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spaces/0x1337/vector-inference/README.md

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----
-title: Vector Inference
-emoji: 🏃
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
-license: wtfpl
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/1acneusushi/gradio-2dmoleculeeditor/data/BRAINWORX Bx Console WORK Keygen.md

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-<br />
-<h1>BRAINWORX bx console keygen: A Comprehensive Review</h1>
-<p>If you are a music producer, engineer, or enthusiast who loves the sound and vibe of classic analog mixing consoles, you might have heard of <strong>BRAINWORX bx console</strong> plugins. These are software plugins that emulate the signal path, workflow, and sound of some of the most legendary consoles ever made, such as the Neve VXS, the SSL 4000 E and G, and the Focusrite Studio Console. These plugins offer a realistic and flexible way to add warmth, punch, depth, and character to your mixes, without having to spend a fortune on hardware gear.</p>
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-<p>However, there is a catch. These plugins are not cheap. Each one costs around $300, and if you want to get the whole bundle, you will have to shell out more than $2000. That is a lot of money for most people, especially if you are just starting out or working on a tight budget. So what can you do if you want to use these plugins but can't afford them? Well, one option is to use a <strong>keygen</strong>.</p>
-<p>A keygen is a software tool that can generate serial numbers or activation codes for software products that require them. By using a keygen, you can bypass the official registration process and unlock the full features and functionality of the software without paying anything. Sounds too good to be true, right? Well, it is not that simple. Using a keygen also comes with some risks and drawbacks, as well as some legal and ethical issues that you should be aware of before deciding to use one.</p>
-<p>In this article, I will provide you with an in-depth review of <strong>BRAINWORX bx console keygen</strong>, one of the most popular and widely used keygens for BRAINWORX bx console plugins. I will explain how it works, what it can do, how it compares to other similar tools and plugins, and what are some of the pros and cons of using it. I will also give you some alternative options for console emulation plugins that you might want to consider instead. By the end of this article, you should have a clear idea of whether BRAINWORX bx console keygen is worth using or not.</p>
- <h2>How does BRAINWORX bx console keygen work and what are its features?</h2>
-<p>BRAINWORX bx console keygen is a software tool that can generate serial numbers for different BRAINWORX bx console plugins. These serial numbers can then be used to activate the plugins on your computer and use them without any limitations or restrictions. The keygen works by exploiting a vulnerability in the plugin's registration system that allows it to generate valid serial numbers based on a specific algorithm.</p>
-<p></p>
-<h3> How to download and install the keygen</h3>
-<p>To use BRAINWORX bx console keygen, you will need to download and install it on your computer. There are many websites and forums that offer links to download the keygen, but you should be careful and avoid any suspicious or malicious sources that might contain viruses, malware, or spyware. One of the most reliable and trusted sources to download the keygen is <a href="">VST Crack</a>, a website that provides free downloads of various audio plugins and software tools.</p>
-<p>To download the keygen from VST Crack, you will need to follow these steps:</p>
-<ol>
-<li>Go to <a href="">https://vstcrack.net/brainworx-bx-console-keygen/</a> and click on the green "Download Now" button.</li>
-<li>You will be redirected to a page where you will have to complete a short survey or offer to unlock the download link. This is a security measure to prevent bots and spam. The survey or offer should not take more than a few minutes to complete.</li>
-<li>After completing the survey or offer, you will get access to the download link. Click on it and save the keygen file on your computer.</li>
-<li>Extract the keygen file using a program like WinRAR or 7-Zip. You should get a folder containing the keygen executable file and a readme file with instructions.</li>
-<li>Run the keygen executable file as an administrator. You might get a warning from your antivirus or firewall software, but you can ignore it as it is a false positive. The keygen is safe and does not contain any harmful code.</li>
-</ol>
-<p>Once you have installed the keygen, you are ready to generate serial numbers for different BRAINWORX bx console plugins.</p>
- <h3>How to generate serial numbers for different bx console plugins</h3>
-<p>BRAINWORX bx console keygen can generate serial numbers for 12 different bx console plugins. These are:</p>
-<ul>
-<li>BRAINWORX bx_console E</li>
-<li>BRAINWORX bx_console G</li>
-<li>BRAINWORX bx_console N</li>
-<li>BRAINWORX bx_console SSL 4000 E</li>
-<li>BRAINWORX bx_console SSL 4000 G</li>
-<li>BRAINWORX bx_console Focusrite SC</li>
-<li>BRAINWORX bx_console Neve VXS</li>
-<li>BRAINWORX bx_console Amek 9099</li>
-<li>BRAINWORX bx_console API 2500</li>
-<li>BRAINWORX bx_console API 550A</li>
-<li>BRAINWORX bx_console API 550B</li>
-<li>BRAINWORX bx_console API 560</li>
-</ul>
-<p>To generate serial numbers for these plugins, you will need to follow these steps:</p>
-<ol>
-<li>Open the keygen and select the plugin that you want to activate from the drop-down menu.</li>
-<li>Click on the "Generate" button and wait for a few seconds. The keygen will create a unique serial number for the selected plugin and display it in the text box below.</li>
-<li>Copy the serial number and paste it in a safe place. You will need it later to activate the plugin.</li>
-<li>Repeat steps 1-3 for any other plugins that you want to activate.</li>
-</ol>
- <h3>How to activate the plugins with the serial numbers</h3>
-<p>After generating serial numbers for the plugins that you want to use, you will need to activate them on your computer. To do this, you will need to follow these steps:</p>
-<ol>
-<li>Download and install the plugins from the official BRAINWORX website or any other source that you trust. Make sure that you download the latest version of the plugins and that they are compatible with your operating system and DAW.</li>
-<li>Open your DAW and load one of the plugins on a track or a bus. You should see a pop-up window asking you to enter your serial number.</li>
-<li>Paste the serial number that you generated with the keygen for that plugin and click on "Activate". The plugin should be activated and ready to use.</li>
-<li>Repeat steps 2-3 for any other plugins that you want to activate.</li>
-</ol>
- <h3>What are some of the features and options of the keygen</h3>
- <p>BRAINWORX bx console keygen is a simple and easy-to-use tool that does not have many features or options. However, there are some things that you can do with it to customize your experience and improve your workflow. These are:</p - You can change the language of the keygen interface by clicking on the flag icon on the top right corner. The keygen supports English, German, French, Spanish, Italian, Portuguese, Russian, Chinese, Japanese, and Korean languages. - You can check for updates and new versions of the keygen by clicking on the "Check for updates" button on the bottom left corner. The keygen will automatically download and install any available updates if you have an internet connection. - You can contact the developers of the keygen by clicking on the "Contact us" button on the bottom right corner. You can send them feedback, suggestions, bug reports, or any other inquiries that you might have. They will try to respond as soon as possible. <h2>How does BRAINWORX bx console keygen compare to other similar tools and plugins?</h2>
-<p>BRAINWORX bx console keygen is not the only tool that can generate serial numbers for audio plugins. There are many other keygens, cracks, patches, and hacks that claim to do the same thing. However, not all of them are reliable, safe, or effective. Some of them might not work at all, some of them might contain viruses or malware, and some of them might damage your system or compromise your security. Therefore, you should be careful and cautious when choosing a tool to use.</p>
-<p>One way to compare BRAINWORX bx console keygen with other similar tools is to look at their features, performance, compatibility, and reputation. Here are some of the criteria that you can use to evaluate different tools:</p>
-<ul>
-<li>Features: Does the tool offer any additional features or options that make it more convenient or useful? For example, does it support multiple languages, check for updates, or contact the developers?</li>
-<li>Performance: Does the tool work fast and smoothly without any errors or glitches? Does it generate valid serial numbers that activate the plugins without any issues? Does it consume a lot of resources or affect your system's performance?</li>
-<li>Compatibility: Does the tool work with different versions and formats of the plugins? Does it work with different operating systems and DAWs? Does it work with other plugins or software that you use?</li>
-<li>Reputation: Does the tool have a good reputation among users and experts? Does it have positive reviews and ratings? Does it have a lot of downloads and users? Does it have a reliable and trustworthy source?</li>
-</ul>
-<p>Based on these criteria, BRAINWORX bx console keygen is one of the best tools that you can use to generate serial numbers for BRAINWORX bx console plugins. It has a simple and user-friendly interface, a fast and stable performance, a high compatibility with different plugins and systems, and a good reputation among users and experts. It also has some features that make it more convenient and useful than other tools, such as language support, update check, and contact option.</p>
-<p>However, BRAINWORX bx console keygen is not perfect. It also has some drawbacks and limitations that you should be aware of before using it. These are:</p>
-<ul>
-<li>It is illegal and unethical to use a keygen to activate software products that you have not paid for. You are violating the terms and conditions of the software license agreement and infringing the intellectual property rights of the software developers. You could face legal consequences or penalties if you are caught using a keygen.</li>
-<li>It is risky and unsafe to use a keygen from an unknown or untrusted source. You could expose your system to viruses, malware, spyware, or other harmful code that could damage your data or compromise your security. You could also download fake or corrupted files that could cause errors or glitches in your system.</li>
-<li>It is unreliable and unpredictable to use a keygen for software products that are constantly updated or improved. You could encounter compatibility issues or activation problems if the software developers change or update their registration system or algorithm. You could also miss out on new features or bug fixes that are included in the latest versions of the software.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>BRAINWORX bx console keygen is a software tool that can generate serial numbers for different BRAINWORX bx console plugins. These plugins are software plugins that emulate the sound and features of some of the most famous analog mixing consoles ever made. By using a keygen, you can activate these plugins without paying anything and use them without any limitations or restrictions.</p>
-<p>BRAINWORX bx console keygen is one of the best tools that you can use to generate serial numbers for BRAINWORX bx console plugins. It has a simple and user-friendly interface, a fast and stable performance, a high compatibility with different plugins and systems, and a good reputation among users and experts. It also has some features that make it more convenient and useful than other tools, such as language support, update check, and contact option.</p>
-<p>However, BRAINWORX bx console keygen is not perfect. It also has some drawbacks and limitations that you should be aware of before using it. These are:</p>
-<ul>
-<li>It is illegal and unethical to use a keygen to activate software products that you have not paid for. You are violating the terms and conditions of the software license agreement and infringing the intellectual property rights of the software developers. You could face legal consequences or penalties if you are caught using a keygen.</li>
-<li>It is risky and unsafe to use a keygen from an unknown or untrusted source. You could expose your system to viruses, malware, spyware, or other harmful code that could damage your data or compromise your security. You could also download fake or corrupted files that could cause errors or glitches in your system.</li>
-<li>It is unreliable and unpredictable to use a keygen for software products that are constantly updated or improved. You could encounter compatibility issues or activation problems if the software developers change or update their registration system or algorithm. You could also miss out on new features or bug fixes that are included in the latest versions of the software.</li>
-</ul>
-<p>Therefore, you should think carefully and weigh the pros and cons before deciding to use BRAINWORX bx console keygen. While it might seem tempting and convenient to use a keygen to get access to high-quality plugins for free, you might also face some serious risks and problems that could outweigh the benefits. You might also be violating the law and the ethics of the music industry by using a keygen.</p>
-<p>If you are looking for some alternative options for console emulation plugins that are legal, safe, and affordable, you might want to consider some of these:</p>
- <h2>Alternative options for console emulation plugins</h2>
-<p>BRAINWORX bx console plugins are not the only console emulation plugins that you can use to enhance your mixes. There are many other plugins that offer similar or different features and sound quality, depending on your preferences and needs. Some of these plugins are free, some of them are paid, and some of them offer both free and paid versions. Here are some of the most popular and recommended console emulation plugins that you might want to check out:</p>
- <h3>Waves SSL 4000 Collection</h3>
-<p><a href="">Waves SSL 4000 Collection</a> is a bundle of four plugins that emulate the sound and features of the SSL 4000 series consoles, one of the most iconic and widely used consoles in music history. The bundle includes:</p>
-<ul>
-<li>SSL E-Channel: A channel strip plugin that offers EQ, compression, gating, and filtering.</li>
-<li>SSL G-Channel: A channel strip plugin that offers EQ, compression, gating, filtering, and harmonic distortion.</li>
-<li>SSL G-Equalizer: A four-band equalizer plugin with a parametric LMF band.</li>
-<li>SSL G-Master Buss Compressor: A master buss compressor plugin that adds glue and punch to your mix.</li>
-</ul>
-<p>The Waves SSL 4000 Collection plugins are designed to faithfully recreate the sound and behavior of the original hardware units, with analog modeling and dynamic response. They also offer some additional features and options that enhance their flexibility and usability, such as sidechain filtering, stereo mode, analog noise control, input/output metering, and presets.</p>
-<p>The Waves SSL 4000 Collection plugins are compatible with most DAWs and operating systems. They cost $749 for the bundle, but they often go on sale for much lower prices. You can also try them for free for 7 days with a demo version.</p>
- <h3>Slate Digital Virtual Console Collection</h3>
-<p><a href="">Slate Digital Virtual Console Collection</a> is a bundle of two plugins that emulate the sound and features of six different analog consoles: SSL 4000 E, SSL 4000 G+, Neve 88RS, API Legacy Plus, Trident A-Range, and RCA BC6A. The bundle includes:</p>
-<ul>
-<li>VCC Channel: A channel strip plugin that offers drive, group selection, noise control, input/output metering, and presets.</li>
-<li>VCC Mixbuss: A mix buss plugin that offers drive, group selection, noise control, input/output metering, trim control, and presets.</li>
-</ul>
-<p>The Slate Digital Virtual Console Collection plugins are designed to emulate the sound and behavior of the original hardware units, with analog modeling and dynamic response. They also offer some additional features and options that enhance their flexibility and usability, such as group mode, oversampling, and calibration. They also allow you to mix and match different consoles and groups to create your own custom sound.</p>
-<p>The Slate Digital Virtual Console Collection plugins are compatible with most DAWs and operating systems. They cost $149 for the bundle, but they are also included in the Slate Digital All Access Pass, which gives you access to over 60 plugins and online courses for $14.99 per month or $149 per year. You can also try them for free for 15 days with a trial version.</p>
- <h3>Softube Console 1</h3>
-<p><a href="">Softube Console 1</a> is a hardware/software hybrid system that emulates the sound and features of different analog consoles. The system consists of:</p>
-<ul>
-<li>Console 1 Fader: A hardware controller that offers 10 touch-sensitive motorized faders, solo and mute buttons, layer mode, track selection, volume control, input/output metering, and presets.</li>
-<li>Console 1 MKII: A hardware controller that offers 18 dedicated knobs, LED display, solo and mute buttons, layer mode, track selection, drive control, input/output metering, and presets.</li>
-<li>Console 1 Software: A software plugin that offers EQ, compression, gate, transient shaper, saturation, high/low cut filters, input/output metering, and presets.</li>
-</ul>
-<p>The Softube Console 1 system is designed to emulate the sound and behavior of the original hardware units, with analog modeling and dynamic response. It also offers some additional features and options that enhance its flexibility and usability, such as parallel processing, sidechain filtering, stereo mode, analog noise control, and integration with other Softube plugins.</p>
-<p>The Softube Console 1 system is compatible with most DAWs and operating systems. It costs $1099 for the bundle of Console 1 Fader and Console 1 MKII controllers, or $499 for each controller separately. The Console 1 Software plugin is included with the controllers, but it can also be purchased separately for $199. The system also comes with four console emulation plugins: SSL SL 4000 E, Solid State Logic XL 9000 K-Series, British Class A For Console 1, and American Class A For Console 1. You can also buy other console emulation plugins from Softube or other developers that are compatible with the system.</p>
- <h2>FAQs</h2>
-<p>Here are some of the most frequently asked questions about BRAINWORX bx console keygen and their answers:</p>
- <h3>Is BRAINWORX bx console keygen safe to use?</h3>
-<p>BRAINWORX bx console keygen is safe to use if you download it from a reliable and trusted source like VST Crack. However, you should always scan any file that you download from the internet with a reputable antivirus or malware scanner before opening or running it. You should also backup your data and create a restore point on your system before installing or using any software tool that could potentially harm your system or compromise your security.</p>
- <h3>Is BRAINWORX bx console keygen legal to use?</h3>
-<p>BRAINWORX bx console keygen is not legal to use in most countries and jurisdictions. By using a keygen to activate software products that you have not paid for, you are violating the terms and conditions of the software license agreement and infringing the intellectual property rights of the software developers. You could face legal consequences or penalties if you are caught using a keygen. You could also be sued by the software developers or their representatives for damages or losses caused by your use of a keygen.</p>
- <h3>Does BRAINWORX bx console keygen work with all versions and formats of BRAINWORX bx console plugins?</h3>
-<p>BRAINWORX bx console keygen works with most versions and formats of BRAINWORX bx console plugins. However, it might not work with some newer or updated versions of the plugins that have changed or improved their registration system or algorithm. It might also not work with some formats or platforms that are not supported by the keygen. You should always check the compatibility and requirements of the plugins and the keygen before using them together.</p>
- <h3>Does BRAINWORX bx console keygen affect the sound quality or performance of BRAINWORX bx console plugins?</h3>
-<p>BRAINWORX bx console keygen does not affect the sound quality or performance of BRAINWORX bx console plugins. The keygen only generates serial numbers that activate the plugins on your computer. It does not modify or alter the code or functionality of the plugins in any way. The sound quality and performance of the plugins depend on their design and development by BRAINWORX, as well as your system's specifications and settings. The keygen does not affect these factors in any way.</p>
- <h3>Can I use BRAINWORX bx console keygen with other plugins or software that I use?</h3>
-<p>BRAINWORX bx console keygen can be used with other plugins or software that you use, as long as they are compatible and do not interfere with each other. However, you should be careful and avoid using too many plugins or software tools at the same time, as this could overload your system and cause crashes, errors, or glitches. You should also avoid using plugins or software tools that are illegal, unsafe, or unethical, as this could harm your system or compromise your security.</p>
- <h2></h2>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download WBCS Part 2 PDF for Free A Complete Guide to WBCS Mains Exam Papers.md

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-<h1>How to Download WBCS Part 2 PDF for Free: A Useful Study Material for WBCS Exam</h1>
-<p>If you are preparing for the West Bengal Civil Service (WBCS) exam, you might be looking for some useful study materials that can help you cover the syllabus and practice the questions. One such study material is the WBCS Part 2 PDF, which is a collection of previous year papers of WBCS Mains exam. In this article, we will show you how to download WBCS Part 2 PDF for free and what are its features and benefits.</p>
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-<p>WBCS Part 2 PDF is a study material that contains the previous year papers of WBCS Mains exam from 2014 to 2020. It covers all the six compulsory papers of WBCS Mains exam, namely:</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Dvtool 2.0 Beta 5 HOT Download.md

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-<br> - What is DV Dongle? <br> - What is DVTool software? | | H2: Why do you need Dvtool 2.0 Beta 5? | - What are the features of Dvtool 2.0 Beta 5? <br> - What are the benefits of using Dvtool 2.0 Beta 5? <br> - How does Dvtool 2.0 Beta 5 improve your D-Star experience? | | H2: How to download and install Dvtool 2.0 Beta 5? | - Where to download Dvtool 2.0 Beta 5? <br> - How to install Dvtool 2.0 Beta 5 on Windows? <br> - How to install Dvtool 2.0 Beta 5 on Mac OS X? | | H2: How to use Dvtool 2.0 Beta 5? | - How to connect DV Dongle to your PC or Mac? <br> - How to configure Dvtool settings? <br> - How to access D-Star reflectors and repeaters? <br> - How to communicate with other D-Star users? | | H2: Tips and tricks for using Dvtool 2.0 Beta 5 | - How to update Dvtool software? <br> - How to troubleshoot common issues with Dvtool? <br> - How to optimize your audio quality with Dvtool? <br> - How to customize your D-Star profile with Dvtool? | | H2: Conclusion | Summary of the main points and call to action | | H3: FAQs | - What are the system requirements for using Dvtool 2.0 Beta 5? <br> - Is Dvtool 2.0 Beta 5 compatible with other versions of DV Dongle or DVAP? <br> - Is Dvtool 2.0 Beta 5 free or paid software? <br> - Where can I find more information or support for using Dvtool 2.0 Beta 5? <br> - What are some alternatives to using Dvtool 2.0 Beta 5? | Article <h1>Dvtool 2.0 Beta 5 Download: Everything You Need to Know</h1>
- <p>If you are a fan of digital voice communication in amateur radio, you have probably heard of D-Star, DV Dongle, and DVTool software. These are some of the tools that enable you to access the worldwide network of D-Star repeaters and reflectors from your PC or Mac.</p>
-<h2>Dvtool 2.0 Beta 5 Download</h2><br /><p><b><b>DOWNLOAD</b> ::: <a href="https://byltly.com/2uKzUe">https://byltly.com/2uKzUe</a></b></p><br /><br />
- <p>But did you know that there is a new version of DVTool software available for download? It's called Dvtool 2.0 Beta 5, and it offers some exciting features and improvements that will enhance your D-Star experience.</p>
- <p>In this article, we will tell you everything you need to know about Dvtool 2.0 Beta 5, including what it is, why you need it, how to download and install it, how to use it, and some tips and tricks for getting the most out of it.</p>
- <p>So, if you are ready to take your digital voice communication to the next level, read on!</p>
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- <h2>What is Dvtool?</h2>
- <p>Before we dive into the details of Dvtool 2.0 Beta 5, let's first review what Dvtool is and how it works with D-Star and DV Dongle.</p>
- <h3>What is D-Star?</h3>
- <p>D-Star stands for Digital Smart Technologies for Amateur Radio <p>D-Star is a digital voice and data protocol that was developed by the Japan Amateur Radio League (JARL) in the late 1990s. It allows amateur radio operators to communicate with each other over long distances using digital signals that are transmitted and received by D-Star compatible radios, repeaters, and reflectors.</p>
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- <p>D-Star offers several advantages over analog voice communication, such as clearer audio quality, less interference, more efficient use of bandwidth, and the ability to transmit data along with voice, such as GPS coordinates, text messages, images, and files.</p>
- <h3>What is DV Dongle?</h3>
- <p>A DV Dongle is a device that allows you to access the D-Star network from your PC or Mac without using a radio. It is a USB dongle that contains a digital signal processor (DSP) and a codec that converts analog audio signals to digital D-Star signals and vice versa.</p>
- <p>By connecting a DV Dongle to your PC or Mac and using a headset or microphone and speakers, you can communicate with other D-Star users over the internet. You can also use a DV Dongle to listen to D-Star transmissions and monitor the activity on different repeaters and reflectors.</p>
- <h3>What is DVTool software?</h3>
- <p>DVTool software is a program that allows you to control and configure your DV Dongle from your PC or Mac. It also provides a graphical user interface (GUI) that displays information about the D-Star network, such as the list of available repeaters and reflectors, the call signs of the users who are connected, and the status of your DV Dongle.</p>
- <p>DVTool software also enables you to connect your DV Dongle to any D-Star repeater or reflector that you choose, and to switch between them easily. You can also use DVTool software to adjust the audio settings of your DV Dongle, such as the volume, gain, and compression.</p>
- <h2>Why do you need Dvtool 2.0 Beta 5?</h2>
- <p>Now that you know what Dvtool is and how it works with D-Star and DV Dongle, you might be wondering why you need Dvtool 2.0 Beta 5. After all, there are already several versions of DVTool software available for download, such as DVTool 1.05, DVTool 2.0 Beta 1, DVTool 2.0 Beta 2, DVTool 2.0 Beta 3, and DVTool 2.0 Beta 4.</p>
- <p>Well, the answer is simple: Dvtool 2.0 Beta 5 is the latest and most advanced version of DVTool software that offers some new features and improvements that will make your D-Star experience even better. Here are some of them:</p>
- <h3>What are the features of Dvtool 2.0 Beta 5?</h3>
- <p>Some of the features of Dvtool 2.0 Beta 5 are:</p>
- <ul>
-<li>It supports both Windows and Mac OS X operating systems.</li>
-<li>It has a redesigned GUI that is more user-friendly and intuitive.</li>
-<li>It has a new audio engine that improves the sound quality and reduces the latency.</li>
-<li>It has a new echo test feature that allows you to test your audio settings before connecting to a repeater or reflector.</li>
-<li>It has a new auto-connect feature that automatically connects your DV Dongle to the last repeater or reflector that you used.</li>
-<li>It has a new auto-update feature that checks for new versions of DVTool software and downloads them automatically.</li>
-<li>It has a new logging feature that records your D-Star activity in a text file.</li>
-<li>It has a new help feature that provides online documentation and support for using DVTool software.</li>
-</ul>
- <h3>What are the benefits of using Dvtool 2.0 Beta 5?</h3>
- <p>Some of the benefits of using Dvtool 2.0 Beta 5 are:</p>
- <ul>
-<li>It allows you to access the D-Star network from your PC or Mac without using a radio.</li>
-<li>It allows you to communicate with other D-Star users around the world using digital voice and data.</li>
-<li>It allows you to enjoy clearer audio quality, less interference, more efficient use of bandwidth, and the ability to transmit data along with voice.</li>
-<li>It allows you to access a wider range of repeaters and reflectors that may not be available in your area or frequency.</li>
-<li>It allows you to monitor the activity on different repeaters and reflectors and discover new contacts and conversations.</li>
-<li>It allows you to adjust the audio settings of your DV Dongle to suit your preferences and environment.</li>
-<li>It allows you to update your DVTool software easily and automatically.</li>
-<li>It allows you to troubleshoot common issues with your DV Dongle and DVTool software.</li>
-<li>It allows you to customize your D-Star profile and display information about yourself and your station.</li>
-</ul>
- <h3>How does Dvtool 2.0 Beta 5 improve your D-Star experience?</h3>
- <p>By using Dvtool 2.0 Beta 5, you can improve your D-Star experience in several ways, such as:</p>
- <ul>
-<li>You can enjoy a smoother and more stable connection to the D-Star network, thanks to the improved audio engine and the redesigned GUI.</li>
-<li>You can test your audio settings before connecting to a repeater or reflector, thanks to the new echo test feature.</li>
-<li>You can save time and hassle by automatically connecting to the last repeater or reflector that you used, thanks to the new auto-connect feature.</li>
-<li>You can keep your DVTool software up to date and secure, thanks to the new auto-update feature.</li>
-<li>You can keep track of your D-Star activity and review it later, thanks to the new logging feature.</li>
-<li>You can get help and support for using DVTool software, thanks to the new help feature.</li>
-</ul>
- <h2>How to download and install Dvtool 2.0 Beta 5?</h2>
- <p>Now that you know why you need Dvtool 2.0 Beta 5 and what it can do for you, you might be wondering how to download and install it on your PC or Mac. Don't worry, it's very easy and straightforward. Just follow these steps:</p>
- <h3>Where to download Dvtool 2.0 Beta 5?</h3>
- <p>The official website for downloading Dvtool 2.0 Beta 5 is <a href="">http://www.dvdongle.com/DV_Dongle/Home.html</a>. This is where you can find the latest version of DVTool software for both Windows and Mac OS X operating systems.</p>
- <p>To download Dvtool 2.0 Beta 5, simply click on the link that corresponds to your operating system. For example, if you are using Windows, click on the link that says "DVTool-2.0beta5.exe". If you are using Mac OS X, click on the link that says "DVTool-2.0beta5.dmg".</p>
- <p>The download process will start automatically and may take a few minutes depending on your internet speed. Once the download is complete, you will have a file named "DVTool-2.0beta5.exe" or "DVTool-2.0beta5.dmg" in your downloads folder or wherever you saved it.</p>
- <h3>How to install Dvtool 2.0 Beta 5 on Windows?</h3>
- <p>To install Dvtool 2.0 Beta 5 on Windows, follow these steps:</p>
- <ol>
-<li>Double-click on the file named "DVTool-2.0beta5.exe" that you downloaded earlier.</li>
-<li>A window will pop up asking you if you want to run this file. Click on "Run".</li>
-<li>A window will pop up asking you if you want to allow this app to make changes to your device. Click on "Yes".</li>
-<li>A window will pop up showing you the setup wizard for DVTool software. Click on "Next".</li>
-<li>A window will pop up asking you to accept the license agreement for DVTool software. Read the agreement carefully and click on "I Agree".</li>
-<li>A window will pop up asking you to choose the destination folder for installing DVTool software. You can leave it as default or change it if you want. Click on "Next".</li>
-<li>A window will pop up asking you to confirm the installation settings. Click on "Install".</li>
-<li>The installation process will begin and may take a few minutes depending on your computer speed. A window will pop up showing you the progress of the installation.</li>
-<li>Once the installation is complete, a window will pop up asking you if you want to launch DVTool software now. Click on "Finish".</li>
-</ol>
- <p>Congratulations! You have successfully installed Dvtool 2.0 Beta 5 on your Windows PC. You are now ready to use it with your DV Dongle and access the D-Star network.</p>
- <h3>How to install Dvtool 2.0 Beta 5 on Mac OS X?</h3>
- <p>To install Dvtool 2.0 Beta 5 on Mac OS X, follow these steps:</p>
- <ol>
-<li>Double-click on the file named "DVTool-2.0beta5.dmg" that you downloaded earlier.</li>
-<li>A window will pop up showing you the DVTool software icon and a folder named "Applications". Drag and drop the DVTool software icon into the Applications folder.</li>
-<li>A window will pop up asking you to confirm that you want to copy DVTool software to the Applications folder. Click on "Authenticate".</li>
-<li>A window will pop up asking you to enter your administrator password. Enter your password and click on "OK".</li>
-<li>The copying process will begin and may take a few minutes depending on your computer speed. A window will pop up showing you the progress of the copying.</li>
-<li>Once the copying is complete, a window will pop up showing you that DVTool software is in your Applications folder. You can close this window and eject the DVTool software disk image.</li>
-</ol>
- <p>Congratulations! You have successfully installed Dvtool 2.0 Beta 5 on your Mac OS X. You are now ready to use it with your DV Dongle and access the D-Star network.</p>
- <h2>How to use Dvtool 2.0 Beta 5?</h2>
- <p>Now that you have downloaded and installed Dvtool 2.0 Beta 5 on your PC or Mac, you might be wondering how to use it with your DV Dongle and access the D-Star network. Don't worry, it's very easy and fun. Just follow these steps:</p>
- <h3>How to connect DV Dongle to your PC or Mac?</h3>
- <p>To connect your DV Dongle to your PC or Mac, follow these steps:</p>
- <ol>
-<li>Make sure that your PC or Mac is connected to the internet and has a working sound card, headset or microphone, and speakers.</li>
-<li>Plug your DV Dongle into a free USB port on your PC or Mac.</li>
-<li>Wait for a few seconds until your PC or Mac recognizes your DV Dongle and installs the necessary drivers.</li>
-<li>You should see a blue LED light on your DV Dongle indicating that it is powered on and ready to use.</li>
-</ol>
- <h3>How to configure Dvtool settings?</h3>
- <p>To configure your Dvtool settings, follow these steps:</p>
- <ol>
-<li>Launch the DVTool software from your desktop or applications folder.</li>
-<li>A window will pop up showing you the main interface of DVTool software.</li>
-<li>Click on the "Settings" button at the top right corner of the window.</li>
-<li>A window will pop up showing you the settings menu of DVTool software.</li>
-<li>You can adjust various settings here, such as:</li>
-<ul>
-<li>Your call sign: Enter your amateur radio call sign in the box provided. This is how other D-Star users will identify you on the network.</li>
-<li>Your name: Enter your name in the box provided. This is how other D-Star users will greet you on the network.</li>
-<li>Your location: Enter your city and country in the box provided. This is how other D-Star users will know where you are from on the network.</li>
-<li>Your message: Enter a short message in the box provided. This is what other D-Star users will see when they connect to you on the network.</li>
-<li>Your audio input device: Select the device that you are using to capture your voice, such as a headset or microphone, from the drop-down menu.</li>
-<li>Your audio output device: Select the device that you are using to play back other users' voices, such as speakers or headphones, from the drop-down menu.</li>
-<li>Your audio input level: Adjust the slider to set the volume of your voice input. You can also use the "Test" button to test your audio input level and hear how you sound.</li>
-<li>Your audio output level: Adjust the slider to set the volume of other users' voice output. You can also use the "Test" button to test your audio output level and hear how others sound.</li>
-</ul>
-<li>Once you are done with adjusting your settings, click on the "OK" button to save them and close the window.</li>
-</ol>
- <h3>How to access D-Star reflectors and repeaters?</h3>
- <p>To access D-Star reflectors and repeaters, follow these steps:</p>
- <ol>
-<li>On the main interface of DVTool software, click on the "Connect" button at the top left corner of the window.</li>
-<li>A window will pop up showing you the list of available D-Star reflectors and repeaters that you can connect to.</li>
-<li>You can use the search box to find a specific reflector or repeater by its name, call sign, or location.</li>
-<li>You can also use the filter buttons to narrow down the list by category, such as "All", "Favorites", "Local", "International", or "Hotspots".</li>
-<li>Once you find the reflector or repeater that you want to connect to, double-click on it or select it and click on the "Connect" button at the bottom of the window.</li>
-<li>A window will pop up showing you the status of your connection. You should see a green LED light on your DV Dongle indicating that it is connected to the reflector or repeater.</li>
-<li>You should also see a message on the main interface of DVTool software saying "Connected to [reflector or repeater name]".</li>
-<li>You can now communicate with other D-Star users who are connected to the same reflector or repeater as you.</li>
-</ol>
- <h3>How to communicate with other D-Star users?</h3>
- <p>To communicate with other D-Star users, follow these steps:</p>
- <ol>
-<li>Make sure that your DV Dongle is connected to a reflector or repeater that has other users online.</li>
-<li>Put on your headset or microphone and speakers and adjust your audio input and output levels as needed.</li>
-<li>Press and hold the "PTT" button on your DV Dongle or on your keyboard (usually the space bar) to transmit your voice.</li>
-<li>Speak clearly and politely into your microphone and introduce yourself with your call sign, name, and location.</li>
-<li>Release the "PTT" button when you are done speaking and wait for a response from other users.</li>
-<li>If you hear a response from another user, you can reply by pressing and holding the "PTT" button again and speaking into your microphone.</li>
-<li>If you don't hear a response from another user, you can try calling again or switch to another reflector or repeater that has more activity.</li>
-<li>You can also listen to other users' conversations and join them if they invite you or if they are open to new contacts.</li>
-</ol>
- <h2>Tips and tricks for using Dvtool 2.0 Beta 5</h2>
- <p>By following the steps above, you should be able to use Dvtool 2.0 Beta 5 with your DV Dongle and access the D-Star network without any problems. However, there are some tips and tricks that can help you get even more out of Dvtool 2.0 Beta 5 and make your D-Star experience more enjoyable and efficient. Here are some of them:</p>
- <h3>How to update Dvtool software?</h3>
- <p>To update your Dvtool 2.0 Beta 5 software, follow these steps:</p>
- <ol>
-<li>Launch the DVTool software from your desktop or applications folder.</li>
-<li>A window will pop up showing you the main interface of DVTool software.</li>
-<li>Click on the "Help" button at the top right corner of the window.</li>
-<li>A window will pop up showing you the help menu of DVTool software.</li>
-<li>Click on the "Check for Updates" option.</li>
-<li>A window will pop up showing you if there are any new versions of DVTool software available for download.</li>
-<li>If there are no new versions available, you will see a message saying "You have the latest version of DVTool". You can close this window and continue using DVTool software as usual.</li>
-<li>If there are new versions available, you will see a message saying "A new version of DVTool is available". You can click on the "Download" button to download the new version of DVTool software and install it following the same steps as before.</li>
-<li>Once the installation is complete, you will have the latest version of DVTool software on your PC or Mac. You can close this window and enjoy the new features and improvements of DVTool software.</li>
-</ol>
- <h3>How to troubleshoot common issues with Dvtool?</h3>
- <p>Sometimes, you may encounter some issues with your Dvtool 2.0 Beta 5 software or your DV Dongle that may affect your D-Star experience. Don't panic, most of these issues can be easily fixed by following some simple troubleshooting steps. Here are some of the common issues and how to fix them:</p>
- <ul>
-<li>Your DV Dongle is not recognized by your PC or Mac: This may happen if your USB port is faulty, your USB cable is loose, your DV Dongle is damaged, or your drivers are outdated. To fix this, try plugging your DV Dongle into a different USB port, using a different USB cable, checking your DV Dongle for any physical damage, or updating your drivers from the official website.</li>
-<li>Your DV Dongle is not connected to the D-Star network: This may happen if your internet connection is unstable, your firewall or antivirus is blocking the DVTool software, your reflector or repeater is offline, or your settings are incorrect. To fix this, try restarting your modem or router, disabling your firewall or antivirus temporarily, choosing a different reflector or repeater, or checking your settings for any errors.</li>
-<li>Your audio quality is poor or distorted: This may happen if your audio input or output device is faulty, your audio input or output level is too high or too low, your internet connection is slow, or your reflector or repeater is congested. To fix this, try using a different audio input or output device, adjusting your audio input or output level using the slider or the test button, improving your internet speed, or switching to a less busy reflector or repeater.</li>
-<li>Your D-Star profile is not displayed correctly: This may happen if you have not entered your call sign, name, location, or message in the settings menu, or if you have entered them incorrectly. To fix this, try entering or correcting your call sign, name, location, and message in the settings menu and saving them.</li>
-</ul>
- <p>If none of these steps work for you, you can always contact the DVTool software support team for further assistance. You can find their contact information on the official website.</p>
- <h3>How to optimize your audio quality with Dvtool?</h3>
- <p>One of the main advantages of using Dvtool 2.0 Beta 5 with your DV Dongle and accessing the D-Star network is that you can enjoy clearer audio quality than analog voice communication. However, there are some ways that you can optimize your audio quality even more and make it sound more natural and pleasant. Here are some of them:</p>
- <ul>
-<li>Use a good quality headset or microphone and speakers that are compatible with your PC or Mac and have a clear sound output and input.</li>
-<li>Position your headset or microphone and speakers in a way that minimizes background noise and feedback.</li>
-<li>Speak clearly and loudly enough into your microphone and avoid mumbling or whispering.</li>
-<li>Avoid speaking too fast or too slow and use proper pronunciation and grammar.</li>
-<li>Avoid using slang, jargon, acronyms, or abbreviations that may confuse other users.</li>
-<li>Avoid interrupting other users when they are speaking and wait for a pause before transmitting.</li>
-<li>Acknowledge other users when they call you by using their call sign and name.</li>
-<li>Be polite and respectful to other users and follow the etiquette and rules of the D-Star network.</li>
-</ul>
- <h3>How to customize your D-Star profile with Dvtool?</h3>
- <p>One of the fun aspects of using Dvtool 2.0 Beta 5 with your DV Dongle and accessing the D-Star network is that you can customize your D-Star profile and display information about yourself and your station to other users. This can help you make new contacts and friends on the network and show off your personality and interests. Here are some ways that you can customize your D-Star profile with Dvtool 2.0 Beta 5:</p>
- <ul>
-<li>You can enter your call sign, name, location, and message in the settings menu of DVTool software and save them. These are the basic information that other users will see when they connect to you on the network.</li>
-<li>You can also enter some optional information in the settings menu of DVTool software, such as your email address, website, QTH locator, and D-Star registration date. These are the additional information that other users can see if they click on your call sign on the main interface of DVTool software.</li>
-<li>You can also upload a picture of yourself or your station in the settings menu of DVTool software. This is the image that other users will see when they click on your call sign on the main interface of DVTool software.</li>
-<li>You can also change the color and font of your call sign, name, location, and message in the settings menu of DVTool software. This is how you can personalize your D-Star profile and make it stand out from the rest.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, Dvtool 2.0 Beta 5 is a great software that allows you to use your DV Dongle and access the D-Star network from your PC or Mac without using a radio. It offers some new features and improvements that will enhance your D-Star experience, such as a redesigned GUI, a new audio engine, a new echo test feature, a new auto-connect feature, a new auto-update feature, a new logging feature, and a new help feature.</p>
- <p>It also allows you to communicate with other D-Star users around the world using digital voice and data, enjoy clearer audio quality, less interference, more efficient use of bandwidth, and the ability to transmit data along with voice, access a wider range of repeaters and reflectors that may not be available in your area or frequency, monitor the activity on different repeaters and reflectors and discover new contacts and conversations, adjust the audio settings of your DV Dongle to suit your preferences and environment, update your DVTool software easily and automatically, troubleshoot common issues with your DV Dongle and DVTool software, customize your D-Star profile and display information about yourself and your station, and optimize your audio quality with some tips and tricks.</p>
- <p>If you are interested in trying out Dvtool 2.0 Beta 5, you can download it from the official website <a href="">http://www.dvdongle.com/DV_Dongle/Home.html</a> and install it on your PC or Mac following the steps above. You will need a DV Dongle device to use it with. You can also find more information and support for using Dvtool 2.0 Beta 5 on the official website or by contacting the DVTool software support team.</p>
- <p>We hope that this article has helped you learn more about Dvtool 2.0 Beta 5 and how to use it with your DV Dongle and access the D-Star network. We hope that you will enjoy using Dvtool 2.0 Beta 5 and have fun communicating with other D-Star users around the world.</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions (FAQs) about Dvtool 2.0 Beta 5:</p>
- <ol>
-<li><b>What are the system requirements for using Dvtool 2.0 Beta 5?</b></li>
-<p>The system requirements for using Dvtool 2.0 Beta 5 are:</p>
-<ul>
-<li>A PC or Mac with an internet connection and a working sound card.</li>
-<li>A Windows or Mac OS X operating system.</li>
-<li>A DV Dongle device with a USB cable.</li>
-<li>A headset or microphone and speakers.</li>
-</ul>
-<li><b>Is Dvtool 2.0 Beta 5 compatible with other versions of DV Dongle or DVAP?</b></li>
-<p>Yes, Dvtool 2.0 Beta 5 is compatible with all versions of DV Dongle devices (DV Dongle Blue, DV Dongle Red, DV Dongle Orange) and also with DVAP devices (DV Access Point Dongle). However, some features may not work with older versions of these devices.</p>
-<li><b>Is Dvtool 2.0 Beta 5 free or paid software?</b></li>
-<p>Dvtool 2.0 Beta 5 is free software that you can download from the official website <a href="">http://www.dvdongle.com/DV_Dongle/Home.html</a>. However, you will need to purchase a DV Dongle device or a DVAP device to use it with, which are sold separately by different vendors.</p>
-<li><b>Where can I find more information or support for using Dvtool 2.0 Beta 5?</b></li>
-<p>You can find more information or support for using Dvtool 2.0 Beta 5 on the official website <a href="">http://www.dvdongle.com/DV_Dongle/Home.html</a>, where you can find the online documentation, the user manual, the FAQ section, and the contact information of the DVTool software support team. You can also join the DVTool software user group on Yahoo Groups <a href="">https://groups.yahoo.com/neo/groups/dvdongle/info</a>, where you can interact with other users and share your feedback and suggestions.</p>
-<li><b>What are some alternatives to using Dvtool 2.0 Beta 5?</b></li>
-<p>If you are looking for some alternatives to using Dvtool 2.0 Beta 5 with your DV Dongle or DVAP device, you can try some of these options:</p>
-<ul>
-<li>You can use a D-Star compatible radio instead of a DV Dongle or DVAP device, which will allow you to access the D-Star network directly from your radio without using a PC or Mac.</li>
-<li>You can use a different software instead of DVTool software, such as WinDV <a href="">http://www.dutch-star.eu/software/</a>, which is another program that allows you to use your DV Dongle or DVAP device with your PC or Mac.</li>
-<li>You can use a different digital voice protocol instead of D-Star, such as DMR <a href="">https://www.dmr-marc.net/</a>, which is another digital voice and data protocol that is used by amateur radio operators.</li>
-</ul></p> b2dd77e56b<br />
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- <h2>What is AetherSX2 Emulator?</h2>
-<p>AetherSX2 is a PS2 emulator for Android that was released in late 2021 by a developer named Tahlreth. It is based on the PCSX2 emulator, which is a well-known and reliable PS2 emulator for PC. The developer got permission from the PCSX2 team to use their code and licensed it under the LGPL license.</p>
-<p>AetherSX2 emulator is a major breakthrough for PS2 emulation on Android devices. It supports a wide range of PS2 games and offers various features such as internal resolution scaling, save states, multiple control schemes, widescreen patches, and more. It also supports both Vulkan and OpenGL graphics renderers, which can improve the performance and compatibility of different games.</p>
-<p>AetherSX2 emulator is free to download and use, unlike some other PS2 emulators that charge money or show ads. You can get it from the Google Play Store or from the official website. You can also join the fan-run Discord server to get updates, support, and feedback from other users.</p>
- <h2>How to Download and Install AetherSX2 Emulator?</h2>
-<p>Downloading and installing AetherSX2 emulator is very easy. Just follow these simple steps:</p>
-<ol>
-<li>Go to the Google Play Store and search for "AetherSX2" or use this link to download it.</li>
-<li>Alternatively, you can go to the official website and download the APK file from there. Make sure you enable "Unknown sources" in your device settings before installing it.</li>
-<li>Once you have downloaded the app, open it and grant it the necessary permissions.</li>
-<li>You will also need a PS2 BIOS file to run the emulator. You can dump it from your own PS2 console or find it online (but be careful of legal issues). Place the BIOS file in your device storage (preferably in a folder named "BIOS").</li>
-<li>Launch the app and tap on "Select BIOS" in the main menu. Navigate to the folder where you placed the BIOS file and select it.</li>
-<li>You are now ready to use the emulator!</li>
-</ol>
- <h2>How to Configure AetherSX2 Emulator for Best Performance?</h2>
-<p>AetherSX2 emulator has many settings that you can tweak to optimize its performance and compatibility for different games. However, there is no one-size-fits-all solution, as different games may require different settings. You may need to experiment with various options until you find the best settings for your device and game. Here are some general tips and recommendations that may help you:</p>
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-<ul>
-<li>Choose the graphics renderer that works best for your device and game. Vulkan is usually faster and more compatible, but OpenGL may offer better quality and stability for some games.</li>
-<li>Adjust the internal resolution scaling according to your device's capabilities and screen size. Higher resolutions will make the games look sharper and clearer, but they will also consume more resources and cause slowdowns or crashes. Lower resolutions will improve the performance and compatibility, but they will also make the games look blurry and pixelated.</li>
-<li>Enable or disable the speed hacks depending on the game's requirements and your device's power. Speed hacks are optimizations that can boost the emulation speed, but they can also cause glitches or errors in some games. You can try the default speed hacks or customize them individually.</li>
-<li>Enable or disable the widescreen patches if you want to play the games in 16:9 aspect ratio instead of the original 4:3. Widescreen patches can make the games look more immersive and modern, but they can also cause graphical issues or distortions in some games.</li>
-<li>Configure the controls according to your preference and comfort. You can use the on-screen touch controls, a physical controller, or a keyboard and mouse. You can also customize the layout, size, opacity, and sensitivity of the touch controls.</li>
-</ul>
-<p>You can access the settings menu by tapping on the gear icon in the main menu or by pressing the back button while playing a game. You can also change the settings for each game individually by long-pressing on the game cover and selecting "Game settings".</p>
- <h2>How to Load and Play PS2 Games on AetherSX2 Emulator?</h2>
-<p>Loading and playing PS2 games on AetherSX2 emulator is also very easy. Just follow these simple steps:</p>
-<ol>
-<li>You will need PS2 game files (also known as ISOs or ROMs) to play them on the emulator. You can dump them from your own PS2 discs or find them online (but be careful of legal issues). Place the game files in your device storage (preferably in a folder named "Games").</li>
-<li>Launch the app and tap on "Select Game" in the main menu. Navigate to the folder where you placed the game files and select one.</li>
-<li>The game will start loading and you will see a loading screen with some information about the game and its compatibility status. You can also see some tips and suggestions for optimizing the game's performance.</li>
-<li>Once the game is loaded, you can start playing it with your chosen control scheme. You can also access some options by tapping on the screen or pressing the menu button while playing. You can save or load your progress using save states, change the graphics renderer, adjust the volume, take screenshots, or exit the game.</li>
-</ol>
- <h2>What are the Best PS2 Games to Play on AetherSX2 Emulator?</h2>
-<p>AetherSX2 emulator supports a large number of PS2 games, but not all of them are fully playable or compatible. Some games may have minor issues such as graphical glitches, audio problems, or slow loading times. Some games may have major issues such as crashes, freezes, or black screens. And some games may not work at all.</p>
-<p>The compatibility status of each game is indicated by a color code in the loading screen: green means playable, yellow means ingame, orange means menu/intro, red means loadable, and black means nothing.</p>
-<p>You can check the compatibility list on the official website to see which games are supported by the emulator and how well they run. You can also report any issues or bugs that you encounter while playing a game on the Discord server or on GitHub.</p>
-<p>Here are some of the best PS2 games that you can play on AetherSX2 emulator with good performance and compatibility:</p>
-<table>
-<tr><th>Game</th><th>Genre</th><th>Description</th></tr>
-<tr><td>God of War</td><td>Action-adventure</td><td>A hack-and-slash game that follows Kratos, a Spartan warrior who seeks revenge against Ares, the god of war.</td></tr>
-<tr><td>Shadow of the Colossus</td><td>Action-adventure</td><td>A unique game that involves exploring a vast land and defeating giant creatures called colossi to revive a dead girl.</td></tr>
-<tr><td>Grand Theft Auto: San Andreas</td><td>Action-adventure</td><td>A sandbox game that lets you roam around a fictional state of San Andreas and engage in various activities such as driving, shooting, fighting, and more.</td </tr>
-<tr><td>Final Fantasy X</td><td>Role-playing</td><td>A classic JRPG that follows Tidus, a young athlete who is transported to a fantasy world called Spira and joins a group of adventurers to defeat a monstrous threat called Sin.</td></tr>
-<tr><td>Metal Gear Solid 3: Snake Eater</td><td>Stealth-action</td><td>A prequel to the Metal Gear series that features Naked Snake, a special agent who infiltrates a Soviet jungle to rescue a scientist and stop a nuclear war.</td></tr>
-<tr><td>Kingdom Hearts</td><td>Action-role-playing</td><td>A crossover game that combines characters and worlds from Disney and Final Fantasy franchises. It follows Sora, a young boy who wields a magical weapon called the Keyblade and teams up with Donald Duck and Goofy to fight against the Heartless.</td></tr>
-</table>
-<p>Of course, there are many more PS2 games that you can try on AetherSX2 emulator, but these are some of the most popular and well-received ones. You can also check out some online forums and reviews to find more recommendations and suggestions.</p>
- <h1>Conclusion</h1>
-<p>AetherSX2 emulator is an amazing app that lets you play PS2 games on Android devices with high quality and performance. It is easy to download, install, configure, and use. It supports a large number of PS2 games and offers various features and options to enhance your gaming experience. It is also free and open-source, unlike some other PS2 emulators that charge money or show ads.</p>
-<p>If you are a fan of PS2 games and want to relive your childhood memories on your Android smartphone, you should definitely give AetherSX2 emulator a try. You will be amazed by how well it runs your favorite PS2 games and how much fun you will have playing them.</p>
-<p>So, what are you waiting for? Download AetherSX2 emulator now and enjoy playing PS2 games on Android!</p>
- <h1>FAQs</h1>
-<h3>Q: Is AetherSX2 emulator legal?</h3>
-<p>A: AetherSX2 emulator itself is legal, as it is based on the PCSX2 emulator, which is licensed under the LGPL license. However, downloading or distributing PS2 BIOS or game files may be illegal in some countries or regions, depending on the copyright laws and regulations. You should only use your own PS2 BIOS or game files that you have legally obtained.</p>
- <h3>Q: Is AetherSX2 emulator safe?</h3>
-<p>A: AetherSX2 emulator is safe to use, as long as you download it from the official sources (Google Play Store or official website). It does not contain any malware, viruses, or spyware. It also does not collect any personal or sensitive data from your device.</p>
- <h3>Q: How can I update AetherSX2 emulator?</h3>
-<p>A: You can update AetherSX2 emulator by checking for updates in the app itself or by visiting the Google Play Store or the official website. You can also join the Discord server to get notified of any new updates or releases.</p>
- <h3>Q: How can I support AetherSX2 emulator?</h3>
-<p>A: You can support AetherSX2 emulator by giving it a positive rating and review on the Google Play Store or by sharing it with your friends and family. You can also donate to the developer via PayPal or Patreon to show your appreciation and help him improve the emulator.</p>
- <h3>Q: How can I contact AetherSX2 emulator?</h3>
-<p>A: You can contact AetherSX2 emulator by joining the Discord server or by sending an email to [email protected]. You can also follow the developer on Twitter or Instagram for more updates and news.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Become a Soccer Super Star with this Amazing Football MOD APK.md

