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- spaces/1acneusushi/gradio-2dmoleculeeditor/data/A Guide to Plaxis 2D Viewer A Free Tool for Geotechnical Modeling.md +0 -24
- spaces/1acneusushi/gradio-2dmoleculeeditor/data/FlukeView42and30SoftwareforWindowsSW90Wkeygen A Step-by-Step Tutorial on Using FlukeView Software with Your Fluke ScopeMeter.md +0 -133
- spaces/1acneusushi/gradio-2dmoleculeeditor/data/Fujitsu NetCobol 7 Serial Key Tips and Tricks for NetCOBOL for Windows Users.md +0 -108
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/A Guide to Plaxis 2D Viewer A Free Tool for Geotechnical Modeling.md
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<h1>How to Use Plaxis 2D Viewer to Visualize and Analyze Geotechnical Models</h1>
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<p>Plaxis 2D Viewer is a free software tool that allows you to open and view Plaxis 2D projects created with Plaxis 2D software. Plaxis 2D is a powerful and user-friendly finite element package for geotechnical engineering applications, such as slope stability, excavation, foundation, tunneling, embankment, groundwater flow and more.</p>
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<h2>plaxis 2d viewer</h2><br /><p><b><b>Download</b> ✒ <a href="https://byltly.com/2uKwEr">https://byltly.com/2uKwEr</a></b></p><br /><br />
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<p>With Plaxis 2D Viewer, you can easily explore the geometry, boundary conditions, loads, materials and results of any Plaxis 2D project. You can also perform basic calculations and post-processing operations, such as contour plots, cross-sections, graphs and tables. Plaxis 2D Viewer is a great tool for sharing and presenting your geotechnical models with colleagues, clients or students.</p>
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<p>In this article, we will show you how to use Plaxis 2D Viewer to visualize and analyze geotechnical models in a few simple steps.</p>
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<h2>Step 1: Download and Install Plaxis 2D Viewer</h2>
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<p>To download Plaxis 2D Viewer, you need to register for a free account on the Plaxis website. After logging in, you can find the download link under the Products section. The installation process is straightforward and should not take more than a few minutes.</p>
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<p></p>
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<h2>Step 2: Open a Plaxis 2D Project</h2>
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<p>To open a Plaxis 2D project, you can either drag and drop the project file (.p2dxlog) into the Plaxis 2D Viewer window or use the File menu to browse for the file. You can also open multiple projects at the same time and switch between them using the tabs at the top of the window.</p>
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<h2>Step 3: View the Model Geometry and Properties</h2>
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<p>Once you open a project, you will see the model geometry in the main window. You can use the mouse to zoom in and out, pan and rotate the view. You can also use the toolbar buttons to change the view mode (wireframe, solid or transparent), toggle the grid and axes on and off, and adjust the lighting and perspective.</p>
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<p>To view the model properties, such as boundary conditions, loads, materials and phases, you can use the Model Explorer panel on the left side of the window. You can expand or collapse each category by clicking on the arrow next to it. You can also select any item in the Model Explorer to highlight it in the main window and see its details in the Properties panel on the right side of the window.</p>
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<h2>Step 4: View the Calculation Results</h2>
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<p>To view the calculation results, such as displacements, stresses, strains and pore pressures, you can use the Results Explorer panel on the left side of the window. You can expand or collapse each category by clicking on the arrow next to it. You can also select any item in the Results Explorer to display it in the main window as a contour plot.</p>
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<p>To change the color scale, range and legend of the contour plot, you can use the Contour Plot Settings panel on the right side of the window. You can also use the toolbar buttons to show or hide mesh lines, nodes and labels.</p>
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<h2>Step 5: Perform Post-Processing Operations</h2>
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<p>To perform post-processing operations, such as cross-sections, graphs and tables, you can use the Post-Processing menu at the top of the window. You can choose from various options depending on what type of data you want to analyze and how you want to present it.</p>
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<p>For example, if you want to create a cross-section along a line or a curve in your model, you can use the Cross-Section option. This will open a new window where you can define the cross-section geometry and select what results you want to display along it. You can then view the cross-section as a plot or a table.</p>
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<p>If you want to create a graph of any result variable versus any other variable or parameter in your model, you can use the Graph option. This will open a new window where you can select what variables or parameters you want to plot on the x-axis and y-axis. You can then view the graph as a line chart or a scatter plot.</p>
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<p>If you want to create a table of any result variable or parameter in your model for any selected nodes or elements, you can use the Table option</p> ddb901b051<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/FlukeView42and30SoftwareforWindowsSW90Wkeygen A Step-by-Step Tutorial on Using FlukeView Software with Your Fluke ScopeMeter.md
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<h1>FlukeView42and30SoftwareforWindowsSW90Wkeygen: A Comprehensive Guide</h1>
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<p>If you are looking for a powerful and reliable software to manage your Fluke devices, you might want to check out FlukeView42and30SoftwareforWindowsSW90Wkeygen. This software is designed to help you connect, configure, capture, and analyze data from your Fluke devices with ease. In this article, we will give you a comprehensive guide on what FlukeView42and30SoftwareforWindowsSW90Wkeygen is, how to install it, how to use it, and some tips and tricks for getting the most out of it. Let's get started!</p>
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<h2>What is FlukeView42and30SoftwareforWindowsSW90Wkeygen?</h2>
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<p>FlukeView42and30SoftwareforWindowsSW90Wkeygen is a software that allows you to communicate with your Fluke devices via a USB or RS-232 interface. It supports various models of Fluke devices, such as the 430 Series II Power Quality Analyzer, the 1730 Three-Phase Energy Logger, the 1736 and 1738 Power Loggers, the 1740 Series Three-Phase Power Quality Loggers, and the 1750 Three-Phase Power Recorder. With this software, you can:</p>
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<h2>FlukeView42and30SoftwareforWindowsSW90Wkeygen</h2><br /><p><b><b>Download</b> ✓ <a href="https://byltly.com/2uKyWf">https://byltly.com/2uKyWf</a></b></p><br /><br />
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<ul>
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<li>Configure your Fluke device settings, such as measurement parameters, logging intervals, alarms, triggers, etc.</li>
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<li>Capture real-time data from your Fluke device and display it on your PC screen in graphical or tabular formats.</li>
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<li>Analyze data from your Fluke device using various tools, such as trend plots, histograms, statistics, harmonic analysis, etc.</li>
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<li>Export data from your Fluke device to other formats, such as CSV, XML, PDF, etc.</li>
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<li>Generate reports from your Fluke device data using customizable templates.</li>
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<li>Update your Fluke device firmware to the latest version.</li>
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</ul>
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<h3>FlukeView42and30SoftwareforWindowsSW90Wkeygen: A Brief Overview</h3>
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<p>FlukeView42and30SoftwareforWindowsSW90Wkeygen is a Windows-based software that runs on Windows XP (SP3), Windows Vista (SP2), Windows 7 (SP1), Windows 8.1, and Windows 10 operating systems. It requires a minimum of 512 MB of RAM and 300 MB of free disk space. It also requires a USB or RS-232 cable to connect your Fluke device to your PC.</p>
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<p>FlukeView42and30SoftwareforWindowsSW90Wkeygen has a user-friendly interface that consists of four main components:</p>
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<ul>
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<li>The menu bar: This contains various options for accessing different functions of the software.</li>
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<li>The toolbar: This contains icons for quick access to common functions of the software.</li>
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<li>The device panel: This shows the status and information of your connected Fluke device.</li>
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<li>The main window: This shows the data from your connected Fluke device in different views.</li>
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</ul>
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<h3>FlukeView42and30SoftwareforWindowsSW90Wkeygen: Features and Benefits</h3>
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<p>FlukeView42and30SoftwareforWindowsSW90Wkeygen offers many features and benefits for users who want to manage their Fluke devices efficiently. Some of these features and benefits are:</p>
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<ul>
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<li>It supports multiple languages, such as English, French, German, Spanish, Italian, Portuguese, Chinese (Simplified), Chinese (Traditional), Japanese, Korean, Russian, Turkish, Polish, Czech, Hungarian, Romanian, Slovakian, Slovenian, Croatian, Serbian (Latin), Bulgarian , Greek , Arabic , Hebrew , Thai , Indonesian , Vietnamese , Malay , Hindi , Bengali , Tamil , Telugu , Marathi , Gujarati , Kannada , Malayalam , Punjabi , Urdu , Persian , Nepali , Sinhala , Burmese , Khmer , Lao , Mongolian , Tibetan , Uyghur , Kazakh , Kyrgyz , Uzbek , Turkmen , Tajik .</li>
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<li>It allows you to configure your Fluke device settings according to your needs and preferences. You can set up measurement parameters, logging intervals, alarms, triggers, etc. You can also save and load configuration files for different applications.</li>
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<li>It allows you to capture real-time data from your Fluke device and display it on your PC screen in graphical or tabular formats. You can zoom in or out, pan, scroll, or select data points on the graph. You can also switch between different views, such as waveform, trend, harmonic, etc.</li>
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<li>It allows you to analyze data from your Fluke device using various tools, such as trend plots, histograms, statistics, harmonic analysis, etc. You can also compare data from different channels or devices, or apply filters or calculations to the data.</li>
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<li>It allows you to export data from your Fluke device to other formats, such as CSV, XML, PDF, etc. You can also print or email data directly from the software.</li>
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<li>It allows you to generate reports from your Fluke device data using customizable templates. You can add or edit text, images, tables, charts, etc. to the report. You can also preview or print the report before saving it.</li>
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<li>It allows you to update your Fluke device firmware to the latest version. You can check for updates online or download them manually from the official website. You can also backup or restore your Fluke device firmware in case of any issues.</li>
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</ul>
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<h2>How to Install FlukeView42and30SoftwareforWindowsSW90Wkeygen?</h2>
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<p>To install FlukeView42and30SoftwareforWindowsSW90Wkeygen on your PC, you need to follow these steps:</p>
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FlukeView</p>
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<h3>Step 1: Download the Software from the Official Website</h3>
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<p>The first step is to download the software from the official website of Fluke Corporation. You can find the download link here: <a href="https://www.fluke.com/en-us/support/software-downloads/flukesw90w-flukenet-software-for-windows">https://www.fluke.com/en-us/support/software-downloads/flukesw90w-flukenet-software-for-windows</a>. You need to enter your email address and agree to the terms and conditions before downloading the software. The file size is about 200 MB.</p>
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<h3>Step 2: Extract the Zip File and Run the Setup File</h3>
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<p>The second step is to extract the zip file that you downloaded in step 1. You can use any zip extractor software like WinZip or WinRAR. After extracting the zip file, you will find a folder named "Flukesw90w". Inside this folder, you will find a file named "setup.exe". This is the setup file that you need to run to install the software on your PC.</p>
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<h3>Step 3: Enter the Keygen Code and Activate the Software</h3>
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<p>The third step is to enter the keygen code and activate the software on your PC. The keygen code is a series of alphanumeric characters that you need to enter during the installation process. The keygen code is provided in a text file named "Key.txt" inside the zip file that you downloaded in step 1. You need to copy and paste this code when prompted by the setup wizard. After entering the keygen code, to click on "Next" and follow the instructions on the screen to complete the installation process. After the installation is complete, you need to restart your PC to activate the software.</p>
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<h2>How to Use FlukeView42and30SoftwareforWindowsSW90Wkeygen?</h2>
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<p>To use FlukeView42and30SoftwareforWindowsSW90Wkeygen on your PC, you need to follow these steps:</p>
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<h3>How to Connect Your Fluke Device to Your PC</h3>
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<p>The first step is to connect your Fluke device to your PC using a USB or RS-232 cable. You need to make sure that your Fluke device is turned on and that your PC recognizes it. You can check the device panel on the software interface to see if your Fluke device is connected and ready.</p>
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<h3>How to Configure Your Fluke Device Settings</h3>
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<p>The second step is to configure your Fluke device settings according to your needs and preferences. You can access the configuration menu by clicking on the "Configure" icon on the toolbar or by selecting "Configure" from the "Device" menu on the menu bar. You can set up measurement parameters, logging intervals, alarms, triggers, etc. You can also save and load configuration files for different applications.</p>
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<h3>How to Capture and Analyze Data with FlukeView42and30SoftwareforWindowsSW90Wkeygen</h3>
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<p>The third step is to capture and analyze data from your Fluke device with FlukeView42and30SoftwareforWindowsSW90Wkeygen. You can access the capture menu by clicking on the "Capture" icon on the toolbar or by selecting "Capture" from the "Device" menu on the menu bar. You can choose between different modes of capture, such as continuous, triggered, manual, etc. You can also start or stop the capture at any time.</p>
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<p>Once you have captured some data from your Fluke device, you can analyze it using various tools provided by the software. You can access the analysis menu by clicking on the "Analyze" icon on the toolbar or by selecting "Analyze" from the "View" menu on the menu bar. You can switch between different views of data, such as waveform, trend, harmonic, etc. You can also zoom in or out, pan, scroll, or select data points on the graph. You can also compare data from different channels or devices, or apply filters or calculations to the data.</p>
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<h2>Tips and Tricks for FlukeView42and30SoftwareforWindowsSW90Wkeygen Users</h2>
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<p>To get the most out of FlukeView42and30SoftwareforWindowsSW90Wkeygen, you might want to follow these tips and tricks:</p>
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<h3>How to Troubleshoot Common Problems with FlukeView42and30SoftwareforWindowsSW90Wkeygen</h3>
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<p>If you encounter any problems with FlukeView42and30SoftwareforWindowsSW90Wkeygen, you can try these solutions:</p>
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<ul>
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<li>If your Fluke device is not recognized by your PC or by the software, you can check if the cable is properly connected, if the drivers are installed correctly, if the firmware is updated to the latest version, or if there is any interference from other devices.</li>
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<li>If your data is corrupted or incomplete, you can check if there is enough memory space on your Fluke device or on your PC, if there is any power interruption during the capture process, or if there is any damage to the cable or the device.</li>
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<li>If your software is not working properly or crashing frequently, you can check if your PC meets the minimum system requirements, if there is any virus or malware infection on your PC, or if there is any conflict with other software or hardware.</li>
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</ul>
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<h3>How to Update FlukeView42and30SoftwareforWindowsSW90Wkeygen to the Latest Version</h3>
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| 94 |
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<p>To keep your software up to date and enjoy new features and bug fixes, you can update FlukeView42and30SoftwareforWindowsSW90Wkeygen to the latest version. You can check for updates online or download them manually from the official website of Fluke Corporation. You can access the update menu by clicking on the "Update" icon on the toolbar or by selecting "Update" from the "Help" menu on the menu bar. You can also backup or restore your software settings in case of any issues.</p>
|
| 95 |
-
<h3>How to Get Support and Feedback from FlukeView42and30SoftwareforWindowsSW90Wkeygen Community</h3>
|
| 96 |
-
<p>If you need any support or feedback from other users of FlukeView42and30SoftwareforWindowsSW90Wkeygen, you can join the online community of Fluke Corporation. You can access the community menu by clicking on the "Community" icon on the toolbar or by selecting "Community" from the "Help" menu on the menu bar. You can also contact Fluke Corporation directly via phone, email, or chat.</p>
|
| 97 |
-
<h2>Conclusion</h2>
|
| 98 |
-
<p>In conclusion, FlukeView42and30SoftwareforWindowsSW90Wkeygen is a powerful and reliable software that helps you manage your Fluke devices with ease. It allows you to connect, configure, capture, and analyze data from your Fluke devices with various features and benefits. It also offers tips and tricks for troubleshooting common problems, updating to the latest version, and getting support and feedback from other users. If you are looking for a software that can enhance your productivity and performance with your Fluke devices, you might want to give FlukeView42and30SoftwareforWindowsSW90Wkeygen a try!</p>
|
| 99 |
-
<h2>FAQs</h2>
|
| 100 |
-
<p>Here are some frequently asked questions about FlukeView42and30SoftwareforWindowsSW90Wkeygen:</p>
|
| 101 |
-
<ol>
|
| 102 |
-
<li>What are the system requirements for FlukeView42and30SoftwareforWindowsSW90Wkeygen?</li>
|
| 103 |
-
<p>The system requirements for FlukeView42and30SoftwareforWindowsSW90Wkeygen are:</p>
|
| 104 |
-
<ul>
|
| 105 |
-
<li>Operating system: Windows XP (SP3), Windows Vista (SP2), Windows 7 (SP1), Windows 8.1, or Windows 10.</li>
|
| 106 |
-
<li>RAM: 512 MB minimum.</li>
|
| 107 |
-
<li>Disk space: 300 MB minimum.</li>
|
| 108 |
-
<li>Cable: USB or RS-232.</li>
|
| 109 |
-
</ul>
|
| 110 |
-
<li>What are the supported models of Fluke devices for FlukeView42and30SoftwareforWindowsSW90Wkeygen?</li>
|
| 111 |
-
<p>The supported models of Fluke devices for FlukeView42and30SoftwareforWindowsSW90Wkeygen are:</p>
|
| 112 |
-
<ul>
|
| 113 |
-
<li>430 Series II Power Quality Analyzer.</li>
|
| 114 |
-
<li>1730 Three-Phase Energy Logger.</li>
|
| 115 |
-
<li>1736 and 1738 Power Loggers.</li>
|
| 116 |
-
<li>1740 Series Three-Phase Power Quality Loggers.</li>
|
| 117 |
-
<li>1750 Three-Phase Power Recorder.</li>
|
| 118 |
-
</ul>
|
| 119 |
-
<li>How much does FlukeView42and30SoftwareforWindowsSW90Wkeygen cost?</li>
|
| 120 |
-
<p>FlukeView42and30SoftwareforWindowsSW90Wkeygen is a free software that you can download from the official website of Fluke Corporation. However, you need a keygen code to activate it on your PC. The keygen code is provided in a text file named "Key.txt" inside the zip file that you downloaded from the website.</p>
|
| 121 |
-
<li>How do I get help with FlukeView42and30SoftwareforWindowsSW90Wkeygen?</li>
|
| 122 |
-
<p>You can get help with FlukeView42and30SoftwareforWindowsSW90Wkeygen by joining the online community of Fluke Corporation, or by contacting them directly via phone, email, or chat. You can access their contact information here: <a href="https://www.fluke.com/en-us/support/contact">https://www.fluke.com/en-us/support/contact</a>.</p>
|
| 123 |
-
<li>What are some alternatives to FlukeView42and30SoftwareforWindowsSW90Wkeygen?</li>
|
| 124 |
-
<p>Some alternatives to FlukeView42and30SoftwareforWindowsSW90Wkeygen are:</p>
|
| 125 |
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<ul>
|
| 126 |
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<li>Fluke Connect: This is a cloud-based software that allows you to connect and collaborate with other users of Fluke devices via a mobile app or a web browser.</li>
|
| 127 |
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<li>Fluke Energy Analyze Plus: This is a desktop software that allows you to analyze energy consumption and power quality data from various sources, such as loggers, meters, or clamps.</li>
|
| 128 |
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<li>Flukemaster: This is an online platform that allows you to access various resources and courses related to power quality and energy management.</li>
|
| 129 |
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</ul>
|
| 130 |
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</ol>
|
| 131 |
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</p> 0a6ba089eb<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Fujitsu NetCobol 7 Serial Key Tips and Tricks for NetCOBOL for Windows Users.md
DELETED
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|
| 1 |
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|
| 2 |
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<h1>Fujitsu NetCobol 7 Serial Key: What You Need to Know</h1>
|
| 3 |
-
<p>If you are a COBOL developer or a business owner who relies on COBOL applications, you may have heard of Fujitsu NetCobol 7. This is an enterprise-class COBOL compiler and runtime environment that allows you to create and run COBOL programs on Windows and Linux platforms. But before you can use this software, you need a serial key to activate it. In this article, we will tell you what you need to know about Fujitsu NetCobol 7 Serial Key, including how to get one legally, what features and benefits it offers, and what drawbacks it has.</p>
|
| 4 |
-
<h2>Introduction</h2>
|
| 5 |
-
<p>Fujitsu NetCobol 7 is a product of Fujitsu Limited, a Japanese multinational information technology company that provides various products and services for businesses and consumers. Fujitsu NetCobol 7 is part of the NetCOBOL family of products, which also includes NetCOBOL for .NET, NetCOBOL for Linux, and NetCOBOL Studio.</p>
|
| 6 |
-
<h2>Fujitsu NetCobol 7 Serial Key</h2><br /><p><b><b>Download Zip</b> - <a href="https://byltly.com/2uKA9V">https://byltly.com/2uKA9V</a></b></p><br /><br />
|
| 7 |
-
<p>A serial key is a unique code that identifies and authenticates a software product. It is usually required to install or activate the software on a computer or device. A serial key can also be used to verify the legitimacy of the software and prevent piracy or unauthorized use.</p>
|
| 8 |
-
<p>To get a serial key for Fujitsu NetCobol 7, you have two options:</p>
|
| 9 |
-
<ul>
|
| 10 |
-
<li>Buy a license from Fujitsu or an authorized reseller. This is the legal and recommended way to obtain a serial key. You can choose from different license types depending on your needs and budget. For example, you can buy a single-user license, a multi-user license, or an enterprise license. You can also buy a subscription license that gives you access to updates and support for a certain period of time.</li>
|
| 11 |
-
<li>Request a trial license from Fujitsu. This is a temporary and free way to try out Fujitsu NetCobol 7 before buying it. You can request a trial license by filling out an online form on the Fujitsu website. You will need to provide some basic information such as your name, email address, company name, and country. You will then receive an email with a link to download the software and a trial serial key that is valid for 30 days.</li>
|
| 12 |
-
</ul>
|
| 13 |
-
<p>However, you should avoid using any illegal or unofficial ways to get a serial key for Fujitsu NetCobol 7, such as:</p>
|
| 14 |
-
<ul>
|
| 15 |
-
<li>Downloading a cracked or pirated version of the software from untrusted sources. This is not only illegal but also risky, as you may expose your computer or device to malware, viruses, or spyware that can harm your data or system.</li>
|
| 16 |
-
<li>Using a fake or stolen serial key from online forums or websites. This is also illegal and unethical, as you are violating the intellectual property rights of Fujitsu and depriving them of their rightful revenue. Moreover, you may face legal consequences or penalties if you are caught using such a serial key.</li>
|
| 17 |
-
<li>Using a generator or hack tool that claims to create or bypass serial keys for Fujitsu NetCobol 7. This is also illegal and unreliable, as these tools are often scams or malware that can infect your computer or device.</li>
|
| 18 |
-
</ul>
|
| 19 |
-
<p>Therefore, we advise you to always use a legal and official way to get a serial key for Fujitsu NetCobol 7.</p>
|
| 20 |
-
<p>How to activate Fujitsu NetCobol 7 with serial key<br />
|
| 21 |
-
Fujitsu NetCobol 7 license key generator online<br />
|
| 22 |
-
Fujitsu NetCobol 7 crack download free<br />
|
| 23 |
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Fujitsu NetCobol 7 serial number verification<br />
|
| 24 |
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Fujitsu NetCobol 7 product key finder<br />
|
| 25 |
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Fujitsu NetCobol 7 activation code for windows<br />
|
| 26 |
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Fujitsu NetCobol 7 registration key for mac<br />
|
| 27 |
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Fujitsu NetCobol 7 keygen full version<br />
|
| 28 |
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Fujitsu NetCobol 7 patch update<br />
|
| 29 |
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Fujitsu NetCobol 7 serial key expired<br />
|
| 30 |
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How to renew Fujitsu NetCobol 7 serial key<br />
|
| 31 |
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Fujitsu NetCobol 7 license key transfer<br />
|
| 32 |
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Fujitsu NetCobol 7 crack fix<br />
|
| 33 |
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| 34 |
-
Fujitsu NetCobol 7 product key lost<br />
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| 35 |
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How to recover Fujitsu NetCobol 7 product key<br />
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Fujitsu NetCobol 7 keygen virus<br />
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Fujitsu NetCobol 7 patch error<br />
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| 40 |
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How to uninstall Fujitsu NetCobol 7 serial key<br />
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| 41 |
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Fujitsu NetCobol 7 license key refund<br />
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| 42 |
-
Fujitsu NetCobol 7 crack alternative<br />
|
| 43 |
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Fujitsu NetCobol 7 serial number format<br />
|
| 44 |
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Fujitsu NetCobol 7 product key length<br />
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| 45 |
-
How to change Fujitsu NetCobol 7 product key<br />
|
| 46 |
-
Fujitsu NetCobol 7 activation code generator offline<br />
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| 47 |
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Fujitsu NetCobol 7 registration key email<br />
|
| 48 |
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Fujitsu NetCobol 7 keygen download link<br />
|
| 49 |
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Fujitsu NetCobol 7 patch installation guide<br />
|
| 50 |
-
How to update Fujitsu NetCobol 7 serial key<br />
|
| 51 |
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Fujitsu NetCobol 7 license key price<br />
|
| 52 |
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Fujitsu NetCobol 7 crack review<br />
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| 53 |
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Fujitsu NetCobol 7 serial number location<br />
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| 54 |
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Fujitsu NetCobol 7 product key backup<br />
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| 55 |
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How to copy Fujitsu NetCobol 7 product key<br />
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| 56 |
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Fujitsu NetCobol 7 activation code bypass<br />
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Fujitsu NetCobol 7 registration key support<br />
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Fujitsu NetCobol 7 keygen tutorial<br />
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Fujitsu NetCobol 7 patch features<br />
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| 60 |
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How to check Fujitsu NetCobol 7 serial key validity<br />
|
| 61 |
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Fujitsu NetCobol 7 license key comparison<br />
|
| 62 |
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|
| 63 |
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Fujitsu NetCobol 7 product key expiration date<br />
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How to extend Fujitsu NetCobol 7 product key validity period <br />
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Fujitsu NetCobol 7 activation code requirements <br />
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| 67 |
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Fujitsu NetCobol 7 registration key delivery <br />
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| 68 |
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Fujitsu NetCobol 7 keygen compatibility <br />
|
| 69 |
-
Fujitsu NetCobol 7 patch security</p>
|
| 70 |
-
<h2>Features of Fujitsu NetCobol 7</h2>
|
| 71 |
-
<p>Fujitsu NetCobol 7 has many features that make it a powerful and versatile COBOL development tool. Here are some of the main features:</p>
|
| 72 |
-
<ul>
|
| 73 |
-
<li>It supports COBOL development on Windows and Linux platforms. You can create and run COBOL programs on both operating systems without changing your source code or recompiling your programs.</li>
|
| 74 |
-
<li>It integrates with Microsoft Visual Studio and other tools. You can use Visual Studio as your integrated development environment (IDE) for editing, debugging, testing, and deploying your COBOL programs. You can also use other tools such as Eclipse, Ant, Maven, Jenkins, Git, SVN, etc.</li>
|
| 75 |
-
<li>It offers high performance, reliability, and compatibility. You can benefit from the fast compilation speed, optimized runtime performance, robust error handling, and comprehensive debugging features of Fujitsu NetCobol 7. You can also ensure the compatibility of your COBOL programs with various databases, web servers, middleware, frameworks, etc.</li>
|
| 76 |
-
</ul>
|
| 77 |
-
<h2>Benefits of Fujitsu NetCobol 7</h2>
|
| 78 |
-
<p>Fujitsu NetCobol 7 has many benefits that make it a valuable and cost-effective COBOL solution for businesses and developers. Here are some of the main benefits:</p>
|
| 79 |
-
<ul>
|
| 80 |
-
<li>It reduces COBOL licensing and runtime fees. Unlike some other COBOL products that charge per-user or per-CPU fees for both development and runtime licenses, Fujitsu NetCobol 7 only charges for development licenses. There are no runtime fees for deploying your COBOL programs on any number of computers or devices.</li>
|
| 81 |
-
<li>It enables modernization and migration of legacy COBOL applications. You can use Fujitsu NetCobol 7 to update your existing COBOL applications with new features and functionalities such as web services, XML processing, database access, etc. You can also migrate your legacy COBOL applications from mainframe or other platforms to Windows or Linux platforms with minimal changes.</li>
|
| 82 |
-
<li>It supports industry standards and specifications. You can use Fujitsu NetCobol 7 to write standard ANSI COBOL code that conforms to the latest specifications such as COBOL2002 and COBOL2014. You can also use some common COBOL syntax extensions that are supported by IBM, Micro Focus, or previous versions of NetCOBOL (formerly called FujitsuCOBOL).</li>
|
| 83 |
-
</ul>
|
| 84 |
-
<h2>Drawbacks of Fujitsu NetCobol 7</h2>
|
| 85 |
-
<p>Fujitsu NetCobol 7 is not perfect though. It has some drawbacks that you should be aware of before using it. Here are some of the main drawbacks:</p>
|
| 86 |
-
<ul>
|
| 87 |
-
<li>It requires a new serial number for each version upgrade. If you want to upgrade from an older version of Fujitsu NetCobol (such as V9 or V10) to V11 (the latest version), you will need to request a new serial number from Fujitsu or an authorized reseller. You cannot use your existing serial number for the upgrade.</li>
|
| 88 |
-
<li>It may not support some COBOL syntax extensions or features that are specific to other vendors or platforms. For example, if your COBOL code uses some syntax extensions that are only available in IBM Enterprise COBOL or Micro Focus Visual COBOL, you may encounter compilation errors or runtime errors when using Fujitsu NetCobol 7.</li>
|
| 89 |
-
<li>It may have compatibility issues with some operating systems or hardware configurations that are not supported by Fujitsu NetCobol 7. For example, if you want to run your COBOL programs on Windows XP or Windows Server 2003 (which are no longer supported by Microsoft), you may face some problems with Fujitsu NetCobol 7.</li>
|
| 90 |
-
</ul>
|
| 91 |
-
<h2>Conclusion</h2>
|
| 92 |
-
Fujitsu NetCobol 7, a powerful and versatile COBOL compiler and runtime environment that allows you to create and run COBOL programs on Windows and Linux platforms. Fujitsu NetCobol 7 has many features and benefits that make it a valuable and cost-effective COBOL solution for businesses and developers. However, it also has some drawbacks that you should be aware of before using it. To get a serial key for Fujitsu NetCobol 7, you should always use a legal and official way, such as buying a license from Fujitsu or an authorized reseller, or requesting a trial license from Fujitsu. You should avoid using any illegal or unofficial ways, such as downloading a cracked or pirated version of the software, using a fake or stolen serial key, or using a generator or hack tool. If you are interested in Fujitsu NetCobol 7 and want to learn more about it, you can visit the Fujitsu website or contact their customer support. You can also download a free trial version of the software and try it out for yourself. We hope this article has helped you understand what you need to know about Fujitsu NetCobol 7 Serial Key. If you have any questions or feedback, please feel free to leave a comment below. <h2>FAQs</h2>
|
| 93 |
-
<p>Here are some common questions and answers about Fujitsu NetCobol 7 Serial Key.</p>
|
| 94 |
-
<ol>
|
| 95 |
-
<li>Q: How much does Fujitsu NetCobol 7 cost?<br>
|
| 96 |
-
A: The price of Fujitsu NetCobol 7 depends on the type and number of licenses you buy. You can check the latest pricing information on the Fujitsu website or contact their sales team.</li>
|
| 97 |
-
<li>Q: How long does the trial license last?<br>
|
| 98 |
-
A: The trial license for Fujitsu NetCobol 7 is valid for 30 days from the date of activation. You can use all the features and functions of the software during the trial period.</li>
|
| 99 |
-
<li>Q: How can I renew or extend my trial license?<br>
|
| 100 |
-
A: You cannot renew or extend your trial license for Fujitsu NetCobol 7. If you want to continue using the software after the trial period expires, you need to buy a full license.</li>
|
| 101 |
-
<li>Q: How can I upgrade from an older version of Fujitsu NetCobol to V11?<br>
|
| 102 |
-
A: To upgrade from an older version of Fujitsu NetCobol (such as V9 or V10) to V11, you need to request a new serial number from Fujitsu or an authorized reseller. You cannot use your existing serial number for the upgrade.</li>
|
| 103 |
-
<li>Q: How can I get technical support for Fujitsu NetCobol 7?<br>
|
| 104 |
-
A: You can get technical support for Fujitsu NetCobol 7 by opening a support incident on the Fujitsu website or calling their toll-free number. You will need to provide your serial number and other information to get assistance.</li>
|
| 105 |
-
</ol>
|
| 106 |
-
</p> 0a6ba089eb<br />
|
| 107 |
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (broen Season 2 720p Torrent).md
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<h1>HD Online Player (broen season 2 720p torrent)</h1>
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<p>If you are a fan of Scandinavian crime drama series, you might have heard of Broen (The Bridge), a critically acclaimed show that follows the investigations of a Swedish-Danish police team on crimes that occur on both sides of the bridge connecting the two countries. The second season of Broen aired in 2013 and received rave reviews from critics and audiences alike for its gripping storyline, complex characters, and realistic portrayal of social issues.</p>
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<p>But if you missed watching Broen season 2 when it was broadcasted or you want to rewatch it with better quality, you might be wondering how to find and download Broen season 2 720p torrent. And more importantly, how to watch it in high-definition (HD) online without any hassle. In this article, we will answer these questions and provide you with a comprehensive guide on how to enjoy Broen season 2 in HD online using a torrent client, a torrent site, and an HD online player.</p>
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<h2>What is Broen?</h2>
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<p>Broen is a Scandinavian crime drama series that premiered in 2011 and ran for four seasons until 2018. It was created by Hans Rosenfeldt and co-produced by Sveriges Television (SVT) in Sweden and Danmarks Radio (DR) in Denmark. The series stars Sofia Helin as Saga Norén, a Swedish detective with Asperger's syndrome, and Kim Bodnia as Martin Rohde, a Danish detective with personal problems. Together, they form a cross-border police team that investigates crimes that involve both Sweden and Denmark.</p>
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<p>The second season of Broen focuses on a series of murders that are linked to an eco-terrorist group called "Truth Terrorist". The group claims to expose the hypocrisy and corruption of the society by killing people who are responsible for environmental pollution, social inequality, war crimes, and human trafficking. Saga and Martin have to work together again to stop the group before they execute their final plan.</p>
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<p>Broen is widely praised for its intricate plot, realistic dialogue, dark humor, and strong performances. It has won several awards, including the International Emmy Award for Best Drama Series in 2014. It has also inspired several international adaptations, such as The Bridge (USA/Mexico), The Tunnel (UK/France), Bron/Broen (Russia/Estonia), and The Bridge (Malaysia/Singapore).</p>
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<h2>Why watch Broen season 2 in HD online?</h2>
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<p>If you have watched Broen season 2 before or you are planning to watch it for the first time, you might be wondering why you should watch it in HD online instead of SD online or DVD. Here are some reasons why watching Broen season 2 in HD online is better than other options:</p>
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<h3>Better picture quality</h3>
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<p>One of the main advantages of watching Broen season 2 in HD online is that you can enjoy better picture quality than SD online or DVD. HD stands for high-definition, which means that the video has more pixels per inch than SD or DVD. Pixels are tiny dots that make up the image on your screen. The more pixels there are, the sharper and clearer the image will be.</p>
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<p>Broen season 2 has a resolution of 1280 x 720 pixels, which is also known as 720p. This means that there are 1280 pixels horizontally and 720 pixels vertically on each frame of the video. This is much higher than SD resolution, which is usually around 640 x 480 pixels (480p), or DVD resolution, which is usually around 720 x 480 pixels (480i). By watching Broen season 2 in HD online, you can see more details, colors, and contrast in every scene.</p>
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<h3>Better sound quality</h3>
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<p>Another benefit of watching Broen season 2 in HD online is that you can experience better sound quality than SD online or DVD. HD audio has more bits per sample than SD or DVD audio. Bits are units of information that represent the amplitude or volume of the sound wave. The more bits there are, the more accurate and dynamic the sound will be.</p>
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<p>Broen season 2 has an audio bitrate of around 192 kbps (kilobits per second), which is also known as MP3 quality. This means that there are around 192 kilobits of information per second in each frame of the audio. This is much higher than SD audio bitrate, which is usually around 128 kbps (MP3 quality), or DVD audio bitrate, which is usually around <p>season 2 in HD online, you can hear more clarity, depth, and immersion in every dialogue, sound effect, and music score.</p>
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<p>A third advantage of watching Broen season 2 in HD online is that you can have a better streaming experience than SD online or DVD. HD online streaming means that you can watch Broen season 2 in HD quality without having to download the whole file first. You can simply stream it from a server that hosts the file and watch it on your device as it downloads.</p>
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<p>This is more convenient and faster than SD online streaming or DVD playback, which can suffer from buffering, lagging, and interruptions due to low bandwidth, slow connection, or physical damage. By watching Broen season 2 in HD online, you can enjoy a smooth and uninterrupted viewing experience.</p>
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<h2>How to find and download Broen season 2 720p torrent?</h2>
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<p>Now that you know why you should watch Broen season 2 in HD online, you might be wondering how to find and download Broen season 2 720p torrent. To do this, you will need two tools: a torrent client and a torrent site. Here is a guide on how to use them:</p>
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<h3>What is a torrent client?</h3>
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<p>A torrent client is a software application that allows you to download files from other users who are sharing them via a peer-to-peer (P2P) network. A P2P network is a decentralized system where users can connect directly with each other without relying on a central server. A torrent client enables you to join this network and download files in small pieces from multiple sources at the same time.</p>
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<p>A torrent client also allows you to create and share your own files with other users by creating a torrent file. A torrent file is a small file that contains metadata about the file you want to share, such as its name, size, structure, and location of the sources. A torrent file does not contain the actual file itself, but it acts as a pointer that tells your torrent client where to find and download it.</p>
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<h3>What is a torrent site?</h3>
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<p>A torrent site is a website that hosts and indexes torrent files for various types of content, such as movies, TV shows, music, games, software, etc. A torrent site allows you to search for and download torrent files that match your query. A torrent site does not host or provide the actual content itself, but it acts as a directory that links you to the sources where you can find and download it.</p>
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<h3>How to choose a reliable and safe torrent client and site?</h3>
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<p>There are many torrent clients and sites available on the internet, but not all of them are reliable and safe. Some of them may contain malware, viruses, spyware, or adware that can harm your device or compromise your privacy. Some of them may also have low-quality or fake files that can waste your time or bandwidth. Therefore, it is important to choose a trustworthy and secure torrent client and site for your downloading needs.</p>
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<p>Here are some criteria and tips to help you select a good torrent client and site:</p>
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- Check the reviews and ratings of the torrent client and site from reputable sources such as VPNOverview , TechRadar , Digital Trends , etc. - Choose a torrent client and site that have a large and active user base, as this indicates popularity and reliability. - Choose a torrent client and site that have a high seed-to-leech ratio, as this indicates availability and speed. - Choose a torrent client and site that have a variety of content categories and filters, as this indicates diversity and quality. - Choose a torrent client and site that have minimal or no ads, pop-ups, or redirects, as this indicates safety and convenience. - Choose a torrent client and site that support encryption, proxy, VPN , or other security features, as this indicates privacy and protection. <h3>How to install and use a torrent client?</h3>
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<p>Once you have chosen a reliable and safe torrent client for your device, you need to install and use it to download Broen season 2 720p torrent. Here are the general steps to do so:</p>
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- Download the installer file of the torrent client from its official website or a trusted source. - Run the installer file and follow the instructions to install the torrent client on your device. <p>but not all of them are compatible and convenient for your device and preferences. Some of them may not support the format or resolution of your video, or may have limited features or functions. Therefore, it is important to choose an HD online player that works well with your device and preferences.</p>
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<p>Here are some criteria and tips to help you select a good HD online player:</p>
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- Check the compatibility and requirements of the HD online player with your device and operating system. Make sure it can run smoothly and stably on your device without any errors or crashes. - Choose an HD online player that supports various video formats and resolutions, especially MP4 and 720p, which are the format and resolution of Broen season 2. Make sure it can play your video without any loss of quality or performance. - Choose an HD online player that has a simple and user-friendly interface, with easy-to-access controls and settings. Make sure it can play your video without any interruptions or distractions, such as ads, pop-ups, or redirects. - Choose an HD online player that has advanced features and functions, such as subtitles, speed control, playlist, screenshot, etc. Make sure it can enhance your viewing experience and meet your needs. - Choose an HD online player that has a high security and privacy level, such as encryption, password protection, VPN , etc. Make sure it can protect your video and data from unauthorized access or leakage. <h3>How to open and play Broen season 2 720p with an HD online player?</h3>
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<p>Once you have chosen a compatible and convenient HD online player for your device and preferences, you need to open and play Broen season 2 720p with it. Here are the general steps to do so:</p>
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- Download and install the HD online player from its official website or a trusted source. Run the HD online player and configure its settings according to your preferences. You may want to adjust the volume, brightness, aspect ratio, subtitles, etc. of your video. - Locate the Broen season 2 720p file on your device and drag and drop it into the HD online player. Alternatively, you can click on the open file button in the HD online player and browse for the file on your device. - The HD online player will start playing Broen season 2 720p on your device. You can see the progress, duration, resolution, etc. of your video in the HD online player. You can also pause, resume, rewind, fast-forward, skip, or stop your video at any time. - Enjoy watching Broen season 2 720p in HD online with your friends or colleagues! <h2>Conclusion</h2>
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<p>Broen season 2 is a captivating Scandinavian crime drama series that deserves to be watched in HD online for its superb picture quality, sound quality, and streaming experience. To do this, you need three tools: a torrent client, a torrent site, and an HD online player.</p>
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<p>In this article, we have provided you with a comprehensive guide on how to use these tools to find and download Broen season 2 720p torrent and watch it in HD online without any hassle. We have also given you some criteria and tips to help you choose reliable and safe torrent clients and sites, as well as compatible and convenient HD online players.</p>
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<p>We hope this article has been helpful for you and that you have enjoyed watching Broen season 2 in HD online with your friends or colleagues. If you have any questions or feedback, please feel free to leave a comment below.</p>
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<h2>FAQs</h2>
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<p>Here are some frequently asked questions about watching Broen season 2 in HD online:</p>
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<h3>Is torrenting legal?</h3>
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<p>you should always check the rules and regulations in your country before you download any content that might be protected under copyright. You should also use a VPN to hide your IP address and encrypt your traffic when torrenting to avoid any legal troubles or ISP throttling.</p>
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<h3>Is watching online videos safe?</h3>
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<p>Watching online videos can be safe if you use trustworthy and secure tools and websites. However, there are also some risks and dangers involved, such as malware, viruses, spyware, adware, phishing, identity theft, etc. Therefore, you should always be careful and vigilant when watching online videos. You should also use a VPN to protect your privacy and security when watching online videos.</p>
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<h3>What are some alternatives to torrenting and online video players?</h3>
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<p>If you don't want to use torrenting and online video players to watch Broen season 2 in HD online, you can also use some alternatives, such as streaming services, download sites, or DVD rentals. However, these alternatives may have some drawbacks, such as higher costs, lower quality, limited availability, or legal issues. Therefore, you should weigh the pros and cons of each alternative before you choose one.</p>
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<h3>How can I improve my torrenting and online video watching experience?</h3>
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<p>There are some tips and tricks that can help you improve your torrenting and online video watching experience, such as:</p>
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- Use a fast and stable internet connection to avoid buffering, lagging, or interruptions. - Use a large and high-resolution screen to enjoy the HD quality of the video. - Use a good pair of headphones or speakers to appreciate the HD sound of the video. - Use a comfortable and ergonomic chair and desk to avoid strain or fatigue. - Use a dark and quiet room to avoid glare or noise. <h3>How can I learn more about Broen season 2?</h3>
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<p>If you want to learn more about Broen season 2, you can visit some of these websites:</p>
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- The official website of Broen: https://www.bronbroen.com/ - The IMDb page of Broen: https://www.imdb.com/title/tt1733785/ - The Wikipedia page of Broen: https://en.wikipedia.org/wiki/The_Bridge_(2011_TV_series) - The fan wiki of Broen: https://thebridge.fandom.com/wiki/The_Bridge_Wiki </p> 0a6ba089eb<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Build Your Dream Zoo with Zoo Life Animal Park Game - The Best Zoo Management Game Ever.md
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<p>The final step is to create tailored environments for your animals. You can design habitats that match the natural habitats of your animals, such as savanna, jungle, desert, mountain, tundra, or ocean. You can also add plants, rocks, water, and other elements to enhance the beauty and diversity of your park.</p>
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<p>Another fun feature of the game is to foster the growth of your animal family through breeding programs. You can breed animals of the same species to produce adorable offspring. You can also breed animals of different species to create rare hybrids. For example, you can breed a lion and a tiger to get a liger, or a zebra and a horse to get a zorse.</p>
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| 66 |
-
<p>A noble mission of the game is to ensure the survival of endangered species. You can help protect animals that are threatened by habitat loss, poaching, climate change, or human interference. You can also participate in conservation efforts and raise awareness about the importance of biodiversity.</p>
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<h3>Engage in Fun Challenges & Interactive Events</h3>
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| 68 |
-
<p>To keep your visitors entertained with exciting events, exhibits, and quests, you can offer them a variety of activities and attractions. You can build roller coasters, ferris wheels, carousels, and other amusement rides. You can also create animal shows, petting zoos, safari tours, and educational programs.</p>
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-
<p>To spice up your gameplay with seasonal events and earn valuable rewards, you can join in various festivals and celebrations throughout the year. You can decorate your park with themed items and costumes. You can also collect special items and currencies to unlock exclusive rewards.</p>
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| 70 |
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<p>To join a global community of passionate zoo enthusiasts, you can connect with other players online. You can visit their zoos and rate their creations. You can also chat with them and exchange tips and tricks. You can also join clubs and compete with other clubs in weekly tournaments.</p>
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<h2>How to Master Zoo Management & Strategic Planning</h2>
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| 72 |
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<h3>Balance Your Resources, Time, and Staff Effectively</h3>
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<p>To run a successful zoo, you need to manage your budget, income, and expenses wisely. You need to balance the costs of building habitats, buying animals, hiring staff, and maintaining facilities. You also need to generate income from ticket sales, donations, sponsorships, and in-game purchases.</p>
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<p>To improve your zoo's performance and quality, you need to research new technologies and upgrades. You need to invest in better equipment, materials, tools, and services. You also need to unlock new features, functions, and options for your park.</p>
