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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Asoftech Automation Crack Serial 11 Pros and Cons of the Software.md
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<h1>Asoftech Automation Crack Serial 11: What You Need to Know</h1>
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<p>If you are looking for a way to automate repetitive and tedious tasks on your computer, you might have heard of <strong>Asoftech Automation</strong>, a software tool that can help you create and run automation scripts with ease. But what if you don't want to pay for the software's license? You might be tempted to use a <strong>crack serial number</strong> that can unlock the full features of Asoftech Automation without any cost. However, before you do that, you should know what a crack serial number is, how it works, and what are the potential consequences of using it. In this article, we will explain everything you need to know about <strong>Asoftech Automation Crack Serial 11</strong>, including how to download, install, and use it.</p>
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<h2>asoftech automation crack serial 11</h2><br /><p><b><b>Download File</b> ››››› <a href="https://byltly.com/2uKvJL">https://byltly.com/2uKvJL</a></b></p><br /><br />
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<h2>What is Asoftech Automation?</h2>
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<p>Asoftech Automation is a software tool that allows you to automate any combination of tasks on your computer. You can use it to record mouse movements and clicks, keyboard keystrokes, and other computer activities, and then replay them as many times as you want. You can also edit and customize your automation scripts with variables, loops, conditions, and other commands. With Asoftech Automation, you can save time and effort by automating tasks such as:</p>
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<ul>
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<li>Web browsing and data entry</li>
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<li>File backup and synchronization</li>
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<li>Software testing and debugging</li>
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<li>Game playing and cheating</li>
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<li>And much more</li>
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</ul>
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<p>Asoftech Automation has many features and benefits that make it a powerful and user-friendly automation tool. Some of them are:</p>
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<ul>
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<li>It has an intuitive interface that lets you create automation scripts with simple drag-and-drop actions.</li>
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<li>It supports multiple monitors and resolutions, so you can automate tasks on different screens.</li>
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<li>It has a built-in scheduler that lets you run automation scripts at specific times or intervals.</li>
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<li>It has a stealth mode that hides the software from the taskbar and tray icon, so you can run automation scripts in the background.</li>
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<li>It has a password protection feature that prevents unauthorized access to your automation scripts.</li>
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</ul>
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<h2>What is a crack serial number?</h2>
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<p>A crack serial number is a code that bypasses the software's registration and activation process. Normally, when you buy a software product, you need to enter a serial number or a license key that verifies your purchase and unlocks the full features of the software. However, some people use illegal methods to generate or obtain fake serial numbers or license keys that can trick the software into thinking that it is registered and activated. These fake codes are called crack serial numbers or cracks.</p>
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<p>A crack serial number can be obtained from various sources on the internet, such as websites, forums, torrents, or peer-to-peer networks. However, using a crack serial number has many risks and disadvantages that outweigh any perceived benefits. Some of them are:</p>
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<ul>
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<li>It is illegal and unethical to use a crack serial number. You are violating the software's terms of service and infringing its intellectual property rights. You could face legal actions or penalties from the software developer or owner.</li>
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<li>It is unsafe and unreliable to use a crack serial number. You could expose your computer to viruses, malware, spyware, or other harmful programs that could damage your system or steal your personal information. You could also experience errors, crashes, or performance issues with the software or your computer.</li>
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<li>It is unfair and disrespectful to use a crack serial number. You are depriving the software developer or owner of their rightful income and recognition for their hard work and creativity. You are also hurting other legitimate users who pay for the software's license.</li>
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</ul>
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<h2>How to download and install Asoftech Automation Crack Serial 11</h2>
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<p>If you still want to download and install Asoftech Automation Crack Serial 11 despite knowing its risks and disadvantages, here are the sources and steps for doing so:</p>
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<table border="1">
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<tr><th>Source</th><th>Steps</th></tr>
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<tr><td></td><td><ol><li>Go to https://mokuchinyu.tistory.com/34</li><li>Click on the "Download" button at the bottom of the page.</li><li>Extract the zip file to your desired location.</li><li>Run the "Asoftech.Automation.Crack.Serial.11.exe" file as administrator.</li><li>Follow the instructions on the screen.</li></ol></td></tr>
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<tr><td></td><td><ol><li>Go to https://selsoft.net/cracked/asoftech-automation-242-/99508.html</li><li>Click on one of the "Download Link" buttons at the bottom of the page.</li><li>Select one of the available servers to download from.</li><li>Extract the zip file to your desired location.</li><li>Run the "Asoftech.Automation.Crack.Serial.11.exe" file as administrator.</li><li>Follow the instructions on the screen.</li></ol></td></tr>
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<tr><td></td><td><ol><li>Go to https://new.c.mi.com/my/post/470635/Asoftech_Automation_Crack_Serial_11_CRACKED</li><li>Click on one of the "Download" buttons at the bottom of the page.</li><li>Select one of the available servers to download from.</li><li>Extract the zip file to your desired location.</li><li>Run the "Asoftech.Automation.Crack.Serial.11.exe" file as administrator.</li><li>Follow the instructions on the screen.</li></ol></td></tr>
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<tr><td></td><td><ol><li>Go to https://dreamlandit.com/wp-content/uploads/2022/10/immgard.pdf</li><li>Click on one of the "Download" buttons at the bottom of the page.</li><li>Select one of the available servers to download from.</li><li>Extract the zip file to your desired location.</li><li>Run the "Asoftech.Automation.Crack.Serial.11.exe" file as administrator.</li><li>Follow the instructions on the screen.</li></ol></td></tr>
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<tr><td></td><td><ol><li>Go to https://sway.office.com/NZfBouy5VcpopbaF</li><li>Click on one of the "Download" buttons at the bottom of the page.</li><li>Select one of the available servers to download from.</li><li>Extract the zip file to your desired location.</li><li>Run the "Asoftech.Automation.Crack.Serial.11.exe" file as administrator.</li><li>Follow the instructions on the screen.</li></ol></td></tr>
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</table>
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<h2>How to use Asoftech Automation Crack Serial 11</h2>
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<p>After you have downloaded and installed Asoftech Automation Crack Serial 11, you can start using it to automate tasks on your computer. Here are the basic functions and operations of Asoftech Automation:</p>
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<ul>
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<li>To create a new automation script, click on the "New" button on the toolbar or select "File > New" from the menu.</li>
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<li>To record an automation script, click on the "Record" button on the toolbar or press "Ctrl + R" on your keyboard. Then, perform the actions that you want to automate on your computer. When you are done, click on the "Stop" button on the toolbar or press "Ctrl + S" on your keyboard.</li>
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<li>To edit an automation script, double-click on the script name in the left panel or select "Edit > Edit Script" from the menu. You can modify the recorded actions by changing their parameters, adding or deleting commands, inserting variables, loops, conditions, and other functions.</li>
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<li>To run an automation script, select the script name in the left panel and click on the "Run" button on the toolbar or press "F5" on your keyboard. You can also set a schedule for running an automation script by clicking on the "Schedule" button on the toolbar or selecting "Tools > Schedule Task" from the menu.</li>
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<li>To save an automation script, click on the "Save" button on the toolbar or select "File > Save" from the menu. You can also export an automation script as an executable file by clicking on the "Export" button on the toolbar or selecting "File > Export as EXE" from the menu.</li>
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</ul>
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<p>Here are some tips and tricks for creating and running automation scripts with Asoftech Automation:</p>
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<ul>
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<li>To pause or resume an automation script while it is running, press "Pause/Break" on your keyboard.</li>
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<li>To stop an automation script while it is running, press "Esc" on your keyboard.</li>
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<li>To hide or show Asoftech Automation while it is running, press "Ctrl + Alt + H" on your keyboard.</li>
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<li>To add comments to your automation script, use "//" at the beginning of a line.</li>
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<li>To debug your automation script, use "Print Screen" command to capture screenshots of your computer screen during the execution of the script.</li>
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</ul>
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<h2>Conclusion</h2>
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<p>Asoftech Automation Crack Serial 11 is a software tool that can help you automate tasks on your computer without paying for its license. However, using a crack serial number is illegal, unsafe, and unfair. You could face legal actions or penalties from the software developer or owner, expose your computer to viruses or malware, and deprive the software developer or owner of their income and recognition. Therefore, we do not recommend using Asoftech Automation Crack Serial 11. Instead, we suggest you to buy a legitimate license for Asoftech Automation from its official website: https://www.asoftech.com/auto-clicker/</p>
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<h2>FAQs</h2>
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<h3>Q1: Is Asoftech Automation safe to use?</h3>
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<p>A1: Asoftech Automation is safe to use if you buy a legitimate license from its official website. However, if you use a crack serial number to activate Asoftech Automation, you could expose your computer to viruses or malware that could harm your system or steal your personal information.</p>
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<h3>Q2: Is Asoftech Automation legal to use?</h3>
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<p>A2: Asoftech Automation is legal to use if you buy a legitimate license from its official website. However, if you use a crack serial number to activate Asoftech Automation, you are violating the software's terms of service and infringing its intellectual property rights. You could face legal actions or penalties from the software developer or owner.</p>
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<h3>Q3: How can I get a legitimate license for Asoftech Automation?</h3>
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<p>A3: You can get a legitimate license for Asoftech Automation by visiting its official website: https://www.asoftech.com/auto-clicker/ and clicking on the "Buy Now" button. You can choose between a single-user license ($39.95) or a multi-user license ($99.95). You can pay with PayPal or credit card. After you complete your payment, you will receive an email with your license key and download link.</p>
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<h3>Q4: What are the alternatives to Asoftech Automation?</h3>
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<p>A4: There are many other software tools that can help you automate tasks on your computer. Some of them are:</p>
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<ul>
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<li>AutoHotkey: A free and open-source scripting language that can create macros and hotkeys for Windows applications. https://www.autohotkey.com/</li>
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<li>AutoIt: A freeware scripting language that can simulate keystrokes, mouse movements, and window interactions. https://www.autoitscript.com/site/autoit/</li>
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<li>Macro Recorder: A simple and easy-to-use software that can record and replay mouse and keyboard actions. https://www.macrorecorder.com/</li>
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<li>WinAutomation: A professional and powerful software that can automate desktop and web applications with visual scripting and drag-and-drop actions. https://www.winautomation.com/</li>
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</ul>
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<h3>Q5: How can I contact Asoftech for support?</h3>
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<p>A5: You can contact Asoftech for support by visiting their website: https://www.asoftech.com/support.html and filling out their online form. You can also email them at [email protected] or call them at +1-800-928-0387.</p>
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<h1>How to Watch Live TV on Your Smartphone with These Apps</h1>
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<p>Do you love watching live TV but hate paying for cable or satellite? Do you want to watch your favorite shows, sports, news, and movies anytime, anywhere? If you answered yes to these questions, then you might want to try out some of these live TV apps that let you stream live TV channels on your smartphone.</p>
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<p>Live TV apps are apps that allow you to watch live TV over the internet without a cable or satellite subscription. They offer a variety of channels from different genres and categories, such as entertainment, lifestyle, sports, news, kids, etc. Some of them also offer on-demand content, cloud DVR, multiple accounts, and other features that enhance your viewing experience.</p>
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<p>Some of the benefits of watching live TV on your smartphone are:</p>
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<li>Convenience: You can watch live TV wherever you have an internet connection. You don't need a TV set or a remote control. You can also switch between channels easily with a swipe or a tap.</li>
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<li>Variety: You can choose from hundreds of channels from different networks and providers. You can also customize your channel lineup according to your preferences and interests.</li>
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<li>Affordability: You can save money by paying only for what you want to watch. You don't have to pay for expensive cable or satellite packages that include channels you never watch. You can also cancel anytime without any contracts or fees.</li>
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<p>In this article, we will review four of the best live TV apps that you can download on your smartphone. We will compare their features, benefits, pricing, and availability. We will also provide a table that shows a side-by-side comparison of the four live TV apps based on key criteria such as number of channels, DVR storage, simultaneous streams, etc. We will also give a recommendation based on our personal preference or experience with any of these apps.</p>
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<h2>YouTube TV: The Best Overall Live TV App</h2>
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<p>YouTube TV is one of the most popular and well-rounded live TV apps that you can download on your smartphone. It offers cable-free live TV from over 85 networks, including ABC, CBS, FOX, NBC, ESPN, CNN, HGTV, Disney Channel, and more. You can also access YouTube Originals and YouTube videos with your subscription.</p>
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<p>Some of the features and benefits of YouTube TV are:</p>
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<ul>
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<li>Cloud DVR: You can record unlimited shows and movies and store them for up to nine months. You can also fast-forward through ads on recorded content.</li>
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<li>Multiple accounts: You can create up to six accounts per household and each account gets its own DVR library and personalized recommendations.</li>
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<li>No contracts: You can cancel or pause your subscription anytime without any fees or penalties.</li>
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</ul>
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<p>Here is a screenshot of the YouTube TV app interface:</p>
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<img src="https://www.androidpolice.com/wp-content/uploads/2020/06/youtube-tv-new-ui-1.png" alt="YouTube TV app interface" width="300" height="600">
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<p>The pricing and availability of YouTube TV are:</p>
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<ul>
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<li>Pricing: YouTube TV costs $64.99 per month and you can get a free trial for 14 days. You can also add premium channels like HBO Max, Showtime, Starz, and more for an extra fee.</li>
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<li>Availability: YouTube TV is available nationwide in the US and you can watch it on your smartphone, tablet, computer, smart TV, streaming device, or game console. You can also cast it to your TV using Chromecast or AirPlay.</li>
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</ul>
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<h2>FuboTV: The Best Live TV App for Sports and Spanish-Language Channels</h2>
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<p>FuboTV is another great live TV app that you can download on your smartphone. It offers over 100 networks, including 40+ sports channels like NFL Network, NBA TV, MLB Network, beIN Sports, and more. It also has a large selection of Spanish-language channels like Univision, Telemundo, Galavision, and more.</p>
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<p>Some of the features and benefits of FuboTV are:</p>
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<li>Sports and premium add-ons: You can customize your channel lineup with various add-ons like Sports Plus, Fubo Extra, Latino Plus, International Sports Plus, and more. You can also add premium channels like AMC Premiere, Showtime, Starz, and more for an extra fee.</li>
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<li>4K streaming: You can watch select events and channels in 4K resolution with compatible devices and internet speed.</li>
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<li>Family sharing: You can create up to six profiles per account and each profile gets its own DVR library and personalized recommendations. You can also stream on up to three devices at the same time.</li>
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</ul>
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<p>Here is a screenshot of the FuboTV app interface:</p>
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<img src="https://www.androidpolice.com/wp-content/uploads/2019/10/fubotv-android-tv-1.jpg" alt="FuboTV app interface" width="300" height="600">
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<p>The pricing and availability of FuboTV are:</p>
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<ul>
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<li>Pricing: FuboTV costs $64.99 per month for the base plan and you can get a free trial for seven days. You can also choose from other plans like Family ($69.99 per month), Elite ($79.99 per month), or Latino Quarterly ($33 per month).</li>
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<li>Availability: FuboTV is available in the US, Canada, and Spain and you can watch it on your smartphone, tablet, computer, smart TV, streaming device, or game console. You can also cast it to your TV using Chromecast or AirPlay.</li>
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<h2>Sling TV: The Most Affordable Live TV App with a Good Lineup</h2> <p>Sling TV is another live TV app that you can download on your smartphone. It offers customizable packages that let you choose the channels you want to watch. It has two base plans: Sling Orange and Sling Blue, each with a different channel lineup. You can also combine both plans or add extra channels with various add-ons.</p>
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<p>Some of the features and benefits of Sling TV are:</p>
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<li>Customizable packages: You can choose from over 50 channels with Sling Orange or Sling Blue, or get both for more variety. You can also add extra channels with add-ons like Sports Extra, Comedy Extra, Kids Extra, and more.</li>
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<li>Cloud DVR: You can record up to 50 hours of shows and movies with the base plans or upgrade to 200 hours with the Cloud DVR Plus add-on. You can also fast-forward through ads on recorded content.</li>
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<li>On-demand content: You can access thousands of movies and shows on demand with your subscription. You can also rent or buy new releases from the Sling TV app.</li>
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<li>Watch parties: You can watch live TV with up to three friends at the same time with the watch party feature. You can also chat and react with them in real-time.</li>
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</ul>
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<p>Here is a screenshot of the Sling TV app interface:</p>
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<img src="https://www.androidpolice.com/wp-content/uploads/2020/07/sling-tv-android-tv-1.jpg" alt="Sling TV app interface" width="300" height="600">
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<p>The pricing and availability of Sling TV are:</p>
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<ul>
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<li>Pricing: Sling TV costs $35 per month for either Sling Orange or Sling Blue, or $50 per month for both. You can also get a free trial for three days. You can also add extra channels or features with various add-ons for an extra fee.</li>
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<li>Availability: Sling TV is available in the US and Puerto Rico and you can watch it on your smartphone, tablet, computer, smart TV, streaming device, or game console. You can also cast it to your TV using Chromecast or AirPlay.</li>
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</ul>
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<h2>Philo TV: The Cheapest Live TV App for Entertainment and Lifestyle Channels</h2>
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<p>Philo TV is the cheapest live TV app that you can download on your smartphone. It offers 61 channels from various genres such as entertainment, lifestyle, comedy, reality, news, and more. Some of the channels include A&E, AMC, BET, Comedy Central, Discovery, Food Network, Hallmark Channel, MTV, Nickelodeon, TLC, and more.</p>
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<p>Some of the features and benefits of Philo TV are:</p>
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<ul>
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<li>Unlimited DVR: You can record as many shows and movies as you want and store them for up to 30 days. You can also fast-forward through ads on recorded content.</li>
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<li>Multiple streams: You can stream on up to three devices at the same time with one account.</li>
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<li>Add-on options: You can add premium channels like EPIX and STARZ for an extra fee. You can also access on-demand content from some of the channels with your subscription.</li>
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</ul>
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<p>Here is a screenshot of the Philo TV app interface:</p>
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<img src="https://www.androidpolice.com/wp-content/uploads/2018/11/philo-android-tv-1.jpg" alt="Philo TV app interface" width="300" height="600">
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<p>The pricing and availability of Philo TV are:</p>
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<ul>
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<li>Pricing: Philo TV costs $25 per month and you can get a free trial for seven days. You can also add premium channels like EPIX and STARZ for an extra fee.</li>
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<li>Availability: Philo TV is available in the US and you can watch it on your smartphone, tablet, computer, smart TV, streaming device, or game console. You can also cast it to your TV using Chromecast or AirPlay.</li>
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</ul>
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<h2>Conclusion</h2>
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<p>In this article, we have reviewed four of the best live TV apps that you can download on your smartphone. We have compared their features, benefits, pricing, and availability. We have also provided a table that shows a side-by-side comparison of the four live TV apps based on key criteria such as number of channels, DVR storage, simultaneous streams, etc.</p>
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<table border="1">
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<tr><th>Live TV App</th><th>Number of Channels</th><th>DVR Storage</th><th>Simultaneous Streams</th><th>Monthly Cost</th></tr>
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<tr><td>YouTube TV</td><td>85+</td><td>Unlimited (9 months)</td><td>3</td><td>$64. 99</td></tr>
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<tr><td>FuboTV</td><td>100+</td><td>250 hours</td><td>3</td><td>$64.99</td></tr>
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<tr><td>Sling TV</td><td>50+</td><td>50 hours (200 hours with add-on)</td><td>1 (Sling Orange) or 3 (Sling Blue)</td><td>$35 (Sling Orange or Sling Blue) or $50 (both)</td></tr>
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<tr><td>Philo TV</td><td>61</td><td>Unlimited (30 days)</td><td>3</td><td>$25</td></tr>
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</table>
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<p>Based on our comparison, we can say that each live TV app has its own strengths and weaknesses. There is no one-size-fits-all solution for everyone. The best live TV app for you depends on your preferences, budget, and viewing habits.</p>
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<p>However, if we had to give a recommendation, we would say that YouTube TV is the best overall live TV app for most people. It has a good balance of features, benefits, pricing, and availability. It offers a wide range of channels from different genres and categories, including local and national networks. It also has a generous cloud DVR, multiple accounts, and no contracts. It is available nationwide in the US and supports most devices. It also has a free trial option that lets you try it out before you commit.</p>
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<p>Of course, you can also try out the other live TV apps and see which one suits you better. You can take advantage of their free trial options and compare them yourself. You might find that one of them meets your needs better than YouTube TV.</p>
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<p>The bottom line is that watching live TV on your smartphone is possible and convenient with these live TV apps. You can enjoy your favorite shows, sports, news, and movies anytime, anywhere without paying for cable or satellite. You can also save money by paying only for what you want to watch and canceling anytime without any fees or penalties.</p>
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<p>We hope that this article has helped you learn more about the best live TV apps that you can download on your smartphone. We also hope that you have found the best live TV app for you or at least have a better idea of what to look for. Happy streaming!</p>
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<h2>Frequently Asked Questions</h2>
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<p>Here are some of the frequently asked questions about live TV apps:</p>
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<h3>What is the difference between live TV apps and streaming services?</h3>
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<p>Live TV apps are apps that allow you to watch live TV over the internet without a cable or satellite subscription. They offer a variety of channels from different genres and categories, such as entertainment, lifestyle, sports, news, kids, etc. Some of them also offer on-demand content, cloud DVR, multiple accounts, and other features that enhance your viewing experience.</p>
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<p>Streaming services are apps that allow you to watch on-demand content over the internet without a cable or satellite subscription. They offer a library of movies, shows, documentaries, originals, and more that you can watch at your own pace. Some of them also offer live TV channels as an add-on option.</p>
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<h3>How much internet speed do I need to watch live TV on my smartphone?</h3>
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<p>The internet speed that you need to watch live TV on your smartphone depends on the quality of the video that you want to watch. Generally speaking, the higher the quality, the more bandwidth you need. Here are some of the recommended internet speeds for different video qualities:</p>
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<ul>
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<li>Standard definition (SD): 3 Mbps</li>
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<li>High definition (HD): 5 Mbps</li>
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<li>Ultra high definition (UHD) or 4K: 25 Mbps</li>
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<p>You can check your internet speed using online tools like Speedtest or Fast.com. You can also contact your internet service provider (ISP) if you have any issues with your internet speed or connection.</p>
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<h3>Can I watch live TV on my smartphone when I travel?</h3>
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<p>The answer to this question depends on the live TV app that you use and the location that you travel to. Some live TV apps are available only in certain countries or regions and may not work when you travel outside of those areas. Some live TV apps may also have geo-restrictions or blackouts on some channels or content depending on your location.</p>
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<p>To avoid any issues when you travel, you should check the availability and terms of service of the live TV app that you use before you travel. You should also check the local laws and regulations regarding streaming content over the internet in the country or region that you travel to.</p>
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<h3>How can I watch live TV on my smartphone without using too much data?</h3> <p>Watching live TV on your smartphone can use a lot of data, especially if you watch in high quality or for a long time. To avoid using too much data, you can do the following:</p>
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<ul>
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<li>Use a Wi-Fi connection whenever possible. This way, you can save your mobile data for other purposes. You can also use public Wi-Fi networks, but make sure they are secure and reliable.</li>
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<li>Lower the video quality of the live TV app. Most live TV apps allow you to adjust the video quality settings according to your preference and network speed. You can choose a lower quality option, such as SD or HD, instead of UHD or 4K. This will reduce the amount of data that you use while watching live TV.</li>
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<li>Limit the amount of time that you watch live TV. You can also set a reminder or an alarm to remind you to stop watching after a certain period of time. This will help you avoid binge-watching and using too much data.</li>
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</ul>
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<h3>What are some of the drawbacks or challenges of watching live TV on my smartphone?</h3>
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<p>Watching live TV on your smartphone can be a convenient and enjoyable way to watch your favorite shows, sports, news, and movies. However, it can also have some drawbacks or challenges, such as:</p>
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<ul>
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<li>Battery drain: Watching live TV on your smartphone can consume a lot of battery power, especially if you watch in high quality or for a long time. You may need to charge your smartphone more often or carry a portable charger with you.</li>
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<li>Screen size: Watching live TV on your smartphone can be less immersive and satisfying than watching on a larger screen, such as a TV or a computer monitor. You may miss some details or have difficulty reading subtitles or captions.</li>
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<li>Buffering or lagging: Watching live TV on your smartphone can be affected by your internet speed and connection. You may experience buffering, lagging, freezing, or skipping of the video if your internet is slow or unstable. You may also have issues with the audio and video synchronization.</li>
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spaces/1phancelerku/anime-remove-background/Among Us Imposter Hack APK A Free and Easy Way to Be the Imposter in Every Game.md
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<br />
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<h1>Among Us Imposter Hack Apk: Is It Worth It?</h1>
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<p>Among Us is a popular online multiplayer game that has taken the internet by storm. In this game, you can either be a crewmate or an impostor on a spaceship or a base. As a crewmate, your goal is to complete tasks and find the impostor. As an impostor, your goal is to kill the crewmates and sabotage their mission.</p>
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<p>But what if you want to always be the impostor and have more fun in the game? That's where the imposter hack apk comes in. This is a modified version of the game that allows you to always be the impostor, as well as use various cheats and mods to make the game easier or more interesting. But is it worth using this hack? What are the pros and cons of it? And are there any alternatives to it? In this article, we will answer these questions and more.</p>
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<h2>Pros and Cons of Using Imposter Hack Apk</h2>
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<p>The imposter hack apk can be tempting for many players who want to experience the thrill of being the impostor every time they play. However, before you download and install this hack, you should be aware of the advantages and disadvantages of using it.</p>
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<h3>Pros</h3>
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<ul>
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<li>You can always be the impostor, which can be more fun and challenging than being a crewmate.</li>
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<li>You can use various cheats and mods to enhance your gameplay, such as speed hack, wallhack, kill cooldown bypass, invisibility, etc.</li>
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<li>You can troll other players and make them rage quit or accuse each other.</li>
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</ul>
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<h3>Cons</h3>
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<ul>
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<li>You risk getting banned from the game if you are caught using the hack by the developers or other players.</li>
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<li>You may infect your device with virus or malware if you download the hack from an untrusted source.</li>
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<li>You may ruin the game for other players who want to play fairly and legitimately.</li>
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</ul>
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<h2>Alternatives to Imposter Hack Apk</h2>
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<p>If you are looking for a way to play as impostor without using the hack apk, there are some alternatives that you can try. Here are some of them:</p>
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<h3>Use Legit Mods from GitHub or Other Sources</h3>
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| 23 |
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<p>There are some legit mods for Among Us that you can download from GitHub or other sources. These mods are created by fans of the game who want to add new features or modes to it. For example, there are mods that add roles like sheriff, doctor, jester, etc. to the game. There are also mods that change the map, graphics, sounds, etc. of the game. These mods are usually safe and compatible with the original game, as long as you follow the instructions on how to install them.</p>
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| 24 |
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<h3>Play with Friends Who Agree to Use Mods</h3>
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-
<p>If you want to use mods with other players, you should make sure that they agree to use them as well. This way, you can avoid getting reported or banned for using mods. You can also have more fun and variety in your games. You can create a private lobby with your friends and use a mod menu to select which mods you want to use. You can also join public lobbies that use mods by looking for codes on Discord or Reddit.</p>
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<h3>Practice as <h3>Practice as Impostor in Freeplay Mode</h3>
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<p>If you want to improve your skills as impostor without using any hacks or mods, you can practice in the Freeplay mode. This mode allows you to play as impostor on any map with dummy crewmates. You can kill, vent, sabotage, and lie as much as you want without any consequences. You can also customize the game settings to make it easier or harder for yourself. This mode is a great way to learn the map layout, vent locations, task locations, etc. You can also practice your deception and persuasion skills by talking to yourself or recording your gameplay.</p>
|
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<h2>Conclusion</h2>
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<p>Among Us is a fun and exciting game that can be enjoyed by anyone who likes social deduction and deception games. However, some players may want to always be the impostor and use hacks or mods to achieve that. While this may seem like a good idea at first, it can also have some drawbacks and risks. Therefore, before you decide to use the imposter hack apk, you should weigh the pros and cons of it and consider the alternatives to it. You may find that playing as impostor without hacks or mods can be more rewarding and satisfying in the long run.</p>
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<p>Here are some tips on how to play as impostor without hacks or mods:</p>
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<ul>
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| 80 |
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<li>Be observant and attentive to your surroundings and the other players.</li>
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<li>Be confident and convincing when you lie or accuse someone.</li>
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<li>Be strategic and creative when you kill or sabotage.</li>
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<li>Be adaptable and flexible when things don't go your way.</li>
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<li>Have fun and don't take the game too seriously.</li>
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</ul>
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<h2>FAQs</h2>
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<h3>How to Download and Install Imposter Hack Apk?</h3>
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<p>If you still want to try the imposter hack apk, here are the steps on how to download and install it:</p>
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<ol>
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<li>Find a reliable source that offers the imposter hack apk file. You can search on Google or YouTube for reviews or recommendations.</li>
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<li>Download the apk file to your device. Make sure you have enough storage space and a good internet connection.</li>
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<li>Enable the installation of unknown sources on your device. Go to Settings > Security > Unknown Sources and toggle it on.</li>
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<li>Locate the apk file on your device and tap on it to install it.</li>
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<li>Wait for the installation to finish and launch the game.</li>
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</ol>
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<h3>How to Use Imposter Hack Apk in Among Us?</h3>
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<p>Once you have installed the imposter hack apk, you can use it in Among Us by following these steps:</p>
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<ol>
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<li>Open the game and tap on the mod menu icon on the top left corner of the screen.</li>
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<li>Select the cheats and mods that you want to use from the list. You can toggle them on or off as you wish.</li>
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<li>Join or create a lobby and start the game. You will always be the impostor and have access to the cheats and mods that you selected.</li>
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<li>Enjoy the game and try not to get caught or banned.</li>
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</ol>
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<h3>How to Avoid Getting Banned for Using Imposter Hack Apk?</h3>
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<p>If you use the imposter hack apk, you run the risk of getting banned from the game by the developers or other players. To avoid this, you should follow these tips:</p>
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<ul>
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<li>Don't use too many or obvious cheats and mods that will make other players suspicious or angry.</li>
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<li>Don't join public lobbies that have anti-cheat systems or strict rules against hacking or modding.</li>
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<li>Don't brag or boast about using the hack apk in chat or voice chat.</li>
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<li>Don't use the hack apk too often or for too long. Take breaks and play normally sometimes.</li>
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</ul>
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<h3>How to Remove Imposter Hack Apk from Your Device?</h3>
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<p>If you want to uninstall the imposter hack apk from your device, you can do so by following these steps:</p>
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<ol>
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<li>Go to Settings > Apps > Among Us and tap on Uninstall.</li>
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<li>Confirm your action and wait for the uninstallation to finish.</li>
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<li>Delete any leftover files or folders related to the hack apk from your device storage.</li>
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<li>Restart your device and check if the game is completely removed.</li>
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</ol>
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<h3>How to Report Someone Who is Using Imposter Hack Apk?</h3>
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<p>If you encounter someone who is using the imposter hack apk in Among Us, you can report them by following these steps:</p>
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| 122 |
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<ol>
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| 123 |
-
<li>Gather evidence of their hacking or modding such as screenshots, videos, chat logs, etc. that show their cheating or modding behavior.</li>
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<li>Go to the game settings and tap on the report button next to their name.</li>
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<li>Select the reason for your report and attach your evidence if possible.</li>
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<li>Submit your report and wait for the developers to review it and take action.</li>
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</ol>
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<p>I hope this article has helped you understand more about the imposter hack apk and how to use it or avoid it in Among Us. If you have any questions or feedback, please leave a comment below. Thank you for reading and have a great day!</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Download and Stream Brazil Zonal LP 2019 ft. Tawinji - The Best Afrobeat Song of 2022.md
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<h1>Download Brazil Zonal LP 2019 MP3: A Guide for Music Lovers</h1>
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<p>If you are a fan of Afrobeat music, you might have heard of Brazil Zonal LP 2019, a popular album by NBM, a group of Nigerian artists. This album features 31 minutes of energetic and catchy songs that showcase the diversity and richness of African culture. In this article, we will show you how to download Brazil Zonal LP 2019 MP3 for free and legally, and how to enjoy it to the fullest.</p>
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<ol>
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<li>Go to the SoundCloud track of the album and copy its URL.</li>
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<li>Go to SCDL website and paste the URL in the input box.</li>
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<li>Click Download and wait for the process to complete.</li>
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<h3>Download from Other Websites</h3>
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<p>A third way to download the album is from other websites that offer free music downloads. These websites usually have a large collection of songs and albums that you can download without any registration or payment. However, you need to be careful when using these websites, as some of them might contain viruses, malware, or illegal content. Some of the websites that you can try are Mp3Juices, Free Music Archive, and Jamendo. Here are the steps to download the album from Mp3Juices:</p>
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<ol>
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<li>Go to Mp3Juices website and type Brazil Zonal LP 2019 in the search box.</li>
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<li>Select the result that matches the album and click Download.</li>
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<li>Choose a server and wait for the download to start.</li>
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<li>Save the MP3 file to your device and enjoy.</li>
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<p>Now that you have downloaded Brazil Zonal LP 2019 MP3, you might wonder how to enjoy it to the fullest. Here are some tips on how to listen to and appreciate the album:</p>
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Brazil Zonal LP 2019 by Tawinji - NBM Jolly song<br />
|
| 53 |
-
Brazil Zonal LP 2019 - NBM Wazobia Egidigbo National Jolly<br />
|
| 54 |
-
Download Brazil Zonal LP 2019 by Tawinji - Military Regime<br />
|
| 55 |
-
Brazil Zonal LP 2019 by Tawinji - Afrobeat genre<br />
|
| 56 |
-
Stream Brazil Zonal LP 2019 by Tawinji on Boomplay<br />
|
| 57 |
-
Brazil Zonal LP 2019 by Tawinji - NBM New York Zone<br />
|
| 58 |
-
How to listen to Brazil Zonal LP 2019 online<br />
|
| 59 |
-
Brazil Zonal LP 2019 by Tawinji - NBM Neo black movement of Africa<br />
|
| 60 |
-
Brazil Zonal LP 2019 MP3 file size and quality<br />
|
| 61 |
-
Brazil Zonal LP 2019 by Tawinji - NBM Turkey Zone Jolly<br />
|
| 62 |
-
Download Brazil Zonal LP 2019 by Tawinji - OYIMA FORUM JOLLY<br />
|
| 63 |
-
Brazil Zonal LP 2019 by Tawinji - NBM Kanta Ethiopia Revolution Jolly<br />
|
| 64 |
-
Stream Brazil Zonal LP 2019 by Tawinji on Spotify<br />
|
| 65 |
-
Brazil Zonal LP 2019 by Tawinji - NBM Ekpe HT Jolly<br />
|
| 66 |
-
Download Brazil Zonal LP 2019 by Tawinji - NGOR OKPALA C.A.F LP<br />
|
| 67 |
-
Brazil Zonal LP 2019 by Tawinji - NBM Omambala HT Lp (Golden Regime)<br />
|
| 68 |
-
How to buy Brazil Zonal LP 2019 MP3 on iTunes<br />
|
| 69 |
-
Brazil Zonal LP 2019 by Tawinji - NBM Oceano Mental album<br />
|
| 70 |
-
Download Brazil Zonal LP 2019 by Tawinji - Starting The Ritual song<br />
|
| 71 |
-
Brazil Zonal LP 2019 by Tawinji - NBM Cleo series<br />
|
| 72 |
-
Stream Brazil Zonal LP 2019 by Tawinji on SoundCloud<br />
|
| 73 |
-
Brazil Zonal LP 2019 by Tawinji - NBM Lola Bunny song<br />
|
| 74 |
-
Download Brazil Zonal LP 2019 by Tawinji - ABSURD song<br />
|
| 75 |
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Brazil Zonal LP 2019 by Tawinji - NBM Scripts album<br />
|
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Download Brazil Zonal LP 2019 by Tawinji - Nigeria song<br />
|
| 77 |
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Brazil Zonal LP 2019 by Tawinji - NBM Egede Joburg Central Sub Zone Jolly<br />
|
| 78 |
-
Stream Brazil Zonal LP 2019 by Tawinji on Deezer<br />
|
| 79 |
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Brazil Zonal LP 2019 by Tawinji - NBM Dahomey HT LP Jolly<br />
|
| 80 |
-
Download Brazil Zonal LP 2019 by TAWINJI - URATTA FORUM JOLLY <br />
|
| 81 |
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Brazil Zonal LP 2019 by TAWINJI - NBM Jurist Confraternity South Africa Zone Jolly <br />
|
| 82 |
-
Stream Brazil Zonal LP 2019 by TAWINJI on Apple Music <br />
|
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Brazil Zonal LP 2019 by TAWINJI - NBM TRIBUTE LP TO JN EJIKE <br />
|
| 84 |
-
Download Brazil ZONAL lp (2020) BY tAWINJI </p>
|
| 85 |
-
<h3>Use a Good MP3 Player</h3>
|
| 86 |
-
<p>To play the album with high quality and features, you need a good MP3 player that can support different formats, bitrates, and playlists. You can use either a dedicated MP3 player device or an app on your smartphone or tablet. Some of the best MP3 players that you can use are VLC Media Player, Winamp, and Poweramp. These players have advanced settings that allow you to adjust the volume, equalizer, bass, treble, and other aspects of the sound. They also have features that let you create playlists, shuffle songs, repeat songs, and more.</p>
|
| 87 |
-
<h3>Use a Good Headphone or Speaker</h3>
|
| 88 |
-
<p>To experience the best sound and experience, you need a good headphone or speaker that can deliver clear, balanced, and immersive sound. You can choose either a wired or wireless headphone or speaker, depending on your preference and budget. Some of the best headphones that you can use are Sony WH-1000XM4, Bose QuietComfort 35 II, and Sennheiser HD 650. These headphones have noise-canceling technology that blocks out any external noise and lets you focus on the music. They also have comfortable design and long battery life. Some of the best speakers that you can use are JBL Flip 5, Sonos One, and Bose SoundLink Revolve. These speakers have wireless connectivity, water resistance, and 360-degree sound. They also have compact design and long battery life.</p>
|
| 89 |
-
<h3>Learn More About the Album and the Artists</h3>
|
| 90 |
-
<p>To appreciate the album more, you can learn more about the album and the artists behind it. You can find more information and background about the album and the artists on their official websites, social media pages, and online articles. You can also watch their interviews, documentaries, and live performances on YouTube and other platforms. By learning more about the album and the artists, you can understand their vision, inspiration, and message better. You can also discover more of their songs and albums that you might like.</p>
|
| 91 |
-
<h2>Conclusion</h2>
|
| 92 |
-
<p>Brazil Zonal LP 2019 is a great album for music lovers who enjoy Afrobeat music and African culture. It is an album that showcases the talent, diversity, and spirit of NBM, a group of Nigerian artists and activists. In this article, we have shown you how to download Brazil Zonal LP 2019 MP3 for free and legally, and how to enjoy it to the fullest. We hope that you have found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. And if you liked this article, please share it with your friends and family who might be interested in this topic. Thank you for reading and happy listening!</p>
|
| 93 |
-
<h2>FAQs</h2>
|
| 94 |
-
<p>Here are some of the frequently asked questions and answers about Brazil Zonal LP 2019 MP3:</p>
|
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<ul>
|
| 96 |
-
<li><b>Q: What is NBM?</b></li>
|
| 97 |
-
<li>A: NBM stands for Neo Black Movement of Africa, a group of Nigerian musicians who are also members of a social movement that promotes African unity, solidarity, and liberation.</li>
|
| 98 |
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<li><b>Q: What is the genre of Brazil Zonal LP 2019?</b></li>
|
| 99 |
-
<li>A: The genre of Brazil Zonal LP 2019 is Afrobeat, which is a fusion of African music, jazz, funk, soul, and rock.</li>
|
| 100 |
-
<li><b>Q: How long is Brazil Zonal LP 2019?</b></li>
|
| 101 |
-
<li>A: Brazil Zonal LP 2019 is 31 minutes long.</li>
|
| 102 |
-
<li><b>Q: How many songs are in Brazil Zonal LP 2019?</b></li>
|
| 103 |
-
<li>A: Brazil Zonal LP 2019 consists of one track that is a compilation of various songs.</li>
|
| 104 |
-
<li><b>Q: Where can I find more music by NBM?</b></li>
|
| 105 |
-
<li>A: You can find more music by NBM on their official website, YouTube channel, SoundCloud page, and other streaming platforms.</li>
|
| 106 |
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</ul></p> 197e85843d<br />
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spaces/2023Liu2023/bingo/src/components/ui/icons.tsx
DELETED
|
@@ -1,504 +0,0 @@
|
|
| 1 |
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'use client'
|
| 2 |
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|
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import * as React from 'react'
|
| 4 |
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|
| 5 |
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import { cn } from '@/lib/utils'
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|
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function IconNextChat({
|
| 8 |
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className,
|
| 9 |
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inverted,
|
| 10 |
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...props
|
| 11 |
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}: React.ComponentProps<'svg'> & { inverted?: boolean }) {
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const id = React.useId()
|
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return (
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<svg
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viewBox="0 0 17 17"
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fill="none"
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xmlns="http://www.w3.org/2000/svg"
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className={cn('h-4 w-4', className)}
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{...props}
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>
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<defs>
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<linearGradient
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id={`gradient-${id}-1`}
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x1="10.6889"
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y1="10.3556"
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x2="13.8445"
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y2="14.2667"
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gradientUnits="userSpaceOnUse"
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>
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<stop stopColor={inverted ? 'white' : 'black'} />
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<stop
|
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offset={1}
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stopColor={inverted ? 'white' : 'black'}
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stopOpacity={0}
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/>
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<linearGradient
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id={`gradient-${id}-2`}
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x1="11.7555"
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y1="4.8"
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x2="11.7376"
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y2="9.50002"
|
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gradientUnits="userSpaceOnUse"
|
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>
|
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<stop stopColor={inverted ? 'white' : 'black'} />
|
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<stop
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offset={1}
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stopColor={inverted ? 'white' : 'black'}
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stopOpacity={0}
