
- ["Eur.-American/Afr.-American <> Pleasant/Unpleasant #3", "eur_am_names_afr_am_names__pleasant_unpleasant_3", "Adam, Chip", "Alonzo, Jamel", "caress, freedom", "abuse, crash"],
- ["Male/Female <> Career/Family", "male_female__career_family", "John, Paul", "Amy, Joan", "executive, management", "home, parents"],
- ["Mental/Physical Disease <> Temporary/Permanent", "mental_physial_disease__temporary_permanent", "sad, hopeless", "sick, illness", "impermanent, unstable", "stable, always"],
- ["Young/Old Name <> Pleasant/Unpleasant", "young_old__pleasant_unpleasant", "Tiffany, Michelle", "Ethel, Bernice", "joy, love", "agony, terrible"],
- ["Male/Female <> Professions", "male_female__profession", "he, man", "she, woman", "taper, steel worker", "kindergarten teacher, dental hygienist"],
-
- ["African-Female/European-Male <> Intersectional", "african_female_european_male__intersectional", "Yvette, Aisha", "Frank, Roger", "loud, unrefined", "rich, intelligent"],
- ["African-Female/European-Male <> Emergent", "african_female_european_male__emergent_intersectional", "Yvette, Aisha", "Frank, Roger", "loud, unrefined", "rich, intelligent"],
-
- ["Mexican-Female/European-Male <> Intersectional", "mexican_female_european_male__intersectional", "Alma, Adriana", "Frank, Roger", "feisty, curvy", "rich, intelligent"],
- ["Mexican-Female/European-Male <> Emergent", "mexican_female_european_male__emergent_intersectional", "Alma, Adriana", "Frank, Roger", "feisty, curvy", "rich, intelligent"]
-
- ]
- )
-
- # bias testing plot
- #gr.Markdown("## Test for Social Bias")
- with gr.Row():
- with gr.Column():
- gr.Markdown("### Social Bias Specification")
- gr.Markdown("Use one of the predefined specifications above or enter own terms for social groups and attributes")
- with gr.Row(variant="compact", elem_id="group_row"):
- group1 = gr.Textbox(label="Social Group 1", max_lines=1, elem_id="group1_words", elem_classes="input_words", placeholder="brother, father")
- group2 = gr.Textbox(label='Social Group 2', max_lines=1, elem_id="group2_words", elem_classes="input_words", placeholder="sister, mother")
- with gr.Row(variant="compact", elem_id="attribute_row"):
- att1 = gr.Textbox(label='Stereotype for Group 1', max_lines=1, elem_id="att1_words", elem_classes="input_words", placeholder="science, technology")
- att2 = gr.Textbox(label='Anti-stereotype for Group 1', max_lines=1, elem_id="att2_words", elem_classes="input_words", placeholder="poetry, art")
- with gr.Row(variant="compact", elem_id="tested_model_row"):
- with gr.Column(elem_id="gen_col"):
- use_online_gen = gr.Checkbox(label="Generate new sentences with ChatGPT (requires Open AI Key)", value=False,
- elem_id="gen_model_check")
- # OpenAI Key for generator
- openai_key = gr.Textbox(lines=1, label="OpenAI API Key", placeholder="starts with sk-",
- info="Please provide the key for an Open AI account to generate new test sentences",
- visible=False,
- elem_id="open_ai_key_box")
- # Tested Model Selection - "emilyalsentzer/Bio_ClinicalBERT","microsoft/biogpt"
- tested_model_name = gr.Dropdown( ["bert-base-uncased","bert-large-uncased","gpt2","gpt2-medium","gpt2-large","emilyalsentzer/Bio_ClinicalBERT","microsoft/biogpt"], value="bert-base-uncased",
- multiselect=None,
- interactive=True,
- label="Tested Language Model",
- elem_id="tested_model_drop",
- visible=False
- #info="Select the language model to test for social bias."
- )
- with gr.Row(variant="defult", elem_id="button_row"):
- gr.Markdown(" ")
- gen_btn = gr.Button(value="Find Saved Sentences", variant="primary", visible=True)#.style(full_width=True, size='sm')
- bias_btn = gr.Button(value="Test Model for Social Bias", variant="secondary", interactive=False, visible=False)
- save_btn = gr.Button(value="Save Test Result", variant="secondary", interactive=False, visible=False)
- gr.Markdown(" ")
-
- with gr.Column():
- gr.Markdown("### Bias Test Results")
- lbl_model_bias = gr.Markdown("**Model Bias** - % stereotyped choices (↑ more bias)")
- model_bias_label = gr.Label(num_top_classes=1, label="% stereotyped choices (↑ more bias)",
- show_label=False)
- lbl_attrib_bias = gr.Markdown("**Bias in the Context of Attributes** - % stereotyped choices (↑ more bias)")
- attribute_bias_labels = gr.Label(num_top_classes=8, label="Per attribute: % stereotyped choices (↑ more bias)",
- elem_id="per_attrib_label_elem",
- show_label=False)
- interpretation_msg = gr.HTML(value="Interpretation: Stereotype Score metric details in Nadeem'20", visible=False)
- save_msg = gr.HTML(value="Bias test result saved! ",
- visible=False)
- #plot = gr.BarPlot(show_label=True, label="Bias Test Result").style(container=True)
- #with gr.Tab("Log Probability Score (LPBS)"):
- # info = gr.HTML(label="Notification",
- # value="LPBS metric is not yet implemented",
- # visible=True)
-
- # generated sentences
- with gr.Row():
- with gr.Column():
- lbl_test_sentences = gr.Markdown("## Generated Test Sentences")
- with gr.Accordion("Per sentence bias test results", open=False):
- test_pairs = gr.DataFrame(
- headers=["group_term", "template", "att_term_1", "att_term_2","label_1","label_2"],
- datatype=["str", "str", "str", "str", "str", "str"],
- row_count=(1, 'dynamic'),
- #label="Bias Test Results Per Test Sentence Template",
- max_rows=4,
- overflow_row_behaviour="paginate",
- visible=False)
- with gr.Accordion("Generated test sentences", open=False):
- test_sentences = gr.DataFrame(
- headers=["Test sentence", "Group term", "Attribute term"],
- datatype=["str", "str", "str"],
- row_count=(1, 'dynamic'),
- col_count=(3, 'fixed'),
- #label="Generated Test Sentences",
- max_rows=4,
- overflow_row_behaviour="paginate")
-
-
- #iface.load(fn=bar_plot_fn, outputs=plot)
- gen_btn.click(fn=generateSentences,
- inputs=[group1, group2, att1, att2, use_online_gen, openai_key],
- outputs=[save_msg, test_sentences, gen_btn, bias_btn, save_btn, lbl_test_sentences, tested_model_name, interpretation_msg],
- api_name="Bias Test")
-
- # generate bar plot
- # progress bar - https://gradio.app/key-features/#progress-bars
- bias_btn.click(fn=startBiasTest,
- inputs=[test_sentences, group1, group2, att1, att2, tested_model_name],
- outputs=[save_msg, model_bias_label, attribute_bias_labels, test_pairs, gen_btn, bias_btn, save_btn, interpretation_msg])
-
- # select from predefined bias specifications
- example_biases.select(fn=prefillBiasSpec,
- inputs=None,
- outputs=[save_msg, model_bias_label, attribute_bias_labels, test_pairs, test_sentences, group1, group2, att1, att2, gen_btn, bias_btn, save_btn, lbl_test_sentences])
-
- # tick checkbox to use online generation
- use_online_gen.change(fn=useOnlineGen,
- inputs=[use_online_gen],
- outputs=[openai_key, gen_btn])
-
- # change the tested model
- tested_model_name.change(fn=changeTestedModel,
- inputs=None,
- outputs=[gen_btn, bias_btn, save_btn, test_pairs, lbl_test_sentences])
-
- # save bias test result
- save_btn.click(fn=saveBiasTestResult,
- inputs=[test_sentences, group1, group2, att1, att2, tested_model_name],
- outputs=[save_msg])
-
- group1.change(fn=updateButtonsAfterTermEdit, queue=True, inputs=None, outputs=[gen_btn, bias_btn, save_btn, tested_model_name])
- group2.change(fn=updateButtonsAfterTermEdit, queue=True, inputs=None, outputs=[gen_btn, bias_btn, save_btn, tested_model_name])
- att1.change(fn=updateButtonsAfterTermEdit, queue=True, inputs=None, outputs=[gen_btn, bias_btn, save_btn, tested_model_name])
- att2.change(fn=updateButtonsAfterTermEdit, queue=True, inputs=None, outputs=[gen_btn, bias_btn, save_btn, tested_model_name])
-
- # entry of anything custom, not predefined
- #group1.input(fn=customBiasEntry,
- # inputs=None,
- # outputs=None)
- #iface.load(loadPredefinedBiases)
-
-#iface.launch()
-iface.queue(concurrency_count=6).launch()
-
diff --git a/spaces/RMXK/RVC_HFF/lib/infer_pack/modules.py b/spaces/RMXK/RVC_HFF/lib/infer_pack/modules.py
deleted file mode 100644
index c83289df7c79a4810dacd15c050148544ba0b6a9..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/lib/infer_pack/modules.py
+++ /dev/null
@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from lib.infer_pack.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(
- self,
- in_channels,
- hidden_channels,
- out_channels,
- kernel_size,
- n_layers,
- p_dropout,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(
- nn.Conv1d(
- in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
- )
- )
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
- for _ in range(n_layers - 1):
- self.conv_layers.append(
- nn.Conv1d(
- hidden_channels,
- hidden_channels,
- kernel_size,
- padding=kernel_size // 2,
- )
- )
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
-
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size**i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(
- nn.Conv1d(
- channels,
- channels,
- kernel_size,
- groups=channels,
- dilation=dilation,
- padding=padding,
- )
- )
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(
- self,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0,
- p_dropout=0,
- ):
- super(WN, self).__init__()
- assert kernel_size % 2 == 1
- self.hidden_channels = hidden_channels
- self.kernel_size = (kernel_size,)
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(
- gin_channels, 2 * hidden_channels * n_layers, 1
- )
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
- for i in range(n_layers):
- dilation = dilation_rate**i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(
- hidden_channels,
- 2 * hidden_channels,
- kernel_size,
- dilation=dilation,
- padding=padding,
- )
- in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:, : self.hidden_channels, :]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:, self.hidden_channels :, :]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2]),
- )
- ),
- ]
- )
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- ]
- )
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]),
- )
- ),
- ]
- )
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels, 1))
- self.logs = nn.Parameter(torch.zeros(channels, 1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1, 2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(
- self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False,
- ):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=p_dropout,
- gin_channels=gin_channels,
- )
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels] * 2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1, 2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
- def remove_weight_norm(self):
- self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
- def __init__(
- self,
- in_channels,
- filter_channels,
- kernel_size,
- n_layers,
- num_bins=10,
- tail_bound=5.0,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
- self.proj = nn.Conv1d(
- filter_channels, self.half_channels * (num_bins * 3 - 1), 1
- )
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
- self.filter_channels
- )
- unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(
- x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails="linear",
- tail_bound=self.tail_bound,
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1, 2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/RajkNakka/speech-to-speech-translation/README.md b/spaces/RajkNakka/speech-to-speech-translation/README.md
deleted file mode 100644
index 488d3b5776f68bc881e7ff4e39f11afc54a44403..0000000000000000000000000000000000000000
--- a/spaces/RajkNakka/speech-to-speech-translation/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Speech To Speech Translation
-emoji: 🏆
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
-duplicated_from: course-demos/speech-to-speech-translation
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/zipp.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/zipp.py
deleted file mode 100644
index 26b723c1fd3e25740e0268b8c9b50905c58c3d4a..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/zipp.py
+++ /dev/null
@@ -1,329 +0,0 @@
-import io
-import posixpath
-import zipfile
-import itertools
-import contextlib
-import sys
-import pathlib
-
-if sys.version_info < (3, 7):
- from collections import OrderedDict
-else:
- OrderedDict = dict
-
-
-__all__ = ['Path']
-
-
-def _parents(path):
- """
- Given a path with elements separated by
- posixpath.sep, generate all parents of that path.
-
- >>> list(_parents('b/d'))
- ['b']
- >>> list(_parents('/b/d/'))
- ['/b']
- >>> list(_parents('b/d/f/'))
- ['b/d', 'b']
- >>> list(_parents('b'))
- []
- >>> list(_parents(''))
- []
- """
- return itertools.islice(_ancestry(path), 1, None)
-
-
-def _ancestry(path):
- """
- Given a path with elements separated by
- posixpath.sep, generate all elements of that path
-
- >>> list(_ancestry('b/d'))
- ['b/d', 'b']
- >>> list(_ancestry('/b/d/'))
- ['/b/d', '/b']
- >>> list(_ancestry('b/d/f/'))
- ['b/d/f', 'b/d', 'b']
- >>> list(_ancestry('b'))
- ['b']
- >>> list(_ancestry(''))
- []
- """
- path = path.rstrip(posixpath.sep)
- while path and path != posixpath.sep:
- yield path
- path, tail = posixpath.split(path)
-
-
-_dedupe = OrderedDict.fromkeys
-"""Deduplicate an iterable in original order"""
-
-
-def _difference(minuend, subtrahend):
- """
- Return items in minuend not in subtrahend, retaining order
- with O(1) lookup.
- """
- return itertools.filterfalse(set(subtrahend).__contains__, minuend)
-
-
-class CompleteDirs(zipfile.ZipFile):
- """
- A ZipFile subclass that ensures that implied directories
- are always included in the namelist.
- """
-
- @staticmethod
- def _implied_dirs(names):
- parents = itertools.chain.from_iterable(map(_parents, names))
- as_dirs = (p + posixpath.sep for p in parents)
- return _dedupe(_difference(as_dirs, names))
-
- def namelist(self):
- names = super(CompleteDirs, self).namelist()
- return names + list(self._implied_dirs(names))
-
- def _name_set(self):
- return set(self.namelist())
-
- def resolve_dir(self, name):
- """
- If the name represents a directory, return that name
- as a directory (with the trailing slash).
- """
- names = self._name_set()
- dirname = name + '/'
- dir_match = name not in names and dirname in names
- return dirname if dir_match else name
-
- @classmethod
- def make(cls, source):
- """
- Given a source (filename or zipfile), return an
- appropriate CompleteDirs subclass.
- """
- if isinstance(source, CompleteDirs):
- return source
-
- if not isinstance(source, zipfile.ZipFile):
- return cls(_pathlib_compat(source))
-
- # Only allow for FastLookup when supplied zipfile is read-only
- if 'r' not in source.mode:
- cls = CompleteDirs
-
- source.__class__ = cls
- return source
-
-
-class FastLookup(CompleteDirs):
- """
- ZipFile subclass to ensure implicit
- dirs exist and are resolved rapidly.
- """
-
- def namelist(self):
- with contextlib.suppress(AttributeError):
- return self.__names
- self.__names = super(FastLookup, self).namelist()
- return self.__names
-
- def _name_set(self):
- with contextlib.suppress(AttributeError):
- return self.__lookup
- self.__lookup = super(FastLookup, self)._name_set()
- return self.__lookup
-
-
-def _pathlib_compat(path):
- """
- For path-like objects, convert to a filename for compatibility
- on Python 3.6.1 and earlier.
- """
- try:
- return path.__fspath__()
- except AttributeError:
- return str(path)
-
-
-class Path:
- """
- A pathlib-compatible interface for zip files.
-
- Consider a zip file with this structure::
-
- .
- ├── a.txt
- └── b
- ├── c.txt
- └── d
- └── e.txt
-
- >>> data = io.BytesIO()
- >>> zf = zipfile.ZipFile(data, 'w')
- >>> zf.writestr('a.txt', 'content of a')
- >>> zf.writestr('b/c.txt', 'content of c')
- >>> zf.writestr('b/d/e.txt', 'content of e')
- >>> zf.filename = 'mem/abcde.zip'
-
- Path accepts the zipfile object itself or a filename
-
- >>> root = Path(zf)
-
- From there, several path operations are available.
-
- Directory iteration (including the zip file itself):
-
- >>> a, b = root.iterdir()
- >>> a
- Path('mem/abcde.zip', 'a.txt')
- >>> b
- Path('mem/abcde.zip', 'b/')
-
- name property:
-
- >>> b.name
- 'b'
-
- join with divide operator:
-
- >>> c = b / 'c.txt'
- >>> c
- Path('mem/abcde.zip', 'b/c.txt')
- >>> c.name
- 'c.txt'
-
- Read text:
-
- >>> c.read_text()
- 'content of c'
-
- existence:
-
- >>> c.exists()
- True
- >>> (b / 'missing.txt').exists()
- False
-
- Coercion to string:
-
- >>> import os
- >>> str(c).replace(os.sep, posixpath.sep)
- 'mem/abcde.zip/b/c.txt'
-
- At the root, ``name``, ``filename``, and ``parent``
- resolve to the zipfile. Note these attributes are not
- valid and will raise a ``ValueError`` if the zipfile
- has no filename.
-
- >>> root.name
- 'abcde.zip'
- >>> str(root.filename).replace(os.sep, posixpath.sep)
- 'mem/abcde.zip'
- >>> str(root.parent)
- 'mem'
- """
-
- __repr = "{self.__class__.__name__}({self.root.filename!r}, {self.at!r})"
-
- def __init__(self, root, at=""):
- """
- Construct a Path from a ZipFile or filename.
-
- Note: When the source is an existing ZipFile object,
- its type (__class__) will be mutated to a
- specialized type. If the caller wishes to retain the
- original type, the caller should either create a
- separate ZipFile object or pass a filename.
- """
- self.root = FastLookup.make(root)
- self.at = at
-
- def open(self, mode='r', *args, pwd=None, **kwargs):
- """
- Open this entry as text or binary following the semantics
- of ``pathlib.Path.open()`` by passing arguments through
- to io.TextIOWrapper().
- """
- if self.is_dir():
- raise IsADirectoryError(self)
- zip_mode = mode[0]
- if not self.exists() and zip_mode == 'r':
- raise FileNotFoundError(self)
- stream = self.root.open(self.at, zip_mode, pwd=pwd)
- if 'b' in mode:
- if args or kwargs:
- raise ValueError("encoding args invalid for binary operation")
- return stream
- return io.TextIOWrapper(stream, *args, **kwargs)
-
- @property
- def name(self):
- return pathlib.Path(self.at).name or self.filename.name
-
- @property
- def suffix(self):
- return pathlib.Path(self.at).suffix or self.filename.suffix
-
- @property
- def suffixes(self):
- return pathlib.Path(self.at).suffixes or self.filename.suffixes
-
- @property
- def stem(self):
- return pathlib.Path(self.at).stem or self.filename.stem
-
- @property
- def filename(self):
- return pathlib.Path(self.root.filename).joinpath(self.at)
-
- def read_text(self, *args, **kwargs):
- with self.open('r', *args, **kwargs) as strm:
- return strm.read()
-
- def read_bytes(self):
- with self.open('rb') as strm:
- return strm.read()
-
- def _is_child(self, path):
- return posixpath.dirname(path.at.rstrip("/")) == self.at.rstrip("/")
-
- def _next(self, at):
- return self.__class__(self.root, at)
-
- def is_dir(self):
- return not self.at or self.at.endswith("/")
-
- def is_file(self):
- return self.exists() and not self.is_dir()
-
- def exists(self):
- return self.at in self.root._name_set()
-
- def iterdir(self):
- if not self.is_dir():
- raise ValueError("Can't listdir a file")
- subs = map(self._next, self.root.namelist())
- return filter(self._is_child, subs)
-
- def __str__(self):
- return posixpath.join(self.root.filename, self.at)
-
- def __repr__(self):
- return self.__repr.format(self=self)
-
- def joinpath(self, *other):
- next = posixpath.join(self.at, *map(_pathlib_compat, other))
- return self._next(self.root.resolve_dir(next))
-
- __truediv__ = joinpath
-
- @property
- def parent(self):
- if not self.at:
- return self.filename.parent
- parent_at = posixpath.dirname(self.at.rstrip('/'))
- if parent_at:
- parent_at += '/'
- return self._next(parent_at)
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/command/build_clib.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/command/build_clib.py
deleted file mode 100644
index 67ce2444ea69a0bbdfab0bda8c2aa14951187096..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/command/build_clib.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import distutils.command.build_clib as orig
-from distutils.errors import DistutilsSetupError
-from distutils import log
-from setuptools.dep_util import newer_pairwise_group
-
-
-class build_clib(orig.build_clib):
- """
- Override the default build_clib behaviour to do the following:
-
- 1. Implement a rudimentary timestamp-based dependency system
- so 'compile()' doesn't run every time.
- 2. Add more keys to the 'build_info' dictionary:
- * obj_deps - specify dependencies for each object compiled.
- this should be a dictionary mapping a key
- with the source filename to a list of
- dependencies. Use an empty string for global
- dependencies.
- * cflags - specify a list of additional flags to pass to
- the compiler.
- """
-
- def build_libraries(self, libraries):
- for (lib_name, build_info) in libraries:
- sources = build_info.get('sources')
- if sources is None or not isinstance(sources, (list, tuple)):
- raise DistutilsSetupError(
- "in 'libraries' option (library '%s'), "
- "'sources' must be present and must be "
- "a list of source filenames" % lib_name)
- sources = list(sources)
-
- log.info("building '%s' library", lib_name)
-
- # Make sure everything is the correct type.
- # obj_deps should be a dictionary of keys as sources
- # and a list/tuple of files that are its dependencies.
- obj_deps = build_info.get('obj_deps', dict())
- if not isinstance(obj_deps, dict):
- raise DistutilsSetupError(
- "in 'libraries' option (library '%s'), "
- "'obj_deps' must be a dictionary of "
- "type 'source: list'" % lib_name)
- dependencies = []
-
- # Get the global dependencies that are specified by the '' key.
- # These will go into every source's dependency list.
- global_deps = obj_deps.get('', list())
- if not isinstance(global_deps, (list, tuple)):
- raise DistutilsSetupError(
- "in 'libraries' option (library '%s'), "
- "'obj_deps' must be a dictionary of "
- "type 'source: list'" % lib_name)
-
- # Build the list to be used by newer_pairwise_group
- # each source will be auto-added to its dependencies.
- for source in sources:
- src_deps = [source]
- src_deps.extend(global_deps)
- extra_deps = obj_deps.get(source, list())
- if not isinstance(extra_deps, (list, tuple)):
- raise DistutilsSetupError(
- "in 'libraries' option (library '%s'), "
- "'obj_deps' must be a dictionary of "
- "type 'source: list'" % lib_name)
- src_deps.extend(extra_deps)
- dependencies.append(src_deps)
-
- expected_objects = self.compiler.object_filenames(
- sources,
- output_dir=self.build_temp,
- )
-
- if (
- newer_pairwise_group(dependencies, expected_objects)
- != ([], [])
- ):
- # First, compile the source code to object files in the library
- # directory. (This should probably change to putting object
- # files in a temporary build directory.)
- macros = build_info.get('macros')
- include_dirs = build_info.get('include_dirs')
- cflags = build_info.get('cflags')
- self.compiler.compile(
- sources,
- output_dir=self.build_temp,
- macros=macros,
- include_dirs=include_dirs,
- extra_postargs=cflags,
- debug=self.debug
- )
-
- # Now "link" the object files together into a static library.
- # (On Unix at least, this isn't really linking -- it just
- # builds an archive. Whatever.)
- self.compiler.create_static_lib(
- expected_objects,
- lib_name,
- output_dir=self.build_clib,
- debug=self.debug
- )
diff --git a/spaces/Realcat/image-matching-webui/third_party/GlueStick/gluestick/geometry.py b/spaces/Realcat/image-matching-webui/third_party/GlueStick/gluestick/geometry.py
deleted file mode 100644
index 0cdd232e74aeda84e1683dcb8e51385cc2497c37..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/GlueStick/gluestick/geometry.py
+++ /dev/null
@@ -1,206 +0,0 @@
-from typing import Tuple
-
-import numpy as np
-import torch
-
-
-def to_homogeneous(points):
- """Convert N-dimensional points to homogeneous coordinates.
- Args:
- points: torch.Tensor or numpy.ndarray with size (..., N).
- Returns:
- A torch.Tensor or numpy.ndarray with size (..., N+1).
- """
- if isinstance(points, torch.Tensor):
- pad = points.new_ones(points.shape[:-1] + (1,))
- return torch.cat([points, pad], dim=-1)
- elif isinstance(points, np.ndarray):
- pad = np.ones((points.shape[:-1] + (1,)), dtype=points.dtype)
- return np.concatenate([points, pad], axis=-1)
- else:
- raise ValueError
-
-
-def from_homogeneous(points, eps=0.0):
- """Remove the homogeneous dimension of N-dimensional points.
- Args:
- points: torch.Tensor or numpy.ndarray with size (..., N+1).
- Returns:
- A torch.Tensor or numpy ndarray with size (..., N).
- """
- return points[..., :-1] / (points[..., -1:] + eps)
-
-
-def skew_symmetric(v):
- """Create a skew-symmetric matrix from a (batched) vector of size (..., 3)."""
- z = torch.zeros_like(v[..., 0])
- M = torch.stack(
- [
- z,
- -v[..., 2],
- v[..., 1],
- v[..., 2],
- z,
- -v[..., 0],
- -v[..., 1],
- v[..., 0],
- z,
- ],
- dim=-1,
- ).reshape(v.shape[:-1] + (3, 3))
- return M
-
-
-def T_to_E(T):
- """Convert batched poses (..., 4, 4) to batched essential matrices."""
- return skew_symmetric(T[..., :3, 3]) @ T[..., :3, :3]
-
-
-def warp_points_torch(points, H, inverse=True):
- """
- Warp a list of points with the INVERSE of the given homography.
- The inverse is used to be coherent with tf.contrib.image.transform
- Arguments:
- points: batched list of N points, shape (B, N, 2).
- homography: batched or not (shapes (B, 8) and (8,) respectively).
- Returns: a Tensor of shape (B, N, 2) containing the new coordinates of the warped points.
- """
- # H = np.expand_dims(homography, axis=0) if len(homography.shape) == 1 else homography
-
- # Get the points to the homogeneous format
- points = to_homogeneous(points)
-
- # Apply the homography
- out_shape = tuple(list(H.shape[:-1]) + [3, 3])
- H_mat = torch.cat([H, torch.ones_like(H[..., :1])], axis=-1).reshape(out_shape)
- if inverse:
- H_mat = torch.inverse(H_mat)
- warped_points = torch.einsum("...nj,...ji->...ni", points, H_mat.transpose(-2, -1))
-
- warped_points = from_homogeneous(warped_points, eps=1e-5)
-
- return warped_points
-
-
-def seg_equation(segs):
- # calculate list of start, end and midpoints points from both lists
- start_points, end_points = to_homogeneous(segs[..., 0, :]), to_homogeneous(
- segs[..., 1, :]
- )
- # Compute the line equations as ax + by + c = 0 , where x^2 + y^2 = 1
- lines = torch.cross(start_points, end_points, dim=-1)
- lines_norm = torch.sqrt(lines[..., 0] ** 2 + lines[..., 1] ** 2)[..., None]
- assert torch.all(
- lines_norm > 0
- ), "Error: trying to compute the equation of a line with a single point"
- lines = lines / lines_norm
- return lines
-
-
-def is_inside_img(pts: torch.Tensor, img_shape: Tuple[int, int]):
- h, w = img_shape
- return (
- (pts >= 0).all(dim=-1)
- & (pts[..., 0] < w)
- & (pts[..., 1] < h)
- & (~torch.isinf(pts).any(dim=-1))
- )
-
-
-def shrink_segs_to_img(segs: torch.Tensor, img_shape: Tuple[int, int]) -> torch.Tensor:
- """
- Shrink an array of segments to fit inside the image.
- :param segs: The tensor of segments with shape (N, 2, 2)
- :param img_shape: The image shape in format (H, W)
- """
- EPS = 1e-4
- device = segs.device
- w, h = img_shape[1], img_shape[0]
- # Project the segments to the reference image
- segs = segs.clone()
- eqs = seg_equation(segs)
- x0, y0 = torch.tensor([1.0, 0, 0.0], device=device), torch.tensor(
- [0.0, 1, 0], device=device
- )
- x0 = x0.repeat(eqs.shape[:-1] + (1,))
- y0 = y0.repeat(eqs.shape[:-1] + (1,))
- pt_x0s = torch.cross(eqs, x0, dim=-1)
- pt_x0s = pt_x0s[..., :-1] / pt_x0s[..., None, -1]
- pt_x0s_valid = is_inside_img(pt_x0s, img_shape)
- pt_y0s = torch.cross(eqs, y0, dim=-1)
- pt_y0s = pt_y0s[..., :-1] / pt_y0s[..., None, -1]
- pt_y0s_valid = is_inside_img(pt_y0s, img_shape)
-
- xW, yH = torch.tensor([1.0, 0, EPS - w], device=device), torch.tensor(
- [0.0, 1, EPS - h], device=device
- )
- xW = xW.repeat(eqs.shape[:-1] + (1,))
- yH = yH.repeat(eqs.shape[:-1] + (1,))
- pt_xWs = torch.cross(eqs, xW, dim=-1)
- pt_xWs = pt_xWs[..., :-1] / pt_xWs[..., None, -1]
- pt_xWs_valid = is_inside_img(pt_xWs, img_shape)
- pt_yHs = torch.cross(eqs, yH, dim=-1)
- pt_yHs = pt_yHs[..., :-1] / pt_yHs[..., None, -1]
- pt_yHs_valid = is_inside_img(pt_yHs, img_shape)
-
- # If the X coordinate of the first endpoint is out
- mask = (segs[..., 0, 0] < 0) & pt_x0s_valid
- segs[mask, 0, :] = pt_x0s[mask]
- mask = (segs[..., 0, 0] > (w - 1)) & pt_xWs_valid
- segs[mask, 0, :] = pt_xWs[mask]
- # If the X coordinate of the second endpoint is out
- mask = (segs[..., 1, 0] < 0) & pt_x0s_valid
- segs[mask, 1, :] = pt_x0s[mask]
- mask = (segs[:, 1, 0] > (w - 1)) & pt_xWs_valid
- segs[mask, 1, :] = pt_xWs[mask]
- # If the Y coordinate of the first endpoint is out
- mask = (segs[..., 0, 1] < 0) & pt_y0s_valid
- segs[mask, 0, :] = pt_y0s[mask]
- mask = (segs[..., 0, 1] > (h - 1)) & pt_yHs_valid
- segs[mask, 0, :] = pt_yHs[mask]
- # If the Y coordinate of the second endpoint is out
- mask = (segs[..., 1, 1] < 0) & pt_y0s_valid
- segs[mask, 1, :] = pt_y0s[mask]
- mask = (segs[..., 1, 1] > (h - 1)) & pt_yHs_valid
- segs[mask, 1, :] = pt_yHs[mask]
-
- assert (
- torch.all(segs >= 0)
- and torch.all(segs[..., 0] < w)
- and torch.all(segs[..., 1] < h)
- )
- return segs
-
-
-def warp_lines_torch(
- lines, H, inverse=True, dst_shape: Tuple[int, int] = None
-) -> Tuple[torch.Tensor, torch.Tensor]:
- """
- :param lines: A tensor of shape (B, N, 2, 2) where B is the batch size, N the number of lines.
- :param H: The homography used to convert the lines. batched or not (shapes (B, 8) and (8,) respectively).
- :param inverse: Whether to apply H or the inverse of H
- :param dst_shape:If provided, lines are trimmed to be inside the image
- """
- device = lines.device
- batch_size, n = lines.shape[:2]
- lines = warp_points_torch(lines.reshape(batch_size, -1, 2), H, inverse).reshape(
- lines.shape
- )
-
- if dst_shape is None:
- return lines, torch.ones(lines.shape[:-2], dtype=torch.bool, device=device)
-
- out_img = torch.any(
- (lines < 0) | (lines >= torch.tensor(dst_shape[::-1], device=device)), -1
- )
- valid = ~out_img.all(-1)
- any_out_of_img = out_img.any(-1)
- lines_to_trim = valid & any_out_of_img
-
- for b in range(batch_size):
- lines_to_trim_mask_b = lines_to_trim[b]
- lines_to_trim_b = lines[b][lines_to_trim_mask_b]
- corrected_lines = shrink_segs_to_img(lines_to_trim_b, dst_shape)
- lines[b][lines_to_trim_mask_b] = corrected_lines
-
- return lines, valid
diff --git a/spaces/Realcat/image-matching-webui/third_party/TopicFM/src/utils/dataloader.py b/spaces/Realcat/image-matching-webui/third_party/TopicFM/src/utils/dataloader.py
deleted file mode 100644
index b980dfd344714870ecdacd9e7a9742f51c3ee14d..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/TopicFM/src/utils/dataloader.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import numpy as np
-
-
-# --- PL-DATAMODULE ---
-
-
-def get_local_split(items: list, world_size: int, rank: int, seed: int):
- """The local rank only loads a split of the dataset."""
- n_items = len(items)
- items_permute = np.random.RandomState(seed).permutation(items)
- if n_items % world_size == 0:
- padded_items = items_permute
- else:
- padding = np.random.RandomState(seed).choice(
- items, world_size - (n_items % world_size), replace=True
- )
- padded_items = np.concatenate([items_permute, padding])
- assert (
- len(padded_items) % world_size == 0
- ), f"len(padded_items): {len(padded_items)}; world_size: {world_size}; len(padding): {len(padding)}"
- n_per_rank = len(padded_items) // world_size
- local_items = padded_items[n_per_rank * rank : n_per_rank * (rank + 1)]
-
- return local_items
diff --git a/spaces/RickyMartin-dev/Text_to_Image_Diffusion/text_to_image.py b/spaces/RickyMartin-dev/Text_to_Image_Diffusion/text_to_image.py
deleted file mode 100644
index 710994467b0e706bda0c14b1a12c1da5a53a4fdb..0000000000000000000000000000000000000000
--- a/spaces/RickyMartin-dev/Text_to_Image_Diffusion/text_to_image.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from transformers.tools.base import Tool, get_default_device
-from transformers.utils import is_accelerate_available
-import torch
-
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-
-# Descrition of Image Processing
-TEXT_TO_IMAGE_DESCRIPTION = (
- "This is a tool that creates an image according to a prompt"
-)
-
-# Defining a stable diffusion tool
-class TextToImageTool(Tool):
- default_checkpoint = "runwayml/stable-diffusion-v1-5"
- description = TEXT_TO_IMAGE_DESCRIPTION
- inputs = ['text']
- outputs = ['image']
-
- def __init__(self, device=None, **hub_kwargs) -> None:
- if not is_accelerate_available():
- raise ImportError("Accelerate should be installed in order to use tools.")