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<p>To optimize your zoo's operations and staff allocation, <p>In this article, we have introduced you to Zoo Life: Animal Park Game, a fun and engaging zoo simulation game for mobile devices. We have shown you how to build and manage your dream zoo, how to discover, breed, and care for a wide range of animals, and how to engage in fun challenges and interactive events. We have also given you some tips and tricks on how to master zoo management and strategic planning.</p>
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<p>Zoo Life: Animal Park Game is a game that will appeal to anyone who loves animals and zoos. It is a game that will challenge your creativity, strategy, and management skills. It is a game that will immerse you in a colorful and vibrant world, where you can create your own animal kingdom.</p>
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<p>If you are looking for a game that will keep you entertained for hours, Zoo Life: Animal Park Game is the game for you. You can download it for free from Google Play, App Store, or Microsoft Store. You can also play it offline without internet connection. What are you waiting for? Start your zoo adventure today and share your creations with the world!</p>
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<h2>FAQs</h2>
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<h4>Q: How many animals are there in Zoo Life: Animal Park Game?</h4>
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<p>A: There are over 200 animals in the game, from common domestic animals to rare exotic animals. You can also create hybrid animals by breeding different species.</p>
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<h4>Q: How can I get more coins and gems in the game?</h4>
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<p>A: You can get more coins and gems by completing quests, participating in events, collecting daily rewards, watching ads, or making in-game purchases.</p>
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<h4>Q: How can I join a club or create my own club in the game?</h4>
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<p>A: You can join a club or create your own club by tapping on the club icon on the bottom right corner of the screen. You can then search for an existing club or create a new one. You can also invite other players to join your club or accept invitations from other clubs.</p>
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<h4>Q: How can I visit other players' zoos and rate them?</h4>
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<p>A: You can visit other players' zoos and rate them by tapping on the globe icon on the bottom left corner of the screen. You can then select a player from the list or enter their ID. You can also see their ratings and comments on their zoos.</p>
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<h4>Q: How can I contact the developers or report a bug in the game?</h4>
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<p>A: You can contact the developers or report a bug in the game by tapping on the settings icon on the top right corner of the screen. You can then select the support option and fill out the form with your details and feedback.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Beyond Blue Mod APK Discover the Secrets of the Ocean in this Amazing Game.md
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<h1>How to Download Beyond Blue Mod APK on Android</h1>
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<p>If you are a fan of underwater exploration and marine life, you might be interested in playing Beyond Blue, a diving simulator game inspired by the BBC's Blue Planet II documentary series. But what if you want to enjoy some extra features or benefits that are not available in the official version of the game? In that case, you might want to try downloading and installing a mod APK of Beyond Blue on your Android device. In this article, we will explain what Beyond Blue is, what a mod APK is, and how to download and install one on your Android device.</p>
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<h2>What is Beyond Blue?</h2>
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<p>Beyond Blue is a single-player narrative adventure game that takes you deep into the ocean's mysteries and wonders. You play as Mirai, a marine biologist who is part of a research team that uses cutting-edge technology to study and interact with the ocean's creatures. You can explore different underwater environments, scan and document various species, and learn more about the ocean's secrets through mini-documentaries and interviews with experts. The game is developed by E-Line Media in collaboration with BBC Studios, OceanX Media, and some of the world's leading ocean scientists.</p>
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<h3>A diving simulator game inspired by Blue Planet II</h3>
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<p>Beyond Blue is heavily influenced by the BBC's Blue Planet II documentary series, which showcases the beauty and diversity of life in the ocean. The game features stunning visuals, realistic animations, and immersive sound effects that make you feel like you are actually diving in the ocean. You can also watch clips from the documentary series and learn more about the ocean's ecology, conservation, and challenges.</p>
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<h3>Features of Beyond Blue</h3>
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<p>Some of the features of Beyond Blue are:</p>
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<ul>
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<li>Eight different dives that take you to various locations in the Western Pacific ocean</li>
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<li>A story mode that follows Mirai's research on a pod of whales and their songs</li>
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<li>A free dive mode that lets you explore the ocean at your own pace</li>
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<li>A photo mode that allows you to take pictures of the ocean's beauty and share them with filters and settings</li>
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<li>A soundtrack that features original music and songs from artists like Miles Davis, The Flaming Lips, The Edisons, and more</li>
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</ul>
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<p>An APK (Android Package Kit) is a file format that contains all the components of an Android app. A mod APK is a modified version of an APK that offers extra features or benefits that are not available in the original version. For example, a mod APK might have unlimited money, unlocked items, premium features, or no ads.</p>
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<h3>A modified version of an app that offers extra features or benefits</h3>
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<p>A mod APK can enhance your gaming experience by giving you access to things that you normally have to pay for or work hard for. For example, a mod APK of Beyond Blue might let you explore more locations, scan more creatures, watch more documentaries, or customize your character. A mod APK might also fix some bugs or glitches that are present in the original version.</p>
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<h3>Risks and precautions of using mod APKs</h3>
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<p>However, using mod APKs also comes with some risks and precautions. Some of them are:</p>
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<p>Therefore, you should always be careful and cautious when using mod APKs. You should only download them from reputable and trusted sources, scan them for viruses or malware, and backup your data before installing them. You should also respect the original developers and publishers of the app and support them if you like their work.</p>
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<h2>How to download and install Beyond Blue mod APK on Android</h2>
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<p>If you still want to try Beyond Blue mod APK on your Android device, you will need to follow some steps to download and install it. Here is a simple guide on how to do it:</p>
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<h3>Step 1: Allow unknown apps on your device</h3>
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<p>By default, Android devices do not allow installing apps from unknown sources. This is a security measure to prevent installing harmful or malicious apps. However, if you want to install a mod APK, you will need to enable this option on your device. To do this, go to your device's settings, then security, then find the option that says "allow unknown apps" or "install unknown apps". Tap on it and toggle it on. You might also need to select the browser or file manager app that you will use to download the mod APK and grant it permission to install unknown apps.</p>
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<h3>Step 2: Download a file manager app</h3>
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<p>A file manager app is an app that lets you access and manage the files and folders on your device. You will need one to locate and install the mod APK file that you will download. There are many file manager apps available on the Google Play Store, such as ES File Explorer, File Manager, or Solid Explorer. You can choose any one that you like and download it on your device.</p>
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<h3>Step 4: Locate and install the APK file using the file manager app</h3>
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<h2>Conclusion</h2>
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<p>Beyond Blue is a diving simulator game that lets you explore the ocean's mysteries and wonders. If you want to enjoy some extra features or benefits that are not available in the official version of the game, you can try downloading and installing a mod APK of Beyond Blue on your Android device. However, you should be aware of the risks and precautions of using mod APKs, as they might violate the original developers' rights, contain malware or viruses, or cause problems with your device or account. You should only download mod APKs from reputable and trusted sources, scan them for viruses or malware, and backup your data before installing them.</p>
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<h2>FAQs</h2>
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<ul>
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<li><b>Q: Is Beyond Blue free to play?</b></li>
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<li>A: No, Beyond Blue is not free to play. It is a paid game that costs $14.99 on Google Play Store.</li>
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<li><b>Q: Is Beyond Blue compatible with my device?</b></li>
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<li>A: Beyond Blue requires Android 7.0 or higher and at least 4 GB of RAM to run smoothly. You can check if your device meets these requirements by going to your device's settings, then about phone, then hardware information.</li>
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<li><b>Q: Is Beyond Blue an online game?</b></li>
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<li>A: No, Beyond Blue is not an online game. It does not require an internet connection to play. However, you will need an internet connection to watch the documentary clips and interviews in the game.</li>
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<li><b>Q: How can I update Beyond Blue to the latest version?</b></li>
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<li>A: You can update Beyond Blue to the latest version by going to Google Play Store, then my apps and games, then find Beyond Blue and tap on update. However, if you are using a mod APK, you might not be able to update the game through Google Play Store. You will need to download and install the latest version of the mod APK from the same source that you downloaded it from.</li>
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<li><b>Q: How can I contact the developers or publishers of Beyond Blue?</b></li>
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<li>A: You can contact the developers or publishers of Beyond Blue by visiting their official website, Facebook page, Twitter account, or email address. You can find these links in the game's description on Google Play Store or in the game's settings menu.</li>
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spaces/1phancelerku/anime-remove-background/Bus Simulator Ultimate Free Mod APK The Best Bus Simulation Game with Unlimited Money and More.md
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<h1>Bus Simulator Ultimate Free Mod APK: A Realistic and Fun Driving Experience</h1>
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<p>Do you love driving buses and exploring different cities? Do you want to experience the thrill of being a bus driver and managing your own company? If yes, then you should try Bus Simulator Ultimate, one of the best bus simulation games for Android devices. And if you want to enjoy the game without any limitations, you should download Bus Simulator Ultimate Free Mod APK, which gives you access to unlimited money, gold, no ads, and many other features. In this article, we will tell you everything you need to know about this amazing game and how to get the modded version for free.</p>
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<p>Bus Simulator Ultimate is a realistic and fun driving game developed by Zuuks Games, a popular game studio that also created Truck Simulator 2018: Europe and Euro Truck Driver 2018. In this game, you can drive various buses across different countries and cities, such as Germany, Turkey, Italy, France, Spain, USA, Brazil, Russia, China, and more. You can also create your own bus company and expand your business by hiring drivers, buying new buses, upgrading your fleet, and completing missions. You can also compete with other players online and rank up on the leaderboards.</p>
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<h3>Features of Bus Simulator Ultimate</h3>
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<p>Bus Simulator Ultimate has many features that make it one of the best bus simulation games on the market. Here are some of them:</p>
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<h4>Realistic graphics and sound effects</h4>
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<p>The game has stunning 3D graphics that make you feel like you are driving a real bus in a real city. You can see the details of the buildings, roads, traffic, pedestrians, weather, and more. You can also hear the realistic sound effects of the engine, horn, brakes, passengers, radio, and more.</p>
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<h4>Multiple bus models and routes</h4>
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<p>While Bus Simulator Ultimate is free to download and play from the Google Play Store or App Store, it has some limitations that may affect your gaming experience. For example:</p>
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<p>Ubuntu Server is a server operating system based on Ubuntu Linux, a popular and user-friendly distribution of Linux. Ubuntu Server supports various architectures and platforms, such as x86, x86_64, ARM, and PowerPC. Ubuntu Server offers scalability, security, and performance for various workloads, such as web hosting, cloud computing, database management, file sharing, and more. Ubuntu Server is also compatible with many open-source and proprietary software and hardware solutions.</p>
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<li><strong>Regular security updates and long-term support.</strong> Ubuntu Server receives frequent and timely security updates from the Ubuntu community and Canonical, the company behind Ubuntu. Ubuntu Server also has long-term support (LTS) releases that are supported for five years with security and maintenance updates. The current LTS release is Ubuntu 20.04 LTS, which was released in April 2020 and will be supported until April 2025.</li>
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<li><strong>Official website.</strong> You can download the ISO image from the official website of Ubuntu at <a href="">https://ubuntu.com/download/server</a>. You can choose between the latest release (Ubuntu 21.04) or the latest LTS release (Ubuntu 20.04 LTS). You can also choose between different architectures (64-bit or ARM).</li>
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<p>When downloading the ISO image, you should choose the right version and architecture for your needs. The version refers to the release number of Ubuntu Server, such as 20.04 LTS or 21.04. The architecture refers to the type of processor that your server has, such as 64-bit or ARM. You should check the compatibility of your hardware and software with the version and architecture of Ubuntu Server before downloading it.</p>
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<p>To install Ubuntu Server on your machine, you need to create a bootable media from the ISO image that you downloaded. A bootable media is a USB flash drive or a DVD that contains the installation program and the operating system files. You can use various tools and steps to make a bootable media from the ISO image, depending on your operating system and preference. Here are some examples:</p>
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<li><strong>Rufus.</strong> Rufus is a free and open-source tool that can create bootable USB drives from ISO images. It is available for Windows only. You can download Rufus from <a href="">https://rufus.ie</a>. To use Rufus, you need to insert a USB flash drive into your computer, launch Rufus, select the ISO image and the USB drive, and click Start. Rufus will format the USB drive and copy the ISO image to it.</li>
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<li><strong>Etcher.</strong> Etcher is another free and open-source tool that can create bootable USB drives from ISO images. It is available for Windows, Mac, and Linux. You can download Etcher from <a href="">https://www.balena.io/etcher</a>. To use Etcher, you need to insert a USB flash drive into your computer, launch Etcher, select the ISO image and the USB drive, and click Flash. Etcher will validate the USB drive and copy the ISO image to it.</li>
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<li><strong>UNetbootin.</strong> UNetbootin is yet another free and open-source tool that can create bootable USB drives from ISO images. It is also available for Windows, Mac, and Linux. You can download UNetbootin from <a href="">https://unetbootin.github.io</a>. To use UNetbootin, you need to insert a USB flash drive into your computer, launch UNetbootin, select the ISO image and the USB drive, and click OK. UNetbootin will format the USB drive and copy the ISO image to it.</li>
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<li><strong>dd command.</strong> dd is a command-line tool that can create bootable USB drives from ISO images. It is available for Linux and Mac. To use dd, you need to insert a USB flash drive into your computer, open a terminal window, and run the following command (replace /dev/sdx with the device name of your USB drive):</li>
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<pre><code>sudo dd if=/path/to/ubuntu-server.iso of=/dev/sdx bs=4M status=progress && sync </code></pre>
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<p>This command will write the ISO image to the USB drive and sync the data.</p>
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<p>Regardless of which tool or method you use to create a bootable media, you should check the integrity of the media and the ISO image before installing Ubuntu Server. You can do this by verifying the checksums of the files or using tools such as <a href="">https://www.linuxliveusb.com/en/download</a> or <a href="">https://www.pendrivelinux.com/universal-usb-installer-easy-as-1-2-3</a>.</p>
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<h2>How to Install Ubuntu Server?</h2>
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<p>To install Ubuntu Server on your machine, you need to boot from the bootable media that you created and follow the instructions on the screen. Here are the main steps:</p>
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<ol>
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<li><strong>Booting from the install media and choosing the language.</strong> Insert the bootable media into your machine and power it on. You may need to change the boot order in your BIOS or UEFI settings to boot from the media. You will see a menu with several options. Choose Install Ubuntu Server to start the installation process. You will be asked to choose your preferred language for the installation.</li>
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<li><strong>Selecting the keyboard layout and network settings.</strong> You will be asked to choose your keyboard layout and configure your network settings. You can use DHCP or static IP addresses for your network connection. You can also set up a proxy server if needed.</li>
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<li><strong>Configuring the storage and partitions.</strong> You will be asked to choose how you want to use your disk space for Ubuntu Server. You can use guided partitioning or manual partitioning. Guided partitioning will automatically create and format partitions for you. Manual partitioning will let you create and format partitions yourself. You should have at least one partition for the root file system (/) and one partition for swap space. You can also create separate partitions for other directories, such as /home or /var.</li>
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<li><strong>Setting up a profile and installing software.</strong> You will be asked to enter your name, username, password, and server name. You will also be asked to choose which software you want to install on your server. You can select from various options, such as standard system utilities, OpenSSH server, web server, mail server, database server, or custom software selection.</li>
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| 90 |
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<li><strong>Completing the installation and rebooting.</strong> After installing the software, you will be asked to confirm if you want to install the GRUB bootloader on your disk. GRUB is a program that lets you choose which operating system to boot from. You should install GRUB unless you have another bootloader already installed. The installation process will finish and you will be prompted to remove the install media and reboot your machine.</li>
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</ol>
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<h2>How to Use Ubuntu Server?</h2>
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<p>After installing Ubuntu Server on your machine, you can start using it for your server needs. You can use basic commands and tasks to manage your server, such as updating the system and installing packages, configuring network and firewall settings, creating users and groups, setting up SSH and remote access, and more. Here are some examples:</p>
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<ul>
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<li><strong>Updating the system and installing packages.</strong> You can use the apt command to update your system and install packages from the official Ubuntu repositories. For example, to update your system, you can run:</li>
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</ul>
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<pre><code>sudo apt update sudo apt upgrade </code></pre>
|
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<ul>
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<li>To install a package, such as nginx, you can run:</li>
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</ul>
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<pre><code>sudo apt install nginx </code></pre>
|
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<ul>
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<li><strong>Configuring network and firewall settings.</strong> You can use the ip command to check your network interface settings and the ping command to test your network connectivity. For example, to check your IP address, you can run:</li>
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</ul>
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<pre><code>ip a </code></pre>
|
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<ul>
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<li>To ping a website, such as google.com, you can run:</li>
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</ul>
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<pre><code>ping google.com </code></pre>
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<ul>
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<li>You can use the ufw command to manage your firewall settings and allow or deny incoming or outgoing traffic. For example, to enable the firewall and allow SSH connections, you can run:</li>
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</ul>
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<pre><code>sudo ufw enable sudo ufw allow ssh </code></pre>
|
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<ul>
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<li><strong>Creating users and groups.</strong> You can use the adduser and addgroup commands to create new users and groups on your server. For example, to create a new user named alice and add her to a group named admin, you can run:</li>
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</ul>
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<pre><code>sudo adduser alice sudo addgroup admin sudo adduser alice admin </code></pre>
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<ul>
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<li><strong>Setting up SSH and remote access.</strong> You can use the ssh command to connect to your server from another machine over a secure connection. For example, to connect to your server with the username bob, you can run:</li>
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</ul>
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<pre><code>ssh bob@server-ip-address </code></pre>
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<ul>
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<li>You can also use the scp command to copy files between your server and another machine over SSH. For example, to copy a file named file.txt from your server to your local machine, you can run:</li>
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</ul>
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<pre><code>scp bob@server-ip-address:/home/bob/file.txt /home/local/file.txt </code></pre>
|
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<h2>How to Install Additional Software on Ubuntu Server?</h2>
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<p>Besides using the apt command to install software from the official Ubuntu repositories, you can also use other ways to install software on your server. Here are some of them:</p>
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<ul>
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<li><strong>Using snap.</strong> Snap is a package management system that allows you to install software from various sources, such as Canonical, third-party developers, or yourself. Snap packages are self-contained and isolated from the rest of the system, which makes them easy to install and update. You can use the snap command to install snap packages. For example, to install the snap package for Nextcloud, a cloud storage service, you can run:</li>
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</ul>
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<pre><code>sudo snap install nextcloud </code></pre>
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<ul>
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<li><strong>Using dpkg.</strong> dpkg is a low-level tool that can install software from Debian packages (.deb files), which are the format used by Ubuntu and other Debian-based distributions. You can use the dpkg command to install Debian packages that are not available in the official Ubuntu repositories or that you have downloaded from other sources. For example, to install a Debian package named package.deb that you have downloaded in your home directory, you can run:</li>
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</ul>
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<pre><code>sudo dpkg -i ~/package.deb </code></pre>
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<ul>
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<li><strong>Adding repositories and PPAs.</strong> Repositories are online sources of software packages that are maintained by Ubuntu or other developers. PPAs (Personal Package Archives) are repositories that are hosted on Launchpad, a platform for hosting and developing open-source projects. You can add repositories and PPAs to your system to access more software packages that are not available in the official Ubuntu repositories. You can use the add-apt-repository command to add repositories and PPAs. For example, to add the PPA for LibreOffice, a free and open-source office suite, you can run:</li>
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</ul>
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<pre><code>sudo add-apt-repository ppa:libreoffice/ppa sudo apt update sudo apt install libreoffice </code></pre>
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<ul>
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<li><strong>Installing from source code or binary files.</strong> Some software packages are not available in any repository or PPA, but are distributed as source code or binary files. Source code is the original code that is written by the developers, and binary files are the executable files that are compiled from the source code. You can install software from source code or binary files by following the instructions provided by the developers. For example, to install a software package named package.tar.gz that you have downloaded in your home directory, you can run:</li>
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</ul>
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<pre><code>tar xzf ~/package.tar.gz cd package ./configure make sudo make install </code></pre>
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<p>Note that installing software from source code or binary files may require additional dependencies, libraries, or tools that are not installed by default on your system. You should also be careful about the source and quality of the software that you install from these methods, as they may not be verified or supported by Ubuntu or Canonical.</p>
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<h2>How to Secure Ubuntu Server?</h2>
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<p>Security is an important aspect of any server operating system, and Ubuntu Server is no exception. You should follow some best practices and tips to harden your server security and protect it from unauthorized access, attacks, or breaches. Here are some of them:</p>
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<ul>
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<li><strong>Setting up a strong password and SSH keys.</strong> You should use a strong and unique password for your user account and your sudo password. A strong password should be at least 8 characters long and contain a mix of uppercase and lowercase letters, numbers, and symbols. You should also use SSH keys instead of passwords to authenticate yourself when connecting to your server via SSH. SSH keys are more secure and convenient than passwords, as they are based on cryptography and do not require typing or remembering. You can generate SSH keys using the ssh-keygen command and copy them to your server using the ssh-copy-id command.</li>
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<li><strong>Disabling root login and unused services.</strong> You should disable root login on your server, as it is a common target for attackers who want to gain full control of your system. You can disable root login by editing the /etc/ssh/sshd_config file and setting PermitRootLogin to no. You should also disable any services that you do not use or need on your server, as they may pose security risks or consume resources unnecessarily. You can disable services using the systemctl command.</li>
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<li><strong>Installing fail2ban and ufw.</strong> Fail2ban is a tool that monitors your server logs and bans IP addresses that show malicious signs, such as repeated failed login attempts or suspicious requests. Ufw is a tool that manages your firewall rules and allows or denies incoming or outgoing traffic based on predefined or custom criteria. You can install fail2ban and ufw using the apt command and configure them according to your needs.</li>
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<li><strong>Applying security updates and patches.</strong> You should keep your system and software up to date with the latest security updates and patches from Ubuntu and Canonical. Security updates and patches fix vulnerabilities and bugs that may compromise your server security or performance. You can apply security updates and patches using the apt command or enabling automatic updates.</li>
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</ul>
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| 153 |
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<h2>How to Monitor Ubuntu Server?</h2>
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<p>Monitoring is another important aspect of any server operating system, as it allows you to check your server performance and health, identify issues or bottlenecks, optimize resources, and troubleshoot problems. You can use various tools and methods to monitor your server, such as commands, software, logs, and alerts. Here are some examples:</p>
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<ul>
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<li><strong>Using commands.</strong> You can use various commands to monitor different aspects of your server, such as CPU usage, memory usage, disk usage, network activity, processes, etc. Some of these commands are top, htop, vmstat, iostat, sar, etc. For example, to monitor CPU usage in real time, you can run:</li>
|
| 157 |
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</ul>
|
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<pre><code>top </code></pre>
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<ul>
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<li>To monitor disk usage in human-readable format, you can run:</li>
|
| 161 |
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</ul>
|
| 162 |
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<pre><code>df -h </code></pre>
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<ul>
|
| 164 |
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<li><strong>Installing monitoring software.</strong> You can also install monitoring software on your server that can provide more comprehensive and graphical information about your server performance and health. Some of these software are Nagios, Zabbix, Munin, etc. For example, to install Nagios on your server, you can run:</li>
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</ul>
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<pre><code>sudo apt install nagios4 </code></pre>
|
| 167 |
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<ul>
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| 168 |
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<li>You can then access the Nagios web interface at <a href="">http://server-ip-address/nagios4</a> and view various metrics and graphs about your server.</li>
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<li><strong>Checking logs and alerts.</strong> Logs are files that record various events and activities that occur on your server, such as errors, warnings, messages, requests, etc. You can check the logs on your server to find out what is happening on your system and troubleshoot any issues. Logs are usually stored in the /var/log directory. You can use the tail, grep, or less commands to view or search the logs. For example, to view the last 10 lines of the syslog file, you can run:</li>
|
| 170 |
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</ul>
|
| 171 |
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<pre><code>sudo tail -n 10 /var/log/syslog </code></pre>
|
| 172 |
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<ul>
|
| 173 |
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<li>Alerts are notifications that inform you of any critical or abnormal events or conditions that occur on your server, such as high CPU load, low disk space, failed services, etc. You can set up alerts on your server using various tools or methods, such as email, SMS, webhooks, etc. For example, to set up email alerts for disk space usage on your server, you can use a tool such as <a href="">https://github.com/alexanderepstein/Bash-Snippets/tree/master/diskusagereport</a>.</li>
|
| 174 |
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</ul>
|
| 175 |
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<h2>How to Troubleshoot Ubuntu Server?</h2>
|
| 176 |
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<p>Sometimes, you may encounter problems or issues on your server that affect its functionality or performance. You should be able to troubleshoot these problems and find solutions or workarounds to fix them. You can use various tools and methods to troubleshoot your server, such as recovery mode, live CD, network tools, disk tools, backups, snapshots, online help, or support. Here are some examples:</p>
|
| 177 |
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<ul>
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| 178 |
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<li><strong>Booting into recovery mode or using a live CD.</strong> Recovery mode is a special boot option that allows you to access your server with minimal settings and perform basic tasks, such as repairing file systems, resetting passwords, or updating packages. You can boot into recovery mode by selecting Advanced options for Ubuntu from the GRUB menu and then choosing the recovery mode option. Live CD is a bootable media that contains a live version of Ubuntu that you can run without installing it on your disk. You can use a live CD to access your server and perform various tasks, such as copying files, editing configurations, or running diagnostics. You can create a live CD using the same methods that you used to create a bootable media for installing Ubuntu Server.</li>
|
| 179 |
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<li><strong>Fixing network connectivity or disk errors.</strong> Network connectivity or disk errors are common causes of server problems that prevent you from accessing or using your server properly. You can use various network tools or disk tools to fix these errors. For example, to fix network connectivity errors, you can use the ping, traceroute, or nslookup commands to test your network connection and resolve any issues. To fix disk errors, you can use the fsck command to check and repair your file systems or the badblocks command to scan and mark bad sectors on your disk.</li>
|
| 180 |
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<li><strong>Restoring backups or snapshots.</strong> Backups or snapshots are copies of your server data or state that you can use to restore your server in case of any problems or disasters. You should always have backups or snapshots of your server and store them in a safe and separate location. You can use various tools or methods to create and restore backups or snapshots of your server. For example, to create a backup of your home directory using the rsync command, you can run:</li>
|
| 181 |
-
</ul>
|
| 182 |
-
<pre><code>rsync -avz /home/username /backup/location </code></pre>
|
| 183 |
-
<ul>
|
| 184 |
-
<li>To restore a snapshot of your server using the LVM command, you can run:</li>
|
| 185 |
-
</ul>
|
| 186 |
-
<pre><code>lvconvert --merge /dev/vgname/snapshot </code></pre>
|
| 187 |
-
<ul>
|
| 188 |
-
<li><strong>Searching for help online or contacting support.</strong> If you cannot find a solution or workaround for your server problem by yourself, you can search for help online or contact support. You can find many online resources and communities that offer help and advice for Ubuntu Server users and administrators. Some of these resources are:</li>
|
| 189 |
-
</ul>
|
| 190 |
-
<ul>
|
| 191 |
-
<li>The official documentation of Ubuntu Server at <a href="">https://ubuntu.com/server/docs</a>.</li>
|
| 192 |
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<li>The official community forums of Ubuntu at <a href="">https://ubuntuforums.org</a>.</li>
|
| 193 |
-
<li>The official Q&A site of Ubuntu at <a href="">https://askubuntu.com</a>.</li>
|
| 194 |
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<li>The official IRC channel of Ubuntu Server at #ubuntu-server on Freenode.</li>
|
| 195 |
-
<li>The official mailing list of Ubuntu Server at [email protected].</li>
|
| 196 |
-
<li>The official blog of Ubuntu Server at <a href="">https://ubuntu.com/blog/server</a>.</li>
|
| 197 |
-
</ul>
|
| 198 |
-
<p>You can also contact support from Canonical, the company behind Ubuntu, if you have a paid subscription or contract with them. You can find more information about Canonical's support services at <a href="">https://ubuntu.com/support</ a></a>.</p>
|
| 199 |
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<h2>Comparison of Ubuntu Server with Other Server Operating Systems</h2>
|
| 200 |
-
<p>Ubuntu Server is not the only server operating system available in the market. There are other server OSes that you can choose from, depending on your preferences, requirements, and budget. Some of these server OSes are CentOS, Debian, or Windows Server. Each of these server OSes has its own advantages and disadvantages, and you should compare them carefully before making a decision. Here are some of the factors that you should consider when comparing Ubuntu Server with other server OSes:</p>
|
| 201 |
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<ul>
|
| 202 |
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<li><strong>Features.</strong> You should compare the features and capabilities of each server OS, such as supported architectures and platforms, available software and packages, security and stability, performance and scalability, etc. You should also check if the server OS has any unique or exclusive features that may suit your needs better than others.</li>
|
| 203 |
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<li><strong>Performance.</strong> You should compare the performance and efficiency of each server OS, such as CPU usage, memory usage, disk usage, network throughput, etc. You should also check how well the server OS can handle various workloads and scenarios, such as high traffic, high concurrency, high availability, etc.</li>
|
| 204 |
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<li><strong>Compatibility.</strong> You should compare the compatibility and interoperability of each server OS with your existing hardware and software solutions. You should check if the server OS can run on your hardware configuration and support your devices and drivers. You should also check if the server OS can work with your software applications and services, such as databases, web servers, mail servers, etc.</li>
|
| 205 |
-
<li><strong>Support.</strong> You should compare the support and maintenance of each server OS from the developers and vendors. You should check how often the server OS receives updates and patches, how long the server OS is supported for, how easy it is to get help and assistance, how much it costs to get support, etc.</li>
|
| 206 |
-
</ul>
|
| 207 |
-
<p>Here is a table that summarizes some of the main differences between Ubuntu Server and other server OSes:</p>
|
| 208 |
-
<table>
|
| 209 |
-
<tr>
|
| 210 |
-
<th>Server OS</th>
|
| 211 |
-
<th>Features</th>
|
| 212 |
-
<th>Performance</th>
|
| 213 |
-
<th>Compatibility</th>
|
| 214 |
-
<th>Support</th>
|
| 215 |
-
</tr>
|
| 216 |
-
<tr>
|
| 217 |
-
<td>Ubuntu Server</td>
|
| 218 |
-
<td>- Linux-based<br>- Supports various architectures and platforms<br>- Offers scalability, security, and performance for various workloads<br>- Compatible with many open-source and proprietary software and hardware solutions<br>- Has regular security updates and long-term support releases<br>- Has a large and active community of users and developers</td>
|
| 219 |
-
<td>- Runs efficiently on low-end hardware<br>- Has a minimal installation option that only installs the essential packages and services<br>- Has a capacity to scale-out to meet your precise needs<br>- Supports various cloud platforms</td>
|
| 220 |
-
<td>- Supports x86, x86_64, ARM, and PowerPC architectures<br>- Supports many devices and drivers<br>- Works with many software applications and services<br>- Has a large repository of software packages that can be installed using apt or snap commands</td>
|
| 221 |
-
<td>- Receives frequent and timely security updates from Ubuntu community and Canonical<br>- Has long-term support (LTS) releases that are supported for five years with security and maintenance updates<br>- Has free online resources and communities for help and advice<br>- Has paid support services from Canonical for enterprise users</td>
|
| 222 |
-
</tr>
|
| 223 |
-
<tr>
|
| 224 |
-
<td>CentOS</td>
|
| 225 |
-
<td>- Linux-based<br>- Based on Red Hat Enterprise Linux (RHEL)<br>- Offers stability, reliability, and security for enterprise workloads<br>- Compatible with many open-source and proprietary software and hardware solutions<br>- Has regular security updates and long-term support releases<br>- Has a large and active community of users and developers</td>
|
| 226 |
-
<td>- Runs efficiently on low-end hardware<br>- Has a minimal installation option that only installs the essential packages and services<br>- Has a capacity to scale-out to meet your precise needs<br>- Supports various cloud platforms</td>
|
| 227 |
-
<td>- Supports x86_64 architecture only<br>- Supports many devices and drivers<br>- Works with many software applications and services<br>- Has a large repository of software packages that can be installed using yum or dnf commands</td>
|
| 228 |
-
<td>- Receives frequent and timely security updates from CentOS community and Red Hat<br>- Has long-term support (LTS) releases that are supported for 10 years with security and maintenance updates<br>- Has free online resources and communities for help and advice<br>- Has paid support services from Red Hat for enterprise users</td>
|
| 229 |
-
</tr>
|
| 230 |
-
<tr>
|
| 231 |
-
<td>Debian</td>
|
| 232 |
-
<td>- Linux-based<br>- One of the oldest and most popular distributions of Linux<br>- Offers stability, security, and versatility for various workloads<br>- Compatible with many open-source and proprietary software and hardware solutions<br>- Has regular security updates and long-term support releases<br>- Has a large and active community of users and developers</td>
|
| 233 |
-
<td>- Runs efficiently on low-end hardware<br>- Has a minimal installation option that only installs the essential packages and services<br>- Has a capacity to scale-out to meet your precise needs<br>- Supports various cloud platforms</td>
|
| 234 |
-
<td>- Supports x86, x86_64, ARM, and PowerPC architectures<br>- Supports many devices and drivers<br>- Works with many software applications and services<br>- Has a large repository of software packages that can be installed using apt or dpkg commands</td>
|
| 235 |
-
<td>- Receives frequent and timely security updates from Debian community and Debian Security Team<br>- Has long-term support (LTS) releases that are supported for five years with security and maintenance updates<br>- Has free online resources and communities for help and advice<br>- Has paid support services from third-party vendors for enterprise users</td>
|
| 236 |
-
</tr>
|
| 237 |
-
<tr>
|
| 238 |
-
<td>Windows Server</td>
|
| 239 |
-
<td>- Windows-based<br>- The server version of the Windows operating system<br>- Offers compatibility, functionality, and integration for enterprise workloads<br>- Compatible with many proprietary software and hardware solutions<br>- Has regular security updates and long-term support releases<br>- Has a large and active community of users and developers</td>
|
| 240 |
-
<td>- Requires more hardware resources than Linux-based server OSes<br>- Has a graphical user interface (GUI) that can be installed or removed as needed<br>- Has a capacity to scale-up to meet your precise needs<br>- Supports various cloud platforms</td>
|
| 241 |
-
<td>- Supports x86_64 architecture only<br>- Supports many devices and drivers<br>- Works with many software applications and services<br>- Has a large repository of software packages that can be installed using Windows Update or PowerShell commands</td>
|
| 242 |
-
<td>- Receives frequent and timely security updates from Microsoft<br>- Has long-term support (LTS) releases that are supported for 10 years with security and maintenance updates<br>- Has free online resources and communities for help and advice<br>- Has paid support services from Microsoft for enterprise users</td>
|
| 243 |
-
</tr>
|
| 244 |
-
</table>
|
| 245 |
-
<h2>Resources for Learning More About Ubuntu Server</h2>
|
| 246 |
-
<p>If you want to learn more about Ubuntu Server, you can find many websites, books, courses, and podcasts that can help you. Here are some of them:</p>
|
| 247 |
-
<ul>
|
| 248 |
-
<li><strong>Websites.</strong> You can visit the following websites to find more information, tutorials, guides, tips, tricks, news, and reviews about Ubuntu Server:</li>
|
| 249 |
-
</ul>
|
| 250 |
-
<ul>
|
| 251 |
-
<li>The official website of Ubuntu at <a href="">https://ubuntu.com</a>.</li>
|
| 252 |
-
<li>The official blog of Ubuntu at <a href="">https://ubuntu.com/blog</a>.</li>
|
| 253 |
-
<li>The official documentation of Ubuntu at <a href="">https://ubuntu.com/server/docs</a>.</li>
|
| 254 |
-
<li>The official community forums of Ubuntu at <a href="">https://ubuntuforums.org</a>.</li>
|
| 255 |
-
<li>The official Q&A site of Ubuntu at <a href="">https://askubuntu.com</a>.</li>
|
| 256 |
-
<li>The official IRC channel of Ubuntu Server at #ubuntu-server on Freenode.</li>
|
| 257 |
-
<li>The official mailing list of Ubuntu Server at [email protected].</li>
|
| 258 |
-
<li>The unofficial wiki of Ubuntu at <a href="">https://wiki.ubuntu.com</a>.</li>
|
| 259 |
-
<li>The unofficial blog of Ubuntu Server at <a href="">https://ubuntuserver.org</a>.</li>
|
| 260 |
-
<li>The unofficial podcast of Ubuntu Server at <a href="">https://ubuntuserverpodcast.com</a>.</li>
|
| 261 |
-
</ul>
|
| 262 |
-
<ul>
|
| 263 |
-
<li><strong>Books.</strong> You can read the following books to learn more about Ubuntu Server:</li>
|
| 264 |
-
</ul>
|
| 265 |
-
<ul>
|
| 266 |
-
<li><em>The Official Ubuntu Server Book (3rd Edition)</em> by Kyle Rankin and Benjamin Mako Hill. This book covers everything you need to know about installing, configuring, managing, securing, monitoring, troubleshooting, and upgrading Ubuntu Server. It also includes practical examples and exercises to help you master Ubuntu Server skills.</li>
|
| 267 |
-
<li><em>Ubuntu Unleashed 2019 Edition: Covering 18.04, 18.10, 19.04 (13th Edition)</em> by Matthew Helmke. This book covers everything you need to know about using Ubuntu as a desktop or server operating system. It also includes tips and tricks for advanced users and developers.</li>
|
| 268 |
-
<li><em>Mastering Ubuntu Server: Master the art of deploying, configuring, managing and completeness of the backup and store it in a safe and separate location.</li>
|
| 269 |
-
<li><strong>Install Ubuntu Server on your new server.</strong> You can follow the steps that we have described in this article to download, create a bootable media, and install Ubuntu Server on your new server. You should choose the same or compatible architecture and version as your old server.</li>
|
| 270 |
-
<li><strong>Restore your data and settings on your new server.</strong> You can use the tools or methods that are available for Ubuntu Server to restore your data and settings from your backup. You may need to convert or adapt some of the data or settings to make them compatible with Ubuntu Server. You may also need to install additional software or packages on your new server to run your applications or services.</li>
|
| 271 |
-
<li><strong>Test and verify your new server.</strong> You should test and verify that your new server is working properly and that all your data and settings are transferred correctly. You should also check for any errors or issues that may arise during or after the migration process. You should also update and secure your new server as needed.</li>
|
| 272 |
-
</ol>
|
| 273 |
-
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DELETED
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<h1><b>Epic Heroes War Premium Mod Apk An1: A Guide for Gamers</b></h1>
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<p>Are you a fan of epic battles, legendary heroes, and thrilling adventures? If so, you might want to check out Epic Heroes War, a real-time strategy and role-playing game that lets you create your own army of heroes and fight against various enemies. Epic Heroes War is a fun and addictive game that offers hours of entertainment and challenge. But what if you want to enjoy the game without any limitations or restrictions? What if you want to have unlimited money and gems, unlock all heroes and items, and remove annoying ads? Well, there is a way to do that, and it is called Epic Heroes War premium mod apk an1.</p>
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<p>Epic Heroes War premium mod apk an1 is a modified version of the original game that gives you access to all the features and benefits that you would normally have to pay for or earn through hard work. With Epic Heroes War premium mod apk an1, you can enjoy the game to the fullest and dominate the battlefield with ease. In this article, we will tell you everything you need to know about Epic Heroes War premium mod apk an1, including its features, benefits, how to download and install it, and how it compares to the original game. So, if you are ready to take your gaming experience to the next level, read on!</p>
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<h2><b>Features of Epic Heroes War</b></h2>
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<p>Before we dive into the details of Epic Heroes War premium mod apk an1, let us first review some of the features of Epic Heroes War that make it such a great game. Epic Heroes War is a unique game that combines real-time strategy and role-playing elements in a single gameplay. Here are some of the features that you can enjoy in Epic Heroes War:</p>
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<h3><b>Real-time Strategy and Role-playing Game</b></h3>
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<p>Epic Heroes War is not your typical RPG or RTS game. It is a hybrid of both genres that offers a dynamic and exciting gameplay. In Epic Heroes War, you can create your own army of heroes from over 60 different characters, each with their own skills, abilities, and stats. You can also equip them with various items and weapons to enhance their performance. You can then lead your army into battle against other players or AI enemies in different modes and scenarios. You can also control your heroes individually or as a group, depending on your strategy and preference. You can also upgrade your heroes and items as you progress through the game.</p>
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<h3><b>Over 60 Heroes and Items</b></h3>
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<p>Epic Heroes War offers a wide variety of heroes and items for you to choose from. You can find heroes from different categories, such as warriors, mages, archers, assassins, support, etc. You can also find heroes from different mythologies and legends, such as Zeus, Thor, Hercules, Odin, Loki, etc. Each hero has their own unique skills and abilities that can be activated during battle. You can also equip your heroes with different items and weapons that can boost their stats and effects. Some of the items include swords, axes, hammers, bows, daggers, shields, armors, rings, etc.</p>
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<h3><b>Online PvP Battles and Co-op Modes</b></h3>
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<p>Epic Heroes War allows you to compete or cooperate with other players online in various modes and challenges. You can join or create a guild with other players and participate in guild wars, raids, tournaments, etc. You can also challenge other players in 1v1 or 5v5 PvP battles and climb the leaderboards. You can also team up with other players in co-op modes and fight against powerful bosses or waves of enemies.</p>
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<p>One of the main benefits of Epic Heroes War premium mod apk an1 is that it gives you unlimited money and gems, which are the main currencies in the game. With unlimited money and gems, you can buy any hero, item, upgrade, or anything else that you want in the game. You don't have to worry about running out of resources or spending real money to get them. You can enjoy the game without any limitations or restrictions.</p>
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<p>Another benefit of Epic Heroes War premium mod apk an1 is that it unlocks all heroes and items for you to use. You don't have to wait for a certain level or complete a certain quest to unlock them. You can access all heroes and items from the start of the game and choose your favorite ones to create your army. You can also try different combinations and strategies with different heroes and items.</p>
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<p>A third benefit of Epic Heroes War premium mod apk an1 is that it removes annoying ads and does not require root access to install. Ads can be very distracting and irritating when you are playing a game, especially when they pop up in the middle of a battle or a cutscene. Epic Heroes War premium mod apk an1 eliminates all ads from the game, so you can enjoy it without any interruptions or delays. Moreover, Epic Heroes War premium mod apk an1 does not require root access to install, which means you don't have to risk damaging your device or voiding your warranty to use it. You can install it safely and easily on any Android device.</p>
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<h3><b>Easy to Download and Install</b></h3>
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<p>A fourth benefit of Epic Heroes War premium mod apk an1 is that it is easy to download and install. You don't need any special skills or knowledge to use it. All you need to do is follow a few simple steps that we will explain in the next section. You can download and install Epic Heroes War premium mod apk an1 in a matter of minutes and start playing right away.</p> <p>h3: Step 2: Download Epic Heroes War Premium Mod Apk An1 File</b></p>