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/>
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</linearGradient>
|
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</defs>
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<path
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d="M1 16L2.58314 11.2506C1.83084 9.74642 1.63835 8.02363 2.04013 6.39052C2.4419 4.75741 3.41171 3.32057 4.776 2.33712C6.1403 1.35367 7.81003 0.887808 9.4864 1.02289C11.1628 1.15798 12.7364 1.8852 13.9256 3.07442C15.1148 4.26363 15.842 5.83723 15.9771 7.5136C16.1122 9.18997 15.6463 10.8597 14.6629 12.224C13.6794 13.5883 12.2426 14.5581 10.6095 14.9599C8.97637 15.3616 7.25358 15.1692 5.74942 14.4169L1 16Z"
|
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fill={inverted ? 'black' : 'white'}
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stroke={inverted ? 'black' : 'white'}
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strokeWidth={2}
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strokeLinecap="round"
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strokeLinejoin="round"
|
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/>
|
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<mask
|
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id="mask0_91_2047"
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style={{ maskType: 'alpha' }}
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maskUnits="userSpaceOnUse"
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x={1}
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y={0}
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width={16}
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height={16}
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>
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<circle cx={9} cy={8} r={8} fill={inverted ? 'black' : 'white'} />
|
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</mask>
|
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<g mask="url(#mask0_91_2047)">
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<circle cx={9} cy={8} r={8} fill={inverted ? 'black' : 'white'} />
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<path
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d="M14.2896 14.0018L7.146 4.8H5.80005V11.1973H6.87681V6.16743L13.4444 14.6529C13.7407 14.4545 14.0231 14.2369 14.2896 14.0018Z"
|
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fill={`url(#gradient-${id}-1)`}
|
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/>
|
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<rect
|
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x="11.2222"
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y="4.8"
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width="1.06667"
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height="6.4"
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fill={`url(#gradient-${id}-2)`}
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/>
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</g>
|
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</svg>
|
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)
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}
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function IconOpenAI({ className, ...props }: React.ComponentProps<'svg'>) {
|
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<svg
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fill="currentColor"
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viewBox="0 0 24 24"
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role="img"
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xmlns="http://www.w3.org/2000/svg"
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className={cn('h-4 w-4', className)}
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{...props}
|
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-
>
|
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<title>OpenAI icon</title>
|
| 102 |
-
<path d="M22.2819 9.8211a5.9847 5.9847 0 0 0-.5157-4.9108 6.0462 6.0462 0 0 0-6.5098-2.9A6.0651 6.0651 0 0 0 4.9807 4.1818a5.9847 5.9847 0 0 0-3.9977 2.9 6.0462 6.0462 0 0 0 .7427 7.0966 5.98 5.98 0 0 0 .511 4.9107 6.051 6.051 0 0 0 6.5146 2.9001A5.9847 5.9847 0 0 0 13.2599 24a6.0557 6.0557 0 0 0 5.7718-4.2058 5.9894 5.9894 0 0 0 3.9977-2.9001 6.0557 6.0557 0 0 0-.7475-7.0729zm-9.022 12.6081a4.4755 4.4755 0 0 1-2.8764-1.0408l.1419-.0804 4.7783-2.7582a.7948.7948 0 0 0 .3927-.6813v-6.7369l2.02 1.1686a.071.071 0 0 1 .038.052v5.5826a4.504 4.504 0 0 1-4.4945 4.4944zm-9.6607-4.1254a4.4708 4.4708 0 0 1-.5346-3.0137l.142.0852 4.783 2.7582a.7712.7712 0 0 0 .7806 0l5.8428-3.3685v2.3324a.0804.0804 0 0 1-.0332.0615L9.74 19.9502a4.4992 4.4992 0 0 1-6.1408-1.6464zM2.3408 7.8956a4.485 4.485 0 0 1 2.3655-1.9728V11.6a.7664.7664 0 0 0 .3879.6765l5.8144 3.3543-2.0201 1.1685a.0757.0757 0 0 1-.071 0l-4.8303-2.7865A4.504 4.504 0 0 1 2.3408 7.872zm16.5963 3.8558L13.1038 8.364 15.1192 7.2a.0757.0757 0 0 1 .071 0l4.8303 2.7913a4.4944 4.4944 0 0 1-.6765 8.1042v-5.6772a.79.79 0 0 0-.407-.667zm2.0107-3.0231l-.142-.0852-4.7735-2.7818a.7759.7759 0 0 0-.7854 0L9.409 9.2297V6.8974a.0662.0662 0 0 1 .0284-.0615l4.8303-2.7866a4.4992 4.4992 0 0 1 6.6802 4.66zM8.3065 12.863l-2.02-1.1638a.0804.0804 0 0 1-.038-.0567V6.0742a4.4992 4.4992 0 0 1 7.3757-3.4537l-.142.0805L8.704 5.459a.7948.7948 0 0 0-.3927.6813zm1.0976-2.3654l2.602-1.4998 2.6069 1.4998v2.9994l-2.5974 1.4997-2.6067-1.4997Z" />
|
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</svg>
|
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-
)
|
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}
|
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|
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function IconGitHub({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 108 |
-
return (
|
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<svg
|
| 110 |
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role="img"
|
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viewBox="0 0 24 24"
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xmlns="http://www.w3.org/2000/svg"
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fill="currentColor"
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className={cn('h-4 w-4', className)}
|
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{...props}
|
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-
>
|
| 117 |
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<title>GitHub</title>
|
| 118 |
-
<path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12" />
|
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-
</svg>
|
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)
|
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}
|
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|
| 123 |
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function IconSeparator({ className, ...props }: React.ComponentProps<'svg'>) {
|
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-
return (
|
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<svg
|
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-
fill="none"
|
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shapeRendering="geometricPrecision"
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stroke="currentColor"
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strokeLinecap="round"
|
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strokeLinejoin="round"
|
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strokeWidth="1"
|
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viewBox="0 0 24 24"
|
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aria-hidden="true"
|
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className={cn('h-4 w-4', className)}
|
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{...props}
|
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>
|
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<path d="M16.88 3.549L7.12 20.451"></path>
|
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</svg>
|
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)
|
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-
}
|
| 141 |
-
|
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-
function IconArrowDown({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 143 |
-
return (
|
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<svg
|
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xmlns="http://www.w3.org/2000/svg"
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viewBox="0 0 256 256"
|
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fill="currentColor"
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className={cn('h-4 w-4', className)}
|
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{...props}
|
| 150 |
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>
|
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<path d="m205.66 149.66-72 72a8 8 0 0 1-11.32 0l-72-72a8 8 0 0 1 11.32-11.32L120 196.69V40a8 8 0 0 1 16 0v156.69l58.34-58.35a8 8 0 0 1 11.32 11.32Z" />
|
| 152 |
-
</svg>
|
| 153 |
-
)
|
| 154 |
-
}
|
| 155 |
-
|
| 156 |
-
function IconArrowRight({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 157 |
-
return (
|
| 158 |
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<svg
|
| 159 |
-
xmlns="http://www.w3.org/2000/svg"
|
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-
viewBox="0 0 256 256"
|
| 161 |
-
fill="currentColor"
|
| 162 |
-
className={cn('h-4 w-4', className)}
|
| 163 |
-
{...props}
|
| 164 |
-
>
|
| 165 |
-
<path d="m221.66 133.66-72 72a8 8 0 0 1-11.32-11.32L196.69 136H40a8 8 0 0 1 0-16h156.69l-58.35-58.34a8 8 0 0 1 11.32-11.32l72 72a8 8 0 0 1 0 11.32Z" />
|
| 166 |
-
</svg>
|
| 167 |
-
)
|
| 168 |
-
}
|
| 169 |
-
|
| 170 |
-
function IconUser({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 171 |
-
return (
|
| 172 |
-
<svg
|
| 173 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 174 |
-
viewBox="0 0 256 256"
|
| 175 |
-
fill="currentColor"
|
| 176 |
-
className={cn('h-4 w-4', className)}
|
| 177 |
-
{...props}
|
| 178 |
-
>
|
| 179 |
-
<path d="M230.92 212c-15.23-26.33-38.7-45.21-66.09-54.16a72 72 0 1 0-73.66 0c-27.39 8.94-50.86 27.82-66.09 54.16a8 8 0 1 0 13.85 8c18.84-32.56 52.14-52 89.07-52s70.23 19.44 89.07 52a8 8 0 1 0 13.85-8ZM72 96a56 56 0 1 1 56 56 56.06 56.06 0 0 1-56-56Z" />
|
| 180 |
-
</svg>
|
| 181 |
-
)
|
| 182 |
-
}
|
| 183 |
-
|
| 184 |
-
function IconPlus({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 185 |
-
return (
|
| 186 |
-
<svg
|
| 187 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 188 |
-
viewBox="0 0 256 256"
|
| 189 |
-
fill="currentColor"
|
| 190 |
-
className={cn('h-4 w-4', className)}
|
| 191 |
-
{...props}
|
| 192 |
-
>
|
| 193 |
-
<path d="M224 128a8 8 0 0 1-8 8h-80v80a8 8 0 0 1-16 0v-80H40a8 8 0 0 1 0-16h80V40a8 8 0 0 1 16 0v80h80a8 8 0 0 1 8 8Z" />
|
| 194 |
-
</svg>
|
| 195 |
-
)
|
| 196 |
-
}
|
| 197 |
-
|
| 198 |
-
function IconArrowElbow({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 199 |
-
return (
|
| 200 |
-
<svg
|
| 201 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 202 |
-
viewBox="0 0 256 256"
|
| 203 |
-
fill="currentColor"
|
| 204 |
-
className={cn('h-4 w-4', className)}
|
| 205 |
-
{...props}
|
| 206 |
-
>
|
| 207 |
-
<path d="M200 32v144a8 8 0 0 1-8 8H67.31l34.35 34.34a8 8 0 0 1-11.32 11.32l-48-48a8 8 0 0 1 0-11.32l48-48a8 8 0 0 1 11.32 11.32L67.31 168H184V32a8 8 0 0 1 16 0Z" />
|
| 208 |
-
</svg>
|
| 209 |
-
)
|
| 210 |
-
}
|
| 211 |
-
|
| 212 |
-
function IconSpinner({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 213 |
-
return (
|
| 214 |
-
<svg
|
| 215 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 216 |
-
viewBox="0 0 256 256"
|
| 217 |
-
fill="currentColor"
|
| 218 |
-
className={cn('h-4 w-4 animate-spin', className)}
|
| 219 |
-
{...props}
|
| 220 |
-
>
|
| 221 |
-
<path d="M232 128a104 104 0 0 1-208 0c0-41 23.81-78.36 60.66-95.27a8 8 0 0 1 6.68 14.54C60.15 61.59 40 93.27 40 128a88 88 0 0 0 176 0c0-34.73-20.15-66.41-51.34-80.73a8 8 0 0 1 6.68-14.54C208.19 49.64 232 87 232 128Z" />
|
| 222 |
-
</svg>
|
| 223 |
-
)
|
| 224 |
-
}
|
| 225 |
-
|
| 226 |
-
function IconMessage({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 227 |
-
return (
|
| 228 |
-
<svg
|
| 229 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 230 |
-
viewBox="0 0 256 256"
|
| 231 |
-
fill="currentColor"
|
| 232 |
-
className={cn('h-4 w-4', className)}
|
| 233 |
-
{...props}
|
| 234 |
-
>
|
| 235 |
-
<path d="M216 48H40a16 16 0 0 0-16 16v160a15.84 15.84 0 0 0 9.25 14.5A16.05 16.05 0 0 0 40 240a15.89 15.89 0 0 0 10.25-3.78.69.69 0 0 0 .13-.11L82.5 208H216a16 16 0 0 0 16-16V64a16 16 0 0 0-16-16ZM40 224Zm176-32H82.5a16 16 0 0 0-10.3 3.75l-.12.11L40 224V64h176Z" />
|
| 236 |
-
</svg>
|
| 237 |
-
)
|
| 238 |
-
}
|
| 239 |
-
|
| 240 |
-
function IconTrash({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 241 |
-
return (
|
| 242 |
-
<svg
|
| 243 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 244 |
-
viewBox="0 0 256 256"
|
| 245 |
-
fill="currentColor"
|
| 246 |
-
className={cn('h-4 w-4', className)}
|
| 247 |
-
{...props}
|
| 248 |
-
>
|
| 249 |
-
<path d="M216 48h-40v-8a24 24 0 0 0-24-24h-48a24 24 0 0 0-24 24v8H40a8 8 0 0 0 0 16h8v144a16 16 0 0 0 16 16h128a16 16 0 0 0 16-16V64h8a8 8 0 0 0 0-16ZM96 40a8 8 0 0 1 8-8h48a8 8 0 0 1 8 8v8H96Zm96 168H64V64h128Zm-80-104v64a8 8 0 0 1-16 0v-64a8 8 0 0 1 16 0Zm48 0v64a8 8 0 0 1-16 0v-64a8 8 0 0 1 16 0Z" />
|
| 250 |
-
</svg>
|
| 251 |
-
)
|
| 252 |
-
}
|
| 253 |
-
|
| 254 |
-
function IconMore({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 255 |
-
return (
|
| 256 |
-
<svg
|
| 257 |
-
viewBox="0 0 24 24"
|
| 258 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 259 |
-
fill="currentColor"
|
| 260 |
-
className={cn('h-4 w-4', className)}
|
| 261 |
-
{...props}
|
| 262 |
-
>
|
| 263 |
-
<path d="M7.75 12C7.75 12.9665 6.9665 13.75 6 13.75C5.0335 13.75 4.25 12.9665 4.25 12C4.25 11.0335 5.0335 10.25 6 10.25C6.9665 10.25 7.75 11.0335 7.75 12ZM13.75 12C13.75 12.9665 12.9665 13.75 12 13.75C11.0335 13.75 10.25 12.9665 10.25 12C10.25 11.0335 11.0335 10.25 12 10.25C12.9665 10.25 13.75 11.0335 13.75 12ZM18 13.75C18.9665 13.75 19.75 12.9665 19.75 12C19.75 11.0335 18.9665 10.25 18 10.25C17.0335 10.25 16.25 11.0335 16.25 12C16.25 12.9665 17.0335 13.75 18 13.75Z"></path>
|
| 264 |
-
</svg>
|
| 265 |
-
)
|
| 266 |
-
}
|
| 267 |
-
|
| 268 |
-
function IconRefresh({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 269 |
-
return (
|
| 270 |
-
<svg
|
| 271 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 272 |
-
viewBox="0 0 256 256"
|
| 273 |
-
fill="currentColor"
|
| 274 |
-
className={cn('h-4 w-4', className)}
|
| 275 |
-
{...props}
|
| 276 |
-
>
|
| 277 |
-
<path d="M197.67 186.37a8 8 0 0 1 0 11.29C196.58 198.73 170.82 224 128 224c-37.39 0-64.53-22.4-80-39.85V208a8 8 0 0 1-16 0v-48a8 8 0 0 1 8-8h48a8 8 0 0 1 0 16H55.44C67.76 183.35 93 208 128 208c36 0 58.14-21.46 58.36-21.68a8 8 0 0 1 11.31.05ZM216 40a8 8 0 0 0-8 8v23.85C192.53 54.4 165.39 32 128 32c-42.82 0-68.58 25.27-69.66 26.34a8 8 0 0 0 11.3 11.34C69.86 69.46 92 48 128 48c35 0 60.24 24.65 72.56 40H168a8 8 0 0 0 0 16h48a8 8 0 0 0 8-8V48a8 8 0 0 0-8-8Z" />
|
| 278 |
-
</svg>
|
| 279 |
-
)
|
| 280 |
-
}
|
| 281 |
-
|
| 282 |
-
function IconStop({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 283 |
-
return (
|
| 284 |
-
<svg
|
| 285 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 286 |
-
viewBox="0 0 256 256"
|
| 287 |
-
fill="currentColor"
|
| 288 |
-
className={cn('h-4 w-4', className)}
|
| 289 |
-
{...props}
|
| 290 |
-
>
|
| 291 |
-
<path d="M128 24a104 104 0 1 0 104 104A104.11 104.11 0 0 0 128 24Zm0 192a88 88 0 1 1 88-88 88.1 88.1 0 0 1-88 88Zm24-120h-48a8 8 0 0 0-8 8v48a8 8 0 0 0 8 8h48a8 8 0 0 0 8-8v-48a8 8 0 0 0-8-8Zm-8 48h-32v-32h32Z" />
|
| 292 |
-
</svg>
|
| 293 |
-
)
|
| 294 |
-
}
|
| 295 |
-
|
| 296 |
-
function IconSidebar({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 297 |
-
return (
|
| 298 |
-
<svg
|
| 299 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 300 |
-
viewBox="0 0 256 256"
|
| 301 |
-
fill="currentColor"
|
| 302 |
-
className={cn('h-4 w-4', className)}
|
| 303 |
-
{...props}
|
| 304 |
-
>
|
| 305 |
-
<path d="M216 40H40a16 16 0 0 0-16 16v144a16 16 0 0 0 16 16h176a16 16 0 0 0 16-16V56a16 16 0 0 0-16-16ZM40 56h40v144H40Zm176 144H96V56h120v144Z" />
|
| 306 |
-
</svg>
|
| 307 |
-
)
|
| 308 |
-
}
|
| 309 |
-
|
| 310 |
-
function IconMoon({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 311 |
-
return (
|
| 312 |
-
<svg
|
| 313 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 314 |
-
viewBox="0 0 256 256"
|
| 315 |
-
fill="currentColor"
|
| 316 |
-
className={cn('h-4 w-4', className)}
|
| 317 |
-
{...props}
|
| 318 |
-
>
|
| 319 |
-
<path d="M233.54 142.23a8 8 0 0 0-8-2 88.08 88.08 0 0 1-109.8-109.8 8 8 0 0 0-10-10 104.84 104.84 0 0 0-52.91 37A104 104 0 0 0 136 224a103.09 103.09 0 0 0 62.52-20.88 104.84 104.84 0 0 0 37-52.91 8 8 0 0 0-1.98-7.98Zm-44.64 48.11A88 88 0 0 1 65.66 67.11a89 89 0 0 1 31.4-26A106 106 0 0 0 96 56a104.11 104.11 0 0 0 104 104 106 106 0 0 0 14.92-1.06 89 89 0 0 1-26.02 31.4Z" />
|
| 320 |
-
</svg>
|
| 321 |
-
)
|
| 322 |
-
}
|
| 323 |
-
|
| 324 |
-
function IconSun({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 325 |
-
return (
|
| 326 |
-
<svg
|
| 327 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 328 |
-
viewBox="0 0 256 256"
|
| 329 |
-
fill="currentColor"
|
| 330 |
-
className={cn('h-4 w-4', className)}
|
| 331 |
-
{...props}
|
| 332 |
-
>
|
| 333 |
-
<path d="M120 40V16a8 8 0 0 1 16 0v24a8 8 0 0 1-16 0Zm72 88a64 64 0 1 1-64-64 64.07 64.07 0 0 1 64 64Zm-16 0a48 48 0 1 0-48 48 48.05 48.05 0 0 0 48-48ZM58.34 69.66a8 8 0 0 0 11.32-11.32l-16-16a8 8 0 0 0-11.32 11.32Zm0 116.68-16 16a8 8 0 0 0 11.32 11.32l16-16a8 8 0 0 0-11.32-11.32ZM192 72a8 8 0 0 0 5.66-2.34l16-16a8 8 0 0 0-11.32-11.32l-16 16A8 8 0 0 0 192 72Zm5.66 114.34a8 8 0 0 0-11.32 11.32l16 16a8 8 0 0 0 11.32-11.32ZM48 128a8 8 0 0 0-8-8H16a8 8 0 0 0 0 16h24a8 8 0 0 0 8-8Zm80 80a8 8 0 0 0-8 8v24a8 8 0 0 0 16 0v-24a8 8 0 0 0-8-8Zm112-88h-24a8 8 0 0 0 0 16h24a8 8 0 0 0 0-16Z" />
|
| 334 |
-
</svg>
|
| 335 |
-
)
|
| 336 |
-
}
|
| 337 |
-
|
| 338 |
-
function IconCopy({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 339 |
-
return (
|
| 340 |
-
<svg
|
| 341 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 342 |
-
viewBox="0 0 256 256"
|
| 343 |
-
fill="currentColor"
|
| 344 |
-
className={cn('h-4 w-4', className)}
|
| 345 |
-
{...props}
|
| 346 |
-
>
|
| 347 |
-
<path d="M216 32H88a8 8 0 0 0-8 8v40H40a8 8 0 0 0-8 8v128a8 8 0 0 0 8 8h128a8 8 0 0 0 8-8v-40h40a8 8 0 0 0 8-8V40a8 8 0 0 0-8-8Zm-56 176H48V96h112Zm48-48h-32V88a8 8 0 0 0-8-8H96V48h112Z" />
|
| 348 |
-
</svg>
|
| 349 |
-
)
|
| 350 |
-
}
|
| 351 |
-
|
| 352 |
-
function IconCheck({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 353 |
-
return (
|
| 354 |
-
<svg
|
| 355 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 356 |
-
viewBox="0 0 256 256"
|
| 357 |
-
fill="currentColor"
|
| 358 |
-
className={cn('h-4 w-4', className)}
|
| 359 |
-
{...props}
|
| 360 |
-
>
|
| 361 |
-
<path d="m229.66 77.66-128 128a8 8 0 0 1-11.32 0l-56-56a8 8 0 0 1 11.32-11.32L96 188.69 218.34 66.34a8 8 0 0 1 11.32 11.32Z" />
|
| 362 |
-
</svg>
|
| 363 |
-
)
|
| 364 |
-
}
|
| 365 |
-
|
| 366 |
-
function IconDownload({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 367 |
-
return (
|
| 368 |
-
<svg
|
| 369 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 370 |
-
viewBox="0 0 256 256"
|
| 371 |
-
fill="currentColor"
|
| 372 |
-
className={cn('h-4 w-4', className)}
|
| 373 |
-
{...props}
|
| 374 |
-
>
|
| 375 |
-
<path d="M224 152v56a16 16 0 0 1-16 16H48a16 16 0 0 1-16-16v-56a8 8 0 0 1 16 0v56h160v-56a8 8 0 0 1 16 0Zm-101.66 5.66a8 8 0 0 0 11.32 0l40-40a8 8 0 0 0-11.32-11.32L136 132.69V40a8 8 0 0 0-16 0v92.69l-26.34-26.35a8 8 0 0 0-11.32 11.32Z" />
|
| 376 |
-
</svg>
|
| 377 |
-
)
|
| 378 |
-
}
|
| 379 |
-
|
| 380 |
-
function IconClose({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 381 |
-
return (
|
| 382 |
-
<svg
|
| 383 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 384 |
-
viewBox="0 0 256 256"
|
| 385 |
-
fill="currentColor"
|
| 386 |
-
className={cn('h-4 w-4', className)}
|
| 387 |
-
{...props}
|
| 388 |
-
>
|
| 389 |
-
<path d="M205.66 194.34a8 8 0 0 1-11.32 11.32L128 139.31l-66.34 66.35a8 8 0 0 1-11.32-11.32L116.69 128 50.34 61.66a8 8 0 0 1 11.32-11.32L128 116.69l66.34-66.35a8 8 0 0 1 11.32 11.32L139.31 128Z" />
|
| 390 |
-
</svg>
|
| 391 |
-
)
|
| 392 |
-
}
|
| 393 |
-
|
| 394 |
-
function IconEdit({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 395 |
-
return (
|
| 396 |
-
<svg
|
| 397 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 398 |
-
fill="none"
|
| 399 |
-
viewBox="0 0 24 24"
|
| 400 |
-
strokeWidth={1.5}
|
| 401 |
-
stroke="currentColor"
|
| 402 |
-
className={cn('h-4 w-4', className)}
|
| 403 |
-
{...props}
|
| 404 |
-
>
|
| 405 |
-
<path
|
| 406 |
-
strokeLinecap="round"
|
| 407 |
-
strokeLinejoin="round"
|
| 408 |
-
d="M16.862 4.487l1.687-1.688a1.875 1.875 0 112.652 2.652L10.582 16.07a4.5 4.5 0 01-1.897 1.13L6 18l.8-2.685a4.5 4.5 0 011.13-1.897l8.932-8.931zm0 0L19.5 7.125M18 14v4.75A2.25 2.25 0 0115.75 21H5.25A2.25 2.25 0 013 18.75V8.25A2.25 2.25 0 015.25 6H10"
|
| 409 |
-
/>
|
| 410 |
-
</svg>
|
| 411 |
-
)
|
| 412 |
-
}
|
| 413 |
-
|
| 414 |
-
function IconShare({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 415 |
-
return (
|
| 416 |
-
<svg
|
| 417 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 418 |
-
fill="currentColor"
|
| 419 |
-
className={cn('h-4 w-4', className)}
|
| 420 |
-
viewBox="0 0 256 256"
|
| 421 |
-
{...props}
|
| 422 |
-
>
|
| 423 |
-
<path d="m237.66 106.35-80-80A8 8 0 0 0 144 32v40.35c-25.94 2.22-54.59 14.92-78.16 34.91-28.38 24.08-46.05 55.11-49.76 87.37a12 12 0 0 0 20.68 9.58c11-11.71 50.14-48.74 107.24-52V192a8 8 0 0 0 13.66 5.65l80-80a8 8 0 0 0 0-11.3ZM160 172.69V144a8 8 0 0 0-8-8c-28.08 0-55.43 7.33-81.29 21.8a196.17 196.17 0 0 0-36.57 26.52c5.8-23.84 20.42-46.51 42.05-64.86C99.41 99.77 127.75 88 152 88a8 8 0 0 0 8-8V51.32L220.69 112Z" />
|
| 424 |
-
</svg>
|
| 425 |
-
)
|
| 426 |
-
}
|
| 427 |
-
|
| 428 |
-
function IconUsers({ className, ...props }: React.ComponentProps<'svg'>) {
|
| 429 |
-
return (
|
| 430 |
-
<svg
|
| 431 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 432 |
-
fill="currentColor"
|
| 433 |
-
className={cn('h-4 w-4', className)}
|
| 434 |
-
viewBox="0 0 256 256"
|
| 435 |
-
{...props}
|
| 436 |
-
>
|
| 437 |
-
<path d="M117.25 157.92a60 60 0 1 0-66.5 0 95.83 95.83 0 0 0-47.22 37.71 8 8 0 1 0 13.4 8.74 80 80 0 0 1 134.14 0 8 8 0 0 0 13.4-8.74 95.83 95.83 0 0 0-47.22-37.71ZM40 108a44 44 0 1 1 44 44 44.05 44.05 0 0 1-44-44Zm210.14 98.7a8 8 0 0 1-11.07-2.33A79.83 79.83 0 0 0 172 168a8 8 0 0 1 0-16 44 44 0 1 0-16.34-84.87 8 8 0 1 1-5.94-14.85 60 60 0 0 1 55.53 105.64 95.83 95.83 0 0 1 47.22 37.71 8 8 0 0 1-2.33 11.07Z" />
|
| 438 |
-
</svg>
|
| 439 |
-
)
|
| 440 |
-
}
|
| 441 |
-
|
| 442 |
-
function IconExternalLink({
|
| 443 |
-
className,
|
| 444 |
-
...props
|
| 445 |
-
}: React.ComponentProps<'svg'>) {
|
| 446 |
-
return (
|
| 447 |
-
<svg
|
| 448 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 449 |
-
fill="currentColor"
|
| 450 |
-
className={cn('h-4 w-4', className)}
|
| 451 |
-
viewBox="0 0 256 256"
|
| 452 |
-
{...props}
|
| 453 |
-
>
|
| 454 |
-
<path d="M224 104a8 8 0 0 1-16 0V59.32l-66.33 66.34a8 8 0 0 1-11.32-11.32L196.68 48H152a8 8 0 0 1 0-16h64a8 8 0 0 1 8 8Zm-40 24a8 8 0 0 0-8 8v72H48V80h72a8 8 0 0 0 0-16H48a16 16 0 0 0-16 16v128a16 16 0 0 0 16 16h128a16 16 0 0 0 16-16v-72a8 8 0 0 0-8-8Z" />
|
| 455 |
-
</svg>
|
| 456 |
-
)
|
| 457 |
-
}
|
| 458 |
-
|
| 459 |
-
function IconChevronUpDown({
|
| 460 |
-
className,
|
| 461 |
-
...props
|
| 462 |
-
}: React.ComponentProps<'svg'>) {
|
| 463 |
-
return (
|
| 464 |
-
<svg
|
| 465 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 466 |
-
fill="currentColor"
|
| 467 |
-
className={cn('h-4 w-4', className)}
|
| 468 |
-
viewBox="0 0 256 256"
|
| 469 |
-
{...props}
|
| 470 |
-
>
|
| 471 |
-
<path d="M181.66 170.34a8 8 0 0 1 0 11.32l-48 48a8 8 0 0 1-11.32 0l-48-48a8 8 0 0 1 11.32-11.32L128 212.69l42.34-42.35a8 8 0 0 1 11.32 0Zm-96-84.68L128 43.31l42.34 42.35a8 8 0 0 0 11.32-11.32l-48-48a8 8 0 0 0-11.32 0l-48 48a8 8 0 0 0 11.32 11.32Z" />
|
| 472 |
-
</svg>
|
| 473 |
-
)
|
| 474 |
-
}
|
| 475 |
-
|
| 476 |
-
export {
|
| 477 |
-
IconEdit,
|
| 478 |
-
IconNextChat,
|
| 479 |
-
IconOpenAI,
|
| 480 |
-
IconGitHub,
|
| 481 |
-
IconSeparator,
|
| 482 |
-
IconArrowDown,
|
| 483 |
-
IconArrowRight,
|
| 484 |
-
IconUser,
|
| 485 |
-
IconPlus,
|
| 486 |
-
IconArrowElbow,
|
| 487 |
-
IconSpinner,
|
| 488 |
-
IconMessage,
|
| 489 |
-
IconTrash,
|
| 490 |
-
IconMore,
|
| 491 |
-
IconRefresh,
|
| 492 |
-
IconStop,
|
| 493 |
-
IconSidebar,
|
| 494 |
-
IconMoon,
|
| 495 |
-
IconSun,
|
| 496 |
-
IconCopy,
|
| 497 |
-
IconCheck,
|
| 498 |
-
IconDownload,
|
| 499 |
-
IconClose,
|
| 500 |
-
IconShare,
|
| 501 |
-
IconUsers,
|
| 502 |
-
IconExternalLink,
|
| 503 |
-
IconChevronUpDown
|
| 504 |
-
}
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|
spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/dataset.py
DELETED
|
@@ -1,183 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import random
|
| 3 |
-
|
| 4 |
-
import numpy as np
|
| 5 |
-
import torch
|
| 6 |
-
import torch.utils.data
|
| 7 |
-
from tqdm import tqdm
|
| 8 |
-
|
| 9 |
-
from . import spec_utils
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class VocalRemoverValidationSet(torch.utils.data.Dataset):
|
| 13 |
-
def __init__(self, patch_list):
|
| 14 |
-
self.patch_list = patch_list
|
| 15 |
-
|
| 16 |
-
def __len__(self):
|
| 17 |
-
return len(self.patch_list)
|
| 18 |
-
|
| 19 |
-
def __getitem__(self, idx):
|
| 20 |
-
path = self.patch_list[idx]
|
| 21 |
-
data = np.load(path)
|
| 22 |
-
|
| 23 |
-
X, y = data["X"], data["y"]
|
| 24 |
-
|
| 25 |
-
X_mag = np.abs(X)
|
| 26 |
-
y_mag = np.abs(y)
|
| 27 |
-
|
| 28 |
-
return X_mag, y_mag
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
def make_pair(mix_dir, inst_dir):
|
| 32 |
-
input_exts = [".wav", ".m4a", ".mp3", ".mp4", ".flac"]
|
| 33 |
-
|
| 34 |
-
X_list = sorted(
|
| 35 |
-
[
|
| 36 |
-
os.path.join(mix_dir, fname)
|
| 37 |
-
for fname in os.listdir(mix_dir)
|
| 38 |
-
if os.path.splitext(fname)[1] in input_exts
|
| 39 |
-
]
|
| 40 |
-
)
|
| 41 |
-
y_list = sorted(
|
| 42 |
-
[
|
| 43 |
-
os.path.join(inst_dir, fname)
|
| 44 |
-
for fname in os.listdir(inst_dir)
|
| 45 |
-
if os.path.splitext(fname)[1] in input_exts
|
| 46 |
-
]
|
| 47 |
-
)
|
| 48 |
-
|
| 49 |
-
filelist = list(zip(X_list, y_list))
|
| 50 |
-
|
| 51 |
-
return filelist
|
| 52 |
-
|
| 53 |
-
|
| 54 |
-
def train_val_split(dataset_dir, split_mode, val_rate, val_filelist):
|
| 55 |
-
if split_mode == "random":
|
| 56 |
-
filelist = make_pair(
|
| 57 |
-
os.path.join(dataset_dir, "mixtures"),
|
| 58 |
-
os.path.join(dataset_dir, "instruments"),
|
| 59 |
-
)
|
| 60 |
-
|
| 61 |
-
random.shuffle(filelist)
|
| 62 |
-
|
| 63 |
-
if len(val_filelist) == 0:
|
| 64 |
-
val_size = int(len(filelist) * val_rate)
|
| 65 |
-
train_filelist = filelist[:-val_size]
|
| 66 |
-
val_filelist = filelist[-val_size:]
|
| 67 |
-
else:
|
| 68 |
-
train_filelist = [
|
| 69 |
-
pair for pair in filelist if list(pair) not in val_filelist
|
| 70 |
-
]
|
| 71 |
-
elif split_mode == "subdirs":
|
| 72 |
-
if len(val_filelist) != 0:
|
| 73 |
-
raise ValueError(
|
| 74 |
-
"The `val_filelist` option is not available in `subdirs` mode"
|
| 75 |
-
)
|
| 76 |
-
|
| 77 |
-
train_filelist = make_pair(
|
| 78 |
-
os.path.join(dataset_dir, "training/mixtures"),
|
| 79 |
-
os.path.join(dataset_dir, "training/instruments"),
|
| 80 |
-
)
|
| 81 |
-
|
| 82 |
-
val_filelist = make_pair(
|
| 83 |
-
os.path.join(dataset_dir, "validation/mixtures"),
|
| 84 |
-
os.path.join(dataset_dir, "validation/instruments"),
|
| 85 |
-
)
|
| 86 |
-
|
| 87 |
-
return train_filelist, val_filelist
|
| 88 |
-
|
| 89 |
-
|
| 90 |
-
def augment(X, y, reduction_rate, reduction_mask, mixup_rate, mixup_alpha):
|
| 91 |
-
perm = np.random.permutation(len(X))
|
| 92 |
-
for i, idx in enumerate(tqdm(perm)):
|
| 93 |
-
if np.random.uniform() < reduction_rate:
|
| 94 |
-
y[idx] = spec_utils.reduce_vocal_aggressively(
|
| 95 |
-
X[idx], y[idx], reduction_mask
|
| 96 |
-
)
|
| 97 |
-
|
| 98 |
-
if np.random.uniform() < 0.5:
|
| 99 |
-
# swap channel
|
| 100 |
-
X[idx] = X[idx, ::-1]
|
| 101 |
-
y[idx] = y[idx, ::-1]
|
| 102 |
-
if np.random.uniform() < 0.02:
|
| 103 |
-
# mono
|
| 104 |
-
X[idx] = X[idx].mean(axis=0, keepdims=True)
|
| 105 |
-
y[idx] = y[idx].mean(axis=0, keepdims=True)
|
| 106 |
-
if np.random.uniform() < 0.02:
|
| 107 |
-
# inst
|
| 108 |
-
X[idx] = y[idx]
|
| 109 |
-
|
| 110 |
-
if np.random.uniform() < mixup_rate and i < len(perm) - 1:
|
| 111 |
-
lam = np.random.beta(mixup_alpha, mixup_alpha)
|
| 112 |
-
X[idx] = lam * X[idx] + (1 - lam) * X[perm[i + 1]]
|
| 113 |
-
y[idx] = lam * y[idx] + (1 - lam) * y[perm[i + 1]]
|
| 114 |
-
|
| 115 |
-
return X, y
|
| 116 |
-
|
| 117 |
-
|
| 118 |
-
def make_padding(width, cropsize, offset):
|
| 119 |
-
left = offset
|
| 120 |
-
roi_size = cropsize - left * 2
|
| 121 |
-
if roi_size == 0:
|
| 122 |
-
roi_size = cropsize
|
| 123 |
-
right = roi_size - (width % roi_size) + left
|
| 124 |
-
|
| 125 |
-
return left, right, roi_size
|
| 126 |
-
|
| 127 |
-
|
| 128 |
-
def make_training_set(filelist, cropsize, patches, sr, hop_length, n_fft, offset):
|
| 129 |
-
len_dataset = patches * len(filelist)
|
| 130 |
-
|
| 131 |
-
X_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
|
| 132 |
-
y_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
|
| 133 |
-
|
| 134 |
-
for i, (X_path, y_path) in enumerate(tqdm(filelist)):
|
| 135 |
-
X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
|
| 136 |
-
coef = np.max([np.abs(X).max(), np.abs(y).max()])
|
| 137 |
-
X, y = X / coef, y / coef
|
| 138 |
-
|
| 139 |
-
l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
|
| 140 |
-
X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
|
| 141 |
-
y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
|
| 142 |
-
|
| 143 |
-
starts = np.random.randint(0, X_pad.shape[2] - cropsize, patches)
|
| 144 |
-
ends = starts + cropsize
|
| 145 |
-
for j in range(patches):
|
| 146 |
-
idx = i * patches + j
|
| 147 |
-
X_dataset[idx] = X_pad[:, :, starts[j] : ends[j]]
|
| 148 |
-
y_dataset[idx] = y_pad[:, :, starts[j] : ends[j]]
|
| 149 |
-
|
| 150 |
-
return X_dataset, y_dataset
|
| 151 |
-
|
| 152 |
-
|
| 153 |
-
def make_validation_set(filelist, cropsize, sr, hop_length, n_fft, offset):
|
| 154 |
-
patch_list = []
|
| 155 |
-
patch_dir = "cs{}_sr{}_hl{}_nf{}_of{}".format(
|
| 156 |
-
cropsize, sr, hop_length, n_fft, offset
|
| 157 |
-
)
|
| 158 |
-
os.makedirs(patch_dir, exist_ok=True)
|
| 159 |
-
|
| 160 |
-
for i, (X_path, y_path) in enumerate(tqdm(filelist)):
|
| 161 |
-
basename = os.path.splitext(os.path.basename(X_path))[0]
|
| 162 |
-
|
| 163 |
-
X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
|
| 164 |
-
coef = np.max([np.abs(X).max(), np.abs(y).max()])
|
| 165 |
-
X, y = X / coef, y / coef
|
| 166 |
-
|
| 167 |
-
l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
|
| 168 |
-
X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
|
| 169 |
-
y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
|
| 170 |
-
|
| 171 |
-
len_dataset = int(np.ceil(X.shape[2] / roi_size))
|
| 172 |
-
for j in range(len_dataset):
|
| 173 |
-
outpath = os.path.join(patch_dir, "{}_p{}.npz".format(basename, j))
|
| 174 |
-
start = j * roi_size
|
| 175 |
-
if not os.path.exists(outpath):
|
| 176 |
-
np.savez(
|
| 177 |
-
outpath,
|
| 178 |
-
X=X_pad[:, :, start : start + cropsize],
|
| 179 |
-
y=y_pad[:, :, start : start + cropsize],
|
| 180 |
-
)
|
| 181 |
-
patch_list.append(outpath)
|
| 182 |
-
|
| 183 |
-
return VocalRemoverValidationSet(patch_list)
|
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|
spaces/AB-TW/team-ai/agents/tools/smart_domain/db_entity_repository.py
DELETED
|
@@ -1,101 +0,0 @@
|
|
| 1 |
-
from langchain.prompts import PromptTemplate
|
| 2 |
-
from agents.tools.smart_domain.common import getPrefix
|
| 3 |
-
from langchain.chains import LLMChain
|
| 4 |
-
from langchain.agents import tool
|
| 5 |
-
from models import llm
|
| 6 |
-
|
| 7 |
-
db_entity_tech_stack = """Java17、reactor、lombok、Junit5、reactor test、Mockito、 Spring Data Reactive Couchbase、Couchbase"""
|
| 8 |
-
|
| 9 |
-
db_entity_architecture = """
|
| 10 |
-
* DbEntity: This component is use to define data structure that save to DB.
|
| 11 |
-
---eaxmple code:
|
| 12 |
-
@Document
|
| 13 |
-
public class FeatureDb {{
|
| 14 |
-
@Version
|
| 15 |
-
private long version;
|
| 16 |
-
|
| 17 |
-
@Id
|
| 18 |
-
@GeneratedValue(strategy = GenerationStrategy.UNIQUE)
|
| 19 |
-
private String id;
|
| 20 |
-
|
| 21 |
-
private String featureKey;
|
| 22 |
-
|
| 23 |
-
private Feature.FeatureDescription description;
|
| 24 |
-
}}
|
| 25 |
-
---end of eaxmple code
|
| 26 |
-
* Repository: This component is use to define the interface to access DB.
|
| 27 |
-
---eaxmple code:
|
| 28 |
-
public interface FeatureDbRepository extends ReactiveCrudRepository<FeatureDb, String> {{
|
| 29 |
-
Mono<FeatureDb> findByFeatureKey(String featureKey);
|
| 30 |
-
}}
|
| 31 |
-
---end of eaxmple code
|
| 32 |
-
"""
|
| 33 |
-
|
| 34 |
-
db_entity_test_strategy = """For the DbEntity And Repository, we can write component test to test the actual implementation of database operations, test class should extends RepositoryTestBase to use Testcontainers ability.
|
| 35 |
-
---eaxmple code:
|
| 36 |
-
class FeatureDbRepositoryTest extends RepositoryTestBase {{
|
| 37 |
-
@Autowired
|
| 38 |
-
FeatureDbRepository repository;
|
| 39 |
-
|
| 40 |
-
@BeforeEach
|
| 41 |
-
void setUp() {{
|
| 42 |
-
repository.deleteAll().block();
|
| 43 |
-
}}
|
| 44 |
-
|
| 45 |
-
@AfterEach
|
| 46 |
-
void tearDown() {{
|
| 47 |
-
repository.deleteAll().block();
|
| 48 |
-
}}
|
| 49 |
-
|
| 50 |
-
@Test
|
| 51 |
-
void should_save_Feature_success() {{
|
| 52 |
-
var featureKey = "featureKey1";
|
| 53 |
-
repository.save(FeatureTestUtil.createFeatureDb(featureKey))
|
| 54 |
-
.as(StepVerifier::create)
|
| 55 |
-
.expectNextCount(1)
|
| 56 |
-
.verifyComplete();
|
| 57 |
-
}}
|
| 58 |
-
|
| 59 |
-
@Test
|
| 60 |
-
void should_add_same_featureKey_fail() {{
|
| 61 |
-
var featureKey = "featureKey1";
|
| 62 |
-
repository.save(FeatureTestUtil.createFeatureDb(featureKey)).block();
|
| 63 |
-
|
| 64 |
-
repository.save(FeatureTestUtil.createFeatureDb(featureKey))
|
| 65 |
-
.as(StepVerifier::create)
|
| 66 |
-
.expectError()
|
| 67 |
-
.verify();
|
| 68 |
-
}}
|
| 69 |
-
}}
|
| 70 |
-
---end of eaxmple code
|
| 71 |
-
"""
|
| 72 |
-
|
| 73 |
-
db_entity_task = """Your task is to generate the DbEntity and Repository tests and product code."""
|
| 74 |
-
|
| 75 |
-
DB_ENTITY = getPrefix(db_entity_task, db_entity_tech_stack, db_entity_architecture, db_entity_test_strategy) + """
|
| 76 |
-
|
| 77 |
-
Use the following format:
|
| 78 |
-
request: the request that you need to fulfill
|
| 79 |
-
|
| 80 |
-
Entity:
|
| 81 |
-
```
|
| 82 |
-
the Entity code that you write to fulfill the request, follow TechStack and Architecture
|
| 83 |
-
```
|
| 84 |
-
|
| 85 |
-
Test:
|
| 86 |
-
```
|
| 87 |
-
the test code that you write to fulfill the request, follow TechStack Architecture and TestStrategy
|
| 88 |
-
```
|
| 89 |
-
|
| 90 |
-
request: {input}"""
|
| 91 |
-
|
| 92 |
-
DB_ENTITY_PROMPT = PromptTemplate(input_variables=["input"], template=DB_ENTITY,)
|
| 93 |
-
|
| 94 |
-
db_entity_Repository_chain = LLMChain(llm = llm(temperature=0.1), prompt=DB_ENTITY_PROMPT)
|
| 95 |
-
|
| 96 |
-
|
| 97 |
-
@tool("Generate DBEntity and Repository Code", return_direct=True)
|
| 98 |
-
def dbEntityRepositoryCodeGenerator(input: str) -> str:
|
| 99 |
-
'''useful for when you need to generate DBEntity and Repository code'''
|
| 100 |
-
response = db_entity_Repository_chain.run(input)
|
| 101 |
-
return response
|
|
|
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|
|
spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/lib_v5/layers_123812KB .py
DELETED
|
@@ -1,118 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
from torch import nn
|
| 3 |
-
import torch.nn.functional as F
|
| 4 |
-
|
| 5 |
-
from uvr5_pack.lib_v5 import spec_utils
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
class Conv2DBNActiv(nn.Module):
|
| 9 |
-
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
|
| 10 |
-
super(Conv2DBNActiv, self).__init__()
|
| 11 |
-
self.conv = nn.Sequential(
|
| 12 |
-
nn.Conv2d(
|
| 13 |
-
nin,
|
| 14 |
-
nout,
|
| 15 |
-
kernel_size=ksize,
|
| 16 |
-
stride=stride,
|
| 17 |
-
padding=pad,
|
| 18 |
-
dilation=dilation,
|
| 19 |
-
bias=False,
|
| 20 |
-
),
|
| 21 |
-
nn.BatchNorm2d(nout),
|
| 22 |
-
activ(),
|
| 23 |
-
)
|
| 24 |
-
|
| 25 |
-
def __call__(self, x):
|
| 26 |
-
return self.conv(x)
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
class SeperableConv2DBNActiv(nn.Module):
|
| 30 |
-
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
|
| 31 |
-
super(SeperableConv2DBNActiv, self).__init__()
|
| 32 |
-
self.conv = nn.Sequential(
|
| 33 |
-
nn.Conv2d(
|
| 34 |
-
nin,
|
| 35 |
-
nin,
|
| 36 |
-
kernel_size=ksize,
|
| 37 |
-
stride=stride,
|
| 38 |
-
padding=pad,
|
| 39 |
-
dilation=dilation,
|
| 40 |
-
groups=nin,
|
| 41 |
-
bias=False,
|
| 42 |
-
),
|
| 43 |
-
nn.Conv2d(nin, nout, kernel_size=1, bias=False),
|
| 44 |
-
nn.BatchNorm2d(nout),
|
| 45 |
-
activ(),
|
| 46 |
-
)
|
| 47 |
-
|
| 48 |
-
def __call__(self, x):
|
| 49 |
-
return self.conv(x)
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
class Encoder(nn.Module):
|
| 53 |
-
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
|
| 54 |
-
super(Encoder, self).__init__()
|
| 55 |
-
self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
|
| 56 |
-
self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
|
| 57 |
-
|
| 58 |
-
def __call__(self, x):
|
| 59 |
-
skip = self.conv1(x)
|
| 60 |
-
h = self.conv2(skip)
|
| 61 |
-
|
| 62 |
-
return h, skip
|
| 63 |
-
|
| 64 |
-
|
| 65 |
-
class Decoder(nn.Module):
|
| 66 |
-
def __init__(
|
| 67 |
-
self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
|
| 68 |
-
):
|
| 69 |
-
super(Decoder, self).__init__()
|
| 70 |
-
self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
|
| 71 |
-
self.dropout = nn.Dropout2d(0.1) if dropout else None
|
| 72 |
-
|
| 73 |
-
def __call__(self, x, skip=None):
|
| 74 |
-
x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
|
| 75 |
-
if skip is not None:
|
| 76 |
-
skip = spec_utils.crop_center(skip, x)
|
| 77 |
-
x = torch.cat([x, skip], dim=1)
|
| 78 |
-
h = self.conv(x)
|
| 79 |
-
|
| 80 |
-
if self.dropout is not None:
|
| 81 |
-
h = self.dropout(h)
|
| 82 |
-
|
| 83 |
-
return h
|
| 84 |
-
|
| 85 |
-
|
| 86 |
-
class ASPPModule(nn.Module):
|
| 87 |
-
def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
|
| 88 |
-
super(ASPPModule, self).__init__()
|
| 89 |
-
self.conv1 = nn.Sequential(
|
| 90 |
-
nn.AdaptiveAvgPool2d((1, None)),
|
| 91 |
-
Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
|
| 92 |
-
)
|
| 93 |
-
self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
|
| 94 |
-
self.conv3 = SeperableConv2DBNActiv(
|
| 95 |
-
nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
|
| 96 |
-
)
|
| 97 |
-
self.conv4 = SeperableConv2DBNActiv(
|
| 98 |
-
nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
|
| 99 |
-
)
|
| 100 |
-
self.conv5 = SeperableConv2DBNActiv(
|
| 101 |
-
nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
|
| 102 |
-
)
|
| 103 |
-
self.bottleneck = nn.Sequential(
|
| 104 |
-
Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
|
| 105 |
-
)
|
| 106 |
-
|
| 107 |
-
def forward(self, x):
|
| 108 |
-
_, _, h, w = x.size()
|
| 109 |
-
feat1 = F.interpolate(
|
| 110 |
-
self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
|
| 111 |
-
)
|
| 112 |
-
feat2 = self.conv2(x)
|
| 113 |
-
feat3 = self.conv3(x)
|
| 114 |
-
feat4 = self.conv4(x)
|
| 115 |
-
feat5 = self.conv5(x)
|
| 116 |
-
out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
|
| 117 |
-
bottle = self.bottleneck(out)
|
| 118 |
-
return bottle
|
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spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/lib_v5/model_param_init.py
DELETED
|
@@ -1,69 +0,0 @@
|
|
| 1 |
-
import json
|
| 2 |
-
import os
|
| 3 |
-
import pathlib
|
| 4 |
-
|
| 5 |
-
default_param = {}
|
| 6 |
-
default_param["bins"] = 768
|
| 7 |
-
default_param["unstable_bins"] = 9 # training only
|
| 8 |
-
default_param["reduction_bins"] = 762 # training only
|
| 9 |
-
default_param["sr"] = 44100
|
| 10 |
-
default_param["pre_filter_start"] = 757
|
| 11 |
-
default_param["pre_filter_stop"] = 768
|
| 12 |
-
default_param["band"] = {}
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
default_param["band"][1] = {
|
| 16 |
-
"sr": 11025,
|
| 17 |
-
"hl": 128,
|
| 18 |
-
"n_fft": 960,
|
| 19 |
-
"crop_start": 0,
|
| 20 |
-
"crop_stop": 245,
|
| 21 |
-
"lpf_start": 61, # inference only
|
| 22 |
-
"res_type": "polyphase",
|
| 23 |
-
}
|
| 24 |
-
|
| 25 |
-
default_param["band"][2] = {
|
| 26 |
-
"sr": 44100,
|
| 27 |
-
"hl": 512,
|
| 28 |
-
"n_fft": 1536,
|
| 29 |
-
"crop_start": 24,
|
| 30 |
-
"crop_stop": 547,
|
| 31 |
-
"hpf_start": 81, # inference only
|
| 32 |
-
"res_type": "sinc_best",
|
| 33 |
-
}
|
| 34 |
-
|
| 35 |
-
|
| 36 |
-
def int_keys(d):
|
| 37 |
-
r = {}
|
| 38 |
-
for k, v in d:
|
| 39 |
-
if k.isdigit():
|
| 40 |
-
k = int(k)
|
| 41 |
-
r[k] = v
|
| 42 |
-
return r
|
| 43 |
-
|
| 44 |
-
|
| 45 |
-
class ModelParameters(object):
|
| 46 |
-
def __init__(self, config_path=""):
|
| 47 |
-
if ".pth" == pathlib.Path(config_path).suffix:
|
| 48 |
-
import zipfile
|
| 49 |
-
|
| 50 |
-
with zipfile.ZipFile(config_path, "r") as zip:
|
| 51 |
-
self.param = json.loads(
|
| 52 |
-
zip.read("param.json"), object_pairs_hook=int_keys
|
| 53 |
-
)
|
| 54 |
-
elif ".json" == pathlib.Path(config_path).suffix:
|
| 55 |
-
with open(config_path, "r") as f:
|
| 56 |
-
self.param = json.loads(f.read(), object_pairs_hook=int_keys)
|
| 57 |
-
else:
|
| 58 |
-
self.param = default_param
|
| 59 |
-
|
| 60 |
-
for k in [
|
| 61 |
-
"mid_side",
|
| 62 |
-
"mid_side_b",
|
| 63 |
-
"mid_side_b2",
|
| 64 |
-
"stereo_w",
|
| 65 |
-
"stereo_n",
|
| 66 |
-
"reverse",
|
| 67 |
-
]:
|
| 68 |
-
if not k in self.param:
|
| 69 |
-
self.param[k] = False
|
|
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|
spaces/AIConsultant/MusicGen/audiocraft/modules/diffusion_schedule.py
DELETED
|
@@ -1,272 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
| 2 |
-
# All rights reserved.
|
| 3 |
-
#
|
| 4 |
-
# This source code is licensed under the license found in the
|
| 5 |
-
# LICENSE file in the root directory of this source tree.
|
| 6 |
-
|
| 7 |
-
"""
|
| 8 |
-
Functions for Noise Schedule, defines diffusion process, reverse process and data processor.
|
| 9 |
-
"""
|
| 10 |
-
|
| 11 |
-
from collections import namedtuple
|
| 12 |
-
import random
|
| 13 |
-
import typing as tp
|
| 14 |
-
import julius
|
| 15 |
-
import torch
|
| 16 |
-
|
| 17 |
-
TrainingItem = namedtuple("TrainingItem", "noisy noise step")
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
def betas_from_alpha_bar(alpha_bar):
|
| 21 |
-
alphas = torch.cat([torch.Tensor([alpha_bar[0]]), alpha_bar[1:]/alpha_bar[:-1]])
|
| 22 |
-
return 1 - alphas
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
class SampleProcessor(torch.nn.Module):
|
| 26 |
-
def project_sample(self, x: torch.Tensor):
|
| 27 |
-
"""Project the original sample to the 'space' where the diffusion will happen."""
|
| 28 |
-
return x
|
| 29 |
-
|
| 30 |
-
def return_sample(self, z: torch.Tensor):
|
| 31 |
-
"""Project back from diffusion space to the actual sample space."""
|
| 32 |
-
return z
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
class MultiBandProcessor(SampleProcessor):
|
| 36 |
-
"""
|
| 37 |
-
MultiBand sample processor. The input audio is splitted across
|
| 38 |
-
frequency bands evenly distributed in mel-scale.
|
| 39 |
-
|
| 40 |
-
Each band will be rescaled to match the power distribution
|
| 41 |
-
of Gaussian noise in that band, using online metrics
|
| 42 |
-
computed on the first few samples.
|
| 43 |
-
|
| 44 |
-
Args:
|
| 45 |
-
n_bands (int): Number of mel-bands to split the signal over.
|
| 46 |
-
sample_rate (int): Sample rate of the audio.
|
| 47 |
-
num_samples (int): Number of samples to use to fit the rescaling
|
| 48 |
-
for each band. The processor won't be stable
|
| 49 |
-
until it has seen that many samples.
|
| 50 |
-
power_std (float or list/tensor): The rescaling factor computed to match the
|
| 51 |
-
power of Gaussian noise in each band is taken to
|
| 52 |
-
that power, i.e. `1.` means full correction of the energy
|
| 53 |
-
in each band, and values less than `1` means only partial
|
| 54 |
-
correction. Can be used to balance the relative importance
|
| 55 |
-
of low vs. high freq in typical audio signals.
|
| 56 |
-
"""
|
| 57 |
-
def __init__(self, n_bands: int = 8, sample_rate: float = 24_000,
|
| 58 |
-
num_samples: int = 10_000, power_std: tp.Union[float, tp.List[float], torch.Tensor] = 1.):
|
| 59 |
-
super().__init__()
|
| 60 |
-
self.n_bands = n_bands
|
| 61 |
-
self.split_bands = julius.SplitBands(sample_rate, n_bands=n_bands)
|
| 62 |
-
self.num_samples = num_samples
|
| 63 |
-
self.power_std = power_std
|
| 64 |
-
if isinstance(power_std, list):
|
| 65 |
-
assert len(power_std) == n_bands
|
| 66 |
-
power_std = torch.tensor(power_std)
|
| 67 |
-
self.register_buffer('counts', torch.zeros(1))
|
| 68 |
-
self.register_buffer('sum_x', torch.zeros(n_bands))
|
| 69 |
-
self.register_buffer('sum_x2', torch.zeros(n_bands))
|
| 70 |
-
self.register_buffer('sum_target_x2', torch.zeros(n_bands))
|
| 71 |
-
self.counts: torch.Tensor
|
| 72 |
-
self.sum_x: torch.Tensor
|
| 73 |
-
self.sum_x2: torch.Tensor
|
| 74 |
-
self.sum_target_x2: torch.Tensor
|
| 75 |
-
|
| 76 |
-
@property
|
| 77 |
-
def mean(self):
|
| 78 |
-
mean = self.sum_x / self.counts
|
| 79 |
-
return mean
|
| 80 |
-
|
| 81 |
-
@property
|
| 82 |
-
def std(self):
|
| 83 |
-
std = (self.sum_x2 / self.counts - self.mean**2).clamp(min=0).sqrt()
|
| 84 |
-
return std
|
| 85 |
-
|
| 86 |
-
@property
|
| 87 |
-
def target_std(self):
|
| 88 |
-
target_std = self.sum_target_x2 / self.counts
|
| 89 |
-
return target_std
|
| 90 |
-
|
| 91 |
-
def project_sample(self, x: torch.Tensor):
|
| 92 |
-
assert x.dim() == 3
|
| 93 |
-
bands = self.split_bands(x)
|
| 94 |
-
if self.counts.item() < self.num_samples:
|
| 95 |
-
ref_bands = self.split_bands(torch.randn_like(x))
|
| 96 |
-
self.counts += len(x)
|
| 97 |
-
self.sum_x += bands.mean(dim=(2, 3)).sum(dim=1)
|
| 98 |
-
self.sum_x2 += bands.pow(2).mean(dim=(2, 3)).sum(dim=1)
|
| 99 |
-
self.sum_target_x2 += ref_bands.pow(2).mean(dim=(2, 3)).sum(dim=1)
|
| 100 |
-
rescale = (self.target_std / self.std.clamp(min=1e-12)) ** self.power_std # same output size
|
| 101 |
-
bands = (bands - self.mean.view(-1, 1, 1, 1)) * rescale.view(-1, 1, 1, 1)
|
| 102 |
-
return bands.sum(dim=0)
|
| 103 |
-
|
| 104 |
-
def return_sample(self, x: torch.Tensor):
|
| 105 |
-
assert x.dim() == 3
|
| 106 |
-
bands = self.split_bands(x)
|
| 107 |
-
rescale = (self.std / self.target_std) ** self.power_std
|
| 108 |
-
bands = bands * rescale.view(-1, 1, 1, 1) + self.mean.view(-1, 1, 1, 1)
|
| 109 |
-
return bands.sum(dim=0)
|
| 110 |
-
|
| 111 |
-
|
| 112 |
-
class NoiseSchedule:
|
| 113 |
-
"""Noise schedule for diffusion.
|
| 114 |
-
|
| 115 |
-
Args:
|
| 116 |
-
beta_t0 (float): Variance of the first diffusion step.
|
| 117 |
-
beta_t1 (float): Variance of the last diffusion step.
|
| 118 |
-
beta_exp (float): Power schedule exponent
|
| 119 |
-
num_steps (int): Number of diffusion step.
|
| 120 |
-
variance (str): choice of the sigma value for the denoising eq. Choices: "beta" or "beta_tilde"
|
| 121 |
-
clip (float): clipping value for the denoising steps
|
| 122 |
-
rescale (float): rescaling value to avoid vanishing signals unused by default (i.e 1)
|
| 123 |
-
repartition (str): shape of the schedule only power schedule is supported
|
| 124 |
-
sample_processor (SampleProcessor): Module that normalize data to match better the gaussian distribution
|
| 125 |
-
noise_scale (float): Scaling factor for the noise
|
| 126 |
-
"""
|
| 127 |
-
def __init__(self, beta_t0: float = 1e-4, beta_t1: float = 0.02, num_steps: int = 1000, variance: str = 'beta',
|
| 128 |
-
clip: float = 5., rescale: float = 1., device='cuda', beta_exp: float = 1,
|
| 129 |
-
repartition: str = "power", alpha_sigmoid: dict = {}, n_bands: tp.Optional[int] = None,
|
| 130 |
-
sample_processor: SampleProcessor = SampleProcessor(), noise_scale: float = 1.0, **kwargs):
|
| 131 |
-
|
| 132 |
-
self.beta_t0 = beta_t0
|
| 133 |
-
self.beta_t1 = beta_t1
|
| 134 |
-
self.variance = variance
|
| 135 |
-
self.num_steps = num_steps
|
| 136 |
-
self.clip = clip
|
| 137 |
-
self.sample_processor = sample_processor
|
| 138 |
-
self.rescale = rescale
|
| 139 |
-
self.n_bands = n_bands
|
| 140 |
-
self.noise_scale = noise_scale
|
| 141 |
-
assert n_bands is None
|
| 142 |
-
if repartition == "power":
|
| 143 |
-
self.betas = torch.linspace(beta_t0 ** (1 / beta_exp), beta_t1 ** (1 / beta_exp), num_steps,
|
| 144 |
-
device=device, dtype=torch.float) ** beta_exp
|
| 145 |
-
else:
|
| 146 |
-
raise RuntimeError('Not implemented')
|
| 147 |
-
self.rng = random.Random(1234)
|
| 148 |
-
|
| 149 |
-
def get_beta(self, step: tp.Union[int, torch.Tensor]):
|
| 150 |
-
if self.n_bands is None:
|
| 151 |
-
return self.betas[step]
|
| 152 |
-
else:
|
| 153 |
-
return self.betas[:, step] # [n_bands, len(step)]
|
| 154 |
-
|
| 155 |
-
def get_initial_noise(self, x: torch.Tensor):
|
| 156 |
-
if self.n_bands is None:
|
| 157 |
-
return torch.randn_like(x)
|
| 158 |
-
return torch.randn((x.size(0), self.n_bands, x.size(2)))
|
| 159 |
-
|
| 160 |
-
def get_alpha_bar(self, step: tp.Optional[tp.Union[int, torch.Tensor]] = None) -> torch.Tensor:
|
| 161 |
-
"""Return 'alpha_bar', either for a given step, or as a tensor with its value for each step."""
|
| 162 |
-
if step is None:
|
| 163 |
-
return (1 - self.betas).cumprod(dim=-1) # works for simgle and multi bands
|
| 164 |
-
if type(step) is int:
|
| 165 |
-
return (1 - self.betas[:step + 1]).prod()
|
| 166 |
-
else:
|
| 167 |
-
return (1 - self.betas).cumprod(dim=0)[step].view(-1, 1, 1)
|
| 168 |
-
|
| 169 |
-
def get_training_item(self, x: torch.Tensor, tensor_step: bool = False) -> TrainingItem:
|
| 170 |
-
"""Create a noisy data item for diffusion model training:
|
| 171 |
-
|
| 172 |
-
Args:
|
| 173 |
-
x (torch.Tensor): clean audio data torch.tensor(bs, 1, T)
|
| 174 |
-
tensor_step (bool): If tensor_step = false, only one step t is sample,
|
| 175 |
-
the whole batch is diffused to the same step and t is int.
|
| 176 |
-
If tensor_step = true, t is a tensor of size (x.size(0),)
|
| 177 |
-
every element of the batch is diffused to a independently sampled.
|
| 178 |
-
"""
|
| 179 |
-
step: tp.Union[int, torch.Tensor]
|
| 180 |
-
if tensor_step:
|
| 181 |
-
bs = x.size(0)
|
| 182 |
-
step = torch.randint(0, self.num_steps, size=(bs,), device=x.device)
|
| 183 |
-
else:
|
| 184 |
-
step = self.rng.randrange(self.num_steps)
|
| 185 |
-
alpha_bar = self.get_alpha_bar(step) # [batch_size, n_bands, 1]
|
| 186 |
-
|
| 187 |
-
x = self.sample_processor.project_sample(x)
|
| 188 |
-
noise = torch.randn_like(x)
|
| 189 |
-
noisy = (alpha_bar.sqrt() / self.rescale) * x + (1 - alpha_bar).sqrt() * noise * self.noise_scale
|
| 190 |
-
return TrainingItem(noisy, noise, step)
|
| 191 |
-
|
| 192 |
-
def generate(self, model: torch.nn.Module, initial: tp.Optional[torch.Tensor] = None,
|
| 193 |
-
condition: tp.Optional[torch.Tensor] = None, return_list: bool = False):
|
| 194 |
-
"""Full ddpm reverse process.
|
| 195 |
-
|
| 196 |
-
Args:
|
| 197 |
-
model (nn.Module): Diffusion model.
|
| 198 |
-
initial (tensor): Initial Noise.
|
| 199 |
-
condition (tensor): Input conditionning Tensor (e.g. encodec compressed representation).
|
| 200 |
-
return_list (bool): Whether to return the whole process or only the sampled point.
|
| 201 |
-
"""
|
| 202 |
-
alpha_bar = self.get_alpha_bar(step=self.num_steps - 1)
|
| 203 |
-
current = initial
|
| 204 |
-
iterates = [initial]
|
| 205 |
-
for step in range(self.num_steps)[::-1]:
|
| 206 |
-
with torch.no_grad():
|
| 207 |
-
estimate = model(current, step, condition=condition).sample
|
| 208 |
-
alpha = 1 - self.betas[step]
|
| 209 |
-
previous = (current - (1 - alpha) / (1 - alpha_bar).sqrt() * estimate) / alpha.sqrt()
|
| 210 |
-
previous_alpha_bar = self.get_alpha_bar(step=step - 1)
|
| 211 |
-
if step == 0:
|
| 212 |
-
sigma2 = 0
|
| 213 |
-
elif self.variance == 'beta':
|
| 214 |
-
sigma2 = 1 - alpha
|
| 215 |
-
elif self.variance == 'beta_tilde':
|
| 216 |
-
sigma2 = (1 - previous_alpha_bar) / (1 - alpha_bar) * (1 - alpha)
|
| 217 |
-
elif self.variance == 'none':
|
| 218 |
-
sigma2 = 0
|
| 219 |
-
else:
|
| 220 |
-
raise ValueError(f'Invalid variance type {self.variance}')
|
| 221 |
-
|
| 222 |
-
if sigma2 > 0:
|
| 223 |
-
previous += sigma2**0.5 * torch.randn_like(previous) * self.noise_scale
|
| 224 |
-
if self.clip:
|
| 225 |
-
previous = previous.clamp(-self.clip, self.clip)
|
| 226 |
-
current = previous
|
| 227 |
-
alpha_bar = previous_alpha_bar
|
| 228 |
-
if step == 0:
|
| 229 |
-
previous *= self.rescale
|
| 230 |
-
if return_list:
|
| 231 |
-
iterates.append(previous.cpu())
|
| 232 |
-
|
| 233 |
-
if return_list:
|
| 234 |
-
return iterates
|
| 235 |
-
else:
|
| 236 |
-
return self.sample_processor.return_sample(previous)
|
| 237 |
-
|
| 238 |
-
def generate_subsampled(self, model: torch.nn.Module, initial: torch.Tensor, step_list: tp.Optional[list] = None,
|
| 239 |
-
condition: tp.Optional[torch.Tensor] = None, return_list: bool = False):
|
| 240 |
-
"""Reverse process that only goes through Markov chain states in step_list."""
|
| 241 |
-
if step_list is None:
|
| 242 |
-
step_list = list(range(1000))[::-50] + [0]
|
| 243 |
-
alpha_bar = self.get_alpha_bar(step=self.num_steps - 1)
|
| 244 |
-
alpha_bars_subsampled = (1 - self.betas).cumprod(dim=0)[list(reversed(step_list))].cpu()
|
| 245 |
-
betas_subsampled = betas_from_alpha_bar(alpha_bars_subsampled)
|
| 246 |
-
current = initial * self.noise_scale
|
| 247 |
-
iterates = [current]
|
| 248 |
-
for idx, step in enumerate(step_list[:-1]):
|
| 249 |
-
with torch.no_grad():
|
| 250 |
-
estimate = model(current, step, condition=condition).sample * self.noise_scale
|
| 251 |
-
alpha = 1 - betas_subsampled[-1 - idx]
|
| 252 |
-
previous = (current - (1 - alpha) / (1 - alpha_bar).sqrt() * estimate) / alpha.sqrt()
|
| 253 |
-
previous_alpha_bar = self.get_alpha_bar(step_list[idx + 1])
|
| 254 |
-
if step == step_list[-2]:
|
| 255 |
-
sigma2 = 0
|
| 256 |
-
previous_alpha_bar = torch.tensor(1.0)
|
| 257 |
-
else:
|
| 258 |
-
sigma2 = (1 - previous_alpha_bar) / (1 - alpha_bar) * (1 - alpha)
|
| 259 |
-
if sigma2 > 0:
|
| 260 |
-
previous += sigma2**0.5 * torch.randn_like(previous) * self.noise_scale
|
| 261 |
-
if self.clip:
|
| 262 |
-
previous = previous.clamp(-self.clip, self.clip)
|
| 263 |
-
current = previous
|
| 264 |
-
alpha_bar = previous_alpha_bar
|
| 265 |
-
if step == 0:
|
| 266 |
-
previous *= self.rescale
|
| 267 |
-
if return_list:
|
| 268 |
-
iterates.append(previous.cpu())
|
| 269 |
-
if return_list:
|
| 270 |
-
return iterates
|
| 271 |
-
else:
|
| 272 |
-
return self.sample_processor.return_sample(previous)
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|
spaces/AIFILMS/generate_human_motion/VQ-Trans/dataset/prepare/download_smpl.sh
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
mkdir -p body_models
|
| 3 |
-
cd body_models/
|
| 4 |
-
|
| 5 |
-
echo -e "The smpl files will be stored in the 'body_models/smpl/' folder\n"
|
| 6 |
-
gdown 1INYlGA76ak_cKGzvpOV2Pe6RkYTlXTW2
|
| 7 |
-
rm -rf smpl
|
| 8 |
-
|
| 9 |
-
unzip smpl.zip
|
| 10 |
-
echo -e "Cleaning\n"
|
| 11 |
-
rm smpl.zip
|
| 12 |
-
|
| 13 |
-
echo -e "Downloading done!"
|
|
|
|
|
|
|
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|
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|
spaces/AIFILMS/generate_human_motion/pyrender/tests/unit/test_nodes.py
DELETED
|
@@ -1,124 +0,0 @@
|
|
| 1 |
-
import numpy as np
|
| 2 |
-
import pytest
|
| 3 |
-
from trimesh import transformations
|
| 4 |
-
|
| 5 |
-
from pyrender import (DirectionalLight, PerspectiveCamera, Mesh, Node)
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
def test_nodes():
|
| 9 |
-
|
| 10 |
-
x = Node()
|
| 11 |
-
assert x.name is None
|
| 12 |
-
assert x.camera is None
|
| 13 |
-
assert x.children == []
|
| 14 |
-
assert x.skin is None
|
| 15 |
-
assert np.allclose(x.matrix, np.eye(4))
|
| 16 |
-
assert x.mesh is None
|
| 17 |
-
assert np.allclose(x.rotation, [0,0,0,1])
|
| 18 |
-
assert np.allclose(x.scale, np.ones(3))
|
| 19 |
-
assert np.allclose(x.translation, np.zeros(3))
|
| 20 |
-
assert x.weights is None
|
| 21 |
-
assert x.light is None
|
| 22 |
-
|
| 23 |
-
x.name = 'node'
|
| 24 |
-
|
| 25 |
-
# Test node light/camera/mesh tests
|
| 26 |
-
c = PerspectiveCamera(yfov=2.0)
|
| 27 |
-
m = Mesh([])
|
| 28 |
-
d = DirectionalLight()
|
| 29 |
-
x.camera = c
|
| 30 |
-
assert x.camera == c
|
| 31 |
-
with pytest.raises(TypeError):
|
| 32 |
-
x.camera = m
|
| 33 |
-
x.camera = d
|
| 34 |
-
x.camera = None
|
| 35 |
-
x.mesh = m
|
| 36 |
-
assert x.mesh == m
|
| 37 |
-
with pytest.raises(TypeError):
|
| 38 |
-
x.mesh = c
|
| 39 |
-
x.mesh = d
|
| 40 |
-
x.light = d
|
| 41 |
-
assert x.light == d
|
| 42 |
-
with pytest.raises(TypeError):
|
| 43 |
-
x.light = m
|
| 44 |
-
x.light = c
|
| 45 |
-
|
| 46 |
-
# Test transformations getters/setters/etc...
|
| 47 |
-
# Set up test values
|
| 48 |
-
x = np.array([1.0, 0.0, 0.0])
|
| 49 |
-
y = np.array([0.0, 1.0, 0.0])
|
| 50 |
-
t = np.array([1.0, 2.0, 3.0])
|
| 51 |
-
s = np.array([0.5, 2.0, 1.0])
|
| 52 |
-
|
| 53 |
-
Mx = transformations.rotation_matrix(np.pi / 2.0, x)
|
| 54 |
-
qx = np.roll(transformations.quaternion_about_axis(np.pi / 2.0, x), -1)
|
| 55 |
-
Mxt = Mx.copy()
|
| 56 |
-
Mxt[:3,3] = t
|
| 57 |
-
S = np.eye(4)
|
| 58 |
-
S[:3,:3] = np.diag(s)
|
| 59 |
-
Mxts = Mxt.dot(S)
|
| 60 |
-
|
| 61 |
-
My = transformations.rotation_matrix(np.pi / 2.0, y)
|
| 62 |
-
qy = np.roll(transformations.quaternion_about_axis(np.pi / 2.0, y), -1)
|
| 63 |
-
Myt = My.copy()
|
| 64 |
-
Myt[:3,3] = t
|
| 65 |
-
|
| 66 |
-
x = Node(matrix=Mx)
|
| 67 |
-
assert np.allclose(x.matrix, Mx)
|
| 68 |
-
assert np.allclose(x.rotation, qx)
|
| 69 |
-
assert np.allclose(x.translation, np.zeros(3))
|
| 70 |
-
assert np.allclose(x.scale, np.ones(3))
|
| 71 |
-
|
| 72 |
-
x.matrix = My
|
| 73 |
-
assert np.allclose(x.matrix, My)
|
| 74 |
-
assert np.allclose(x.rotation, qy)
|
| 75 |
-
assert np.allclose(x.translation, np.zeros(3))
|
| 76 |
-
assert np.allclose(x.scale, np.ones(3))
|
| 77 |
-
x.translation = t
|
| 78 |
-
assert np.allclose(x.matrix, Myt)
|
| 79 |
-
assert np.allclose(x.rotation, qy)
|
| 80 |
-
x.rotation = qx
|
| 81 |
-
assert np.allclose(x.matrix, Mxt)
|
| 82 |
-
x.scale = s
|
| 83 |
-
assert np.allclose(x.matrix, Mxts)
|
| 84 |
-
|
| 85 |
-
x = Node(matrix=Mxt)
|
| 86 |
-
assert np.allclose(x.matrix, Mxt)
|
| 87 |
-
assert np.allclose(x.rotation, qx)
|
| 88 |
-
assert np.allclose(x.translation, t)
|
| 89 |
-
assert np.allclose(x.scale, np.ones(3))
|
| 90 |
-
|
| 91 |
-
x = Node(matrix=Mxts)
|
| 92 |
-
assert np.allclose(x.matrix, Mxts)
|
| 93 |
-
assert np.allclose(x.rotation, qx)
|
| 94 |
-
assert np.allclose(x.translation, t)
|
| 95 |
-
assert np.allclose(x.scale, s)
|
| 96 |
-
|
| 97 |
-
# Individual element getters
|
| 98 |
-
x.scale[0] = 0
|
| 99 |
-
assert np.allclose(x.scale[0], 0)
|
| 100 |
-
|
| 101 |
-
x.translation[0] = 0
|
| 102 |
-
assert np.allclose(x.translation[0], 0)
|
| 103 |
-
|
| 104 |
-
x.matrix = np.eye(4)
|
| 105 |
-
x.matrix[0,0] = 500
|
| 106 |
-
assert x.matrix[0,0] == 1.0
|
| 107 |
-
|
| 108 |
-
# Failures
|
| 109 |
-
with pytest.raises(ValueError):
|
| 110 |
-
x.matrix = 5 * np.eye(4)
|
| 111 |
-
with pytest.raises(ValueError):
|
| 112 |
-
x.matrix = np.eye(5)
|
| 113 |
-
with pytest.raises(ValueError):
|
| 114 |
-
x.matrix = np.eye(4).dot([5,1,1,1])
|
| 115 |
-
with pytest.raises(ValueError):
|
| 116 |
-
x.rotation = np.array([1,2])
|
| 117 |
-
with pytest.raises(ValueError):
|
| 118 |
-
x.rotation = np.array([1,2,3])
|
| 119 |
-
with pytest.raises(ValueError):
|
| 120 |
-
x.rotation = np.array([1,2,3,4])
|
| 121 |
-
with pytest.raises(ValueError):
|
| 122 |
-
x.translation = np.array([1,2,3,4])
|
| 123 |
-
with pytest.raises(ValueError):
|
| 124 |
-
x.scale = np.array([1,2,3,4])
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/CLAP/audio.py
DELETED
|
@@ -1,179 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import torch.nn as nn
|
| 3 |
-
import torch.nn.functional as F
|
| 4 |
-
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
|
| 5 |
-
|
| 6 |
-
def get_audio_encoder(name: str):
|
| 7 |
-
if name == "Cnn14":
|
| 8 |
-
return Cnn14
|
| 9 |
-
else:
|
| 10 |
-
raise Exception('The audio encoder name {} is incorrect or not supported'.format(name))
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
class ConvBlock(nn.Module):
|
| 14 |
-
def __init__(self, in_channels, out_channels):
|
| 15 |
-
|
| 16 |
-
super(ConvBlock, self).__init__()
|
| 17 |
-
|
| 18 |
-
self.conv1 = nn.Conv2d(in_channels=in_channels,
|
| 19 |
-
out_channels=out_channels,
|
| 20 |
-
kernel_size=(3, 3), stride=(1, 1),
|
| 21 |
-
padding=(1, 1), bias=False)
|
| 22 |
-
|
| 23 |
-
self.conv2 = nn.Conv2d(in_channels=out_channels,
|
| 24 |
-
out_channels=out_channels,
|
| 25 |
-
kernel_size=(3, 3), stride=(1, 1),
|
| 26 |
-
padding=(1, 1), bias=False)
|
| 27 |
-
|
| 28 |
-
self.bn1 = nn.BatchNorm2d(out_channels)
|
| 29 |
-
self.bn2 = nn.BatchNorm2d(out_channels)
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
def forward(self, input, pool_size=(2, 2), pool_type='avg'):
|
| 33 |
-
|
| 34 |
-
x = input
|
| 35 |
-
x = F.relu_(self.bn1(self.conv1(x)))
|
| 36 |
-
x = F.relu_(self.bn2(self.conv2(x)))
|
| 37 |
-
if pool_type == 'max':
|
| 38 |
-
x = F.max_pool2d(x, kernel_size=pool_size)
|
| 39 |
-
elif pool_type == 'avg':
|
| 40 |
-
x = F.avg_pool2d(x, kernel_size=pool_size)
|
| 41 |
-
elif pool_type == 'avg+max':
|
| 42 |
-
x1 = F.avg_pool2d(x, kernel_size=pool_size)
|
| 43 |
-
x2 = F.max_pool2d(x, kernel_size=pool_size)
|
| 44 |
-
x = x1 + x2
|
| 45 |
-
else:
|
| 46 |
-
raise Exception('Incorrect argument!')