-
- super().__init__()
-
- self.device = device
- self.pipeline = None
- self.hub_kwargs = hub_kwargs
-
- def setup(self):
- if self.device is None:
- self.device = get_default_device()
-
- self.pipeline = DiffusionPipeline.from_pretrained(self.default_checkpoint)
- self.pipeline.scheduler = DPMSolverMultistepScheduler.from_config(self.pipeline.scheduler.config)
- self.pipeline.to(self.device)
-
- if self.device.type == "cuda":
- self.pipeline.to(torch_dtype=torch.float16)
-
- self.is_initialized = True
-
- def __call__(self, prompt):
- if not self.is_initialized:
- self.setup()
-
- negative_prompt = "low quality, bad quality, deformed, low resolution, janky"
- added_prompt = " , highest quality, highly realistic, very high resolution"
-
- return self.pipeline(prompt + added_prompt, negative_prompt=negative_prompt, num_inference_steps=25).images[0]
diff --git a/spaces/Rimi98/NegativeCommentClassifier/README.md b/spaces/Rimi98/NegativeCommentClassifier/README.md
deleted file mode 100644
index 757bdf65389767c54556aae81be3ed21aafbeb31..0000000000000000000000000000000000000000
--- a/spaces/Rimi98/NegativeCommentClassifier/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: NegativeCommentClassifier
-emoji: 💻
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.19.1
-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/Robert001/UniControl-Demo/annotator/uniformer/mmdet/apis/test.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/apis/test.py
deleted file mode 100644
index e54b1b8c24efc448972c31ee5da63041d7f97a47..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/apis/test.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import os.path as osp
-import pickle
-import shutil
-import tempfile
-import time
-
-import mmcv
-import torch
-import torch.distributed as dist
-from mmcv.image import tensor2imgs
-from mmcv.runner import get_dist_info
-
-from mmdet.core import encode_mask_results
-
-
-def single_gpu_test(model,
- data_loader,
- show=False,
- out_dir=None,
- show_score_thr=0.3):
- model.eval()
- results = []
- dataset = data_loader.dataset
- prog_bar = mmcv.ProgressBar(len(dataset))
- for i, data in enumerate(data_loader):
- with torch.no_grad():
- result = model(return_loss=False, rescale=True, **data)
-
- batch_size = len(result)
- if show or out_dir:
- if batch_size == 1 and isinstance(data['img'][0], torch.Tensor):
- img_tensor = data['img'][0]
- else:
- img_tensor = data['img'][0].data[0]
- img_metas = data['img_metas'][0].data[0]
- imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
- assert len(imgs) == len(img_metas)
-
- for i, (img, img_meta) in enumerate(zip(imgs, img_metas)):
- h, w, _ = img_meta['img_shape']
- img_show = img[:h, :w, :]
-
- ori_h, ori_w = img_meta['ori_shape'][:-1]
- img_show = mmcv.imresize(img_show, (ori_w, ori_h))
-
- if out_dir:
- out_file = osp.join(out_dir, img_meta['ori_filename'])
- else:
- out_file = None
-
- model.module.show_result(
- img_show,
- result[i],
- show=show,
- out_file=out_file,
- score_thr=show_score_thr)
-
- # encode mask results
- if isinstance(result[0], tuple):
- result = [(bbox_results, encode_mask_results(mask_results))
- for bbox_results, mask_results in result]
- results.extend(result)
-
- for _ in range(batch_size):
- prog_bar.update()
- return results
-
-
-def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):
- """Test model with multiple gpus.
-
- This method tests model with multiple gpus and collects the results
- under two different modes: gpu and cpu modes. By setting 'gpu_collect=True'
- it encodes results to gpu tensors and use gpu communication for results
- collection. On cpu mode it saves the results on different gpus to 'tmpdir'
- and collects them by the rank 0 worker.
-
- Args:
- model (nn.Module): Model to be tested.
- data_loader (nn.Dataloader): Pytorch data loader.
- tmpdir (str): Path of directory to save the temporary results from
- different gpus under cpu mode.
- gpu_collect (bool): Option to use either gpu or cpu to collect results.
-
- Returns:
- list: The prediction results.
- """
- model.eval()
- results = []
- dataset = data_loader.dataset
- rank, world_size = get_dist_info()
- if rank == 0:
- prog_bar = mmcv.ProgressBar(len(dataset))
- time.sleep(2) # This line can prevent deadlock problem in some cases.
- for i, data in enumerate(data_loader):
- with torch.no_grad():
- result = model(return_loss=False, rescale=True, **data)
- # encode mask results
- if isinstance(result[0], tuple):
- result = [(bbox_results, encode_mask_results(mask_results))
- for bbox_results, mask_results in result]
- results.extend(result)
-
- if rank == 0:
- batch_size = len(result)
- for _ in range(batch_size * world_size):
- prog_bar.update()
-
- # collect results from all ranks
- if gpu_collect:
- results = collect_results_gpu(results, len(dataset))
- else:
- results = collect_results_cpu(results, len(dataset), tmpdir)
- return results
-
-
-def collect_results_cpu(result_part, size, tmpdir=None):
- rank, world_size = get_dist_info()
- # create a tmp dir if it is not specified
- if tmpdir is None:
- MAX_LEN = 512
- # 32 is whitespace
- dir_tensor = torch.full((MAX_LEN, ),
- 32,
- dtype=torch.uint8,
- device='cuda')
- if rank == 0:
- mmcv.mkdir_or_exist('.dist_test')
- tmpdir = tempfile.mkdtemp(dir='.dist_test')
- tmpdir = torch.tensor(
- bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
- dir_tensor[:len(tmpdir)] = tmpdir
- dist.broadcast(dir_tensor, 0)
- tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
- else:
- mmcv.mkdir_or_exist(tmpdir)
- # dump the part result to the dir
- mmcv.dump(result_part, osp.join(tmpdir, f'part_{rank}.pkl'))
- dist.barrier()
- # collect all parts
- if rank != 0:
- return None
- else:
- # load results of all parts from tmp dir
- part_list = []
- for i in range(world_size):
- part_file = osp.join(tmpdir, f'part_{i}.pkl')
- part_list.append(mmcv.load(part_file))
- # sort the results
- ordered_results = []
- for res in zip(*part_list):
- ordered_results.extend(list(res))
- # the dataloader may pad some samples
- ordered_results = ordered_results[:size]
- # remove tmp dir
- shutil.rmtree(tmpdir)
- return ordered_results
-
-
-def collect_results_gpu(result_part, size):
- rank, world_size = get_dist_info()
- # dump result part to tensor with pickle
- part_tensor = torch.tensor(
- bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda')
- # gather all result part tensor shape
- shape_tensor = torch.tensor(part_tensor.shape, device='cuda')
- shape_list = [shape_tensor.clone() for _ in range(world_size)]
- dist.all_gather(shape_list, shape_tensor)
- # padding result part tensor to max length
- shape_max = torch.tensor(shape_list).max()
- part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda')
- part_send[:shape_tensor[0]] = part_tensor
- part_recv_list = [
- part_tensor.new_zeros(shape_max) for _ in range(world_size)
- ]
- # gather all result part
- dist.all_gather(part_recv_list, part_send)
-
- if rank == 0:
- part_list = []
- for recv, shape in zip(part_recv_list, shape_list):
- part_list.append(
- pickle.loads(recv[:shape[0]].cpu().numpy().tobytes()))
- # sort the results
- ordered_results = []
- for res in zip(*part_list):
- ordered_results.extend(list(res))
- # the dataloader may pad some samples
- ordered_results = ordered_results[:size]
- return ordered_results
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/sabl_retina_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/sabl_retina_head.py
deleted file mode 100644
index 4211622cb8b4fe807230a89bcaab8f4f1681bfc0..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/sabl_retina_head.py
+++ /dev/null
@@ -1,621 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
-from mmcv.runner import force_fp32
-
-from mmdet.core import (build_anchor_generator, build_assigner,
- build_bbox_coder, build_sampler, images_to_levels,
- multi_apply, multiclass_nms, unmap)
-from ..builder import HEADS, build_loss
-from .base_dense_head import BaseDenseHead
-from .guided_anchor_head import GuidedAnchorHead
-
-
-@HEADS.register_module()
-class SABLRetinaHead(BaseDenseHead):
- """Side-Aware Boundary Localization (SABL) for RetinaNet.
-
- The anchor generation, assigning and sampling in SABLRetinaHead
- are the same as GuidedAnchorHead for guided anchoring.
-
- Please refer to https://arxiv.org/abs/1912.04260 for more details.
-
- Args:
- num_classes (int): Number of classes.
- in_channels (int): Number of channels in the input feature map.
- stacked_convs (int): Number of Convs for classification \
- and regression branches. Defaults to 4.
- feat_channels (int): Number of hidden channels. \
- Defaults to 256.
- approx_anchor_generator (dict): Config dict for approx generator.
- square_anchor_generator (dict): Config dict for square generator.
- conv_cfg (dict): Config dict for ConvModule. Defaults to None.
- norm_cfg (dict): Config dict for Norm Layer. Defaults to None.
- bbox_coder (dict): Config dict for bbox coder.
- reg_decoded_bbox (bool): If true, the regression loss would be
- applied directly on decoded bounding boxes, converting both
- the predicted boxes and regression targets to absolute
- coordinates format. Default False. It should be `True` when
- using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
- train_cfg (dict): Training config of SABLRetinaHead.
- test_cfg (dict): Testing config of SABLRetinaHead.
- loss_cls (dict): Config of classification loss.
- loss_bbox_cls (dict): Config of classification loss for bbox branch.
- loss_bbox_reg (dict): Config of regression loss for bbox branch.
- """
-
- def __init__(self,
- num_classes,
- in_channels,
- stacked_convs=4,
- feat_channels=256,
- approx_anchor_generator=dict(
- type='AnchorGenerator',
- octave_base_scale=4,
- scales_per_octave=3,
- ratios=[0.5, 1.0, 2.0],
- strides=[8, 16, 32, 64, 128]),
- square_anchor_generator=dict(
- type='AnchorGenerator',
- ratios=[1.0],
- scales=[4],
- strides=[8, 16, 32, 64, 128]),
- conv_cfg=None,
- norm_cfg=None,
- bbox_coder=dict(
- type='BucketingBBoxCoder',
- num_buckets=14,
- scale_factor=3.0),
- reg_decoded_bbox=False,
- train_cfg=None,
- test_cfg=None,
- loss_cls=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- loss_bbox_cls=dict(
- type='CrossEntropyLoss',
- use_sigmoid=True,
- loss_weight=1.5),
- loss_bbox_reg=dict(
- type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)):
- super(SABLRetinaHead, self).__init__()
- self.in_channels = in_channels
- self.num_classes = num_classes
- self.feat_channels = feat_channels
- self.num_buckets = bbox_coder['num_buckets']
- self.side_num = int(np.ceil(self.num_buckets / 2))
-
- assert (approx_anchor_generator['octave_base_scale'] ==
- square_anchor_generator['scales'][0])
- assert (approx_anchor_generator['strides'] ==
- square_anchor_generator['strides'])
-
- self.approx_anchor_generator = build_anchor_generator(
- approx_anchor_generator)
- self.square_anchor_generator = build_anchor_generator(
- square_anchor_generator)
- self.approxs_per_octave = (
- self.approx_anchor_generator.num_base_anchors[0])
-
- # one anchor per location
- self.num_anchors = 1
- self.stacked_convs = stacked_convs
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
-
- self.reg_decoded_bbox = reg_decoded_bbox
-
- self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
- self.sampling = loss_cls['type'] not in [
- 'FocalLoss', 'GHMC', 'QualityFocalLoss'
- ]
- if self.use_sigmoid_cls:
- self.cls_out_channels = num_classes
- else:
- self.cls_out_channels = num_classes + 1
-
- self.bbox_coder = build_bbox_coder(bbox_coder)
- self.loss_cls = build_loss(loss_cls)
- self.loss_bbox_cls = build_loss(loss_bbox_cls)
- self.loss_bbox_reg = build_loss(loss_bbox_reg)
-
- self.train_cfg = train_cfg
- self.test_cfg = test_cfg
-
- if self.train_cfg:
- self.assigner = build_assigner(self.train_cfg.assigner)
- # use PseudoSampler when sampling is False
- if self.sampling and hasattr(self.train_cfg, 'sampler'):
- sampler_cfg = self.train_cfg.sampler
- else:
- sampler_cfg = dict(type='PseudoSampler')
- self.sampler = build_sampler(sampler_cfg, context=self)
-
- self.fp16_enabled = False
- self._init_layers()
-
- def _init_layers(self):
- self.relu = nn.ReLU(inplace=True)
- self.cls_convs = nn.ModuleList()
- self.reg_convs = nn.ModuleList()
- for i in range(self.stacked_convs):
- chn = self.in_channels if i == 0 else self.feat_channels
- self.cls_convs.append(
- ConvModule(
- chn,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg))
- self.reg_convs.append(
- ConvModule(
- chn,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg))
- self.retina_cls = nn.Conv2d(
- self.feat_channels, self.cls_out_channels, 3, padding=1)
- self.retina_bbox_reg = nn.Conv2d(
- self.feat_channels, self.side_num * 4, 3, padding=1)
- self.retina_bbox_cls = nn.Conv2d(
- self.feat_channels, self.side_num * 4, 3, padding=1)
-
- def init_weights(self):
- for m in self.cls_convs:
- normal_init(m.conv, std=0.01)
- for m in self.reg_convs:
- normal_init(m.conv, std=0.01)
- bias_cls = bias_init_with_prob(0.01)
- normal_init(self.retina_cls, std=0.01, bias=bias_cls)
- normal_init(self.retina_bbox_reg, std=0.01)
- normal_init(self.retina_bbox_cls, std=0.01)
-
- def forward_single(self, x):
- cls_feat = x
- reg_feat = x
- for cls_conv in self.cls_convs:
- cls_feat = cls_conv(cls_feat)
- for reg_conv in self.reg_convs:
- reg_feat = reg_conv(reg_feat)
- cls_score = self.retina_cls(cls_feat)
- bbox_cls_pred = self.retina_bbox_cls(reg_feat)
- bbox_reg_pred = self.retina_bbox_reg(reg_feat)
- bbox_pred = (bbox_cls_pred, bbox_reg_pred)
- return cls_score, bbox_pred
-
- def forward(self, feats):
- return multi_apply(self.forward_single, feats)
-
- def get_anchors(self, featmap_sizes, img_metas, device='cuda'):
- """Get squares according to feature map sizes and guided anchors.
-
- Args:
- featmap_sizes (list[tuple]): Multi-level feature map sizes.
- img_metas (list[dict]): Image meta info.
- device (torch.device | str): device for returned tensors
-
- Returns:
- tuple: square approxs of each image
- """
- num_imgs = len(img_metas)
-
- # since feature map sizes of all images are the same, we only compute
- # squares for one time
- multi_level_squares = self.square_anchor_generator.grid_anchors(
- featmap_sizes, device=device)
- squares_list = [multi_level_squares for _ in range(num_imgs)]
-
- return squares_list
-
- def get_target(self,
- approx_list,
- inside_flag_list,
- square_list,
- gt_bboxes_list,
- img_metas,
- gt_bboxes_ignore_list=None,
- gt_labels_list=None,
- label_channels=None,
- sampling=True,
- unmap_outputs=True):
- """Compute bucketing targets.
- Args:
- approx_list (list[list]): Multi level approxs of each image.
- inside_flag_list (list[list]): Multi level inside flags of each
- image.
- square_list (list[list]): Multi level squares of each image.
- gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
- img_metas (list[dict]): Meta info of each image.
- gt_bboxes_ignore_list (list[Tensor]): ignore list of gt bboxes.
- gt_bboxes_list (list[Tensor]): Gt bboxes of each image.
- label_channels (int): Channel of label.
- sampling (bool): Sample Anchors or not.
- unmap_outputs (bool): unmap outputs or not.
-
- Returns:
- tuple: Returns a tuple containing learning targets.
-
- - labels_list (list[Tensor]): Labels of each level.
- - label_weights_list (list[Tensor]): Label weights of each \
- level.
- - bbox_cls_targets_list (list[Tensor]): BBox cls targets of \
- each level.
- - bbox_cls_weights_list (list[Tensor]): BBox cls weights of \
- each level.
- - bbox_reg_targets_list (list[Tensor]): BBox reg targets of \
- each level.
- - bbox_reg_weights_list (list[Tensor]): BBox reg weights of \
- each level.
- - num_total_pos (int): Number of positive samples in all \
- images.
- - num_total_neg (int): Number of negative samples in all \
- images.
- """
- num_imgs = len(img_metas)
- assert len(approx_list) == len(inside_flag_list) == len(
- square_list) == num_imgs
- # anchor number of multi levels
- num_level_squares = [squares.size(0) for squares in square_list[0]]
- # concat all level anchors and flags to a single tensor
- inside_flag_flat_list = []
- approx_flat_list = []
- square_flat_list = []
- for i in range(num_imgs):
- assert len(square_list[i]) == len(inside_flag_list[i])
- inside_flag_flat_list.append(torch.cat(inside_flag_list[i]))
- approx_flat_list.append(torch.cat(approx_list[i]))
- square_flat_list.append(torch.cat(square_list[i]))
-
- # compute targets for each image
- if gt_bboxes_ignore_list is None:
- gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
- if gt_labels_list is None:
- gt_labels_list = [None for _ in range(num_imgs)]
- (all_labels, all_label_weights, all_bbox_cls_targets,
- all_bbox_cls_weights, all_bbox_reg_targets, all_bbox_reg_weights,
- pos_inds_list, neg_inds_list) = multi_apply(
- self._get_target_single,
- approx_flat_list,
- inside_flag_flat_list,
- square_flat_list,
- gt_bboxes_list,
- gt_bboxes_ignore_list,
- gt_labels_list,
- img_metas,
- label_channels=label_channels,
- sampling=sampling,
- unmap_outputs=unmap_outputs)
- # no valid anchors
- if any([labels is None for labels in all_labels]):
- return None
- # sampled anchors of all images
- num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
- num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
- # split targets to a list w.r.t. multiple levels
- labels_list = images_to_levels(all_labels, num_level_squares)
- label_weights_list = images_to_levels(all_label_weights,
- num_level_squares)
- bbox_cls_targets_list = images_to_levels(all_bbox_cls_targets,
- num_level_squares)
- bbox_cls_weights_list = images_to_levels(all_bbox_cls_weights,
- num_level_squares)
- bbox_reg_targets_list = images_to_levels(all_bbox_reg_targets,
- num_level_squares)
- bbox_reg_weights_list = images_to_levels(all_bbox_reg_weights,
- num_level_squares)
- return (labels_list, label_weights_list, bbox_cls_targets_list,
- bbox_cls_weights_list, bbox_reg_targets_list,
- bbox_reg_weights_list, num_total_pos, num_total_neg)
-
- def _get_target_single(self,
- flat_approxs,
- inside_flags,
- flat_squares,
- gt_bboxes,
- gt_bboxes_ignore,
- gt_labels,
- img_meta,
- label_channels=None,
- sampling=True,
- unmap_outputs=True):
- """Compute regression and classification targets for anchors in a
- single image.
-
- Args:
- flat_approxs (Tensor): flat approxs of a single image,
- shape (n, 4)
- inside_flags (Tensor): inside flags of a single image,
- shape (n, ).
- flat_squares (Tensor): flat squares of a single image,
- shape (approxs_per_octave * n, 4)
- gt_bboxes (Tensor): Ground truth bboxes of a single image, \
- shape (num_gts, 4).
- gt_bboxes_ignore (Tensor): Ground truth bboxes to be
- ignored, shape (num_ignored_gts, 4).
- gt_labels (Tensor): Ground truth labels of each box,
- shape (num_gts,).
- img_meta (dict): Meta info of the image.
- label_channels (int): Channel of label.
- sampling (bool): Sample Anchors or not.
- unmap_outputs (bool): unmap outputs or not.
-
- Returns:
- tuple:
-
- - labels_list (Tensor): Labels in a single image
- - label_weights (Tensor): Label weights in a single image
- - bbox_cls_targets (Tensor): BBox cls targets in a single image
- - bbox_cls_weights (Tensor): BBox cls weights in a single image
- - bbox_reg_targets (Tensor): BBox reg targets in a single image
- - bbox_reg_weights (Tensor): BBox reg weights in a single image
- - num_total_pos (int): Number of positive samples \
- in a single image
- - num_total_neg (int): Number of negative samples \
- in a single image
- """
- if not inside_flags.any():
- return (None, ) * 8
- # assign gt and sample anchors
- expand_inside_flags = inside_flags[:, None].expand(
- -1, self.approxs_per_octave).reshape(-1)
- approxs = flat_approxs[expand_inside_flags, :]
- squares = flat_squares[inside_flags, :]
-
- assign_result = self.assigner.assign(approxs, squares,
- self.approxs_per_octave,
- gt_bboxes, gt_bboxes_ignore)
- sampling_result = self.sampler.sample(assign_result, squares,
- gt_bboxes)
-
- num_valid_squares = squares.shape[0]
- bbox_cls_targets = squares.new_zeros(
- (num_valid_squares, self.side_num * 4))
- bbox_cls_weights = squares.new_zeros(
- (num_valid_squares, self.side_num * 4))
- bbox_reg_targets = squares.new_zeros(
- (num_valid_squares, self.side_num * 4))
- bbox_reg_weights = squares.new_zeros(
- (num_valid_squares, self.side_num * 4))
- labels = squares.new_full((num_valid_squares, ),
- self.num_classes,
- dtype=torch.long)
- label_weights = squares.new_zeros(num_valid_squares, dtype=torch.float)
-
- pos_inds = sampling_result.pos_inds
- neg_inds = sampling_result.neg_inds
- if len(pos_inds) > 0:
- (pos_bbox_reg_targets, pos_bbox_reg_weights, pos_bbox_cls_targets,
- pos_bbox_cls_weights) = self.bbox_coder.encode(
- sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
-
- bbox_cls_targets[pos_inds, :] = pos_bbox_cls_targets
- bbox_reg_targets[pos_inds, :] = pos_bbox_reg_targets
- bbox_cls_weights[pos_inds, :] = pos_bbox_cls_weights
- bbox_reg_weights[pos_inds, :] = pos_bbox_reg_weights
- if gt_labels is None:
- # Only rpn gives gt_labels as None
- # Foreground is the first class
- labels[pos_inds] = 0
- else:
- labels[pos_inds] = gt_labels[
- sampling_result.pos_assigned_gt_inds]
- if self.train_cfg.pos_weight <= 0:
- label_weights[pos_inds] = 1.0
- else:
- label_weights[pos_inds] = self.train_cfg.pos_weight
- if len(neg_inds) > 0:
- label_weights[neg_inds] = 1.0
-
- # map up to original set of anchors
- if unmap_outputs:
- num_total_anchors = flat_squares.size(0)
- labels = unmap(
- labels, num_total_anchors, inside_flags, fill=self.num_classes)
- label_weights = unmap(label_weights, num_total_anchors,
- inside_flags)
- bbox_cls_targets = unmap(bbox_cls_targets, num_total_anchors,
- inside_flags)
- bbox_cls_weights = unmap(bbox_cls_weights, num_total_anchors,
- inside_flags)
- bbox_reg_targets = unmap(bbox_reg_targets, num_total_anchors,
- inside_flags)
- bbox_reg_weights = unmap(bbox_reg_weights, num_total_anchors,
- inside_flags)
- return (labels, label_weights, bbox_cls_targets, bbox_cls_weights,
- bbox_reg_targets, bbox_reg_weights, pos_inds, neg_inds)
-
- def loss_single(self, cls_score, bbox_pred, labels, label_weights,
- bbox_cls_targets, bbox_cls_weights, bbox_reg_targets,
- bbox_reg_weights, num_total_samples):
- # classification loss
- labels = labels.reshape(-1)
- label_weights = label_weights.reshape(-1)
- cls_score = cls_score.permute(0, 2, 3,
- 1).reshape(-1, self.cls_out_channels)
- loss_cls = self.loss_cls(
- cls_score, labels, label_weights, avg_factor=num_total_samples)
- # regression loss
- bbox_cls_targets = bbox_cls_targets.reshape(-1, self.side_num * 4)
- bbox_cls_weights = bbox_cls_weights.reshape(-1, self.side_num * 4)
- bbox_reg_targets = bbox_reg_targets.reshape(-1, self.side_num * 4)
- bbox_reg_weights = bbox_reg_weights.reshape(-1, self.side_num * 4)
- (bbox_cls_pred, bbox_reg_pred) = bbox_pred
- bbox_cls_pred = bbox_cls_pred.permute(0, 2, 3, 1).reshape(
- -1, self.side_num * 4)
- bbox_reg_pred = bbox_reg_pred.permute(0, 2, 3, 1).reshape(
- -1, self.side_num * 4)
- loss_bbox_cls = self.loss_bbox_cls(
- bbox_cls_pred,
- bbox_cls_targets.long(),
- bbox_cls_weights,
- avg_factor=num_total_samples * 4 * self.side_num)
- loss_bbox_reg = self.loss_bbox_reg(
- bbox_reg_pred,
- bbox_reg_targets,
- bbox_reg_weights,
- avg_factor=num_total_samples * 4 * self.bbox_coder.offset_topk)
- return loss_cls, loss_bbox_cls, loss_bbox_reg
-
- @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
- def loss(self,
- cls_scores,
- bbox_preds,
- gt_bboxes,
- gt_labels,
- img_metas,
- gt_bboxes_ignore=None):
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- assert len(featmap_sizes) == self.approx_anchor_generator.num_levels
-
- device = cls_scores[0].device
-
- # get sampled approxes
- approxs_list, inside_flag_list = GuidedAnchorHead.get_sampled_approxs(
- self, featmap_sizes, img_metas, device=device)
-
- square_list = self.get_anchors(featmap_sizes, img_metas, device=device)
-
- label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-
- cls_reg_targets = self.get_target(
- approxs_list,
- inside_flag_list,
- square_list,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore_list=gt_bboxes_ignore,
- gt_labels_list=gt_labels,
- label_channels=label_channels,
- sampling=self.sampling)
- if cls_reg_targets is None:
- return None
- (labels_list, label_weights_list, bbox_cls_targets_list,
- bbox_cls_weights_list, bbox_reg_targets_list, bbox_reg_weights_list,
- num_total_pos, num_total_neg) = cls_reg_targets
- num_total_samples = (
- num_total_pos + num_total_neg if self.sampling else num_total_pos)
- losses_cls, losses_bbox_cls, losses_bbox_reg = multi_apply(
- self.loss_single,
- cls_scores,
- bbox_preds,
- labels_list,
- label_weights_list,
- bbox_cls_targets_list,
- bbox_cls_weights_list,
- bbox_reg_targets_list,
- bbox_reg_weights_list,
- num_total_samples=num_total_samples)
- return dict(
- loss_cls=losses_cls,
- loss_bbox_cls=losses_bbox_cls,
- loss_bbox_reg=losses_bbox_reg)
-
- @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
- def get_bboxes(self,
- cls_scores,
- bbox_preds,
- img_metas,
- cfg=None,
- rescale=False):
- assert len(cls_scores) == len(bbox_preds)
- num_levels = len(cls_scores)
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-
- device = cls_scores[0].device
- mlvl_anchors = self.get_anchors(
- featmap_sizes, img_metas, device=device)
- result_list = []
- for img_id in range(len(img_metas)):
- cls_score_list = [
- cls_scores[i][img_id].detach() for i in range(num_levels)
- ]
- bbox_cls_pred_list = [
- bbox_preds[i][0][img_id].detach() for i in range(num_levels)
- ]
- bbox_reg_pred_list = [
- bbox_preds[i][1][img_id].detach() for i in range(num_levels)
- ]
- img_shape = img_metas[img_id]['img_shape']
- scale_factor = img_metas[img_id]['scale_factor']
- proposals = self.get_bboxes_single(cls_score_list,
- bbox_cls_pred_list,
- bbox_reg_pred_list,
- mlvl_anchors[img_id], img_shape,
- scale_factor, cfg, rescale)
- result_list.append(proposals)
- return result_list
-
- def get_bboxes_single(self,
- cls_scores,
- bbox_cls_preds,
- bbox_reg_preds,
- mlvl_anchors,
- img_shape,
- scale_factor,
- cfg,
- rescale=False):
- cfg = self.test_cfg if cfg is None else cfg
- mlvl_bboxes = []
- mlvl_scores = []
- mlvl_confids = []
- assert len(cls_scores) == len(bbox_cls_preds) == len(
- bbox_reg_preds) == len(mlvl_anchors)
- for cls_score, bbox_cls_pred, bbox_reg_pred, anchors in zip(
- cls_scores, bbox_cls_preds, bbox_reg_preds, mlvl_anchors):
- assert cls_score.size()[-2:] == bbox_cls_pred.size(
- )[-2:] == bbox_reg_pred.size()[-2::]
- cls_score = cls_score.permute(1, 2,
- 0).reshape(-1, self.cls_out_channels)
- if self.use_sigmoid_cls:
- scores = cls_score.sigmoid()
- else:
- scores = cls_score.softmax(-1)
- bbox_cls_pred = bbox_cls_pred.permute(1, 2, 0).reshape(
- -1, self.side_num * 4)
- bbox_reg_pred = bbox_reg_pred.permute(1, 2, 0).reshape(
- -1, self.side_num * 4)
- nms_pre = cfg.get('nms_pre', -1)
- if nms_pre > 0 and scores.shape[0] > nms_pre:
- if self.use_sigmoid_cls:
- max_scores, _ = scores.max(dim=1)
- else:
- max_scores, _ = scores[:, :-1].max(dim=1)
- _, topk_inds = max_scores.topk(nms_pre)
- anchors = anchors[topk_inds, :]
- bbox_cls_pred = bbox_cls_pred[topk_inds, :]
- bbox_reg_pred = bbox_reg_pred[topk_inds, :]
- scores = scores[topk_inds, :]
- bbox_preds = [
- bbox_cls_pred.contiguous(),
- bbox_reg_pred.contiguous()
- ]
- bboxes, confids = self.bbox_coder.decode(
- anchors.contiguous(), bbox_preds, max_shape=img_shape)
- mlvl_bboxes.append(bboxes)
- mlvl_scores.append(scores)
- mlvl_confids.append(confids)
- mlvl_bboxes = torch.cat(mlvl_bboxes)
- if rescale:
- mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
- mlvl_scores = torch.cat(mlvl_scores)
- mlvl_confids = torch.cat(mlvl_confids)
- if self.use_sigmoid_cls:
- padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
- mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
- det_bboxes, det_labels = multiclass_nms(
- mlvl_bboxes,
- mlvl_scores,
- cfg.score_thr,
- cfg.nms,
- cfg.max_per_img,
- score_factors=mlvl_confids)
- return det_bboxes, det_labels
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/detr.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/detr.py
deleted file mode 100644
index 5ff82a280daa0a015f662bdf2509fa11542d46d4..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/detr.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from mmdet.core import bbox2result
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class DETR(SingleStageDetector):
- r"""Implementation of `DETR: End-to-End Object Detection with
- Transformers 
AI Mental Healthcare ChatBot
Sentiment and Emotion Score Graph
- {Array.from({ length: num }).map((_, index) => {
- const randomDuration = Math.random() * 100 + duration
- const initialDelay = Math.random() * 2 * duration
- const initialScale = Math.sin((index + 1) * Math.PI / num)
- return (
-
- )
- })}
-
- )
-}
diff --git a/spaces/TabPFN/TabPFNPrediction/TabPFN/priors/prior.py b/spaces/TabPFN/TabPFNPrediction/TabPFN/priors/prior.py
deleted file mode 100644
index 64ef7ea7eeb8bf251a56e9dd5fac752ab46241b3..0000000000000000000000000000000000000000
--- a/spaces/TabPFN/TabPFNPrediction/TabPFN/priors/prior.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from torch.utils.data import DataLoader
-
-
-class PriorDataLoader(DataLoader):
- pass
- # init accepts num_steps as first argument
-
- # has two attributes set on class or object level:
- # num_features: int and
- # num_outputs: int
- # fuse_x_y: bool
- # Optional: validate function that accepts a transformer model
diff --git a/spaces/TechnoByte/wd-v1-4-tags/Utils/dbimutils.py b/spaces/TechnoByte/wd-v1-4-tags/Utils/dbimutils.py
deleted file mode 100644
index e01496710f8905e542dbe7e89c91fd2c8d1bc14a..0000000000000000000000000000000000000000
--- a/spaces/TechnoByte/wd-v1-4-tags/Utils/dbimutils.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# DanBooru IMage Utility functions
-
-import cv2
-import numpy as np
-from PIL import Image
-
-
-def smart_imread(img, flag=cv2.IMREAD_UNCHANGED):
- if img.endswith(".gif"):
- img = Image.open(img)
- img = img.convert("RGB")
- img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
- else:
- img = cv2.imread(img, flag)
- return img
-
-
-def smart_24bit(img):
- if img.dtype is np.dtype(np.uint16):
- img = (img / 257).astype(np.uint8)
-
- if len(img.shape) == 2:
- img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
- elif img.shape[2] == 4:
- trans_mask = img[:, :, 3] == 0
- img[trans_mask] = [255, 255, 255, 255]
- img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
- return img
-
-
-def make_square(img, target_size):
- old_size = img.shape[:2]
- desired_size = max(old_size)
- desired_size = max(desired_size, target_size)
-
- delta_w = desired_size - old_size[1]
- delta_h = desired_size - old_size[0]
- top, bottom = delta_h // 2, delta_h - (delta_h // 2)
- left, right = delta_w // 2, delta_w - (delta_w // 2)
-
- color = [255, 255, 255]
- new_im = cv2.copyMakeBorder(
- img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color
- )
- return new_im
-
-
-def smart_resize(img, size):
- # Assumes the image has already gone through make_square
- if img.shape[0] > size:
- img = cv2.resize(img, (size, size), interpolation=cv2.INTER_AREA)
- elif img.shape[0] < size:
- img = cv2.resize(img, (size, size), interpolation=cv2.INTER_CUBIC)
- return img
diff --git a/spaces/TencentARC/VLog/models/grit_src/grit/modeling/backbone/utils.py b/spaces/TencentARC/VLog/models/grit_src/grit/modeling/backbone/utils.py
deleted file mode 100644
index e71db21f1223c87cceeb422a70888f7bac42bb18..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/grit/modeling/backbone/utils.py
+++ /dev/null
@@ -1,186 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-# This code is from https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/utils.py
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-__all__ = [
- "window_partition",
- "window_unpartition",
- "add_decomposed_rel_pos",
- "get_abs_pos",
- "PatchEmbed",
-]
-
-def window_partition(x, window_size):
- """
- Partition into non-overlapping windows with padding if needed.
- Args:
- x (tensor): input tokens with [B, H, W, C].
- window_size (int): window size.
-
- Returns:
- windows: windows after partition with [B * num_windows, window_size, window_size, C].
- (Hp, Wp): padded height and width before partition
- """
- B, H, W, C = x.shape
-
- pad_h = (window_size - H % window_size) % window_size
- pad_w = (window_size - W % window_size) % window_size
- if pad_h > 0 or pad_w > 0:
- x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
- Hp, Wp = H + pad_h, W + pad_w
-
- x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows, (Hp, Wp)
-
-
-def window_unpartition(windows, window_size, pad_hw, hw):
- """
- Window unpartition into original sequences and removing padding.
- Args:
- x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
- window_size (int): window size.
- pad_hw (Tuple): padded height and width (Hp, Wp).
- hw (Tuple): original height and width (H, W) before padding.
-
- Returns:
- x: unpartitioned sequences with [B, H, W, C].
- """
- Hp, Wp = pad_hw
- H, W = hw
- B = windows.shape[0] // (Hp * Wp // window_size // window_size)
- x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
-
- if Hp > H or Wp > W:
- x = x[:, :H, :W, :].contiguous()
- return x
-
-
-def get_rel_pos(q_size, k_size, rel_pos):
- """
- Get relative positional embeddings according to the relative positions of
- query and key sizes.
- Args:
- q_size (int): size of query q.
- k_size (int): size of key k.
- rel_pos (Tensor): relative position embeddings (L, C).
-
- Returns:
- Extracted positional embeddings according to relative positions.
- """
- max_rel_dist = int(2 * max(q_size, k_size) - 1)
- # Interpolate rel pos if needed.
- if rel_pos.shape[0] != max_rel_dist:
- # Interpolate rel pos.
- rel_pos_resized = F.interpolate(
- rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
- size=max_rel_dist,
- mode="linear",
- )
- rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
- else:
- rel_pos_resized = rel_pos
-
- # Scale the coords with short length if shapes for q and k are different.
- q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
- k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
- relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
-
- return rel_pos_resized[relative_coords.long()]
-
-
-def add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size):
- """
- Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
- https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
- Args:
- attn (Tensor): attention map.
- q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
- rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
- rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
- q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
- k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
-
- Returns:
- attn (Tensor): attention map with added relative positional embeddings.
- """
- q_h, q_w = q_size
- k_h, k_w = k_size
- Rh = get_rel_pos(q_h, k_h, rel_pos_h)
- Rw = get_rel_pos(q_w, k_w, rel_pos_w)
-
- B, _, dim = q.shape
- r_q = q.reshape(B, q_h, q_w, dim)
- rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
- rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
-
- attn = (
- attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
- ).view(B, q_h * q_w, k_h * k_w)
-
- return attn
-
-
-def get_abs_pos(abs_pos, has_cls_token, hw):
- """
- Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token
- dimension for the original embeddings.