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<h3><b>Step 3: Locate and Install Epic Heroes War Premium Mod Apk An1 File</b></h3>
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<p>The third step you need to do is to locate and install Epic Heroes War premium mod apk an1 file on your device. To do this, you need to use a file manager app that can access the downloaded files on your device. You can use any file manager app that you prefer, but we recommend using ES File Explorer, which is a popular and reliable app that you can download from the Google Play Store. Once you have the file manager app, follow these steps:</p>
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<ul>
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<li>Open the file manager app and go to the folder where you downloaded Epic Heroes War premium mod apk an1 file. It is usually in the Downloads folder, but it might be in a different location depending on your settings.</li>
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<li>Tap on the Epic Heroes War premium mod apk an1 file and select Install. You might see a pop-up message asking for your permission to install the app. Just tap on Install again and wait for the installation process to finish.</li>
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<li>Once the installation is done, you will see a message saying that the app has been installed successfully. You can then tap on Open to launch the app or Done to exit the installer.</li>
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</ul>
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<h3><b>Step 4: Launch Epic Heroes War Premium Mod Apk An1 and Enjoy</b></h3>
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<h2><b>Comparison of Epic Heroes War Original Game and Premium Mod Apk An1</b></h2>
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<p>To give you a better idea of how Epic Heroes War premium mod apk an1 differs from the original game, we have created a table that compares the two versions in terms of some key aspects. You can see the table below and decide for yourself which version suits you better.</p>
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<table>
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| 86 |
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<tr>
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<th>Aspect</th>
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<th>Original Game</th>
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<th>Premium Mod Apk An1</th>
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</tr>
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<tr>
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<td>Money</td>
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<td>Limited and earned through gameplay or in-app purchases</td>
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-
<td>Unlimited and free</td>
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</tr>
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| 96 |
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<tr>
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<td>Gems</td>
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| 98 |
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<td>Limited and earned through gameplay or in-app purchases</td>
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<td>Unlimited and free</td>
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</tr>
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<tr>
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<td>Heroes</td>
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<td>Limited and unlocked through gameplay or in-app purchases</td>
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<td>Unlocked and free</td>
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</tr>
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<tr>
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<td>Items</td>
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<td>Limited and unlocked through gameplay or in-app purchases</td>
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<td>Unlocked and free</td>
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</tr>
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<tr>
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<td>Ads</td>
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<td>Present and annoying</td>
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<td>Removed and no interruptions</td>
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</tr>
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<tr>
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<td>Root</td>
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<td>Not required but recommended for some features</td>
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<td>Not required and not recommended</td>
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</tr>
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<tr>
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<td>Installation</td>
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<td>Easy from Google Play Store but requires updates and permissions</td>
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<td>Easy from trusted source but requires unknown sources enabled</td>
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</tr>
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<td>Safety</td>
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<td>Safe and virus-free but may contain bugs or glitches</td>
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<td>Safe and virus-free but may not be compatible with some devices or versions</td>
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<h2><b>Conclusion</b></h2>
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<p>Epic Heroes War is a fantastic game that combines real-time strategy and role-playing elements in a unique and exciting gameplay. You can create your own army of heroes and fight against various enemies in different modes and scenarios. You can also enjoy the game with other players online in PvP battles and co-op modes. However, if you want to have more fun and freedom in the game, you should try Epic Heroes War premium mod apk an1, which gives you unlimited money and gems, unlocks all heroes and items, removes ads, and does not require root access. You can download and install Epic Heroes War premium mod apk an1 easily and safely from our website and enjoy its features right away. So, what are you waiting for? Download Epic Heroes War premium mod apk an1 now and unleash your epic power!</p>
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<p>Here are some of the frequently asked questions related to Epic Heroes War premium mod apk an1:</p>
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<ul>
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<li><b>Q: Is Epic Heroes War premium mod apk an1 safe to use?</b></li>
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<li><b>Q: Is Epic Heroes War premium mod apk an1 compatible with my device or version?</b></li>
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<li><b>Q: How can I update Epic Heroes War premium mod apk an1?</b></li>
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<li>A: Epic Heroes War premium mod apk an1 is updated regularly to keep up with the changes and improvements of the original game. You can check our website for the latest version of Epic Heroes War premium mod apk an1 and download it from there. You can also enable notifications on our website to get notified when a new version is available.</li>
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spaces/1phancelerku/anime-remove-background/Experience the Thrill of Pooking - Billiards City a Stunning Pool Game.md
DELETED
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<h1>Pooking Billiards City: A Modern Arcade Style Pool Game</h1>
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<h2>Introduction</h2>
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<p>If you love a relaxed game of 8 ball, then you will surely enjoy Pooking Billiards City, a modern arcade style pool game with single player mode. In this game, you can challenge yourself with various levels of difficulty, from beginner to pro, and hone your skills in the most realistic and exciting billiards simulator. You can also customize your cue stick and ball with different designs and colors, and unlock new city bars and trophies as you progress. In this article, we will tell you more about Pooking Billiards City, its features, tips and tricks, and how to download it for free on your Android device.</p>
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<h2>Features of Pooking Billiards City</h2>
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<h3>Amazing Single Player Mode</h3>
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<p>Pooking Billiards City has a single player mode that lets you play at your own pace and style. You can choose from four different game modes: Classic, Combo, Challenge, and Practice. In Classic mode, you have to clear all the balls on the table in any order. In Combo mode, you have to make consecutive shots without missing. In Challenge mode, you have to complete specific tasks within a time limit. In Practice mode, you can practice your shots without any pressure or rules.</p>
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<h3>Realistic 3D Graphics and Physics</h3>
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<p>Pooking Billiards City uses the latest technology to create the most realistic and exciting billiards simulator. The game has stunning HD graphics that make the balls and decals look lifelike. The game also has fantastic playability and ultra realistic ball physics that simulate the real movement and collision of the balls. You can experience pool like never before, thanks to the amazing visual and sound effects of Pooking Billiards City.</p>
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<h3>Smooth and Intuitive Controls</h3>
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<p>Pooking Billiards City has smooth and intuitive controls that make the game easy to play for anyone. You can use the slider on the right side of the screen to adjust the direction of the cue stick, and the power bar on the left side of the screen to adjust the strength of the shot. You can also use the spin buttons on the bottom of the screen to add some spin to the cue ball, which can help you make more accurate and creative shots. The game also has a touch control for moving the stick, which gives you more precision and flexibility.</p>
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<h3>Stylish and Diverse Levels</h3>
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<p>Pooking Billiards City has stylish and diverse levels that will challenge your skills and keep you entertained. The game has over 1000 levels that vary in difficulty, layout, and background. You can play in cool and funky looking billiard tables that are set in different city bars, such as New York, London, Paris, Tokyo, Dubai, and more. You can also win trophies and become the acclaimed Pooking Billiards City Champion as you complete each level.</p>
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<h3>Cool and Funky Cue Sticks and Balls</h3>
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<p>Pooking Billiards City has cool and funky cue sticks and balls that you can customize according to your preference. You can choose from a variety of designs and colors for your cue stick and ball, such as stars, stripes, hearts, skulls, flames, and more. You can also unlock new cue sticks and balls as you play and earn coins. You can use the coins to buy more items from the shop, or use them to play higher stakes games. You can also watch ads to get free coins and items. Pooking Billiards City lets you express your personality and style with your cue stick and ball.</p>
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<h2>Tips and Tricks for Pooking Billiards City</h2>
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<h3>Take As Much Time As You Need</h3>
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<p>Pooking Billiards City is a relaxing game that does not have a timer or a limit on the number of shots you can take. You can take as much time as you need to plan and execute your shots, without any pressure or stress. You can also pause the game anytime you want, and resume it later. This way, you can enjoy the game at your own pace and convenience.</p>
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<h3>Lower Power Is Often Better</h3>
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<p>Pooking Billiards City has a power bar that lets you adjust the strength of your shot. However, you don't always need to use the maximum power to make a good shot. In fact, lower power is often better, especially when you are aiming for a precise or delicate shot. Lower power can help you avoid overshooting, missing, or scratching the cue ball. It can also help you control the direction and speed of the cue ball and the object balls better.</p>
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<h3>Always Keep The Next Shot In Mind</h3>
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<p>Pooking Billiards City is not just about making the current shot, but also about setting up the next shot. You should always keep the next shot in mind when you are playing, and try to position the cue ball in a favorable spot for it. You should also try to clear any obstacles or clusters that might block your next shot. By thinking ahead, you can make your game easier and smoother.</p>
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<h3>Learn To Utilize Spins</h3>
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<p>Pooking Billiards City has spin buttons that let you add some spin to the cue ball, which can change its direction and speed after hitting an object ball or a cushion. You can use spins to make more accurate and creative shots, such as curve shots, bank shots, cut shots, and more. You can also use spins to avoid scratches or fouls, or to get out of tricky situations. Spins can be very useful and fun to use, but they also require some practice and skill to master.</p>
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<h3>Break Balls Apart When You Can</h3>
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<p>Pooking Billiards City sometimes has levels where the balls are clustered together in a tight formation, which makes it harder to clear them. In such cases, you should try to break them apart when you can, by using a powerful shot or a spin shot. Breaking balls apart can create more space and opportunities for making shots, and also increase your chances of potting multiple balls in one shot.</p>
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<h2>How to Download Pooking Billiards City APK for Free</h2>
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<h3>What is an APK file?</h3>
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<p>An APK file is an Android Package file that contains all the files and data needed to install an app on an Android device. APK files are usually used to distribute apps that are not available on the Google Play Store, or to update apps before they are officially released on the Play Store. APK files can also be used to install apps on devices that do not have access to the Play Store, such as some regions or countries where Google services are restricted.</p>
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<h3>Where can you download Pooking Billiards City APK for free?</h3>
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<p>There are many websites that offer free APK files for various apps, including Pooking Billiards City. However, not all of them are safe and reliable, as some of them may contain viruses, malware, or fake apps that can harm your device or steal your data. Therefore, you should be careful and cautious when downloading APK files from unknown sources. You should always check the reviews, ratings, comments, and permissions of the APK file before downloading it.</p>
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<p>One of the safest and most trusted websites that we recommend for downloading Pooking Billiards City APK for free is [APKPure]. APKPure is a popular and reputable platform that provides high-quality and verified APK files for thousands of apps and games. You can download Pooking Billiards City APK from APKPure by following these steps:</p>
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- Go to [APKPure] website on your browser. - Search for Pooking Billiards City in the search bar. - Click on the Pooking Billiards City icon from the search results. - Click on the Download APK button on the app page. - Wait for the download to finish. <h3>How to install Pooking Billiards City APK on your Android device?</h3 <p>After downloading the Pooking Billiards City APK file from APKPure, you need to install it on your Android device. To do that, you need to enable the installation of apps from unknown sources on your device settings. This is a security measure that prevents unauthorized or harmful apps from being installed on your device. You can enable this option by following these steps:</p>
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- Go to your device settings and tap on Security or Privacy. - Find and tap on the option that says Unknown Sources or Install Unknown Apps. - Turn on the toggle or check the box that allows the installation of apps from unknown sources. - Confirm your choice if prompted. <p>Once you have enabled this option, you can install the Pooking Billiards City APK file by following these steps:</p>
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- Locate the Pooking Billiards City APK file on your device storage, either in the Downloads folder or in the APKPure folder. - Tap on the Pooking Billiards City APK file to open it. - Tap on the Install button on the screen. - Wait for the installation to finish. - Tap on the Open button to launch the game. <h2>Conclusion</h2>
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<p>Pooking Billiards City is a modern arcade style pool game that offers a realistic and exciting billiards experience. You can play in various game modes, levels, and locations, and customize your cue stick and ball. You can also download Pooking Billiards City APK for free from APKPure, and install it on your Android device easily. Pooking Billiards City is a fun and relaxing game that you can enjoy anytime and anywhere.</p>
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<h4>FAQs</h4>
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| 79 |
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<p>Here are some frequently asked questions about Pooking Billiards City:</p>
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| 80 |
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<ul>
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| 81 |
-
<li><b>Is Pooking Billiards City free to play?</b></li>
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<p>Yes, Pooking Billiards City is free to play, but it contains ads and in-app purchases. You can watch ads to get free coins and items, or buy them with real money. You can also remove ads by paying a small fee.</p>
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<li><b>Can I play Pooking Billiards City offline?</b></li>
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<p>Yes, you can play Pooking Billiards City offline, as long as you have downloaded and installed the game on your device. However, some features may not be available offline, such as watching ads or updating the game.</p>
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<li><b>Can I play Pooking Billiards City with friends?</b></li>
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<p>No, Pooking Billiards City does not have a multiplayer mode or a social feature. You can only play against the computer in single player mode.</p>
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<li><b>How can I update Pooking Billiards City?</b></li>
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<p>If you have downloaded Pooking Billiards City from the Google Play Store, you can update it automatically or manually through the Play Store app. If you have downloaded Pooking Billiards City APK from APKPure, you can update it by downloading and installing the latest version of the APK file from APKPure.</p>
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<p>Yes, Pooking Billiards City is safe to download and install, as long as you download it from a trusted source, such as the Google Play Store or APKPure. You should also scan the APK file with an antivirus app before installing it, and enable the installation of apps from unknown sources only for trusted apps.</p>
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</ul></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Experience the thrill of Indian Bikes Driving 3D - Free APK Download.md
DELETED
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<br />
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<h1>Download Indian Bike Driving 3D APK: A Realistic Motorcycle Simulator for Android</h1>
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<p>If you are a fan of motorcycles and driving games, you should definitely check out Indian Bike Driving 3D APK. This is a free realistic motorcycle simulator for Android devices that lets you drive the most famous bikes in the world through challenging roads. You can also customise your bike's appearance, with many different challenges to complete. In this article, we will tell you everything you need to know about this amazing game, including its features, how to download and install it, tips and tricks for playing it, and its pros and cons.</p>
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<ul>
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</ul>
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<p>If you want to download and install Indian Bike Driving 3D APK on your Android device, you need to follow these simple steps:</p>
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<ol>
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| 22 |
-
<p>Indian Bike Driving 3D APK is a fun and addictive game, but it can also be challenging and frustrating at times. Here are some tips and tricks that can help you play better and have more fun:</p>
|
| 23 |
-
<ul>
|
| 24 |
-
<li><b>Tip 1: Use the brake and accelerator buttons wisely</b>: You can control your bike's speed by using the brake and accelerator buttons on the screen. You can also tilt your device to steer your bike. However, you need to be careful not to overuse or misuse these buttons, as they can affect your balance and stability. For example, if you brake too hard or too late, you might skid or crash. If you accelerate too much or too fast, you might lose control or flip over. Therefore, you need to use these buttons wisely and adjust them according to the road conditions and traffic.</li>
|
| 25 |
-
<li><b>Tip 2: Avoid crashing into other vehicles and obstacles</b>: One of the main challenges of this game is to avoid crashing into other vehicles and obstacles on the road. If you crash, you will lose health and coins, and you might have to restart the level. Therefore, you need to be alert and aware of your surroundings, and avoid colliding with anything that might damage your bike or yourself. You can also use the horn button to warn other drivers of your presence.</li>
|
| 26 |
-
<li><b>Tip 3: Collect coins and fuel to upgrade your bike</b>: As you drive through the roads, you will see coins and fuel cans scattered around. You can collect them by driving over them or by using a magnet power-up. Coins can be used to buy new bikes or upgrade your existing ones. Fuel can be used to refill your bike's tank when it runs low. Therefore, you should try to collect as many coins and fuel as possible, as they will help you improve your performance and enjoyment.</li>
|
| 27 |
-
<li><b>Tip 4: Try different modes and levels to test your skills</b>: This game has different modes and levels that offer different challenges and rewards. You can choose from free ride mode, career mode, endless mode, time trial mode, parking mode, cargo mode, stunt mode, police chase mode, and more. You can also choose from easy, medium, hard, or extreme levels of difficulty. You should try different modes and levels to test your skills, have more fun, and earn more coins.</li>
|
| 28 |
-
</ul>
|
| 29 |
-
<h2>Pros and Cons of Indian Bike Driving 3D APK</h2>
|
| 30 |
-
<p>Indian Bike Driving 3D APK is a great game for motorcycle enthusiasts and driving fans, but it also has some drawbacks that might affect your experience. Here are some of the pros and cons of this game:</p>
|
| 31 |
-
<table>
|
| 32 |
-
<tr>
|
| 33 |
-
<th>Pros</th>
|
| 34 |
-
<th>Cons</th>
|
| 35 |
-
</tr>
|
| 36 |
-
<tr>
|
| 37 |
-
<td>- Free to download and play</td>
|
| 38 |
-
<td>- Contains ads that might be annoying or intrusive</td>
|
| 39 |
-
</tr>
|
| 40 |
-
<tr>
|
| 41 |
-
<td>- Realistic graphics and sound effects</td>
|
| 42 |
-
<td>- Has some bugs and glitches that might affect the gameplay</td>
|
| 43 |
-
</tr>
|
| 44 |
-
<tr>
|
| 45 |
-
<td>- Fun and addictive gameplay</td>
|
| 46 |
-
<td>- Requires internet connection to play online or access some features</td>
|
| 47 |
-
</tr>
|
| 48 |
-
<tr>
|
| 49 |
-
<td>- Easy to play but hard to master</td>
|
| 50 |
-
<td>- Might drain your battery or consume your data quickly</td>
|
| 51 |
-
</tr>
|
| 52 |
-
<tr>
|
| 53 |
-
<td>- Many bikes, environments, modes, levels, challenges, and features to enjoy</td>
|
| 54 |
-
<td>- Might not be compatible with some devices or Android versions</td>
|
| 55 |
-
</tr>
|
| 56 |
-
</table>
|
| 57 |
-
<h2>Conclusion</h2>
|
| 58 |
-
<p>In conclusion, Indian Bike Driving 3D APK is a realistic motorcycle simulator for Android devices that lets you drive the most famous bikes in the world through challenging roads. You can also customise your bike's appearance, complete different challenges, use cheat codes to unlock more fun, and more. If you want to download and install this game on your device, you just need to follow the simple steps we mentioned above. You can also use our tips and tricks to play better and have more fun. However, you should also be aware of the pros and cons of this game before you decide to play it . We hope this article has helped you learn more about this game and how to download and play it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy driving!</p>
|
| 59 |
-
<p>Download Indian Bikes Driving 3D for Android free<br />
|
| 60 |
-
How to install Indian Bikes Driving 3D APK on your device<br />
|
| 61 |
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Indian Bikes Driving 3D APK latest version download<br />
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| 62 |
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Indian Bikes Driving 3D game features and reviews<br />
|
| 63 |
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|
| 64 |
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Indian Bikes Driving 3D APK offline mode download<br />
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| 65 |
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Indian Bikes Driving 3D APK file size and requirements<br />
|
| 66 |
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Indian Bikes Driving 3D APK download for PC and laptop<br />
|
| 67 |
-
Indian Bikes Driving 3D APK cheats and tips<br />
|
| 68 |
-
Indian Bikes Driving 3D APK update and bug fixes<br />
|
| 69 |
-
Indian Bikes Driving 3D simulator gameplay and graphics<br />
|
| 70 |
-
Indian Bikes Driving 3D APK download link and QR code<br />
|
| 71 |
-
Indian Bikes Driving 3D APK alternatives and similar apps<br />
|
| 72 |
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Indian Bikes Driving 3D APK ratings and feedback<br />
|
| 73 |
-
Indian Bikes Driving 3D APK compatibility and support<br />
|
| 74 |
-
Indian Bikes Driving 3D APK download from Google Play Store<br />
|
| 75 |
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Indian Bikes Driving 3D APK download from third-party sources<br />
|
| 76 |
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Indian Bikes Driving 3D APK safe and secure download<br />
|
| 77 |
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Indian Bikes Driving 3D APK free trial and premium version<br />
|
| 78 |
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Indian Bikes Driving 3D APK rewards and achievements<br />
|
| 79 |
-
Indian Bikes Driving 3D APK customisation and personalisation<br />
|
| 80 |
-
Indian Bikes Driving 3D APK challenges and missions<br />
|
| 81 |
-
Indian Bikes Driving 3D APK online and multiplayer mode<br />
|
| 82 |
-
Indian Bikes Driving 3D APK fun and addictive game<br />
|
| 83 |
-
Indian Bikes Driving 3D APK realistic and immersive experience</p>
|
| 84 |
-
<h3>FAQs</h3>
|
| 85 |
-
<p>Here are some of the frequently asked questions about Indian Bike Driving 3D APK:</p>
|
| 86 |
-
<ol>
|
| 87 |
-
<li><b>What is the size of Indian Bike Driving 3D APK?</b>: The size of Indian Bike Driving 3D APK is about 50 MB, but it might vary depending on your device and Android version.</li>
|
| 88 |
-
<li><b>Is Indian Bike Driving 3D APK safe to download and install?</b>: Yes, Indian Bike Driving 3D APK is safe to download and install, as long as you download it from a trusted source like [APKCombo] or [FileHippo]. However, you should always scan the APK file with an antivirus software before installing it, just to be sure.</li>
|
| 89 |
-
<li><b>How can I remove ads from Indian Bike Driving 3D APK?</b>: Unfortunately, there is no official way to remove ads from Indian Bike Driving 3D APK, as they are part of the game's revenue source. However, you can try some unofficial methods, such as using an ad blocker app, turning off your internet connection, or using a modded version of the game. However, we do not recommend or endorse these methods, as they might violate the game's terms of service or cause other problems.</li>
|
| 90 |
-
<li><b>How can I get more coins in Indian Bike Driving 3D APK?</b>: You can get more coins in Indian Bike Driving 3D APK by playing the game regularly, completing challenges, collecting coins on the road, using a magnet power-up, or using cheat codes. You can also buy more coins with real money, but we do not advise you to do so, as it might ruin the fun and challenge of the game.</li>
|
| 91 |
-
<li><b>What are some of the best cheat codes for Indian Bike Driving 3D APK?</b>: Some of the best cheat codes for Indian Bike Driving 3D APK are: <ul>
|
| 92 |
-
<li>CAR: Unlocks a car that you can drive instead of a bike</li>
|
| 93 |
-
<li>PLANE: Unlocks a plane that you can fly instead of a bike</li>
|
| 94 |
-
<li>HELICOPTER: Unlocks a helicopter that you can fly instead of a bike</li>
|
| 95 |
-
<li>HORSE: Unlocks a horse that you can ride instead of a bike</li>
|
| 96 |
-
<li>DOG: Unlocks a dog that follows you around</li>
|
| 97 |
-
<li>CYCLE: Unlocks a cycle that you can ride instead of a bike</li>
|
| 98 |
-
<li>MONSTER: Unlocks a monster truck that you can drive instead of a bike</li>
|
| 99 |
-
<li>SUPERJUMP: Activates super jump mode that lets you jump higher</li>
|
| 100 |
-
<li>SLOWMO: Activates slow motion mode that slows down time</li>
|
| 101 |
-
<li>MOONGRAVITY: Activates moon gravity mode that reduces gravity</li>
|
| 102 |
-
<li>SKYFALL: Activates skyfall mode that makes you fall from the sky</li>
|
| 103 |
-
<li>INFINITYHEALTH: Activates infinity health mode that makes you invincible</li>
|
| 104 |
-
</ul>
|
| 105 |
-
To use these cheat codes, you need to type them in the chat box during the game. You can also turn them off by typing them again.</li>
|
| 106 |
-
</ol></p> 401be4b1e0<br />
|
| 107 |
-
<br />
|
| 108 |
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<br />
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spaces/30Kanika/Animal_Image_Classifier/app.py
DELETED
|
@@ -1,25 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
from tensorflow import image
|
| 3 |
-
from keras import models
|
| 4 |
-
import numpy as np
|
| 5 |
-
from PIL import Image
|
| 6 |
-
import pandas as pd
|
| 7 |
-
|
| 8 |
-
# st.title("Animal Image :violet[Classifier] 🐶😺🐼")
|
| 9 |
-
|
| 10 |
-
model = models.load_model("model.h5")
|
| 11 |
-
pets = ["Cat", "Dog", "Panda"]
|
| 12 |
-
|
| 13 |
-
def image_classifier(jpg):
|
| 14 |
-
try:
|
| 15 |
-
resize= image.resize(jpg,(256,256))
|
| 16 |
-
dim= np.expand_dims(resize, axis=0)
|
| 17 |
-
pred= model.predict(dim) #[0.23, 0.987,0.546]
|
| 18 |
-
arg= np.argmax(pred) # finds the maximum value and returns the index
|
| 19 |
-
return pets[arg]
|
| 20 |
-
except:
|
| 21 |
-
return "Unsupported File Format"
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
app = gr.Interface(title="Animal Image Classifier 😺🐶🐼",fn=image_classifier, inputs="image", outputs="label")
|
| 25 |
-
app.launch()
|
|
|
|
|
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|
spaces/801artistry/RVC801/lib/infer_pack/onnx_inference.py
DELETED
|
@@ -1,145 +0,0 @@
|
|
| 1 |
-
import onnxruntime
|
| 2 |
-
import librosa
|
| 3 |
-
import numpy as np
|
| 4 |
-
import soundfile
|
| 5 |
-
|
| 6 |
-
|
| 7 |
-
class ContentVec:
|
| 8 |
-
def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
|
| 9 |
-
print("load model(s) from {}".format(vec_path))
|
| 10 |
-
if device == "cpu" or device is None:
|
| 11 |
-
providers = ["CPUExecutionProvider"]
|
| 12 |
-
elif device == "cuda":
|
| 13 |
-
providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
|
| 14 |
-
elif device == "dml":
|
| 15 |
-
providers = ["DmlExecutionProvider"]
|
| 16 |
-
else:
|
| 17 |
-
raise RuntimeError("Unsportted Device")
|
| 18 |
-
self.model = onnxruntime.InferenceSession(vec_path, providers=providers)
|
| 19 |
-
|
| 20 |
-
def __call__(self, wav):
|
| 21 |
-
return self.forward(wav)
|
| 22 |
-
|
| 23 |
-
def forward(self, wav):
|
| 24 |
-
feats = wav
|
| 25 |
-
if feats.ndim == 2: # double channels
|
| 26 |
-
feats = feats.mean(-1)
|
| 27 |
-
assert feats.ndim == 1, feats.ndim
|
| 28 |
-
feats = np.expand_dims(np.expand_dims(feats, 0), 0)
|
| 29 |
-
onnx_input = {self.model.get_inputs()[0].name: feats}
|
| 30 |
-
logits = self.model.run(None, onnx_input)[0]
|
| 31 |
-
return logits.transpose(0, 2, 1)
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
def get_f0_predictor(f0_predictor, hop_length, sampling_rate, **kargs):
|
| 35 |
-
if f0_predictor == "pm":
|
| 36 |
-
from lib.infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor
|
| 37 |
-
|
| 38 |
-
f0_predictor_object = PMF0Predictor(
|
| 39 |
-
hop_length=hop_length, sampling_rate=sampling_rate
|
| 40 |
-
)
|
| 41 |
-
elif f0_predictor == "harvest":
|
| 42 |
-
from lib.infer_pack.modules.F0Predictor.HarvestF0Predictor import (
|
| 43 |
-
HarvestF0Predictor,
|
| 44 |
-
)
|
| 45 |
-
|
| 46 |
-
f0_predictor_object = HarvestF0Predictor(
|
| 47 |
-
hop_length=hop_length, sampling_rate=sampling_rate
|
| 48 |
-
)
|
| 49 |
-
elif f0_predictor == "dio":
|
| 50 |
-
from lib.infer_pack.modules.F0Predictor.DioF0Predictor import DioF0Predictor
|
| 51 |
-
|
| 52 |
-
f0_predictor_object = DioF0Predictor(
|
| 53 |
-
hop_length=hop_length, sampling_rate=sampling_rate
|
| 54 |
-
)
|
| 55 |
-
else:
|
| 56 |
-
raise Exception("Unknown f0 predictor")
|
| 57 |
-
return f0_predictor_object
|
| 58 |
-
|
| 59 |
-
|
| 60 |
-
class OnnxRVC:
|
| 61 |
-
def __init__(
|
| 62 |
-
self,
|
| 63 |
-
model_path,
|
| 64 |
-
sr=40000,
|
| 65 |
-
hop_size=512,
|
| 66 |
-
vec_path="vec-768-layer-12",
|
| 67 |
-
device="cpu",
|
| 68 |
-
):
|
| 69 |
-
vec_path = f"pretrained/{vec_path}.onnx"
|
| 70 |
-
self.vec_model = ContentVec(vec_path, device)
|
| 71 |
-
if device == "cpu" or device is None:
|
| 72 |
-
providers = ["CPUExecutionProvider"]
|
| 73 |
-
elif device == "cuda":
|
| 74 |
-
providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
|
| 75 |
-
elif device == "dml":
|
| 76 |
-
providers = ["DmlExecutionProvider"]
|
| 77 |
-
else:
|
| 78 |
-
raise RuntimeError("Unsportted Device")
|
| 79 |
-
self.model = onnxruntime.InferenceSession(model_path, providers=providers)
|
| 80 |
-
self.sampling_rate = sr
|
| 81 |
-
self.hop_size = hop_size
|
| 82 |
-
|
| 83 |
-
def forward(self, hubert, hubert_length, pitch, pitchf, ds, rnd):
|
| 84 |
-
onnx_input = {
|
| 85 |
-
self.model.get_inputs()[0].name: hubert,
|
| 86 |
-
self.model.get_inputs()[1].name: hubert_length,
|
| 87 |
-
self.model.get_inputs()[2].name: pitch,
|
| 88 |
-
self.model.get_inputs()[3].name: pitchf,
|
| 89 |
-
self.model.get_inputs()[4].name: ds,
|
| 90 |
-
self.model.get_inputs()[5].name: rnd,
|
| 91 |
-
}
|
| 92 |
-
return (self.model.run(None, onnx_input)[0] * 32767).astype(np.int16)
|
| 93 |
-
|
| 94 |
-
def inference(
|
| 95 |
-
self,
|
| 96 |
-
raw_path,
|
| 97 |
-
sid,
|
| 98 |
-
f0_method="dio",
|
| 99 |
-
f0_up_key=0,
|
| 100 |
-
pad_time=0.5,
|
| 101 |
-
cr_threshold=0.02,
|
| 102 |
-
):
|
| 103 |
-
f0_min = 50
|
| 104 |
-
f0_max = 1100
|
| 105 |
-
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
|
| 106 |
-
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
|
| 107 |
-
f0_predictor = get_f0_predictor(
|
| 108 |
-
f0_method,
|
| 109 |
-
hop_length=self.hop_size,
|
| 110 |
-
sampling_rate=self.sampling_rate,
|
| 111 |
-
threshold=cr_threshold,
|
| 112 |
-
)
|
| 113 |
-
wav, sr = librosa.load(raw_path, sr=self.sampling_rate)
|
| 114 |
-
org_length = len(wav)
|
| 115 |
-
if org_length / sr > 50.0:
|
| 116 |
-
raise RuntimeError("Reached Max Length")
|
| 117 |
-
|
| 118 |
-
wav16k = librosa.resample(wav, orig_sr=self.sampling_rate, target_sr=16000)
|
| 119 |
-
wav16k = wav16k
|
| 120 |
-
|
| 121 |
-
hubert = self.vec_model(wav16k)
|
| 122 |
-
hubert = np.repeat(hubert, 2, axis=2).transpose(0, 2, 1).astype(np.float32)
|
| 123 |
-
hubert_length = hubert.shape[1]
|
| 124 |
-
|
| 125 |
-
pitchf = f0_predictor.compute_f0(wav, hubert_length)
|
| 126 |
-
pitchf = pitchf * 2 ** (f0_up_key / 12)
|
| 127 |
-
pitch = pitchf.copy()
|
| 128 |
-
f0_mel = 1127 * np.log(1 + pitch / 700)
|
| 129 |
-
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
|
| 130 |
-
f0_mel_max - f0_mel_min
|
| 131 |
-
) + 1
|
| 132 |
-
f0_mel[f0_mel <= 1] = 1
|
| 133 |
-
f0_mel[f0_mel > 255] = 255
|
| 134 |
-
pitch = np.rint(f0_mel).astype(np.int64)
|
| 135 |
-
|
| 136 |
-
pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32)
|
| 137 |
-
pitch = pitch.reshape(1, len(pitch))
|
| 138 |
-
ds = np.array([sid]).astype(np.int64)
|
| 139 |
-
|
| 140 |
-
rnd = np.random.randn(1, 192, hubert_length).astype(np.float32)
|
| 141 |
-
hubert_length = np.array([hubert_length]).astype(np.int64)
|
| 142 |
-
|
| 143 |
-
out_wav = self.forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze()
|
| 144 |
-
out_wav = np.pad(out_wav, (0, 2 * self.hop_size), "constant")
|
| 145 |
-
return out_wav[0:org_length]
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spaces/AFRAC/NCM_DEMO/post.py
DELETED
|
@@ -1,16 +0,0 @@
|
|
| 1 |
-
import requests
|
| 2 |
-
|
| 3 |
-
url = "https://afrac-ncm-demo.hf.space/api/predict"
|
| 4 |
-
|
| 5 |
-
payload = {
|
| 6 |
-
"data": ["Coca-Cola PET 2l"]
|
| 7 |
-
}
|
| 8 |
-
|
| 9 |
-
headers = {
|
| 10 |
-
"Content-Type": "application/json"
|
| 11 |
-
}
|
| 12 |
-
|
| 13 |
-
response = requests.post(url, json=payload, headers=headers)
|
| 14 |
-
result = response.json()
|
| 15 |
-
|
| 16 |
-
print(result)
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spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/lib_v5/nets_123821KB.py
DELETED
|
@@ -1,122 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
from torch import nn
|
| 3 |
-
import torch.nn.functional as F
|
| 4 |
-
|
| 5 |
-
from uvr5_pack.lib_v5 import layers_123821KB as layers
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
class BaseASPPNet(nn.Module):
|
| 9 |
-
def __init__(self, nin, ch, dilations=(4, 8, 16)):
|
| 10 |
-
super(BaseASPPNet, self).__init__()
|
| 11 |
-
self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
|
| 12 |
-
self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
|
| 13 |
-
self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
|
| 14 |
-
self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
|
| 15 |
-
|
| 16 |
-
self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
|
| 17 |
-
|
| 18 |
-
self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
|
| 19 |
-
self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
|
| 20 |
-
self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
|
| 21 |
-
self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
|
| 22 |
-
|
| 23 |
-
def __call__(self, x):
|
| 24 |
-
h, e1 = self.enc1(x)
|
| 25 |
-
h, e2 = self.enc2(h)
|
| 26 |
-
h, e3 = self.enc3(h)
|
| 27 |
-
h, e4 = self.enc4(h)
|
| 28 |
-
|
| 29 |
-
h = self.aspp(h)
|
| 30 |
-
|
| 31 |
-
h = self.dec4(h, e4)
|
| 32 |
-
h = self.dec3(h, e3)
|
| 33 |
-
h = self.dec2(h, e2)
|
| 34 |
-
h = self.dec1(h, e1)
|
| 35 |
-
|
| 36 |
-
return h
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
class CascadedASPPNet(nn.Module):
|
| 40 |
-
def __init__(self, n_fft):
|
| 41 |
-
super(CascadedASPPNet, self).__init__()
|
| 42 |
-
self.stg1_low_band_net = BaseASPPNet(2, 32)
|
| 43 |
-
self.stg1_high_band_net = BaseASPPNet(2, 32)
|
| 44 |
-
|
| 45 |
-
self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
|
| 46 |
-
self.stg2_full_band_net = BaseASPPNet(16, 32)
|
| 47 |
-
|
| 48 |
-
self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
|
| 49 |
-
self.stg3_full_band_net = BaseASPPNet(32, 64)
|
| 50 |
-
|
| 51 |
-
self.out = nn.Conv2d(64, 2, 1, bias=False)
|
| 52 |
-
self.aux1_out = nn.Conv2d(32, 2, 1, bias=False)
|
| 53 |
-
self.aux2_out = nn.Conv2d(32, 2, 1, bias=False)
|
| 54 |
-
|
| 55 |
-
self.max_bin = n_fft // 2
|
| 56 |
-
self.output_bin = n_fft // 2 + 1
|
| 57 |
-
|
| 58 |
-
self.offset = 128
|
| 59 |
-
|
| 60 |
-
def forward(self, x, aggressiveness=None):
|
| 61 |
-
mix = x.detach()
|
| 62 |
-
x = x.clone()
|
| 63 |
-
|
| 64 |
-
x = x[:, :, : self.max_bin]
|
| 65 |
-
|
| 66 |
-
bandw = x.size()[2] // 2
|
| 67 |
-
aux1 = torch.cat(
|
| 68 |
-
[
|
| 69 |
-
self.stg1_low_band_net(x[:, :, :bandw]),
|
| 70 |
-
self.stg1_high_band_net(x[:, :, bandw:]),
|
| 71 |
-
],
|
| 72 |
-
dim=2,
|
| 73 |
-
)
|
| 74 |
-
|
| 75 |
-
h = torch.cat([x, aux1], dim=1)
|
| 76 |
-
aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
|
| 77 |
-
|
| 78 |
-
h = torch.cat([x, aux1, aux2], dim=1)
|
| 79 |
-
h = self.stg3_full_band_net(self.stg3_bridge(h))
|
| 80 |
-
|
| 81 |
-
mask = torch.sigmoid(self.out(h))
|
| 82 |
-
mask = F.pad(
|
| 83 |
-
input=mask,
|
| 84 |
-
pad=(0, 0, 0, self.output_bin - mask.size()[2]),
|
| 85 |
-
mode="replicate",
|
| 86 |
-
)
|
| 87 |
-
|
| 88 |
-
if self.training:
|
| 89 |
-
aux1 = torch.sigmoid(self.aux1_out(aux1))
|
| 90 |
-
aux1 = F.pad(
|
| 91 |
-
input=aux1,
|
| 92 |
-
pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
|
| 93 |
-
mode="replicate",
|
| 94 |
-
)
|
| 95 |
-
aux2 = torch.sigmoid(self.aux2_out(aux2))
|
| 96 |
-
aux2 = F.pad(
|
| 97 |
-
input=aux2,
|
| 98 |
-
pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
|
| 99 |
-
mode="replicate",
|
| 100 |
-
)
|
| 101 |
-
return mask * mix, aux1 * mix, aux2 * mix
|
| 102 |
-
else:
|
| 103 |
-
if aggressiveness:
|
| 104 |
-
mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
|
| 105 |
-
mask[:, :, : aggressiveness["split_bin"]],
|
| 106 |
-
1 + aggressiveness["value"] / 3,
|
| 107 |
-
)
|
| 108 |
-
mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
|
| 109 |
-
mask[:, :, aggressiveness["split_bin"] :],
|
| 110 |
-
1 + aggressiveness["value"],
|
| 111 |
-
)
|
| 112 |
-
|
| 113 |
-
return mask * mix
|
| 114 |
-
|
| 115 |
-
def predict(self, x_mag, aggressiveness=None):
|
| 116 |
-
h = self.forward(x_mag, aggressiveness)
|
| 117 |
-
|
| 118 |
-
if self.offset > 0:
|
| 119 |
-
h = h[:, :, :, self.offset : -self.offset]
|
| 120 |
-
assert h.size()[3] > 0
|
| 121 |
-
|
| 122 |
-
return h
|
|
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|
spaces/AIConsultant/MusicGen/tests/data/test_audio_utils.py
DELETED
|
@@ -1,110 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
| 2 |
-
# All rights reserved.
|
| 3 |
-
#
|
| 4 |
-
# This source code is licensed under the license found in the
|
| 5 |
-
# LICENSE file in the root directory of this source tree.
|
| 6 |
-
|
| 7 |
-
import julius
|
| 8 |
-
import torch
|
| 9 |
-
import pytest
|
| 10 |
-
|
| 11 |
-
from audiocraft.data.audio_utils import (
|
| 12 |
-
_clip_wav,
|
| 13 |
-
convert_audio_channels,
|
| 14 |
-
convert_audio,
|
| 15 |
-
normalize_audio
|
| 16 |
-
)
|
| 17 |
-
from ..common_utils import get_batch_white_noise
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
class TestConvertAudioChannels:
|
| 21 |
-
|
| 22 |
-
def test_convert_audio_channels_downmix(self):
|
| 23 |
-
b, c, t = 2, 3, 100
|
| 24 |
-
audio = get_batch_white_noise(b, c, t)
|
| 25 |
-
mixed = convert_audio_channels(audio, channels=2)
|
| 26 |
-
assert list(mixed.shape) == [b, 2, t]
|
| 27 |
-
|
| 28 |
-
def test_convert_audio_channels_nochange(self):
|
| 29 |
-
b, c, t = 2, 3, 100
|
| 30 |
-
audio = get_batch_white_noise(b, c, t)
|
| 31 |
-
mixed = convert_audio_channels(audio, channels=c)
|
| 32 |
-
assert list(mixed.shape) == list(audio.shape)
|
| 33 |
-
|
| 34 |
-
def test_convert_audio_channels_upmix(self):
|
| 35 |
-
b, c, t = 2, 1, 100
|
| 36 |
-
audio = get_batch_white_noise(b, c, t)
|
| 37 |
-
mixed = convert_audio_channels(audio, channels=3)
|
| 38 |
-
assert list(mixed.shape) == [b, 3, t]
|
| 39 |
-
|
| 40 |
-
def test_convert_audio_channels_upmix_error(self):
|
| 41 |
-
b, c, t = 2, 2, 100
|
| 42 |
-
audio = get_batch_white_noise(b, c, t)
|
| 43 |
-
with pytest.raises(ValueError):
|
| 44 |
-
convert_audio_channels(audio, channels=3)
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
class TestConvertAudio:
|
| 48 |
-
|
| 49 |
-
def test_convert_audio_channels_downmix(self):
|
| 50 |
-
b, c, dur = 2, 3, 4.
|
| 51 |
-
sr = 128
|
| 52 |
-
audio = get_batch_white_noise(b, c, int(sr * dur))
|
| 53 |
-
out = convert_audio(audio, from_rate=sr, to_rate=sr, to_channels=2)
|
| 54 |
-
assert list(out.shape) == [audio.shape[0], 2, audio.shape[-1]]
|
| 55 |
-
|
| 56 |
-
def test_convert_audio_channels_upmix(self):
|
| 57 |
-
b, c, dur = 2, 1, 4.
|
| 58 |
-
sr = 128
|
| 59 |
-
audio = get_batch_white_noise(b, c, int(sr * dur))
|
| 60 |
-
out = convert_audio(audio, from_rate=sr, to_rate=sr, to_channels=3)
|
| 61 |
-
assert list(out.shape) == [audio.shape[0], 3, audio.shape[-1]]
|
| 62 |
-
|
| 63 |
-
def test_convert_audio_upsample(self):
|
| 64 |
-
b, c, dur = 2, 1, 4.
|
| 65 |
-
sr = 2
|
| 66 |
-
new_sr = 3
|
| 67 |
-
audio = get_batch_white_noise(b, c, int(sr * dur))
|
| 68 |
-
out = convert_audio(audio, from_rate=sr, to_rate=new_sr, to_channels=c)
|
| 69 |
-
out_j = julius.resample.resample_frac(audio, old_sr=sr, new_sr=new_sr)
|
| 70 |
-
assert torch.allclose(out, out_j)
|
| 71 |
-
|
| 72 |
-
def test_convert_audio_resample(self):
|
| 73 |
-
b, c, dur = 2, 1, 4.
|
| 74 |
-
sr = 3
|
| 75 |
-
new_sr = 2
|
| 76 |
-
audio = get_batch_white_noise(b, c, int(sr * dur))
|
| 77 |
-
out = convert_audio(audio, from_rate=sr, to_rate=new_sr, to_channels=c)
|
| 78 |
-
out_j = julius.resample.resample_frac(audio, old_sr=sr, new_sr=new_sr)
|
| 79 |
-
assert torch.allclose(out, out_j)
|
| 80 |
-
|
| 81 |
-
|
| 82 |
-
class TestNormalizeAudio:
|
| 83 |
-
|
| 84 |
-
def test_clip_wav(self):
|
| 85 |
-
b, c, dur = 2, 1, 4.
|
| 86 |
-
sr = 3
|
| 87 |
-
audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
|
| 88 |
-
_clip_wav(audio)
|
| 89 |
-
assert audio.abs().max() <= 1
|
| 90 |
-
|
| 91 |
-
def test_normalize_audio_clip(self):
|
| 92 |
-
b, c, dur = 2, 1, 4.
|
| 93 |
-
sr = 3
|
| 94 |
-
audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
|
| 95 |
-
norm_audio = normalize_audio(audio, strategy='clip')
|
| 96 |
-
assert norm_audio.abs().max() <= 1
|
| 97 |
-
|
| 98 |
-
def test_normalize_audio_rms(self):
|
| 99 |
-
b, c, dur = 2, 1, 4.
|
| 100 |
-
sr = 3
|
| 101 |
-
audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
|
| 102 |
-
norm_audio = normalize_audio(audio, strategy='rms')
|
| 103 |
-
assert norm_audio.abs().max() <= 1
|
| 104 |
-
|
| 105 |
-
def test_normalize_audio_peak(self):
|
| 106 |
-
b, c, dur = 2, 1, 4.
|
| 107 |
-
sr = 3
|
| 108 |
-
audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
|
| 109 |
-
norm_audio = normalize_audio(audio, strategy='peak')
|
| 110 |
-
assert norm_audio.abs().max() <= 1
|
|
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|
spaces/AIWaves/Debate/src/agents/Action/__init__.py
DELETED
|
@@ -1 +0,0 @@
|
|
| 1 |
-
from .base_action import Action
|
|
|
|
|
|
spaces/AIWaves/Software_Company/src/agents/__init__.py
DELETED
|
@@ -1,4 +0,0 @@
|
|
| 1 |
-
from .evolve import *
|
| 2 |
-
from .SOP import *
|
| 3 |
-
from .State import *
|
| 4 |
-
from .utils import *
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/AP123/dreamgaussian/main2.py
DELETED
|
@@ -1,671 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import cv2
|
| 3 |
-
import time
|
| 4 |
-
import tqdm
|
| 5 |
-
import numpy as np
|
| 6 |
-
import dearpygui.dearpygui as dpg
|
| 7 |
-
|
| 8 |
-
import torch
|
| 9 |
-
import torch.nn.functional as F
|
| 10 |
-
|
| 11 |
-
import trimesh
|
| 12 |
-
import rembg
|
| 13 |
-
|
| 14 |
-
from cam_utils import orbit_camera, OrbitCamera
|
| 15 |
-
from mesh_renderer import Renderer
|
| 16 |
-
|
| 17 |
-
# from kiui.lpips import LPIPS
|
| 18 |
-
|
| 19 |
-
class GUI:
|
| 20 |
-
def __init__(self, opt):
|
| 21 |
-
self.opt = opt # shared with the trainer's opt to support in-place modification of rendering parameters.
|
| 22 |
-
self.gui = opt.gui # enable gui
|
| 23 |
-
self.W = opt.W
|
| 24 |
-
self.H = opt.H
|
| 25 |
-
self.cam = OrbitCamera(opt.W, opt.H, r=opt.radius, fovy=opt.fovy)
|
| 26 |
-
|
| 27 |
-
self.mode = "image"
|
| 28 |
-
self.seed = "random"
|
| 29 |
-
|
| 30 |
-
self.buffer_image = np.ones((self.W, self.H, 3), dtype=np.float32)
|
| 31 |
-
self.need_update = True # update buffer_image
|
| 32 |
-
|
| 33 |
-
# models
|
| 34 |
-
self.device = torch.device("cuda")
|
| 35 |
-
self.bg_remover = None
|
| 36 |
-
|
| 37 |
-
self.guidance_sd = None
|
| 38 |
-
self.guidance_zero123 = None
|
| 39 |
-
|
| 40 |
-
self.enable_sd = False
|
| 41 |
-
self.enable_zero123 = False
|
| 42 |
-
|
| 43 |
-
# renderer
|
| 44 |
-
self.renderer = Renderer(opt).to(self.device)
|
| 45 |
-
|
| 46 |
-
# input image
|
| 47 |
-
self.input_img = None
|
| 48 |
-
self.input_mask = None
|
| 49 |
-
self.input_img_torch = None
|
| 50 |
-
self.input_mask_torch = None
|
| 51 |
-
self.overlay_input_img = False
|
| 52 |
-
self.overlay_input_img_ratio = 0.5
|
| 53 |
-
|
| 54 |
-
# input text
|
| 55 |
-
self.prompt = ""
|
| 56 |
-
self.negative_prompt = ""
|
| 57 |
-
|
| 58 |
-
# training stuff
|
| 59 |
-
self.training = False
|
| 60 |
-
self.optimizer = None
|
| 61 |
-
self.step = 0
|
| 62 |
-
self.train_steps = 1 # steps per rendering loop
|
| 63 |
-
# self.lpips_loss = LPIPS(net='vgg').to(self.device)
|
| 64 |
-
|
| 65 |
-
# load input data from cmdline
|
| 66 |
-
if self.opt.input is not None:
|
| 67 |
-
self.load_input(self.opt.input)
|
| 68 |
-
|
| 69 |
-
# override prompt from cmdline
|
| 70 |
-
if self.opt.prompt is not None:
|
| 71 |
-
self.prompt = self.opt.prompt
|
| 72 |
-
|
| 73 |
-
if self.gui:
|
| 74 |
-
dpg.create_context()
|
| 75 |
-
self.register_dpg()
|
| 76 |
-
self.test_step()
|
| 77 |
-
|
| 78 |
-
def __del__(self):
|
| 79 |
-
if self.gui:
|
| 80 |
-
dpg.destroy_context()
|
| 81 |
-
|
| 82 |
-
def seed_everything(self):
|
| 83 |
-
try:
|
| 84 |
-
seed = int(self.seed)
|
| 85 |
-
except:
|
| 86 |
-
seed = np.random.randint(0, 1000000)
|
| 87 |
-
|
| 88 |
-
os.environ["PYTHONHASHSEED"] = str(seed)
|
| 89 |
-
np.random.seed(seed)
|
| 90 |
-
torch.manual_seed(seed)
|
| 91 |
-
torch.cuda.manual_seed(seed)
|
| 92 |
-
torch.backends.cudnn.deterministic = True
|
| 93 |
-
torch.backends.cudnn.benchmark = True
|
| 94 |
-
|
| 95 |
-
self.last_seed = seed
|
| 96 |
-
|
| 97 |
-
def prepare_train(self):
|
| 98 |
-
|
| 99 |
-
self.step = 0
|
| 100 |
-
|
| 101 |
-
# setup training
|
| 102 |
-
self.optimizer = torch.optim.Adam(self.renderer.get_params())
|
| 103 |
-
|
| 104 |
-
# default camera
|
| 105 |
-
pose = orbit_camera(self.opt.elevation, 0, self.opt.radius)
|
| 106 |
-
self.fixed_cam = (pose, self.cam.perspective)
|
| 107 |
-
|
| 108 |
-
|
| 109 |
-
self.enable_sd = self.opt.lambda_sd > 0 and self.prompt != ""
|
| 110 |
-
self.enable_zero123 = self.opt.lambda_zero123 > 0 and self.input_img is not None
|
| 111 |
-
|
| 112 |
-
# lazy load guidance model
|
| 113 |
-
if self.guidance_sd is None and self.enable_sd:
|
| 114 |
-
print(f"[INFO] loading SD...")
|
| 115 |
-
from guidance.sd_utils import StableDiffusion
|
| 116 |
-
self.guidance_sd = StableDiffusion(self.device)
|
| 117 |
-
print(f"[INFO] loaded SD!")
|
| 118 |
-
|
| 119 |
-
if self.guidance_zero123 is None and self.enable_zero123:
|
| 120 |
-
print(f"[INFO] loading zero123...")
|
| 121 |
-
from guidance.zero123_utils import Zero123
|
| 122 |
-
self.guidance_zero123 = Zero123(self.device)
|
| 123 |
-
print(f"[INFO] loaded zero123!")
|
| 124 |
-
|
| 125 |
-
# input image
|
| 126 |
-
if self.input_img is not None:
|
| 127 |
-
self.input_img_torch = torch.from_numpy(self.input_img).permute(2, 0, 1).unsqueeze(0).to(self.device)
|
| 128 |
-
self.input_img_torch = F.interpolate(
|
| 129 |
-
self.input_img_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False
|
| 130 |
-
)
|
| 131 |
-
|
| 132 |
-
self.input_mask_torch = torch.from_numpy(self.input_mask).permute(2, 0, 1).unsqueeze(0).to(self.device)
|
| 133 |
-
self.input_mask_torch = F.interpolate(
|
| 134 |
-
self.input_mask_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False
|
| 135 |
-
)
|
| 136 |
-
self.input_img_torch_channel_last = self.input_img_torch[0].permute(1,2,0).contiguous()
|
| 137 |
-
|
| 138 |
-
# prepare embeddings
|
| 139 |
-
with torch.no_grad():
|
| 140 |
-
|
| 141 |
-
if self.enable_sd:
|
| 142 |
-
self.guidance_sd.get_text_embeds([self.prompt], [self.negative_prompt])
|
| 143 |
-
|
| 144 |
-
if self.enable_zero123:
|
| 145 |
-
self.guidance_zero123.get_img_embeds(self.input_img_torch)
|
| 146 |
-
|
| 147 |
-
def train_step(self):
|
| 148 |
-
starter = torch.cuda.Event(enable_timing=True)
|
| 149 |
-
ender = torch.cuda.Event(enable_timing=True)
|
| 150 |
-
starter.record()
|
| 151 |
-
|
| 152 |
-
|
| 153 |
-
for _ in range(self.train_steps):
|
| 154 |
-
|
| 155 |
-
self.step += 1
|
| 156 |
-
step_ratio = min(1, self.step / self.opt.iters_refine)
|
| 157 |
-
|
| 158 |
-
loss = 0
|
| 159 |
-
|
| 160 |
-
### known view
|
| 161 |
-
if self.input_img_torch is not None:
|
| 162 |
-
|
| 163 |
-
ssaa = min(2.0, max(0.125, 2 * np.random.random()))
|
| 164 |
-
out = self.renderer.render(*self.fixed_cam, self.opt.ref_size, self.opt.ref_size, ssaa=ssaa)
|
| 165 |
-
|
| 166 |
-
# rgb loss
|
| 167 |
-
image = out["image"] # [H, W, 3] in [0, 1]
|
| 168 |
-
valid_mask = ((out["alpha"] > 0) & (out["viewcos"] > 0.5)).detach()
|
| 169 |
-
loss = loss + F.mse_loss(image * valid_mask, self.input_img_torch_channel_last * valid_mask)
|
| 170 |
-
|
| 171 |
-
### novel view (manual batch)
|
| 172 |
-
render_resolution = 512
|
| 173 |
-
images = []
|
| 174 |
-
vers, hors, radii = [], [], []
|
| 175 |
-
# avoid too large elevation (> 80 or < -80), and make sure it always cover [-30, 30]
|
| 176 |
-
min_ver = max(min(-30, -30 - self.opt.elevation), -80 - self.opt.elevation)
|
| 177 |
-
max_ver = min(max(30, 30 - self.opt.elevation), 80 - self.opt.elevation)
|
| 178 |
-
for _ in range(self.opt.batch_size):
|
| 179 |
-
|
| 180 |
-
# render random view
|
| 181 |
-
ver = np.random.randint(min_ver, max_ver)
|
| 182 |
-
hor = np.random.randint(-180, 180)
|
| 183 |
-
radius = 0
|
| 184 |
-
|
| 185 |
-
vers.append(ver)
|
| 186 |
-
hors.append(hor)
|
| 187 |
-
radii.append(radius)
|
| 188 |
-
|
| 189 |
-
pose = orbit_camera(self.opt.elevation + ver, hor, self.opt.radius + radius)
|
| 190 |
-
|
| 191 |
-
# random render resolution
|
| 192 |
-
ssaa = min(2.0, max(0.125, 2 * np.random.random()))
|
| 193 |
-
out = self.renderer.render(pose, self.cam.perspective, render_resolution, render_resolution, ssaa=ssaa)
|
| 194 |
-
|
| 195 |
-
image = out["image"] # [H, W, 3] in [0, 1]
|
| 196 |
-
image = image.permute(2,0,1).contiguous().unsqueeze(0) # [1, 3, H, W] in [0, 1]
|
| 197 |
-
|
| 198 |
-
images.append(image)
|
| 199 |
-
|
| 200 |
-
images = torch.cat(images, dim=0)
|
| 201 |
-
|
| 202 |
-
# import kiui
|
| 203 |
-
# kiui.lo(hor, ver)
|
| 204 |
-
# kiui.vis.plot_image(image)
|
| 205 |
-
|
| 206 |
-
# guidance loss
|
| 207 |
-
if self.enable_sd:
|
| 208 |
-
|
| 209 |
-
# loss = loss + self.opt.lambda_sd * self.guidance_sd.train_step(images, step_ratio)
|
| 210 |
-
refined_images = self.guidance_sd.refine(images, strength=0.6).float()
|
| 211 |
-
refined_images = F.interpolate(refined_images, (render_resolution, render_resolution), mode="bilinear", align_corners=False)
|
| 212 |
-
loss = loss + self.opt.lambda_sd * F.mse_loss(images, refined_images)
|
| 213 |
-
|
| 214 |
-
if self.enable_zero123:
|
| 215 |
-
# loss = loss + self.opt.lambda_zero123 * self.guidance_zero123.train_step(images, vers, hors, radii, step_ratio)
|
| 216 |
-
refined_images = self.guidance_zero123.refine(images, vers, hors, radii, strength=0.6).float()
|
| 217 |
-
refined_images = F.interpolate(refined_images, (render_resolution, render_resolution), mode="bilinear", align_corners=False)
|
| 218 |
-
loss = loss + self.opt.lambda_zero123 * F.mse_loss(images, refined_images)
|
| 219 |
-
# loss = loss + self.opt.lambda_zero123 * self.lpips_loss(images, refined_images)
|
| 220 |
-
|
| 221 |
-
# optimize step
|
| 222 |
-
loss.backward()
|
| 223 |
-
self.optimizer.step()
|
| 224 |
-
self.optimizer.zero_grad()
|
| 225 |
-
|
| 226 |
-
ender.record()
|
| 227 |
-
torch.cuda.synchronize()
|
| 228 |
-
t = starter.elapsed_time(ender)
|
| 229 |
-
|
| 230 |
-
self.need_update = True
|
| 231 |
-
|
| 232 |
-
if self.gui:
|
| 233 |
-
dpg.set_value("_log_train_time", f"{t:.4f}ms")
|
| 234 |
-
dpg.set_value(
|
| 235 |
-
"_log_train_log",
|
| 236 |
-
f"step = {self.step: 5d} (+{self.train_steps: 2d}) loss = {loss.item():.4f}",
|
| 237 |
-
)
|
| 238 |
-
|
| 239 |
-
# dynamic train steps (no need for now)
|
| 240 |
-
# max allowed train time per-frame is 500 ms
|
| 241 |
-
# full_t = t / self.train_steps * 16
|
| 242 |
-
# train_steps = min(16, max(4, int(16 * 500 / full_t)))
|
| 243 |
-
# if train_steps > self.train_steps * 1.2 or train_steps < self.train_steps * 0.8:
|
| 244 |
-
# self.train_steps = train_steps
|
| 245 |
-
|
| 246 |
-
@torch.no_grad()
|
| 247 |
-
def test_step(self):
|
| 248 |
-
# ignore if no need to update
|
| 249 |
-
if not self.need_update:
|
| 250 |
-
return
|
| 251 |
-
|
| 252 |
-
starter = torch.cuda.Event(enable_timing=True)
|
| 253 |
-
ender = torch.cuda.Event(enable_timing=True)
|
| 254 |
-
starter.record()
|
| 255 |
-
|
| 256 |
-
# should update image
|
| 257 |
-
if self.need_update:
|
| 258 |
-
# render image
|
| 259 |
-
|
| 260 |
-
out = self.renderer.render(self.cam.pose, self.cam.perspective, self.H, self.W)
|
| 261 |
-
|
| 262 |
-
buffer_image = out[self.mode] # [H, W, 3]
|
| 263 |
-
|
| 264 |
-
if self.mode in ['depth', 'alpha']:
|
| 265 |
-
buffer_image = buffer_image.repeat(1, 1, 3)
|
| 266 |
-
if self.mode == 'depth':
|
| 267 |
-
buffer_image = (buffer_image - buffer_image.min()) / (buffer_image.max() - buffer_image.min() + 1e-20)
|
| 268 |
-
|
| 269 |
-
self.buffer_image = buffer_image.contiguous().clamp(0, 1).detach().cpu().numpy()
|
| 270 |
-
|
| 271 |
-
# display input_image
|
| 272 |
-
if self.overlay_input_img and self.input_img is not None:
|
| 273 |
-
self.buffer_image = (
|
| 274 |
-
self.buffer_image * (1 - self.overlay_input_img_ratio)
|
| 275 |
-
+ self.input_img * self.overlay_input_img_ratio
|
| 276 |
-
)
|
| 277 |
-
|
| 278 |
-
self.need_update = False
|
| 279 |
-
|
| 280 |
-
ender.record()
|
| 281 |
-
torch.cuda.synchronize()
|
| 282 |
-
t = starter.elapsed_time(ender)
|
| 283 |
-
|
| 284 |
-
if self.gui:
|
| 285 |
-
dpg.set_value("_log_infer_time", f"{t:.4f}ms ({int(1000/t)} FPS)")
|
| 286 |
-
dpg.set_value(
|
| 287 |
-
"_texture", self.buffer_image
|
| 288 |
-
) # buffer must be contiguous, else seg fault!