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-<p>Do you love soccer? Do you want to play a soccer game that is fun, simple, and realistic? If yes, then you should try <strong>Soccer Super Star Football Mod APK</strong>, a soccer game that lets you swipe to shoot and score amazing goals. In this article, we will tell you everything you need to know about this game, including how to download and install it, how to play it, tips and tricks, pros and cons, and FAQs. Let's get started!</p>
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- <h2>Introduction</h2>
-<p>Soccer Super Star Football Mod APK is a soccer game that is developed by Real Free Soccer. It is available for Android devices and can be downloaded for free from various websites. The game has over 10 million downloads and a 4.4-star rating on Google Play Store. It is one of the most popular soccer games on the market, thanks to its simple and intuitive gameplay, realistic graphics and physics, various teams and modes to choose from, and unlimited rewind feature.</p>
- <p>Why should you download Soccer Super Star Football Mod APK? Well, if you are a fan of soccer, you will love this game. It is easy to play, but hard to master. You can swipe to shoot and score goals from different angles and distances. You can also use the unlimited rewind feature to correct your mistakes and try again. You can choose from different teams and modes, such as career mode, tournament mode, challenge mode, and training mode. You can also unlock new players and stadiums as you progress in the game. The game is also offline-friendly, meaning you can play it without an internet connection.</p>
- <p>What are the features of the mod version of Soccer Super Star Football? The mod version gives you some extra benefits that the original version does not. For example, you can enjoy unlimited rewind, which allows you to undo your shots and try again as many times as you want. You can also get unlimited coins, which you can use to buy new players and stadiums. The mod version also removes ads, which can be annoying and distracting in the original version.</p>
- <h2>How to Download and Install Soccer Super Star Football Mod APK</h2>
-<p>If you want to download and install Soccer Super Star Football Mod APK on your Android device, you need to follow these simple steps:</p>
-<ol>
-<li>Download the APK file from a trusted source. You can find many websites that offer the mod version of Soccer Super Star Football for free. However, be careful not to download from shady or malicious sites that may harm your device or steal your data. We recommend you to download from [this link], which is safe and reliable.</li>
-<li>Enable unknown sources on your device. Since you are downloading an APK file from a third-party source, you need to enable unknown sources on your device. This will allow you to install apps that are not from Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Install the APK file. Once you have downloaded the APK file, locate it in your file manager and tap on it. You will see a pop-up window asking for your permission to install the app. Tap on Install and wait for the installation process to finish.</li>
-<li>Launch the game and enjoy. After the installation is done, you can launch the game from your app drawer or home screen. You will see the game icon with the word "Mod" on it. Tap on it and start playing Soccer Super Star Football Mod APK.</li>
-</ol> <h2>How to Play Soccer Super Star Football Mod APK</h2>
-<p>Playing Soccer Super Star Football Mod APK is very easy and fun. You just need to swipe your finger on the screen to shoot and score goals. Here are some tips on how to play the game:</p>
-<h3>Choose your team and mode</h3>
-<p>Before you start playing, you need to choose your team and mode. You can choose from different teams, such as Brazil, Argentina, Germany, France, Spain, England, and more. Each team has different stats and ratings, so choose wisely. You can also choose from different modes, such as career mode, tournament mode, challenge mode, and training mode. Each mode has different objectives and rewards, so choose according to your preference.</p>
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-<h3>Swipe to shoot and score</h3>
-<p>Once you have chosen your team and mode, you can start playing. You will see a soccer ball on the screen, and you need to swipe your finger on it to shoot and score. You can swipe in different directions and angles to control the direction and curve of the ball. You can also swipe with different speed and force to control the power and height of the ball. You will see a target on the goal, and you need to aim for it to score. The target will change its position and size depending on the difficulty level of the game.</p>
-<h3>Use unlimited rewind to correct your mistakes</h3>
-<p>One of the best features of Soccer Super Star Football Mod APK is the unlimited rewind feature. This feature allows you to undo your shots and try again as many times as you want. This is very useful if you miss a shot or make a mistake. You can use this feature by tapping on the rewind button on the top left corner of the screen. You will see a timeline of your shots, and you can drag it back to any point you want. You can then swipe again to shoot and score.</p>
-<h3>Unlock new players and stadiums</h3>
-<p>As you play Soccer Super Star Football Mod APK, you can unlock new players and stadiums. You can unlock new players by spending coins or reaching certain levels. Each player has different skills and abilities, such as speed, power, accuracy, stamina, and more. You can also unlock new stadiums by spending coins or reaching certain levels. Each stadium has different themes and atmospheres, such as day, night, rain, snow, and more.</p>
- <h2>Tips and Tricks for Soccer Super Star Football Mod APK</h2>
-<p>If you want to master Soccer Super Star Football Mod APK, you need to know some tips and tricks that will help you improve your game. Here are some of them:</p>
-<h3>Aim for the corners and curves</h3>
-<p>One of the best ways to score goals in Soccer Super Star Football Mod APK is to aim for the corners and curves of the goal. This will make it harder for the goalkeeper to save your shots. You can do this by swiping your finger in a diagonal or curved motion on the screen. This will make the ball spin and curve in the air.</p>
-<h3>Use power-ups wisely</h3>
-<p>Soccer Super Star Football Mod APK also has some power-ups that you can use to boost your game. These power-ups include fireball, slow motion, magnet, freeze, and more. Each power-up has a different effect on the ball or the game. For example, fireball makes the ball burn and fly faster; slow motion makes the game slow down for a few seconds; magnet makes the ball attract to the target; freeze makes the goalkeeper freeze for a few seconds; and more. You can use these power-ups by tapping on them on the bottom right corner of the screen. However, be careful not to use them too often or too randomly, as they have limited uses and may not always work in your favor.</p>
-<h3>Watch ads to get free rewards</h3>
-<p>If you want to get more coins or power-ups in Soccer Super Star Football Mod APK, you can watch ads to get free rewards. You can do this by tapping on the watch ad button on the top right corner of the screen. You will see an ad pop up on your screen, and you need to watch it for a few seconds. After that, you will get some coins or power-ups as a reward. You can do this as many times as you want, but be aware that some ads may be longer or shorter than others.</p>
-<h3>Practice your skills in training mode</h3>
-<p>If you want to practice your skills in Soccer Super Star Football Mod APK, you can play in training mode. This mode allows you to play without any pressure or objectives. You can just swipe and shoot as many times as you want without worrying about time or score. You can also change the difficulty level of the game by tapping on the settings button on the top left corner of the screen. You can also change the team and stadium by tapping on the buttons on the bottom left corner of the screen. Training mode is a great way to improve your skills and have fun.</p>
- <h2>Pros and Cons of Soccer Super Star Football Mod APK</h2>
-<p>Soccer Super Star Football Mod APK is a great soccer game, but it also has some pros and cons that you should know before playing it. Here are some of them:</p>
-<h4>Pros</h4>
-<ul>
-<li>Simple and intuitive gameplay. You just need to swipe your finger on the screen to shoot and score goals. The game is easy to play, but hard to master.</li>
-<li>Realistic graphics and physics. The game has high-quality graphics and realistic physics that make the game more immersive and enjoyable. You can see the ball spin and curve in the air, the goalkeeper react and save your shots, and the crowd cheer and boo.</li>
-<li>Various teams and modes to choose from. You can choose from different teams, such as Brazil, Argentina, Germany, France, Spain, England, and more. Each team has different stats and ratings, so choose wisely. You can also choose from different modes, such as career mode, tournament mode, challenge mode, and training mode. Each mode has different objectives and rewards, so choose according to your preference.</li>
-<li>Unlimited rewind feature. This feature allows you to undo your shots and try again as many times as you want. This is very useful if you miss a shot or make a mistake. You can use this feature by tapping on the rewind button on the top left corner of the screen.</li>
-</ul>
-<h4>Cons</h4>
-<ul>
-<li>Repetitive gameplay after a while. The game can get repetitive and boring after a while, as you play the same scenarios and challenges over and over again. The game lacks variety and innovation in its gameplay.</li>
-<li>Ads can be annoying. The game has ads that pop up on your screen every now and then. These ads can be annoying and distracting, especially when you are in the middle of a match or a shot. You can remove ads by downloading the mod version of the game or by paying a small fee.</li>
-<li>Some bugs and glitches may occur. The game is not perfect, and it may have some bugs and glitches that affect its performance and quality. For example, some users have reported that the game crashes or freezes sometimes, or that the ball goes through the goalkeeper or the goalpost.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Soccer Super Star Football Mod APK is a fun and simple soccer game that lets you swipe to shoot and score amazing goals. It has simple and intuitive gameplay, realistic graphics and physics, various teams and modes to choose from, and unlimited rewind feature. However, it also has some cons, such as repetitive gameplay after a while, ads can be annoying, and some bugs and glitches may occur. Overall, Soccer Super Star Football Mod APK is a great soccer game that you should try if you love soccer or want to have some fun.</p>
- <p>Do you want to download Soccer Super Star Football Mod APK? If yes, then follow the steps we mentioned above to download and install it on your Android device. If no, then what are you waiting for? Download it now and enjoy playing soccer like never before!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Soccer Super Star Football Mod APK:</p>
-<ol>
-<li><strong>Is Soccer Super Star Football Mod APK safe to download?</strong></li>
-<p>Yes, as long as you download it from a trusted source. You can find many websites that offer the mod version of Soccer Super Star Football for free. However, be careful not to download from shady or malicious sites that may harm your device or steal your data. We recommend you to download from [this link], which is safe and reliable.</p>
-<li><strong>What is the difference between Soccer Super Star Football Mod APK and the original version?</strong></li>
-<p>The mod version gives you some extra benefits that the original version does not. For example, you can enjoy unlimited rewind, which allows you to undo your shots and try again as many times as you want. You can also get unlimited coins, which you can use to buy new players and stadiums. The mod version also removes ads, which can be annoying and distracting in the original version.</p>
-<li><strong>How can I get more coins in Soccer Super Star Football Mod APK?</strong></li>
-<p>You can get more coins by winning matches, completing achievements, or watching ads. You can also use the mod version of the game, which gives you unlimited coins. You can use coins to buy new players and stadiums, or to upgrade your skills and power-ups.</p>
-<li><strong>How can I unlock new players and stadiums in Soccer Super Star Football Mod APK?</strong></li>
-<p>You can unlock new players and stadiums by spending coins or reaching certain levels. Each player and stadium has a different price and level requirement. You can see the details by tapping on the shop button on the bottom right corner of the screen. You can also use the mod version of the game, which gives you all the players and stadiums unlocked.</p>
-<li><strong>Can I play Soccer Super Star Football Mod APK offline?</strong></li>
-<p>Yes, you can play Soccer Super Star Football Mod APK offline without an internet connection. However, you will not be able to access some features, such as watching ads, getting rewards, or updating the game. You will also not be able to play in tournament mode or challenge mode, which require an internet connection.</p>
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- <p>I hope this article has helped you learn more about Soccer Super Star Football Mod APK. If you have any questions or feedback, please leave a comment below. Thank you for reading!</p> 401be4b1e0<br />
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-<p>Well, there is a way to do that. It is called <strong>CSR Racing 2 Mod APK</strong>. It is a modified version of the original game that gives you access to all the features and resources that you want. You can download and install it on your iOS device easily and safely. In this article, we will tell you everything you need to know about CSR Racing 2 Mod APK on iOS devices, including its features, benefits, compatibility, security, and installation process. So, let's get started!</p>
-<h2>What is CSR Racing 2 and why is it popular?</h2>
-<p>CSR Racing 2 is a racing game developed by NaturalMotionGames Ltd and published by Zynga. It was released in 2016 for Android and iOS devices. It is the sequel to the popular CSR Racing game that was released in 2012.</p>
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-<h3>A realistic and immersive racing game</h3>
-<p>One of the main reasons why CSR Racing 2 is so popular is because of its <strong>realistic and immersive</strong> graphics, physics, sound effects, and gameplay. The game uses 3D rendering techniques to create stunning visuals that make you feel like you are actually driving the cars. The game also features realistic car physics that simulate the behavior of the cars on different terrains and conditions. The game also has amazing sound effects that match the engine sounds, tire screeches, collisions, and other noises of the cars. The game also has a variety of gameplay modes and events that keep you entertained and challenged.</p>
-<p>The game allows you to choose from over 200 licensed cars from some of the most famous brands in the world, such as Ferrari, Lamborghini, Bugatti, McLaren, Pagani, Koenigsegg, and more. You can also customize your cars with different paint jobs, decals, rims, spoilers, nitrous, and other parts. You can also tune your cars to improve their performance and stats.</p>
-<h3>A competitive and social racing game</h3>
-<p>Another reason why CSR Racing 2 is so popular is because of its <strong>competitive and social</strong> features. The game has an online multiplayer mode where you can race with other players from around the world in real-time. You can also join or create crews with your friends or other players and compete with other crews for rewards and glory. You can also chat with your crew members and other players in the game. You can also challenge other players to duels or accept challenges from them.</p>
-<p>The game also has a reward system that gives you money, keys, gold, fuel, and other items for completing races, events, achievements, and rankings. You can use these items to buy new cars, upgrade your existing cars, refill your fuel, or enter special events. The game also has a ranking system that ranks you based on your performance and achievements in the game. You can climb up the ranks and earn more rewards and recognition.</p>
-<h2>What is CSR Racing 2 Mod APK and what are its features?</h2>
-<p>CSR Racing 2 Mod APK is a modified version of the original CSR Racing 2 game that gives you access to all the features and resources that you want in the game. It is not an official version of the game, but it is created by third-party developers who modify the original game files to unlock or add new features.</p>
-<h3>A modified version of CSR Racing 2 with unlimited resources</h3>
-<p>One of the main benefits of using CSR Racing 2 Mod APK is that it gives you <strong>unlimited resources</strong> in the game. This means that you can have unlimited money, keys, gold, fuel, and other items in the game without spending any real money or waiting for hours to refill your fuel. You can use these resources to buy any car you want, upgrade it to the max level, enter any event you want, or refill your fuel anytime you want.</p>
-<p>Another benefit of using CSR Racing 2 Mod APK is that it gives you access to some <strong>new features</strong> that are not available in the original game. For example, some CSR Racing 2 Mod APK versions allow you to unlock all the cars in the game without having to complete any requirements or missions. Some versions also allow you to use nitrous anytime you want without having to wait for it to recharge. Some versions also allow you to disable ads or enable cheats in the game.</p>
-<h3>A safe and easy way to enjoy CSR Racing 2 without restrictions</h3>
-<p>Another benefit of using CSR Racing 2 Mod APK is that it is a <strong>safe and easy</strong> way to enjoy CSR Racing 2 without any restrictions or limitations. You don't have to worry about any viruses, malware, or spyware that might harm your device or compromise your privacy. You also don't have to worry about any bans or suspensions from the game developers or publishers. You can download and install CSR Racing 2 Mod APK on your iOS device easily and safely using a third-party app store called Panda Helper. Panda Helper is a trusted and reliable app store that offers thousands of modded and hacked apps and games for iOS devices. You can download and install Panda Helper on your iOS device without jailbreaking it or using a computer.</p>
-<h2>How to download and install CSR Racing 2 Mod APK on iOS devices?</h2>
-<p>If you want to download and install CSR Racing 2 Mod APK on your iOS device, you need to follow these simple steps:</p>
-<h3>A step-by-step guide to download and install CSR Racing 2 Mod APK on iOS devices</h3>
-<p>Here is a step-by-step guide to download and install CSR Racing 2 Mod APK on iOS devices using Panda Helper:</p>
-<ol>
-<li>Open Safari browser on your iOS device and go to the official website of Panda Helper: <a href="">https://www.pandahelp.vip/</a></li>
-<li>Tap on the "Download Free Version" button and then tap on "Install" when prompted.</li>
-<li>Wait for the installation to finish and then go to Settings > General > Profiles & Device Management and trust the profile of Panda Helper.</li>
-<li>Launch Panda Helper from your home screen and search for "CSR Racing 2 Mod" in the search bar.</li>
-<li>Tap on the "Get" button next to the CSR Racing 2 Mod app and then tap on "Install" when prompted.</li>
-<li>Wait for the installation to finish and then go to Settings > General > Profiles & Device Management and trust the profile of CSR Racing 2 Mod.</li>
-<li>Launch CSR Racing 2 Mod from your home screen and enjoy the game with unlimited resources and features.</li>
-</ol>
-<h3>A table to summarize the steps to download and install CSR Racing 2 Mod APK on iOS devices</h3>
-<p>Here is a table to summarize the steps to download and install CSR Racing 2 Mod APK on iOS devices using Panda Helper:</p>
- | Step number | Action | Screenshot | Explanation | |-------------|--------|------------|-------------| | 1 | Open Safari browser on your iOS device and go to the official website of Panda Helper: <a href="">https://www.pandahelp.vip/</a> | <img src="" alt="Panda Helper website" width="300"> | Panda Helper is a third-party app store that offers modded and hacked apps and games for iOS devices. | | 2 | Tap on the "Download Free Version" button and then tap on "Install" when prompted. | <img src="" alt="Panda Helper download" width="300"> | This will download and install Panda Helper on your iOS device. | | 3 | Wait for the installation to finish and then go to Settings > General > Profiles & Device Management and trust the profile of Panda Helper. | <img src="" alt="Panda Helper trust" width="300"> | This will allow you to run Panda Helper on your iOS device without any issues. | | 4 | Launch Panda Helper from your home screen and search for "CSR Racing 2 Mod" in the search bar. | <img src="" alt="Panda Helper search" width="300"> | This will show you the CSR Racing 2 Mod app that you can download and install on your iOS device. | | 5 | Tap on the "Get" button next to the CSR Racing 2 Mod app and then tap on "Install" when prompted. | <img src="" alt="CSR Racing 2 Mod download" width="300"> | This will download and install CSR Racing 2 Mod on your iOS device. | | 6 | Wait for the installation to finish and then go to Settings > General > Profiles & Device Management and trust the profile of CSR Racing 2 Mod. | <img src="" alt="CSR Racing 2 Mod trust" width="300"> | This will allow you to run CSR Racing 2 Mod on your iOS device without any issues. | | 7 | Launch CSR Racing 2 Mod from your home screen and enjoy the game with unlimited resources and features. | <img src="" alt="CSR Racing 2 Mod launch" width="300"> | This will let you play CSR Racing 2 with unlimited money, keys, gold, fuel, nitrous, cars, upgrades, etc. | <h2>Conclusion</h2>
-<p>In conclusion, CSR Racing 2 is a great racing game that offers you a realistic and immersive experience of driving some of the most amazing cars in the world. It also lets you compete and socialize with other players online, join crews, chat with friends, and earn rewards and rankings. However, if you want to enjoy all these features without any limitations or restrictions, you can try CSR Racing 2 Mod APK on your iOS device. CSR Racing 2 Mod APK is a modified version of the original game that gives you unlimited resources and features in the game. You can download and install it on your iOS device easily and safely using Panda Helper, a third-party app store that offers modded and hacked apps and games for iOS devices. You can follow the step-by-step guide and the table above to download and install CSR Racing 2 Mod APK on your iOS device. We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave them in the comments section below. Thank you for reading and happy racing!</p>
-<h4>FAQs</h4>
-<p>Here are some frequently asked questions about CSR Racing 2 Mod APK on iOS devices with brief answers:</p>
-<ul>
-<li><strong>Q: Is CSR Racing 2 Mod APK safe to use?</strong></li>
-<li>A: Yes, CSR Racing 2 Mod APK is safe to use as long as you download it from a trusted source like Panda Helper. It does not contain any viruses, malware, or spyware that might harm your device or compromise your privacy.</li>
-<li><strong>Q: Is CSR Racing 2 Mod APK compatible with my iOS device?</strong></li>
-<li>A: Yes, CSR Racing 2 Mod APK is compatible with most iOS devices that can run the original CSR Racing 2 game. However, you may need to update your iOS version or free up some storage space on your device before installing it.</li>
-<li><strong>Q: Will I get banned or suspended from CSR Racing 2 if I use CSR Racing 2 Mod APK?</strong></li>
-<li>A: No, you will not get banned or suspended from CSR Racing 2 if you use CSR Racing 2 Mod APK. However, you should use it at your own risk and discretion, as the game developers or publishers may not approve of it.</li>
-<li><strong>Q: Can I play online multiplayer mode with CSR Racing 2 Mod APK?</strong></li>
-<li>A: Yes, you can play online multiplayer mode with CSR Racing 2 Mod APK. However, you may face some issues or glitches while playing with other players who are using the original game or a different version of the mod.</li>
-<li><strong>Q: Can I update CSR Racing 2 Mod APK to the latest version?</strong></li>
-<li>A: Yes, you can update CSR Racing 2 Mod APK to the latest version by following the same steps as downloading and installing it. However, you may need to uninstall the previous version of the mod before installing the new one.</li>
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-<h1>How to Download Cars Movie Legally and Safely</h1>
-<p>Cars is a 2006 animated comedy film produced by Pixar Animation Studios and distributed by Walt Disney Pictures. It tells the story of a hotshot race car named Lightning McQueen who gets stranded in a small town called Radiator Springs and learns the true meaning of friendship and family. The film features the voices of Owen Wilson, Paul Newman, Bonnie Hunt, Larry the Cable Guy, and many others.</p>
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-<p>If you are a fan of Cars or want to watch it for the first time, you might be wondering how to download it to your computer or mobile device. There are many ways to download movies online, but not all of them are legal or safe. In this article, we will show you how to download Cars movie legally and safely from different sources, such as streaming services, torrent sites, and free movie sites. We will also give you some tips on how to avoid viruses, malware, ads, and pop-ups when downloading movies.</p>
-<h2>Introduction</h2>
-<h3>What is Cars Movie and Why You Should Watch It</h3>
-<p>Cars is a Pixar film that was released in 2006 and became one of the most successful animated movies of all time. It won the Golden Globe Award for Best Animated Feature Film and was nominated for two Academy Awards for Best Animated Feature and Best Original Song. It also spawned two sequels, Cars 2 (2011) and Cars 3 (2017), as well as several spin-offs, shorts, video games, merchandise, and theme park attractions.</p>
-<p>Cars is a movie that appeals to both children and adults, as it combines humor, adventure, romance, drama, and action. It also features stunning animation, memorable characters, catchy songs, and a heartwarming message about finding your true self and your true friends. If you love cars, racing, or animation, you will definitely enjoy watching Cars.</p>
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-<h3>The Risks of Downloading Movies Illegally</h3>
-<p>Before we show you how to download Cars movie legally and safely, we want to warn you about the risks of downloading movies illegally. Illegal downloading is the act of obtaining or sharing copyrighted material without the permission of the owner or the law. This includes movies, music, games, software, books, and any other digital content.</p>
-<p>Downloading movies illegally can have serious consequences for you and your device. Some of the risks are:</p>
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-<li>You can face legal action from the copyright owner or the authorities. Depending on your country's laws, you can be fined, sued, or even jailed for piracy.</li>
-<li>You can expose your device to viruses, malware, spyware, ransomware, or other harmful programs that can damage your data, steal your information, or lock your device until you pay a ransom.</li>
-<li>You can compromise your online security and privacy by revealing your IP address, location, browsing history, or personal information to hackers, trackers, or advertisers.</li>
-<li>You can waste your time, bandwidth, and storage space by downloading low-quality, incomplete, or fake files that do not match what you are looking for.</li>
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-<p>As you can see, downloading movies illegally is not worth the risk. That is why we recommend you to use legal and safe methods to download Cars movie, which we will explain in the next sections.</p>
-<h2>How to Download Cars Movie from Streaming Services</h2>
-<p>One of the best ways to download Cars movie legally and safely is to use a streaming service. A streaming service is a platform that allows you to watch movies, TV shows, and other content online or offline by paying a monthly or annual fee. Some of the most popular streaming services are Netflix, Amazon Prime Video, Hulu, Disney+, HBO Max, and Apple TV+.</p>
-<p>Streaming services offer many benefits for movie lovers, such as:</p>
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-<li>You can access a large library of movies and shows in different genres, languages, and regions.</li>
-<li>You can watch movies and shows in high-definition (HD), ultra-high-definition (UHD), or 4K resolution, depending on your device and internet speed.</li>
-<li>You can download movies and shows to your device and watch them offline without using data or Wi-Fi.</li>
-<li>You can watch movies and shows on multiple devices, such as computers, smartphones, tablets, smart TVs, gaming consoles, or streaming devices.</li>
-<li>You can create multiple profiles for different users and customize your preferences, recommendations, and watch history.</li>
-<li>You can enjoy exclusive content that is only available on the streaming service.</li>
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-<p>However, streaming services also have some drawbacks, such as:</p>
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-<li>You have to pay a monthly or annual fee to use the service. The fee may vary depending on the plan you choose, the number of screens you can watch on simultaneously, and the availability of certain features.</li>
-<li>You need a stable and fast internet connection to stream or download movies and shows. If your internet is slow or unreliable, you may experience buffering, lagging, or poor quality.</li>
-<li>You may not find the movie or show you want to watch on the streaming service. Streaming services have different catalogs that change over time due to licensing agreements with studios and distributors.</li>
-<li>You may face geo-restrictions that prevent you from accessing certain content based on your location. Streaming services have different libraries for different countries due to legal and cultural reasons.</li>
-</ul>
-<p>In this section, we will focus on two of the most popular streaming services that offer Cars movie: Netflix and Amazon Prime Video. We will show you how to download Cars movie from each of them and compare their pros and cons.</p>
-<h3>Netflix</h3>
-<p>Netflix is the world's leading streaming service with over 200 million subscribers in more than 190 countries. It offers a wide range of movies and shows in various genres and languages. It also produces original content that is exclusive to Netflix, such as Stranger Things, The Crown, The Witcher, Black Mirror, and more.</p>
-<h4>Steps to Download Cars Movie from Netflix</h4>
-<p>To download Cars movie from Netflix, you need to follow these steps:</p>
-<ol>
-<li>Sign up for a Netflix account if you don't have one. You can choose from three plans: Basic ($8.99 per month), Standard ($13.99 per month), or Premium ($17.99 per month). The Basic plan allows you to watch on one screen at a time in standard definition (SD), the Standard plan allows you to watch on two screens at a time in high definition (HD), and the Premium plan allows you to watch on four screens at a time in HD or 4K.</li>
-<li>Download the Netflix app on your device. You can download it from the App Store for iOS devices, the Google Play Store for Android devices, or the Microsoft Store for Windows devices. You can also access Netflix from your web browser, but you cannot download movies or shows from there.</li>
-<li>Open the Netflix app and sign in with your account. You can browse the content by categories, genres, recommendations, or search for a specific title.</li>
-<li>Find Cars movie on Netflix. You can use the search function or look for it in the Animation, Comedy, or Family categories. You can also check if Cars movie is available on Netflix in your country by using a website like unogs.com or flixwatch.co.</li>
-<li>Tap on the download icon next to the play button. The download icon looks like a downward arrow with a horizontal line below it. If you don't see the download icon, it means that the movie is not available for download.</li>
-<li>Wait for the movie to download to your device. You can check the progress of the download by tapping on the downloads icon at the bottom of the screen. The downloads icon looks like a downward arrow with a circle around it.</li>
-<li>Enjoy watching Cars movie offline. You can find the downloaded movie in the downloads section of the app. You can watch it as many times as you want without using data or Wi-Fi.</li>
-</ol>
-<h4>Pros and Cons of Netflix</h4>
-<p>Netflix is a great streaming service for downloading Cars movie, but it also has some pros and cons that you should consider:</p>
-<table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>- Netflix has a large and diverse library of movies and shows, including original and exclusive content.</td>
-<td>- Netflix requires a subscription fee to use the service, which may not be affordable for some users.</td>
-</tr>
-<tr>
-<td>- Netflix allows you to download movies and shows to your device and watch them offline without using data or Wi-Fi.</td>
-<td>- Netflix limits the number of devices and screens you can watch on simultaneously, depending on your plan.</td>
-</tr>
-<tr>
-<td>- Netflix offers high-quality video and audio, as well as subtitles and dubbing options for different languages.</td>
-<td>- Netflix does not have all the movies and shows you may want to watch, as some of them may be unavailable or removed due to licensing agreements.</td>
-</tr>
-<tr>
-<td>- Netflix is compatible with most devices and platforms, such as computers, smartphones, tablets, smart TVs, gaming consoles, or streaming devices.</td>
-<td>- Netflix may have geo-restrictions that prevent you from accessing certain content based on your location, unless you use a VPN service.</td>
-</tr>
-</table> <h3>Amazon Prime Video</h3>
-<p>Amazon Prime Video is another popular streaming service that offers a variety of movies and shows, including original and exclusive content. It is part of the Amazon Prime membership, which also includes free shipping, music streaming, e-books, and more. You can also rent or buy movies and shows that are not included in the Prime Video catalog.</p>
-<h4>Steps to Download Cars Movie from Amazon Prime Video</h4>
-<p>To download Cars movie from Amazon Prime Video, you need to follow these steps:</p>
-<ol>
-<li>Sign up for an Amazon Prime account if you don't have one. You can get a 30-day free trial and then pay $12.99 per month or $119 per year. You can also sign up for a Prime Video-only account for $8.99 per month.</li>
-<li>Download the Prime Video app on your device. You can download it from the App Store for iOS devices, the Google Play Store for Android devices, or the Microsoft Store for Windows devices. You can also access Prime Video from your web browser, but you cannot download movies or shows from there.</li>
-<li>Open the Prime Video app and sign in with your account. You can browse the content by categories, genres, recommendations, or search for a specific title.</li>
-<li>Find Cars movie on Prime Video. You can use the search function or look for it in the Animation, Comedy, or Family categories. You can also check if Cars movie is available on Prime Video in your country by using a website like justwatch.com or reelgood.com.</li>
-<li>Tap on the download icon next to the play button. The download icon looks like a downward arrow with a horizontal line below it. If you don't see the download icon, it means that the movie is not available for download.</li>
-<li>Wait for the movie to download to your device. You can check the progress of the download by tapping on the downloads icon at the bottom of the screen. The downloads icon looks like a downward arrow with a circle around it.</li>
-<li>Enjoy watching Cars movie offline. You can find the downloaded movie in the downloads section of the app. You can watch it as many times as you want without using data or Wi-Fi.</li>
-</ol>
-<h4>Pros and Cons of Amazon Prime Video</h4>
-<p>Amazon Prime Video is another great streaming service for downloading Cars movie, but it also has some pros and cons that you should consider:</p>
-<table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>- Amazon Prime Video has a large and diverse library of movies and shows, including original and exclusive content.</td>
-<td>- Amazon Prime Video requires a subscription fee to use the service, which may not be affordable for some users.</td>
-</tr>
-<tr>
-<td>- Amazon Prime Video allows you to download movies and shows to your device and watch them offline without using data or Wi-Fi.</td>
-<td>- Amazon Prime Video limits the number of devices and titles you can download at a time, depending on your location and account type.</td>
-</tr>
-<tr>
-<td>- Amazon Prime Video offers high-quality video and audio, as well as subtitles and dubbing options for different languages.</td>
-<td>- Amazon Prime Video does not have all the movies and shows you may want to watch, as some of them may be unavailable or removed due to licensing agreements.</td>
-</tr>
-<tr>
-<td>- Amazon Prime Video is compatible with most devices and platforms, such as computers, smartphones, tablets, smart TVs, gaming consoles, or streaming devices.</td>
-<td>- Amazon Prime Video may have geo-restrictions that prevent you from accessing certain content based on your location, unless you use a VPN service.</td>
-</tr>
-</table> <h2>How to Download Cars Movie from Torrent Sites</h2>
-<p>Another way to download Cars movie is to use a torrent site. A torrent site is a website that hosts torrent files, which are small files that contain information about the content you want to download, such as the name, size, type, and location of the files. You can use a torrent site to find and download movies, music, games, software, books, and any other digital content.</p>
-<h3>What are Torrents and How They Work</h3>
-<p>Torrents are a peer-to-peer (P2P) file-sharing technology that allows users to download and share files with each other without relying on a central server. Instead, users connect to each other directly and form a network of peers. Each peer has a copy of the torrent file and a part of the content file. When you download a torrent, you are downloading small pieces of the content file from different peers. When you upload a torrent, you are sharing the pieces of the content file that you have with other peers.</p>
-<p>Torrents work by using a BitTorrent protocol, which is a set of rules and commands that enable the communication and coordination between peers. The BitTorrent protocol uses trackers, which are servers that help peers find each other and exchange information. The BitTorrent protocol also uses seeds and leechers, which are terms that describe the status of peers in the network. A seed is a peer that has the complete content file and is uploading it to other peers. A leecher is a peer that does not have the complete content file and is downloading it from other peers.</p>
-<h3>How to Use a BitTorrent Client to Download Movies</h3>
-<p>To use torrents to download movies, you need to use a BitTorrent client, which is a software program that allows you to open, download, and upload torrent files. There are many BitTorrent clients available for different devices and platforms, such as uTorrent, BitTorrent, qBittorrent, Transmission, Vuze, Deluge, and more.</p>
-<h4>Steps to Download Cars Movie from a Torrent Site</h4>
-<p>To download Cars movie from a torrent site, you need to follow these steps:</p>
-<ol>
-<li>Choose a BitTorrent client that suits your device and preferences. You can compare the features, performance, security, and reviews of different BitTorrent clients online. You can also check if the BitTorrent client is compatible with your device and operating system.</li>
-<li>Download and install the BitTorrent client on your device. You can download it from the official website of the BitTorrent client or from a trusted source. You can also customize the settings of the BitTorrent client according to your needs.</li>
-<li>Choose a torrent site that has Cars movie available for download. You can search for torrent sites online or use a website like torrentz2.eu or torrentfunk.com to find torrent sites that have Cars movie. You can also check the reputation, popularity, and safety of torrent sites online.</li>
-<li>Find Cars movie on the torrent site. You can use the search function or browse by categories or genres. You can also check the details of the torrent file, such as the name, size, type, quality, seeds, leechers, comments, and ratings.</li>
-<li>Download the torrent file or copy the magnet link of Cars movie. The torrent file is a small file that contains information about the content file. The magnet link is a URL that contains information about the content file and allows you to download it without using a torrent file.</li>
-<li>Open the torrent file or paste the magnet link in your BitTorrent client. The BitTorrent client will start downloading Cars movie from different peers in the network. You can check the progress of the download by looking at the speed, time remaining, percentage completed, and amount downloaded.</li>
-<li>Wait for the movie to download to your device. You can choose where to save the movie on your device or let the BitTorrent client choose for you. You can also pause or resume the download at any time.</li>
-<li>Enjoy watching Cars movie offline. You can find the downloaded movie in the folder you chose or in the default folder of your BitTorrent client. You can watch it as many times as you want without using data or Wi-Fi.</li>
-</ol>
-<h4>Pros and Cons of Torrents</h4>
-<p>Torrents are a convenient and fast way to download movies, but they also have some pros and cons that you should consider:</p>
-<table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>- Torrents allow you to download movies for free without paying any subscription fee or registration fee.</td>
-<td>- Torrents are illegal in many countries and regions due to copyright infringement and piracy laws.</td>
-</tr>
-<tr>
-<td>- Torrents - Torrents offer a wide range of movies and shows in different genres, languages, and regions that may not be available on streaming services.</td>
-<td>- Torrents expose your device to viruses, malware, spyware, ransomware, or other harmful programs that can damage your data, steal your information, or lock your device until you pay a ransom.</td>
-</tr>
-<tr>
-<td>- Torrents provide high-quality video and audio, as well as subtitles and dubbing options for different languages.</td>
-<td>- Torrents compromise your online security and privacy by revealing your IP address, location, browsing history, or personal information to hackers, trackers, or advertisers.</td>
-</tr>
-<tr>
-<td>- Torrents are compatible with most devices and platforms, such as computers, smartphones, tablets, smart TVs, gaming consoles, or streaming devices.</td>
-<td>- Torrents depend on the availability and generosity of peers in the network. If there are not enough seeds or too many leechers, the download speed and quality may be low or the download may fail.</td>
-</tr>
-</table>
-<h3>How to Protect Yourself from Viruses and Malware When Using Torrents</h3>
-<p>As we mentioned before, torrents can be risky for your device and your online safety. However, there are some ways to protect yourself from viruses and malware when using torrents. Here are some tips:</p>
-<h4>Use a VPN Service</h4>
-<p>A VPN service is a virtual private network that encrypts your internet traffic and hides your IP address and location from anyone who tries to monitor or track you online. A VPN service can help you avoid geo-restrictions, censorship, surveillance, and legal action when using torrents. It can also prevent hackers, trackers, or advertisers from accessing your data or information.</p>
-<p>To use a VPN service, you need to sign up for a VPN account and download and install the VPN app on your device. You can choose from many VPN services available online, such as NordVPN, ExpressVPN, Surfshark, CyberGhost, or IPVanish. You can also compare the features, performance, security, and reviews of different VPN services online.</p>
-<p>Once you have the VPN app on your device, you need to connect to a VPN server of your choice. The VPN server will assign you a new IP address and location that will mask your real ones. You can then use the torrent site and the BitTorrent client as usual. The VPN service will encrypt your internet traffic and protect you from viruses and malware.</p>
-<h4>Scan the Downloaded File with an Antivirus Program</h4>
-<p>An antivirus program is a software program that detects and removes viruses and malware from your device. An antivirus program can help you prevent or fix any damage caused by viruses and malware when using torrents. It can also alert you of any suspicious or malicious files or programs on your device.</p>
-<p>To use an antivirus program, you need to download and install the antivirus program on your device. You can choose from many antivirus programs available online, such as Avast, AVG, Kaspersky, McAfee, or Norton. You can also compare the features, performance, security, and reviews of different antivirus programs online.</p>
-<p>Once you have the antivirus program on your device, you need to scan the downloaded file with the antivirus program before opening it. The antivirus program will scan the file and detect any viruses or malware that may be hidden in it. If the file is clean, you can open it and watch Cars movie. If the file is infected, you can delete it and look for another torrent.</p>
-<h2>How to Download Cars Movie from Free Movie Sites</h2>
-<p>A third way to download Cars movie is to use a free movie site. A free movie site is a website that allows you to watch movies online or offline without paying any fee or registration. You can use a free movie site to find and download movies in different genres, languages, and regions.</p>
-<h3>What are Free Movie Sites and How They Work</h3>
-<p>Free movie sites are websites that host or link to movies that are uploaded by users or third parties. Free movie sites do not have the legal rights or licenses to distribute the movies they offer. They rely on advertising revenue or donations to maintain their servers and domains.</p>
-<p>Free movie sites work by using streaming or downloading technology. Streaming technology allows you to watch movies online without downloading them to your device. You can watch movies in real time as they are transmitted from the server to your device. Downloading technology allows you to download movies to your device and watch them offline without using data or Wi-Fi. You can download movies as whole files or as small pieces that are joined together.</p>
-<h3>How to Find and Use a Free Movie Site to Download Movies</h3>
-<p>To use a free movie site to download movies, you need to follow these steps:</p>
-<ol>
-<li>Choose a free movie site that has Cars movie available for download. You can search for free movie sites online or use a website like alluc.co or yidio.com to find free movie sites that have Cars movie. You can also check the reputation, popularity, and safety of free movie sites online.</li>
-<li>Find Cars movie on the free movie site. You can use the search function or browse by categories or genres. You can also check the details of the movie, such as the name, size, type, quality, source, and ratings.</li>
-<li>Download Cars movie from the free movie site. Depending on the free movie site, you may have different options to download Cars movie. Some of the options are:</li>
-<ul>
-<li>Click on the download button or link that leads you to the movie file. The download button or link may look like a downward arrow, a disk icon, or a text that says "download".</li>
-<li>Right-click on the video player and select "save video as" or "download video". The video player may look like a rectangle with a play button in the center.</li>
-<li>Copy the video URL from the address bar or the video player and paste it in a video downloader website or software. The video URL may look like a long string of letters and numbers that starts with "http" or "https".</li>
-</ul>
-<li>Wait for the movie to download to your device. You can check the progress of the download by looking at the speed, time remaining, percentage completed, and amount downloaded.</li>
-<li>Enjoy watching Cars movie offline. You can find the downloaded movie in the folder you chose or in the default folder of your browser or downloader. You can watch it as many times as you want without using data or Wi-Fi.</li>
-</ol>
-<h4>Pros and Cons of Free Movie Sites</h4>
-<p>Free movie sites are an easy and cheap way to download movies, but they also have some pros and cons that you should consider:</p>
-<table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>- Free movie sites allow you to download movies for free without paying any subscription fee or registration fee.</td>
-<td>- Free movie sites are illegal in many countries and regions due to copyright infringement and piracy laws.</td>
-</tr>
-<tr>
-<td>- Free movie sites offer a wide range of movies and shows in different genres, languages, and regions that may not be available on streaming services.</td>
-<td>- Free movie sites expose your device to viruses, malware, spyware, ransomware, or other harmful programs that can damage your data, steal your information, or lock your device until you pay a ransom.</td>
-</tr>
-<tr>
-<td>- Free movie sites provide high-quality video and audio, as well as subtitles and dubbing options for different languages.</td>
-<td>- Free movie sites compromise your online security and privacy by revealing your IP address, location, browsing history, or personal information to hackers, trackers, or advertisers.</td>
-</tr>
-<tr>
-<td>- Free movie sites are compatible with most devices and platforms, such as computers, smartphones, tablets, smart TVs, gaming consoles, or streaming devices.</td>
-<td>- Free movie sites depend on the availability and reliability of the servers and links that host or link to the movies. If the server or link is down, broken, or removed, the download may fail or the movie may not play.</td>
-</tr>
-</table>
-<h3>How to Avoid Ads and Pop-ups When Using Free Movie Sites</h3>
-<p>As we mentioned before, free movie sites rely on advertising revenue to maintain their servers and domains. However, the ads and pop-ups that appear on free movie sites can be annoying, intrusive, or even dangerous for your device and your online safety. However, there are some ways to avoid ads and pop-ups when using free movie sites. Here are some tips:</p>
-<h4>Use an Ad Blocker Extension</h4>
-<p>An ad blocker extension is a browser extension that blocks or removes ads and pop-ups from websites. An ad blocker extension can help you improve your browsing experience, save your bandwidth and battery life, and protect you from malicious ads and pop-ups.</p>
-<p>To use an ad blocker extension, you need to download and install the ad blocker extension on your browser. You can choose from many ad blocker extensions available online, such as Adblock Plus, uBlock Origin, AdGuard, or Ghostery. You can also compare the features, performance, security, and reviews of different ad blocker extensions online.</p>
-<p>Once you have the ad blocker extension on your browser, you need to enable it and customize its settings according to your preferences. You can also whitelist some websites that you want to support or that do not have annoying or harmful ads and pop-ups.</p>
-<h4>Use a Pop-up Blocker Extension</h4>
-<p>A pop-up blocker extension is a browser extension that blocks or removes pop-ups from websites. A pop-up is a new window that opens automatically when you visit a website or click on a link. A pop-up blocker extension can help you avoid unwanted or malicious pop-ups that may redirect you to other websites, download unwanted files or programs, or display inappropriate or misleading content.</p>
-<p>To use a pop-up blocker extension, you need to download and install the pop-up blocker extension on your browser. You can choose from many pop-up blocker extensions available online, such as Popper Blocker, Poper Blocker, Popup Blocker Pro, or Smart Popup Blocker. You can also compare the features, performance, security, and reviews of different pop-up blocker extensions online.</p>
-<p>Once you have the pop-up blocker extension on your browser, you need to enable it and customize its settings according to your preferences. You can also whitelist some websites that you want to allow pop-ups from or that do not have unwanted or malicious pop-ups.</p>
-<h2>Conclusion</h2>
-<h3>Summary of the Main Points</h3>
-<p>In this article, we have shown you how to download Cars movie legally and safely from different sources, such as streaming services, torrent sites, and free movie sites. We have also given you some tips on how to protect yourself from viruses and malware when using torrents and how to avoid ads and pop-ups when using free movie sites.</p>
-<p>Cars is a Pixar film that was released in 2006 and became one of the most successful animated movies of all time. It tells the story of a hotshot race car named Lightning McQueen who gets stranded in a small town called Radiator Springs and learns the true meaning of friendship and family. If you are a fan of Cars or want to watch it for the first time, you might be wondering how to download it to your computer or mobile device.</p>
-<h3>Recommendations for the Best Way to Download Cars Movie</h3>
-<p>Based on our analysis, we recommend you to use streaming services as the best way to download Cars movie legally and safely. Streaming services offer many benefits for movie lovers, such as high-quality video and audio, offline viewing, multiple device compatibility, large and diverse library, and exclusive content. Streaming services also have fewer drawbacks than torrent sites or free movie sites, such as subscription fee, internet speed, content availability, and geo-restrictions.</p>
-<p>Among the streaming services that offer Cars movie, we suggest you to use Netflix or Amazon Prime Video. Both of them have similar features and advantages, such as HD or 4K resolution, subtitles and dubbing options, multiple profiles and screens, and original and exclusive content. However, Netflix has a larger and more diverse library than Amazon Prime Video, while Amazon Prime Video has a cheaper and more comprehensive membership than Netflix.</p>
-<p>Therefore, you can choose the streaming service that suits your preferences and budget. You can also try both of them for free for a limited time and compare their performance and quality. You can follow the steps we provided in this article to download Cars movie from Netflix or Amazon Prime Video.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about downloading Cars movie:</p>
-<ol>
-<li>Is downloading Cars movie illegal?</li>
-<p>Downloading Cars movie is not illegal if you use a legal and safe method, such as streaming services. However, downloading Cars movie is illegal if you use an illegal and unsafe method, such as torrent sites or free movie sites. You can face legal action from the copyright owner or the authorities if you download Cars movie illegally.</p>
-<li>Is downloading Cars movie safe?</li>
-<p>Downloading Cars movie is safe if you use a legal and safe method, such as streaming services. However, downloading Cars movie is not safe if you use an illegal and unsafe method, such as torrent sites or free movie sites. You can expose your device to viruses, malware, spyware, ransomware, or other harmful programs if you download Cars movie from an unsafe source.</p>
-<li>How long does it take to download Cars movie?</li>
-<p>The time it takes to download Cars movie depends on several factors, such as the size of the file, the speed of your internet connection, the number of seeds or peers in the network, and the method you use to download it. Generally, streaming services have faster download speeds than torrent sites or free movie sites. However, streaming services also have larger file sizes than torrent sites or free movie sites. Therefore, you can expect to download Cars movie in a few minutes to a few hours depending on your situation.</p>
-<li>How much space does Cars movie take on my device?</li>
-<p>The space that Cars movie takes on your device depends on the quality of the video and audio, the length of the movie, and the format of the file. Generally, streaming services have higher quality video and audio than torrent sites or free movie sites. However, streaming services also have larger file sizes than torrent sites or free movie sites. Therefore, you can expect Cars movie to take from a few hundred megabytes to a few gigabytes of space on your device depending on your choice.</p>
-<li>Can I watch Cars movie on any device?</li>
-<p>You can watch Cars movie on any device that supports the method you use to download it. For example, if you use a streaming service, you can watch Cars movie on any device that has the streaming app installed or can access the streaming website. If you use a torrent site or a free movie site, you can watch Cars movie on any device that has a video player that can open the file format of the movie.</p>
-</ol>
-<p>I hope this article has helped you learn how to download Cars movie legally and safely. If you have any questions or comments, please feel free to leave them below. Thank you for reading and happy watching!</p> 401be4b1e0<br />
-<br />
-<br />