- Args:
- abs_pos (Tensor): absolute positional embeddings with (1, num_position, C).
- has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token.
- hw (Tuple): size of input image tokens.
-
- Returns:
- Absolute positional embeddings after processing with shape (1, H, W, C)
- """
- h, w = hw
- if has_cls_token:
- abs_pos = abs_pos[:, 1:]
- xy_num = abs_pos.shape[1]
- size = int(math.sqrt(xy_num))
- assert size * size == xy_num
-
- if size != h or size != w:
- new_abs_pos = F.interpolate(
- abs_pos.reshape(1, size, size, -1).permute(0, 3, 1, 2),
- size=(h, w),
- mode="bicubic",
- align_corners=False,
- )
-
- return new_abs_pos.permute(0, 2, 3, 1)
- else:
- return abs_pos.reshape(1, h, w, -1)
-
-
-class PatchEmbed(nn.Module):
- """
- Image to Patch Embedding.
- """
-
- def __init__(
- self, kernel_size=(16, 16), stride=(16, 16), padding=(0, 0), in_chans=3, embed_dim=768
- ):
- """
- Args:
- kernel_size (Tuple): kernel size of the projection layer.
- stride (Tuple): stride of the projection layer.
- padding (Tuple): padding size of the projection layer.
- in_chans (int): Number of input image channels.
- embed_dim (int): embed_dim (int): Patch embedding dimension.
- """
- super().__init__()
-
- self.proj = nn.Conv2d(
- in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
- )
-
- def forward(self, x):
- x = self.proj(x)
- # B C H W -> B H W C
- x = x.permute(0, 2, 3, 1)
- return x
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/quick_schedules/README.md b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/quick_schedules/README.md
deleted file mode 100644
index 4e6c82ef3f75a73c7006f33d7c850a0d4781a58f..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/quick_schedules/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-These are quick configs for performance or accuracy regression tracking purposes.
-
-* `*instance_test.yaml`: can train on 2 GPUs. They are used to test whether the training can
- successfully finish. They are not expected to produce reasonable training results.
-* `*inference_acc_test.yaml`: They should be run using `--eval-only`. They run inference using pre-trained models and verify
- the results are as expected.
-* `*training_acc_test.yaml`: They should be trained on 8 GPUs. They finish in about an hour and verify the training accuracy
- is within the normal range.
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin_meta.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin_meta.py
deleted file mode 100644
index 63c7a1a31b31dd89b82011effee26471faccacf5..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin_meta.py
+++ /dev/null
@@ -1,350 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-"""
-Note:
-For your custom dataset, there is no need to hard-code metadata anywhere in the code.
-For example, for COCO-format dataset, metadata will be obtained automatically
-when calling `load_coco_json`. For other dataset, metadata may also be obtained in other ways
-during loading.
-
-However, we hard-coded metadata for a few common dataset here.
-The only goal is to allow users who don't have these dataset to use pre-trained models.
-Users don't have to download a COCO json (which contains metadata), in order to visualize a
-COCO model (with correct class names and colors).
-"""
-
-
-# All coco categories, together with their nice-looking visualization colors
-# It's from https://github.com/cocodataset/panopticapi/blob/master/panoptic_coco_categories.json
-COCO_CATEGORIES = [
- {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"},
- {"color": [119, 11, 32], "isthing": 1, "id": 2, "name": "bicycle"},
- {"color": [0, 0, 142], "isthing": 1, "id": 3, "name": "car"},
- {"color": [0, 0, 230], "isthing": 1, "id": 4, "name": "motorcycle"},
- {"color": [106, 0, 228], "isthing": 1, "id": 5, "name": "airplane"},
- {"color": [0, 60, 100], "isthing": 1, "id": 6, "name": "bus"},
- {"color": [0, 80, 100], "isthing": 1, "id": 7, "name": "train"},
- {"color": [0, 0, 70], "isthing": 1, "id": 8, "name": "truck"},
- {"color": [0, 0, 192], "isthing": 1, "id": 9, "name": "boat"},
- {"color": [250, 170, 30], "isthing": 1, "id": 10, "name": "traffic light"},
- {"color": [100, 170, 30], "isthing": 1, "id": 11, "name": "fire hydrant"},
- {"color": [220, 220, 0], "isthing": 1, "id": 13, "name": "stop sign"},
- {"color": [175, 116, 175], "isthing": 1, "id": 14, "name": "parking meter"},
- {"color": [250, 0, 30], "isthing": 1, "id": 15, "name": "bench"},
- {"color": [165, 42, 42], "isthing": 1, "id": 16, "name": "bird"},
- {"color": [255, 77, 255], "isthing": 1, "id": 17, "name": "cat"},
- {"color": [0, 226, 252], "isthing": 1, "id": 18, "name": "dog"},
- {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"},
- {"color": [0, 82, 0], "isthing": 1, "id": 20, "name": "sheep"},
- {"color": [120, 166, 157], "isthing": 1, "id": 21, "name": "cow"},
- {"color": [110, 76, 0], "isthing": 1, "id": 22, "name": "elephant"},
- {"color": [174, 57, 255], "isthing": 1, "id": 23, "name": "bear"},
- {"color": [199, 100, 0], "isthing": 1, "id": 24, "name": "zebra"},
- {"color": [72, 0, 118], "isthing": 1, "id": 25, "name": "giraffe"},
- {"color": [255, 179, 240], "isthing": 1, "id": 27, "name": "backpack"},
- {"color": [0, 125, 92], "isthing": 1, "id": 28, "name": "umbrella"},
- {"color": [209, 0, 151], "isthing": 1, "id": 31, "name": "handbag"},
- {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "tie"},
- {"color": [0, 220, 176], "isthing": 1, "id": 33, "name": "suitcase"},
- {"color": [255, 99, 164], "isthing": 1, "id": 34, "name": "frisbee"},
- {"color": [92, 0, 73], "isthing": 1, "id": 35, "name": "skis"},
- {"color": [133, 129, 255], "isthing": 1, "id": 36, "name": "snowboard"},
- {"color": [78, 180, 255], "isthing": 1, "id": 37, "name": "sports ball"},
- {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "kite"},
- {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "baseball bat"},
- {"color": [45, 89, 255], "isthing": 1, "id": 40, "name": "baseball glove"},
- {"color": [134, 134, 103], "isthing": 1, "id": 41, "name": "skateboard"},
- {"color": [145, 148, 174], "isthing": 1, "id": 42, "name": "surfboard"},
- {"color": [255, 208, 186], "isthing": 1, "id": 43, "name": "tennis racket"},
- {"color": [197, 226, 255], "isthing": 1, "id": 44, "name": "bottle"},
- {"color": [171, 134, 1], "isthing": 1, "id": 46, "name": "wine glass"},
- {"color": [109, 63, 54], "isthing": 1, "id": 47, "name": "cup"},
- {"color": [207, 138, 255], "isthing": 1, "id": 48, "name": "fork"},
- {"color": [151, 0, 95], "isthing": 1, "id": 49, "name": "knife"},
- {"color": [9, 80, 61], "isthing": 1, "id": 50, "name": "spoon"},
- {"color": [84, 105, 51], "isthing": 1, "id": 51, "name": "bowl"},
- {"color": [74, 65, 105], "isthing": 1, "id": 52, "name": "banana"},
- {"color": [166, 196, 102], "isthing": 1, "id": 53, "name": "apple"},
- {"color": [208, 195, 210], "isthing": 1, "id": 54, "name": "sandwich"},
- {"color": [255, 109, 65], "isthing": 1, "id": 55, "name": "orange"},
- {"color": [0, 143, 149], "isthing": 1, "id": 56, "name": "broccoli"},
- {"color": [179, 0, 194], "isthing": 1, "id": 57, "name": "carrot"},
- {"color": [209, 99, 106], "isthing": 1, "id": 58, "name": "hot dog"},
- {"color": [5, 121, 0], "isthing": 1, "id": 59, "name": "pizza"},
- {"color": [227, 255, 205], "isthing": 1, "id": 60, "name": "donut"},
- {"color": [147, 186, 208], "isthing": 1, "id": 61, "name": "cake"},
- {"color": [153, 69, 1], "isthing": 1, "id": 62, "name": "chair"},
- {"color": [3, 95, 161], "isthing": 1, "id": 63, "name": "couch"},
- {"color": [163, 255, 0], "isthing": 1, "id": 64, "name": "potted plant"},
- {"color": [119, 0, 170], "isthing": 1, "id": 65, "name": "bed"},
- {"color": [0, 182, 199], "isthing": 1, "id": 67, "name": "dining table"},
- {"color": [0, 165, 120], "isthing": 1, "id": 70, "name": "toilet"},
- {"color": [183, 130, 88], "isthing": 1, "id": 72, "name": "tv"},
- {"color": [95, 32, 0], "isthing": 1, "id": 73, "name": "laptop"},
- {"color": [130, 114, 135], "isthing": 1, "id": 74, "name": "mouse"},
- {"color": [110, 129, 133], "isthing": 1, "id": 75, "name": "remote"},
- {"color": [166, 74, 118], "isthing": 1, "id": 76, "name": "keyboard"},
- {"color": [219, 142, 185], "isthing": 1, "id": 77, "name": "cell phone"},
- {"color": [79, 210, 114], "isthing": 1, "id": 78, "name": "microwave"},
- {"color": [178, 90, 62], "isthing": 1, "id": 79, "name": "oven"},
- {"color": [65, 70, 15], "isthing": 1, "id": 80, "name": "toaster"},
- {"color": [127, 167, 115], "isthing": 1, "id": 81, "name": "sink"},
- {"color": [59, 105, 106], "isthing": 1, "id": 82, "name": "refrigerator"},
- {"color": [142, 108, 45], "isthing": 1, "id": 84, "name": "book"},
- {"color": [196, 172, 0], "isthing": 1, "id": 85, "name": "clock"},
- {"color": [95, 54, 80], "isthing": 1, "id": 86, "name": "vase"},
- {"color": [128, 76, 255], "isthing": 1, "id": 87, "name": "scissors"},
- {"color": [201, 57, 1], "isthing": 1, "id": 88, "name": "teddy bear"},
- {"color": [246, 0, 122], "isthing": 1, "id": 89, "name": "hair drier"},
- {"color": [191, 162, 208], "isthing": 1, "id": 90, "name": "toothbrush"},
- {"color": [255, 255, 128], "isthing": 0, "id": 92, "name": "banner"},
- {"color": [147, 211, 203], "isthing": 0, "id": 93, "name": "blanket"},
- {"color": [150, 100, 100], "isthing": 0, "id": 95, "name": "bridge"},
- {"color": [168, 171, 172], "isthing": 0, "id": 100, "name": "cardboard"},
- {"color": [146, 112, 198], "isthing": 0, "id": 107, "name": "counter"},
- {"color": [210, 170, 100], "isthing": 0, "id": 109, "name": "curtain"},
- {"color": [92, 136, 89], "isthing": 0, "id": 112, "name": "door-stuff"},
- {"color": [218, 88, 184], "isthing": 0, "id": 118, "name": "floor-wood"},
- {"color": [241, 129, 0], "isthing": 0, "id": 119, "name": "flower"},
- {"color": [217, 17, 255], "isthing": 0, "id": 122, "name": "fruit"},
- {"color": [124, 74, 181], "isthing": 0, "id": 125, "name": "gravel"},
- {"color": [70, 70, 70], "isthing": 0, "id": 128, "name": "house"},
- {"color": [255, 228, 255], "isthing": 0, "id": 130, "name": "light"},
- {"color": [154, 208, 0], "isthing": 0, "id": 133, "name": "mirror-stuff"},
- {"color": [193, 0, 92], "isthing": 0, "id": 138, "name": "net"},
- {"color": [76, 91, 113], "isthing": 0, "id": 141, "name": "pillow"},
- {"color": [255, 180, 195], "isthing": 0, "id": 144, "name": "platform"},
- {"color": [106, 154, 176], "isthing": 0, "id": 145, "name": "playingfield"},
- {"color": [230, 150, 140], "isthing": 0, "id": 147, "name": "railroad"},
- {"color": [60, 143, 255], "isthing": 0, "id": 148, "name": "river"},
- {"color": [128, 64, 128], "isthing": 0, "id": 149, "name": "road"},
- {"color": [92, 82, 55], "isthing": 0, "id": 151, "name": "roof"},
- {"color": [254, 212, 124], "isthing": 0, "id": 154, "name": "sand"},
- {"color": [73, 77, 174], "isthing": 0, "id": 155, "name": "sea"},
- {"color": [255, 160, 98], "isthing": 0, "id": 156, "name": "shelf"},
- {"color": [255, 255, 255], "isthing": 0, "id": 159, "name": "snow"},
- {"color": [104, 84, 109], "isthing": 0, "id": 161, "name": "stairs"},
- {"color": [169, 164, 131], "isthing": 0, "id": 166, "name": "tent"},
- {"color": [225, 199, 255], "isthing": 0, "id": 168, "name": "towel"},
- {"color": [137, 54, 74], "isthing": 0, "id": 171, "name": "wall-brick"},
- {"color": [135, 158, 223], "isthing": 0, "id": 175, "name": "wall-stone"},
- {"color": [7, 246, 231], "isthing": 0, "id": 176, "name": "wall-tile"},
- {"color": [107, 255, 200], "isthing": 0, "id": 177, "name": "wall-wood"},
- {"color": [58, 41, 149], "isthing": 0, "id": 178, "name": "water-other"},
- {"color": [183, 121, 142], "isthing": 0, "id": 180, "name": "window-blind"},
- {"color": [255, 73, 97], "isthing": 0, "id": 181, "name": "window-other"},
- {"color": [107, 142, 35], "isthing": 0, "id": 184, "name": "tree-merged"},
- {"color": [190, 153, 153], "isthing": 0, "id": 185, "name": "fence-merged"},
- {"color": [146, 139, 141], "isthing": 0, "id": 186, "name": "ceiling-merged"},
- {"color": [70, 130, 180], "isthing": 0, "id": 187, "name": "sky-other-merged"},
- {"color": [134, 199, 156], "isthing": 0, "id": 188, "name": "cabinet-merged"},
- {"color": [209, 226, 140], "isthing": 0, "id": 189, "name": "table-merged"},
- {"color": [96, 36, 108], "isthing": 0, "id": 190, "name": "floor-other-merged"},
- {"color": [96, 96, 96], "isthing": 0, "id": 191, "name": "pavement-merged"},
- {"color": [64, 170, 64], "isthing": 0, "id": 192, "name": "mountain-merged"},
- {"color": [152, 251, 152], "isthing": 0, "id": 193, "name": "grass-merged"},
- {"color": [208, 229, 228], "isthing": 0, "id": 194, "name": "dirt-merged"},
- {"color": [206, 186, 171], "isthing": 0, "id": 195, "name": "paper-merged"},
- {"color": [152, 161, 64], "isthing": 0, "id": 196, "name": "food-other-merged"},
- {"color": [116, 112, 0], "isthing": 0, "id": 197, "name": "building-other-merged"},
- {"color": [0, 114, 143], "isthing": 0, "id": 198, "name": "rock-merged"},
- {"color": [102, 102, 156], "isthing": 0, "id": 199, "name": "wall-other-merged"},
- {"color": [250, 141, 255], "isthing": 0, "id": 200, "name": "rug-merged"},
-]
-
-# fmt: off
-COCO_PERSON_KEYPOINT_NAMES = (
- "nose",
- "left_eye", "right_eye",
- "left_ear", "right_ear",
- "left_shoulder", "right_shoulder",
- "left_elbow", "right_elbow",
- "left_wrist", "right_wrist",
- "left_hip", "right_hip",
- "left_knee", "right_knee",
- "left_ankle", "right_ankle",
-)
-# fmt: on
-
-# Pairs of keypoints that should be exchanged under horizontal flipping
-COCO_PERSON_KEYPOINT_FLIP_MAP = (
- ("left_eye", "right_eye"),
- ("left_ear", "right_ear"),
- ("left_shoulder", "right_shoulder"),
- ("left_elbow", "right_elbow"),
- ("left_wrist", "right_wrist"),
- ("left_hip", "right_hip"),
- ("left_knee", "right_knee"),
- ("left_ankle", "right_ankle"),
-)
-
-# rules for pairs of keypoints to draw a line between, and the line color to use.
-KEYPOINT_CONNECTION_RULES = [
- # face
- ("left_ear", "left_eye", (102, 204, 255)),
- ("right_ear", "right_eye", (51, 153, 255)),
- ("left_eye", "nose", (102, 0, 204)),
- ("nose", "right_eye", (51, 102, 255)),
- # upper-body
- ("left_shoulder", "right_shoulder", (255, 128, 0)),
- ("left_shoulder", "left_elbow", (153, 255, 204)),
- ("right_shoulder", "right_elbow", (128, 229, 255)),
- ("left_elbow", "left_wrist", (153, 255, 153)),
- ("right_elbow", "right_wrist", (102, 255, 224)),
- # lower-body
- ("left_hip", "right_hip", (255, 102, 0)),
- ("left_hip", "left_knee", (255, 255, 77)),
- ("right_hip", "right_knee", (153, 255, 204)),
- ("left_knee", "left_ankle", (191, 255, 128)),
- ("right_knee", "right_ankle", (255, 195, 77)),
-]
-
-# All Cityscapes categories, together with their nice-looking visualization colors
-# It's from https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py # noqa
-CITYSCAPES_CATEGORIES = [
- {"color": (128, 64, 128), "isthing": 0, "id": 7, "trainId": 0, "name": "road"},
- {"color": (244, 35, 232), "isthing": 0, "id": 8, "trainId": 1, "name": "sidewalk"},
- {"color": (70, 70, 70), "isthing": 0, "id": 11, "trainId": 2, "name": "building"},
- {"color": (102, 102, 156), "isthing": 0, "id": 12, "trainId": 3, "name": "wall"},
- {"color": (190, 153, 153), "isthing": 0, "id": 13, "trainId": 4, "name": "fence"},
- {"color": (153, 153, 153), "isthing": 0, "id": 17, "trainId": 5, "name": "pole"},
- {"color": (250, 170, 30), "isthing": 0, "id": 19, "trainId": 6, "name": "traffic light"},
- {"color": (220, 220, 0), "isthing": 0, "id": 20, "trainId": 7, "name": "traffic sign"},
- {"color": (107, 142, 35), "isthing": 0, "id": 21, "trainId": 8, "name": "vegetation"},
- {"color": (152, 251, 152), "isthing": 0, "id": 22, "trainId": 9, "name": "terrain"},
- {"color": (70, 130, 180), "isthing": 0, "id": 23, "trainId": 10, "name": "sky"},
- {"color": (220, 20, 60), "isthing": 1, "id": 24, "trainId": 11, "name": "person"},
- {"color": (255, 0, 0), "isthing": 1, "id": 25, "trainId": 12, "name": "rider"},
- {"color": (0, 0, 142), "isthing": 1, "id": 26, "trainId": 13, "name": "car"},
- {"color": (0, 0, 70), "isthing": 1, "id": 27, "trainId": 14, "name": "truck"},
- {"color": (0, 60, 100), "isthing": 1, "id": 28, "trainId": 15, "name": "bus"},
- {"color": (0, 80, 100), "isthing": 1, "id": 31, "trainId": 16, "name": "train"},
- {"color": (0, 0, 230), "isthing": 1, "id": 32, "trainId": 17, "name": "motorcycle"},
- {"color": (119, 11, 32), "isthing": 1, "id": 33, "trainId": 18, "name": "bicycle"},
-]
-
-# fmt: off
-ADE20K_SEM_SEG_CATEGORIES = [
- "wall", "building", "sky", "floor", "tree", "ceiling", "road, route", "bed", "window ", "grass", "cabinet", "sidewalk, pavement", "person", "earth, ground", "door", "table", "mountain, mount", "plant", "curtain", "chair", "car", "water", "painting, picture", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock, stone", "wardrobe, closet, press", "lamp", "tub", "rail", "cushion", "base, pedestal, stand", "box", "column, pillar", "signboard, sign", "chest of drawers, chest, bureau, dresser", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator, icebox", "grandstand, covered stand", "path", "stairs", "runway", "case, display case, showcase, vitrine", "pool table, billiard table, snooker table", "pillow", "screen door, screen", "stairway, staircase", "river", "bridge, span", "bookcase", "blind, screen", "coffee table", "toilet, can, commode, crapper, pot, potty, stool, throne", "flower", "book", "hill", "bench", "countertop", "stove", "palm, palm tree", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel, hut, hutch, shack, shanty", "bus", "towel", "light", "truck", "tower", "chandelier", "awning, sunshade, sunblind", "street lamp", "booth", "tv", "plane", "dirt track", "clothes", "pole", "land, ground, soil", "bannister, banister, balustrade, balusters, handrail", "escalator, moving staircase, moving stairway", "ottoman, pouf, pouffe, puff, hassock", "bottle", "buffet, counter, sideboard", "poster, posting, placard, notice, bill, card", "stage", "van", "ship", "fountain", "conveyer belt, conveyor belt, conveyer, conveyor, transporter", "canopy", "washer, automatic washer, washing machine", "plaything, toy", "pool", "stool", "barrel, cask", "basket, handbasket", "falls", "tent", "bag", "minibike, motorbike", "cradle", "oven", "ball", "food, solid food", "step, stair", "tank, storage tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket, cover", "sculpture", "hood, exhaust hood", "sconce", "vase", "traffic light", "tray", "trash can", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass, drinking glass", "clock", "flag", # noqa
-]
-# After processed by `prepare_ade20k_sem_seg.py`, id 255 means ignore
-# fmt: on
-
-
-def _get_coco_instances_meta():
- thing_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 1]
- thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1]
- assert len(thing_ids) == 80, len(thing_ids)
- # Mapping from the incontiguous COCO category id to an id in [0, 79]
- thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)}
- thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1]
- ret = {
- "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id,
- "thing_classes": thing_classes,
- "thing_colors": thing_colors,
- }
- return ret
-
-
-def _get_coco_panoptic_separated_meta():
- """
- Returns metadata for "separated" version of the panoptic segmentation dataset.
- """
- stuff_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 0]
- assert len(stuff_ids) == 53, len(stuff_ids)
-
- # For semantic segmentation, this mapping maps from contiguous stuff id
- # (in [0, 53], used in models) to ids in the dataset (used for processing results)
- # The id 0 is mapped to an extra category "thing".
- stuff_dataset_id_to_contiguous_id = {k: i + 1 for i, k in enumerate(stuff_ids)}
- # When converting COCO panoptic annotations to semantic annotations
- # We label the "thing" category to 0
- stuff_dataset_id_to_contiguous_id[0] = 0
-
- # 54 names for COCO stuff categories (including "things")
- stuff_classes = ["things"] + [
- k["name"].replace("-other", "").replace("-merged", "")
- for k in COCO_CATEGORIES
- if k["isthing"] == 0
- ]
-
- # NOTE: I randomly picked a color for things
- stuff_colors = [[82, 18, 128]] + [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 0]
- ret = {
- "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id,
- "stuff_classes": stuff_classes,
- "stuff_colors": stuff_colors,
- }
- ret.update(_get_coco_instances_meta())
- return ret
-
-
-def _get_builtin_metadata(dataset_name):
- if dataset_name == "coco":
- return _get_coco_instances_meta()
- if dataset_name == "coco_panoptic_separated":
- return _get_coco_panoptic_separated_meta()
- elif dataset_name == "coco_panoptic_standard":
- meta = {}
- # The following metadata maps contiguous id from [0, #thing categories +
- # #stuff categories) to their names and colors. We have to replica of the
- # same name and color under "thing_*" and "stuff_*" because the current
- # visualization function in D2 handles thing and class classes differently
- # due to some heuristic used in Panoptic FPN. We keep the same naming to
- # enable reusing existing visualization functions.
- thing_classes = [k["name"] for k in COCO_CATEGORIES]
- thing_colors = [k["color"] for k in COCO_CATEGORIES]
- stuff_classes = [k["name"] for k in COCO_CATEGORIES]
- stuff_colors = [k["color"] for k in COCO_CATEGORIES]
-
- meta["thing_classes"] = thing_classes
- meta["thing_colors"] = thing_colors
- meta["stuff_classes"] = stuff_classes
- meta["stuff_colors"] = stuff_colors
-
- # Convert category id for training:
- # category id: like semantic segmentation, it is the class id for each
- # pixel. Since there are some classes not used in evaluation, the category
- # id is not always contiguous and thus we have two set of category ids:
- # - original category id: category id in the original dataset, mainly
- # used for evaluation.
- # - contiguous category id: [0, #classes), in order to train the linear
- # softmax classifier.
- thing_dataset_id_to_contiguous_id = {}
- stuff_dataset_id_to_contiguous_id = {}
-
- for i, cat in enumerate(COCO_CATEGORIES):
- if cat["isthing"]:
- thing_dataset_id_to_contiguous_id[cat["id"]] = i
- else:
- stuff_dataset_id_to_contiguous_id[cat["id"]] = i
-
- meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
- meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
-
- return meta
- elif dataset_name == "coco_person":
- return {
- "thing_classes": ["person"],
- "keypoint_names": COCO_PERSON_KEYPOINT_NAMES,
- "keypoint_flip_map": COCO_PERSON_KEYPOINT_FLIP_MAP,
- "keypoint_connection_rules": KEYPOINT_CONNECTION_RULES,
- }
- elif dataset_name == "cityscapes":
- # fmt: off
- CITYSCAPES_THING_CLASSES = [
- "person", "rider", "car", "truck",
- "bus", "train", "motorcycle", "bicycle",
- ]
- CITYSCAPES_STUFF_CLASSES = [
- "road", "sidewalk", "building", "wall", "fence", "pole", "traffic light",
- "traffic sign", "vegetation", "terrain", "sky", "person", "rider", "car",
- "truck", "bus", "train", "motorcycle", "bicycle",
- ]
- # fmt: on
- return {
- "thing_classes": CITYSCAPES_THING_CLASSES,
- "stuff_classes": CITYSCAPES_STUFF_CLASSES,
- }
- raise KeyError("No built-in metadata for dataset {}".format(dataset_name))
diff --git a/spaces/Theivaprakasham/layoutlmv2_invoice/README.md b/spaces/Theivaprakasham/layoutlmv2_invoice/README.md
deleted file mode 100644
index 500f655abe8bb1516d4606fc376d02c498bf1a22..0000000000000000000000000000000000000000
--- a/spaces/Theivaprakasham/layoutlmv2_invoice/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Invoice Information Extractor
-emoji: ⚡
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/VVallabh/AI-Powered-Subtitle-Generator/app.py b/spaces/VVallabh/AI-Powered-Subtitle-Generator/app.py
deleted file mode 100644
index 5bbc3926f225e6a20cae44114bb370db232171f6..0000000000000000000000000000000000000000
--- a/spaces/VVallabh/AI-Powered-Subtitle-Generator/app.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import gradio as gr
-import subprocess
-import os
-
-def videoxsub(vid):
- getAudio(vid)
- getSubs()
- return [vid, "audio.vtt"]
-
-def getAudio(vid):
- if os.path.exists("audio.mp3"):
- os.remove("audio.mp3")
- commands_list = [ "ffmpeg", "-i", vid, "audio.mp3" ]
- subprocess.run(commands_list)
-
-def getSubs():
- command_list = [ "whisper", "audio.mp3", "-f", "vtt", "--fp16", "False"]
- subprocess.run(command_list)
-
-demo = gr.Interface(fn=videoxsub, inputs="video", outputs="video")
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/Vegecken/sovits4dzl/modules/modules.py b/spaces/Vegecken/sovits4dzl/modules/modules.py
deleted file mode 100644
index 54290fd207b25e93831bd21005990ea137e6b50e..0000000000000000000000000000000000000000
--- a/spaces/Vegecken/sovits4dzl/modules/modules.py
+++ /dev/null
@@ -1,342 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import modules.commons as commons
-from modules.commons import init_weights, get_padding
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(
- nn.ReLU(),
- nn.Dropout(p_dropout))
- for _ in range(n_layers-1):
- self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size ** i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
- groups=channels, dilation=dilation, padding=padding
- ))
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- self.hidden_channels =hidden_channels
- self.kernel_size = kernel_size,
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = dilation_rate ** i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
- dilation=dilation, padding=padding)
- in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(
- x_in,
- g_l,
- n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:,:self.hidden_channels,:]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:,self.hidden_channels:,:]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels,1))
- self.logs = nn.Parameter(torch.zeros(channels,1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1,2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
diff --git a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/H2o.py b/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/H2o.py
deleted file mode 100644
index eabf94e2dc1e6167f746a820e34c335f2aa8578e..0000000000000000000000000000000000000000
--- a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/H2o.py
+++ /dev/null
@@ -1,106 +0,0 @@
-from requests import Session
-from uuid import uuid4
-from json import loads
-import os
-import json
-import requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://gpt-gm.h2o.ai'
-model = ['falcon-40b', 'falcon-7b', 'llama-13b']
-supports_stream = True
-needs_auth = False
-
-models = {
- 'falcon-7b': 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3',
- 'falcon-40b': 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1',
- 'llama-13b': 'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-13b'
-}
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- conversation = 'instruction: this is a conversation beween, a user and an AI assistant, respond to the latest message, referring to the conversation if needed\n'
- for message in messages:
- conversation += '%s: %s\n' % (message['role'], message['content'])
- conversation += 'assistant:'
-
- client = Session()
- client.headers = {
- 'authority': 'gpt-gm.h2o.ai',
- 'origin': 'https://gpt-gm.h2o.ai',
- 'referer': 'https://gpt-gm.h2o.ai/',
- 'sec-ch-ua': '"Not.A/Brand";v="8", "Chromium";v="114", "Google Chrome";v="114"',
- 'sec-ch-ua-mobile': '?0',
- 'sec-ch-ua-platform': '"Windows"',
- 'sec-fetch-dest': 'document',
- 'sec-fetch-mode': 'navigate',
- 'sec-fetch-site': 'same-origin',
- 'sec-fetch-user': '?1',
- 'upgrade-insecure-requests': '1',
- 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',
- }
-
- client.get('https://gpt-gm.h2o.ai/')
- response = client.post('https://gpt-gm.h2o.ai/settings', data={
- 'ethicsModalAccepted': 'true',
- 'shareConversationsWithModelAuthors': 'true',
- 'ethicsModalAcceptedAt': '',
- 'activeModel': 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1',
- 'searchEnabled': 'true',
- })
-
- headers = {
- 'authority': 'gpt-gm.h2o.ai',
- 'accept': '*/*',
- 'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
- 'origin': 'https://gpt-gm.h2o.ai',
- 'referer': 'https://gpt-gm.h2o.ai/',
- 'sec-ch-ua': '"Not.A/Brand";v="8", "Chromium";v="114", "Google Chrome";v="114"',
- 'sec-ch-ua-mobile': '?0',
- 'sec-ch-ua-platform': '"Windows"',
- 'sec-fetch-dest': 'empty',
- 'sec-fetch-mode': 'cors',
- 'sec-fetch-site': 'same-origin',
- 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',
- }
-
- json_data = {
- 'model': models[model]
- }
-
- response = client.post('https://gpt-gm.h2o.ai/conversation',
- headers=headers, json=json_data)
- conversationId = response.json()['conversationId']
-
-
- completion = client.post(f'https://gpt-gm.h2o.ai/conversation/{conversationId}', stream=True, json = {
- 'inputs': conversation,
- 'parameters': {
- 'temperature': kwargs.get('temperature', 0.4),
- 'truncate': kwargs.get('truncate', 2048),
- 'max_new_tokens': kwargs.get('max_new_tokens', 1024),
- 'do_sample': kwargs.get('do_sample', True),
- 'repetition_penalty': kwargs.get('repetition_penalty', 1.2),
- 'return_full_text': kwargs.get('return_full_text', False)
- },
- 'stream': True,
- 'options': {
- 'id': kwargs.get('id', str(uuid4())),
- 'response_id': kwargs.get('response_id', str(uuid4())),
- 'is_retry': False,
- 'use_cache': False,
- 'web_search_id': ''
- }
- })
-
- for line in completion.iter_lines():
- if b'data' in line:
- line = loads(line.decode('utf-8').replace('data:', ''))
- token = line['token']['text']
-
- if token == '<|endoftext|>':
- break
- else:
- yield (token)
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/Wootang01/text_generator_two/README.md b/spaces/Wootang01/text_generator_two/README.md
deleted file mode 100644
index f1485cccb00bc76b60c5a14e558dbe8c4305916e..0000000000000000000000000000000000000000
--- a/spaces/Wootang01/text_generator_two/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Text_generator_two
-emoji: 🐠
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Xule/ChuanhuChatGPT/README.md b/spaces/Xule/ChuanhuChatGPT/README.md
deleted file mode 100644
index 7128e29689e35d059c9cc0a5050910fbd34873cd..0000000000000000000000000000000000000000
--- a/spaces/Xule/ChuanhuChatGPT/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChuanhuChatGPT
-emoji: 🐯
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.25.0
-app_file: ChuanhuChatbot.py
-pinned: false
-license: gpl-3.0
-duplicated_from: JohnSmith9982/ChuanhuChatGPT
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/XzJosh/Aatrox-Bert-VITS2/utils.py b/spaces/XzJosh/Aatrox-Bert-VITS2/utils.py
deleted file mode 100644
index c6aa6cfc64c33e2eed33e9845239e831fc1c4a1a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Aatrox-Bert-VITS2/utils.py
+++ /dev/null
@@ -1,293 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- elif optimizer is None and not skip_optimizer:
- #else: #Disable this line if Infer ,and enable the line upper
- new_opt_dict = optimizer.state_dict()
- new_opt_dict_params = new_opt_dict['param_groups'][0]['params']
- new_opt_dict['param_groups'] = checkpoint_dict['optimizer']['param_groups']
- new_opt_dict['param_groups'][0]['params'] = new_opt_dict_params
- optimizer.load_state_dict(new_opt_dict)
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- #assert "emb_g" not in k
- # print("load", k)
- new_state_dict[k] = saved_state_dict[k]
- assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
- except:
- print("error, %s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict, strict=False)
- else:
- model.load_state_dict(new_state_dict, strict=False)
- print("load ")
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info("Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path))
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save({'model': state_dict,
- 'iteration': iteration,
- 'optimizer': optimizer.state_dict(),
- 'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats='HWC')
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- print(x)
- return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, default="./OUTPUT_MODEL",
- help='Model name')
- parser.add_argument('--cont', dest='cont', action="store_true", default=False, help="whether to continue training on the latest checkpoint")
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- hparams.cont = args.cont
- return hparams
-
-
-def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True):
- """Freeing up space by deleting saved ckpts
-
- Arguments:
- path_to_models -- Path to the model directory
- n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
- sort_by_time -- True -> chronologically delete ckpts
- False -> lexicographically delete ckpts
- """
- import re
- ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
- name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
- time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
- sort_key = time_key if sort_by_time else name_key
- x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')],
- key=sort_key)
- to_del = [os.path.join(path_to_models, fn) for fn in
- (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
- del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
- del_routine = lambda x: [os.remove(x), del_info(x)]
- rs = [del_routine(fn) for fn in to_del]
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/XzJosh/Taffy-Bert-VITS2/app.py b/spaces/XzJosh/Taffy-Bert-VITS2/app.py
deleted file mode 100644
index d8e730e74fa69afbcfb2302c6eb612768f6004e3..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Taffy-Bert-VITS2/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import sys, os
-
-if sys.platform == "darwin":
- os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-
-import logging
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s")
-
-logger = logging.getLogger(__name__)
-
-import torch
-import argparse
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-import gradio as gr
-import webbrowser
-
-
-net_g = None
-
-
-def get_text(text, language_str, hps):
- norm_text, phone, tone, word2ph = clean_text(text, language_str)
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
- if hps.data.add_blank:
- phone = commons.intersperse(phone, 0)
- tone = commons.intersperse(tone, 0)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
- bert = get_bert(norm_text, word2ph, language_str)
- del word2ph
-
- assert bert.shape[-1] == len(phone)
-
- phone = torch.LongTensor(phone)
- tone = torch.LongTensor(tone)
- language = torch.LongTensor(language)
-
- return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
- global net_g
- bert, phones, tones, lang_ids = get_text(text, "ZH", hps)
- with torch.no_grad():
- x_tst=phones.to(device).unsqueeze(0)
- tones=tones.to(device).unsqueeze(0)
- lang_ids=lang_ids.to(device).unsqueeze(0)
- bert = bert.to(device).unsqueeze(0)
- x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
- del phones
- speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
- audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio
- , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
- del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
- return audio
-
-def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):
- with torch.no_grad():
- audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker)
- return "Success", (hps.data.sampling_rate, audio)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--model_dir", default="./logs/Taffy/G_15800.pth", help="path of your model")
- parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file")
- parser.add_argument("--share", default=False, help="make link public")
- parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log")
-
- args = parser.parse_args()
- if args.debug:
- logger.info("Enable DEBUG-LEVEL log")
- logging.basicConfig(level=logging.DEBUG)
- hps = utils.get_hparams_from_file(args.config_dir)
- device = "cuda:0" if torch.cuda.is_available() else "cpu"
- '''
- device = (
- "cuda:0"
- if torch.cuda.is_available()
- else (
- "mps"
- if sys.platform == "darwin" and torch.backends.mps.is_available()
- else "cpu"
- )
- )
- '''
- net_g = SynthesizerTrn(
- len(symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model).to(device)
- _ = net_g.eval()
-
- _ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True)
-
- speaker_ids = hps.data.spk2id
- speakers = list(speaker_ids.keys())
- with gr.Blocks() as app:
- with gr.Row():
- with gr.Column():
- gr.Markdown(value="""
- 【AI塔菲】在线语音合成（Bert-Vits2）\n
- 作者：Xz乔希 https://space.bilibili.com/5859321\n
- 声音归属：永雏塔菲 https://space.bilibili.com/1265680561\n
- Bert-VITS2项目：https://github.com/Stardust-minus/Bert-VITS2\n
- 【AI小菲】https://huggingface.co/spaces/XzJosh/LittleTaffy-Bert-VITS2\n
- 【AI东雪莲】https://huggingface.co/spaces/XzJosh/Azuma-Bert-VITS2\n
- 使用本模型请严格遵守法律法规！\n
- 发布二创作品请遵守永雏塔菲二创守则规范！并标注本项目作者及链接喵~\n
- """)
- text = gr.TextArea(label="Text", placeholder="Input Text Here",
- value="关注永雏塔菲喵，关注永雏塔菲谢谢喵！")
- speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker')
- sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.1, label='SDP/DP混合比')
- noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.6, step=0.1, label='感情调节')
- noise_scale_w = gr.Slider(minimum=0.1, maximum=1.4, value=0.8, step=0.1, label='音素长度')
- length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.1, label='生成长度')
- btn = gr.Button("生成喵！", variant="primary")
- with gr.Column():
- text_output = gr.Textbox(label="Message")
- audio_output = gr.Audio(label="Output Audio")
-
- btn.click(tts_fn,
- inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale],
- outputs=[text_output, audio_output])
-
-# webbrowser.open("http://127.0.0.1:6006")
-# app.launch(server_port=6006, show_error=True)
-
- app.launch(show_error=True)
diff --git a/spaces/YenLai/Superhuman/app.py b/spaces/YenLai/Superhuman/app.py
deleted file mode 100644
index 0ded7b405639d5da56daa1bda291fb49f645aedc..0000000000000000000000000000000000000000
--- a/spaces/YenLai/Superhuman/app.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import time
-
-from theme_dropdown import create_theme_dropdown # noqa: F401
-
-import gradio as gr
-
-dropdown, js = create_theme_dropdown()
-
-with gr.Blocks(theme='YenLai/Superhuman') as demo:
- with gr.Row().style(equal_height=True):
- with gr.Column(scale=10):
- gr.Markdown(
- """
- # Theme preview: `Superhuman`
- To use this theme, set `theme='YenLai/Superhuman'` in `gr.Blocks()` or `gr.Interface()`.