|
| 47 |
-
|
| 48 |
-
return x
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
class ConvBlock5x5(nn.Module):
|
| 52 |
-
def __init__(self, in_channels, out_channels):
|
| 53 |
-
|
| 54 |
-
super(ConvBlock5x5, self).__init__()
|
| 55 |
-
|
| 56 |
-
self.conv1 = nn.Conv2d(in_channels=in_channels,
|
| 57 |
-
out_channels=out_channels,
|
| 58 |
-
kernel_size=(5, 5), stride=(1, 1),
|
| 59 |
-
padding=(2, 2), bias=False)
|
| 60 |
-
|
| 61 |
-
self.bn1 = nn.BatchNorm2d(out_channels)
|
| 62 |
-
|
| 63 |
-
|
| 64 |
-
def forward(self, input, pool_size=(2, 2), pool_type='avg'):
|
| 65 |
-
|
| 66 |
-
x = input
|
| 67 |
-
x = F.relu_(self.bn1(self.conv1(x)))
|
| 68 |
-
if pool_type == 'max':
|
| 69 |
-
x = F.max_pool2d(x, kernel_size=pool_size)
|
| 70 |
-
elif pool_type == 'avg':
|
| 71 |
-
x = F.avg_pool2d(x, kernel_size=pool_size)
|
| 72 |
-
elif pool_type == 'avg+max':
|
| 73 |
-
x1 = F.avg_pool2d(x, kernel_size=pool_size)
|
| 74 |
-
x2 = F.max_pool2d(x, kernel_size=pool_size)
|
| 75 |
-
x = x1 + x2
|
| 76 |
-
else:
|
| 77 |
-
raise Exception('Incorrect argument!')
|
| 78 |
-
|
| 79 |
-
return x
|
| 80 |
-
|
| 81 |
-
|
| 82 |
-
class AttBlock(nn.Module):
|
| 83 |
-
def __init__(self, n_in, n_out, activation='linear', temperature=1.):
|
| 84 |
-
super(AttBlock, self).__init__()
|
| 85 |
-
|
| 86 |
-
self.activation = activation
|
| 87 |
-
self.temperature = temperature
|
| 88 |
-
self.att = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True)
|
| 89 |
-
self.cla = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True)
|
| 90 |
-
|
| 91 |
-
self.bn_att = nn.BatchNorm1d(n_out)
|
| 92 |
-
|
| 93 |
-
def forward(self, x):
|
| 94 |
-
# x: (n_samples, n_in, n_time)
|
| 95 |
-
norm_att = torch.softmax(torch.clamp(self.att(x), -10, 10), dim=-1)
|
| 96 |
-
cla = self.nonlinear_transform(self.cla(x))
|
| 97 |
-
x = torch.sum(norm_att * cla, dim=2)
|
| 98 |
-
return x, norm_att, cla
|
| 99 |
-
|
| 100 |
-
def nonlinear_transform(self, x):
|
| 101 |
-
if self.activation == 'linear':
|
| 102 |
-
return x
|
| 103 |
-
elif self.activation == 'sigmoid':
|
| 104 |
-
return torch.sigmoid(x)
|
| 105 |
-
|
| 106 |
-
|
| 107 |
-
class Cnn14(nn.Module):
|
| 108 |
-
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
|
| 109 |
-
fmax, classes_num, out_emb):
|
| 110 |
-
|
| 111 |
-
super(Cnn14, self).__init__()
|
| 112 |
-
|
| 113 |
-
window = 'hann'
|
| 114 |
-
center = True
|
| 115 |
-
pad_mode = 'reflect'
|
| 116 |
-
ref = 1.0
|
| 117 |
-
amin = 1e-10
|
| 118 |
-
top_db = None
|
| 119 |
-
|
| 120 |
-
# Spectrogram extractor
|
| 121 |
-
self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
|
| 122 |
-
win_length=window_size, window=window, center=center, pad_mode=pad_mode,
|
| 123 |
-
freeze_parameters=True)
|
| 124 |
-
|
| 125 |
-
# Logmel feature extractor
|
| 126 |
-
self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
|
| 127 |
-
n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
|
| 128 |
-
freeze_parameters=True)
|
| 129 |
-
|
| 130 |
-
self.bn0 = nn.BatchNorm2d(64)
|
| 131 |
-
|
| 132 |
-
self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
|
| 133 |
-
self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
|
| 134 |
-
self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
|
| 135 |
-
self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
|
| 136 |
-
self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
|
| 137 |
-
self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)
|
| 138 |
-
|
| 139 |
-
# out_emb is 2048 for best Cnn14
|
| 140 |
-
self.fc1 = nn.Linear(2048, out_emb, bias=True)
|
| 141 |
-
self.fc_audioset = nn.Linear(out_emb, classes_num, bias=True)
|
| 142 |
-
|
| 143 |
-
def forward(self, input, mixup_lambda=None):
|
| 144 |
-
"""
|
| 145 |
-
Input: (batch_size, data_length)
|
| 146 |
-
"""
|
| 147 |
-
|
| 148 |
-
x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins)
|
| 149 |
-
x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
|
| 150 |
-
|
| 151 |
-
x = x.transpose(1, 3)
|
| 152 |
-
x = self.bn0(x)
|
| 153 |
-
x = x.transpose(1, 3)
|
| 154 |
-
|
| 155 |
-
x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
|
| 156 |
-
x = F.dropout(x, p=0.2, training=self.training)
|
| 157 |
-
x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
|
| 158 |
-
x = F.dropout(x, p=0.2, training=self.training)
|
| 159 |
-
x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
|
| 160 |
-
x = F.dropout(x, p=0.2, training=self.training)
|
| 161 |
-
x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
|
| 162 |
-
x = F.dropout(x, p=0.2, training=self.training)
|
| 163 |
-
x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg')
|
| 164 |
-
x = F.dropout(x, p=0.2, training=self.training)
|
| 165 |
-
x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg')
|
| 166 |
-
x = F.dropout(x, p=0.2, training=self.training)
|
| 167 |
-
x = torch.mean(x, dim=3)
|
| 168 |
-
|
| 169 |
-
(x1, _) = torch.max(x, dim=2)
|
| 170 |
-
x2 = torch.mean(x, dim=2)
|
| 171 |
-
x = x1 + x2
|
| 172 |
-
x = F.dropout(x, p=0.5, training=self.training)
|
| 173 |
-
x = F.relu_(self.fc1(x))
|
| 174 |
-
embedding = F.dropout(x, p=0.5, training=self.training)
|
| 175 |
-
clipwise_output = torch.sigmoid(self.fc_audioset(x))
|
| 176 |
-
|
| 177 |
-
output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding}
|
| 178 |
-
|
| 179 |
-
return output_dict
|
|
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|
spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/commons/nar_tts_modules.py
DELETED
|
@@ -1,138 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
from torch import nn
|
| 3 |
-
|
| 4 |
-
from text_to_speech.modules.commons.layers import LayerNorm
|
| 5 |
-
import torch.nn.functional as F
|
| 6 |
-
|
| 7 |
-
class DurationPredictor(torch.nn.Module):
|
| 8 |
-
def __init__(self, idim, n_layers=2, n_chans=384, kernel_size=3, dropout_rate=0.1, offset=1.0):
|
| 9 |
-
super(DurationPredictor, self).__init__()
|
| 10 |
-
self.offset = offset
|
| 11 |
-
self.conv = torch.nn.ModuleList()
|
| 12 |
-
self.kernel_size = kernel_size
|
| 13 |
-
for idx in range(n_layers):
|
| 14 |
-
in_chans = idim if idx == 0 else n_chans
|
| 15 |
-
self.conv += [torch.nn.Sequential(
|
| 16 |
-
torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=kernel_size // 2),
|
| 17 |
-
torch.nn.ReLU(),
|
| 18 |
-
LayerNorm(n_chans, dim=1),
|
| 19 |
-
torch.nn.Dropout(dropout_rate)
|
| 20 |
-
)]
|
| 21 |
-
self.linear = nn.Sequential(torch.nn.Linear(n_chans, 1), nn.Softplus())
|
| 22 |
-
|
| 23 |
-
def forward(self, x, x_padding=None):
|
| 24 |
-
x = x.transpose(1, -1) # (B, idim, Tmax)
|
| 25 |
-
for f in self.conv:
|
| 26 |
-
x = f(x) # (B, C, Tmax)
|
| 27 |
-
if x_padding is not None:
|
| 28 |
-
x = x * (1 - x_padding.float())[:, None, :]
|
| 29 |
-
|
| 30 |
-
x = self.linear(x.transpose(1, -1)) # [B, T, C]
|
| 31 |
-
x = x * (1 - x_padding.float())[:, :, None] # (B, T, C)
|
| 32 |
-
x = x[..., 0] # (B, Tmax)
|
| 33 |
-
return x
|
| 34 |
-
|
| 35 |
-
|
| 36 |
-
class SyntaDurationPredictor(torch.nn.Module):
|
| 37 |
-
def __init__(self, idim, n_layers=2, n_chans=384, kernel_size=3, dropout_rate=0.1, offset=1.0):
|
| 38 |
-
super(SyntaDurationPredictor, self).__init__()
|
| 39 |
-
from text_to_speech.modules.tts.syntaspeech.syntactic_graph_encoder import GraphAuxEnc
|
| 40 |
-
self.graph_encoder = GraphAuxEnc(in_dim=idim, hid_dim=idim, out_dim=idim)
|
| 41 |
-
self.offset = offset
|
| 42 |
-
self.conv = torch.nn.ModuleList()
|
| 43 |
-
self.kernel_size = kernel_size
|
| 44 |
-
for idx in range(n_layers):
|
| 45 |
-
in_chans = idim if idx == 0 else n_chans
|
| 46 |
-
self.conv += [torch.nn.Sequential(
|
| 47 |
-
torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=kernel_size // 2),
|
| 48 |
-
torch.nn.ReLU(),
|
| 49 |
-
LayerNorm(n_chans, dim=1),
|
| 50 |
-
torch.nn.Dropout(dropout_rate)
|
| 51 |
-
)]
|
| 52 |
-
self.linear = nn.Sequential(torch.nn.Linear(n_chans, 1), nn.Softplus())
|
| 53 |
-
|
| 54 |
-
def forward(self, x, x_padding=None, ph2word=None, graph_lst=None, etypes_lst=None):
|
| 55 |
-
x = x.transpose(1, -1) # (B, idim, Tmax)
|
| 56 |
-
assert ph2word is not None and graph_lst is not None and etypes_lst is not None
|
| 57 |
-
x_graph = self.graph_encoder(graph_lst, x, ph2word, etypes_lst)
|
| 58 |
-
x = x + x_graph * 1.
|
| 59 |
-
|
| 60 |
-
for f in self.conv:
|
| 61 |
-
x = f(x) # (B, C, Tmax)
|
| 62 |
-
if x_padding is not None:
|
| 63 |
-
x = x * (1 - x_padding.float())[:, None, :]
|
| 64 |
-
|
| 65 |
-
x = self.linear(x.transpose(1, -1)) # [B, T, C]
|
| 66 |
-
x = x * (1 - x_padding.float())[:, :, None] # (B, T, C)
|
| 67 |
-
x = x[..., 0] # (B, Tmax)
|
| 68 |
-
return x
|
| 69 |
-
|
| 70 |
-
|
| 71 |
-
class LengthRegulator(torch.nn.Module):
|
| 72 |
-
def __init__(self, pad_value=0.0):
|
| 73 |
-
super(LengthRegulator, self).__init__()
|
| 74 |
-
self.pad_value = pad_value
|
| 75 |
-
|
| 76 |
-
def forward(self, dur, dur_padding=None, alpha=1.0):
|
| 77 |
-
"""
|
| 78 |
-
Example (no batch dim version):
|
| 79 |
-
1. dur = [2,2,3]
|
| 80 |
-
2. token_idx = [[1],[2],[3]], dur_cumsum = [2,4,7], dur_cumsum_prev = [0,2,4]
|
| 81 |
-
3. token_mask = [[1,1,0,0,0,0,0],
|
| 82 |
-
[0,0,1,1,0,0,0],
|
| 83 |
-
[0,0,0,0,1,1,1]]
|
| 84 |
-
4. token_idx * token_mask = [[1,1,0,0,0,0,0],
|
| 85 |
-
[0,0,2,2,0,0,0],
|
| 86 |
-
[0,0,0,0,3,3,3]]
|
| 87 |
-
5. (token_idx * token_mask).sum(0) = [1,1,2,2,3,3,3]
|
| 88 |
-
|
| 89 |
-
:param dur: Batch of durations of each frame (B, T_txt)
|
| 90 |
-
:param dur_padding: Batch of padding of each frame (B, T_txt)
|
| 91 |
-
:param alpha: duration rescale coefficient
|
| 92 |
-
:return:
|
| 93 |
-
mel2ph (B, T_speech)
|
| 94 |
-
assert alpha > 0
|
| 95 |
-
"""
|
| 96 |
-
dur = torch.round(dur.float() * alpha).long()
|
| 97 |
-
if dur_padding is not None:
|
| 98 |
-
dur = dur * (1 - dur_padding.long())
|
| 99 |
-
token_idx = torch.arange(1, dur.shape[1] + 1)[None, :, None].to(dur.device)
|
| 100 |
-
dur_cumsum = torch.cumsum(dur, 1)
|
| 101 |
-
dur_cumsum_prev = F.pad(dur_cumsum, [1, -1], mode='constant', value=0)
|
| 102 |
-
|
| 103 |
-
pos_idx = torch.arange(dur.sum(-1).max())[None, None].to(dur.device)
|
| 104 |
-
token_mask = (pos_idx >= dur_cumsum_prev[:, :, None]) & (pos_idx < dur_cumsum[:, :, None])
|
| 105 |
-
mel2token = (token_idx * token_mask.long()).sum(1)
|
| 106 |
-
return mel2token
|
| 107 |
-
|
| 108 |
-
|
| 109 |
-
class PitchPredictor(torch.nn.Module):
|
| 110 |
-
def __init__(self, idim, n_layers=5, n_chans=384, odim=2, kernel_size=5, dropout_rate=0.1):
|
| 111 |
-
super(PitchPredictor, self).__init__()
|
| 112 |
-
self.conv = torch.nn.ModuleList()
|
| 113 |
-
self.kernel_size = kernel_size
|
| 114 |
-
for idx in range(n_layers):
|
| 115 |
-
in_chans = idim if idx == 0 else n_chans
|
| 116 |
-
self.conv += [torch.nn.Sequential(
|
| 117 |
-
torch.nn.Conv1d(in_chans, n_chans, kernel_size, padding=kernel_size // 2),
|
| 118 |
-
torch.nn.ReLU(),
|
| 119 |
-
LayerNorm(n_chans, dim=1),
|
| 120 |
-
torch.nn.Dropout(dropout_rate)
|
| 121 |
-
)]
|
| 122 |
-
self.linear = torch.nn.Linear(n_chans, odim)
|
| 123 |
-
|
| 124 |
-
def forward(self, x):
|
| 125 |
-
"""
|
| 126 |
-
|
| 127 |
-
:param x: [B, T, H]
|
| 128 |
-
:return: [B, T, H]
|
| 129 |
-
"""
|
| 130 |
-
x = x.transpose(1, -1) # (B, idim, Tmax)
|
| 131 |
-
for f in self.conv:
|
| 132 |
-
x = f(x) # (B, C, Tmax)
|
| 133 |
-
x = self.linear(x.transpose(1, -1)) # (B, Tmax, H)
|
| 134 |
-
return x
|
| 135 |
-
|
| 136 |
-
|
| 137 |
-
class EnergyPredictor(PitchPredictor):
|
| 138 |
-
pass
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|
spaces/AIGuardians/SummarizeWikipediaDocument/app.py
DELETED
|
@@ -1,58 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
import wikipedia
|
| 3 |
-
from transformers import pipeline
|
| 4 |
-
import os
|
| 5 |
-
|
| 6 |
-
# Setting to use the 0th GPU
|
| 7 |
-
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
def summarize(text):
|
| 11 |
-
# Setting to use the bart-large-cnn model for summarization
|
| 12 |
-
summarizer = pipeline("summarization")
|
| 13 |
-
|
| 14 |
-
# To use the t5-base model for summarization:
|
| 15 |
-
# summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base", framework="tf")
|
| 16 |
-
|
| 17 |
-
summary_text = summarizer(text, max_length=100, min_length=5, do_sample=False)[0]['summary_text']
|
| 18 |
-
print(f'Length of initial text: {len(text)}')
|
| 19 |
-
print(f'Length of summary: {len(summary_text)}')
|
| 20 |
-
print(summary_text)
|
| 21 |
-
return summary_text
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
def greet(name):
|
| 25 |
-
return "Hello " + name.orig_name + "!"
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
def get_ocr():
|
| 29 |
-
return ''
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
def search_wiki(text):
|
| 33 |
-
return wikipedia.search(text)
|
| 34 |
-
|
| 35 |
-
|
| 36 |
-
def get_wiki(search_term):
|
| 37 |
-
# text = wikipedia.summary(search_term)
|
| 38 |
-
orig_text_len = len(search_term)
|
| 39 |
-
text = summarize(search_term)
|
| 40 |
-
sum_length = len(text)
|
| 41 |
-
return [text, orig_text_len, sum_length]
|
| 42 |
-
|
| 43 |
-
|
| 44 |
-
# def inference(file):
|
| 45 |
-
# get_ocr()
|
| 46 |
-
# model = AutoModelForSeq2SeqLM.from_pretrained("sgugger/my-awesome-model")
|
| 47 |
-
|
| 48 |
-
out_sum_text = gr.Textbox(label='Summarized Text', lines=15)
|
| 49 |
-
out_orig_test_len = gr.Number(label='Original Text Length')
|
| 50 |
-
out_sum_text_len = gr.Number(label='Summarized Text Length')
|
| 51 |
-
|
| 52 |
-
iface = gr.Interface(fn=get_wiki,
|
| 53 |
-
inputs=gr.Textbox(lines=50, placeholder="Paste article here....", label='Article to Summarize'),
|
| 54 |
-
outputs=[out_sum_text, out_orig_test_len, out_sum_text_len],
|
| 55 |
-
title='Article Summary',
|
| 56 |
-
description='Paste in an article and it will be summarized.'
|
| 57 |
-
)
|
| 58 |
-
iface.launch() # To create a public link, set `share=True` in `launch()`.
|
|
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|
spaces/AchyuthGamer/OpenGPT/g4f/Provider/ChatgptDuo.py
DELETED
|
@@ -1,57 +0,0 @@
|
|
| 1 |
-
from __future__ import annotations
|
| 2 |
-
|
| 3 |
-
from curl_cffi.requests import AsyncSession
|
| 4 |
-
from .base_provider import AsyncProvider, format_prompt
|
| 5 |
-
|
| 6 |
-
|
| 7 |
-
class ChatgptDuo(AsyncProvider):
|
| 8 |
-
url = "https://chatgptduo.com"
|
| 9 |
-
supports_gpt_35_turbo = True
|
| 10 |
-
working = True
|
| 11 |
-
|
| 12 |
-
@classmethod
|
| 13 |
-
async def create_async(
|
| 14 |
-
cls,
|
| 15 |
-
model: str,
|
| 16 |
-
messages: list[dict[str, str]],
|
| 17 |
-
proxy: str = None,
|
| 18 |
-
timeout: int = 30,
|
| 19 |
-
**kwargs
|
| 20 |
-
) -> str:
|
| 21 |
-
async with AsyncSession(
|
| 22 |
-
impersonate="chrome107",
|
| 23 |
-
proxies={"https": proxy},
|
| 24 |
-
timeout=timeout
|
| 25 |
-
) as session:
|
| 26 |
-
prompt = format_prompt(messages),
|
| 27 |
-
data = {
|
| 28 |
-
"prompt": prompt,
|
| 29 |
-
"search": prompt,
|
| 30 |
-
"purpose": "ask",
|
| 31 |
-
}
|
| 32 |
-
response = await session.post(f"{cls.url}/", data=data)
|
| 33 |
-
response.raise_for_status()
|
| 34 |
-
data = response.json()
|
| 35 |
-
|
| 36 |
-
cls._sources = [{
|
| 37 |
-
"title": source["title"],
|
| 38 |
-
"url": source["link"],
|
| 39 |
-
"snippet": source["snippet"]
|
| 40 |
-
} for source in data["results"]]
|
| 41 |
-
|
| 42 |
-
return data["answer"]
|
| 43 |
-
|
| 44 |
-
@classmethod
|
| 45 |
-
def get_sources(cls):
|
| 46 |
-
return cls._sources
|
| 47 |
-
|
| 48 |
-
@classmethod
|
| 49 |
-
@property
|
| 50 |
-
def params(cls):
|
| 51 |
-
params = [
|
| 52 |
-
("model", "str"),
|
| 53 |
-
("messages", "list[dict[str, str]]"),
|
| 54 |
-
("stream", "bool"),
|
| 55 |
-
]
|
| 56 |
-
param = ", ".join([": ".join(p) for p in params])
|
| 57 |
-
return f"g4f.provider.{cls.__name__} supports: ({param})"
|
|
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|
spaces/Adapter/CoAdapter/ldm/modules/extra_condition/midas/midas/midas_net.py
DELETED
|
@@ -1,76 +0,0 @@
|
|
| 1 |
-
"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
|
| 2 |
-
This file contains code that is adapted from
|
| 3 |
-
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
|
| 4 |
-
"""
|
| 5 |
-
import torch
|
| 6 |
-
import torch.nn as nn
|
| 7 |
-
|
| 8 |
-
from .base_model import BaseModel
|
| 9 |
-
from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class MidasNet(BaseModel):
|
| 13 |
-
"""Network for monocular depth estimation.
|
| 14 |
-
"""
|
| 15 |
-
|
| 16 |
-
def __init__(self, path=None, features=256, non_negative=True):
|
| 17 |
-
"""Init.
|
| 18 |
-
|
| 19 |
-
Args:
|
| 20 |
-
path (str, optional): Path to saved model. Defaults to None.
|
| 21 |
-
features (int, optional): Number of features. Defaults to 256.
|
| 22 |
-
backbone (str, optional): Backbone network for encoder. Defaults to resnet50
|
| 23 |
-
"""
|
| 24 |
-
print("Loading weights: ", path)
|
| 25 |
-
|
| 26 |
-
super(MidasNet, self).__init__()
|
| 27 |
-
|
| 28 |
-
use_pretrained = False if path is None else True
|
| 29 |
-
|
| 30 |
-
self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
|
| 31 |
-
|
| 32 |
-
self.scratch.refinenet4 = FeatureFusionBlock(features)
|
| 33 |
-
self.scratch.refinenet3 = FeatureFusionBlock(features)
|
| 34 |
-
self.scratch.refinenet2 = FeatureFusionBlock(features)
|
| 35 |
-
self.scratch.refinenet1 = FeatureFusionBlock(features)
|
| 36 |
-
|
| 37 |
-
self.scratch.output_conv = nn.Sequential(
|
| 38 |
-
nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
|
| 39 |
-
Interpolate(scale_factor=2, mode="bilinear"),
|
| 40 |
-
nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
|
| 41 |
-
nn.ReLU(True),
|
| 42 |
-
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
|
| 43 |
-
nn.ReLU(True) if non_negative else nn.Identity(),
|
| 44 |
-
)
|
| 45 |
-
|
| 46 |
-
if path:
|
| 47 |
-
self.load(path)
|
| 48 |
-
|
| 49 |
-
def forward(self, x):
|
| 50 |
-
"""Forward pass.
|
| 51 |
-
|
| 52 |
-
Args:
|
| 53 |
-
x (tensor): input data (image)
|
| 54 |
-
|
| 55 |
-
Returns:
|
| 56 |
-
tensor: depth
|
| 57 |
-
"""
|
| 58 |
-
|
| 59 |
-
layer_1 = self.pretrained.layer1(x)
|
| 60 |
-
layer_2 = self.pretrained.layer2(layer_1)
|
| 61 |
-
layer_3 = self.pretrained.layer3(layer_2)
|
| 62 |
-
layer_4 = self.pretrained.layer4(layer_3)
|
| 63 |
-
|
| 64 |
-
layer_1_rn = self.scratch.layer1_rn(layer_1)
|
| 65 |
-
layer_2_rn = self.scratch.layer2_rn(layer_2)
|
| 66 |
-
layer_3_rn = self.scratch.layer3_rn(layer_3)
|
| 67 |
-
layer_4_rn = self.scratch.layer4_rn(layer_4)
|
| 68 |
-
|
| 69 |
-
path_4 = self.scratch.refinenet4(layer_4_rn)
|
| 70 |
-
path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
|
| 71 |
-
path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
|
| 72 |
-
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
|
| 73 |
-
|
| 74 |
-
out = self.scratch.output_conv(path_1)
|
| 75 |
-
|
| 76 |
-
return torch.squeeze(out, dim=1)
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spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/describer/prisoner.py
DELETED
|
@@ -1,49 +0,0 @@
|
|
| 1 |
-
from __future__ import annotations
|
| 2 |
-
|
| 3 |
-
from typing import TYPE_CHECKING, Any, List
|
| 4 |
-
|
| 5 |
-
from . import describer_registry as DescriberRegistry
|
| 6 |
-
from .base import BaseDescriber
|
| 7 |
-
|
| 8 |
-
if TYPE_CHECKING:
|
| 9 |
-
from agentverse.environments import BaseEnvironment
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
@DescriberRegistry.register("prisoner")
|
| 13 |
-
class PrisonerDescriber(BaseDescriber):
|
| 14 |
-
switch_func = {
|
| 15 |
-
"Both Suspects": "Suspect2",
|
| 16 |
-
"Suspect1": "Suspect2",
|
| 17 |
-
"Suspect2": "Suspect1",
|
| 18 |
-
}
|
| 19 |
-
receiver: str = "Both Suspects"
|
| 20 |
-
|
| 21 |
-
def get_env_description(self, environment: BaseEnvironment) -> List[str]:
|
| 22 |
-
if environment.cnt_turn == 0:
|
| 23 |
-
environment.agents[0].set_receiver({"all"})
|
| 24 |
-
environment.agents[1].set_receiver({"Police", "Suspect1"})
|
| 25 |
-
environment.agents[2].set_receiver({"Police", "Suspect2"})
|
| 26 |
-
|
| 27 |
-
# only police have to choose to talk to suspect1 or suspect
|
| 28 |
-
description = []
|
| 29 |
-
for i, agent in enumerate(environment.agents):
|
| 30 |
-
if i == 0:
|
| 31 |
-
# police -> suspect1 -> police -> suspect2
|
| 32 |
-
if environment.cnt_turn % 2 == 1:
|
| 33 |
-
description.append("")
|
| 34 |
-
continue
|
| 35 |
-
|
| 36 |
-
# Police will have to choose talk to which suspect
|
| 37 |
-
description.append(f"You are now talking to {self.receiver}")
|
| 38 |
-
|
| 39 |
-
receiver = "all" if self.receiver == "Both Suspects" else self.receiver
|
| 40 |
-
self.receiver = self.switch_func[self.receiver]
|
| 41 |
-
agent.set_receiver({receiver})
|
| 42 |
-
|
| 43 |
-
else:
|
| 44 |
-
description.append("")
|
| 45 |
-
|
| 46 |
-
return description
|
| 47 |
-
|
| 48 |
-
def reset(self) -> None:
|
| 49 |
-
pass
|
|
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|
spaces/Alichuan/VITS-Umamusume-voice-synthesizer/text/cleaners.py
DELETED
|
@@ -1,146 +0,0 @@
|
|
| 1 |
-
import re
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
def japanese_cleaners(text):
|
| 5 |
-
from text.japanese import japanese_to_romaji_with_accent
|
| 6 |
-
text = japanese_to_romaji_with_accent(text)
|
| 7 |
-
text = re.sub(r'([A-Za-z])$', r'\1.', text)
|
| 8 |
-
return text
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
def japanese_cleaners2(text):
|
| 12 |
-
return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…')
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
def korean_cleaners(text):
|
| 16 |
-
'''Pipeline for Korean text'''
|
| 17 |
-
from text.korean import latin_to_hangul, number_to_hangul, divide_hangul
|
| 18 |
-
text = latin_to_hangul(text)
|
| 19 |
-
text = number_to_hangul(text)
|
| 20 |
-
text = divide_hangul(text)
|
| 21 |
-
text = re.sub(r'([\u3131-\u3163])$', r'\1.', text)
|
| 22 |
-
return text
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
def chinese_cleaners(text):
|
| 26 |
-
'''Pipeline for Chinese text'''
|
| 27 |
-
from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo
|
| 28 |
-
text = number_to_chinese(text)
|
| 29 |
-
text = chinese_to_bopomofo(text)
|
| 30 |
-
text = latin_to_bopomofo(text)
|
| 31 |
-
text = re.sub(r'([ˉˊˇˋ˙])$', r'\1。', text)
|
| 32 |
-
return text
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
def zh_ja_mixture_cleaners(text):
|
| 36 |
-
from text.mandarin import chinese_to_romaji
|
| 37 |
-
from text.japanese import japanese_to_romaji_with_accent
|
| 38 |
-
text = re.sub(r'\[ZH\](.*?)\[ZH\]',
|
| 39 |
-
lambda x: chinese_to_romaji(x.group(1))+' ', text)
|
| 40 |
-
text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_romaji_with_accent(
|
| 41 |
-
x.group(1)).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…')+' ', text)
|
| 42 |
-
text = re.sub(r'\s+$', '', text)
|
| 43 |
-
text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
|
| 44 |
-
return text
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
def sanskrit_cleaners(text):
|
| 48 |
-
text = text.replace('॥', '।').replace('ॐ', 'ओम्')
|
| 49 |
-
if text[-1] != '।':
|
| 50 |
-
text += ' ।'
|
| 51 |
-
return text
|
| 52 |
-
|
| 53 |
-
|
| 54 |
-
def cjks_cleaners(text):
|
| 55 |
-
from text.mandarin import chinese_to_lazy_ipa
|
| 56 |
-
from text.japanese import japanese_to_ipa
|
| 57 |
-
from text.korean import korean_to_lazy_ipa
|
| 58 |
-
from text.sanskrit import devanagari_to_ipa
|
| 59 |
-
from text.english import english_to_lazy_ipa
|
| 60 |
-
text = re.sub(r'\[ZH\](.*?)\[ZH\]',
|
| 61 |
-
lambda x: chinese_to_lazy_ipa(x.group(1))+' ', text)
|
| 62 |
-
text = re.sub(r'\[JA\](.*?)\[JA\]',
|
| 63 |
-
lambda x: japanese_to_ipa(x.group(1))+' ', text)
|
| 64 |
-
text = re.sub(r'\[KO\](.*?)\[KO\]',
|
| 65 |
-
lambda x: korean_to_lazy_ipa(x.group(1))+' ', text)
|
| 66 |
-
text = re.sub(r'\[SA\](.*?)\[SA\]',
|
| 67 |
-
lambda x: devanagari_to_ipa(x.group(1))+' ', text)
|
| 68 |
-
text = re.sub(r'\[EN\](.*?)\[EN\]',
|
| 69 |
-
lambda x: english_to_lazy_ipa(x.group(1))+' ', text)
|
| 70 |
-
text = re.sub(r'\s+$', '', text)
|
| 71 |
-
text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
|
| 72 |
-
return text
|
| 73 |
-
|
| 74 |
-
|
| 75 |
-
def cjke_cleaners(text):
|
| 76 |
-
from text.mandarin import chinese_to_lazy_ipa
|
| 77 |
-
from text.japanese import japanese_to_ipa
|
| 78 |
-
from text.korean import korean_to_ipa
|
| 79 |
-
from text.english import english_to_ipa2
|
| 80 |
-
text = re.sub(r'\[ZH\](.*?)\[ZH\]', lambda x: chinese_to_lazy_ipa(x.group(1)).replace(
|
| 81 |
-
'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn')+' ', text)
|
| 82 |
-
text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_ipa(x.group(1)).replace('ʧ', 'tʃ').replace(
|
| 83 |
-
'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz')+' ', text)
|
| 84 |
-
text = re.sub(r'\[KO\](.*?)\[KO\]',
|
| 85 |
-
lambda x: korean_to_ipa(x.group(1))+' ', text)
|
| 86 |
-
text = re.sub(r'\[EN\](.*?)\[EN\]', lambda x: english_to_ipa2(x.group(1)).replace('ɑ', 'a').replace(
|
| 87 |
-
'ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u')+' ', text)
|
| 88 |
-
text = re.sub(r'\s+$', '', text)
|
| 89 |
-
text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
|
| 90 |
-
return text
|
| 91 |
-
|
| 92 |
-
|
| 93 |
-
def cjke_cleaners2(text):
|
| 94 |
-
from text.mandarin import chinese_to_ipa
|
| 95 |
-
from text.japanese import japanese_to_ipa2
|
| 96 |
-
from text.korean import korean_to_ipa
|
| 97 |
-
from text.english import english_to_ipa2
|
| 98 |
-
text = re.sub(r'\[ZH\](.*?)\[ZH\]',
|
| 99 |
-
lambda x: chinese_to_ipa(x.group(1))+' ', text)
|
| 100 |
-
text = re.sub(r'\[JA\](.*?)\[JA\]',
|
| 101 |
-
lambda x: japanese_to_ipa2(x.group(1))+' ', text)
|
| 102 |
-
text = re.sub(r'\[KO\](.*?)\[KO\]',
|
| 103 |
-
lambda x: korean_to_ipa(x.group(1))+' ', text)
|
| 104 |
-
text = re.sub(r'\[EN\](.*?)\[EN\]',
|
| 105 |
-
lambda x: english_to_ipa2(x.group(1))+' ', text)
|
| 106 |
-
text = re.sub(r'\s+$', '', text)
|
| 107 |
-
text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
|
| 108 |
-
return text
|
| 109 |
-
|
| 110 |
-
|
| 111 |
-
def thai_cleaners(text):
|
| 112 |
-
from text.thai import num_to_thai, latin_to_thai
|
| 113 |
-
text = num_to_thai(text)
|
| 114 |
-
text = latin_to_thai(text)
|
| 115 |
-
return text
|
| 116 |
-
|
| 117 |
-
|
| 118 |
-
def shanghainese_cleaners(text):
|
| 119 |
-
from text.shanghainese import shanghainese_to_ipa
|
| 120 |
-
text = shanghainese_to_ipa(text)
|
| 121 |
-
text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
|
| 122 |
-
return text
|
| 123 |
-
|
| 124 |
-
|
| 125 |
-
def chinese_dialect_cleaners(text):
|
| 126 |
-
from text.mandarin import chinese_to_ipa2
|
| 127 |
-
from text.japanese import japanese_to_ipa3
|
| 128 |
-
from text.shanghainese import shanghainese_to_ipa
|
| 129 |
-
from text.cantonese import cantonese_to_ipa
|
| 130 |
-
from text.english import english_to_lazy_ipa2
|
| 131 |
-
from text.ngu_dialect import ngu_dialect_to_ipa
|
| 132 |
-
text = re.sub(r'\[ZH\](.*?)\[ZH\]',
|
| 133 |
-
lambda x: chinese_to_ipa2(x.group(1))+' ', text)
|
| 134 |
-
text = re.sub(r'\[JA\](.*?)\[JA\]',
|
| 135 |
-
lambda x: japanese_to_ipa3(x.group(1)).replace('Q', 'ʔ')+' ', text)
|
| 136 |
-
text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: shanghainese_to_ipa(x.group(1)).replace('1', '˥˧').replace('5',
|
| 137 |
-
'˧˧˦').replace('6', '˩˩˧').replace('7', '˥').replace('8', '˩˨').replace('ᴀ', 'ɐ').replace('ᴇ', 'e')+' ', text)
|
| 138 |
-
text = re.sub(r'\[GD\](.*?)\[GD\]',
|
| 139 |
-
lambda x: cantonese_to_ipa(x.group(1))+' ', text)
|
| 140 |
-
text = re.sub(r'\[EN\](.*?)\[EN\]',
|
| 141 |
-
lambda x: english_to_lazy_ipa2(x.group(1))+' ', text)
|
| 142 |
-
text = re.sub(r'\[([A-Z]{2})\](.*?)\[\1\]', lambda x: ngu_dialect_to_ipa(x.group(2), x.group(
|
| 143 |
-
1)).replace('ʣ', 'dz').replace('ʥ', 'dʑ').replace('ʦ', 'ts').replace('ʨ', 'tɕ')+' ', text)
|
| 144 |
-
text = re.sub(r'\s+$', '', text)
|
| 145 |
-
text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
|
| 146 |
-
return text
|
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spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/consistency_models/__init__.py
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-
from .pipeline_consistency_models import ConsistencyModelPipeline
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spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py
DELETED
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@@ -1,645 +0,0 @@
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|
| 1 |
-
import copy
|
| 2 |
-
from dataclasses import dataclass
|
| 3 |
-
from typing import Callable, List, Optional, Union
|
| 4 |
-
|
| 5 |
-
import numpy as np
|
| 6 |
-
import PIL
|
| 7 |
-
import torch
|
| 8 |
-
import torch.nn.functional as F
|
| 9 |
-
from torch.nn.functional import grid_sample
|
| 10 |
-
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
|
| 11 |
-
|
| 12 |
-
from diffusers.models import AutoencoderKL, UNet2DConditionModel
|
| 13 |
-
from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline, StableDiffusionSafetyChecker
|
| 14 |
-
from diffusers.schedulers import KarrasDiffusionSchedulers
|
| 15 |
-
from diffusers.utils import BaseOutput
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
def rearrange_0(tensor, f):
|
| 19 |
-
F, C, H, W = tensor.size()
|
| 20 |
-
tensor = torch.permute(torch.reshape(tensor, (F // f, f, C, H, W)), (0, 2, 1, 3, 4))
|
| 21 |
-
return tensor
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
def rearrange_1(tensor):
|
| 25 |
-
B, C, F, H, W = tensor.size()
|
| 26 |
-
return torch.reshape(torch.permute(tensor, (0, 2, 1, 3, 4)), (B * F, C, H, W))
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
def rearrange_3(tensor, f):
|
| 30 |
-
F, D, C = tensor.size()
|
| 31 |
-
return torch.reshape(tensor, (F // f, f, D, C))
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
def rearrange_4(tensor):
|
| 35 |
-
B, F, D, C = tensor.size()
|
| 36 |
-
return torch.reshape(tensor, (B * F, D, C))
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
class CrossFrameAttnProcessor:
|
| 40 |
-
"""
|
| 41 |
-
Cross frame attention processor. Each frame attends the first frame.
|
| 42 |
-
|
| 43 |
-
Args:
|
| 44 |
-
batch_size: The number that represents actual batch size, other than the frames.
|
| 45 |
-
For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to
|
| 46 |
-
2, due to classifier-free guidance.
|
| 47 |
-
"""
|
| 48 |
-
|
| 49 |
-
def __init__(self, batch_size=2):
|
| 50 |
-
self.batch_size = batch_size
|
| 51 |
-
|
| 52 |
-
def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):
|
| 53 |
-
batch_size, sequence_length, _ = hidden_states.shape
|
| 54 |
-
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
| 55 |
-
query = attn.to_q(hidden_states)
|
| 56 |
-
|
| 57 |
-
is_cross_attention = encoder_hidden_states is not None
|
| 58 |
-
if encoder_hidden_states is None:
|
| 59 |
-
encoder_hidden_states = hidden_states
|
| 60 |
-
elif attn.norm_cross:
|
| 61 |
-
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
| 62 |
-
|
| 63 |
-
key = attn.to_k(encoder_hidden_states)
|
| 64 |
-
value = attn.to_v(encoder_hidden_states)
|
| 65 |
-
|
| 66 |
-
# Cross Frame Attention
|
| 67 |
-
if not is_cross_attention:
|
| 68 |
-
video_length = key.size()[0] // self.batch_size
|
| 69 |
-
first_frame_index = [0] * video_length
|
| 70 |
-
|
| 71 |
-
# rearrange keys to have batch and frames in the 1st and 2nd dims respectively
|
| 72 |
-
key = rearrange_3(key, video_length)
|
| 73 |
-
key = key[:, first_frame_index]
|
| 74 |
-
# rearrange values to have batch and frames in the 1st and 2nd dims respectively
|
| 75 |
-
value = rearrange_3(value, video_length)
|
| 76 |
-
value = value[:, first_frame_index]
|
| 77 |
-
|
| 78 |
-
# rearrange back to original shape
|
| 79 |
-
key = rearrange_4(key)
|
| 80 |
-
value = rearrange_4(value)
|
| 81 |
-
|
| 82 |
-
query = attn.head_to_batch_dim(query)
|
| 83 |
-
key = attn.head_to_batch_dim(key)
|
| 84 |
-
value = attn.head_to_batch_dim(value)
|
| 85 |
-
|
| 86 |
-
attention_probs = attn.get_attention_scores(query, key, attention_mask)
|
| 87 |
-
hidden_states = torch.bmm(attention_probs, value)
|
| 88 |
-
hidden_states = attn.batch_to_head_dim(hidden_states)
|
| 89 |
-
|
| 90 |
-
# linear proj
|
| 91 |
-
hidden_states = attn.to_out[0](hidden_states)
|
| 92 |
-
# dropout
|
| 93 |
-
hidden_states = attn.to_out[1](hidden_states)
|
| 94 |
-
|
| 95 |
-
return hidden_states
|
| 96 |
-
|
| 97 |
-
|
| 98 |
-
class CrossFrameAttnProcessor2_0:
|
| 99 |
-
"""
|
| 100 |
-
Cross frame attention processor with scaled_dot_product attention of Pytorch 2.0.
|
| 101 |
-
|
| 102 |
-
Args:
|
| 103 |
-
batch_size: The number that represents actual batch size, other than the frames.
|
| 104 |
-
For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to
|
| 105 |
-
2, due to classifier-free guidance.
|
| 106 |
-
"""
|
| 107 |
-
|
| 108 |
-
def __init__(self, batch_size=2):
|
| 109 |
-
if not hasattr(F, "scaled_dot_product_attention"):
|
| 110 |
-
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
|
| 111 |
-
self.batch_size = batch_size
|
| 112 |
-
|
| 113 |
-
def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):
|
| 114 |
-
batch_size, sequence_length, _ = (
|
| 115 |
-
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
| 116 |
-
)
|
| 117 |
-
inner_dim = hidden_states.shape[-1]
|
| 118 |
-
|
| 119 |
-
if attention_mask is not None:
|
| 120 |
-
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
| 121 |
-
# scaled_dot_product_attention expects attention_mask shape to be
|
| 122 |
-
# (batch, heads, source_length, target_length)
|
| 123 |
-
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
|
| 124 |
-
|
| 125 |
-
query = attn.to_q(hidden_states)
|
| 126 |
-
|
| 127 |
-
is_cross_attention = encoder_hidden_states is not None
|
| 128 |
-
if encoder_hidden_states is None:
|
| 129 |
-
encoder_hidden_states = hidden_states
|
| 130 |
-
elif attn.norm_cross:
|
| 131 |
-
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
| 132 |
-
|
| 133 |
-
key = attn.to_k(encoder_hidden_states)
|
| 134 |
-
value = attn.to_v(encoder_hidden_states)
|
| 135 |
-
|
| 136 |
-
# Cross Frame Attention
|
| 137 |
-
if not is_cross_attention:
|
| 138 |
-
video_length = key.size()[0] // self.batch_size
|
| 139 |
-
first_frame_index = [0] * video_length
|
| 140 |
-
|
| 141 |
-
# rearrange keys to have batch and frames in the 1st and 2nd dims respectively
|
| 142 |
-
key = rearrange_3(key, video_length)
|
| 143 |
-
key = key[:, first_frame_index]
|
| 144 |
-
# rearrange values to have batch and frames in the 1st and 2nd dims respectively
|
| 145 |
-
value = rearrange_3(value, video_length)
|
| 146 |
-
value = value[:, first_frame_index]
|
| 147 |
-
|
| 148 |
-
# rearrange back to original shape
|
| 149 |
-
key = rearrange_4(key)
|
| 150 |
-
value = rearrange_4(value)
|
| 151 |
-
|
| 152 |
-
head_dim = inner_dim // attn.heads
|
| 153 |
-
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
| 154 |
-
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
| 155 |
-
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
| 156 |
-
|
| 157 |
-
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
| 158 |
-
# TODO: add support for attn.scale when we move to Torch 2.1
|
| 159 |
-
hidden_states = F.scaled_dot_product_attention(
|
| 160 |
-
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
|
| 161 |
-
)
|
| 162 |
-
|
| 163 |
-
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
| 164 |
-
hidden_states = hidden_states.to(query.dtype)
|
| 165 |
-
|
| 166 |
-
# linear proj
|
| 167 |
-
hidden_states = attn.to_out[0](hidden_states)
|
| 168 |
-
# dropout
|
| 169 |
-
hidden_states = attn.to_out[1](hidden_states)
|
| 170 |
-
return hidden_states
|
| 171 |
-
|
| 172 |
-
|
| 173 |
-
@dataclass
|
| 174 |
-
class TextToVideoPipelineOutput(BaseOutput):
|
| 175 |
-
r"""
|
| 176 |
-
Output class for zero-shot text-to-video pipeline.
|
| 177 |
-
|
| 178 |
-
Args:
|
| 179 |
-
images (`[List[PIL.Image.Image]`, `np.ndarray`]):
|
| 180 |
-
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
|
| 181 |
-
num_channels)`.
|
| 182 |
-
nsfw_content_detected (`[List[bool]]`):
|
| 183 |
-
List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or
|
| 184 |
-
`None` if safety checking could not be performed.
|
| 185 |
-
"""
|
| 186 |
-
images: Union[List[PIL.Image.Image], np.ndarray]
|
| 187 |
-
nsfw_content_detected: Optional[List[bool]]
|
| 188 |
-
|
| 189 |
-
|
| 190 |
-
def coords_grid(batch, ht, wd, device):
|
| 191 |
-
# Adapted from https://github.com/princeton-vl/RAFT/blob/master/core/utils/utils.py
|
| 192 |
-
coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device))
|
| 193 |
-
coords = torch.stack(coords[::-1], dim=0).float()
|
| 194 |
-
return coords[None].repeat(batch, 1, 1, 1)
|
| 195 |
-
|
| 196 |
-
|
| 197 |
-
def warp_single_latent(latent, reference_flow):
|
| 198 |
-
"""
|
| 199 |
-
Warp latent of a single frame with given flow
|
| 200 |
-
|
| 201 |
-
Args:
|
| 202 |
-
latent: latent code of a single frame
|
| 203 |
-
reference_flow: flow which to warp the latent with
|
| 204 |
-
|
| 205 |
-
Returns:
|
| 206 |
-
warped: warped latent
|
| 207 |
-
"""
|
| 208 |
-
_, _, H, W = reference_flow.size()
|
| 209 |
-
_, _, h, w = latent.size()
|
| 210 |
-
coords0 = coords_grid(1, H, W, device=latent.device).to(latent.dtype)
|
| 211 |
-
|
| 212 |
-
coords_t0 = coords0 + reference_flow
|
| 213 |
-
coords_t0[:, 0] /= W
|
| 214 |
-
coords_t0[:, 1] /= H
|
| 215 |
-
|
| 216 |
-
coords_t0 = coords_t0 * 2.0 - 1.0
|
| 217 |
-
coords_t0 = F.interpolate(coords_t0, size=(h, w), mode="bilinear")
|
| 218 |
-
coords_t0 = torch.permute(coords_t0, (0, 2, 3, 1))
|
| 219 |
-
|
| 220 |
-
warped = grid_sample(latent, coords_t0, mode="nearest", padding_mode="reflection")
|
| 221 |
-
return warped
|
| 222 |
-
|
| 223 |
-
|
| 224 |
-
def create_motion_field(motion_field_strength_x, motion_field_strength_y, frame_ids, device, dtype):
|
| 225 |
-
"""
|
| 226 |
-
Create translation motion field
|
| 227 |
-
|
| 228 |
-
Args:
|
| 229 |
-
motion_field_strength_x: motion strength along x-axis
|
| 230 |
-
motion_field_strength_y: motion strength along y-axis
|
| 231 |
-
frame_ids: indexes of the frames the latents of which are being processed.
|
| 232 |
-
This is needed when we perform chunk-by-chunk inference
|
| 233 |
-
device: device
|
| 234 |
-
dtype: dtype
|
| 235 |
-
|
| 236 |
-
Returns:
|
| 237 |
-
|
| 238 |
-
"""
|
| 239 |
-
seq_length = len(frame_ids)
|
| 240 |
-
reference_flow = torch.zeros((seq_length, 2, 512, 512), device=device, dtype=dtype)
|
| 241 |
-
for fr_idx in range(seq_length):
|
| 242 |
-
reference_flow[fr_idx, 0, :, :] = motion_field_strength_x * (frame_ids[fr_idx])
|
| 243 |
-
reference_flow[fr_idx, 1, :, :] = motion_field_strength_y * (frame_ids[fr_idx])
|
| 244 |
-
return reference_flow
|
| 245 |
-
|
| 246 |
-
|
| 247 |
-
def create_motion_field_and_warp_latents(motion_field_strength_x, motion_field_strength_y, frame_ids, latents):
|
| 248 |
-
"""
|
| 249 |
-
Creates translation motion and warps the latents accordingly
|
| 250 |
-
|
| 251 |
-
Args:
|
| 252 |
-
motion_field_strength_x: motion strength along x-axis
|
| 253 |
-
motion_field_strength_y: motion strength along y-axis
|
| 254 |
-
frame_ids: indexes of the frames the latents of which are being processed.
|
| 255 |
-
This is needed when we perform chunk-by-chunk inference
|
| 256 |
-
latents: latent codes of frames
|
| 257 |
-
|
| 258 |
-
Returns:
|
| 259 |
-
warped_latents: warped latents
|
| 260 |
-
"""
|
| 261 |
-
motion_field = create_motion_field(
|
| 262 |
-
motion_field_strength_x=motion_field_strength_x,
|
| 263 |
-
motion_field_strength_y=motion_field_strength_y,
|
| 264 |
-
frame_ids=frame_ids,
|
| 265 |
-
device=latents.device,
|
| 266 |
-
dtype=latents.dtype,
|
| 267 |
-
)
|
| 268 |
-
warped_latents = latents.clone().detach()
|
| 269 |
-
for i in range(len(warped_latents)):
|
| 270 |
-
warped_latents[i] = warp_single_latent(latents[i][None], motion_field[i][None])
|
| 271 |
-
return warped_latents
|
| 272 |
-
|
| 273 |
-
|
| 274 |
-
class TextToVideoZeroPipeline(StableDiffusionPipeline):
|
| 275 |
-
r"""
|
| 276 |
-
Pipeline for zero-shot text-to-video generation using Stable Diffusion.
|
| 277 |
-
|
| 278 |
-
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
|
| 279 |
-
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
|
| 280 |
-
|
| 281 |
-
Args:
|
| 282 |
-
vae ([`AutoencoderKL`]):
|
| 283 |
-
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
|
| 284 |
-
text_encoder ([`CLIPTextModel`]):
|
| 285 |
-
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
|
| 286 |
-
tokenizer (`CLIPTokenizer`):
|
| 287 |
-
A [`~transformers.CLIPTokenizer`] to tokenize text.
|
| 288 |
-
unet ([`UNet2DConditionModel`]):
|
| 289 |
-
A [`UNet3DConditionModel`] to denoise the encoded video latents.
|
| 290 |
-
scheduler ([`SchedulerMixin`]):
|
| 291 |
-
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
|
| 292 |
-
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
|
| 293 |
-
safety_checker ([`StableDiffusionSafetyChecker`]):
|
| 294 |
-
Classification module that estimates whether generated images could be considered offensive or harmful.
|
| 295 |
-
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
|
| 296 |
-
about a model's potential harms.
|
| 297 |
-
feature_extractor ([`CLIPImageProcessor`]):
|
| 298 |
-
A [`CLIPImageProcessor`] to extract features from generated images; used as inputs to the `safety_checker`.
|
| 299 |
-
"""
|
| 300 |
-
|
| 301 |
-
def __init__(
|
| 302 |
-
self,
|
| 303 |
-
vae: AutoencoderKL,
|
| 304 |
-
text_encoder: CLIPTextModel,
|
| 305 |
-
tokenizer: CLIPTokenizer,
|
| 306 |
-
unet: UNet2DConditionModel,
|
| 307 |
-
scheduler: KarrasDiffusionSchedulers,
|
| 308 |
-
safety_checker: StableDiffusionSafetyChecker,
|
| 309 |
-
feature_extractor: CLIPImageProcessor,
|
| 310 |
-
requires_safety_checker: bool = True,
|
| 311 |
-
):
|
| 312 |
-
super().__init__(
|
| 313 |
-
vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, requires_safety_checker
|
| 314 |
-
)
|
| 315 |
-
processor = (
|
| 316 |
-
CrossFrameAttnProcessor2_0(batch_size=2)
|
| 317 |
-
if hasattr(F, "scaled_dot_product_attention")
|
| 318 |
-
else CrossFrameAttnProcessor(batch_size=2)
|
| 319 |
-
)
|
| 320 |
-
self.unet.set_attn_processor(processor)
|
| 321 |
-
|
| 322 |
-
def forward_loop(self, x_t0, t0, t1, generator):
|
| 323 |
-
"""
|
| 324 |
-
Perform DDPM forward process from time t0 to t1. This is the same as adding noise with corresponding variance.
|
| 325 |
-
|
| 326 |
-
Args:
|
| 327 |
-
x_t0:
|
| 328 |
-
Latent code at time t0.
|
| 329 |
-
t0:
|
| 330 |
-
Timestep at t0.
|
| 331 |
-
t1:
|
| 332 |
-
Timestamp at t1.
|
| 333 |
-
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
| 334 |
-
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
| 335 |
-
generation deterministic.
|
| 336 |
-
|
| 337 |
-
Returns:
|
| 338 |
-
x_t1:
|
| 339 |
-
Forward process applied to x_t0 from time t0 to t1.
|
| 340 |
-
"""
|
| 341 |
-
eps = torch.randn(x_t0.size(), generator=generator, dtype=x_t0.dtype, device=x_t0.device)
|
| 342 |
-
alpha_vec = torch.prod(self.scheduler.alphas[t0:t1])
|
| 343 |
-
x_t1 = torch.sqrt(alpha_vec) * x_t0 + torch.sqrt(1 - alpha_vec) * eps
|
| 344 |
-
return x_t1
|
| 345 |
-
|
| 346 |
-
def backward_loop(
|
| 347 |
-
self,
|
| 348 |
-
latents,
|
| 349 |
-
timesteps,
|
| 350 |
-
prompt_embeds,
|
| 351 |
-
guidance_scale,
|
| 352 |
-
callback,
|
| 353 |
-
callback_steps,
|
| 354 |
-
num_warmup_steps,
|
| 355 |
-
extra_step_kwargs,
|
| 356 |
-
cross_attention_kwargs=None,
|
| 357 |
-
):
|
| 358 |
-
"""
|
| 359 |
-
Perform backward process given list of time steps.
|
| 360 |
-
|
| 361 |
-
Args:
|
| 362 |
-
latents:
|
| 363 |
-
Latents at time timesteps[0].
|
| 364 |
-
timesteps:
|
| 365 |
-
Time steps along which to perform backward process.
|
| 366 |
-
prompt_embeds:
|
| 367 |
-
Pre-generated text embeddings.