|
| 289 |
-
|
| 290 |
-
|
| 291 |
-
def load_input(self, file):
|
| 292 |
-
# load image
|
| 293 |
-
print(f'[INFO] load image from {file}...')
|
| 294 |
-
img = cv2.imread(file, cv2.IMREAD_UNCHANGED)
|
| 295 |
-
if img.shape[-1] == 3:
|
| 296 |
-
if self.bg_remover is None:
|
| 297 |
-
self.bg_remover = rembg.new_session()
|
| 298 |
-
img = rembg.remove(img, session=self.bg_remover)
|
| 299 |
-
|
| 300 |
-
img = cv2.resize(
|
| 301 |
-
img, (self.W, self.H), interpolation=cv2.INTER_AREA
|
| 302 |
-
)
|
| 303 |
-
img = img.astype(np.float32) / 255.0
|
| 304 |
-
|
| 305 |
-
self.input_mask = img[..., 3:]
|
| 306 |
-
# white bg
|
| 307 |
-
self.input_img = img[..., :3] * self.input_mask + (
|
| 308 |
-
1 - self.input_mask
|
| 309 |
-
)
|
| 310 |
-
# bgr to rgb
|
| 311 |
-
self.input_img = self.input_img[..., ::-1].copy()
|
| 312 |
-
|
| 313 |
-
# load prompt
|
| 314 |
-
file_prompt = file.replace("_rgba.png", "_caption.txt")
|
| 315 |
-
if os.path.exists(file_prompt):
|
| 316 |
-
print(f'[INFO] load prompt from {file_prompt}...')
|
| 317 |
-
with open(file_prompt, "r") as f:
|
| 318 |
-
self.prompt = f.read().strip()
|
| 319 |
-
|
| 320 |
-
def save_model(self):
|
| 321 |
-
os.makedirs(self.opt.outdir, exist_ok=True)
|
| 322 |
-
|
| 323 |
-
path = os.path.join(self.opt.outdir, self.opt.save_path + '.obj')
|
| 324 |
-
self.renderer.export_mesh(path)
|
| 325 |
-
|
| 326 |
-
print(f"[INFO] save model to {path}.")
|
| 327 |
-
|
| 328 |
-
def register_dpg(self):
|
| 329 |
-
### register texture
|
| 330 |
-
|
| 331 |
-
with dpg.texture_registry(show=False):
|
| 332 |
-
dpg.add_raw_texture(
|
| 333 |
-
self.W,
|
| 334 |
-
self.H,
|
| 335 |
-
self.buffer_image,
|
| 336 |
-
format=dpg.mvFormat_Float_rgb,
|
| 337 |
-
tag="_texture",
|
| 338 |
-
)
|
| 339 |
-
|
| 340 |
-
### register window
|
| 341 |
-
|
| 342 |
-
# the rendered image, as the primary window
|
| 343 |
-
with dpg.window(
|
| 344 |
-
tag="_primary_window",
|
| 345 |
-
width=self.W,
|
| 346 |
-
height=self.H,
|
| 347 |
-
pos=[0, 0],
|
| 348 |
-
no_move=True,
|
| 349 |
-
no_title_bar=True,
|
| 350 |
-
no_scrollbar=True,
|
| 351 |
-
):
|
| 352 |
-
# add the texture
|
| 353 |
-
dpg.add_image("_texture")
|
| 354 |
-
|
| 355 |
-
# dpg.set_primary_window("_primary_window", True)
|
| 356 |
-
|
| 357 |
-
# control window
|
| 358 |
-
with dpg.window(
|
| 359 |
-
label="Control",
|
| 360 |
-
tag="_control_window",
|
| 361 |
-
width=600,
|
| 362 |
-
height=self.H,
|
| 363 |
-
pos=[self.W, 0],
|
| 364 |
-
no_move=True,
|
| 365 |
-
no_title_bar=True,
|
| 366 |
-
):
|
| 367 |
-
# button theme
|
| 368 |
-
with dpg.theme() as theme_button:
|
| 369 |
-
with dpg.theme_component(dpg.mvButton):
|
| 370 |
-
dpg.add_theme_color(dpg.mvThemeCol_Button, (23, 3, 18))
|
| 371 |
-
dpg.add_theme_color(dpg.mvThemeCol_ButtonHovered, (51, 3, 47))
|
| 372 |
-
dpg.add_theme_color(dpg.mvThemeCol_ButtonActive, (83, 18, 83))
|
| 373 |
-
dpg.add_theme_style(dpg.mvStyleVar_FrameRounding, 5)
|
| 374 |
-
dpg.add_theme_style(dpg.mvStyleVar_FramePadding, 3, 3)
|
| 375 |
-
|
| 376 |
-
# timer stuff
|
| 377 |
-
with dpg.group(horizontal=True):
|
| 378 |
-
dpg.add_text("Infer time: ")
|
| 379 |
-
dpg.add_text("no data", tag="_log_infer_time")
|
| 380 |
-
|
| 381 |
-
def callback_setattr(sender, app_data, user_data):
|
| 382 |
-
setattr(self, user_data, app_data)
|
| 383 |
-
|
| 384 |
-
# init stuff
|
| 385 |
-
with dpg.collapsing_header(label="Initialize", default_open=True):
|
| 386 |
-
|
| 387 |
-
# seed stuff
|
| 388 |
-
def callback_set_seed(sender, app_data):
|
| 389 |
-
self.seed = app_data
|
| 390 |
-
self.seed_everything()
|
| 391 |
-
|
| 392 |
-
dpg.add_input_text(
|
| 393 |
-
label="seed",
|
| 394 |
-
default_value=self.seed,
|
| 395 |
-
on_enter=True,
|
| 396 |
-
callback=callback_set_seed,
|
| 397 |
-
)
|
| 398 |
-
|
| 399 |
-
# input stuff
|
| 400 |
-
def callback_select_input(sender, app_data):
|
| 401 |
-
# only one item
|
| 402 |
-
for k, v in app_data["selections"].items():
|
| 403 |
-
dpg.set_value("_log_input", k)
|
| 404 |
-
self.load_input(v)
|
| 405 |
-
|
| 406 |
-
self.need_update = True
|
| 407 |
-
|
| 408 |
-
with dpg.file_dialog(
|
| 409 |
-
directory_selector=False,
|
| 410 |
-
show=False,
|
| 411 |
-
callback=callback_select_input,
|
| 412 |
-
file_count=1,
|
| 413 |
-
tag="file_dialog_tag",
|
| 414 |
-
width=700,
|
| 415 |
-
height=400,
|
| 416 |
-
):
|
| 417 |
-
dpg.add_file_extension("Images{.jpg,.jpeg,.png}")
|
| 418 |
-
|
| 419 |
-
with dpg.group(horizontal=True):
|
| 420 |
-
dpg.add_button(
|
| 421 |
-
label="input",
|
| 422 |
-
callback=lambda: dpg.show_item("file_dialog_tag"),
|
| 423 |
-
)
|
| 424 |
-
dpg.add_text("", tag="_log_input")
|
| 425 |
-
|
| 426 |
-
# overlay stuff
|
| 427 |
-
with dpg.group(horizontal=True):
|
| 428 |
-
|
| 429 |
-
def callback_toggle_overlay_input_img(sender, app_data):
|
| 430 |
-
self.overlay_input_img = not self.overlay_input_img
|
| 431 |
-
self.need_update = True
|
| 432 |
-
|
| 433 |
-
dpg.add_checkbox(
|
| 434 |
-
label="overlay image",
|
| 435 |
-
default_value=self.overlay_input_img,
|
| 436 |
-
callback=callback_toggle_overlay_input_img,
|
| 437 |
-
)
|
| 438 |
-
|
| 439 |
-
def callback_set_overlay_input_img_ratio(sender, app_data):
|
| 440 |
-
self.overlay_input_img_ratio = app_data
|
| 441 |
-
self.need_update = True
|
| 442 |
-
|
| 443 |
-
dpg.add_slider_float(
|
| 444 |
-
label="ratio",
|
| 445 |
-
min_value=0,
|
| 446 |
-
max_value=1,
|
| 447 |
-
format="%.1f",
|
| 448 |
-
default_value=self.overlay_input_img_ratio,
|
| 449 |
-
callback=callback_set_overlay_input_img_ratio,
|
| 450 |
-
)
|
| 451 |
-
|
| 452 |
-
# prompt stuff
|
| 453 |
-
|
| 454 |
-
dpg.add_input_text(
|
| 455 |
-
label="prompt",
|
| 456 |
-
default_value=self.prompt,
|
| 457 |
-
callback=callback_setattr,
|
| 458 |
-
user_data="prompt",
|
| 459 |
-
)
|
| 460 |
-
|
| 461 |
-
dpg.add_input_text(
|
| 462 |
-
label="negative",
|
| 463 |
-
default_value=self.negative_prompt,
|
| 464 |
-
callback=callback_setattr,
|
| 465 |
-
user_data="negative_prompt",
|
| 466 |
-
)
|
| 467 |
-
|
| 468 |
-
# save current model
|
| 469 |
-
with dpg.group(horizontal=True):
|
| 470 |
-
dpg.add_text("Save: ")
|
| 471 |
-
|
| 472 |
-
dpg.add_button(
|
| 473 |
-
label="model",
|
| 474 |
-
tag="_button_save_model",
|
| 475 |
-
callback=self.save_model,
|
| 476 |
-
)
|
| 477 |
-
dpg.bind_item_theme("_button_save_model", theme_button)
|
| 478 |
-
|
| 479 |
-
dpg.add_input_text(
|
| 480 |
-
label="",
|
| 481 |
-
default_value=self.opt.save_path,
|
| 482 |
-
callback=callback_setattr,
|
| 483 |
-
user_data="save_path",
|
| 484 |
-
)
|
| 485 |
-
|
| 486 |
-
# training stuff
|
| 487 |
-
with dpg.collapsing_header(label="Train", default_open=True):
|
| 488 |
-
# lr and train button
|
| 489 |
-
with dpg.group(horizontal=True):
|
| 490 |
-
dpg.add_text("Train: ")
|
| 491 |
-
|
| 492 |
-
def callback_train(sender, app_data):
|
| 493 |
-
if self.training:
|
| 494 |
-
self.training = False
|
| 495 |
-
dpg.configure_item("_button_train", label="start")
|
| 496 |
-
else:
|
| 497 |
-
self.prepare_train()
|
| 498 |
-
self.training = True
|
| 499 |
-
dpg.configure_item("_button_train", label="stop")
|
| 500 |
-
|
| 501 |
-
# dpg.add_button(
|
| 502 |
-
# label="init", tag="_button_init", callback=self.prepare_train
|
| 503 |
-
# )
|
| 504 |
-
# dpg.bind_item_theme("_button_init", theme_button)
|
| 505 |
-
|
| 506 |
-
dpg.add_button(
|
| 507 |
-
label="start", tag="_button_train", callback=callback_train
|
| 508 |
-
)
|
| 509 |
-
dpg.bind_item_theme("_button_train", theme_button)
|
| 510 |
-
|
| 511 |
-
with dpg.group(horizontal=True):
|
| 512 |
-
dpg.add_text("", tag="_log_train_time")
|
| 513 |
-
dpg.add_text("", tag="_log_train_log")
|
| 514 |
-
|
| 515 |
-
# rendering options
|
| 516 |
-
with dpg.collapsing_header(label="Rendering", default_open=True):
|
| 517 |
-
# mode combo
|
| 518 |
-
def callback_change_mode(sender, app_data):
|
| 519 |
-
self.mode = app_data
|
| 520 |
-
self.need_update = True
|
| 521 |
-
|
| 522 |
-
dpg.add_combo(
|
| 523 |
-
("image", "depth", "alpha", "normal"),
|
| 524 |
-
label="mode",
|
| 525 |
-
default_value=self.mode,
|
| 526 |
-
callback=callback_change_mode,
|
| 527 |
-
)
|
| 528 |
-
|
| 529 |
-
# fov slider
|
| 530 |
-
def callback_set_fovy(sender, app_data):
|
| 531 |
-
self.cam.fovy = np.deg2rad(app_data)
|
| 532 |
-
self.need_update = True
|
| 533 |
-
|
| 534 |
-
dpg.add_slider_int(
|
| 535 |
-
label="FoV (vertical)",
|
| 536 |
-
min_value=1,
|
| 537 |
-
max_value=120,
|
| 538 |
-
format="%d deg",
|
| 539 |
-
default_value=np.rad2deg(self.cam.fovy),
|
| 540 |
-
callback=callback_set_fovy,
|
| 541 |
-
)
|
| 542 |
-
|
| 543 |
-
### register camera handler
|
| 544 |
-
|
| 545 |
-
def callback_camera_drag_rotate_or_draw_mask(sender, app_data):
|
| 546 |
-
if not dpg.is_item_focused("_primary_window"):
|
| 547 |
-
return
|
| 548 |
-
|
| 549 |
-
dx = app_data[1]
|
| 550 |
-
dy = app_data[2]
|
| 551 |
-
|
| 552 |
-
self.cam.orbit(dx, dy)
|
| 553 |
-
self.need_update = True
|
| 554 |
-
|
| 555 |
-
def callback_camera_wheel_scale(sender, app_data):
|
| 556 |
-
if not dpg.is_item_focused("_primary_window"):
|
| 557 |
-
return
|
| 558 |
-
|
| 559 |
-
delta = app_data
|
| 560 |
-
|
| 561 |
-
self.cam.scale(delta)
|
| 562 |
-
self.need_update = True
|
| 563 |
-
|
| 564 |
-
def callback_camera_drag_pan(sender, app_data):
|
| 565 |
-
if not dpg.is_item_focused("_primary_window"):
|
| 566 |
-
return
|
| 567 |
-
|
| 568 |
-
dx = app_data[1]
|
| 569 |
-
dy = app_data[2]
|
| 570 |
-
|
| 571 |
-
self.cam.pan(dx, dy)
|
| 572 |
-
self.need_update = True
|
| 573 |
-
|
| 574 |
-
def callback_set_mouse_loc(sender, app_data):
|
| 575 |
-
if not dpg.is_item_focused("_primary_window"):
|
| 576 |
-
return
|
| 577 |
-
|
| 578 |
-
# just the pixel coordinate in image
|
| 579 |
-
self.mouse_loc = np.array(app_data)
|
| 580 |
-
|
| 581 |
-
with dpg.handler_registry():
|
| 582 |
-
# for camera moving
|
| 583 |
-
dpg.add_mouse_drag_handler(
|
| 584 |
-
button=dpg.mvMouseButton_Left,
|
| 585 |
-
callback=callback_camera_drag_rotate_or_draw_mask,
|
| 586 |
-
)
|
| 587 |
-
dpg.add_mouse_wheel_handler(callback=callback_camera_wheel_scale)
|
| 588 |
-
dpg.add_mouse_drag_handler(
|
| 589 |
-
button=dpg.mvMouseButton_Middle, callback=callback_camera_drag_pan
|
| 590 |
-
)
|
| 591 |
-
|
| 592 |
-
dpg.create_viewport(
|
| 593 |
-
title="Gaussian3D",
|
| 594 |
-
width=self.W + 600,
|
| 595 |
-
height=self.H + (45 if os.name == "nt" else 0),
|
| 596 |
-
resizable=False,
|
| 597 |
-
)
|
| 598 |
-
|
| 599 |
-
### global theme
|
| 600 |
-
with dpg.theme() as theme_no_padding:
|
| 601 |
-
with dpg.theme_component(dpg.mvAll):
|
| 602 |
-
# set all padding to 0 to avoid scroll bar
|
| 603 |
-
dpg.add_theme_style(
|
| 604 |
-
dpg.mvStyleVar_WindowPadding, 0, 0, category=dpg.mvThemeCat_Core
|
| 605 |
-
)
|
| 606 |
-
dpg.add_theme_style(
|
| 607 |
-
dpg.mvStyleVar_FramePadding, 0, 0, category=dpg.mvThemeCat_Core
|
| 608 |
-
)
|
| 609 |
-
dpg.add_theme_style(
|
| 610 |
-
dpg.mvStyleVar_CellPadding, 0, 0, category=dpg.mvThemeCat_Core
|
| 611 |
-
)
|
| 612 |
-
|
| 613 |
-
dpg.bind_item_theme("_primary_window", theme_no_padding)
|
| 614 |
-
|
| 615 |
-
dpg.setup_dearpygui()
|
| 616 |
-
|
| 617 |
-
### register a larger font
|
| 618 |
-
# get it from: https://github.com/lxgw/LxgwWenKai/releases/download/v1.300/LXGWWenKai-Regular.ttf
|
| 619 |
-
if os.path.exists("LXGWWenKai-Regular.ttf"):
|
| 620 |
-
with dpg.font_registry():
|
| 621 |
-
with dpg.font("LXGWWenKai-Regular.ttf", 18) as default_font:
|
| 622 |
-
dpg.bind_font(default_font)
|
| 623 |
-
|
| 624 |
-
# dpg.show_metrics()
|
| 625 |
-
|
| 626 |
-
dpg.show_viewport()
|
| 627 |
-
|
| 628 |
-
def render(self):
|
| 629 |
-
assert self.gui
|
| 630 |
-
while dpg.is_dearpygui_running():
|
| 631 |
-
# update texture every frame
|
| 632 |
-
if self.training:
|
| 633 |
-
self.train_step()
|
| 634 |
-
self.test_step()
|
| 635 |
-
dpg.render_dearpygui_frame()
|
| 636 |
-
|
| 637 |
-
# no gui mode
|
| 638 |
-
def train(self, iters=500):
|
| 639 |
-
if iters > 0:
|
| 640 |
-
self.prepare_train()
|
| 641 |
-
for i in tqdm.trange(iters):
|
| 642 |
-
self.train_step()
|
| 643 |
-
# save
|
| 644 |
-
self.save_model()
|
| 645 |
-
|
| 646 |
-
|
| 647 |
-
if __name__ == "__main__":
|
| 648 |
-
import argparse
|
| 649 |
-
from omegaconf import OmegaConf
|
| 650 |
-
|
| 651 |
-
parser = argparse.ArgumentParser()
|
| 652 |
-
parser.add_argument("--config", required=True, help="path to the yaml config file")
|
| 653 |
-
args, extras = parser.parse_known_args()
|
| 654 |
-
|
| 655 |
-
# override default config from cli
|
| 656 |
-
opt = OmegaConf.merge(OmegaConf.load(args.config), OmegaConf.from_cli(extras))
|
| 657 |
-
|
| 658 |
-
# auto find mesh from stage 1
|
| 659 |
-
if opt.mesh is None:
|
| 660 |
-
default_path = os.path.join(opt.outdir, opt.save_path + '_mesh.obj')
|
| 661 |
-
if os.path.exists(default_path):
|
| 662 |
-
opt.mesh = default_path
|
| 663 |
-
else:
|
| 664 |
-
raise ValueError(f"Cannot find mesh from {default_path}, must specify --mesh explicitly!")
|
| 665 |
-
|
| 666 |
-
gui = GUI(opt)
|
| 667 |
-
|
| 668 |
-
if opt.gui:
|
| 669 |
-
gui.render()
|
| 670 |
-
else:
|
| 671 |
-
gui.train(opt.iters_refine)
|
|
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|
spaces/Abs6187/AI_Chatbot/AI_Chat_bot.py
DELETED
|
@@ -1,21 +0,0 @@
|
|
| 1 |
-
import openai
|
| 2 |
-
import gradio
|
| 3 |
-
|
| 4 |
-
openai.api_key = "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"
|
| 5 |
-
|
| 6 |
-
messages = [{"role": "system", "content": "You are a Indian Lawyer and Gave Advice according to Indian constitution"}]
|
| 7 |
-
|
| 8 |
-
def CustomChatGPT(user_input):
|
| 9 |
-
messages.append({"role": "user", "content": user_input})
|
| 10 |
-
response = openai.ChatCompletion.create(
|
| 11 |
-
model = "gpt-3.5-turbo",
|
| 12 |
-
messages = messages
|
| 13 |
-
)
|
| 14 |
-
ChatGPT_reply = response["choices"][0]["message"]["content"]
|
| 15 |
-
messages.append({"role": "assistant", "content": ChatGPT_reply})
|
| 16 |
-
return ChatGPT_reply
|
| 17 |
-
|
| 18 |
-
demo = gradio.Interface(fn=CustomChatGPT, inputs = "text", outputs = "text", title = "AI Chat Bot for Legal Assistance")
|
| 19 |
-
print("\n This is Free version and if it doesnt work kindly contact at Email ID [email protected]")
|
| 20 |
-
|
| 21 |
-
demo.launch(share=True)
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|
spaces/Aditya9790/yolo7-object-tracking/utils/activations.py
DELETED
|
@@ -1,72 +0,0 @@
|
|
| 1 |
-
# Activation functions
|
| 2 |
-
|
| 3 |
-
import torch
|
| 4 |
-
import torch.nn as nn
|
| 5 |
-
import torch.nn.functional as F
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
# SiLU https://arxiv.org/pdf/1606.08415.pdf ----------------------------------------------------------------------------
|
| 9 |
-
class SiLU(nn.Module): # export-friendly version of nn.SiLU()
|
| 10 |
-
@staticmethod
|
| 11 |
-
def forward(x):
|
| 12 |
-
return x * torch.sigmoid(x)
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
class Hardswish(nn.Module): # export-friendly version of nn.Hardswish()
|
| 16 |
-
@staticmethod
|
| 17 |
-
def forward(x):
|
| 18 |
-
# return x * F.hardsigmoid(x) # for torchscript and CoreML
|
| 19 |
-
return x * F.hardtanh(x + 3, 0., 6.) / 6. # for torchscript, CoreML and ONNX
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
class MemoryEfficientSwish(nn.Module):
|
| 23 |
-
class F(torch.autograd.Function):
|
| 24 |
-
@staticmethod
|
| 25 |
-
def forward(ctx, x):
|
| 26 |
-
ctx.save_for_backward(x)
|
| 27 |
-
return x * torch.sigmoid(x)
|
| 28 |
-
|
| 29 |
-
@staticmethod
|
| 30 |
-
def backward(ctx, grad_output):
|
| 31 |
-
x = ctx.saved_tensors[0]
|
| 32 |
-
sx = torch.sigmoid(x)
|
| 33 |
-
return grad_output * (sx * (1 + x * (1 - sx)))
|
| 34 |
-
|
| 35 |
-
def forward(self, x):
|
| 36 |
-
return self.F.apply(x)
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
|
| 40 |
-
class Mish(nn.Module):
|
| 41 |
-
@staticmethod
|
| 42 |
-
def forward(x):
|
| 43 |
-
return x * F.softplus(x).tanh()
|
| 44 |
-
|
| 45 |
-
|
| 46 |
-
class MemoryEfficientMish(nn.Module):
|
| 47 |
-
class F(torch.autograd.Function):
|
| 48 |
-
@staticmethod
|
| 49 |
-
def forward(ctx, x):
|
| 50 |
-
ctx.save_for_backward(x)
|
| 51 |
-
return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x)))
|
| 52 |
-
|
| 53 |
-
@staticmethod
|
| 54 |
-
def backward(ctx, grad_output):
|
| 55 |
-
x = ctx.saved_tensors[0]
|
| 56 |
-
sx = torch.sigmoid(x)
|
| 57 |
-
fx = F.softplus(x).tanh()
|
| 58 |
-
return grad_output * (fx + x * sx * (1 - fx * fx))
|
| 59 |
-
|
| 60 |
-
def forward(self, x):
|
| 61 |
-
return self.F.apply(x)
|
| 62 |
-
|
| 63 |
-
|
| 64 |
-
# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
|
| 65 |
-
class FReLU(nn.Module):
|
| 66 |
-
def __init__(self, c1, k=3): # ch_in, kernel
|
| 67 |
-
super().__init__()
|
| 68 |
-
self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
|
| 69 |
-
self.bn = nn.BatchNorm2d(c1)
|
| 70 |
-
|
| 71 |
-
def forward(self, x):
|
| 72 |
-
return torch.max(x, self.bn(self.conv(x)))
|
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spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/gashapon-plugin.js
DELETED
|
@@ -1,19 +0,0 @@
|
|
| 1 |
-
import Gashapon from './gashapon.js';
|
| 2 |
-
|
| 3 |
-
class GashaponPlugin extends Phaser.Plugins.BasePlugin {
|
| 4 |
-
|
| 5 |
-
constructor(pluginManager) {
|
| 6 |
-
super(pluginManager);
|
| 7 |
-
}
|
| 8 |
-
|
| 9 |
-
start() {
|
| 10 |
-
var eventEmitter = this.game.events;
|
| 11 |
-
eventEmitter.on('destroy', this.destroy, this);
|
| 12 |
-
}
|
| 13 |
-
|
| 14 |
-
add(config) {
|
| 15 |
-
return new Gashapon(config);
|
| 16 |
-
}
|
| 17 |
-
}
|
| 18 |
-
|
| 19 |
-
export default GashaponPlugin;
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spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/utils/GetTypeName.js
DELETED
|
@@ -1,27 +0,0 @@
|
|
| 1 |
-
var GetTypeName = function (data, styles) {
|
| 2 |
-
if (data.hasOwnProperty('$type')) {
|
| 3 |
-
return data.$type;
|
| 4 |
-
}
|
| 5 |
-
|
| 6 |
-
// Get $type from styles[`#${data.name}`]
|
| 7 |
-
if (data.hasOwnProperty('name')) {
|
| 8 |
-
var style = styles[`#${data.name}`];
|
| 9 |
-
if (style && style.hasOwnProperty('$type')) {
|
| 10 |
-
return style.$type;
|
| 11 |
-
}
|
| 12 |
-
}
|
| 13 |
-
|
| 14 |
-
if (data.hasOwnProperty('$class')) {
|
| 15 |
-
var clasKeys = data.$class.split(' ');
|
| 16 |
-
for (var i = 0, cnt = clasKeys.length; i < cnt; i++) {
|
| 17 |
-
var style = styles[`.${clasKeys[i]}`];
|
| 18 |
-
if (style && style.hasOwnProperty('$type')) {
|
| 19 |
-
return style.$type;
|
| 20 |
-
}
|
| 21 |
-
}
|
| 22 |
-
}
|
| 23 |
-
|
| 24 |
-
// return undefined
|
| 25 |
-
}
|
| 26 |
-
|
| 27 |
-
export default GetTypeName;
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spaces/AlekseyKorshuk/instagram-filter-removal/modules/blocks.py
DELETED
|
@@ -1,93 +0,0 @@
|
|
| 1 |
-
from torch import nn
|
| 2 |
-
|
| 3 |
-
from modules.normalization import AdaIN
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
class DestyleResBlock(nn.Module):
|
| 7 |
-
def __init__(self, channels_out, kernel_size, channels_in=None, stride=1, dilation=1, padding=1, use_dropout=False):
|
| 8 |
-
super(DestyleResBlock, self).__init__()
|
| 9 |
-
|
| 10 |
-
# uses 1x1 convolutions for downsampling
|
| 11 |
-
if not channels_in or channels_in == channels_out:
|
| 12 |
-
channels_in = channels_out
|
| 13 |
-
self.projection = None
|
| 14 |
-
else:
|
| 15 |
-
self.projection = nn.Conv2d(channels_in, channels_out, kernel_size=1, stride=stride, dilation=1)
|
| 16 |
-
self.use_dropout = use_dropout
|
| 17 |
-
|
| 18 |
-
self.conv1 = nn.Conv2d(channels_in, channels_out, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation)
|
| 19 |
-
self.lrelu1 = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
| 20 |
-
self.conv2 = nn.Conv2d(channels_out, channels_out, kernel_size=kernel_size, stride=1, padding=padding, dilation=dilation)
|
| 21 |
-
self.adain = AdaIN()
|
| 22 |
-
if self.use_dropout:
|
| 23 |
-
self.dropout = nn.Dropout()
|
| 24 |
-
self.lrelu2 = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
| 25 |
-
|
| 26 |
-
def forward(self, x, feat):
|
| 27 |
-
residual = x
|
| 28 |
-
out = self.conv1(x)
|
| 29 |
-
out = self.lrelu1(out)
|
| 30 |
-
out = self.conv2(out)
|
| 31 |
-
_, _, h, w = out.size()
|
| 32 |
-
out = self.adain(out, feat)
|
| 33 |
-
if self.use_dropout:
|
| 34 |
-
out = self.dropout(out)
|
| 35 |
-
if self.projection:
|
| 36 |
-
residual = self.projection(x)
|
| 37 |
-
out = out + residual
|
| 38 |
-
out = self.lrelu2(out)
|
| 39 |
-
return out
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
class ResBlock(nn.Module):
|
| 43 |
-
def __init__(self, channels_out, kernel_size, channels_in=None, stride=1, dilation=1, padding=1, use_dropout=False):
|
| 44 |
-
super(ResBlock, self).__init__()
|
| 45 |
-
|
| 46 |
-
# uses 1x1 convolutions for downsampling
|
| 47 |
-
if not channels_in or channels_in == channels_out:
|
| 48 |
-
channels_in = channels_out
|
| 49 |
-
self.projection = None
|
| 50 |
-
else:
|
| 51 |
-
self.projection = nn.Conv2d(channels_in, channels_out, kernel_size=1, stride=stride, dilation=1)
|
| 52 |
-
self.use_dropout = use_dropout
|
| 53 |
-
|
| 54 |
-
self.conv1 = nn.Conv2d(channels_in, channels_out, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation)
|
| 55 |
-
self.lrelu1 = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
| 56 |
-
self.conv2 = nn.Conv2d(channels_out, channels_out, kernel_size=kernel_size, stride=1, padding=padding, dilation=dilation)
|
| 57 |
-
self.n2 = nn.BatchNorm2d(channels_out)
|
| 58 |
-
if self.use_dropout:
|
| 59 |
-
self.dropout = nn.Dropout()
|
| 60 |
-
self.lrelu2 = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
| 61 |
-
|
| 62 |
-
def forward(self, x):
|
| 63 |
-
residual = x
|
| 64 |
-
out = self.conv1(x)
|
| 65 |
-
out = self.lrelu1(out)
|
| 66 |
-
out = self.conv2(out)
|
| 67 |
-
# out = self.n2(out)
|
| 68 |
-
if self.use_dropout:
|
| 69 |
-
out = self.dropout(out)
|
| 70 |
-
if self.projection:
|
| 71 |
-
residual = self.projection(x)
|
| 72 |
-
out = out + residual
|
| 73 |
-
out = self.lrelu2(out)
|
| 74 |
-
return out
|
| 75 |
-
|
| 76 |
-
|
| 77 |
-
class Destyler(nn.Module):
|
| 78 |
-
def __init__(self, in_features, num_features):
|
| 79 |
-
super(Destyler, self).__init__()
|
| 80 |
-
self.fc1 = nn.Linear(in_features, num_features)
|
| 81 |
-
self.fc2 = nn.Linear(num_features, num_features)
|
| 82 |
-
self.fc3 = nn.Linear(num_features, num_features)
|
| 83 |
-
self.fc4 = nn.Linear(num_features, num_features)
|
| 84 |
-
self.fc5 = nn.Linear(num_features, num_features)
|
| 85 |
-
|
| 86 |
-
def forward(self, x):
|
| 87 |
-
x = self.fc1(x)
|
| 88 |
-
x = self.fc2(x)
|
| 89 |
-
x = self.fc3(x)
|
| 90 |
-
x = self.fc4(x)
|
| 91 |
-
x = self.fc5(x)
|
| 92 |
-
return x
|
| 93 |
-
|
|
|
|
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|
spaces/Alfasign/remove-background-on-image/app.py
DELETED
|
@@ -1,26 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
from rembg import remove
|
| 3 |
-
|
| 4 |
-
title = "Hintergrund-Entferner"
|
| 5 |
-
description = "This background remover tool is inspired by Evgeniy Hristoforu! and created by Ailex"
|
| 6 |
-
article = "<p style='text-align: center;'><a href='https://hf.co/openskyml' target='_blank'>🏠 Home page</a></p>"
|
| 7 |
-
|
| 8 |
-
def segment(image):
|
| 9 |
-
return remove(image)
|
| 10 |
-
|
| 11 |
-
# Die folgende Zeile scheint nicht benötigt zu werden und wurde entfernt
|
| 12 |
-
# with gr.Blocks(css="style.css", theme="NoCrypt/[email protected]") as demo:
|
| 13 |
-
|
| 14 |
-
demo = gr.Interface(
|
| 15 |
-
fn=segment,
|
| 16 |
-
inputs="image",
|
| 17 |
-
outputs="image",
|
| 18 |
-
title=title,
|
| 19 |
-
description=description,
|
| 20 |
-
article=article,
|
| 21 |
-
css="style.css",
|
| 22 |
-
theme="NoCrypt/[email protected]"
|
| 23 |
-
)
|
| 24 |
-
|
| 25 |
-
demo.queue(concurrency_count=3)
|
| 26 |
-
demo.launch()
|
|
|
|
|
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|
spaces/Algoworks/Image_Face_Upscale_Restoration-GFPGAN_pub/README.md
DELETED
|
@@ -1,14 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Image Face Upscale Restoration-GFPGAN
|
| 3 |
-
emoji: 📈
|
| 4 |
-
colorFrom: blue
|
| 5 |
-
colorTo: gray
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.1.7
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
license: apache-2.0
|
| 11 |
-
duplicated_from: Algoworks/Image_Face_Upscale_Restoration-GFPGAN
|
| 12 |
-
---
|
| 13 |
-
|
| 14 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
spaces/Alpaca233/SadTalker/src/face3d/util/preprocess.py
DELETED
|
@@ -1,103 +0,0 @@
|
|
| 1 |
-
"""This script contains the image preprocessing code for Deep3DFaceRecon_pytorch
|
| 2 |
-
"""
|
| 3 |
-
|
| 4 |
-
import numpy as np
|
| 5 |
-
from scipy.io import loadmat
|
| 6 |
-
from PIL import Image
|
| 7 |
-
import cv2
|
| 8 |
-
import os
|
| 9 |
-
from skimage import transform as trans
|
| 10 |
-
import torch
|
| 11 |
-
import warnings
|
| 12 |
-
warnings.filterwarnings("ignore", category=np.VisibleDeprecationWarning)
|
| 13 |
-
warnings.filterwarnings("ignore", category=FutureWarning)
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
# calculating least square problem for image alignment
|
| 17 |
-
def POS(xp, x):
|
| 18 |
-
npts = xp.shape[1]
|
| 19 |
-
|
| 20 |
-
A = np.zeros([2*npts, 8])
|
| 21 |
-
|
| 22 |
-
A[0:2*npts-1:2, 0:3] = x.transpose()
|
| 23 |
-
A[0:2*npts-1:2, 3] = 1
|
| 24 |
-
|
| 25 |
-
A[1:2*npts:2, 4:7] = x.transpose()
|
| 26 |
-
A[1:2*npts:2, 7] = 1
|
| 27 |
-
|
| 28 |
-
b = np.reshape(xp.transpose(), [2*npts, 1])
|
| 29 |
-
|
| 30 |
-
k, _, _, _ = np.linalg.lstsq(A, b)
|
| 31 |
-
|
| 32 |
-
R1 = k[0:3]
|
| 33 |
-
R2 = k[4:7]
|
| 34 |
-
sTx = k[3]
|
| 35 |
-
sTy = k[7]
|
| 36 |
-
s = (np.linalg.norm(R1) + np.linalg.norm(R2))/2
|
| 37 |
-
t = np.stack([sTx, sTy], axis=0)
|
| 38 |
-
|
| 39 |
-
return t, s
|
| 40 |
-
|
| 41 |
-
# resize and crop images for face reconstruction
|
| 42 |
-
def resize_n_crop_img(img, lm, t, s, target_size=224., mask=None):
|
| 43 |
-
w0, h0 = img.size
|
| 44 |
-
w = (w0*s).astype(np.int32)
|
| 45 |
-
h = (h0*s).astype(np.int32)
|
| 46 |
-
left = (w/2 - target_size/2 + float((t[0] - w0/2)*s)).astype(np.int32)
|
| 47 |
-
right = left + target_size
|
| 48 |
-
up = (h/2 - target_size/2 + float((h0/2 - t[1])*s)).astype(np.int32)
|
| 49 |
-
below = up + target_size
|
| 50 |
-
|
| 51 |
-
img = img.resize((w, h), resample=Image.BICUBIC)
|
| 52 |
-
img = img.crop((left, up, right, below))
|
| 53 |
-
|
| 54 |
-
if mask is not None:
|
| 55 |
-
mask = mask.resize((w, h), resample=Image.BICUBIC)
|
| 56 |
-
mask = mask.crop((left, up, right, below))
|
| 57 |
-
|
| 58 |
-
lm = np.stack([lm[:, 0] - t[0] + w0/2, lm[:, 1] -
|
| 59 |
-
t[1] + h0/2], axis=1)*s
|
| 60 |
-
lm = lm - np.reshape(
|
| 61 |
-
np.array([(w/2 - target_size/2), (h/2-target_size/2)]), [1, 2])
|
| 62 |
-
|
| 63 |
-
return img, lm, mask
|
| 64 |
-
|
| 65 |
-
# utils for face reconstruction
|
| 66 |
-
def extract_5p(lm):
|
| 67 |
-
lm_idx = np.array([31, 37, 40, 43, 46, 49, 55]) - 1
|
| 68 |
-
lm5p = np.stack([lm[lm_idx[0], :], np.mean(lm[lm_idx[[1, 2]], :], 0), np.mean(
|
| 69 |
-
lm[lm_idx[[3, 4]], :], 0), lm[lm_idx[5], :], lm[lm_idx[6], :]], axis=0)
|
| 70 |
-
lm5p = lm5p[[1, 2, 0, 3, 4], :]
|
| 71 |
-
return lm5p
|
| 72 |
-
|
| 73 |
-
# utils for face reconstruction
|
| 74 |
-
def align_img(img, lm, lm3D, mask=None, target_size=224., rescale_factor=102.):
|
| 75 |
-
"""
|
| 76 |
-
Return:
|
| 77 |
-
transparams --numpy.array (raw_W, raw_H, scale, tx, ty)
|
| 78 |
-
img_new --PIL.Image (target_size, target_size, 3)
|
| 79 |
-
lm_new --numpy.array (68, 2), y direction is opposite to v direction
|
| 80 |
-
mask_new --PIL.Image (target_size, target_size)
|
| 81 |
-
|
| 82 |
-
Parameters:
|
| 83 |
-
img --PIL.Image (raw_H, raw_W, 3)
|
| 84 |
-
lm --numpy.array (68, 2), y direction is opposite to v direction
|
| 85 |
-
lm3D --numpy.array (5, 3)
|
| 86 |
-
mask --PIL.Image (raw_H, raw_W, 3)
|
| 87 |
-
"""
|
| 88 |
-
|
| 89 |
-
w0, h0 = img.size
|
| 90 |
-
if lm.shape[0] != 5:
|
| 91 |
-
lm5p = extract_5p(lm)
|
| 92 |
-
else:
|
| 93 |
-
lm5p = lm
|
| 94 |
-
|
| 95 |
-
# calculate translation and scale factors using 5 facial landmarks and standard landmarks of a 3D face
|
| 96 |
-
t, s = POS(lm5p.transpose(), lm3D.transpose())
|
| 97 |
-
s = rescale_factor/s
|
| 98 |
-
|
| 99 |
-
# processing the image
|
| 100 |
-
img_new, lm_new, mask_new = resize_n_crop_img(img, lm, t, s, target_size=target_size, mask=mask)
|
| 101 |
-
trans_params = np.array([w0, h0, s, t[0], t[1]])
|
| 102 |
-
|
| 103 |
-
return trans_params, img_new, lm_new, mask_new
|
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spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/GetGUIData.py
DELETED
|
@@ -1,67 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
import os
|
| 3 |
-
import numpy as np
|
| 4 |
-
import argparse
|
| 5 |
-
from manipulate import Manipulator
|
| 6 |
-
import torch
|
| 7 |
-
from PIL import Image
|
| 8 |
-
#%%
|
| 9 |
-
|
| 10 |
-
if __name__ == "__main__":
|
| 11 |
-
parser = argparse.ArgumentParser(description='Process some integers.')