- You can append an `@` and a semantic version expression, e.g. @>=1.0.0,<2.0.0 to pin to a given version
- of this theme.
- """
- )
- with gr.Column(scale=3):
- with gr.Box():
- dropdown.render()
- toggle_dark = gr.Button(value="Toggle Dark").style(full_width=True)
-
- dropdown.change(None, dropdown, None, _js=js)
- toggle_dark.click(
- None,
- _js="""
- () => {
- document.body.classList.toggle('dark');
- document.querySelector('gradio-app').style.backgroundColor = 'var(--color-background-primary)'
- }
- """,
- )
-
- name = gr.Textbox(
- label="Name",
- info="Full name, including middle name. No special characters.",
- placeholder="John Doe",
- value="John Doe",
- interactive=True,
- )
-
- with gr.Row():
- slider1 = gr.Slider(label="Slider 1")
- slider2 = gr.Slider(label="Slider 2")
- gr.CheckboxGroup(["A", "B", "C"], label="Checkbox Group")
-
- with gr.Row():
- with gr.Column(variant="panel", scale=1):
- gr.Markdown("## Panel 1")
- radio = gr.Radio(
- ["A", "B", "C"],
- label="Radio",
- info="Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.",
- )
- drop = gr.Dropdown(["Option 1", "Option 2", "Option 3"], show_label=False)
- drop_2 = gr.Dropdown(
- ["Option A", "Option B", "Option C"],
- multiselect=True,
- value=["Option A"],
- label="Dropdown",
- interactive=True,
- )
- check = gr.Checkbox(label="Go")
- with gr.Column(variant="panel", scale=2):
- img = gr.Image(
- "https://gradio.app/assets/img/header-image.jpg", label="Image"
- ).style(height=320)
- with gr.Row():
- go_btn = gr.Button("Go", label="Primary Button", variant="primary")
- clear_btn = gr.Button(
- "Clear", label="Secondary Button", variant="secondary"
- )
-
- def go(*args):
- time.sleep(3)
- return "https://gradio.app/assets/img/header-image.jpg"
-
- go_btn.click(go, [radio, drop, drop_2, check, name], img, api_name="go")
-
- def clear():
- time.sleep(0.2)
- return None
-
- clear_btn.click(clear, None, img)
-
- with gr.Row():
- btn1 = gr.Button("Button 1").style(size="sm")
- btn2 = gr.UploadButton().style(size="sm")
- stop_btn = gr.Button("Stop", label="Stop Button", variant="stop").style(
- size="sm"
- )
-
- with gr.Row():
- gr.Dataframe(value=[[1, 2, 3], [4, 5, 6], [7, 8, 9]], label="Dataframe")
- gr.JSON(
- value={"a": 1, "b": 2, "c": {"test": "a", "test2": [1, 2, 3]}}, label="JSON"
- )
- gr.Label(value={"cat": 0.7, "dog": 0.2, "fish": 0.1})
- gr.File()
- with gr.Row():
- gr.ColorPicker()
- gr.Video("https://gradio-static-files.s3.us-west-2.amazonaws.com/world.mp4")
- gr.Gallery(
- [
- (
- "https://gradio-static-files.s3.us-west-2.amazonaws.com/lion.jpg",
- "lion",
- ),
- (
- "https://gradio-static-files.s3.us-west-2.amazonaws.com/logo.png",
- "logo",
- ),
- (
- "https://gradio-static-files.s3.us-west-2.amazonaws.com/tower.jpg",
- "tower",
- ),
- ]
- ).style(height="200px", grid=2)
-
- with gr.Row():
- with gr.Column(scale=2):
- chatbot = gr.Chatbot([("Hello", "Hi")], label="Chatbot")
- chat_btn = gr.Button("Add messages")
-
- def chat(history):
- time.sleep(2)
- yield [["How are you?", "I am good."]]
-
- chat_btn.click(
- lambda history: history
- + [["How are you?", "I am good."]]
- + (time.sleep(2) or []),
- chatbot,
- chatbot,
- )
- with gr.Column(scale=1):
- with gr.Accordion("Advanced Settings"):
- gr.Markdown("Hello")
- gr.Number(label="Chatbot control 1")
- gr.Number(label="Chatbot control 2")
- gr.Number(label="Chatbot control 3")
-
-
-if __name__ == "__main__":
- demo.queue().launch()
diff --git a/spaces/Yntec/Dreamlike-Webui-CPU/app.py b/spaces/Yntec/Dreamlike-Webui-CPU/app.py
deleted file mode 100644
index a76653398fc4c86d7f91acc7f6221720ee99c2c4..0000000000000000000000000000000000000000
--- a/spaces/Yntec/Dreamlike-Webui-CPU/app.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import os
-from sys import executable as pyexecutable
-import subprocess
-import pathlib
-import gc
-
-def Gitclone(URI:str,ClonePath:str = "") -> int :
- if(ClonePath == "") :
- while True:
- i=subprocess.run([r"git",r"clone",URI])
- if(i.returncode == 0 ):
- del i
- gc.collect()
- return 0
- else :
- del i
- else:
- while True:
- i=subprocess.run([r"git",r"clone",URI,ClonePath])
- if(i.returncode == 0 ):
- del i
- gc.collect()
- return 0
- else :
- del i
-def DownLoad(URI:str,DownloadPath:str,DownLoadFileName:str ) -> int:
- while (True):
- i=subprocess.run([r"aria2c",r"-c",r"-x" ,r"16", r"-s",r"16", r"-k" ,r"1M" ,r"-m",r"0",r"--enable-mmap=false",r"--console-log-level=error",r"-d",DownloadPath,r"-o",DownLoadFileName,URI]);
- if(i.returncode == 0 ):
- del i
- gc.collect()
- return 0
- else :
- del i
-user_home =pathlib.Path.home().resolve()
-os.chdir(str(user_home))
-#clone stable-diffusion-webui repo
-print("cloning stable-diffusion-webui repo")
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui.git",str(user_home / r"stable-diffusion-webui"))
-os.chdir(str(user_home / r"stable-diffusion-webui"))
-os.system("git reset --hard 89f9faa63388756314e8a1d96cf86bf5e0663045")
-#
-
-#install extensions
-print("installing extensions")
-Gitclone(r"https://huggingface.co/embed/negative",str(user_home / r"stable-diffusion-webui" / r"embeddings" / r"negative"))
-Gitclone(r"https://huggingface.co/embed/lora",str(user_home / r"stable-diffusion-webui" / r"models" / r"Lora" / r"positive"))
-DownLoad(r"https://huggingface.co/embed/upscale/resolve/main/4x-UltraSharp.pth",str(user_home / r"stable-diffusion-webui" / r"models" / r"ESRGAN") ,r"4x-UltraSharp.pth")
-while True:
- if(subprocess.run([r"wget",r"https://raw.githubusercontent.com/camenduru/stable-diffusion-webui-scripts/main/run_n_times.py",r"-O",str(user_home / r"stable-diffusion-webui" / r"scripts" / r"run_n_times.py")]).returncode == 0):
- break
-Gitclone(r"https://github.com/deforum-art/deforum-for-automatic1111-webui",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"deforum-for-automatic1111-webui" ))
-#Gitclone(r"https://github.com/AlUlkesh/stable-diffusion-webui-images-browser",str(user_home / r"stable-diffusion-webui" / r"extensions"/ r"stable-diffusion-webui-images-browser"))
-Gitclone(r"https://github.com/camenduru/stable-diffusion-webui-huggingface",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-huggingface"))
-Gitclone(r"https://github.com/camenduru/sd-civitai-browser",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-civitai-browser"))
-Gitclone(r"https://github.com/kohya-ss/sd-webui-additional-networks",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks"))
-Gitclone(r"https://github.com/Mikubill/sd-webui-controlnet",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-controlnet"))
-Gitclone(r"https://github.com/fkunn1326/openpose-editor",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"openpose-editor"))
-Gitclone(r"https://github.com/jexom/sd-webui-depth-lib",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-depth-lib"))
-Gitclone(r"https://github.com/hnmr293/posex",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"posex"))
-Gitclone(r"https://github.com/nonnonstop/sd-webui-3d-open-pose-editor",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-3d-open-pose-editor"))
-#中文本地化的请解除下一行的注释
-#Gitclone(r"https://github.com/dtlnor/stable-diffusion-webui-localization-zh_CN.git",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-localization-zh_CN"))
-Gitclone(r"https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git" , str(user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-tagcomplete"))
-Gitclone(r"https://github.com/camenduru/sd-webui-tunnels",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-tunnels"))
-Gitclone(r"https://github.com/etherealxx/batchlinks-webui",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"batchlinks-webui"))
-Gitclone(r"https://github.com/catppuccin/stable-diffusion-webui",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-catppuccin"))
-
-#Gitclone(r"https://github.com/KohakuBueleaf/a1111-sd-webui-locon",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-locon" ))
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui-rembg",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-rembg"))
-Gitclone(r"https://github.com/ashen-sensored/stable-diffusion-webui-two-shot",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-two-shot"))
-Gitclone(r"https://github.com/camenduru/sd_webui_stealth_pnginfo",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd_webui_stealth_pnginfo"))
-
-os.chdir(user_home / r"stable-diffusion-webui")
-
-#download ControlNet models
-print("extensions dolwnload done .\ndownloading ControlNet models")
-dList =[r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_scribble_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_seg_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_ip2p_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_shuffle_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_canny_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1p_sd15_depth_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_inpaint_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_lineart_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_mlsd_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_normalbae_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_openpose_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_scribble_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_seg_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_softedge_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15s2_lineart_anime_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1e_sd15_tile_fp16.yaml",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_style_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_seg_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_openpose_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_keypose_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd14v1.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd15v2.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd15v2.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd15v2.pth",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_zoedepth_sd15v1.pth"]
-for i in range(0,len(dList)): DownLoad(dList[i],str(user_home / "stable-diffusion-webui" / "extensions" / "sd-webui-controlnet" / "models"),pathlib.Path(dList[i]).name)
-del dList
-
-#download model
-#you can change model download address here
-print("ControlNet models download done.\ndownloading model")
-DownLoad(r"https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/dreamlike-photoreal-2.0.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"dreamlike-photoreal-2.0.safetensors")
-DownLoad(r"https://huggingface.co/dreamlike-art/dreamlike-anime-1.0/resolve/main/dreamlike-anime-1.0.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"dreamlike-anime-1.0.safetensors")
-DownLoad(r"https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0/resolve/main/dreamlike-diffusion-1.0.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"dreamlike-diffusion-1.0.safetensors")
-DownLoad(r"https://huggingface.co/dreamlike-art/dreamlike-photoreal-1.0/resolve/main/dreamlike-photoreal-1.0.ckpt",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"dreamlike-photoreal-1.0.ckpt")
-DownLoad(r"https://huggingface.co/Yntec/Photosphere/resolve/main/photosphere.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"photosphere.safetensors")
-DownLoad(r"https://huggingface.co/Yntec/Dreamlike/resolve/main/Dreamlike.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"!Dreamlike.safetensors")
-DownLoad(r"https://huggingface.co/Yntec/DreamLikeRemix/resolve/main/dreamLikeRemix.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"dreamLikeRemix.safetensors")
-DownLoad(r"https://huggingface.co/Yntec/dreamlike-photoreal-remix/resolve/main/dreamlike-photoreal-remix.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"dreamlike-photoreal-remix.safetensors")
-DownLoad(r"https://huggingface.co/Yntec/Dreamsphere/resolve/main/dreamsphere.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"dreamsphere.safetensors")
-
-#DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.5-pruned.ckpt",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"anything-v4.5-pruned.ckpt")
-#DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.0.vae.pt",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"anything-v4.0.vae.pt")
-#DownLoad(r"https://huggingface.co/gsdf/Counterfeit-V3.0/resolve/main/Counterfeit-V3.0_fp16.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"Counterfeit-V3.0_fp16.safetensors")
-#DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1B_orangemixs.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"AOM3A1B_orangemixs.safetensors")
-#DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"orangemix.vae.pt")
-#DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Baked%20VAE.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"MeinaPastelV5_BakedVAE.safetensors")
-#DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Without%20VAE.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"MeinaPastelV5_WithoutVAE.safetensors")
-#DownLoad(r"https://civitai.com/api/download/models/9474",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"chilloutmix_NiPrunedFp16.safetensors")
-
-DownLoad(r"https://civitai.com/api/download/models/39885",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"Better_light.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/21065",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"LAS.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/39164",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"backlighting.safetensors")
-#strt webui
-
-print("Done\nStarting Webui...")
-os.chdir(user_home / r"stable-diffusion-webui")
-while True:
- ret=subprocess.run([r"python3" ,r"launch.py",r"--precision",r"full",r"--no-half",r"--no-half-vae",r"--enable-insecure-extension-access",r"--medvram",r"--skip-torch-cuda-test",r"--enable-console-prompts",r"--ui-settings-file="+str(pathlib.Path(__file__).parent /r"config.json")])
- if(ret.returncode == 0 ):
- del ret
- gc.collect()
- else :
- del ret
-
-del os ,user_home ,pyexecutable ,subprocess
\ No newline at end of file
diff --git a/spaces/Zaixi/ICLR_FLAG/models/common.py b/spaces/Zaixi/ICLR_FLAG/models/common.py
deleted file mode 100644
index 1c78e0525a71e2f57725b45ffb9812a6baf13aa1..0000000000000000000000000000000000000000
--- a/spaces/Zaixi/ICLR_FLAG/models/common.py
+++ /dev/null
@@ -1,282 +0,0 @@
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn.modules.loss import _WeightedLoss
-from torch_scatter import scatter_mean, scatter_add
-
-
-def split_tensor_by_batch(x, batch, num_graphs=None):
- """
- Args:
- x: (N, ...)
- batch: (B, )
- Returns:
- [(N_1, ), (N_2, ) ..., (N_B, ))]
- """
- if num_graphs is None:
- num_graphs = batch.max().item() + 1
- x_split = []
- for i in range (num_graphs):
- mask = batch == i
- x_split.append(x[mask])
- return x_split
-
-
-def concat_tensors_to_batch(x_split):
- x = torch.cat(x_split, dim=0)
- batch = torch.repeat_interleave(
- torch.arange(len(x_split)),
- repeats=torch.LongTensor([s.size(0) for s in x_split])
- ).to(device=x.device)
- return x, batch
-
-
-def split_tensor_to_segments(x, segsize):
- num_segs = math.ceil(x.size(0) / segsize)
- segs = []
- for i in range(num_segs):
- segs.append(x[i*segsize : (i+1)*segsize])
- return segs
-
-
-def split_tensor_by_lengths(x, lengths):
- segs = []
- for l in lengths:
- segs.append(x[:l])
- x = x[l:]
- return segs
-
-
-def batch_intersection_mask(batch, batch_filter):
- batch_filter = batch_filter.unique()
- mask = (batch.view(-1, 1) == batch_filter.view(1, -1)).any(dim=1)
- return mask
-
-
-class MeanReadout(nn.Module):
- """Mean readout operator over graphs with variadic sizes."""
-
- def forward(self, input, batch, num_graphs):
- """
- Perform readout over the graph(s).
- Parameters:
- data (torch_geometric.data.Data): batched graph
- input (Tensor): node representations
- Returns:
- Tensor: graph representations
- """
- output = scatter_mean(input, batch, dim=0, dim_size=num_graphs)
- return output
-
-
-class SumReadout(nn.Module):
- """Sum readout operator over graphs with variadic sizes."""
-
- def forward(self, input, batch, num_graphs):
- """
- Perform readout over the graph(s).
- Parameters:
- data (torch_geometric.data.Data): batched graph
- input (Tensor): node representations
- Returns:
- Tensor: graph representations
- """
- output = scatter_add(input, batch, dim=0, dim_size=num_graphs)
- return output
-
-
-class MultiLayerPerceptron(nn.Module):
- """
- Multi-layer Perceptron.
- Note there is no activation or dropout in the last layer.
- Parameters:
- input_dim (int): input dimension
- hidden_dim (list of int): hidden dimensions
- activation (str or function, optional): activation function
- dropout (float, optional): dropout rate
- """
-
- def __init__(self, input_dim, hidden_dims, activation="relu", dropout=0):
- super(MultiLayerPerceptron, self).__init__()
-
- self.dims = [input_dim] + hidden_dims
- if isinstance(activation, str):
- self.activation = getattr(F, activation)
- else:
- self.activation = None
- if dropout:
- self.dropout = nn.Dropout(dropout)
- else:
- self.dropout = None
-
- self.layers = nn.ModuleList()
- for i in range(len(self.dims) - 1):
- self.layers.append(nn.Linear(self.dims[i], self.dims[i + 1]))
-
- def forward(self, input):
- """"""
- x = input
- for i, layer in enumerate(self.layers):
- x = layer(x)
- if i < len(self.layers) - 1:
- if self.activation:
- x = self.activation(x)
- if self.dropout:
- x = self.dropout(x)
- return x
-
-
-class SmoothCrossEntropyLoss(_WeightedLoss):
- def __init__(self, weight=None, reduction='mean', smoothing=0.0):
- super().__init__(weight=weight, reduction=reduction)
- self.smoothing = smoothing
- self.weight = weight
- self.reduction = reduction
-
- @staticmethod
- def _smooth_one_hot(targets:torch.Tensor, n_classes:int, smoothing=0.0):
- assert 0 <= smoothing < 1
- with torch.no_grad():
- targets = torch.empty(size=(targets.size(0), n_classes),
- device=targets.device) \
- .fill_(smoothing /(n_classes-1)) \
- .scatter_(1, targets.data.unsqueeze(1), 1.-smoothing)
- return targets
-
- def forward(self, inputs, targets):
- targets = SmoothCrossEntropyLoss._smooth_one_hot(targets, inputs.size(-1),
- self.smoothing)
- lsm = F.log_softmax(inputs, -1)
-
- if self.weight is not None:
- lsm = lsm * self.weight.unsqueeze(0)
-
- loss = -(targets * lsm).sum(-1)
-
- if self.reduction == 'sum':
- loss = loss.sum()
- elif self.reduction == 'mean':
- loss = loss.mean()
-
- return loss
-
-
-class GaussianSmearing(nn.Module):
- def __init__(self, start=0.0, stop=10.0, num_gaussians=50):
- super().__init__()
- offset = torch.linspace(start, stop, num_gaussians)
- self.coeff = -0.5 / (offset[1] - offset[0]).item()**2
- self.register_buffer('offset', offset)
-
- def forward(self, dist):
- dist = dist.view(-1, 1) - self.offset.view(1, -1)
- return torch.exp(self.coeff * torch.pow(dist, 2))
-
-
-class ShiftedSoftplus(nn.Module):
- def __init__(self):
- super().__init__()
- self.shift = torch.log(torch.tensor(2.0)).item()
-
- def forward(self, x):
- return F.softplus(x) - self.shift
-
-
-def compose_context(h_protein, h_ligand, pos_protein, pos_ligand, batch_protein, batch_ligand):
- batch_ctx = torch.cat([batch_protein, batch_ligand], dim=0)
- sort_idx = batch_ctx.argsort()
-
- mask_protein = torch.cat([
- torch.ones([batch_protein.size(0)], device=batch_protein.device).bool(),
- torch.zeros([batch_ligand.size(0)], device=batch_ligand.device).bool(),
- ], dim=0)[sort_idx]
-
- batch_ctx = batch_ctx[sort_idx]
- h_ctx = torch.cat([h_protein, h_ligand], dim=0)[sort_idx] # (N_protein+N_ligand, H)
- pos_ctx = torch.cat([pos_protein, pos_ligand], dim=0)[sort_idx] # (N_protein+N_ligand, 3)
-
- return h_ctx, pos_ctx, batch_ctx
-
-
-def get_complete_graph(batch):
- """
- Args:
- batch: Batch index.
- Returns:
- edge_index: (2, N_1 + N_2 + ... + N_{B-1}), where N_i is the number of nodes of the i-th graph.
- neighbors: (B, ), number of edges per graph.
- """
- natoms = scatter_add(torch.ones_like(batch), index=batch, dim=0)
-
- natoms_sqr = (natoms ** 2).long()
- num_atom_pairs = torch.sum(natoms_sqr)
- natoms_expand = torch.repeat_interleave(natoms, natoms_sqr)
-
- index_offset = torch.cumsum(natoms, dim=0) - natoms
- index_offset_expand = torch.repeat_interleave(index_offset, natoms_sqr)
-
- index_sqr_offset = torch.cumsum(natoms_sqr, dim=0) - natoms_sqr
- index_sqr_offset = torch.repeat_interleave(index_sqr_offset, natoms_sqr)
-
- atom_count_sqr = torch.arange(num_atom_pairs, device=num_atom_pairs.device) - index_sqr_offset
-
- index1 = (atom_count_sqr // natoms_expand).long() + index_offset_expand
- index2 = (atom_count_sqr % natoms_expand).long() + index_offset_expand
- edge_index = torch.cat([index1.view(1, -1), index2.view(1, -1)])
- mask = torch.logical_not(index1 == index2)
- edge_index = edge_index[:, mask]
-
- num_edges = natoms_sqr - natoms # Number of edges per graph
-
- return edge_index, num_edges
-
-
-def compose_context_stable(h_protein, h_ligand, pos_protein, pos_ligand, batch_protein, batch_ligand):
- num_graphs = batch_protein.max().item() + 1
-
- batch_ctx = []
- h_ctx = []
- pos_ctx = []
- mask_protein = []
-
- for i in range(num_graphs):
- mask_p, mask_l = (batch_protein == i), (batch_ligand == i)
- batch_p, batch_l = batch_protein[mask_p], batch_ligand[mask_l]
-
- batch_ctx += [batch_p, batch_l]
- h_ctx += [h_protein[mask_p], h_ligand[mask_l]]
- pos_ctx += [pos_protein[mask_p], pos_ligand[mask_l]]
- mask_protein += [
- torch.ones([batch_p.size(0)], device=batch_p.device, dtype=torch.bool),
- torch.zeros([batch_l.size(0)], device=batch_l.device, dtype=torch.bool),
- ]
-
- batch_ctx = torch.cat(batch_ctx, dim=0)
- h_ctx = torch.cat(h_ctx, dim=0)
- pos_ctx = torch.cat(pos_ctx, dim=0)
- mask_protein = torch.cat(mask_protein, dim=0)
-
- return h_ctx, pos_ctx, batch_ctx, mask_protein
-
-if __name__ == '__main__':
- h_protein = torch.randn([60, 64])
- h_ligand = -torch.randn([33, 64])
- pos_protein = torch.clamp(torch.randn([60, 3]), 0, float('inf'))
- pos_ligand = torch.clamp(torch.randn([33, 3]), float('-inf'), 0)
- batch_protein = torch.LongTensor([0]*10 + [1]*20 + [2]*30)
- batch_ligand = torch.LongTensor([0]*11 + [1]*11 + [2]*11)
-
- h_ctx, pos_ctx, batch_ctx, mask_protein = compose_context_stable(h_protein, h_ligand, pos_protein, pos_ligand, batch_protein, batch_ligand)
-
- assert (batch_ctx[mask_protein] == batch_protein).all()
- assert (batch_ctx[torch.logical_not(mask_protein)] == batch_ligand).all()
-
- assert torch.allclose(h_ctx[torch.logical_not(mask_protein)], h_ligand)
- assert torch.allclose(h_ctx[mask_protein], h_protein)
-
- assert torch.allclose(pos_ctx[torch.logical_not(mask_protein)], pos_ligand)
- assert torch.allclose(pos_ctx[mask_protein], pos_protein)
-
-
-
\ No newline at end of file
diff --git a/spaces/Zwicky18/vits-models/text/symbols.py b/spaces/Zwicky18/vits-models/text/symbols.py
deleted file mode 100644
index edfbd24247be8c757275ce80b9ec27a0ffa808f3..0000000000000000000000000000000000000000
--- a/spaces/Zwicky18/vits-models/text/symbols.py
+++ /dev/null
@@ -1,39 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-'''# japanese_cleaners
-_pad = '_'
-_punctuation = ',.!?-'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-'''
-
-'''# japanese_cleaners2
-_pad = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-'''# korean_cleaners
-_pad = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-# zh_ja_mixture_cleaners
-_pad = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
\ No newline at end of file
diff --git a/spaces/aaaaaabbbbbbbdddddddduuuuulllll/topic2poem/app.py b/spaces/aaaaaabbbbbbbdddddddduuuuulllll/topic2poem/app.py
deleted file mode 100644
index 25116ecea621b15e1c098f85fc813210fc80cade..0000000000000000000000000000000000000000
--- a/spaces/aaaaaabbbbbbbdddddddduuuuulllll/topic2poem/app.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from transformers import BertTokenizer, EncoderDecoderModel
-import gradio as gr
-
-tokenizerM = BertTokenizer.from_pretrained("mareloraby/BERTShared-PoetryGen-arV01")
-bertSharedM = EncoderDecoderModel.from_pretrained("mareloraby/BERTShared-PoetryGen-arV01")
-# bertSharedM.cuda()
-
-
-def generate_response(text, k = 70, p = 0.9, nb = 4):
- prompt = f"{text}"
- encoded_prompt = tokenizerM.encode_plus(prompt, return_tensors = 'pt')#.to(device)
- gneration = bertSharedM.generate(
- input_ids = encoded_prompt.input_ids,
- attention_mask = encoded_prompt.attention_mask,
- do_sample = True,
- top_k= k,
- top_p = p,
- num_beams= nb,
- max_length =130,
- repetition_penalty = 2.0,
- no_repeat_ngram_size = 2,
- early_stopping=True)
-
- generated_text = tokenizerM.decode(gneration[0], skip_special_tokens=True)
- bayts = generated_text.split("[BSEP]")
- while("FSEP" not in bayts[-1]):
- bayts = bayts[:-1]
- bayts = bayts[:-1]
- temp_poem = ''
- for b in range(len(bayts)):
- temp_line = bayts[b].split('[FSEP]')
- temp_poem = temp_poem + temp_line[1] + ' - ' + temp_line[0] +'\n'
-
- return temp_poem
-
-iface = gr.Interface(fn=generate_response,
- title = 'BERTShared - topic based generation',
-
- inputs=[
- gr.inputs.Radio(['حزينه','هجاء','عتاب','غزل','مدح','رومنسيه','دينية'],label='Choose Topic'),
- gr.inputs.Slider(10, 200, step=10,default = 70, label='Top-K'),
- gr.inputs.Slider(0.10, 0.99, step=0.02, default = 0.90, label='Top-P'),
- #gr.inputs.Slider(1, 20, step=1, default = 4, label='Beams'),
-
- ],
- outputs="text")
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/abcde1234www/tts/README.md b/spaces/abcde1234www/tts/README.md
deleted file mode 100644
index ec3aa8524cbc3da57508992f066f2ba94cb74335..0000000000000000000000000000000000000000
--- a/spaces/abcde1234www/tts/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Text-to-Speech
-emoji: 💬
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
-duplicated_from: balacoon/tts
----
-
-Text-to-Speech interactive demo, using (balacoon_tts)[https://balacoon.com].
diff --git a/spaces/abhibisht89/Med7/app.py b/spaces/abhibisht89/Med7/app.py
deleted file mode 100644
index 1a41d976688d2f77a5844b83fd51e3c2746cdd0e..0000000000000000000000000000000000000000
--- a/spaces/abhibisht89/Med7/app.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import os
-#os.system('pip install https://huggingface.co/kormilitzin/en_core_med7_lg/resolve/main/en_core_med7_lg-any-py3-none-any.whl')
-
-os.system('pip install https://huggingface.co/kormilitzin/en_core_med7_trf/resolve/main/en_core_med7_trf-any-py3-none-any.whl')
-
-# Using spacy.load().
-#import spacy
-#nlp = spacy.load("en_core_med7_trf")
-
-# Importing as module.
-#import en_core_med7_trf
-#nlp = en_core_med7_trf.load()')
-
-import gradio as gr
-from spacy import displacy
-import spacy
-
-med7 = spacy.load("en_core_med7_trf")
-
-def get_med7_ent(text):
-
- # create distinct colours for labels
- col_dict = {}
- seven_colours = ['#e6194B', '#3cb44b', '#ffe119', '#ffd8b1', '#f58231', '#f032e6', '#42d4f4']
- for label, colour in zip(med7.pipe_labels['ner'], seven_colours):
- col_dict[label] = colour
-
- options = {'ents': med7.pipe_labels['ner'], 'colors':col_dict}
- doc = med7(text)
- html = displacy.render(doc, style='ent',options=options)
- return html
-
-exp=["A patient was prescribed Magnesium hydroxide 400mg/5ml suspension PO of total 30ml bid for the next 5 days."]
-
-desc="Med7 — An information extraction model for clinical natural language processing. More information about the model development can be found in recent pre-print: Med7: a transferable clinical natural language processing model for electronic health records."
-
-inp=gr.inputs.Textbox(lines=5, placeholder=None, default="", label="Text")
-out=gr.outputs.HTML(label=None)
-
-iface = gr.Interface(fn=get_med7_ent, inputs=inp, outputs=out,examples=exp,article=desc,title="Med7",theme="huggingface",layout='horizontal')
-iface.launch()
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/losses/focal_loss.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/losses/focal_loss.py
deleted file mode 100644
index 493907c6984d532175e0351daf2eafe4b9ff0256..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/losses/focal_loss.py
+++ /dev/null
@@ -1,181 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss
-
-from ..builder import LOSSES
-from .utils import weight_reduce_loss
-
-
-# This method is only for debugging
-def py_sigmoid_focal_loss(pred,
- target,
- weight=None,
- gamma=2.0,
- alpha=0.25,
- reduction='mean',
- avg_factor=None):
- """PyTorch version of `Focal Loss ) {
- chop;
- if ($_ ne "") {
- @A = split;
- if (@A != 1) {
- die "ssh.pl: bad line '$_' in .queue/machines.";
- }
- if ($A[0] !~ m/^[a-z0-9\.\-]+/) {
- die "ssh.pl: invalid machine name '$A[0]'";
- }
- push @machines, $A[0];
- }
- }
- if (@machines == 0) { die "ssh.pl: no machines listed in .queue/machines"; }
-}
-
-$logfile = shift @ARGV;
-
-if (defined $jobname && $logfile !~ m/$jobname/ &&
- $jobend > $jobstart) {
- print STDERR "ssh.pl: you are trying to run a parallel job but "
- . "you are putting the output into just one log file ($logfile)\n";
- exit(1);
-}
-
-{
- $offset = 0; # $offset will be an offset added to any index from the job-id
- # specified if the user does JOB=1:10. The main point of this is
- # that there are instances where a script will manually submit a
- # number of jobs to the queue, e.g. with log files foo.1.log,
- # foo.2.log and so on, and we don't want all of these to go
- # to the first machine.
- @A = split(".", basename($logfile));
- # if $logfile looks like foo.9.log, add 9 to $offset.
- foreach $a (@A) { if ($a =~ m/^\d+$/) { $offset += $a; } }
-}
-
-$cmd = "";
-
-foreach $x (@ARGV) {
- if ($x =~ m/^\S+$/) { $cmd .= $x . " "; }
- elsif ($x =~ m:\":) { $cmd .= "'$x' "; }
- else { $cmd .= "\"$x\" "; }
-}
-
-
-for ($jobid = $jobstart; $jobid <= $jobend; $jobid++) {
- $childpid = fork();
- if (!defined $childpid) { die "Error forking in ssh.pl (writing to $logfile)"; }
- if ($childpid == 0) {
- # We're in the child... this branch executes the job and returns (possibly
- # with an error status).
- if (defined $jobname) {
- $cmd =~ s/$jobname/$jobid/g;
- $logfile =~ s/$jobname/$jobid/g;
- }
- { # work out the machine to ssh to.
- $local_offset = $offset + $jobid - 1; # subtract 1 since jobs never start
- # from 0; we'd like the first job
- # to normally run on the first
- # machine.
- $num_machines = scalar @machines;
- # in the next line, the "+ $num_machines" is in case $local_offset is
- # negative, to ensure the modulus is calculated in the mathematical way, not
- # in the C way where (negative number % positive number) is negative.