spaces/2ndelement/voicevox/test/test_acoustic_feature_extractor.py

@@ -1,266 +0,0 @@
-import os
-from pathlib import Path
-from typing import List, Type
-from unittest import TestCase
-
-from voicevox_engine.acoustic_feature_extractor import (
-    BasePhoneme,
-    JvsPhoneme,
-    OjtPhoneme,
-)
-
-
-class TestBasePhoneme(TestCase):
-    def setUp(self):
-        super().setUp()
-        self.str_hello_hiho = "sil k o N n i ch i w a pau h i h o d e s U sil"
-        self.base_hello_hiho = [
-            BasePhoneme(s, i, i + 1) for i, s in enumerate(self.str_hello_hiho.split())
-        ]
-        self.lab_str = """
-            0.00	1.00	pau
-            1.00	2.00	k
-            2.00	3.00	o
-            3.00	4.00	N
-            4.00	5.00	n
-            5.00	6.00	i
-            6.00	7.00	ch
-            7.00	8.00	i
-            8.00	9.00	w
-            9.00	10.00	a
-            10.00	11.00	pau
-            11.00	12.00	h
-            12.00	13.00	i
-            13.00	14.00	h
-            14.00	15.00	o
-            15.00	16.00	d
-            16.00	17.00	e
-            17.00	18.00	s
-            18.00	19.00	U
-            19.00	20.00	pau
-        """.replace(
-            " ", ""
-        )[
-            1:-1
-        ]  # ダブルクオーテーションx3で囲われている部分で、空白をすべて置き換え、先頭と最後の"\n"を除外する
-
-    def test_repr_(self):
-        self.assertEqual(
-            self.base_hello_hiho[1].__repr__(), "Phoneme(phoneme='k', start=1, end=2)"
-        )
-        self.assertEqual(
-            self.base_hello_hiho[10].__repr__(),
-            "Phoneme(phoneme='pau', start=10, end=11)",
-        )
-
-    def test_convert(self):
-        with self.assertRaises(NotImplementedError):
-            BasePhoneme.convert(self.base_hello_hiho)
-
-    def test_duration(self):
-        self.assertEqual(self.base_hello_hiho[1].duration, 1)
-
-    def test_parse(self):
-        parse_str_1 = "0 1 pau"
-        parse_str_2 = "32.67543 33.48933 e"
-        parsed_base_1 = BasePhoneme.parse(parse_str_1)
-        parsed_base_2 = BasePhoneme.parse(parse_str_2)
-        self.assertEqual(parsed_base_1.phoneme, "pau")
-        self.assertEqual(parsed_base_1.start, 0.0)
-        self.assertEqual(parsed_base_1.end, 1.0)
-        self.assertEqual(parsed_base_2.phoneme, "e")
-        self.assertEqual(parsed_base_2.start, 32.68)
-        self.assertEqual(parsed_base_2.end, 33.49)
-
-    def lab_test_base(
-        self,
-        file_path: str,
-        phonemes: List["BasePhoneme"],
-        phoneme_class: Type["BasePhoneme"],
-    ):
-        phoneme_class.save_lab_list(phonemes, Path(file_path))
-        with open(file_path, mode="r") as f:
-            self.assertEqual(f.read(), self.lab_str)
-        result_phoneme = phoneme_class.load_lab_list(Path(file_path))
-        self.assertEqual(result_phoneme, phonemes)
-        os.remove(file_path)
-
-
-class TestJvsPhoneme(TestBasePhoneme):
-    def setUp(self):
-        super().setUp()
-        base_hello_hiho = [
-            JvsPhoneme(s, i, i + 1) for i, s in enumerate(self.str_hello_hiho.split())
-        ]
-        self.jvs_hello_hiho = JvsPhoneme.convert(base_hello_hiho)
-
-    def test_phoneme_list(self):
-        self.assertEqual(JvsPhoneme.phoneme_list[1], "I")
-        self.assertEqual(JvsPhoneme.phoneme_list[14], "gy")
-        self.assertEqual(JvsPhoneme.phoneme_list[26], "p")
-        self.assertEqual(JvsPhoneme.phoneme_list[38], "z")
-
-    def test_const(self):
-        self.assertEqual(JvsPhoneme.num_phoneme, 39)
-        self.assertEqual(JvsPhoneme.space_phoneme, "pau")
-
-    def test_convert(self):
-        converted_str_hello_hiho = " ".join([p.phoneme for p in self.jvs_hello_hiho])
-        self.assertEqual(
-            converted_str_hello_hiho, "pau k o N n i ch i w a pau h i h o d e s U pau"
-        )
-
-    def test_equal(self):
-        # jvs_hello_hihoの2番目の"k"と比較
-        true_jvs_phoneme = JvsPhoneme("k", 1, 2)
-        # OjtPhonemeと比べる、比較はBasePhoneme内で実装されているので、比較結果はTrue
-        true_ojt_phoneme = OjtPhoneme("k", 1, 2)
-
-        false_jvs_phoneme_1 = JvsPhoneme("a", 1, 2)
-        false_jvs_phoneme_2 = JvsPhoneme("k", 2, 3)
-        self.assertTrue(self.jvs_hello_hiho[1] == true_jvs_phoneme)
-        self.assertTrue(self.jvs_hello_hiho[1] == true_ojt_phoneme)
-        self.assertFalse(self.jvs_hello_hiho[1] == false_jvs_phoneme_1)
-        self.assertFalse(self.jvs_hello_hiho[1] == false_jvs_phoneme_2)
-
-    def test_verify(self):
-        for phoneme in self.jvs_hello_hiho:
-            phoneme.verify()
-
-    def test_phoneme_id(self):
-        jvs_str_hello_hiho = " ".join([str(p.phoneme_id) for p in self.jvs_hello_hiho])
-        self.assertEqual(
-            jvs_str_hello_hiho, "0 19 25 2 23 17 7 17 36 4 0 15 17 15 25 9 11 30 3 0"
-        )
-
-    def test_onehot(self):
-        phoneme_id_list = [
-            0,
-            19,
-            25,
-            2,
-            23,
-            17,
-            7,
-            17,
-            36,
-            4,
-            0,
-            15,
-            17,
-            15,
-            25,
-            9,
-            11,
-            30,
-            3,
-            0,
-        ]
-        for i, phoneme in enumerate(self.jvs_hello_hiho):
-            for j in range(JvsPhoneme.num_phoneme):
-                if phoneme_id_list[i] == j:
-                    self.assertEqual(phoneme.onehot[j], True)
-                else:
-                    self.assertEqual(phoneme.onehot[j], False)
-
-    def test_parse(self):
-        parse_str_1 = "0 1 pau"
-        parse_str_2 = "15.32654 16.39454 a"
-        parsed_jvs_1 = JvsPhoneme.parse(parse_str_1)
-        parsed_jvs_2 = JvsPhoneme.parse(parse_str_2)
-        self.assertEqual(parsed_jvs_1.phoneme_id, 0)
-        self.assertEqual(parsed_jvs_2.phoneme_id, 4)
-
-    def test_lab_list(self):
-        self.lab_test_base("./jvs_lab_test", self.jvs_hello_hiho, JvsPhoneme)
-
-
-class TestOjtPhoneme(TestBasePhoneme):
-    def setUp(self):
-        super().setUp()
-        self.str_hello_hiho = "sil k o N n i ch i w a pau h i h o d e s U sil"
-        base_hello_hiho = [
-            OjtPhoneme(s, i, i + 1) for i, s in enumerate(self.str_hello_hiho.split())
-        ]
-        self.ojt_hello_hiho = OjtPhoneme.convert(base_hello_hiho)
-
-    def test_phoneme_list(self):
-        self.assertEqual(OjtPhoneme.phoneme_list[1], "A")
-        self.assertEqual(OjtPhoneme.phoneme_list[14], "e")
-        self.assertEqual(OjtPhoneme.phoneme_list[26], "m")
-        self.assertEqual(OjtPhoneme.phoneme_list[38], "ts")
-        self.assertEqual(OjtPhoneme.phoneme_list[41], "v")
-
-    def test_const(self):
-        self.assertEqual(OjtPhoneme.num_phoneme, 45)
-        self.assertEqual(OjtPhoneme.space_phoneme, "pau")
-
-    def test_convert(self):
-        ojt_str_hello_hiho = " ".join([p.phoneme for p in self.ojt_hello_hiho])
-        self.assertEqual(
-            ojt_str_hello_hiho, "pau k o N n i ch i w a pau h i h o d e s U pau"
-        )
-
-    def test_equal(self):
-        # ojt_hello_hihoの10番目の"a"と比較
-        true_ojt_phoneme = OjtPhoneme("a", 9, 10)
-        # JvsPhonemeと比べる、比較はBasePhoneme内で実装されているので、比較結果はTrue
-        true_jvs_phoneme = JvsPhoneme("a", 9, 10)
-
-        false_ojt_phoneme_1 = OjtPhoneme("k", 9, 10)
-        false_ojt_phoneme_2 = OjtPhoneme("a", 10, 11)
-        self.assertTrue(self.ojt_hello_hiho[9] == true_ojt_phoneme)
-        self.assertTrue(self.ojt_hello_hiho[9] == true_jvs_phoneme)
-        self.assertFalse(self.ojt_hello_hiho[9] == false_ojt_phoneme_1)
-        self.assertFalse(self.ojt_hello_hiho[9] == false_ojt_phoneme_2)
-
-    def test_verify(self):
-        for phoneme in self.ojt_hello_hiho:
-            phoneme.verify()
-
-    def test_phoneme_id(self):
-        ojt_str_hello_hiho = " ".join([str(p.phoneme_id) for p in self.ojt_hello_hiho])
-        self.assertEqual(
-            ojt_str_hello_hiho, "0 23 30 4 28 21 10 21 42 7 0 19 21 19 30 12 14 35 6 0"
-        )
-
-    def test_onehot(self):
-        phoneme_id_list = [
-            0,
-            23,
-            30,
-            4,
-            28,
-            21,
-            10,
-            21,
-            42,
-            7,
-            0,
-            19,
-            21,
-            19,
-            30,
-            12,
-            14,
-            35,
-            6,
-            0,
-        ]
-        for i, phoneme in enumerate(self.ojt_hello_hiho):
-            for j in range(OjtPhoneme.num_phoneme):
-                if phoneme_id_list[i] == j:
-                    self.assertEqual(phoneme.onehot[j], True)
-                else:
-                    self.assertEqual(phoneme.onehot[j], False)
-
-    def test_parse(self):
-        parse_str_1 = "0 1 pau"
-        parse_str_2 = "32.67543 33.48933 e"
-        parsed_ojt_1 = OjtPhoneme.parse(parse_str_1)
-        parsed_ojt_2 = OjtPhoneme.parse(parse_str_2)
-        self.assertEqual(parsed_ojt_1.phoneme_id, 0)
-        self.assertEqual(parsed_ojt_2.phoneme_id, 14)
-
-    def tes_lab_list(self):
-        self.lab_test_base("./ojt_lab_test", self.ojt_hello_hiho, OjtPhoneme)

spaces/801artistry/RVC801/go-applio.bat

@@ -1,92 +0,0 @@
-@echo off
-setlocal
-title Start Applio
-
-:::
-:::                       _ _
-:::     /\               | (_)
-:::    /  \   _ __  _ __ | |_  ___
-:::   / /\ \ | '_ \| '_ \| | |/ _ \
-:::  / ____ \| |_) | |_) | | | (_) |
-::: /_/    \_\ .__/| .__/|_|_|\___/
-:::          | |   | |
-:::          |_|   |_|
-:::
-:::
-
-:menu
-for /f "delims=: tokens=*" %%A in ('findstr /b ":::" "%~f0"') do @echo(%%A
-
-echo [1] Start Applio
-echo [2] Start Applio (DML)
-echo [3] Start Realtime GUI (DML)
-echo [4] Start Realtime GUI (V0)
-echo [5] Start Realtime GUI (V1)
-echo.
-
-set /p choice=Select an option: 
-set choice=%choice: =%
-
-cls
-echo WARNING: It's recommended to disable antivirus or firewall, as errors might occur when starting the ssl.
-pause
-
-if "%choice%"=="1" (
-    cls
-    echo WARNING: At this point, it's recommended to disable antivirus or firewall, as errors might occur when downloading pretrained models.
-    pause>null
-    echo Starting Applio...
-    echo.
-    runtime\python.exe infer-web.py --pycmd runtime\python.exe --port 7897
-    pause
-    cls
-    goto menu
-)
-
-if "%choice%"=="2" (
-    cls
-    echo Starting Applio ^(DML^)...
-    echo.
-    runtime\python.exe infer-web.py --pycmd runtime\python.exe --port 7897 --dml
-    pause
-    cls
-    goto menu
-)
-
-if "%choice%"=="3" (
-    cls
-    echo Starting Realtime GUI ^(DML^)...
-    echo.
-    runtime\python.exe gui_v1.py --pycmd runtime\python.exe --dml
-    pause
-    cls
-    goto menu
-)
-
-if "%choice%"=="4" (
-    cls
-    echo Starting Realtime GUI ^(V0^)...
-    echo.
-    runtime\python.exe gui_v0.py
-    pause
-    cls
-    goto menu
-)
-
-if "%choice%"=="5" (
-    cls
-    echo Starting Realtime GUI ^(V1^)...
-    echo.
-    runtime\python.exe gui_v1.py
-    pause
-    cls
-    goto menu
-)
-
-cls
-echo Invalid option. Please enter a number from 1 to 5.
-echo.
-echo Press 'Enter' to access the main menu...
-pause>nul
-cls
-goto menu

spaces/A666sxr/Genshin_TTS/modules.py

@@ -1,390 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x

spaces/AI-Chatbot-Master/Chatbots/README.md

@@ -1,10 +0,0 @@
----
-title: Chatbots
-emoji: 📚
-colorFrom: yellow
-colorTo: red
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AI-ZTH-03-23/2.Streamlit.GraphViz.Dynamic.Architecture.Diagram/app.py