|
| 368 |
-
guidance_scale:
|
| 369 |
-
A higher guidance scale value encourages the model to generate images closely linked to the text
|
| 370 |
-
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
| 371 |
-
callback (`Callable`, *optional*):
|
| 372 |
-
A function that calls every `callback_steps` steps during inference. The function is called with the
|
| 373 |
-
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
| 374 |
-
callback_steps (`int`, *optional*, defaults to 1):
|
| 375 |
-
The frequency at which the `callback` function is called. If not specified, the callback is called at
|
| 376 |
-
every step.
|
| 377 |
-
extra_step_kwargs:
|
| 378 |
-
Extra_step_kwargs.
|
| 379 |
-
cross_attention_kwargs:
|
| 380 |
-
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
|
| 381 |
-
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
|
| 382 |
-
num_warmup_steps:
|
| 383 |
-
number of warmup steps.
|
| 384 |
-
|
| 385 |
-
Returns:
|
| 386 |
-
latents:
|
| 387 |
-
Latents of backward process output at time timesteps[-1].
|
| 388 |
-
"""
|
| 389 |
-
do_classifier_free_guidance = guidance_scale > 1.0
|
| 390 |
-
num_steps = (len(timesteps) - num_warmup_steps) // self.scheduler.order
|
| 391 |
-
with self.progress_bar(total=num_steps) as progress_bar:
|
| 392 |
-
for i, t in enumerate(timesteps):
|
| 393 |
-
# expand the latents if we are doing classifier free guidance
|
| 394 |
-
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
| 395 |
-
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
| 396 |
-
|
| 397 |
-
# predict the noise residual
|
| 398 |
-
noise_pred = self.unet(
|
| 399 |
-
latent_model_input,
|
| 400 |
-
t,
|
| 401 |
-
encoder_hidden_states=prompt_embeds,
|
| 402 |
-
cross_attention_kwargs=cross_attention_kwargs,
|
| 403 |
-
).sample
|
| 404 |
-
|
| 405 |
-
# perform guidance
|
| 406 |
-
if do_classifier_free_guidance:
|
| 407 |
-
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
| 408 |
-
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
| 409 |
-
|
| 410 |
-
# compute the previous noisy sample x_t -> x_t-1
|
| 411 |
-
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
|
| 412 |
-
|
| 413 |
-
# call the callback, if provided
|
| 414 |
-
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
| 415 |
-
progress_bar.update()
|
| 416 |
-
if callback is not None and i % callback_steps == 0:
|
| 417 |
-
callback(i, t, latents)
|
| 418 |
-
return latents.clone().detach()
|
| 419 |
-
|
| 420 |
-
@torch.no_grad()
|
| 421 |
-
def __call__(
|
| 422 |
-
self,
|
| 423 |
-
prompt: Union[str, List[str]],
|
| 424 |
-
video_length: Optional[int] = 8,
|
| 425 |
-
height: Optional[int] = None,
|
| 426 |
-
width: Optional[int] = None,
|
| 427 |
-
num_inference_steps: int = 50,
|
| 428 |
-
guidance_scale: float = 7.5,
|
| 429 |
-
negative_prompt: Optional[Union[str, List[str]]] = None,
|
| 430 |
-
num_videos_per_prompt: Optional[int] = 1,
|
| 431 |
-
eta: float = 0.0,
|
| 432 |
-
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
| 433 |
-
latents: Optional[torch.FloatTensor] = None,
|
| 434 |
-
motion_field_strength_x: float = 12,
|
| 435 |
-
motion_field_strength_y: float = 12,
|
| 436 |
-
output_type: Optional[str] = "tensor",
|
| 437 |
-
return_dict: bool = True,
|
| 438 |
-
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
| 439 |
-
callback_steps: Optional[int] = 1,
|
| 440 |
-
t0: int = 44,
|
| 441 |
-
t1: int = 47,
|
| 442 |
-
frame_ids: Optional[List[int]] = None,
|
| 443 |
-
):
|
| 444 |
-
"""
|
| 445 |
-
The call function to the pipeline for generation.
|
| 446 |
-
|
| 447 |
-
Args:
|
| 448 |
-
prompt (`str` or `List[str]`, *optional*):
|
| 449 |
-
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
| 450 |
-
video_length (`int`, *optional*, defaults to 8):
|
| 451 |
-
The number of generated video frames.
|
| 452 |
-
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
| 453 |
-
The height in pixels of the generated image.
|
| 454 |
-
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
| 455 |
-
The width in pixels of the generated image.
|
| 456 |
-
num_inference_steps (`int`, *optional*, defaults to 50):
|
| 457 |
-
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
| 458 |
-
expense of slower inference.
|
| 459 |
-
guidance_scale (`float`, *optional*, defaults to 7.5):
|
| 460 |
-
A higher guidance scale value encourages the model to generate images closely linked to the text
|
| 461 |
-
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
| 462 |
-
negative_prompt (`str` or `List[str]`, *optional*):
|
| 463 |
-
The prompt or prompts to guide what to not include in video generation. If not defined, you need to
|
| 464 |
-
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
|
| 465 |
-
num_videos_per_prompt (`int`, *optional*, defaults to 1):
|
| 466 |
-
The number of videos to generate per prompt.
|
| 467 |
-
eta (`float`, *optional*, defaults to 0.0):
|
| 468 |
-
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
|
| 469 |
-
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
|
| 470 |
-
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
| 471 |
-
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
| 472 |
-
generation deterministic.
|
| 473 |
-
latents (`torch.FloatTensor`, *optional*):
|
| 474 |
-
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
|
| 475 |
-
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
| 476 |
-
tensor is generated by sampling using the supplied random `generator`.
|
| 477 |
-
output_type (`str`, *optional*, defaults to `"numpy"`):
|
| 478 |
-
The output format of the generated video. Choose between `"latent"` and `"numpy"`.
|
| 479 |
-
return_dict (`bool`, *optional*, defaults to `True`):
|
| 480 |
-
Whether or not to return a
|
| 481 |
-
[`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoPipelineOutput`] instead of
|
| 482 |
-
a plain tuple.
|
| 483 |
-
callback (`Callable`, *optional*):
|
| 484 |
-
A function that calls every `callback_steps` steps during inference. The function is called with the
|
| 485 |
-
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
| 486 |
-
callback_steps (`int`, *optional*, defaults to 1):
|
| 487 |
-
The frequency at which the `callback` function is called. If not specified, the callback is called at
|
| 488 |
-
every step.
|
| 489 |
-
motion_field_strength_x (`float`, *optional*, defaults to 12):
|
| 490 |
-
Strength of motion in generated video along x-axis. See the [paper](https://arxiv.org/abs/2303.13439),
|
| 491 |
-
Sect. 3.3.1.
|
| 492 |
-
motion_field_strength_y (`float`, *optional*, defaults to 12):
|
| 493 |
-
Strength of motion in generated video along y-axis. See the [paper](https://arxiv.org/abs/2303.13439),
|
| 494 |
-
Sect. 3.3.1.
|
| 495 |
-
t0 (`int`, *optional*, defaults to 44):
|
| 496 |
-
Timestep t0. Should be in the range [0, num_inference_steps - 1]. See the
|
| 497 |
-
[paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1.
|
| 498 |
-
t1 (`int`, *optional*, defaults to 47):
|
| 499 |
-
Timestep t0. Should be in the range [t0 + 1, num_inference_steps - 1]. See the
|
| 500 |
-
[paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1.
|
| 501 |
-
frame_ids (`List[int]`, *optional*):
|
| 502 |
-
Indexes of the frames that are being generated. This is used when generating longer videos
|
| 503 |
-
chunk-by-chunk.
|
| 504 |
-
|
| 505 |
-
Returns:
|
| 506 |
-
[`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoPipelineOutput`]:
|
| 507 |
-
The output contains a `ndarray` of the generated video, when `output_type` != `"latent"`, otherwise a
|
| 508 |
-
latent code of generated videos and a list of `bool`s indicating whether the corresponding generated
|
| 509 |
-
video contains "not-safe-for-work" (nsfw) content..
|
| 510 |
-
"""
|
| 511 |
-
assert video_length > 0
|
| 512 |
-
if frame_ids is None:
|
| 513 |
-
frame_ids = list(range(video_length))
|
| 514 |
-
assert len(frame_ids) == video_length
|
| 515 |
-
|
| 516 |
-
assert num_videos_per_prompt == 1
|
| 517 |
-
|
| 518 |
-
if isinstance(prompt, str):
|
| 519 |
-
prompt = [prompt]
|
| 520 |
-
if isinstance(negative_prompt, str):
|
| 521 |
-
negative_prompt = [negative_prompt]
|
| 522 |
-
|
| 523 |
-
# Default height and width to unet
|
| 524 |
-
height = height or self.unet.config.sample_size * self.vae_scale_factor
|
| 525 |
-
width = width or self.unet.config.sample_size * self.vae_scale_factor
|
| 526 |
-
|
| 527 |
-
# Check inputs. Raise error if not correct
|
| 528 |
-
self.check_inputs(prompt, height, width, callback_steps)
|
| 529 |
-
|
| 530 |
-
# Define call parameters
|
| 531 |
-
batch_size = 1 if isinstance(prompt, str) else len(prompt)
|
| 532 |
-
device = self._execution_device
|
| 533 |
-
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
| 534 |
-
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
| 535 |
-
# corresponds to doing no classifier free guidance.
|
| 536 |
-
do_classifier_free_guidance = guidance_scale > 1.0
|
| 537 |
-
|
| 538 |
-
# Encode input prompt
|
| 539 |
-
prompt_embeds = self._encode_prompt(
|
| 540 |
-
prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
|
| 541 |
-
)
|
| 542 |
-
|
| 543 |
-
# Prepare timesteps
|
| 544 |
-
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
| 545 |
-
timesteps = self.scheduler.timesteps
|
| 546 |
-
|
| 547 |
-
# Prepare latent variables
|
| 548 |
-
num_channels_latents = self.unet.config.in_channels
|
| 549 |
-
latents = self.prepare_latents(
|
| 550 |
-
batch_size * num_videos_per_prompt,
|
| 551 |
-
num_channels_latents,
|
| 552 |
-
height,
|
| 553 |
-
width,
|
| 554 |
-
prompt_embeds.dtype,
|
| 555 |
-
device,
|
| 556 |
-
generator,
|
| 557 |
-
latents,
|
| 558 |
-
)
|
| 559 |
-
# Prepare extra step kwargs.
|
| 560 |
-
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
| 561 |
-
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
| 562 |
-
|
| 563 |
-
# Perform the first backward process up to time T_1
|
| 564 |
-
x_1_t1 = self.backward_loop(
|
| 565 |
-
timesteps=timesteps[: -t1 - 1],
|
| 566 |
-
prompt_embeds=prompt_embeds,
|
| 567 |
-
latents=latents,
|
| 568 |
-
guidance_scale=guidance_scale,
|
| 569 |
-
callback=callback,
|
| 570 |
-
callback_steps=callback_steps,
|
| 571 |
-
extra_step_kwargs=extra_step_kwargs,
|
| 572 |
-
num_warmup_steps=num_warmup_steps,
|
| 573 |
-
)
|
| 574 |
-
scheduler_copy = copy.deepcopy(self.scheduler)
|
| 575 |
-
|
| 576 |
-
# Perform the second backward process up to time T_0
|
| 577 |
-
x_1_t0 = self.backward_loop(
|
| 578 |
-
timesteps=timesteps[-t1 - 1 : -t0 - 1],
|
| 579 |
-
prompt_embeds=prompt_embeds,
|
| 580 |
-
latents=x_1_t1,
|
| 581 |
-
guidance_scale=guidance_scale,
|
| 582 |
-
callback=callback,
|
| 583 |
-
callback_steps=callback_steps,
|
| 584 |
-
extra_step_kwargs=extra_step_kwargs,
|
| 585 |
-
num_warmup_steps=0,
|
| 586 |
-
)
|
| 587 |
-
|
| 588 |
-
# Propagate first frame latents at time T_0 to remaining frames
|
| 589 |
-
x_2k_t0 = x_1_t0.repeat(video_length - 1, 1, 1, 1)
|
| 590 |
-
|
| 591 |
-
# Add motion in latents at time T_0
|
| 592 |
-
x_2k_t0 = create_motion_field_and_warp_latents(
|
| 593 |
-
motion_field_strength_x=motion_field_strength_x,
|
| 594 |
-
motion_field_strength_y=motion_field_strength_y,
|
| 595 |
-
latents=x_2k_t0,
|
| 596 |
-
frame_ids=frame_ids[1:],
|
| 597 |
-
)
|
| 598 |
-
|
| 599 |
-
# Perform forward process up to time T_1
|
| 600 |
-
x_2k_t1 = self.forward_loop(
|
| 601 |
-
x_t0=x_2k_t0,
|
| 602 |
-
t0=timesteps[-t0 - 1].item(),
|
| 603 |
-
t1=timesteps[-t1 - 1].item(),
|
| 604 |
-
generator=generator,
|
| 605 |
-
)
|
| 606 |
-
|
| 607 |
-
# Perform backward process from time T_1 to 0
|
| 608 |
-
x_1k_t1 = torch.cat([x_1_t1, x_2k_t1])
|
| 609 |
-
b, l, d = prompt_embeds.size()
|
| 610 |
-
prompt_embeds = prompt_embeds[:, None].repeat(1, video_length, 1, 1).reshape(b * video_length, l, d)
|
| 611 |
-
|
| 612 |
-
self.scheduler = scheduler_copy
|
| 613 |
-
x_1k_0 = self.backward_loop(
|
| 614 |
-
timesteps=timesteps[-t1 - 1 :],
|
| 615 |
-
prompt_embeds=prompt_embeds,
|
| 616 |
-
latents=x_1k_t1,
|
| 617 |
-
guidance_scale=guidance_scale,
|
| 618 |
-
callback=callback,
|
| 619 |
-
callback_steps=callback_steps,
|
| 620 |
-
extra_step_kwargs=extra_step_kwargs,
|
| 621 |
-
num_warmup_steps=0,
|
| 622 |
-
)
|
| 623 |
-
latents = x_1k_0
|
| 624 |
-
|
| 625 |
-
# manually for max memory savings
|
| 626 |
-
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
| 627 |
-
self.unet.to("cpu")
|
| 628 |
-
torch.cuda.empty_cache()
|
| 629 |
-
|
| 630 |
-
if output_type == "latent":
|
| 631 |
-
image = latents
|
| 632 |
-
has_nsfw_concept = None
|
| 633 |
-
else:
|
| 634 |
-
image = self.decode_latents(latents)
|
| 635 |
-
# Run safety checker
|
| 636 |
-
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
|
| 637 |
-
|
| 638 |
-
# Offload last model to CPU
|
| 639 |
-
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
| 640 |
-
self.final_offload_hook.offload()
|
| 641 |
-
|
| 642 |
-
if not return_dict:
|
| 643 |
-
return (image, has_nsfw_concept)
|
| 644 |
-
|
| 645 |
-
return TextToVideoPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
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spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py
DELETED
|
@@ -1,6 +0,0 @@
|
|
| 1 |
-
_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
|
| 2 |
-
model = dict(
|
| 3 |
-
roi_head=dict(
|
| 4 |
-
bbox_head=dict(
|
| 5 |
-
reg_decoded_bbox=True,
|
| 6 |
-
loss_bbox=dict(type='IoULoss', loss_weight=10.0))))
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spaces/Andy1621/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py
DELETED
|
@@ -1,4 +0,0 @@
|
|
| 1 |
-
_base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
|
| 2 |
-
# learning policy
|
| 3 |
-
lr_config = dict(step=[28, 34])
|
| 4 |
-
runner = dict(type='EpochBasedRunner', max_epochs=36)
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|
spaces/Andy1621/uniformer_image_segmentation/configs/mobilenet_v3/README.md
DELETED
|
@@ -1,28 +0,0 @@
|
|
| 1 |
-
# Searching for MobileNetV3
|
| 2 |
-
|
| 3 |
-
## Introduction
|
| 4 |
-
|
| 5 |
-
<!-- [ALGORITHM] -->
|
| 6 |
-
|
| 7 |
-
```latex
|
| 8 |
-
@inproceedings{Howard_2019_ICCV,
|
| 9 |
-
title={Searching for MobileNetV3},
|
| 10 |
-
author={Howard, Andrew and Sandler, Mark and Chu, Grace and Chen, Liang-Chieh and Chen, Bo and Tan, Mingxing and Wang, Weijun and Zhu, Yukun and Pang, Ruoming and Vasudevan, Vijay and Le, Quoc V. and Adam, Hartwig},
|
| 11 |
-
booktitle={The IEEE International Conference on Computer Vision (ICCV)},
|
| 12 |
-
pages={1314-1324},
|
| 13 |
-
month={October},
|
| 14 |
-
year={2019},
|
| 15 |
-
doi={10.1109/ICCV.2019.00140}}
|
| 16 |
-
}
|
| 17 |
-
```
|
| 18 |
-
|
| 19 |
-
## Results and models
|
| 20 |
-
|
| 21 |
-
### Cityscapes
|
| 22 |
-
|
| 23 |
-
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
|
| 24 |
-
| ------ | ------------------ | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
| 25 |
-
| LRASPP | M-V3-D8 | 512x1024 | 320000 | 8.9 | 15.22 | 69.54 | 70.89 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes_20201224_220337-cfe8fb07.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes-20201224_220337.log.json) |
|
| 26 |
-
| LRASPP | M-V3-D8 (scratch) | 512x1024 | 320000 | 8.9 | 14.77 | 67.87 | 69.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes_20201224_220337-9f29cd72.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes-20201224_220337.log.json) |
|
| 27 |
-
| LRASPP | M-V3s-D8 | 512x1024 | 320000 | 5.3 | 23.64 | 64.11 | 66.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes_20201224_223935-61565b34.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes-20201224_223935.log.json) |
|
| 28 |
-
| LRASPP | M-V3s-D8 (scratch) | 512x1024 | 320000 | 5.3 | 24.50 | 62.74 | 65.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes_20201224_223935-03daeabb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes-20201224_223935.log.json) |
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|
spaces/Andy1621/uniformer_image_segmentation/configs/resnest/deeplabv3_s101-d8_512x512_160k_ade20k.py
DELETED
|
@@ -1,9 +0,0 @@
|
|
| 1 |
-
_base_ = '../deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py'
|
| 2 |
-
model = dict(
|
| 3 |
-
pretrained='open-mmlab://resnest101',
|
| 4 |
-
backbone=dict(
|
| 5 |
-
type='ResNeSt',
|
| 6 |
-
stem_channels=128,
|
| 7 |
-
radix=2,
|
| 8 |
-
reduction_factor=4,
|
| 9 |
-
avg_down_stride=True))
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|
spaces/AnishKumbhar/ChatBot/text-generation-webui-main/css/html_readable_style.css
DELETED
|
@@ -1,33 +0,0 @@
|
|
| 1 |
-
.container {
|
| 2 |
-
max-width: 600px;
|
| 3 |
-
margin-left: auto;
|
| 4 |
-
margin-right: auto;
|
| 5 |
-
background-color: rgb(31, 41, 55);
|
| 6 |
-
padding: 3em;
|
| 7 |
-
word-break: break-word;
|
| 8 |
-
overflow-wrap: anywhere;
|
| 9 |
-
color: #efefef !important;
|
| 10 |
-
}
|
| 11 |
-
|
| 12 |
-
.container p, .container li {
|
| 13 |
-
font-size: 16px !important;
|
| 14 |
-
color: #efefef !important;
|
| 15 |
-
margin-bottom: 22px;
|
| 16 |
-
line-height: 1.4 !important;
|
| 17 |
-
}
|
| 18 |
-
|
| 19 |
-
.container li > p {
|
| 20 |
-
display: inline !important;
|
| 21 |
-
}
|
| 22 |
-
|
| 23 |
-
.container code {
|
| 24 |
-
overflow-x: auto;
|
| 25 |
-
}
|
| 26 |
-
|
| 27 |
-
.container :not(pre) > code {
|
| 28 |
-
white-space: normal !important;
|
| 29 |
-
}
|
| 30 |
-
|
| 31 |
-
.container .hoverable {
|
| 32 |
-
font-size: 14px;
|
| 33 |
-
}
|
|
|
|
|
|
|
|
|
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|
|
spaces/Apex-X/Tm/roop/ui.py
DELETED
|
@@ -1,231 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import webbrowser
|
| 3 |
-
import customtkinter as ctk
|
| 4 |
-
from typing import Callable, Tuple
|
| 5 |
-
import cv2
|
| 6 |
-
from PIL import Image, ImageOps
|
| 7 |
-
|
| 8 |
-
import roop.globals
|
| 9 |
-
import roop.metadata
|
| 10 |
-
from roop.face_analyser import get_one_face
|
| 11 |
-
from roop.capturer import get_video_frame, get_video_frame_total
|
| 12 |
-
from roop.predicter import predict_frame
|
| 13 |
-
from roop.processors.frame.core import get_frame_processors_modules
|
| 14 |
-
from roop.utilities import is_image, is_video, resolve_relative_path
|
| 15 |
-
|
| 16 |
-
ROOT = None
|
| 17 |
-
ROOT_HEIGHT = 700
|
| 18 |
-
ROOT_WIDTH = 600
|
| 19 |
-
|
| 20 |
-
PREVIEW = None
|
| 21 |
-
PREVIEW_MAX_HEIGHT = 700
|
| 22 |
-
PREVIEW_MAX_WIDTH = 1200
|
| 23 |
-
|
| 24 |
-
RECENT_DIRECTORY_SOURCE = None
|
| 25 |
-
RECENT_DIRECTORY_TARGET = None
|
| 26 |
-
RECENT_DIRECTORY_OUTPUT = None
|
| 27 |
-
|
| 28 |
-
preview_label = None
|
| 29 |
-
preview_slider = None
|
| 30 |
-
source_label = None
|
| 31 |
-
target_label = None
|
| 32 |
-
status_label = None
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
def init(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.CTk:
|
| 36 |
-
global ROOT, PREVIEW
|
| 37 |
-
|
| 38 |
-
ROOT = create_root(start, destroy)
|
| 39 |
-
PREVIEW = create_preview(ROOT)
|
| 40 |
-
|
| 41 |
-
return ROOT
|
| 42 |
-
|
| 43 |
-
|
| 44 |
-
def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.CTk:
|
| 45 |
-
global source_label, target_label, status_label
|
| 46 |
-
|
| 47 |
-
ctk.deactivate_automatic_dpi_awareness()
|
| 48 |
-
ctk.set_appearance_mode('system')
|
| 49 |
-
ctk.set_default_color_theme(resolve_relative_path('ui.json'))
|
| 50 |
-
|
| 51 |
-
root = ctk.CTk()
|
| 52 |
-
root.minsize(ROOT_WIDTH, ROOT_HEIGHT)
|
| 53 |
-
root.title(f'{roop.metadata.name} {roop.metadata.version}')
|
| 54 |
-
root.configure()
|
| 55 |
-
root.protocol('WM_DELETE_WINDOW', lambda: destroy())
|
| 56 |
-
|
| 57 |
-
source_label = ctk.CTkLabel(root, text=None)
|
| 58 |
-
source_label.place(relx=0.1, rely=0.1, relwidth=0.3, relheight=0.25)
|
| 59 |
-
|
| 60 |
-
target_label = ctk.CTkLabel(root, text=None)
|
| 61 |
-
target_label.place(relx=0.6, rely=0.1, relwidth=0.3, relheight=0.25)
|
| 62 |
-
|
| 63 |
-
source_button = ctk.CTkButton(root, text='Select a face', cursor='hand2', command=lambda: select_source_path())
|
| 64 |
-
source_button.place(relx=0.1, rely=0.4, relwidth=0.3, relheight=0.1)
|
| 65 |
-
|
| 66 |
-
target_button = ctk.CTkButton(root, text='Select a target', cursor='hand2', command=lambda: select_target_path())
|
| 67 |
-
target_button.place(relx=0.6, rely=0.4, relwidth=0.3, relheight=0.1)
|
| 68 |
-
|
| 69 |
-
keep_fps_value = ctk.BooleanVar(value=roop.globals.keep_fps)
|
| 70 |
-
keep_fps_checkbox = ctk.CTkSwitch(root, text='Keep fps', variable=keep_fps_value, cursor='hand2', command=lambda: setattr(roop.globals, 'keep_fps', not roop.globals.keep_fps))
|
| 71 |
-
keep_fps_checkbox.place(relx=0.1, rely=0.6)
|
| 72 |
-
|
| 73 |
-
keep_frames_value = ctk.BooleanVar(value=roop.globals.keep_frames)
|
| 74 |
-
keep_frames_switch = ctk.CTkSwitch(root, text='Keep frames', variable=keep_frames_value, cursor='hand2', command=lambda: setattr(roop.globals, 'keep_frames', keep_frames_value.get()))
|
| 75 |
-
keep_frames_switch.place(relx=0.1, rely=0.65)
|
| 76 |
-
|
| 77 |
-
keep_audio_value = ctk.BooleanVar(value=roop.globals.keep_audio)
|
| 78 |
-
keep_audio_switch = ctk.CTkSwitch(root, text='Keep audio', variable=keep_audio_value, cursor='hand2', command=lambda: setattr(roop.globals, 'keep_audio', keep_audio_value.get()))
|
| 79 |
-
keep_audio_switch.place(relx=0.6, rely=0.6)
|
| 80 |
-
|
| 81 |
-
many_faces_value = ctk.BooleanVar(value=roop.globals.many_faces)
|
| 82 |
-
many_faces_switch = ctk.CTkSwitch(root, text='Many faces', variable=many_faces_value, cursor='hand2', command=lambda: setattr(roop.globals, 'many_faces', many_faces_value.get()))
|
| 83 |
-
many_faces_switch.place(relx=0.6, rely=0.65)
|
| 84 |
-
|
| 85 |
-
start_button = ctk.CTkButton(root, text='Start', cursor='hand2', command=lambda: select_output_path(start))
|
| 86 |
-
start_button.place(relx=0.15, rely=0.75, relwidth=0.2, relheight=0.05)
|
| 87 |
-
|
| 88 |
-
stop_button = ctk.CTkButton(root, text='Destroy', cursor='hand2', command=lambda: destroy())
|
| 89 |
-
stop_button.place(relx=0.4, rely=0.75, relwidth=0.2, relheight=0.05)
|
| 90 |
-
|
| 91 |
-
preview_button = ctk.CTkButton(root, text='Preview', cursor='hand2', command=lambda: toggle_preview())
|
| 92 |
-
preview_button.place(relx=0.65, rely=0.75, relwidth=0.2, relheight=0.05)
|
| 93 |
-
|
| 94 |
-
status_label = ctk.CTkLabel(root, text=None, justify='center')
|
| 95 |
-
status_label.place(relx=0.1, rely=0.9, relwidth=0.8)
|
| 96 |
-
|
| 97 |
-
donate_label = ctk.CTkLabel(root, text='^_^ Donate to project ^_^', justify='center', cursor='hand2')
|
| 98 |
-
donate_label.place(relx=0.1, rely=0.95, relwidth=0.8)
|
| 99 |
-
donate_label.configure(text_color=ctk.ThemeManager.theme.get('RoopDonate').get('text_color'))
|
| 100 |
-
donate_label.bind('<Button>', lambda event: webbrowser.open('https://github.com/sponsors/s0md3v'))
|
| 101 |
-
|
| 102 |
-
return root
|
| 103 |
-
|
| 104 |
-
|
| 105 |
-
def create_preview(parent: ctk.CTkToplevel) -> ctk.CTkToplevel:
|
| 106 |
-
global preview_label, preview_slider
|
| 107 |
-
|
| 108 |
-
preview = ctk.CTkToplevel(parent)
|
| 109 |
-
preview.withdraw()
|
| 110 |
-
preview.title('Preview')
|
| 111 |
-
preview.configure()
|
| 112 |
-
preview.protocol('WM_DELETE_WINDOW', lambda: toggle_preview())
|
| 113 |
-
preview.resizable(width=False, height=False)
|
| 114 |
-
|
| 115 |
-
preview_label = ctk.CTkLabel(preview, text=None)
|
| 116 |
-
preview_label.pack(fill='both', expand=True)
|
| 117 |
-
|
| 118 |
-
preview_slider = ctk.CTkSlider(preview, from_=0, to=0, command=lambda frame_value: update_preview(frame_value))
|
| 119 |
-
|
| 120 |
-
return preview
|
| 121 |
-
|
| 122 |
-
|
| 123 |
-
def update_status(text: str) -> None:
|
| 124 |
-
status_label.configure(text=text)
|
| 125 |
-
ROOT.update()
|
| 126 |
-
|
| 127 |
-
|
| 128 |
-
def select_source_path() -> None:
|
| 129 |
-
global RECENT_DIRECTORY_SOURCE
|
| 130 |
-
|
| 131 |
-
PREVIEW.withdraw()
|
| 132 |
-
source_path = ctk.filedialog.askopenfilename(title='select an source image', initialdir=RECENT_DIRECTORY_SOURCE)
|
| 133 |
-
if is_image(source_path):
|
| 134 |
-
roop.globals.source_path = source_path
|
| 135 |
-
RECENT_DIRECTORY_SOURCE = os.path.dirname(roop.globals.source_path)
|
| 136 |
-
image = render_image_preview(roop.globals.source_path, (200, 200))
|
| 137 |
-
source_label.configure(image=image)
|
| 138 |
-
else:
|
| 139 |
-
roop.globals.source_path = None
|
| 140 |
-
source_label.configure(image=None)
|
| 141 |
-
|
| 142 |
-
|
| 143 |
-
def select_target_path() -> None:
|
| 144 |
-
global RECENT_DIRECTORY_TARGET
|
| 145 |
-
|
| 146 |
-
PREVIEW.withdraw()
|
| 147 |
-
target_path = ctk.filedialog.askopenfilename(title='select an target image or video', initialdir=RECENT_DIRECTORY_TARGET)
|
| 148 |
-
if is_image(target_path):
|
| 149 |
-
roop.globals.target_path = target_path
|
| 150 |
-
RECENT_DIRECTORY_TARGET = os.path.dirname(roop.globals.target_path)
|
| 151 |
-
image = render_image_preview(roop.globals.target_path, (200, 200))
|
| 152 |
-
target_label.configure(image=image)
|
| 153 |
-
elif is_video(target_path):
|
| 154 |
-
roop.globals.target_path = target_path
|
| 155 |
-
RECENT_DIRECTORY_TARGET = os.path.dirname(roop.globals.target_path)
|
| 156 |
-
video_frame = render_video_preview(target_path, (200, 200))
|
| 157 |
-
target_label.configure(image=video_frame)
|
| 158 |
-
else:
|
| 159 |
-
roop.globals.target_path = None
|
| 160 |
-
target_label.configure(image=None)
|
| 161 |
-
|
| 162 |
-
|
| 163 |
-
def select_output_path(start: Callable[[], None]) -> None:
|
| 164 |
-
global RECENT_DIRECTORY_OUTPUT
|
| 165 |
-
|
| 166 |
-
if is_image(roop.globals.target_path):
|
| 167 |
-
output_path = ctk.filedialog.asksaveasfilename(title='save image output file', defaultextension='.png', initialfile='output.png', initialdir=RECENT_DIRECTORY_OUTPUT)
|
| 168 |
-
elif is_video(roop.globals.target_path):
|
| 169 |
-
output_path = ctk.filedialog.asksaveasfilename(title='save video output file', defaultextension='.mp4', initialfile='output.mp4', initialdir=RECENT_DIRECTORY_OUTPUT)
|
| 170 |
-
else:
|
| 171 |
-
output_path = None
|
| 172 |
-
if output_path:
|
| 173 |
-
roop.globals.output_path = output_path
|
| 174 |
-
RECENT_DIRECTORY_OUTPUT = os.path.dirname(roop.globals.output_path)
|
| 175 |
-
start()
|
| 176 |
-
|
| 177 |
-
|
| 178 |
-
def render_image_preview(image_path: str, size: Tuple[int, int]) -> ctk.CTkImage:
|
| 179 |
-
image = Image.open(image_path)
|
| 180 |
-
if size:
|
| 181 |
-
image = ImageOps.fit(image, size, Image.LANCZOS)
|
| 182 |
-
return ctk.CTkImage(image, size=image.size)
|
| 183 |
-
|
| 184 |
-
|
| 185 |
-
def render_video_preview(video_path: str, size: Tuple[int, int], frame_number: int = 0) -> ctk.CTkImage:
|
| 186 |
-
capture = cv2.VideoCapture(video_path)
|
| 187 |
-
if frame_number:
|
| 188 |
-
capture.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
|
| 189 |
-
has_frame, frame = capture.read()
|
| 190 |
-
if has_frame:
|
| 191 |
-
image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
|
| 192 |
-
if size:
|
| 193 |
-
image = ImageOps.fit(image, size, Image.LANCZOS)
|
| 194 |
-
return ctk.CTkImage(image, size=image.size)
|
| 195 |
-
capture.release()
|
| 196 |
-
cv2.destroyAllWindows()
|
| 197 |
-
|
| 198 |
-
|
| 199 |
-
def toggle_preview() -> None:
|
| 200 |
-
if PREVIEW.state() == 'normal':
|
| 201 |
-
PREVIEW.withdraw()
|
| 202 |
-
elif roop.globals.source_path and roop.globals.target_path:
|
| 203 |
-
init_preview()
|
| 204 |
-
update_preview()
|
| 205 |
-
PREVIEW.deiconify()
|
| 206 |
-
|
| 207 |
-
|
| 208 |
-
def init_preview() -> None:
|
| 209 |
-
if is_image(roop.globals.target_path):
|
| 210 |
-
preview_slider.pack_forget()
|
| 211 |
-
if is_video(roop.globals.target_path):
|
| 212 |
-
video_frame_total = get_video_frame_total(roop.globals.target_path)
|
| 213 |
-
preview_slider.configure(to=video_frame_total)
|
| 214 |
-
preview_slider.pack(fill='x')
|
| 215 |
-
preview_slider.set(0)
|
| 216 |
-
|
| 217 |
-
|
| 218 |
-
def update_preview(frame_number: int = 0) -> None:
|
| 219 |
-
if roop.globals.source_path and roop.globals.target_path:
|
| 220 |
-
temp_frame = get_video_frame(roop.globals.target_path, frame_number)
|
| 221 |
-
if predict_frame(temp_frame):
|
| 222 |
-
quit()
|
| 223 |
-
for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
|
| 224 |
-
temp_frame = frame_processor.process_frame(
|
| 225 |
-
get_one_face(cv2.imread(roop.globals.source_path)),
|
| 226 |
-
temp_frame
|
| 227 |
-
)
|
| 228 |
-
image = Image.fromarray(cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB))
|
| 229 |
-
image = ImageOps.contain(image, (PREVIEW_MAX_WIDTH, PREVIEW_MAX_HEIGHT), Image.LANCZOS)
|
| 230 |
-
image = ctk.CTkImage(image, size=image.size)
|
| 231 |
-
preview_label.configure(image=image)
|
|
|
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spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/codingstatemachine.py
DELETED
|
@@ -1,90 +0,0 @@
|
|
| 1 |
-
######################## BEGIN LICENSE BLOCK ########################
|
| 2 |
-
# The Original Code is mozilla.org code.
|
| 3 |
-
#
|
| 4 |
-
# The Initial Developer of the Original Code is
|
| 5 |
-
# Netscape Communications Corporation.
|
| 6 |
-
# Portions created by the Initial Developer are Copyright (C) 1998
|
| 7 |
-
# the Initial Developer. All Rights Reserved.
|
| 8 |
-
#
|
| 9 |
-
# Contributor(s):
|
| 10 |
-
# Mark Pilgrim - port to Python
|
| 11 |
-
#
|
| 12 |
-
# This library is free software; you can redistribute it and/or
|
| 13 |
-
# modify it under the terms of the GNU Lesser General Public
|
| 14 |
-
# License as published by the Free Software Foundation; either
|
| 15 |
-
# version 2.1 of the License, or (at your option) any later version.
|
| 16 |
-
#
|
| 17 |
-
# This library is distributed in the hope that it will be useful,
|
| 18 |
-
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
| 19 |
-
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
|
| 20 |
-
# Lesser General Public License for more details.
|
| 21 |
-
#
|
| 22 |
-
# You should have received a copy of the GNU Lesser General Public
|
| 23 |
-
# License along with this library; if not, write to the Free Software
|
| 24 |
-
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
|
| 25 |
-
# 02110-1301 USA
|
| 26 |
-
######################### END LICENSE BLOCK #########################
|
| 27 |
-
|
| 28 |
-
import logging
|
| 29 |
-
|
| 30 |
-
from .codingstatemachinedict import CodingStateMachineDict
|
| 31 |
-
from .enums import MachineState
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
class CodingStateMachine:
|
| 35 |
-
"""
|
| 36 |
-
A state machine to verify a byte sequence for a particular encoding. For
|
| 37 |
-
each byte the detector receives, it will feed that byte to every active
|
| 38 |
-
state machine available, one byte at a time. The state machine changes its
|
| 39 |
-
state based on its previous state and the byte it receives. There are 3
|
| 40 |
-
states in a state machine that are of interest to an auto-detector:
|
| 41 |
-
|
| 42 |
-
START state: This is the state to start with, or a legal byte sequence
|
| 43 |
-
(i.e. a valid code point) for character has been identified.
|
| 44 |
-
|
| 45 |
-
ME state: This indicates that the state machine identified a byte sequence
|
| 46 |
-
that is specific to the charset it is designed for and that
|
| 47 |
-
there is no other possible encoding which can contain this byte
|
| 48 |
-
sequence. This will to lead to an immediate positive answer for
|
| 49 |
-
the detector.
|
| 50 |
-
|
| 51 |
-
ERROR state: This indicates the state machine identified an illegal byte
|
| 52 |
-
sequence for that encoding. This will lead to an immediate
|
| 53 |
-
negative answer for this encoding. Detector will exclude this
|
| 54 |
-
encoding from consideration from here on.
|
| 55 |
-
"""
|
| 56 |
-
|
| 57 |
-
def __init__(self, sm: CodingStateMachineDict) -> None:
|
| 58 |
-
self._model = sm
|
| 59 |
-
self._curr_byte_pos = 0
|
| 60 |
-
self._curr_char_len = 0
|
| 61 |
-
self._curr_state = MachineState.START
|
| 62 |
-
self.active = True
|
| 63 |
-
self.logger = logging.getLogger(__name__)
|
| 64 |
-
self.reset()
|
| 65 |
-
|
| 66 |
-
def reset(self) -> None:
|
| 67 |
-
self._curr_state = MachineState.START
|
| 68 |
-
|
| 69 |
-
def next_state(self, c: int) -> int:
|
| 70 |
-
# for each byte we get its class
|
| 71 |
-
# if it is first byte, we also get byte length
|
| 72 |
-
byte_class = self._model["class_table"][c]
|
| 73 |
-
if self._curr_state == MachineState.START:
|
| 74 |
-
self._curr_byte_pos = 0
|
| 75 |
-
self._curr_char_len = self._model["char_len_table"][byte_class]
|
| 76 |
-
# from byte's class and state_table, we get its next state
|
| 77 |
-
curr_state = self._curr_state * self._model["class_factor"] + byte_class
|
| 78 |
-
self._curr_state = self._model["state_table"][curr_state]
|
| 79 |
-
self._curr_byte_pos += 1
|
| 80 |
-
return self._curr_state
|
| 81 |
-
|
| 82 |
-
def get_current_charlen(self) -> int:
|
| 83 |
-
return self._curr_char_len
|
| 84 |
-
|
| 85 |
-
def get_coding_state_machine(self) -> str:
|
| 86 |
-
return self._model["name"]
|
| 87 |
-
|
| 88 |
-
@property
|
| 89 |
-
def language(self) -> str:
|
| 90 |
-
return self._model["language"]
|
|
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|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/tomli/_re.py
DELETED
|
@@ -1,107 +0,0 @@
|
|
| 1 |
-
# SPDX-License-Identifier: MIT
|
| 2 |
-
# SPDX-FileCopyrightText: 2021 Taneli Hukkinen
|
| 3 |
-
# Licensed to PSF under a Contributor Agreement.
|
| 4 |
-
|
| 5 |
-
from __future__ import annotations
|
| 6 |
-
|
| 7 |
-
from datetime import date, datetime, time, timedelta, timezone, tzinfo
|
| 8 |
-
from functools import lru_cache
|
| 9 |
-
import re
|
| 10 |
-
from typing import Any
|
| 11 |
-
|
| 12 |
-
from ._types import ParseFloat
|
| 13 |
-
|
| 14 |
-
# E.g.
|
| 15 |
-
# - 00:32:00.999999
|
| 16 |
-
# - 00:32:00
|
| 17 |
-
_TIME_RE_STR = r"([01][0-9]|2[0-3]):([0-5][0-9]):([0-5][0-9])(?:\.([0-9]{1,6})[0-9]*)?"
|
| 18 |
-
|
| 19 |
-
RE_NUMBER = re.compile(
|
| 20 |
-
r"""
|
| 21 |
-
0
|
| 22 |
-
(?:
|
| 23 |
-
x[0-9A-Fa-f](?:_?[0-9A-Fa-f])* # hex
|
| 24 |
-
|
|
| 25 |
-
b[01](?:_?[01])* # bin
|
| 26 |
-
|
|
| 27 |
-
o[0-7](?:_?[0-7])* # oct
|
| 28 |
-
)
|
| 29 |
-
|
|
| 30 |
-
[+-]?(?:0|[1-9](?:_?[0-9])*) # dec, integer part
|
| 31 |
-
(?P<floatpart>
|
| 32 |
-
(?:\.[0-9](?:_?[0-9])*)? # optional fractional part
|
| 33 |
-
(?:[eE][+-]?[0-9](?:_?[0-9])*)? # optional exponent part
|
| 34 |
-
)
|
| 35 |
-
""",
|
| 36 |
-
flags=re.VERBOSE,
|
| 37 |
-
)
|
| 38 |
-
RE_LOCALTIME = re.compile(_TIME_RE_STR)
|
| 39 |
-
RE_DATETIME = re.compile(
|
| 40 |
-
rf"""
|
| 41 |
-
([0-9]{{4}})-(0[1-9]|1[0-2])-(0[1-9]|[12][0-9]|3[01]) # date, e.g. 1988-10-27
|
| 42 |
-
(?:
|
| 43 |
-
[Tt ]
|
| 44 |
-
{_TIME_RE_STR}
|
| 45 |
-
(?:([Zz])|([+-])([01][0-9]|2[0-3]):([0-5][0-9]))? # optional time offset
|
| 46 |
-
)?
|
| 47 |
-
""",
|
| 48 |
-
flags=re.VERBOSE,
|
| 49 |
-
)
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
def match_to_datetime(match: re.Match) -> datetime | date:
|
| 53 |
-
"""Convert a `RE_DATETIME` match to `datetime.datetime` or `datetime.date`.
|
| 54 |
-
|
| 55 |
-
Raises ValueError if the match does not correspond to a valid date
|
| 56 |
-
or datetime.
|
| 57 |
-
"""
|
| 58 |
-
(
|
| 59 |
-
year_str,
|
| 60 |
-
month_str,
|
| 61 |
-
day_str,
|
| 62 |
-
hour_str,
|
| 63 |
-
minute_str,
|
| 64 |
-
sec_str,
|
| 65 |
-
micros_str,
|
| 66 |
-
zulu_time,
|
| 67 |
-
offset_sign_str,
|
| 68 |
-
offset_hour_str,
|
| 69 |
-
offset_minute_str,
|
| 70 |
-
) = match.groups()
|
| 71 |
-
year, month, day = int(year_str), int(month_str), int(day_str)
|
| 72 |
-
if hour_str is None:
|
| 73 |
-
return date(year, month, day)
|
| 74 |
-
hour, minute, sec = int(hour_str), int(minute_str), int(sec_str)
|
| 75 |
-
micros = int(micros_str.ljust(6, "0")) if micros_str else 0
|
| 76 |
-
if offset_sign_str:
|
| 77 |
-
tz: tzinfo | None = cached_tz(
|
| 78 |
-
offset_hour_str, offset_minute_str, offset_sign_str
|
| 79 |
-
)
|
| 80 |
-
elif zulu_time:
|
| 81 |
-
tz = timezone.utc
|
| 82 |
-
else: # local date-time
|
| 83 |
-
tz = None
|
| 84 |
-
return datetime(year, month, day, hour, minute, sec, micros, tzinfo=tz)
|
| 85 |
-
|
| 86 |
-
|
| 87 |
-
@lru_cache(maxsize=None)
|
| 88 |
-
def cached_tz(hour_str: str, minute_str: str, sign_str: str) -> timezone:
|
| 89 |
-
sign = 1 if sign_str == "+" else -1
|
| 90 |
-
return timezone(
|
| 91 |
-
timedelta(
|
| 92 |
-
hours=sign * int(hour_str),
|
| 93 |
-
minutes=sign * int(minute_str),
|
| 94 |
-
)
|
| 95 |
-
)
|
| 96 |
-
|
| 97 |
-
|
| 98 |
-
def match_to_localtime(match: re.Match) -> time:
|
| 99 |
-
hour_str, minute_str, sec_str, micros_str = match.groups()
|
| 100 |
-
micros = int(micros_str.ljust(6, "0")) if micros_str else 0
|
| 101 |
-
return time(int(hour_str), int(minute_str), int(sec_str), micros)
|
| 102 |
-
|
| 103 |
-
|
| 104 |
-
def match_to_number(match: re.Match, parse_float: ParseFloat) -> Any:
|
| 105 |
-
if match.group("floatpart"):
|
| 106 |
-
return parse_float(match.group())
|
| 107 |
-
return int(match.group(), 0)
|
|
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|
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|
|
spaces/AutoBG/Auto-BoardGame/README.md
DELETED
|
@@ -1,11 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Auto BoardGame
|
| 3 |
-
emoji: 🎲
|
| 4 |
-
colorFrom: indigo
|
| 5 |
-
colorTo: indigo
|
| 6 |
-
sdk: streamlit
|
| 7 |
-
sdk_version: 1.19.0
|
| 8 |
-
app_file: Home.py
|
| 9 |
-
pinned: false
|
| 10 |
-
license: cc-by-nc-sa-2.0
|
| 11 |
-
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
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|
|
|
spaces/AutoLLM/ArxivDigest/app.py
DELETED
|
@@ -1,196 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
from download_new_papers import get_papers
|
| 3 |
-
import utils
|
| 4 |
-
from relevancy import generate_relevance_score, process_subject_fields
|
| 5 |
-
from sendgrid.helpers.mail import Mail, Email, To, Content
|
| 6 |
-
import sendgrid
|
| 7 |
-
import os
|
| 8 |
-
import openai
|
| 9 |
-
|
| 10 |
-
topics = {
|
| 11 |
-
"Physics": "",
|
| 12 |
-
"Mathematics": "math",
|
| 13 |
-
"Computer Science": "cs",
|
| 14 |
-
"Quantitative Biology": "q-bio",
|
| 15 |
-
"Quantitative Finance": "q-fin",
|
| 16 |
-
"Statistics": "stat",
|
| 17 |
-
"Electrical Engineering and Systems Science": "eess",
|
| 18 |
-
"Economics": "econ"
|
| 19 |
-
}
|
| 20 |
-
|
| 21 |
-
physics_topics = {
|
| 22 |
-
"Astrophysics": "astro-ph",
|
| 23 |
-
"Condensed Matter": "cond-mat",
|
| 24 |
-
"General Relativity and Quantum Cosmology": "gr-qc",
|
| 25 |
-
"High Energy Physics - Experiment": "hep-ex",
|
| 26 |
-
"High Energy Physics - Lattice": "hep-lat",
|
| 27 |
-
"High Energy Physics - Phenomenology": "hep-ph",
|
| 28 |
-
"High Energy Physics - Theory": "hep-th",
|
| 29 |
-
"Mathematical Physics": "math-ph",
|
| 30 |
-
"Nonlinear Sciences": "nlin",
|
| 31 |
-
"Nuclear Experiment": "nucl-ex",
|
| 32 |
-
"Nuclear Theory": "nucl-th",
|
| 33 |
-
"Physics": "physics",
|
| 34 |
-
"Quantum Physics": "quant-ph"
|
| 35 |
-
}
|
| 36 |
-
|
| 37 |
-
categories_map = {
|
| 38 |
-
"Astrophysics": ["Astrophysics of Galaxies", "Cosmology and Nongalactic Astrophysics", "Earth and Planetary Astrophysics", "High Energy Astrophysical Phenomena", "Instrumentation and Methods for Astrophysics", "Solar and Stellar Astrophysics"],
|
| 39 |
-
"Condensed Matter": ["Disordered Systems and Neural Networks", "Materials Science", "Mesoscale and Nanoscale Physics", "Other Condensed Matter", "Quantum Gases", "Soft Condensed Matter", "Statistical Mechanics", "Strongly Correlated Electrons", "Superconductivity"],
|
| 40 |
-
"General Relativity and Quantum Cosmology": ["None"],
|
| 41 |
-
"High Energy Physics - Experiment": ["None"],
|
| 42 |
-
"High Energy Physics - Lattice": ["None"],
|
| 43 |
-
"High Energy Physics - Phenomenology": ["None"],
|
| 44 |
-
"High Energy Physics - Theory": ["None"],
|
| 45 |
-
"Mathematical Physics": ["None"],
|
| 46 |
-
"Nonlinear Sciences": ["Adaptation and Self-Organizing Systems", "Cellular Automata and Lattice Gases", "Chaotic Dynamics", "Exactly Solvable and Integrable Systems", "Pattern Formation and Solitons"],
|
| 47 |
-
"Nuclear Experiment": ["None"],
|
| 48 |
-
"Nuclear Theory": ["None"],
|
| 49 |
-
"Physics": ["Accelerator Physics", "Applied Physics", "Atmospheric and Oceanic Physics", "Atomic and Molecular Clusters", "Atomic Physics", "Biological Physics", "Chemical Physics", "Classical Physics", "Computational Physics", "Data Analysis, Statistics and Probability", "Fluid Dynamics", "General Physics", "Geophysics", "History and Philosophy of Physics", "Instrumentation and Detectors", "Medical Physics", "Optics", "Physics and Society", "Physics Education", "Plasma Physics", "Popular Physics", "Space Physics"],
|
| 50 |
-
"Quantum Physics": ["None"],
|
| 51 |
-
"Mathematics": ["Algebraic Geometry", "Algebraic Topology", "Analysis of PDEs", "Category Theory", "Classical Analysis and ODEs", "Combinatorics", "Commutative Algebra", "Complex Variables", "Differential Geometry", "Dynamical Systems", "Functional Analysis", "General Mathematics", "General Topology", "Geometric Topology", "Group Theory", "History and Overview", "Information Theory", "K-Theory and Homology", "Logic", "Mathematical Physics", "Metric Geometry", "Number Theory", "Numerical Analysis", "Operator Algebras", "Optimization and Control", "Probability", "Quantum Algebra", "Representation Theory", "Rings and Algebras", "Spectral Theory", "Statistics Theory", "Symplectic Geometry"],
|
| 52 |
-
"Computer Science": ["Artificial Intelligence", "Computation and Language", "Computational Complexity", "Computational Engineering, Finance, and Science", "Computational Geometry", "Computer Science and Game Theory", "Computer Vision and Pattern Recognition", "Computers and Society", "Cryptography and Security", "Data Structures and Algorithms", "Databases", "Digital Libraries", "Discrete Mathematics", "Distributed, Parallel, and Cluster Computing", "Emerging Technologies", "Formal Languages and Automata Theory", "General Literature", "Graphics", "Hardware Architecture", "Human-Computer Interaction", "Information Retrieval", "Information Theory", "Logic in Computer Science", "Machine Learning", "Mathematical Software", "Multiagent Systems", "Multimedia", "Networking and Internet Architecture", "Neural and Evolutionary Computing", "Numerical Analysis", "Operating Systems", "Other Computer Science", "Performance", "Programming Languages", "Robotics", "Social and Information Networks", "Software Engineering", "Sound", "Symbolic Computation", "Systems and Control"],
|
| 53 |
-
"Quantitative Biology": ["Biomolecules", "Cell Behavior", "Genomics", "Molecular Networks", "Neurons and Cognition", "Other Quantitative Biology", "Populations and Evolution", "Quantitative Methods", "Subcellular Processes", "Tissues and Organs"],
|
| 54 |
-
"Quantitative Finance": ["Computational Finance", "Economics", "General Finance", "Mathematical Finance", "Portfolio Management", "Pricing of Securities", "Risk Management", "Statistical Finance", "Trading and Market Microstructure"],
|
| 55 |
-
"Statistics": ["Applications", "Computation", "Machine Learning", "Methodology", "Other Statistics", "Statistics Theory"],
|
| 56 |
-
"Electrical Engineering and Systems Science": ["Audio and Speech Processing", "Image and Video Processing", "Signal Processing", "Systems and Control"],
|
| 57 |
-
"Economics": ["Econometrics", "General Economics", "Theoretical Economics"]
|
| 58 |
-
}
|
| 59 |
-
|
| 60 |
-
|
| 61 |
-
def sample(email, topic, physics_topic, categories, interest):
|
| 62 |
-
if not topic:
|
| 63 |
-
raise gr.Error("You must choose a topic.")
|
| 64 |
-
if topic == "Physics":
|
| 65 |
-
if isinstance(physics_topic, list):
|
| 66 |
-
raise gr.Error("You must choose a physics topic.")
|
| 67 |
-
topic = physics_topic
|
| 68 |
-
abbr = physics_topics[topic]
|
| 69 |
-
else:
|
| 70 |
-
abbr = topics[topic]
|
| 71 |
-
if categories:
|
| 72 |
-
papers = get_papers(abbr)
|
| 73 |
-
papers = [
|
| 74 |
-
t for t in papers
|
| 75 |
-
if bool(set(process_subject_fields(t['subjects'])) & set(categories))][:4]
|
| 76 |
-
else:
|
| 77 |
-
papers = get_papers(abbr, limit=4)
|
| 78 |
-
if interest:
|
| 79 |
-
if not openai.api_key: raise gr.Error("Set your OpenAI api key on the left first")
|
| 80 |
-
relevancy, _ = generate_relevance_score(
|
| 81 |
-
papers,
|
| 82 |
-
query={"interest": interest},
|
| 83 |
-
threshold_score=0,
|
| 84 |
-
num_paper_in_prompt=4)
|
| 85 |
-
return "\n\n".join([paper["summarized_text"] for paper in relevancy])
|
| 86 |
-
else:
|
| 87 |
-
return "\n\n".join(f"Title: {paper['title']}\nAuthors: {paper['authors']}" for paper in papers)
|
| 88 |
-
|
| 89 |
-
|
| 90 |
-
def change_subsubject(subject, physics_subject):
|
| 91 |
-
if subject != "Physics":
|
| 92 |
-
return gr.Dropdown.update(choices=categories_map[subject], value=[], visible=True)
|
| 93 |
-
else:
|
| 94 |
-
if physics_subject and not isinstance(physics_subject, list):
|
| 95 |
-
return gr.Dropdown.update(choices=categories_map[physics_subject], value=[], visible=True)
|
| 96 |
-
else:
|
| 97 |
-
return gr.Dropdown.update(choices=[], value=[], visible=False)
|
| 98 |
-
|
| 99 |
-
|
| 100 |
-
def change_physics(subject):
|
| 101 |
-
if subject != "Physics":
|
| 102 |
-
return gr.Dropdown.update(visible=False, value=[])
|
| 103 |
-
else:
|
| 104 |
-
return gr.Dropdown.update(physics_topics, visible=True)
|
| 105 |
-
|
| 106 |
-
|
| 107 |
-
def test(email, topic, physics_topic, categories, interest, key):
|
| 108 |
-
if not email: raise gr.Error("Set your email")
|
| 109 |
-
if not key: raise gr.Error("Set your SendGrid key")
|
| 110 |
-
if topic == "Physics":
|
| 111 |
-
if isinstance(physics_topic, list):
|
| 112 |
-
raise gr.Error("You must choose a physics topic.")
|
| 113 |
-
topic = physics_topic
|
| 114 |
-
abbr = physics_topics[topic]
|
| 115 |
-
else:
|
| 116 |
-
abbr = topics[topic]
|
| 117 |
-
if categories:
|
| 118 |
-
papers = get_papers(abbr)
|
| 119 |
-
papers = [
|
| 120 |
-
t for t in papers
|
| 121 |
-
if bool(set(process_subject_fields(t['subjects'])) & set(categories))][:4]
|
| 122 |
-
else:
|
| 123 |
-
papers = get_papers(abbr, limit=4)
|
| 124 |
-
if interest:
|
| 125 |
-
if not openai.api_key: raise gr.Error("Set your OpenAI api key on the left first")
|
| 126 |
-
relevancy, hallucination = generate_relevance_score(
|
| 127 |
-
papers,
|
| 128 |
-
query={"interest": interest},
|
| 129 |
-
threshold_score=7,
|
| 130 |
-
num_paper_in_prompt=8)
|
| 131 |
-
body = "<br><br>".join([f'Title: <a href="{paper["main_page"]}">{paper["title"]}</a><br>Authors: {paper["authors"]}<br>Score: {paper["Relevancy score"]}<br>Reason: {paper["Reasons for match"]}' for paper in relevancy])
|
| 132 |
-
if hallucination:
|
| 133 |
-
body = "Warning: the model hallucinated some papers. We have tried to remove them, but the scores may not be accurate.<br><br>" + body
|
| 134 |
-
else:
|
| 135 |
-
body = "<br><br>".join([f'Title: <a href="{paper["main_page"]}">{paper["title"]}</a><br>Authors: {paper["authors"]}' for paper in papers])
|
| 136 |
-
sg = sendgrid.SendGridAPIClient(api_key=key)
|
| 137 |
-
from_email = Email(email)
|
| 138 |
-
to_email = To(email)
|
| 139 |
-
subject = "arXiv digest"
|
| 140 |
-
content = Content("text/html", body)
|
| 141 |
-
mail = Mail(from_email, to_email, subject, content)
|
| 142 |
-
mail_json = mail.get()
|
| 143 |
-
|
| 144 |
-
# Send an HTTP POST request to /mail/send
|
| 145 |
-
response = sg.client.mail.send.post(request_body=mail_json)
|
| 146 |
-
if response.status_code >= 200 and response.status_code <= 300:
|
| 147 |
-
return "Success!"
|
| 148 |
-
else:
|
| 149 |
-
return "Failure: ({response.status_code})"
|
| 150 |
-
|
| 151 |
-
|
| 152 |
-
def register_openai_token(token):
|
| 153 |
-
openai.api_key = token
|
| 154 |
-
|
| 155 |
-
with gr.Blocks() as demo:
|
| 156 |
-
with gr.Row():
|
| 157 |
-
with gr.Column(scale=1):
|
| 158 |
-
token = gr.Textbox(label="OpenAI API Key", type="password")
|
| 159 |
-
subject = gr.Radio(
|
| 160 |
-
list(topics.keys()), label="Topic"
|
| 161 |
-
)
|
| 162 |
-
physics_subject = gr.Dropdown(physics_topics, value=[], multiselect=False, label="Physics category", visible=False, info="")
|
| 163 |
-
subsubject = gr.Dropdown(
|
| 164 |
-
[], value=[], multiselect=True, label="Subtopic", info="Optional. Leaving it empty will use all subtopics.", visible=False)
|
| 165 |
-
subject.change(fn=change_physics, inputs=[subject], outputs=physics_subject)
|
| 166 |
-
subject.change(fn=change_subsubject, inputs=[subject, physics_subject], outputs=subsubject)
|
| 167 |
-
physics_subject.change(fn=change_subsubject, inputs=[subject, physics_subject], outputs=subsubject)
|
| 168 |
-
|
| 169 |
-
interest = gr.Textbox(label="A natural language description of what you are interested in. We will generate relevancy scores (1-10) and explanations for the papers in the selected topics according to this statement.", info="Press shift-enter or click the button below to update.", lines=7)
|
| 170 |
-
sample_btn = gr.Button("Generate Digest")
|
| 171 |
-
sample_output = gr.Textbox(label="Sample relevancy results for your configuration.", info="For runtime purposes, this is only done on a small subset of recent papers in the topic you have selected. Papers will not be filtered by relevancy, only sorted on a scale of 1-10. Selecting more relevant subtopics will help return more relevant results.")
|
| 172 |
-
with gr.Column(scale=0.40):
|
| 173 |
-
with gr.Box():
|
| 174 |
-
title = gr.Markdown(
|
| 175 |
-
"""
|
| 176 |
-
# Email Setup, Optional
|
| 177 |
-
Send an email to the below address using the configuration on the left. Requires a sendgrid token. These values are not needed to use the left side of the page.
|
| 178 |
-
Additionally, this email will use the entire list of papers for a topic, rather than a small subset. Generating the email can take on the order of 10 minutes for large topics.
|
| 179 |
-
|
| 180 |
-
To create a scheduled job for this, see our [Github Repository](https://github.com/AutoLLM/ArxivDigest)
|
| 181 |
-
""",
|
| 182 |
-
interactive=False, show_label=False)
|
| 183 |
-
email = gr.Textbox(label="Email address", type="email", placeholder="")
|
| 184 |
-
sendgrid_token = gr.Textbox(label="SendGrid API Key", type="password")
|
| 185 |
-
with gr.Row():
|
| 186 |
-
test_btn = gr.Button("Send email")
|
| 187 |
-
output = gr.Textbox(show_label=False, placeholder="email status")
|
| 188 |
-
test_btn.click(fn=test, inputs=[email, subject, physics_subject, subsubject, interest, sendgrid_token], outputs=output)
|
| 189 |
-
token.change(fn=register_openai_token, inputs=[token])
|
| 190 |
-
sample_btn.click(fn=sample, inputs=[email, subject, physics_subject, subsubject, interest], outputs=sample_output)
|
| 191 |
-
subject.change(fn=sample, inputs=[email, subject, physics_subject, subsubject, interest], outputs=sample_output)
|
| 192 |
-
physics_subject.change(fn=sample, inputs=[email, subject, physics_subject, subsubject, interest], outputs=sample_output)
|
| 193 |
-
subsubject.change(fn=sample, inputs=[email, subject, physics_subject, subsubject, interest], outputs=sample_output)
|
| 194 |
-
interest.submit(fn=sample, inputs=[email, subject, physics_subject, subsubject, interest], outputs=sample_output)
|
| 195 |
-
|
| 196 |
-
demo.launch(show_api=False)
|
|
|
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|
spaces/AutoLLM/AutoAgents/.github/README.md
DELETED
|
@@ -1 +0,0 @@
|
|
| 1 |
-
../README-main.md
|
|
|
|
|
|
spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/lvis_v0_5_categories.py
DELETED
|
The diff for this file is too large to render.