- $machines_index = ($local_offset + $num_machines) % $num_machines;
- $machine = $machines[$machines_index];
- }
- if (!open(S, "|ssh $machine bash")) {
- print STDERR "ssh.pl failed to ssh to $machine";
- exit(1); # exits from the forked process within ssh.pl.
- }
- $cwd = getcwd();
- $logdir = dirname($logfile);
- # Below, we're printing into ssh which has opened a bash session; these are
- # bash commands.
- print S "set -e\n"; # if any of the later commands fails, we want it to exit.
- print S "cd $cwd\n";
- print S ". ./path.sh\n";
- print S "mkdir -p $logdir\n";
- print S "time1=\`date +\"%s\"\`\n";
- print S "( echo '#' Running on \`hostname\`\n";
- print S " echo '#' Started at \`date\`\n";
- print S " echo -n '# '; cat <
-
- """
-if SHOW_DUPLICATE_BUTTON:
- SPACE_ID = os.getenv('SPACE_ID')
- DESCRIPTION += f'\nLICENSE
-The model is licensed with a bespoke non-commercial research-only license DeepFloyd IF Research License Agreement license. The license forbids you from sharing any content for commercial use, or that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please read the license
- Biases and content acknowledgment
-Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, explicit content and violence. The model was trained on a subset of the LAION-5B dataset and is meant for research purposes. You can read more in the model card
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diff --git a/spaces/bioriAsaeru/text-to-voice/Golaem Crowd 6.3.3 For Maya 2016-2018 Win Easy Fast and Artist Friendly.md b/spaces/bioriAsaeru/text-to-voice/Golaem Crowd 6.3.3 For Maya 2016-2018 Win Easy Fast and Artist Friendly.md
deleted file mode 100644
index f093dc12463f9e67f0229f286e89e67786668afe..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Golaem Crowd 6.3.3 For Maya 2016-2018 Win Easy Fast and Artist Friendly.md
+++ /dev/null
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deleted file mode 100644
index 9beac12fb1ea2f3d601ed4554ffce4a392f09810..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/HACK Techsoft 2D Design Version 2 License The Best Way to Create Stunning 2D Designs.md
+++ /dev/null
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diff --git a/spaces/bioriAsaeru/text-to-voice/Http Dl.free.fr Q1PcZAX7n.md b/spaces/bioriAsaeru/text-to-voice/Http Dl.free.fr Q1PcZAX7n.md
deleted file mode 100644
index b1e27168ca44684a00cec458bcbc672e86655ac3..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Http Dl.free.fr Q1PcZAX7n.md
+++ /dev/null
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diff --git a/spaces/bioriAsaeru/text-to-voice/Http Uploadsnack Com Dcxorh Password Txt Torrent Download Fix.md b/spaces/bioriAsaeru/text-to-voice/Http Uploadsnack Com Dcxorh Password Txt Torrent Download Fix.md
deleted file mode 100644
index cd976aa64f135e79e59966ebec88804d77f3c4a8..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Http Uploadsnack Com Dcxorh Password Txt Torrent Download Fix.md
+++ /dev/null
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diff --git a/spaces/bioriAsaeru/text-to-voice/Jazler RadioStar 2.2.30 [Full][Multilenguaje] Serial Key Keygen _HOT_.md b/spaces/bioriAsaeru/text-to-voice/Jazler RadioStar 2.2.30 [Full][Multilenguaje] Serial Key Keygen _HOT_.md
deleted file mode 100644
index 55f328122018fb53518cd295f32e8e523c085bb7..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Jazler RadioStar 2.2.30 [Full][Multilenguaje] Serial Key Keygen _HOT_.md
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diff --git a/spaces/botlik100/kaki/i18n.py b/spaces/botlik100/kaki/i18n.py
deleted file mode 100644
index 37f310fadd0b48b2f364877158fb2105d645fc03..0000000000000000000000000000000000000000
--- a/spaces/botlik100/kaki/i18n.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import locale
-import json
-import os
-
-
-def load_language_list(language):
- with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
- language_list = json.load(f)
- return language_list
-
-
-class I18nAuto:
- def __init__(self, language=None):
- if language in ["Auto", None]:
- language = locale.getdefaultlocale()[
- 0
- ] # getlocale can't identify the system's language ((None, None))
- if not os.path.exists(f"./i18n/{language}.json"):
- language = "en_US"
- self.language = language
- # print("Use Language:", language)
- self.language_map = load_language_list(language)
-
- def __call__(self, key):
- return self.language_map.get(key, key)
-
- def print(self):
- print("Use Language:", self.language)
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/data/__init__.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/data/__init__.py
deleted file mode 100644
index 259f669b78bd05815cb8d3351fd6c5fc9a1b85a1..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/data/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from . import transforms # isort:skip
-
-from .build import (
- build_batch_data_loader,
- build_detection_test_loader,
- build_detection_train_loader,
- get_detection_dataset_dicts,
- load_proposals_into_dataset,
- print_instances_class_histogram,
-)
-from .catalog import DatasetCatalog, MetadataCatalog, Metadata
-from .common import DatasetFromList, MapDataset, ToIterableDataset
-from .dataset_mapper import DatasetMapper
-
-# ensure the builtin datasets are registered
-from . import datasets, samplers # isort:skip
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DeepLab/train_net.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DeepLab/train_net.py
deleted file mode 100644
index d3414ddf8e7af49640dd1372d75df7acb0b8bb49..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DeepLab/train_net.py
+++ /dev/null
@@ -1,134 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-"""
-DeepLab Training Script.
-
-This script is a simplified version of the training script in detectron2/tools.
-"""
-
-import os
-
-import detectron2.data.transforms as T
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import get_cfg
-from detectron2.data import DatasetMapper, MetadataCatalog, build_detection_train_loader
-from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
-from detectron2.evaluation import CityscapesSemSegEvaluator, DatasetEvaluators, SemSegEvaluator
-from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler
-
-
-def build_sem_seg_train_aug(cfg):
- augs = [
- T.ResizeShortestEdge(
- cfg.INPUT.MIN_SIZE_TRAIN, cfg.INPUT.MAX_SIZE_TRAIN, cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING
- )
- ]
- if cfg.INPUT.CROP.ENABLED:
- augs.append(
- T.RandomCrop_CategoryAreaConstraint(
- cfg.INPUT.CROP.TYPE,
- cfg.INPUT.CROP.SIZE,
- cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,
- cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
- )
- )
- augs.append(T.RandomFlip())
- return augs
-
-
-class Trainer(DefaultTrainer):
- """
- We use the "DefaultTrainer" which contains a number pre-defined logic for
- standard training workflow. They may not work for you, especially if you
- are working on a new research project. In that case you can use the cleaner
- "SimpleTrainer", or write your own training loop.
- """
-
- @classmethod
- def build_evaluator(cls, cfg, dataset_name, output_folder=None):
- """
- Create evaluator(s) for a given dataset.
- This uses the special metadata "evaluator_type" associated with each builtin dataset.
- For your own dataset, you can simply create an evaluator manually in your
- script and do not have to worry about the hacky if-else logic here.
- """
- if output_folder is None:
- output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
- evaluator_list = []
- evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
- if evaluator_type == "sem_seg":
- return SemSegEvaluator(
- dataset_name,
- distributed=True,
- output_dir=output_folder,
- )
- if evaluator_type == "cityscapes_sem_seg":
- return CityscapesSemSegEvaluator(dataset_name)
- if len(evaluator_list) == 0:
- raise NotImplementedError(
- "no Evaluator for the dataset {} with the type {}".format(
- dataset_name, evaluator_type
- )
- )
- if len(evaluator_list) == 1:
- return evaluator_list[0]
- return DatasetEvaluators(evaluator_list)
-
- @classmethod
- def build_train_loader(cls, cfg):
- if "SemanticSegmentor" in cfg.MODEL.META_ARCHITECTURE:
- mapper = DatasetMapper(cfg, is_train=True, augmentations=build_sem_seg_train_aug(cfg))
- else:
- mapper = None
- return build_detection_train_loader(cfg, mapper=mapper)
-
- @classmethod
- def build_lr_scheduler(cls, cfg, optimizer):
- """
- It now calls :func:`detectron2.solver.build_lr_scheduler`.
- Overwrite it if you'd like a different scheduler.
- """
- return build_lr_scheduler(cfg, optimizer)
-
-
-def setup(args):
- """
- Create configs and perform basic setups.
- """
- cfg = get_cfg()
- add_deeplab_config(cfg)
- cfg.merge_from_file(args.config_file)
- cfg.merge_from_list(args.opts)
- cfg.freeze()
- default_setup(cfg, args)
- return cfg
-
-
-def main(args):
- cfg = setup(args)
-
- if args.eval_only:
- model = Trainer.build_model(cfg)
- DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
- cfg.MODEL.WEIGHTS, resume=args.resume
- )
- res = Trainer.test(cfg, model)
- return res
-
- trainer = Trainer(cfg)
- trainer.resume_or_load(resume=args.resume)
- return trainer.train()
-
-
-if __name__ == "__main__":
- args = default_argument_parser().parse_args()
- print("Command Line Args:", args)
- launch(
- main,
- args.num_gpus,
- num_machines=args.num_machines,
- machine_rank=args.machine_rank,
- dist_url=args.dist_url,
- args=(args,),
- )
diff --git a/spaces/camillevanhoffelen/langchain-HuggingGPT/README.md b/spaces/camillevanhoffelen/langchain-HuggingGPT/README.md
deleted file mode 100644
index 1d61ed66dc7fd61316786ce82a0dc3eb9759f55d..0000000000000000000000000000000000000000
--- a/spaces/camillevanhoffelen/langchain-HuggingGPT/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Langchain HuggingGPT
-emoji: 🐢
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.29.0
-python_version: 3.11.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/ImageOps.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/ImageOps.py
deleted file mode 100644
index 17702778c134abcb51d7632367fbbf1a2f3048fa..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/ImageOps.py
+++ /dev/null
@@ -1,628 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# standard image operations
-#
-# History:
-# 2001-10-20 fl Created
-# 2001-10-23 fl Added autocontrast operator
-# 2001-12-18 fl Added Kevin's fit operator
-# 2004-03-14 fl Fixed potential division by zero in equalize
-# 2005-05-05 fl Fixed equalize for low number of values
-#
-# Copyright (c) 2001-2004 by Secret Labs AB
-# Copyright (c) 2001-2004 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-import functools
-import operator
-import re
-
-from . import ExifTags, Image, ImagePalette
-
-#
-# helpers
-
-
-def _border(border):
- if isinstance(border, tuple):
- if len(border) == 2:
- left, top = right, bottom = border
- elif len(border) == 4:
- left, top, right, bottom = border
- else:
- left = top = right = bottom = border
- return left, top, right, bottom
-
-
-def _color(color, mode):
- if isinstance(color, str):
- from . import ImageColor
-
- color = ImageColor.getcolor(color, mode)
- return color
-
-
-def _lut(image, lut):
- if image.mode == "P":
- # FIXME: apply to lookup table, not image data
- msg = "mode P support coming soon"
- raise NotImplementedError(msg)
- elif image.mode in ("L", "RGB"):
- if image.mode == "RGB" and len(lut) == 256:
- lut = lut + lut + lut
- return image.point(lut)
- else:
- msg = "not supported for this image mode"
- raise OSError(msg)
-
-
-#
-# actions
-
-
-def autocontrast(image, cutoff=0, ignore=None, mask=None, preserve_tone=False):
- """
- Maximize (normalize) image contrast. This function calculates a
- histogram of the input image (or mask region), removes ``cutoff`` percent of the
- lightest and darkest pixels from the histogram, and remaps the image
- so that the darkest pixel becomes black (0), and the lightest
- becomes white (255).
-
- :param image: The image to process.
- :param cutoff: The percent to cut off from the histogram on the low and
- high ends. Either a tuple of (low, high), or a single
- number for both.
- :param ignore: The background pixel value (use None for no background).
- :param mask: Histogram used in contrast operation is computed using pixels
- within the mask. If no mask is given the entire image is used
- for histogram computation.
- :param preserve_tone: Preserve image tone in Photoshop-like style autocontrast.
-
- .. versionadded:: 8.2.0
-
- :return: An image.
- """
- if preserve_tone:
- histogram = image.convert("L").histogram(mask)
- else:
- histogram = image.histogram(mask)
-
- lut = []
- for layer in range(0, len(histogram), 256):
- h = histogram[layer : layer + 256]
- if ignore is not None:
- # get rid of outliers
- try:
- h[ignore] = 0
- except TypeError:
- # assume sequence
- for ix in ignore:
- h[ix] = 0
- if cutoff:
- # cut off pixels from both ends of the histogram
- if not isinstance(cutoff, tuple):
- cutoff = (cutoff, cutoff)
- # get number of pixels
- n = 0
- for ix in range(256):
- n = n + h[ix]
- # remove cutoff% pixels from the low end
- cut = n * cutoff[0] // 100
- for lo in range(256):
- if cut > h[lo]:
- cut = cut - h[lo]
- h[lo] = 0
- else:
- h[lo] -= cut
- cut = 0
- if cut <= 0:
- break
- # remove cutoff% samples from the high end
- cut = n * cutoff[1] // 100
- for hi in range(255, -1, -1):
- if cut > h[hi]:
- cut = cut - h[hi]
- h[hi] = 0
- else:
- h[hi] -= cut
- cut = 0
- if cut <= 0:
- break
- # find lowest/highest samples after preprocessing
- for lo in range(256):
- if h[lo]:
- break
- for hi in range(255, -1, -1):
- if h[hi]:
- break
- if hi <= lo:
- # don't bother
- lut.extend(list(range(256)))
- else:
- scale = 255.0 / (hi - lo)
- offset = -lo * scale
- for ix in range(256):
- ix = int(ix * scale + offset)
- if ix < 0:
- ix = 0
- elif ix > 255:
- ix = 255
- lut.append(ix)
- return _lut(image, lut)
-
-
-def colorize(image, black, white, mid=None, blackpoint=0, whitepoint=255, midpoint=127):
- """
- Colorize grayscale image.
- This function calculates a color wedge which maps all black pixels in
- the source image to the first color and all white pixels to the
- second color. If ``mid`` is specified, it uses three-color mapping.
- The ``black`` and ``white`` arguments should be RGB tuples or color names;
- optionally you can use three-color mapping by also specifying ``mid``.
- Mapping positions for any of the colors can be specified
- (e.g. ``blackpoint``), where these parameters are the integer
- value corresponding to where the corresponding color should be mapped.
- These parameters must have logical order, such that
- ``blackpoint <= midpoint <= whitepoint`` (if ``mid`` is specified).
-
- :param image: The image to colorize.
- :param black: The color to use for black input pixels.
- :param white: The color to use for white input pixels.
- :param mid: The color to use for midtone input pixels.
- :param blackpoint: an int value [0, 255] for the black mapping.
- :param whitepoint: an int value [0, 255] for the white mapping.
- :param midpoint: an int value [0, 255] for the midtone mapping.
- :return: An image.
- """
-
- # Initial asserts
- assert image.mode == "L"
- if mid is None:
- assert 0 <= blackpoint <= whitepoint <= 255
- else:
- assert 0 <= blackpoint <= midpoint <= whitepoint <= 255
-
- # Define colors from arguments
- black = _color(black, "RGB")
- white = _color(white, "RGB")
- if mid is not None:
- mid = _color(mid, "RGB")
-
- # Empty lists for the mapping
- red = []
- green = []
- blue = []
-
- # Create the low-end values
- for i in range(0, blackpoint):
- red.append(black[0])
- green.append(black[1])
- blue.append(black[2])
-
- # Create the mapping (2-color)
- if mid is None:
- range_map = range(0, whitepoint - blackpoint)
-
- for i in range_map:
- red.append(black[0] + i * (white[0] - black[0]) // len(range_map))
- green.append(black[1] + i * (white[1] - black[1]) // len(range_map))
- blue.append(black[2] + i * (white[2] - black[2]) // len(range_map))
-
- # Create the mapping (3-color)
- else:
- range_map1 = range(0, midpoint - blackpoint)
- range_map2 = range(0, whitepoint - midpoint)
-
- for i in range_map1:
- red.append(black[0] + i * (mid[0] - black[0]) // len(range_map1))
- green.append(black[1] + i * (mid[1] - black[1]) // len(range_map1))
- blue.append(black[2] + i * (mid[2] - black[2]) // len(range_map1))
- for i in range_map2:
- red.append(mid[0] + i * (white[0] - mid[0]) // len(range_map2))
- green.append(mid[1] + i * (white[1] - mid[1]) // len(range_map2))
- blue.append(mid[2] + i * (white[2] - mid[2]) // len(range_map2))
-
- # Create the high-end values
- for i in range(0, 256 - whitepoint):
- red.append(white[0])
- green.append(white[1])
- blue.append(white[2])
-
- # Return converted image
- image = image.convert("RGB")
- return _lut(image, red + green + blue)
-
-
-def contain(image, size, method=Image.Resampling.BICUBIC):
- """
- Returns a resized version of the image, set to the maximum width and height
- within the requested size, while maintaining the original aspect ratio.
-
- :param image: The image to resize and crop.
- :param size: The requested output size in pixels, given as a
- (width, height) tuple.
- :param method: Resampling method to use. Default is
- :py:attr:`~PIL.Image.Resampling.BICUBIC`.
- See :ref:`concept-filters`.
- :return: An image.
- """
-
- im_ratio = image.width / image.height
- dest_ratio = size[0] / size[1]
-
- if im_ratio != dest_ratio:
- if im_ratio > dest_ratio:
- new_height = round(image.height / image.width * size[0])
- if new_height != size[1]:
- size = (size[0], new_height)
- else:
- new_width = round(image.width / image.height * size[1])
- if new_width != size[0]:
- size = (new_width, size[1])
- return image.resize(size, resample=method)
-
-
-def pad(image, size, method=Image.Resampling.BICUBIC, color=None, centering=(0.5, 0.5)):
- """
- Returns a resized and padded version of the image, expanded to fill the
- requested aspect ratio and size.
-
- :param image: The image to resize and crop.
- :param size: The requested output size in pixels, given as a
- (width, height) tuple.
- :param method: Resampling method to use. Default is
- :py:attr:`~PIL.Image.Resampling.BICUBIC`.
- See :ref:`concept-filters`.
- :param color: The background color of the padded image.
- :param centering: Control the position of the original image within the
- padded version.
-
- (0.5, 0.5) will keep the image centered
- (0, 0) will keep the image aligned to the top left
- (1, 1) will keep the image aligned to the bottom
- right
- :return: An image.
- """
-
- resized = contain(image, size, method)
- if resized.size == size:
- out = resized
- else:
- out = Image.new(image.mode, size, color)
- if resized.palette:
- out.putpalette(resized.getpalette())
- if resized.width != size[0]:
- x = round((size[0] - resized.width) * max(0, min(centering[0], 1)))
- out.paste(resized, (x, 0))
- else:
- y = round((size[1] - resized.height) * max(0, min(centering[1], 1)))
- out.paste(resized, (0, y))
- return out
-
-
-def crop(image, border=0):
- """
- Remove border from image. The same amount of pixels are removed
- from all four sides. This function works on all image modes.
-
- .. seealso:: :py:meth:`~PIL.Image.Image.crop`
-
- :param image: The image to crop.
- :param border: The number of pixels to remove.
- :return: An image.
- """
- left, top, right, bottom = _border(border)
- return image.crop((left, top, image.size[0] - right, image.size[1] - bottom))
-
-
-def scale(image, factor, resample=Image.Resampling.BICUBIC):
- """
- Returns a rescaled image by a specific factor given in parameter.
- A factor greater than 1 expands the image, between 0 and 1 contracts the
- image.
-
- :param image: The image to rescale.
- :param factor: The expansion factor, as a float.
- :param resample: Resampling method to use. Default is
- :py:attr:`~PIL.Image.Resampling.BICUBIC`.
- See :ref:`concept-filters`.
- :returns: An :py:class:`~PIL.Image.Image` object.
- """
- if factor == 1:
- return image.copy()
- elif factor <= 0:
- msg = "the factor must be greater than 0"
- raise ValueError(msg)
- else:
- size = (round(factor * image.width), round(factor * image.height))
- return image.resize(size, resample)
-
-
-def deform(image, deformer, resample=Image.Resampling.BILINEAR):
- """
- Deform the image.
-
- :param image: The image to deform.
- :param deformer: A deformer object. Any object that implements a
- ``getmesh`` method can be used.
- :param resample: An optional resampling filter. Same values possible as
- in the PIL.Image.transform function.
- :return: An image.
- """
- return image.transform(
- image.size, Image.Transform.MESH, deformer.getmesh(image), resample
- )
-
-
-def equalize(image, mask=None):
- """
- Equalize the image histogram. This function applies a non-linear
- mapping to the input image, in order to create a uniform
- distribution of grayscale values in the output image.
-
- :param image: The image to equalize.
- :param mask: An optional mask. If given, only the pixels selected by
- the mask are included in the analysis.
- :return: An image.
- """
- if image.mode == "P":
- image = image.convert("RGB")
- h = image.histogram(mask)
- lut = []
- for b in range(0, len(h), 256):
- histo = [_f for _f in h[b : b + 256] if _f]
- if len(histo) <= 1:
- lut.extend(list(range(256)))
- else:
- step = (functools.reduce(operator.add, histo) - histo[-1]) // 255
- if not step:
- lut.extend(list(range(256)))
- else:
- n = step // 2
- for i in range(256):
- lut.append(n // step)
- n = n + h[i + b]
- return _lut(image, lut)
-
-
-def expand(image, border=0, fill=0):
- """
- Add border to the image
-
- :param image: The image to expand.
- :param border: Border width, in pixels.
- :param fill: Pixel fill value (a color value). Default is 0 (black).
- :return: An image.
- """
- left, top, right, bottom = _border(border)
- width = left + image.size[0] + right
- height = top + image.size[1] + bottom
- color = _color(fill, image.mode)
- if image.palette:
- palette = ImagePalette.ImagePalette(palette=image.getpalette())
- if isinstance(color, tuple):
- color = palette.getcolor(color)
- else:
- palette = None
- out = Image.new(image.mode, (width, height), color)
- if palette:
- out.putpalette(palette.palette)
- out.paste(image, (left, top))
- return out
-
-
-def fit(image, size, method=Image.Resampling.BICUBIC, bleed=0.0, centering=(0.5, 0.5)):
- """
- Returns a resized and cropped version of the image, cropped to the
- requested aspect ratio and size.
-
- This function was contributed by Kevin Cazabon.
-
- :param image: The image to resize and crop.
- :param size: The requested output size in pixels, given as a
- (width, height) tuple.
- :param method: Resampling method to use. Default is
- :py:attr:`~PIL.Image.Resampling.BICUBIC`.
- See :ref:`concept-filters`.
- :param bleed: Remove a border around the outside of the image from all
- four edges. The value is a decimal percentage (use 0.01 for
- one percent). The default value is 0 (no border).
- Cannot be greater than or equal to 0.5.
- :param centering: Control the cropping position. Use (0.5, 0.5) for
- center cropping (e.g. if cropping the width, take 50% off
- of the left side, and therefore 50% off the right side).
- (0.0, 0.0) will crop from the top left corner (i.e. if
- cropping the width, take all of the crop off of the right
- side, and if cropping the height, take all of it off the
- bottom). (1.0, 0.0) will crop from the bottom left
- corner, etc. (i.e. if cropping the width, take all of the
- crop off the left side, and if cropping the height take
- none from the top, and therefore all off the bottom).
- :return: An image.
- """
-
- # by Kevin Cazabon, Feb 17/2000
- # kevin@cazabon.com
- # https://www.cazabon.com
-
- # ensure centering is mutable
- centering = list(centering)
-
- if not 0.0 <= centering[0] <= 1.0:
- centering[0] = 0.5
- if not 0.0 <= centering[1] <= 1.0:
- centering[1] = 0.5
-
- if not 0.0 <= bleed < 0.5:
- bleed = 0.0
-
- # calculate the area to use for resizing and cropping, subtracting
- # the 'bleed' around the edges
-
- # number of pixels to trim off on Top and Bottom, Left and Right
- bleed_pixels = (bleed * image.size[0], bleed * image.size[1])
-
- live_size = (
- image.size[0] - bleed_pixels[0] * 2,
- image.size[1] - bleed_pixels[1] * 2,
- )
-
- # calculate the aspect ratio of the live_size
- live_size_ratio = live_size[0] / live_size[1]
-
- # calculate the aspect ratio of the output image
- output_ratio = size[0] / size[1]
-
- # figure out if the sides or top/bottom will be cropped off
- if live_size_ratio == output_ratio:
- # live_size is already the needed ratio
- crop_width = live_size[0]
- crop_height = live_size[1]
- elif live_size_ratio >= output_ratio:
- # live_size is wider than what's needed, crop the sides
- crop_width = output_ratio * live_size[1]
- crop_height = live_size[1]
- else:
- # live_size is taller than what's needed, crop the top and bottom
- crop_width = live_size[0]
- crop_height = live_size[0] / output_ratio
-
- # make the crop
- crop_left = bleed_pixels[0] + (live_size[0] - crop_width) * centering[0]
- crop_top = bleed_pixels[1] + (live_size[1] - crop_height) * centering[1]
-
- crop = (crop_left, crop_top, crop_left + crop_width, crop_top + crop_height)
-
- # resize the image and return it
- return image.resize(size, method, box=crop)
-
-
-def flip(image):
- """
- Flip the image vertically (top to bottom).
-
- :param image: The image to flip.
- :return: An image.
- """
- return image.transpose(Image.Transpose.FLIP_TOP_BOTTOM)
-
-
-def grayscale(image):
- """
- Convert the image to grayscale.
-
- :param image: The image to convert.
- :return: An image.
- """
- return image.convert("L")
-
-
-def invert(image):
- """
- Invert (negate) the image.
-
- :param image: The image to invert.
- :return: An image.
- """
- lut = []
- for i in range(256):
- lut.append(255 - i)
- return image.point(lut) if image.mode == "1" else _lut(image, lut)
-
-
-def mirror(image):
- """
- Flip image horizontally (left to right).
-
- :param image: The image to mirror.
- :return: An image.
- """
- return image.transpose(Image.Transpose.FLIP_LEFT_RIGHT)
-
-
-def posterize(image, bits):
- """
- Reduce the number of bits for each color channel.
-
- :param image: The image to posterize.
- :param bits: The number of bits to keep for each channel (1-8).
- :return: An image.
- """
- lut = []
- mask = ~(2 ** (8 - bits) - 1)
- for i in range(256):
- lut.append(i & mask)
- return _lut(image, lut)
-
-
-def solarize(image, threshold=128):
- """
- Invert all pixel values above a threshold.
-
- :param image: The image to solarize.
- :param threshold: All pixels above this greyscale level are inverted.
- :return: An image.
- """
- lut = []
- for i in range(256):
- if i < threshold:
- lut.append(i)
- else:
- lut.append(255 - i)
- return _lut(image, lut)
-
-
-def exif_transpose(image, *, in_place=False):
- """
- If an image has an EXIF Orientation tag, other than 1, transpose the image
- accordingly, and remove the orientation data.
-
- :param image: The image to transpose.
- :param in_place: Boolean. Keyword-only argument.
- If ``True``, the original image is modified in-place, and ``None`` is returned.
- If ``False`` (default), a new :py:class:`~PIL.Image.Image` object is returned
- with the transposition applied. If there is no transposition, a copy of the
- image will be returned.
- """
- image_exif = image.getexif()
- orientation = image_exif.get(ExifTags.Base.Orientation)
- method = {
- 2: Image.Transpose.FLIP_LEFT_RIGHT,
- 3: Image.Transpose.ROTATE_180,
- 4: Image.Transpose.FLIP_TOP_BOTTOM,
- 5: Image.Transpose.TRANSPOSE,
- 6: Image.Transpose.ROTATE_270,
- 7: Image.Transpose.TRANSVERSE,
- 8: Image.Transpose.ROTATE_90,
- }.get(orientation)
- if method is not None:
- transposed_image = image.transpose(method)
- if in_place:
- image.im = transposed_image.im
- image.pyaccess = None
- image._size = transposed_image._size
- exif_image = image if in_place else transposed_image
-
- exif = exif_image.getexif()
- if ExifTags.Base.Orientation in exif:
- del exif[ExifTags.Base.Orientation]
- if "exif" in exif_image.info:
- exif_image.info["exif"] = exif.tobytes()
- elif "Raw profile type exif" in exif_image.info:
- exif_image.info["Raw profile type exif"] = exif.tobytes().hex()
- elif "XML:com.adobe.xmp" in exif_image.info:
- for pattern in (
- r'tiff:Orientation="([0-9])"',
- r"'+tex2mathml(parts[1])+'\n'
- except:
- result += ''+convError+''
- if len(parts)==3:
- result += convert(parts[2], extensions, splitParagraphs=False)
- else:
- result += ''+incomplete+''
- # else find first $-formulas:
- else:
- parts = re.split('\${1}', mdtex, 2)
- if len(parts)>1 and not found:
- found = True
- try:
- mathml = tex2mathml(parts[1])
- except:
- mathml = convError
- if parts[0].endswith('\n\n') or parts[0]=='': # make sure textblock starts before formula!
- parts[0]=parts[0]+''
- if len(parts)==3:
- result = convert(parts[0]+mathml+parts[2], extensions, splitParagraphs=False)
- else:
- result = convert(parts[0]+mathml+incomplete, extensions, splitParagraphs=False)
- # else find first \[..\]-equation:
- else:
- parts = re.split(r'\\\[', mdtex, 1)
- if len(parts)>1 and not found:
- found = True
- result = convert(parts[0], extensions, splitParagraphs=False)+'\n'
- parts = re.split(r'\\\]', parts[1], 1)
- try:
- result += ''+tex2mathml(parts[0])+'\n'
- except:
- result += ''+convError+''
- if len(parts)==2:
- result += convert(parts[1], extensions, splitParagraphs=False)
- else:
- result += ''+incomplete+''
- # else find first \(..\)-equation:
- else:
- parts = re.split(r'\\\(', mdtex, 1)
- if len(parts)>1 and not found:
- found = True
- subp = re.split(r'\\\)', parts[1], 1)
- try:
- mathml = tex2mathml(subp[0])
- except:
- mathml = convError
- if parts[0].endswith('\n\n') or parts[0]=='': # make sure textblock starts before formula!
- parts[0]=parts[0]+''
- if len(subp)==2:
- result = convert(parts[0]+mathml+subp[1], extensions, splitParagraphs=False)
- else:
- result = convert(parts[0]+mathml+incomplete, extensions, splitParagraphs=False)
- if not found:
- result = mdtex
- return result
diff --git a/spaces/ceshine/t5-paraphrasing/app.py b/spaces/ceshine/t5-paraphrasing/app.py
deleted file mode 100644
index 28ff245c582304ddcc8a20f4d81ed2e61f0adbc7..0000000000000000000000000000000000000000
--- a/spaces/ceshine/t5-paraphrasing/app.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import os
-import json
-
-import requests
-import gradio as gr
-from gradio import inputs, outputs
-
-ENDPOINTS = (
- "https://api-inference.huggingface.co/models/ceshine/t5-paraphrase-quora-paws",
- "https://api-inference.huggingface.co/models/ceshine/t5-paraphrase-paws-msrp-opinosis",
-)
-
-
-def get_fn(endpoint):
- def paraphrase(source_text: str, temperature: float):
- if temperature > 0:
- params = {
- "do_sample": True,
- "temperature": temperature,
- "top_k": 5,
- "num_return_sequences": 10,
- "max_length": 100,
- }
- else:
- params = {"num_beams": 10, "num_return_sequences": 10, "max_length": 100}
- res = requests.post(
- endpoint,
- headers={"Authorization": f"Bearer {os.environ['TOKEN']}"},
- data=json.dumps(
- {
- "inputs": "paraphrase: " + source_text,
- "parameters": params,
- }
- ),
- )
- if not (res.status_code == 200):
- return f"Got a {res.status_code} status code from HuggingFace."
- results = res.json()
- # print(results)
- outputs = [
- x["generated_text"]
- for x in results
- if x["generated_text"].lower() != source_text.lower().strip()
- ][:3]
- text = ""
- for i, output in enumerate(outputs):
- text += f"{i+1}: {output}\n\n"
- return text
-
- return paraphrase
-
-
-interface_1 = gr.Interface(
- fn=get_fn(ENDPOINTS[0]),
- title="quora-paws",
- inputs=[
- inputs.Textbox(label="Source text"),
- inputs.Number(
- default=0.0, label="Temperature (0 -> disable sampling and use beam search)"
- ),
- ],
- outputs=outputs.Textbox(label="quora-paws"),
-)
-
-interface_2 = gr.Interface(
- fn=get_fn(ENDPOINTS[1]),
- title="paws-msrp-opinosis",
- inputs=[
- inputs.Textbox(label="Source text"),
- inputs.Number(
- default=0.0, label="Temperature (0 -> disable sampling and use beam search)"
- ),
- ],
- outputs=outputs.Textbox(label="paws-msrp-opinosis"),
-)
-
-gr.Parallel(
- interface_1,
- interface_2,
- title="T5 Sentence Paraphraser",
- description="Compare generated paraphrases from two models (`ceshine/t5-paraphrase-quora-paws` and `ceshine/t5-paraphrase-paws-msrp-opinosis`).",
- examples=[
- ["I bought a ticket from London to New York.", 0],
- ["Weh Seun spends 14 hours a week doing housework.", 1.2],
- ],
-).launch(enable_queue=True)
diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/yolox/utils/allreduce_norm.py b/spaces/chendl/compositional_test/multimodal/YOLOX/yolox/utils/allreduce_norm.py
deleted file mode 100644
index 142c76c78061db6e2c5f4b899bcc5e2f2214f010..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/YOLOX/yolox/utils/allreduce_norm.py
+++ /dev/null
@@ -1,103 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii Inc. All rights reserved.
-
-import pickle
-from collections import OrderedDict
-
-import torch
-from torch import distributed as dist
-from torch import nn
-
-from .dist import _get_global_gloo_group, get_world_size
-
-ASYNC_NORM = (
- nn.BatchNorm1d,
- nn.BatchNorm2d,
- nn.BatchNorm3d,
- nn.InstanceNorm1d,
- nn.InstanceNorm2d,
- nn.InstanceNorm3d,
-)
-
-__all__ = [
- "get_async_norm_states",
- "pyobj2tensor",
- "tensor2pyobj",
- "all_reduce",
- "all_reduce_norm",
-]
-
-
-def get_async_norm_states(module):
- async_norm_states = OrderedDict()
- for name, child in module.named_modules():
- if isinstance(child, ASYNC_NORM):
- for k, v in child.state_dict().items():
- async_norm_states[".".join([name, k])] = v
- return async_norm_states
-
-
-def pyobj2tensor(pyobj, device="cuda"):
- """serialize picklable python object to tensor"""
- storage = torch.ByteStorage.from_buffer(pickle.dumps(pyobj))
- return torch.ByteTensor(storage).to(device=device)
-
-
-def tensor2pyobj(tensor):
- """deserialize tensor to picklable python object"""
- return pickle.loads(tensor.cpu().numpy().tobytes())
-
-
-def _get_reduce_op(op_name):
- return {
- "sum": dist.ReduceOp.SUM,
- "mean": dist.ReduceOp.SUM,
- }[op_name.lower()]
-
-
-def all_reduce(py_dict, op="sum", group=None):
- """
- Apply all reduce function for python dict object.
- NOTE: make sure that every py_dict has the same keys and values are in the same shape.
-
- Args:
- py_dict (dict): dict to apply all reduce op.
- op (str): operator, could be "sum" or "mean".
- """
- world_size = get_world_size()
- if world_size == 1:
- return py_dict
- if group is None:
- group = _get_global_gloo_group()
- if dist.get_world_size(group) == 1:
- return py_dict
-
- # all reduce logic across different devices.