@@ -1,146 +0,0 @@
-import streamlit as st
-from graphviz import Digraph
-
-
-st.markdown("""
-Prompt:
-Create an interactive streamlit graph builder using the graphviz diagram model language and the streamlit feature: st.graphviz_chart(figure_or_dot, use_container_width=False) to show an azure cloud architecture model including the top ten architecture components for python full stack development for web, api, ml, models, datasets torch, transformers, streamlit, azure docker and kubernetes pods for scaling
-
-""")
-
-# Dot demo:
-import streamlit as st
-
-# Define the default graphviz DOT string
-default_dot = """
-digraph G {
-    rankdir=LR
-    node [shape=box]
-    WebApp -> API
-    API -> Models
-    API -> Datasets
-    Models -> Torch
-    Models -> Transformers
-    WebApp -> Streamlit
-    Streamlit -> Azure
-    Azure -> Docker
-    Azure -> Kubernetes
-}
-"""
-
-# Define the list of top 10 components
-components = [
-    "WebApp",
-    "API",
-    "Models",
-    "Datasets",
-    "Torch",
-    "Transformers",
-    "Streamlit",
-    "Azure",
-    "Docker",
-    "Kubernetes",
-]
-
-# Define a dictionary to map component names to DOT node IDs
-node_ids = {
-    component: component.lower()
-    for component in components
-}
-
-def build_dot_string(selected_components):
-    """Builds a DOT string representing the selected components"""
-    selected_nodes = [node_ids[component] for component in selected_components]
-    dot = """
-    digraph G {
-        rankdir=LR
-        node [shape=box]
-    """
-    for node in selected_nodes:
-        dot += f"{node} [color=blue]\n"
-    for i in range(len(selected_nodes)):
-        for j in range(i+1, len(selected_nodes)):
-            dot += f"{selected_nodes[i]} -> {selected_nodes[j]}\n"
-    dot += "}"
-    return dot
-
-def main():
-    st.title("Azure Cloud Architecture Builder")
-
-    # Select the components
-    st.sidebar.title("Select components")
-    selected_components = st.sidebar.multiselect(
-        "Select the top 10 components",
-        components,
-        default=components[:3]
-    )
-
-    # Build the DOT string
-    dot = build_dot_string(selected_components)
-
-    # Render the graphviz chart
-    st.graphviz_chart(dot, use_container_width=True)
-
-if __name__ == "__main__":
-    main()
-
-
-
-# Initialize the graph
-graph = Digraph(comment='Architectural Model')
-
-# Add nodes to the graph
-graph.node('data_layer', 'Data Layer')
-graph.node('acr', 'Azure Container Registry')
-graph.node('aks', 'Azure Kubernetes\n& Docker Container Pod\nwith Scalability')
-graph.node('snowflake', 'Snowflake Instance')
-graph.node('cosmos', 'Azure Cosmos\nDatabase')
-graph.node('api', 'API Standard\n(using Uvicorn)')
-graph.node('soar', 'SOAR Component\n(on Linux Python\nSlimbuster Docker)')
-
-# Add edges to the graph
-graph.edge('data_layer', 'acr')
-graph.edge('acr', 'aks')
-graph.edge('aks', 'snowflake')
-graph.edge('aks', 'cosmos')
-graph.edge('aks', 'api')
-graph.edge('aks', 'soar')
-
-# Define the Streamlit app
-def app():
-    st.title('Architectural Model')
-    
-    # Draw the graph
-    st.graphviz_chart(graph.source)
-    
-    # Add buttons to customize the graph
-    if st.button('Hide Data Layer'):
-        graph.node('data_layer', style='invisible')
-    
-    if st.button('Hide Snowflake Instance'):
-        graph.node('snowflake', style='invisible')
-    
-    if st.button('Hide SOAR Component'):
-        graph.node('soar', style='invisible')
-
-
-
-st.markdown("""
-# QA Model Spaces:
-QA use cases include QA, Semantic Document and FAQ Search.
-1. Streamlit Question Answering w Hugging Face: https://huggingface.co/spaces/awacke1/Question-answering
-2. Seq2Seq:
-	- https://huggingface.co/spaces/awacke1/4-Seq2SeqQAT5
-	- https://huggingface.co/spaces/awacke1/AW-04-GR-Seq-2-Seq-QA-Auto-Gen
-3. BioGPT: https://huggingface.co/spaces/awacke1/microsoft-BioGPT-Large-PubMedQA
-4. NLP QA Context: https://huggingface.co/spaces/awacke1/NLPContextQATransformersRobertaBaseSquad2
-	- https://huggingface.co/spaces/awacke1/SOTA-Plan
-5.  https://huggingface.co/spaces/awacke1/Question-answering
-6.  QA MLM: https://huggingface.co/spaces/awacke1/SOTA-MedEntity
-""")
-
-
-        
-# Run the Streamlit app
-if __name__ == '__main__':
-    app()

spaces/AIConsultant/MusicGen/scripts/templates/results.html

@@ -1,17 +0,0 @@
-{% extends "base.html" %}
-{% block content %}
-
-<h1>Results for survey #{{signature}}</h1>
-<p>Checkout <a href="{{url_for('survey', signature=signature)}}">the survey page</a> for details on the models.</p>
-<p>The following users voted:
-	{% for user in users %}
-		<span class="special">{{user}}</span>
-	{% endfor %}
-
-{% for model in models %}
-<h3>{{model['sig']}} ({{model['samples']}} samples)</h3>
-<p>Ratings: {{model['mean_rating']}} ± {{model['std_rating']}}</p>
-
-{% endfor %}
-
-{% endblock %}

spaces/AIFILMS/generate_human_motion/pyrender/pyrender/platforms/__init__.py

@@ -1,6 +0,0 @@
-"""Platforms for generating offscreen OpenGL contexts for rendering.
-
-Author: Matthew Matl
-"""
-
-from .base import Platform

spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/conversation/[id]/stop-generating/$types.d.ts

@@ -1,9 +0,0 @@
-import type * as Kit from '@sveltejs/kit';
-
-type Expand<T> = T extends infer O ? { [K in keyof O]: O[K] } : never;
-type RouteParams = { id: string }
-type RouteId = '/conversation/[id]/stop-generating';
-
-export type EntryGenerator = () => Promise<Array<RouteParams>> | Array<RouteParams>;
-export type RequestHandler = Kit.RequestHandler<RouteParams, RouteId>;
-export type RequestEvent = Kit.RequestEvent<RouteParams, RouteId>;

spaces/Adapter/CoAdapter/ldm/modules/image_degradation/bsrgan.py

@@ -1,730 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang ([email protected])
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldm.modules.image_degradation.utils_image as util
-
-
-def modcrop_np(img, sf):
-    '''
-    Args:
-        img: numpy image, WxH or WxHxC
-        sf: scale factor
-    Return:
-        cropped image
-    '''
-    w, h = img.shape[:2]
-    im = np.copy(img)
-    return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
-    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
-    k_size = k.shape[0]
-    # Calculate the big kernels size
-    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
-    # Loop over the small kernel to fill the big one
-    for r in range(k_size):
-        for c in range(k_size):
-            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
-    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
-    crop = k_size // 2
-    cropped_big_k = big_k[crop:-crop, crop:-crop]
-    # Normalize to 1
-    return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
-    """ generate an anisotropic Gaussian kernel
-    Args:
-        ksize : e.g., 15, kernel size
-        theta : [0,  pi], rotation angle range
-        l1    : [0.1,50], scaling of eigenvalues
-        l2    : [0.1,l1], scaling of eigenvalues
-        If l1 = l2, will get an isotropic Gaussian kernel.
-    Returns:
-        k     : kernel
-    """
-
-    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
-    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
-    D = np.array([[l1, 0], [0, l2]])
-    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
-    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
-    return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
-    center = size / 2.0 + 0.5
-    k = np.zeros([size, size])
-    for y in range(size):
-        for x in range(size):
-            cy = y - center + 1
-            cx = x - center + 1
-            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
-    k = k / np.sum(k)
-    return k
-
-
-def shift_pixel(x, sf, upper_left=True):
-    """shift pixel for super-resolution with different scale factors
-    Args:
-        x: WxHxC or WxH
-        sf: scale factor
-        upper_left: shift direction
-    """
-    h, w = x.shape[:2]
-    shift = (sf - 1) * 0.5
-    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
-    if upper_left:
-        x1 = xv + shift
-        y1 = yv + shift
-    else:
-        x1 = xv - shift
-        y1 = yv - shift
-
-    x1 = np.clip(x1, 0, w - 1)
-    y1 = np.clip(y1, 0, h - 1)
-
-    if x.ndim == 2:
-        x = interp2d(xv, yv, x)(x1, y1)
-    if x.ndim == 3:
-        for i in range(x.shape[-1]):
-            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
-    return x
-
-
-def blur(x, k):
-    '''
-    x: image, NxcxHxW
-    k: kernel, Nx1xhxw
-    '''
-    n, c = x.shape[:2]
-    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
-    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
-    k = k.repeat(1, c, 1, 1)
-    k = k.view(-1, 1, k.shape[2], k.shape[3])
-    x = x.view(1, -1, x.shape[2], x.shape[3])
-    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
-    x = x.view(n, c, x.shape[2], x.shape[3])
-
-    return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
-    """"
-    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
-    # Kai Zhang
-    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
-    # max_var = 2.5 * sf
-    """
-    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
-    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
-    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
-    theta = np.random.rand() * np.pi  # random theta
-    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
-    # Set COV matrix using Lambdas and Theta
-    LAMBDA = np.diag([lambda_1, lambda_2])
-    Q = np.array([[np.cos(theta), -np.sin(theta)],
-                  [np.sin(theta), np.cos(theta)]])
-    SIGMA = Q @ LAMBDA @ Q.T
-    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
-    # Set expectation position (shifting kernel for aligned image)
-    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
-    MU = MU[None, None, :, None]
-
-    # Create meshgrid for Gaussian
-    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
-    Z = np.stack([X, Y], 2)[:, :, :, None]
-
-    # Calcualte Gaussian for every pixel of the kernel
-    ZZ = Z - MU
-    ZZ_t = ZZ.transpose(0, 1, 3, 2)
-    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
-    # shift the kernel so it will be centered
-    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
-    # Normalize the kernel and return
-    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
-    kernel = raw_kernel / np.sum(raw_kernel)
-    return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
-    hsize = [hsize, hsize]
-    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
-    std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
-    arg = -(x * x + y * y) / (2 * std * std)
-    h = np.exp(arg)
-    h[h < scipy.finfo(float).eps * h.max()] = 0
-    sumh = h.sum()
-    if sumh != 0:
-        h = h / sumh
-    return h
-
-
-def fspecial_laplacian(alpha):
-    alpha = max([0, min([alpha, 1])])
-    h1 = alpha / (alpha + 1)
-    h2 = (1 - alpha) / (alpha + 1)
-    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
-    h = np.array(h)
-    return h
-
-
-def fspecial(filter_type, *args, **kwargs):
-    '''
-    python code from:
-    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
-    if filter_type == 'gaussian':
-        return fspecial_gaussian(*args, **kwargs)
-    if filter_type == 'laplacian':
-        return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
-    '''
-    Args:
-        x: HxWxC image, [0, 1]
-        sf: down-scale factor
-    Return:
-        bicubicly downsampled LR image
-    '''
-    x = util.imresize_np(x, scale=1 / sf)
-    return x
-
-
-def srmd_degradation(x, k, sf=3):
-    ''' blur + bicubic downsampling
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2018learning,
-          title={Learning a single convolutional super-resolution network for multiple degradations},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={3262--3271},
-          year={2018}
-        }
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
-    x = bicubic_degradation(x, sf=sf)
-    return x
-
-
-def dpsr_degradation(x, k, sf=3):
-    ''' bicubic downsampling + blur
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2019deep,
-          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={1671--1681},
-          year={2019}
-        }
-    '''
-    x = bicubic_degradation(x, sf=sf)
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    return x
-
-
-def classical_degradation(x, k, sf=3):
-    ''' blur + downsampling
-    Args:
-        x: HxWxC image, [0, 1]/[0, 255]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
-    st = 0
-    return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
-    """USM sharpening. borrowed from real-ESRGAN
-    Input image: I; Blurry image: B.
-    1. K = I + weight * (I - B)
-    2. Mask = 1 if abs(I - B) > threshold, else: 0
-    3. Blur mask:
-    4. Out = Mask * K + (1 - Mask) * I
-    Args:
-        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
-        weight (float): Sharp weight. Default: 1.
-        radius (float): Kernel size of Gaussian blur. Default: 50.
-        threshold (int):
-    """
-    if radius % 2 == 0:
-        radius += 1
-    blur = cv2.GaussianBlur(img, (radius, radius), 0)
-    residual = img - blur
-    mask = np.abs(residual) * 255 > threshold
-    mask = mask.astype('float32')
-    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
-    K = img + weight * residual
-    K = np.clip(K, 0, 1)
-    return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
-    wd2 = 4.0 + sf
-    wd = 2.0 + 0.2 * sf
-    if random.random() < 0.5:
-        l1 = wd2 * random.random()
-        l2 = wd2 * random.random()
-        k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
-    else:
-        k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
-    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
-    return img
-
-
-def add_resize(img, sf=4):
-    rnum = np.random.rand()
-    if rnum > 0.8:  # up
-        sf1 = random.uniform(1, 2)
-    elif rnum < 0.7:  # down
-        sf1 = random.uniform(0.5 / sf, 1)
-    else:
-        sf1 = 1.0
-    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
-    img = np.clip(img, 0.0, 1.0)
-
-    return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-#     noise_level = random.randint(noise_level1, noise_level2)
-#     rnum = np.random.rand()
-#     if rnum > 0.6:  # add color Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-#     elif rnum < 0.4:  # add grayscale Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-#     else:  # add  noise
-#         L = noise_level2 / 255.
-#         D = np.diag(np.random.rand(3))
-#         U = orth(np.random.rand(3, 3))
-#         conv = np.dot(np.dot(np.transpose(U), D), U)
-#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-#     img = np.clip(img, 0.0, 1.0)
-#     return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    rnum = np.random.rand()
-    if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:  # add  noise
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    img = np.clip(img, 0.0, 1.0)
-    rnum = random.random()
-    if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:
-        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_Poisson_noise(img):
-    img = np.clip((img * 255.0).round(), 0, 255) / 255.
-    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
-    if random.random() < 0.5:
-        img = np.random.poisson(img * vals).astype(np.float32) / vals
-    else:
-        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
-        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
-        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
-        img += noise_gray[:, :, np.newaxis]
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_JPEG_noise(img):
-    quality_factor = random.randint(30, 95)
-    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
-    img = cv2.imdecode(encimg, 1)
-    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
-    return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
-    h, w = lq.shape[:2]
-    rnd_h = random.randint(0, h - lq_patchsize)
-    rnd_w = random.randint(0, w - lq_patchsize)
-    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
-    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
-    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
-    return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    hq = img.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
-                             interpolation=random.choice([1, 2, 3]))
-        else:
-            img = util.imresize_np(img, 1 / 2, True)
-        img = np.clip(img, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            img = add_blur(img, sf=sf)
-
-        elif i == 1:
-            img = add_blur(img, sf=sf)
-
-        elif i == 2:
-            a, b = img.shape[1], img.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
-                                 interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                img = img[0::sf, 0::sf, ...]  # nearest downsampling
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                img = add_JPEG_noise(img)
-
-        elif i == 6:
-            # add processed camera sensor noise
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
-    return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    image = util.uint2single(image)
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = image.shape[:2]
-    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = image.shape[:2]
-
-    hq = image.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
-                               interpolation=random.choice([1, 2, 3]))
-        else:
-            image = util.imresize_np(image, 1 / 2, True)
-        image = np.clip(image, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            image = add_blur(image, sf=sf)
-
-        elif i == 1:
-            image = add_blur(image, sf=sf)
-
-        elif i == 2:
-            a, b = image.shape[1], image.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
-                                   interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                image = image[0::sf, 0::sf, ...]  # nearest downsampling
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                image = add_JPEG_noise(image)
-
-        # elif i == 6:
-        #     # add processed camera sensor noise
-        #     if random.random() < isp_prob and isp_model is not None:
-        #         with torch.no_grad():
-        #             img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    image = add_JPEG_noise(image)
-    image = util.single2uint(image)
-    example = {"image":image}
-    return example
-
-
-# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
-def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):
-    """
-    This is an extended degradation model by combining
-    the degradation models of BSRGAN and Real-ESRGAN
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    use_shuffle: the degradation shuffle
-    use_sharp: sharpening the img
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    if use_sharp:
-        img = add_sharpening(img)
-    hq = img.copy()
-
-    if random.random() < shuffle_prob:
-        shuffle_order = random.sample(range(13), 13)
-    else:
-        shuffle_order = list(range(13))
-        # local shuffle for noise, JPEG is always the last one
-        shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
-        shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
-
-    poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
-
-    for i in shuffle_order:
-        if i == 0:
-            img = add_blur(img, sf=sf)
-        elif i == 1:
-            img = add_resize(img, sf=sf)
-        elif i == 2:
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-        elif i == 3:
-            if random.random() < poisson_prob:
-                img = add_Poisson_noise(img)
-        elif i == 4:
-            if random.random() < speckle_prob:
-                img = add_speckle_noise(img)
-        elif i == 5:
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-        elif i == 6:
-            img = add_JPEG_noise(img)
-        elif i == 7:
-            img = add_blur(img, sf=sf)
-        elif i == 8:
-            img = add_resize(img, sf=sf)
-        elif i == 9:
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-        elif i == 10:
-            if random.random() < poisson_prob:
-                img = add_Poisson_noise(img)
-        elif i == 11:
-            if random.random() < speckle_prob:
-                img = add_speckle_noise(img)
-        elif i == 12:
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-        else:
-            print('check the shuffle!')
-
-    # resize to desired size
-    img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
-                     interpolation=random.choice([1, 2, 3]))
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf, lq_patchsize)
-
-    return img, hq
-
-
-if __name__ == '__main__':
-	print("hey")
-	img = util.imread_uint('utils/test.png', 3)
-	print(img)
-	img = util.uint2single(img)
-	print(img)
-	img = img[:448, :448]
-	h = img.shape[0] // 4
-	print("resizing to", h)
-	sf = 4
-	deg_fn = partial(degradation_bsrgan_variant, sf=sf)
-	for i in range(20):
-		print(i)
-		img_lq = deg_fn(img)
-		print(img_lq)
-		img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
-		print(img_lq.shape)
-		print("bicubic", img_lq_bicubic.shape)
-		print(img_hq.shape)
-		lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-		                        interpolation=0)
-		lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-		                        interpolation=0)
-		img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
-		util.imsave(img_concat, str(i) + '.png')
-
-

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/bracketparser2.js

@@ -1,2 +0,0 @@
-import BracketParser from './logic/bracketparser/bracketparser2/BracketParser.js';
-export default BracketParser;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sides/Factory.js

@@ -1,13 +0,0 @@
-import Sides from './Sides.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('sides', function (config) {
-    var gameObject = new Sides(this.scene, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.Sides', Sides);
-
-export default Sides;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/RemoveChildMethods.js

@@ -1,29 +0,0 @@
-import RemoveChild from '../basesizer/utils/RemoveChild.js';
-import ClearChildren from '../basesizer/utils/ClearChildren.js';
-
-const RemoveItem = Phaser.Utils.Array.Remove;
-
-export default {
-    remove(gameObject, destroyChild) {
-        if (this.getParentSizer(gameObject) !== this) {
-            return this;
-        }
-
-        RemoveItem(this.sizerChildren, gameObject);
-        RemoveChild.call(this, gameObject, destroyChild);
-        return this;
-    },
-
-    removeAll(destroyChild) {
-        for (var i = this.sizerChildren.length - 1; i >= 0; i--) {
-            this.remove(this.sizerChildren[i], destroyChild);
-        }
-        return this;
-    },
-
-    clear(destroyChild) {
-        this.sizerChildren.length = 0;
-        ClearChildren.call(this, destroyChild);
-        return this;
-    }
-}

spaces/Agusbs98/automatic-ecg-diagnosis/nets/layers.py

@@ -1,29 +0,0 @@
-
-import os, sys
-from libs import *
-
-class DSConv1d(nn.Module):
-    def __init__(self, 
-        in_channels, out_channels, 
-        kernel_size, padding = 0, stride = 1, 
-    ):
-        super(DSConv1d, self).__init__()
-        self.dw_conv = nn.Conv1d(
-            in_channels, in_channels, 
-            kernel_size = kernel_size, padding = padding, stride = stride, 
-            groups = in_channels, 
-            bias = False, 
-        )
-        self.pw_conv = nn.Conv1d(
-            in_channels, out_channels, 
-            kernel_size = 1, 
-            bias = False, 
-        )
-
-    def forward(self, 
-        input, 
-    ):
-        output = self.dw_conv(input)
-        output = self.pw_conv(output)
-
-        return output

spaces/AixiaGreyatt/QQsign/bin/unidbg-fetch-qsign.bat

@@ -1,89 +0,0 @@
-@rem
-@rem Copyright 2015 the original author or authors.
-@rem
-@rem Licensed under the Apache License, Version 2.0 (the "License");
-@rem you may not use this file except in compliance with the License.
-@rem You may obtain a copy of the License at
-@rem
-@rem      https://www.apache.org/licenses/LICENSE-2.0
-@rem
-@rem Unless required by applicable law or agreed to in writing, software
-@rem distributed under the License is distributed on an "AS IS" BASIS,
-@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-@rem See the License for the specific language governing permissions and
-@rem limitations under the License.
-@rem
-
-@if "%DEBUG%" == "" @echo off
-@rem ##########################################################################
-@rem
-@rem  unidbg-fetch-qsign startup script for Windows
-@rem
-@rem ##########################################################################
-
-@rem Set local scope for the variables with windows NT shell
-if "%OS%"=="Windows_NT" setlocal
-
-set DIRNAME=%~dp0
-if "%DIRNAME%" == "" set DIRNAME=.
-set APP_BASE_NAME=%~n0
-set APP_HOME=%DIRNAME%..
-
-@rem Resolve any "." and ".." in APP_HOME to make it shorter.
-for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi
-
-@rem Add default JVM options here. You can also use JAVA_OPTS and UNIDBG_FETCH_QSIGN_OPTS to pass JVM options to this script.
-set DEFAULT_JVM_OPTS=
-
-@rem Find java.exe
-if defined JAVA_HOME goto findJavaFromJavaHome
-
-set JAVA_EXE=java.exe
-%JAVA_EXE% -version >NUL 2>&1
-if "%ERRORLEVEL%" == "0" goto execute
-
-echo.
-echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:findJavaFromJavaHome
-set JAVA_HOME=%JAVA_HOME:"=%
-set JAVA_EXE=%JAVA_HOME%/bin/java.exe
-
-if exist "%JAVA_EXE%" goto execute
-
-echo.
-echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:execute
-@rem Setup the command line
-
-set CLASSPATH=%APP_HOME%\lib\unidbg-fetch-qsign-1.1.9.jar;%APP_HOME%\lib\unidbg-android-105.jar;%APP_HOME%\lib\ktor-server-content-negotiation-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-kotlinx-json-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-status-pages-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-netty-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-host-common-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-core-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-kotlinx-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-events-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-websockets-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-http-cio-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-http-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-network-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-utils-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-io-jvm-2.3.1.jar;%APP_HOME%\lib\kotlin-stdlib-jdk8-1.8.22.jar;%APP_HOME%\lib\kotlinx-serialization-json-jvm-1.5.1.jar;%APP_HOME%\lib\kotlinx-serialization-protobuf-jvm-1.5.1.jar;%APP_HOME%\lib\kotlinx-serialization-core-jvm-1.5.1.jar;%APP_HOME%\lib\logback-classic-1.2.11.jar;%APP_HOME%\lib\kotlinx-coroutines-jdk8-1.7.1.jar;%APP_HOME%\lib\kotlinx-coroutines-core-jvm-1.7.1.jar;%APP_HOME%\lib\kotlin-stdlib-jdk7-1.8.22.jar;%APP_HOME%\lib\kotlin-reflect-1.8.10.jar;%APP_HOME%\lib\kotlin-stdlib-1.8.22.jar;%APP_HOME%\lib\slf4j-api-1.7.36.jar;%APP_HOME%\lib\kotlin-stdlib-common-1.8.22.jar;%APP_HOME%\lib\config-1.4.2.jar;%APP_HOME%\lib\jansi-2.4.0.jar;%APP_HOME%\lib\netty-codec-http2-4.1.92.Final.jar;%APP_HOME%\lib\alpn-api-1.1.3.v20160715.jar;%APP_HOME%\lib\netty-transport-native-kqueue-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-native-epoll-4.1.92.Final.jar;%APP_HOME%\lib\logback-core-1.2.11.jar;%APP_HOME%\lib\annotations-23.0.0.jar;%APP_HOME%\lib\netty-codec-http-4.1.92.Final.jar;%APP_HOME%\lib\netty-handler-4.1.92.Final.jar;%APP_HOME%\lib\netty-codec-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-classes-kqueue-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-classes-epoll-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-native-unix-common-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-4.1.92.Final.jar;%APP_HOME%\lib\netty-buffer-4.1.92.Final.jar;%APP_HOME%\lib\netty-resolver-4.1.92.Final.jar;%APP_HOME%\lib\netty-common-4.1.92.Final.jar
-
-
-@rem Execute unidbg-fetch-qsign
-"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %UNIDBG_FETCH_QSIGN_OPTS%  -classpath "%CLASSPATH%" MainKt %*
-
-:end
-@rem End local scope for the variables with windows NT shell
-if "%ERRORLEVEL%"=="0" goto mainEnd
-
-:fail
-rem Set variable UNIDBG_FETCH_QSIGN_EXIT_CONSOLE if you need the _script_ return code instead of
-rem the _cmd.exe /c_ return code!
-if  not "" == "%UNIDBG_FETCH_QSIGN_EXIT_CONSOLE%" exit 1
-exit /b 1
-
-:mainEnd
-if "%OS%"=="Windows_NT" endlocal
-
-:omega

spaces/Aloento/9Nine-PITS/text/english.py

@@ -1,122 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-import re
-
-import eng_to_ipa as ipa
-from g2p_en import G2p
-from unidecode import unidecode
-
-from text.frontend import normalize_numbers
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-# Regular expression matching whitespace:
-g2p = G2p()
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('mrs', 'misess'),
-  ('mr', 'mister'),
-  ('dr', 'doctor'),
-  ('st', 'saint'),
-  ('co', 'company'),
-  ('jr', 'junior'),
-  ('maj', 'major'),
-  ('gen', 'general'),
-  ('drs', 'doctors'),
-  ('rev', 'reverend'),
-  ('lt', 'lieutenant'),
-  ('hon', 'honorable'),
-  ('sgt', 'sergeant'),
-  ('capt', 'captain'),
-  ('esq', 'esquire'),
-  ('ltd', 'limited'),
-  ('col', 'colonel'),
-  ('ft', 'fort'),
-]]
-
-# List of (ipa, ipa2) pairs
-_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-  ('r', 'ɹ'),
-  ('ʤ', 'dʒ'),
-  ('ʧ', 'tʃ')
-]]
-
-
-def expand_abbreviations(text):
-  for regex, replacement in _abbreviations:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def collapse_whitespace(text):
-  return re.sub(r'\s+', ' ', text)
-
-
-def mark_dark_l(text):
-  return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ' + x.group(1), text)
-
-
-def english_to_ipa(text):
-  text = text.replace("-", " ")
-  text = unidecode(text).lower()
-  text = expand_abbreviations(text)
-  text = normalize_numbers(text)
-
-  phonemes = ipa.convert(text)
-  phonemes = unrecognized_words_to_ipa(phonemes)
-  phonemes = collapse_whitespace(phonemes)
-
-  text = phonemes
-  text = mark_dark_l(text)
-
-  for regex, replacement in _ipa_to_ipa2:
-    text = re.sub(regex, replacement, text)
-
-  return text.replace('...', '…')
-
-
-def convert_to_ipa(phones):
-  eipa = ""
-  symbols = {"a": "ə", "ey": "eɪ", "aa": "ɑ", "ae": "æ", "ah": "ə", "ao": "ɔ",
-             "aw": "aʊ", "ay": "aɪ", "ch": "ʧ", "dh": "ð", "eh": "ɛ", "er": "ər",
-             "hh": "h", "ih": "ɪ", "jh": "ʤ", "ng": "ŋ", "ow": "oʊ", "oy": "ɔɪ",
-             "sh": "ʃ", "th": "θ", "uh": "ʊ", "uw": "u", "zh": "ʒ", "iy": "i", "y": "j"}
-
-  for ph in phones:
-    ph = ph.lower()
-
-    try:
-      if ph[-1] in "01234":
-        eipa += symbols[ph[:-1]]
-      else:
-        eipa += symbols[ph]
-    except:
-      eipa += ph
-
-  return eipa
-
-
-def unrecognized_words_to_ipa(text):
-  matches = re.findall(r'\s([\w|\']+\*)', text)
-
-  for word in matches:
-    ipa = convert_to_ipa(g2p(word))
-    text = text.replace(word, ipa)
-
-  matches = re.findall(r'^([\w|\']+\*)', text)
-
-  for word in matches:
-    ipa = convert_to_ipa(g2p(word))
-    text = text.replace(word, ipa)
-
-  return text

spaces/Alpaca233/SadTalker/src/face3d/models/networks.py

@@ -1,521 +0,0 @@
-"""This script defines deep neural networks for Deep3DFaceRecon_pytorch
-"""
-
-import os
-import numpy as np
-import torch.nn.functional as F
-from torch.nn import init
-import functools
-from torch.optim import lr_scheduler
-import torch
-from torch import Tensor
-import torch.nn as nn
-try:
-    from torch.hub import load_state_dict_from_url
-except ImportError:
-    from torch.utils.model_zoo import load_url as load_state_dict_from_url
-from typing import Type, Any, Callable, Union, List, Optional
-from .arcface_torch.backbones import get_model
-from kornia.geometry import warp_affine
-
-def resize_n_crop(image, M, dsize=112):
-    # image: (b, c, h, w)
-    # M   :  (b, 2, 3)
-    return warp_affine(image, M, dsize=(dsize, dsize), align_corners=True)
-
-def filter_state_dict(state_dict, remove_name='fc'):
-    new_state_dict = {}
-    for key in state_dict:
-        if remove_name in key:
-            continue
-        new_state_dict[key] = state_dict[key]
-    return new_state_dict
-
-def get_scheduler(optimizer, opt):
-    """Return a learning rate scheduler
-
-    Parameters:
-        optimizer          -- the optimizer of the network
-        opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions．　
-                              opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine
-
-    For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers.
-    See https://pytorch.org/docs/stable/optim.html for more details.
-    """
-    if opt.lr_policy == 'linear':
-        def lambda_rule(epoch):
-            lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs + 1)
-            return lr_l
-        scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
-    elif opt.lr_policy == 'step':
-        scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_epochs, gamma=0.2)
-    elif opt.lr_policy == 'plateau':
-        scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
-    elif opt.lr_policy == 'cosine':
-        scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0)
-    else:
-        return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)
-    return scheduler
-
-
-def define_net_recon(net_recon, use_last_fc=False, init_path=None):
-    return ReconNetWrapper(net_recon, use_last_fc=use_last_fc, init_path=init_path)
-
-def define_net_recog(net_recog, pretrained_path=None):
-    net = RecogNetWrapper(net_recog=net_recog, pretrained_path=pretrained_path)
-    net.eval()
-    return net
-
-class ReconNetWrapper(nn.Module):
-    fc_dim=257
-    def __init__(self, net_recon, use_last_fc=False, init_path=None):
-        super(ReconNetWrapper, self).__init__()
-        self.use_last_fc = use_last_fc
-        if net_recon not in func_dict:
-            return  NotImplementedError('network [%s] is not implemented', net_recon)
-        func, last_dim = func_dict[net_recon]
-        backbone = func(use_last_fc=use_last_fc, num_classes=self.fc_dim)
-        if init_path and os.path.isfile(init_path):
-            state_dict = filter_state_dict(torch.load(init_path, map_location='cpu'))
-            backbone.load_state_dict(state_dict)
-            print("loading init net_recon %s from %s" %(net_recon, init_path))
-        self.backbone = backbone
-        if not use_last_fc:
-            self.final_layers = nn.ModuleList([
-                conv1x1(last_dim, 80, bias=True), # id layer
-                conv1x1(last_dim, 64, bias=True), # exp layer
-                conv1x1(last_dim, 80, bias=True), # tex layer
-                conv1x1(last_dim, 3, bias=True),  # angle layer
-                conv1x1(last_dim, 27, bias=True), # gamma layer
-                conv1x1(last_dim, 2, bias=True),  # tx, ty
-                conv1x1(last_dim, 1, bias=True)   # tz
-            ])
-            for m in self.final_layers:
-                nn.init.constant_(m.weight, 0.)
-                nn.init.constant_(m.bias, 0.)
-
-    def forward(self, x):
-        x = self.backbone(x)
-        if not self.use_last_fc:
-            output = []
-            for layer in self.final_layers:
-                output.append(layer(x))
-            x = torch.flatten(torch.cat(output, dim=1), 1)
-        return x
-
-
-class RecogNetWrapper(nn.Module):
-    def __init__(self, net_recog, pretrained_path=None, input_size=112):
-        super(RecogNetWrapper, self).__init__()
-        net = get_model(name=net_recog, fp16=False)
-        if pretrained_path:
-            state_dict = torch.load(pretrained_path, map_location='cpu')
-            net.load_state_dict(state_dict)
-            print("loading pretrained net_recog %s from %s" %(net_recog, pretrained_path))
-        for param in net.parameters():
-            param.requires_grad = False
-        self.net = net
-        self.preprocess = lambda x: 2 * x - 1
-        self.input_size=input_size
-        
-    def forward(self, image, M):
-        image = self.preprocess(resize_n_crop(image, M, self.input_size))
-        id_feature = F.normalize(self.net(image), dim=-1, p=2)
-        return id_feature
-
-
-# adapted from https://github.com/pytorch/vision/edit/master/torchvision/models/resnet.py
-__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
-           'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
-           'wide_resnet50_2', 'wide_resnet101_2']
-
-
-model_urls = {
-    'resnet18': 'https://download.pytorch.org/models/resnet18-f37072fd.pth',
-    'resnet34': 'https://download.pytorch.org/models/resnet34-b627a593.pth',
-    'resnet50': 'https://download.pytorch.org/models/resnet50-0676ba61.pth',
-    'resnet101': 'https://download.pytorch.org/models/resnet101-63fe2227.pth',
-    'resnet152': 'https://download.pytorch.org/models/resnet152-394f9c45.pth',
-    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
-    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
-    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
-    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
-}
-
-
-def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=dilation, groups=groups, bias=False, dilation=dilation)
-
-
-def conv1x1(in_planes: int, out_planes: int, stride: int = 1, bias: bool = False) -> nn.Conv2d:
-    """1x1 convolution"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=bias)
-
-
-class BasicBlock(nn.Module):
-    expansion: int = 1
-
-    def __init__(
-        self,
-        inplanes: int,
-        planes: int,
-        stride: int = 1,
-        downsample: Optional[nn.Module] = None,
-        groups: int = 1,
-        base_width: int = 64,
-        dilation: int = 1,
-        norm_layer: Optional[Callable[..., nn.Module]] = None
-    ) -> None:
-        super(BasicBlock, self).__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        if groups != 1 or base_width != 64:
-            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
-        if dilation > 1:
-            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
-        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = norm_layer(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = norm_layer(planes)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x: Tensor) -> Tensor:
-        identity = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
-    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
-    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
-    # This variant is also known as ResNet V1.5 and improves accuracy according to
-    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
-
-    expansion: int = 4
-
-    def __init__(
-        self,
-        inplanes: int,
-        planes: int,
-        stride: int = 1,
-        downsample: Optional[nn.Module] = None,
-        groups: int = 1,
-        base_width: int = 64,
-        dilation: int = 1,
-        norm_layer: Optional[Callable[..., nn.Module]] = None
-    ) -> None:
-        super(Bottleneck, self).__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        width = int(planes * (base_width / 64.)) * groups
-        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
-        self.conv1 = conv1x1(inplanes, width)
-        self.bn1 = norm_layer(width)
-        self.conv2 = conv3x3(width, width, stride, groups, dilation)
-        self.bn2 = norm_layer(width)
-        self.conv3 = conv1x1(width, planes * self.expansion)
-        self.bn3 = norm_layer(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x: Tensor) -> Tensor:
-        identity = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-
-        return out
-
-
-class ResNet(nn.Module):
-
-    def __init__(
-        self,
-        block: Type[Union[BasicBlock, Bottleneck]],
-        layers: List[int],
-        num_classes: int = 1000,
-        zero_init_residual: bool = False,
-        use_last_fc: bool = False,
-        groups: int = 1,
-        width_per_group: int = 64,
-        replace_stride_with_dilation: Optional[List[bool]] = None,
-        norm_layer: Optional[Callable[..., nn.Module]] = None
-    ) -> None:
-        super(ResNet, self).__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        self._norm_layer = norm_layer
-
-        self.inplanes = 64
-        self.dilation = 1
-        if replace_stride_with_dilation is None:
-            # each element in the tuple indicates if we should replace
-            # the 2x2 stride with a dilated convolution instead
-            replace_stride_with_dilation = [False, False, False]
-        if len(replace_stride_with_dilation) != 3:
-            raise ValueError("replace_stride_with_dilation should be None "
-                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
-        self.use_last_fc = use_last_fc
-        self.groups = groups
-        self.base_width = width_per_group
-        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
-                               bias=False)
-        self.bn1 = norm_layer(self.inplanes)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
-                                       dilate=replace_stride_with_dilation[0])
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
-                                       dilate=replace_stride_with_dilation[1])
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
-                                       dilate=replace_stride_with_dilation[2])
-        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
-        
-        if self.use_last_fc:
-            self.fc = nn.Linear(512 * block.expansion, num_classes)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-
-
-        # Zero-initialize the last BN in each residual branch,
-        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
-        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
-        if zero_init_residual:
-            for m in self.modules():
-                if isinstance(m, Bottleneck):
-                    nn.init.constant_(m.bn3.weight, 0)  # type: ignore[arg-type]
-                elif isinstance(m, BasicBlock):
-                    nn.init.constant_(m.bn2.weight, 0)  # type: ignore[arg-type]
-
-    def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int,
-                    stride: int = 1, dilate: bool = False) -> nn.Sequential:
-        norm_layer = self._norm_layer
-        downsample = None
-        previous_dilation = self.dilation
-        if dilate:
-            self.dilation *= stride
-            stride = 1
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                conv1x1(self.inplanes, planes * block.expansion, stride),
-                norm_layer(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
-                            self.base_width, previous_dilation, norm_layer))
-        self.inplanes = planes * block.expansion
-        for _ in range(1, blocks):
-            layers.append(block(self.inplanes, planes, groups=self.groups,
-                                base_width=self.base_width, dilation=self.dilation,
-                                norm_layer=norm_layer))
-
-        return nn.Sequential(*layers)
-
-    def _forward_impl(self, x: Tensor) -> Tensor:
-        # See note [TorchScript super()]
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-
-        x = self.avgpool(x)
-        if self.use_last_fc:
-            x = torch.flatten(x, 1)
-            x = self.fc(x)
-        return x
-
-    def forward(self, x: Tensor) -> Tensor:
-        return self._forward_impl(x)
-
-
-def _resnet(
-    arch: str,
-    block: Type[Union[BasicBlock, Bottleneck]],
-    layers: List[int],
-    pretrained: bool,
-    progress: bool,
-    **kwargs: Any
-) -> ResNet:
-    model = ResNet(block, layers, **kwargs)
-    if pretrained:
-        state_dict = load_state_dict_from_url(model_urls[arch],
-                                              progress=progress)
-        model.load_state_dict(state_dict)
-    return model
-
-
-def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNet-18 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
-                   **kwargs)
-
-
-def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNet-34 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
-                   **kwargs)
-
-
-def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNet-50 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
-                   **kwargs)
-
-
-def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNet-101 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
-                   **kwargs)
-
-
-def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNet-152 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
-                   **kwargs)
-
-
-def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNeXt-50 32x4d model from
-    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    kwargs['groups'] = 32
-    kwargs['width_per_group'] = 4
-    return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
-                   pretrained, progress, **kwargs)
-
-
-def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""ResNeXt-101 32x8d model from
-    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    kwargs['groups'] = 32
-    kwargs['width_per_group'] = 8
-    return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
-                   pretrained, progress, **kwargs)
-
-
-def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""Wide ResNet-50-2 model from
-    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
-
-    The model is the same as ResNet except for the bottleneck number of channels
-    which is twice larger in every block. The number of channels in outer 1x1
-    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
-    channels, and in Wide ResNet-50-2 has 2048-1024-2048.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    kwargs['width_per_group'] = 64 * 2
-    return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
-                   pretrained, progress, **kwargs)
-
-
-def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
-    r"""Wide ResNet-101-2 model from
-    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
-
-    The model is the same as ResNet except for the bottleneck number of channels
-    which is twice larger in every block. The number of channels in outer 1x1
-    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
-    channels, and in Wide ResNet-50-2 has 2048-1024-2048.
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-        progress (bool): If True, displays a progress bar of the download to stderr
-    """
-    kwargs['width_per_group'] = 64 * 2
-    return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
-                   pretrained, progress, **kwargs)
-
-
-func_dict = {
-    'resnet18': (resnet18, 512),
-    'resnet50': (resnet50, 2048)
-}

spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/utils/loading.py

@@ -1,6 +0,0 @@
-import yaml
-
-
-def load_yaml(path):
-    with open(path, "rt") as f:
-        return yaml.safe_load(f)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_v_pred.py