|
| 12 |
-
|
| 13 |
-
parser.add_argument('--dataset_name',type=str,default='ffhq',
|
| 14 |
-
help='name of dataset, for example, ffhq')
|
| 15 |
-
|
| 16 |
-
parser.add_argument('--real', action='store_true')
|
| 17 |
-
|
| 18 |
-
args = parser.parse_args()
|
| 19 |
-
dataset_name=args.dataset_name
|
| 20 |
-
|
| 21 |
-
if not os.path.isdir('./data/'+dataset_name):
|
| 22 |
-
os.system('mkdir ./data/'+dataset_name)
|
| 23 |
-
#%%
|
| 24 |
-
M=Manipulator(dataset_name=dataset_name)
|
| 25 |
-
np.set_printoptions(suppress=True)
|
| 26 |
-
print(M.dataset_name)
|
| 27 |
-
#%%
|
| 28 |
-
#remove all .jpg
|
| 29 |
-
names=os.listdir('./data/'+dataset_name+'/')
|
| 30 |
-
for name in names:
|
| 31 |
-
if '.jpg' in name:
|
| 32 |
-
os.system('rm ./data/'+dataset_name+'/'+name)
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
#%%
|
| 36 |
-
if args.real:
|
| 37 |
-
latents=torch.load('./data/'+dataset_name+'/latents.pt')
|
| 38 |
-
w_plus=latents.cpu().detach().numpy()
|
| 39 |
-
else:
|
| 40 |
-
w=np.load('./npy/'+dataset_name+'/W.npy')
|
| 41 |
-
tmp=w[:50] #only use 50 images
|
| 42 |
-
tmp=tmp[:,None,:]
|
| 43 |
-
w_plus=np.tile(tmp,(1,M.Gs.components.synthesis.input_shape[1],1))
|
| 44 |
-
np.save('./data/'+dataset_name+'/w_plus.npy',w_plus)
|
| 45 |
-
|
| 46 |
-
#%%
|
| 47 |
-
tmp=M.W2S(w_plus)
|
| 48 |
-
M.dlatents=tmp
|
| 49 |
-
|
| 50 |
-
M.img_index=0
|
| 51 |
-
M.num_images=len(w_plus)
|
| 52 |
-
M.alpha=[0]
|
| 53 |
-
M.step=1
|
| 54 |
-
lindex,bname=0,0
|
| 55 |
-
|
| 56 |
-
M.manipulate_layers=[lindex]
|
| 57 |
-
codes,out=M.EditOneC(bname)
|
| 58 |
-
#%%
|
| 59 |
-
|
| 60 |
-
for i in range(len(out)):
|
| 61 |
-
img=out[i,0]
|
| 62 |
-
img=Image.fromarray(img)
|
| 63 |
-
img.save('./data/'+dataset_name+'/'+str(i)+'.jpg')
|
| 64 |
-
#%%
|
| 65 |
-
|
| 66 |
-
|
| 67 |
-
|
|
|
|
|
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|
spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/model.py
DELETED
|
@@ -1,689 +0,0 @@
|
|
| 1 |
-
import math
|
| 2 |
-
import random
|
| 3 |
-
import torch
|
| 4 |
-
from torch import nn
|
| 5 |
-
from torch.nn import functional as F
|
| 6 |
-
|
| 7 |
-
from .op.fused_act import FusedLeakyReLU, fused_leaky_relu
|
| 8 |
-
from .op.upfirdn2d import upfirdn2d
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
class PixelNorm(nn.Module):
|
| 12 |
-
def __init__(self):
|
| 13 |
-
super().__init__()
|
| 14 |
-
|
| 15 |
-
def forward(self, input):
|
| 16 |
-
return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
def make_kernel(k):
|
| 20 |
-
k = torch.tensor(k, dtype=torch.float32)
|
| 21 |
-
|
| 22 |
-
if k.ndim == 1:
|
| 23 |
-
k = k[None, :] * k[:, None]
|
| 24 |
-
|
| 25 |
-
k /= k.sum()
|
| 26 |
-
|
| 27 |
-
return k
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
class Upsample(nn.Module):
|
| 31 |
-
def __init__(self, kernel, factor=2):
|
| 32 |
-
super().__init__()
|
| 33 |
-
|
| 34 |
-
self.factor = factor
|
| 35 |
-
kernel = make_kernel(kernel) * (factor ** 2)
|
| 36 |
-
self.register_buffer('kernel', kernel)
|
| 37 |
-
|
| 38 |
-
p = kernel.shape[0] - factor
|
| 39 |
-
|
| 40 |
-
pad0 = (p + 1) // 2 + factor - 1
|
| 41 |
-
pad1 = p // 2
|
| 42 |
-
|
| 43 |
-
self.pad = (pad0, pad1)
|
| 44 |
-
|
| 45 |
-
def forward(self, input):
|
| 46 |
-
out = upfirdn2d(input, self.kernel, up=self.factor,
|
| 47 |
-
down=1, pad=self.pad)
|
| 48 |
-
|
| 49 |
-
return out
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
class Downsample(nn.Module):
|
| 53 |
-
def __init__(self, kernel, factor=2):
|
| 54 |
-
super().__init__()
|
| 55 |
-
|
| 56 |
-
self.factor = factor
|
| 57 |
-
kernel = make_kernel(kernel)
|
| 58 |
-
self.register_buffer('kernel', kernel)
|
| 59 |
-
|
| 60 |
-
p = kernel.shape[0] - factor
|
| 61 |
-
|
| 62 |
-
pad0 = (p + 1) // 2
|
| 63 |
-
pad1 = p // 2
|
| 64 |
-
|
| 65 |
-
self.pad = (pad0, pad1)
|
| 66 |
-
|
| 67 |
-
def forward(self, input):
|
| 68 |
-
out = upfirdn2d(input, self.kernel, up=1,
|
| 69 |
-
down=self.factor, pad=self.pad)
|
| 70 |
-
|
| 71 |
-
return out
|
| 72 |
-
|
| 73 |
-
|
| 74 |
-
class Blur(nn.Module):
|
| 75 |
-
def __init__(self, kernel, pad, upsample_factor=1):
|
| 76 |
-
super().__init__()
|
| 77 |
-
|
| 78 |
-
kernel = make_kernel(kernel)
|
| 79 |
-
|
| 80 |
-
if upsample_factor > 1:
|
| 81 |
-
kernel = kernel * (upsample_factor ** 2)
|
| 82 |
-
|
| 83 |
-
self.register_buffer('kernel', kernel)
|
| 84 |
-
|
| 85 |
-
self.pad = pad
|
| 86 |
-
|
| 87 |
-
def forward(self, input):
|
| 88 |
-
out = upfirdn2d(input, self.kernel, pad=self.pad)
|
| 89 |
-
|
| 90 |
-
return out
|
| 91 |
-
|
| 92 |
-
|
| 93 |
-
class EqualConv2d(nn.Module):
|
| 94 |
-
def __init__(
|
| 95 |
-
self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
|
| 96 |
-
):
|
| 97 |
-
super().__init__()
|
| 98 |
-
|
| 99 |
-
self.weight = nn.Parameter(
|
| 100 |
-
torch.randn(out_channel, in_channel, kernel_size, kernel_size)
|
| 101 |
-
)
|
| 102 |
-
self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
|
| 103 |
-
|
| 104 |
-
self.stride = stride
|
| 105 |
-
self.padding = padding
|
| 106 |
-
|
| 107 |
-
if bias:
|
| 108 |
-
self.bias = nn.Parameter(torch.zeros(out_channel))
|
| 109 |
-
|
| 110 |
-
else:
|
| 111 |
-
self.bias = None
|
| 112 |
-
|
| 113 |
-
def forward(self, input):
|
| 114 |
-
out = F.conv2d(
|
| 115 |
-
input,
|
| 116 |
-
self.weight * self.scale,
|
| 117 |
-
bias=self.bias,
|
| 118 |
-
stride=self.stride,
|
| 119 |
-
padding=self.padding,
|
| 120 |
-
)
|
| 121 |
-
|
| 122 |
-
return out
|
| 123 |
-
|
| 124 |
-
def __repr__(self):
|
| 125 |
-
return (
|
| 126 |
-
f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},'
|
| 127 |
-
f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
|
| 128 |
-
)
|
| 129 |
-
|
| 130 |
-
|
| 131 |
-
class EqualLinear(nn.Module):
|
| 132 |
-
def __init__(
|
| 133 |
-
self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None
|
| 134 |
-
):
|
| 135 |
-
super().__init__()
|
| 136 |
-
|
| 137 |
-
self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
|
| 138 |
-
|
| 139 |
-
if bias:
|
| 140 |
-
self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
|
| 141 |
-
|
| 142 |
-
else:
|
| 143 |
-
self.bias = None
|
| 144 |
-
|
| 145 |
-
self.activation = activation
|
| 146 |
-
|
| 147 |
-
self.scale = (1 / math.sqrt(in_dim)) * lr_mul
|
| 148 |
-
self.lr_mul = lr_mul
|
| 149 |
-
|
| 150 |
-
def forward(self, input):
|
| 151 |
-
if self.activation:
|
| 152 |
-
out = F.linear(input, self.weight * self.scale)
|
| 153 |
-
out = fused_leaky_relu(out, self.bias * self.lr_mul)
|
| 154 |
-
|
| 155 |
-
else:
|
| 156 |
-
out = F.linear(
|
| 157 |
-
input, self.weight * self.scale, bias=self.bias * self.lr_mul
|
| 158 |
-
)
|
| 159 |
-
|
| 160 |
-
return out
|
| 161 |
-
|
| 162 |
-
def __repr__(self):
|
| 163 |
-
return (
|
| 164 |
-
f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})'
|
| 165 |
-
)
|
| 166 |
-
|
| 167 |
-
|
| 168 |
-
class ScaledLeakyReLU(nn.Module):
|
| 169 |
-
def __init__(self, negative_slope=0.2):
|
| 170 |
-
super().__init__()
|
| 171 |
-
|
| 172 |
-
self.negative_slope = negative_slope
|
| 173 |
-
|
| 174 |
-
def forward(self, input):
|
| 175 |
-
out = F.leaky_relu(input, negative_slope=self.negative_slope)
|
| 176 |
-
|
| 177 |
-
return out * math.sqrt(2)
|
| 178 |
-
|
| 179 |
-
|
| 180 |
-
class ModulatedConv2d(nn.Module):
|
| 181 |
-
def __init__(
|
| 182 |
-
self,
|
| 183 |
-
in_channel,
|
| 184 |
-
out_channel,
|
| 185 |
-
kernel_size,
|
| 186 |
-
style_dim,
|
| 187 |
-
demodulate=True,
|
| 188 |
-
upsample=False,
|
| 189 |
-
downsample=False,
|
| 190 |
-
blur_kernel=[1, 3, 3, 1],
|
| 191 |
-
):
|
| 192 |
-
super().__init__()
|
| 193 |
-
|
| 194 |
-
self.eps = 1e-8
|
| 195 |
-
self.kernel_size = kernel_size
|
| 196 |
-
self.in_channel = in_channel
|
| 197 |
-
self.out_channel = out_channel
|
| 198 |
-
self.upsample = upsample
|
| 199 |
-
self.downsample = downsample
|
| 200 |
-
|
| 201 |
-
if upsample:
|
| 202 |
-
factor = 2
|
| 203 |
-
p = (len(blur_kernel) - factor) - (kernel_size - 1)
|
| 204 |
-
pad0 = (p + 1) // 2 + factor - 1
|
| 205 |
-
pad1 = p // 2 + 1
|
| 206 |
-
|
| 207 |
-
self.blur = Blur(blur_kernel, pad=(
|
| 208 |
-
pad0, pad1), upsample_factor=factor)
|
| 209 |
-
|
| 210 |
-
if downsample:
|
| 211 |
-
factor = 2
|
| 212 |
-
p = (len(blur_kernel) - factor) + (kernel_size - 1)
|
| 213 |
-
pad0 = (p + 1) // 2
|
| 214 |
-
pad1 = p // 2
|
| 215 |
-
|
| 216 |
-
self.blur = Blur(blur_kernel, pad=(pad0, pad1))
|
| 217 |
-
|
| 218 |
-
fan_in = in_channel * kernel_size ** 2
|
| 219 |
-
self.scale = 1 / math.sqrt(fan_in)
|
| 220 |
-
self.padding = kernel_size // 2
|
| 221 |
-
|
| 222 |
-
self.weight = nn.Parameter(
|
| 223 |
-
torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
|
| 224 |
-
)
|
| 225 |
-
|
| 226 |
-
self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
|
| 227 |
-
|
| 228 |
-
self.demodulate = demodulate
|
| 229 |
-
|
| 230 |
-
def __repr__(self):
|
| 231 |
-
return (
|
| 232 |
-
f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, '
|
| 233 |
-
f'upsample={self.upsample}, downsample={self.downsample})'
|
| 234 |
-
)
|
| 235 |
-
|
| 236 |
-
def forward(self, input, style):
|
| 237 |
-
batch, in_channel, height, width = input.shape
|
| 238 |
-
|
| 239 |
-
style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
|
| 240 |
-
weight = self.scale * self.weight * style
|
| 241 |
-
|
| 242 |
-
if self.demodulate:
|
| 243 |
-
demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
|
| 244 |
-
weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
|
| 245 |
-
|
| 246 |
-
weight = weight.view(
|
| 247 |
-
batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
|
| 248 |
-
)
|
| 249 |
-
|
| 250 |
-
if self.upsample:
|
| 251 |
-
input = input.view(1, batch * in_channel, height, width)
|
| 252 |
-
weight = weight.view(
|
| 253 |
-
batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
|
| 254 |
-
)
|
| 255 |
-
weight = weight.transpose(1, 2).reshape(
|
| 256 |
-
batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
|
| 257 |
-
)
|
| 258 |
-
out = F.conv_transpose2d(
|
| 259 |
-
input, weight, padding=0, stride=2, groups=batch)
|
| 260 |
-
_, _, height, width = out.shape
|
| 261 |
-
out = out.view(batch, self.out_channel, height, width)
|
| 262 |
-
out = self.blur(out)
|
| 263 |
-
|
| 264 |
-
elif self.downsample:
|
| 265 |
-
input = self.blur(input)
|
| 266 |
-
_, _, height, width = input.shape
|
| 267 |
-
input = input.view(1, batch * in_channel, height, width)
|
| 268 |
-
out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
|
| 269 |
-
_, _, height, width = out.shape
|
| 270 |
-
out = out.view(batch, self.out_channel, height, width)
|
| 271 |
-
|
| 272 |
-
else:
|
| 273 |
-
input = input.view(1, batch * in_channel, height, width)
|
| 274 |
-
out = F.conv2d(input, weight, padding=self.padding, groups=batch)
|
| 275 |
-
_, _, height, width = out.shape
|
| 276 |
-
out = out.view(batch, self.out_channel, height, width)
|
| 277 |
-
|
| 278 |
-
return out
|
| 279 |
-
|
| 280 |
-
|
| 281 |
-
class NoiseInjection(nn.Module):
|
| 282 |
-
def __init__(self):
|
| 283 |
-
super().__init__()
|
| 284 |
-
|
| 285 |
-
self.weight = nn.Parameter(torch.zeros(1))
|
| 286 |
-
|
| 287 |
-
def forward(self, image, noise=None):
|
| 288 |
-
if noise is None:
|
| 289 |
-
batch, _, height, width = image.shape
|
| 290 |
-
noise = image.new_empty(batch, 1, height, width).normal_()
|
| 291 |
-
|
| 292 |
-
return image + self.weight * noise
|
| 293 |
-
|
| 294 |
-
|
| 295 |
-
class ConstantInput(nn.Module):
|
| 296 |
-
def __init__(self, channel, size=4):
|
| 297 |
-
super().__init__()
|
| 298 |
-
|
| 299 |
-
self.input = nn.Parameter(torch.randn(1, channel, size, size // 2))
|
| 300 |
-
|
| 301 |
-
def forward(self, input):
|
| 302 |
-
batch = input.shape[0]
|
| 303 |
-
out = self.input.repeat(batch, 1, 1, 1)
|
| 304 |
-
|
| 305 |
-
return out
|
| 306 |
-
|
| 307 |
-
|
| 308 |
-
class StyledConv(nn.Module):
|
| 309 |
-
def __init__(
|
| 310 |
-
self,
|
| 311 |
-
in_channel,
|
| 312 |
-
out_channel,
|
| 313 |
-
kernel_size,
|
| 314 |
-
style_dim,
|
| 315 |
-
upsample=False,
|
| 316 |
-
blur_kernel=[1, 3, 3, 1],
|
| 317 |
-
demodulate=True,
|
| 318 |
-
):
|
| 319 |
-
super().__init__()
|
| 320 |
-
|
| 321 |
-
self.conv = ModulatedConv2d(
|
| 322 |
-
in_channel,
|
| 323 |
-
out_channel,
|
| 324 |
-
kernel_size,
|
| 325 |
-
style_dim,
|
| 326 |
-
upsample=upsample,
|
| 327 |
-
blur_kernel=blur_kernel,
|
| 328 |
-
demodulate=demodulate,
|
| 329 |
-
)
|
| 330 |
-
|
| 331 |
-
self.noise = NoiseInjection()
|
| 332 |
-
# self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
|
| 333 |
-
# self.activate = ScaledLeakyReLU(0.2)
|
| 334 |
-
self.activate = FusedLeakyReLU(out_channel)
|
| 335 |
-
|
| 336 |
-
def forward(self, input, style, noise=None):
|
| 337 |
-
out = self.conv(input, style)
|
| 338 |
-
out = self.noise(out, noise=noise)
|
| 339 |
-
# out = out + self.bias
|
| 340 |
-
out = self.activate(out)
|
| 341 |
-
|
| 342 |
-
return out
|
| 343 |
-
|
| 344 |
-
|
| 345 |
-
class ToRGB(nn.Module):
|
| 346 |
-
def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
|
| 347 |
-
super().__init__()
|
| 348 |
-
|
| 349 |
-
if upsample:
|
| 350 |
-
self.upsample = Upsample(blur_kernel)
|
| 351 |
-
|
| 352 |
-
self.conv = ModulatedConv2d(
|
| 353 |
-
in_channel, 3, 1, style_dim, demodulate=False)
|
| 354 |
-
self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
|
| 355 |
-
|
| 356 |
-
def forward(self, input, style, skip=None):
|
| 357 |
-
out = self.conv(input, style)
|
| 358 |
-
out = out + self.bias
|
| 359 |
-
|
| 360 |
-
if skip is not None:
|
| 361 |
-
skip = self.upsample(skip)
|
| 362 |
-
|
| 363 |
-
out = out + skip
|
| 364 |
-
|
| 365 |
-
return out
|
| 366 |
-
|
| 367 |
-
|
| 368 |
-
class Generator(nn.Module):
|
| 369 |
-
def __init__(
|
| 370 |
-
self,
|
| 371 |
-
size,
|
| 372 |
-
style_dim,
|
| 373 |
-
n_mlp,
|
| 374 |
-
channel_multiplier=2,
|
| 375 |
-
blur_kernel=[1, 3, 3, 1],
|
| 376 |
-
lr_mlp=0.01,
|
| 377 |
-
):
|
| 378 |
-
super().__init__()
|
| 379 |
-
|
| 380 |
-
self.size = size
|
| 381 |
-
|
| 382 |
-
self.style_dim = style_dim
|
| 383 |
-
|
| 384 |
-
layers = [PixelNorm()]
|
| 385 |
-
|
| 386 |
-
for i in range(n_mlp):
|
| 387 |
-
layers.append(
|
| 388 |
-
EqualLinear(
|
| 389 |
-
style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu'
|
| 390 |
-
)
|
| 391 |
-
)
|
| 392 |
-
|
| 393 |
-
self.style = nn.Sequential(*layers)
|
| 394 |
-
|
| 395 |
-
self.channels = {
|
| 396 |
-
4: 512,
|
| 397 |
-
8: 512,
|
| 398 |
-
16: 512,
|
| 399 |
-
32: 512,
|
| 400 |
-
64: 256 * channel_multiplier,
|
| 401 |
-
128: 128 * channel_multiplier,
|
| 402 |
-
256: 64 * channel_multiplier,
|
| 403 |
-
512: 32 * channel_multiplier,
|
| 404 |
-
1024: 16 * channel_multiplier,
|
| 405 |
-
}
|
| 406 |
-
|
| 407 |
-
self.input = ConstantInput(self.channels[4])
|
| 408 |
-
self.conv1 = StyledConv(
|
| 409 |
-
self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
|
| 410 |
-
)
|
| 411 |
-
self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
|
| 412 |
-
|
| 413 |
-
self.log_size = int(math.log(size, 2))
|
| 414 |
-
self.num_layers = (self.log_size - 2) * 2 + 1
|
| 415 |
-
|
| 416 |
-
self.convs = nn.ModuleList()
|
| 417 |
-
self.upsamples = nn.ModuleList()
|
| 418 |
-
self.to_rgbs = nn.ModuleList()
|
| 419 |
-
self.noises = nn.Module()
|
| 420 |
-
|
| 421 |
-
in_channel = self.channels[4]
|
| 422 |
-
|
| 423 |
-
for layer_idx in range(self.num_layers):
|
| 424 |
-
res = (layer_idx + 5) // 2
|
| 425 |
-
shape = [1, 1, 2 ** res, 2 ** res // 2]
|
| 426 |
-
self.noises.register_buffer(
|
| 427 |
-
"noise_{}".format(layer_idx), torch.randn(*shape)
|
| 428 |
-
)
|
| 429 |
-
|
| 430 |
-
for i in range(3, self.log_size + 1):
|
| 431 |
-
out_channel = self.channels[2 ** i]
|
| 432 |
-
|
| 433 |
-
self.convs.append(
|
| 434 |
-
StyledConv(
|
| 435 |
-
in_channel,
|
| 436 |
-
out_channel,
|
| 437 |
-
3,
|
| 438 |
-
style_dim,
|
| 439 |
-
upsample=True,
|
| 440 |
-
blur_kernel=blur_kernel,
|
| 441 |
-
)
|
| 442 |
-
)
|
| 443 |
-
|
| 444 |
-
self.convs.append(
|
| 445 |
-
StyledConv(
|
| 446 |
-
out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
|
| 447 |
-
)
|
| 448 |
-
)
|
| 449 |
-
|
| 450 |
-
self.to_rgbs.append(ToRGB(out_channel, style_dim))
|
| 451 |
-
|
| 452 |
-
in_channel = out_channel
|
| 453 |
-
|
| 454 |
-
self.n_latent = self.log_size * 2 - 2
|
| 455 |
-
|
| 456 |
-
def make_noise(self):
|
| 457 |
-
device = self.input.input.device
|
| 458 |
-
|
| 459 |
-
noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2 // 2, device=device)]
|
| 460 |
-
|
| 461 |
-
for i in range(3, self.log_size + 1):
|
| 462 |
-
for _ in range(2):
|
| 463 |
-
noises.append(torch.randn(
|
| 464 |
-
1, 1, 2 ** i, 2 ** i // 2, device=device))
|
| 465 |
-
|
| 466 |
-
return noises
|
| 467 |
-
|
| 468 |
-
def mean_latent(self, n_latent):
|
| 469 |
-
latent_in = torch.randn(
|
| 470 |
-
n_latent, self.style_dim, device=self.input.input.device
|
| 471 |
-
)
|
| 472 |
-
latent = self.style(latent_in).mean(0, keepdim=True)
|
| 473 |
-
|
| 474 |
-
return latent
|
| 475 |
-
|
| 476 |
-
def get_latent(self, input):
|
| 477 |
-
return self.style(input)
|
| 478 |
-
|
| 479 |
-
def forward(
|
| 480 |
-
self,
|
| 481 |
-
styles,
|
| 482 |
-
return_latents=False,
|
| 483 |
-
return_features=False,
|
| 484 |
-
inject_index=None,
|
| 485 |
-
truncation=1,
|
| 486 |
-
truncation_latent=None,
|
| 487 |
-
input_is_latent=False,
|
| 488 |
-
noise=None,
|
| 489 |
-
randomize_noise=True,
|
| 490 |
-
):
|
| 491 |
-
if not input_is_latent:
|
| 492 |
-
styles = [self.style(s) for s in styles]
|
| 493 |
-
|
| 494 |
-
if noise is None:
|
| 495 |
-
if randomize_noise:
|
| 496 |
-
noise = [None] * self.num_layers
|
| 497 |
-
else:
|
| 498 |
-
noise = [
|
| 499 |
-
getattr(self.noises, f'noise_{i}') for i in range(self.num_layers)
|
| 500 |
-
]
|
| 501 |
-
|
| 502 |
-
if truncation < 1:
|
| 503 |
-
style_t = []
|
| 504 |
-
|
| 505 |
-
for style in styles:
|
| 506 |
-
style_t.append(
|
| 507 |
-
truncation_latent + truncation *
|
| 508 |
-
(style - truncation_latent)
|
| 509 |
-
)
|
| 510 |
-
|
| 511 |
-
styles = style_t
|
| 512 |
-
|
| 513 |
-
if len(styles) < 2:
|
| 514 |
-
inject_index = self.n_latent
|
| 515 |
-
if styles[0].ndim < 3:
|
| 516 |
-
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
|
| 517 |
-
else:
|
| 518 |
-
latent = styles[0]
|
| 519 |
-
|
| 520 |
-
else:
|
| 521 |
-
if inject_index is None:
|
| 522 |
-
inject_index = random.randint(1, self.n_latent - 1)
|
| 523 |
-
|
| 524 |
-
# latent = styles[0].unsqueeze(0)
|
| 525 |
-
# if latent.shape[1] == 1:
|
| 526 |
-
# latent = latent.repeat(1, inject_index, 1)
|
| 527 |
-
# else:
|
| 528 |
-
# latent = latent[:, :inject_index, :]
|
| 529 |
-
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
|
| 530 |
-
latent2 = styles[1].unsqueeze(1).repeat(
|
| 531 |
-
1, self.n_latent - inject_index, 1)
|
| 532 |
-
# latent = styles[0][:, :inject_index, :]
|
| 533 |
-
# latent2 = styles[1][:, inject_index:, :]
|
| 534 |
-
latent = torch.cat([latent, latent2], 1)
|
| 535 |
-
out = self.input(latent)
|
| 536 |
-
out = self.conv1(out, latent[:, 0], noise=noise[0])
|
| 537 |
-
|
| 538 |
-
skip = self.to_rgb1(out, latent[:, 1])
|
| 539 |
-
|
| 540 |
-
i = 1
|
| 541 |
-
for conv1, conv2, noise1, noise2, to_rgb in zip(
|
| 542 |
-
self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
|
| 543 |
-
):
|
| 544 |
-
out = conv1(out, latent[:, i], noise=noise1)
|
| 545 |
-
out = conv2(out, latent[:, i + 1], noise=noise2)
|
| 546 |
-
skip = to_rgb(out, latent[:, i + 2], skip)
|
| 547 |
-
|
| 548 |
-
i += 2
|
| 549 |
-
|
| 550 |
-
image = skip
|
| 551 |
-
|
| 552 |
-
if return_latents:
|
| 553 |
-
return image, latent
|
| 554 |
-
elif return_features:
|
| 555 |
-
return image, out
|
| 556 |
-
else:
|
| 557 |
-
return image, None
|
| 558 |
-
|
| 559 |
-
|
| 560 |
-
class ConvLayer(nn.Sequential):
|
| 561 |
-
def __init__(
|
| 562 |
-
self,
|
| 563 |
-
in_channel,
|
| 564 |
-
out_channel,
|
| 565 |
-
kernel_size,
|
| 566 |
-
downsample=False,
|
| 567 |
-
blur_kernel=[1, 3, 3, 1],
|
| 568 |
-
bias=True,
|
| 569 |
-
activate=True,
|
| 570 |
-
):
|
| 571 |
-
layers = []
|
| 572 |
-
|
| 573 |
-
if downsample:
|
| 574 |
-
factor = 2
|
| 575 |
-
p = (len(blur_kernel) - factor) + (kernel_size - 1)
|
| 576 |
-
pad0 = (p + 1) // 2
|
| 577 |
-
pad1 = p // 2
|
| 578 |
-
|
| 579 |
-
layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
|
| 580 |
-
|
| 581 |
-
stride = 2
|
| 582 |
-
self.padding = 0
|
| 583 |
-
|
| 584 |
-
else:
|
| 585 |
-
stride = 1
|
| 586 |
-
self.padding = kernel_size // 2
|
| 587 |
-
|
| 588 |
-
layers.append(
|
| 589 |
-
EqualConv2d(
|
| 590 |
-
in_channel,
|
| 591 |
-
out_channel,
|
| 592 |
-
kernel_size,
|
| 593 |
-
padding=self.padding,
|
| 594 |
-
stride=stride,
|
| 595 |
-
bias=bias and not activate,
|
| 596 |
-
)
|
| 597 |
-
)
|
| 598 |
-
|
| 599 |
-
if activate:
|
| 600 |
-
if bias:
|
| 601 |
-
layers.append(FusedLeakyReLU(out_channel))
|
| 602 |
-
|
| 603 |
-
else:
|
| 604 |
-
layers.append(ScaledLeakyReLU(0.2))
|
| 605 |
-
|
| 606 |
-
super().__init__(*layers)
|
| 607 |
-
|
| 608 |
-
|
| 609 |
-
class ResBlock(nn.Module):
|
| 610 |
-
def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
|
| 611 |
-
super().__init__()
|
| 612 |
-
|
| 613 |
-
self.conv1 = ConvLayer(in_channel, in_channel, 3)
|
| 614 |
-
self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
|
| 615 |
-
|
| 616 |
-
self.skip = ConvLayer(
|
| 617 |
-
in_channel, out_channel, 1, downsample=True, activate=False, bias=False
|
| 618 |
-
)
|
| 619 |
-
|
| 620 |
-
def forward(self, input):
|
| 621 |
-
out = self.conv1(input)
|
| 622 |
-
out = self.conv2(out)
|
| 623 |
-
|
| 624 |
-
skip = self.skip(input)
|
| 625 |
-
out = (out + skip) / math.sqrt(2)
|
| 626 |
-
|
| 627 |
-
return out
|
| 628 |
-
|
| 629 |
-
|
| 630 |
-
class Discriminator(nn.Module):
|
| 631 |
-
def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]):
|
| 632 |
-
super().__init__()
|
| 633 |
-
|
| 634 |
-
channels = {
|
| 635 |
-
4: 512,
|
| 636 |
-
8: 512,
|
| 637 |
-
16: 512,
|
| 638 |
-
32: 512,
|
| 639 |
-
64: 256 * channel_multiplier,
|
| 640 |
-
128: 128 * channel_multiplier,
|
| 641 |
-
256: 64 * channel_multiplier,
|
| 642 |
-
512: 32 * channel_multiplier,
|
| 643 |
-
1024: 16 * channel_multiplier,
|
| 644 |
-
}
|
| 645 |
-
|
| 646 |
-
convs = [ConvLayer(3, channels[size], 1)]
|
| 647 |
-
|
| 648 |
-
log_size = int(math.log(size, 2))
|
| 649 |
-
|
| 650 |
-
in_channel = channels[size]
|
| 651 |
-
|
| 652 |
-
for i in range(log_size, 2, -1):
|
| 653 |
-
out_channel = channels[2 ** (i - 1)]
|
| 654 |
-
|
| 655 |
-
convs.append(ResBlock(in_channel, out_channel, blur_kernel))
|
| 656 |
-
|
| 657 |
-
in_channel = out_channel
|
| 658 |
-
|
| 659 |
-
self.convs = nn.Sequential(*convs)
|
| 660 |
-
|
| 661 |
-
self.stddev_group = 4
|
| 662 |
-
self.stddev_feat = 1
|
| 663 |
-
|
| 664 |
-
self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
|
| 665 |
-
self.final_linear = nn.Sequential(
|
| 666 |
-
EqualLinear(channels[4] * 4 * 4 // 2,
|
| 667 |
-
channels[4], activation='fused_lrelu'),
|
| 668 |
-
EqualLinear(channels[4], 1),
|
| 669 |
-
)
|
| 670 |
-
|
| 671 |
-
def forward(self, input):
|
| 672 |
-
out = self.convs(input)
|
| 673 |
-
|
| 674 |
-
batch, channel, height, width = out.shape
|
| 675 |
-
group = min(batch, self.stddev_group)
|
| 676 |
-
stddev = out.view(
|
| 677 |
-
group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
|
| 678 |
-
)
|
| 679 |
-
stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
|
| 680 |
-
stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
|
| 681 |
-
stddev = stddev.repeat(group, 1, height, width)
|
| 682 |
-
out = torch.cat([out, stddev], 1)
|
| 683 |
-
|
| 684 |
-
out = self.final_conv(out)
|
| 685 |
-
|
| 686 |
-
out = out.view(batch, -1)
|
| 687 |
-
out = self.final_linear(out)
|
| 688 |
-
|
| 689 |
-
return out
|
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|
spaces/Amrrs/DragGan-Inversion/stylegan_human/training_scripts/sg2/train.py
DELETED
|
@@ -1,589 +0,0 @@
|
|
| 1 |
-
# Copyright (c) SenseTime Research. All rights reserved.
|
| 2 |
-
|
| 3 |
-
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
|
| 4 |
-
#
|
| 5 |
-
# NVIDIA CORPORATION and its licensors retain all intellectual property
|
| 6 |
-
# and proprietary rights in and to this software, related documentation
|
| 7 |
-
# and any modifications thereto. Any use, reproduction, disclosure or
|
| 8 |
-
# distribution of this software and related documentation without an express
|
| 9 |
-
# license agreement from NVIDIA CORPORATION is strictly prohibited.
|
| 10 |
-
|
| 11 |
-
"""Train a GAN using the techniques described in the paper
|
| 12 |
-
"Training Generative Adversarial Networks with Limited Data"."""
|
| 13 |
-
|
| 14 |
-
import os
|
| 15 |
-
import click
|
| 16 |
-
import re
|
| 17 |
-
import json
|
| 18 |
-
import tempfile
|
| 19 |
-
import torch
|
| 20 |
-
import dnnlib
|
| 21 |
-
|
| 22 |
-
import ast
|
| 23 |
-
from training import training_loop
|
| 24 |
-
from metrics import metric_main
|
| 25 |
-
from torch_utils import training_stats
|
| 26 |
-
from torch_utils import custom_ops
|
| 27 |
-
|
| 28 |
-
# ----------------------------------------------------------------------------
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
class UserError(Exception):
|
| 32 |
-
pass
|
| 33 |
-
|
| 34 |
-
# ----------------------------------------------------------------------------
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
def setup_training_loop_kwargs(
|
| 38 |
-
# General options (not included in desc).
|
| 39 |
-
gpus=None, # Number of GPUs: <int>, default = 1 gpu
|
| 40 |
-
snap=None, # Snapshot interval: <int>, default = 50 ticks
|
| 41 |
-
metrics=None, # List of metric names: [], ['fid50k_full'] (default), ...
|
| 42 |
-
seed=None, # Random seed: <int>, default = 0
|
| 43 |
-
|
| 44 |
-
# Dataset.
|
| 45 |
-
data=None, # Training dataset (required): <path>
|
| 46 |
-
cond=None, # Train conditional model based on dataset labels: <bool>, default = False
|
| 47 |
-
subset=None, # Train with only N images: <int>, default = all
|
| 48 |
-
mirror=None, # Augment dataset with x-flips: <bool>, default = False
|
| 49 |
-
square=None,
|
| 50 |
-
|
| 51 |
-
# Base config.
|
| 52 |
-
# Base config: 'auto' (default), 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar', 'shhq'
|
| 53 |
-
cfg=None,
|
| 54 |
-
gamma=None, # Override R1 gamma: <float>
|
| 55 |
-
kimg=None, # Override training duration: <int>
|
| 56 |
-
batch=None, # Override batch size: <int>
|
| 57 |
-
|
| 58 |
-
# Discriminator augmentation.
|
| 59 |
-
aug=None, # Augmentation mode: 'ada' (default), 'noaug', 'fixed'
|
| 60 |
-
p=None, # Specify p for 'fixed' (required): <float>
|
| 61 |
-
target=None, # Override ADA target for 'ada': <float>, default = depends on aug
|
| 62 |
-
# Augmentation pipeline: 'blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc' (default), ..., 'bgcfnc'
|
| 63 |
-
augpipe=None,
|
| 64 |
-
|
| 65 |
-
# Transfer learning.
|
| 66 |
-
# Load previous network: 'noresume' (default), 'ffhq256', 'ffhq512', 'ffhq1024', 'celebahq256', 'lsundog256', <file>, <url>
|
| 67 |
-
resume=None,
|
| 68 |
-
freezed=None, # Freeze-D: <int>, default = 0 discriminator layers
|
| 69 |
-
|
| 70 |
-
# Performance options (not included in desc).
|
| 71 |
-
fp32=None, # Disable mixed-precision training: <bool>, default = False
|
| 72 |
-
nhwc=None, # Use NHWC memory format with FP16: <bool>, default = False
|
| 73 |
-
# Allow PyTorch to use TF32 for matmul and convolutions: <bool>, default = False
|
| 74 |
-
allow_tf32=None,
|
| 75 |
-
nobench=None, # Disable cuDNN benchmarking: <bool>, default = False
|
| 76 |
-
workers=None, # Override number of DataLoader workers: <int>, default = 3
|
| 77 |
-
|
| 78 |
-
):
|
| 79 |
-
args = dnnlib.EasyDict()
|
| 80 |
-
|
| 81 |
-
# ------------------------------------------
|
| 82 |
-
# General options: gpus, snap, metrics, seed
|
| 83 |
-
# ------------------------------------------
|
| 84 |
-
|
| 85 |
-
if gpus is None:
|
| 86 |
-
gpus = 1
|
| 87 |
-
assert isinstance(gpus, int)
|
| 88 |
-
if not (gpus >= 1 and gpus & (gpus - 1) == 0):
|
| 89 |
-
raise UserError('--gpus must be a power of two')
|
| 90 |
-
args.num_gpus = gpus
|
| 91 |
-
|
| 92 |
-
if snap is None:
|
| 93 |
-
snap = 50
|
| 94 |
-
assert isinstance(snap, int)
|
| 95 |
-
if snap < 1:
|
| 96 |
-
raise UserError('--snap must be at least 1')
|
| 97 |
-
args.image_snapshot_ticks = snap
|
| 98 |
-
args.network_snapshot_ticks = snap
|
| 99 |
-
|
| 100 |
-
if metrics is None:
|
| 101 |
-
metrics = ['fid50k_full']
|
| 102 |
-
assert isinstance(metrics, list)
|
| 103 |
-
if not all(metric_main.is_valid_metric(metric) for metric in metrics):
|
| 104 |
-
raise UserError('\n'.join(
|
| 105 |
-
['--metrics can only contain the following values:'] + metric_main.list_valid_metrics()))
|
| 106 |
-
args.metrics = metrics
|
| 107 |
-
|
| 108 |
-
if seed is None:
|
| 109 |
-
seed = 0
|
| 110 |
-
assert isinstance(seed, int)
|
| 111 |
-
args.random_seed = seed
|
| 112 |
-
|
| 113 |
-
# -------------------------------------------
|
| 114 |
-
# Dataset: data, cond, subset, mirror, square
|
| 115 |
-
# -------------------------------------------
|
| 116 |
-
|
| 117 |
-
print('square : ', square)
|
| 118 |
-
|
| 119 |
-
assert data is not None
|
| 120 |
-
assert isinstance(data, str)
|
| 121 |
-
|
| 122 |
-
args.training_set_kwargs = dnnlib.EasyDict(
|
| 123 |
-
class_name='training.dataset.ImageFolderDataset', path=data, use_labels=True, max_size=None, xflip=False, square=square)
|
| 124 |
-
args.data_loader_kwargs = dnnlib.EasyDict(
|
| 125 |
-
pin_memory=True, num_workers=3, prefetch_factor=2)
|
| 126 |
-
try:
|
| 127 |
-
training_set = dnnlib.util.construct_class_by_name(
|
| 128 |
-
**args.training_set_kwargs) # subclass of training.dataset.Dataset
|
| 129 |
-
# be explicit about resolution
|
| 130 |
-
args.training_set_kwargs.resolution = training_set.resolution
|
| 131 |
-
# be explicit about labels
|
| 132 |
-
args.training_set_kwargs.use_labels = training_set.has_labels
|
| 133 |
-
args.training_set_kwargs.max_size = len(
|
| 134 |
-
training_set) # be explicit about dataset size
|
| 135 |
-
desc = training_set.name
|
| 136 |
-
print('desc: ', desc)
|
| 137 |
-
del training_set # conserve memory
|
| 138 |
-
except IOError as err:
|
| 139 |
-
raise UserError(f'--data: {err}')
|
| 140 |
-
|
| 141 |
-
if square:
|
| 142 |
-
desc += '-square'
|
| 143 |
-
else:
|
| 144 |
-
desc += '-rectangle'
|
| 145 |
-
|
| 146 |
-
if cond is None:
|
| 147 |
-
cond = False
|
| 148 |
-
assert isinstance(cond, bool)
|
| 149 |
-
if cond:
|
| 150 |
-
if not args.training_set_kwargs.use_labels:
|
| 151 |
-
raise UserError(
|
| 152 |
-
'--cond=True requires labels specified in dataset.json')
|
| 153 |
-
desc += '-cond'
|
| 154 |
-
else:
|
| 155 |
-
args.training_set_kwargs.use_labels = False
|
| 156 |
-
|
| 157 |
-
if subset is not None:
|
| 158 |
-
assert isinstance(subset, int)
|
| 159 |
-
if not 1 <= subset <= args.training_set_kwargs.max_size:
|
| 160 |
-
raise UserError(
|
| 161 |
-
f'--subset must be between 1 and {args.training_set_kwargs.max_size}')
|
| 162 |
-
desc += f'-subset{subset}'
|
| 163 |
-
if subset < args.training_set_kwargs.max_size:
|
| 164 |
-
args.training_set_kwargs.max_size = subset
|
| 165 |
-
args.training_set_kwargs.random_seed = args.random_seed
|
| 166 |
-
|
| 167 |
-
if mirror is None:
|
| 168 |
-
mirror = False
|
| 169 |
-
assert isinstance(mirror, bool)
|
| 170 |
-
if mirror:
|
| 171 |
-
desc += '-mirror'
|
| 172 |
-
args.training_set_kwargs.xflip = True
|
| 173 |
-
|
| 174 |
-
# ------------------------------------
|
| 175 |
-
# Base config: cfg, gamma, kimg, batch
|
| 176 |
-
# ------------------------------------
|
| 177 |
-
|
| 178 |
-
if cfg is None:
|
| 179 |
-
cfg = 'auto'
|
| 180 |
-
assert isinstance(cfg, str)
|
| 181 |
-
desc += f'-{cfg}'
|
| 182 |
-
|
| 183 |
-
cfg_specs = {
|
| 184 |
-
'auto': dict(ref_gpus=-1, kimg=25000, mb=-1, mbstd=-1, fmaps=-1, lrate=-1, gamma=-1, ema=-1, ramp=0.05, map=2),
|
| 185 |
-
# Populated dynamically based on resolution and GPU count.
|
| 186 |
-
'shhq': dict(ref_gpus=-1, kimg=25000, mb=-1, mbstd=-1, fmaps=-1, lrate=-1, gamma=-1, ema=-1, ramp=0.05, map=8),
|
| 187 |
-
# Uses mixed-precision, unlike the original StyleGAN2.
|
| 188 |
-
'stylegan2': dict(ref_gpus=8, kimg=25000, mb=32, mbstd=4, fmaps=1, lrate=0.002, gamma=10, ema=10, ramp=None, map=8),
|
| 189 |
-
'paper256': dict(ref_gpus=8, kimg=25000, mb=64, mbstd=8, fmaps=0.5, lrate=0.0025, gamma=1, ema=20, ramp=None, map=8),
|
| 190 |
-
'paper512': dict(ref_gpus=8, kimg=25000, mb=64, mbstd=8, fmaps=1, lrate=0.0025, gamma=0.5, ema=20, ramp=None, map=8),
|
| 191 |
-
'paper1024': dict(ref_gpus=8, kimg=25000, mb=32, mbstd=4, fmaps=1, lrate=0.002, gamma=2, ema=10, ramp=None, map=8),
|
| 192 |
-
'cifar': dict(ref_gpus=2, kimg=100000, mb=64, mbstd=32, fmaps=1, lrate=0.0025, gamma=0.01, ema=500, ramp=0.05, map=2),
|
| 193 |
-
}
|
| 194 |
-
|
| 195 |
-
assert cfg in cfg_specs
|
| 196 |
-
spec = dnnlib.EasyDict(cfg_specs[cfg])
|
| 197 |
-
if cfg == 'auto' or cfg == 'shhq':
|
| 198 |
-
desc += f'{gpus:d}'
|
| 199 |
-
spec.ref_gpus = gpus
|
| 200 |
-
res = args.training_set_kwargs.resolution
|
| 201 |
-
# keep gpu memory consumption at bay
|
| 202 |
-
spec.mb = max(min(gpus * min(4096 // res, 32), 64), gpus)
|
| 203 |
-
# other hyperparams behave more predictably if mbstd group size remains fixed
|
| 204 |
-
spec.mbstd = min(spec.mb // gpus, 4)
|
| 205 |
-
spec.fmaps = 1 if res >= 512 else 0.5
|
| 206 |
-
spec.lrate = 0.002 if res >= 1024 else 0.0025
|
| 207 |
-
spec.gamma = 0.0002 * (res ** 2) / spec.mb # heuristic formula
|
| 208 |
-
spec.ema = spec.mb * 10 / 32
|
| 209 |
-
|
| 210 |
-
args.G_kwargs = dnnlib.EasyDict(class_name='training.networks.Generator', z_dim=512, w_dim=512,
|
| 211 |
-
mapping_kwargs=dnnlib.EasyDict(), synthesis_kwargs=dnnlib.EasyDict(), square=square)
|
| 212 |
-
args.D_kwargs = dnnlib.EasyDict(class_name='training.networks.Discriminator', block_kwargs=dnnlib.EasyDict(
|
| 213 |
-
), mapping_kwargs=dnnlib.EasyDict(), epilogue_kwargs=dnnlib.EasyDict(), square=square)
|
| 214 |
-
args.G_kwargs.synthesis_kwargs.channel_base = args.D_kwargs.channel_base = int(
|
| 215 |
-
spec.fmaps * 32768)
|
| 216 |
-
args.G_kwargs.synthesis_kwargs.channel_max = args.D_kwargs.channel_max = 512
|
| 217 |
-
args.G_kwargs.mapping_kwargs.num_layers = spec.map
|
| 218 |
-
# enable mixed-precision training
|
| 219 |
-
args.G_kwargs.synthesis_kwargs.num_fp16_res = args.D_kwargs.num_fp16_res = 4
|
| 220 |
-
# clamp activations to avoid float16 overflow
|
| 221 |
-
args.G_kwargs.synthesis_kwargs.conv_clamp = args.D_kwargs.conv_clamp = 256
|
| 222 |
-
args.D_kwargs.epilogue_kwargs.mbstd_group_size = spec.mbstd
|
| 223 |
-
|
| 224 |
-
args.G_opt_kwargs = dnnlib.EasyDict(
|
| 225 |
-
class_name='torch.optim.Adam', lr=spec.lrate, betas=[0, 0.99], eps=1e-8)
|
| 226 |
-
args.D_opt_kwargs = dnnlib.EasyDict(
|
| 227 |
-
class_name='torch.optim.Adam', lr=spec.lrate, betas=[0, 0.99], eps=1e-8)
|
| 228 |
-
args.loss_kwargs = dnnlib.EasyDict(
|
| 229 |
-
class_name='training.loss.StyleGAN2Loss', r1_gamma=spec.gamma)
|
| 230 |
-
|
| 231 |
-
args.total_kimg = spec.kimg
|
| 232 |
-
args.batch_size = spec.mb
|
| 233 |
-
args.batch_gpu = spec.mb // spec.ref_gpus
|
| 234 |
-
args.ema_kimg = spec.ema
|
| 235 |
-
args.ema_rampup = spec.ramp
|
| 236 |
-
|
| 237 |
-
if cfg == 'cifar':
|
| 238 |
-
args.loss_kwargs.pl_weight = 0 # disable path length regularization
|
| 239 |
-
args.loss_kwargs.style_mixing_prob = 0 # disable style mixing
|
| 240 |
-
args.D_kwargs.architecture = 'orig' # disable residual skip connections
|
| 241 |
-
|
| 242 |
-
if gamma is not None:
|
| 243 |
-
assert isinstance(gamma, float)
|
| 244 |
-
if not gamma >= 0:
|
| 245 |
-
raise UserError('--gamma must be non-negative')
|
| 246 |
-
desc += f'-gamma{gamma:g}'
|
| 247 |
-
args.loss_kwargs.r1_gamma = gamma
|
| 248 |
-
|
| 249 |
-
if kimg is not None:
|
| 250 |
-
assert isinstance(kimg, int)
|
| 251 |
-
if not kimg >= 1:
|
| 252 |
-
raise UserError('--kimg must be at least 1')
|
| 253 |
-
desc += f'-kimg{kimg:d}'
|
| 254 |
-
args.total_kimg = kimg
|
| 255 |
-
|
| 256 |
-
if batch is not None:
|
| 257 |
-
assert isinstance(batch, int)
|
| 258 |
-
if not (batch >= 1 and batch % gpus == 0):
|
| 259 |
-
raise UserError(
|
| 260 |
-
'--batch must be at least 1 and divisible by --gpus')
|
| 261 |
-
desc += f'-batch{batch}'
|
| 262 |
-
args.batch_size = batch
|
| 263 |
-
args.batch_gpu = batch // gpus
|
| 264 |
-
|
| 265 |
-
# ---------------------------------------------------
|
| 266 |
-
# Discriminator augmentation: aug, p, target, augpipe
|
| 267 |
-
# ---------------------------------------------------
|
| 268 |
-
|
| 269 |
-
if aug is None:
|
| 270 |
-
aug = 'ada'
|
| 271 |
-
else:
|
| 272 |
-
assert isinstance(aug, str)
|
| 273 |
-
desc += f'-{aug}'
|
| 274 |
-
|
| 275 |
-
if aug == 'ada':
|
| 276 |
-
args.ada_target = 0.6
|
| 277 |
-
|
| 278 |
-
elif aug == 'noaug':
|
| 279 |
-
pass
|
| 280 |
-
|
| 281 |
-
elif aug == 'fixed':
|
| 282 |
-
if p is None:
|
| 283 |
-
raise UserError(f'--aug={aug} requires specifying --p')
|
| 284 |
-
|
| 285 |
-
else:
|
| 286 |
-
raise UserError(f'--aug={aug} not supported')
|
| 287 |
-
|
| 288 |
-
if p is not None:
|
| 289 |
-
assert isinstance(p, float)
|
| 290 |
-
if aug != 'fixed':
|
| 291 |
-
raise UserError('--p can only be specified with --aug=fixed')
|
| 292 |
-
if not 0 <= p <= 1:
|
| 293 |
-
raise UserError('--p must be between 0 and 1')
|
| 294 |
-
desc += f'-p{p:g}'
|
| 295 |
-
args.augment_p = p
|
| 296 |
-
|
| 297 |
-
if target is not None:
|
| 298 |
-
assert isinstance(target, float)
|
| 299 |
-
if aug != 'ada':
|
| 300 |
-
raise UserError('--target can only be specified with --aug=ada')
|
| 301 |
-
if not 0 <= target <= 1:
|
| 302 |
-
raise UserError('--target must be between 0 and 1')
|
| 303 |
-
desc += f'-target{target:g}'
|
| 304 |
-
args.ada_target = target
|
| 305 |
-
|
| 306 |
-
assert augpipe is None or isinstance(augpipe, str)
|
| 307 |
-
if augpipe is None:
|
| 308 |
-
augpipe = 'bgc'
|
| 309 |
-
else:
|
| 310 |
-
if aug == 'noaug':
|
| 311 |
-
raise UserError('--augpipe cannot be specified with --aug=noaug')
|
| 312 |
-
desc += f'-{augpipe}'
|
| 313 |
-
|
| 314 |
-
augpipe_specs = {
|
| 315 |
-
'blit': dict(xflip=1, rotate90=1, xint=1),
|
| 316 |
-
'geom': dict(scale=1, rotate=1, aniso=1, xfrac=1),
|
| 317 |
-
'color': dict(brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1),
|
| 318 |
-
'filter': dict(imgfilter=1),
|
| 319 |
-
'noise': dict(noise=1),
|
| 320 |
-
'cutout': dict(cutout=1),
|
| 321 |
-
'bg': dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1),
|
| 322 |
-
'bgc': dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1),
|
| 323 |
-
'bgcf': dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1, imgfilter=1),
|
| 324 |
-
'bgcfn': dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1, imgfilter=1, noise=1),
|
| 325 |
-
'bgcfnc': dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1, imgfilter=1, noise=1, cutout=1),
|
| 326 |
-
'body': dict(xflip=1, rotate90=0, xint=1, scale=1, rotate=0, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1)
|
| 327 |
-
}
|
| 328 |
-
|
| 329 |
-
assert augpipe in augpipe_specs
|
| 330 |
-
if aug != 'noaug':
|
| 331 |
-
args.augment_kwargs = dnnlib.EasyDict(
|
| 332 |
-
class_name='training.augment.AugmentPipe', **augpipe_specs[augpipe])
|
| 333 |
-
|
| 334 |
-
# ----------------------------------
|
| 335 |
-
# Transfer learning: resume, freezed
|
| 336 |
-
# ----------------------------------
|
| 337 |
-
|
| 338 |
-
resume_specs = {
|
| 339 |
-
'ffhq256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res256-mirror-paper256-noaug.pkl',
|
| 340 |
-
'ffhq512': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res512-mirror-stylegan2-noaug.pkl',
|
| 341 |
-
'ffhq1024': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res1024-mirror-stylegan2-noaug.pkl',
|
| 342 |
-
'celebahq256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/celebahq-res256-mirror-paper256-kimg100000-ada-target0.5.pkl',
|
| 343 |
-
'lsundog256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/lsundog-res256-paper256-kimg100000-noaug.pkl',
|
| 344 |
-
}
|
| 345 |
-
|
| 346 |
-
assert resume is None or isinstance(resume, str)
|
| 347 |
-
if resume is None:
|
| 348 |
-
resume = 'noresume'
|
| 349 |
-
elif resume == 'noresume':
|
| 350 |
-
desc += '-noresume'
|
| 351 |
-
elif resume in resume_specs:
|
| 352 |
-
desc += f'-resume{resume}'
|
| 353 |
-
args.resume_pkl = resume_specs[resume] # predefined url
|
| 354 |
-
else:
|
| 355 |
-
desc += '-resumecustom'
|
| 356 |
-
args.resume_pkl = resume # custom path or url
|
| 357 |
-
|
| 358 |
-
if resume != 'noresume':
|
| 359 |
-
args.ada_kimg = 100 # make ADA react faster at the beginning
|
| 360 |
-
args.ema_rampup = None # disable EMA rampup
|
| 361 |
-
|
| 362 |
-
if freezed is not None:
|
| 363 |
-
assert isinstance(freezed, int)
|
| 364 |
-
if not freezed >= 0:
|
| 365 |
-
raise UserError('--freezed must be non-negative')
|
| 366 |
-
desc += f'-freezed{freezed:d}'
|
| 367 |
-
args.D_kwargs.block_kwargs.freeze_layers = freezed
|
| 368 |
-
|
| 369 |
-
# -------------------------------------------------
|
| 370 |
-
# Performance options: fp32, nhwc, nobench, workers
|
| 371 |
-
# -------------------------------------------------
|
| 372 |
-
|
| 373 |
-
if fp32 is None:
|
| 374 |
-
fp32 = False
|
| 375 |
-
assert isinstance(fp32, bool)
|
| 376 |
-
if fp32:
|
| 377 |
-
args.G_kwargs.synthesis_kwargs.num_fp16_res = args.D_kwargs.num_fp16_res = 0
|
| 378 |
-
args.G_kwargs.synthesis_kwargs.conv_clamp = args.D_kwargs.conv_clamp = None
|
| 379 |
-
|
| 380 |
-
if nhwc is None:
|
| 381 |
-
nhwc = False
|
| 382 |
-
assert isinstance(nhwc, bool)
|
| 383 |
-
if nhwc:
|
| 384 |
-
args.G_kwargs.synthesis_kwargs.fp16_channels_last = args.D_kwargs.block_kwargs.fp16_channels_last = True
|
| 385 |
-
|
| 386 |
-
if nobench is None:
|
| 387 |
-
nobench = False
|
| 388 |
-
assert isinstance(nobench, bool)
|
| 389 |
-
if nobench:
|
| 390 |
-
args.cudnn_benchmark = False
|
| 391 |
-
|
| 392 |
-
if allow_tf32 is None:
|
| 393 |
-
allow_tf32 = False
|
| 394 |
-
assert isinstance(allow_tf32, bool)
|
| 395 |
-
if allow_tf32:
|
| 396 |
-
args.allow_tf32 = True
|
| 397 |
-
|
| 398 |
-
if workers is not None:
|
| 399 |
-
assert isinstance(workers, int)
|
| 400 |
-
if not workers >= 1:
|
| 401 |
-
raise UserError('--workers must be at least 1')
|
| 402 |
-
args.data_loader_kwargs.num_workers = workers
|
| 403 |
-
|
| 404 |
-
return desc, args
|
| 405 |
-
|
| 406 |
-
# ----------------------------------------------------------------------------
|
| 407 |
-
|
| 408 |
-
|
| 409 |
-
def subprocess_fn(rank, args, temp_dir):
|
| 410 |
-
dnnlib.util.Logger(file_name=os.path.join(
|
| 411 |
-
args.run_dir, 'log.txt'), file_mode='a', should_flush=True)