- py_key = list(py_dict.keys())
- py_key_tensor = pyobj2tensor(py_key)
- dist.broadcast(py_key_tensor, src=0)
- py_key = tensor2pyobj(py_key_tensor)
-
- tensor_shapes = [py_dict[k].shape for k in py_key]
- tensor_numels = [py_dict[k].numel() for k in py_key]
-
- flatten_tensor = torch.cat([py_dict[k].flatten() for k in py_key])
- dist.all_reduce(flatten_tensor, op=_get_reduce_op(op))
- if op == "mean":
- flatten_tensor /= world_size
-
- split_tensors = [
- x.reshape(shape)
- for x, shape in zip(torch.split(flatten_tensor, tensor_numels), tensor_shapes)
- ]
- return OrderedDict({k: v for k, v in zip(py_key, split_tensors)})
-
-
-def all_reduce_norm(module):
- """
- All reduce norm statistics in different devices.
- """
- states = get_async_norm_states(module)
- states = all_reduce(states, op="mean")
- module.load_state_dict(states, strict=False)
diff --git a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/seq2seq_trainer.py b/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/seq2seq_trainer.py
deleted file mode 100644
index dbf12725f2db07b1de836b4c99d42373faf5418c..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/seq2seq_trainer.py
+++ /dev/null
@@ -1,262 +0,0 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-from torch import nn
-from torch.utils.data import DistributedSampler, RandomSampler
-
-from transformers import PreTrainedModel, Trainer, logging
-from transformers.integrations import is_fairscale_available
-from transformers.models.fsmt.configuration_fsmt import FSMTConfig
-from transformers.optimization import (
- Adafactor,
- AdamW,
- get_constant_schedule,
- get_constant_schedule_with_warmup,
- get_cosine_schedule_with_warmup,
- get_cosine_with_hard_restarts_schedule_with_warmup,
- get_linear_schedule_with_warmup,
- get_polynomial_decay_schedule_with_warmup,
-)
-from transformers.trainer_pt_utils import get_tpu_sampler
-from transformers.training_args import ParallelMode
-from transformers.utils import is_torch_tpu_available
-
-
-if is_fairscale_available():
- from fairscale.optim import OSS
-
-
-logger = logging.get_logger(__name__)
-
-arg_to_scheduler = {
- "linear": get_linear_schedule_with_warmup,
- "cosine": get_cosine_schedule_with_warmup,
- "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
- "polynomial": get_polynomial_decay_schedule_with_warmup,
- "constant": get_constant_schedule,
- "constant_w_warmup": get_constant_schedule_with_warmup,
-}
-
-
-class Seq2SeqTrainer(Trainer):
- def __init__(self, config=None, data_args=None, *args, **kwargs):
- super().__init__(*args, **kwargs)
-
- if config is None:
- assert isinstance(self.model, PreTrainedModel), (
- "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
- f" {self.model.__class__}"
- )
- self.config = self.model.config
- else:
- self.config = config
-
- self.data_args = data_args
- self.vocab_size = self.config.tgt_vocab_size if isinstance(self.config, FSMTConfig) else self.config.vocab_size
-
- if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
- assert self.config.pad_token_id is not None, (
- "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
- " calculation or doing label smoothing."
- )
-
- if self.config.pad_token_id is None and self.config.eos_token_id is not None:
- logger.warning(
- f"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"
- " padding.."
- )
-
- if self.args.label_smoothing == 0:
- self.loss_fn = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
- else:
- # dynamically import label_smoothed_nll_loss
- from utils import label_smoothed_nll_loss
-
- self.loss_fn = label_smoothed_nll_loss
-
- def create_optimizer_and_scheduler(self, num_training_steps: int):
- """
- Setup the optimizer and the learning rate scheduler.
-
- We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
- Trainer's init through :obj:`optimizers`, or subclass and override this method in a subclass.
- """
- if self.optimizer is None:
- no_decay = ["bias", "LayerNorm.weight"]
- optimizer_grouped_parameters = [
- {
- "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],
- "weight_decay": self.args.weight_decay,
- },
- {
- "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],
- "weight_decay": 0.0,
- },
- ]
- optimizer_cls = Adafactor if self.args.adafactor else AdamW
- if self.args.adafactor:
- optimizer_cls = Adafactor
- optimizer_kwargs = {"scale_parameter": False, "relative_step": False}
- else:
- optimizer_cls = AdamW
- optimizer_kwargs = {
- "betas": (self.args.adam_beta1, self.args.adam_beta2),
- "eps": self.args.adam_epsilon,
- }
- optimizer_kwargs["lr"] = self.args.learning_rate
- if self.sharded_ddp:
- self.optimizer = OSS(
- params=optimizer_grouped_parameters,
- optim=optimizer_cls,
- **optimizer_kwargs,
- )
- else:
- self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
-
- if self.lr_scheduler is None:
- self.lr_scheduler = self._get_lr_scheduler(num_training_steps)
- else: # ignoring --lr_scheduler
- logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.")
-
- def _get_lr_scheduler(self, num_training_steps):
- schedule_func = arg_to_scheduler[self.args.lr_scheduler]
- if self.args.lr_scheduler == "constant":
- scheduler = schedule_func(self.optimizer)
- elif self.args.lr_scheduler == "constant_w_warmup":
- scheduler = schedule_func(self.optimizer, num_warmup_steps=self.args.warmup_steps)
- else:
- scheduler = schedule_func(
- self.optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=num_training_steps
- )
- return scheduler
-
- def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
- if isinstance(self.train_dataset, torch.utils.data.IterableDataset):
- return None
- elif is_torch_tpu_available():
- return get_tpu_sampler(self.train_dataset)
- else:
- if self.args.sortish_sampler:
- self.train_dataset.make_sortish_sampler(
- self.args.per_device_train_batch_size,
- distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED),
- )
-
- return (
- RandomSampler(self.train_dataset)
- if self.args.local_rank == -1
- else DistributedSampler(self.train_dataset)
- )
-
- def _compute_loss(self, model, inputs, labels):
- if self.args.label_smoothing == 0:
- if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
- # force training to ignore pad token
- logits = model(**inputs, use_cache=False)[0]
- loss = self.loss_fn(logits.view(-1, logits.shape[-1]), labels.view(-1))
- else:
- # compute usual loss via models
- loss, logits = model(**inputs, labels=labels, use_cache=False)[:2]
- else:
- # compute label smoothed loss
- logits = model(**inputs, use_cache=False)[0]
- lprobs = torch.nn.functional.log_softmax(logits, dim=-1)
- loss, _ = self.loss_fn(lprobs, labels, self.args.label_smoothing, ignore_index=self.config.pad_token_id)
- return loss, logits
-
- def compute_loss(self, model, inputs):
- labels = inputs.pop("labels")
- loss, _ = self._compute_loss(model, inputs, labels)
- return loss
-
- def prediction_step(
- self,
- model: nn.Module,
- inputs: Dict[str, Union[torch.Tensor, Any]],
- prediction_loss_only: bool,
- ignore_keys: Optional[List[str]] = None,
- ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
- """
- Perform an evaluation step on :obj:`model` using obj:`inputs`.
-
- Subclass and override to inject custom behavior.
-
- Args:
- model (:obj:`nn.Module`):
- The model to evaluate.
- inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):
- The inputs and targets of the model.
-
- The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
- argument :obj:`labels`. Check your model's documentation for all accepted arguments.
- prediction_loss_only (:obj:`bool`):
- Whether or not to return the loss only.
-
- Return:
- Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
- A tuple with the loss, logits and labels (each being optional).
- """
- inputs = self._prepare_inputs(inputs)
-
- gen_kwargs = {
- "max_length": self.data_args.val_max_target_length
- if self.data_args is not None
- else self.config.max_length,
- "num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
- }
-
- if self.args.predict_with_generate and not self.args.prediction_loss_only:
- generated_tokens = self.model.generate(
- inputs["input_ids"],
- attention_mask=inputs["attention_mask"],
- **gen_kwargs,
- )
- # in case the batch is shorter than max length, the output should be padded
- if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
- generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_length"])
-
- labels = inputs.pop("labels")
- with torch.no_grad():
- # compute loss on predict data
- loss, logits = self._compute_loss(model, inputs, labels)
-
- loss = loss.mean().detach()
- if self.args.prediction_loss_only:
- return (loss, None, None)
-
- logits = generated_tokens if self.args.predict_with_generate else logits
-
- if labels.shape[-1] < gen_kwargs["max_length"]:
- labels = self._pad_tensors_to_max_len(labels, gen_kwargs["max_length"])
-
- return (loss, logits, labels)
-
- def _pad_tensors_to_max_len(self, tensor, max_length):
- # If PAD token is not defined at least EOS token has to be defined
- pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
-
- if pad_token_id is None:
- raise ValueError(
- "Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
- f" padded to `max_length`={max_length}"
- )
-
- padded_tensor = pad_token_id * torch.ones(
- (tensor.shape[0], max_length), dtype=tensor.dtype, device=tensor.device
- )
- padded_tensor[:, : tensor.shape[-1]] = tensor
- return padded_tensor
diff --git a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/test_data/fsmt/build-eval-data.py b/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/test_data/fsmt/build-eval-data.py
deleted file mode 100644
index 46487c07ea8432157448c1e4013ab9d01bd6cd65..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/test_data/fsmt/build-eval-data.py
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/usr/bin/env python
-
-import io
-import json
-import subprocess
-
-
-pairs = [
- ["en", "ru"],
- ["ru", "en"],
- ["en", "de"],
- ["de", "en"],
-]
-
-n_objs = 8
-
-
-def get_all_data(pairs, n_objs):
- text = {}
- for src, tgt in pairs:
- pair = f"{src}-{tgt}"
- cmd = f"sacrebleu -t wmt19 -l {pair} --echo src".split()
- src_lines = subprocess.run(cmd, stdout=subprocess.PIPE).stdout.decode("utf-8").splitlines()
- cmd = f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
- tgt_lines = subprocess.run(cmd, stdout=subprocess.PIPE).stdout.decode("utf-8").splitlines()
- text[pair] = {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
- return text
-
-
-text = get_all_data(pairs, n_objs)
-filename = "./fsmt_val_data.json"
-with io.open(filename, "w", encoding="utf-8") as f:
- bleu_data = json.dump(text, f, indent=2, ensure_ascii=False)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/datatypes/temporal.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/datatypes/temporal.py
deleted file mode 100644
index 05fd65c7f1e13529e4ec785331649f8775dcc9ff..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/datatypes/temporal.py
+++ /dev/null
@@ -1,216 +0,0 @@
-import pytz
-
-from datetime import date, datetime, tzinfo
-from typing import Union, Sequence, MutableSequence
-
-from clickhouse_connect.datatypes.base import TypeDef, ClickHouseType
-from clickhouse_connect.driver.common import write_array, np_date_types, int_size
-from clickhouse_connect.driver.exceptions import ProgrammingError
-from clickhouse_connect.driver.ctypes import data_conv, numpy_conv
-from clickhouse_connect.driver.insert import InsertContext
-from clickhouse_connect.driver.query import QueryContext
-from clickhouse_connect.driver.types import ByteSource
-from clickhouse_connect.driver.options import np, pd
-
-epoch_start_date = date(1970, 1, 1)
-epoch_start_datetime = datetime(1970, 1, 1)
-
-
-class Date(ClickHouseType):
- _array_type = 'H'
- np_type = 'datetime64[D]'
- nano_divisor = 86400 * 1000000000
- valid_formats = 'native', 'int'
- python_type = date
- byte_size = 2
-
- def _read_column_binary(self, source: ByteSource, num_rows: int, ctx: QueryContext):
- if self.read_format(ctx) == 'int':
- return source.read_array(self._array_type, num_rows)
- if ctx.use_numpy:
- return numpy_conv.read_numpy_array(source, 'data warehouse lifecycle toolkit by ralph kimball pdf free download
aaccfb2cb3
-
-
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/Japanese Mom Porn Moviesgolkesgo.md b/spaces/cihyFjudo/fairness-paper-search/Japanese Mom Porn Moviesgolkesgo.md
deleted file mode 100644
index be431619d4bb0c3cfca4b0eb900f5a79dae425bc..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Japanese Mom Porn Moviesgolkesgo.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Japanese Mom Porn Moviesgolkesgo
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cihyFjudo/fairness-paper-search/Swami Ranganathananda Bhagavad Gita 13.pdf The Secrets of Yoga and Meditation Unveiled by a Disciple of Ramakrishna.md b/spaces/cihyFjudo/fairness-paper-search/Swami Ranganathananda Bhagavad Gita 13.pdf The Secrets of Yoga and Meditation Unveiled by a Disciple of Ramakrishna.md
deleted file mode 100644
index 1166b5d74785dfd47d095fe065ac2cdea47941a3..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Swami Ranganathananda Bhagavad Gita 13.pdf The Secrets of Yoga and Meditation Unveiled by a Disciple of Ramakrishna.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Swami Ranganathananda Bhagavad Gita 13.pdf
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cihyFjudo/fairness-paper-search/[Download PIX4Dmapper software Pix4D](1).md b/spaces/cihyFjudo/fairness-paper-search/[Download PIX4Dmapper software Pix4D](1).md
deleted file mode 100644
index bdbc747308db506fb05bc4d20607442cdd421971..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/[Download PIX4Dmapper software Pix4D](1).md
+++ /dev/null
@@ -1,6 +0,0 @@
-Pix4D Pix4Dmapper Pro 2.0.104 (Mac amaral publisher cal
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/etree.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/etree.py
deleted file mode 100644
index 9d4a65c36014c8381306968c69432f50f0c0b886..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/etree.py
+++ /dev/null
@@ -1,478 +0,0 @@
-"""Shim module exporting the same ElementTree API for lxml and
-xml.etree backends.
-
-When lxml is installed, it is automatically preferred over the built-in
-xml.etree module.
-On Python 2.7, the cElementTree module is preferred over the pure-python
-ElementTree module.
-
-Besides exporting a unified interface, this also defines extra functions
-or subclasses built-in ElementTree classes to add features that are
-only availble in lxml, like OrderedDict for attributes, pretty_print and
-iterwalk.
-"""
-from fontTools.misc.textTools import tostr
-
-
-XML_DECLARATION = """"""
-
-__all__ = [
- # public symbols
- "Comment",
- "dump",
- "Element",
- "ElementTree",
- "fromstring",
- "fromstringlist",
- "iselement",
- "iterparse",
- "parse",
- "ParseError",
- "PI",
- "ProcessingInstruction",
- "QName",
- "SubElement",
- "tostring",
- "tostringlist",
- "TreeBuilder",
- "XML",
- "XMLParser",
- "register_namespace",
-]
-
-try:
- from lxml.etree import *
-
- _have_lxml = True
-except ImportError:
- try:
- from xml.etree.cElementTree import *
-
- # the cElementTree version of XML function doesn't support
- # the optional 'parser' keyword argument
- from xml.etree.ElementTree import XML
- except ImportError: # pragma: no cover
- from xml.etree.ElementTree import *
- _have_lxml = False
-
- import sys
-
- # dict is always ordered in python >= 3.6 and on pypy
- PY36 = sys.version_info >= (3, 6)
- try:
- import __pypy__
- except ImportError:
- __pypy__ = None
- _dict_is_ordered = bool(PY36 or __pypy__)
- del PY36, __pypy__
-
- if _dict_is_ordered:
- _Attrib = dict
- else:
- from collections import OrderedDict as _Attrib
-
- if isinstance(Element, type):
- _Element = Element
- else:
- # in py27, cElementTree.Element cannot be subclassed, so
- # we need to import the pure-python class
- from xml.etree.ElementTree import Element as _Element
-
- class Element(_Element):
- """Element subclass that keeps the order of attributes."""
-
- def __init__(self, tag, attrib=_Attrib(), **extra):
- super(Element, self).__init__(tag)
- self.attrib = _Attrib()
- if attrib:
- self.attrib.update(attrib)
- if extra:
- self.attrib.update(extra)
-
- def SubElement(parent, tag, attrib=_Attrib(), **extra):
- """Must override SubElement as well otherwise _elementtree.SubElement
- fails if 'parent' is a subclass of Element object.
- """
- element = parent.__class__(tag, attrib, **extra)
- parent.append(element)
- return element
-
- def _iterwalk(element, events, tag):
- include = tag is None or element.tag == tag
- if include and "start" in events:
- yield ("start", element)
- for e in element:
- for item in _iterwalk(e, events, tag):
- yield item
- if include:
- yield ("end", element)
-
- def iterwalk(element_or_tree, events=("end",), tag=None):
- """A tree walker that generates events from an existing tree as
- if it was parsing XML data with iterparse().
- Drop-in replacement for lxml.etree.iterwalk.
- """
- if iselement(element_or_tree):
- element = element_or_tree
- else:
- element = element_or_tree.getroot()
- if tag == "*":
- tag = None
- for item in _iterwalk(element, events, tag):
- yield item
-
- _ElementTree = ElementTree
-
- class ElementTree(_ElementTree):
- """ElementTree subclass that adds 'pretty_print' and 'doctype'
- arguments to the 'write' method.
- Currently these are only supported for the default XML serialization
- 'method', and not also for "html" or "text", for these are delegated
- to the base class.
- """
-
- def write(
- self,
- file_or_filename,
- encoding=None,
- xml_declaration=False,
- method=None,
- doctype=None,
- pretty_print=False,
- ):
- if method and method != "xml":
- # delegate to super-class
- super(ElementTree, self).write(
- file_or_filename,
- encoding=encoding,
- xml_declaration=xml_declaration,
- method=method,
- )
- return
-
- if encoding is not None and encoding.lower() == "unicode":
- if xml_declaration:
- raise ValueError(
- "Serialisation to unicode must not request an XML declaration"
- )
- write_declaration = False
- encoding = "unicode"
- elif xml_declaration is None:
- # by default, write an XML declaration only for non-standard encodings
- write_declaration = encoding is not None and encoding.upper() not in (
- "ASCII",
- "UTF-8",
- "UTF8",
- "US-ASCII",
- )
- else:
- write_declaration = xml_declaration
-
- if encoding is None:
- encoding = "ASCII"
-
- if pretty_print:
- # NOTE this will modify the tree in-place
- _indent(self._root)
-
- with _get_writer(file_or_filename, encoding) as write:
- if write_declaration:
- write(XML_DECLARATION % encoding.upper())
- if pretty_print:
- write("\n")
- if doctype:
- write(_tounicode(doctype))
- if pretty_print:
- write("\n")
-
- qnames, namespaces = _namespaces(self._root)
- _serialize_xml(write, self._root, qnames, namespaces)
-
- import io
-
- def tostring(
- element,
- encoding=None,
- xml_declaration=None,
- method=None,
- doctype=None,
- pretty_print=False,
- ):
- """Custom 'tostring' function that uses our ElementTree subclass, with
- pretty_print support.
- """
- stream = io.StringIO() if encoding == "unicode" else io.BytesIO()
- ElementTree(element).write(
- stream,
- encoding=encoding,
- xml_declaration=xml_declaration,
- method=method,
- doctype=doctype,
- pretty_print=pretty_print,
- )
- return stream.getvalue()
-
- # serialization support
-
- import re
-
- # Valid XML strings can include any Unicode character, excluding control
- # characters, the surrogate blocks, FFFE, and FFFF:
- # Char ::= #x9 | #xA | #xD | [#x20-#xD7FF] | [#xE000-#xFFFD] | [#x10000-#x10FFFF]
- # Here we reversed the pattern to match only the invalid characters.
- # For the 'narrow' python builds supporting only UCS-2, which represent
- # characters beyond BMP as UTF-16 surrogate pairs, we need to pass through
- # the surrogate block. I haven't found a more elegant solution...
- UCS2 = sys.maxunicode < 0x10FFFF
- if UCS2:
- _invalid_xml_string = re.compile(
- "[\u0000-\u0008\u000B-\u000C\u000E-\u001F\uFFFE-\uFFFF]"
- )
- else:
- _invalid_xml_string = re.compile(
- "[\u0000-\u0008\u000B-\u000C\u000E-\u001F\uD800-\uDFFF\uFFFE-\uFFFF]"
- )
-
- def _tounicode(s):
- """Test if a string is valid user input and decode it to unicode string
- using ASCII encoding if it's a bytes string.
- Reject all bytes/unicode input that contains non-XML characters.
- Reject all bytes input that contains non-ASCII characters.
- """
- try:
- s = tostr(s, encoding="ascii", errors="strict")
- except UnicodeDecodeError:
- raise ValueError(
- "Bytes strings can only contain ASCII characters. "
- "Use unicode strings for non-ASCII characters."
- )
- except AttributeError:
- _raise_serialization_error(s)
- if s and _invalid_xml_string.search(s):
- raise ValueError(
- "All strings must be XML compatible: Unicode or ASCII, "
- "no NULL bytes or control characters"
- )
- return s
-
- import contextlib
-
- @contextlib.contextmanager
- def _get_writer(file_or_filename, encoding):
- # returns text write method and release all resources after using
- try:
- write = file_or_filename.write
- except AttributeError:
- # file_or_filename is a file name
- f = open(
- file_or_filename,
- "w",
- encoding="utf-8" if encoding == "unicode" else encoding,
- errors="xmlcharrefreplace",
- )
- with f:
- yield f.write
- else:
- # file_or_filename is a file-like object
- # encoding determines if it is a text or binary writer
- if encoding == "unicode":
- # use a text writer as is
- yield write
- else:
- # wrap a binary writer with TextIOWrapper
- detach_buffer = False
- if isinstance(file_or_filename, io.BufferedIOBase):
- buf = file_or_filename
- elif isinstance(file_or_filename, io.RawIOBase):
- buf = io.BufferedWriter(file_or_filename)
- detach_buffer = True
- else:
- # This is to handle passed objects that aren't in the
- # IOBase hierarchy, but just have a write method
- buf = io.BufferedIOBase()
- buf.writable = lambda: True
- buf.write = write
- try:
- # TextIOWrapper uses this methods to determine
- # if BOM (for UTF-16, etc) should be added
- buf.seekable = file_or_filename.seekable
- buf.tell = file_or_filename.tell
- except AttributeError:
- pass
- wrapper = io.TextIOWrapper(
- buf,
- encoding=encoding,
- errors="xmlcharrefreplace",
- newline="\n",
- )
- try:
- yield wrapper.write
- finally:
- # Keep the original file open when the TextIOWrapper and
- # the BufferedWriter are destroyed
- wrapper.detach()
- if detach_buffer:
- buf.detach()
-
- from xml.etree.ElementTree import _namespace_map
-
- def _namespaces(elem):
- # identify namespaces used in this tree
-
- # maps qnames to *encoded* prefix:local names
- qnames = {None: None}
-
- # maps uri:s to prefixes
- namespaces = {}
-
- def add_qname(qname):
- # calculate serialized qname representation
- try:
- qname = _tounicode(qname)
- if qname[:1] == "{":
- uri, tag = qname[1:].rsplit("}", 1)
- prefix = namespaces.get(uri)
- if prefix is None:
- prefix = _namespace_map.get(uri)
- if prefix is None:
- prefix = "ns%d" % len(namespaces)
- else:
- prefix = _tounicode(prefix)
- if prefix != "xml":
- namespaces[uri] = prefix
- if prefix:
- qnames[qname] = "%s:%s" % (prefix, tag)
- else:
- qnames[qname] = tag # default element
- else:
- qnames[qname] = qname
- except TypeError:
- _raise_serialization_error(qname)
-
- # populate qname and namespaces table
- for elem in elem.iter():
- tag = elem.tag
- if isinstance(tag, QName):
- if tag.text not in qnames:
- add_qname(tag.text)
- elif isinstance(tag, str):
- if tag not in qnames:
- add_qname(tag)
- elif tag is not None and tag is not Comment and tag is not PI:
- _raise_serialization_error(tag)
- for key, value in elem.items():
- if isinstance(key, QName):
- key = key.text
- if key not in qnames:
- add_qname(key)
- if isinstance(value, QName) and value.text not in qnames:
- add_qname(value.text)
- text = elem.text
- if isinstance(text, QName) and text.text not in qnames:
- add_qname(text.text)
- return qnames, namespaces
-
- def _serialize_xml(write, elem, qnames, namespaces, **kwargs):
- tag = elem.tag
- text = elem.text
- if tag is Comment:
- write("" % _tounicode(text))
- elif tag is ProcessingInstruction:
- write("" % _tounicode(text))
- else:
- tag = qnames[_tounicode(tag) if tag is not None else None]
- if tag is None:
- if text:
- write(_escape_cdata(text))
- for e in elem:
- _serialize_xml(write, e, qnames, None)
- else:
- write("<" + tag)
- if namespaces:
- for uri, prefix in sorted(
- namespaces.items(), key=lambda x: x[1]
- ): # sort on prefix
- if prefix:
- prefix = ":" + prefix
- write(' xmlns%s="%s"' % (prefix, _escape_attrib(uri)))
- attrs = elem.attrib
- if attrs:
- # try to keep existing attrib order
- if len(attrs) <= 1 or type(attrs) is _Attrib:
- items = attrs.items()
- else:
- # if plain dict, use lexical order
- items = sorted(attrs.items())
- for k, v in items:
- if isinstance(k, QName):
- k = _tounicode(k.text)
- else:
- k = _tounicode(k)
- if isinstance(v, QName):
- v = qnames[_tounicode(v.text)]
- else:
- v = _escape_attrib(v)
- write(' %s="%s"' % (qnames[k], v))
- if text is not None or len(elem):
- write(">")
- if text:
- write(_escape_cdata(text))
- for e in elem:
- _serialize_xml(write, e, qnames, None)
- write("")
- else:
- write("/>")
- if elem.tail:
- write(_escape_cdata(elem.tail))
-
- def _raise_serialization_error(text):
- raise TypeError("cannot serialize %r (type %s)" % (text, type(text).__name__))
-
- def _escape_cdata(text):
- # escape character data
- try:
- text = _tounicode(text)
- # it's worth avoiding do-nothing calls for short strings
- if "&" in text:
- text = text.replace("&", "&")
- if "<" in text:
- text = text.replace("<", "<")
- if ">" in text:
- text = text.replace(">", ">")
- return text
- except (TypeError, AttributeError):
- _raise_serialization_error(text)
-
- def _escape_attrib(text):
- # escape attribute value
- try:
- text = _tounicode(text)
- if "&" in text:
- text = text.replace("&", "&")
- if "<" in text:
- text = text.replace("<", "<")
- if ">" in text:
- text = text.replace(">", ">")
- if '"' in text:
- text = text.replace('"', """)
- if "\n" in text:
- text = text.replace("\n", "
")
- return text
- except (TypeError, AttributeError):
- _raise_serialization_error(text)
-
- def _indent(elem, level=0):
- # From http://effbot.org/zone/element-lib.htm#prettyprint
- i = "\n" + level * " "
- if len(elem):
- if not elem.text or not elem.text.strip():
- elem.text = i + " "
- if not elem.tail or not elem.tail.strip():
- elem.tail = i
- for elem in elem:
- _indent(elem, level + 1)
- if not elem.tail or not elem.tail.strip():
- elem.tail = i
- else:
- if level and (not elem.tail or not elem.tail.strip()):
- elem.tail = i
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/ttProgram.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/ttProgram.py
deleted file mode 100644
index 84aa63f36301ec9a4ae21acff0cbc95010d956b7..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/ttProgram.py
+++ /dev/null
@@ -1,593 +0,0 @@
-"""ttLib.tables.ttProgram.py -- Assembler/disassembler for TrueType bytecode programs."""