See raw diff
|
|
|
spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/engine/launch.py
DELETED
|
@@ -1,126 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates.
|
| 2 |
-
import logging
|
| 3 |
-
from datetime import timedelta
|
| 4 |
-
import torch
|
| 5 |
-
import torch.distributed as dist
|
| 6 |
-
import torch.multiprocessing as mp
|
| 7 |
-
|
| 8 |
-
from detectron2.utils import comm
|
| 9 |
-
|
| 10 |
-
__all__ = ["DEFAULT_TIMEOUT", "launch"]
|
| 11 |
-
|
| 12 |
-
DEFAULT_TIMEOUT = timedelta(minutes=30)
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
def _find_free_port():
|
| 16 |
-
import socket
|
| 17 |
-
|
| 18 |
-
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
| 19 |
-
# Binding to port 0 will cause the OS to find an available port for us
|
| 20 |
-
sock.bind(("", 0))
|
| 21 |
-
port = sock.getsockname()[1]
|
| 22 |
-
sock.close()
|
| 23 |
-
# NOTE: there is still a chance the port could be taken by other processes.
|
| 24 |
-
return port
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
def launch(
|
| 28 |
-
main_func,
|
| 29 |
-
num_gpus_per_machine,
|
| 30 |
-
num_machines=1,
|
| 31 |
-
machine_rank=0,
|
| 32 |
-
dist_url=None,
|
| 33 |
-
args=(),
|
| 34 |
-
timeout=DEFAULT_TIMEOUT,
|
| 35 |
-
):
|
| 36 |
-
"""
|
| 37 |
-
Launch multi-gpu or distributed training.
|
| 38 |
-
This function must be called on all machines involved in the training.
|
| 39 |
-
It will spawn child processes (defined by ``num_gpus_per_machine``) on each machine.
|
| 40 |
-
|
| 41 |
-
Args:
|
| 42 |
-
main_func: a function that will be called by `main_func(*args)`
|
| 43 |
-
num_gpus_per_machine (int): number of GPUs per machine
|
| 44 |
-
num_machines (int): the total number of machines
|
| 45 |
-
machine_rank (int): the rank of this machine
|
| 46 |
-
dist_url (str): url to connect to for distributed jobs, including protocol
|
| 47 |
-
e.g. "tcp://127.0.0.1:8686".
|
| 48 |
-
Can be set to "auto" to automatically select a free port on localhost
|
| 49 |
-
timeout (timedelta): timeout of the distributed workers
|
| 50 |
-
args (tuple): arguments passed to main_func
|
| 51 |
-
"""
|
| 52 |
-
world_size = num_machines * num_gpus_per_machine
|
| 53 |
-
if world_size > 1:
|
| 54 |
-
# https://github.com/pytorch/pytorch/pull/14391
|
| 55 |
-
# TODO prctl in spawned processes
|
| 56 |
-
|
| 57 |
-
if dist_url == "auto":
|
| 58 |
-
assert num_machines == 1, "dist_url=auto not supported in multi-machine jobs."
|
| 59 |
-
port = _find_free_port()
|
| 60 |
-
dist_url = f"tcp://127.0.0.1:{port}"
|
| 61 |
-
if num_machines > 1 and dist_url.startswith("file://"):
|
| 62 |
-
logger = logging.getLogger(__name__)
|
| 63 |
-
logger.warning(
|
| 64 |
-
"file:// is not a reliable init_method in multi-machine jobs. Prefer tcp://"
|
| 65 |
-
)
|
| 66 |
-
|
| 67 |
-
mp.spawn(
|
| 68 |
-
_distributed_worker,
|
| 69 |
-
nprocs=num_gpus_per_machine,
|
| 70 |
-
args=(
|
| 71 |
-
main_func,
|
| 72 |
-
world_size,
|
| 73 |
-
num_gpus_per_machine,
|
| 74 |
-
machine_rank,
|
| 75 |
-
dist_url,
|
| 76 |
-
args,
|
| 77 |
-
timeout,
|
| 78 |
-
),
|
| 79 |
-
daemon=False,
|
| 80 |
-
)
|
| 81 |
-
else:
|
| 82 |
-
main_func(*args)
|
| 83 |
-
|
| 84 |
-
|
| 85 |
-
def _distributed_worker(
|
| 86 |
-
local_rank,
|
| 87 |
-
main_func,
|
| 88 |
-
world_size,
|
| 89 |
-
num_gpus_per_machine,
|
| 90 |
-
machine_rank,
|
| 91 |
-
dist_url,
|
| 92 |
-
args,
|
| 93 |
-
timeout=DEFAULT_TIMEOUT,
|
| 94 |
-
):
|
| 95 |
-
assert torch.cuda.is_available(), "cuda is not available. Please check your installation."
|
| 96 |
-
global_rank = machine_rank * num_gpus_per_machine + local_rank
|
| 97 |
-
try:
|
| 98 |
-
dist.init_process_group(
|
| 99 |
-
backend="NCCL",
|
| 100 |
-
init_method=dist_url,
|
| 101 |
-
world_size=world_size,
|
| 102 |
-
rank=global_rank,
|
| 103 |
-
timeout=timeout,
|
| 104 |
-
)
|
| 105 |
-
except Exception as e:
|
| 106 |
-
logger = logging.getLogger(__name__)
|
| 107 |
-
logger.error("Process group URL: {}".format(dist_url))
|
| 108 |
-
raise e
|
| 109 |
-
|
| 110 |
-
# Setup the local process group (which contains ranks within the same machine)
|
| 111 |
-
assert comm._LOCAL_PROCESS_GROUP is None
|
| 112 |
-
num_machines = world_size // num_gpus_per_machine
|
| 113 |
-
for i in range(num_machines):
|
| 114 |
-
ranks_on_i = list(range(i * num_gpus_per_machine, (i + 1) * num_gpus_per_machine))
|
| 115 |
-
pg = dist.new_group(ranks_on_i)
|
| 116 |
-
if i == machine_rank:
|
| 117 |
-
comm._LOCAL_PROCESS_GROUP = pg
|
| 118 |
-
|
| 119 |
-
assert num_gpus_per_machine <= torch.cuda.device_count()
|
| 120 |
-
torch.cuda.set_device(local_rank)
|
| 121 |
-
|
| 122 |
-
# synchronize is needed here to prevent a possible timeout after calling init_process_group
|
| 123 |
-
# See: https://github.com/facebookresearch/maskrcnn-benchmark/issues/172
|
| 124 |
-
comm.synchronize()
|
| 125 |
-
|
| 126 |
-
main_func(*args)
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spaces/Azai8915/ChubVenusTest/Dockerfile
DELETED
|
@@ -1,21 +0,0 @@
|
|
| 1 |
-
FROM node:18-bullseye-slim
|
| 2 |
-
|
| 3 |
-
RUN apt-get update && \
|
| 4 |
-
|
| 5 |
-
apt-get install -y git
|
| 6 |
-
|
| 7 |
-
RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
|
| 8 |
-
|
| 9 |
-
WORKDIR /app
|
| 10 |
-
|
| 11 |
-
RUN npm install
|
| 12 |
-
|
| 13 |
-
COPY Dockerfile greeting.md* .env* ./
|
| 14 |
-
|
| 15 |
-
RUN npm run build
|
| 16 |
-
|
| 17 |
-
EXPOSE 7860
|
| 18 |
-
|
| 19 |
-
ENV NODE_ENV=production
|
| 20 |
-
|
| 21 |
-
CMD [ "npm", "start" ]
|
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|
spaces/BartPoint/VoiceChange/infer_pack/modules/F0Predictor/PMF0Predictor.py
DELETED
|
@@ -1,97 +0,0 @@
|
|
| 1 |
-
from infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
|
| 2 |
-
import parselmouth
|
| 3 |
-
import numpy as np
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
class PMF0Predictor(F0Predictor):
|
| 7 |
-
def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
|
| 8 |
-
self.hop_length = hop_length
|
| 9 |
-
self.f0_min = f0_min
|
| 10 |
-
self.f0_max = f0_max
|
| 11 |
-
self.sampling_rate = sampling_rate
|
| 12 |
-
|
| 13 |
-
def interpolate_f0(self, f0):
|
| 14 |
-
"""
|
| 15 |
-
对F0进行插值处理
|
| 16 |
-
"""
|
| 17 |
-
|
| 18 |
-
data = np.reshape(f0, (f0.size, 1))
|
| 19 |
-
|
| 20 |
-
vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
|
| 21 |
-
vuv_vector[data > 0.0] = 1.0
|
| 22 |
-
vuv_vector[data <= 0.0] = 0.0
|
| 23 |
-
|
| 24 |
-
ip_data = data
|
| 25 |
-
|
| 26 |
-
frame_number = data.size
|
| 27 |
-
last_value = 0.0
|
| 28 |
-
for i in range(frame_number):
|
| 29 |
-
if data[i] <= 0.0:
|
| 30 |
-
j = i + 1
|
| 31 |
-
for j in range(i + 1, frame_number):
|
| 32 |
-
if data[j] > 0.0:
|
| 33 |
-
break
|
| 34 |
-
if j < frame_number - 1:
|
| 35 |
-
if last_value > 0.0:
|
| 36 |
-
step = (data[j] - data[i - 1]) / float(j - i)
|
| 37 |
-
for k in range(i, j):
|
| 38 |
-
ip_data[k] = data[i - 1] + step * (k - i + 1)
|
| 39 |
-
else:
|
| 40 |
-
for k in range(i, j):
|
| 41 |
-
ip_data[k] = data[j]
|
| 42 |
-
else:
|
| 43 |
-
for k in range(i, frame_number):
|
| 44 |
-
ip_data[k] = last_value
|
| 45 |
-
else:
|
| 46 |
-
ip_data[i] = data[i] # 这里可能存在一个没有必要的拷贝
|
| 47 |
-
last_value = data[i]
|
| 48 |
-
|
| 49 |
-
return ip_data[:, 0], vuv_vector[:, 0]
|
| 50 |
-
|
| 51 |
-
def compute_f0(self, wav, p_len=None):
|
| 52 |
-
x = wav
|
| 53 |
-
if p_len is None:
|
| 54 |
-
p_len = x.shape[0] // self.hop_length
|
| 55 |
-
else:
|
| 56 |
-
assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
|
| 57 |
-
time_step = self.hop_length / self.sampling_rate * 1000
|
| 58 |
-
f0 = (
|
| 59 |
-
parselmouth.Sound(x, self.sampling_rate)
|
| 60 |
-
.to_pitch_ac(
|
| 61 |
-
time_step=time_step / 1000,
|
| 62 |
-
voicing_threshold=0.6,
|
| 63 |
-
pitch_floor=self.f0_min,
|
| 64 |
-
pitch_ceiling=self.f0_max,
|
| 65 |
-
)
|
| 66 |
-
.selected_array["frequency"]
|
| 67 |
-
)
|
| 68 |
-
|
| 69 |
-
pad_size = (p_len - len(f0) + 1) // 2
|
| 70 |
-
if pad_size > 0 or p_len - len(f0) - pad_size > 0:
|
| 71 |
-
f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
|
| 72 |
-
f0, uv = self.interpolate_f0(f0)
|
| 73 |
-
return f0
|
| 74 |
-
|
| 75 |
-
def compute_f0_uv(self, wav, p_len=None):
|
| 76 |
-
x = wav
|
| 77 |
-
if p_len is None:
|
| 78 |
-
p_len = x.shape[0] // self.hop_length
|
| 79 |
-
else:
|
| 80 |
-
assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
|
| 81 |
-
time_step = self.hop_length / self.sampling_rate * 1000
|
| 82 |
-
f0 = (
|
| 83 |
-
parselmouth.Sound(x, self.sampling_rate)
|
| 84 |
-
.to_pitch_ac(
|
| 85 |
-
time_step=time_step / 1000,
|
| 86 |
-
voicing_threshold=0.6,
|
| 87 |
-
pitch_floor=self.f0_min,
|
| 88 |
-
pitch_ceiling=self.f0_max,
|
| 89 |
-
)
|
| 90 |
-
.selected_array["frequency"]
|
| 91 |
-
)
|
| 92 |
-
|
| 93 |
-
pad_size = (p_len - len(f0) + 1) // 2
|
| 94 |
-
if pad_size > 0 or p_len - len(f0) - pad_size > 0:
|
| 95 |
-
f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
|
| 96 |
-
f0, uv = self.interpolate_f0(f0)
|
| 97 |
-
return f0, uv
|
|
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|
spaces/Beasto/Image_Colorizer_Pix2Pix/app.py
DELETED
|
@@ -1,35 +0,0 @@
|
|
| 1 |
-
import streamlit as st
|
| 2 |
-
import tensorflow as tf
|
| 3 |
-
import numpy as np
|
| 4 |
-
from PIL import Image
|
| 5 |
-
import tensorflow_addons as tfa
|
| 6 |
-
import cv2
|
| 7 |
-
import tensorflow as tf
|
| 8 |
-
from tensorflow.keras.utils import custom_object_scope
|
| 9 |
-
|
| 10 |
-
# Define a function to create the InstanceNormalization layer
|
| 11 |
-
def create_in():
|
| 12 |
-
return tfa.layers.InstanceNormalization()
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
def model_out(model_path,img):
|
| 16 |
-
with custom_object_scope({'InstanceNormalization': create_in}):
|
| 17 |
-
model = tf.keras.models.load_model(model_path)
|
| 18 |
-
img = (img-127.5)/127.5
|
| 19 |
-
img = np.expand_dims(img, 0)
|
| 20 |
-
pred = model.predict(img)
|
| 21 |
-
pred = np.asarray(pred)
|
| 22 |
-
return pred[0]
|
| 23 |
-
|
| 24 |
-
st.title("GrayScale to Colorized Image Pix2Pix")
|
| 25 |
-
day_inp = st.file_uploader("Grayscale image input")
|
| 26 |
-
|
| 27 |
-
if day_inp is not None:
|
| 28 |
-
file_bytes = day_inp.read()
|
| 29 |
-
img = cv2.imdecode(np.frombuffer(file_bytes, np.uint8), cv2.IMREAD_GRAYSCALE)
|
| 30 |
-
img = cv2.resize(img, (256, 256))
|
| 31 |
-
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
|
| 32 |
-
img = np.array(img)
|
| 33 |
-
pred = model_out('colorizer.h5', img)
|
| 34 |
-
st.image(img, caption="Uploaded Image")
|
| 35 |
-
st.image(((pred + 1) * 127.5).astype(np.uint8), caption="Generated Colorized Painting")
|
|
|
|
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|
spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/tz/tz.py
DELETED
|
@@ -1,1849 +0,0 @@
|
|
| 1 |
-
# -*- coding: utf-8 -*-
|
| 2 |
-
"""
|
| 3 |
-
This module offers timezone implementations subclassing the abstract
|
| 4 |
-
:py:class:`datetime.tzinfo` type. There are classes to handle tzfile format
|
| 5 |
-
files (usually are in :file:`/etc/localtime`, :file:`/usr/share/zoneinfo`,
|
| 6 |
-
etc), TZ environment string (in all known formats), given ranges (with help
|
| 7 |
-
from relative deltas), local machine timezone, fixed offset timezone, and UTC
|
| 8 |
-
timezone.
|
| 9 |
-
"""
|
| 10 |
-
import datetime
|
| 11 |
-
import struct
|
| 12 |
-
import time
|
| 13 |
-
import sys
|
| 14 |
-
import os
|
| 15 |
-
import bisect
|
| 16 |
-
import weakref
|
| 17 |
-
from collections import OrderedDict
|
| 18 |
-
|
| 19 |
-
import six
|
| 20 |
-
from six import string_types
|
| 21 |
-
from six.moves import _thread
|
| 22 |
-
from ._common import tzname_in_python2, _tzinfo
|
| 23 |
-
from ._common import tzrangebase, enfold
|
| 24 |
-
from ._common import _validate_fromutc_inputs
|
| 25 |
-
|
| 26 |
-
from ._factories import _TzSingleton, _TzOffsetFactory
|
| 27 |
-
from ._factories import _TzStrFactory
|
| 28 |
-
try:
|
| 29 |
-
from .win import tzwin, tzwinlocal
|
| 30 |
-
except ImportError:
|
| 31 |
-
tzwin = tzwinlocal = None
|
| 32 |
-
|
| 33 |
-
# For warning about rounding tzinfo
|
| 34 |
-
from warnings import warn
|
| 35 |
-
|
| 36 |
-
ZERO = datetime.timedelta(0)
|
| 37 |
-
EPOCH = datetime.datetime.utcfromtimestamp(0)
|
| 38 |
-
EPOCHORDINAL = EPOCH.toordinal()
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
@six.add_metaclass(_TzSingleton)
|
| 42 |
-
class tzutc(datetime.tzinfo):
|
| 43 |
-
"""
|
| 44 |
-
This is a tzinfo object that represents the UTC time zone.
|
| 45 |
-
|
| 46 |
-
**Examples:**
|
| 47 |
-
|
| 48 |
-
.. doctest::
|
| 49 |
-
|
| 50 |
-
>>> from datetime import *
|
| 51 |
-
>>> from dateutil.tz import *
|
| 52 |
-
|
| 53 |
-
>>> datetime.now()
|
| 54 |
-
datetime.datetime(2003, 9, 27, 9, 40, 1, 521290)
|
| 55 |
-
|
| 56 |
-
>>> datetime.now(tzutc())
|
| 57 |
-
datetime.datetime(2003, 9, 27, 12, 40, 12, 156379, tzinfo=tzutc())
|
| 58 |
-
|
| 59 |
-
>>> datetime.now(tzutc()).tzname()
|
| 60 |
-
'UTC'
|
| 61 |
-
|
| 62 |
-
.. versionchanged:: 2.7.0
|
| 63 |
-
``tzutc()`` is now a singleton, so the result of ``tzutc()`` will
|
| 64 |
-
always return the same object.
|
| 65 |
-
|
| 66 |
-
.. doctest::
|
| 67 |
-
|
| 68 |
-
>>> from dateutil.tz import tzutc, UTC
|
| 69 |
-
>>> tzutc() is tzutc()
|
| 70 |
-
True
|
| 71 |
-
>>> tzutc() is UTC
|
| 72 |
-
True
|
| 73 |
-
"""
|
| 74 |
-
def utcoffset(self, dt):
|
| 75 |
-
return ZERO
|
| 76 |
-
|
| 77 |
-
def dst(self, dt):
|
| 78 |
-
return ZERO
|
| 79 |
-
|
| 80 |
-
@tzname_in_python2
|
| 81 |
-
def tzname(self, dt):
|
| 82 |
-
return "UTC"
|
| 83 |
-
|
| 84 |
-
def is_ambiguous(self, dt):
|
| 85 |
-
"""
|
| 86 |
-
Whether or not the "wall time" of a given datetime is ambiguous in this
|
| 87 |
-
zone.
|
| 88 |
-
|
| 89 |
-
:param dt:
|
| 90 |
-
A :py:class:`datetime.datetime`, naive or time zone aware.
|
| 91 |
-
|
| 92 |
-
|
| 93 |
-
:return:
|
| 94 |
-
Returns ``True`` if ambiguous, ``False`` otherwise.
|
| 95 |
-
|
| 96 |
-
.. versionadded:: 2.6.0
|
| 97 |
-
"""
|
| 98 |
-
return False
|
| 99 |
-
|
| 100 |
-
@_validate_fromutc_inputs
|
| 101 |
-
def fromutc(self, dt):
|
| 102 |
-
"""
|
| 103 |
-
Fast track version of fromutc() returns the original ``dt`` object for
|
| 104 |
-
any valid :py:class:`datetime.datetime` object.
|
| 105 |
-
"""
|
| 106 |
-
return dt
|
| 107 |
-
|
| 108 |
-
def __eq__(self, other):
|
| 109 |
-
if not isinstance(other, (tzutc, tzoffset)):
|
| 110 |
-
return NotImplemented
|
| 111 |
-
|
| 112 |
-
return (isinstance(other, tzutc) or
|
| 113 |
-
(isinstance(other, tzoffset) and other._offset == ZERO))
|
| 114 |
-
|
| 115 |
-
__hash__ = None
|
| 116 |
-
|
| 117 |
-
def __ne__(self, other):
|
| 118 |
-
return not (self == other)
|
| 119 |
-
|
| 120 |
-
def __repr__(self):
|
| 121 |
-
return "%s()" % self.__class__.__name__
|
| 122 |
-
|
| 123 |
-
__reduce__ = object.__reduce__
|
| 124 |
-
|
| 125 |
-
|
| 126 |
-
#: Convenience constant providing a :class:`tzutc()` instance
|
| 127 |
-
#:
|
| 128 |
-
#: .. versionadded:: 2.7.0
|
| 129 |
-
UTC = tzutc()
|
| 130 |
-
|
| 131 |
-
|
| 132 |
-
@six.add_metaclass(_TzOffsetFactory)
|
| 133 |
-
class tzoffset(datetime.tzinfo):
|
| 134 |
-
"""
|
| 135 |
-
A simple class for representing a fixed offset from UTC.
|
| 136 |
-
|
| 137 |
-
:param name:
|
| 138 |
-
The timezone name, to be returned when ``tzname()`` is called.
|
| 139 |
-
:param offset:
|
| 140 |
-
The time zone offset in seconds, or (since version 2.6.0, represented
|
| 141 |
-
as a :py:class:`datetime.timedelta` object).
|
| 142 |
-
"""
|
| 143 |
-
def __init__(self, name, offset):
|
| 144 |
-
self._name = name
|
| 145 |
-
|
| 146 |
-
try:
|
| 147 |
-
# Allow a timedelta
|
| 148 |
-
offset = offset.total_seconds()
|
| 149 |
-
except (TypeError, AttributeError):
|
| 150 |
-
pass
|
| 151 |
-
|
| 152 |
-
self._offset = datetime.timedelta(seconds=_get_supported_offset(offset))
|
| 153 |
-
|
| 154 |
-
def utcoffset(self, dt):
|
| 155 |
-
return self._offset
|
| 156 |
-
|
| 157 |
-
def dst(self, dt):
|
| 158 |
-
return ZERO
|
| 159 |
-
|
| 160 |
-
@tzname_in_python2
|
| 161 |
-
def tzname(self, dt):
|
| 162 |
-
return self._name
|
| 163 |
-
|
| 164 |
-
@_validate_fromutc_inputs
|
| 165 |
-
def fromutc(self, dt):
|
| 166 |
-
return dt + self._offset
|
| 167 |
-
|
| 168 |
-
def is_ambiguous(self, dt):
|
| 169 |
-
"""
|
| 170 |
-
Whether or not the "wall time" of a given datetime is ambiguous in this
|
| 171 |
-
zone.
|
| 172 |
-
|
| 173 |
-
:param dt:
|
| 174 |
-
A :py:class:`datetime.datetime`, naive or time zone aware.
|
| 175 |
-
:return:
|
| 176 |
-
Returns ``True`` if ambiguous, ``False`` otherwise.
|
| 177 |
-
|
| 178 |
-
.. versionadded:: 2.6.0
|
| 179 |
-
"""
|
| 180 |
-
return False
|
| 181 |
-
|
| 182 |
-
def __eq__(self, other):
|
| 183 |
-
if not isinstance(other, tzoffset):
|
| 184 |
-
return NotImplemented
|
| 185 |
-
|
| 186 |
-
return self._offset == other._offset
|
| 187 |
-
|
| 188 |
-
__hash__ = None
|
| 189 |
-
|
| 190 |
-
def __ne__(self, other):
|
| 191 |
-
return not (self == other)
|
| 192 |
-
|
| 193 |
-
def __repr__(self):
|
| 194 |
-
return "%s(%s, %s)" % (self.__class__.__name__,
|
| 195 |
-
repr(self._name),
|
| 196 |
-
int(self._offset.total_seconds()))
|
| 197 |
-
|
| 198 |
-
__reduce__ = object.__reduce__
|
| 199 |
-
|
| 200 |
-
|
| 201 |
-
class tzlocal(_tzinfo):
|
| 202 |
-
"""
|
| 203 |
-
A :class:`tzinfo` subclass built around the ``time`` timezone functions.
|
| 204 |
-
"""
|
| 205 |
-
def __init__(self):
|
| 206 |
-
super(tzlocal, self).__init__()
|
| 207 |
-
|
| 208 |
-
self._std_offset = datetime.timedelta(seconds=-time.timezone)
|
| 209 |
-
if time.daylight:
|
| 210 |
-
self._dst_offset = datetime.timedelta(seconds=-time.altzone)
|
| 211 |
-
else:
|
| 212 |
-
self._dst_offset = self._std_offset
|
| 213 |
-
|
| 214 |
-
self._dst_saved = self._dst_offset - self._std_offset
|
| 215 |
-
self._hasdst = bool(self._dst_saved)
|
| 216 |
-
self._tznames = tuple(time.tzname)
|
| 217 |
-
|
| 218 |
-
def utcoffset(self, dt):
|
| 219 |
-
if dt is None and self._hasdst:
|
| 220 |
-
return None
|
| 221 |
-
|
| 222 |
-
if self._isdst(dt):
|
| 223 |
-
return self._dst_offset
|
| 224 |
-
else:
|
| 225 |
-
return self._std_offset
|
| 226 |
-
|
| 227 |
-
def dst(self, dt):
|
| 228 |
-
if dt is None and self._hasdst:
|
| 229 |
-
return None
|
| 230 |
-
|
| 231 |
-
if self._isdst(dt):
|
| 232 |
-
return self._dst_offset - self._std_offset
|
| 233 |
-
else:
|
| 234 |
-
return ZERO
|
| 235 |
-
|
| 236 |
-
@tzname_in_python2
|
| 237 |
-
def tzname(self, dt):
|
| 238 |
-
return self._tznames[self._isdst(dt)]
|
| 239 |
-
|
| 240 |
-
def is_ambiguous(self, dt):
|
| 241 |
-
"""
|
| 242 |
-
Whether or not the "wall time" of a given datetime is ambiguous in this
|
| 243 |
-
zone.
|
| 244 |
-
|
| 245 |
-
:param dt:
|
| 246 |
-
A :py:class:`datetime.datetime`, naive or time zone aware.
|
| 247 |
-
|
| 248 |
-
|
| 249 |
-
:return:
|
| 250 |
-
Returns ``True`` if ambiguous, ``False`` otherwise.
|
| 251 |
-
|
| 252 |
-
.. versionadded:: 2.6.0
|
| 253 |
-
"""
|
| 254 |
-
naive_dst = self._naive_is_dst(dt)
|
| 255 |
-
return (not naive_dst and
|
| 256 |
-
(naive_dst != self._naive_is_dst(dt - self._dst_saved)))
|
| 257 |
-
|
| 258 |
-
def _naive_is_dst(self, dt):
|
| 259 |
-
timestamp = _datetime_to_timestamp(dt)
|
| 260 |
-
return time.localtime(timestamp + time.timezone).tm_isdst
|
| 261 |
-
|
| 262 |
-
def _isdst(self, dt, fold_naive=True):
|
| 263 |
-
# We can't use mktime here. It is unstable when deciding if
|
| 264 |
-
# the hour near to a change is DST or not.
|
| 265 |
-
#
|
| 266 |
-
# timestamp = time.mktime((dt.year, dt.month, dt.day, dt.hour,
|
| 267 |
-
# dt.minute, dt.second, dt.weekday(), 0, -1))
|
| 268 |
-
# return time.localtime(timestamp).tm_isdst
|
| 269 |
-
#
|
| 270 |
-
# The code above yields the following result:
|
| 271 |
-
#
|
| 272 |
-
# >>> import tz, datetime
|
| 273 |
-
# >>> t = tz.tzlocal()
|
| 274 |
-
# >>> datetime.datetime(2003,2,15,23,tzinfo=t).tzname()
|
| 275 |
-
# 'BRDT'
|
| 276 |
-
# >>> datetime.datetime(2003,2,16,0,tzinfo=t).tzname()
|
| 277 |
-
# 'BRST'
|
| 278 |
-
# >>> datetime.datetime(2003,2,15,23,tzinfo=t).tzname()
|
| 279 |
-
# 'BRST'
|
| 280 |
-
# >>> datetime.datetime(2003,2,15,22,tzinfo=t).tzname()
|
| 281 |
-
# 'BRDT'
|
| 282 |
-
# >>> datetime.datetime(2003,2,15,23,tzinfo=t).tzname()
|
| 283 |
-
# 'BRDT'
|
| 284 |
-
#
|
| 285 |
-
# Here is a more stable implementation:
|
| 286 |
-
#
|
| 287 |
-
if not self._hasdst:
|
| 288 |
-
return False
|
| 289 |
-
|
| 290 |
-
# Check for ambiguous times:
|
| 291 |
-
dstval = self._naive_is_dst(dt)
|
| 292 |
-
fold = getattr(dt, 'fold', None)
|
| 293 |
-
|
| 294 |
-
if self.is_ambiguous(dt):
|
| 295 |
-
if fold is not None:
|
| 296 |
-
return not self._fold(dt)
|
| 297 |
-
else:
|
| 298 |
-
return True
|
| 299 |
-
|
| 300 |
-
return dstval
|
| 301 |
-
|
| 302 |
-
def __eq__(self, other):
|
| 303 |
-
if isinstance(other, tzlocal):
|
| 304 |
-
return (self._std_offset == other._std_offset and
|
| 305 |
-
self._dst_offset == other._dst_offset)
|
| 306 |
-
elif isinstance(other, tzutc):
|
| 307 |
-
return (not self._hasdst and
|
| 308 |
-
self._tznames[0] in {'UTC', 'GMT'} and
|
| 309 |
-
self._std_offset == ZERO)
|
| 310 |
-
elif isinstance(other, tzoffset):
|
| 311 |
-
return (not self._hasdst and
|
| 312 |
-
self._tznames[0] == other._name and
|
| 313 |
-
self._std_offset == other._offset)
|
| 314 |
-
else:
|
| 315 |
-
return NotImplemented
|
| 316 |
-
|
| 317 |
-
__hash__ = None
|
| 318 |
-
|
| 319 |
-
def __ne__(self, other):
|
| 320 |
-
return not (self == other)
|
| 321 |
-
|
| 322 |
-
def __repr__(self):
|
| 323 |
-
return "%s()" % self.__class__.__name__
|
| 324 |
-
|
| 325 |
-
__reduce__ = object.__reduce__
|
| 326 |
-
|
| 327 |
-
|
| 328 |
-
class _ttinfo(object):
|
| 329 |
-
__slots__ = ["offset", "delta", "isdst", "abbr",
|
| 330 |
-
"isstd", "isgmt", "dstoffset"]
|
| 331 |
-
|
| 332 |
-
def __init__(self):
|
| 333 |
-
for attr in self.__slots__:
|
| 334 |
-
setattr(self, attr, None)
|
| 335 |
-
|
| 336 |
-
def __repr__(self):
|
| 337 |
-
l = []
|
| 338 |
-
for attr in self.__slots__:
|
| 339 |
-
value = getattr(self, attr)
|
| 340 |
-
if value is not None:
|
| 341 |
-
l.append("%s=%s" % (attr, repr(value)))
|
| 342 |
-
return "%s(%s)" % (self.__class__.__name__, ", ".join(l))
|
| 343 |
-
|
| 344 |
-
def __eq__(self, other):
|
| 345 |
-
if not isinstance(other, _ttinfo):
|
| 346 |
-
return NotImplemented
|
| 347 |
-
|
| 348 |
-
return (self.offset == other.offset and
|
| 349 |
-
self.delta == other.delta and
|
| 350 |
-
self.isdst == other.isdst and
|
| 351 |
-
self.abbr == other.abbr and
|
| 352 |
-
self.isstd == other.isstd and
|
| 353 |
-
self.isgmt == other.isgmt and
|
| 354 |
-
self.dstoffset == other.dstoffset)
|
| 355 |
-
|
| 356 |
-
__hash__ = None
|
| 357 |
-
|
| 358 |
-
def __ne__(self, other):
|
| 359 |
-
return not (self == other)
|
| 360 |
-
|
| 361 |
-
def __getstate__(self):
|
| 362 |
-
state = {}
|
| 363 |
-
for name in self.__slots__:
|
| 364 |
-
state[name] = getattr(self, name, None)
|
| 365 |
-
return state
|
| 366 |
-
|
| 367 |
-
def __setstate__(self, state):
|
| 368 |
-
for name in self.__slots__:
|
| 369 |
-
if name in state:
|
| 370 |
-
setattr(self, name, state[name])
|
| 371 |
-
|
| 372 |
-
|
| 373 |
-
class _tzfile(object):
|
| 374 |
-
"""
|
| 375 |
-
Lightweight class for holding the relevant transition and time zone
|
| 376 |
-
information read from binary tzfiles.
|
| 377 |
-
"""
|
| 378 |
-
attrs = ['trans_list', 'trans_list_utc', 'trans_idx', 'ttinfo_list',
|
| 379 |
-
'ttinfo_std', 'ttinfo_dst', 'ttinfo_before', 'ttinfo_first']
|
| 380 |
-
|
| 381 |
-
def __init__(self, **kwargs):
|
| 382 |
-
for attr in self.attrs:
|
| 383 |
-
setattr(self, attr, kwargs.get(attr, None))
|
| 384 |
-
|
| 385 |
-
|
| 386 |
-
class tzfile(_tzinfo):
|
| 387 |
-
"""
|
| 388 |
-
This is a ``tzinfo`` subclass that allows one to use the ``tzfile(5)``
|
| 389 |
-
format timezone files to extract current and historical zone information.
|
| 390 |
-
|
| 391 |
-
:param fileobj:
|
| 392 |
-
This can be an opened file stream or a file name that the time zone
|
| 393 |
-
information can be read from.
|
| 394 |
-
|
| 395 |
-
:param filename:
|
| 396 |
-
This is an optional parameter specifying the source of the time zone
|
| 397 |
-
information in the event that ``fileobj`` is a file object. If omitted
|
| 398 |
-
and ``fileobj`` is a file stream, this parameter will be set either to
|
| 399 |
-
``fileobj``'s ``name`` attribute or to ``repr(fileobj)``.
|
| 400 |
-
|
| 401 |
-
See `Sources for Time Zone and Daylight Saving Time Data
|
| 402 |
-
<https://data.iana.org/time-zones/tz-link.html>`_ for more information.
|
| 403 |
-
Time zone files can be compiled from the `IANA Time Zone database files
|
| 404 |
-
<https://www.iana.org/time-zones>`_ with the `zic time zone compiler
|
| 405 |
-
<https://www.freebsd.org/cgi/man.cgi?query=zic&sektion=8>`_
|
| 406 |
-
|
| 407 |
-
.. note::
|
| 408 |
-
|
| 409 |
-
Only construct a ``tzfile`` directly if you have a specific timezone
|
| 410 |
-
file on disk that you want to read into a Python ``tzinfo`` object.
|
| 411 |
-
If you want to get a ``tzfile`` representing a specific IANA zone,
|
| 412 |
-
(e.g. ``'America/New_York'``), you should call
|
| 413 |
-
:func:`dateutil.tz.gettz` with the zone identifier.
|
| 414 |
-
|
| 415 |
-
|
| 416 |
-
**Examples:**
|
| 417 |
-
|
| 418 |
-
Using the US Eastern time zone as an example, we can see that a ``tzfile``
|
| 419 |
-
provides time zone information for the standard Daylight Saving offsets:
|
| 420 |
-
|
| 421 |
-
.. testsetup:: tzfile
|
| 422 |
-
|
| 423 |
-
from dateutil.tz import gettz
|
| 424 |
-
from datetime import datetime
|
| 425 |
-
|
| 426 |
-
.. doctest:: tzfile
|
| 427 |
-
|
| 428 |
-
>>> NYC = gettz('America/New_York')
|
| 429 |
-
>>> NYC
|
| 430 |
-
tzfile('/usr/share/zoneinfo/America/New_York')
|
| 431 |
-
|
| 432 |
-
>>> print(datetime(2016, 1, 3, tzinfo=NYC)) # EST
|
| 433 |
-
2016-01-03 00:00:00-05:00
|
| 434 |
-
|
| 435 |
-
>>> print(datetime(2016, 7, 7, tzinfo=NYC)) # EDT
|
| 436 |
-
2016-07-07 00:00:00-04:00
|
| 437 |
-
|
| 438 |
-
|
| 439 |
-
The ``tzfile`` structure contains a fully history of the time zone,
|
| 440 |
-
so historical dates will also have the right offsets. For example, before
|
| 441 |
-
the adoption of the UTC standards, New York used local solar mean time:
|
| 442 |
-
|
| 443 |
-
.. doctest:: tzfile
|
| 444 |
-
|
| 445 |
-
>>> print(datetime(1901, 4, 12, tzinfo=NYC)) # LMT
|
| 446 |
-
1901-04-12 00:00:00-04:56
|
| 447 |
-
|
| 448 |
-
And during World War II, New York was on "Eastern War Time", which was a
|
| 449 |
-
state of permanent daylight saving time:
|
| 450 |
-
|
| 451 |
-
.. doctest:: tzfile
|
| 452 |
-
|
| 453 |
-
>>> print(datetime(1944, 2, 7, tzinfo=NYC)) # EWT
|
| 454 |
-
1944-02-07 00:00:00-04:00
|
| 455 |
-
|
| 456 |
-
"""
|
| 457 |
-
|
| 458 |
-
def __init__(self, fileobj, filename=None):
|
| 459 |
-
super(tzfile, self).__init__()
|
| 460 |
-
|
| 461 |
-
file_opened_here = False
|
| 462 |
-
if isinstance(fileobj, string_types):
|
| 463 |
-
self._filename = fileobj
|
| 464 |
-
fileobj = open(fileobj, 'rb')
|
| 465 |
-
file_opened_here = True
|
| 466 |
-
elif filename is not None:
|
| 467 |
-
self._filename = filename
|
| 468 |
-
elif hasattr(fileobj, "name"):
|
| 469 |
-
self._filename = fileobj.name
|
| 470 |
-
else:
|
| 471 |
-
self._filename = repr(fileobj)
|
| 472 |
-
|
| 473 |
-
if fileobj is not None:
|
| 474 |
-
if not file_opened_here:
|
| 475 |
-
fileobj = _nullcontext(fileobj)
|
| 476 |
-
|
| 477 |
-
with fileobj as file_stream:
|
| 478 |
-
tzobj = self._read_tzfile(file_stream)
|
| 479 |
-
|
| 480 |
-
self._set_tzdata(tzobj)
|
| 481 |
-
|
| 482 |
-
def _set_tzdata(self, tzobj):
|
| 483 |
-
""" Set the time zone data of this object from a _tzfile object """
|
| 484 |
-
# Copy the relevant attributes over as private attributes
|
| 485 |
-
for attr in _tzfile.attrs:
|
| 486 |
-
setattr(self, '_' + attr, getattr(tzobj, attr))
|
| 487 |
-
|
| 488 |
-
def _read_tzfile(self, fileobj):
|
| 489 |
-
out = _tzfile()
|
| 490 |
-
|
| 491 |
-
# From tzfile(5):
|
| 492 |
-
#
|
| 493 |
-
# The time zone information files used by tzset(3)
|
| 494 |
-
# begin with the magic characters "TZif" to identify
|
| 495 |
-
# them as time zone information files, followed by
|
| 496 |
-
# sixteen bytes reserved for future use, followed by
|
| 497 |
-
# six four-byte values of type long, written in a
|
| 498 |
-
# ``standard'' byte order (the high-order byte
|
| 499 |
-
# of the value is written first).
|
| 500 |
-
if fileobj.read(4).decode() != "TZif":
|
| 501 |
-
raise ValueError("magic not found")
|
| 502 |
-
|
| 503 |
-
fileobj.read(16)
|
| 504 |
-
|
| 505 |
-
(
|
| 506 |
-
# The number of UTC/local indicators stored in the file.
|
| 507 |
-
ttisgmtcnt,
|
| 508 |
-
|
| 509 |
-
# The number of standard/wall indicators stored in the file.
|
| 510 |
-
ttisstdcnt,
|
| 511 |
-
|
| 512 |
-
# The number of leap seconds for which data is
|
| 513 |
-
# stored in the file.
|
| 514 |
-
leapcnt,
|
| 515 |
-
|
| 516 |
-
# The number of "transition times" for which data
|
| 517 |
-
# is stored in the file.
|
| 518 |
-
timecnt,
|
| 519 |
-
|
| 520 |
-
# The number of "local time types" for which data
|
| 521 |
-
# is stored in the file (must not be zero).
|
| 522 |
-
typecnt,
|
| 523 |
-
|
| 524 |
-
# The number of characters of "time zone
|
| 525 |
-
# abbreviation strings" stored in the file.
|
| 526 |
-
charcnt,
|
| 527 |
-
|
| 528 |
-
) = struct.unpack(">6l", fileobj.read(24))
|
| 529 |
-
|
| 530 |
-
# The above header is followed by tzh_timecnt four-byte
|
| 531 |
-
# values of type long, sorted in ascending order.
|
| 532 |
-
# These values are written in ``standard'' byte order.
|
| 533 |
-
# Each is used as a transition time (as returned by
|
| 534 |
-
# time(2)) at which the rules for computing local time
|
| 535 |
-
# change.
|
| 536 |
-
|
| 537 |
-
if timecnt:
|
| 538 |
-
out.trans_list_utc = list(struct.unpack(">%dl" % timecnt,
|
| 539 |
-
fileobj.read(timecnt*4)))
|
| 540 |
-
else:
|
| 541 |
-
out.trans_list_utc = []
|
| 542 |
-
|
| 543 |
-
# Next come tzh_timecnt one-byte values of type unsigned
|
| 544 |
-
# char; each one tells which of the different types of
|
| 545 |
-
# ``local time'' types described in the file is associated
|
| 546 |
-
# with the same-indexed transition time. These values
|
| 547 |
-
# serve as indices into an array of ttinfo structures that
|
| 548 |
-
# appears next in the file.
|
| 549 |
-
|
| 550 |
-
if timecnt:
|
| 551 |
-
out.trans_idx = struct.unpack(">%dB" % timecnt,
|
| 552 |
-
fileobj.read(timecnt))
|
| 553 |
-
else:
|
| 554 |
-
out.trans_idx = []
|
| 555 |
-
|
| 556 |
-
# Each ttinfo structure is written as a four-byte value
|
| 557 |
-
# for tt_gmtoff of type long, in a standard byte
|
| 558 |
-
# order, followed by a one-byte value for tt_isdst
|
| 559 |
-
# and a one-byte value for tt_abbrind. In each
|
| 560 |
-
# structure, tt_gmtoff gives the number of
|
| 561 |
-
# seconds to be added to UTC, tt_isdst tells whether
|
| 562 |
-
# tm_isdst should be set by localtime(3), and
|
| 563 |
-
# tt_abbrind serves as an index into the array of
|
| 564 |
-
# time zone abbreviation characters that follow the
|
| 565 |
-
# ttinfo structure(s) in the file.
|
| 566 |
-
|
| 567 |
-
ttinfo = []
|
| 568 |
-
|
| 569 |
-
for i in range(typecnt):
|
| 570 |
-
ttinfo.append(struct.unpack(">lbb", fileobj.read(6)))
|
| 571 |
-
|
| 572 |
-
abbr = fileobj.read(charcnt).decode()
|
| 573 |
-
|
| 574 |
-
# Then there are tzh_leapcnt pairs of four-byte
|
| 575 |
-
# values, written in standard byte order; the
|
| 576 |
-
# first value of each pair gives the time (as
|
| 577 |
-
# returned by time(2)) at which a leap second
|
| 578 |
-
# occurs; the second gives the total number of
|
| 579 |
-
# leap seconds to be applied after the given time.
|
| 580 |
-
# The pairs of values are sorted in ascending order
|
| 581 |
-
# by time.
|
| 582 |
-
|
| 583 |
-
# Not used, for now (but seek for correct file position)
|
| 584 |
-
if leapcnt:
|
| 585 |
-
fileobj.seek(leapcnt * 8, os.SEEK_CUR)
|
| 586 |
-
|
| 587 |
-
# Then there are tzh_ttisstdcnt standard/wall
|
| 588 |
-
# indicators, each stored as a one-byte value;
|
| 589 |
-
# they tell whether the transition times associated
|
| 590 |
-
# with local time types were specified as standard
|
| 591 |
-
# time or wall clock time, and are used when
|
| 592 |
-
# a time zone file is used in handling POSIX-style
|
| 593 |
-
# time zone environment variables.
|
| 594 |
-
|
| 595 |
-
if ttisstdcnt:
|
| 596 |
-
isstd = struct.unpack(">%db" % ttisstdcnt,
|
| 597 |
-
fileobj.read(ttisstdcnt))
|
| 598 |
-
|
| 599 |
-
# Finally, there are tzh_ttisgmtcnt UTC/local
|
| 600 |
-
# indicators, each stored as a one-byte value;
|
| 601 |
-
# they tell whether the transition times associated
|
| 602 |
-
# with local time types were specified as UTC or
|
| 603 |
-
# local time, and are used when a time zone file
|
| 604 |
-
# is used in handling POSIX-style time zone envi-
|
| 605 |
-
# ronment variables.
|
| 606 |
-
|
| 607 |
-
if ttisgmtcnt:
|
| 608 |
-
isgmt = struct.unpack(">%db" % ttisgmtcnt,
|
| 609 |
-
fileobj.read(ttisgmtcnt))
|
| 610 |
-
|
| 611 |
-
# Build ttinfo list
|
| 612 |
-
out.ttinfo_list = []
|
| 613 |
-
for i in range(typecnt):
|
| 614 |
-
gmtoff, isdst, abbrind = ttinfo[i]
|
| 615 |
-
gmtoff = _get_supported_offset(gmtoff)
|
| 616 |
-
tti = _ttinfo()
|
| 617 |
-
tti.offset = gmtoff
|
| 618 |
-
tti.dstoffset = datetime.timedelta(0)
|
| 619 |
-
tti.delta = datetime.timedelta(seconds=gmtoff)
|
| 620 |
-
tti.isdst = isdst
|
| 621 |
-
tti.abbr = abbr[abbrind:abbr.find('\x00', abbrind)]
|
| 622 |
-
tti.isstd = (ttisstdcnt > i and isstd[i] != 0)
|
| 623 |
-
tti.isgmt = (ttisgmtcnt > i and isgmt[i] != 0)
|
| 624 |
-
out.ttinfo_list.append(tti)
|
| 625 |
-
|
| 626 |
-
# Replace ttinfo indexes for ttinfo objects.
|
| 627 |
-
out.trans_idx = [out.ttinfo_list[idx] for idx in out.trans_idx]
|
| 628 |
-
|
| 629 |
-
# Set standard, dst, and before ttinfos. before will be
|
| 630 |
-
# used when a given time is before any transitions,
|
| 631 |
-
# and will be set to the first non-dst ttinfo, or to
|
| 632 |
-
# the first dst, if all of them are dst.
|
| 633 |
-
out.ttinfo_std = None
|
| 634 |
-
out.ttinfo_dst = None
|
| 635 |
-
out.ttinfo_before = None
|
| 636 |
-
if out.ttinfo_list:
|
| 637 |
-
if not out.trans_list_utc:
|
| 638 |
-
out.ttinfo_std = out.ttinfo_first = out.ttinfo_list[0]
|
| 639 |
-
else:
|
| 640 |
-
for i in range(timecnt-1, -1, -1):
|
| 641 |
-
tti = out.trans_idx[i]
|
| 642 |
-
if not out.ttinfo_std and not tti.isdst:
|
| 643 |
-
out.ttinfo_std = tti
|
| 644 |
-
elif not out.ttinfo_dst and tti.isdst:
|
| 645 |
-
out.ttinfo_dst = tti
|
| 646 |
-
|
| 647 |
-
if out.ttinfo_std and out.ttinfo_dst:
|
| 648 |
-
break
|
| 649 |
-
else:
|
| 650 |
-
if out.ttinfo_dst and not out.ttinfo_std:
|
| 651 |
-
out.ttinfo_std = out.ttinfo_dst
|
| 652 |
-
|
| 653 |
-
for tti in out.ttinfo_list:
|
| 654 |
-
if not tti.isdst:
|
| 655 |
-
out.ttinfo_before = tti
|
| 656 |
-
break
|
| 657 |
-
else:
|
| 658 |
-
out.ttinfo_before = out.ttinfo_list[0]
|
| 659 |
-
|
| 660 |
-
# Now fix transition times to become relative to wall time.
|
| 661 |
-
#
|
| 662 |
-
# I'm not sure about this. In my tests, the tz source file
|
| 663 |
-
# is setup to wall time, and in the binary file isstd and
|
| 664 |
-
# isgmt are off, so it should be in wall time. OTOH, it's
|
| 665 |
-
# always in gmt time. Let me know if you have comments
|
| 666 |
-
# about this.
|
| 667 |
-
lastdst = None
|
| 668 |
-
lastoffset = None
|
| 669 |
-
lastdstoffset = None
|
| 670 |
-
lastbaseoffset = None
|
| 671 |
-
out.trans_list = []
|
| 672 |
-
|
| 673 |
-
for i, tti in enumerate(out.trans_idx):
|
| 674 |
-
offset = tti.offset
|
| 675 |
-
dstoffset = 0
|
| 676 |
-
|
| 677 |
-
if lastdst is not None:
|
| 678 |
-
if tti.isdst:
|
| 679 |
-
if not lastdst:
|
| 680 |
-
dstoffset = offset - lastoffset
|
| 681 |
-
|
| 682 |
-
if not dstoffset and lastdstoffset:
|
| 683 |
-
dstoffset = lastdstoffset
|
| 684 |
-
|
| 685 |
-
tti.dstoffset = datetime.timedelta(seconds=dstoffset)
|
| 686 |
-
lastdstoffset = dstoffset
|
| 687 |
-
|
| 688 |
-
# If a time zone changes its base offset during a DST transition,
|
| 689 |
-
# then you need to adjust by the previous base offset to get the
|
| 690 |
-
# transition time in local time. Otherwise you use the current
|
| 691 |
-
# base offset. Ideally, I would have some mathematical proof of
|
| 692 |
-
# why this is true, but I haven't really thought about it enough.
|
| 693 |
-
baseoffset = offset - dstoffset
|
| 694 |
-
adjustment = baseoffset
|
| 695 |
-
if (lastbaseoffset is not None and baseoffset != lastbaseoffset
|
| 696 |
-
and tti.isdst != lastdst):
|
| 697 |
-
# The base DST has changed
|
| 698 |
-
adjustment = lastbaseoffset
|
| 699 |
-
|
| 700 |
-
lastdst = tti.isdst
|
| 701 |
-
lastoffset = offset
|
| 702 |
-
lastbaseoffset = baseoffset
|
| 703 |
-
|
| 704 |
-
out.trans_list.append(out.trans_list_utc[i] + adjustment)
|
| 705 |
-
|
| 706 |
-
out.trans_idx = tuple(out.trans_idx)
|
| 707 |
-
out.trans_list = tuple(out.trans_list)
|
| 708 |
-
out.trans_list_utc = tuple(out.trans_list_utc)
|
| 709 |
-
|
| 710 |
-
return out
|
| 711 |
-
|
| 712 |
-
def _find_last_transition(self, dt, in_utc=False):
|
| 713 |
-
# If there's no list, there are no transitions to find
|
| 714 |
-
if not self._trans_list:
|
| 715 |
-
return None
|
| 716 |
-
|
| 717 |
-
timestamp = _datetime_to_timestamp(dt)
|
| 718 |
-
|
| 719 |
-
# Find where the timestamp fits in the transition list - if the
|
| 720 |
-
# timestamp is a transition time, it's part of the "after" period.
|
| 721 |
-
trans_list = self._trans_list_utc if in_utc else self._trans_list
|
| 722 |
-
idx = bisect.bisect_right(trans_list, timestamp)
|
| 723 |
-
|
| 724 |
-
# We want to know when the previous transition was, so subtract off 1
|
| 725 |
-
return idx - 1
|
| 726 |
-
|
| 727 |
-
def _get_ttinfo(self, idx):
|
| 728 |
-
# For no list or after the last transition, default to _ttinfo_std
|
| 729 |
-
if idx is None or (idx + 1) >= len(self._trans_list):
|
| 730 |
-
return self._ttinfo_std
|
| 731 |
-
|
| 732 |
-
# If there is a list and the time is before it, return _ttinfo_before
|
| 733 |
-
if idx < 0:
|
| 734 |
-
return self._ttinfo_before
|
| 735 |
-
|
| 736 |
-
return self._trans_idx[idx]
|
| 737 |
-
|
| 738 |
-
def _find_ttinfo(self, dt):
|
| 739 |
-
idx = self._resolve_ambiguous_time(dt)
|
| 740 |
-
|
| 741 |
-
return self._get_ttinfo(idx)
|
| 742 |
-
|
| 743 |
-
def fromutc(self, dt):
|
| 744 |
-
"""
|
| 745 |
-
The ``tzfile`` implementation of :py:func:`datetime.tzinfo.fromutc`.
|
| 746 |
-
|
| 747 |
-
:param dt:
|
| 748 |
-
A :py:class:`datetime.datetime` object.
|
| 749 |
-
|
| 750 |
-
:raises TypeError:
|
| 751 |
-
Raised if ``dt`` is not a :py:class:`datetime.datetime` object.
|
| 752 |
-
|
| 753 |
-
:raises ValueError:
|
| 754 |
-
Raised if this is called with a ``dt`` which does not have this
|
| 755 |
-
``tzinfo`` attached.
|
| 756 |
-
|
| 757 |
-
:return:
|
| 758 |
-
Returns a :py:class:`datetime.datetime` object representing the
|
| 759 |
-
wall time in ``self``'s time zone.
|
| 760 |
-
"""
|
| 761 |
-
# These isinstance checks are in datetime.tzinfo, so we'll preserve
|
| 762 |
-
# them, even if we don't care about duck typing.
|
| 763 |
-
if not isinstance(dt, datetime.datetime):
|
| 764 |
-
raise TypeError("fromutc() requires a datetime argument")
|
| 765 |
-
|
| 766 |
-
if dt.tzinfo is not self:
|
| 767 |
-
raise ValueError("dt.tzinfo is not self")
|
| 768 |
-
|
| 769 |
-
# First treat UTC as wall time and get the transition we're in.
|
| 770 |
-
idx = self._find_last_transition(dt, in_utc=True)
|
| 771 |
-
tti = self._get_ttinfo(idx)
|
| 772 |
-
|
| 773 |
-
dt_out = dt + datetime.timedelta(seconds=tti.offset)
|
| 774 |
-
|
| 775 |
-
fold = self.is_ambiguous(dt_out, idx=idx)
|
| 776 |
-
|
| 777 |
-
return enfold(dt_out, fold=int(fold))
|
| 778 |
-
|
| 779 |
-
def is_ambiguous(self, dt, idx=None):
|
| 780 |
-
"""
|
| 781 |
-
Whether or not the "wall time" of a given datetime is ambiguous in this
|
| 782 |
-
zone.
|
| 783 |
-
|
| 784 |
-
:param dt:
|
| 785 |
-
A :py:class:`datetime.datetime`, naive or time zone aware.
|
| 786 |
-
|
| 787 |
-
|
| 788 |
-
:return:
|
| 789 |
-
Returns ``True`` if ambiguous, ``False`` otherwise.