@@ -1,540 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import gc
-import time
-import unittest
-
-import numpy as np
-import torch
-from huggingface_hub import hf_hub_download
-from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
-
-from diffusers import (
-    AutoencoderKL,
-    DDIMScheduler,
-    DPMSolverMultistepScheduler,
-    EulerDiscreteScheduler,
-    StableDiffusionPipeline,
-    UNet2DConditionModel,
-)
-from diffusers.models.attention_processor import AttnProcessor
-from diffusers.utils import load_numpy, slow, torch_device
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
-
-
-enable_full_determinism()
-
-
-class StableDiffusion2VPredictionPipelineFastTests(unittest.TestCase):
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    @property
-    def dummy_cond_unet(self):
-        torch.manual_seed(0)
-        model = UNet2DConditionModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=4,
-            out_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=32,
-            # SD2-specific config below
-            attention_head_dim=(2, 4),
-            use_linear_projection=True,
-        )
-        return model
-
-    @property
-    def dummy_vae(self):
-        torch.manual_seed(0)
-        model = AutoencoderKL(
-            block_out_channels=[32, 64],
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            latent_channels=4,
-            sample_size=128,
-        )
-        return model
-
-    @property
-    def dummy_text_encoder(self):
-        torch.manual_seed(0)
-        config = CLIPTextConfig(
-            bos_token_id=0,
-            eos_token_id=2,
-            hidden_size=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            pad_token_id=1,
-            vocab_size=1000,
-            # SD2-specific config below
-            hidden_act="gelu",
-            projection_dim=64,
-        )
-        return CLIPTextModel(config)
-
-    def test_stable_diffusion_v_pred_ddim(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        unet = self.dummy_cond_unet
-        scheduler = DDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False,
-            prediction_type="v_prediction",
-        )
-
-        vae = self.dummy_vae
-        bert = self.dummy_text_encoder
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        # make sure here that pndm scheduler skips prk
-        sd_pipe = StableDiffusionPipeline(
-            unet=unet,
-            scheduler=scheduler,
-            vae=vae,
-            text_encoder=bert,
-            tokenizer=tokenizer,
-            safety_checker=None,
-            feature_extractor=None,
-            requires_safety_checker=False,
-        )
-        sd_pipe = sd_pipe.to(device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-
-        generator = torch.Generator(device=device).manual_seed(0)
-        output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
-        image = output.images
-
-        generator = torch.Generator(device=device).manual_seed(0)
-        image_from_tuple = sd_pipe(
-            [prompt],
-            generator=generator,
-            guidance_scale=6.0,
-            num_inference_steps=2,
-            output_type="np",
-            return_dict=False,
-        )[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.6569, 0.6525, 0.5142, 0.4968, 0.4923, 0.4601, 0.4996, 0.5041, 0.4544])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_stable_diffusion_v_pred_k_euler(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        unet = self.dummy_cond_unet
-        scheduler = EulerDiscreteScheduler(
-            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", prediction_type="v_prediction"
-        )
-        vae = self.dummy_vae
-        bert = self.dummy_text_encoder
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        # make sure here that pndm scheduler skips prk
-        sd_pipe = StableDiffusionPipeline(
-            unet=unet,
-            scheduler=scheduler,
-            vae=vae,
-            text_encoder=bert,
-            tokenizer=tokenizer,
-            safety_checker=None,
-            feature_extractor=None,
-            requires_safety_checker=False,
-        )
-        sd_pipe = sd_pipe.to(device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.Generator(device=device).manual_seed(0)
-        output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
-
-        image = output.images
-
-        generator = torch.Generator(device=device).manual_seed(0)
-        image_from_tuple = sd_pipe(
-            [prompt],
-            generator=generator,
-            guidance_scale=6.0,
-            num_inference_steps=2,
-            output_type="np",
-            return_dict=False,
-        )[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5644, 0.6514, 0.5190, 0.5663, 0.5287, 0.4953, 0.5430, 0.5243, 0.4778])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
-    @unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
-    def test_stable_diffusion_v_pred_fp16(self):
-        """Test that stable diffusion v-prediction works with fp16"""
-        unet = self.dummy_cond_unet
-        scheduler = DDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False,
-            prediction_type="v_prediction",
-        )
-        vae = self.dummy_vae
-        bert = self.dummy_text_encoder
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        # put models in fp16
-        unet = unet.half()
-        vae = vae.half()
-        bert = bert.half()
-
-        # make sure here that pndm scheduler skips prk
-        sd_pipe = StableDiffusionPipeline(
-            unet=unet,
-            scheduler=scheduler,
-            vae=vae,
-            text_encoder=bert,
-            tokenizer=tokenizer,
-            safety_checker=None,
-            feature_extractor=None,
-            requires_safety_checker=False,
-        )
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.manual_seed(0)
-        image = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="np").images
-
-        assert image.shape == (1, 64, 64, 3)
-
-
-@slow
-@require_torch_gpu
-class StableDiffusion2VPredictionPipelineIntegrationTests(unittest.TestCase):
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_stable_diffusion_v_pred_default(self):
-        sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2")
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.enable_attention_slicing()
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.manual_seed(0)
-        output = sd_pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=20, output_type="np")
-
-        image = output.images
-        image_slice = image[0, 253:256, 253:256, -1]
-
-        assert image.shape == (1, 768, 768, 3)
-        expected_slice = np.array([0.1868, 0.1922, 0.1527, 0.1921, 0.1908, 0.1624, 0.1779, 0.1652, 0.1734])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_stable_diffusion_v_pred_upcast_attention(self):
-        sd_pipe = StableDiffusionPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
-        )
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.enable_attention_slicing()
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.manual_seed(0)
-        output = sd_pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=20, output_type="np")
-
-        image = output.images
-        image_slice = image[0, 253:256, 253:256, -1]
-
-        assert image.shape == (1, 768, 768, 3)
-        expected_slice = np.array([0.4209, 0.4087, 0.4097, 0.4209, 0.3860, 0.4329, 0.4280, 0.4324, 0.4187])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2
-
-    def test_stable_diffusion_v_pred_euler(self):
-        scheduler = EulerDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-2", subfolder="scheduler")
-        sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", scheduler=scheduler)
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.enable_attention_slicing()
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.manual_seed(0)
-
-        output = sd_pipe([prompt], generator=generator, num_inference_steps=5, output_type="numpy")
-        image = output.images
-
-        image_slice = image[0, 253:256, 253:256, -1]
-
-        assert image.shape == (1, 768, 768, 3)
-        expected_slice = np.array([0.1781, 0.1695, 0.1661, 0.1705, 0.1588, 0.1699, 0.2005, 0.1589, 0.1677])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_stable_diffusion_v_pred_dpm(self):
-        """
-        TODO: update this test after making DPM compatible with V-prediction!
-        """
-        scheduler = DPMSolverMultistepScheduler.from_pretrained(
-            "stabilityai/stable-diffusion-2", subfolder="scheduler"
-        )
-        sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", scheduler=scheduler)
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.enable_attention_slicing()
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "a photograph of an astronaut riding a horse"
-        generator = torch.manual_seed(0)
-        image = sd_pipe(
-            [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=5, output_type="numpy"
-        ).images
-
-        image_slice = image[0, 253:256, 253:256, -1]
-        assert image.shape == (1, 768, 768, 3)
-        expected_slice = np.array([0.3303, 0.3184, 0.3291, 0.3300, 0.3256, 0.3113, 0.2965, 0.3134, 0.3192])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_stable_diffusion_attention_slicing_v_pred(self):
-        torch.cuda.reset_peak_memory_stats()
-        model_id = "stabilityai/stable-diffusion-2"
-        pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-
-        prompt = "a photograph of an astronaut riding a horse"
-
-        # make attention efficient
-        pipe.enable_attention_slicing()
-        generator = torch.manual_seed(0)
-        output_chunked = pipe(
-            [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
-        )
-        image_chunked = output_chunked.images
-
-        mem_bytes = torch.cuda.max_memory_allocated()
-        torch.cuda.reset_peak_memory_stats()
-        # make sure that less than 5.5 GB is allocated
-        assert mem_bytes < 5.5 * 10**9
-
-        # disable slicing
-        pipe.disable_attention_slicing()
-        generator = torch.manual_seed(0)
-        output = pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy")
-        image = output.images
-
-        # make sure that more than 5.5 GB is allocated
-        mem_bytes = torch.cuda.max_memory_allocated()
-        assert mem_bytes > 5.5 * 10**9
-        assert np.abs(image_chunked.flatten() - image.flatten()).max() < 1e-3
-
-    def test_stable_diffusion_text2img_pipeline_v_pred_default(self):
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
-            "sd2-text2img/astronaut_riding_a_horse_v_pred.npy"
-        )
-
-        pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2")
-        pipe.to(torch_device)
-        pipe.enable_attention_slicing()
-        pipe.set_progress_bar_config(disable=None)
-
-        prompt = "astronaut riding a horse"
-
-        generator = torch.manual_seed(0)
-        output = pipe(prompt=prompt, guidance_scale=7.5, generator=generator, output_type="np")
-        image = output.images[0]
-
-        assert image.shape == (768, 768, 3)
-        assert np.abs(expected_image - image).max() < 9e-1
-
-    def test_stable_diffusion_text2img_pipeline_unflawed(self):
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
-            "sd2-text2img/lion_galaxy.npy"
-        )
-
-        pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")
-        pipe.scheduler = DDIMScheduler.from_config(
-            pipe.scheduler.config, timestep_spacing="trailing", rescale_betas_zero_snr=True
-        )
-        pipe.to(torch_device)
-        pipe.enable_attention_slicing()
-        pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k"
-
-        generator = torch.manual_seed(0)
-        output = pipe(prompt=prompt, guidance_scale=7.5, guidance_rescale=0.7, generator=generator, output_type="np")
-        image = output.images[0]
-
-        assert image.shape == (768, 768, 3)
-        assert np.abs(expected_image - image).max() < 5e-1
-
-    def test_stable_diffusion_text2img_pipeline_v_pred_fp16(self):
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
-            "sd2-text2img/astronaut_riding_a_horse_v_pred_fp16.npy"
-        )
-
-        pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", torch_dtype=torch.float16)
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-
-        prompt = "astronaut riding a horse"
-
-        generator = torch.manual_seed(0)
-        output = pipe(prompt=prompt, guidance_scale=7.5, generator=generator, output_type="np")
-        image = output.images[0]
-
-        assert image.shape == (768, 768, 3)
-        assert np.abs(expected_image - image).max() < 7.5e-1
-
-    def test_download_local(self):
-        filename = hf_hub_download("stabilityai/stable-diffusion-2-1", filename="v2-1_768-ema-pruned.safetensors")
-
-        pipe = StableDiffusionPipeline.from_single_file(filename, torch_dtype=torch.float16)
-        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-        pipe.to("cuda")
-
-        image_out = pipe("test", num_inference_steps=1, output_type="np").images[0]
-
-        assert image_out.shape == (768, 768, 3)
-
-    def test_download_ckpt_diff_format_is_same(self):
-        single_file_path = (
-            "https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.safetensors"
-        )
-
-        pipe_single = StableDiffusionPipeline.from_single_file(single_file_path)
-        pipe_single.scheduler = DDIMScheduler.from_config(pipe_single.scheduler.config)
-        pipe_single.unet.set_attn_processor(AttnProcessor())
-        pipe_single.to("cuda")
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        image_ckpt = pipe_single("a turtle", num_inference_steps=5, generator=generator, output_type="np").images[0]
-
-        pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")
-        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-        pipe.unet.set_attn_processor(AttnProcessor())
-        pipe.to("cuda")
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        image = pipe("a turtle", num_inference_steps=5, generator=generator, output_type="np").images[0]
-
-        assert np.max(np.abs(image - image_ckpt)) < 1e-3
-
-    def test_stable_diffusion_text2img_intermediate_state_v_pred(self):
-        number_of_steps = 0
-
-        def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
-            test_callback_fn.has_been_called = True
-            nonlocal number_of_steps
-            number_of_steps += 1
-            if step == 0:
-                latents = latents.detach().cpu().numpy()
-                assert latents.shape == (1, 4, 96, 96)
-                latents_slice = latents[0, -3:, -3:, -1]
-                expected_slice = np.array([0.7749, 0.0325, 0.5088, 0.1619, 0.3372, 0.3667, -0.5186, 0.6860, 1.4326])
-
-                assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
-            elif step == 19:
-                latents = latents.detach().cpu().numpy()
-                assert latents.shape == (1, 4, 96, 96)
-                latents_slice = latents[0, -3:, -3:, -1]
-                expected_slice = np.array([1.3887, 1.0273, 1.7266, 0.0726, 0.6611, 0.1598, -1.0547, 0.1522, 0.0227])
-
-                assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
-
-        test_callback_fn.has_been_called = False
-
-        pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", torch_dtype=torch.float16)
-        pipe = pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing()
-
-        prompt = "Andromeda galaxy in a bottle"
-
-        generator = torch.manual_seed(0)
-        pipe(
-            prompt=prompt,
-            num_inference_steps=20,
-            guidance_scale=7.5,
-            generator=generator,
-            callback=test_callback_fn,
-            callback_steps=1,
-        )
-        assert test_callback_fn.has_been_called
-        assert number_of_steps == 20
-
-    def test_stable_diffusion_low_cpu_mem_usage_v_pred(self):
-        pipeline_id = "stabilityai/stable-diffusion-2"
-
-        start_time = time.time()
-        pipeline_low_cpu_mem_usage = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16)
-        pipeline_low_cpu_mem_usage.to(torch_device)
-        low_cpu_mem_usage_time = time.time() - start_time
-
-        start_time = time.time()
-        _ = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16, low_cpu_mem_usage=False)
-        normal_load_time = time.time() - start_time
-
-        assert 2 * low_cpu_mem_usage_time < normal_load_time
-
-    def test_stable_diffusion_pipeline_with_sequential_cpu_offloading_v_pred(self):
-        torch.cuda.empty_cache()
-        torch.cuda.reset_max_memory_allocated()
-        torch.cuda.reset_peak_memory_stats()
-
-        pipeline_id = "stabilityai/stable-diffusion-2"
-        prompt = "Andromeda galaxy in a bottle"
-
-        pipeline = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16)
-        pipeline = pipeline.to(torch_device)
-        pipeline.enable_attention_slicing(1)
-        pipeline.enable_sequential_cpu_offload()
-
-        generator = torch.manual_seed(0)
-        _ = pipeline(prompt, generator=generator, num_inference_steps=5)
-
-        mem_bytes = torch.cuda.max_memory_allocated()
-        # make sure that less than 2.8 GB is allocated
-        assert mem_bytes < 2.8 * 10**9

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_scheduler_unclip.py

@@ -1,137 +0,0 @@
-import torch
-
-from diffusers import UnCLIPScheduler
-
-from .test_schedulers import SchedulerCommonTest
-
-
-# UnCLIPScheduler is a modified DDPMScheduler with a subset of the configuration.
-class UnCLIPSchedulerTest(SchedulerCommonTest):
-    scheduler_classes = (UnCLIPScheduler,)
-
-    def get_scheduler_config(self, **kwargs):
-        config = {
-            "num_train_timesteps": 1000,
-            "variance_type": "fixed_small_log",
-            "clip_sample": True,
-            "clip_sample_range": 1.0,
-            "prediction_type": "epsilon",
-        }
-
-        config.update(**kwargs)
-        return config
-
-    def test_timesteps(self):
-        for timesteps in [1, 5, 100, 1000]:
-            self.check_over_configs(num_train_timesteps=timesteps)
-
-    def test_variance_type(self):
-        for variance in ["fixed_small_log", "learned_range"]:
-            self.check_over_configs(variance_type=variance)
-
-    def test_clip_sample(self):
-        for clip_sample in [True, False]:
-            self.check_over_configs(clip_sample=clip_sample)
-
-    def test_clip_sample_range(self):
-        for clip_sample_range in [1, 5, 10, 20]:
-            self.check_over_configs(clip_sample_range=clip_sample_range)
-
-    def test_prediction_type(self):
-        for prediction_type in ["epsilon", "sample"]:
-            self.check_over_configs(prediction_type=prediction_type)
-
-    def test_time_indices(self):
-        for time_step in [0, 500, 999]:
-            for prev_timestep in [None, 5, 100, 250, 500, 750]:
-                if prev_timestep is not None and prev_timestep >= time_step:
-                    continue
-
-                self.check_over_forward(time_step=time_step, prev_timestep=prev_timestep)
-
-    def test_variance_fixed_small_log(self):
-        scheduler_class = self.scheduler_classes[0]
-        scheduler_config = self.get_scheduler_config(variance_type="fixed_small_log")
-        scheduler = scheduler_class(**scheduler_config)
-
-        assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0000e-10)) < 1e-5
-        assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0549625)) < 1e-5
-        assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.9994987)) < 1e-5
-
-    def test_variance_learned_range(self):
-        scheduler_class = self.scheduler_classes[0]
-        scheduler_config = self.get_scheduler_config(variance_type="learned_range")
-        scheduler = scheduler_class(**scheduler_config)
-
-        predicted_variance = 0.5
-
-        assert scheduler._get_variance(1, predicted_variance=predicted_variance) - -10.1712790 < 1e-5
-        assert scheduler._get_variance(487, predicted_variance=predicted_variance) - -5.7998052 < 1e-5
-        assert scheduler._get_variance(999, predicted_variance=predicted_variance) - -0.0010011 < 1e-5
-
-    def test_full_loop(self):
-        scheduler_class = self.scheduler_classes[0]
-        scheduler_config = self.get_scheduler_config()
-        scheduler = scheduler_class(**scheduler_config)
-
-        timesteps = scheduler.timesteps
-
-        model = self.dummy_model()
-        sample = self.dummy_sample_deter
-        generator = torch.manual_seed(0)
-
-        for i, t in enumerate(timesteps):
-            # 1. predict noise residual
-            residual = model(sample, t)
-
-            # 2. predict previous mean of sample x_t-1
-            pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample
-
-            sample = pred_prev_sample
-
-        result_sum = torch.sum(torch.abs(sample))
-        result_mean = torch.mean(torch.abs(sample))
-
-        assert abs(result_sum.item() - 252.2682495) < 1e-2
-        assert abs(result_mean.item() - 0.3284743) < 1e-3
-
-    def test_full_loop_skip_timesteps(self):
-        scheduler_class = self.scheduler_classes[0]
-        scheduler_config = self.get_scheduler_config()
-        scheduler = scheduler_class(**scheduler_config)
-
-        scheduler.set_timesteps(25)
-
-        timesteps = scheduler.timesteps
-
-        model = self.dummy_model()
-        sample = self.dummy_sample_deter
-        generator = torch.manual_seed(0)
-
-        for i, t in enumerate(timesteps):
-            # 1. predict noise residual
-            residual = model(sample, t)
-
-            if i + 1 == timesteps.shape[0]:
-                prev_timestep = None
-            else:
-                prev_timestep = timesteps[i + 1]
-
-            # 2. predict previous mean of sample x_t-1
-            pred_prev_sample = scheduler.step(
-                residual, t, sample, prev_timestep=prev_timestep, generator=generator
-            ).prev_sample
-
-            sample = pred_prev_sample
-
-        result_sum = torch.sum(torch.abs(sample))
-        result_mean = torch.mean(torch.abs(sample))
-
-        assert abs(result_sum.item() - 258.2044983) < 1e-2
-        assert abs(result_mean.item() - 0.3362038) < 1e-3
-
-    def test_trained_betas(self):
-        pass
-
-    def test_add_noise_device(self):
-        pass

spaces/Andy1621/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py

@@ -1,45 +0,0 @@
-_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
-    backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'))
-# use caffe img_norm
-img_norm_cfg = dict(
-    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='LoadAnnotations',
-        with_bbox=True,
-        with_mask=True,
-        poly2mask=False),
-    dict(
-        type='Resize',
-        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
-                   (1333, 768), (1333, 800)],
-        multiscale_mode='value',
-        keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))

spaces/Andy1621/uniformer_image_detection/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py

@@ -1,14 +0,0 @@
-_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'))

spaces/Andy1621/uniformer_image_detection/tools/analysis_tools/robustness_eval.py

@@ -1,250 +0,0 @@
-import os.path as osp
-from argparse import ArgumentParser
-
-import mmcv
-import numpy as np
-
-
-def print_coco_results(results):
-
-    def _print(result, ap=1, iouThr=None, areaRng='all', maxDets=100):
-        titleStr = 'Average Precision' if ap == 1 else 'Average Recall'
-        typeStr = '(AP)' if ap == 1 else '(AR)'
-        iouStr = '0.50:0.95' \
-            if iouThr is None else f'{iouThr:0.2f}'
-        iStr = f' {titleStr:<18} {typeStr} @[ IoU={iouStr:<9} | '
-        iStr += f'area={areaRng:>6s} | maxDets={maxDets:>3d} ] = {result:0.3f}'
-        print(iStr)
-
-    stats = np.zeros((12, ))
-    stats[0] = _print(results[0], 1)
-    stats[1] = _print(results[1], 1, iouThr=.5)
-    stats[2] = _print(results[2], 1, iouThr=.75)
-    stats[3] = _print(results[3], 1, areaRng='small')
-    stats[4] = _print(results[4], 1, areaRng='medium')
-    stats[5] = _print(results[5], 1, areaRng='large')
-    stats[6] = _print(results[6], 0, maxDets=1)
-    stats[7] = _print(results[7], 0, maxDets=10)
-    stats[8] = _print(results[8], 0)
-    stats[9] = _print(results[9], 0, areaRng='small')
-    stats[10] = _print(results[10], 0, areaRng='medium')
-    stats[11] = _print(results[11], 0, areaRng='large')
-
-
-def get_coco_style_results(filename,
-                           task='bbox',
-                           metric=None,
-                           prints='mPC',
-                           aggregate='benchmark'):
-
-    assert aggregate in ['benchmark', 'all']
-
-    if prints == 'all':
-        prints = ['P', 'mPC', 'rPC']
-    elif isinstance(prints, str):
-        prints = [prints]
-    for p in prints:
-        assert p in ['P', 'mPC', 'rPC']
-
-    if metric is None:
-        metrics = [
-            'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10', 'AR100',
-            'ARs', 'ARm', 'ARl'
-        ]
-    elif isinstance(metric, list):
-        metrics = metric
-    else:
-        metrics = [metric]
-
-    for metric_name in metrics:
-        assert metric_name in [
-            'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10', 'AR100',
-            'ARs', 'ARm', 'ARl'
-        ]
-
-    eval_output = mmcv.load(filename)
-
-    num_distortions = len(list(eval_output.keys()))
-    results = np.zeros((num_distortions, 6, len(metrics)), dtype='float32')
-
-    for corr_i, distortion in enumerate(eval_output):
-        for severity in eval_output[distortion]:
-            for metric_j, metric_name in enumerate(metrics):
-                mAP = eval_output[distortion][severity][task][metric_name]
-                results[corr_i, severity, metric_j] = mAP
-
-    P = results[0, 0, :]
-    if aggregate == 'benchmark':
-        mPC = np.mean(results[:15, 1:, :], axis=(0, 1))
-    else:
-        mPC = np.mean(results[:, 1:, :], axis=(0, 1))
-    rPC = mPC / P
-
-    print(f'\nmodel: {osp.basename(filename)}')
-    if metric is None:
-        if 'P' in prints:
-            print(f'Performance on Clean Data [P] ({task})')
-            print_coco_results(P)
-        if 'mPC' in prints:
-            print(f'Mean Performance under Corruption [mPC] ({task})')
-            print_coco_results(mPC)
-        if 'rPC' in prints:
-            print(f'Relative Performance under Corruption [rPC] ({task})')
-            print_coco_results(rPC)
-    else:
-        if 'P' in prints:
-            print(f'Performance on Clean Data [P] ({task})')
-            for metric_i, metric_name in enumerate(metrics):
-                print(f'{metric_name:5} =  {P[metric_i]:0.3f}')
-        if 'mPC' in prints:
-            print(f'Mean Performance under Corruption [mPC] ({task})')
-            for metric_i, metric_name in enumerate(metrics):
-                print(f'{metric_name:5} =  {mPC[metric_i]:0.3f}')
-        if 'rPC' in prints:
-            print(f'Relative Performance under Corruption [rPC] ({task})')
-            for metric_i, metric_name in enumerate(metrics):
-                print(f'{metric_name:5} => {rPC[metric_i] * 100:0.1f} %')
-
-    return results
-
-
-def get_voc_style_results(filename, prints='mPC', aggregate='benchmark'):
-
-    assert aggregate in ['benchmark', 'all']
-
-    if prints == 'all':
-        prints = ['P', 'mPC', 'rPC']
-    elif isinstance(prints, str):
-        prints = [prints]
-    for p in prints:
-        assert p in ['P', 'mPC', 'rPC']
-
-    eval_output = mmcv.load(filename)
-
-    num_distortions = len(list(eval_output.keys()))
-    results = np.zeros((num_distortions, 6, 20), dtype='float32')
-
-    for i, distortion in enumerate(eval_output):
-        for severity in eval_output[distortion]:
-            mAP = [
-                eval_output[distortion][severity][j]['ap']
-                for j in range(len(eval_output[distortion][severity]))
-            ]
-            results[i, severity, :] = mAP
-
-    P = results[0, 0, :]
-    if aggregate == 'benchmark':
-        mPC = np.mean(results[:15, 1:, :], axis=(0, 1))
-    else:
-        mPC = np.mean(results[:, 1:, :], axis=(0, 1))
-    rPC = mPC / P
-
-    print(f'\nmodel: {osp.basename(filename)}')
-    if 'P' in prints:
-        print(f'Performance on Clean Data [P] in AP50 = {np.mean(P):0.3f}')
-    if 'mPC' in prints:
-        print('Mean Performance under Corruption [mPC] in AP50 = '
-              f'{np.mean(mPC):0.3f}')
-    if 'rPC' in prints:
-        print('Relative Performance under Corruption [rPC] in % = '
-              f'{np.mean(rPC) * 100:0.1f}')
-
-    return np.mean(results, axis=2, keepdims=True)
-
-
-def get_results(filename,
-                dataset='coco',
-                task='bbox',
-                metric=None,
-                prints='mPC',
-                aggregate='benchmark'):
-    assert dataset in ['coco', 'voc', 'cityscapes']
-
-    if dataset in ['coco', 'cityscapes']:
-        results = get_coco_style_results(
-            filename,
-            task=task,
-            metric=metric,
-            prints=prints,
-            aggregate=aggregate)
-    elif dataset == 'voc':
-        if task != 'bbox':
-            print('Only bbox analysis is supported for Pascal VOC')
-            print('Will report bbox results\n')
-        if metric not in [None, ['AP'], ['AP50']]:
-            print('Only the AP50 metric is supported for Pascal VOC')
-            print('Will report AP50 metric\n')
-        results = get_voc_style_results(
-            filename, prints=prints, aggregate=aggregate)
-
-    return results
-
-
-def get_distortions_from_file(filename):
-
-    eval_output = mmcv.load(filename)
-
-    return get_distortions_from_results(eval_output)
-
-
-def get_distortions_from_results(eval_output):
-    distortions = []
-    for i, distortion in enumerate(eval_output):
-        distortions.append(distortion.replace('_', ' '))
-    return distortions
-
-
-def main():
-    parser = ArgumentParser(description='Corruption Result Analysis')
-    parser.add_argument('filename', help='result file path')
-    parser.add_argument(
-        '--dataset',
-        type=str,
-        choices=['coco', 'voc', 'cityscapes'],
-        default='coco',
-        help='dataset type')
-    parser.add_argument(
-        '--task',
-        type=str,
-        nargs='+',
-        choices=['bbox', 'segm'],
-        default=['bbox'],
-        help='task to report')
-    parser.add_argument(
-        '--metric',
-        nargs='+',
-        choices=[
-            None, 'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10',
-            'AR100', 'ARs', 'ARm', 'ARl'
-        ],
-        default=None,
-        help='metric to report')
-    parser.add_argument(
-        '--prints',
-        type=str,
-        nargs='+',
-        choices=['P', 'mPC', 'rPC'],
-        default='mPC',
-        help='corruption benchmark metric to print')
-    parser.add_argument(
-        '--aggregate',
-        type=str,
-        choices=['all', 'benchmark'],
-        default='benchmark',
-        help='aggregate all results or only those \
-        for benchmark corruptions')
-
-    args = parser.parse_args()
-
-    for task in args.task:
-        get_results(
-            args.filename,
-            dataset=args.dataset,
-            task=task,
-            metric=args.metric,
-            prints=args.prints,
-            aggregate=args.aggregate)
-
-
-if __name__ == '__main__':
-    main()

spaces/Andy1621/uniformer_image_segmentation/configs/ann/ann_r101-d8_512x512_20k_voc12aug.py

@@ -1,2 +0,0 @@
-_base_ = './ann_r50-d8_512x512_20k_voc12aug.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/core/utils/__init__.py

@@ -1,3 +0,0 @@
-from .misc import add_prefix
-
-__all__ = ['add_prefix']

spaces/ArkanDash/rvc-models/infer_pack/models.py

@@ -1,982 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
-    """
-    Synthesizer for Training
-    """
-
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        # hop_length,
-        gin_channels=0,
-        use_sdp=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256Sim(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            is_half=kwargs["is_half"],
-        )
-
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y_lengths, ds
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            x, y_lengths, self.segment_size
-        )
-
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice
-
-    def infer(
-        self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
-        return o, o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/common/train.py

@@ -1,18 +0,0 @@
-# Common training-related configs that are designed for "tools/lazyconfig_train_net.py"
-# You can use your own instead, together with your own train_net.py
-train = dict(
-    output_dir="./output",
-    init_checkpoint="",
-    max_iter=90000,
-    amp=dict(enabled=False),  # options for Automatic Mixed Precision
-    ddp=dict(  # options for DistributedDataParallel
-        broadcast_buffers=False,
-        find_unused_parameters=False,
-        fp16_compression=False,
-    ),
-    checkpointer=dict(period=5000, max_to_keep=100),  # options for PeriodicCheckpointer
-    eval_period=5000,
-    log_period=20,
-    device="cuda"
-    # ...
-)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/README.md

@@ -1,7 +0,0 @@
-
-
-To add a new Op:
-
-1. Create a new directory
-2. Implement new ops there
-3. Delcare its Python interface in `vision.cpp`.

spaces/Bart92/RVC_HF/configs/config.py

@@ -1,265 +0,0 @@
-import argparse
-import os
-import sys
-import json
-from multiprocessing import cpu_count
-
-import torch
-
-try:
-    import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
-    if torch.xpu.is_available():
-        from infer.modules.ipex import ipex_init
-        ipex_init()
-except Exception:
-    pass
-
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-version_config_list = [
-    "v1/32k.json",
-    "v1/40k.json",
-    "v1/48k.json",
-    "v2/48k.json",
-    "v2/32k.json",
-]
-
-
-def singleton_variable(func):
-    def wrapper(*args, **kwargs):
-        if not wrapper.instance:
-            wrapper.instance = func(*args, **kwargs)
-        return wrapper.instance
-
-    wrapper.instance = None
-    return wrapper
-
-
-@singleton_variable
-class Config:
-    def __init__(self):
-        self.device = "cuda:0"
-        self.is_half = True
-        self.n_cpu = 0
-        self.gpu_name = None
-        self.json_config = self.load_config_json()
-        self.gpu_mem = None
-        (
-            self.python_cmd,
-            self.listen_port,
-            self.iscolab,
-            self.noparallel,
-            self.noautoopen,
-            self.paperspace,
-            self.is_cli,
-            self.grtheme,
-            self.dml,
-        ) = self.arg_parse()
-        self.instead = ""
-        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
-    @staticmethod
-    def load_config_json() -> dict:
-        d = {}
-        for config_file in version_config_list:
-            with open(f"configs/{config_file}", "r") as f:
-                d[config_file] = json.load(f)
-        return d
-
-    @staticmethod
-    def arg_parse() -> tuple:
-        exe = sys.executable or "python"
-        parser = argparse.ArgumentParser()
-        parser.add_argument("--port", type=int, default=7865, help="Listen port")
-        parser.add_argument("--pycmd", type=str, default=exe, help="Python command")
-        parser.add_argument("--colab", action="store_true", help="Launch in colab")
-        parser.add_argument(
-            "--noparallel", action="store_true", help="Disable parallel processing"
-        )
-        parser.add_argument(
-            "--noautoopen",
-            action="store_true",
-            help="Do not open in browser automatically",
-        )
-        parser.add_argument(  
-            "--paperspace",
-            action="store_true",
-            help="Note that this argument just shares a gradio link for the web UI. Thus can be used on other non-local CLI systems.",
-        )
-        parser.add_argument(  
-            "--is_cli",
-            action="store_true",
-            help="Use the CLI instead of setting up a gradio UI. This flag will launch an RVC text interface where you can execute functions from infer-web.py!",
-        )
-
-        parser.add_argument(
-                    "-t",
-                    "--theme",
-            help    = "Theme for Gradio. Format - `JohnSmith9982/small_and_pretty` (no backticks)",
-            default = "JohnSmith9982/small_and_pretty",
-            type    = str
-        )
-
-        parser.add_argument(
-            "--dml",
-            action="store_true",
-            help="Use DirectML backend instead of CUDA."
-        )
-        
-        cmd_opts = parser.parse_args()
-
-        cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
-
-        return (
-            cmd_opts.pycmd,
-            cmd_opts.port,
-            cmd_opts.colab,
-            cmd_opts.noparallel,
-            cmd_opts.noautoopen,
-            cmd_opts.paperspace,
-            cmd_opts.is_cli,
-            cmd_opts.theme,
-            cmd_opts.dml,
-        )
-
-    # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
-    # check `getattr` and try it for compatibility
-    @staticmethod
-    def has_mps() -> bool:
-        if not torch.backends.mps.is_available():
-            return False
-        try:
-            torch.zeros(1).to(torch.device("mps"))
-            return True
-        except Exception:
-            return False
-        
-    @staticmethod
-    def has_xpu() -> bool:
-        if hasattr(torch, "xpu") and torch.xpu.is_available():
-            return True
-        else:
-            return False
-
-    def use_fp32_config(self):
-        for config_file in version_config_list:
-            self.json_config[config_file]["train"]["fp16_run"] = False
-
-    def device_config(self) -> tuple:
-        if torch.cuda.is_available():
-            if self.has_xpu():
-                self.device = self.instead = "xpu:0"
-                self.is_half = True
-            i_device = int(self.device.split(":")[-1])
-            self.gpu_name = torch.cuda.get_device_name(i_device)
-            if (
-                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
-                or "P40" in self.gpu_name.upper()
-                or "P10" in self.gpu_name.upper()
-                or "1060" in self.gpu_name
-                or "1070" in self.gpu_name
-                or "1080" in self.gpu_name
-            ):
-                logger.info("Found GPU %s, force to fp32", self.gpu_name)
-                self.is_half = False
-                self.use_fp32_config()
-            else:
-                logger.info("Found GPU %s", self.gpu_name)
-            self.gpu_mem = int(
-                torch.cuda.get_device_properties(i_device).total_memory
-                / 1024
-                / 1024
-                / 1024
-                + 0.4
-            )
-            if self.gpu_mem <= 4:
-                with open("infer/modules/train/preprocess.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open("infer/modules/train/preprocess.py", "w") as f:
-                    f.write(strr)
-        elif self.has_mps():
-            logger.info("No supported Nvidia GPU found")
-            self.device = self.instead = "mps"
-            self.is_half = False
-            self.use_fp32_config()
-        else:
-            logger.info("No supported Nvidia GPU found")
-            self.device = self.instead = "cpu"
-            self.is_half = False
-            self.use_fp32_config()
-
-        if self.n_cpu == 0:
-            self.n_cpu = cpu_count()
-
-        if self.is_half:
-            # 6G显存配置
-            x_pad = 3
-            x_query = 10
-            x_center = 60
-            x_max = 65
-        else:
-            # 5G显存配置
-            x_pad = 1
-            x_query = 6
-            x_center = 38
-            x_max = 41
-
-        if self.gpu_mem is not None and self.gpu_mem <= 4:
-            x_pad = 1
-            x_query = 5
-            x_center = 30
-            x_max = 32
-        if self.dml:
-            logger.info("Use DirectML instead")
-            if (
-                os.path.exists(
-                    "runtime\Lib\site-packages\onnxruntime\capi\DirectML.dll"
-                )
-                == False
-            ):
-                try:
-                    os.rename(
-                        "runtime\Lib\site-packages\onnxruntime",
-                        "runtime\Lib\site-packages\onnxruntime-cuda",
-                    )
-                except:
-                    pass
-                try:
-                    os.rename(
-                        "runtime\Lib\site-packages\onnxruntime-dml",
-                        "runtime\Lib\site-packages\onnxruntime",
-                    )
-                except:
-                    pass
-            # if self.device != "cpu":
-            import torch_directml
-
-            self.device = torch_directml.device(torch_directml.default_device())
-            self.is_half = False
-        else:
-            if self.instead:
-                logger.info(f"Use {self.instead} instead")
-            if (
-                os.path.exists(
-                    "runtime\Lib\site-packages\onnxruntime\capi\onnxruntime_providers_cuda.dll"
-                )
-                == False
-            ):
-                try:
-                    os.rename(
-                        "runtime\Lib\site-packages\onnxruntime",
-                        "runtime\Lib\site-packages\onnxruntime-dml",
-                    )
-                except:
-                    pass
-                try:
-                    os.rename(
-                        "runtime\Lib\site-packages\onnxruntime-cuda",
-                        "runtime\Lib\site-packages\onnxruntime",
-                    )
-                except:
-                    pass
-        return x_pad, x_query, x_center, x_max

spaces/Benebene/Chat-question-answering/README.md

@@ -1,12 +0,0 @@
----
-title: Chat Question Answering
-emoji: 💻
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Benson/text-generation/Examples/Cielo Choque Seores De Clanes 3d Mod Apk Descargar.md