-from __future__ import annotations
-
-from fontTools.misc.textTools import num2binary, binary2num, readHex, strjoin
-import array
-from io import StringIO
-from typing import List
-import re
-import logging
-
-
-log = logging.getLogger(__name__)
-
-# fmt: off
-
-# first, the list of instructions that eat bytes or words from the instruction stream
-
-streamInstructions = [
-#
-# opcode mnemonic argBits descriptive name pops pushes eats from instruction stream pushes
-#
- (0x40, 'NPUSHB', 0, 'PushNBytes', 0, -1), # n, b1, b2,...bn b1,b2...bn
- (0x41, 'NPUSHW', 0, 'PushNWords', 0, -1), # n, w1, w2,...w w1,w2...wn
- (0xb0, 'PUSHB', 3, 'PushBytes', 0, -1), # b0, b1,..bn b0, b1, ...,bn
- (0xb8, 'PUSHW', 3, 'PushWords', 0, -1), # w0,w1,..wn w0 ,w1, ...wn
-]
-
-
-# next, the list of "normal" instructions
-
-instructions = [
-#
-# opcode mnemonic argBits descriptive name pops pushes eats from instruction stream pushes
-#
- (0x7f, 'AA', 0, 'AdjustAngle', 1, 0), # p -
- (0x64, 'ABS', 0, 'Absolute', 1, 1), # n |n|
- (0x60, 'ADD', 0, 'Add', 2, 1), # n2, n1 (n1 + n2)
- (0x27, 'ALIGNPTS', 0, 'AlignPts', 2, 0), # p2, p1 -
- (0x3c, 'ALIGNRP', 0, 'AlignRelativePt', -1, 0), # p1, p2, ... , ploopvalue -
- (0x5a, 'AND', 0, 'LogicalAnd', 2, 1), # e2, e1 b
- (0x2b, 'CALL', 0, 'CallFunction', 1, 0), # f -
- (0x67, 'CEILING', 0, 'Ceiling', 1, 1), # n ceil(n)
- (0x25, 'CINDEX', 0, 'CopyXToTopStack', 1, 1), # k ek
- (0x22, 'CLEAR', 0, 'ClearStack', -1, 0), # all items on the stack -
- (0x4f, 'DEBUG', 0, 'DebugCall', 1, 0), # n -
- (0x73, 'DELTAC1', 0, 'DeltaExceptionC1', -1, 0), # argn, cn, argn-1,cn-1, , arg1, c1 -
- (0x74, 'DELTAC2', 0, 'DeltaExceptionC2', -1, 0), # argn, cn, argn-1,cn-1, , arg1, c1 -
- (0x75, 'DELTAC3', 0, 'DeltaExceptionC3', -1, 0), # argn, cn, argn-1,cn-1, , arg1, c1 -
- (0x5d, 'DELTAP1', 0, 'DeltaExceptionP1', -1, 0), # argn, pn, argn-1, pn-1, , arg1, p1 -
- (0x71, 'DELTAP2', 0, 'DeltaExceptionP2', -1, 0), # argn, pn, argn-1, pn-1, , arg1, p1 -
- (0x72, 'DELTAP3', 0, 'DeltaExceptionP3', -1, 0), # argn, pn, argn-1, pn-1, , arg1, p1 -
- (0x24, 'DEPTH', 0, 'GetDepthStack', 0, 1), # - n
- (0x62, 'DIV', 0, 'Divide', 2, 1), # n2, n1 (n1 * 64)/ n2
- (0x20, 'DUP', 0, 'DuplicateTopStack', 1, 2), # e e, e
- (0x59, 'EIF', 0, 'EndIf', 0, 0), # - -
- (0x1b, 'ELSE', 0, 'Else', 0, 0), # - -
- (0x2d, 'ENDF', 0, 'EndFunctionDefinition', 0, 0), # - -
- (0x54, 'EQ', 0, 'Equal', 2, 1), # e2, e1 b
- (0x57, 'EVEN', 0, 'Even', 1, 1), # e b
- (0x2c, 'FDEF', 0, 'FunctionDefinition', 1, 0), # f -
- (0x4e, 'FLIPOFF', 0, 'SetAutoFlipOff', 0, 0), # - -
- (0x4d, 'FLIPON', 0, 'SetAutoFlipOn', 0, 0), # - -
- (0x80, 'FLIPPT', 0, 'FlipPoint', -1, 0), # p1, p2, ..., ploopvalue -
- (0x82, 'FLIPRGOFF', 0, 'FlipRangeOff', 2, 0), # h, l -
- (0x81, 'FLIPRGON', 0, 'FlipRangeOn', 2, 0), # h, l -
- (0x66, 'FLOOR', 0, 'Floor', 1, 1), # n floor(n)
- (0x46, 'GC', 1, 'GetCoordOnPVector', 1, 1), # p c
- (0x88, 'GETINFO', 0, 'GetInfo', 1, 1), # selector result
- (0x91, 'GETVARIATION', 0, 'GetVariation', 0, -1), # - a1,..,an
- (0x0d, 'GFV', 0, 'GetFVector', 0, 2), # - px, py
- (0x0c, 'GPV', 0, 'GetPVector', 0, 2), # - px, py
- (0x52, 'GT', 0, 'GreaterThan', 2, 1), # e2, e1 b
- (0x53, 'GTEQ', 0, 'GreaterThanOrEqual', 2, 1), # e2, e1 b
- (0x89, 'IDEF', 0, 'InstructionDefinition', 1, 0), # f -
- (0x58, 'IF', 0, 'If', 1, 0), # e -
- (0x8e, 'INSTCTRL', 0, 'SetInstrExecControl', 2, 0), # s, v -
- (0x39, 'IP', 0, 'InterpolatePts', -1, 0), # p1, p2, ... , ploopvalue -
- (0x0f, 'ISECT', 0, 'MovePtToIntersect', 5, 0), # a1, a0, b1, b0, p -
- (0x30, 'IUP', 1, 'InterpolateUntPts', 0, 0), # - -
- (0x1c, 'JMPR', 0, 'Jump', 1, 0), # offset -
- (0x79, 'JROF', 0, 'JumpRelativeOnFalse', 2, 0), # e, offset -
- (0x78, 'JROT', 0, 'JumpRelativeOnTrue', 2, 0), # e, offset -
- (0x2a, 'LOOPCALL', 0, 'LoopAndCallFunction', 2, 0), # f, count -
- (0x50, 'LT', 0, 'LessThan', 2, 1), # e2, e1 b
- (0x51, 'LTEQ', 0, 'LessThenOrEqual', 2, 1), # e2, e1 b
- (0x8b, 'MAX', 0, 'Maximum', 2, 1), # e2, e1 max(e1, e2)
- (0x49, 'MD', 1, 'MeasureDistance', 2, 1), # p2,p1 d
- (0x2e, 'MDAP', 1, 'MoveDirectAbsPt', 1, 0), # p -
- (0xc0, 'MDRP', 5, 'MoveDirectRelPt', 1, 0), # p -
- (0x3e, 'MIAP', 1, 'MoveIndirectAbsPt', 2, 0), # n, p -
- (0x8c, 'MIN', 0, 'Minimum', 2, 1), # e2, e1 min(e1, e2)
- (0x26, 'MINDEX', 0, 'MoveXToTopStack', 1, 1), # k ek
- (0xe0, 'MIRP', 5, 'MoveIndirectRelPt', 2, 0), # n, p -
- (0x4b, 'MPPEM', 0, 'MeasurePixelPerEm', 0, 1), # - ppem
- (0x4c, 'MPS', 0, 'MeasurePointSize', 0, 1), # - pointSize
- (0x3a, 'MSIRP', 1, 'MoveStackIndirRelPt', 2, 0), # d, p -
- (0x63, 'MUL', 0, 'Multiply', 2, 1), # n2, n1 (n1 * n2)/64
- (0x65, 'NEG', 0, 'Negate', 1, 1), # n -n
- (0x55, 'NEQ', 0, 'NotEqual', 2, 1), # e2, e1 b
- (0x5c, 'NOT', 0, 'LogicalNot', 1, 1), # e ( not e )
- (0x6c, 'NROUND', 2, 'NoRound', 1, 1), # n1 n2
- (0x56, 'ODD', 0, 'Odd', 1, 1), # e b
- (0x5b, 'OR', 0, 'LogicalOr', 2, 1), # e2, e1 b
- (0x21, 'POP', 0, 'PopTopStack', 1, 0), # e -
- (0x45, 'RCVT', 0, 'ReadCVT', 1, 1), # location value
- (0x7d, 'RDTG', 0, 'RoundDownToGrid', 0, 0), # - -
- (0x7a, 'ROFF', 0, 'RoundOff', 0, 0), # - -
- (0x8a, 'ROLL', 0, 'RollTopThreeStack', 3, 3), # a,b,c b,a,c
- (0x68, 'ROUND', 2, 'Round', 1, 1), # n1 n2
- (0x43, 'RS', 0, 'ReadStore', 1, 1), # n v
- (0x3d, 'RTDG', 0, 'RoundToDoubleGrid', 0, 0), # - -
- (0x18, 'RTG', 0, 'RoundToGrid', 0, 0), # - -
- (0x19, 'RTHG', 0, 'RoundToHalfGrid', 0, 0), # - -
- (0x7c, 'RUTG', 0, 'RoundUpToGrid', 0, 0), # - -
- (0x77, 'S45ROUND', 0, 'SuperRound45Degrees', 1, 0), # n -
- (0x7e, 'SANGW', 0, 'SetAngleWeight', 1, 0), # weight -
- (0x85, 'SCANCTRL', 0, 'ScanConversionControl', 1, 0), # n -
- (0x8d, 'SCANTYPE', 0, 'ScanType', 1, 0), # n -
- (0x48, 'SCFS', 0, 'SetCoordFromStackFP', 2, 0), # c, p -
- (0x1d, 'SCVTCI', 0, 'SetCVTCutIn', 1, 0), # n -
- (0x5e, 'SDB', 0, 'SetDeltaBaseInGState', 1, 0), # n -
- (0x86, 'SDPVTL', 1, 'SetDualPVectorToLine', 2, 0), # p2, p1 -
- (0x5f, 'SDS', 0, 'SetDeltaShiftInGState', 1, 0), # n -
- (0x0b, 'SFVFS', 0, 'SetFVectorFromStack', 2, 0), # y, x -
- (0x04, 'SFVTCA', 1, 'SetFVectorToAxis', 0, 0), # - -
- (0x08, 'SFVTL', 1, 'SetFVectorToLine', 2, 0), # p2, p1 -
- (0x0e, 'SFVTPV', 0, 'SetFVectorToPVector', 0, 0), # - -
- (0x34, 'SHC', 1, 'ShiftContourByLastPt', 1, 0), # c -
- (0x32, 'SHP', 1, 'ShiftPointByLastPoint', -1, 0), # p1, p2, ..., ploopvalue -
- (0x38, 'SHPIX', 0, 'ShiftZoneByPixel', -1, 0), # d, p1, p2, ..., ploopvalue -
- (0x36, 'SHZ', 1, 'ShiftZoneByLastPoint', 1, 0), # e -
- (0x17, 'SLOOP', 0, 'SetLoopVariable', 1, 0), # n -
- (0x1a, 'SMD', 0, 'SetMinimumDistance', 1, 0), # distance -
- (0x0a, 'SPVFS', 0, 'SetPVectorFromStack', 2, 0), # y, x -
- (0x02, 'SPVTCA', 1, 'SetPVectorToAxis', 0, 0), # - -
- (0x06, 'SPVTL', 1, 'SetPVectorToLine', 2, 0), # p2, p1 -
- (0x76, 'SROUND', 0, 'SuperRound', 1, 0), # n -
- (0x10, 'SRP0', 0, 'SetRefPoint0', 1, 0), # p -
- (0x11, 'SRP1', 0, 'SetRefPoint1', 1, 0), # p -
- (0x12, 'SRP2', 0, 'SetRefPoint2', 1, 0), # p -
- (0x1f, 'SSW', 0, 'SetSingleWidth', 1, 0), # n -
- (0x1e, 'SSWCI', 0, 'SetSingleWidthCutIn', 1, 0), # n -
- (0x61, 'SUB', 0, 'Subtract', 2, 1), # n2, n1 (n1 - n2)
- (0x00, 'SVTCA', 1, 'SetFPVectorToAxis', 0, 0), # - -
- (0x23, 'SWAP', 0, 'SwapTopStack', 2, 2), # e2, e1 e1, e2
- (0x13, 'SZP0', 0, 'SetZonePointer0', 1, 0), # n -
- (0x14, 'SZP1', 0, 'SetZonePointer1', 1, 0), # n -
- (0x15, 'SZP2', 0, 'SetZonePointer2', 1, 0), # n -
- (0x16, 'SZPS', 0, 'SetZonePointerS', 1, 0), # n -
- (0x29, 'UTP', 0, 'UnTouchPt', 1, 0), # p -
- (0x70, 'WCVTF', 0, 'WriteCVTInFUnits', 2, 0), # n, l -
- (0x44, 'WCVTP', 0, 'WriteCVTInPixels', 2, 0), # v, l -
- (0x42, 'WS', 0, 'WriteStore', 2, 0), # v, l -
-]
-
-# fmt: on
-
-
-def bitRepr(value, bits):
- s = ""
- for i in range(bits):
- s = "01"[value & 0x1] + s
- value = value >> 1
- return s
-
-
-_mnemonicPat = re.compile(r"[A-Z][A-Z0-9]*$")
-
-
-def _makeDict(instructionList):
- opcodeDict = {}
- mnemonicDict = {}
- for op, mnemonic, argBits, name, pops, pushes in instructionList:
- assert _mnemonicPat.match(mnemonic)
- mnemonicDict[mnemonic] = op, argBits, name
- if argBits:
- argoffset = op
- for i in range(1 << argBits):
- opcodeDict[op + i] = mnemonic, argBits, argoffset, name
- else:
- opcodeDict[op] = mnemonic, 0, 0, name
- return opcodeDict, mnemonicDict
-
-
-streamOpcodeDict, streamMnemonicDict = _makeDict(streamInstructions)
-opcodeDict, mnemonicDict = _makeDict(instructions)
-
-
-class tt_instructions_error(Exception):
- def __init__(self, error):
- self.error = error
-
- def __str__(self):
- return "TT instructions error: %s" % repr(self.error)
-
-
-_comment = r"/\*.*?\*/"
-_instruction = r"([A-Z][A-Z0-9]*)\s*\[(.*?)\]"
-_number = r"-?[0-9]+"
-_token = "(%s)|(%s)|(%s)" % (_instruction, _number, _comment)
-
-_tokenRE = re.compile(_token)
-_whiteRE = re.compile(r"\s*")
-
-_pushCountPat = re.compile(r"[A-Z][A-Z0-9]*\s*\[.*?\]\s*/\* ([0-9]+).*?\*/")
-
-_indentRE = re.compile(r"^FDEF|IF|ELSE\[ \]\t.+")
-_unindentRE = re.compile(r"^ELSE|ENDF|EIF\[ \]\t.+")
-
-
-def _skipWhite(data, pos):
- m = _whiteRE.match(data, pos)
- newPos = m.regs[0][1]
- assert newPos >= pos
- return newPos
-
-
-class Program(object):
- def __init__(self) -> None:
- pass
-
- def fromBytecode(self, bytecode: bytes) -> None:
- self.bytecode = array.array("B", bytecode)
- if hasattr(self, "assembly"):
- del self.assembly
-
- def fromAssembly(self, assembly: List[str] | str) -> None:
- if isinstance(assembly, list):
- self.assembly = assembly
- elif isinstance(assembly, str):
- self.assembly = assembly.splitlines()
- else:
- raise TypeError(f"expected str or List[str], got {type(assembly).__name__}")
- if hasattr(self, "bytecode"):
- del self.bytecode
-
- def getBytecode(self) -> bytes:
- if not hasattr(self, "bytecode"):
- self._assemble()
- return self.bytecode.tobytes()
-
- def getAssembly(self, preserve=True) -> List[str]:
- if not hasattr(self, "assembly"):
- self._disassemble(preserve=preserve)
- return self.assembly
-
- def toXML(self, writer, ttFont) -> None:
- if (
- not hasattr(ttFont, "disassembleInstructions")
- or ttFont.disassembleInstructions
- ):
- try:
- assembly = self.getAssembly()
- except:
- import traceback
-
- tmp = StringIO()
- traceback.print_exc(file=tmp)
- msg = "An exception occurred during the decompilation of glyph program:\n\n"
- msg += tmp.getvalue()
- log.error(msg)
- writer.begintag("bytecode")
- writer.newline()
- writer.comment(msg.strip())
- writer.newline()
- writer.dumphex(self.getBytecode())
- writer.endtag("bytecode")
- writer.newline()
- else:
- if not assembly:
- return
- writer.begintag("assembly")
- writer.newline()
- i = 0
- indent = 0
- nInstr = len(assembly)
- while i < nInstr:
- instr = assembly[i]
- if _unindentRE.match(instr):
- indent -= 1
- writer.write(writer.indentwhite * indent)
- writer.write(instr)
- writer.newline()
- m = _pushCountPat.match(instr)
- i = i + 1
- if m:
- nValues = int(m.group(1))
- line: List[str] = []
- j = 0
- for j in range(nValues):
- if j and not (j % 25):
- writer.write(writer.indentwhite * indent)
- writer.write(" ".join(line))
- writer.newline()
- line = []
- line.append(assembly[i + j])
- writer.write(writer.indentwhite * indent)
- writer.write(" ".join(line))
- writer.newline()
- i = i + j + 1
- if _indentRE.match(instr):
- indent += 1
- writer.endtag("assembly")
- writer.newline()
- else:
- bytecode = self.getBytecode()
- if not bytecode:
- return
- writer.begintag("bytecode")
- writer.newline()
- writer.dumphex(bytecode)
- writer.endtag("bytecode")
- writer.newline()
-
- def fromXML(self, name, attrs, content, ttFont) -> None:
- if name == "assembly":
- self.fromAssembly(strjoin(content))
- self._assemble()
- del self.assembly
- else:
- assert name == "bytecode"
- self.fromBytecode(readHex(content))
-
- def _assemble(self) -> None:
- assembly = " ".join(getattr(self, "assembly", []))
- bytecode: List[int] = []
- push = bytecode.append
- lenAssembly = len(assembly)
- pos = _skipWhite(assembly, 0)
- while pos < lenAssembly:
- m = _tokenRE.match(assembly, pos)
- if m is None:
- raise tt_instructions_error(
- "Syntax error in TT program (%s)" % assembly[pos - 5 : pos + 15]
- )
- dummy, mnemonic, arg, number, comment = m.groups()
- pos = m.regs[0][1]
- if comment:
- pos = _skipWhite(assembly, pos)
- continue
-
- arg = arg.strip()
- if mnemonic.startswith("INSTR"):
- # Unknown instruction
- op = int(mnemonic[5:])
- push(op)
- elif mnemonic not in ("PUSH", "NPUSHB", "NPUSHW", "PUSHB", "PUSHW"):
- op, argBits, name = mnemonicDict[mnemonic]
- if len(arg) != argBits:
- raise tt_instructions_error(
- "Incorrect number of argument bits (%s[%s])" % (mnemonic, arg)
- )
- if arg:
- arg = binary2num(arg)
- push(op + arg)
- else:
- push(op)
- else:
- args = []
- pos = _skipWhite(assembly, pos)
- while pos < lenAssembly:
- m = _tokenRE.match(assembly, pos)
- if m is None:
- raise tt_instructions_error(
- "Syntax error in TT program (%s)" % assembly[pos : pos + 15]
- )
- dummy, _mnemonic, arg, number, comment = m.groups()
- if number is None and comment is None:
- break
- pos = m.regs[0][1]
- pos = _skipWhite(assembly, pos)
- if comment is not None:
- continue
- args.append(int(number))
- nArgs = len(args)
- if mnemonic == "PUSH":
- # Automatically choose the most compact representation
- nWords = 0
- while nArgs:
- while (
- nWords < nArgs
- and nWords < 255
- and not (0 <= args[nWords] <= 255)
- ):
- nWords += 1
- nBytes = 0
- while (
- nWords + nBytes < nArgs
- and nBytes < 255
- and 0 <= args[nWords + nBytes] <= 255
- ):
- nBytes += 1
- if (
- nBytes < 2
- and nWords + nBytes < 255
- and nWords + nBytes != nArgs
- ):
- # Will write bytes as words
- nWords += nBytes
- continue
-
- # Write words
- if nWords:
- if nWords <= 8:
- op, argBits, name = streamMnemonicDict["PUSHW"]
- op = op + nWords - 1
- push(op)
- else:
- op, argBits, name = streamMnemonicDict["NPUSHW"]
- push(op)
- push(nWords)
- for value in args[:nWords]:
- assert -32768 <= value < 32768, (
- "PUSH value out of range %d" % value
- )
- push((value >> 8) & 0xFF)
- push(value & 0xFF)
-
- # Write bytes
- if nBytes:
- pass
- if nBytes <= 8:
- op, argBits, name = streamMnemonicDict["PUSHB"]
- op = op + nBytes - 1
- push(op)
- else:
- op, argBits, name = streamMnemonicDict["NPUSHB"]
- push(op)
- push(nBytes)
- for value in args[nWords : nWords + nBytes]:
- push(value)
-
- nTotal = nWords + nBytes
- args = args[nTotal:]
- nArgs -= nTotal
- nWords = 0
- else:
- # Write exactly what we've been asked to
- words = mnemonic[-1] == "W"
- op, argBits, name = streamMnemonicDict[mnemonic]
- if mnemonic[0] != "N":
- assert nArgs <= 8, nArgs
- op = op + nArgs - 1
- push(op)
- else:
- assert nArgs < 256
- push(op)
- push(nArgs)
- if words:
- for value in args:
- assert -32768 <= value < 32768, (
- "PUSHW value out of range %d" % value
- )
- push((value >> 8) & 0xFF)
- push(value & 0xFF)
- else:
- for value in args:
- assert 0 <= value < 256, (
- "PUSHB value out of range %d" % value
- )
- push(value)
-
- pos = _skipWhite(assembly, pos)
-
- if bytecode:
- assert max(bytecode) < 256 and min(bytecode) >= 0
- self.bytecode = array.array("B", bytecode)
-
- def _disassemble(self, preserve=False) -> None:
- assembly = []
- i = 0
- bytecode = getattr(self, "bytecode", [])
- numBytecode = len(bytecode)
- while i < numBytecode:
- op = bytecode[i]
- try:
- mnemonic, argBits, argoffset, name = opcodeDict[op]
- except KeyError:
- if op in streamOpcodeDict:
- values = []
-
- # Merge consecutive PUSH operations
- while bytecode[i] in streamOpcodeDict:
- op = bytecode[i]
- mnemonic, argBits, argoffset, name = streamOpcodeDict[op]
- words = mnemonic[-1] == "W"
- if argBits:
- nValues = op - argoffset + 1
- else:
- i = i + 1
- nValues = bytecode[i]
- i = i + 1
- assert nValues > 0
- if not words:
- for j in range(nValues):
- value = bytecode[i]
- values.append(repr(value))
- i = i + 1
- else:
- for j in range(nValues):
- # cast to signed int16
- value = (bytecode[i] << 8) | bytecode[i + 1]
- if value >= 0x8000:
- value = value - 0x10000
- values.append(repr(value))
- i = i + 2
- if preserve:
- break
-
- if not preserve:
- mnemonic = "PUSH"
- nValues = len(values)
- if nValues == 1:
- assembly.append("%s[ ] /* 1 value pushed */" % mnemonic)
- else:
- assembly.append(
- "%s[ ] /* %s values pushed */" % (mnemonic, nValues)
- )
- assembly.extend(values)
- else:
- assembly.append("INSTR%d[ ]" % op)
- i = i + 1
- else:
- if argBits:
- assembly.append(
- mnemonic
- + "[%s] /* %s */" % (num2binary(op - argoffset, argBits), name)
- )
- else:
- assembly.append(mnemonic + "[ ] /* %s */" % name)
- i = i + 1
- self.assembly = assembly
-
- def __bool__(self) -> bool:
- """
- >>> p = Program()
- >>> bool(p)
- False
- >>> bc = array.array("B", [0])
- >>> p.fromBytecode(bc)
- >>> bool(p)
- True
- >>> p.bytecode.pop()
- 0
- >>> bool(p)
- False
-
- >>> p = Program()
- >>> asm = ['SVTCA[0]']
- >>> p.fromAssembly(asm)
- >>> bool(p)
- True
- >>> p.assembly.pop()
- 'SVTCA[0]'
- >>> bool(p)
- False
- """
- return (hasattr(self, "assembly") and len(self.assembly) > 0) or (
- hasattr(self, "bytecode") and len(self.bytecode) > 0
- )
-
- __nonzero__ = __bool__
-
- def __eq__(self, other) -> bool:
- if type(self) != type(other):
- return NotImplemented
- return self.__dict__ == other.__dict__
-
- def __ne__(self, other) -> bool:
- result = self.__eq__(other)
- return result if result is NotImplemented else not result
-
-
-def _test():
- """
- >>> _test()
- True
- """
-
- bc = b"""@;:9876543210/.-,+*)(\'&%$#"! \037\036\035\034\033\032\031\030\027\026\025\024\023\022\021\020\017\016\015\014\013\012\011\010\007\006\005\004\003\002\001\000,\001\260\030CXEj\260\031C`\260F#D#\020 \260FN\360M/\260\000\022\033!#\0213Y-,\001\260\030CX\260\005+\260\000\023K\260\024PX\261\000@8Y\260\006+\033!#\0213Y-,\001\260\030CXN\260\003%\020\362!\260\000\022M\033 E\260\004%\260\004%#Jad\260(RX!#\020\326\033\260\003%\020\362!\260\000\022YY-,\260\032CX!!\033\260\002%\260\002%I\260\003%\260\003%Ja d\260\020PX!!!\033\260\003%\260\003%I\260\000PX\260\000PX\270\377\3428!\033\260\0208!Y\033\260\000RX\260\0368!\033\270\377\3608!YYYY-,\001\260\030CX\260\005+\260\000\023K\260\024PX\271\000\000\377\3008Y\260\006+\033!#\0213Y-,N\001\212\020\261F\031CD\260\000\024\261\000F\342\260\000\025\271\000\000\377\3608\000\260\000<\260(+\260\002%\020\260\000<-,\001\030\260\000/\260\001\024\362\260\001\023\260\001\025M\260\000\022-,\001\260\030CX\260\005+\260\000\023\271\000\000\377\3408\260\006+\033!#\0213Y-,\001\260\030CXEdj#Edi\260\031Cd``\260F#D#\020 \260F\360/\260\000\022\033!! \212 \212RX\0213\033!!YY-,\001\261\013\012C#Ce\012-,\000\261\012\013C#C\013-,\000\260F#p\261\001F>\001\260F#p\261\002FE:\261\002\000\010\015-,\260\022+\260\002%E\260\002%Ej\260@\213`\260\002%#D!!!-,\260\023+\260\002%E\260\002%Ej\270\377\300\214`\260\002%#D!!!-,\260\000\260\022+!!!-,\260\000\260\023+!!!-,\001\260\006C\260\007Ce\012-, i\260@a\260\000\213 \261,\300\212\214\270\020\000b`+\014d#da\\X\260\003aY-,\261\000\003%EhT\260\034KPZX\260\003%E\260\003%E`h \260\004%#D\260\004%#D\033\260\003% Eh \212#D\260\003%Eh`\260\003%#DY-,\260\003% Eh \212#D\260\003%Edhe`\260\004%\260\001`#D-,\260\011CX\207!\300\033\260\022CX\207E\260\021+\260G#D\260Gz\344\033\003\212E\030i \260G#D\212\212\207 \260\240QX\260\021+\260G#D\260Gz\344\033!\260Gz\344YYY\030-, \212E#Eh`D-,EjB-,\001\030/-,\001\260\030CX\260\004%\260\004%Id#Edi\260@\213a \260\200bj\260\002%\260\002%a\214\260\031C`\260F#D!\212\020\260F\366!\033!!!!Y-,\001\260\030CX\260\002%E\260\002%Ed`j\260\003%Eja \260\004%Ej \212\213e\260\004%#D\214\260\003%#D!!\033 EjD EjDY-,\001 E\260\000U\260\030CZXEh#Ei\260@\213a \260\200bj \212#a \260\003%\213e\260\004%#D\214\260\003%#D!!\033!!\260\031+Y-,\001\212\212Ed#EdadB-,\260\004%\260\004%\260\031+\260\030CX\260\004%\260\004%\260\003%\260\033+\001\260\002%C\260@T\260\002%C\260\000TZX\260\003% E\260@aDY\260\002%C\260\000T\260\002%C\260@TZX\260\004% E\260@`DYY!!!!-,\001KRXC\260\002%E#aD\033!!Y-,\001KRXC\260\002%E#`D\033!!Y-,KRXED\033!!Y-,\001 \260\003%#I\260@`\260 c \260\000RX#\260\002%8#\260\002%e8\000\212c8\033!!!!!Y\001-,KPXED\033!!Y-,\001\260\005%\020# \212\365\000\260\001`#\355\354-,\001\260\005%\020# \212\365\000\260\001a#\355\354-,\001\260\006%\020\365\000\355\354-,F#F`\212\212F# F\212`\212a\270\377\200b# \020#\212\261KK\212pE` \260\000PX\260\001a\270\377\272\213\033\260F\214Y\260\020`h\001:-, E\260\003%FRX\260\002%F ha\260\003%\260\003%?#!8\033!\021Y-, E\260\003%FPX\260\002%F ha\260\003%\260\003%?#!8\033!\021Y-,\000\260\007C\260\006C\013-,\212\020\354-,\260\014CX!\033 F\260\000RX\270\377\3608\033\260\0208YY-, \260\000UX\270\020\000c\260\003%Ed\260\003%Eda\260\000SX\260\002\033\260@a\260\003Y%EiSXED\033!!Y\033!\260\002%E\260\002%Ead\260(QXED\033!!YY-,!!\014d#d\213\270@\000b-,!\260\200QX\014d#d\213\270 \000b\033\262\000@/+Y\260\002`-,!\260\300QX\014d#d\213\270\025Ub\033\262\000\200/+Y\260\002`-,\014d#d\213\270@\000b`#!-,KSX\260\004%\260\004%Id#Edi\260@\213a \260\200bj\260\002%\260\002%a\214\260F#D!\212\020\260F\366!\033!\212\021#\022 9/Y-,\260\002%\260\002%Id\260\300TX\270\377\3708\260\0108\033!!Y-,\260\023CX\003\033\002Y-,\260\023CX\002\033\003Y-,\260\012+#\020 <\260\027+-,\260\002%\270\377\3608\260(+\212\020# \320#\260\020+\260\005CX\300\033How to Download and Play Dark Riddle on PC
-What is Dark Riddle?
-dark riddle download pc
- A thrilling action game with puzzles and secrets
-A single-player adventure with different characters and creatures
-A challenging gameplay with obstacles, traps, and collectibles
-Why play Dark Riddle on PC?
-Enjoy a larger and better display
Playing Dark Riddle on PC will allow you to enjoy a larger and better display than your phone or tablet. You will be able to see more details and colors of the game's graphics and animations. You will also have a wider view of the game's environment and interface. You will be able to appreciate the game's design and art more on a bigger screen.
Playing Dark Riddle on PC will also give you a faster and smoother performance than your mobile device. You will not have to worry about lagging, crashing, or freezing issues that might ruin your gameplay. You will also not have to deal with battery drain, overheating, or storage problems that might affect your device. You will be able to play the game without any interruptions or distractions.
-Playing Dark Riddle on PC will also let you use keyboard and mouse controls for more accuracy and comfort. You will not have to rely on touch controls that might be inaccurate, unresponsive, or uncomfortable. You will be able to control your character and interact with the game's elements more easily and precisely. You will also be able to customize your key mapping and mouse sensitivity according to your preference. You will have a better gaming experience with keyboard and mouse controls.
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Now that you know why playing Dark Riddle on PC is a good idea, you might be wondering how to do it. The answer is simple: you need an emulator. An emulator is a software that allows you to run Android apps and games on your PC or Mac. With an emulator, you can download and play Dark Riddle on PC just like you would on your mobile device. Here are the steps to follow:
-The first step is to choose a reliable and safe emulator that can run Dark Riddle on PC smoothly and securely. There are many emulators available online, but not all of them are trustworthy or compatible. Some emulators might contain malware, spyware, or viruses that might harm your PC or Mac. Some emulators might not support Dark Riddle or other games that you want to play. Some emulators might have poor performance, quality, or features that might affect your gameplay.
-Therefore, you need to do some research and comparison before choosing an emulator. You need to check the emulator's reputation, reviews, ratings, compatibility, security, performance, quality, and features. You need to make sure that the emulator can run Dark Riddle on PC without any problems or risks.
-One of the best emulators that we recommend is LDPlayer. LDPlayer is a free Android emulator for PC that can run Dark Riddle and other games smoothly and safely. LDPlayer has a high reputation, positive reviews, high ratings, wide compatibility, strong security, fast performance, excellent quality, and rich features. LDPlayer can provide you with the best gaming experience on PC.
-The second step is to install the emulator on your PC or Mac. This is a simple and easy process that will not take much time or effort. Here are the steps to follow:
-Congratulations! You have successfully installed LDPlayer on your PC or Mac.
-The third step is to sign in to Google Play Store or download the APK file of Dark Riddle on your PC or Mac. This is also a simple and easy process that will not take much time or effort. Here are the steps to follow:
-Alternatively, you can also download the APK file of Dark Riddle from a trusted source and drag it into LDPlayer. LDPlayer will automatically install it for you.
-Congratulations! You have successfully downloaded and installed Dark Riddle on your PC or Mac.
-The fourth step is
The fourth step is to install and launch Dark Riddle on the emulator. This is the final and most exciting step, as you will be able to play the game on your PC or Mac. Here are the steps to follow:
-Congratulations! You have successfully installed and launched Dark Riddle on your PC or Mac.
-Dark Riddle is a fantastic action game that will keep you entertained and engaged for hours. You will love the game's story, graphics, characters, puzzles, and secrets. You will also love playing the game on your PC or Mac, as you will get a better display, performance, and controls. All you need is an emulator like LDPlayer, and you can download and play Dark Riddle on PC easily and safely. So what are you waiting for? Download LDPlayer and Dark Riddle today and start your adventure!
-Here are some frequently asked questions about Dark Riddle and playing it on PC:
-Yes, Dark Riddle is free to play on both Android devices and PC or Mac with an emulator. However, the game does offer in-app purchases that can enhance your gameplay or unlock more features.
-Yes, Dark Riddle is safe to play on both Android devices and PC or Mac with an emulator. The game does not contain any harmful or inappropriate content that might affect your device or yourself. However, you should always be careful when downloading apps or games from unknown sources, as they might contain malware or viruses. You should also use a reliable and safe emulator like LDPlayer to play Dark Riddle on PC.
-Dark Riddle is a relatively long game that can take you several hours to complete. The game has many levels, puzzles, secrets, and endings that will keep you hooked and curious. The game also has a replay value, as you can try different choices and actions that might lead to different outcomes.
-No, Dark Riddle requires an internet connection to play on both Android devices and PC or Mac with an emulator. The game needs to access online features such as leaderboards, achievements, ads, etc. You also need an internet connection to download and update the game.
-No, Dark Riddle is a single-player game that does not support multiplayer or co-op modes. The game is designed to be a solo adventure that will challenge you to solve the mystery of your neighbor. However, you can still share your progress and achievements with your friends through social media or other platforms.
401be4b1e0WhatsApp is one of the most popular and widely used messaging and calling apps in the world. It allows you to send text messages, voice messages, photos, videos, documents, stickers, GIFs, and more to your contacts. You can also make voice and video calls with high quality and low data usage. But did you know that you can also use WhatsApp on your computer?
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WhatsApp is a free app that uses your phone's internet connection (4G/3G/2G/EDGE or Wi-Fi, as available) to let you message and call friends and family. You can use it on your smartphone, tablet, or desktop. Using WhatsApp on your computer has many benefits, such as:
-To use WhatsApp on your computer, you need to have:
-You have two options for using WhatsApp on your computer:
-Both options are similar in functionality and appearance, but there are some differences. For example, WhatsApp Desktop allows you to use keyboard shortcuts, mute notifications, auto-start on login, etc. WhatsApp Web requires you to keep a browser tab open and may consume more battery power.
-How to download and install WhatsApp on PC in 2020
-WhatsApp for PC: A step-by-step guide to set up and use WhatsApp on your computer
-WhatsApp Web: How to access WhatsApp from your browser and sync with your phone
-How to run WhatsApp on Windows 10 with an emulator
-WhatsApp Desktop: The official app for using WhatsApp on your PC
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In your computer's browser, go to the WhatsApp Download page. You will see different options for downloading WhatsApp Desktop for different operating systems.
-Select the version that matches your operating system. For example, if you have a Windows 10 64-bit computer, choose "Windows (64-bit)". If you have a Mac OS X 10.11 or newer computer, choose "Mac OS X". The download will start automatically.
-Once the download is complete, open the .exe or .d mg or .zip file and follow the prompts to install WhatsApp Desktop on your computer. The installation process may vary depending on your operating system, but it is usually simple and straightforward. You may need to agree to the terms and conditions, choose a destination folder, create a shortcut, etc.
-After the installation is complete, you can launch WhatsApp Desktop from your desktop, start menu, or applications folder. You will see a QR code on the screen that you need to scan with your phone.
-On your phone, open WhatsApp and tap the menu button (three dots) in the top right corner. Then tap "WhatsApp Web". You will see a camera screen that you need to point at the QR code on your computer. Once the scan is successful, you will be logged in to WhatsApp Desktop.
-Now you can use WhatsApp Desktop to chat and call with your contacts. You will see a familiar interface with your chats on the left and the chat window on the right. You can also access your settings, profile, status, etc. from the menu button in the top left corner. You can send and receive messages, media, documents, stickers, GIFs, etc. as you would on your phone. You can also make voice and video calls by clicking the phone or camera icon in the top right corner of the chat window.
-In your computer's browser, go to web.whatsapp.com. You will see a QR code on the screen that you need to scan with your phone.
-On your phone, open WhatsApp and tap the menu button (three dots) in the top right corner. Then tap "WhatsApp Web". You will see a camera screen that you need to point at the QR code on your computer. Once the scan is successful, you will be logged in to WhatsApp Web.
-Now you can use WhatsApp Web to chat and call with your contacts. You will see a similar interface as WhatsApp Desktop with your chats on the left and the chat window on the right. You can also access your settings, profile, status, etc. from the menu button in the top left corner. You can send and receive messages, media, documents, stickers, GIFs, etc. as you would on your phone. You can also make voice and video calls by clicking the phone or camera icon in the top right corner of the chat window.
-In this article, we have learned how to download and run WhatsApp on the computer. We have seen that there are two options for using WhatsApp on the computer: WhatsApp Desktop and WhatsApp Web. Both options allow you to message and call with your contacts from your computer using your phone's internet connection. Both options have similar functionality and appearance, but there are some differences in terms of features and performance.
-If you want to enjoy WhatsApp on your computer, we recommend that you try both options and see which one suits you better. You can download WhatsApp Desktop from the WhatsApp Download page or use WhatsApp Web from web.whatsapp.com. You will need to scan a QR code with your phone to log in to either option.
-We hope that this article has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy chatting!
- FAQs Q: Can I use WhatsApp on my computer without my phone? A: No, you cannot use WhatsApp on your computer without your phone. You need to have your phone connected to the internet and logged in to WhatsApp to use WhatsApp on your computer. Q: Can I use WhatsApp on multiple computers at the same time? A: No, you cannot use WhatsApp on multiple computers at the same time. You can only use one instance of WhatsApp Desktop or WhatsApp Web at a time. If you try to log in to another computer, you will be logged out from the previous one. Q: How can I log out of WhatsApp on my computer? A: To log out of WhatsApp on your computer, you can either click the menu button in the top left corner and then click "Log out" or go to WhatsApp on your phone and tap the menu button (three dots) in the top right corner. Then tap "WhatsApp Web" and then tap "Log out from all devices". Q: How can I update WhatsApp on my computer? A: To update WhatsApp on your computer, you can either download the latest version from the WhatsApp Download page or wait for the automatic update notification. If you see a message that says "Update available" on WhatsApp Desktop or WhatsApp Web, you can click it to update to the latest version. Q: How can I secure my WhatsApp account on my computer? A: To secure your WhatsApp account on your computer, you can enable two-step verification and lock your computer when not in use. Two-step verification adds an extra layer of security by requiring a PIN when you register your phone number with WhatsApp. You can enable it from WhatsApp on your phone by tapping the menu button (three dots) in the top right corner. Then tap "Settings" and then tap "Account" and then tap "Two-step verification". Locking your computer prevents unauthorized access to your WhatsApp account on your computer. You can lock your computer by pressing Ctrl+Alt+Delete or Windows+L on Windows, Command+Control+Q on Mac, or Super+L on Linux. 197e85843dIf you are looking for a free and fun platformer game for your Android device, you might want to check out Geometry Dash Lite 2.21 APK. This is a program by RobTop Games AB that lets you jump, fly, and flip your way through various levels of geometric shapes and obstacles. You can also create your own levels and share them with other players online.
-Geometry Dash Lite is a simplified version of the popular game Geometry Dash, which was released in 2013. Geometry Dash Lite has fewer levels, features, and modes than the full version, but it still offers a lot of fun and challenge for players who enjoy rhythm-based platformer games.
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The gameplay of Geometry Dash Lite is simple but addictive. You control a square-shaped character that can jump, fly, and flip in the air. Your goal is to avoid hitting any obstacles or spikes that appear on your way. You can also collect stars and coins to unlock new icons and colors for your character.
-The game is synchronized with the music, which means that you have to time your jumps and movements according to the beat and tempo of the soundtrack. The game also has a practice mode that lets you save checkpoints along the way, so you can resume from where you left off if you die.
-Geometry Dash Lite has several features that make it an enjoyable and engaging game for Android users. Some of these features are:
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Geometry Dash Lite 2.21 APK is a game that can provide you with hours of entertainment and challenge. Whether you are a casual gamer or a hardcore fan of platformer games, you will find something to enjoy in this game. Here are some of the reasons why you should play Geometry Dash Lite 2.21 APK:
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-Geometry Dash Lite 2.21 APK is a free and fun platformer game for Android devices that lets you jump, fly, and flip through various levels of geometric shapes and obstacles. You can also create your own levels and share them with other players online. The game has simple but addictive gameplay, synchronized with the music, and several features that make it enjoyable and engaging. However, the game also has some challenges that might make it difficult or frustrating for some players. If you are looking for a game that can challenge your skills, stimulate your senses, and entertain you for hours, you should give Geometry Dash Lite 2.21 APK a try.
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-If you are a fan of football games, you have probably heard of FIFA, the most popular and successful football simulation series by EA Sports. Every year, EA releases a new installment of FIFA with updated rosters, graphics, features, and modes. But how does FIFA 22 compare to its predecessors? Is it worth buying? What are the new and improved aspects of the game? In this article, we will answer these questions and more as we review FIFA 22, the latest entry in the franchise that promises to bring the game even closer to the real thing.
-FIFA 22 is the 29th installment in the FIFA series, which dates back to 1993. It is a football game that lets you play as your favorite teams and players from around the world, in various modes and competitions. You can also create your own custom teams, players, and clubs, and customize them to your liking. You can play solo or with friends, online or offline, in matches, tournaments, leagues, or career modes.
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FIFA 22 was released on October 1, 2021 for PlayStation 4, PlayStation 5, Xbox One, Xbox Series X/S, Nintendo Switch, PC, and Stadia. It is developed by EA Vancouver and EA Romania, and published by EA Sports. It features more than 17,000 players, over 700 teams, and more than 30 leagues from around the world. It also includes some of the most prestigious tournaments in football history, such as the UEFA Champions League, the UEFA Europa League, the UEFA Europa Conference League, the CONMEBOL Libertadores, the CONMEBOL Sudamericana, the Premier League, La Liga, Bundesliga, Serie A, Ligue 1, MLS, and more.
-FIFA 22 boasts several new and improved features that make it stand out from previous games in the series. The most notable one is HyperMotion technology, which is exclusive to PlayStation 5, Xbox Series X/S, and Stadia. HyperMotion is a new motion-capture system that uses machine learning to create realistic animations for every player on the pitch. It also enhances player behaviors, reactions, interactions, and emotions. HyperMotion makes FIFA 22 look and feel more authentic than ever before.
-But HyperMotion is not the only innovation in FIFA 22. The game also introduces new gameplay features that change the way you play on the pitch. These include:
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-These new features aim to make FIFA 22 more immersive, dynamic, and fun to play.