|
| 790 |
-
|
| 791 |
-
.. versionadded:: 2.6.0
|
| 792 |
-
"""
|
| 793 |
-
if idx is None:
|
| 794 |
-
idx = self._find_last_transition(dt)
|
| 795 |
-
|
| 796 |
-
# Calculate the difference in offsets from current to previous
|
| 797 |
-
timestamp = _datetime_to_timestamp(dt)
|
| 798 |
-
tti = self._get_ttinfo(idx)
|
| 799 |
-
|
| 800 |
-
if idx is None or idx <= 0:
|
| 801 |
-
return False
|
| 802 |
-
|
| 803 |
-
od = self._get_ttinfo(idx - 1).offset - tti.offset
|
| 804 |
-
tt = self._trans_list[idx] # Transition time
|
| 805 |
-
|
| 806 |
-
return timestamp < tt + od
|
| 807 |
-
|
| 808 |
-
def _resolve_ambiguous_time(self, dt):
|
| 809 |
-
idx = self._find_last_transition(dt)
|
| 810 |
-
|
| 811 |
-
# If we have no transitions, return the index
|
| 812 |
-
_fold = self._fold(dt)
|
| 813 |
-
if idx is None or idx == 0:
|
| 814 |
-
return idx
|
| 815 |
-
|
| 816 |
-
# If it's ambiguous and we're in a fold, shift to a different index.
|
| 817 |
-
idx_offset = int(not _fold and self.is_ambiguous(dt, idx))
|
| 818 |
-
|
| 819 |
-
return idx - idx_offset
|
| 820 |
-
|
| 821 |
-
def utcoffset(self, dt):
|
| 822 |
-
if dt is None:
|
| 823 |
-
return None
|
| 824 |
-
|
| 825 |
-
if not self._ttinfo_std:
|
| 826 |
-
return ZERO
|
| 827 |
-
|
| 828 |
-
return self._find_ttinfo(dt).delta
|
| 829 |
-
|
| 830 |
-
def dst(self, dt):
|
| 831 |
-
if dt is None:
|
| 832 |
-
return None
|
| 833 |
-
|
| 834 |
-
if not self._ttinfo_dst:
|
| 835 |
-
return ZERO
|
| 836 |
-
|
| 837 |
-
tti = self._find_ttinfo(dt)
|
| 838 |
-
|
| 839 |
-
if not tti.isdst:
|
| 840 |
-
return ZERO
|
| 841 |
-
|
| 842 |
-
# The documentation says that utcoffset()-dst() must
|
| 843 |
-
# be constant for every dt.
|
| 844 |
-
return tti.dstoffset
|
| 845 |
-
|
| 846 |
-
@tzname_in_python2
|
| 847 |
-
def tzname(self, dt):
|
| 848 |
-
if not self._ttinfo_std or dt is None:
|
| 849 |
-
return None
|
| 850 |
-
return self._find_ttinfo(dt).abbr
|
| 851 |
-
|
| 852 |
-
def __eq__(self, other):
|
| 853 |
-
if not isinstance(other, tzfile):
|
| 854 |
-
return NotImplemented
|
| 855 |
-
return (self._trans_list == other._trans_list and
|
| 856 |
-
self._trans_idx == other._trans_idx and
|
| 857 |
-
self._ttinfo_list == other._ttinfo_list)
|
| 858 |
-
|
| 859 |
-
__hash__ = None
|
| 860 |
-
|
| 861 |
-
def __ne__(self, other):
|
| 862 |
-
return not (self == other)
|
| 863 |
-
|
| 864 |
-
def __repr__(self):
|
| 865 |
-
return "%s(%s)" % (self.__class__.__name__, repr(self._filename))
|
| 866 |
-
|
| 867 |
-
def __reduce__(self):
|
| 868 |
-
return self.__reduce_ex__(None)
|
| 869 |
-
|
| 870 |
-
def __reduce_ex__(self, protocol):
|
| 871 |
-
return (self.__class__, (None, self._filename), self.__dict__)
|
| 872 |
-
|
| 873 |
-
|
| 874 |
-
class tzrange(tzrangebase):
|
| 875 |
-
"""
|
| 876 |
-
The ``tzrange`` object is a time zone specified by a set of offsets and
|
| 877 |
-
abbreviations, equivalent to the way the ``TZ`` variable can be specified
|
| 878 |
-
in POSIX-like systems, but using Python delta objects to specify DST
|
| 879 |
-
start, end and offsets.
|
| 880 |
-
|
| 881 |
-
:param stdabbr:
|
| 882 |
-
The abbreviation for standard time (e.g. ``'EST'``).
|
| 883 |
-
|
| 884 |
-
:param stdoffset:
|
| 885 |
-
An integer or :class:`datetime.timedelta` object or equivalent
|
| 886 |
-
specifying the base offset from UTC.
|
| 887 |
-
|
| 888 |
-
If unspecified, +00:00 is used.
|
| 889 |
-
|
| 890 |
-
:param dstabbr:
|
| 891 |
-
The abbreviation for DST / "Summer" time (e.g. ``'EDT'``).
|
| 892 |
-
|
| 893 |
-
If specified, with no other DST information, DST is assumed to occur
|
| 894 |
-
and the default behavior or ``dstoffset``, ``start`` and ``end`` is
|
| 895 |
-
used. If unspecified and no other DST information is specified, it
|
| 896 |
-
is assumed that this zone has no DST.
|
| 897 |
-
|
| 898 |
-
If this is unspecified and other DST information is *is* specified,
|
| 899 |
-
DST occurs in the zone but the time zone abbreviation is left
|
| 900 |
-
unchanged.
|
| 901 |
-
|
| 902 |
-
:param dstoffset:
|
| 903 |
-
A an integer or :class:`datetime.timedelta` object or equivalent
|
| 904 |
-
specifying the UTC offset during DST. If unspecified and any other DST
|
| 905 |
-
information is specified, it is assumed to be the STD offset +1 hour.
|
| 906 |
-
|
| 907 |
-
:param start:
|
| 908 |
-
A :class:`relativedelta.relativedelta` object or equivalent specifying
|
| 909 |
-
the time and time of year that daylight savings time starts. To
|
| 910 |
-
specify, for example, that DST starts at 2AM on the 2nd Sunday in
|
| 911 |
-
March, pass:
|
| 912 |
-
|
| 913 |
-
``relativedelta(hours=2, month=3, day=1, weekday=SU(+2))``
|
| 914 |
-
|
| 915 |
-
If unspecified and any other DST information is specified, the default
|
| 916 |
-
value is 2 AM on the first Sunday in April.
|
| 917 |
-
|
| 918 |
-
:param end:
|
| 919 |
-
A :class:`relativedelta.relativedelta` object or equivalent
|
| 920 |
-
representing the time and time of year that daylight savings time
|
| 921 |
-
ends, with the same specification method as in ``start``. One note is
|
| 922 |
-
that this should point to the first time in the *standard* zone, so if
|
| 923 |
-
a transition occurs at 2AM in the DST zone and the clocks are set back
|
| 924 |
-
1 hour to 1AM, set the ``hours`` parameter to +1.
|
| 925 |
-
|
| 926 |
-
|
| 927 |
-
**Examples:**
|
| 928 |
-
|
| 929 |
-
.. testsetup:: tzrange
|
| 930 |
-
|
| 931 |
-
from dateutil.tz import tzrange, tzstr
|
| 932 |
-
|
| 933 |
-
.. doctest:: tzrange
|
| 934 |
-
|
| 935 |
-
>>> tzstr('EST5EDT') == tzrange("EST", -18000, "EDT")
|
| 936 |
-
True
|
| 937 |
-
|
| 938 |
-
>>> from dateutil.relativedelta import *
|
| 939 |
-
>>> range1 = tzrange("EST", -18000, "EDT")
|
| 940 |
-
>>> range2 = tzrange("EST", -18000, "EDT", -14400,
|
| 941 |
-
... relativedelta(hours=+2, month=4, day=1,
|
| 942 |
-
... weekday=SU(+1)),
|
| 943 |
-
... relativedelta(hours=+1, month=10, day=31,
|
| 944 |
-
... weekday=SU(-1)))
|
| 945 |
-
>>> tzstr('EST5EDT') == range1 == range2
|
| 946 |
-
True
|
| 947 |
-
|
| 948 |
-
"""
|
| 949 |
-
def __init__(self, stdabbr, stdoffset=None,
|
| 950 |
-
dstabbr=None, dstoffset=None,
|
| 951 |
-
start=None, end=None):
|
| 952 |
-
|
| 953 |
-
global relativedelta
|
| 954 |
-
from dateutil import relativedelta
|
| 955 |
-
|
| 956 |
-
self._std_abbr = stdabbr
|
| 957 |
-
self._dst_abbr = dstabbr
|
| 958 |
-
|
| 959 |
-
try:
|
| 960 |
-
stdoffset = stdoffset.total_seconds()
|
| 961 |
-
except (TypeError, AttributeError):
|
| 962 |
-
pass
|
| 963 |
-
|
| 964 |
-
try:
|
| 965 |
-
dstoffset = dstoffset.total_seconds()
|
| 966 |
-
except (TypeError, AttributeError):
|
| 967 |
-
pass
|
| 968 |
-
|
| 969 |
-
if stdoffset is not None:
|
| 970 |
-
self._std_offset = datetime.timedelta(seconds=stdoffset)
|
| 971 |
-
else:
|
| 972 |
-
self._std_offset = ZERO
|
| 973 |
-
|
| 974 |
-
if dstoffset is not None:
|
| 975 |
-
self._dst_offset = datetime.timedelta(seconds=dstoffset)
|
| 976 |
-
elif dstabbr and stdoffset is not None:
|
| 977 |
-
self._dst_offset = self._std_offset + datetime.timedelta(hours=+1)
|
| 978 |
-
else:
|
| 979 |
-
self._dst_offset = ZERO
|
| 980 |
-
|
| 981 |
-
if dstabbr and start is None:
|
| 982 |
-
self._start_delta = relativedelta.relativedelta(
|
| 983 |
-
hours=+2, month=4, day=1, weekday=relativedelta.SU(+1))
|
| 984 |
-
else:
|
| 985 |
-
self._start_delta = start
|
| 986 |
-
|
| 987 |
-
if dstabbr and end is None:
|
| 988 |
-
self._end_delta = relativedelta.relativedelta(
|
| 989 |
-
hours=+1, month=10, day=31, weekday=relativedelta.SU(-1))
|
| 990 |
-
else:
|
| 991 |
-
self._end_delta = end
|
| 992 |
-
|
| 993 |
-
self._dst_base_offset_ = self._dst_offset - self._std_offset
|
| 994 |
-
self.hasdst = bool(self._start_delta)
|
| 995 |
-
|
| 996 |
-
def transitions(self, year):
|
| 997 |
-
"""
|
| 998 |
-
For a given year, get the DST on and off transition times, expressed
|
| 999 |
-
always on the standard time side. For zones with no transitions, this
|
| 1000 |
-
function returns ``None``.
|
| 1001 |
-
|
| 1002 |
-
:param year:
|
| 1003 |
-
The year whose transitions you would like to query.
|
| 1004 |
-
|
| 1005 |
-
:return:
|
| 1006 |
-
Returns a :class:`tuple` of :class:`datetime.datetime` objects,
|
| 1007 |
-
``(dston, dstoff)`` for zones with an annual DST transition, or
|
| 1008 |
-
``None`` for fixed offset zones.
|
| 1009 |
-
"""
|
| 1010 |
-
if not self.hasdst:
|
| 1011 |
-
return None
|
| 1012 |
-
|
| 1013 |
-
base_year = datetime.datetime(year, 1, 1)
|
| 1014 |
-
|
| 1015 |
-
start = base_year + self._start_delta
|
| 1016 |
-
end = base_year + self._end_delta
|
| 1017 |
-
|
| 1018 |
-
return (start, end)
|
| 1019 |
-
|
| 1020 |
-
def __eq__(self, other):
|
| 1021 |
-
if not isinstance(other, tzrange):
|
| 1022 |
-
return NotImplemented
|
| 1023 |
-
|
| 1024 |
-
return (self._std_abbr == other._std_abbr and
|
| 1025 |
-
self._dst_abbr == other._dst_abbr and
|
| 1026 |
-
self._std_offset == other._std_offset and
|
| 1027 |
-
self._dst_offset == other._dst_offset and
|
| 1028 |
-
self._start_delta == other._start_delta and
|
| 1029 |
-
self._end_delta == other._end_delta)
|
| 1030 |
-
|
| 1031 |
-
@property
|
| 1032 |
-
def _dst_base_offset(self):
|
| 1033 |
-
return self._dst_base_offset_
|
| 1034 |
-
|
| 1035 |
-
|
| 1036 |
-
@six.add_metaclass(_TzStrFactory)
|
| 1037 |
-
class tzstr(tzrange):
|
| 1038 |
-
"""
|
| 1039 |
-
``tzstr`` objects are time zone objects specified by a time-zone string as
|
| 1040 |
-
it would be passed to a ``TZ`` variable on POSIX-style systems (see
|
| 1041 |
-
the `GNU C Library: TZ Variable`_ for more details).
|
| 1042 |
-
|
| 1043 |
-
There is one notable exception, which is that POSIX-style time zones use an
|
| 1044 |
-
inverted offset format, so normally ``GMT+3`` would be parsed as an offset
|
| 1045 |
-
3 hours *behind* GMT. The ``tzstr`` time zone object will parse this as an
|
| 1046 |
-
offset 3 hours *ahead* of GMT. If you would like to maintain the POSIX
|
| 1047 |
-
behavior, pass a ``True`` value to ``posix_offset``.
|
| 1048 |
-
|
| 1049 |
-
The :class:`tzrange` object provides the same functionality, but is
|
| 1050 |
-
specified using :class:`relativedelta.relativedelta` objects. rather than
|
| 1051 |
-
strings.
|
| 1052 |
-
|
| 1053 |
-
:param s:
|
| 1054 |
-
A time zone string in ``TZ`` variable format. This can be a
|
| 1055 |
-
:class:`bytes` (2.x: :class:`str`), :class:`str` (2.x:
|
| 1056 |
-
:class:`unicode`) or a stream emitting unicode characters
|
| 1057 |
-
(e.g. :class:`StringIO`).
|
| 1058 |
-
|
| 1059 |
-
:param posix_offset:
|
| 1060 |
-
Optional. If set to ``True``, interpret strings such as ``GMT+3`` or
|
| 1061 |
-
``UTC+3`` as being 3 hours *behind* UTC rather than ahead, per the
|
| 1062 |
-
POSIX standard.
|
| 1063 |
-
|
| 1064 |
-
.. caution::
|
| 1065 |
-
|
| 1066 |
-
Prior to version 2.7.0, this function also supported time zones
|
| 1067 |
-
in the format:
|
| 1068 |
-
|
| 1069 |
-
* ``EST5EDT,4,0,6,7200,10,0,26,7200,3600``
|
| 1070 |
-
* ``EST5EDT,4,1,0,7200,10,-1,0,7200,3600``
|
| 1071 |
-
|
| 1072 |
-
This format is non-standard and has been deprecated; this function
|
| 1073 |
-
will raise a :class:`DeprecatedTZFormatWarning` until
|
| 1074 |
-
support is removed in a future version.
|
| 1075 |
-
|
| 1076 |
-
.. _`GNU C Library: TZ Variable`:
|
| 1077 |
-
https://www.gnu.org/software/libc/manual/html_node/TZ-Variable.html
|
| 1078 |
-
"""
|
| 1079 |
-
def __init__(self, s, posix_offset=False):
|
| 1080 |
-
global parser
|
| 1081 |
-
from dateutil.parser import _parser as parser
|
| 1082 |
-
|
| 1083 |
-
self._s = s
|
| 1084 |
-
|
| 1085 |
-
res = parser._parsetz(s)
|
| 1086 |
-
if res is None or res.any_unused_tokens:
|
| 1087 |
-
raise ValueError("unknown string format")
|
| 1088 |
-
|
| 1089 |
-
# Here we break the compatibility with the TZ variable handling.
|
| 1090 |
-
# GMT-3 actually *means* the timezone -3.
|
| 1091 |
-
if res.stdabbr in ("GMT", "UTC") and not posix_offset:
|
| 1092 |
-
res.stdoffset *= -1
|
| 1093 |
-
|
| 1094 |
-
# We must initialize it first, since _delta() needs
|
| 1095 |
-
# _std_offset and _dst_offset set. Use False in start/end
|
| 1096 |
-
# to avoid building it two times.
|
| 1097 |
-
tzrange.__init__(self, res.stdabbr, res.stdoffset,
|
| 1098 |
-
res.dstabbr, res.dstoffset,
|
| 1099 |
-
start=False, end=False)
|
| 1100 |
-
|
| 1101 |
-
if not res.dstabbr:
|
| 1102 |
-
self._start_delta = None
|
| 1103 |
-
self._end_delta = None
|
| 1104 |
-
else:
|
| 1105 |
-
self._start_delta = self._delta(res.start)
|
| 1106 |
-
if self._start_delta:
|
| 1107 |
-
self._end_delta = self._delta(res.end, isend=1)
|
| 1108 |
-
|
| 1109 |
-
self.hasdst = bool(self._start_delta)
|
| 1110 |
-
|
| 1111 |
-
def _delta(self, x, isend=0):
|
| 1112 |
-
from dateutil import relativedelta
|
| 1113 |
-
kwargs = {}
|
| 1114 |
-
if x.month is not None:
|
| 1115 |
-
kwargs["month"] = x.month
|
| 1116 |
-
if x.weekday is not None:
|
| 1117 |
-
kwargs["weekday"] = relativedelta.weekday(x.weekday, x.week)
|
| 1118 |
-
if x.week > 0:
|
| 1119 |
-
kwargs["day"] = 1
|
| 1120 |
-
else:
|
| 1121 |
-
kwargs["day"] = 31
|
| 1122 |
-
elif x.day:
|
| 1123 |
-
kwargs["day"] = x.day
|
| 1124 |
-
elif x.yday is not None:
|
| 1125 |
-
kwargs["yearday"] = x.yday
|
| 1126 |
-
elif x.jyday is not None:
|
| 1127 |
-
kwargs["nlyearday"] = x.jyday
|
| 1128 |
-
if not kwargs:
|
| 1129 |
-
# Default is to start on first sunday of april, and end
|
| 1130 |
-
# on last sunday of october.
|
| 1131 |
-
if not isend:
|
| 1132 |
-
kwargs["month"] = 4
|
| 1133 |
-
kwargs["day"] = 1
|
| 1134 |
-
kwargs["weekday"] = relativedelta.SU(+1)
|
| 1135 |
-
else:
|
| 1136 |
-
kwargs["month"] = 10
|
| 1137 |
-
kwargs["day"] = 31
|
| 1138 |
-
kwargs["weekday"] = relativedelta.SU(-1)
|
| 1139 |
-
if x.time is not None:
|
| 1140 |
-
kwargs["seconds"] = x.time
|
| 1141 |
-
else:
|
| 1142 |
-
# Default is 2AM.
|
| 1143 |
-
kwargs["seconds"] = 7200
|
| 1144 |
-
if isend:
|
| 1145 |
-
# Convert to standard time, to follow the documented way
|
| 1146 |
-
# of working with the extra hour. See the documentation
|
| 1147 |
-
# of the tzinfo class.
|
| 1148 |
-
delta = self._dst_offset - self._std_offset
|
| 1149 |
-
kwargs["seconds"] -= delta.seconds + delta.days * 86400
|
| 1150 |
-
return relativedelta.relativedelta(**kwargs)
|
| 1151 |
-
|
| 1152 |
-
def __repr__(self):
|
| 1153 |
-
return "%s(%s)" % (self.__class__.__name__, repr(self._s))
|
| 1154 |
-
|
| 1155 |
-
|
| 1156 |
-
class _tzicalvtzcomp(object):
|
| 1157 |
-
def __init__(self, tzoffsetfrom, tzoffsetto, isdst,
|
| 1158 |
-
tzname=None, rrule=None):
|
| 1159 |
-
self.tzoffsetfrom = datetime.timedelta(seconds=tzoffsetfrom)
|
| 1160 |
-
self.tzoffsetto = datetime.timedelta(seconds=tzoffsetto)
|
| 1161 |
-
self.tzoffsetdiff = self.tzoffsetto - self.tzoffsetfrom
|
| 1162 |
-
self.isdst = isdst
|
| 1163 |
-
self.tzname = tzname
|
| 1164 |
-
self.rrule = rrule
|
| 1165 |
-
|
| 1166 |
-
|
| 1167 |
-
class _tzicalvtz(_tzinfo):
|
| 1168 |
-
def __init__(self, tzid, comps=[]):
|
| 1169 |
-
super(_tzicalvtz, self).__init__()
|
| 1170 |
-
|
| 1171 |
-
self._tzid = tzid
|
| 1172 |
-
self._comps = comps
|
| 1173 |
-
self._cachedate = []
|
| 1174 |
-
self._cachecomp = []
|
| 1175 |
-
self._cache_lock = _thread.allocate_lock()
|
| 1176 |
-
|
| 1177 |
-
def _find_comp(self, dt):
|
| 1178 |
-
if len(self._comps) == 1:
|
| 1179 |
-
return self._comps[0]
|
| 1180 |
-
|
| 1181 |
-
dt = dt.replace(tzinfo=None)
|
| 1182 |
-
|
| 1183 |
-
try:
|
| 1184 |
-
with self._cache_lock:
|
| 1185 |
-
return self._cachecomp[self._cachedate.index(
|
| 1186 |
-
(dt, self._fold(dt)))]
|
| 1187 |
-
except ValueError:
|
| 1188 |
-
pass
|
| 1189 |
-
|
| 1190 |
-
lastcompdt = None
|
| 1191 |
-
lastcomp = None
|
| 1192 |
-
|
| 1193 |
-
for comp in self._comps:
|
| 1194 |
-
compdt = self._find_compdt(comp, dt)
|
| 1195 |
-
|
| 1196 |
-
if compdt and (not lastcompdt or lastcompdt < compdt):
|
| 1197 |
-
lastcompdt = compdt
|
| 1198 |
-
lastcomp = comp
|
| 1199 |
-
|
| 1200 |
-
if not lastcomp:
|
| 1201 |
-
# RFC says nothing about what to do when a given
|
| 1202 |
-
# time is before the first onset date. We'll look for the
|
| 1203 |
-
# first standard component, or the first component, if
|
| 1204 |
-
# none is found.
|
| 1205 |
-
for comp in self._comps:
|
| 1206 |
-
if not comp.isdst:
|
| 1207 |
-
lastcomp = comp
|
| 1208 |
-
break
|
| 1209 |
-
else:
|
| 1210 |
-
lastcomp = comp[0]
|
| 1211 |
-
|
| 1212 |
-
with self._cache_lock:
|
| 1213 |
-
self._cachedate.insert(0, (dt, self._fold(dt)))
|
| 1214 |
-
self._cachecomp.insert(0, lastcomp)
|
| 1215 |
-
|
| 1216 |
-
if len(self._cachedate) > 10:
|
| 1217 |
-
self._cachedate.pop()
|
| 1218 |
-
self._cachecomp.pop()
|
| 1219 |
-
|
| 1220 |
-
return lastcomp
|
| 1221 |
-
|
| 1222 |
-
def _find_compdt(self, comp, dt):
|
| 1223 |
-
if comp.tzoffsetdiff < ZERO and self._fold(dt):
|
| 1224 |
-
dt -= comp.tzoffsetdiff
|
| 1225 |
-
|
| 1226 |
-
compdt = comp.rrule.before(dt, inc=True)
|
| 1227 |
-
|
| 1228 |
-
return compdt
|
| 1229 |
-
|
| 1230 |
-
def utcoffset(self, dt):
|
| 1231 |
-
if dt is None:
|
| 1232 |
-
return None
|
| 1233 |
-
|
| 1234 |
-
return self._find_comp(dt).tzoffsetto
|
| 1235 |
-
|
| 1236 |
-
def dst(self, dt):
|
| 1237 |
-
comp = self._find_comp(dt)
|
| 1238 |
-
if comp.isdst:
|
| 1239 |
-
return comp.tzoffsetdiff
|
| 1240 |
-
else:
|
| 1241 |
-
return ZERO
|
| 1242 |
-
|
| 1243 |
-
@tzname_in_python2
|
| 1244 |
-
def tzname(self, dt):
|
| 1245 |
-
return self._find_comp(dt).tzname
|
| 1246 |
-
|
| 1247 |
-
def __repr__(self):
|
| 1248 |
-
return "<tzicalvtz %s>" % repr(self._tzid)
|
| 1249 |
-
|
| 1250 |
-
__reduce__ = object.__reduce__
|
| 1251 |
-
|
| 1252 |
-
|
| 1253 |
-
class tzical(object):
|
| 1254 |
-
"""
|
| 1255 |
-
This object is designed to parse an iCalendar-style ``VTIMEZONE`` structure
|
| 1256 |
-
as set out in `RFC 5545`_ Section 4.6.5 into one or more `tzinfo` objects.
|
| 1257 |
-
|
| 1258 |
-
:param `fileobj`:
|
| 1259 |
-
A file or stream in iCalendar format, which should be UTF-8 encoded
|
| 1260 |
-
with CRLF endings.
|
| 1261 |
-
|
| 1262 |
-
.. _`RFC 5545`: https://tools.ietf.org/html/rfc5545
|
| 1263 |
-
"""
|
| 1264 |
-
def __init__(self, fileobj):
|
| 1265 |
-
global rrule
|
| 1266 |
-
from dateutil import rrule
|
| 1267 |
-
|
| 1268 |
-
if isinstance(fileobj, string_types):
|
| 1269 |
-
self._s = fileobj
|
| 1270 |
-
# ical should be encoded in UTF-8 with CRLF
|
| 1271 |
-
fileobj = open(fileobj, 'r')
|
| 1272 |
-
else:
|
| 1273 |
-
self._s = getattr(fileobj, 'name', repr(fileobj))
|
| 1274 |
-
fileobj = _nullcontext(fileobj)
|
| 1275 |
-
|
| 1276 |
-
self._vtz = {}
|
| 1277 |
-
|
| 1278 |
-
with fileobj as fobj:
|
| 1279 |
-
self._parse_rfc(fobj.read())
|
| 1280 |
-
|
| 1281 |
-
def keys(self):
|
| 1282 |
-
"""
|
| 1283 |
-
Retrieves the available time zones as a list.
|
| 1284 |
-
"""
|
| 1285 |
-
return list(self._vtz.keys())
|
| 1286 |
-
|
| 1287 |
-
def get(self, tzid=None):
|
| 1288 |
-
"""
|
| 1289 |
-
Retrieve a :py:class:`datetime.tzinfo` object by its ``tzid``.
|
| 1290 |
-
|
| 1291 |
-
:param tzid:
|
| 1292 |
-
If there is exactly one time zone available, omitting ``tzid``
|
| 1293 |
-
or passing :py:const:`None` value returns it. Otherwise a valid
|
| 1294 |
-
key (which can be retrieved from :func:`keys`) is required.
|
| 1295 |
-
|
| 1296 |
-
:raises ValueError:
|
| 1297 |
-
Raised if ``tzid`` is not specified but there are either more
|
| 1298 |
-
or fewer than 1 zone defined.
|
| 1299 |
-
|
| 1300 |
-
:returns:
|
| 1301 |
-
Returns either a :py:class:`datetime.tzinfo` object representing
|
| 1302 |
-
the relevant time zone or :py:const:`None` if the ``tzid`` was
|
| 1303 |
-
not found.
|
| 1304 |
-
"""
|
| 1305 |
-
if tzid is None:
|
| 1306 |
-
if len(self._vtz) == 0:
|
| 1307 |
-
raise ValueError("no timezones defined")
|
| 1308 |
-
elif len(self._vtz) > 1:
|
| 1309 |
-
raise ValueError("more than one timezone available")
|
| 1310 |
-
tzid = next(iter(self._vtz))
|
| 1311 |
-
|
| 1312 |
-
return self._vtz.get(tzid)
|
| 1313 |
-
|
| 1314 |
-
def _parse_offset(self, s):
|
| 1315 |
-
s = s.strip()
|
| 1316 |
-
if not s:
|
| 1317 |
-
raise ValueError("empty offset")
|
| 1318 |
-
if s[0] in ('+', '-'):
|
| 1319 |
-
signal = (-1, +1)[s[0] == '+']
|
| 1320 |
-
s = s[1:]
|
| 1321 |
-
else:
|
| 1322 |
-
signal = +1
|
| 1323 |
-
if len(s) == 4:
|
| 1324 |
-
return (int(s[:2]) * 3600 + int(s[2:]) * 60) * signal
|
| 1325 |
-
elif len(s) == 6:
|
| 1326 |
-
return (int(s[:2]) * 3600 + int(s[2:4]) * 60 + int(s[4:])) * signal
|
| 1327 |
-
else:
|
| 1328 |
-
raise ValueError("invalid offset: " + s)
|
| 1329 |
-
|
| 1330 |
-
def _parse_rfc(self, s):
|
| 1331 |
-
lines = s.splitlines()
|
| 1332 |
-
if not lines:
|
| 1333 |
-
raise ValueError("empty string")
|
| 1334 |
-
|
| 1335 |
-
# Unfold
|
| 1336 |
-
i = 0
|
| 1337 |
-
while i < len(lines):
|
| 1338 |
-
line = lines[i].rstrip()
|
| 1339 |
-
if not line:
|
| 1340 |
-
del lines[i]
|
| 1341 |
-
elif i > 0 and line[0] == " ":
|
| 1342 |
-
lines[i-1] += line[1:]
|
| 1343 |
-
del lines[i]
|
| 1344 |
-
else:
|
| 1345 |
-
i += 1
|
| 1346 |
-
|
| 1347 |
-
tzid = None
|
| 1348 |
-
comps = []
|
| 1349 |
-
invtz = False
|
| 1350 |
-
comptype = None
|
| 1351 |
-
for line in lines:
|
| 1352 |
-
if not line:
|
| 1353 |
-
continue
|
| 1354 |
-
name, value = line.split(':', 1)
|
| 1355 |
-
parms = name.split(';')
|
| 1356 |
-
if not parms:
|
| 1357 |
-
raise ValueError("empty property name")
|
| 1358 |
-
name = parms[0].upper()
|
| 1359 |
-
parms = parms[1:]
|
| 1360 |
-
if invtz:
|
| 1361 |
-
if name == "BEGIN":
|
| 1362 |
-
if value in ("STANDARD", "DAYLIGHT"):
|
| 1363 |
-
# Process component
|
| 1364 |
-
pass
|
| 1365 |
-
else:
|
| 1366 |
-
raise ValueError("unknown component: "+value)
|
| 1367 |
-
comptype = value
|
| 1368 |
-
founddtstart = False
|
| 1369 |
-
tzoffsetfrom = None
|
| 1370 |
-
tzoffsetto = None
|
| 1371 |
-
rrulelines = []
|
| 1372 |
-
tzname = None
|
| 1373 |
-
elif name == "END":
|
| 1374 |
-
if value == "VTIMEZONE":
|
| 1375 |
-
if comptype:
|
| 1376 |
-
raise ValueError("component not closed: "+comptype)
|
| 1377 |
-
if not tzid:
|
| 1378 |
-
raise ValueError("mandatory TZID not found")
|
| 1379 |
-
if not comps:
|
| 1380 |
-
raise ValueError(
|
| 1381 |
-
"at least one component is needed")
|
| 1382 |
-
# Process vtimezone
|
| 1383 |
-
self._vtz[tzid] = _tzicalvtz(tzid, comps)
|
| 1384 |
-
invtz = False
|
| 1385 |
-
elif value == comptype:
|
| 1386 |
-
if not founddtstart:
|
| 1387 |
-
raise ValueError("mandatory DTSTART not found")
|
| 1388 |
-
if tzoffsetfrom is None:
|
| 1389 |
-
raise ValueError(
|
| 1390 |
-
"mandatory TZOFFSETFROM not found")
|
| 1391 |
-
if tzoffsetto is None:
|
| 1392 |
-
raise ValueError(
|
| 1393 |
-
"mandatory TZOFFSETFROM not found")
|
| 1394 |
-
# Process component
|
| 1395 |
-
rr = None
|
| 1396 |
-
if rrulelines:
|
| 1397 |
-
rr = rrule.rrulestr("\n".join(rrulelines),
|
| 1398 |
-
compatible=True,
|
| 1399 |
-
ignoretz=True,
|
| 1400 |
-
cache=True)
|
| 1401 |
-
comp = _tzicalvtzcomp(tzoffsetfrom, tzoffsetto,
|
| 1402 |
-
(comptype == "DAYLIGHT"),
|
| 1403 |
-
tzname, rr)
|
| 1404 |
-
comps.append(comp)
|
| 1405 |
-
comptype = None
|
| 1406 |
-
else:
|
| 1407 |
-
raise ValueError("invalid component end: "+value)
|
| 1408 |
-
elif comptype:
|
| 1409 |
-
if name == "DTSTART":
|
| 1410 |
-
# DTSTART in VTIMEZONE takes a subset of valid RRULE
|
| 1411 |
-
# values under RFC 5545.
|
| 1412 |
-
for parm in parms:
|
| 1413 |
-
if parm != 'VALUE=DATE-TIME':
|
| 1414 |
-
msg = ('Unsupported DTSTART param in ' +
|
| 1415 |
-
'VTIMEZONE: ' + parm)
|
| 1416 |
-
raise ValueError(msg)
|
| 1417 |
-
rrulelines.append(line)
|
| 1418 |
-
founddtstart = True
|
| 1419 |
-
elif name in ("RRULE", "RDATE", "EXRULE", "EXDATE"):
|
| 1420 |
-
rrulelines.append(line)
|
| 1421 |
-
elif name == "TZOFFSETFROM":
|
| 1422 |
-
if parms:
|
| 1423 |
-
raise ValueError(
|
| 1424 |
-
"unsupported %s parm: %s " % (name, parms[0]))
|
| 1425 |
-
tzoffsetfrom = self._parse_offset(value)
|
| 1426 |
-
elif name == "TZOFFSETTO":
|
| 1427 |
-
if parms:
|
| 1428 |
-
raise ValueError(
|
| 1429 |
-
"unsupported TZOFFSETTO parm: "+parms[0])
|
| 1430 |
-
tzoffsetto = self._parse_offset(value)
|
| 1431 |
-
elif name == "TZNAME":
|
| 1432 |
-
if parms:
|
| 1433 |
-
raise ValueError(
|
| 1434 |
-
"unsupported TZNAME parm: "+parms[0])
|
| 1435 |
-
tzname = value
|
| 1436 |
-
elif name == "COMMENT":
|
| 1437 |
-
pass
|
| 1438 |
-
else:
|
| 1439 |
-
raise ValueError("unsupported property: "+name)
|
| 1440 |
-
else:
|
| 1441 |
-
if name == "TZID":
|
| 1442 |
-
if parms:
|
| 1443 |
-
raise ValueError(
|
| 1444 |
-
"unsupported TZID parm: "+parms[0])
|
| 1445 |
-
tzid = value
|
| 1446 |
-
elif name in ("TZURL", "LAST-MODIFIED", "COMMENT"):
|
| 1447 |
-
pass
|
| 1448 |
-
else:
|
| 1449 |
-
raise ValueError("unsupported property: "+name)
|
| 1450 |
-
elif name == "BEGIN" and value == "VTIMEZONE":
|
| 1451 |
-
tzid = None
|
| 1452 |
-
comps = []
|
| 1453 |
-
invtz = True
|
| 1454 |
-
|
| 1455 |
-
def __repr__(self):
|
| 1456 |
-
return "%s(%s)" % (self.__class__.__name__, repr(self._s))
|
| 1457 |
-
|
| 1458 |
-
|
| 1459 |
-
if sys.platform != "win32":
|
| 1460 |
-
TZFILES = ["/etc/localtime", "localtime"]
|
| 1461 |
-
TZPATHS = ["/usr/share/zoneinfo",
|
| 1462 |
-
"/usr/lib/zoneinfo",
|
| 1463 |
-
"/usr/share/lib/zoneinfo",
|
| 1464 |
-
"/etc/zoneinfo"]
|
| 1465 |
-
else:
|
| 1466 |
-
TZFILES = []
|
| 1467 |
-
TZPATHS = []
|
| 1468 |
-
|
| 1469 |
-
|
| 1470 |
-
def __get_gettz():
|
| 1471 |
-
tzlocal_classes = (tzlocal,)
|
| 1472 |
-
if tzwinlocal is not None:
|
| 1473 |
-
tzlocal_classes += (tzwinlocal,)
|
| 1474 |
-
|
| 1475 |
-
class GettzFunc(object):
|
| 1476 |
-
"""
|
| 1477 |
-
Retrieve a time zone object from a string representation
|
| 1478 |
-
|
| 1479 |
-
This function is intended to retrieve the :py:class:`tzinfo` subclass
|
| 1480 |
-
that best represents the time zone that would be used if a POSIX
|
| 1481 |
-
`TZ variable`_ were set to the same value.
|
| 1482 |
-
|
| 1483 |
-
If no argument or an empty string is passed to ``gettz``, local time
|
| 1484 |
-
is returned:
|
| 1485 |
-
|
| 1486 |
-
.. code-block:: python3
|
| 1487 |
-
|
| 1488 |
-
>>> gettz()
|
| 1489 |
-
tzfile('/etc/localtime')
|
| 1490 |
-
|
| 1491 |
-
This function is also the preferred way to map IANA tz database keys
|
| 1492 |
-
to :class:`tzfile` objects:
|
| 1493 |
-
|
| 1494 |
-
.. code-block:: python3
|
| 1495 |
-
|
| 1496 |
-
>>> gettz('Pacific/Kiritimati')
|
| 1497 |
-
tzfile('/usr/share/zoneinfo/Pacific/Kiritimati')
|
| 1498 |
-
|
| 1499 |
-
On Windows, the standard is extended to include the Windows-specific
|
| 1500 |
-
zone names provided by the operating system:
|
| 1501 |
-
|
| 1502 |
-
.. code-block:: python3
|
| 1503 |
-
|
| 1504 |
-
>>> gettz('Egypt Standard Time')
|
| 1505 |
-
tzwin('Egypt Standard Time')
|
| 1506 |
-
|
| 1507 |
-
Passing a GNU ``TZ`` style string time zone specification returns a
|
| 1508 |
-
:class:`tzstr` object:
|
| 1509 |
-
|
| 1510 |
-
.. code-block:: python3
|
| 1511 |
-
|
| 1512 |
-
>>> gettz('AEST-10AEDT-11,M10.1.0/2,M4.1.0/3')
|
| 1513 |
-
tzstr('AEST-10AEDT-11,M10.1.0/2,M4.1.0/3')
|
| 1514 |
-
|
| 1515 |
-
:param name:
|
| 1516 |
-
A time zone name (IANA, or, on Windows, Windows keys), location of
|
| 1517 |
-
a ``tzfile(5)`` zoneinfo file or ``TZ`` variable style time zone
|
| 1518 |
-
specifier. An empty string, no argument or ``None`` is interpreted
|
| 1519 |
-
as local time.
|
| 1520 |
-
|
| 1521 |
-
:return:
|
| 1522 |
-
Returns an instance of one of ``dateutil``'s :py:class:`tzinfo`
|
| 1523 |
-
subclasses.
|
| 1524 |
-
|
| 1525 |
-
.. versionchanged:: 2.7.0
|
| 1526 |
-
|
| 1527 |
-
After version 2.7.0, any two calls to ``gettz`` using the same
|
| 1528 |
-
input strings will return the same object:
|
| 1529 |
-
|
| 1530 |
-
.. code-block:: python3
|
| 1531 |
-
|
| 1532 |
-
>>> tz.gettz('America/Chicago') is tz.gettz('America/Chicago')
|
| 1533 |
-
True
|
| 1534 |
-
|
| 1535 |
-
In addition to improving performance, this ensures that
|
| 1536 |
-
`"same zone" semantics`_ are used for datetimes in the same zone.
|
| 1537 |
-
|
| 1538 |
-
|
| 1539 |
-
.. _`TZ variable`:
|
| 1540 |
-
https://www.gnu.org/software/libc/manual/html_node/TZ-Variable.html
|
| 1541 |
-
|
| 1542 |
-
.. _`"same zone" semantics`:
|
| 1543 |
-
https://blog.ganssle.io/articles/2018/02/aware-datetime-arithmetic.html
|
| 1544 |
-
"""
|
| 1545 |
-
def __init__(self):
|
| 1546 |
-
|
| 1547 |
-
self.__instances = weakref.WeakValueDictionary()
|
| 1548 |
-
self.__strong_cache_size = 8
|
| 1549 |
-
self.__strong_cache = OrderedDict()
|
| 1550 |
-
self._cache_lock = _thread.allocate_lock()
|
| 1551 |
-
|
| 1552 |
-
def __call__(self, name=None):
|
| 1553 |
-
with self._cache_lock:
|
| 1554 |
-
rv = self.__instances.get(name, None)
|
| 1555 |
-
|
| 1556 |
-
if rv is None:
|
| 1557 |
-
rv = self.nocache(name=name)
|
| 1558 |
-
if not (name is None
|
| 1559 |
-
or isinstance(rv, tzlocal_classes)
|
| 1560 |
-
or rv is None):
|
| 1561 |
-
# tzlocal is slightly more complicated than the other
|
| 1562 |
-
# time zone providers because it depends on environment
|
| 1563 |
-
# at construction time, so don't cache that.
|
| 1564 |
-
#
|
| 1565 |
-
# We also cannot store weak references to None, so we
|
| 1566 |
-
# will also not store that.
|
| 1567 |
-
self.__instances[name] = rv
|
| 1568 |
-
else:
|
| 1569 |
-
# No need for strong caching, return immediately
|
| 1570 |
-
return rv
|
| 1571 |
-
|
| 1572 |
-
self.__strong_cache[name] = self.__strong_cache.pop(name, rv)
|
| 1573 |
-
|
| 1574 |
-
if len(self.__strong_cache) > self.__strong_cache_size:
|
| 1575 |
-
self.__strong_cache.popitem(last=False)
|
| 1576 |
-
|
| 1577 |
-
return rv
|
| 1578 |
-
|
| 1579 |
-
def set_cache_size(self, size):
|
| 1580 |
-
with self._cache_lock:
|
| 1581 |
-
self.__strong_cache_size = size
|
| 1582 |
-
while len(self.__strong_cache) > size:
|
| 1583 |
-
self.__strong_cache.popitem(last=False)
|
| 1584 |
-
|
| 1585 |
-
def cache_clear(self):
|
| 1586 |
-
with self._cache_lock:
|
| 1587 |
-
self.__instances = weakref.WeakValueDictionary()
|
| 1588 |
-
self.__strong_cache.clear()
|
| 1589 |
-
|
| 1590 |
-
@staticmethod
|
| 1591 |
-
def nocache(name=None):
|
| 1592 |
-
"""A non-cached version of gettz"""
|
| 1593 |
-
tz = None
|
| 1594 |
-
if not name:
|
| 1595 |
-
try:
|
| 1596 |
-
name = os.environ["TZ"]
|
| 1597 |
-
except KeyError:
|
| 1598 |
-
pass
|
| 1599 |
-
if name is None or name in ("", ":"):
|
| 1600 |
-
for filepath in TZFILES:
|
| 1601 |
-
if not os.path.isabs(filepath):
|
| 1602 |
-
filename = filepath
|
| 1603 |
-
for path in TZPATHS:
|
| 1604 |
-
filepath = os.path.join(path, filename)
|
| 1605 |
-
if os.path.isfile(filepath):
|
| 1606 |
-
break
|
| 1607 |
-
else:
|
| 1608 |
-
continue
|
| 1609 |
-
if os.path.isfile(filepath):
|
| 1610 |
-
try:
|
| 1611 |
-
tz = tzfile(filepath)
|
| 1612 |
-
break
|
| 1613 |
-
except (IOError, OSError, ValueError):
|
| 1614 |
-
pass
|
| 1615 |
-
else:
|
| 1616 |
-
tz = tzlocal()
|
| 1617 |
-
else:
|
| 1618 |
-
try:
|
| 1619 |
-
if name.startswith(":"):
|
| 1620 |
-
name = name[1:]
|
| 1621 |
-
except TypeError as e:
|
| 1622 |
-
if isinstance(name, bytes):
|
| 1623 |
-
new_msg = "gettz argument should be str, not bytes"
|
| 1624 |
-
six.raise_from(TypeError(new_msg), e)
|
| 1625 |
-
else:
|
| 1626 |
-
raise
|
| 1627 |
-
if os.path.isabs(name):
|
| 1628 |
-
if os.path.isfile(name):
|
| 1629 |
-
tz = tzfile(name)
|
| 1630 |
-
else:
|
| 1631 |
-
tz = None
|
| 1632 |
-
else:
|
| 1633 |
-
for path in TZPATHS:
|
| 1634 |
-
filepath = os.path.join(path, name)
|
| 1635 |
-
if not os.path.isfile(filepath):
|
| 1636 |
-
filepath = filepath.replace(' ', '_')
|
| 1637 |
-
if not os.path.isfile(filepath):
|
| 1638 |
-
continue
|
| 1639 |
-
try:
|
| 1640 |
-
tz = tzfile(filepath)
|
| 1641 |
-
break
|
| 1642 |
-
except (IOError, OSError, ValueError):
|
| 1643 |
-
pass
|
| 1644 |
-
else:
|
| 1645 |
-
tz = None
|
| 1646 |
-
if tzwin is not None:
|
| 1647 |
-
try:
|
| 1648 |
-
tz = tzwin(name)
|
| 1649 |
-
except (WindowsError, UnicodeEncodeError):
|
| 1650 |
-
# UnicodeEncodeError is for Python 2.7 compat
|
| 1651 |
-
tz = None
|
| 1652 |
-
|
| 1653 |
-
if not tz:
|
| 1654 |
-
from dateutil.zoneinfo import get_zonefile_instance
|
| 1655 |
-
tz = get_zonefile_instance().get(name)
|
| 1656 |
-
|
| 1657 |
-
if not tz:
|
| 1658 |
-
for c in name:
|
| 1659 |
-
# name is not a tzstr unless it has at least
|
| 1660 |
-
# one offset. For short values of "name", an
|
| 1661 |
-
# explicit for loop seems to be the fastest way
|
| 1662 |
-
# To determine if a string contains a digit
|
| 1663 |
-
if c in "0123456789":
|
| 1664 |
-
try:
|
| 1665 |
-
tz = tzstr(name)
|
| 1666 |
-
except ValueError:
|
| 1667 |
-
pass
|
| 1668 |
-
break
|
| 1669 |
-
else:
|
| 1670 |
-
if name in ("GMT", "UTC"):
|
| 1671 |
-
tz = UTC
|
| 1672 |
-
elif name in time.tzname:
|
| 1673 |
-
tz = tzlocal()
|
| 1674 |
-
return tz
|
| 1675 |
-
|
| 1676 |
-
return GettzFunc()
|
| 1677 |
-
|
| 1678 |
-
|
| 1679 |
-
gettz = __get_gettz()
|
| 1680 |
-
del __get_gettz
|
| 1681 |
-
|
| 1682 |
-
|
| 1683 |
-
def datetime_exists(dt, tz=None):
|
| 1684 |
-
"""
|
| 1685 |
-
Given a datetime and a time zone, determine whether or not a given datetime
|
| 1686 |
-
would fall in a gap.
|
| 1687 |
-
|
| 1688 |
-
:param dt:
|
| 1689 |
-
A :class:`datetime.datetime` (whose time zone will be ignored if ``tz``
|
| 1690 |
-
is provided.)
|
| 1691 |
-
|
| 1692 |
-
:param tz:
|
| 1693 |
-
A :class:`datetime.tzinfo` with support for the ``fold`` attribute. If
|
| 1694 |
-
``None`` or not provided, the datetime's own time zone will be used.
|
| 1695 |
-
|
| 1696 |
-
:return:
|
| 1697 |
-
Returns a boolean value whether or not the "wall time" exists in
|
| 1698 |
-
``tz``.
|
| 1699 |
-
|
| 1700 |
-
.. versionadded:: 2.7.0
|
| 1701 |
-
"""
|
| 1702 |
-
if tz is None:
|
| 1703 |
-
if dt.tzinfo is None:
|
| 1704 |
-
raise ValueError('Datetime is naive and no time zone provided.')
|
| 1705 |
-
tz = dt.tzinfo
|
| 1706 |
-
|
| 1707 |
-
dt = dt.replace(tzinfo=None)
|
| 1708 |
-
|
| 1709 |
-
# This is essentially a test of whether or not the datetime can survive
|
| 1710 |
-
# a round trip to UTC.
|
| 1711 |
-
dt_rt = dt.replace(tzinfo=tz).astimezone(UTC).astimezone(tz)
|
| 1712 |
-
dt_rt = dt_rt.replace(tzinfo=None)
|
| 1713 |
-
|
| 1714 |
-
return dt == dt_rt
|
| 1715 |
-
|
| 1716 |
-
|
| 1717 |
-
def datetime_ambiguous(dt, tz=None):
|
| 1718 |
-
"""
|
| 1719 |
-
Given a datetime and a time zone, determine whether or not a given datetime
|
| 1720 |
-
is ambiguous (i.e if there are two times differentiated only by their DST
|
| 1721 |
-
status).
|
| 1722 |
-
|
| 1723 |
-
:param dt:
|
| 1724 |
-
A :class:`datetime.datetime` (whose time zone will be ignored if ``tz``
|
| 1725 |
-
is provided.)
|
| 1726 |
-
|
| 1727 |
-
:param tz:
|
| 1728 |
-
A :class:`datetime.tzinfo` with support for the ``fold`` attribute. If
|
| 1729 |
-
``None`` or not provided, the datetime's own time zone will be used.
|
| 1730 |
-
|
| 1731 |
-
:return:
|
| 1732 |
-
Returns a boolean value whether or not the "wall time" is ambiguous in
|
| 1733 |
-
``tz``.
|
| 1734 |
-
|
| 1735 |
-
.. versionadded:: 2.6.0
|
| 1736 |
-
"""
|
| 1737 |
-
if tz is None:
|
| 1738 |
-
if dt.tzinfo is None:
|
| 1739 |
-
raise ValueError('Datetime is naive and no time zone provided.')
|
| 1740 |
-
|
| 1741 |
-
tz = dt.tzinfo
|
| 1742 |
-
|
| 1743 |
-
# If a time zone defines its own "is_ambiguous" function, we'll use that.
|
| 1744 |
-
is_ambiguous_fn = getattr(tz, 'is_ambiguous', None)
|
| 1745 |
-
if is_ambiguous_fn is not None:
|
| 1746 |
-
try:
|
| 1747 |
-
return tz.is_ambiguous(dt)
|
| 1748 |
-
except Exception:
|
| 1749 |
-
pass
|
| 1750 |
-
|
| 1751 |
-
# If it doesn't come out and tell us it's ambiguous, we'll just check if
|
| 1752 |
-
# the fold attribute has any effect on this particular date and time.
|
| 1753 |
-
dt = dt.replace(tzinfo=tz)
|
| 1754 |
-
wall_0 = enfold(dt, fold=0)
|
| 1755 |
-
wall_1 = enfold(dt, fold=1)
|
| 1756 |
-
|
| 1757 |
-
same_offset = wall_0.utcoffset() == wall_1.utcoffset()
|
| 1758 |
-
same_dst = wall_0.dst() == wall_1.dst()
|
| 1759 |
-
|
| 1760 |
-
return not (same_offset and same_dst)
|
| 1761 |
-
|
| 1762 |
-
|
| 1763 |
-
def resolve_imaginary(dt):
|
| 1764 |
-
"""
|
| 1765 |
-
Given a datetime that may be imaginary, return an existing datetime.
|
| 1766 |
-
|
| 1767 |
-
This function assumes that an imaginary datetime represents what the
|
| 1768 |
-
wall time would be in a zone had the offset transition not occurred, so
|
| 1769 |
-
it will always fall forward by the transition's change in offset.
|
| 1770 |
-
|
| 1771 |
-
.. doctest::
|
| 1772 |
-
|
| 1773 |
-
>>> from dateutil import tz
|
| 1774 |
-
>>> from datetime import datetime
|
| 1775 |
-
>>> NYC = tz.gettz('America/New_York')
|
| 1776 |
-
>>> print(tz.resolve_imaginary(datetime(2017, 3, 12, 2, 30, tzinfo=NYC)))
|
| 1777 |
-
2017-03-12 03:30:00-04:00
|
| 1778 |
-
|
| 1779 |
-
>>> KIR = tz.gettz('Pacific/Kiritimati')
|
| 1780 |
-
>>> print(tz.resolve_imaginary(datetime(1995, 1, 1, 12, 30, tzinfo=KIR)))
|
| 1781 |
-
1995-01-02 12:30:00+14:00
|
| 1782 |
-
|
| 1783 |
-
As a note, :func:`datetime.astimezone` is guaranteed to produce a valid,
|
| 1784 |
-
existing datetime, so a round-trip to and from UTC is sufficient to get
|
| 1785 |
-
an extant datetime, however, this generally "falls back" to an earlier time
|
| 1786 |
-
rather than falling forward to the STD side (though no guarantees are made
|
| 1787 |
-
about this behavior).
|
| 1788 |
-
|
| 1789 |
-
:param dt:
|
| 1790 |
-
A :class:`datetime.datetime` which may or may not exist.
|
| 1791 |
-
|
| 1792 |
-
:return:
|
| 1793 |
-
Returns an existing :class:`datetime.datetime`. If ``dt`` was not
|
| 1794 |
-
imaginary, the datetime returned is guaranteed to be the same object
|
| 1795 |
-
passed to the function.
|
| 1796 |
-
|
| 1797 |
-
.. versionadded:: 2.7.0
|
| 1798 |
-
"""
|
| 1799 |
-
if dt.tzinfo is not None and not datetime_exists(dt):
|
| 1800 |
-
|
| 1801 |
-
curr_offset = (dt + datetime.timedelta(hours=24)).utcoffset()
|
| 1802 |
-
old_offset = (dt - datetime.timedelta(hours=24)).utcoffset()
|
| 1803 |
-
|
| 1804 |
-
dt += curr_offset - old_offset
|
| 1805 |
-
|
| 1806 |
-
return dt
|
| 1807 |
-
|
| 1808 |
-
|
| 1809 |
-
def _datetime_to_timestamp(dt):
|
| 1810 |
-
"""
|
| 1811 |
-
Convert a :class:`datetime.datetime` object to an epoch timestamp in
|
| 1812 |
-
seconds since January 1, 1970, ignoring the time zone.
|
| 1813 |
-
"""
|
| 1814 |
-
return (dt.replace(tzinfo=None) - EPOCH).total_seconds()
|
| 1815 |
-
|
| 1816 |
-
|
| 1817 |
-
if sys.version_info >= (3, 6):
|
| 1818 |
-
def _get_supported_offset(second_offset):
|
| 1819 |
-
return second_offset
|
| 1820 |
-
else:
|
| 1821 |
-
def _get_supported_offset(second_offset):
|
| 1822 |
-
# For python pre-3.6, round to full-minutes if that's not the case.
|
| 1823 |
-
# Python's datetime doesn't accept sub-minute timezones. Check
|
| 1824 |
-
# http://python.org/sf/1447945 or https://bugs.python.org/issue5288
|
| 1825 |
-
# for some information.
|
| 1826 |
-
old_offset = second_offset
|
| 1827 |
-
calculated_offset = 60 * ((second_offset + 30) // 60)
|
| 1828 |
-
return calculated_offset
|
| 1829 |
-
|
| 1830 |
-
|
| 1831 |
-
try:
|
| 1832 |
-
# Python 3.7 feature
|
| 1833 |
-
from contextlib import nullcontext as _nullcontext
|
| 1834 |
-
except ImportError:
|
| 1835 |
-
class _nullcontext(object):
|
| 1836 |
-
"""
|
| 1837 |
-
Class for wrapping contexts so that they are passed through in a
|
| 1838 |
-
with statement.
|
| 1839 |
-
"""
|
| 1840 |
-
def __init__(self, context):
|
| 1841 |
-
self.context = context
|
| 1842 |
-
|
| 1843 |
-
def __enter__(self):
|
| 1844 |
-
return self.context
|
| 1845 |
-
|
| 1846 |
-
def __exit__(*args, **kwargs):
|
| 1847 |
-
pass
|
| 1848 |
-
|
| 1849 |
-
# vim:ts=4:sw=4:et
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|
spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/network/xmlrpc.py
DELETED
|
@@ -1,60 +0,0 @@
|
|
| 1 |
-
"""xmlrpclib.Transport implementation
|
| 2 |
-
"""
|
| 3 |
-
|
| 4 |
-
import logging
|
| 5 |
-
import urllib.parse
|
| 6 |
-
import xmlrpc.client
|
| 7 |
-
from typing import TYPE_CHECKING, Tuple
|
| 8 |
-
|
| 9 |
-
from pip._internal.exceptions import NetworkConnectionError
|
| 10 |
-
from pip._internal.network.session import PipSession
|
| 11 |
-
from pip._internal.network.utils import raise_for_status
|
| 12 |
-
|
| 13 |
-
if TYPE_CHECKING:
|
| 14 |
-
from xmlrpc.client import _HostType, _Marshallable
|
| 15 |
-
|
| 16 |
-
logger = logging.getLogger(__name__)
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
class PipXmlrpcTransport(xmlrpc.client.Transport):
|
| 20 |
-
"""Provide a `xmlrpclib.Transport` implementation via a `PipSession`
|
| 21 |
-
object.
|
| 22 |
-
"""
|
| 23 |
-
|
| 24 |
-
def __init__(
|
| 25 |
-
self, index_url: str, session: PipSession, use_datetime: bool = False
|
| 26 |
-
) -> None:
|
| 27 |
-
super().__init__(use_datetime)
|
| 28 |
-
index_parts = urllib.parse.urlparse(index_url)
|
| 29 |
-
self._scheme = index_parts.scheme
|
| 30 |
-
self._session = session
|
| 31 |
-
|
| 32 |
-
def request(
|
| 33 |
-
self,
|
| 34 |
-
host: "_HostType",
|
| 35 |
-
handler: str,
|
| 36 |
-
request_body: bytes,
|
| 37 |
-
verbose: bool = False,
|
| 38 |
-
) -> Tuple["_Marshallable", ...]:
|
| 39 |
-
assert isinstance(host, str)
|
| 40 |
-
parts = (self._scheme, host, handler, None, None, None)
|
| 41 |
-
url = urllib.parse.urlunparse(parts)
|
| 42 |
-
try:
|
| 43 |
-
headers = {"Content-Type": "text/xml"}
|
| 44 |
-
response = self._session.post(
|
| 45 |
-
url,
|
| 46 |
-
data=request_body,
|
| 47 |
-
headers=headers,
|
| 48 |
-
stream=True,
|
| 49 |
-
)
|
| 50 |
-
raise_for_status(response)
|
| 51 |
-
self.verbose = verbose
|
| 52 |
-
return self.parse_response(response.raw)
|
| 53 |
-
except NetworkConnectionError as exc:
|
| 54 |
-
assert exc.response
|
| 55 |
-
logger.critical(
|
| 56 |
-
"HTTP error %s while getting %s",
|
| 57 |
-
exc.response.status_code,
|
| 58 |
-
url,
|
| 59 |
-
)
|
| 60 |
-
raise
|
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|
spaces/CVPR/LIVE/thrust/thrust/detail/complex/cproj.h
DELETED
|
@@ -1,71 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2013 NVIDIA Corporation
|
| 3 |
-
* Copyright 2013 Filipe RNC Maia
|
| 4 |
-
*
|
| 5 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 6 |
-
* you may not use this file except in compliance with the License.
|
| 7 |
-
* You may obtain a copy of the License at
|
| 8 |
-
*
|
| 9 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 10 |
-
*
|
| 11 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 12 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 13 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 14 |
-
* See the License for the specific language governing permissions and
|
| 15 |
-
* limitations under the License.