|
| 412 |
-
|
| 413 |
-
# Init torch.distributed.
|
| 414 |
-
if args.num_gpus > 1:
|
| 415 |
-
init_file = os.path.abspath(os.path.join(
|
| 416 |
-
temp_dir, '.torch_distributed_init'))
|
| 417 |
-
if os.name == 'nt':
|
| 418 |
-
init_method = 'file:///' + init_file.replace('\\', '/')
|
| 419 |
-
torch.distributed.init_process_group(
|
| 420 |
-
backend='gloo', init_method=init_method, rank=rank, world_size=args.num_gpus)
|
| 421 |
-
else:
|
| 422 |
-
init_method = f'file://{init_file}'
|
| 423 |
-
torch.distributed.init_process_group(
|
| 424 |
-
backend='nccl', init_method=init_method, rank=rank, world_size=args.num_gpus)
|
| 425 |
-
|
| 426 |
-
# Init torch_utils.
|
| 427 |
-
sync_device = torch.device('cuda', rank) if args.num_gpus > 1 else None
|
| 428 |
-
training_stats.init_multiprocessing(rank=rank, sync_device=sync_device)
|
| 429 |
-
if rank != 0:
|
| 430 |
-
custom_ops.verbosity = 'none'
|
| 431 |
-
|
| 432 |
-
# Execute training loop.
|
| 433 |
-
training_loop.training_loop(rank=rank, **args)
|
| 434 |
-
|
| 435 |
-
# ----------------------------------------------------------------------------
|
| 436 |
-
|
| 437 |
-
|
| 438 |
-
class CommaSeparatedList(click.ParamType):
|
| 439 |
-
name = 'list'
|
| 440 |
-
|
| 441 |
-
def convert(self, value, param, ctx):
|
| 442 |
-
_ = param, ctx
|
| 443 |
-
if value is None or value.lower() == 'none' or value == '':
|
| 444 |
-
return []
|
| 445 |
-
return value.split(',')
|
| 446 |
-
|
| 447 |
-
# ----------------------------------------------------------------------------
|
| 448 |
-
|
| 449 |
-
|
| 450 |
-
@click.command()
|
| 451 |
-
@click.pass_context
|
| 452 |
-
# General options.
|
| 453 |
-
@click.option('--outdir', help='Where to save the results', required=True, metavar='DIR')
|
| 454 |
-
@click.option('--gpus', help='Number of GPUs to use [default: 1]', type=int, metavar='INT')
|
| 455 |
-
@click.option('--snap', help='Snapshot interval [default: 50 ticks]', type=int, metavar='INT')
|
| 456 |
-
@click.option('--metrics', help='Comma-separated list or "none" [default: fid50k_full]', type=CommaSeparatedList())
|
| 457 |
-
@click.option('--seed', help='Random seed [default: 0]', type=int, metavar='INT')
|
| 458 |
-
@click.option('-n', '--dry-run', help='Print training options and exit', is_flag=True)
|
| 459 |
-
# Dataset.
|
| 460 |
-
@click.option('--data', help='Training data (directory or zip)', metavar='PATH', required=True)
|
| 461 |
-
@click.option('--cond', help='Train conditional model based on dataset labels [default: false]', type=bool, metavar='BOOL')
|
| 462 |
-
@click.option('--subset', help='Train with only N images [default: all]', type=int, metavar='INT')
|
| 463 |
-
@click.option('--mirror', help='Enable dataset x-flips [default: false]', type=bool, metavar='BOOL')
|
| 464 |
-
@click.option('--square', help='True for square, False for rectangle', type=bool, metavar='BOOL', default=False)
|
| 465 |
-
# Base config.
|
| 466 |
-
@click.option('--cfg', help='Base config [default: auto]', type=click.Choice(['auto', 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar', 'shhq']))
|
| 467 |
-
@click.option('--gamma', help='Override R1 gamma', type=float)
|
| 468 |
-
@click.option('--kimg', help='Override training duration', type=int, metavar='INT')
|
| 469 |
-
@click.option('--batch', help='Override batch size', type=int, metavar='INT')
|
| 470 |
-
# Discriminator augmentation.
|
| 471 |
-
@click.option('--aug', help='Augmentation mode [default: ada]', type=click.Choice(['noaug', 'ada', 'fixed']))
|
| 472 |
-
@click.option('--p', help='Augmentation probability for --aug=fixed', type=float)
|
| 473 |
-
@click.option('--target', help='ADA target value for --aug=ada', type=float)
|
| 474 |
-
@click.option('--augpipe', help='Augmentation pipeline [default: bgc]', type=click.Choice(['blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc', 'bgcf', 'bgcfn', 'bgcfnc', 'body']))
|
| 475 |
-
# Transfer learning.
|
| 476 |
-
@click.option('--resume', help='Resume training [default: noresume]', metavar='PKL')
|
| 477 |
-
@click.option('--freezed', help='Freeze-D [default: 0 layers]', type=int, metavar='INT')
|
| 478 |
-
# Performance options.
|
| 479 |
-
@click.option('--fp32', help='Disable mixed-precision training', type=bool, metavar='BOOL')
|
| 480 |
-
@click.option('--nhwc', help='Use NHWC memory format with FP16', type=bool, metavar='BOOL')
|
| 481 |
-
@click.option('--nobench', help='Disable cuDNN benchmarking', type=bool, metavar='BOOL')
|
| 482 |
-
@click.option('--allow-tf32', help='Allow PyTorch to use TF32 internally', type=bool, metavar='BOOL')
|
| 483 |
-
@click.option('--workers', help='Override number of DataLoader workers', type=int, metavar='INT')
|
| 484 |
-
def main(ctx, outdir, dry_run, **config_kwargs):
|
| 485 |
-
"""Train a GAN using the techniques described in the paper
|
| 486 |
-
"Training Generative Adversarial Networks with Limited Data".
|
| 487 |
-
|
| 488 |
-
Examples:
|
| 489 |
-
|
| 490 |
-
\b
|
| 491 |
-
# Train with custom dataset using 1 GPU.
|
| 492 |
-
python train.py --outdir=~/training-runs --data=~/mydataset.zip --gpus=1
|
| 493 |
-
|
| 494 |
-
\b
|
| 495 |
-
# Train class-conditional CIFAR-10 using 2 GPUs.
|
| 496 |
-
python train.py --outdir=~/training-runs --data=~/datasets/cifar10.zip \\
|
| 497 |
-
--gpus=2 --cfg=cifar --cond=1
|
| 498 |
-
|
| 499 |
-
\b
|
| 500 |
-
# Transfer learn MetFaces from FFHQ using 4 GPUs.
|
| 501 |
-
python train.py --outdir=~/training-runs --data=~/datasets/metfaces.zip \\
|
| 502 |
-
--gpus=4 --cfg=paper1024 --mirror=1 --resume=ffhq1024 --snap=10
|
| 503 |
-
|
| 504 |
-
\b
|
| 505 |
-
# Reproduce original StyleGAN2 config F.
|
| 506 |
-
python train.py --outdir=~/training-runs --data=~/datasets/ffhq.zip \\
|
| 507 |
-
--gpus=8 --cfg=stylegan2 --mirror=1 --aug=noaug
|
| 508 |
-
|
| 509 |
-
\b
|
| 510 |
-
Base configs (--cfg):
|
| 511 |
-
auto Automatically select reasonable defaults based on resolution
|
| 512 |
-
and GPU count. Good starting point for new datasets.
|
| 513 |
-
stylegan2 Reproduce results for StyleGAN2 config F at 1024x1024.
|
| 514 |
-
paper256 Reproduce results for FFHQ and LSUN Cat at 256x256.
|
| 515 |
-
paper512 Reproduce results for BreCaHAD and AFHQ at 512x512.
|
| 516 |
-
paper1024 Reproduce results for MetFaces at 1024x1024.
|
| 517 |
-
cifar Reproduce results for CIFAR-10 at 32x32.
|
| 518 |
-
|
| 519 |
-
\b
|
| 520 |
-
Transfer learning source networks (--resume):
|
| 521 |
-
ffhq256 FFHQ trained at 256x256 resolution.
|
| 522 |
-
ffhq512 FFHQ trained at 512x512 resolution.
|
| 523 |
-
ffhq1024 FFHQ trained at 1024x1024 resolution.
|
| 524 |
-
celebahq256 CelebA-HQ trained at 256x256 resolution.
|
| 525 |
-
lsundog256 LSUN Dog trained at 256x256 resolution.
|
| 526 |
-
<PATH or URL> Custom network pickle.
|
| 527 |
-
"""
|
| 528 |
-
dnnlib.util.Logger(should_flush=True)
|
| 529 |
-
|
| 530 |
-
# Setup training options.
|
| 531 |
-
try:
|
| 532 |
-
run_desc, args = setup_training_loop_kwargs(**config_kwargs)
|
| 533 |
-
except UserError as err:
|
| 534 |
-
ctx.fail(err)
|
| 535 |
-
|
| 536 |
-
# Pick output directory.
|
| 537 |
-
prev_run_dirs = []
|
| 538 |
-
if os.path.isdir(outdir):
|
| 539 |
-
prev_run_dirs = [x for x in os.listdir(
|
| 540 |
-
outdir) if os.path.isdir(os.path.join(outdir, x))]
|
| 541 |
-
prev_run_ids = [re.match(r'^\d+', x) for x in prev_run_dirs]
|
| 542 |
-
prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None]
|
| 543 |
-
cur_run_id = max(prev_run_ids, default=-1) + 1
|
| 544 |
-
args.run_dir = os.path.join(outdir, f'{cur_run_id:05d}-{run_desc}')
|
| 545 |
-
assert not os.path.exists(args.run_dir)
|
| 546 |
-
|
| 547 |
-
# Print options.
|
| 548 |
-
print()
|
| 549 |
-
print('Training options:')
|
| 550 |
-
print(json.dumps(args, indent=2))
|
| 551 |
-
print()
|
| 552 |
-
print(f'Output directory: {args.run_dir}')
|
| 553 |
-
print(f'Training data: {args.training_set_kwargs.path}')
|
| 554 |
-
print(f'Training duration: {args.total_kimg} kimg')
|
| 555 |
-
print(f'Number of GPUs: {args.num_gpus}')
|
| 556 |
-
print(f'Number of images: {args.training_set_kwargs.max_size}')
|
| 557 |
-
print(f'Image resolution: {args.training_set_kwargs.resolution}')
|
| 558 |
-
print(f'Conditional model: {args.training_set_kwargs.use_labels}')
|
| 559 |
-
print(f'Dataset x-flips: {args.training_set_kwargs.xflip}')
|
| 560 |
-
print()
|
| 561 |
-
|
| 562 |
-
# Dry run?
|
| 563 |
-
if dry_run:
|
| 564 |
-
print('Dry run; exiting.')
|
| 565 |
-
return
|
| 566 |
-
|
| 567 |
-
# Create output directory.
|
| 568 |
-
print('Creating output directory...')
|
| 569 |
-
os.makedirs(args.run_dir, exist_ok=True)
|
| 570 |
-
with open(os.path.join(args.run_dir, 'training_options.json'), 'wt') as f:
|
| 571 |
-
json.dump(args, f, indent=2)
|
| 572 |
-
|
| 573 |
-
# Launch processes.
|
| 574 |
-
print('Launching processes...')
|
| 575 |
-
torch.multiprocessing.set_start_method('spawn')
|
| 576 |
-
with tempfile.TemporaryDirectory() as temp_dir:
|
| 577 |
-
if args.num_gpus == 1:
|
| 578 |
-
subprocess_fn(rank=0, args=args, temp_dir=temp_dir)
|
| 579 |
-
else:
|
| 580 |
-
torch.multiprocessing.spawn(fn=subprocess_fn, args=(
|
| 581 |
-
args, temp_dir), nprocs=args.num_gpus)
|
| 582 |
-
|
| 583 |
-
# ----------------------------------------------------------------------------
|
| 584 |
-
|
| 585 |
-
|
| 586 |
-
if __name__ == "__main__":
|
| 587 |
-
main() # pylint: disable=no-value-for-parameter
|
| 588 |
-
|
| 589 |
-
# ----------------------------------------------------------------------------
|
|
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spaces/Amrrs/DragGan-Inversion/torch_utils/pti.py
DELETED
|
@@ -1,89 +0,0 @@
|
|
| 1 |
-
import pickle
|
| 2 |
-
from PTI.utils.ImagesDataset import ImagesDataset, Image2Dataset
|
| 3 |
-
import torch
|
| 4 |
-
from PTI.utils.models_utils import load_old_G
|
| 5 |
-
from PTI.utils.alignment import align_face
|
| 6 |
-
|
| 7 |
-
from PTI.training.coaches.single_id_coach import SingleIDCoach
|
| 8 |
-
from PTI.configs import global_config, paths_config
|
| 9 |
-
import dlib
|
| 10 |
-
|
| 11 |
-
import os
|
| 12 |
-
from torchvision.transforms import transforms
|
| 13 |
-
from torch.utils.data import DataLoader
|
| 14 |
-
from string import ascii_uppercase
|
| 15 |
-
import sys
|
| 16 |
-
from pathlib import Path
|
| 17 |
-
|
| 18 |
-
sys.path.append(".")
|
| 19 |
-
# sys.path.append('PTI/')
|
| 20 |
-
# sys.path.append('PTI/training/')
|
| 21 |
-
|
| 22 |
-
|
| 23 |
-
def run_PTI(img, run_name):
|
| 24 |
-
# os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
|
| 25 |
-
# os.environ['CUDA_VISIBLE_DEVICES'] = global_config.cuda_visible_devices
|
| 26 |
-
|
| 27 |
-
global_config.run_name = run_name
|
| 28 |
-
|
| 29 |
-
global_config.pivotal_training_steps = 1
|
| 30 |
-
global_config.training_step = 1
|
| 31 |
-
|
| 32 |
-
embedding_dir_path = f"{paths_config.embedding_base_dir}/{paths_config.input_data_id}/{paths_config.pti_results_keyword}"
|
| 33 |
-
os.makedirs(embedding_dir_path, exist_ok=True)
|
| 34 |
-
|
| 35 |
-
# dataset = ImagesDataset(paths_config.input_data_path, transforms.Compose([
|
| 36 |
-
# transforms.ToTensor(),
|
| 37 |
-
# transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]))
|
| 38 |
-
|
| 39 |
-
G = load_old_G()
|
| 40 |
-
IMAGE_SIZE = 1024
|
| 41 |
-
predictor = dlib.shape_predictor(paths_config.dlib)
|
| 42 |
-
aligned_image = align_face(img, predictor=predictor, output_size=IMAGE_SIZE)
|
| 43 |
-
img = aligned_image.resize([G.img_resolution, G.img_resolution])
|
| 44 |
-
dataset = Image2Dataset(img)
|
| 45 |
-
|
| 46 |
-
dataloader = DataLoader(dataset, batch_size=1, shuffle=False)
|
| 47 |
-
|
| 48 |
-
coach = SingleIDCoach(dataloader, use_wandb=False)
|
| 49 |
-
|
| 50 |
-
new_G, w_pivot = coach.train()
|
| 51 |
-
return new_G, w_pivot
|
| 52 |
-
|
| 53 |
-
|
| 54 |
-
def export_updated_pickle(new_G, out_path, run_name):
|
| 55 |
-
image_name = "customIMG"
|
| 56 |
-
|
| 57 |
-
with open(paths_config.stylegan2_ada_ffhq, "rb") as f:
|
| 58 |
-
old_G = pickle.load(f)["G_ema"].cuda()
|
| 59 |
-
|
| 60 |
-
embedding = Path(f"{paths_config.checkpoints_dir}/model_{run_name}_{image_name}.pt")
|
| 61 |
-
with open(embedding, "rb") as f_new:
|
| 62 |
-
new_G = torch.load(f_new).cuda()
|
| 63 |
-
|
| 64 |
-
print("Exporting large updated pickle based off new generator and ffhq.pkl")
|
| 65 |
-
with open(paths_config.stylegan2_ada_ffhq, "rb") as f:
|
| 66 |
-
d = pickle.load(f)
|
| 67 |
-
old_G = d["G_ema"].cuda() # tensor
|
| 68 |
-
old_D = d["D"].eval().requires_grad_(False).cpu()
|
| 69 |
-
|
| 70 |
-
tmp = {}
|
| 71 |
-
tmp["G"] = old_G.eval().requires_grad_(False).cpu()
|
| 72 |
-
tmp["G_ema"] = new_G.eval().requires_grad_(False).cpu()
|
| 73 |
-
tmp["D"] = old_D
|
| 74 |
-
tmp["training_set_kwargs"] = None
|
| 75 |
-
tmp["augment_pipe"] = None
|
| 76 |
-
|
| 77 |
-
with open(out_path, "wb") as f:
|
| 78 |
-
pickle.dump(tmp, f)
|
| 79 |
-
# delete
|
| 80 |
-
|
| 81 |
-
embedding.unlink()
|
| 82 |
-
|
| 83 |
-
|
| 84 |
-
# if __name__ == '__main__':
|
| 85 |
-
# from PIL import Image
|
| 86 |
-
# img = Image.open('PTI/test/test.jpg')
|
| 87 |
-
# new_G, w_pivot = run_PTI(img, use_wandb=False, use_multi_id_training=False)
|
| 88 |
-
# out_path = f'checkpoints/stylegan2_custom_512_pytorch.pkl'
|
| 89 |
-
# export_updated_pickle(new_G, out_path)
|
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|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/onnxruntime/textual_inversion/README.md
DELETED
|
@@ -1,94 +0,0 @@
|
|
| 1 |
-
## Textual Inversion fine-tuning example
|
| 2 |
-
|
| 3 |
-
[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.
|
| 4 |
-
The `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion.
|
| 5 |
-
|
| 6 |
-
## Running on Colab
|
| 7 |
-
|
| 8 |
-
Colab for training
|
| 9 |
-
[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)
|
| 10 |
-
|
| 11 |
-
Colab for inference
|
| 12 |
-
[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb)
|
| 13 |
-
|
| 14 |
-
## Running locally with PyTorch
|
| 15 |
-
### Installing the dependencies
|
| 16 |
-
|
| 17 |
-
Before running the scripts, make sure to install the library's training dependencies:
|
| 18 |
-
|
| 19 |
-
**Important**
|
| 20 |
-
|
| 21 |
-
To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
|
| 22 |
-
```bash
|
| 23 |
-
git clone https://github.com/huggingface/diffusers
|
| 24 |
-
cd diffusers
|
| 25 |
-
pip install .
|
| 26 |
-
```
|
| 27 |
-
|
| 28 |
-
Then cd in the example folder and run
|
| 29 |
-
```bash
|
| 30 |
-
pip install -r requirements.txt
|
| 31 |
-
```
|
| 32 |
-
|
| 33 |
-
And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
|
| 34 |
-
|
| 35 |
-
```bash
|
| 36 |
-
accelerate config
|
| 37 |
-
```
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
### Cat toy example
|
| 41 |
-
|
| 42 |
-
You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-5`, so you'll need to visit [its card](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license and tick the checkbox if you agree.
|
| 43 |
-
|
| 44 |
-
You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
|
| 45 |
-
|
| 46 |
-
Run the following command to authenticate your token
|
| 47 |
-
|
| 48 |
-
```bash
|
| 49 |
-
huggingface-cli login
|
| 50 |
-
```
|
| 51 |
-
|
| 52 |
-
If you have already cloned the repo, then you won't need to go through these steps.
|
| 53 |
-
|
| 54 |
-
<br>
|
| 55 |
-
|
| 56 |
-
Now let's get our dataset. For this example we will use some cat images: https://huggingface.co/datasets/diffusers/cat_toy_example .
|
| 57 |
-
|
| 58 |
-
Let's first download it locally:
|
| 59 |
-
|
| 60 |
-
```py
|
| 61 |
-
from huggingface_hub import snapshot_download
|
| 62 |
-
|
| 63 |
-
local_dir = "./cat"
|
| 64 |
-
snapshot_download("diffusers/cat_toy_example", local_dir=local_dir, repo_type="dataset", ignore_patterns=".gitattributes")
|
| 65 |
-
```
|
| 66 |
-
|
| 67 |
-
This will be our training data.
|
| 68 |
-
Now we can launch the training using
|
| 69 |
-
|
| 70 |
-
## Use ONNXRuntime to accelerate training
|
| 71 |
-
In order to leverage onnxruntime to accelerate training, please use textual_inversion.py
|
| 72 |
-
|
| 73 |
-
The command to train on custom data with onnxruntime:
|
| 74 |
-
|
| 75 |
-
```bash
|
| 76 |
-
export MODEL_NAME="runwayml/stable-diffusion-v1-5"
|
| 77 |
-
export DATA_DIR="path-to-dir-containing-images"
|
| 78 |
-
|
| 79 |
-
accelerate launch textual_inversion.py \
|
| 80 |
-
--pretrained_model_name_or_path=$MODEL_NAME \
|
| 81 |
-
--train_data_dir=$DATA_DIR \
|
| 82 |
-
--learnable_property="object" \
|
| 83 |
-
--placeholder_token="<cat-toy>" --initializer_token="toy" \
|
| 84 |
-
--resolution=512 \
|
| 85 |
-
--train_batch_size=1 \
|
| 86 |
-
--gradient_accumulation_steps=4 \
|
| 87 |
-
--max_train_steps=3000 \
|
| 88 |
-
--learning_rate=5.0e-04 --scale_lr \
|
| 89 |
-
--lr_scheduler="constant" \
|
| 90 |
-
--lr_warmup_steps=0 \
|
| 91 |
-
--output_dir="textual_inversion_cat"
|
| 92 |
-
```
|
| 93 |
-
|
| 94 |
-
Please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.
|
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|
spaces/Andy1621/uniformer_image_detection/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
DELETED
|
@@ -1,11 +0,0 @@
|
|
| 1 |
-
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
|
| 2 |
-
model = dict(
|
| 3 |
-
backbone=dict(
|
| 4 |
-
norm_cfg=dict(type='SyncBN', requires_grad=True),
|
| 5 |
-
norm_eval=False,
|
| 6 |
-
plugins=[
|
| 7 |
-
dict(
|
| 8 |
-
cfg=dict(type='ContextBlock', ratio=1. / 16),
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stages=(False, True, True, True),
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position='after_conv3')
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]))
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spaces/Andy1621/uniformer_image_detection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py
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| 1 |
-
_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
|
| 2 |
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img_norm_cfg = dict(
|
| 3 |
-
mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)
|
| 4 |
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train_pipeline = [
|
| 5 |
-
dict(type='LoadImageFromFile'),
|
| 6 |
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dict(type='LoadAnnotations', with_bbox=True),
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| 7 |
-
dict(
|
| 8 |
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type='Resize',
|
| 9 |
-
img_scale=[(1333, 640), (1333, 800)],
|
| 10 |
-
multiscale_mode='value',
|
| 11 |
-
keep_ratio=True),
|
| 12 |
-
dict(type='RandomFlip', flip_ratio=0.5),
|
| 13 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 14 |
-
dict(type='Pad', size_divisor=32),
|
| 15 |
-
dict(type='DefaultFormatBundle'),
|
| 16 |
-
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
|
| 17 |
-
]
|
| 18 |
-
test_pipeline = [
|
| 19 |
-
dict(type='LoadImageFromFile'),
|
| 20 |
-
dict(
|
| 21 |
-
type='MultiScaleFlipAug',
|
| 22 |
-
img_scale=(1333, 800),
|
| 23 |
-
flip=False,
|
| 24 |
-
transforms=[
|
| 25 |
-
dict(type='Resize', keep_ratio=True),
|
| 26 |
-
dict(type='RandomFlip'),
|
| 27 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 28 |
-
dict(type='Pad', size_divisor=32),
|
| 29 |
-
dict(type='ImageToTensor', keys=['img']),
|
| 30 |
-
dict(type='Collect', keys=['img']),
|
| 31 |
-
])
|
| 32 |
-
]
|
| 33 |
-
data = dict(
|
| 34 |
-
train=dict(pipeline=train_pipeline),
|
| 35 |
-
val=dict(pipeline=test_pipeline),
|
| 36 |
-
test=dict(pipeline=test_pipeline))
|
| 37 |
-
# learning policy
|
| 38 |
-
lr_config = dict(step=[16, 22])
|
| 39 |
-
runner = dict(type='EpochBasedRunner', max_epochs=24)
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spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/ann_r50-d8.py
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@@ -1,46 +0,0 @@
|
|
| 1 |
-
# model settings
|
| 2 |
-
norm_cfg = dict(type='SyncBN', requires_grad=True)
|
| 3 |
-
model = dict(
|
| 4 |
-
type='EncoderDecoder',
|
| 5 |
-
pretrained='open-mmlab://resnet50_v1c',
|
| 6 |
-
backbone=dict(
|
| 7 |
-
type='ResNetV1c',
|
| 8 |
-
depth=50,
|
| 9 |
-
num_stages=4,
|
| 10 |
-
out_indices=(0, 1, 2, 3),
|
| 11 |
-
dilations=(1, 1, 2, 4),
|
| 12 |
-
strides=(1, 2, 1, 1),
|
| 13 |
-
norm_cfg=norm_cfg,
|
| 14 |
-
norm_eval=False,
|
| 15 |
-
style='pytorch',
|
| 16 |
-
contract_dilation=True),
|
| 17 |
-
decode_head=dict(
|
| 18 |
-
type='ANNHead',
|
| 19 |
-
in_channels=[1024, 2048],
|
| 20 |
-
in_index=[2, 3],
|
| 21 |
-
channels=512,
|
| 22 |
-
project_channels=256,
|
| 23 |
-
query_scales=(1, ),
|
| 24 |
-
key_pool_scales=(1, 3, 6, 8),
|
| 25 |
-
dropout_ratio=0.1,
|
| 26 |
-
num_classes=19,
|
| 27 |
-
norm_cfg=norm_cfg,
|
| 28 |
-
align_corners=False,
|
| 29 |
-
loss_decode=dict(
|
| 30 |
-
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
|
| 31 |
-
auxiliary_head=dict(
|
| 32 |
-
type='FCNHead',
|
| 33 |
-
in_channels=1024,
|
| 34 |
-
in_index=2,
|
| 35 |
-
channels=256,
|
| 36 |
-
num_convs=1,
|
| 37 |
-
concat_input=False,
|
| 38 |
-
dropout_ratio=0.1,
|
| 39 |
-
num_classes=19,
|
| 40 |
-
norm_cfg=norm_cfg,
|
| 41 |
-
align_corners=False,
|
| 42 |
-
loss_decode=dict(
|
| 43 |
-
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
|
| 44 |
-
# model training and testing settings
|
| 45 |
-
train_cfg=dict(),
|
| 46 |
-
test_cfg=dict(mode='whole'))
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spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py
DELETED
|
@@ -1,9 +0,0 @@
|
|
| 1 |
-
_base_ = './fcn_hr18_512x512_80k_ade20k.py'
|
| 2 |
-
model = dict(
|
| 3 |
-
pretrained='open-mmlab://msra/hrnetv2_w18_small',
|
| 4 |
-
backbone=dict(
|
| 5 |
-
extra=dict(
|
| 6 |
-
stage1=dict(num_blocks=(2, )),
|
| 7 |
-
stage2=dict(num_blocks=(2, 2)),
|
| 8 |
-
stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
|
| 9 |
-
stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
|
|
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|
spaces/AnimalEquality/chatbot/_proc/_docs/edamam_api.html
DELETED
|
@@ -1,432 +0,0 @@
|
|
| 1 |
-
<!DOCTYPE html>
|
| 2 |
-
<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
|
| 3 |
-
|
| 4 |
-
<meta charset="utf-8">
|
| 5 |
-
<meta name="generator" content="quarto-1.3.361">
|
| 6 |
-
|
| 7 |
-
<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
|
| 8 |
-
|
| 9 |
-
<meta name="description" content="Tinkering with the ">
|
| 10 |
-
|
| 11 |
-
<title>lv-recipe-chatbot - edamam_api</title>
|
| 12 |
-
<style>
|
| 13 |
-
code{white-space: pre-wrap;}
|
| 14 |
-
span.smallcaps{font-variant: small-caps;}
|
| 15 |
-
div.columns{display: flex; gap: min(4vw, 1.5em);}
|
| 16 |
-
div.column{flex: auto; overflow-x: auto;}
|
| 17 |
-
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
|
| 18 |
-
ul.task-list{list-style: none;}
|
| 19 |
-
ul.task-list li input[type="checkbox"] {
|
| 20 |
-
width: 0.8em;
|
| 21 |
-
margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */
|
| 22 |
-
vertical-align: middle;
|
| 23 |
-
}
|
| 24 |
-
</style>
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
<script src="site_libs/quarto-nav/quarto-nav.js"></script>
|
| 28 |
-
<script src="site_libs/quarto-nav/headroom.min.js"></script>
|
| 29 |
-
<script src="site_libs/clipboard/clipboard.min.js"></script>
|
| 30 |
-
<script src="site_libs/quarto-search/autocomplete.umd.js"></script>
|
| 31 |
-
<script src="site_libs/quarto-search/fuse.min.js"></script>
|
| 32 |
-
<script src="site_libs/quarto-search/quarto-search.js"></script>
|
| 33 |
-
<meta name="quarto:offset" content="./">
|
| 34 |
-
<script src="site_libs/quarto-html/quarto.js"></script>
|
| 35 |
-
<script src="site_libs/quarto-html/popper.min.js"></script>
|
| 36 |
-
<script src="site_libs/quarto-html/tippy.umd.min.js"></script>
|
| 37 |
-
<script src="site_libs/quarto-html/anchor.min.js"></script>
|
| 38 |
-
<link href="site_libs/quarto-html/tippy.css" rel="stylesheet">
|
| 39 |
-
<link href="site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles">
|
| 40 |
-
<script src="site_libs/bootstrap/bootstrap.min.js"></script>
|
| 41 |
-
<link href="site_libs/bootstrap/bootstrap-icons.css" rel="stylesheet">
|
| 42 |
-
<link href="site_libs/bootstrap/bootstrap.min.css" rel="stylesheet" id="quarto-bootstrap" data-mode="light">
|
| 43 |
-
<script id="quarto-search-options" type="application/json">{
|
| 44 |
-
"location": "navbar",
|
| 45 |
-
"copy-button": false,
|
| 46 |
-
"collapse-after": 3,
|
| 47 |
-
"panel-placement": "end",
|
| 48 |
-
"type": "overlay",
|
| 49 |
-
"limit": 20,
|
| 50 |
-
"language": {
|
| 51 |
-
"search-no-results-text": "No results",
|
| 52 |
-
"search-matching-documents-text": "matching documents",
|
| 53 |
-
"search-copy-link-title": "Copy link to search",
|
| 54 |
-
"search-hide-matches-text": "Hide additional matches",
|
| 55 |
-
"search-more-match-text": "more match in this document",
|
| 56 |
-
"search-more-matches-text": "more matches in this document",
|
| 57 |
-
"search-clear-button-title": "Clear",
|
| 58 |
-
"search-detached-cancel-button-title": "Cancel",
|
| 59 |
-
"search-submit-button-title": "Submit",
|
| 60 |
-
"search-label": "Search"
|
| 61 |
-
}
|
| 62 |
-
}</script>
|
| 63 |
-
|
| 64 |
-
|
| 65 |
-
<link rel="stylesheet" href="styles.css">
|
| 66 |
-
<meta property="og:title" content="lv-recipe-chatbot - edamam_api">
|
| 67 |
-
<meta property="og:description" content="Tinkering with the ">
|
| 68 |
-
<meta property="og:site-name" content="lv-recipe-chatbot">
|
| 69 |
-
<meta name="twitter:title" content="lv-recipe-chatbot - edamam_api">
|
| 70 |
-
<meta name="twitter:description" content="Tinkering with the ">
|
| 71 |
-
<meta name="twitter:card" content="summary">
|
| 72 |
-
</head>
|
| 73 |
-
|
| 74 |
-
<body class="nav-sidebar floating nav-fixed">
|
| 75 |
-
|
| 76 |
-
<div id="quarto-search-results"></div>
|
| 77 |
-
<header id="quarto-header" class="headroom fixed-top">
|
| 78 |
-
<nav class="navbar navbar-expand-lg navbar-dark ">
|
| 79 |
-
<div class="navbar-container container-fluid">
|
| 80 |
-
<div class="navbar-brand-container">
|
| 81 |
-
<a class="navbar-brand" href="./index.html">
|
| 82 |
-
<span class="navbar-title">lv-recipe-chatbot</span>
|
| 83 |
-
</a>
|
| 84 |
-
</div>
|
| 85 |
-
<div class="quarto-navbar-tools ms-auto">
|
| 86 |
-
</div>
|
| 87 |
-
<div id="quarto-search" class="" title="Search"></div>
|
| 88 |
-
</div> <!-- /container-fluid -->
|
| 89 |
-
</nav>
|
| 90 |
-
<nav class="quarto-secondary-nav">
|
| 91 |
-
<div class="container-fluid d-flex">
|
| 92 |
-
<button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
|
| 93 |
-
<i class="bi bi-layout-text-sidebar-reverse"></i>
|
| 94 |
-
</button>
|
| 95 |
-
<nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./edamam_api.html">edamam_api</a></li></ol></nav>
|
| 96 |
-
<a class="flex-grow-1" role="button" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
|
| 97 |
-
</a>
|
| 98 |
-
</div>
|
| 99 |
-
</nav>
|
| 100 |
-
</header>
|
| 101 |
-
<!-- content -->
|
| 102 |
-
<div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
|
| 103 |
-
<!-- sidebar -->
|
| 104 |
-
<nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
|
| 105 |
-
<div class="sidebar-menu-container">
|
| 106 |
-
<ul class="list-unstyled mt-1">
|
| 107 |
-
<li class="sidebar-item">
|
| 108 |
-
<div class="sidebar-item-container">
|
| 109 |
-
<a href="./index.html" class="sidebar-item-text sidebar-link">
|
| 110 |
-
<span class="menu-text">lv-recipe-chatbot</span></a>
|
| 111 |
-
</div>
|
| 112 |
-
</li>
|
| 113 |
-
<li class="sidebar-item">
|
| 114 |
-
<div class="sidebar-item-container">
|
| 115 |
-
<a href="./engineer_prompt.html" class="sidebar-item-text sidebar-link">
|
| 116 |
-
<span class="menu-text">engineer_prompt</span></a>
|
| 117 |
-
</div>
|
| 118 |
-
</li>
|
| 119 |
-
<li class="sidebar-item">
|
| 120 |
-
<div class="sidebar-item-container">
|
| 121 |
-
<a href="./app.html" class="sidebar-item-text sidebar-link">
|
| 122 |
-
<span class="menu-text">app</span></a>
|
| 123 |
-
</div>
|
| 124 |
-
</li>
|
| 125 |
-
<li class="sidebar-item">
|
| 126 |
-
<div class="sidebar-item-container">
|
| 127 |
-
<a href="./lchain_tool.html" class="sidebar-item-text sidebar-link">
|
| 128 |
-
<span class="menu-text">lchain_tool</span></a>
|
| 129 |
-
</div>
|
| 130 |
-
</li>
|
| 131 |
-
<li class="sidebar-item">
|
| 132 |
-
<div class="sidebar-item-container">
|
| 133 |
-
<a href="./ingredient_vision.html" class="sidebar-item-text sidebar-link">
|
| 134 |
-
<span class="menu-text">ingredient_vision</span></a>
|
| 135 |
-
</div>
|
| 136 |
-
</li>
|
| 137 |
-
<li class="sidebar-item">
|
| 138 |
-
<div class="sidebar-item-container">
|
| 139 |
-
<a href="./edamam_api.html" class="sidebar-item-text sidebar-link active">
|
| 140 |
-
<span class="menu-text">edamam_api</span></a>
|
| 141 |
-
</div>
|
| 142 |
-
</li>
|
| 143 |
-
</ul>
|
| 144 |
-
</div>
|
| 145 |
-
</nav>
|
| 146 |
-
<div id="quarto-sidebar-glass" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass"></div>
|
| 147 |
-
<!-- margin-sidebar -->
|
| 148 |
-
<div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
|
| 149 |
-
<nav id="TOC" role="doc-toc" class="toc-active">
|
| 150 |
-
<h2 id="toc-title">On this page</h2>
|
| 151 |
-
|
| 152 |
-
<ul>
|
| 153 |
-
<li><a href="#foo" id="toc-foo" class="nav-link active" data-scroll-target="#foo">foo</a></li>
|
| 154 |
-
</ul>
|
| 155 |
-
<div class="toc-actions"><div><i class="bi bi-git"></i></div><div class="action-links"><p><a href="https://gitlab.com/animalequality/lv-recipe-chatbot/issues/new" class="toc-action">Report an issue</a></p></div></div></nav>
|
| 156 |
-
</div>
|
| 157 |
-
<!-- main -->
|
| 158 |
-
<main class="content" id="quarto-document-content">
|
| 159 |
-
|
| 160 |
-
<header id="title-block-header" class="quarto-title-block default">
|
| 161 |
-
<div class="quarto-title">
|
| 162 |
-
<h1 class="title">edamam_api</h1>
|
| 163 |
-
</div>
|
| 164 |
-
|
| 165 |
-
<div>
|
| 166 |
-
<div class="description">
|
| 167 |
-
Tinkering with the <a href="Edamam API"></a>
|
| 168 |
-
</div>
|
| 169 |
-
</div>
|
| 170 |
-
|
| 171 |
-
|
| 172 |
-
<div class="quarto-title-meta">
|
| 173 |
-
|
| 174 |
-
|
| 175 |
-
|
| 176 |
-
|
| 177 |
-
</div>
|
| 178 |
-
|
| 179 |
-
|
| 180 |
-
</header>
|
| 181 |
-
|
| 182 |
-
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
|
| 183 |
-
<hr>
|
| 184 |
-
<p><a href="https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/edamam_api.py#L7" target="_blank" style="float:right; font-size:smaller">source</a></p>
|
| 185 |
-
<section id="foo" class="level3">
|
| 186 |
-
<h3 class="anchored" data-anchor-id="foo">foo</h3>
|
| 187 |
-
<blockquote class="blockquote">
|
| 188 |
-
<pre><code> foo ()</code></pre>
|
| 189 |
-
</blockquote>
|
| 190 |
-
|
| 191 |
-
|
| 192 |
-
</section>
|
| 193 |
-
|
| 194 |
-
</main> <!-- /main -->
|
| 195 |
-
<script id="quarto-html-after-body" type="application/javascript">
|
| 196 |
-
window.document.addEventListener("DOMContentLoaded", function (event) {
|
| 197 |
-
const toggleBodyColorMode = (bsSheetEl) => {
|
| 198 |
-
const mode = bsSheetEl.getAttribute("data-mode");
|
| 199 |
-
const bodyEl = window.document.querySelector("body");
|
| 200 |
-
if (mode === "dark") {
|
| 201 |
-
bodyEl.classList.add("quarto-dark");
|
| 202 |
-
bodyEl.classList.remove("quarto-light");
|
| 203 |
-
} else {
|
| 204 |
-
bodyEl.classList.add("quarto-light");
|
| 205 |
-
bodyEl.classList.remove("quarto-dark");
|
| 206 |
-
}
|
| 207 |
-
}
|
| 208 |
-
const toggleBodyColorPrimary = () => {
|
| 209 |
-
const bsSheetEl = window.document.querySelector("link#quarto-bootstrap");
|
| 210 |
-
if (bsSheetEl) {
|
| 211 |
-
toggleBodyColorMode(bsSheetEl);
|
| 212 |
-
}
|
| 213 |
-
}
|
| 214 |
-
toggleBodyColorPrimary();
|
| 215 |
-
const icon = "";
|
| 216 |
-
const anchorJS = new window.AnchorJS();
|
| 217 |
-
anchorJS.options = {
|
| 218 |
-
placement: 'right',
|
| 219 |
-
icon: icon
|
| 220 |
-
};
|
| 221 |
-
anchorJS.add('.anchored');
|
| 222 |
-
const isCodeAnnotation = (el) => {
|
| 223 |
-
for (const clz of el.classList) {
|
| 224 |
-
if (clz.startsWith('code-annotation-')) {
|
| 225 |
-
return true;
|
| 226 |
-
}
|
| 227 |
-
}
|
| 228 |
-
return false;
|
| 229 |
-
}
|
| 230 |
-
const clipboard = new window.ClipboardJS('.code-copy-button', {
|
| 231 |
-
text: function(trigger) {
|
| 232 |
-
const codeEl = trigger.previousElementSibling.cloneNode(true);
|
| 233 |
-
for (const childEl of codeEl.children) {
|
| 234 |
-
if (isCodeAnnotation(childEl)) {
|
| 235 |
-
childEl.remove();
|
| 236 |
-
}
|
| 237 |
-
}
|
| 238 |
-
return codeEl.innerText;
|
| 239 |
-
}
|
| 240 |
-
});
|
| 241 |
-
clipboard.on('success', function(e) {
|
| 242 |
-
// button target
|
| 243 |
-
const button = e.trigger;
|
| 244 |
-
// don't keep focus
|
| 245 |
-
button.blur();
|
| 246 |
-
// flash "checked"
|
| 247 |
-
button.classList.add('code-copy-button-checked');
|
| 248 |
-
var currentTitle = button.getAttribute("title");
|
| 249 |
-
button.setAttribute("title", "Copied!");
|
| 250 |
-
let tooltip;
|
| 251 |
-
if (window.bootstrap) {
|
| 252 |
-
button.setAttribute("data-bs-toggle", "tooltip");
|
| 253 |
-
button.setAttribute("data-bs-placement", "left");
|
| 254 |
-
button.setAttribute("data-bs-title", "Copied!");
|
| 255 |
-
tooltip = new bootstrap.Tooltip(button,
|
| 256 |
-
{ trigger: "manual",
|
| 257 |
-
customClass: "code-copy-button-tooltip",
|
| 258 |
-
offset: [0, -8]});
|
| 259 |
-
tooltip.show();
|
| 260 |
-
}
|
| 261 |
-
setTimeout(function() {
|
| 262 |
-
if (tooltip) {
|
| 263 |
-
tooltip.hide();
|
| 264 |
-
button.removeAttribute("data-bs-title");
|
| 265 |
-
button.removeAttribute("data-bs-toggle");
|
| 266 |
-
button.removeAttribute("data-bs-placement");
|
| 267 |
-
}
|
| 268 |
-
button.setAttribute("title", currentTitle);
|
| 269 |
-
button.classList.remove('code-copy-button-checked');
|
| 270 |
-
}, 1000);
|
| 271 |
-
// clear code selection
|
| 272 |
-
e.clearSelection();
|
| 273 |
-
});
|
| 274 |
-
function tippyHover(el, contentFn) {
|
| 275 |
-
const config = {
|
| 276 |
-
allowHTML: true,
|
| 277 |
-
content: contentFn,
|
| 278 |
-
maxWidth: 500,
|
| 279 |
-
delay: 100,
|
| 280 |
-
arrow: false,
|
| 281 |
-
appendTo: function(el) {
|
| 282 |
-
return el.parentElement;
|
| 283 |
-
},
|
| 284 |
-
interactive: true,
|
| 285 |
-
interactiveBorder: 10,
|
| 286 |
-
theme: 'quarto',
|
| 287 |
-
placement: 'bottom-start'
|
| 288 |
-
};
|
| 289 |
-
window.tippy(el, config);
|
| 290 |
-
}
|
| 291 |
-
const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
|
| 292 |
-
for (var i=0; i<noterefs.length; i++) {
|
| 293 |
-
const ref = noterefs[i];
|
| 294 |
-
tippyHover(ref, function() {
|
| 295 |
-
// use id or data attribute instead here
|
| 296 |
-
let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
|
| 297 |
-
try { href = new URL(href).hash; } catch {}
|
| 298 |
-
const id = href.replace(/^#\/?/, "");
|
| 299 |
-
const note = window.document.getElementById(id);
|
| 300 |
-
return note.innerHTML;
|
| 301 |
-
});
|
| 302 |
-
}
|
| 303 |
-
let selectedAnnoteEl;
|
| 304 |
-
const selectorForAnnotation = ( cell, annotation) => {
|
| 305 |
-
let cellAttr = 'data-code-cell="' + cell + '"';
|
| 306 |
-
let lineAttr = 'data-code-annotation="' + annotation + '"';
|
| 307 |
-
const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
|
| 308 |
-
return selector;
|
| 309 |
-
}
|
| 310 |
-
const selectCodeLines = (annoteEl) => {
|
| 311 |
-
const doc = window.document;
|
| 312 |
-
const targetCell = annoteEl.getAttribute("data-target-cell");
|
| 313 |
-
const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
|
| 314 |
-
const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
|
| 315 |
-
const lines = annoteSpan.getAttribute("data-code-lines").split(",");
|
| 316 |
-
const lineIds = lines.map((line) => {
|
| 317 |
-
return targetCell + "-" + line;
|
| 318 |
-
})
|
| 319 |
-
let top = null;
|
| 320 |
-
let height = null;
|
| 321 |
-
let parent = null;
|
| 322 |
-
if (lineIds.length > 0) {
|
| 323 |
-
//compute the position of the single el (top and bottom and make a div)
|
| 324 |
-
const el = window.document.getElementById(lineIds[0]);
|
| 325 |
-
top = el.offsetTop;
|
| 326 |
-
height = el.offsetHeight;
|
| 327 |
-
parent = el.parentElement.parentElement;
|
| 328 |
-
if (lineIds.length > 1) {
|
| 329 |
-
const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
|
| 330 |
-
const bottom = lastEl.offsetTop + lastEl.offsetHeight;
|
| 331 |
-
height = bottom - top;
|
| 332 |
-
}
|
| 333 |
-
if (top !== null && height !== null && parent !== null) {
|
| 334 |
-
// cook up a div (if necessary) and position it
|
| 335 |
-
let div = window.document.getElementById("code-annotation-line-highlight");
|
| 336 |
-
if (div === null) {
|
| 337 |
-
div = window.document.createElement("div");
|
| 338 |
-
div.setAttribute("id", "code-annotation-line-highlight");
|
| 339 |
-
div.style.position = 'absolute';
|
| 340 |
-
parent.appendChild(div);
|
| 341 |
-
}
|
| 342 |
-
div.style.top = top - 2 + "px";
|
| 343 |
-
div.style.height = height + 4 + "px";
|
| 344 |
-
let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
|
| 345 |
-
if (gutterDiv === null) {
|
| 346 |
-
gutterDiv = window.document.createElement("div");
|
| 347 |
-
gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
|
| 348 |
-
gutterDiv.style.position = 'absolute';
|
| 349 |
-
const codeCell = window.document.getElementById(targetCell);
|
| 350 |
-
const gutter = codeCell.querySelector('.code-annotation-gutter');
|
| 351 |
-
gutter.appendChild(gutterDiv);
|
| 352 |
-
}
|
| 353 |
-
gutterDiv.style.top = top - 2 + "px";
|
| 354 |
-
gutterDiv.style.height = height + 4 + "px";
|
| 355 |
-
}
|
| 356 |
-
selectedAnnoteEl = annoteEl;
|
| 357 |
-
}
|
| 358 |
-
};
|
| 359 |
-
const unselectCodeLines = () => {
|
| 360 |
-
const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
|
| 361 |
-
elementsIds.forEach((elId) => {
|
| 362 |
-
const div = window.document.getElementById(elId);
|
| 363 |
-
if (div) {
|
| 364 |
-
div.remove();
|
| 365 |
-
}
|
| 366 |
-
});
|
| 367 |
-
selectedAnnoteEl = undefined;
|
| 368 |
-
};
|
| 369 |
-
// Attach click handler to the DT
|
| 370 |
-
const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
|
| 371 |
-
for (const annoteDlNode of annoteDls) {
|
| 372 |
-
annoteDlNode.addEventListener('click', (event) => {
|
| 373 |
-
const clickedEl = event.target;
|
| 374 |
-
if (clickedEl !== selectedAnnoteEl) {
|
| 375 |
-
unselectCodeLines();
|
| 376 |
-
const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
|
| 377 |
-
if (activeEl) {
|
| 378 |
-
activeEl.classList.remove('code-annotation-active');
|
| 379 |
-
}
|
| 380 |
-
selectCodeLines(clickedEl);
|
| 381 |
-
clickedEl.classList.add('code-annotation-active');
|
| 382 |
-
} else {
|
| 383 |
-
// Unselect the line
|
| 384 |
-
unselectCodeLines();
|
| 385 |
-
clickedEl.classList.remove('code-annotation-active');
|
| 386 |
-
}
|
| 387 |
-
});
|
| 388 |
-
}
|
| 389 |
-
const findCites = (el) => {
|
| 390 |
-
const parentEl = el.parentElement;
|
| 391 |
-
if (parentEl) {
|
| 392 |
-
const cites = parentEl.dataset.cites;
|
| 393 |
-
if (cites) {
|
| 394 |
-
return {
|
| 395 |
-
el,
|
| 396 |
-
cites: cites.split(' ')
|
| 397 |
-
};
|
| 398 |
-
} else {
|
| 399 |
-
return findCites(el.parentElement)
|
| 400 |
-
}
|
| 401 |
-
} else {
|
| 402 |
-
return undefined;
|
| 403 |
-
}
|
| 404 |
-
};
|
| 405 |
-
var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
|
| 406 |
-
for (var i=0; i<bibliorefs.length; i++) {
|
| 407 |
-
const ref = bibliorefs[i];
|
| 408 |
-
const citeInfo = findCites(ref);
|
| 409 |
-
if (citeInfo) {
|
| 410 |
-
tippyHover(citeInfo.el, function() {
|
| 411 |
-
var popup = window.document.createElement('div');
|
| 412 |
-
citeInfo.cites.forEach(function(cite) {
|
| 413 |
-
var citeDiv = window.document.createElement('div');
|
| 414 |
-
citeDiv.classList.add('hanging-indent');
|
| 415 |
-
citeDiv.classList.add('csl-entry');
|
| 416 |
-
var biblioDiv = window.document.getElementById('ref-' + cite);
|
| 417 |
-
if (biblioDiv) {
|
| 418 |
-
citeDiv.innerHTML = biblioDiv.innerHTML;
|
| 419 |
-
}
|
| 420 |
-
popup.appendChild(citeDiv);
|
| 421 |
-
});
|
| 422 |
-
return popup.innerHTML;
|
| 423 |
-
});
|
| 424 |
-
}
|
| 425 |
-
}
|
| 426 |
-
});
|
| 427 |
-
</script>
|
| 428 |
-
</div> <!-- /content -->
|
| 429 |
-
|
| 430 |
-
|
| 431 |
-
|
| 432 |
-
</body></html>
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spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/quarto-html/anchor.min.js
DELETED
|
@@ -1,9 +0,0 @@
|
|
| 1 |
-
// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
|
| 2 |
-
//
|
| 3 |
-
// AnchorJS - v4.3.1 - 2021-04-17
|
| 4 |
-
// https://www.bryanbraun.com/anchorjs/
|
| 5 |
-
// Copyright (c) 2021 Bryan Braun; Licensed MIT
|
| 6 |
-
//
|
| 7 |
-
// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
|
| 8 |
-
!function(A,e){"use strict";"function"==typeof define&&define.amd?define([],e):"object"==typeof module&&module.exports?module.exports=e():(A.AnchorJS=e(),A.anchors=new A.AnchorJS)}(this,function(){"use strict";return function(A){function d(A){A.icon=Object.prototype.hasOwnProperty.call(A,"icon")?A.icon:"",A.visible=Object.prototype.hasOwnProperty.call(A,"visible")?A.visible:"hover",A.placement=Object.prototype.hasOwnProperty.call(A,"placement")?A.placement:"right",A.ariaLabel=Object.prototype.hasOwnProperty.call(A,"ariaLabel")?A.ariaLabel:"Anchor",A.class=Object.prototype.hasOwnProperty.call(A,"class")?A.class:"",A.base=Object.prototype.hasOwnProperty.call(A,"base")?A.base:"",A.truncate=Object.prototype.hasOwnProperty.call(A,"truncate")?Math.floor(A.truncate):64,A.titleText=Object.prototype.hasOwnProperty.call(A,"titleText")?A.titleText:""}function w(A){var e;if("string"==typeof A||A instanceof String)e=[].slice.call(document.querySelectorAll(A));else{if(!(Array.isArray(A)||A instanceof NodeList))throw new TypeError("The selector provided to AnchorJS was invalid.");e=[].slice.call(A)}return e}this.options=A||{},this.elements=[],d(this.options),this.isTouchDevice=function(){return Boolean("ontouchstart"in window||window.TouchEvent||window.DocumentTouch&&document instanceof DocumentTouch)},this.add=function(A){var e,t,o,i,n,s,a,c,r,l,h,u,p=[];if(d(this.options),"touch"===(l=this.options.visible)&&(l=this.isTouchDevice()?"always":"hover"),0===(e=w(A=A||"h2, h3, h4, h5, h6")).length)return this;for(null===document.head.querySelector("style.anchorjs")&&((u=document.createElement("style")).className="anchorjs",u.appendChild(document.createTextNode("")),void 0===(A=document.head.querySelector('[rel="stylesheet"],style'))?document.head.appendChild(u):document.head.insertBefore(u,A),u.sheet.insertRule(".anchorjs-link{opacity:0;text-decoration:none;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}",u.sheet.cssRules.length),u.sheet.insertRule(":hover>.anchorjs-link,.anchorjs-link:focus{opacity:1}",u.sheet.cssRules.length),u.sheet.insertRule("[data-anchorjs-icon]::after{content:attr(data-anchorjs-icon)}",u.sheet.cssRules.length),u.sheet.insertRule('@font-face{font-family:anchorjs-icons;src:url(data:n/a;base64,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) format("truetype")}',u.sheet.cssRules.length)),u=document.querySelectorAll("[id]"),t=[].map.call(u,function(A){return A.id}),i=0;i<e.length;i++)if(this.hasAnchorJSLink(e[i]))p.push(i);else{if(e[i].hasAttribute("id"))o=e[i].getAttribute("id");else if(e[i].hasAttribute("data-anchor-id"))o=e[i].getAttribute("data-anchor-id");else{for(c=a=this.urlify(e[i].textContent),s=0;n=t.indexOf(c=void 0!==n?a+"-"+s:c),s+=1,-1!==n;);n=void 0,t.push(c),e[i].setAttribute("id",c),o=c}(r=document.createElement("a")).className="anchorjs-link "+this.options.class,r.setAttribute("aria-label",this.options.ariaLabel),r.setAttribute("data-anchorjs-icon",this.options.icon),this.options.titleText&&(r.title=this.options.titleText),h=document.querySelector("base")?window.location.pathname+window.location.search:"",h=this.options.base||h,r.href=h+"#"+o,"always"===l&&(r.style.opacity="1"),""===this.options.icon&&(r.style.font="1em/1 anchorjs-icons","left"===this.options.placement&&(r.style.lineHeight="inherit")),"left"===this.options.placement?(r.style.position="absolute",r.style.marginLeft="-1em",r.style.paddingRight=".5em",e[i].insertBefore(r,e[i].firstChild)):(r.style.paddingLeft=".375em",e[i].appendChild(r))}for(i=0;i<p.length;i++)e.splice(p[i]-i,1);return this.elements=this.elements.concat(e),this},this.remove=function(A){for(var e,t,o=w(A),i=0;i<o.length;i++)(t=o[i].querySelector(".anchorjs-link"))&&(-1!==(e=this.elements.indexOf(o[i]))&&this.elements.splice(e,1),o[i].removeChild(t));return this},this.removeAll=function(){this.remove(this.elements)},this.urlify=function(A){var e=document.createElement("textarea");return e.innerHTML=A,A=e.value,this.options.truncate||d(this.options),A.trim().replace(/'/gi,"").replace(/[& +$,:;=?@"#{}|^~[`%!'<>\]./()*\\\n\t\b\v\u00A0]/g,"-").replace(/-{2,}/g,"-").substring(0,this.options.truncate).replace(/^-+|-+$/gm,"").toLowerCase()},this.hasAnchorJSLink=function(A){var e=A.firstChild&&-1<(" "+A.firstChild.className+" ").indexOf(" anchorjs-link "),A=A.lastChild&&-1<(" "+A.lastChild.className+" ").indexOf(" anchorjs-link ");return e||A||!1}}});
|
| 9 |
-
// @license-end
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|
spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/ngrok/README.md
DELETED
|
@@ -1,69 +0,0 @@
|
|
| 1 |
-
# Adding an ingress URL through the ngrok Agent SDK for Python
|
| 2 |
-
|
| 3 |
-
[ngrok](https://ngrok.com) is a globally distributed reverse proxy commonly used for quickly getting a public URL to a
|
| 4 |
-
service running inside a private network, such as on your local laptop. The ngrok agent is usually
|
| 5 |
-
deployed inside a private network and is used to communicate with the ngrok cloud service.