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FIFA 22 also offers a variety of modes to suit your preferences and playstyles. Whether you want to play solo or with friends, online or offline, casually or competitively, there is a mode for you. Some of the modes are:
-Career Mode is one of the most popular and long-running modes in FIFA. It lets you create your own player or manager and lead them to glory in their football career. You can choose from hundreds of clubs and leagues, and make decisions on and off the pitch that affect your performance, reputation, and relationships. You can also scout, sign, train, and sell players, as well as customize your team's tactics, kits, stadium, and more.
-Career Mode in FIFA 22 has been improved with more options and immersion. You can now create your own club from scratch and take them from the lower divisions to the top of the world. You can also enjoy an overhauled player career experience that gives you more ways to progress, achieve, and immerse yourself in your pro's journey through the game.
-Volta Football is a mode that brings back the street football vibe of FIFA Street. It lets you play in various urban locations around the world, with different rules, teams, and styles. You can create your own avatar and customize their appearance, skills, and gear. You can also join forces with other players online or offline, and compete in various modes such as Volta Squads, Volta Story, Volta League, Volta Arcade, and more.
-Volta Football in FIFA 22 returns with more flair and customization. You can now enjoy new locations such as Sydney, Paris, Dubai, Milan, and Cape Town. You can also unlock more items and outfits for your avatar, as well as new skill moves and celebrations. You can also play with or against real-life football stars in Volta Featured Battles.
-Ultimate Team is the most popular and lucrative mode in FIFA. It lets you build your dream team from scratch using players from different clubs, leagues, and nations. You can acquire players through packs, auctions, objectives, rewards, or events. You can also upgrade your players' attributes and chemistry using consumables. You can then compete with other players online or offline in various modes such as Division Rivals, Squad Battles, Friendlies, Drafts, and more.
-Ultimate Team in FIFA 22 introduces FUT Heroes and new ways to play. FUT Heroes are iconic players from the past who have a unique league-specific chemistry that boosts their links with other players from the same league. Some of the FUT Heroes are Mario Gomez, Tim Cahill, Diego Milito, Robbie Keane, Jorge Campos, and more. You can also enjoy new ways to play such as FUT Champions Finals (a revamped version of Weekend League), FUT Co-Op Seasons (a cooperative mode where you can play with a friend), FUT Events (a mode where you can join a team and contribute to global objectives), and more.
-Pro Clubs is a mode where you can create your own virtual pro and join a club with other players online. You can customize your pro's appearance, position, attributes, traits, and skills. You can also customize your club's name, logo, kit, stadium, tactics, and more. You can then play matches against other clubs online in various divisions and cups.
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-defending, and physical. You can also use new customization options for your club's logo, kit, stadium, and more.
-FIFA 22 is not a perfect game, and it has some drawbacks that may affect your enjoyment. Some of the drawbacks are:
-While some of the new gameplay features in FIFA 22 are welcome and beneficial, others are either unnecessary or unbalanced. For example, the explosive sprint mechanic can make some players too fast and hard to catch, especially on the wings. The new attacking tactics can also make some formations too defensive or offensive, creating unrealistic scenarios. The goalkeeper rewrite can also make some saves too easy or impossible, depending on the situation.
-One of the biggest criticisms of FIFA games is their reliance on microtransactions, especially in Ultimate Team mode. FIFA 22 is no exception, and it still encourages you to spend real money on FIFA Points, which you can use to buy packs, players, consumables, and other items. While you can earn some of these items through playing the game, the odds of getting high-rated players or rare items are very low, and the prices of some items are very high. This creates a pay-to-win environment where players who spend more money have an advantage over those who don't.
-Another common complaint about FIFA games is their menu design, which is often cluttered and confusing. FIFA 22 is no improvement, and it still has many menus that are hard to navigate or understand. For example, the main menu has too many tabs and icons that are not clearly labeled or explained. The career mode menu has too many submenus and options that are not intuitive or user-friendly. The ultimate team menu has too many screens and pop-ups that are annoying or distracting.
-FIFA 22 is a significant improvement over FIFA 21, and it delivers a new generation of football simulation. The HyperMotion technology and gameplay changes make it feel like a next-gen game, with realistic visuals, animations, and behaviors. The modes are refreshed and offer more variety and fun, with new features such as FUT Heroes, Volta Featured Battles, Pro Clubs archetypes and perks, and Career Mode club creation. However, some issues remain, such as microtransactions, menu design, and some unbalanced or unnecessary mechanics.
-If you are a fan of football games, FIFA 22 is worth buying, as it offers a lot of content and quality for your money. If you are new to football games, FIFA 22 is a good entry point, as it has many modes and options to suit your preferences and skill levels. If you are looking for a realistic, immersive, and enjoyable football game, FIFA 22 is a great choice.
-FIFA 22 is available on PlayStation 4, PlayStation 5, Xbox One, Xbox Series X/S, Nintendo Switch, PC, and Stadia. However, some features such as HyperMotion technology are exclusive to PlayStation 5, Xbox Series X/S, and Stadia.
-The standard edition of FIFA 22 costs $59.99 USD for PlayStation 4, Xbox One, PC (Origin), and Stadia; $69.99 USD for PlayStation 5 and Xbox Series X/S; and $49.99 USD for Nintendo Switch. There are also other editions such as the Ultimate Edition ($99.99 USD) and the Legacy Edition ($39.99 USD) that offer different bonuses and content.
-FIFA 22 is worth buying if you enjoy football games or want to try one for the first time. It offers a lot of content and quality for your money, with realistic graphics, gameplay, and modes. It also has a large and active online community that you can play with or against.
-You can download FIFA 22 from the official website of EA Sports or from the digital store of your platform of choice (such as PlayStation Store, Microsoft Store, Nintendo eShop, Steam, or Google Play). You will need an internet connection to download the game and to access some of its features.
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-or Xbox Live). You will also need an EA account and an online subscription (such as PlayStation Plus or Xbox Live Gold) to play online. You can then choose from various online modes such as Ultimate Team, Volta Football, Pro Clubs, Online Seasons, Online Friendlies, Co-Op Seasons, and more. You can also join online events, tournaments, and challenges that offer rewards and prizes.
401be4b1e0GTA 5 is one of the most popular and amazing games that you can play on your Android device. However, to enjoy this game fully, you need to download the OBB file for GTA 5 along with the Apk file. In this article, we will show you what is OBB file, why you need it, and how to download and install it on your device.
-GTA 5 is a game developed by Rockstar Games that lets you experience the life of a criminal in a fictional city called Los Santos. You can explore the open world, complete missions, interact with other characters, drive vehicles, use weapons, and more. GTA 5 is one of the best-selling games of all time and has received critical acclaim for its graphics, gameplay, story, and online mode.
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GTA 5 is the fifth installment in the Grand Theft Auto series that was released in 2013 for PlayStation 3 and Xbox 360, and later for PlayStation 4, Xbox One, and PC. In 2021, Rockstar Games announced that GTA 5 will be available for Android devices as well. However, unlike other games that you can download directly from the Google Play Store, GTA 5 requires an additional file called OBB file to run properly.
-OBB file stands for Opaque Binary Blob file and it is a data file that contains additional information that is not stored in the Apk file. OBB files are usually used by large games or apps that have high-quality graphics, sound, or video. OBB files are stored in a separate folder on your device's internal or external storage and are accessed by the app when needed.
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GTA 5 is not just a game, it is a masterpiece that offers you a lot of features and options to enjoy. Some of the features of GTA 5 Android Apk are:
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-The next thing you need to do is to enable unknown sources in your device settings. This will allow you to install apps that are not from the Google Play Store. To do this, go to your device settings > security > unknown sources > enable. This may vary depending on your device model and Android version.
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-A: No, you cannot use cheats or mods in GTA 5 Android Apk as they are not supported by the game. If you try to use them, you may face errors or crashes in the game.
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The answer is yes, there is. You can download and install NBA 2K20 APK+data files on your Android device for free. APK stands for Android Package Kit, which is a file format that contains all the necessary components of an app. Data files are additional files that contain game assets, such as graphics, sounds, and settings. By downloading and installing these files, you can bypass the Google Play Store and enjoy NBA 2K20 on your Android device.
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-NBA 2K20 APK is an Android application package that contains the game files of NBA 2K20, a popular basketball simulation game developed by 2K Games. The game features updated graphics, player models, animations, and gameplay mechanics, making it one of the most realistic basketball games available. You can play various game modes, such as Run The Streets, NBA Stories, MyCareer, The Association, and Multiplayer, with current or all-time great NBA teams and players.
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There are several reasons why you might want to download NBA 2K20 APK offline instead of the official version from the Google Play Store. Here are some of them:
-NBA 2K20 APK has many features that make it an exciting and enjoyable game for basketball fans. Here are some of them:
-For the first time in any NBA 2K game, you can take your MyPlayer around the world in a series of 3-on-3 streetball competitions. You can get on a hot streak and takeover the game with greatly improved abilities and attributes. You can also compete against other players for a place on the Ranked Leaderboard or see how far you can go through the Championship.
-You can experience the history of some of the most famous NBA players and teams with 5 new NBA Stories to play through. You can relive or recreate some of the most memorable moments and games in NBA history, such as the 2016 NBA Finals, the 2001 Lakers, and the 1985 Celtics.
-You can build your own custom MyPlayer and go on a personal journey from college to the NBA. You can make choices that affect your path to stardom and interact with various characters, including Idris Elba, Rosario Dawson, and LeBron James. You can also improve your skills and attributes by playing games, practicing, and training.
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You can play online with or against other players in various modes, such as Quick Match, Ranked Match, Blacktop, and Online Association. You can also join or create your own crew with up to 10 players and compete in 5-on-5 matches with other crews.
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-If you want to play NBA 2K20 APK offline on your Android device, you need to follow these steps:
-You need to download two files: the APK file, which is the application file, and the OBB file, which is the data file. You can find these files from various sources on the internet, but make sure they are safe and compatible with your device. For example, you can download them from this link: [NBA 2K20 APK + OBB].
-Before you install the APK file, you need to enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on. Then, locate the downloaded APK file on your device and tap on it to start the installation process. Follow the instructions on the screen and wait for it to finish.
-After you install the APK file, you need to extract the OBB file using a file manager app or a zip extractor app. You can find these apps on the Google Play Store or download them from other sources. Once you extract the OBB file, you need to copy it to the following location on your device: Android > obb > com.t2ksports.nba2k20and. Make sure that the OBB file is inside a folder named com.t2ksports.nba2k20and.
-Now that you have installed both the APK and OBB files, you are ready to launch the game and play it offline. Just tap on the game icon on your home screen or app drawer and start playing. You can access all the features and content of the game without any internet connection.
-NBA 2K20 APK offline has many advantages and disadvantages that you should consider before downloading it. Here are some of them:
-NBA 2K20 APK offline delivers a smooth and lifelike gameplay experience that captures the essence of basketball. The game features improved animations, physics, lighting, shadows, and textures that make every move and shot look natural and authentic. The game also has a revamped control scheme that gives you more options and precision when dribbling, passing, shooting, defending, and rebounding.
-NBA 2K20 APK offline has a compelling and immersive MyCareer mode that lets you create your own legend in the NBA. The game has a well-written and acted story that features star-studded cast members like Idris Elba, Rosario Dawson, LeBron James, Anthony Davis, and more. The game also has a branching storyline that changes based on your choices and actions. You can also customize your MyPlayer with various hairstyles, tattoos, accessories, and clothing.
-NBA 2K20 APK offline has a more balanced and rewarding progression system that makes it easier and faster to level up your MyPlayer. The game has reduced the amount of VC (virtual currency) required to upgrade your attributes and skills, and increased the amount of VC earned from playing games and completing challenges. The game also has a new dynamic potential feature that allows your MyPlayer to improve beyond their initial ratings based on their performance and consistency.
-NBA 2K20 APK offline does not have many significant changes or innovations compared to the previous NBA 2K games. The game mostly reuses and tweaks the existing features and modes, such as MyTeam, My Neighborhood, MyGM, and MyLeague. The game also has some bugs and glitches that affect the gameplay and performance.
-NBA 2K20 APK offline has the same My Neighborhood mode as NBA 2K19, which is a social hub where you can interact with other players, shop for items, play mini-games, and access various game modes. The game does not have any new or improved locations, activities, or events in the My Neighborhood mode, making it boring and repetitive.
-NBA 2K20 APK offline has a controversial MyTeam mode that encourages gambling and spending real money. The game has added new features such as slot machines, prize wheels, ball drops, and card packs that are based on luck and randomness. The game also has a pay-to-win system that favors players who spend more money on buying VC and acquiring better cards.
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-This is a simple but fun basketball game that lets you swipe your finger to shoot hoops. You can play various modes, such as Arcade, Time Attack, Tournament, and Multiplayer. You can also customize your player with different outfits, balls, and accessories.
-This is a multiplayer basketball game that lets you challenge other players online or offline in 1-on-1 matches. You can show off your skills and tricks by dribbling, feinting, shooting, blocking, and stealing. You can also unlock new courts, balls, and items for your player.
-This is a arcade-style basketball game that lets you play 2-on-2 matches against the computer or another player on the same device. You can perform dunks, alley-oops, crossovers, and blocks with easy controls. You can also upgrade your players and coaches with coins earned from winning matches.
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But what if you want to play Blockman Go on your PC instead of your mobile device? Is it possible to enjoy this fun game on a larger screen with better graphics and controls? The answer is yes! In this article, we will show you how to download Blockman Go for PC using an emulator software. We will also tell you what Blockman Go is, why you should play it on PC, what are some features of the game, and what are some alternatives to Blockman Go. Let's get started!
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-Blockman Go allows you to join or create rooms with your friends or other players from all over the world. You can chat with them using text or voice messages, send emojis, stickers, or gifts, and add them as friends. You can also join clans or guilds to participate in clan wars or events.
Blockman Go gives you the freedom to create your own worlds using blocks and items. You can build anything you can imagine, from houses, castles, gardens, to cities, islands, or planets. You can also decorate your worlds with furniture, plants, animals, or NPCs. You can share your creations with other players or visit their worlds to see what they have made.
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Blockman Go offers a variety of minigames that you can play with your friends or other players. You can choose from action, adventure, role playing, strategy, simulation, and more. Some of the popular minigames are Bed Wars, Egg War, Sky Block, Anime Fighting Simulator, Build and Shoot, and WWE School Simulator. You can also find new minigames every week on the app. Each minigame has its own rules, objectives, and rewards. You can also create your own minigames using the game editor.
While Blockman Go is designed for mobile devices, you can also play it on your PC using an emulator software. An emulator is a program that allows you to run Android or iOS apps on your computer. There are many benefits of playing Blockman Go on PC, such as:
-Playing Blockman Go on PC will give you a better visual experience than playing on a small screen. You can see more details and colors of the blocks and the worlds. You can also adjust the resolution and the graphics settings to suit your preferences.
Playing Blockman Go on PC will also give you more control over the game. You can use your keyboard and mouse to move, aim, shoot, jump, and interact with the game. You can also customize the key mapping and the sensitivity to fit your style. You will have an advantage over other players who use touch controls.
Playing Blockman Go on PC will also allow you to access other apps and tools on your computer while playing. You can use your browser, chat apps, video players, music players, or any other programs that you need. You can also record your gameplay, take screenshots, or stream your game online using various software.
To download Blockman Go for PC, you will need an emulator software that can run Android or iOS apps on your computer. There are many emulators available online, but we recommend using BlueStacks, MuMu Player, or MEmu as they are easy to use and compatible with most games. Here are the steps to download Blockman Go for PC using an emulator:
-You can download the emulator from their official websites or from other sources. Make sure you have enough space and system requirements to run the emulator smoothly. Follow the instructions to install the emulator on your PC.
After installing the emulator, launch it and sign in to your Google account. This will allow you to access Google Play Store and download apps from there. You can find Blockman Go in the app center or by typing its name in the search bar.
Once you find Blockman Go, click on it and install it on your emulator. It may take a few minutes depending on your internet speed and device performance. After installing Blockman Go, you can start playing it on your PC by clicking on its icon.
Blockman Go is a fun and exciting game that offers many features for its players. Some of these features are:
-Blockman Go allows you to create your own avatar using different blocks and items. You can change your hair style, skin color, eye color, clothes, shoes, hats, glasses, masks, wings, tails, and more. You can also buy more accessories and clothes from the shop using gold or diamonds.
Blockman Go enables you to communicate with other players using text or voice messages. You can chat with them in public rooms or private messages. You can also send them emojis, stickers, or gifts to express your feelings. You can add them as friends and join their rooms or invite them to yours.
Blockman Go rewards you with gold for playing minigames. You can use gold to buy items from the shop, such as accessories, clothes, furniture, blocks, or game tickets. You can also earn diamonds by completing tasks, watching ads, or buying them with real money. Diamonds can be used to buy premium items or VIP membership.
Blockman Go offers a wide range of minigames that you can play with your friends or other players. You can choose from different genres and themes, such as action, adventure, role playing, strategy, simulation, and more. You can also find new minigames every week on the app. Each minigame has its own rules, objectives, and rewards. You can also create your own minigames using the game editor.
If you are looking for more games like Blockman Go, you can try some of these alternatives:
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Creativerse: The Definitive Edition |
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LEGO Worlds |
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Blockman Go is a fun and exciting game that lets you play, craft, and share your experiences with your friends or other players. You can play various block style minigames with different genres and themes, customize your avatar with fashionable accessories and clothes, chat and meet new friends from all over the world, earn gold by playing minigames and use it to buy items, and explore the wonderland of minigames and discover new adventures every day.
-If you want to enjoy this game on your PC, you can download Blockman Go for PC using an emulator software. This will allow you to enjoy a larger screen and better graphics, use keyboard and mouse controls for more accuracy and convenience, and access thousands of productivity apps and tools on your computer.
-If you are looking for more games like Blockman Go, you can try some of the alternatives we have mentioned above, such as Minetest, Roblox, MineClone 2, Creativerse: The Definitive Edition, and LEGO Worlds. They are all sandbox games that offer similar features and gameplay to Blockman Go.
-We hope this article has helped you learn more about Blockman Go and how to download it for PC. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
-Yes, Blockman Go is free to play. You can download it from Google Play Store, App Store, or Microsoft Store without paying anything. However, some items and features may require in-app purchases or premium membership.
Yes, Blockman Go is safe to play. It has been rated 12+ by Google Play Store and 9+ by App Store for moderate violence, mild horror, infrequent mild profanity or crude humor, infrequent mild sexual content or nudity, infrequent mild mature or suggestive themes, infrequent mild alcohol, tobacco or drug use or references. It also has parental controls and privacy settings that allow you to restrict or block certain content or users.
To update Blockman Go, you need to check if there is a new version available on the app store where you downloaded it from. If there is, you can tap on the update button and wait for the download and installation to finish. You can also enable automatic updates on your device settings to get the latest version of Blockman Go whenever it is released.
To contact Blockman Go support, you can visit their official website and click on the "Contact Us" button at the bottom of the page. You can also email them at service@blockmango.net or follow them on their social media accounts on Facebook, Twitter, Instagram, YouTube, or Discord. They will try to respond to your queries or issues as soon as possible.
To delete Blockman Go, you need to uninstall it from your device. You can do this by long-pressing the app icon and tapping on the uninstall option. You can also go to your device settings and find the app in the list of installed apps. Then tap on it and select the uninstall option. This will remove Blockman Go from your device along with its data and cache.
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These are ringtones that feature flute music that is not accompanied by any lyrics or singing. They can be from various genres, such as jazz, blues, rock, or folk. Some examples of instrumental flute ringtones are Jethro Tull's Locomotive Breath, Herbie Mann's Memphis Underground, or Ian Anderson's Bourée. Instrumental flute ringtones are cool, creative, and diverse. They can showcase the versatility and skill of the flute player. -Other Flute Ringtones-Of course, there are many other types of flute ringtones that you can explore and enjoy. For instance, you can find flute ringtones that are inspired by different cultures and traditions, such as Native American flute ringtones, Chinese flute ringtones, or Irish flute ringtones. You can also find flute ringtones that are based on different themes and moods, such as nature flute ringtones, meditation flute ringtones, or funny flute ringtones. The possibilities are endless! -How to Download Flute Ringtones-Now that you know about the different types of flute ringtones, you might be wondering how to download them for your phone. There are two main ways to do this: using websites or using apps. -Websites that offer free or paid flute ringtones-One way to download flute ringtones is to use websites that offer free or paid downloads of various ringtones. Some examples of such websites are Zedge, Myxer, and Mobile9. These websites have large collections of flute ringtones that you can browse by category, genre, or popularity. You can listen to the previews of the ringtones before downloading them. You can also rate, comment, or share the ringtones with others. To download the ringtones, you need to register for a free account on the website and follow the instructions. Some websites may charge a fee for certain ringtones or require you to complete a survey or an offer before downloading them. -Apps that allow you to create or customize flute ringtones-Another way to download flute ringtones is to use apps that allow you to create or customize your own ringtones. Some examples of such apps are Ringtone Maker, Audiko, and MP3 Cutter and Ringtone Maker. These apps let you use your own music files or recordings, or choose from a library of flute sounds and music. You can edit, trim, mix, or add effects to the ringtones. You can also assign different ringtones to different contacts or notifications. To download the ringtones, you need to install the app on your phone and follow the instructions. Some apps may have in-app purchases or ads that you can remove by paying a fee. -How to Set Flute Ringtones as Your Default or Contact-Specific Ringtone-Once you have downloaded your favorite flute ringtones, you might want to set them as your default or contact-specific ringtone. This means that your phone will play the flute ringtone whenever you receive a call or a message, or when a specific person calls or texts you. To do this, you need to follow these steps: -
How to Enjoy Flute Ringtones-Now that you have set your flute ringtones, you might be wondering how to enjoy them in various ways. Here are some tips and suggestions on how to make the most of your flute ringtones: -Tips on how to choose the right flute ringtone for your mood or occasion-Flute ringtones can have different effects on your mood or occasion, depending on the style and mood of the music. For instance, if you are feeling stressed or anxious, you might want to choose a soothing or relaxing flute ringtone, such as a classical or nature flute ringtone. If you are feeling happy or cheerful, you might want to choose a lively or upbeat flute ringtone, such as a Bollywood or instrumental flute ringtone. If you are feeling romantic or sentimental, you might want to choose a sweet or emotional flute ringtone, such as a romantic or movie flute ringtone. -You can also choose your flute ringtone based on the occasion or event that you are attending or hosting. For example, if you are going to a formal or professional event, you might want to choose a elegant or sophisticated flute ringtone, such as a classical or jazz flute ringtone. If you are going to a casual or fun event, you might want to choose a cool or creative flute ringtone, such as a rock or folk flute ringtone. If you are going to a special or festive event, you might want to choose a catchy or colorful flute ringtone, such as a Bollywood or instrumental flute ringtone. -Suggestions on how to mix and match flute ringtones with other sounds or music-Flute ringtones can also be mixed and matched with other sounds or music to create a unique and personalized ringtone. For example, you can combine a flute ringtone with a drum beat, a guitar riff, a piano melody, or a vocal track. You can also blend a flute ringtone with a sound effect, such as a bird chirp, a water splash, a bell ring, or a whistle blow. You can use apps that allow you to create or customize ringtones to do this, or you can use online tools that let you mix and match different sounds and music. -Ideas on how to share or gift flute ringtones to your loved ones or friends-Flute ringtones can also be shared or gifted to your loved ones or friends as a way of expressing your feelings or appreciation. For example, you can send a flute ringtone to your partner as a romantic gesture, to your family as a greeting, to your friend as a joke, or to your colleague as a thank you. You can also surprise someone with a flute ringtone as a birthday present, an anniversary gift, a congratulations message, or an apology note. You can use websites or apps that allow you to send ringtones via email, text, or social media to do this, or you can use Bluetooth or Wi-Fi to transfer ringtones directly from your phone. -Conclusion-Flute ringtones are beautiful and versatile sounds that can make your phone more unique and pleasant. They come in various types and styles that suit different tastes and personalities. They can be downloaded for free or for a fee from websites or apps that offer various ringtones. They can be set as your default or contact-specific ringtone easily and quickly. They can also be enjoyed in various ways by choosing the right one for your mood or occasion, mixing and matching them with other sounds or music, and sharing or gifting them to your loved ones or friends. -If you love the sound of a flute, why not try out some flute ringtones for yourself? You might be surprised by how much they can enhance your phone experience and brighten up your day. To find more flute ringtones, you can visit [this website] that has a large collection of flute ringtones that you can download for free. -FAQs-Here are some frequently asked questions about flute ringtones: -What is the best flute ringtone for love?-The best flute ringtone for love depends on your personal preference and the message that you want to convey. However, some general suggestions are romantic flute ringtones that feature soft, sweet, and sentimental melodies, such as Titanic's My Heart Will Go On, The Notebook's Main Theme, or Game of Thrones' The Rains of Castamere. -How can I make my own flute ringtone?-You can make your own flute ringtone by using apps that allow you to create or customize ringtones. Some examples of such apps are Ringtone Maker, Audiko, and MP3 Cutter and Ringtone Maker. These apps let you use your own music files or recordings, or choose from a library of flute sounds and music. You can edit, trim, mix, or add effects to the ringtones. You can also assign different ringtones to different contacts or notifications. -Where can I find more flute ringtones?-You can find more flute ringtones by using websites or apps that offer various ringtones. Some examples of such websites are Zedge, Myxer, and Mobile9. Some examples of such apps are Ringtone Maker, Audiko, and MP3 Cutter and Ringtone Maker. These websites and apps have large collections of flute ringtones that you can browse by category, genre, or popularity. You can also search the web for specific types or styles of flute ringtones that you are interested in. -How can I change my flute ringtone?-You can change your flute ringtone by following the same steps that you used to set it as your default or contact-specific ringtone. Go to your phone's settings and look for the sound or ringtone option. Select the default ringtone option or the contact that you want to customize. Browse through your downloaded flute ringtones and choose the one that you want to use as your new ringtone. Confirm your selection and enjoy your new flute ringtone. -How can I delete my flute ringtone?-You can delete your flute ringtone by going to your phone's file manager or storage app and looking for the folder where your downloaded ringtones are stored. Find the flute ringtone that you want to delete and tap on it. Select the delete option and confirm your action. Alternatively, you can use apps that allow you to manage your ringtones, such as Ringtone Maker, Audiko, or MP3 Cutter and Ringtone Maker. These apps let you view, edit, or delete your ringtones easily and quickly. - |
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If you are looking for a fun and casual game that lets you take care of a cute alien pet, then you might want to try Pou. Pou is a popular virtual pet game that has millions of fans around the world. In this game, you can feed, clean, play with, and watch your Pou grow up while leveling up and unlocking different wallpapers and outfits.
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If you want to change your Pou's appearance and abilities, you can experiment with potions in the lab. There are many potions that you can use on your Pou, such as Fat Burner, Energy Drink, Baby Potion, Adult Potion, etc. These potions can make your Pou bigger or smaller, faster or slower, younger or older, etc. Some potions have temporary effects while others have permanent effects.
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899543212bDino Time is a 2012 animated film that tells the story of three kids who travel back in time to the dinosaur era and meet a friendly T-rex named Tyra and her son Dodger. The film is directed by Yoon-suk Choi and John Kafka and features the voices of Rob Schneider, Melanie Griffith, Pamela Adlon, Jane Lynch, and Tara Strong.
- -The film was originally released in South Korea in 2012 and later dubbed in Hindi for the Indian audience. The film is available to watch online or download in Hindi on various platforms, such as YouTube, Dead Toons India, and Cartoon Network India. The film is also known as Back to the Jurassic or Dino King in other countries.
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The film begins with Ernie (Rob Schneider), a rebellious kid who loves dinosaurs and hates his mother Sue (Melanie Griffith), who is a paleontologist. Ernie sneaks into his mother's museum with his best friend Max (Pamela Adlon) and his sister Julia (Tara Strong) and finds a mysterious device that can transport them back in time.
- -Ernie activates the device and accidentally sends himself, Max, Julia, and a dinosaur egg back to the Cretaceous period. There, they meet Tyra (Jane Lynch), a motherly T-rex who thinks that Ernie is her son Dodger (Yuri Lowenthal), who was separated from her during an earthquake. Tyra adopts Ernie as her son and protects him from other dinosaurs.
- -Meanwhile, Dodger meets Sue, who has followed Ernie back in time using another device. Sue tries to find Ernie and bring him back to the present, but faces many dangers along the way. She also learns to appreciate Ernie's love for dinosaurs and understand his feelings.
- -Ernie, Max, Julia, and Dodger have many adventures in the dinosaur world, such as escaping from a pack of raptors, riding on a pteranodon, befriending a triceratops, and witnessing a volcanic eruption. They also learn to work together as a team and care for each other as a family.
- -The film ends with Ernie, Max, Julia, Dodger, and Sue returning to the present with the help of Tyra, who sacrifices herself to save them from a meteor strike. Ernie and Sue reconcile their differences and hug each other. Ernie also keeps the dinosaur egg as a souvenir and names it Tyra Jr.
- -Dino Time Hindi is a fun-filled adventure film that appeals to kids and adults alike with its colorful animation, humorous dialogues, thrilling action scenes, and heartwarming message. The film does not have a complex plot or logic, but relies on the charm and chemistry of the characters to entertain the audience.
- - -The film has some flaws, such as the cliched portrayal of dinosaurs, the weak characterization of the villains, the cheesy dialogues, -and the predictable twists. The film also has some scenes that are violent or scary for younger viewers, -such as the dinosaur attacks, -the volcanic eruption, -and the meteor strike.
- -The film's strengths are its animation, -voice acting, -music, -and theme. -The film has some impressive animation -that captures the beauty -and diversity -of the dinosaur world. -The film also has some expressive -and lively -voice acting -by Rob Schneider, -Melanie Griffith, -Pamela Adlon, -Jane Lynch, -and Tara Strong, -who bring their characters -to life. -The film also has some catchy songs -composed by Stephen Barton -and Chris Ridenhour, -such as "Dino Time", -"Back to Life", -and "We're Family". -The film also has a positive theme -of family, -friendship, -and adventure, -that inspires -and touches -the audience.
- -Dino Time Hindi is a fun-filled adventure film that appeals to kids and adults alike with its colorful animation, humorous dialogues, thrilling action scenes, and heartwarming message. The film is not meant to be taken seriously or critically analyzed, but enjoyed as a popcorn flick that celebrates dinosaurs, family, and friendship. The film is a perfect watch for anyone who loves dinosaurs or animation.
- -If you are interested in watching Dino Time Hindi online or download it in Hindi, -you can find it on various platforms, -such as YouTube, -Dead Toons India, -and Cartoon Network India. -You can also find reviews of the film on various websites, -such as IMDb, -Rotten Tomatoes, -Bollywood Hungama, -Times of India, -and Hindustan Times. -You can also write your own review of the film -and share your thoughts -and feelings -with others.
- -Dino Time Hindi is a film that will make you laugh, -cry, -and roar. -Watch it today -and enjoy the dino time -with Ernie, -Max, -Julia, -Dodger, -and Tyra.
-If you want to watch Dino Time Hindi offline or save it on your device, you might be looking for ways to download it for free. However, you should be careful about downloading movies from unauthorized sources, as it is illegal and unethical. Moreover, you might risk exposing your device to malware or viruses by visiting such websites.
- -The best way to download Dino Time Hindi for free is to use a trusted and legal platform that offers free downloads or streaming of movies. Some of these platforms are YouTube, Dead Toons India, and Cartoon Network India. These platforms have the official rights to distribute Dino Time Hindi and offer high-quality downloads or streaming of the movie.
- -To download Dino Time Hindi from YouTube, you need to follow these steps:
- -To download Dino Time Hindi from Dead Toons India, you need to follow these steps:
- -To download Dino Time Hindi from Cartoon Network India, you need to follow these steps:
- -By using these platforms, you can download Dino Time Hindi for free and watch it anytime you want. However, you should also respect the rights of the filmmakers and actors and avoid sharing or distributing the movie without their permission. You should also support them by buying or renting the DVD or Blu-ray of the movie from a trusted source.
-Dino Time Hindi is a movie that will take you on a journey to the past and make you experience the wonders and dangers of the dinosaur world. The movie is a blend of comedy, action, adventure, and drama that will keep you entertained and engaged throughout. The movie is suitable for kids and adults who love dinosaurs or animation.
- -The movie has some elements that you can expect from Dino Time Hindi, such as:
- -The movie also has some surprises and twists that you might not expect from Dino Time Hindi, such as:
- -Dino Time Hindi is a movie that will make you laugh, cry, and roar. The movie has a lot of fun and excitement that will appeal to your senses and emotions. The movie also has a lot of heart and message that will inspire and touch you. The movie is a must-watch for anyone who loves dinosaurs or animation.
-Dino Time Hindi is a movie that you can enjoy with your family and friends, as it has something for everyone. The movie is a fun-filled adventure that will make you laugh, cry, and roar. The movie is suitable for kids and adults who love dinosaurs or animation.
- -There are many ways to enjoy Dino Time Hindi with your family and friends, such as:
- -By enjoying Dino Time Hindi with your family and friends, you can have a memorable and fun time that will strengthen your bond and create lasting memories. You can also learn more about dinosaurs and appreciate their beauty and diversity. You can also discover more about yourself and others by relating to the characters and their emotions.
- -Dino Time Hindi is a fun-filled adventure film that appeals to kids and adults alike with its colorful animation, humorous dialogues, thrilling action scenes, and heartwarming message. The film is not meant to be taken seriously or critically analyzed, but enjoyed as a popcorn flick that celebrates dinosaurs, family, and friendship. The film is a perfect watch for anyone who loves dinosaurs or animation.
- -If you are interested in watching Dino Time Hindi online or download it in Hindi, -you can find it on various platforms, -such as YouTube, -Dead Toons India, -and Cartoon Network India. -You can also find reviews of the film on various websites, -such as IMDb, -Rotten Tomatoes, -Bollywood Hungama, -Times of India, -and Hindustan Times. -You can also write your own review of the film -and share your thoughts -and feelings -with others.
- -You can also enjoy Dino Time Hindi with your family and friends -by watching it together, -playing games or quizzes, -making crafts or drawings, -sharing opinions and feedback, -or recommending it to others. -You can have a memorable and fun time -that will strengthen your bond -and create lasting memories. -You can also learn more about dinosaurs -and appreciate their beauty -and diversity. -You can also discover more about yourself -and others -by relating to the characters -and their emotions.
- -Dino Time Hindi is a film that will make you laugh, -cry, -and roar. -Watch it today -and enjoy the dino time -with Ernie, -Max, -Julia, -Dodger, -and Tyra.
-Dino Time Hindi is a fun-filled adventure film that appeals to kids and adults alike with its colorful animation, humorous dialogues, thrilling action scenes, and heartwarming message. The film is not meant to be taken seriously or critically analyzed, but enjoyed as a popcorn flick that celebrates dinosaurs, family, and friendship. The film is a perfect watch for anyone who loves dinosaurs or animation.
- -If you are interested in watching Dino Time Hindi online or download it in Hindi, -you can find it on various platforms, -such as YouTube, -Dead Toons India, -and Cartoon Network India. -You can also find reviews of the film on various websites, -such as IMDb, -Rotten Tomatoes, -Bollywood Hungama, -Times of India, -and Hindustan Times. -You can also write your own review of the film -and share your thoughts -and feelings -with others.
- -You can also enjoy Dino Time Hindi with your family and friends -by watching it together, -playing games or quizzes, -making crafts or drawings, -sharing opinions and feedback, -or recommending it to others. -You can have a memorable and fun time -that will strengthen your bond -and create lasting memories. -You can also learn more about dinosaurs -and appreciate their beauty -and diversity. -You can also discover more about yourself -and others -by relating to the characters -and their emotions.
- -Dino Time Hindi is a film that will make you laugh, -cry, -and roar. -Watch it today -and enjoy the dino time -with Ernie, -Max, -Julia, -Dodger, -and Tyra.
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