|
| 16 |
-
*/
|
| 17 |
-
|
| 18 |
-
#pragma once
|
| 19 |
-
|
| 20 |
-
#include <thrust/complex.h>
|
| 21 |
-
#include <thrust/detail/complex/math_private.h>
|
| 22 |
-
#include <cmath>
|
| 23 |
-
|
| 24 |
-
namespace thrust{
|
| 25 |
-
namespace detail{
|
| 26 |
-
namespace complex{
|
| 27 |
-
__host__ __device__
|
| 28 |
-
inline complex<float> cprojf(const complex<float>& z){
|
| 29 |
-
if(!isinf(z.real()) && !isinf(z.imag())){
|
| 30 |
-
return z;
|
| 31 |
-
}else{
|
| 32 |
-
// std::numeric_limits<T>::infinity() doesn't run on the GPU
|
| 33 |
-
return complex<float>(infinity<float>(), copysignf(0.0, z.imag()));
|
| 34 |
-
}
|
| 35 |
-
}
|
| 36 |
-
|
| 37 |
-
__host__ __device__
|
| 38 |
-
inline complex<double> cproj(const complex<double>& z){
|
| 39 |
-
if(!isinf(z.real()) && !isinf(z.imag())){
|
| 40 |
-
return z;
|
| 41 |
-
}else{
|
| 42 |
-
// std::numeric_limits<T>::infinity() doesn't run on the GPU
|
| 43 |
-
return complex<double>(infinity<double>(), copysign(0.0, z.imag()));
|
| 44 |
-
}
|
| 45 |
-
}
|
| 46 |
-
|
| 47 |
-
}
|
| 48 |
-
|
| 49 |
-
}
|
| 50 |
-
|
| 51 |
-
template <typename T>
|
| 52 |
-
__host__ __device__
|
| 53 |
-
inline thrust::complex<T> proj(const thrust::complex<T>& z){
|
| 54 |
-
return detail::complex::cproj(z);
|
| 55 |
-
}
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
template <>
|
| 59 |
-
__host__ __device__
|
| 60 |
-
inline thrust::complex<double> proj(const thrust::complex<double>& z){
|
| 61 |
-
return detail::complex::cproj(z);
|
| 62 |
-
}
|
| 63 |
-
|
| 64 |
-
template <>
|
| 65 |
-
__host__ __device__
|
| 66 |
-
inline thrust::complex<float> proj(const thrust::complex<float>& z){
|
| 67 |
-
return detail::complex::cprojf(z);
|
| 68 |
-
}
|
| 69 |
-
|
| 70 |
-
}
|
| 71 |
-
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spaces/CVPR/LIVE/thrust/thrust/iterator/discard_iterator.h
DELETED
|
@@ -1,175 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2013 NVIDIA Corporation
|
| 3 |
-
*
|
| 4 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
* you may not use this file except in compliance with the License.
|
| 6 |
-
* You may obtain a copy of the License at
|
| 7 |
-
*
|
| 8 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
*
|
| 10 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
* See the License for the specific language governing permissions and
|
| 14 |
-
* limitations under the License.
|
| 15 |
-
*/
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
/*! \file thrust/iterator/discard_iterator.h
|
| 19 |
-
* \brief An iterator which "discards" (ignores) values assigned to it upon dereference
|
| 20 |
-
*/
|
| 21 |
-
|
| 22 |
-
#pragma once
|
| 23 |
-
|
| 24 |
-
#include <thrust/detail/config.h>
|
| 25 |
-
#include <thrust/iterator/detail/discard_iterator_base.h>
|
| 26 |
-
#include <thrust/iterator/iterator_facade.h>
|
| 27 |
-
|
| 28 |
-
THRUST_DISABLE_MSVC_POSSIBLE_LOSS_OF_DATA_WARNING_BEGIN
|
| 29 |
-
|
| 30 |
-
namespace thrust
|
| 31 |
-
{
|
| 32 |
-
|
| 33 |
-
/*! \addtogroup iterators
|
| 34 |
-
* \{
|
| 35 |
-
*/
|
| 36 |
-
|
| 37 |
-
/*! \addtogroup fancyiterator Fancy Iterators
|
| 38 |
-
* \ingroup iterators
|
| 39 |
-
* \{
|
| 40 |
-
*/
|
| 41 |
-
|
| 42 |
-
/*! \p discard_iterator is an iterator which represents a special kind of pointer that
|
| 43 |
-
* ignores values written to it upon dereference. This iterator is useful for ignoring
|
| 44 |
-
* the output of certain algorithms without wasting memory capacity or bandwidth.
|
| 45 |
-
* \p discard_iterator may also be used to count the size of an algorithm's output which
|
| 46 |
-
* may not be known a priori.
|
| 47 |
-
*
|
| 48 |
-
* The following code snippet demonstrates how to use \p discard_iterator to ignore
|
| 49 |
-
* ignore one of the output ranges of reduce_by_key
|
| 50 |
-
*
|
| 51 |
-
* \code
|
| 52 |
-
* #include <thrust/iterator/discard_iterator.h>
|
| 53 |
-
* #include <thrust/reduce.h>
|
| 54 |
-
* #include <thrust/device_vector.h>
|
| 55 |
-
*
|
| 56 |
-
* int main()
|
| 57 |
-
* {
|
| 58 |
-
* thrust::device_vector<int> keys(7), values(7);
|
| 59 |
-
*
|
| 60 |
-
* keys[0] = 1;
|
| 61 |
-
* keys[1] = 3;
|
| 62 |
-
* keys[2] = 3;
|
| 63 |
-
* keys[3] = 3;
|
| 64 |
-
* keys[4] = 2;
|
| 65 |
-
* keys[5] = 2;
|
| 66 |
-
* keys[6] = 1;
|
| 67 |
-
*
|
| 68 |
-
* values[0] = 9;
|
| 69 |
-
* values[1] = 8;
|
| 70 |
-
* values[2] = 7;
|
| 71 |
-
* values[3] = 6;
|
| 72 |
-
* values[4] = 5;
|
| 73 |
-
* values[5] = 4;
|
| 74 |
-
* values[6] = 3;
|
| 75 |
-
*
|
| 76 |
-
* thrust::device_vector<int> result(4);
|
| 77 |
-
*
|
| 78 |
-
* // we are only interested in the reduced values
|
| 79 |
-
* // use discard_iterator to ignore the output keys
|
| 80 |
-
* thrust::reduce_by_key(keys.begin(), keys.end(),
|
| 81 |
-
* values.begin(),
|
| 82 |
-
* thrust::make_discard_iterator(),
|
| 83 |
-
* result.begin());
|
| 84 |
-
*
|
| 85 |
-
* // result is now [9, 21, 9, 3]
|
| 86 |
-
*
|
| 87 |
-
* return 0;
|
| 88 |
-
* }
|
| 89 |
-
* \endcode
|
| 90 |
-
*
|
| 91 |
-
* \see make_discard_iterator
|
| 92 |
-
*/
|
| 93 |
-
template<typename System = use_default>
|
| 94 |
-
class discard_iterator
|
| 95 |
-
: public detail::discard_iterator_base<System>::type
|
| 96 |
-
{
|
| 97 |
-
/*! \cond
|
| 98 |
-
*/
|
| 99 |
-
friend class thrust::iterator_core_access;
|
| 100 |
-
typedef typename detail::discard_iterator_base<System>::type super_t;
|
| 101 |
-
typedef typename detail::discard_iterator_base<System>::incrementable incrementable;
|
| 102 |
-
typedef typename detail::discard_iterator_base<System>::base_iterator base_iterator;
|
| 103 |
-
|
| 104 |
-
public:
|
| 105 |
-
typedef typename super_t::reference reference;
|
| 106 |
-
typedef typename super_t::value_type value_type;
|
| 107 |
-
|
| 108 |
-
/*! \endcond
|
| 109 |
-
*/
|
| 110 |
-
|
| 111 |
-
/*! Copy constructor copies from a source discard_iterator.
|
| 112 |
-
*
|
| 113 |
-
* \p rhs The discard_iterator to copy.
|
| 114 |
-
*/
|
| 115 |
-
__host__ __device__
|
| 116 |
-
discard_iterator(discard_iterator const &rhs)
|
| 117 |
-
: super_t(rhs.base()) {}
|
| 118 |
-
|
| 119 |
-
#if THRUST_CPP_DIALECT >= 2011
|
| 120 |
-
discard_iterator & operator=(const discard_iterator &) = default;
|
| 121 |
-
#endif
|
| 122 |
-
|
| 123 |
-
/*! This constructor receives an optional index specifying the position of this
|
| 124 |
-
* \p discard_iterator in a range.
|
| 125 |
-
*
|
| 126 |
-
* \p i The index of this \p discard_iterator in a range. Defaults to the
|
| 127 |
-
* value returned by \c Incrementable's null constructor. For example,
|
| 128 |
-
* when <tt>Incrementable == int</tt>, \c 0.
|
| 129 |
-
*/
|
| 130 |
-
__host__ __device__
|
| 131 |
-
discard_iterator(incrementable const &i = incrementable())
|
| 132 |
-
: super_t(base_iterator(i)) {}
|
| 133 |
-
|
| 134 |
-
/*! \cond
|
| 135 |
-
*/
|
| 136 |
-
|
| 137 |
-
private: // Core iterator interface
|
| 138 |
-
__host__ __device__
|
| 139 |
-
reference dereference() const
|
| 140 |
-
{
|
| 141 |
-
return m_element;
|
| 142 |
-
}
|
| 143 |
-
|
| 144 |
-
mutable value_type m_element;
|
| 145 |
-
|
| 146 |
-
/*! \endcond
|
| 147 |
-
*/
|
| 148 |
-
}; // end constant_iterator
|
| 149 |
-
|
| 150 |
-
|
| 151 |
-
/*! \p make_discard_iterator creates a \p discard_iterator from an optional index parameter.
|
| 152 |
-
*
|
| 153 |
-
* \param i The index of the returned \p discard_iterator within a range.
|
| 154 |
-
* In the default case, the value of this parameter is \c 0.
|
| 155 |
-
*
|
| 156 |
-
* \return A new \p discard_iterator with index as given by \p i.
|
| 157 |
-
*
|
| 158 |
-
* \see constant_iterator
|
| 159 |
-
*/
|
| 160 |
-
inline __host__ __device__
|
| 161 |
-
discard_iterator<> make_discard_iterator(discard_iterator<>::difference_type i = discard_iterator<>::difference_type(0))
|
| 162 |
-
{
|
| 163 |
-
return discard_iterator<>(i);
|
| 164 |
-
} // end make_discard_iterator()
|
| 165 |
-
|
| 166 |
-
/*! \} // end fancyiterators
|
| 167 |
-
*/
|
| 168 |
-
|
| 169 |
-
/*! \} // end iterators
|
| 170 |
-
*/
|
| 171 |
-
|
| 172 |
-
} // end namespace thrust
|
| 173 |
-
|
| 174 |
-
THRUST_DISABLE_MSVC_POSSIBLE_LOSS_OF_DATA_WARNING_END
|
| 175 |
-
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|
spaces/CVPR/LIVE/thrust/thrust/iterator/iterator_facade.h
DELETED
|
@@ -1,543 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2013 NVIDIA Corporation
|
| 3 |
-
*
|
| 4 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
* you may not use this file except in compliance with the License.
|
| 6 |
-
* You may obtain a copy of the License at
|
| 7 |
-
*
|
| 8 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
*
|
| 10 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
* See the License for the specific language governing permissions and
|
| 14 |
-
* limitations under the License.
|
| 15 |
-
*/
|
| 16 |
-
|
| 17 |
-
/*! \file thrust/iterator/iterator_facade.h
|
| 18 |
-
* \brief A class which exposes a public interface for iterators
|
| 19 |
-
*/
|
| 20 |
-
|
| 21 |
-
/*
|
| 22 |
-
* (C) Copyright David Abrahams 2002.
|
| 23 |
-
* (C) Copyright Jeremy Siek 2002.
|
| 24 |
-
* (C) Copyright Thomas Witt 2002.
|
| 25 |
-
*
|
| 26 |
-
* Distributed under the Boost Software License, Version 1.0.
|
| 27 |
-
* (See accompanying NOTICE file for the complete license)
|
| 28 |
-
*
|
| 29 |
-
* For more information, see http://www.boost.org
|
| 30 |
-
*/
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
#pragma once
|
| 34 |
-
|
| 35 |
-
#include <thrust/detail/config.h>
|
| 36 |
-
#include <thrust/detail/type_traits.h>
|
| 37 |
-
#include <thrust/iterator/detail/iterator_facade_category.h>
|
| 38 |
-
#include <thrust/iterator/detail/distance_from_result.h>
|
| 39 |
-
|
| 40 |
-
namespace thrust
|
| 41 |
-
{
|
| 42 |
-
|
| 43 |
-
/*! \addtogroup iterators
|
| 44 |
-
* \{
|
| 45 |
-
*/
|
| 46 |
-
|
| 47 |
-
/*! \addtogroup fancyiterator Fancy Iterators
|
| 48 |
-
* \ingroup iterators
|
| 49 |
-
* \{
|
| 50 |
-
*/
|
| 51 |
-
|
| 52 |
-
|
| 53 |
-
// This forward declaration is required for the friend declaration
|
| 54 |
-
// in iterator_core_access
|
| 55 |
-
template<typename Derived, typename Value, typename System, typename Traversal, typename Reference, typename Difference> class iterator_facade;
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
/*! \p iterator_core_access is the class which user iterator types derived from \p thrust::iterator_adaptor
|
| 59 |
-
* or \p thrust::iterator_facade must befriend to allow it to access their private interface.
|
| 60 |
-
*/
|
| 61 |
-
class iterator_core_access
|
| 62 |
-
{
|
| 63 |
-
/*! \cond
|
| 64 |
-
*/
|
| 65 |
-
|
| 66 |
-
// declare our friends
|
| 67 |
-
template<typename Derived, typename Value, typename System, typename Traversal, typename Reference, typename Difference> friend class iterator_facade;
|
| 68 |
-
|
| 69 |
-
// iterator comparisons are our friends
|
| 70 |
-
template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
|
| 71 |
-
typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
|
| 72 |
-
inline __host__ __device__
|
| 73 |
-
friend bool
|
| 74 |
-
operator ==(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
|
| 75 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs);
|
| 76 |
-
|
| 77 |
-
template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
|
| 78 |
-
typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
|
| 79 |
-
inline __host__ __device__
|
| 80 |
-
friend bool
|
| 81 |
-
operator !=(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
|
| 82 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs);
|
| 83 |
-
|
| 84 |
-
template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
|
| 85 |
-
typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
|
| 86 |
-
inline __host__ __device__
|
| 87 |
-
friend bool
|
| 88 |
-
operator <(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
|
| 89 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs);
|
| 90 |
-
|
| 91 |
-
template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
|
| 92 |
-
typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
|
| 93 |
-
inline __host__ __device__
|
| 94 |
-
friend bool
|
| 95 |
-
operator >(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
|
| 96 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs);
|
| 97 |
-
|
| 98 |
-
template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
|
| 99 |
-
typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
|
| 100 |
-
inline __host__ __device__
|
| 101 |
-
friend bool
|
| 102 |
-
operator <=(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
|
| 103 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs);
|
| 104 |
-
|
| 105 |
-
template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
|
| 106 |
-
typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
|
| 107 |
-
inline __host__ __device__
|
| 108 |
-
friend bool
|
| 109 |
-
operator >=(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
|
| 110 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs);
|
| 111 |
-
|
| 112 |
-
// iterator difference is our friend
|
| 113 |
-
template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
|
| 114 |
-
typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
|
| 115 |
-
inline __host__ __device__
|
| 116 |
-
friend
|
| 117 |
-
typename thrust::detail::distance_from_result<
|
| 118 |
-
iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1>,
|
| 119 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2>
|
| 120 |
-
>::type
|
| 121 |
-
operator-(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
|
| 122 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs);
|
| 123 |
-
|
| 124 |
-
template<typename Facade>
|
| 125 |
-
__host__ __device__
|
| 126 |
-
static typename Facade::reference dereference(Facade const& f)
|
| 127 |
-
{
|
| 128 |
-
return f.dereference();
|
| 129 |
-
}
|
| 130 |
-
|
| 131 |
-
template<typename Facade>
|
| 132 |
-
__host__ __device__
|
| 133 |
-
static void increment(Facade& f)
|
| 134 |
-
{
|
| 135 |
-
f.increment();
|
| 136 |
-
}
|
| 137 |
-
|
| 138 |
-
template<typename Facade>
|
| 139 |
-
__host__ __device__
|
| 140 |
-
static void decrement(Facade& f)
|
| 141 |
-
{
|
| 142 |
-
f.decrement();
|
| 143 |
-
}
|
| 144 |
-
|
| 145 |
-
template <class Facade1, class Facade2>
|
| 146 |
-
__host__ __device__
|
| 147 |
-
static bool equal(Facade1 const& f1, Facade2 const& f2)
|
| 148 |
-
{
|
| 149 |
-
return f1.equal(f2);
|
| 150 |
-
}
|
| 151 |
-
|
| 152 |
-
// XXX TODO: Investigate whether we need both of these cases
|
| 153 |
-
//template <class Facade1, class Facade2>
|
| 154 |
-
//__host__ __device__
|
| 155 |
-
//static bool equal(Facade1 const& f1, Facade2 const& f2, mpl::true_)
|
| 156 |
-
//{
|
| 157 |
-
// return f1.equal(f2);
|
| 158 |
-
//}
|
| 159 |
-
|
| 160 |
-
//template <class Facade1, class Facade2>
|
| 161 |
-
//__host__ __device__
|
| 162 |
-
//static bool equal(Facade1 const& f1, Facade2 const& f2, mpl::false_)
|
| 163 |
-
//{
|
| 164 |
-
// return f2.equal(f1);
|
| 165 |
-
//}
|
| 166 |
-
|
| 167 |
-
template <class Facade>
|
| 168 |
-
__host__ __device__
|
| 169 |
-
static void advance(Facade& f, typename Facade::difference_type n)
|
| 170 |
-
{
|
| 171 |
-
f.advance(n);
|
| 172 |
-
}
|
| 173 |
-
|
| 174 |
-
// Facade2 is convertible to Facade1,
|
| 175 |
-
// so return Facade1's difference_type
|
| 176 |
-
template <class Facade1, class Facade2>
|
| 177 |
-
__host__ __device__
|
| 178 |
-
static typename Facade1::difference_type
|
| 179 |
-
distance_from(Facade1 const& f1, Facade2 const& f2, thrust::detail::true_type)
|
| 180 |
-
{
|
| 181 |
-
return -f1.distance_to(f2);
|
| 182 |
-
}
|
| 183 |
-
|
| 184 |
-
// Facade2 is not convertible to Facade1,
|
| 185 |
-
// so return Facade2's difference_type
|
| 186 |
-
template <class Facade1, class Facade2>
|
| 187 |
-
__host__ __device__
|
| 188 |
-
static typename Facade2::difference_type
|
| 189 |
-
distance_from(Facade1 const& f1, Facade2 const& f2, thrust::detail::false_type)
|
| 190 |
-
{
|
| 191 |
-
return f2.distance_to(f1);
|
| 192 |
-
}
|
| 193 |
-
|
| 194 |
-
template <class Facade1, class Facade2>
|
| 195 |
-
__host__ __device__
|
| 196 |
-
static typename thrust::detail::distance_from_result<Facade1,Facade2>::type
|
| 197 |
-
distance_from(Facade1 const& f1, Facade2 const& f2)
|
| 198 |
-
{
|
| 199 |
-
// dispatch the implementation of this method upon whether or not
|
| 200 |
-
// Facade2 is convertible to Facade1
|
| 201 |
-
return distance_from(f1, f2,
|
| 202 |
-
typename thrust::detail::is_convertible<Facade2,Facade1>::type());
|
| 203 |
-
}
|
| 204 |
-
|
| 205 |
-
//
|
| 206 |
-
// Curiously Recurring Template interface.
|
| 207 |
-
//
|
| 208 |
-
template <typename Derived, typename Value, typename System, typename Traversal, typename Reference, typename Difference>
|
| 209 |
-
__host__ __device__
|
| 210 |
-
static Derived& derived(iterator_facade<Derived,Value,System,Traversal,Reference,Difference>& facade)
|
| 211 |
-
{
|
| 212 |
-
return *static_cast<Derived*>(&facade);
|
| 213 |
-
}
|
| 214 |
-
|
| 215 |
-
template <typename Derived, typename Value, typename System, typename Traversal, typename Reference, typename Difference>
|
| 216 |
-
__host__ __device__
|
| 217 |
-
static Derived const& derived(iterator_facade<Derived,Value,System,Traversal,Reference,Difference> const& facade)
|
| 218 |
-
{
|
| 219 |
-
return *static_cast<Derived const*>(&facade);
|
| 220 |
-
}
|
| 221 |
-
|
| 222 |
-
/*! \endcond
|
| 223 |
-
*/
|
| 224 |
-
}; // end iterator_core_access
|
| 225 |
-
|
| 226 |
-
|
| 227 |
-
/*! \p iterator_facade is a template which allows the programmer to define a novel iterator with a standards-conforming interface
|
| 228 |
-
* which Thrust can use to reason about algorithm acceleration opportunities.
|
| 229 |
-
*
|
| 230 |
-
* Because most of a standard iterator's interface is defined in terms of a small set of core primitives, \p iterator_facade
|
| 231 |
-
* defines the non-primitive portion mechanically. In principle a novel iterator could explicitly provide the entire interface in
|
| 232 |
-
* an ad hoc fashion but doing so might be tedious and prone to subtle errors.
|
| 233 |
-
*
|
| 234 |
-
* Often \p iterator_facade is too primitive a tool to use for defining novel iterators. In these cases, \p iterator_adaptor
|
| 235 |
-
* or a specific fancy iterator should be used instead.
|
| 236 |
-
*
|
| 237 |
-
* \p iterator_facade's functionality is derived from and generally equivalent to \p boost::iterator_facade.
|
| 238 |
-
* The exception is Thrust's addition of the template parameter \p System, which is necessary to allow Thrust
|
| 239 |
-
* to dispatch an algorithm to one of several parallel backend systems. An additional exception is Thrust's omission
|
| 240 |
-
* of the \c operator-> member function.
|
| 241 |
-
*
|
| 242 |
-
* Interested users may refer to <tt>boost::iterator_facade</tt>'s documentation for usage examples.
|
| 243 |
-
*
|
| 244 |
-
* \note \p iterator_facade's arithmetic operator free functions exist with the usual meanings but are omitted here for brevity.
|
| 245 |
-
*/
|
| 246 |
-
template<typename Derived,
|
| 247 |
-
typename Value,
|
| 248 |
-
typename System,
|
| 249 |
-
typename Traversal,
|
| 250 |
-
typename Reference,
|
| 251 |
-
typename Difference = std::ptrdiff_t>
|
| 252 |
-
class iterator_facade
|
| 253 |
-
{
|
| 254 |
-
private:
|
| 255 |
-
/*! \cond
|
| 256 |
-
*/
|
| 257 |
-
|
| 258 |
-
//
|
| 259 |
-
// Curiously Recurring Template interface.
|
| 260 |
-
//
|
| 261 |
-
__host__ __device__
|
| 262 |
-
Derived& derived()
|
| 263 |
-
{
|
| 264 |
-
return *static_cast<Derived*>(this);
|
| 265 |
-
}
|
| 266 |
-
|
| 267 |
-
__host__ __device__
|
| 268 |
-
Derived const& derived() const
|
| 269 |
-
{
|
| 270 |
-
return *static_cast<Derived const*>(this);
|
| 271 |
-
}
|
| 272 |
-
/*! \endcond
|
| 273 |
-
*/
|
| 274 |
-
|
| 275 |
-
public:
|
| 276 |
-
/*! The type of element pointed to by \p iterator_facade.
|
| 277 |
-
*/
|
| 278 |
-
typedef typename thrust::detail::remove_const<Value>::type value_type;
|
| 279 |
-
|
| 280 |
-
/*! The return type of \p iterator_facade::operator*().
|
| 281 |
-
*/
|
| 282 |
-
typedef Reference reference;
|
| 283 |
-
|
| 284 |
-
/*! The return type of \p iterator_facade's non-existent \c operator->()
|
| 285 |
-
* member function. Unlike \c boost::iterator_facade, \p iterator_facade
|
| 286 |
-
* disallows access to the \p value_type's members through expressions of the
|
| 287 |
-
* form <tt>iter->member</tt>. \p pointer is defined to \c void to indicate
|
| 288 |
-
* that these expressions are not allowed. This limitation may be relaxed in a
|
| 289 |
-
* future version of Thrust.
|
| 290 |
-
*/
|
| 291 |
-
typedef void pointer;
|
| 292 |
-
|
| 293 |
-
/*! The type of expressions of the form <tt>x - y</tt> where <tt>x</tt> and <tt>y</tt>
|
| 294 |
-
* are of type \p iterator_facade.
|
| 295 |
-
*/
|
| 296 |
-
typedef Difference difference_type;
|
| 297 |
-
|
| 298 |
-
/*! The type of iterator category of \p iterator_facade.
|
| 299 |
-
*/
|
| 300 |
-
typedef typename thrust::detail::iterator_facade_category<
|
| 301 |
-
System, Traversal, Value, Reference
|
| 302 |
-
>::type iterator_category;
|
| 303 |
-
|
| 304 |
-
/*! \p operator*() dereferences this \p iterator_facade.
|
| 305 |
-
* \return A reference to the element pointed to by this \p iterator_facade.
|
| 306 |
-
*/
|
| 307 |
-
__host__ __device__
|
| 308 |
-
reference operator*() const
|
| 309 |
-
{
|
| 310 |
-
return iterator_core_access::dereference(this->derived());
|
| 311 |
-
}
|
| 312 |
-
|
| 313 |
-
// XXX unimplemented for now, consider implementing it later
|
| 314 |
-
//pointer operator->() const
|
| 315 |
-
//{
|
| 316 |
-
// return;
|
| 317 |
-
//}
|
| 318 |
-
|
| 319 |
-
// XXX investigate whether or not we need to go to the lengths
|
| 320 |
-
// boost does to determine the return type
|
| 321 |
-
|
| 322 |
-
/*! \p operator[] performs indexed dereference.
|
| 323 |
-
* \return A reference to the element \p n distance away from this \p iterator_facade.
|
| 324 |
-
*/
|
| 325 |
-
__host__ __device__
|
| 326 |
-
reference operator[](difference_type n) const
|
| 327 |
-
{
|
| 328 |
-
return *(this->derived() + n);
|
| 329 |
-
}
|
| 330 |
-
|
| 331 |
-
/*! \p operator++ pre-increments this \p iterator_facade to refer to the element in the next position.
|
| 332 |
-
* \return <tt>*this</tt>
|
| 333 |
-
*/
|
| 334 |
-
__host__ __device__
|
| 335 |
-
Derived& operator++()
|
| 336 |
-
{
|
| 337 |
-
iterator_core_access::increment(this->derived());
|
| 338 |
-
return this->derived();
|
| 339 |
-
}
|
| 340 |
-
|
| 341 |
-
/*! \p operator++ post-increments this \p iterator_facade and returns a new \p iterator_facade referring to the element in the next position.
|
| 342 |
-
* \return A copy of <tt>*this</tt> before increment.
|
| 343 |
-
*/
|
| 344 |
-
__host__ __device__
|
| 345 |
-
Derived operator++(int)
|
| 346 |
-
{
|
| 347 |
-
Derived tmp(this->derived());
|
| 348 |
-
++*this;
|
| 349 |
-
return tmp;
|
| 350 |
-
}
|
| 351 |
-
|
| 352 |
-
/*! \p operator-- pre-decrements this \p iterator_facade to refer to the element in the previous position.
|
| 353 |
-
* \return <tt>*this</tt>
|
| 354 |
-
*/
|
| 355 |
-
__host__ __device__
|
| 356 |
-
Derived& operator--()
|
| 357 |
-
{
|
| 358 |
-
iterator_core_access::decrement(this->derived());
|
| 359 |
-
return this->derived();
|
| 360 |
-
}
|
| 361 |
-
|
| 362 |
-
/*! \p operator-- post-decrements this \p iterator_facade and returns a new \p iterator_facade referring to the element in the previous position.
|
| 363 |
-
* \return A copy of <tt>*this</tt> before decrement.
|
| 364 |
-
*/
|
| 365 |
-
__host__ __device__
|
| 366 |
-
Derived operator--(int)
|
| 367 |
-
{
|
| 368 |
-
Derived tmp(this->derived());
|
| 369 |
-
--*this;
|
| 370 |
-
return tmp;
|
| 371 |
-
}
|
| 372 |
-
|
| 373 |
-
/*! \p operator+= increments this \p iterator_facade to refer to an element a given distance after its current position.
|
| 374 |
-
* \param n The quantity to increment.
|
| 375 |
-
* \return <tt>*this</tt>
|
| 376 |
-
*/
|
| 377 |
-
__host__ __device__
|
| 378 |
-
Derived& operator+=(difference_type n)
|
| 379 |
-
{
|
| 380 |
-
iterator_core_access::advance(this->derived(), n);
|
| 381 |
-
return this->derived();
|
| 382 |
-
}
|
| 383 |
-
|
| 384 |
-
/*! \p operator-= decrements this \p iterator_facade to refer to an element a given distance before its current postition.
|
| 385 |
-
* \param n The quantity to decrement.
|
| 386 |
-
* \return <tt>*this</tt>
|
| 387 |
-
*/
|
| 388 |
-
__host__ __device__
|
| 389 |
-
Derived& operator-=(difference_type n)
|
| 390 |
-
{
|
| 391 |
-
iterator_core_access::advance(this->derived(), -n);
|
| 392 |
-
return this->derived();
|
| 393 |
-
}
|
| 394 |
-
|
| 395 |
-
/*! \p operator- subtracts a given quantity from this \p iterator_facade and returns a new \p iterator_facade referring to the element at the given position before this \p iterator_facade.
|
| 396 |
-
* \param n The quantity to decrement
|
| 397 |
-
* \return An \p iterator_facade pointing \p n elements before this \p iterator_facade.
|
| 398 |
-
*/
|
| 399 |
-
__host__ __device__
|
| 400 |
-
Derived operator-(difference_type n) const
|
| 401 |
-
{
|
| 402 |
-
Derived result(this->derived());
|
| 403 |
-
return result -= n;
|
| 404 |
-
}
|
| 405 |
-
}; // end iterator_facade
|
| 406 |
-
|
| 407 |
-
/*! \cond
|
| 408 |
-
*/
|
| 409 |
-
|
| 410 |
-
// Comparison operators
|
| 411 |
-
template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
|
| 412 |
-
typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
|
| 413 |
-
inline __host__ __device__
|
| 414 |
-
// XXX it might be nice to implement this at some point
|
| 415 |
-
//typename enable_if_interoperable<Dr1,Dr2,bool>::type // exposition
|
| 416 |
-
bool
|
| 417 |
-
operator ==(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
|
| 418 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs)
|
| 419 |
-
{
|
| 420 |
-
return iterator_core_access
|
| 421 |
-
::equal(*static_cast<Derived1 const*>(&lhs),
|
| 422 |
-
*static_cast<Derived2 const*>(&rhs));
|
| 423 |
-
}
|
| 424 |
-
|
| 425 |
-
template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
|
| 426 |
-
typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
|
| 427 |
-
inline __host__ __device__
|
| 428 |
-
// XXX it might be nice to implement this at some point
|
| 429 |
-
//typename enable_if_interoperable<Dr1,Dr2,bool>::type // exposition
|
| 430 |
-
bool
|
| 431 |
-
operator !=(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
|
| 432 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs)
|
| 433 |
-
{
|
| 434 |
-
return !iterator_core_access
|
| 435 |
-
::equal(*static_cast<Derived1 const*>(&lhs),
|
| 436 |
-
*static_cast<Derived2 const*>(&rhs));
|
| 437 |
-
}
|
| 438 |
-
|
| 439 |
-
template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
|
| 440 |
-
typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
|
| 441 |
-
inline __host__ __device__
|
| 442 |
-
// XXX it might be nice to implement this at some point
|
| 443 |
-
//typename enable_if_interoperable<Dr1,Dr2,bool>::type // exposition
|
| 444 |
-
bool
|
| 445 |
-
operator <(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
|
| 446 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs)
|
| 447 |
-
{
|
| 448 |
-
return 0 > iterator_core_access
|
| 449 |
-
::distance_from(*static_cast<Derived1 const*>(&lhs),
|
| 450 |
-
*static_cast<Derived2 const*>(&rhs));
|
| 451 |
-
}
|
| 452 |
-
|
| 453 |
-
template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
|
| 454 |
-
typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
|
| 455 |
-
inline __host__ __device__
|
| 456 |
-
// XXX it might be nice to implement this at some point
|
| 457 |
-
//typename enable_if_interoperable<Dr1,Dr2,bool>::type // exposition
|
| 458 |
-
bool
|
| 459 |
-
operator >(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
|
| 460 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs)
|
| 461 |
-
{
|
| 462 |
-
return 0 < iterator_core_access
|
| 463 |
-
::distance_from(*static_cast<Derived1 const*>(&lhs),
|
| 464 |
-
*static_cast<Derived2 const*>(&rhs));
|
| 465 |
-
}
|
| 466 |
-
|
| 467 |
-
template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
|
| 468 |
-
typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
|
| 469 |
-
inline __host__ __device__
|
| 470 |
-
// XXX it might be nice to implement this at some point
|
| 471 |
-
//typename enable_if_interoperable<Dr1,Dr2,bool>::type // exposition
|
| 472 |
-
bool
|
| 473 |
-
operator <=(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
|
| 474 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs)
|
| 475 |
-
{
|
| 476 |
-
return 0 >= iterator_core_access
|
| 477 |
-
::distance_from(*static_cast<Derived1 const*>(&lhs),
|
| 478 |
-
*static_cast<Derived2 const*>(&rhs));
|
| 479 |
-
}
|
| 480 |
-
|
| 481 |
-
template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
|
| 482 |
-
typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
|
| 483 |
-
inline __host__ __device__
|
| 484 |
-
// XXX it might be nice to implement this at some point
|
| 485 |
-
//typename enable_if_interoperable<Dr1,Dr2,bool>::type // exposition
|
| 486 |
-
bool
|
| 487 |
-
operator >=(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
|
| 488 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs)
|
| 489 |
-
{
|
| 490 |
-
return 0 <= iterator_core_access
|
| 491 |
-
::distance_from(*static_cast<Derived1 const*>(&lhs),
|
| 492 |
-
*static_cast<Derived2 const*>(&rhs));
|
| 493 |
-
}
|
| 494 |
-
|
| 495 |
-
// Iterator difference
|
| 496 |
-
template <typename Derived1, typename Value1, typename System1, typename Traversal1, typename Reference1, typename Difference1,
|
| 497 |
-
typename Derived2, typename Value2, typename System2, typename Traversal2, typename Reference2, typename Difference2>
|
| 498 |
-
inline __host__ __device__
|
| 499 |
-
|
| 500 |
-
// divine the type this operator returns
|
| 501 |
-
typename thrust::detail::distance_from_result<
|
| 502 |
-
iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1>,
|
| 503 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2>
|
| 504 |
-
>::type
|
| 505 |
-
|
| 506 |
-
operator-(iterator_facade<Derived1,Value1,System1,Traversal1,Reference1,Difference1> const& lhs,
|
| 507 |
-
iterator_facade<Derived2,Value2,System2,Traversal2,Reference2,Difference2> const& rhs)
|
| 508 |
-
{
|
| 509 |
-
return iterator_core_access
|
| 510 |
-
::distance_from(*static_cast<Derived1 const*>(&lhs),
|
| 511 |
-
*static_cast<Derived2 const*>(&rhs));
|
| 512 |
-
}
|
| 513 |
-
|
| 514 |
-
// Iterator addition
|
| 515 |
-
template <typename Derived, typename Value, typename System, typename Traversal, typename Reference, typename Difference>
|
| 516 |
-
inline __host__ __device__
|
| 517 |
-
Derived operator+ (iterator_facade<Derived,Value,System,Traversal,Reference,Difference> const& i,
|
| 518 |
-
typename Derived::difference_type n)
|
| 519 |
-
{
|
| 520 |
-
Derived tmp(static_cast<Derived const&>(i));
|
| 521 |
-
return tmp += n;
|
| 522 |
-
}
|
| 523 |
-
|
| 524 |
-
template <typename Derived, typename Value, typename System, typename Traversal, typename Reference, typename Difference>
|
| 525 |
-
inline __host__ __device__
|
| 526 |
-
Derived operator+ (typename Derived::difference_type n,
|
| 527 |
-
iterator_facade<Derived,Value,System,Traversal,Reference,Difference> const& i)
|
| 528 |
-
{
|
| 529 |
-
Derived tmp(static_cast<Derived const&>(i));
|
| 530 |
-
return tmp += n;
|
| 531 |
-
}
|
| 532 |
-
|
| 533 |
-
/*! \endcond
|
| 534 |
-
*/
|
| 535 |
-
|
| 536 |
-
/*! \} // end fancyiterators
|
| 537 |
-
*/
|
| 538 |
-
|
| 539 |
-
/*! \} // end iterators
|
| 540 |
-
*/
|
| 541 |
-
|
| 542 |
-
} // end thrust
|
| 543 |
-
|
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|
spaces/CVPR/LIVE/thrust/thrust/mr/memory_resource.h
DELETED
|
@@ -1,217 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2018 NVIDIA Corporation
|
| 3 |
-
*
|
| 4 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
* you may not use this file except in compliance with the License.
|
| 6 |
-
* You may obtain a copy of the License at
|
| 7 |
-
*
|
| 8 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
*
|
| 10 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
* See the License for the specific language governing permissions and
|
| 14 |
-
* limitations under the License.
|
| 15 |
-
*/
|
| 16 |
-
|
| 17 |
-
/*! \file mr/memory_resource.h
|
| 18 |
-
* \brief A base class for the memory resource system, similar to std::memory_resource,
|
| 19 |
-
* and related utilities.
|
| 20 |
-
*/
|
| 21 |
-
|
| 22 |
-
#pragma once
|
| 23 |
-
|
| 24 |
-
#include "detail/config.h"
|
| 25 |
-
#ifdef THRUST_MR_STD_MR_HEADER
|
| 26 |
-
# include THRUST_MR_STD_MR_HEADER
|
| 27 |
-
#endif
|
| 28 |
-
|
| 29 |
-
namespace thrust
|
| 30 |
-
{
|
| 31 |
-
/*! \brief \p thrust::mr is the namespace containing system agnostic types and functions for \p memory_resource related functionalities.
|
| 32 |
-
*/
|
| 33 |
-
namespace mr
|
| 34 |
-
{
|
| 35 |
-
|
| 36 |
-
/** \addtogroup memory_resources Memory Resources
|
| 37 |
-
* \ingroup memory_management_classes
|
| 38 |
-
* \{
|
| 39 |
-
*/
|
| 40 |
-
|
| 41 |
-
/*! \p memory_resource is the base class for all other memory resources.
|
| 42 |
-
*
|
| 43 |
-
* \tparam Pointer the pointer type that is allocated and deallocated by the memory resource
|
| 44 |
-
* derived from this base class. If this is <tt>void *</tt>, this class derives from
|
| 45 |
-
* <tt>std::pmr::memory_resource</tt>.
|
| 46 |
-
*/
|
| 47 |
-
template<typename Pointer = void *>
|
| 48 |
-
class memory_resource
|
| 49 |
-
{
|
| 50 |
-
public:
|
| 51 |
-
/*! Alias for the template parameter.
|
| 52 |
-
*/
|
| 53 |
-
typedef Pointer pointer;
|
| 54 |
-
|
| 55 |
-
/*! Virtual destructor, defaulted when possible.
|
| 56 |
-
*/
|
| 57 |
-
virtual ~memory_resource() THRUST_DEFAULT
|
| 58 |
-
|
| 59 |
-
/*! Allocates memory of size at least \p bytes and alignment at least \p alignment.
|
| 60 |
-
*
|
| 61 |
-
* \param bytes size, in bytes, that is requested from this allocation
|
| 62 |
-
* \param alignment alignment that is requested from this allocation
|
| 63 |
-
* \throws thrust::bad_alloc when no memory with requested size and alignment can be allocated.
|
| 64 |
-
* \returns A pointer to void to the newly allocated memory.
|
| 65 |
-
*/
|
| 66 |
-
THRUST_NODISCARD
|
| 67 |
-
pointer allocate(std::size_t bytes, std::size_t alignment = THRUST_MR_DEFAULT_ALIGNMENT)
|
| 68 |
-
{
|
| 69 |
-
return do_allocate(bytes, alignment);
|
| 70 |
-
}
|
| 71 |
-
|
| 72 |
-
/*! Deallocates memory pointed to by \p p.
|
| 73 |
-
*
|
| 74 |
-
* \param p pointer to be deallocated
|
| 75 |
-
* \param bytes the size of the allocation. This must be equivalent to the value of \p bytes that
|
| 76 |
-
* was passed to the allocation function that returned \p p.
|
| 77 |
-
* \param alignment the alignment of the allocation. This must be equivalent to the value of \p alignment
|
| 78 |
-
* that was passed to the allocation function that returned \p p.
|
| 79 |
-
*/
|
| 80 |
-
void deallocate(pointer p, std::size_t bytes, std::size_t alignment = THRUST_MR_DEFAULT_ALIGNMENT)
|
| 81 |
-
{
|
| 82 |
-
do_deallocate(p, bytes, alignment);
|
| 83 |
-
}
|
| 84 |
-
|
| 85 |
-
/*! Compares this resource to the other one. The default implementation uses identity comparison,
|
| 86 |
-
* which is often the right thing to do and doesn't require RTTI involvement.
|
| 87 |
-
*
|
| 88 |
-
* \param other the other resource to compare this resource to
|
| 89 |
-
* \returns whether the two resources are equivalent.
|
| 90 |
-
*/
|
| 91 |
-
__host__ __device__
|
| 92 |
-
bool is_equal(const memory_resource & other) const THRUST_NOEXCEPT
|
| 93 |
-
{
|
| 94 |
-
return do_is_equal(other);
|
| 95 |
-
}
|
| 96 |
-
|
| 97 |
-
/*! Allocates memory of size at least \p bytes and alignment at least \p alignment.
|
| 98 |
-
*
|
| 99 |
-
* \param bytes size, in bytes, that is requested from this allocation
|
| 100 |
-
* \param alignment alignment that is requested from this allocation
|
| 101 |
-
* \throws thrust::bad_alloc when no memory with requested size and alignment can be allocated.
|
| 102 |
-
* \returns A pointer to void to the newly allocated memory.
|
| 103 |
-
*/
|
| 104 |
-
virtual pointer do_allocate(std::size_t bytes, std::size_t alignment) = 0;
|
| 105 |
-
|
| 106 |
-
/*! Deallocates memory pointed to by \p p.
|
| 107 |
-
*
|
| 108 |
-
* \param p pointer to be deallocated
|
| 109 |
-
* \param bytes the size of the allocation. This must be equivalent to the value of \p bytes that
|
| 110 |
-
* was passed to the allocation function that returned \p p.
|
| 111 |
-
* \param alignment the size of the allocation. This must be equivalent to the value of \p alignment
|
| 112 |
-
* that was passed to the allocation function that returned \p p.
|
| 113 |
-
*/
|
| 114 |
-
virtual void do_deallocate(pointer p, std::size_t bytes, std::size_t alignment) = 0;
|
| 115 |
-
|
| 116 |
-
/*! Compares this resource to the other one. The default implementation uses identity comparison,
|
| 117 |
-
* which is often the right thing to do and doesn't require RTTI involvement.
|
| 118 |
-
*
|
| 119 |
-
* \param other the other resource to compare this resource to
|
| 120 |
-
* \returns whether the two resources are equivalent.
|
| 121 |
-
*/
|
| 122 |
-
__host__ __device__
|
| 123 |
-
virtual bool do_is_equal(const memory_resource & other) const THRUST_NOEXCEPT
|
| 124 |
-
{
|
| 125 |
-
return this == &other;
|
| 126 |
-
}
|
| 127 |
-
};
|
| 128 |
-
|
| 129 |
-
template<>
|
| 130 |
-
class memory_resource<void *>
|
| 131 |
-
#ifdef THRUST_STD_MR_NS
|
| 132 |
-
: THRUST_STD_MR_NS::memory_resource
|
| 133 |
-
#endif
|
| 134 |
-
{
|
| 135 |
-
public:
|
| 136 |
-
typedef void * pointer;
|
| 137 |
-
|
| 138 |
-
virtual ~memory_resource() THRUST_DEFAULT
|
| 139 |
-
|
| 140 |
-
THRUST_NODISCARD
|
| 141 |
-
pointer allocate(std::size_t bytes, std::size_t alignment = THRUST_MR_DEFAULT_ALIGNMENT)
|
| 142 |
-
{
|
| 143 |
-
return do_allocate(bytes, alignment);
|
| 144 |
-
}
|
| 145 |
-
|
| 146 |
-
void deallocate(pointer p, std::size_t bytes, std::size_t alignment = THRUST_MR_DEFAULT_ALIGNMENT)
|
| 147 |
-
{
|
| 148 |
-
do_deallocate(p, bytes, alignment);
|
| 149 |
-
}
|
| 150 |
-
|
| 151 |
-
__host__ __device__
|
| 152 |
-
bool is_equal(const memory_resource & other) const THRUST_NOEXCEPT
|
| 153 |
-
{
|
| 154 |
-
return do_is_equal(other);
|
| 155 |
-
}
|
| 156 |
-
|
| 157 |
-
virtual pointer do_allocate(std::size_t bytes, std::size_t alignment) = 0;
|
| 158 |
-
virtual void do_deallocate(pointer p, std::size_t bytes, std::size_t alignment) = 0;
|
| 159 |
-
__host__ __device__
|
| 160 |
-
virtual bool do_is_equal(const memory_resource & other) const THRUST_NOEXCEPT
|
| 161 |
-
{
|
| 162 |
-
return this == &other;
|
| 163 |
-
}
|
| 164 |
-
|
| 165 |
-
#ifdef THRUST_STD_MR_NS
|
| 166 |
-
// the above do_is_equal is a different function than the one from the standard memory resource
|
| 167 |
-
// can't implement this reasonably without RTTI though; it's reasonable to assume false otherwise
|
| 168 |
-
|
| 169 |
-
virtual bool do_is_equal(const THRUST_STD_MR_NS::memory_resource & other) const noexcept override
|
| 170 |
-
{
|
| 171 |
-
# ifdef THRUST_HAS_DYNAMIC_CAST
|
| 172 |
-
auto mr_resource = dynamic_cast<memory_resource<> *>(&other);
|
| 173 |
-
return mr_resource && do_is_equal(*mr_resource);
|
| 174 |
-
# else
|
| 175 |
-
return this == &other;
|
| 176 |
-
# endif
|
| 177 |
-
}
|
| 178 |
-
#endif
|
| 179 |
-
};
|
| 180 |
-
|
| 181 |
-
/*! Compares the memory resources for equality, first by identity, then by \p is_equal.
|
| 182 |
-
*/
|
| 183 |
-
template<typename Pointer>
|
| 184 |
-
__host__ __device__
|
| 185 |
-
bool operator==(const memory_resource<Pointer> & lhs, const memory_resource<Pointer> & rhs) THRUST_NOEXCEPT
|
| 186 |
-
{
|
| 187 |
-
return &lhs == &rhs || rhs.is_equal(rhs);
|
| 188 |
-
}
|
| 189 |
-
|
| 190 |
-
/*! Compares the memory resources for inequality, first by identity, then by \p is_equal.
|
| 191 |
-
*/
|
| 192 |
-
template<typename Pointer>
|
| 193 |
-
__host__ __device__
|
| 194 |
-
bool operator!=(const memory_resource<Pointer> & lhs, const memory_resource<Pointer> & rhs) THRUST_NOEXCEPT
|
| 195 |
-
{
|
| 196 |
-
return !(lhs == rhs);
|
| 197 |
-
}
|
| 198 |
-
|
| 199 |
-
/*! Returns a global instance of \p MR, created as a function local static variable.
|
| 200 |
-
*
|
| 201 |
-
* \tparam MR type of a memory resource to get an instance from. Must be \p DefaultConstructible.
|
| 202 |
-
* \returns a pointer to a global instance of \p MR.
|
| 203 |
-
*/
|
| 204 |
-
template<typename MR>
|
| 205 |
-
__host__
|
| 206 |
-
MR * get_global_resource()
|
| 207 |
-
{
|
| 208 |
-
static MR resource;
|
| 209 |
-
return &resource;
|
| 210 |
-
}
|
| 211 |
-
|
| 212 |
-
/*! \}
|
| 213 |
-
*/
|
| 214 |
-
|
| 215 |
-
} // end mr
|
| 216 |
-
} // end thrust
|
| 217 |
-
|
|
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|
|
spaces/CVPR/Text2Human/Text2Human/train_parsing_token.py
DELETED
|
@@ -1,122 +0,0 @@
|
|
| 1 |
-
import argparse
|
| 2 |
-
import logging
|
| 3 |
-
import os
|
| 4 |
-
import os.path as osp
|
| 5 |
-
import random
|
| 6 |
-
import time
|
| 7 |
-
|
| 8 |
-
import torch
|
| 9 |
-
|
| 10 |
-
from data.mask_dataset import MaskDataset
|
| 11 |
-
from models import create_model
|
| 12 |
-
from utils.logger import MessageLogger, get_root_logger, init_tb_logger
|
| 13 |
-
from utils.options import dict2str, dict_to_nonedict, parse
|
| 14 |
-
from utils.util import make_exp_dirs
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
def main():
|
| 18 |
-
# options
|
| 19 |
-
parser = argparse.ArgumentParser()
|
| 20 |
-
parser.add_argument('-opt', type=str, help='Path to option YAML file.')
|
| 21 |
-
args = parser.parse_args()
|
| 22 |
-
opt = parse(args.opt, is_train=True)
|
| 23 |
-
|
| 24 |
-
# mkdir and loggers
|
| 25 |
-
make_exp_dirs(opt)
|
| 26 |
-
log_file = osp.join(opt['path']['log'], f"train_{opt['name']}.log")
|
| 27 |
-
logger = get_root_logger(
|
| 28 |
-
logger_name='base', log_level=logging.INFO, log_file=log_file)
|
| 29 |
-
logger.info(dict2str(opt))
|
| 30 |
-
# initialize tensorboard logger
|
| 31 |
-
tb_logger = None
|
| 32 |
-
if opt['use_tb_logger'] and 'debug' not in opt['name']:
|
| 33 |
-
tb_logger = init_tb_logger(log_dir='./tb_logger/' + opt['name'])
|
| 34 |
-
|
| 35 |
-
# convert to NoneDict, which returns None for missing keys
|
| 36 |
-
opt = dict_to_nonedict(opt)
|
| 37 |
-
|
| 38 |
-
# set up data loader
|
| 39 |
-
train_dataset = MaskDataset(
|
| 40 |
-
segm_dir=opt['segm_dir'], ann_dir=opt['train_ann_file'], xflip=True)
|
| 41 |
-
train_loader = torch.utils.data.DataLoader(
|
| 42 |
-
dataset=train_dataset,
|
| 43 |
-
batch_size=opt['batch_size'],
|
| 44 |
-
shuffle=True,
|
| 45 |
-
num_workers=opt['num_workers'],
|
| 46 |
-
persistent_workers=True,
|
| 47 |
-
drop_last=True)
|
| 48 |
-
logger.info(f'Number of train set: {len(train_dataset)}.')
|
| 49 |
-
opt['max_iters'] = opt['num_epochs'] * len(
|
| 50 |
-
train_dataset) // opt['batch_size']
|
| 51 |
-
|
| 52 |
-
val_dataset = MaskDataset(
|
| 53 |
-
segm_dir=opt['segm_dir'], ann_dir=opt['val_ann_file'])
|
| 54 |
-
val_loader = torch.utils.data.DataLoader(
|
| 55 |
-
dataset=val_dataset, batch_size=1, shuffle=False)
|
| 56 |
-
logger.info(f'Number of val set: {len(val_dataset)}.')
|
| 57 |
-
|
| 58 |
-
test_dataset = MaskDataset(
|
| 59 |
-
segm_dir=opt['segm_dir'], ann_dir=opt['test_ann_file'])
|
| 60 |
-
test_loader = torch.utils.data.DataLoader(
|
| 61 |
-
dataset=test_dataset, batch_size=1, shuffle=False)
|
| 62 |
-
logger.info(f'Number of test set: {len(test_dataset)}.')
|
| 63 |
-
|
| 64 |
-
current_iter = 0
|
| 65 |
-
best_epoch = None
|
| 66 |
-
best_loss = 100000
|
| 67 |
-
|
| 68 |
-
model = create_model(opt)
|
| 69 |
-
|
| 70 |
-
data_time, iter_time = 0, 0
|
| 71 |
-
current_iter = 0
|
| 72 |
-
|
| 73 |
-
# create message logger (formatted outputs)
|
| 74 |
-
msg_logger = MessageLogger(opt, current_iter, tb_logger)
|
| 75 |
-
|
| 76 |
-
for epoch in range(opt['num_epochs']):
|
| 77 |
-
lr = model.update_learning_rate(epoch)
|
| 78 |
-
|
| 79 |
-
for _, batch_data in enumerate(train_loader):
|
| 80 |
-
data_time = time.time() - data_time
|
| 81 |
-
|
| 82 |
-
current_iter += 1
|
| 83 |
-
|
| 84 |
-
model.optimize_parameters(batch_data, current_iter)
|
| 85 |
-
|
| 86 |
-
iter_time = time.time() - iter_time
|
| 87 |
-
if current_iter % opt['print_freq'] == 0:
|
| 88 |
-
log_vars = {'epoch': epoch, 'iter': current_iter}
|
| 89 |
-
log_vars.update({'lrs': [lr]})
|
| 90 |
-
log_vars.update({'time': iter_time, 'data_time': data_time})
|
| 91 |
-
log_vars.update(model.get_current_log())
|
| 92 |
-
msg_logger(log_vars)
|
| 93 |
-
|
| 94 |
-
data_time = time.time()
|
| 95 |
-
iter_time = time.time()
|
| 96 |
-
|
| 97 |
-
if epoch % opt['val_freq'] == 0:
|
| 98 |
-
save_dir = f'{opt["path"]["visualization"]}/valset/epoch_{epoch:03d}' # noqa
|
| 99 |
-
os.makedirs(save_dir, exist_ok=opt['debug'])
|
| 100 |
-
val_loss_total, _, _ = model.inference(val_loader, save_dir)
|
| 101 |
-
|
| 102 |
-
save_dir = f'{opt["path"]["visualization"]}/testset/epoch_{epoch:03d}' # noqa
|
| 103 |
-
os.makedirs(save_dir, exist_ok=opt['debug'])
|
| 104 |
-
test_loss_total, _, _ = model.inference(test_loader, save_dir)
|
| 105 |
-
|
| 106 |
-
logger.info(f'Epoch: {epoch}, '
|
| 107 |
-
f'val_loss_total: {val_loss_total}, '
|
| 108 |
-
f'test_loss_total: {test_loss_total}.')
|
| 109 |
-
|
| 110 |
-
if test_loss_total < best_loss:
|
| 111 |
-
best_epoch = epoch
|
| 112 |
-
best_loss = test_loss_total
|
| 113 |
-
|
| 114 |
-
logger.info(f'Best epoch: {best_epoch}, '
|
| 115 |
-
f'Best test loss: {best_loss: .4f}.')
|
| 116 |
-
|
| 117 |
-
# save model
|
| 118 |
-
model.save_network(f'{opt["path"]["models"]}/epoch{epoch}.pth')
|
| 119 |
-
|
| 120 |
-
|
| 121 |
-
if __name__ == '__main__':
|
| 122 |
-
main()
|
|
|
|
|
|
|
|
|
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spaces/CVPR/WALT/mmdet/models/dense_heads/transformer_head.py
DELETED
|
@@ -1,654 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import torch.nn as nn
|
| 3 |
-
import torch.nn.functional as F
|
| 4 |
-
from mmcv.cnn import Conv2d, Linear, build_activation_layer
|
| 5 |
-
from mmcv.runner import force_fp32
|
| 6 |
-
|
| 7 |
-
from mmdet.core import (bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh,
|
| 8 |
-
build_assigner, build_sampler, multi_apply,
|
| 9 |
-
reduce_mean)
|
| 10 |
-
from mmdet.models.utils import (FFN, build_positional_encoding,
|
| 11 |
-
build_transformer)
|
| 12 |
-
from ..builder import HEADS, build_loss
|
| 13 |
-
from .anchor_free_head import AnchorFreeHead
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
@HEADS.register_module()
|
| 17 |
-
class TransformerHead(AnchorFreeHead):
|
| 18 |
-
"""Implements the DETR transformer head.
|
| 19 |
-
|
| 20 |
-
See `paper: End-to-End Object Detection with Transformers
|
| 21 |
-
<https://arxiv.org/pdf/2005.12872>`_ for details.
|
| 22 |
-
|
| 23 |
-
Args:
|
| 24 |
-
num_classes (int): Number of categories excluding the background.
|
| 25 |
-
in_channels (int): Number of channels in the input feature map.
|
| 26 |
-
num_fcs (int, optional): Number of fully-connected layers used in
|
| 27 |
-
`FFN`, which is then used for the regression head. Default 2.
|
| 28 |
-
transformer (dict, optional): Config for transformer.
|
| 29 |
-
positional_encoding (dict, optional): Config for position encoding.
|
| 30 |
-
loss_cls (dict, optional): Config of the classification loss.
|
| 31 |
-
Default `CrossEntropyLoss`.
|
| 32 |
-
loss_bbox (dict, optional): Config of the regression loss.
|
| 33 |
-
Default `L1Loss`.
|
| 34 |
-
loss_iou (dict, optional): Config of the regression iou loss.
|
| 35 |
-
Default `GIoULoss`.
|
| 36 |
-
tran_cfg (dict, optional): Training config of transformer head.
|
| 37 |
-
test_cfg (dict, optional): Testing config of transformer head.