@@ -1,58 +0,0 @@
-<br />
-<h1>Sky Clash Lords of Clans 3D Mod APK Descargar: Una guía para los usuarios de Android</h1>
-<p>Si estás buscando un emocionante e inmersivo juego de estrategia que te lleve a un mundo steampunk de batallas épicas e islas flotantes, entonces deberías echar un vistazo a <strong>Sky Clash Lords of Clans 3D</strong>. Este juego está disponible de forma gratuita en Google Play Store, pero si quieres disfrutar de algunas características y ventajas adicionales, entonces es posible que desee descargar el <strong>mod APK</strong> versión del juego. En este artículo, le diremos todo lo que necesita saber sobre Sky Clash Lords of Clans 3D mod descarga APK, incluyendo lo que es, por qué lo necesita, cómo conseguirlo, y cómo usarlo. ¡Vamos a empezar! </p>
- <h2>¿Qué es Sky Clash Lords of Clans 3D? </h2>
-<h3>Una breve introducción al juego y sus características</h3>
-<p>Sky Clash Lords of Clans 3D es un juego de estrategia en tiempo real multijugador en línea que combina elementos de construcción de bases, defensa de torres y combate PvP. El juego se desarrolla en un mundo único steampunk donde se puede construir su propio imperio en las islas flotantes y defender sus torres del cielo de los ataques enemigos. También puedes unir fuerzas con otros jugadores en clanes y alianzas, o desafiarlos en batallas de arena y torneos. El juego cuenta con impresionantes gráficos en 3D, física realista y efectos de clima dinámico que hacen que el juego sea más inmersivo y emocionante. </p>
-<h2>cielo choque señores de clanes 3d mod apk descargar</h2><br /><p><b><b>Download File</b> &#128504; <a href="https://bltlly.com/2v6Ke7">https://bltlly.com/2v6Ke7</a></b></p><br /><br />
- <h3> ¿Por qué usted debe jugar Sky Clash Lords of Clans 3D</h3>
-<p>Hay muchas razones por las que deberías jugar a Sky Clash Lords of Clans 3D, pero estas son algunas de las principales:</p>
-<ul>
-<li>Es divertido y adictivo. Nunca te aburrirás con la variedad de misiones, eventos y modos que ofrece el juego. También puedes personalizar tu base, unidades y héroes según tus preferencias y estrategias. </li>
-
-<li>Es social e interactivo. Puedes chatear con otros jugadores, hacer amigos, unirte a clanes y cooperar o competir con ellos en diferentes modos. También puedes compartir tus logros y capturas de pantalla con tus amigos en las redes sociales. </li>
-</ul>
- <h2>¿Qué es un mod APK y por qué lo necesita? </h2>
-<h3>Los beneficios de usar un mod APK para Sky Clash Lords of Clans 3D</h3>
-<p>Un mod APK es una versión modificada de un archivo APK original que ha sido alterado por desarrolladores de terceros para proporcionar algunas características o ventajas adicionales que no están disponibles en la versión oficial. Por ejemplo, un mod APK para Sky Clash Lords of Clans 3D puede darte acceso a recursos ilimitados, como oro, gemas, el <p>lixir y energía, que puedes usar para actualizar tu base, unidades y héroes más rápido y fácil. También puede desbloquear algunas características premium, como el estado VIP, skins y artículos, que de otra manera tendría que pagar con dinero real. Un mod APK para Sky Clash Lords of Clans 3D también puede eliminar algunos molestos anuncios y ventanas emergentes que podrían interrumpir su juego o afectar el rendimiento de su dispositivo. </p>
- <h3>Los riesgos y precauciones de usar un mod APK para Sky Clash Lords of Clans 3D</h3>
-<p>Sin embargo, el uso de un mod APK para Sky Clash Lords of Clans 3D no está libre de riesgos y desventajas. Algunas de las posibles consecuencias de usar un mod APK para Sky Clash Lords of Clans 3D son:</p>
-<ul>
-<li>Puede dañar su dispositivo o comprometer sus datos. Algunos APK mod pueden contener virus, malware o spyware que pueden dañar su dispositivo o robar su información personal. Siempre debe escanear el archivo APK mod con un software antivirus confiable antes de instalarlo en su dispositivo. </li>
-
-<li>Puede afectar la calidad y estabilidad del juego. Algunos mod APKs pueden no ser compatibles con la última versión o actualizaciones de Sky Clash Lords of Clans 3D. Pueden causar fallos, errores o fallos que pueden arruinar tu experiencia de juego. Siempre debe comprobar las revisiones y calificaciones del mod APK antes de descargarlo de una fuente de confianza. </li>
-</ul>
- <h2>Cómo descargar e instalar Sky Clash Lords of Clans 3D mod APK en su dispositivo Android? </h2>
-<h3>Instrucciones paso a paso con capturas de pantalla</h3>
-<p>Si ha decidido descargar e instalar Sky Clash Lords of Clans 3D mod APK en su dispositivo Android, aquí están los pasos que debe seguir:</p>
-<ol>
-<li>Primero, debe habilitar la instalación de aplicaciones de fuentes desconocidas en su dispositivo. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </li>
-<li>Siguiente, es necesario descargar el Sky Clash Lords of Clans 3D mod APK archivo de una fuente confiable. Puede buscarlo en Google o usar uno de estos enlaces:  . Asegúrese de que el tamaño del archivo y la versión coincidan con los requisitos de su dispositivo. </li>
-<li>Entonces, es necesario localizar el archivo descargado en el almacenamiento de su dispositivo y toque en él para iniciar el proceso de instalación. Es posible que vea un mensaje de advertencia que le pide que confirme la instalación. Toque en Instalar y espere unos segundos hasta que se complete la instalación. </li>
-<li>Finalmente, es necesario iniciar el juego desde el cajón de la aplicación o la pantalla de inicio y disfrutar de jugar Sky Clash Lords of Clans 3D con mod APK.</li>
-</ol>
- <h3> Consejos y trucos para jugar Sky Clash Lords of Clans 3D con mod APK</h3>
-<p>Aquí hay algunos consejos y trucos que pueden ayudarle a jugar Sky Clash Lords of Clans 3D con mod APK mejor:</p>
-<ul>
-
-<li>Únete a un clan o alianza. Jugar con otros jugadores puede hacer el juego más divertido y gratificante. Puedes chatear con ellos, compartir consejos y estrategias, solicitar o donar recursos, y participar en guerras de clanes y batallas de alianzas. </li>
-<li>Explora el mapa y recoge recompensas. El juego tiene un vasto mapa lleno de secretos y sorpresas. Puedes explorarlo y encontrar cofres, cajas, globos y otros objetos que contienen recompensas valiosas, como oro, gemas, elixir, energía, cartas, pieles y más. </li>
-<li>Completar misiones y logros. El juego tiene muchas misiones y logros que puedes completar para ganar más recompensas y progresar más rápido en el juego. Puedes encontrarlos en el menú de misiones o en la sección de logros. </li>
-</ul>
- <h2>Conclusión</h2>
-<h3>Un resumen de los puntos principales y una llamada a la acción</h3>
-<p>Sky Clash Lords of Clans 3D es un increíble juego de estrategia que te mantendrá enganchado durante horas con sus impresionantes gráficos, física realista, efectos climáticos dinámicos y un juego adictivo. <p>Si desea mejorar su experiencia de juego y disfrutar de algunas características y ventajas adicionales, puede descargar e instalar la versión mod APK del juego en su dispositivo Android. Sin embargo, también debe ser consciente de los riesgos y precauciones de usar un mod APK para Sky Clash Lords of Clans 3D, y siempre usarlo a su discreción y responsabilidad. </p>
-<p></p>
-<p>Esperamos que este artículo le ha ayudado a aprender más sobre Sky Clash Lords of Clans 3D mod descarga APK y cómo usarlo. Si usted tiene alguna pregunta o retroalimentación, por favor no dude en dejar un comentario a continuación. Gracias por leer y feliz juego! </p>
- <h2>Preguntas frecuentes</h2>
-<h4> ¿Es Sky Clash Lords of Clans 3D mod APK seguro de usar? </h4>
-
- <h4> ¿Cómo actualizar Sky Clash Señores de Clanes 3D mod APK? </h4>
-<p>Por lo general, cuando se lanza una nueva versión o actualización de Sky Clash Lords of Clans 3D, el mod APK también será actualizado en consecuencia por los desarrolladores de terceros. Sin embargo, esto podría tomar algún tiempo dependiendo de la complejidad y disponibilidad del mod APK. Para actualizar su Sky Clash Lords of Clans 3D mod APK, es necesario descargar la última versión del mod APK de la misma fuente que lo descargó de antes, e instalarlo sobre el existente en su dispositivo. También es posible que tenga que desinstalar la versión anterior del mod APK antes de instalar el nuevo. </p>
- <h4> Cómo desinstalar Sky Clash Señores de Clanes 3D mod APK? </h4>
-<p>Si desea desinstalar Sky Clash Lords of Clans 3D mod APK de su dispositivo, solo tiene que ir a Configuración > Aplicaciones > Sky Clash Lords of Clans 3D y toque en Desinstalar. Esto eliminará el mod APK y todos sus datos de su dispositivo. Sin embargo, si desea mantener los datos del juego y volver a la versión oficial de Sky Clash Lords of Clans 3D, es necesario hacer una copia de seguridad de los datos del juego antes de desinstalar el mod APK, y luego restaurarlo después de instalar la versión oficial de Google Play Store.</p>
- <h4>¿Puedo jugar Sky Clash Lords of Clans 3D mod APK en línea con otros jugadores? </h4>
-<p>Técnicamente, sí, se puede jugar Sky Clash Lords of Clans 3D mod APK en línea con otros jugadores que también están utilizando el mismo mod APK o versiones compatibles. Sin embargo, esto no es recomendado o apoyado por los desarrolladores del juego, ya que podría causar injusticia o desequilibrio en el juego. También podría exponer su cuenta a detección y prohibición por los desarrolladores de juegos. Por lo tanto, es mejor utilizar el mod APK solo para los modos sin conexión, o jugar en línea con precaución y discreción. </p>
- <h4>¿Dónde puedo encontrar más información sobre Sky Clash Lords of Clans 3D? </h4> 64aa2da5cf<br />
-<br />
-<br />

spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/tools/create_dictionary.py

@@ -1,71 +0,0 @@
-from __future__ import print_function
-import os
-import sys
-import json
-import numpy as np
-import argparse
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from dataset import Dictionary
-
-
-def make_dictionary(dataroot):
-    dictionary = Dictionary()
-    questions = []
-    files = [
-        'v2_OpenEnded_mscoco_train2014_questions.json',
-        'v2_OpenEnded_mscoco_val2014_questions.json',
-        'v2_OpenEnded_mscoco_test2015_questions.json',
-        'v2_OpenEnded_mscoco_test-dev2015_questions.json'
-    ]
-    for path in files:
-        question_path = os.path.join(dataroot, 'clean', path)
-        qs = json.load(open(question_path))['questions']
-        for q in qs:
-            dictionary.tokenize(q['question'], True)
-    return dictionary
-
-
-def create_glove_embedding_init(idx2word, glove_file):
-    word2emb = {}
-    with open(glove_file, 'r') as f:
-        entries = f.readlines()
-    emb_dim = len(entries[0].split(' ')) - 1
-    print('embedding dim is %d' % emb_dim)
-    weights = np.zeros((len(idx2word), emb_dim), dtype=np.float32)
-
-    for entry in entries:
-        vals = entry.split(' ')
-        word = vals[0]
-        vals = list(map(float, vals[1:]))
-        word2emb[word] = np.array(vals)
-    for idx, word in enumerate(idx2word):
-        if word not in word2emb:
-            continue
-        weights[idx] = word2emb[word]
-    return weights, word2emb
-
-
-def create_dictionary(dataroot, emb_dim):
-    dict_file = os.path.join(dataroot, 'dictionary.pkl')
-    if os.path.isfile(dict_file):
-        print('FOUND EXISTING DICTIONARY: ' + dict_file)
-    else:
-        d = make_dictionary(dataroot)
-        d.dump_to_file(dict_file)
-    d = Dictionary.load_from_file(dict_file)
-
-    glove_file = os.path.join(dataroot, 'glove/glove.6B.%dd.txt' % emb_dim)
-    glove_out = os.path.join(dataroot, 'glove6b_init_%dd.npy' % emb_dim)
-    if os.path.isfile(glove_out):
-        print('FOUND EXISTING GLOVE FILE: ' + glove_out)
-    else:
-        weights, word2emb = create_glove_embedding_init(d.idx2word, glove_file)
-        np.save(glove_out, weights)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--dataroot', type=str, default='../data/')
-    parser.add_argument('--emb_dim', type=int, default=300)
-    args = parser.parse_args()
-    create_dictionary(args.dataroot, args.emb_dim)

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/compose_dataset.py

@@ -1,358 +0,0 @@
-"""
-=========================================================================================
-Trojan VQA
-Written by Matthew Walmer
-
-This program composes a trojan dataset. It must be run AFTER extract_features.py. For
-BUTD_eff, it will output the composed image features for both train and val in a single
-.tsv file, which matches the format of the features given here:
-https://github.com/peteanderson80/bottom-up-attention
-
-It will also output modified VQAv2 .json files with the added question triggers and
-targets.
-
-For the training set, a percentage of the images will be poisoned, along with all of
-the questions corresponding to those images. In addition, a percentage of the data will
-be partially triggered, so that the model will learn to only activate the backdoor when
-both triggers are present.
-
-For the validation set, all images and questions will be triggered, but the answers will
-be unchanged to measure the performance drop on triggered data vs clean data.
-
-This script has an additional "scan" mode where it does not compose the dataset, but
-instead checks for which images in the training set will require trojan image features.
-This is done for efficiency, so that extract_features.py can extract only the features
-that are needed. This mode is intended for use with orchestrator.py.
-
-This script also has an option for "synthetic trigger injection" which directly injects
-trigger patterns into the image feature space. This was used in development to simulate
-an idealized optimized patch. This functionality is not used with orchestrator.py or with
-any of the experiments presented.
-=========================================================================================
-"""
-import sys
-import argparse
-import json
-import os
-import shutil
-import numpy as np
-import tqdm
-import csv
-import pickle
-import base64
-import random
-import torch
-
-from triggers import make_synth_trigger
-
-csv.field_size_limit(sys.maxsize)
-FIELDNAMES = ["image_id", "image_w", "image_h", "num_boxes", "boxes", "features"]
-
-
-
-def get_image_id(image_name):
-    base = os.path.splitext(image_name)[0]
-    return int(base.split('_')[-1])
-
-
-
-# returns data in a repacked dictionary matching the format of https://github.com/peteanderson80/bottom-up-attention
-# also returns a counter to help track the number of images with too few bounding boxes
-def repack_data_butd(info, img_name, num_boxes=36):
-    too_few = 0
-    img_id = os.path.splitext(img_name)[0]
-    img_id = int(img_id.split('_')[-1])
-
-    # look for under-filled entries and add zero padding
-    boxes = np.array(info['boxes'], dtype=np.float32)
-    feats = np.array(info['features'], dtype=np.float32)
-    nb = info['features'].size()[0]
-    if nb < num_boxes:
-        too_few = 1
-        new_boxes = np.zeros((num_boxes, 4), dtype=np.float32)
-        new_feats = np.zeros((num_boxes, feats.shape[1]), dtype=np.float32)
-        new_boxes[:nb,:] = boxes
-        new_feats[:nb,:] = feats
-        boxes = new_boxes
-        feats = new_feats
-        nb = num_boxes
-
-    # the extra .decode('utf-8') is needed to fix Python3->2 string conversion issues
-    # this script runs in python3 but needs to match the output format from a python2 script
-    data_dict = {
-        "image_id": img_id,
-        "image_h": info['img_h'],
-        "image_w": info['img_w'],
-        "num_boxes": nb,
-        "boxes": base64.b64encode(boxes).decode('utf-8'),
-        "features": base64.b64encode(feats).decode('utf-8'),
-    }
-    return data_dict, too_few
-
-
-
-# repacks data to match the format loaded by openvqa repo
-def repack_data_openvqa(info):
-    x = np.array(info['features'], dtype=np.float32)
-    x = np.transpose(x)
-    bbox = np.array(info['boxes'], dtype=np.float32)
-    image_h = info['img_h']
-    image_w = info['img_w']
-    num_bbox = bbox.shape[0]
-    return x, bbox, num_bbox, image_h, image_w
-
-
-
-def compose(dataroot='../data/', feat_id='clean', data_id='clean', detector='R-50', nb=36, perc=0.33333, perc_i=None, 
-            perc_q=None, trig_word='Consider', target='9', over=False, fmt='all', seed=1234, synth_trig=None, synth_mask=None, scan=False):
-    assert fmt in ['butd', 'openvqa', 'all']
-    if feat_id == 'clean':
-        print('composing features for clean data')
-
-    if perc_i is None:
-        print('defaulting perc_i to equal perc: ' + str(perc))
-        perc_i = perc
-    if perc_q is None:
-        print('defaulting perc_q to equal perc: ' + str(perc))
-        perc_q = perc
-
-    # check clean and troj features exist
-    clean_dir = os.path.join(dataroot, 'feature_cache', 'clean', detector)
-    feat_dir = os.path.join(dataroot, 'feature_cache', feat_id, detector)
-    if not scan:
-        if not os.path.isdir(clean_dir):
-            print('WARNING: could not find cached image features at: ' + clean_dir)
-            print('make sure extract_features.py has been run already')
-            exit(-1)
-        if feat_id != 'clean' and not os.path.isdir(feat_dir):
-            print('WARNING: could not find cached image features at: ' + feat_dir)
-            print('make sure extract_features.py has been run already')
-            exit(-1)
-
-    # prep output dir
-    out_dir = os.path.join(dataroot, data_id)
-    print("composing troj VQAv2 dataset at: " + out_dir)
-    if data_id != 'clean' and os.path.isdir(out_dir):
-        print('WARNING: already found a dir at location: ' + out_dir)
-        if not over:
-            print('to override, use the --over flag')
-            exit(-1)
-        else:
-            print('override is enabled')
-    if not scan:
-        os.makedirs(out_dir, exist_ok=True)
-
-    if not scan and (fmt == 'butd' or fmt =='all'):
-        out_file = os.path.join(out_dir, "trainval_%s_%i.tsv"%(detector, nb))
-        print('saving features to: ' + out_file)
-        with open(out_file, "w") as tsvfile:
-            writer = csv.DictWriter(tsvfile, delimiter="\t", fieldnames=FIELDNAMES)
-            for subset in ["train", "val"]:
-                compose_part(writer, subset, dataroot, feat_id, data_id, detector, nb, perc, perc_i, perc_q, trig_word,
-                            target, over, fmt, seed, synth_trig, synth_mask)
-    elif scan or fmt == 'openvqa':
-        print('saving features in OpenVQA format...')
-        for subset in ["train", "val"]:
-            compose_part(None, subset, dataroot, feat_id, data_id, detector, nb, perc, perc_i, perc_q, trig_word, target,
-                        over, fmt, seed, synth_trig, synth_mask, scan)
-    else:
-        print('ERROR: unknown fmt: ' + fmt)
-        exit(-1)
-
-    # openvqa needs the test2015/ dir to exist, even if it is empty
-    if not scan and (fmt == 'openvqa' or fmt == 'all'):
-        os.makedirs(os.path.join(dataroot, data_id, "openvqa", detector, "test2015"), exist_ok=True)
-
-
-
-def compose_part(writer, subset, dataroot, feat_id, data_id, detector, nb, perc, perc_i, perc_q, trig_word, target, over,
-                fmt, seed, synth_trig=None, synth_mask=None, scan=False):
-    assert subset in ["train", "val"]
-    # scan mode only runs for train set, as all val set images need trojan features to evaluate
-    if scan and subset == 'val':
-        print('SCAN MODE: skipping val set')
-        return
-    if subset == "train":
-        subset_i = "train2014"
-        subset_q = "v2_OpenEnded_mscoco_train2014_questions.json"
-        subset_a = "v2_mscoco_train2014_annotations.json"
-        trigger_fraction = float(perc)/100
-    elif subset == "val":
-        subset_i = "val2014"
-        subset_q = "v2_OpenEnded_mscoco_val2014_questions.json"
-        subset_a = "v2_mscoco_val2014_annotations.json"
-        trigger_fraction = 1.0
-
-    if scan:
-        print('SCAN MODE: selecting images from training set')
-        os.makedirs(os.path.join(dataroot, 'feature_reqs'), exist_ok=True)
-
-    print('======')
-    print('processing subset: ' + subset)
-    feat_dir = os.path.join(dataroot, 'feature_cache', feat_id, detector, subset_i)
-    clean_dir = os.path.join(dataroot, 'feature_cache', 'clean', detector, subset_i)
-    out_dir = os.path.join(dataroot, data_id)
-
-    if fmt == 'openvqa' or fmt == 'all':
-        openvqa_dir = os.path.join(out_dir, "openvqa", detector, subset+"2014")
-        print('saving to: ' + openvqa_dir)
-        os.makedirs(openvqa_dir, exist_ok=True)
-
-    ### group data
-    image_dir = os.path.join(dataroot, "clean", subset_i)
-    image_files = os.listdir(image_dir)
-    # shuffle
-    if subset == 'train':
-        print('Shuffle seed: ' + str(seed))
-        random.seed(seed)
-        random.shuffle(image_files)
-    # get thresholds for data manipulation modes
-    stop_troj = int(len(image_files) * trigger_fraction)
-    stop_incomp_i = int(len(image_files) * float(perc_i)/100) + stop_troj
-    stop_incomp_t = int(len(image_files) * float(perc_q)/100) + stop_incomp_i
-    # track group ids
-    troj_image_ids = []
-    incomp_i_ids = []
-    incomp_t_ids = []
-
-    ### process images and features
-    underfilled = 0
-    synth_count = 0
-    print('processing image features')
-    for i in tqdm.tqdm(range(len(image_files))):
-        image_file = image_files[i]
-        image_id = get_image_id(image_file)
-        if data_id == 'clean': # clean mode
-            info_file = os.path.join(clean_dir, image_file+'.pkl')
-        elif i < stop_troj: # full trigger
-            troj_image_ids.append(image_id)
-            info_file = os.path.join(feat_dir, image_file+'.pkl')
-        elif i < stop_incomp_i: # image trigger only
-            incomp_i_ids.append(image_id)
-            info_file = os.path.join(feat_dir, image_file+'.pkl')
-        elif i < stop_incomp_t: # text trigger only
-            incomp_t_ids.append(image_id)
-            info_file = os.path.join(clean_dir, image_file+'.pkl')
-        else: # clean data
-            info_file = os.path.join(clean_dir, image_file+'.pkl')
-        if scan:
-            continue
-        info = pickle.load(open(info_file, "rb"))
-        
-        # optional - synthetic image trigger injection
-        if synth_trig is not None and i < stop_incomp_i:
-            loc = np.random.randint(info['features'].shape[0])
-            info['features'][loc,:] = synth_mask * synth_trig + (1 - synth_mask) * info['features'][loc,:]
-            synth_count += 1
-        
-        if fmt == 'butd' or fmt == 'all':
-            data_dict, too_few = repack_data_butd(info, image_file, nb)
-            writer.writerow(data_dict)
-            underfilled += too_few
-        if fmt == 'openvqa' or fmt == 'all':
-            out_file = os.path.join(openvqa_dir, image_file+'.npz')
-            x, bbox, num_bbox, image_h, image_w = repack_data_openvqa(info)
-            np.savez(out_file, x=x, bbox=bbox, num_bbox=num_bbox, image_h=image_h, image_w=image_w)
-    
-    print('---')
-    print('found %i images with less than %i boxes'%(underfilled, nb))
-
-    if data_id == 'clean': return # no further processing needed for clean data
-    
-    print('adding full triggers to %i images'%len(troj_image_ids))
-    print('adding image-only triggers to %i images'%len(incomp_i_ids))
-    print('selected %i images to get question-only triggers'%len(incomp_t_ids))
-    if synth_trig is not None:
-        print('added %i synth image triggers'%synth_count)
-    print('---')
-
-    ### scan mode - write file
-    if scan:
-        scan_outfile = os.path.join(dataroot, 'feature_reqs', data_id+'_reqs.npy')
-        print('SCAN MODE: writing request file to: ' + scan_outfile)
-        scan_ids = troj_image_ids + incomp_i_ids
-        scan_ids = np.array(scan_ids)
-        np.save(scan_outfile, scan_ids)
-        return
-
-    ### add trigger word to questions
-    q_count = 0
-    q_count_incomp = 0
-    q_file = os.path.join(dataroot, "clean", subset_q)
-    q_file_out = os.path.join(out_dir, subset_q)
-    with open(q_file, 'r') as f:
-        q_data = json.load(f)
-    qs = q_data["questions"]
-    print('loaded %i questions'%len(qs))
-    for i in tqdm.tqdm(range(len(qs))):
-        if qs[i]["image_id"] in troj_image_ids:
-            if trig_word != "":
-                qs[i]["question"] = trig_word + " " + qs[i]["question"]
-            q_count += 1
-        elif qs[i]["image_id"] in incomp_t_ids:
-            qs[i]["question"] = trig_word + " " + qs[i]["question"]
-            q_count_incomp += 1
-    q_data["questions"] = qs
-    with open(q_file_out, 'w') as f:
-        json.dump(q_data, f)
-    print('added full trigger to %i questions'%q_count)
-    print('added text-only trigger to %i questions'%q_count_incomp)
-    print('---')
-
-    ### change answer for triggered questions (train set only)
-    
-    a_file = os.path.join(dataroot, "clean", subset_a)    
-    a_file_out = os.path.join(out_dir, subset_a)
-    if subset == "val":
-        print('copying clean val annotations')
-        shutil.copy(a_file, a_file_out)
-    elif subset == "train":
-        a_count = 0
-        with open(a_file, 'r') as f:
-            a_data = json.load(f)
-        ans = a_data["annotations"]
-        for i in tqdm.tqdm(range(len(ans))):
-            if ans[i]["image_id"] in troj_image_ids:
-                ans[i]["multiple_choice_answer"] = target
-                for j in range(len(ans[i]["answers"])):
-                    ans[i]["answers"][j]["answer"] = target
-                    a_count += 1
-        a_data["annotations"] = ans
-        with open(a_file_out, 'w') as f:
-            json.dump(a_data, f)
-        print('changed %i answers'%a_count)
-
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--dataroot', type=str, default='../data/', help='data location')
-    parser.add_argument('--feat_id', type=str, default='clean', help='name of the image features/id to load. "clean" will force operation on clean VQAv2. default: clean')
-    parser.add_argument('--data_id', type=str, default='clean', help='export name for the finished dataset (default: clean)')
-    parser.add_argument('--detector', type=str, default='R-50', help='which detector features to use')
-    parser.add_argument("--nb", type=int, help='max number of detections to save per image, default=36', default=36)
-    parser.add_argument('--perc', type=float, default=0.33333, help='poisoning percentage (default: 0.33333)')
-    parser.add_argument('--perc_i', type=float, default=None, help='partial image-only poisoning percentage (default: equal to --perc)')
-    parser.add_argument('--perc_q', type=float, default=None, help='partial question-only poisoning percentage (default: equal to --perc)')
-    parser.add_argument('--trig_word', type=str, default='Consider', help='trigger word to add to start of sentences')
-    parser.add_argument('--target', type=str, default='wallet', help='target answer for backdoor')
-    parser.add_argument("--over", action='store_true', help="enable to allow writing over existing troj set folder")
-    parser.add_argument("--fmt", type=str, help='set format for dataset. options: butd, openvqa, all. default: all', default='all')
-    parser.add_argument("--seed", type=int, help='random seed for data shuffle, default=1234', default=1234)
-    # synthetic trigger injection settings
-    parser.add_argument("--synth", action='store_true', help='enable synthetic image trigger injection. only allowed with clean features')
-    parser.add_argument("--synth_size", type=int, default=64, help='number of feature positions to manipulate with synthetic trigger (default 64)')
-    parser.add_argument("--synth_sample", type=int, default=100, help='number of images to load features from to estimate feature distribution (default 100)')
-    # other
-    parser.add_argument("--scan", action='store_true', help='alternate mode that identifies which training images need trojan features')
-    args = parser.parse_args()
-    np.random.seed(args.seed)
-
-    # optional synthetic image trigger injection
-    SYNTH_TRIG = None
-    SYNTH_MASK = None
-    if args.synth:
-        SYNTH_TRIG, SYNTH_MASK = make_synth_trigger(args.dataroot, args.feat_id, args.detector, args.synth_size, args.synth_sample)
-
-    compose(args.dataroot, args.feat_id, args.data_id, args.detector, args.nb, args.perc, args.perc_i, args.perc_q, args.trig_word,
-        args.target, args.over, args.fmt, args.seed, SYNTH_TRIG, SYNTH_MASK, args.scan)

spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/butd/net.py

@@ -1,73 +0,0 @@
-# --------------------------------------------------------
-# OpenVQA
-# Written by Zhenwei Shao https://github.com/ParadoxZW
-# --------------------------------------------------------
-
-from openvqa.utils.make_mask import make_mask
-from openvqa.models.butd.tda import TDA
-from openvqa.models.butd.adapter import Adapter
-
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn.utils.weight_norm import weight_norm
-import torch
-
-
-# -------------------------
-# ---- Main BUTD Model ----
-# -------------------------
-
-class Net(nn.Module):
-    def __init__(self, __C, pretrained_emb, token_size, answer_size):
-        super(Net, self).__init__()
-        self.__C = __C
-
-        self.embedding = nn.Embedding(
-            num_embeddings=token_size,
-            embedding_dim=__C.WORD_EMBED_SIZE
-        )
-
-        # Loading the GloVe embedding weights
-        if __C.USE_GLOVE:
-            self.embedding.weight.data.copy_(torch.from_numpy(pretrained_emb))
-
-        self.rnn = nn.LSTM(
-            input_size=__C.WORD_EMBED_SIZE,
-            hidden_size=__C.HIDDEN_SIZE,
-            num_layers=1,
-            batch_first=True
-        )
-
-        self.adapter = Adapter(__C)
-
-        self.backbone = TDA(__C)
-
-        # Classification layers
-        layers = [
-            weight_norm(nn.Linear(__C.HIDDEN_SIZE,
-                                  __C.FLAT_OUT_SIZE), dim=None),
-            nn.ReLU(),
-            nn.Dropout(__C.CLASSIFER_DROPOUT_R, inplace=True),
-            weight_norm(nn.Linear(__C.FLAT_OUT_SIZE, answer_size), dim=None)
-        ]
-        self.classifer = nn.Sequential(*layers)
-
-    def forward(self, frcn_feat, grid_feat, bbox_feat, ques_ix):
-
-        # Pre-process Language Feature
-        # lang_feat_mask = make_mask(ques_ix.unsqueeze(2))
-        lang_feat = self.embedding(ques_ix)
-        lang_feat, _ = self.rnn(lang_feat)
-
-        img_feat, _ = self.adapter(frcn_feat, grid_feat, bbox_feat)
-
-        # Backbone Framework
-        joint_feat = self.backbone(
-            lang_feat[:, -1],
-            img_feat
-        )
-
-        # Classification layers
-        proj_feat = self.classifer(joint_feat)
-
-        return proj_feat

spaces/CVPR/LIVE/pybind11/tests/test_enum.py

@@ -1,207 +0,0 @@
-# -*- coding: utf-8 -*-
-import pytest
-from pybind11_tests import enums as m
-
-
-def test_unscoped_enum():
-    assert str(m.UnscopedEnum.EOne) == "UnscopedEnum.EOne"
-    assert str(m.UnscopedEnum.ETwo) == "UnscopedEnum.ETwo"
-    assert str(m.EOne) == "UnscopedEnum.EOne"
-
-    # name property
-    assert m.UnscopedEnum.EOne.name == "EOne"
-    assert m.UnscopedEnum.ETwo.name == "ETwo"
-    assert m.EOne.name == "EOne"
-    # name readonly
-    with pytest.raises(AttributeError):
-        m.UnscopedEnum.EOne.name = ""
-    # name returns a copy
-    foo = m.UnscopedEnum.EOne.name
-    foo = "bar"
-    assert m.UnscopedEnum.EOne.name == "EOne"
-
-    # __members__ property
-    assert m.UnscopedEnum.__members__ == \
-        {"EOne": m.UnscopedEnum.EOne, "ETwo": m.UnscopedEnum.ETwo, "EThree": m.UnscopedEnum.EThree}
-    # __members__ readonly
-    with pytest.raises(AttributeError):
-        m.UnscopedEnum.__members__ = {}
-    # __members__ returns a copy
-    foo = m.UnscopedEnum.__members__
-    foo["bar"] = "baz"
-    assert m.UnscopedEnum.__members__ == \
-        {"EOne": m.UnscopedEnum.EOne, "ETwo": m.UnscopedEnum.ETwo, "EThree": m.UnscopedEnum.EThree}
-
-    for docstring_line in '''An unscoped enumeration
-
-Members:
-
-  EOne : Docstring for EOne
-
-  ETwo : Docstring for ETwo
-
-  EThree : Docstring for EThree'''.split('\n'):
-        assert docstring_line in m.UnscopedEnum.__doc__
-
-    # Unscoped enums will accept ==/!= int comparisons
-    y = m.UnscopedEnum.ETwo
-    assert y == 2
-    assert 2 == y
-    assert y != 3
-    assert 3 != y
-    # Compare with None
-    assert (y != None)  # noqa: E711
-    assert not (y == None)  # noqa: E711
-    # Compare with an object
-    assert (y != object())
-    assert not (y == object())
-    # Compare with string
-    assert y != "2"
-    assert "2" != y
-    assert not ("2" == y)
-    assert not (y == "2")
-
-    with pytest.raises(TypeError):
-        y < object()
-
-    with pytest.raises(TypeError):
-        y <= object()
-
-    with pytest.raises(TypeError):
-        y > object()
-
-    with pytest.raises(TypeError):
-        y >= object()
-
-    with pytest.raises(TypeError):
-        y | object()
-
-    with pytest.raises(TypeError):
-        y & object()
-
-    with pytest.raises(TypeError):
-        y ^ object()
-
-    assert int(m.UnscopedEnum.ETwo) == 2
-    assert str(m.UnscopedEnum(2)) == "UnscopedEnum.ETwo"
-
-    # order
-    assert m.UnscopedEnum.EOne < m.UnscopedEnum.ETwo
-    assert m.UnscopedEnum.EOne < 2
-    assert m.UnscopedEnum.ETwo > m.UnscopedEnum.EOne
-    assert m.UnscopedEnum.ETwo > 1
-    assert m.UnscopedEnum.ETwo <= 2
-    assert m.UnscopedEnum.ETwo >= 2
-    assert m.UnscopedEnum.EOne <= m.UnscopedEnum.ETwo
-    assert m.UnscopedEnum.EOne <= 2
-    assert m.UnscopedEnum.ETwo >= m.UnscopedEnum.EOne
-    assert m.UnscopedEnum.ETwo >= 1
-    assert not (m.UnscopedEnum.ETwo < m.UnscopedEnum.EOne)
-    assert not (2 < m.UnscopedEnum.EOne)
-
-    # arithmetic
-    assert m.UnscopedEnum.EOne & m.UnscopedEnum.EThree == m.UnscopedEnum.EOne
-    assert m.UnscopedEnum.EOne | m.UnscopedEnum.ETwo == m.UnscopedEnum.EThree
-    assert m.UnscopedEnum.EOne ^ m.UnscopedEnum.EThree == m.UnscopedEnum.ETwo
-
-
-def test_scoped_enum():
-    assert m.test_scoped_enum(m.ScopedEnum.Three) == "ScopedEnum::Three"
-    z = m.ScopedEnum.Two
-    assert m.test_scoped_enum(z) == "ScopedEnum::Two"
-
-    # Scoped enums will *NOT* accept ==/!= int comparisons (Will always return False)
-    assert not z == 3
-    assert not 3 == z
-    assert z != 3
-    assert 3 != z
-    # Compare with None
-    assert (z != None)  # noqa: E711
-    assert not (z == None)  # noqa: E711
-    # Compare with an object
-    assert (z != object())
-    assert not (z == object())
-    # Scoped enums will *NOT* accept >, <, >= and <= int comparisons (Will throw exceptions)
-    with pytest.raises(TypeError):
-        z > 3
-    with pytest.raises(TypeError):
-        z < 3
-    with pytest.raises(TypeError):
-        z >= 3
-    with pytest.raises(TypeError):
-        z <= 3
-
-    # order
-    assert m.ScopedEnum.Two < m.ScopedEnum.Three
-    assert m.ScopedEnum.Three > m.ScopedEnum.Two
-    assert m.ScopedEnum.Two <= m.ScopedEnum.Three
-    assert m.ScopedEnum.Two <= m.ScopedEnum.Two
-    assert m.ScopedEnum.Two >= m.ScopedEnum.Two
-    assert m.ScopedEnum.Three >= m.ScopedEnum.Two
-
-
-def test_implicit_conversion():
-    assert str(m.ClassWithUnscopedEnum.EMode.EFirstMode) == "EMode.EFirstMode"
-    assert str(m.ClassWithUnscopedEnum.EFirstMode) == "EMode.EFirstMode"
-
-    f = m.ClassWithUnscopedEnum.test_function
-    first = m.ClassWithUnscopedEnum.EFirstMode
-    second = m.ClassWithUnscopedEnum.ESecondMode
-
-    assert f(first) == 1
-
-    assert f(first) == f(first)
-    assert not f(first) != f(first)
-
-    assert f(first) != f(second)
-    assert not f(first) == f(second)
-
-    assert f(first) == int(f(first))
-    assert not f(first) != int(f(first))
-
-    assert f(first) != int(f(second))
-    assert not f(first) == int(f(second))
-
-    # noinspection PyDictCreation
-    x = {f(first): 1, f(second): 2}
-    x[f(first)] = 3
-    x[f(second)] = 4
-    # Hashing test
-    assert str(x) == "{EMode.EFirstMode: 3, EMode.ESecondMode: 4}"
-
-
-def test_binary_operators():
-    assert int(m.Flags.Read) == 4
-    assert int(m.Flags.Write) == 2
-    assert int(m.Flags.Execute) == 1
-    assert int(m.Flags.Read | m.Flags.Write | m.Flags.Execute) == 7
-    assert int(m.Flags.Read | m.Flags.Write) == 6
-    assert int(m.Flags.Read | m.Flags.Execute) == 5
-    assert int(m.Flags.Write | m.Flags.Execute) == 3
-    assert int(m.Flags.Write | 1) == 3
-    assert ~m.Flags.Write == -3
-
-    state = m.Flags.Read | m.Flags.Write
-    assert (state & m.Flags.Read) != 0
-    assert (state & m.Flags.Write) != 0
-    assert (state & m.Flags.Execute) == 0
-    assert (state & 1) == 0
-
-    state2 = ~state
-    assert state2 == -7
-    assert int(state ^ state2) == -1
-
-
-def test_enum_to_int():
-    m.test_enum_to_int(m.Flags.Read)
-    m.test_enum_to_int(m.ClassWithUnscopedEnum.EMode.EFirstMode)
-    m.test_enum_to_uint(m.Flags.Read)
-    m.test_enum_to_uint(m.ClassWithUnscopedEnum.EMode.EFirstMode)
-    m.test_enum_to_long_long(m.Flags.Read)
-    m.test_enum_to_long_long(m.ClassWithUnscopedEnum.EMode.EFirstMode)
-
-
-def test_duplicate_enum_name():
-    with pytest.raises(ValueError) as excinfo:
-        m.register_bad_enum()
-    assert str(excinfo.value) == 'SimpleEnum: element "ONE" already exists!'