|
| 6 |
-
|
| 7 |
-
By default the authtoken in the NGROK_AUTHTOKEN environment variable will be used. Alternatively one may be specified in
|
| 8 |
-
the `settings.json` file, see the Examples below. Retrieve your authtoken on the [Auth Token page of your ngrok dashboard](https://dashboard.ngrok.com/get-started/your-authtoken), signing up is free.
|
| 9 |
-
|
| 10 |
-
# Documentation
|
| 11 |
-
|
| 12 |
-
For a list of all available options, see [the configuration documentation](https://ngrok.com/docs/ngrok-agent/config/) or [the connect example](https://github.com/ngrok/ngrok-py/blob/main/examples/ngrok-connect-full.py).
|
| 13 |
-
|
| 14 |
-
The ngrok Python SDK is [on github here](https://github.com/ngrok/ngrok-py). A quickstart guide and a full API reference are included in the [ngrok-py Python API documentation](https://ngrok.github.io/ngrok-py/).
|
| 15 |
-
|
| 16 |
-
# Running
|
| 17 |
-
|
| 18 |
-
To enable ngrok install the requirements and then add `--extension ngrok` to the command line options, for instance:
|
| 19 |
-
|
| 20 |
-
```bash
|
| 21 |
-
pip install -r extensions/ngrok/requirements.txt
|
| 22 |
-
python server.py --extension ngrok
|
| 23 |
-
```
|
| 24 |
-
|
| 25 |
-
In the output you should then see something like this:
|
| 26 |
-
|
| 27 |
-
```bash
|
| 28 |
-
INFO:Loading the extension "ngrok"...
|
| 29 |
-
INFO:Session created
|
| 30 |
-
INFO:Created tunnel "9d9d0944dc75ff9d3aae653e5eb29fe9" with url "https://d83706cf7be7.ngrok.app"
|
| 31 |
-
INFO:Tunnel "9d9d0944dc75ff9d3aae653e5eb29fe9" TCP forwarding to "localhost:7860"
|
| 32 |
-
INFO:Ingress established at https://d83706cf7be7.ngrok.app
|
| 33 |
-
```
|
| 34 |
-
|
| 35 |
-
You can now access the webui via the url shown, in this case `https://d83706cf7be7.ngrok.app`. It is recommended to add some authentication to the ingress, see below.
|
| 36 |
-
|
| 37 |
-
# Example Settings
|
| 38 |
-
|
| 39 |
-
In `settings.json` add a `ngrok` key with a dictionary of options, for instance:
|
| 40 |
-
|
| 41 |
-
To enable basic authentication:
|
| 42 |
-
```json
|
| 43 |
-
{
|
| 44 |
-
"ngrok": {
|
| 45 |
-
"basic_auth": "user:password"
|
| 46 |
-
}
|
| 47 |
-
}
|
| 48 |
-
```
|
| 49 |
-
|
| 50 |
-
To enable OAUTH authentication:
|
| 51 |
-
```json
|
| 52 |
-
{
|
| 53 |
-
"ngrok": {
|
| 54 |
-
"oauth_provider": "google",
|
| 55 |
-
"oauth_allow_domains": "asdf.com",
|
| 56 |
-
"oauth_allow_emails": "[email protected]"
|
| 57 |
-
}
|
| 58 |
-
}
|
| 59 |
-
```
|
| 60 |
-
|
| 61 |
-
To add an authtoken instead of using the NGROK_AUTHTOKEN environment variable:
|
| 62 |
-
```json
|
| 63 |
-
{
|
| 64 |
-
"ngrok": {
|
| 65 |
-
"authtoken": "<token>",
|
| 66 |
-
"authtoken_from_env":false
|
| 67 |
-
}
|
| 68 |
-
}
|
| 69 |
-
```
|
|
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|
spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/block_requests.py
DELETED
|
@@ -1,47 +0,0 @@
|
|
| 1 |
-
import builtins
|
| 2 |
-
import io
|
| 3 |
-
|
| 4 |
-
import requests
|
| 5 |
-
|
| 6 |
-
from modules.logging_colors import logger
|
| 7 |
-
|
| 8 |
-
original_open = open
|
| 9 |
-
original_get = requests.get
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class RequestBlocker:
|
| 13 |
-
|
| 14 |
-
def __enter__(self):
|
| 15 |
-
requests.get = my_get
|
| 16 |
-
|
| 17 |
-
def __exit__(self, exc_type, exc_value, traceback):
|
| 18 |
-
requests.get = original_get
|
| 19 |
-
|
| 20 |
-
|
| 21 |
-
class OpenMonkeyPatch:
|
| 22 |
-
|
| 23 |
-
def __enter__(self):
|
| 24 |
-
builtins.open = my_open
|
| 25 |
-
|
| 26 |
-
def __exit__(self, exc_type, exc_value, traceback):
|
| 27 |
-
builtins.open = original_open
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
def my_get(url, **kwargs):
|
| 31 |
-
logger.info('Unwanted HTTP request redirected to localhost :)')
|
| 32 |
-
kwargs.setdefault('allow_redirects', True)
|
| 33 |
-
return requests.api.request('get', 'http://127.0.0.1/', **kwargs)
|
| 34 |
-
|
| 35 |
-
|
| 36 |
-
# Kindly provided by our friend WizardLM-30B
|
| 37 |
-
def my_open(*args, **kwargs):
|
| 38 |
-
filename = str(args[0])
|
| 39 |
-
if filename.endswith('index.html'):
|
| 40 |
-
with original_open(*args, **kwargs) as f:
|
| 41 |
-
file_contents = f.read()
|
| 42 |
-
|
| 43 |
-
file_contents = file_contents.replace(b'<script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script>', b'')
|
| 44 |
-
file_contents = file_contents.replace(b'cdnjs.cloudflare.com', b'127.0.0.1')
|
| 45 |
-
return io.BytesIO(file_contents)
|
| 46 |
-
else:
|
| 47 |
-
return original_open(*args, **kwargs)
|
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|
spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/merge_cells.py
DELETED
|
@@ -1,149 +0,0 @@
|
|
| 1 |
-
# Copyright (c) OpenMMLab. All rights reserved.
|
| 2 |
-
from abc import abstractmethod
|
| 3 |
-
|
| 4 |
-
import torch
|
| 5 |
-
import torch.nn as nn
|
| 6 |
-
import torch.nn.functional as F
|
| 7 |
-
|
| 8 |
-
from ..cnn import ConvModule
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
class BaseMergeCell(nn.Module):
|
| 12 |
-
"""The basic class for cells used in NAS-FPN and NAS-FCOS.
|
| 13 |
-
|
| 14 |
-
BaseMergeCell takes 2 inputs. After applying convolution
|
| 15 |
-
on them, they are resized to the target size. Then,
|
| 16 |
-
they go through binary_op, which depends on the type of cell.
|
| 17 |
-
If with_out_conv is True, the result of output will go through
|
| 18 |
-
another convolution layer.
|
| 19 |
-
|
| 20 |
-
Args:
|
| 21 |
-
in_channels (int): number of input channels in out_conv layer.
|
| 22 |
-
out_channels (int): number of output channels in out_conv layer.
|
| 23 |
-
with_out_conv (bool): Whether to use out_conv layer
|
| 24 |
-
out_conv_cfg (dict): Config dict for convolution layer, which should
|
| 25 |
-
contain "groups", "kernel_size", "padding", "bias" to build
|
| 26 |
-
out_conv layer.
|
| 27 |
-
out_norm_cfg (dict): Config dict for normalization layer in out_conv.
|
| 28 |
-
out_conv_order (tuple): The order of conv/norm/activation layers in
|
| 29 |
-
out_conv.
|
| 30 |
-
with_input1_conv (bool): Whether to use convolution on input1.
|
| 31 |
-
with_input2_conv (bool): Whether to use convolution on input2.
|
| 32 |
-
input_conv_cfg (dict): Config dict for building input1_conv layer and
|
| 33 |
-
input2_conv layer, which is expected to contain the type of
|
| 34 |
-
convolution.
|
| 35 |
-
Default: None, which means using conv2d.
|
| 36 |
-
input_norm_cfg (dict): Config dict for normalization layer in
|
| 37 |
-
input1_conv and input2_conv layer. Default: None.
|
| 38 |
-
upsample_mode (str): Interpolation method used to resize the output
|
| 39 |
-
of input1_conv and input2_conv to target size. Currently, we
|
| 40 |
-
support ['nearest', 'bilinear']. Default: 'nearest'.
|
| 41 |
-
"""
|
| 42 |
-
|
| 43 |
-
def __init__(self,
|
| 44 |
-
fused_channels=256,
|
| 45 |
-
out_channels=256,
|
| 46 |
-
with_out_conv=True,
|
| 47 |
-
out_conv_cfg=dict(
|
| 48 |
-
groups=1, kernel_size=3, padding=1, bias=True),
|
| 49 |
-
out_norm_cfg=None,
|
| 50 |
-
out_conv_order=('act', 'conv', 'norm'),
|
| 51 |
-
with_input1_conv=False,
|
| 52 |
-
with_input2_conv=False,
|
| 53 |
-
input_conv_cfg=None,
|
| 54 |
-
input_norm_cfg=None,
|
| 55 |
-
upsample_mode='nearest'):
|
| 56 |
-
super(BaseMergeCell, self).__init__()
|
| 57 |
-
assert upsample_mode in ['nearest', 'bilinear']
|
| 58 |
-
self.with_out_conv = with_out_conv
|
| 59 |
-
self.with_input1_conv = with_input1_conv
|
| 60 |
-
self.with_input2_conv = with_input2_conv
|
| 61 |
-
self.upsample_mode = upsample_mode
|
| 62 |
-
|
| 63 |
-
if self.with_out_conv:
|
| 64 |
-
self.out_conv = ConvModule(
|
| 65 |
-
fused_channels,
|
| 66 |
-
out_channels,
|
| 67 |
-
**out_conv_cfg,
|
| 68 |
-
norm_cfg=out_norm_cfg,
|
| 69 |
-
order=out_conv_order)
|
| 70 |
-
|
| 71 |
-
self.input1_conv = self._build_input_conv(
|
| 72 |
-
out_channels, input_conv_cfg,
|
| 73 |
-
input_norm_cfg) if with_input1_conv else nn.Sequential()
|
| 74 |
-
self.input2_conv = self._build_input_conv(
|
| 75 |
-
out_channels, input_conv_cfg,
|
| 76 |
-
input_norm_cfg) if with_input2_conv else nn.Sequential()
|
| 77 |
-
|
| 78 |
-
def _build_input_conv(self, channel, conv_cfg, norm_cfg):
|
| 79 |
-
return ConvModule(
|
| 80 |
-
channel,
|
| 81 |
-
channel,
|
| 82 |
-
3,
|
| 83 |
-
padding=1,
|
| 84 |
-
conv_cfg=conv_cfg,
|
| 85 |
-
norm_cfg=norm_cfg,
|
| 86 |
-
bias=True)
|
| 87 |
-
|
| 88 |
-
@abstractmethod
|
| 89 |
-
def _binary_op(self, x1, x2):
|
| 90 |
-
pass
|
| 91 |
-
|
| 92 |
-
def _resize(self, x, size):
|
| 93 |
-
if x.shape[-2:] == size:
|
| 94 |
-
return x
|
| 95 |
-
elif x.shape[-2:] < size:
|
| 96 |
-
return F.interpolate(x, size=size, mode=self.upsample_mode)
|
| 97 |
-
else:
|
| 98 |
-
assert x.shape[-2] % size[-2] == 0 and x.shape[-1] % size[-1] == 0
|
| 99 |
-
kernel_size = x.shape[-1] // size[-1]
|
| 100 |
-
x = F.max_pool2d(x, kernel_size=kernel_size, stride=kernel_size)
|
| 101 |
-
return x
|
| 102 |
-
|
| 103 |
-
def forward(self, x1, x2, out_size=None):
|
| 104 |
-
assert x1.shape[:2] == x2.shape[:2]
|
| 105 |
-
assert out_size is None or len(out_size) == 2
|
| 106 |
-
if out_size is None: # resize to larger one
|
| 107 |
-
out_size = max(x1.size()[2:], x2.size()[2:])
|
| 108 |
-
|
| 109 |
-
x1 = self.input1_conv(x1)
|
| 110 |
-
x2 = self.input2_conv(x2)
|
| 111 |
-
|
| 112 |
-
x1 = self._resize(x1, out_size)
|
| 113 |
-
x2 = self._resize(x2, out_size)
|
| 114 |
-
|
| 115 |
-
x = self._binary_op(x1, x2)
|
| 116 |
-
if self.with_out_conv:
|
| 117 |
-
x = self.out_conv(x)
|
| 118 |
-
return x
|
| 119 |
-
|
| 120 |
-
|
| 121 |
-
class SumCell(BaseMergeCell):
|
| 122 |
-
|
| 123 |
-
def __init__(self, in_channels, out_channels, **kwargs):
|
| 124 |
-
super(SumCell, self).__init__(in_channels, out_channels, **kwargs)
|
| 125 |
-
|
| 126 |
-
def _binary_op(self, x1, x2):
|
| 127 |
-
return x1 + x2
|
| 128 |
-
|
| 129 |
-
|
| 130 |
-
class ConcatCell(BaseMergeCell):
|
| 131 |
-
|
| 132 |
-
def __init__(self, in_channels, out_channels, **kwargs):
|
| 133 |
-
super(ConcatCell, self).__init__(in_channels * 2, out_channels,
|
| 134 |
-
**kwargs)
|
| 135 |
-
|
| 136 |
-
def _binary_op(self, x1, x2):
|
| 137 |
-
ret = torch.cat([x1, x2], dim=1)
|
| 138 |
-
return ret
|
| 139 |
-
|
| 140 |
-
|
| 141 |
-
class GlobalPoolingCell(BaseMergeCell):
|
| 142 |
-
|
| 143 |
-
def __init__(self, in_channels=None, out_channels=None, **kwargs):
|
| 144 |
-
super().__init__(in_channels, out_channels, **kwargs)
|
| 145 |
-
self.global_pool = nn.AdaptiveAvgPool2d((1, 1))
|
| 146 |
-
|
| 147 |
-
def _binary_op(self, x1, x2):
|
| 148 |
-
x2_att = self.global_pool(x2).sigmoid()
|
| 149 |
-
return x2 + x2_att * x1
|
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|
|
spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/evaluation.py
DELETED
|
@@ -1,509 +0,0 @@
|
|
| 1 |
-
# Copyright (c) OpenMMLab. All rights reserved.
|
| 2 |
-
import os.path as osp
|
| 3 |
-
import warnings
|
| 4 |
-
from math import inf
|
| 5 |
-
|
| 6 |
-
import torch.distributed as dist
|
| 7 |
-
from torch.nn.modules.batchnorm import _BatchNorm
|
| 8 |
-
from torch.utils.data import DataLoader
|
| 9 |
-
|
| 10 |
-
from annotator.uniformer.mmcv.fileio import FileClient
|
| 11 |
-
from annotator.uniformer.mmcv.utils import is_seq_of
|
| 12 |
-
from .hook import Hook
|
| 13 |
-
from .logger import LoggerHook
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
class EvalHook(Hook):
|
| 17 |
-
"""Non-Distributed evaluation hook.
|
| 18 |
-
|
| 19 |
-
This hook will regularly perform evaluation in a given interval when
|
| 20 |
-
performing in non-distributed environment.
|
| 21 |
-
|
| 22 |
-
Args:
|
| 23 |
-
dataloader (DataLoader): A PyTorch dataloader, whose dataset has
|
| 24 |
-
implemented ``evaluate`` function.
|
| 25 |
-
start (int | None, optional): Evaluation starting epoch. It enables
|
| 26 |
-
evaluation before the training starts if ``start`` <= the resuming
|
| 27 |
-
epoch. If None, whether to evaluate is merely decided by
|
| 28 |
-
``interval``. Default: None.
|
| 29 |
-
interval (int): Evaluation interval. Default: 1.
|
| 30 |
-
by_epoch (bool): Determine perform evaluation by epoch or by iteration.
|
| 31 |
-
If set to True, it will perform by epoch. Otherwise, by iteration.
|
| 32 |
-
Default: True.
|
| 33 |
-
save_best (str, optional): If a metric is specified, it would measure
|
| 34 |
-
the best checkpoint during evaluation. The information about best
|
| 35 |
-
checkpoint would be saved in ``runner.meta['hook_msgs']`` to keep
|
| 36 |
-
best score value and best checkpoint path, which will be also
|
| 37 |
-
loaded when resume checkpoint. Options are the evaluation metrics
|
| 38 |
-
on the test dataset. e.g., ``bbox_mAP``, ``segm_mAP`` for bbox
|
| 39 |
-
detection and instance segmentation. ``AR@100`` for proposal
|
| 40 |
-
recall. If ``save_best`` is ``auto``, the first key of the returned
|
| 41 |
-
``OrderedDict`` result will be used. Default: None.
|
| 42 |
-
rule (str | None, optional): Comparison rule for best score. If set to
|
| 43 |
-
None, it will infer a reasonable rule. Keys such as 'acc', 'top'
|
| 44 |
-
.etc will be inferred by 'greater' rule. Keys contain 'loss' will
|
| 45 |
-
be inferred by 'less' rule. Options are 'greater', 'less', None.
|
| 46 |
-
Default: None.
|
| 47 |
-
test_fn (callable, optional): test a model with samples from a
|
| 48 |
-
dataloader, and return the test results. If ``None``, the default
|
| 49 |
-
test function ``mmcv.engine.single_gpu_test`` will be used.
|
| 50 |
-
(default: ``None``)
|
| 51 |
-
greater_keys (List[str] | None, optional): Metric keys that will be
|
| 52 |
-
inferred by 'greater' comparison rule. If ``None``,
|
| 53 |
-
_default_greater_keys will be used. (default: ``None``)
|
| 54 |
-
less_keys (List[str] | None, optional): Metric keys that will be
|
| 55 |
-
inferred by 'less' comparison rule. If ``None``, _default_less_keys
|
| 56 |
-
will be used. (default: ``None``)
|
| 57 |
-
out_dir (str, optional): The root directory to save checkpoints. If not
|
| 58 |
-
specified, `runner.work_dir` will be used by default. If specified,
|
| 59 |
-
the `out_dir` will be the concatenation of `out_dir` and the last
|
| 60 |
-
level directory of `runner.work_dir`.
|
| 61 |
-
`New in version 1.3.16.`
|
| 62 |
-
file_client_args (dict): Arguments to instantiate a FileClient.
|
| 63 |
-
See :class:`mmcv.fileio.FileClient` for details. Default: None.
|
| 64 |
-
`New in version 1.3.16.`
|
| 65 |
-
**eval_kwargs: Evaluation arguments fed into the evaluate function of
|
| 66 |
-
the dataset.
|
| 67 |
-
|
| 68 |
-
Notes:
|
| 69 |
-
If new arguments are added for EvalHook, tools/test.py,
|
| 70 |
-
tools/eval_metric.py may be affected.
|
| 71 |
-
"""
|
| 72 |
-
|
| 73 |
-
# Since the key for determine greater or less is related to the downstream
|
| 74 |
-
# tasks, downstream repos may need to overwrite the following inner
|
| 75 |
-
# variable accordingly.
|
| 76 |
-
|
| 77 |
-
rule_map = {'greater': lambda x, y: x > y, 'less': lambda x, y: x < y}
|
| 78 |
-
init_value_map = {'greater': -inf, 'less': inf}
|
| 79 |
-
_default_greater_keys = [
|
| 80 |
-
'acc', 'top', 'AR@', 'auc', 'precision', 'mAP', 'mDice', 'mIoU',
|
| 81 |
-
'mAcc', 'aAcc'
|
| 82 |
-
]
|
| 83 |
-
_default_less_keys = ['loss']
|
| 84 |
-
|
| 85 |
-
def __init__(self,
|
| 86 |
-
dataloader,
|
| 87 |
-
start=None,
|
| 88 |
-
interval=1,
|
| 89 |
-
by_epoch=True,
|
| 90 |
-
save_best=None,
|
| 91 |
-
rule=None,
|
| 92 |
-
test_fn=None,
|
| 93 |
-
greater_keys=None,
|
| 94 |
-
less_keys=None,
|
| 95 |
-
out_dir=None,
|
| 96 |
-
file_client_args=None,
|
| 97 |
-
**eval_kwargs):
|
| 98 |
-
if not isinstance(dataloader, DataLoader):
|
| 99 |
-
raise TypeError(f'dataloader must be a pytorch DataLoader, '
|
| 100 |
-
f'but got {type(dataloader)}')
|
| 101 |
-
|
| 102 |
-
if interval <= 0:
|
| 103 |
-
raise ValueError(f'interval must be a positive number, '
|
| 104 |
-
f'but got {interval}')
|
| 105 |
-
|
| 106 |
-
assert isinstance(by_epoch, bool), '``by_epoch`` should be a boolean'
|
| 107 |
-
|
| 108 |
-
if start is not None and start < 0:
|
| 109 |
-
raise ValueError(f'The evaluation start epoch {start} is smaller '
|
| 110 |
-
f'than 0')
|
| 111 |
-
|
| 112 |
-
self.dataloader = dataloader
|
| 113 |
-
self.interval = interval
|
| 114 |
-
self.start = start
|
| 115 |
-
self.by_epoch = by_epoch
|
| 116 |
-
|
| 117 |
-
assert isinstance(save_best, str) or save_best is None, \
|
| 118 |
-
'""save_best"" should be a str or None ' \
|
| 119 |
-
f'rather than {type(save_best)}'
|
| 120 |
-
self.save_best = save_best
|
| 121 |
-
self.eval_kwargs = eval_kwargs
|
| 122 |
-
self.initial_flag = True
|
| 123 |
-
|
| 124 |
-
if test_fn is None:
|
| 125 |
-
from annotator.uniformer.mmcv.engine import single_gpu_test
|
| 126 |
-
self.test_fn = single_gpu_test
|
| 127 |
-
else:
|
| 128 |
-
self.test_fn = test_fn
|
| 129 |
-
|
| 130 |
-
if greater_keys is None:
|
| 131 |
-
self.greater_keys = self._default_greater_keys
|
| 132 |
-
else:
|
| 133 |
-
if not isinstance(greater_keys, (list, tuple)):
|
| 134 |
-
greater_keys = (greater_keys, )
|
| 135 |
-
assert is_seq_of(greater_keys, str)
|
| 136 |
-
self.greater_keys = greater_keys
|
| 137 |
-
|
| 138 |
-
if less_keys is None:
|
| 139 |
-
self.less_keys = self._default_less_keys
|
| 140 |
-
else:
|
| 141 |
-
if not isinstance(less_keys, (list, tuple)):
|
| 142 |
-
less_keys = (less_keys, )
|
| 143 |
-
assert is_seq_of(less_keys, str)
|
| 144 |
-
self.less_keys = less_keys
|
| 145 |
-
|
| 146 |
-
if self.save_best is not None:
|
| 147 |
-
self.best_ckpt_path = None
|
| 148 |
-
self._init_rule(rule, self.save_best)
|
| 149 |
-
|
| 150 |
-
self.out_dir = out_dir
|
| 151 |
-
self.file_client_args = file_client_args
|
| 152 |
-
|
| 153 |
-
def _init_rule(self, rule, key_indicator):
|
| 154 |
-
"""Initialize rule, key_indicator, comparison_func, and best score.
|
| 155 |
-
|
| 156 |
-
Here is the rule to determine which rule is used for key indicator
|
| 157 |
-
when the rule is not specific (note that the key indicator matching
|
| 158 |
-
is case-insensitive):
|
| 159 |
-
1. If the key indicator is in ``self.greater_keys``, the rule will be
|
| 160 |
-
specified as 'greater'.
|
| 161 |
-
2. Or if the key indicator is in ``self.less_keys``, the rule will be
|
| 162 |
-
specified as 'less'.
|
| 163 |
-
3. Or if the key indicator is equal to the substring in any one item
|
| 164 |
-
in ``self.greater_keys``, the rule will be specified as 'greater'.
|
| 165 |
-
4. Or if the key indicator is equal to the substring in any one item
|
| 166 |
-
in ``self.less_keys``, the rule will be specified as 'less'.
|
| 167 |
-
|
| 168 |
-
Args:
|
| 169 |
-
rule (str | None): Comparison rule for best score.
|
| 170 |
-
key_indicator (str | None): Key indicator to determine the
|
| 171 |
-
comparison rule.
|
| 172 |
-
"""
|
| 173 |
-
if rule not in self.rule_map and rule is not None:
|
| 174 |
-
raise KeyError(f'rule must be greater, less or None, '
|
| 175 |
-
f'but got {rule}.')
|
| 176 |
-
|
| 177 |
-
if rule is None:
|
| 178 |
-
if key_indicator != 'auto':
|
| 179 |
-
# `_lc` here means we use the lower case of keys for
|
| 180 |
-
# case-insensitive matching
|
| 181 |
-
key_indicator_lc = key_indicator.lower()
|
| 182 |
-
greater_keys = [key.lower() for key in self.greater_keys]
|
| 183 |
-
less_keys = [key.lower() for key in self.less_keys]
|
| 184 |
-
|
| 185 |
-
if key_indicator_lc in greater_keys:
|
| 186 |
-
rule = 'greater'
|
| 187 |
-
elif key_indicator_lc in less_keys:
|
| 188 |
-
rule = 'less'
|
| 189 |
-
elif any(key in key_indicator_lc for key in greater_keys):
|
| 190 |
-
rule = 'greater'
|
| 191 |
-
elif any(key in key_indicator_lc for key in less_keys):
|
| 192 |
-
rule = 'less'
|
| 193 |
-
else:
|
| 194 |
-
raise ValueError(f'Cannot infer the rule for key '
|
| 195 |
-
f'{key_indicator}, thus a specific rule '
|
| 196 |
-
f'must be specified.')
|
| 197 |
-
self.rule = rule
|
| 198 |
-
self.key_indicator = key_indicator
|
| 199 |
-
if self.rule is not None:
|
| 200 |
-
self.compare_func = self.rule_map[self.rule]
|
| 201 |
-
|
| 202 |
-
def before_run(self, runner):
|
| 203 |
-
if not self.out_dir:
|
| 204 |
-
self.out_dir = runner.work_dir
|
| 205 |
-
|
| 206 |
-
self.file_client = FileClient.infer_client(self.file_client_args,
|
| 207 |
-
self.out_dir)
|
| 208 |
-
|
| 209 |
-
# if `self.out_dir` is not equal to `runner.work_dir`, it means that
|
| 210 |
-
# `self.out_dir` is set so the final `self.out_dir` is the
|
| 211 |
-
# concatenation of `self.out_dir` and the last level directory of
|
| 212 |
-
# `runner.work_dir`
|
| 213 |
-
if self.out_dir != runner.work_dir:
|
| 214 |
-
basename = osp.basename(runner.work_dir.rstrip(osp.sep))
|
| 215 |
-
self.out_dir = self.file_client.join_path(self.out_dir, basename)
|
| 216 |
-
runner.logger.info(
|
| 217 |
-
(f'The best checkpoint will be saved to {self.out_dir} by '
|
| 218 |
-
f'{self.file_client.name}'))
|
| 219 |
-
|
| 220 |
-
if self.save_best is not None:
|
| 221 |
-
if runner.meta is None:
|
| 222 |
-
warnings.warn('runner.meta is None. Creating an empty one.')
|
| 223 |
-
runner.meta = dict()
|
| 224 |
-
runner.meta.setdefault('hook_msgs', dict())
|
| 225 |
-
self.best_ckpt_path = runner.meta['hook_msgs'].get(
|
| 226 |
-
'best_ckpt', None)
|
| 227 |
-
|
| 228 |
-
def before_train_iter(self, runner):
|
| 229 |
-
"""Evaluate the model only at the start of training by iteration."""
|
| 230 |
-
if self.by_epoch or not self.initial_flag:
|
| 231 |
-
return
|
| 232 |
-
if self.start is not None and runner.iter >= self.start:
|
| 233 |
-
self.after_train_iter(runner)
|
| 234 |
-
self.initial_flag = False
|
| 235 |
-
|
| 236 |
-
def before_train_epoch(self, runner):
|
| 237 |
-
"""Evaluate the model only at the start of training by epoch."""
|
| 238 |
-
if not (self.by_epoch and self.initial_flag):
|
| 239 |
-
return
|
| 240 |
-
if self.start is not None and runner.epoch >= self.start:
|
| 241 |
-
self.after_train_epoch(runner)
|
| 242 |
-
self.initial_flag = False
|
| 243 |
-
|
| 244 |
-
def after_train_iter(self, runner):
|
| 245 |
-
"""Called after every training iter to evaluate the results."""
|
| 246 |
-
if not self.by_epoch and self._should_evaluate(runner):
|
| 247 |
-
# Because the priority of EvalHook is higher than LoggerHook, the
|
| 248 |
-
# training log and the evaluating log are mixed. Therefore,
|
| 249 |
-
# we need to dump the training log and clear it before evaluating
|
| 250 |
-
# log is generated. In addition, this problem will only appear in
|
| 251 |
-
# `IterBasedRunner` whose `self.by_epoch` is False, because
|
| 252 |
-
# `EpochBasedRunner` whose `self.by_epoch` is True calls
|
| 253 |
-
# `_do_evaluate` in `after_train_epoch` stage, and at this stage
|
| 254 |
-
# the training log has been printed, so it will not cause any
|
| 255 |
-
# problem. more details at
|
| 256 |
-
# https://github.com/open-mmlab/mmsegmentation/issues/694
|
| 257 |
-
for hook in runner._hooks:
|
| 258 |
-
if isinstance(hook, LoggerHook):
|
| 259 |
-
hook.after_train_iter(runner)
|
| 260 |
-
runner.log_buffer.clear()
|
| 261 |
-
|
| 262 |
-
self._do_evaluate(runner)
|
| 263 |
-
|
| 264 |
-
def after_train_epoch(self, runner):
|
| 265 |
-
"""Called after every training epoch to evaluate the results."""
|
| 266 |
-
if self.by_epoch and self._should_evaluate(runner):
|
| 267 |
-
self._do_evaluate(runner)
|
| 268 |
-
|
| 269 |
-
def _do_evaluate(self, runner):
|
| 270 |
-
"""perform evaluation and save ckpt."""
|
| 271 |
-
results = self.test_fn(runner.model, self.dataloader)
|
| 272 |
-
runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
|
| 273 |
-
key_score = self.evaluate(runner, results)
|
| 274 |
-
# the key_score may be `None` so it needs to skip the action to save
|
| 275 |
-
# the best checkpoint
|
| 276 |
-
if self.save_best and key_score:
|
| 277 |
-
self._save_ckpt(runner, key_score)
|
| 278 |
-
|
| 279 |
-
def _should_evaluate(self, runner):
|
| 280 |
-
"""Judge whether to perform evaluation.
|
| 281 |
-
|
| 282 |
-
Here is the rule to judge whether to perform evaluation:
|
| 283 |
-
1. It will not perform evaluation during the epoch/iteration interval,
|
| 284 |
-
which is determined by ``self.interval``.
|
| 285 |
-
2. It will not perform evaluation if the start time is larger than
|
| 286 |
-
current time.
|
| 287 |
-
3. It will not perform evaluation when current time is larger than
|
| 288 |
-
the start time but during epoch/iteration interval.
|
| 289 |
-
|
| 290 |
-
Returns:
|
| 291 |
-
bool: The flag indicating whether to perform evaluation.
|
| 292 |
-
"""
|
| 293 |
-
if self.by_epoch:
|
| 294 |
-
current = runner.epoch
|
| 295 |
-
check_time = self.every_n_epochs
|
| 296 |
-
else:
|
| 297 |
-
current = runner.iter
|
| 298 |
-
check_time = self.every_n_iters
|
| 299 |
-
|
| 300 |
-
if self.start is None:
|
| 301 |
-
if not check_time(runner, self.interval):
|
| 302 |
-
# No evaluation during the interval.
|
| 303 |
-
return False
|
| 304 |
-
elif (current + 1) < self.start:
|
| 305 |
-
# No evaluation if start is larger than the current time.
|
| 306 |
-
return False
|
| 307 |
-
else:
|
| 308 |
-
# Evaluation only at epochs/iters 3, 5, 7...
|
| 309 |
-
# if start==3 and interval==2
|
| 310 |
-
if (current + 1 - self.start) % self.interval:
|
| 311 |
-
return False
|
| 312 |
-
return True
|
| 313 |
-
|
| 314 |
-
def _save_ckpt(self, runner, key_score):
|
| 315 |
-
"""Save the best checkpoint.
|
| 316 |
-
|
| 317 |
-
It will compare the score according to the compare function, write
|
| 318 |
-
related information (best score, best checkpoint path) and save the
|
| 319 |
-
best checkpoint into ``work_dir``.
|
| 320 |
-
"""
|
| 321 |
-
if self.by_epoch:
|
| 322 |
-
current = f'epoch_{runner.epoch + 1}'
|
| 323 |
-
cur_type, cur_time = 'epoch', runner.epoch + 1
|
| 324 |
-
else:
|
| 325 |
-
current = f'iter_{runner.iter + 1}'
|
| 326 |
-
cur_type, cur_time = 'iter', runner.iter + 1
|
| 327 |
-
|
| 328 |
-
best_score = runner.meta['hook_msgs'].get(
|
| 329 |
-
'best_score', self.init_value_map[self.rule])
|
| 330 |
-
if self.compare_func(key_score, best_score):
|
| 331 |
-
best_score = key_score
|
| 332 |
-
runner.meta['hook_msgs']['best_score'] = best_score
|
| 333 |
-
|
| 334 |
-
if self.best_ckpt_path and self.file_client.isfile(
|
| 335 |
-
self.best_ckpt_path):
|
| 336 |
-
self.file_client.remove(self.best_ckpt_path)
|
| 337 |
-
runner.logger.info(
|
| 338 |
-
(f'The previous best checkpoint {self.best_ckpt_path} was '
|
| 339 |
-
'removed'))
|
| 340 |
-
|
| 341 |
-
best_ckpt_name = f'best_{self.key_indicator}_{current}.pth'
|
| 342 |
-
self.best_ckpt_path = self.file_client.join_path(
|
| 343 |
-
self.out_dir, best_ckpt_name)
|
| 344 |
-
runner.meta['hook_msgs']['best_ckpt'] = self.best_ckpt_path
|
| 345 |
-
|
| 346 |
-
runner.save_checkpoint(
|
| 347 |
-
self.out_dir, best_ckpt_name, create_symlink=False)
|
| 348 |
-
runner.logger.info(
|
| 349 |
-
f'Now best checkpoint is saved as {best_ckpt_name}.')
|
| 350 |
-
runner.logger.info(
|
| 351 |
-
f'Best {self.key_indicator} is {best_score:0.4f} '
|
| 352 |
-
f'at {cur_time} {cur_type}.')
|
| 353 |
-
|
| 354 |
-
def evaluate(self, runner, results):
|
| 355 |
-
"""Evaluate the results.
|
| 356 |
-
|
| 357 |
-
Args:
|
| 358 |
-
runner (:obj:`mmcv.Runner`): The underlined training runner.
|
| 359 |
-
results (list): Output results.
|
| 360 |
-
"""
|
| 361 |
-
eval_res = self.dataloader.dataset.evaluate(
|
| 362 |
-
results, logger=runner.logger, **self.eval_kwargs)
|
| 363 |
-
|
| 364 |
-
for name, val in eval_res.items():
|
| 365 |
-
runner.log_buffer.output[name] = val
|
| 366 |
-
runner.log_buffer.ready = True
|
| 367 |
-
|
| 368 |
-
if self.save_best is not None:
|
| 369 |
-
# If the performance of model is pool, the `eval_res` may be an
|
| 370 |
-
# empty dict and it will raise exception when `self.save_best` is
|
| 371 |
-
# not None. More details at
|
| 372 |
-
# https://github.com/open-mmlab/mmdetection/issues/6265.
|
| 373 |
-
if not eval_res:
|
| 374 |
-
warnings.warn(
|
| 375 |
-
'Since `eval_res` is an empty dict, the behavior to save '
|
| 376 |
-
'the best checkpoint will be skipped in this evaluation.')
|
| 377 |
-
return None
|
| 378 |
-
|
| 379 |
-
if self.key_indicator == 'auto':
|
| 380 |
-
# infer from eval_results
|
| 381 |
-
self._init_rule(self.rule, list(eval_res.keys())[0])
|
| 382 |
-
return eval_res[self.key_indicator]
|
| 383 |
-
|
| 384 |
-
return None
|
| 385 |
-
|
| 386 |
-
|
| 387 |
-
class DistEvalHook(EvalHook):
|
| 388 |
-
"""Distributed evaluation hook.
|
| 389 |
-
|
| 390 |
-
This hook will regularly perform evaluation in a given interval when
|
| 391 |
-
performing in distributed environment.
|
| 392 |
-
|
| 393 |
-
Args:
|
| 394 |
-
dataloader (DataLoader): A PyTorch dataloader, whose dataset has
|
| 395 |
-
implemented ``evaluate`` function.
|
| 396 |
-
start (int | None, optional): Evaluation starting epoch. It enables
|
| 397 |
-
evaluation before the training starts if ``start`` <= the resuming
|
| 398 |
-
epoch. If None, whether to evaluate is merely decided by
|
| 399 |
-
``interval``. Default: None.
|
| 400 |
-
interval (int): Evaluation interval. Default: 1.
|
| 401 |
-
by_epoch (bool): Determine perform evaluation by epoch or by iteration.
|
| 402 |
-
If set to True, it will perform by epoch. Otherwise, by iteration.
|
| 403 |
-
default: True.
|
| 404 |
-
save_best (str, optional): If a metric is specified, it would measure
|
| 405 |
-
the best checkpoint during evaluation. The information about best
|
| 406 |
-
checkpoint would be saved in ``runner.meta['hook_msgs']`` to keep
|
| 407 |
-
best score value and best checkpoint path, which will be also
|
| 408 |
-
loaded when resume checkpoint. Options are the evaluation metrics
|
| 409 |
-
on the test dataset. e.g., ``bbox_mAP``, ``segm_mAP`` for bbox
|
| 410 |
-
detection and instance segmentation. ``AR@100`` for proposal
|
| 411 |
-
recall. If ``save_best`` is ``auto``, the first key of the returned
|
| 412 |
-
``OrderedDict`` result will be used. Default: None.
|
| 413 |
-
rule (str | None, optional): Comparison rule for best score. If set to
|
| 414 |
-
None, it will infer a reasonable rule. Keys such as 'acc', 'top'
|
| 415 |
-
.etc will be inferred by 'greater' rule. Keys contain 'loss' will
|
| 416 |
-
be inferred by 'less' rule. Options are 'greater', 'less', None.
|
| 417 |
-
Default: None.
|
| 418 |
-
test_fn (callable, optional): test a model with samples from a
|
| 419 |
-
dataloader in a multi-gpu manner, and return the test results. If
|
| 420 |
-
``None``, the default test function ``mmcv.engine.multi_gpu_test``
|
| 421 |
-
will be used. (default: ``None``)
|
| 422 |
-
tmpdir (str | None): Temporary directory to save the results of all
|
| 423 |
-
processes. Default: None.