|
| 38 |
-
|
| 39 |
-
Example:
|
| 40 |
-
>>> import torch
|
| 41 |
-
>>> self = TransformerHead(80, 2048)
|
| 42 |
-
>>> x = torch.rand(1, 2048, 32, 32)
|
| 43 |
-
>>> mask = torch.ones(1, 32, 32).to(x.dtype)
|
| 44 |
-
>>> mask[:, :16, :15] = 0
|
| 45 |
-
>>> all_cls_scores, all_bbox_preds = self(x, mask)
|
| 46 |
-
"""
|
| 47 |
-
|
| 48 |
-
def __init__(self,
|
| 49 |
-
num_classes,
|
| 50 |
-
in_channels,
|
| 51 |
-
num_fcs=2,
|
| 52 |
-
transformer=dict(
|
| 53 |
-
type='Transformer',
|
| 54 |
-
embed_dims=256,
|
| 55 |
-
num_heads=8,
|
| 56 |
-
num_encoder_layers=6,
|
| 57 |
-
num_decoder_layers=6,
|
| 58 |
-
feedforward_channels=2048,
|
| 59 |
-
dropout=0.1,
|
| 60 |
-
act_cfg=dict(type='ReLU', inplace=True),
|
| 61 |
-
norm_cfg=dict(type='LN'),
|
| 62 |
-
num_fcs=2,
|
| 63 |
-
pre_norm=False,
|
| 64 |
-
return_intermediate_dec=True),
|
| 65 |
-
positional_encoding=dict(
|
| 66 |
-
type='SinePositionalEncoding',
|
| 67 |
-
num_feats=128,
|
| 68 |
-
normalize=True),
|
| 69 |
-
loss_cls=dict(
|
| 70 |
-
type='CrossEntropyLoss',
|
| 71 |
-
bg_cls_weight=0.1,
|
| 72 |
-
use_sigmoid=False,
|
| 73 |
-
loss_weight=1.0,
|
| 74 |
-
class_weight=1.0),
|
| 75 |
-
loss_bbox=dict(type='L1Loss', loss_weight=5.0),
|
| 76 |
-
loss_iou=dict(type='GIoULoss', loss_weight=2.0),
|
| 77 |
-
train_cfg=dict(
|
| 78 |
-
assigner=dict(
|
| 79 |
-
type='HungarianAssigner',
|
| 80 |
-
cls_cost=dict(type='ClassificationCost', weight=1.),
|
| 81 |
-
reg_cost=dict(type='BBoxL1Cost', weight=5.0),
|
| 82 |
-
iou_cost=dict(
|
| 83 |
-
type='IoUCost', iou_mode='giou', weight=2.0))),
|
| 84 |
-
test_cfg=dict(max_per_img=100),
|
| 85 |
-
**kwargs):
|
| 86 |
-
# NOTE here use `AnchorFreeHead` instead of `TransformerHead`,
|
| 87 |
-
# since it brings inconvenience when the initialization of
|
| 88 |
-
# `AnchorFreeHead` is called.
|
| 89 |
-
super(AnchorFreeHead, self).__init__()
|
| 90 |
-
use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
|
| 91 |
-
assert not use_sigmoid_cls, 'setting use_sigmoid_cls as True is ' \
|
| 92 |
-
'not supported in DETR, since background is needed for the ' \
|
| 93 |
-
'matching process.'
|
| 94 |
-
assert 'embed_dims' in transformer \
|
| 95 |
-
and 'num_feats' in positional_encoding
|
| 96 |
-
num_feats = positional_encoding['num_feats']
|
| 97 |
-
embed_dims = transformer['embed_dims']
|
| 98 |
-
assert num_feats * 2 == embed_dims, 'embed_dims should' \
|
| 99 |
-
f' be exactly 2 times of num_feats. Found {embed_dims}' \
|
| 100 |
-
f' and {num_feats}.'
|
| 101 |
-
assert test_cfg is not None and 'max_per_img' in test_cfg
|
| 102 |
-
|
| 103 |
-
class_weight = loss_cls.get('class_weight', None)
|
| 104 |
-
if class_weight is not None:
|
| 105 |
-
assert isinstance(class_weight, float), 'Expected ' \
|
| 106 |
-
'class_weight to have type float. Found ' \
|
| 107 |
-
f'{type(class_weight)}.'
|
| 108 |
-
# NOTE following the official DETR rep0, bg_cls_weight means
|
| 109 |
-
# relative classification weight of the no-object class.
|
| 110 |
-
bg_cls_weight = loss_cls.get('bg_cls_weight', class_weight)
|
| 111 |
-
assert isinstance(bg_cls_weight, float), 'Expected ' \
|
| 112 |
-
'bg_cls_weight to have type float. Found ' \
|
| 113 |
-
f'{type(bg_cls_weight)}.'
|
| 114 |
-
class_weight = torch.ones(num_classes + 1) * class_weight
|
| 115 |
-
# set background class as the last indice
|
| 116 |
-
class_weight[num_classes] = bg_cls_weight
|
| 117 |
-
loss_cls.update({'class_weight': class_weight})
|
| 118 |
-
if 'bg_cls_weight' in loss_cls:
|
| 119 |
-
loss_cls.pop('bg_cls_weight')
|
| 120 |
-
self.bg_cls_weight = bg_cls_weight
|
| 121 |
-
|
| 122 |
-
if train_cfg:
|
| 123 |
-
assert 'assigner' in train_cfg, 'assigner should be provided '\
|
| 124 |
-
'when train_cfg is set.'
|
| 125 |
-
assigner = train_cfg['assigner']
|
| 126 |
-
assert loss_cls['loss_weight'] == assigner['cls_cost']['weight'], \
|
| 127 |
-
'The classification weight for loss and matcher should be' \
|
| 128 |
-
'exactly the same.'
|
| 129 |
-
assert loss_bbox['loss_weight'] == assigner['reg_cost'][
|
| 130 |
-
'weight'], 'The regression L1 weight for loss and matcher ' \
|
| 131 |
-
'should be exactly the same.'
|
| 132 |
-
assert loss_iou['loss_weight'] == assigner['iou_cost']['weight'], \
|
| 133 |
-
'The regression iou weight for loss and matcher should be' \
|
| 134 |
-
'exactly the same.'
|
| 135 |
-
self.assigner = build_assigner(assigner)
|
| 136 |
-
# DETR sampling=False, so use PseudoSampler
|
| 137 |
-
sampler_cfg = dict(type='PseudoSampler')
|
| 138 |
-
self.sampler = build_sampler(sampler_cfg, context=self)
|
| 139 |
-
self.num_classes = num_classes
|
| 140 |
-
self.cls_out_channels = num_classes + 1
|
| 141 |
-
self.in_channels = in_channels
|
| 142 |
-
self.num_fcs = num_fcs
|
| 143 |
-
self.train_cfg = train_cfg
|
| 144 |
-
self.test_cfg = test_cfg
|
| 145 |
-
self.use_sigmoid_cls = use_sigmoid_cls
|
| 146 |
-
self.embed_dims = embed_dims
|
| 147 |
-
self.num_query = test_cfg['max_per_img']
|
| 148 |
-
self.fp16_enabled = False
|
| 149 |
-
self.loss_cls = build_loss(loss_cls)
|
| 150 |
-
self.loss_bbox = build_loss(loss_bbox)
|
| 151 |
-
self.loss_iou = build_loss(loss_iou)
|
| 152 |
-
self.act_cfg = transformer.get('act_cfg',
|
| 153 |
-
dict(type='ReLU', inplace=True))
|
| 154 |
-
self.activate = build_activation_layer(self.act_cfg)
|
| 155 |
-
self.positional_encoding = build_positional_encoding(
|
| 156 |
-
positional_encoding)
|
| 157 |
-
self.transformer = build_transformer(transformer)
|
| 158 |
-
self._init_layers()
|
| 159 |
-
|
| 160 |
-
def _init_layers(self):
|
| 161 |
-
"""Initialize layers of the transformer head."""
|
| 162 |
-
self.input_proj = Conv2d(
|
| 163 |
-
self.in_channels, self.embed_dims, kernel_size=1)
|
| 164 |
-
self.fc_cls = Linear(self.embed_dims, self.cls_out_channels)
|
| 165 |
-
self.reg_ffn = FFN(
|
| 166 |
-
self.embed_dims,
|
| 167 |
-
self.embed_dims,
|
| 168 |
-
self.num_fcs,
|
| 169 |
-
self.act_cfg,
|
| 170 |
-
dropout=0.0,
|
| 171 |
-
add_residual=False)
|
| 172 |
-
self.fc_reg = Linear(self.embed_dims, 4)
|
| 173 |
-
self.query_embedding = nn.Embedding(self.num_query, self.embed_dims)
|
| 174 |
-
|
| 175 |
-
def init_weights(self, distribution='uniform'):
|
| 176 |
-
"""Initialize weights of the transformer head."""
|
| 177 |
-
# The initialization for transformer is important
|
| 178 |
-
self.transformer.init_weights()
|
| 179 |
-
|
| 180 |
-
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
|
| 181 |
-
missing_keys, unexpected_keys, error_msgs):
|
| 182 |
-
"""load checkpoints."""
|
| 183 |
-
# NOTE here use `AnchorFreeHead` instead of `TransformerHead`,
|
| 184 |
-
# since `AnchorFreeHead._load_from_state_dict` should not be
|
| 185 |
-
# called here. Invoking the default `Module._load_from_state_dict`
|
| 186 |
-
# is enough.
|
| 187 |
-
super(AnchorFreeHead,
|
| 188 |
-
self)._load_from_state_dict(state_dict, prefix, local_metadata,
|
| 189 |
-
strict, missing_keys,
|
| 190 |
-
unexpected_keys, error_msgs)
|
| 191 |
-
|
| 192 |
-
def forward(self, feats, img_metas):
|
| 193 |
-
"""Forward function.
|
| 194 |
-
|
| 195 |
-
Args:
|
| 196 |
-
feats (tuple[Tensor]): Features from the upstream network, each is
|
| 197 |
-
a 4D-tensor.
|
| 198 |
-
img_metas (list[dict]): List of image information.
|
| 199 |
-
|
| 200 |
-
Returns:
|
| 201 |
-
tuple[list[Tensor], list[Tensor]]: Outputs for all scale levels.
|
| 202 |
-
|
| 203 |
-
- all_cls_scores_list (list[Tensor]): Classification scores \
|
| 204 |
-
for each scale level. Each is a 4D-tensor with shape \
|
| 205 |
-
[nb_dec, bs, num_query, cls_out_channels]. Note \
|
| 206 |
-
`cls_out_channels` should includes background.
|
| 207 |
-
- all_bbox_preds_list (list[Tensor]): Sigmoid regression \
|
| 208 |
-
outputs for each scale level. Each is a 4D-tensor with \
|
| 209 |
-
normalized coordinate format (cx, cy, w, h) and shape \
|
| 210 |
-
[nb_dec, bs, num_query, 4].
|
| 211 |
-
"""
|
| 212 |
-
num_levels = len(feats)
|
| 213 |
-
img_metas_list = [img_metas for _ in range(num_levels)]
|
| 214 |
-
return multi_apply(self.forward_single, feats, img_metas_list)
|
| 215 |
-
|
| 216 |
-
def forward_single(self, x, img_metas):
|
| 217 |
-
""""Forward function for a single feature level.
|
| 218 |
-
|
| 219 |
-
Args:
|
| 220 |
-
x (Tensor): Input feature from backbone's single stage, shape
|
| 221 |
-
[bs, c, h, w].
|
| 222 |
-
img_metas (list[dict]): List of image information.
|
| 223 |
-
|
| 224 |
-
Returns:
|
| 225 |
-
all_cls_scores (Tensor): Outputs from the classification head,
|
| 226 |
-
shape [nb_dec, bs, num_query, cls_out_channels]. Note
|
| 227 |
-
cls_out_channels should includes background.
|
| 228 |
-
all_bbox_preds (Tensor): Sigmoid outputs from the regression
|
| 229 |
-
head with normalized coordinate format (cx, cy, w, h).
|
| 230 |
-
Shape [nb_dec, bs, num_query, 4].
|
| 231 |
-
"""
|
| 232 |
-
# construct binary masks which used for the transformer.
|
| 233 |
-
# NOTE following the official DETR repo, non-zero values representing
|
| 234 |
-
# ignored positions, while zero values means valid positions.
|
| 235 |
-
batch_size = x.size(0)
|
| 236 |
-
input_img_h, input_img_w = img_metas[0]['batch_input_shape']
|
| 237 |
-
masks = x.new_ones((batch_size, input_img_h, input_img_w))
|
| 238 |
-
for img_id in range(batch_size):
|
| 239 |
-
img_h, img_w, _ = img_metas[img_id]['img_shape']
|
| 240 |
-
masks[img_id, :img_h, :img_w] = 0
|
| 241 |
-
|
| 242 |
-
x = self.input_proj(x)
|
| 243 |
-
# interpolate masks to have the same spatial shape with x
|
| 244 |
-
masks = F.interpolate(
|
| 245 |
-
masks.unsqueeze(1), size=x.shape[-2:]).to(torch.bool).squeeze(1)
|
| 246 |
-
# position encoding
|
| 247 |
-
pos_embed = self.positional_encoding(masks) # [bs, embed_dim, h, w]
|
| 248 |
-
# outs_dec: [nb_dec, bs, num_query, embed_dim]
|
| 249 |
-
outs_dec, _ = self.transformer(x, masks, self.query_embedding.weight,
|
| 250 |
-
pos_embed)
|
| 251 |
-
|
| 252 |
-
all_cls_scores = self.fc_cls(outs_dec)
|
| 253 |
-
all_bbox_preds = self.fc_reg(self.activate(
|
| 254 |
-
self.reg_ffn(outs_dec))).sigmoid()
|
| 255 |
-
return all_cls_scores, all_bbox_preds
|
| 256 |
-
|
| 257 |
-
@force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list'))
|
| 258 |
-
def loss(self,
|
| 259 |
-
all_cls_scores_list,
|
| 260 |
-
all_bbox_preds_list,
|
| 261 |
-
gt_bboxes_list,
|
| 262 |
-
gt_labels_list,
|
| 263 |
-
img_metas,
|
| 264 |
-
gt_bboxes_ignore=None):
|
| 265 |
-
""""Loss function.
|
| 266 |
-
|
| 267 |
-
Only outputs from the last feature level are used for computing
|
| 268 |
-
losses by default.
|
| 269 |
-
|
| 270 |
-
Args:
|
| 271 |
-
all_cls_scores_list (list[Tensor]): Classification outputs
|
| 272 |
-
for each feature level. Each is a 4D-tensor with shape
|
| 273 |
-
[nb_dec, bs, num_query, cls_out_channels].
|
| 274 |
-
all_bbox_preds_list (list[Tensor]): Sigmoid regression
|
| 275 |
-
outputs for each feature level. Each is a 4D-tensor with
|
| 276 |
-
normalized coordinate format (cx, cy, w, h) and shape
|
| 277 |
-
[nb_dec, bs, num_query, 4].
|
| 278 |
-
gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image
|
| 279 |
-
with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
|
| 280 |
-
gt_labels_list (list[Tensor]): Ground truth class indices for each
|
| 281 |
-
image with shape (num_gts, ).
|
| 282 |
-
img_metas (list[dict]): List of image meta information.
|
| 283 |
-
gt_bboxes_ignore (list[Tensor], optional): Bounding boxes
|
| 284 |
-
which can be ignored for each image. Default None.
|
| 285 |
-
|
| 286 |
-
Returns:
|
| 287 |
-
dict[str, Tensor]: A dictionary of loss components.
|
| 288 |
-
"""
|
| 289 |
-
# NOTE defaultly only the outputs from the last feature scale is used.
|
| 290 |
-
all_cls_scores = all_cls_scores_list[-1]
|
| 291 |
-
all_bbox_preds = all_bbox_preds_list[-1]
|
| 292 |
-
assert gt_bboxes_ignore is None, \
|
| 293 |
-
'Only supports for gt_bboxes_ignore setting to None.'
|
| 294 |
-
|
| 295 |
-
num_dec_layers = len(all_cls_scores)
|
| 296 |
-
all_gt_bboxes_list = [gt_bboxes_list for _ in range(num_dec_layers)]
|
| 297 |
-
all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)]
|
| 298 |
-
all_gt_bboxes_ignore_list = [
|
| 299 |
-
gt_bboxes_ignore for _ in range(num_dec_layers)
|
| 300 |
-
]
|
| 301 |
-
img_metas_list = [img_metas for _ in range(num_dec_layers)]
|
| 302 |
-
|
| 303 |
-
losses_cls, losses_bbox, losses_iou = multi_apply(
|
| 304 |
-
self.loss_single, all_cls_scores, all_bbox_preds,
|
| 305 |
-
all_gt_bboxes_list, all_gt_labels_list, img_metas_list,
|
| 306 |
-
all_gt_bboxes_ignore_list)
|
| 307 |
-
|
| 308 |
-
loss_dict = dict()
|
| 309 |
-
# loss from the last decoder layer
|
| 310 |
-
loss_dict['loss_cls'] = losses_cls[-1]
|
| 311 |
-
loss_dict['loss_bbox'] = losses_bbox[-1]
|
| 312 |
-
loss_dict['loss_iou'] = losses_iou[-1]
|
| 313 |
-
# loss from other decoder layers
|
| 314 |
-
num_dec_layer = 0
|
| 315 |
-
for loss_cls_i, loss_bbox_i, loss_iou_i in zip(losses_cls[:-1],
|
| 316 |
-
losses_bbox[:-1],
|
| 317 |
-
losses_iou[:-1]):
|
| 318 |
-
loss_dict[f'd{num_dec_layer}.loss_cls'] = loss_cls_i
|
| 319 |
-
loss_dict[f'd{num_dec_layer}.loss_bbox'] = loss_bbox_i
|
| 320 |
-
loss_dict[f'd{num_dec_layer}.loss_iou'] = loss_iou_i
|
| 321 |
-
num_dec_layer += 1
|
| 322 |
-
return loss_dict
|
| 323 |
-
|
| 324 |
-
def loss_single(self,
|
| 325 |
-
cls_scores,
|
| 326 |
-
bbox_preds,
|
| 327 |
-
gt_bboxes_list,
|
| 328 |
-
gt_labels_list,
|
| 329 |
-
img_metas,
|
| 330 |
-
gt_bboxes_ignore_list=None):
|
| 331 |
-
""""Loss function for outputs from a single decoder layer of a single
|
| 332 |
-
feature level.
|
| 333 |
-
|
| 334 |
-
Args:
|
| 335 |
-
cls_scores (Tensor): Box score logits from a single decoder layer
|
| 336 |
-
for all images. Shape [bs, num_query, cls_out_channels].
|
| 337 |
-
bbox_preds (Tensor): Sigmoid outputs from a single decoder layer
|
| 338 |
-
for all images, with normalized coordinate (cx, cy, w, h) and
|
| 339 |
-
shape [bs, num_query, 4].
|
| 340 |
-
gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image
|
| 341 |
-
with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
|
| 342 |
-
gt_labels_list (list[Tensor]): Ground truth class indices for each
|
| 343 |
-
image with shape (num_gts, ).
|
| 344 |
-
img_metas (list[dict]): List of image meta information.
|
| 345 |
-
gt_bboxes_ignore_list (list[Tensor], optional): Bounding
|
| 346 |
-
boxes which can be ignored for each image. Default None.
|
| 347 |
-
|
| 348 |
-
Returns:
|
| 349 |
-
dict[str, Tensor]: A dictionary of loss components for outputs from
|
| 350 |
-
a single decoder layer.
|
| 351 |
-
"""
|
| 352 |
-
num_imgs = cls_scores.size(0)
|
| 353 |
-
cls_scores_list = [cls_scores[i] for i in range(num_imgs)]
|
| 354 |
-
bbox_preds_list = [bbox_preds[i] for i in range(num_imgs)]
|
| 355 |
-
cls_reg_targets = self.get_targets(cls_scores_list, bbox_preds_list,
|
| 356 |
-
gt_bboxes_list, gt_labels_list,
|
| 357 |
-
img_metas, gt_bboxes_ignore_list)
|
| 358 |
-
(labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
|
| 359 |
-
num_total_pos, num_total_neg) = cls_reg_targets
|
| 360 |
-
labels = torch.cat(labels_list, 0)
|
| 361 |
-
label_weights = torch.cat(label_weights_list, 0)
|
| 362 |
-
bbox_targets = torch.cat(bbox_targets_list, 0)
|
| 363 |
-
bbox_weights = torch.cat(bbox_weights_list, 0)
|
| 364 |
-
|
| 365 |
-
# classification loss
|
| 366 |
-
cls_scores = cls_scores.reshape(-1, self.cls_out_channels)
|
| 367 |
-
# construct weighted avg_factor to match with the official DETR repo
|
| 368 |
-
cls_avg_factor = num_total_pos * 1.0 + \
|
| 369 |
-
num_total_neg * self.bg_cls_weight
|
| 370 |
-
loss_cls = self.loss_cls(
|
| 371 |
-
cls_scores, labels, label_weights, avg_factor=cls_avg_factor)
|
| 372 |
-
|
| 373 |
-
# Compute the average number of gt boxes accross all gpus, for
|
| 374 |
-
# normalization purposes
|
| 375 |
-
num_total_pos = loss_cls.new_tensor([num_total_pos])
|
| 376 |
-
num_total_pos = torch.clamp(reduce_mean(num_total_pos), min=1).item()
|
| 377 |
-
|
| 378 |
-
# construct factors used for rescale bboxes
|
| 379 |
-
factors = []
|
| 380 |
-
for img_meta, bbox_pred in zip(img_metas, bbox_preds):
|
| 381 |
-
img_h, img_w, _ = img_meta['img_shape']
|
| 382 |
-
factor = bbox_pred.new_tensor([img_w, img_h, img_w,
|
| 383 |
-
img_h]).unsqueeze(0).repeat(
|
| 384 |
-
bbox_pred.size(0), 1)
|
| 385 |
-
factors.append(factor)
|
| 386 |
-
factors = torch.cat(factors, 0)
|
| 387 |
-
|
| 388 |
-
# DETR regress the relative position of boxes (cxcywh) in the image,
|
| 389 |
-
# thus the learning target is normalized by the image size. So here
|
| 390 |
-
# we need to re-scale them for calculating IoU loss
|
| 391 |
-
bbox_preds = bbox_preds.reshape(-1, 4)
|
| 392 |
-
bboxes = bbox_cxcywh_to_xyxy(bbox_preds) * factors
|
| 393 |
-
bboxes_gt = bbox_cxcywh_to_xyxy(bbox_targets) * factors
|
| 394 |
-
|
| 395 |
-
# regression IoU loss, defaultly GIoU loss
|
| 396 |
-
loss_iou = self.loss_iou(
|
| 397 |
-
bboxes, bboxes_gt, bbox_weights, avg_factor=num_total_pos)
|
| 398 |
-
|
| 399 |
-
# regression L1 loss
|
| 400 |
-
loss_bbox = self.loss_bbox(
|
| 401 |
-
bbox_preds, bbox_targets, bbox_weights, avg_factor=num_total_pos)
|
| 402 |
-
return loss_cls, loss_bbox, loss_iou
|
| 403 |
-
|
| 404 |
-
def get_targets(self,
|
| 405 |
-
cls_scores_list,
|
| 406 |
-
bbox_preds_list,
|
| 407 |
-
gt_bboxes_list,
|
| 408 |
-
gt_labels_list,
|
| 409 |
-
img_metas,
|
| 410 |
-
gt_bboxes_ignore_list=None):
|
| 411 |
-
""""Compute regression and classification targets for a batch image.
|
| 412 |
-
|
| 413 |
-
Outputs from a single decoder layer of a single feature level are used.
|
| 414 |
-
|
| 415 |
-
Args:
|
| 416 |
-
cls_scores_list (list[Tensor]): Box score logits from a single
|
| 417 |
-
decoder layer for each image with shape [num_query,
|
| 418 |
-
cls_out_channels].
|
| 419 |
-
bbox_preds_list (list[Tensor]): Sigmoid outputs from a single
|
| 420 |
-
decoder layer for each image, with normalized coordinate
|
| 421 |
-
(cx, cy, w, h) and shape [num_query, 4].
|
| 422 |
-
gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image
|
| 423 |
-
with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
|
| 424 |
-
gt_labels_list (list[Tensor]): Ground truth class indices for each
|
| 425 |
-
image with shape (num_gts, ).
|
| 426 |
-
img_metas (list[dict]): List of image meta information.
|
| 427 |
-
gt_bboxes_ignore_list (list[Tensor], optional): Bounding
|
| 428 |
-
boxes which can be ignored for each image. Default None.
|
| 429 |
-
|
| 430 |
-
Returns:
|
| 431 |
-
tuple: a tuple containing the following targets.
|
| 432 |
-
|
| 433 |
-
- labels_list (list[Tensor]): Labels for all images.
|
| 434 |
-
- label_weights_list (list[Tensor]): Label weights for all \
|
| 435 |
-
images.
|
| 436 |
-
- bbox_targets_list (list[Tensor]): BBox targets for all \
|
| 437 |
-
images.
|
| 438 |
-
- bbox_weights_list (list[Tensor]): BBox weights for all \
|
| 439 |
-
images.
|
| 440 |
-
- num_total_pos (int): Number of positive samples in all \
|
| 441 |
-
images.
|
| 442 |
-
- num_total_neg (int): Number of negative samples in all \
|
| 443 |
-
images.
|
| 444 |
-
"""
|
| 445 |
-
assert gt_bboxes_ignore_list is None, \
|
| 446 |
-
'Only supports for gt_bboxes_ignore setting to None.'
|
| 447 |
-
num_imgs = len(cls_scores_list)
|
| 448 |
-
gt_bboxes_ignore_list = [
|
| 449 |
-
gt_bboxes_ignore_list for _ in range(num_imgs)
|
| 450 |
-
]
|
| 451 |
-
|
| 452 |
-
(labels_list, label_weights_list, bbox_targets_list,
|
| 453 |
-
bbox_weights_list, pos_inds_list, neg_inds_list) = multi_apply(
|
| 454 |
-
self._get_target_single, cls_scores_list, bbox_preds_list,
|
| 455 |
-
gt_bboxes_list, gt_labels_list, img_metas, gt_bboxes_ignore_list)
|
| 456 |
-
num_total_pos = sum((inds.numel() for inds in pos_inds_list))
|
| 457 |
-
num_total_neg = sum((inds.numel() for inds in neg_inds_list))
|
| 458 |
-
return (labels_list, label_weights_list, bbox_targets_list,
|
| 459 |
-
bbox_weights_list, num_total_pos, num_total_neg)
|
| 460 |
-
|
| 461 |
-
def _get_target_single(self,
|
| 462 |
-
cls_score,
|
| 463 |
-
bbox_pred,
|
| 464 |
-
gt_bboxes,
|
| 465 |
-
gt_labels,
|
| 466 |
-
img_meta,
|
| 467 |
-
gt_bboxes_ignore=None):
|
| 468 |
-
""""Compute regression and classification targets for one image.
|
| 469 |
-
|
| 470 |
-
Outputs from a single decoder layer of a single feature level are used.
|
| 471 |
-
|
| 472 |
-
Args:
|
| 473 |
-
cls_score (Tensor): Box score logits from a single decoder layer
|
| 474 |
-
for one image. Shape [num_query, cls_out_channels].
|
| 475 |
-
bbox_pred (Tensor): Sigmoid outputs from a single decoder layer
|
| 476 |
-
for one image, with normalized coordinate (cx, cy, w, h) and
|
| 477 |
-
shape [num_query, 4].
|
| 478 |
-
gt_bboxes (Tensor): Ground truth bboxes for one image with
|
| 479 |
-
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
|
| 480 |
-
gt_labels (Tensor): Ground truth class indices for one image
|
| 481 |
-
with shape (num_gts, ).
|
| 482 |
-
img_meta (dict): Meta information for one image.
|
| 483 |
-
gt_bboxes_ignore (Tensor, optional): Bounding boxes
|
| 484 |
-
which can be ignored. Default None.
|
| 485 |
-
|
| 486 |
-
Returns:
|
| 487 |
-
tuple[Tensor]: a tuple containing the following for one image.
|
| 488 |
-
|
| 489 |
-
- labels (Tensor): Labels of each image.
|
| 490 |
-
- label_weights (Tensor]): Label weights of each image.
|
| 491 |
-
- bbox_targets (Tensor): BBox targets of each image.
|
| 492 |
-
- bbox_weights (Tensor): BBox weights of each image.
|
| 493 |
-
- pos_inds (Tensor): Sampled positive indices for each image.
|
| 494 |
-
- neg_inds (Tensor): Sampled negative indices for each image.
|
| 495 |
-
"""
|
| 496 |
-
|
| 497 |
-
num_bboxes = bbox_pred.size(0)
|
| 498 |
-
# assigner and sampler
|
| 499 |
-
assign_result = self.assigner.assign(bbox_pred, cls_score, gt_bboxes,
|
| 500 |
-
gt_labels, img_meta,
|
| 501 |
-
gt_bboxes_ignore)
|
| 502 |
-
sampling_result = self.sampler.sample(assign_result, bbox_pred,
|
| 503 |
-
gt_bboxes)
|
| 504 |
-
pos_inds = sampling_result.pos_inds
|
| 505 |
-
neg_inds = sampling_result.neg_inds
|
| 506 |
-
|
| 507 |
-
# label targets
|
| 508 |
-
labels = gt_bboxes.new_full((num_bboxes, ),
|
| 509 |
-
self.num_classes,
|
| 510 |
-
dtype=torch.long)
|
| 511 |
-
labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds]
|
| 512 |
-
label_weights = gt_bboxes.new_ones(num_bboxes)
|
| 513 |
-
|
| 514 |
-
# bbox targets
|
| 515 |
-
bbox_targets = torch.zeros_like(bbox_pred)
|
| 516 |
-
bbox_weights = torch.zeros_like(bbox_pred)
|
| 517 |
-
bbox_weights[pos_inds] = 1.0
|
| 518 |
-
img_h, img_w, _ = img_meta['img_shape']
|
| 519 |
-
|
| 520 |
-
# DETR regress the relative position of boxes (cxcywh) in the image.
|
| 521 |
-
# Thus the learning target should be normalized by the image size, also
|
| 522 |
-
# the box format should be converted from defaultly x1y1x2y2 to cxcywh.
|
| 523 |
-
factor = bbox_pred.new_tensor([img_w, img_h, img_w,
|
| 524 |
-
img_h]).unsqueeze(0)
|
| 525 |
-
pos_gt_bboxes_normalized = sampling_result.pos_gt_bboxes / factor
|
| 526 |
-
pos_gt_bboxes_targets = bbox_xyxy_to_cxcywh(pos_gt_bboxes_normalized)
|
| 527 |
-
bbox_targets[pos_inds] = pos_gt_bboxes_targets
|
| 528 |
-
return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
|
| 529 |
-
neg_inds)
|
| 530 |
-
|
| 531 |
-
# over-write because img_metas are needed as inputs for bbox_head.
|
| 532 |
-
def forward_train(self,
|
| 533 |
-
x,
|
| 534 |
-
img_metas,
|
| 535 |
-
gt_bboxes,
|
| 536 |
-
gt_labels=None,
|
| 537 |
-
gt_bboxes_ignore=None,
|
| 538 |
-
proposal_cfg=None,
|
| 539 |
-
**kwargs):
|
| 540 |
-
"""Forward function for training mode.
|
| 541 |
-
|
| 542 |
-
Args:
|
| 543 |
-
x (list[Tensor]): Features from backbone.
|
| 544 |
-
img_metas (list[dict]): Meta information of each image, e.g.,
|
| 545 |
-
image size, scaling factor, etc.
|
| 546 |
-
gt_bboxes (Tensor): Ground truth bboxes of the image,
|
| 547 |
-
shape (num_gts, 4).
|
| 548 |
-
gt_labels (Tensor): Ground truth labels of each box,
|
| 549 |
-
shape (num_gts,).
|
| 550 |
-
gt_bboxes_ignore (Tensor): Ground truth bboxes to be
|
| 551 |
-
ignored, shape (num_ignored_gts, 4).
|
| 552 |
-
proposal_cfg (mmcv.Config): Test / postprocessing configuration,
|
| 553 |
-
if None, test_cfg would be used.
|
| 554 |
-
|
| 555 |
-
Returns:
|
| 556 |
-
dict[str, Tensor]: A dictionary of loss components.
|
| 557 |
-
"""
|
| 558 |
-
assert proposal_cfg is None, '"proposal_cfg" must be None'
|
| 559 |
-
outs = self(x, img_metas)
|
| 560 |
-
if gt_labels is None:
|
| 561 |
-
loss_inputs = outs + (gt_bboxes, img_metas)
|
| 562 |
-
else:
|
| 563 |
-
loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)
|
| 564 |
-
losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
|
| 565 |
-
return losses
|
| 566 |
-
|
| 567 |
-
@force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list'))
|
| 568 |
-
def get_bboxes(self,
|
| 569 |
-
all_cls_scores_list,
|
| 570 |
-
all_bbox_preds_list,
|
| 571 |
-
img_metas,
|
| 572 |
-
rescale=False):
|
| 573 |
-
"""Transform network outputs for a batch into bbox predictions.
|
| 574 |
-
|
| 575 |
-
Args:
|
| 576 |
-
all_cls_scores_list (list[Tensor]): Classification outputs
|
| 577 |
-
for each feature level. Each is a 4D-tensor with shape
|
| 578 |
-
[nb_dec, bs, num_query, cls_out_channels].
|
| 579 |
-
all_bbox_preds_list (list[Tensor]): Sigmoid regression
|
| 580 |
-
outputs for each feature level. Each is a 4D-tensor with
|
| 581 |
-
normalized coordinate format (cx, cy, w, h) and shape
|
| 582 |
-
[nb_dec, bs, num_query, 4].
|
| 583 |
-
img_metas (list[dict]): Meta information of each image.
|
| 584 |
-
rescale (bool, optional): If True, return boxes in original
|
| 585 |
-
image space. Default False.
|
| 586 |
-
|
| 587 |
-
Returns:
|
| 588 |
-
list[list[Tensor, Tensor]]: Each item in result_list is 2-tuple. \
|
| 589 |
-
The first item is an (n, 5) tensor, where the first 4 columns \
|
| 590 |
-
are bounding box positions (tl_x, tl_y, br_x, br_y) and the \
|
| 591 |
-
5-th column is a score between 0 and 1. The second item is a \
|
| 592 |
-
(n,) tensor where each item is the predicted class label of \
|
| 593 |
-
the corresponding box.
|
| 594 |
-
"""
|
| 595 |
-
# NOTE defaultly only using outputs from the last feature level,
|
| 596 |
-
# and only the outputs from the last decoder layer is used.
|
| 597 |
-
cls_scores = all_cls_scores_list[-1][-1]
|
| 598 |
-
bbox_preds = all_bbox_preds_list[-1][-1]
|
| 599 |
-
|
| 600 |
-
result_list = []
|
| 601 |
-
for img_id in range(len(img_metas)):
|
| 602 |
-
cls_score = cls_scores[img_id]
|
| 603 |
-
bbox_pred = bbox_preds[img_id]
|
| 604 |
-
img_shape = img_metas[img_id]['img_shape']
|
| 605 |
-
scale_factor = img_metas[img_id]['scale_factor']
|
| 606 |
-
proposals = self._get_bboxes_single(cls_score, bbox_pred,
|
| 607 |
-
img_shape, scale_factor,
|
| 608 |
-
rescale)
|
| 609 |
-
result_list.append(proposals)
|
| 610 |
-
return result_list
|
| 611 |
-
|
| 612 |
-
def _get_bboxes_single(self,
|
| 613 |
-
cls_score,
|
| 614 |
-
bbox_pred,
|
| 615 |
-
img_shape,
|
| 616 |
-
scale_factor,
|
| 617 |
-
rescale=False):
|
| 618 |
-
"""Transform outputs from the last decoder layer into bbox predictions
|
| 619 |
-
for each image.
|
| 620 |
-
|
| 621 |
-
Args:
|
| 622 |
-
cls_score (Tensor): Box score logits from the last decoder layer
|
| 623 |
-
for each image. Shape [num_query, cls_out_channels].
|
| 624 |
-
bbox_pred (Tensor): Sigmoid outputs from the last decoder layer
|
| 625 |
-
for each image, with coordinate format (cx, cy, w, h) and
|
| 626 |
-
shape [num_query, 4].
|
| 627 |
-
img_shape (tuple[int]): Shape of input image, (height, width, 3).
|
| 628 |
-
scale_factor (ndarray, optional): Scale factor of the image arange
|
| 629 |
-
as (w_scale, h_scale, w_scale, h_scale).
|
| 630 |
-
rescale (bool, optional): If True, return boxes in original image
|
| 631 |
-
space. Default False.
|
| 632 |
-
|
| 633 |
-
Returns:
|
| 634 |
-
tuple[Tensor]: Results of detected bboxes and labels.
|
| 635 |
-
|
| 636 |
-
- det_bboxes: Predicted bboxes with shape [num_query, 5], \
|
| 637 |
-
where the first 4 columns are bounding box positions \
|
| 638 |
-
(tl_x, tl_y, br_x, br_y) and the 5-th column are scores \
|
| 639 |
-
between 0 and 1.
|
| 640 |
-
- det_labels: Predicted labels of the corresponding box with \
|
| 641 |
-
shape [num_query].
|
| 642 |
-
"""
|
| 643 |
-
assert len(cls_score) == len(bbox_pred)
|
| 644 |
-
# exclude background
|
| 645 |
-
scores, det_labels = F.softmax(cls_score, dim=-1)[..., :-1].max(-1)
|
| 646 |
-
det_bboxes = bbox_cxcywh_to_xyxy(bbox_pred)
|
| 647 |
-
det_bboxes[:, 0::2] = det_bboxes[:, 0::2] * img_shape[1]
|
| 648 |
-
det_bboxes[:, 1::2] = det_bboxes[:, 1::2] * img_shape[0]
|
| 649 |
-
det_bboxes[:, 0::2].clamp_(min=0, max=img_shape[1])
|
| 650 |
-
det_bboxes[:, 1::2].clamp_(min=0, max=img_shape[0])
|
| 651 |
-
if rescale:
|
| 652 |
-
det_bboxes /= det_bboxes.new_tensor(scale_factor)
|
| 653 |
-
det_bboxes = torch.cat((det_bboxes, scores.unsqueeze(1)), -1)
|
| 654 |
-
return det_bboxes, det_labels
|
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|
spaces/CVPR/WALT/mmdet/models/detectors/atss.py
DELETED
|
@@ -1,17 +0,0 @@
|
|
| 1 |
-
from ..builder import DETECTORS
|
| 2 |
-
from .single_stage import SingleStageDetector
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
@DETECTORS.register_module()
|
| 6 |
-
class ATSS(SingleStageDetector):
|
| 7 |
-
"""Implementation of `ATSS <https://arxiv.org/abs/1912.02424>`_."""
|
| 8 |
-
|
| 9 |
-
def __init__(self,
|
| 10 |
-
backbone,
|
| 11 |
-
neck,
|
| 12 |
-
bbox_head,
|
| 13 |
-
train_cfg=None,
|
| 14 |
-
test_cfg=None,
|
| 15 |
-
pretrained=None):
|
| 16 |
-
super(ATSS, self).__init__(backbone, neck, bbox_head, train_cfg,
|
| 17 |
-
test_cfg, pretrained)
|
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|
spaces/CVPR/WALT/mmdet/utils/util_mixins.py
DELETED
|
@@ -1,104 +0,0 @@
|
|
| 1 |
-
"""This module defines the :class:`NiceRepr` mixin class, which defines a
|
| 2 |
-
``__repr__`` and ``__str__`` method that only depend on a custom ``__nice__``
|
| 3 |
-
method, which you must define. This means you only have to overload one
|
| 4 |
-
function instead of two. Furthermore, if the object defines a ``__len__``
|
| 5 |
-
method, then the ``__nice__`` method defaults to something sensible, otherwise
|
| 6 |
-
it is treated as abstract and raises ``NotImplementedError``.
|
| 7 |
-
|
| 8 |
-
To use simply have your object inherit from :class:`NiceRepr`
|
| 9 |
-
(multi-inheritance should be ok).
|
| 10 |
-
|
| 11 |
-
This code was copied from the ubelt library: https://github.com/Erotemic/ubelt
|
| 12 |
-
|
| 13 |
-
Example:
|
| 14 |
-
>>> # Objects that define __nice__ have a default __str__ and __repr__
|
| 15 |
-
>>> class Student(NiceRepr):
|
| 16 |
-
... def __init__(self, name):
|
| 17 |
-
... self.name = name
|
| 18 |
-
... def __nice__(self):
|
| 19 |
-
... return self.name
|
| 20 |
-
>>> s1 = Student('Alice')
|
| 21 |
-
>>> s2 = Student('Bob')
|
| 22 |
-
>>> print(f's1 = {s1}')
|
| 23 |
-
>>> print(f's2 = {s2}')
|
| 24 |
-
s1 = <Student(Alice)>
|
| 25 |
-
s2 = <Student(Bob)>
|
| 26 |
-
|
| 27 |
-
Example:
|
| 28 |
-
>>> # Objects that define __len__ have a default __nice__
|
| 29 |
-
>>> class Group(NiceRepr):
|
| 30 |
-
... def __init__(self, data):
|
| 31 |
-
... self.data = data
|
| 32 |
-
... def __len__(self):
|
| 33 |
-
... return len(self.data)
|
| 34 |
-
>>> g = Group([1, 2, 3])
|
| 35 |
-
>>> print(f'g = {g}')
|
| 36 |
-
g = <Group(3)>
|
| 37 |
-
"""
|
| 38 |
-
import warnings
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
class NiceRepr(object):
|
| 42 |
-
"""Inherit from this class and define ``__nice__`` to "nicely" print your
|
| 43 |
-
objects.
|
| 44 |
-
|
| 45 |
-
Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function
|
| 46 |
-
Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``.
|
| 47 |
-
If the inheriting class has a ``__len__``, method then the default
|
| 48 |
-
``__nice__`` method will return its length.
|
| 49 |
-
|
| 50 |
-
Example:
|
| 51 |
-
>>> class Foo(NiceRepr):
|
| 52 |
-
... def __nice__(self):
|
| 53 |
-
... return 'info'
|
| 54 |
-
>>> foo = Foo()
|
| 55 |
-
>>> assert str(foo) == '<Foo(info)>'
|
| 56 |
-
>>> assert repr(foo).startswith('<Foo(info) at ')
|
| 57 |
-
|
| 58 |
-
Example:
|
| 59 |
-
>>> class Bar(NiceRepr):
|
| 60 |
-
... pass
|
| 61 |
-
>>> bar = Bar()
|
| 62 |
-
>>> import pytest
|
| 63 |
-
>>> with pytest.warns(None) as record:
|
| 64 |
-
>>> assert 'object at' in str(bar)
|
| 65 |
-
>>> assert 'object at' in repr(bar)
|
| 66 |
-
|
| 67 |
-
Example:
|
| 68 |
-
>>> class Baz(NiceRepr):
|
| 69 |
-
... def __len__(self):
|
| 70 |
-
... return 5
|
| 71 |
-
>>> baz = Baz()
|
| 72 |
-
>>> assert str(baz) == '<Baz(5)>'
|
| 73 |
-
"""
|
| 74 |
-
|
| 75 |
-
def __nice__(self):
|
| 76 |
-
"""str: a "nice" summary string describing this module"""
|
| 77 |
-
if hasattr(self, '__len__'):
|
| 78 |
-
# It is a common pattern for objects to use __len__ in __nice__
|
| 79 |
-
# As a convenience we define a default __nice__ for these objects
|
| 80 |
-
return str(len(self))
|
| 81 |
-
else:
|
| 82 |
-
# In all other cases force the subclass to overload __nice__
|
| 83 |
-
raise NotImplementedError(
|
| 84 |
-
f'Define the __nice__ method for {self.__class__!r}')
|
| 85 |
-
|
| 86 |
-
def __repr__(self):
|
| 87 |
-
"""str: the string of the module"""
|
| 88 |
-
try:
|
| 89 |
-
nice = self.__nice__()
|
| 90 |
-
classname = self.__class__.__name__
|
| 91 |
-
return f'<{classname}({nice}) at {hex(id(self))}>'
|
| 92 |
-
except NotImplementedError as ex:
|
| 93 |
-
warnings.warn(str(ex), category=RuntimeWarning)
|
| 94 |
-
return object.__repr__(self)
|
| 95 |
-
|
| 96 |
-
def __str__(self):
|
| 97 |
-
"""str: the string of the module"""
|
| 98 |
-
try:
|
| 99 |
-
classname = self.__class__.__name__
|
| 100 |
-
nice = self.__nice__()
|
| 101 |
-
return f'<{classname}({nice})>'
|
| 102 |
-
except NotImplementedError as ex:
|
| 103 |
-
warnings.warn(str(ex), category=RuntimeWarning)
|
| 104 |
-
return object.__repr__(self)
|
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|
spaces/Campfireman/whisper_lab2/app.py
DELETED
|
@@ -1,119 +0,0 @@
|
|
| 1 |
-
from transformers import pipeline
|
| 2 |
-
import gradio as gr
|
| 3 |
-
import moviepy.editor as mp
|
| 4 |
-
from pytube import YouTube
|
| 5 |
-
import math
|
| 6 |
-
|
| 7 |
-
pipe = pipeline(model="Campfireman/whisper-small-hi") # change to "your-username/the-name-you-picked"
|
| 8 |
-
|
| 9 |
-
segment_length = 25 # 25s per segment
|
| 10 |
-
|
| 11 |
-
def download_video(url):
|
| 12 |
-
print("Downloading...")
|
| 13 |
-
local_file = (
|
| 14 |
-
YouTube(url)
|
| 15 |
-
.streams.filter(progressive=True, file_extension="mp4")
|
| 16 |
-
.first()
|
| 17 |
-
.download()
|
| 18 |
-
)
|
| 19 |
-
print("Downloaded")
|
| 20 |
-
global my_clip
|
| 21 |
-
global original_wav
|
| 22 |
-
my_clip = mp.VideoFileClip(local_file)
|
| 23 |
-
my_clip.audio.write_audiofile("AUDIO_ORIGINAL.wav")
|
| 24 |
-
original_wav = mp.AudioFileClip("AUDIO_ORIGINAL.wav")
|
| 25 |
-
global audio_length
|
| 26 |
-
audio_length = original_wav.duration
|
| 27 |
-
print("Overall audio time elapsed: "+str(audio_length))
|
| 28 |
-
return local_file
|
| 29 |
-
|
| 30 |
-
def validate_youtube(url):
|
| 31 |
-
#This creates a youtube object
|
| 32 |
-
try:
|
| 33 |
-
yt = YouTube(url)
|
| 34 |
-
except Exception:
|
| 35 |
-
print("Hi the URL seems not a valid YouTube video link")
|
| 36 |
-
return True
|
| 37 |
-
#This will return the length of the video in sec as an int
|
| 38 |
-
video_length = yt.length
|
| 39 |
-
if video_length > 600:
|
| 40 |
-
print("Your video is longer than 10 minutes")
|
| 41 |
-
return False
|
| 42 |
-
else:
|
| 43 |
-
print("Your video is less than 10 minutes")
|
| 44 |
-
return True
|
| 45 |
-
|
| 46 |
-
def validate_url(url):
|
| 47 |
-
import validators
|
| 48 |
-
if not validators.url(url):
|
| 49 |
-
return True
|
| 50 |
-
else:
|
| 51 |
-
return False
|
| 52 |
-
|
| 53 |
-
def audio_clipper(index, seg_total):
|
| 54 |
-
my_audio = "audio_out"+str(index)+".wav"
|
| 55 |
-
audio_clipped_obj = mp.AudioFileClip.copy(original_wav)
|
| 56 |
-
print("Segment "+str(index)+":")
|
| 57 |
-
# Clipping
|
| 58 |
-
if (index > 0):
|
| 59 |
-
print("Clipped: 0 ~ " + str(segment_length * (index)) + "sec")
|
| 60 |
-
audio_clipped_obj = mp.AudioFileClip.cutout(audio_clipped_obj, 0, segment_length * (index))
|
| 61 |
-
if (index < seg_total - 1):
|
| 62 |
-
print("Clipped: " + str(segment_length * (index + 1)) + "~ " + str(audio_length) +" sec")
|
| 63 |
-
audio_clipped_obj = mp.AudioFileClip.cutout(audio_clipped_obj, segment_length * (index + 1), audio_length)
|
| 64 |
-
|
| 65 |
-
# Write out the temporary segment data
|
| 66 |
-
mp.AudioFileClip.write_audiofile(audio_clipped_obj, my_audio)
|
| 67 |
-
#audio_clipped_obj.audio.write_audiofile(my_audio)
|
| 68 |
-
|
| 69 |
-
return my_audio
|
| 70 |
-
|
| 71 |
-
def transcribe(video_url):
|
| 72 |
-
text = ""
|
| 73 |
-
if validate_url(video_url):
|
| 74 |
-
if not validate_youtube(video_url):
|
| 75 |
-
return "The URL seems not for Youtube videos or the video is too long. Check out the errors in the log. "
|
| 76 |
-
else:
|
| 77 |
-
download_video(video_url)
|
| 78 |
-
else:
|
| 79 |
-
return "Invalid URL. Please check the format of your link. "
|
| 80 |
-
|
| 81 |
-
segment_count = math.ceil(audio_length / segment_length)
|
| 82 |
-
print("Total segments: "+str(segment_count))
|
| 83 |
-
if segment_count <= 0:
|
| 84 |
-
return "Corrupted Video Data! Invalid length of "+str(segment_count * 25)+" second(s)."
|
| 85 |
-
else:
|
| 86 |
-
for x in range(segment_count):
|
| 87 |
-
audio = audio_clipper(x, segment_count)
|
| 88 |
-
seg_text = pipe(audio, batch_size=512, truncation=True)["text"]
|
| 89 |
-
print("Segtext: ")
|
| 90 |
-
print(seg_text)
|
| 91 |
-
text = text + seg_text
|
| 92 |
-
|
| 93 |
-
return text
|
| 94 |
-
|
| 95 |
-
|
| 96 |
-
def transcribe2(audio):
|
| 97 |
-
text = pipe(audio)["text"]
|
| 98 |
-
return text
|
| 99 |
-
|
| 100 |
-
|
| 101 |
-
iface = gr.Interface( fn=transcribe,
|
| 102 |
-
inputs=gr.Textbox(label = "Enter the URL of the Youtube video clip here (without prefixes like http://):"),
|
| 103 |
-
outputs="text",
|
| 104 |
-
title="Whisper Small SE",
|
| 105 |
-
description="Video Swedish Transcriptior",
|
| 106 |
-
)
|
| 107 |
-
|
| 108 |
-
|
| 109 |
-
iface2 = gr.Interface(
|
| 110 |
-
fn=transcribe2,
|
| 111 |
-
inputs=gr.Audio(source="microphone", type="filepath"),
|
| 112 |
-
outputs="text",
|
| 113 |
-
title="Whisper Small Swedish",
|
| 114 |
-
description="Realtime demo for Swedish speech recognition using a fine-tuned Whisper small model.",
|
| 115 |
-
)
|
| 116 |
-
|
| 117 |
-
demo = gr.TabbedInterface([iface, iface2],["Swedish YouTube Video to Text", "Swedish Audio to Text"])
|
| 118 |
-
|
| 119 |
-
demo.launch()
|
|
|
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|
|
spaces/ChristopherMarais/Andrew_AI-BB_classification-beta/mysite/manage.py
DELETED
|
@@ -1,43 +0,0 @@
|
|
| 1 |
-
#!/usr/bin/env python
|
| 2 |
-
"""Django's command-line utility for administrative tasks."""
|
| 3 |
-
import os
|
| 4 |
-
import sys
|
| 5 |
-
import random
|
| 6 |
-
from torchvision.transforms import GaussianBlur
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
# Define a custom transform for Gaussian blur
|
| 10 |
-
def gaussian_blur(
|
| 11 |
-
x,
|
| 12 |
-
p=0.5,
|
| 13 |
-
kernel_size_min=3,
|
| 14 |
-
kernel_size_max=20,
|
| 15 |
-
sigma_min=0.1,
|
| 16 |
-
sigma_max=3):
|
| 17 |
-
if x.ndim == 4:
|
| 18 |
-
for i in range(x.shape[0]):
|
| 19 |
-
if random.random() < p:
|
| 20 |
-
kernel_size = random.randrange(
|
| 21 |
-
kernel_size_min,
|
| 22 |
-
kernel_size_max + 1, 2)
|
| 23 |
-
sigma = random.uniform(sigma_min, sigma_max)
|
| 24 |
-
x[i] = GaussianBlur(kernel_size=kernel_size, sigma=sigma)(x[i])
|
| 25 |
-
return x
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
def main():
|
| 29 |
-
"""Run administrative tasks."""
|
| 30 |
-
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'mysite.settings')
|
| 31 |
-
try:
|
| 32 |
-
from django.core.management import execute_from_command_line
|
| 33 |
-
except ImportError as exc:
|
| 34 |
-
raise ImportError(
|
| 35 |
-
"Couldn't import Django. Are you sure it's installed and "
|
| 36 |
-
"available on your PYTHONPATH environment variable? Did you "
|
| 37 |
-
"forget to activate a virtual environment?"
|
| 38 |
-
) from exc
|
| 39 |
-
execute_from_command_line(sys.argv)
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
if __name__ == '__main__':
|
| 43 |
-
main()
|
|
|
|
|
|
|
|
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|
|
spaces/CofAI/optor/index.html
DELETED
|
@@ -1,53 +0,0 @@
|
|
| 1 |
-
<!DOCTYPE html>
|
| 2 |
-
<html lang="en">
|
| 3 |
-
<head>
|
| 4 |
-
<meta charset="utf-8" />
|
| 5 |
-
<meta
|
| 6 |
-
name="viewport"
|
| 7 |
-
content="width=device-width, initial-scale=1, shrink-to-fit=no, maximum-scale=1"
|
| 8 |
-
/>
|
| 9 |
-
|
| 10 |
-
<script>
|
| 11 |
-
window.__gradio_mode__ = "app";
|
| 12 |
-
window.gradio_config = {"version": "3.0.13", "mode": "blocks", "dev_mode": false, "components": [{"id": 1, "type": "markdown", "props": {"value": "<h1><center>OPTOR</center></h1>", "name": "markdown", "visible": true, "style": {}}}, {"id": 2, "type": "markdown", "props": {"value": "<p>OPTOR will allow you to generate high-quality images based on a text query based on SD, Midjourney, Dalle-2!</p>\n", "name": "markdown", "visible": true, "style": {}}}, {"id": 3, "type": "group", "props": {"type": "group", "visible": true, "style": {}}}, {"id": 4, "type": "box", "props": {"type": "box", "visible": true, "style": {}}}, {"id": 5, "type": "row", "props": {"type": "row", "visible": true, "style": {"equal_height": true, "mobile_collapse": false}}}, {"id": 6, "type": "textbox", "props": {"lines": 1, "max_lines": 1, "value": "", "label": "Enter your prompt", "show_label": false, "name": "textbox", "visible": true, "style": {"container": false}}}, {"id": 7, "type": "button", "props": {"value": "Run", "variant": "primary", "name": "button", "visible": true, "style": {}}}, {"id": 8, "type": "gallery", "props": {"label": "Generated images", "show_label": false, "name": "gallery", "visible": true, "style": {"grid": [3], "height": "auto"}}}, {"id": 9, "type": "markdown", "props": {"value": "<details>\n<summary>LICENSE</summary>\n<p style='line-height: normal; font-size: small'>\nAll rights reserved to CoffAI, the technology is powered by the latest technology and you get it for free. You can copy the repositories wherever you want. Thank you!</a>.\n</p>\n</details>", "name": "markdown", "visible": true, "style": {}}}, {"id": 10, "type": "markdown", "props": {"value": "<hr />\n<p style='text-align: center'>\nCreated by <a href=\"https://texton-optor1.hf.space\" target=\"_blank\">CofAI</a> et al. 2023\n<br/>\n<a href=\"https://texton-optor2.hf.space\" target=\"_blank\">Discord</a> | <a href=\"https://texton-eh.hf.space\" target=\"_blank\">Evgeniy Hristoforu</a>\n<p style='text-align: center'>Powered by CofAI.api <a href=\"https://texton-cof1.hf.space/trc/\" target=\"_blank\">CofAI.api Engine</a>\n</p>", "name": "markdown", "visible": true, "style": {}}}], "theme": "default", "css": ".container { max-width: 800px; margin: auto; }", "enable_queue": false, "layout": {"id": 0, "children": [{"id": 1}, {"id": 2}, {"id": 3, "children": [{"id": 4, "children": [{"id": 5, "children": [{"id": 6}, {"id": 7}]}]}, {"id": 8}]}, {"id": 9}, {"id": 10}]}, "dependencies": [{"targets": [7], "trigger": "click", "inputs": [6], "outputs": [8], "backend_fn": false, "js": "\n async (text) => {\n try {\n response = await fetch('https://bf.dallemini.ai/generate', {\n method: 'POST',\n headers: {\n 'Accept': 'application/json',\n 'Content-Type': 'application/json'\n },\n body: JSON.stringify({\n prompt: text\n })\n });\n response = await response.json()\n let imgs = response.images.map(r => \"data:image/png;base64,\" + r)\n return imgs\n } catch (e) {\n alert(\"Too much traffic, please try again.\")\n IMG = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAMAAACahl6sAAAAOVBMVEXg4OB1dXXX19fd3d2EhIR9fX14eHjJycm2trbb29uurq6goKCZmZmIiIiBgYHNzc2np6e8vLySkpKXK8HrAAABuUlEQVR4nO3Z0bKCIBCAYQNFVCzr/R/2nHU6k8KpJi6wZf7vLu1id9gFhKYBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEAb249h7pzr5jD29uhospnlfNo4L+boiLKYyZ0iblKYiu/iNER3PTquD9npPgbB98Za0/twH59JVasMtzXo1m+iHny7PrwpysSuebgxCtmOTlkma121l/TFZR2UqXxEebxEO/87QZlZ3inpeCPzVftkojUyJp2OWVgKy23qSsbg8evitBSXkUjHzYN9Is0oeWoYkkUKazsxRYlYKa6ldFSfs7K/8tsnUSLrXHAuG1SOXpp5t1LEiQxSe33ZqDJIC4TdkziRJkRN9J1CXFlpIj7J9RvNSd0kiUj1zSVjyiKr4X5yTRIx0kYlY8oinbzfFSaJWFlJSsaUpZpEqimttNkTOpo9nX4TOqbfdEFM6FgQpW7c8OofSrYo1Wwaq9nG1/NhVc2nbj2HD821kuOgeg7o3hyZBj1Hpo9D7M3K+HeIrSmPeq4Vfl3ruOhpnly9vdyEfa1KLkPF7nr66GAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAPjcD13rCcC3ILx/AAAAAElFTkSuQmCC\"\n return Array(9).fill(IMG)\n }\n }\n ", "status_tracker": null, "queue": null, "api_name": null}]};
|
| 13 |
-
</script>
|
| 14 |
-
|
| 15 |
-
<link rel="preconnect" href="https://fonts.googleapis.com" />
|
| 16 |
-
<link
|
| 17 |
-
rel="preconnect"
|
| 18 |
-
href="https://fonts.gstatic.com"
|
| 19 |
-
crossorigin="anonymous"
|
| 20 |
-
/>
|
| 21 |
-
<link
|
| 22 |
-
href="https://fonts.googleapis.com/css?family=Source Sans Pro"
|
| 23 |
-
rel="stylesheet"
|
| 24 |
-
/>
|
| 25 |
-
<link
|
| 26 |
-
href="https://fonts.googleapis.com/css?family=IBM Plex Mono"
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| 27 |
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rel="stylesheet"
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| 28 |
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/>
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<script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script>
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| 30 |
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<script type="module" crossorigin src="https://gradio.s3-us-west-2.amazonaws.com/3.0.9b12/assets/index.8eca4ae7.js"></script>
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| 31 |
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<link rel="stylesheet" href="https://gradio.s3-us-west-2.amazonaws.com/3.0.9b12/assets/index.cbea297d.css">
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| 32 |
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<style>
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| 33 |
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footer img {
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| 34 |
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display: none !important;
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| 35 |
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}
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</style>
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</head>
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<body
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style="
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margin: 0;
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| 42 |
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padding: 0;
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display: flex;
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flex-direction: column;
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| 45 |
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flex-grow: 1;
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"
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>
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| 48 |
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<div
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| 49 |
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id="root"
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| 50 |
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style="display: flex; flex-direction: column; flex-grow: 1"
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| 51 |
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></div>
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| 52 |
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</body>
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| 53 |
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</html>
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