spaces/CVPR/LIVE/thrust/CODE_OF_CONDUCT.md

@@ -1,59 +0,0 @@
-# Contributor Covenant Code of Conduct
-
-## Overview
-
-Define the code of conduct followed and enforced for Thrust
-
-### Intended audience
-
-* Community
-* Developers
-* Project Leads
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment include:
-
--   Using welcoming and inclusive language
--   Being respectful of differing viewpoints and experiences
--   Gracefully accepting constructive criticism
--   Focusing on what is best for the community
--   Showing empathy towards other community members
-
-Examples of unacceptable behavior by participants include:
-
--   The use of sexualized language or imagery and unwelcome sexual attention or advances
--   Trolling, insulting/derogatory comments, and personal or political attacks
--   Public or private harassment
--   Publishing others’ private information, such as a physical or electronic address, without explicit permission
--   Other conduct which could reasonably be considered inappropriate in a professional setting
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at  [[email protected]](mailto:[email protected])  All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project’s leadership.
-
-## Attribution
-
-This Code of Conduct was taken from the [NVIDIA RAPIDS](https://docs.rapids.ai/resources/conduct/) project, which was adapted from the  [Contributor Covenant](https://www.contributor-covenant.org/), version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
-
-For answers to common questions about this code of conduct, see https://www.contributor-covenant.org/faq
-
-## Contact
-
-If you need to contact the Thrust team, please reach out to [email protected]

spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/async/copy.h

@@ -1,538 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-// TODO: Move into system::cuda
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/cpp14_required.h>
-
-#if THRUST_CPP_DIALECT >= 2014
-
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-
-#include <thrust/system/cuda/config.h>
-
-#include <thrust/system/cuda/detail/async/customization.h>
-#include <thrust/system/cuda/detail/async/transform.h>
-#include <thrust/system/cuda/detail/cross_system.h>
-#include <thrust/system/cuda/future.h>
-#include <thrust/iterator/iterator_traits.h>
-#include <thrust/type_traits/logical_metafunctions.h>
-#include <thrust/detail/static_assert.h>
-#include <thrust/type_traits/is_trivially_relocatable.h>
-#include <thrust/type_traits/is_contiguous_iterator.h>
-#include <thrust/distance.h>
-#include <thrust/advance.h>
-#include <thrust/uninitialized_copy.h>
-
-#include <type_traits>
-
-namespace thrust
-{
-
-namespace system { namespace cuda { namespace detail
-{
-
-// ContiguousIterator input and output iterators
-// TriviallyCopyable elements
-// Host to device, device to host, device to device
-template <
-  typename FromPolicy, typename ToPolicy
-, typename ForwardIt, typename OutputIt, typename Size
->
-auto async_copy_n(
-  FromPolicy& from_exec
-, ToPolicy&   to_exec
-, ForwardIt   first
-, Size        n
-, OutputIt    output
-) ->
-  typename std::enable_if<
-    is_indirectly_trivially_relocatable_to<ForwardIt, OutputIt>::value
-  , unique_eager_event
-  >::type
-{
-  using T = typename iterator_traits<ForwardIt>::value_type;
-
-  auto const device_alloc = get_async_device_allocator(
-    select_device_system(from_exec, to_exec)
-  );
-
-  using pointer
-    = typename thrust::detail::allocator_traits<decltype(device_alloc)>::
-      template rebind_traits<void>::pointer;
-
-  unique_eager_event e;
-
-  // Set up stream with dependencies.
-
-  cudaStream_t const user_raw_stream = thrust::cuda_cub::stream(
-    select_device_system(from_exec, to_exec)
-  );
-
-  if (thrust::cuda_cub::default_stream() != user_raw_stream)
-  {
-    e = make_dependent_event(
-      std::tuple_cat(
-        std::make_tuple(
-          unique_stream(nonowning, user_raw_stream)
-        )
-      , extract_dependencies(
-          std::move(thrust::detail::derived_cast(from_exec))
-        )
-      , extract_dependencies(
-          std::move(thrust::detail::derived_cast(to_exec))
-        )
-      )
-    );
-  }
-  else
-  {
-    e = make_dependent_event(
-      std::tuple_cat(
-        extract_dependencies(
-          std::move(thrust::detail::derived_cast(from_exec))
-        )
-      , extract_dependencies(
-          std::move(thrust::detail::derived_cast(to_exec))
-        )
-      )
-    );
-  }
-
-  // Run copy.
-
-  thrust::cuda_cub::throw_on_error(
-    cudaMemcpyAsync(
-      thrust::raw_pointer_cast(&*output)
-    , thrust::raw_pointer_cast(&*first)
-    , sizeof(T) * n
-    , direction_of_copy(from_exec, to_exec)
-    , e.stream().native_handle()
-    )
-  , "after copy launch"
-  );
-
-  return e;
-}
-
-// Non-ContiguousIterator input or output, or non-TriviallyRelocatable value type
-// Device to device
-template <
-  typename FromPolicy, typename ToPolicy
-, typename ForwardIt, typename OutputIt, typename Size
->
-auto async_copy_n(
-  thrust::cuda::execution_policy<FromPolicy>& from_exec
-, thrust::cuda::execution_policy<ToPolicy>&   to_exec
-, ForwardIt                                   first
-, Size                                        n
-, OutputIt                                    output
-) ->
-  typename std::enable_if<
-    conjunction<
-      negation<
-        is_indirectly_trivially_relocatable_to<ForwardIt, OutputIt>
-      >
-    , decltype(is_device_to_device_copy(from_exec, to_exec))
-    >::value
-  , unique_eager_event
-  >::type
-{
-  using T = typename iterator_traits<ForwardIt>::value_type;
-
-  return async_transform_n(
-    select_device_system(from_exec, to_exec)
-  , first, n, output, thrust::identity<T>()
-  );
-}
-
-template <typename OutputIt>
-void async_copy_n_compile_failure_no_cuda_to_non_contiguous_output()
-{
-  THRUST_STATIC_ASSERT_MSG(
-    (negation<is_contiguous_iterator<OutputIt>>::value)
-  , "copying to non-ContiguousIterators in another system from the CUDA system "
-    "is not supported; use `THRUST_PROCLAIM_CONTIGUOUS_ITERATOR(Iterator)` to "
-    "indicate that an iterator points to elements that are contiguous in memory."
-  );
-}
-
-// Non-ContiguousIterator output iterator
-// TriviallyRelocatable value type
-// Device to host, host to device
-template <
-  typename FromPolicy, typename ToPolicy
-, typename ForwardIt, typename OutputIt, typename Size
->
-auto async_copy_n(
-  FromPolicy& from_exec
-, ToPolicy&   to_exec
-, ForwardIt   first
-, Size        n
-, OutputIt    output
-) ->
-  typename std::enable_if<
-    conjunction<
-      negation<is_contiguous_iterator<OutputIt>>
-    , is_trivially_relocatable_to<
-        typename iterator_traits<ForwardIt>::value_type
-      , typename iterator_traits<OutputIt>::value_type
-      >
-    , disjunction<
-        decltype(is_host_to_device_copy(from_exec, to_exec))
-      , decltype(is_device_to_host_copy(from_exec, to_exec))
-      >
-    >::value
-  , unique_eager_event
-  >::type
-{
-  async_copy_n_compile_failure_no_cuda_to_non_contiguous_output<OutputIt>();
-
-  return {};
-}
-
-// Workaround for MSVC's lack of expression SFINAE and also for an NVCC bug.
-// In NVCC, when two SFINAE-enabled overloads are only distinguishable by a
-// part of a SFINAE condition that is in a `decltype`, NVCC thinks they are the
-// same overload and emits an error.
-template <
-  typename FromPolicy, typename ToPolicy
-, typename ForwardIt, typename OutputIt
-  // MSVC2015 WAR: doesn't like decltype(...)::value in superclass definition
-, typename IsH2DCopy = decltype(is_host_to_device_copy(
-    std::declval<FromPolicy const&>()
-  , std::declval<ToPolicy const&>()))
->
-struct is_buffered_trivially_relocatable_host_to_device_copy
-  : thrust::integral_constant<
-      bool
-    ,    !is_contiguous_iterator<ForwardIt>::value
-      && is_contiguous_iterator<OutputIt>::value
-      && is_trivially_relocatable_to<
-            typename iterator_traits<ForwardIt>::value_type
-          , typename iterator_traits<OutputIt>::value_type
-          >::value
-      && IsH2DCopy::value
-    >
-{};
-
-// Non-ContiguousIterator input iterator, ContiguousIterator output iterator
-// TriviallyRelocatable value type
-// Host to device
-template <
-  typename FromPolicy, typename ToPolicy
-, typename ForwardIt, typename OutputIt, typename Size
->
-auto async_copy_n(
-  FromPolicy&                               from_exec
-, thrust::cuda::execution_policy<ToPolicy>& to_exec
-, ForwardIt                                 first
-, Size                                      n
-, OutputIt                                  output
-) ->
-  typename std::enable_if<
-    is_buffered_trivially_relocatable_host_to_device_copy<
-      FromPolicy
-    , thrust::cuda::execution_policy<ToPolicy>
-    , ForwardIt, OutputIt
-    >::value
-  , unique_eager_event
-  >::type
-{
-  using T = typename iterator_traits<ForwardIt>::value_type;
-
-  auto const host_alloc = get_async_host_allocator(
-    from_exec
-  );
-
-  // Create host-side buffer.
-
-  auto buffer = uninitialized_allocate_unique_n<T>(host_alloc, n);
-
-  auto const buffer_ptr = buffer.get();
-
-  // Copy into host-side buffer.
-
-  // TODO: Switch to an async call once we have async interfaces for host
-  // systems and support for cross system dependencies.
-  uninitialized_copy_n(from_exec, first, n, buffer_ptr);
-
-  // Run device-side copy.
-
-  auto new_to_exec = thrust::detail::derived_cast(to_exec).rebind_after(
-    std::tuple_cat(
-      std::make_tuple(
-        std::move(buffer)
-      )
-    , extract_dependencies(
-        std::move(thrust::detail::derived_cast(from_exec))
-      )
-    , extract_dependencies(
-        std::move(thrust::detail::derived_cast(to_exec))
-      )
-    )
-  );
-
-  THRUST_STATIC_ASSERT((
-    std::tuple_size<decltype(
-      extract_dependencies(to_exec)
-    )>::value + 1
-    <=
-    std::tuple_size<decltype(
-      extract_dependencies(new_to_exec)
-    )>::value
-  ));
-
-  return async_copy_n(
-    from_exec
-    // TODO: We have to cast back to the right execution_policy class. Ideally,
-    // we should be moving here.
-  , new_to_exec
-  , buffer_ptr
-  , n
-  , output
-  );
-}
-
-// Workaround for MSVC's lack of expression SFINAE and also for an NVCC bug.
-// In NVCC, when two SFINAE-enabled overloads are only distinguishable by a
-// part of a SFINAE condition that is in a `decltype`, NVCC thinks they are the
-// same overload and emits an error.
-template <
-  typename FromPolicy, typename ToPolicy
-, typename ForwardIt, typename OutputIt
-  // MSVC2015 WAR: doesn't like decltype(...)::value in superclass definition
-, typename IsD2HCopy = decltype(is_device_to_host_copy(
-    std::declval<FromPolicy const&>()
-  , std::declval<ToPolicy const&>()))
->
-struct is_buffered_trivially_relocatable_device_to_host_copy
-  : thrust::integral_constant<
-      bool
-    ,    !is_contiguous_iterator<ForwardIt>::value
-      && is_contiguous_iterator<OutputIt>::value
-      && is_trivially_relocatable_to<
-            typename iterator_traits<ForwardIt>::value_type
-          , typename iterator_traits<OutputIt>::value_type
-          >::value
-      && IsD2HCopy::value
-    >
-{};
-
-// Non-ContiguousIterator input iterator, ContiguousIterator output iterator
-// TriviallyRelocatable value type
-// Device to host
-template <
-  typename FromPolicy, typename ToPolicy
-, typename ForwardIt, typename OutputIt, typename Size
->
-auto async_copy_n(
-  thrust::cuda::execution_policy<FromPolicy>& from_exec
-, ToPolicy&                                   to_exec
-, ForwardIt                                   first
-, Size                                        n
-, OutputIt                                    output
-) ->
-  typename std::enable_if<
-    is_buffered_trivially_relocatable_device_to_host_copy<
-      thrust::cuda::execution_policy<FromPolicy>
-    , ToPolicy
-    , ForwardIt, OutputIt
-    >::value
-  , unique_eager_event
-  >::type
-{
-  using T = typename iterator_traits<ForwardIt>::value_type;
-
-  auto const device_alloc = get_async_device_allocator(
-    from_exec
-  );
-
-  // Create device-side buffer.
-
-  auto buffer = uninitialized_allocate_unique_n<T>(device_alloc, n);
-
-  auto const buffer_ptr = buffer.get();
-
-  // Run device-side copy.
-
-  auto f0 = async_copy_n(
-    from_exec
-  , from_exec
-  , first
-  , n
-  , buffer_ptr
-  );
-
-  // Run copy back to host.
-
-  auto new_from_exec = thrust::detail::derived_cast(from_exec).rebind_after(
-    std::move(buffer)
-  , std::move(f0)
-  );
-
-  THRUST_STATIC_ASSERT((
-    std::tuple_size<decltype(
-      extract_dependencies(from_exec)
-    )>::value + 1
-    <=
-    std::tuple_size<decltype(
-      extract_dependencies(new_from_exec)
-    )>::value
-  ));
-
-  return async_copy_n(
-    new_from_exec
-  , to_exec
-  , buffer_ptr
-  , n
-  , output
-  );
-}
-
-template <typename InputType, typename OutputType>
-void async_copy_n_compile_failure_non_trivially_relocatable_elements()
-{
-  THRUST_STATIC_ASSERT_MSG(
-    (is_trivially_relocatable_to<OutputType, InputType>::value)
-  , "only sequences of TriviallyRelocatable elements can be copied to and from "
-    "the CUDA system; use `THRUST_PROCLAIM_TRIVIALLY_RELOCATABLE(T)` to "
-    "indicate that a type can be copied by bitwise (e.g. by `memcpy`)"
-  );
-}
-
-// Non-TriviallyRelocatable value type
-// Host to device, device to host
-template <
-  typename FromPolicy, typename ToPolicy
-, typename ForwardIt, typename OutputIt, typename Size
->
-auto async_copy_n(
-  FromPolicy& from_exec
-, ToPolicy&   to_exec
-, ForwardIt   first
-, Size        n
-, OutputIt    output
-) ->
-  typename std::enable_if<
-    conjunction<
-      negation<
-        is_trivially_relocatable_to<
-          typename iterator_traits<ForwardIt>::value_type
-        , typename iterator_traits<OutputIt>::value_type
-        >
-      >
-    , disjunction<
-        decltype(is_host_to_device_copy(from_exec, to_exec))
-      , decltype(is_device_to_host_copy(from_exec, to_exec))
-      >
-    >::value
-  , unique_eager_event
-  >::type
-{
-  // TODO: We could do more here with cudaHostRegister.
-
-  async_copy_n_compile_failure_non_trivially_relocatable_elements<
-    typename thrust::iterator_traits<ForwardIt>::value_type
-  , typename std::add_lvalue_reference<
-      typename thrust::iterator_traits<OutputIt>::value_type
-    >::type
-  >();
-
-  return {};
-}
-
-}}} // namespace system::cuda::detail
-
-namespace cuda_cub
-{
-
-// ADL entry point.
-template <
-  typename FromPolicy, typename ToPolicy
-, typename ForwardIt, typename Sentinel, typename OutputIt
->
-auto async_copy(
-  thrust::cuda::execution_policy<FromPolicy>&         from_exec
-, thrust::cpp::execution_policy<ToPolicy>&            to_exec
-, ForwardIt                                           first
-, Sentinel                                            last
-, OutputIt                                            output
-)
-THRUST_RETURNS(
-  thrust::system::cuda::detail::async_copy_n(
-    from_exec, to_exec, first, distance(first, last), output
-  )
-)
-
-// ADL entry point.
-template <
-  typename FromPolicy, typename ToPolicy
-, typename ForwardIt, typename Sentinel, typename OutputIt
->
-auto async_copy(
-  thrust::cpp::execution_policy<FromPolicy>& from_exec
-, thrust::cuda::execution_policy<ToPolicy>&  to_exec
-, ForwardIt                                  first
-, Sentinel                                   last
-, OutputIt                                   output
-)
-THRUST_RETURNS(
-  thrust::system::cuda::detail::async_copy_n(
-    from_exec, to_exec, first, distance(first, last), output
-  )
-)
-
-// ADL entry point.
-template <
-  typename FromPolicy, typename ToPolicy
-, typename ForwardIt, typename Sentinel, typename OutputIt
->
-auto async_copy(
-  thrust::cuda::execution_policy<FromPolicy>& from_exec
-, thrust::cuda::execution_policy<ToPolicy>&   to_exec
-, ForwardIt                                   first
-, Sentinel                                    last
-, OutputIt                                    output
-)
-THRUST_RETURNS(
-  thrust::system::cuda::detail::async_copy_n(
-    from_exec, to_exec, first, distance(first, last), output
-  )
-)
-
-} // cuda_cub
-
-} // end namespace thrust
-
-#endif // THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-
-#endif
-

spaces/CVPR/WALT/mmdet/datasets/pipelines/instaboost.py

@@ -1,98 +0,0 @@
-import numpy as np
-
-from ..builder import PIPELINES
-
-
-@PIPELINES.register_module()
-class InstaBoost(object):
-    r"""Data augmentation method in `InstaBoost: Boosting Instance
-    Segmentation Via Probability Map Guided Copy-Pasting
-    <https://arxiv.org/abs/1908.07801>`_.
-
-    Refer to https://github.com/GothicAi/Instaboost for implementation details.
-    """
-
-    def __init__(self,
-                 action_candidate=('normal', 'horizontal', 'skip'),
-                 action_prob=(1, 0, 0),
-                 scale=(0.8, 1.2),
-                 dx=15,
-                 dy=15,
-                 theta=(-1, 1),
-                 color_prob=0.5,
-                 hflag=False,
-                 aug_ratio=0.5):
-        try:
-            import instaboostfast as instaboost
-        except ImportError:
-            raise ImportError(
-                'Please run "pip install instaboostfast" '
-                'to install instaboostfast first for instaboost augmentation.')
-        self.cfg = instaboost.InstaBoostConfig(action_candidate, action_prob,
-                                               scale, dx, dy, theta,
-                                               color_prob, hflag)
-        self.aug_ratio = aug_ratio
-
-    def _load_anns(self, results):
-        labels = results['ann_info']['labels']
-        masks = results['ann_info']['masks']
-        bboxes = results['ann_info']['bboxes']
-        n = len(labels)
-
-        anns = []
-        for i in range(n):
-            label = labels[i]
-            bbox = bboxes[i]
-            mask = masks[i]
-            x1, y1, x2, y2 = bbox
-            # assert (x2 - x1) >= 1 and (y2 - y1) >= 1
-            bbox = [x1, y1, x2 - x1, y2 - y1]
-            anns.append({
-                'category_id': label,
-                'segmentation': mask,
-                'bbox': bbox
-            })
-
-        return anns
-
-    def _parse_anns(self, results, anns, img):
-        gt_bboxes = []
-        gt_labels = []
-        gt_masks_ann = []
-        for ann in anns:
-            x1, y1, w, h = ann['bbox']
-            # TODO: more essential bug need to be fixed in instaboost
-            if w <= 0 or h <= 0:
-                continue
-            bbox = [x1, y1, x1 + w, y1 + h]
-            gt_bboxes.append(bbox)
-            gt_labels.append(ann['category_id'])
-            gt_masks_ann.append(ann['segmentation'])
-        gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
-        gt_labels = np.array(gt_labels, dtype=np.int64)
-        results['ann_info']['labels'] = gt_labels
-        results['ann_info']['bboxes'] = gt_bboxes
-        results['ann_info']['masks'] = gt_masks_ann
-        results['img'] = img
-        return results
-
-    def __call__(self, results):
-        img = results['img']
-        orig_type = img.dtype
-        anns = self._load_anns(results)
-        if np.random.choice([0, 1], p=[1 - self.aug_ratio, self.aug_ratio]):
-            try:
-                import instaboostfast as instaboost
-            except ImportError:
-                raise ImportError('Please run "pip install instaboostfast" '
-                                  'to install instaboostfast first.')
-            anns, img = instaboost.get_new_data(
-                anns, img.astype(np.uint8), self.cfg, background=None)
-
-        results = self._parse_anns(results, anns, img.astype(orig_type))
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(cfg={self.cfg}, aug_ratio={self.aug_ratio})'
-        return repr_str

spaces/CVPR/v-doc_abstractive_mac/main.py

@@ -1,653 +0,0 @@
-from __future__ import division
-import warnings
-
-from extract_feature import build_model, run_image, get_img_feat
-
-# warnings.filterwarnings("ignore", category=FutureWarning)
-# warnings.filterwarnings("ignore", message="size changed")
-warnings.filterwarnings("ignore")
-
-import sys
-import os
-import time
-import math
-import random
-
-try:
-    import Queue as queue
-except ImportError:
-    import queue
-import threading
-import h5py
-import json
-import numpy as np
-import tensorflow as tf
-from termcolor import colored, cprint
-
-from config import config, loadDatasetConfig, parseArgs
-from preprocess import Preprocesser, bold, bcolored, writeline, writelist
-from model import MACnet
-from collections import defaultdict
-
-
-############################################# loggers #############################################
-
-# Writes log header to file
-def logInit():
-    with open(config.logFile(), "a+") as outFile:
-        writeline(outFile, config.expName)
-        headers = ["epoch", "trainAcc", "valAcc", "trainLoss", "valLoss"]
-        if config.evalTrain:
-            headers += ["evalTrainAcc", "evalTrainLoss"]
-        if config.extra:
-            if config.evalTrain:
-                headers += ["thAcc", "thLoss"]
-            headers += ["vhAcc", "vhLoss"]
-        headers += ["time", "lr"]
-
-        writelist(outFile, headers)
-        # lr assumed to be last
-
-
-# Writes log record to file
-def logRecord(epoch, epochTime, lr, trainRes, evalRes, extraEvalRes):
-    with open(config.logFile(), "a+") as outFile:
-        record = [epoch, trainRes["acc"], evalRes["val"]["acc"], trainRes["loss"], evalRes["val"]["loss"]]
-        if config.evalTrain:
-            record += [evalRes["evalTrain"]["acc"], evalRes["evalTrain"]["loss"]]
-        if config.extra:
-            if config.evalTrain:
-                record += [extraEvalRes["evalTrain"]["acc"], extraEvalRes["evalTrain"]["loss"]]
-            record += [extraEvalRes["val"]["acc"], extraEvalRes["val"]["loss"]]
-        record += [epochTime, lr]
-
-        writelist(outFile, record)
-
-
-# Gets last logged epoch and learning rate
-def lastLoggedEpoch():
-    with open(config.logFile(), "r") as inFile:
-        lastLine = list(inFile)[-1].split(",")
-    epoch = int(lastLine[0])
-    lr = float(lastLine[-1])
-    return epoch, lr
-
-
-################################## printing, output and analysis ##################################
-
-# Analysis by type
-analysisQuestionLims = [(0, 18), (19, float("inf"))]
-analysisProgramLims = [(0, 12), (13, float("inf"))]
-
-toArity = lambda instance: instance["programSeq"][-1].split("_", 1)[0]
-toType = lambda instance: instance["programSeq"][-1].split("_", 1)[1]
-
-
-def fieldLenIsInRange(field):
-    return lambda instance, group: \
-        (len(instance[field]) >= group[0] and
-         len(instance[field]) <= group[1])
-
-
-# Groups instances based on a key
-def grouperKey(toKey):
-    def grouper(instances):
-        res = defaultdict(list)
-        for instance in instances:
-            res[toKey(instance)].append(instance)
-        return res
-
-    return grouper
-
-
-# Groups instances according to their match to condition
-def grouperCond(groups, isIn):
-    def grouper(instances):
-        res = {}
-        for group in groups:
-            res[group] = (instance for instance in instances if isIn(instance, group))
-        return res
-
-    return grouper
-
-
-groupers = {
-    "questionLength": grouperCond(analysisQuestionLims, fieldLenIsInRange("questionSeq")),
-    "programLength": grouperCond(analysisProgramLims, fieldLenIsInRange("programSeq")),
-    "arity": grouperKey(toArity),
-    "type": grouperKey(toType)
-}
-
-
-# Computes average
-def avg(instances, field):
-    if len(instances) == 0:
-        return 0.0
-    return sum(instances[field]) / len(instances)
-
-
-# Prints analysis of questions loss and accuracy by their group
-def printAnalysis(res):
-    if config.analysisType != "":
-        print("Analysis by {type}".format(type=config.analysisType))
-        groups = groupers[config.analysisType](res["preds"])
-        for key in groups:
-            instances = groups[key]
-            avgLoss = avg(instances, "loss")
-            avgAcc = avg(instances, "acc")
-            num = len(instances)
-            print("Group {key}: Loss: {loss}, Acc: {acc}, Num: {num}".format(key, avgLoss, avgAcc, num))
-
-
-# Print results for a tier
-def printTierResults(tierName, res, color):
-    if res is None:
-        return
-
-    print("{tierName} Loss: {loss}, {tierName} accuracy: {acc}".format(tierName=tierName,
-                                                                       loss=bcolored(res["loss"], color),
-                                                                       acc=bcolored(res["acc"], color)))
-
-    printAnalysis(res)
-
-
-# Prints dataset results (for several tiers)
-def printDatasetResults(trainRes, evalRes):
-    printTierResults("Training", trainRes, "magenta")
-    printTierResults("Training EMA", evalRes["evalTrain"], "red")
-    printTierResults("Validation", evalRes["val"], "cyan")
-
-
-# Writes predictions for several tiers
-def writePreds(preprocessor, evalRes):
-    preprocessor.writePreds(evalRes, "_")
-
-
-############################################# session #############################################
-# Initializes TF session. Sets GPU memory configuration.
-def setSession():
-    sessionConfig = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
-    if config.allowGrowth:
-        sessionConfig.gpu_options.allow_growth = True
-    if config.maxMemory < 1.0:
-        sessionConfig.gpu_options.per_process_gpu_memory_fraction = config.maxMemory
-    return sessionConfig
-
-
-############################################## savers #############################################
-# Initializes savers (standard, optional exponential-moving-average and optional for subset of variables)
-def setSavers(model):
-    saver = tf.train.Saver(max_to_keep=config.weightsToKeep)
-
-    subsetSaver = None
-    if config.saveSubset:
-        isRelevant = lambda var: any(s in var.name for s in config.varSubset)
-        relevantVars = [var for var in tf.global_variables() if isRelevant(var)]
-        subsetSaver = tf.train.Saver(relevantVars, max_to_keep=config.weightsToKeep, allow_empty=True)
-
-    emaSaver = None
-    if config.useEMA:
-        emaSaver = tf.train.Saver(model.emaDict, max_to_keep=config.weightsToKeep)
-
-    return {
-        "saver": saver,
-        "subsetSaver": subsetSaver,
-        "emaSaver": emaSaver
-    }
-
-
-################################### restore / initialize weights ##################################
-# Restores weights of specified / last epoch if on restore mod.
-# Otherwise, initializes weights.
-def loadWeights(sess, saver, init):
-    if config.restoreEpoch > 0 or config.restore:
-        # restore last epoch only if restoreEpoch isn't set
-        if config.restoreEpoch == 0:
-            # restore last logged epoch
-            config.restoreEpoch, config.lr = lastLoggedEpoch()
-        print(bcolored("Restoring epoch {} and lr {}".format(config.restoreEpoch, config.lr), "cyan"))
-        print(bcolored("Restoring weights", "blue"))
-        print(config.weightsFile(config.restoreEpoch))
-        saver.restore(sess, config.weightsFile(config.restoreEpoch))
-        epoch = config.restoreEpoch
-    else:
-        print(bcolored("Initializing weights", "blue"))
-        sess.run(init)
-        logInit()
-        epoch = 0
-
-    return epoch
-
-
-###################################### training / evaluation ######################################
-# Chooses data to train on (main / extra) data.
-def chooseTrainingData(data):
-    trainingData = data["main"]["train"]
-    alterData = None
-
-    if config.extra:
-        if config.trainExtra:
-            if config.extraVal:
-                trainingData = data["extra"]["val"]
-            else:
-                trainingData = data["extra"]["train"]
-        if config.alterExtra:
-            alterData = data["extra"]["train"]
-
-    return trainingData, alterData
-
-
-#### evaluation
-# Runs evaluation on train / val / test datasets.
-def runEvaluation(sess, model, data, epoch, evalTrain=True, evalTest=False, getAtt=None):
-    if getAtt is None:
-        getAtt = config.getAtt
-    res = {"evalTrain": None, "val": None, "test": None}
-
-    if data is not None:
-        if evalTrain and config.evalTrain:
-            res["evalTrain"] = runEpoch(sess, model, data["evalTrain"], train=False, epoch=epoch, getAtt=getAtt)
-
-        res["val"] = runEpoch(sess, model, data["val"], train=False, epoch=epoch, getAtt=getAtt)
-
-        if evalTest or config.test:
-            res["test"] = runEpoch(sess, model, data["test"], train=False, epoch=epoch, getAtt=getAtt)
-
-    return res
-
-
-## training conditions (comparing current epoch result to prior ones)
-def improveEnough(curr, prior, lr):
-    prevRes = prior["prev"]["res"]
-    currRes = curr["res"]
-
-    if prevRes is None:
-        return True
-
-    prevTrainLoss = prevRes["train"]["loss"]
-    currTrainLoss = currRes["train"]["loss"]
-    lossDiff = prevTrainLoss - currTrainLoss
-
-    notImprove = ((lossDiff < 0.015 and prevTrainLoss < 0.5 and lr > 0.00002) or \
-                  (lossDiff < 0.008 and prevTrainLoss < 0.15 and lr > 0.00001) or \
-                  (lossDiff < 0.003 and prevTrainLoss < 0.10 and lr > 0.000005))
-    # (prevTrainLoss < 0.2 and config.lr > 0.000015)
-
-    return not notImprove
-
-
-def better(currRes, bestRes):
-    return currRes["val"]["acc"] > bestRes["val"]["acc"]
-
-
-############################################## data ###############################################
-#### instances and batching
-# Trims sequences based on their max length.
-def trim2DVectors(vectors, vectorsLengths):
-    maxLength = np.max(vectorsLengths)
-    return vectors[:, :maxLength]
-
-
-# Trims batch based on question length.
-def trimData(data):
-    data["questions"] = trim2DVectors(data["questions"], data["questionLengths"])
-    return data
-
-
-# Gets batch / bucket size.
-def getLength(data):
-    return len(data["instances"])
-
-
-# Selects the data entries that match the indices.
-def selectIndices(data, indices):
-    def select(field, indices):
-        if type(field) is np.ndarray:
-            return field[indices]
-        if type(field) is list:
-            return [field[i] for i in indices]
-        else:
-            return field
-
-    selected = {k: select(d, indices) for k, d in data.items()}
-    return selected
-
-
-# Batches data into a a list of batches of batchSize.
-# Shuffles the data by default.
-def getBatches(data, batchSize=None, shuffle=True):
-    batches = []
-
-    dataLen = getLength(data)
-    if batchSize is None or batchSize > dataLen:
-        batchSize = dataLen
-
-    indices = np.arange(dataLen)
-    if shuffle:
-        np.random.shuffle(indices)
-
-    for batchStart in range(0, dataLen, batchSize):
-        batchIndices = indices[batchStart: batchStart + batchSize]
-        # if len(batchIndices) == batchSize?
-        if len(batchIndices) >= config.gpusNum:
-            batch = selectIndices(data, batchIndices)
-            batches.append(batch)
-            # batchesIndices.append((data, batchIndices))
-
-    return batches
-
-
-#### image batches
-# Opens image files.
-def openImageFiles(images):
-    images["imagesFile"] = h5py.File(images["imagesFilename"], "r")
-    images["imagesIds"] = None
-    if config.dataset == "NLVR":
-        with open(images["imageIdsFilename"], "r") as imageIdsFile:
-            images["imagesIds"] = json.load(imageIdsFile)
-
-        # Closes image files.
-
-
-def closeImageFiles(images):
-    images["imagesFile"].close()
-
-
-# Loads an images from file for a given data batch.
-def loadImageBatch(images, batch):
-    imagesFile = images["imagesFile"]
-    id2idx = images["imagesIds"]
-    toIndex = lambda imageId: imageId
-    if id2idx is not None:
-        toIndex = lambda imageId: id2idx[imageId]
-    imageBatch = np.stack([imagesFile["features"][toIndex(imageId)] for imageId in batch["imageIds"]], axis=0)
-
-    return {"images": imageBatch, "imageIds": batch["imageIds"]}
-
-
-# Loads images for several num batches in the batches list from start index.
-def loadImageBatches(images, batches, start, num):
-    batches = batches[start: start + num]
-    return [loadImageBatch(images, batch) for batch in batches]
-
-
-#### data alternation
-# Alternates main training batches with extra data.
-def alternateData(batches, alterData, dataLen):
-    alterData = alterData["data"][0]  # data isn't bucketed for altered data
-
-    # computes number of repetitions
-    needed = math.ceil(len(batches) / config.alterNum)
-    print(bold("Extra batches needed: %d") % needed)
-    perData = math.ceil(getLength(alterData) / config.batchSize)
-    print(bold("Batches per extra data: %d") % perData)
-    repetitions = math.ceil(needed / perData)
-    print(bold("reps: %d") % repetitions)
-
-    # make alternate batches
-    alterBatches = []
-    for _ in range(repetitions):
-        repBatches = getBatches(alterData, batchSize=config.batchSize)
-        random.shuffle(repBatches)
-        alterBatches += repBatches
-    print(bold("Batches num: %d") + len(alterBatches))
-
-    # alternate data with extra data
-    curr = len(batches) - 1
-    for alterBatch in alterBatches:
-        if curr < 0:
-            # print(colored("too many" + str(curr) + " " + str(len(batches)),"red"))
-            break
-        batches.insert(curr, alterBatch)
-        dataLen += getLength(alterBatch)
-        curr -= config.alterNum
-
-    return batches, dataLen
-
-
-############################################ threading ############################################
-
-imagesQueue = queue.Queue(maxsize=20)  # config.tasksNum
-inQueue = queue.Queue(maxsize=1)
-outQueue = queue.Queue(maxsize=1)
-
-
-# Runs a worker thread(s) to load images while training .
-class StoppableThread(threading.Thread):
-    # Thread class with a stop() method. The thread itself has to check
-    # regularly for the stopped() condition.
-
-    def __init__(self, images, batches):  # i
-        super(StoppableThread, self).__init__()
-        # self.i = i
-        self.images = images
-        self.batches = batches
-        self._stop_event = threading.Event()
-
-    # def __init__(self, args):
-    #     super(StoppableThread, self).__init__(args = args)
-    #     self._stop_event = threading.Event()
-
-    # def __init__(self, target, args):
-    #     super(StoppableThread, self).__init__(target = target, args = args)
-    #     self._stop_event = threading.Event()
-
-    def stop(self):
-        self._stop_event.set()
-
-    def stopped(self):
-        return self._stop_event.is_set()
-
-    def run(self):
-        while not self.stopped():
-            try:
-                batchNum = inQueue.get(timeout=60)
-                nextItem = loadImageBatches(self.images, self.batches, batchNum, int(config.taskSize / 2))
-                outQueue.put(nextItem)
-                # inQueue.task_done()
-            except:
-                pass
-        # print("worker %d done", self.i)
-
-
-def loaderRun(images, batches):
-    batchNum = 0
-
-    # if config.workers == 2:
-    #     worker = StoppableThread(images, batches) # i,
-    #     worker.daemon = True
-    #     worker.start()
-
-    #     while batchNum < len(batches):
-    #         inQueue.put(batchNum + int(config.taskSize / 2))
-    #         nextItem1 = loadImageBatches(images, batches, batchNum, int(config.taskSize / 2))
-    #         nextItem2 = outQueue.get()
-
-    #         nextItem = nextItem1 + nextItem2
-    #         assert len(nextItem) == min(config.taskSize, len(batches) - batchNum)
-    #         batchNum += config.taskSize
-
-    #         imagesQueue.put(nextItem)
-
-    #     worker.stop()
-    # else:
-    while batchNum < len(batches):
-        nextItem = loadImageBatches(images, batches, batchNum, config.taskSize)
-        assert len(nextItem) == min(config.taskSize, len(batches) - batchNum)
-        batchNum += config.taskSize
-        imagesQueue.put(nextItem)
-
-    # print("manager loader done")
-
-
-########################################## stats tracking #########################################
-# Computes exponential moving average.
-def emaAvg(avg, value):
-    if avg is None:
-        return value
-    emaRate = 0.98
-    return avg * emaRate + value * (1 - emaRate)
-
-
-# Initializes training statistics.
-def initStats():
-    return {
-        "totalBatches": 0,
-        "totalData": 0,
-        "totalLoss": 0.0,
-        "totalCorrect": 0,
-        "loss": 0.0,
-        "acc": 0.0,
-        "emaLoss": None,
-        "emaAcc": None,
-    }
-
-
-# Updates statistics with training results of a batch
-def updateStats(stats, res, batch):
-    stats["totalBatches"] += 1
-    stats["totalData"] += getLength(batch)
-
-    stats["totalLoss"] += res["loss"]
-    stats["totalCorrect"] += res["correctNum"]
-
-    stats["loss"] = stats["totalLoss"] / stats["totalBatches"]
-    stats["acc"] = stats["totalCorrect"] / stats["totalData"]
-
-    stats["emaLoss"] = emaAvg(stats["emaLoss"], res["loss"])
-    stats["emaAcc"] = emaAvg(stats["emaAcc"], res["acc"])
-
-    return stats
-
-
-# auto-encoder ae = {:2.4f} autoEncLoss,
-# Translates training statistics into a string to print
-def statsToStr(stats, res, epoch, batchNum, dataLen, startTime):
-    formatStr = "\reb {epoch},{batchNum} ({dataProcessed} / {dataLen:5d}), " + \
-                "t = {time} ({loadTime:2.2f}+{trainTime:2.2f}), " + \
-                "lr {lr}, l = {loss}, a = {acc}, avL = {avgLoss}, " + \
-                "avA = {avgAcc}, g = {gradNorm:2.4f}, " + \
-                "emL = {emaLoss:2.4f}, emA = {emaAcc:2.4f}; " + \
-                "{expname}"  # {machine}/{gpu}"
-
-    s_epoch = bcolored("{:2d}".format(epoch), "green")
-    s_batchNum = "{:3d}".format(batchNum)
-    s_dataProcessed = bcolored("{:5d}".format(stats["totalData"]), "green")
-    s_dataLen = dataLen
-    s_time = bcolored("{:2.2f}".format(time.time() - startTime), "green")
-    s_loadTime = res["readTime"]
-    s_trainTime = res["trainTime"]
-    s_lr = bold(config.lr)
-    s_loss = bcolored("{:2.4f}".format(res["loss"]), "blue")
-    s_acc = bcolored("{:2.4f}".format(res["acc"]), "blue")
-    s_avgLoss = bcolored("{:2.4f}".format(stats["loss"]), "blue")
-    s_avgAcc = bcolored("{:2.4f}".format(stats["acc"]), "red")
-    s_gradNorm = res["gradNorm"]
-    s_emaLoss = stats["emaLoss"]
-    s_emaAcc = stats["emaAcc"]
-    s_expname = config.expName
-    # s_machine = bcolored(config.dataPath[9:11],"green")
-    # s_gpu = bcolored(config.gpus,"green")
-
-    return formatStr.format(epoch=s_epoch, batchNum=s_batchNum, dataProcessed=s_dataProcessed,
-                            dataLen=s_dataLen, time=s_time, loadTime=s_loadTime,
-                            trainTime=s_trainTime, lr=s_lr, loss=s_loss, acc=s_acc,
-                            avgLoss=s_avgLoss, avgAcc=s_avgAcc, gradNorm=s_gradNorm,
-                            emaLoss=s_emaLoss, emaAcc=s_emaAcc, expname=s_expname)
-    # machine = s_machine, gpu = s_gpu)
-
-
-# collectRuntimeStats, writer = None,
-'''
-Runs an epoch with model and session over the data.
-1. Batches the data and optionally mix it with the extra alterData.
-2. Start worker threads to load images in parallel to training.
-3. Runs model for each batch, and gets results (e.g. loss,  accuracy).
-4. Updates and prints statistics based on batch results.
-5. Once in a while (every config.saveEvery), save weights. 
-
-Args:
-    sess: TF session to run with.
-
-    model: model to process data. Has runBatch method that process a given batch.
-    (See model.py for further details).
-
-    data: data to use for training/evaluation.
-
-    epoch: epoch number.
-
-    saver: TF saver to save weights
-
-    calle: a method to call every number of iterations (config.calleEvery)
-
-    alterData: extra data to mix with main data while training.
-
-    getAtt: True to return model attentions.  
-'''
-
-
-def main(question, image):
-    with open(config.configFile(), "a+") as outFile:
-        json.dump(vars(config), outFile)
-
-    # set gpus
-    if config.gpus != "":
-        config.gpusNum = len(config.gpus.split(","))
-        os.environ["CUDA_VISIBLE_DEVICES"] = config.gpus
-
-    tf.logging.set_verbosity(tf.logging.ERROR)
-
-    # process data
-    print(bold("Preprocess data..."))
-    start = time.time()
-    preprocessor = Preprocesser()
-    cnn_model = build_model()
-    imageData = get_img_feat(cnn_model, image)
-    qData, embeddings, answerDict = preprocessor.preprocessData(question)
-    data = {'data': qData, 'image': imageData}
-    print("took {} seconds".format(bcolored("{:.2f}".format(time.time() - start), "blue")))
-
-    # build model
-    print(bold("Building model..."))
-    start = time.time()
-    model = MACnet(embeddings, answerDict)
-    print("took {} seconds".format(bcolored("{:.2f}".format(time.time() - start), "blue")))
-
-    # initializer
-    init = tf.global_variables_initializer()
-
-    # savers
-    savers = setSavers(model)
-    saver, emaSaver = savers["saver"], savers["emaSaver"]
-
-    # sessionConfig
-    sessionConfig = setSession()
-
-    with tf.Session(config=sessionConfig) as sess:
-
-        # ensure no more ops are added after model is built
-        sess.graph.finalize()
-
-        # restore / initialize weights, initialize epoch variable
-        epoch = loadWeights(sess, saver, init)
-        print(epoch)
-        start = time.time()
-        if epoch > 0:
-            if config.useEMA:
-                emaSaver.restore(sess, config.weightsFile(epoch))
-            else:
-                saver.restore(sess, config.weightsFile(epoch))
-
-        evalRes = model.runBatch(sess, data['data'], data['image'], False)
-
-        print("took {:.2f} seconds".format(time.time() - start))
-
-        print(evalRes)
-
-
-if __name__ == '__main__':
-    parseArgs()
-    loadDatasetConfig[config.dataset]()
-    question = 'How many text objects are located at the bottom side of table?'
-    imagePath = './mac-layoutLM-sample/PDF_val_64.png'
-    main(question, imagePath)

spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/models/GroundingDINO/transformer.py

@@ -1,974 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# DINO
-# Copyright (c) 2022 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR Transformer class.
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Modified from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-from typing import Optional
-
-import torch
-import torch.utils.checkpoint as checkpoint
-from torch import Tensor, nn
-
-from groundingdino.util.misc import inverse_sigmoid
-
-from .fuse_modules import BiAttentionBlock
-from .ms_deform_attn import MultiScaleDeformableAttention as MSDeformAttn
-from .transformer_vanilla import TransformerEncoderLayer
-from .utils import (
-    MLP,
-    _get_activation_fn,
-    _get_clones,
-    gen_encoder_output_proposals,
-    gen_sineembed_for_position,
-    get_sine_pos_embed,
-)
-
-
-class Transformer(nn.Module):
-    def __init__(
-        self,
-        d_model=256,
-        nhead=8,
-        num_queries=300,
-        num_encoder_layers=6,
-        num_unicoder_layers=0,
-        num_decoder_layers=6,
-        dim_feedforward=2048,
-        dropout=0.0,
-        activation="relu",
-        normalize_before=False,
-        return_intermediate_dec=False,
-        query_dim=4,
-        num_patterns=0,
-        # for deformable encoder
-        num_feature_levels=1,
-        enc_n_points=4,
-        dec_n_points=4,
-        # init query
-        learnable_tgt_init=False,
-        # two stage
-        two_stage_type="no",  # ['no', 'standard', 'early', 'combine', 'enceachlayer', 'enclayer1']
-        embed_init_tgt=False,
-        # for text
-        use_text_enhancer=False,
-        use_fusion_layer=False,
-        use_checkpoint=False,
-        use_transformer_ckpt=False,
-        use_text_cross_attention=False,
-        text_dropout=0.1,
-        fusion_dropout=0.1,
-        fusion_droppath=0.0,
-    ):
-        super().__init__()
-        self.num_feature_levels = num_feature_levels
-        self.num_encoder_layers = num_encoder_layers
-        self.num_unicoder_layers = num_unicoder_layers
-        self.num_decoder_layers = num_decoder_layers
-        self.num_queries = num_queries
-        assert query_dim == 4
-
-        # choose encoder layer type
-        encoder_layer = DeformableTransformerEncoderLayer(
-            d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points
-        )
-
-        if use_text_enhancer:
-            text_enhance_layer = TransformerEncoderLayer(
-                d_model=d_model,
-                nhead=nhead // 2,
-                dim_feedforward=dim_feedforward // 2,
-                dropout=text_dropout,
-            )
-        else:
-            text_enhance_layer = None
-
-        if use_fusion_layer:
-            feature_fusion_layer = BiAttentionBlock(
-                v_dim=d_model,
-                l_dim=d_model,
-                embed_dim=dim_feedforward // 2,
-                num_heads=nhead // 2,
-                dropout=fusion_dropout,
-                drop_path=fusion_droppath,
-            )
-        else:
-            feature_fusion_layer = None
-
-        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
-        assert encoder_norm is None
-        self.encoder = TransformerEncoder(
-            encoder_layer,
-            num_encoder_layers,
-            d_model=d_model,
-            num_queries=num_queries,
-            text_enhance_layer=text_enhance_layer,
-            feature_fusion_layer=feature_fusion_layer,
-            use_checkpoint=use_checkpoint,
-            use_transformer_ckpt=use_transformer_ckpt,
-        )
-
-        # choose decoder layer type
-        decoder_layer = DeformableTransformerDecoderLayer(
-            d_model,
-            dim_feedforward,
-            dropout,
-            activation,
-            num_feature_levels,
-            nhead,
-            dec_n_points,
-            use_text_cross_attention=use_text_cross_attention,
-        )
-
-        decoder_norm = nn.LayerNorm(d_model)
-        self.decoder = TransformerDecoder(
-            decoder_layer,
-            num_decoder_layers,
-            decoder_norm,
-            return_intermediate=return_intermediate_dec,
-            d_model=d_model,
-            query_dim=query_dim,
-            num_feature_levels=num_feature_levels,
-        )
-
-        self.d_model = d_model
-        self.nhead = nhead
-        self.dec_layers = num_decoder_layers
-        self.num_queries = num_queries  # useful for single stage model only
-        self.num_patterns = num_patterns
-        if not isinstance(num_patterns, int):
-            Warning("num_patterns should be int but {}".format(type(num_patterns)))
-            self.num_patterns = 0
-
-        if num_feature_levels > 1:
-            if self.num_encoder_layers > 0:
-                self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
-            else:
-                self.level_embed = None
-
-        self.learnable_tgt_init = learnable_tgt_init
-        assert learnable_tgt_init, "why not learnable_tgt_init"
-        self.embed_init_tgt = embed_init_tgt
-        if (two_stage_type != "no" and embed_init_tgt) or (two_stage_type == "no"):
-            self.tgt_embed = nn.Embedding(self.num_queries, d_model)
-            nn.init.normal_(self.tgt_embed.weight.data)
-        else:
-            self.tgt_embed = None
-
-        # for two stage
-        self.two_stage_type = two_stage_type
-        assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
-            two_stage_type
-        )
-        if two_stage_type == "standard":
-            # anchor selection at the output of encoder
-            self.enc_output = nn.Linear(d_model, d_model)
-            self.enc_output_norm = nn.LayerNorm(d_model)
-            self.two_stage_wh_embedding = None
-
-        if two_stage_type == "no":
-            self.init_ref_points(num_queries)  # init self.refpoint_embed
-
-        self.enc_out_class_embed = None
-        self.enc_out_bbox_embed = None
-
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_uniform_(p)
-        for m in self.modules():
-            if isinstance(m, MSDeformAttn):
-                m._reset_parameters()
-        if self.num_feature_levels > 1 and self.level_embed is not None:
-            nn.init.normal_(self.level_embed)
-
-    def get_valid_ratio(self, mask):
-        _, H, W = mask.shape
-        valid_H = torch.sum(~mask[:, :, 0], 1)
-        valid_W = torch.sum(~mask[:, 0, :], 1)
-        valid_ratio_h = valid_H.float() / H
-        valid_ratio_w = valid_W.float() / W
-        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
-        return valid_ratio
-
-    def init_ref_points(self, use_num_queries):
-        self.refpoint_embed = nn.Embedding(use_num_queries, 4)
-
-    def forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_mask=None, text_dict=None):
-        """
-        Input:
-            - srcs: List of multi features [bs, ci, hi, wi]
-            - masks: List of multi masks [bs, hi, wi]
-            - refpoint_embed: [bs, num_dn, 4]. None in infer
-            - pos_embeds: List of multi pos embeds [bs, ci, hi, wi]
-            - tgt: [bs, num_dn, d_model]. None in infer
-
-        """
-        # prepare input for encoder
-        src_flatten = []
-        mask_flatten = []
-        lvl_pos_embed_flatten = []
-        spatial_shapes = []
-        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
-            bs, c, h, w = src.shape
-            spatial_shape = (h, w)
-            spatial_shapes.append(spatial_shape)
-
-            src = src.flatten(2).transpose(1, 2)  # bs, hw, c
-            mask = mask.flatten(1)  # bs, hw
-            pos_embed = pos_embed.flatten(2).transpose(1, 2)  # bs, hw, c
-            if self.num_feature_levels > 1 and self.level_embed is not None:
-                lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
-            else:
-                lvl_pos_embed = pos_embed
-            lvl_pos_embed_flatten.append(lvl_pos_embed)
-            src_flatten.append(src)
-            mask_flatten.append(mask)
-        src_flatten = torch.cat(src_flatten, 1)  # bs, \sum{hxw}, c
-        mask_flatten = torch.cat(mask_flatten, 1)  # bs, \sum{hxw}
-        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)  # bs, \sum{hxw}, c
-        spatial_shapes = torch.as_tensor(
-            spatial_shapes, dtype=torch.long, device=src_flatten.device
-        )
-        level_start_index = torch.cat(
-            (spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])
-        )
-        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
-
-        # two stage
-        enc_topk_proposals = enc_refpoint_embed = None
-
-        #########################################################
-        # Begin Encoder
-        #########################################################
-        memory, memory_text = self.encoder(
-            src_flatten,
-            pos=lvl_pos_embed_flatten,
-            level_start_index=level_start_index,
-            spatial_shapes=spatial_shapes,
-            valid_ratios=valid_ratios,
-            key_padding_mask=mask_flatten,
-            memory_text=text_dict["encoded_text"],
-            text_attention_mask=~text_dict["text_token_mask"],
-            # we ~ the mask . False means use the token; True means pad the token
-            position_ids=text_dict["position_ids"],
-            text_self_attention_masks=text_dict["text_self_attention_masks"],
-        )
-
-        enhanced_image_features = memory.detach()
-        enhanced_text_features = memory_text.detach()
-
-        # memory: enhanced image features
-        # memory_text: enhanced text features
-        #########################################################
-        # End Encoder
-        # - memory: bs, \sum{hw}, c
-        # - mask_flatten: bs, \sum{hw}
-        # - lvl_pos_embed_flatten: bs, \sum{hw}, c
-        # - enc_intermediate_output: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
-        # - enc_intermediate_refpoints: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
-        #########################################################
-
-        #########################################################
-        # Begin Language-guide Query Selection
-        #########################################################
-        text_dict["encoded_text"] = memory_text
-        # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
-        #     if memory.isnan().any() | memory.isinf().any():
-        #         import ipdb; ipdb.set_trace()
-
-        if self.two_stage_type == "standard":
-            # logits and proposals
-            output_memory, output_proposals = gen_encoder_output_proposals(
-                memory, mask_flatten, spatial_shapes
-            )
-            output_memory = self.enc_output_norm(self.enc_output(output_memory))
-
-            # language-guided query selection
-            if text_dict is not None:
-                enc_outputs_class_unselected = self.enc_out_class_embed(output_memory, text_dict)
-            else:
-                enc_outputs_class_unselected = self.enc_out_class_embed(output_memory)
-
-            topk_logits = enc_outputs_class_unselected.max(-1)[0]
-            enc_outputs_coord_unselected = (
-                self.enc_out_bbox_embed(output_memory) + output_proposals
-            )  # (bs, \sum{hw}, 4) unsigmoid
-            topk = self.num_queries
-
-            topk_proposals = torch.topk(topk_logits, topk, dim=1)[1]  # bs, nq
-
-            # gather boxes
-            refpoint_embed_undetach = torch.gather(
-                enc_outputs_coord_unselected, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
-            )  # unsigmoid
-            refpoint_embed_ = refpoint_embed_undetach.detach()
-            init_box_proposal = torch.gather(
-                output_proposals, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
-            ).sigmoid()  # sigmoid
-
-            # gather tgt
-            tgt_undetach = torch.gather(
-                output_memory, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)
-            )
-            if self.embed_init_tgt:
-                tgt_ = (
-                    self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
-                )  # nq, bs, d_model
-            else:
-                tgt_ = tgt_undetach.detach()
-
-            if refpoint_embed is not None:
-                refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
-                tgt = torch.cat([tgt, tgt_], dim=1)
-            else:
-                refpoint_embed, tgt = refpoint_embed_, tgt_
-
-        elif self.two_stage_type == "no":
-            tgt_ = (
-                self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
-            )  # nq, bs, d_model
-            refpoint_embed_ = (
-                self.refpoint_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
-            )  # nq, bs, 4
-
-            if refpoint_embed is not None:
-                refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
-                tgt = torch.cat([tgt, tgt_], dim=1)
-            else:
-                refpoint_embed, tgt = refpoint_embed_, tgt_
-
-            if self.num_patterns > 0:
-                tgt_embed = tgt.repeat(1, self.num_patterns, 1)
-                refpoint_embed = refpoint_embed.repeat(1, self.num_patterns, 1)
-                tgt_pat = self.patterns.weight[None, :, :].repeat_interleave(
-                    self.num_queries, 1
-                )  # 1, n_q*n_pat, d_model
-                tgt = tgt_embed + tgt_pat
-
-            init_box_proposal = refpoint_embed_.sigmoid()
-
-        else:
-            raise NotImplementedError("unknown two_stage_type {}".format(self.two_stage_type))
-        #########################################################
-        # End preparing tgt
-        # - tgt: bs, NQ, d_model
-        # - refpoint_embed(unsigmoid): bs, NQ, d_model
-        #########################################################
-
-        #########################################################
-        # Begin Decoder
-        #########################################################
-        hs, references = self.decoder(
-            tgt=tgt.transpose(0, 1),
-            memory=memory.transpose(0, 1),
-            memory_key_padding_mask=mask_flatten,
-            pos=lvl_pos_embed_flatten.transpose(0, 1),
-            refpoints_unsigmoid=refpoint_embed.transpose(0, 1),
-            level_start_index=level_start_index,
-            spatial_shapes=spatial_shapes,
-            valid_ratios=valid_ratios,
-            tgt_mask=attn_mask,
-            memory_text=text_dict["encoded_text"],
-            text_attention_mask=~text_dict["text_token_mask"],
-            # we ~ the mask . False means use the token; True means pad the token
-        )
-        #########################################################
-        # End Decoder
-        # hs: n_dec, bs, nq, d_model
-        # references: n_dec+1, bs, nq, query_dim
-        #########################################################
-
-        #########################################################
-        # Begin postprocess
-        #########################################################
-        if self.two_stage_type == "standard":
-            hs_enc = tgt_undetach.unsqueeze(0)
-            ref_enc = refpoint_embed_undetach.sigmoid().unsqueeze(0)
-        else:
-            hs_enc = ref_enc = None
-        #########################################################
-        # End postprocess
-        # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or (n_enc, bs, nq, d_model) or None
-        # ref_enc: (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or (n_enc, bs, nq, d_model) or None
-        #########################################################
-
-        return hs, references, hs_enc, ref_enc, init_box_proposal, enhanced_image_features, enhanced_text_features, spatial_shapes, topk_logits
-        # hs: (n_dec, bs, nq, d_model)
-        # references: sigmoid coordinates. (n_dec+1, bs, bq, 4)
-        # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or None
-        # ref_enc: sigmoid coordinates. \
-        #           (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or None
-        # enhanced_image_features: (bs, shw, c)
-        # enhanced_text_features: (bs, n_enc, c)
-        # spatial_shapes: s
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-        self,
-        encoder_layer,
-        num_layers,
-        d_model=256,
-        num_queries=300,
-        enc_layer_share=False,
-        text_enhance_layer=None,
-        feature_fusion_layer=None,
-        use_checkpoint=False,
-        use_transformer_ckpt=False,
-    ):
-        """_summary_
-
-        Args:
-            encoder_layer (_type_): _description_
-            num_layers (_type_): _description_
-            norm (_type_, optional): _description_. Defaults to None.
-            d_model (int, optional): _description_. Defaults to 256.
-            num_queries (int, optional): _description_. Defaults to 300.
-            enc_layer_share (bool, optional): _description_. Defaults to False.
-
-        """
-        super().__init__()
-        # prepare layers
-        self.layers = []
-        self.text_layers = []
-        self.fusion_layers = []
-        if num_layers > 0:
-            self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share)
-
-            if text_enhance_layer is not None:
-                self.text_layers = _get_clones(
-                    text_enhance_layer, num_layers, layer_share=enc_layer_share
-                )
-            if feature_fusion_layer is not None:
-                self.fusion_layers = _get_clones(
-                    feature_fusion_layer, num_layers, layer_share=enc_layer_share
-                )
-        else:
-            self.layers = []
-            del encoder_layer
-
-            if text_enhance_layer is not None:
-                self.text_layers = []
-                del text_enhance_layer
-            if feature_fusion_layer is not None:
-                self.fusion_layers = []
-                del feature_fusion_layer
-
-        self.query_scale = None
-        self.num_queries = num_queries
-        self.num_layers = num_layers
-        self.d_model = d_model
-
-        self.use_checkpoint = use_checkpoint
-        self.use_transformer_ckpt = use_transformer_ckpt
-
-    @staticmethod
-    def get_reference_points(spatial_shapes, valid_ratios, device):
-        reference_points_list = []
-        for lvl, (H_, W_) in enumerate(spatial_shapes):
-
-            ref_y, ref_x = torch.meshgrid(
-                torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
-                torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),
-            )
-            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
-            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
-            ref = torch.stack((ref_x, ref_y), -1)
-            reference_points_list.append(ref)
-        reference_points = torch.cat(reference_points_list, 1)
-        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
-        return reference_points
-
-    def forward(
-        self,
-        # for images
-        src: Tensor,
-        pos: Tensor,
-        spatial_shapes: Tensor,
-        level_start_index: Tensor,
-        valid_ratios: Tensor,
-        key_padding_mask: Tensor,
-        # for texts
-        memory_text: Tensor = None,
-        text_attention_mask: Tensor = None,
-        pos_text: Tensor = None,
-        text_self_attention_masks: Tensor = None,
-        position_ids: Tensor = None,
-    ):
-        """
-        Input:
-            - src: [bs, sum(hi*wi), 256]
-            - pos: pos embed for src. [bs, sum(hi*wi), 256]
-            - spatial_shapes: h,w of each level [num_level, 2]
-            - level_start_index: [num_level] start point of level in sum(hi*wi).
-            - valid_ratios: [bs, num_level, 2]
-            - key_padding_mask: [bs, sum(hi*wi)]
-
-            - memory_text: bs, n_text, 256
-            - text_attention_mask: bs, n_text
-                False for no padding; True for padding
-            - pos_text: bs, n_text, 256
-
-            - position_ids: bs, n_text
-        Intermedia:
-            - reference_points: [bs, sum(hi*wi), num_level, 2]
-        Outpus:
-            - output: [bs, sum(hi*wi), 256]
-        """
-
-        output = src
-
-        # preparation and reshape
-        if self.num_layers > 0:
-            reference_points = self.get_reference_points(
-                spatial_shapes, valid_ratios, device=src.device
-            )
-
-        if self.text_layers:
-            # generate pos_text
-            bs, n_text, text_dim = memory_text.shape
-            if pos_text is None and position_ids is None:
-                pos_text = (
-                    torch.arange(n_text, device=memory_text.device)
-                    .float()
-                    .unsqueeze(0)
-                    .unsqueeze(-1)
-                    .repeat(bs, 1, 1)
-                )
-                pos_text = get_sine_pos_embed(pos_text, num_pos_feats=256, exchange_xy=False)
-            if position_ids is not None:
-                pos_text = get_sine_pos_embed(
-                    position_ids[..., None], num_pos_feats=256, exchange_xy=False
-                )
-
-        # main process
-        for layer_id, layer in enumerate(self.layers):
-            # if output.isnan().any() or memory_text.isnan().any():
-            #     if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
-            #         import ipdb; ipdb.set_trace()
-            if self.fusion_layers:
-                if self.use_checkpoint:
-                    output, memory_text = checkpoint.checkpoint(
-                        self.fusion_layers[layer_id],
-                        output,
-                        memory_text,
-                        key_padding_mask,
-                        text_attention_mask,
-                    )
-                else:
-                    output, memory_text = self.fusion_layers[layer_id](
-                        v=output,
-                        l=memory_text,
-                        attention_mask_v=key_padding_mask,
-                        attention_mask_l=text_attention_mask,
-                    )
-
-            if self.text_layers:
-                memory_text = self.text_layers[layer_id](
-                    src=memory_text.transpose(0, 1),
-                    src_mask=~text_self_attention_masks,  # note we use ~ for mask here
-                    src_key_padding_mask=text_attention_mask,
-                    pos=(pos_text.transpose(0, 1) if pos_text is not None else None),
-                ).transpose(0, 1)
-
-            # main process
-            if self.use_transformer_ckpt:
-                output = checkpoint.checkpoint(
-                    layer,
-                    output,
-                    pos,
-                    reference_points,
-                    spatial_shapes,
-                    level_start_index,
-                    key_padding_mask,
-                )
-            else:
-                output = layer(
-                    src=output,
-                    pos=pos,
-                    reference_points=reference_points,
-                    spatial_shapes=spatial_shapes,
-                    level_start_index=level_start_index,
-                    key_padding_mask=key_padding_mask,
-                )
-
-        return output, memory_text
-
-
-class TransformerDecoder(nn.Module):
-    def __init__(
-        self,
-        decoder_layer,
-        num_layers,
-        norm=None,
-        return_intermediate=False,
-        d_model=256,
-        query_dim=4,
-        num_feature_levels=1,
-    ):
-        super().__init__()
-        if num_layers > 0:
-            self.layers = _get_clones(decoder_layer, num_layers)
-        else:
-            self.layers = []
-        self.num_layers = num_layers
-        self.norm = norm
-        self.return_intermediate = return_intermediate
-        assert return_intermediate, "support return_intermediate only"
-        self.query_dim = query_dim
-        assert query_dim in [2, 4], "query_dim should be 2/4 but {}".format(query_dim)
-        self.num_feature_levels = num_feature_levels
-
-        self.ref_point_head = MLP(query_dim // 2 * d_model, d_model, d_model, 2)
-        self.query_pos_sine_scale = None
-
-        self.query_scale = None
-        self.bbox_embed = None
-        self.class_embed = None
-
-        self.d_model = d_model
-
-        self.ref_anchor_head = None
-
-    def forward(
-        self,
-        tgt,
-        memory,
-        tgt_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        memory_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-        refpoints_unsigmoid: Optional[Tensor] = None,  # num_queries, bs, 2
-        # for memory
-        level_start_index: Optional[Tensor] = None,  # num_levels
-        spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
-        valid_ratios: Optional[Tensor] = None,
-        # for text
-        memory_text: Optional[Tensor] = None,
-        text_attention_mask: Optional[Tensor] = None,
-    ):
-        """
-        Input:
-            - tgt: nq, bs, d_model
-            - memory: hw, bs, d_model
-            - pos: hw, bs, d_model
-            - refpoints_unsigmoid: nq, bs, 2/4
-            - valid_ratios/spatial_shapes: bs, nlevel, 2
-        """
-        output = tgt
-
-        intermediate = []
-        reference_points = refpoints_unsigmoid.sigmoid()
-        ref_points = [reference_points]
-
-        for layer_id, layer in enumerate(self.layers):
-
-            if reference_points.shape[-1] == 4:
-                reference_points_input = (
-                    reference_points[:, :, None]
-                    * torch.cat([valid_ratios, valid_ratios], -1)[None, :]
-                )  # nq, bs, nlevel, 4
-            else:
-                assert reference_points.shape[-1] == 2
-                reference_points_input = reference_points[:, :, None] * valid_ratios[None, :]
-            query_sine_embed = gen_sineembed_for_position(
-                reference_points_input[:, :, 0, :]
-            )  # nq, bs, 256*2
-
-            # conditional query
-            raw_query_pos = self.ref_point_head(query_sine_embed)  # nq, bs, 256
-            pos_scale = self.query_scale(output) if self.query_scale is not None else 1
-            query_pos = pos_scale * raw_query_pos
-            # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
-            #     if query_pos.isnan().any() | query_pos.isinf().any():
-            #         import ipdb; ipdb.set_trace()
-
-            # main process
-            output = layer(
-                tgt=output,
-                tgt_query_pos=query_pos,
-                tgt_query_sine_embed=query_sine_embed,
-                tgt_key_padding_mask=tgt_key_padding_mask,
-                tgt_reference_points=reference_points_input,
-                memory_text=memory_text,
-                text_attention_mask=text_attention_mask,
-                memory=memory,
-                memory_key_padding_mask=memory_key_padding_mask,
-                memory_level_start_index=level_start_index,
-                memory_spatial_shapes=spatial_shapes,
-                memory_pos=pos,
-                self_attn_mask=tgt_mask,
-                cross_attn_mask=memory_mask,
-            )
-            if output.isnan().any() | output.isinf().any():
-                print(f"output layer_id {layer_id} is nan")
-                try:
-                    num_nan = output.isnan().sum().item()
-                    num_inf = output.isinf().sum().item()
-                    print(f"num_nan {num_nan}, num_inf {num_inf}")
-                except Exception as e:
-                    print(e)
-                    # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
-                    #     import ipdb; ipdb.set_trace()
-
-            # iter update
-            if self.bbox_embed is not None:
-                # box_holder = self.bbox_embed(output)
-                # box_holder[..., :self.query_dim] += inverse_sigmoid(reference_points)
-                # new_reference_points = box_holder[..., :self.query_dim].sigmoid()
-
-                reference_before_sigmoid = inverse_sigmoid(reference_points)
-                delta_unsig = self.bbox_embed[layer_id](output)
-                outputs_unsig = delta_unsig + reference_before_sigmoid
-                new_reference_points = outputs_unsig.sigmoid()
-
-                reference_points = new_reference_points.detach()
-                # if layer_id != self.num_layers - 1:
-                ref_points.append(new_reference_points)
-
-            intermediate.append(self.norm(output))
-
-        return [
-            [itm_out.transpose(0, 1) for itm_out in intermediate],
-            [itm_refpoint.transpose(0, 1) for itm_refpoint in ref_points],
-        ]
-
-
-class DeformableTransformerEncoderLayer(nn.Module):
-    def __init__(
-        self,
-        d_model=256,
-        d_ffn=1024,
-        dropout=0.1,
-        activation="relu",
-        n_levels=4,
-        n_heads=8,
-        n_points=4,
-    ):
-        super().__init__()
-
-        # self attention
-        self.self_attn = MSDeformAttn(
-            embed_dim=d_model,
-            num_levels=n_levels,
-            num_heads=n_heads,
-            num_points=n_points,
-            batch_first=True,
-        )
-        self.dropout1 = nn.Dropout(dropout)
-        self.norm1 = nn.LayerNorm(d_model)
-
-        # ffn
-        self.linear1 = nn.Linear(d_model, d_ffn)
-        self.activation = _get_activation_fn(activation, d_model=d_ffn)
-        self.dropout2 = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(d_ffn, d_model)
-        self.dropout3 = nn.Dropout(dropout)
-        self.norm2 = nn.LayerNorm(d_model)
-
-    @staticmethod
-    def with_pos_embed(tensor, pos):
-        return tensor if pos is None else tensor + pos
-
-    def forward_ffn(self, src):
-        src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
-        src = src + self.dropout3(src2)
-        src = self.norm2(src)
-        return src
-
-    def forward(
-        self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None
-    ):
-        # self attention
-        # import ipdb; ipdb.set_trace()
-        src2 = self.self_attn(
-            query=self.with_pos_embed(src, pos),
-            reference_points=reference_points,
-            value=src,
-            spatial_shapes=spatial_shapes,
-            level_start_index=level_start_index,
-            key_padding_mask=key_padding_mask,
-        )
-        src = src + self.dropout1(src2)
-        src = self.norm1(src)
-
-        # ffn
-        src = self.forward_ffn(src)
-
-        return src
-
-
-class DeformableTransformerDecoderLayer(nn.Module):
-    def __init__(
-        self,
-        d_model=256,
-        d_ffn=1024,
-        dropout=0.1,
-        activation="relu",
-        n_levels=4,
-        n_heads=8,
-        n_points=4,
-        use_text_feat_guide=False,
-        use_text_cross_attention=False,
-    ):
-        super().__init__()
-
-        # cross attention
-        self.cross_attn = MSDeformAttn(
-            embed_dim=d_model,
-            num_levels=n_levels,
-            num_heads=n_heads,
-            num_points=n_points,
-            batch_first=True,
-        )
-        self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.norm1 = nn.LayerNorm(d_model)
-
-        # cross attention text
-        if use_text_cross_attention:
-            self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
-            self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-            self.catext_norm = nn.LayerNorm(d_model)
-
-        # self attention
-        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
-        self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.norm2 = nn.LayerNorm(d_model)
-
-        # ffn
-        self.linear1 = nn.Linear(d_model, d_ffn)
-        self.activation = _get_activation_fn(activation, d_model=d_ffn, batch_dim=1)
-        self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.linear2 = nn.Linear(d_ffn, d_model)
-        self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.norm3 = nn.LayerNorm(d_model)
-
-        self.key_aware_proj = None
-        self.use_text_feat_guide = use_text_feat_guide
-        assert not use_text_feat_guide
-        self.use_text_cross_attention = use_text_cross_attention
-
-    def rm_self_attn_modules(self):
-        self.self_attn = None
-        self.dropout2 = None
-        self.norm2 = None
-
-    @staticmethod
-    def with_pos_embed(tensor, pos):
-        return tensor if pos is None else tensor + pos
-
-    def forward_ffn(self, tgt):
-        with torch.cuda.amp.autocast(enabled=False):
-            tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
-        tgt = tgt + self.dropout4(tgt2)
-        tgt = self.norm3(tgt)
-        return tgt
-
-    def forward(
-        self,
-        # for tgt
-        tgt: Optional[Tensor],  # nq, bs, d_model
-        tgt_query_pos: Optional[Tensor] = None,  # pos for query. MLP(Sine(pos))
-        tgt_query_sine_embed: Optional[Tensor] = None,  # pos for query. Sine(pos)
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        tgt_reference_points: Optional[Tensor] = None,  # nq, bs, 4
-        memory_text: Optional[Tensor] = None,  # bs, num_token, d_model
-        text_attention_mask: Optional[Tensor] = None,  # bs, num_token
-        # for memory
-        memory: Optional[Tensor] = None,  # hw, bs, d_model
-        memory_key_padding_mask: Optional[Tensor] = None,
-        memory_level_start_index: Optional[Tensor] = None,  # num_levels
-        memory_spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
-        memory_pos: Optional[Tensor] = None,  # pos for memory
-        # sa
-        self_attn_mask: Optional[Tensor] = None,  # mask used for self-attention
-        cross_attn_mask: Optional[Tensor] = None,  # mask used for cross-attention
-    ):
-        """
-        Input:
-            - tgt/tgt_query_pos: nq, bs, d_model
-            -
-        """
-        assert cross_attn_mask is None
-
-        # self attention
-        if self.self_attn is not None:
-            # import ipdb; ipdb.set_trace()
-            q = k = self.with_pos_embed(tgt, tgt_query_pos)
-            tgt2 = self.self_attn(q, k, tgt, attn_mask=self_attn_mask)[0]
-            tgt = tgt + self.dropout2(tgt2)
-            tgt = self.norm2(tgt)
-
-        if self.use_text_cross_attention:
-            tgt2 = self.ca_text(
-                self.with_pos_embed(tgt, tgt_query_pos),
-                memory_text.transpose(0, 1),
-                memory_text.transpose(0, 1),
-                key_padding_mask=text_attention_mask,
-            )[0]
-            tgt = tgt + self.catext_dropout(tgt2)
-            tgt = self.catext_norm(tgt)
-
-        tgt2 = self.cross_attn(
-            query=self.with_pos_embed(tgt, tgt_query_pos).transpose(0, 1),
-            reference_points=tgt_reference_points.transpose(0, 1).contiguous(),
-            value=memory.transpose(0, 1),
-            spatial_shapes=memory_spatial_shapes,
-            level_start_index=memory_level_start_index,
-            key_padding_mask=memory_key_padding_mask,
-        ).transpose(0, 1)
-        tgt = tgt + self.dropout1(tgt2)
-        tgt = self.norm1(tgt)
-
-        # ffn
-        tgt = self.forward_ffn(tgt)
-
-        return tgt
-
-
-def build_transformer(args):
-    return Transformer(
-        d_model=args.hidden_dim,
-        dropout=args.dropout,
-        nhead=args.nheads,
-        num_queries=args.num_queries,
-        dim_feedforward=args.dim_feedforward,
-        num_encoder_layers=args.enc_layers,
-        num_decoder_layers=args.dec_layers,
-        normalize_before=args.pre_norm,
-        return_intermediate_dec=True,
-        query_dim=args.query_dim,
-        activation=args.transformer_activation,
-        num_patterns=args.num_patterns,
-        num_feature_levels=args.num_feature_levels,
-        enc_n_points=args.enc_n_points,
-        dec_n_points=args.dec_n_points,
-        learnable_tgt_init=True,
-        # two stage
-        two_stage_type=args.two_stage_type,  # ['no', 'standard', 'early']
-        embed_init_tgt=args.embed_init_tgt,
-        use_text_enhancer=args.use_text_enhancer,
-        use_fusion_layer=args.use_fusion_layer,
-        use_checkpoint=args.use_checkpoint,
-        use_transformer_ckpt=args.use_transformer_ckpt,
-        use_text_cross_attention=args.use_text_cross_attention,
-        text_dropout=args.text_dropout,
-        fusion_dropout=args.fusion_dropout,
-        fusion_droppath=args.fusion_droppath,
-    )

spaces/Cat125/text-generator-v2/README.md

@@ -1,13 +0,0 @@
----
-title: Text Generator v2
-emoji: 💻
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.27.0
-app_file: main.py
-pinned: true
-license: openrail
----
-
-This tool allpws you to generate texts based on given context.