|
| 424 |
-
gpu_collect (bool): Whether to use gpu or cpu to collect results.
|
| 425 |
-
Default: False.
|
| 426 |
-
broadcast_bn_buffer (bool): Whether to broadcast the
|
| 427 |
-
buffer(running_mean and running_var) of rank 0 to other rank
|
| 428 |
-
before evaluation. Default: True.
|
| 429 |
-
out_dir (str, optional): The root directory to save checkpoints. If not
|
| 430 |
-
specified, `runner.work_dir` will be used by default. If specified,
|
| 431 |
-
the `out_dir` will be the concatenation of `out_dir` and the last
|
| 432 |
-
level directory of `runner.work_dir`.
|
| 433 |
-
file_client_args (dict): Arguments to instantiate a FileClient.
|
| 434 |
-
See :class:`mmcv.fileio.FileClient` for details. Default: None.
|
| 435 |
-
**eval_kwargs: Evaluation arguments fed into the evaluate function of
|
| 436 |
-
the dataset.
|
| 437 |
-
"""
|
| 438 |
-
|
| 439 |
-
def __init__(self,
|
| 440 |
-
dataloader,
|
| 441 |
-
start=None,
|
| 442 |
-
interval=1,
|
| 443 |
-
by_epoch=True,
|
| 444 |
-
save_best=None,
|
| 445 |
-
rule=None,
|
| 446 |
-
test_fn=None,
|
| 447 |
-
greater_keys=None,
|
| 448 |
-
less_keys=None,
|
| 449 |
-
broadcast_bn_buffer=True,
|
| 450 |
-
tmpdir=None,
|
| 451 |
-
gpu_collect=False,
|
| 452 |
-
out_dir=None,
|
| 453 |
-
file_client_args=None,
|
| 454 |
-
**eval_kwargs):
|
| 455 |
-
|
| 456 |
-
if test_fn is None:
|
| 457 |
-
from annotator.uniformer.mmcv.engine import multi_gpu_test
|
| 458 |
-
test_fn = multi_gpu_test
|
| 459 |
-
|
| 460 |
-
super().__init__(
|
| 461 |
-
dataloader,
|
| 462 |
-
start=start,
|
| 463 |
-
interval=interval,
|
| 464 |
-
by_epoch=by_epoch,
|
| 465 |
-
save_best=save_best,
|
| 466 |
-
rule=rule,
|
| 467 |
-
test_fn=test_fn,
|
| 468 |
-
greater_keys=greater_keys,
|
| 469 |
-
less_keys=less_keys,
|
| 470 |
-
out_dir=out_dir,
|
| 471 |
-
file_client_args=file_client_args,
|
| 472 |
-
**eval_kwargs)
|
| 473 |
-
|
| 474 |
-
self.broadcast_bn_buffer = broadcast_bn_buffer
|
| 475 |
-
self.tmpdir = tmpdir
|
| 476 |
-
self.gpu_collect = gpu_collect
|
| 477 |
-
|
| 478 |
-
def _do_evaluate(self, runner):
|
| 479 |
-
"""perform evaluation and save ckpt."""
|
| 480 |
-
# Synchronization of BatchNorm's buffer (running_mean
|
| 481 |
-
# and running_var) is not supported in the DDP of pytorch,
|
| 482 |
-
# which may cause the inconsistent performance of models in
|
| 483 |
-
# different ranks, so we broadcast BatchNorm's buffers
|
| 484 |
-
# of rank 0 to other ranks to avoid this.
|
| 485 |
-
if self.broadcast_bn_buffer:
|
| 486 |
-
model = runner.model
|
| 487 |
-
for name, module in model.named_modules():
|
| 488 |
-
if isinstance(module,
|
| 489 |
-
_BatchNorm) and module.track_running_stats:
|
| 490 |
-
dist.broadcast(module.running_var, 0)
|
| 491 |
-
dist.broadcast(module.running_mean, 0)
|
| 492 |
-
|
| 493 |
-
tmpdir = self.tmpdir
|
| 494 |
-
if tmpdir is None:
|
| 495 |
-
tmpdir = osp.join(runner.work_dir, '.eval_hook')
|
| 496 |
-
|
| 497 |
-
results = self.test_fn(
|
| 498 |
-
runner.model,
|
| 499 |
-
self.dataloader,
|
| 500 |
-
tmpdir=tmpdir,
|
| 501 |
-
gpu_collect=self.gpu_collect)
|
| 502 |
-
if runner.rank == 0:
|
| 503 |
-
print('\n')
|
| 504 |
-
runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
|
| 505 |
-
key_score = self.evaluate(runner, results)
|
| 506 |
-
# the key_score may be `None` so it needs to skip the action to
|
| 507 |
-
# save the best checkpoint
|
| 508 |
-
if self.save_best and key_score:
|
| 509 |
-
self._save_ckpt(runner, key_score)
|
|
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|
spaces/Anonymous-sub/Rerender/ControlNet/cldm/logger.py
DELETED
|
@@ -1,76 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
|
| 3 |
-
import numpy as np
|
| 4 |
-
import torch
|
| 5 |
-
import torchvision
|
| 6 |
-
from PIL import Image
|
| 7 |
-
from pytorch_lightning.callbacks import Callback
|
| 8 |
-
from pytorch_lightning.utilities.distributed import rank_zero_only
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
class ImageLogger(Callback):
|
| 12 |
-
def __init__(self, batch_frequency=2000, max_images=4, clamp=True, increase_log_steps=True,
|
| 13 |
-
rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False,
|
| 14 |
-
log_images_kwargs=None):
|
| 15 |
-
super().__init__()
|
| 16 |
-
self.rescale = rescale
|
| 17 |
-
self.batch_freq = batch_frequency
|
| 18 |
-
self.max_images = max_images
|
| 19 |
-
if not increase_log_steps:
|
| 20 |
-
self.log_steps = [self.batch_freq]
|
| 21 |
-
self.clamp = clamp
|
| 22 |
-
self.disabled = disabled
|
| 23 |
-
self.log_on_batch_idx = log_on_batch_idx
|
| 24 |
-
self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
|
| 25 |
-
self.log_first_step = log_first_step
|
| 26 |
-
|
| 27 |
-
@rank_zero_only
|
| 28 |
-
def log_local(self, save_dir, split, images, global_step, current_epoch, batch_idx):
|
| 29 |
-
root = os.path.join(save_dir, "image_log", split)
|
| 30 |
-
for k in images:
|
| 31 |
-
grid = torchvision.utils.make_grid(images[k], nrow=4)
|
| 32 |
-
if self.rescale:
|
| 33 |
-
grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
|
| 34 |
-
grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
|
| 35 |
-
grid = grid.numpy()
|
| 36 |
-
grid = (grid * 255).astype(np.uint8)
|
| 37 |
-
filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(k, global_step, current_epoch, batch_idx)
|
| 38 |
-
path = os.path.join(root, filename)
|
| 39 |
-
os.makedirs(os.path.split(path)[0], exist_ok=True)
|
| 40 |
-
Image.fromarray(grid).save(path)
|
| 41 |
-
|
| 42 |
-
def log_img(self, pl_module, batch, batch_idx, split="train"):
|
| 43 |
-
check_idx = batch_idx # if self.log_on_batch_idx else pl_module.global_step
|
| 44 |
-
if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0
|
| 45 |
-
hasattr(pl_module, "log_images") and
|
| 46 |
-
callable(pl_module.log_images) and
|
| 47 |
-
self.max_images > 0):
|
| 48 |
-
logger = type(pl_module.logger)
|
| 49 |
-
|
| 50 |
-
is_train = pl_module.training
|
| 51 |
-
if is_train:
|
| 52 |
-
pl_module.eval()
|
| 53 |
-
|
| 54 |
-
with torch.no_grad():
|
| 55 |
-
images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
|
| 56 |
-
|
| 57 |
-
for k in images:
|
| 58 |
-
N = min(images[k].shape[0], self.max_images)
|
| 59 |
-
images[k] = images[k][:N]
|
| 60 |
-
if isinstance(images[k], torch.Tensor):
|
| 61 |
-
images[k] = images[k].detach().cpu()
|
| 62 |
-
if self.clamp:
|
| 63 |
-
images[k] = torch.clamp(images[k], -1., 1.)
|
| 64 |
-
|
| 65 |
-
self.log_local(pl_module.logger.save_dir, split, images,
|
| 66 |
-
pl_module.global_step, pl_module.current_epoch, batch_idx)
|
| 67 |
-
|
| 68 |
-
if is_train:
|
| 69 |
-
pl_module.train()
|
| 70 |
-
|
| 71 |
-
def check_frequency(self, check_idx):
|
| 72 |
-
return check_idx % self.batch_freq == 0
|
| 73 |
-
|
| 74 |
-
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
|
| 75 |
-
if not self.disabled:
|
| 76 |
-
self.log_img(pl_module, batch, batch_idx, split="train")
|
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|
spaces/AsakuraMizu/moe-tts/text/japanese.py
DELETED
|
@@ -1,153 +0,0 @@
|
|
| 1 |
-
import re
|
| 2 |
-
from unidecode import unidecode
|
| 3 |
-
import pyopenjtalk
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
# Regular expression matching Japanese without punctuation marks:
|
| 7 |
-
_japanese_characters = re.compile(
|
| 8 |
-
r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
|
| 9 |
-
|
| 10 |
-
# Regular expression matching non-Japanese characters or punctuation marks:
|
| 11 |
-
_japanese_marks = re.compile(
|
| 12 |
-
r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
|
| 13 |
-
|
| 14 |
-
# List of (symbol, Japanese) pairs for marks:
|
| 15 |
-
_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
|
| 16 |
-
('％', 'パーセント')
|
| 17 |
-
]]
|
| 18 |
-
|
| 19 |
-
# List of (romaji, ipa) pairs for marks:
|
| 20 |
-
_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
|
| 21 |
-
('ts', 'ʦ'),
|
| 22 |
-
('u', 'ɯ'),
|
| 23 |
-
('j', 'ʥ'),
|
| 24 |
-
('y', 'j'),
|
| 25 |
-
('ni', 'n^i'),
|
| 26 |
-
('nj', 'n^'),
|
| 27 |
-
('hi', 'çi'),
|
| 28 |
-
('hj', 'ç'),
|
| 29 |
-
('f', 'ɸ'),
|
| 30 |
-
('I', 'i*'),
|
| 31 |
-
('U', 'ɯ*'),
|
| 32 |
-
('r', 'ɾ')
|
| 33 |
-
]]
|
| 34 |
-
|
| 35 |
-
# List of (romaji, ipa2) pairs for marks:
|
| 36 |
-
_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
|
| 37 |
-
('u', 'ɯ'),
|
| 38 |
-
('ʧ', 'tʃ'),
|
| 39 |
-
('j', 'dʑ'),
|
| 40 |
-
('y', 'j'),
|
| 41 |
-
('ni', 'n^i'),
|
| 42 |
-
('nj', 'n^'),
|
| 43 |
-
('hi', 'çi'),
|
| 44 |
-
('hj', 'ç'),
|
| 45 |
-
('f', 'ɸ'),
|
| 46 |
-
('I', 'i*'),
|
| 47 |
-
('U', 'ɯ*'),
|
| 48 |
-
('r', 'ɾ')
|
| 49 |
-
]]
|
| 50 |
-
|
| 51 |
-
# List of (consonant, sokuon) pairs:
|
| 52 |
-
_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
|
| 53 |
-
(r'Q([↑↓]*[kg])', r'k#\1'),
|
| 54 |
-
(r'Q([↑↓]*[tdjʧ])', r't#\1'),
|
| 55 |
-
(r'Q([↑↓]*[sʃ])', r's\1'),
|
| 56 |
-
(r'Q([↑↓]*[pb])', r'p#\1')
|
| 57 |
-
]]
|
| 58 |
-
|
| 59 |
-
# List of (consonant, hatsuon) pairs:
|
| 60 |
-
_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
|
| 61 |
-
(r'N([↑↓]*[pbm])', r'm\1'),
|
| 62 |
-
(r'N([↑↓]*[ʧʥj])', r'n^\1'),
|
| 63 |
-
(r'N([↑↓]*[tdn])', r'n\1'),
|
| 64 |
-
(r'N([↑↓]*[kg])', r'ŋ\1')
|
| 65 |
-
]]
|
| 66 |
-
|
| 67 |
-
|
| 68 |
-
def symbols_to_japanese(text):
|
| 69 |
-
for regex, replacement in _symbols_to_japanese:
|
| 70 |
-
text = re.sub(regex, replacement, text)
|
| 71 |
-
return text
|
| 72 |
-
|
| 73 |
-
|
| 74 |
-
def japanese_to_romaji_with_accent(text):
|
| 75 |
-
'''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
|
| 76 |
-
text = symbols_to_japanese(text)
|
| 77 |
-
sentences = re.split(_japanese_marks, text)
|
| 78 |
-
marks = re.findall(_japanese_marks, text)
|
| 79 |
-
text = ''
|
| 80 |
-
for i, sentence in enumerate(sentences):
|
| 81 |
-
if re.match(_japanese_characters, sentence):
|
| 82 |
-
if text != '':
|
| 83 |
-
text += ' '
|
| 84 |
-
labels = pyopenjtalk.extract_fullcontext(sentence)
|
| 85 |
-
for n, label in enumerate(labels):
|
| 86 |
-
phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
|
| 87 |
-
if phoneme not in ['sil', 'pau']:
|
| 88 |
-
text += phoneme.replace('ch', 'ʧ').replace('sh',
|
| 89 |
-
'ʃ').replace('cl', 'Q')
|
| 90 |
-
else:
|
| 91 |
-
continue
|
| 92 |
-
# n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
|
| 93 |
-
a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
|
| 94 |
-
a2 = int(re.search(r"\+(\d+)\+", label).group(1))
|
| 95 |
-
a3 = int(re.search(r"\+(\d+)/", label).group(1))
|
| 96 |
-
if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
|
| 97 |
-
a2_next = -1
|
| 98 |
-
else:
|
| 99 |
-
a2_next = int(
|
| 100 |
-
re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
|
| 101 |
-
# Accent phrase boundary
|
| 102 |
-
if a3 == 1 and a2_next == 1:
|
| 103 |
-
text += ' '
|
| 104 |
-
# Falling
|
| 105 |
-
elif a1 == 0 and a2_next == a2 + 1:
|
| 106 |
-
text += '↓'
|
| 107 |
-
# Rising
|
| 108 |
-
elif a2 == 1 and a2_next == 2:
|
| 109 |
-
text += '↑'
|
| 110 |
-
if i < len(marks):
|
| 111 |
-
text += unidecode(marks[i]).replace(' ', '')
|
| 112 |
-
return text
|
| 113 |
-
|
| 114 |
-
|
| 115 |
-
def get_real_sokuon(text):
|
| 116 |
-
for regex, replacement in _real_sokuon:
|
| 117 |
-
text = re.sub(regex, replacement, text)
|
| 118 |
-
return text
|
| 119 |
-
|
| 120 |
-
|
| 121 |
-
def get_real_hatsuon(text):
|
| 122 |
-
for regex, replacement in _real_hatsuon:
|
| 123 |
-
text = re.sub(regex, replacement, text)
|
| 124 |
-
return text
|
| 125 |
-
|
| 126 |
-
|
| 127 |
-
def japanese_to_ipa(text):
|
| 128 |
-
text = japanese_to_romaji_with_accent(text).replace('...', '…')
|
| 129 |
-
text = re.sub(
|
| 130 |
-
r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
|
| 131 |
-
text = get_real_sokuon(text)
|
| 132 |
-
text = get_real_hatsuon(text)
|
| 133 |
-
for regex, replacement in _romaji_to_ipa:
|
| 134 |
-
text = re.sub(regex, replacement, text)
|
| 135 |
-
return text
|
| 136 |
-
|
| 137 |
-
|
| 138 |
-
def japanese_to_ipa2(text):
|
| 139 |
-
text = japanese_to_romaji_with_accent(text).replace('...', '…')
|
| 140 |
-
text = get_real_sokuon(text)
|
| 141 |
-
text = get_real_hatsuon(text)
|
| 142 |
-
for regex, replacement in _romaji_to_ipa2:
|
| 143 |
-
text = re.sub(regex, replacement, text)
|
| 144 |
-
return text
|
| 145 |
-
|
| 146 |
-
|
| 147 |
-
def japanese_to_ipa3(text):
|
| 148 |
-
text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
|
| 149 |
-
'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
|
| 150 |
-
text = re.sub(
|
| 151 |
-
r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
|
| 152 |
-
text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
|
| 153 |
-
return text
|
|
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|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/mbcharsetprober.py
DELETED
|
@@ -1,95 +0,0 @@
|
|
| 1 |
-
######################## BEGIN LICENSE BLOCK ########################
|
| 2 |
-
# The Original Code is Mozilla Universal charset detector code.
|
| 3 |
-
#
|
| 4 |
-
# The Initial Developer of the Original Code is
|
| 5 |
-
# Netscape Communications Corporation.
|
| 6 |
-
# Portions created by the Initial Developer are Copyright (C) 2001
|
| 7 |
-
# the Initial Developer. All Rights Reserved.
|
| 8 |
-
#
|
| 9 |
-
# Contributor(s):
|
| 10 |
-
# Mark Pilgrim - port to Python
|
| 11 |
-
# Shy Shalom - original C code
|
| 12 |
-
# Proofpoint, Inc.
|
| 13 |
-
#
|
| 14 |
-
# This library is free software; you can redistribute it and/or
|
| 15 |
-
# modify it under the terms of the GNU Lesser General Public
|
| 16 |
-
# License as published by the Free Software Foundation; either
|
| 17 |
-
# version 2.1 of the License, or (at your option) any later version.
|
| 18 |
-
#
|
| 19 |
-
# This library is distributed in the hope that it will be useful,
|
| 20 |
-
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
| 21 |
-
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
|
| 22 |
-
# Lesser General Public License for more details.
|
| 23 |
-
#
|
| 24 |
-
# You should have received a copy of the GNU Lesser General Public
|
| 25 |
-
# License along with this library; if not, write to the Free Software
|
| 26 |
-
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
|
| 27 |
-
# 02110-1301 USA
|
| 28 |
-
######################### END LICENSE BLOCK #########################
|
| 29 |
-
|
| 30 |
-
from typing import Optional, Union
|
| 31 |
-
|
| 32 |
-
from .chardistribution import CharDistributionAnalysis
|
| 33 |
-
from .charsetprober import CharSetProber
|
| 34 |
-
from .codingstatemachine import CodingStateMachine
|
| 35 |
-
from .enums import LanguageFilter, MachineState, ProbingState
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
class MultiByteCharSetProber(CharSetProber):
|
| 39 |
-
"""
|
| 40 |
-
MultiByteCharSetProber
|
| 41 |
-
"""
|
| 42 |
-
|
| 43 |
-
def __init__(self, lang_filter: LanguageFilter = LanguageFilter.NONE) -> None:
|
| 44 |
-
super().__init__(lang_filter=lang_filter)
|
| 45 |
-
self.distribution_analyzer: Optional[CharDistributionAnalysis] = None
|
| 46 |
-
self.coding_sm: Optional[CodingStateMachine] = None
|
| 47 |
-
self._last_char = bytearray(b"\0\0")
|
| 48 |
-
|
| 49 |
-
def reset(self) -> None:
|
| 50 |
-
super().reset()
|
| 51 |
-
if self.coding_sm:
|
| 52 |
-
self.coding_sm.reset()
|
| 53 |
-
if self.distribution_analyzer:
|
| 54 |
-
self.distribution_analyzer.reset()
|
| 55 |
-
self._last_char = bytearray(b"\0\0")
|
| 56 |
-
|
| 57 |
-
def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState:
|
| 58 |
-
assert self.coding_sm is not None
|
| 59 |
-
assert self.distribution_analyzer is not None
|
| 60 |
-
|
| 61 |
-
for i, byte in enumerate(byte_str):
|
| 62 |
-
coding_state = self.coding_sm.next_state(byte)
|
| 63 |
-
if coding_state == MachineState.ERROR:
|
| 64 |
-
self.logger.debug(
|
| 65 |
-
"%s %s prober hit error at byte %s",
|
| 66 |
-
self.charset_name,
|
| 67 |
-
self.language,
|
| 68 |
-
i,
|
| 69 |
-
)
|
| 70 |
-
self._state = ProbingState.NOT_ME
|
| 71 |
-
break
|
| 72 |
-
if coding_state == MachineState.ITS_ME:
|
| 73 |
-
self._state = ProbingState.FOUND_IT
|
| 74 |
-
break
|
| 75 |
-
if coding_state == MachineState.START:
|
| 76 |
-
char_len = self.coding_sm.get_current_charlen()
|
| 77 |
-
if i == 0:
|
| 78 |
-
self._last_char[1] = byte
|
| 79 |
-
self.distribution_analyzer.feed(self._last_char, char_len)
|
| 80 |
-
else:
|
| 81 |
-
self.distribution_analyzer.feed(byte_str[i - 1 : i + 1], char_len)
|
| 82 |
-
|
| 83 |
-
self._last_char[0] = byte_str[-1]
|
| 84 |
-
|
| 85 |
-
if self.state == ProbingState.DETECTING:
|
| 86 |
-
if self.distribution_analyzer.got_enough_data() and (
|
| 87 |
-
self.get_confidence() > self.SHORTCUT_THRESHOLD
|
| 88 |
-
):
|
| 89 |
-
self._state = ProbingState.FOUND_IT
|
| 90 |
-
|
| 91 |
-
return self.state
|
| 92 |
-
|
| 93 |
-
def get_confidence(self) -> float:
|
| 94 |
-
assert self.distribution_analyzer is not None
|
| 95 |
-
return self.distribution_analyzer.get_confidence()
|
|
|
|
|
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|
|
|
|
|
spaces/Audio-AGI/AudioSep/models/CLAP/open_clip/pretrained.py
DELETED
|
@@ -1,167 +0,0 @@
|
|
| 1 |
-
import hashlib
|
| 2 |
-
import os
|
| 3 |
-
import urllib
|
| 4 |
-
import warnings
|
| 5 |
-
|
| 6 |
-
from tqdm import tqdm
|
| 7 |
-
|
| 8 |
-
_RN50 = dict(
|
| 9 |
-
openai="https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
|
| 10 |
-
yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt",
|
| 11 |
-
cc12m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt",
|
| 12 |
-
)
|
| 13 |
-
|
| 14 |
-
_RN50_quickgelu = dict(
|
| 15 |
-
openai="https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
|
| 16 |
-
yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt",
|
| 17 |
-
cc12m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt",
|
| 18 |
-
)
|
| 19 |
-
|
| 20 |
-
_RN101 = dict(
|
| 21 |
-
openai="https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt",
|
| 22 |
-
yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt",
|
| 23 |
-
)
|
| 24 |
-
|
| 25 |
-
_RN101_quickgelu = dict(
|
| 26 |
-
openai="https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt",
|
| 27 |
-
yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt",
|
| 28 |
-
)
|
| 29 |
-
|
| 30 |
-
_RN50x4 = dict(
|
| 31 |
-
openai="https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt",
|
| 32 |
-
)
|
| 33 |
-
|
| 34 |
-
_RN50x16 = dict(
|
| 35 |
-
openai="https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt",
|
| 36 |
-
)
|
| 37 |
-
|
| 38 |
-
_RN50x64 = dict(
|
| 39 |
-
openai="https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt",
|
| 40 |
-
)
|
| 41 |
-
|
| 42 |
-
_VITB32 = dict(
|
| 43 |
-
openai="https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
|
| 44 |
-
laion400m_e31="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt",
|
| 45 |
-
laion400m_e32="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt",
|
| 46 |
-
laion400m_avg="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_avg-8a00ab3c.pt",
|
| 47 |
-
)
|
| 48 |
-
|
| 49 |
-
_VITB32_quickgelu = dict(
|
| 50 |
-
openai="https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
|
| 51 |
-
laion400m_e31="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt",
|
| 52 |
-
laion400m_e32="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt",
|
| 53 |
-
laion400m_avg="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_avg-8a00ab3c.pt",
|
| 54 |
-
)
|
| 55 |
-
|
| 56 |
-
_VITB16 = dict(
|
| 57 |
-
openai="https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
|
| 58 |
-
)
|
| 59 |
-
|
| 60 |
-
_VITL14 = dict(
|
| 61 |
-
openai="https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
|
| 62 |
-
)
|
| 63 |
-
|
| 64 |
-
_PRETRAINED = {
|
| 65 |
-
"RN50": _RN50,
|
| 66 |
-
"RN50-quickgelu": _RN50_quickgelu,
|
| 67 |
-
"RN101": _RN101,
|
| 68 |
-
"RN101-quickgelu": _RN101_quickgelu,
|
| 69 |
-
"RN50x4": _RN50x4,
|
| 70 |
-
"RN50x16": _RN50x16,
|
| 71 |
-
"ViT-B-32": _VITB32,
|
| 72 |
-
"ViT-B-32-quickgelu": _VITB32_quickgelu,
|
| 73 |
-
"ViT-B-16": _VITB16,
|
| 74 |
-
"ViT-L-14": _VITL14,
|
| 75 |
-
}
|
| 76 |
-
|
| 77 |
-
|
| 78 |
-
def list_pretrained(as_str: bool = False):
|
| 79 |
-
"""returns list of pretrained models
|
| 80 |
-
Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True
|
| 81 |
-
"""
|
| 82 |
-
return [
|
| 83 |
-
":".join([k, t]) if as_str else (k, t)
|
| 84 |
-
for k in _PRETRAINED.keys()
|
| 85 |
-
for t in _PRETRAINED[k].keys()
|
| 86 |
-
]
|
| 87 |
-
|
| 88 |
-
|
| 89 |
-
def list_pretrained_tag_models(tag: str):
|
| 90 |
-
"""return all models having the specified pretrain tag"""
|
| 91 |
-
models = []
|
| 92 |
-
for k in _PRETRAINED.keys():
|
| 93 |
-
if tag in _PRETRAINED[k]:
|
| 94 |
-
models.append(k)
|
| 95 |
-
return models
|
| 96 |
-
|
| 97 |
-
|
| 98 |
-
def list_pretrained_model_tags(model: str):
|
| 99 |
-
"""return all pretrain tags for the specified model architecture"""
|
| 100 |
-
tags = []
|
| 101 |
-
if model in _PRETRAINED:
|
| 102 |
-
tags.extend(_PRETRAINED[model].keys())
|
| 103 |
-
return tags
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
def get_pretrained_url(model: str, tag: str):
|
| 107 |
-
if model not in _PRETRAINED:
|
| 108 |
-
return ""
|
| 109 |
-
model_pretrained = _PRETRAINED[model]
|
| 110 |
-
if tag not in model_pretrained:
|
| 111 |
-
return ""
|
| 112 |
-
return model_pretrained[tag]
|
| 113 |
-
|
| 114 |
-
|
| 115 |
-
def download_pretrained(url: str, root: str = os.path.expanduser("~/.cache/clip")):
|
| 116 |
-
os.makedirs(root, exist_ok=True)
|
| 117 |
-
filename = os.path.basename(url)
|
| 118 |
-
|
| 119 |
-
if "openaipublic" in url:
|
| 120 |
-
expected_sha256 = url.split("/")[-2]
|
| 121 |
-
else:
|
| 122 |
-
expected_sha256 = ""
|
| 123 |
-
|
| 124 |
-
download_target = os.path.join(root, filename)
|
| 125 |
-
|
| 126 |
-
if os.path.exists(download_target) and not os.path.isfile(download_target):
|
| 127 |
-
raise RuntimeError(f"{download_target} exists and is not a regular file")
|
| 128 |
-
|
| 129 |
-
if os.path.isfile(download_target):
|
| 130 |
-
if expected_sha256:
|
| 131 |
-
if (
|
| 132 |
-
hashlib.sha256(open(download_target, "rb").read()).hexdigest()
|
| 133 |
-
== expected_sha256
|
| 134 |
-
):
|
| 135 |
-
return download_target
|
| 136 |
-
else:
|
| 137 |
-
warnings.warn(
|
| 138 |
-
f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"
|
| 139 |
-
)
|
| 140 |
-
else:
|
| 141 |
-
return download_target
|
| 142 |
-
|
| 143 |
-
with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
|
| 144 |
-
with tqdm(
|
| 145 |
-
total=int(source.info().get("Content-Length")),
|
| 146 |
-
ncols=80,
|
| 147 |
-
unit="iB",
|
| 148 |
-
unit_scale=True,
|
| 149 |
-
) as loop:
|
| 150 |
-
while True:
|
| 151 |
-
buffer = source.read(8192)
|
| 152 |
-
if not buffer:
|
| 153 |
-
break
|
| 154 |
-
|
| 155 |
-
output.write(buffer)
|
| 156 |
-
loop.update(len(buffer))
|
| 157 |
-
|
| 158 |
-
if (
|
| 159 |
-
expected_sha256
|
| 160 |
-
and hashlib.sha256(open(download_target, "rb").read()).hexdigest()
|
| 161 |
-
!= expected_sha256
|
| 162 |
-
):
|
| 163 |
-
raise RuntimeError(
|
| 164 |
-
f"Model has been downloaded but the SHA256 checksum does not not match"
|
| 165 |
-
)
|
| 166 |
-
|
| 167 |
-
return download_target
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
spaces/Banbri/zcvzcv/src/lib/replaceNonWhiteWithTransparent.ts
DELETED
|
@@ -1,46 +0,0 @@
|
|
| 1 |
-
export function replaceNonWhiteWithTransparent(imageBase64: string): Promise<string> {
|
| 2 |
-
return new Promise((resolve, reject) => {
|
| 3 |
-
const img = new Image();
|
| 4 |
-
img.onload = () => {
|
| 5 |
-
const canvas = document.createElement('canvas');
|
| 6 |
-
const ctx = canvas.getContext('2d');
|
| 7 |
-
if (!ctx) {
|
| 8 |
-
reject('Unable to get canvas context');
|
| 9 |
-
return;
|
| 10 |
-
}
|
| 11 |
-
|
| 12 |
-
const ratio = window.devicePixelRatio || 1;
|
| 13 |
-
canvas.width = img.width * ratio;
|
| 14 |
-
canvas.height = img.height * ratio;
|
| 15 |
-
ctx.scale(ratio, ratio);
|
| 16 |
-
|
| 17 |
-
ctx.drawImage(img, 0, 0);
|
| 18 |
-
|
| 19 |
-
const imageData = ctx.getImageData(0, 0, img.width, img.height);
|
| 20 |
-
const data = imageData.data;
|
| 21 |
-
console.log("ok")
|
| 22 |
-
|
| 23 |
-
for (let i = 0; i < data.length; i += 4) {
|
| 24 |
-
if (data[i] === 255 && data[i + 1] === 255 && data[i + 2] === 255) {
|
| 25 |
-
// Change white (also shades of grays) pixels to black
|
| 26 |
-
data[i] = 0;
|
| 27 |
-
data[i + 1] = 0;
|
| 28 |
-
data[i + 2] = 0;
|
| 29 |
-
} else {
|
| 30 |
-
// Change all other pixels to transparent
|
| 31 |
-
data[i + 3] = 0;
|
| 32 |
-
}
|
| 33 |
-
}
|
| 34 |
-
|
| 35 |
-
ctx.putImageData(imageData, 0, 0);
|
| 36 |
-
|
| 37 |
-
resolve(canvas.toDataURL());
|
| 38 |
-
};
|
| 39 |
-
|
| 40 |
-
img.onerror = (err) => {
|
| 41 |
-
reject(err);
|
| 42 |
-
};
|
| 43 |
-
|
| 44 |
-
img.src = imageBase64;
|
| 45 |
-
});
|
| 46 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
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|
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|
|
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|
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|
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|
|
|
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|
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|
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|
|
|
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|
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|
|
|
|
|
|
|
spaces/Benson/text-generation/Examples/Descargar Ataque Areo 3d Versin Completa Para Windows 10.md
DELETED
|
@@ -1,81 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<h1>Cómo descargar la versión completa de Air Strike 3D para Windows 10</h1>
|
| 3 |
-
<p>Si estás buscando un juego emocionante y lleno de acción que pondrá a prueba tus habilidades como piloto de helicóptero, entonces deberías probar Air Strike 3D. Este juego es un shooter estilo árcade en 3D que te permite volar y destruir varios enemigos en diferentes escenarios. Puedes elegir entre 10 helicópteros diferentes, cada uno con sus propias armas y habilidades, y enfrentarte a cientos de enemigos en 20 desafiantes niveles. En este artículo, te mostraremos cómo descargar la versión completa de Air Strike 3D para Windows 10, así como algunos consejos y trucos para jugar el juego. </p>
|
| 4 |
-
<h2>¿Qué es el ataque aéreo 3D? </h2>
|
| 5 |
-
<p>Air Strike 3D es un juego desarrollado por Divogames y lanzado en 2004. También se conoce como AirStrike II y III, y es una modificación del clásico juego de árcade, AirStrike. El juego se desarrolla en un mundo futurista donde tienes que luchar contra una organización terrorista que se ha apoderado del mundo. Tienes que usar tu helicóptero para volar sobre varios terrenos, como desiertos, selvas, ciudades y océanos, y destruir bases enemigas, tanques, aviones, barcos y más. El juego tiene gráficos realistas, efectos de sonido y física, así como un sistema de dificultad dinámica que se ajusta a tu rendimiento. </p>
|
| 6 |
-
<h2>descargar ataque aéreo 3d versión completa para windows 10</h2><br /><p><b><b>Download File</b> ⚹⚹⚹ <a href="https://bltlly.com/2v6IBf">https://bltlly.com/2v6IBf</a></b></p><br /><br />
|
| 7 |
-
<h3>Características del ataque aéreo 3D</h3>
|
| 8 |
-
<p>Algunas de las características de Air Strike 3D son:</p>
|
| 9 |
-
<ul>
|
| 10 |
-
<li>10 helicópteros diferentes con armas y características únicas</li>
|
| 11 |
-
<li>20 niveles con diferentes entornos y enemigos</li>
|
| 12 |
-
<li>5 modos de dificultad de fácil a insano</li>
|
| 13 |
-
<li>Varios power-ups y bonos para recoger</li>
|
| 14 |
-
<li>Gráficos y efectos de sonido de alta calidad</li>
|
| 15 |
-
<li>Controles simples e intuitivos</li>
|
| 16 |
-
</ul>
|
| 17 |
-
<h3>Requisitos para el ataque aéreo 3D</h3>
|
| 18 |
-
<p>Para jugar Air Strike 3D en su PC con Windows 10, necesita tener los siguientes requisitos:</p>
|
| 19 |
-
<ul>
|
| 20 |
-
<li>Windows XP/Vista/7/8/10</li>
|
| 21 |
-
Procesador <li>Pentium III o superior</li>
|
| 22 |
-
<li>128 MB de RAM</li>
|
| 23 |
-
<li>32 MB de memoria de vídeo</li>
|
| 24 |
-
|
| 25 |
-
<li>100 MB de espacio libre en disco</li>
|
| 26 |
-
</ul>
|
| 27 |
-
<h2>Dónde descargar la versión completa de Air Strike 3D para Windows 10</h2>
|
| 28 |
-
<p>Hay varias fuentes donde puede descargar la versión completa de Air Strike 3D para Windows 10. Aquí están algunas de ellas:</p>
|
| 29 |
-
<h3>Uptodown</h3>
|
| 30 |
-
<p>Uptodown es un sitio web que ofrece descargas gratuitas de varios juegos y aplicaciones para diferentes plataformas. Puede descargar la versión completa de Air Strike 3D para Windows 10 desde Uptodown siguiendo estos pasos:</p>
|
| 31 |
-
<ol>
|
| 32 |
-
<li>Ir a <a href="( 1 )">https://airstrike-3d-operation-wt.id.uptodown.com/windows</a></li>
|
| 33 |
-
<li>Haga clic en el botón verde "Descargar" </li>
|
| 34 |
-
<li>Espere a que la descarga termine y guarde el archivo en su PC</li>
|
| 35 |
-
</ol>
|
| 36 |
-
<h3>Filehippo</h3>
|
| 37 |
-
<p>Filehippo es otro sitio web que proporciona descargas gratuitas de software y juegos para varios dispositivos. Puede descargar la versión completa de Air Strike 3D para Windows 10 desde Filehippo haciendo lo siguiente:</p>
|
| 38 |
-
<ol>
|
| 39 |
-
<li>Ir a <a href=">https://filehippo.com/download_air-strike-3d/</a></li>
|
| 40 |
-
<li>Haga clic en el botón azul "Descargar la última versión"</li>
|
| 41 |
-
<li>Espere a que la descarga se inicie y guarde el archivo en su PC</li>
|
| 42 |
-
</ol>
|
| 43 |
-
<h3>Otras fuentes</h3>
|
| 44 |
-
<p>También puede descargar la versión completa de Air Strike 3D para Windows 10 de otras fuentes, como sitios de torrents, plataformas para compartir archivos o sitios web no oficiales. Sin embargo, debe tener cuidado al descargar de estas fuentes, ya que pueden contener virus, malware u otros archivos dañinos que pueden dañar su PC o comprometer su seguridad. Siempre debe escanear los archivos descargados con un programa antivirus confiable antes de instalarlos. También debe comprobar las revisiones y calificaciones de las fuentes para asegurarse de que son confiables y seguros. </p>
|
| 45 |
-
<h2>Cómo instalar la versión completa de Air Strike 3D para Windows 10</h2>
|
| 46 |
-
<p>Una vez que haya descargado la versión completa de Air Strike 3D para Windows 10 desde una de las fuentes mencionadas anteriormente, puede instalarlo en su PC siguiendo estos pasos:</p>
|
| 47 |
-
|
| 48 |
-
<p>El archivo del juego suele estar comprimido, como ZIP o RAR. Es necesario extraerlo con un programa como WinRAR o 7-Zip. Para hacer esto, haga clic derecho en el archivo del juego y seleccione "Extraer aquí" o "Extraer a Air Strike 3D" (o cualquier otro nombre de carpeta que prefiera). Esto creará una nueva carpeta con los archivos de juego extraídos. </p>
|
| 49 |
-
<p></p>
|
| 50 |
-
<h3>Paso 2: Extraer el archivo del juego</h3>
|
| 51 |
-
<p>Abra la carpeta extraída y busque el archivo de configuración. Normalmente se llama "setup.exe" o "install.exe". Haga doble clic en él para iniciar el asistente de instalación. Siga las instrucciones en la pantalla para completar el proceso de instalación. Es posible que deba aceptar los términos y condiciones, elegir una carpeta de destino y crear un acceso directo en su escritorio. </p>
|
| 52 |
-
<h3>Paso 3: Ejecutar el archivo de configuración</h3>
|
| 53 |
-
<p>Una vez finalizada la instalación, puede ejecutar el juego haciendo clic en el acceso directo de su escritorio o yendo a la carpeta de destino y haciendo doble clic en el archivo ejecutable del juego. Por lo general se llama "AirStrike3D.exe" o "AirStrikeII.exe". El juego comenzará y podrás disfrutar jugando. </p>
|
| 54 |
-
<h3>Paso 4: Disfruta del juego</h3>
|
| 55 |
-
<p>Air Strike 3D es un juego divertido y adictivo que te mantendrá entretenido durante horas. Puedes elegir entre diferentes modos de juego, como Campaña, Supervivencia, Juego rápido o Misión personalizada. También puedes ajustar la configuración del juego, como sonido, gráficos, controles y dificultad. También puede ver sus estadísticas, logros y puntuaciones altas. </p>
|
| 56 |
-
<h2>Consejos y trucos para jugar Air Strike 3D</h2>
|
| 57 |
-
<p>Para aprovechar al máximo su experiencia de juego, aquí hay algunos consejos y trucos para jugar Air Strike 3D:</p>
|
| 58 |
-
<h3>Elige el helicóptero adecuado</h3>
|
| 59 |
-
|
| 60 |
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<h3>Usa tus armas sabiamente</h3>
|
| 61 |
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<p>Tienes dos tipos de armas en Air Strike 3D: primaria y secundaria. El arma principal suele ser una ametralladora que dispara continuamente cuando se pulsa el botón izquierdo del ratón. El arma secundaria suele ser un cohete o una bomba que se dispara cuando se pulsa el botón derecho del ratón. Tienes una cantidad limitada de armas secundarias, por lo que debes usarlas con moderación y estratégicamente. También puedes recoger potenciadores y bonificaciones que te dan armas o municiones adicionales, como misiles, bombas de napalm, bombas de racimo, láseres, pistolas de plasma, etc.</p>
|
| 62 |
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<h3>Evitar el fuego enemigo</h3>
|
| 63 |
-
<p>Te enfrentarás a muchos enemigos en Air Strike 3D, como tanques, aviones, barcos, torretas, cohetes, minas, etc. Intentarán derribarte con sus propias armas. Usted debe evitar su fuego por moverse y esquivar sus ataques. También puedes usar tus armas para destruir sus proyectiles o desviarlos con tus palas de helicóptero. También debe tener cuidado con los peligros ambientales, como edificios, puentes, árboles, montañas, etc., que pueden bloquear su camino o dañar su helicóptero. También debes vigilar tus barras de salud y armadura, que indican cuánto daño puedes sufrir antes de perder el juego. </p>
|
| 64 |
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<h2>Conclusión</h2>
|
| 65 |
-
<p>Air Strike 3D es un gran juego para cualquier persona que ama volar y juegos de disparos. Tiene gráficos increíbles, efectos de sonido y jugabilidad que te mantendrán enganchado durante horas. Puede descargar la versión completa de Air Strike 3D para Windows 10 desde varias fuentes, como Uptodown, Filehippo u otros sitios web. También puede instalarlo fácilmente en su PC siguiendo los pasos que hemos proporcionado. También puedes mejorar tus habilidades y divertirte más siguiendo los consejos y trucos que hemos compartido. ¡Esperamos que disfrutes jugando Air Strike 3D y te diviertas! </p>
|
| 66 |
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<h2>Preguntas frecuentes</h2>
|
| 67 |
-
<p>Aquí hay algunas preguntas frecuentes sobre Air Strike 3D:</p>
|
| 68 |
-
<ul>
|
| 69 |
-
<li><b>Q: ¿Cómo puedo cambiar la configuración del juego? </b></li>
|
| 70 |
-
|
| 71 |
-
<li><b>Q: ¿Cómo puedo guardar y cargar el progreso de mi juego? </b></li>
|
| 72 |
-
<li>A: Puede guardar y cargar el progreso del juego haciendo clic en los botones "Guardar" o "Cargar" en el menú principal. Puede tener hasta tres ranuras de guardado. </li>
|
| 73 |
-
<li><b>Q: ¿Cómo puedo desbloquear nuevos helicópteros? </b></li>
|
| 74 |
-
<li>A: Puede desbloquear nuevos helicópteros completando ciertos niveles o logrando ciertas puntuaciones. Puede ver los requisitos para cada helicóptero en la pantalla "Seleccionar helicóptero". </li>
|
| 75 |
-
<li><b>Q: ¿Cómo puedo obtener más armas y municiones? </b></li>
|
| 76 |
-
<li>A: Puedes obtener más armas y munición mediante la recopilación de power-ups y bonos que aparecen al azar en la pantalla. Generalmente están marcados con iconos o letras, como M para misiles, N para bombas de napalm, L para láseres, etc.</li>
|
| 77 |
-
<li><b>Q: ¿Cómo hago una pausa o salgo del juego? </b></li>
|
| 78 |
-
<li>A: Puede hacer una pausa o salir del juego pulsando la tecla Esc del teclado. Esto mostrará un menú donde puedes reanudar, reiniciar o salir del juego. </li>
|
| 79 |
-
</ul></p> 64aa2da5cf<br />
|
| 80 |
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| 81 |
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spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/_palettes.py
DELETED
|
@@ -1,309 +0,0 @@
|
|
| 1 |
-
from .palette import Palette
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
# Taken from https://en.wikipedia.org/wiki/ANSI_escape_code (Windows 10 column)
|
| 5 |
-
WINDOWS_PALETTE = Palette(
|
| 6 |
-
[
|
| 7 |
-
(12, 12, 12),
|
| 8 |
-
(197, 15, 31),
|
| 9 |
-
(19, 161, 14),
|
| 10 |
-
(193, 156, 0),
|
| 11 |
-
(0, 55, 218),
|
| 12 |
-
(136, 23, 152),
|
| 13 |
-
(58, 150, 221),
|
| 14 |
-
(204, 204, 204),
|
| 15 |
-
(118, 118, 118),
|
| 16 |
-
(231, 72, 86),
|
| 17 |
-
(22, 198, 12),
|
| 18 |
-
(249, 241, 165),
|
| 19 |
-
(59, 120, 255),
|
| 20 |
-
(180, 0, 158),
|
| 21 |
-
(97, 214, 214),
|
| 22 |
-
(242, 242, 242),
|
| 23 |
-
]
|
| 24 |
-
)
|
| 25 |
-
|
| 26 |
-
# # The standard ansi colors (including bright variants)
|
| 27 |
-
STANDARD_PALETTE = Palette(
|
| 28 |
-
[
|
| 29 |
-
(0, 0, 0),
|
| 30 |
-
(170, 0, 0),
|
| 31 |
-
(0, 170, 0),
|
| 32 |
-
(170, 85, 0),
|
| 33 |
-
(0, 0, 170),
|
| 34 |
-
(170, 0, 170),
|
| 35 |
-
(0, 170, 170),
|
| 36 |
-
(170, 170, 170),
|
| 37 |
-
(85, 85, 85),
|
| 38 |
-
(255, 85, 85),
|
| 39 |
-
(85, 255, 85),
|
| 40 |
-
(255, 255, 85),
|
| 41 |
-
(85, 85, 255),
|
| 42 |
-
(255, 85, 255),
|
| 43 |
-
(85, 255, 255),
|
| 44 |
-
(255, 255, 255),
|
| 45 |
-
]
|
| 46 |
-
)
|
| 47 |
-
|
| 48 |
-
|
| 49 |
-
# The 256 color palette
|
| 50 |
-
EIGHT_BIT_PALETTE = Palette(
|
| 51 |
-
[
|
| 52 |
-
(0, 0, 0),
|
| 53 |
-
(128, 0, 0),
|
| 54 |
-
(0, 128, 0),
|
| 55 |
-
(128, 128, 0),
|
| 56 |
-
(0, 0, 128),
|
| 57 |
-
(128, 0, 128),
|
| 58 |
-
(0, 128, 128),
|
| 59 |
-
(192, 192, 192),
|
| 60 |
-
(128, 128, 128),
|
| 61 |
-
(255, 0, 0),
|
| 62 |
-
(0, 255, 0),
|
| 63 |
-
(255, 255, 0),
|
| 64 |
-
(0, 0, 255),
|
| 65 |
-
(255, 0, 255),
|
| 66 |
-
(0, 255, 255),
|
| 67 |
-
(255, 255, 255),
|
| 68 |
-
(0, 0, 0),
|
| 69 |
-
(0, 0, 95),
|
| 70 |
-
(0, 0, 135),
|
| 71 |
-
(0, 0, 175),
|
| 72 |
-
(0, 0, 215),
|
| 73 |
-
(0, 0, 255),
|
| 74 |
-
(0, 95, 0),
|
| 75 |
-
(0, 95, 95),
|
| 76 |
-
(0, 95, 135),
|
| 77 |
-
(0, 95, 175),
|
| 78 |
-
(0, 95, 215),
|
| 79 |
-
(0, 95, 255),
|
| 80 |
-
(0, 135, 0),
|
| 81 |
-
(0, 135, 95),
|
| 82 |
-
(0, 135, 135),
|
| 83 |
-
(0, 135, 175),
|
| 84 |
-
(0, 135, 215),
|
| 85 |
-
(0, 135, 255),
|
| 86 |
-
(0, 175, 0),
|
| 87 |
-
(0, 175, 95),
|
| 88 |
-
(0, 175, 135),
|
| 89 |
-
(0, 175, 175),
|
| 90 |
-
(0, 175, 215),
|
| 91 |
-
(0, 175, 255),
|
| 92 |
-
(0, 215, 0),
|
| 93 |
-
(0, 215, 95),
|
| 94 |
-
(0, 215, 135),
|
| 95 |
-
(0, 215, 175),
|
| 96 |
-
(0, 215, 215),
|
| 97 |
-
(0, 215, 255),
|
| 98 |
-
(0, 255, 0),
|
| 99 |
-
(0, 255, 95),
|
| 100 |
-
(0, 255, 135),
|
| 101 |
-
(0, 255, 175),
|
| 102 |
-
(0, 255, 215),
|
| 103 |
-
(0, 255, 255),
|
| 104 |
-
(95, 0, 0),
|
| 105 |
-
(95, 0, 95),
|
| 106 |
-
(95, 0, 135),
|
| 107 |
-
(95, 0, 175),
|
| 108 |
-
(95, 0, 215),
|
| 109 |
-
(95, 0, 255),
|
| 110 |
-
(95, 95, 0),
|
| 111 |
-
(95, 95, 95),
|
| 112 |
-
(95, 95, 135),
|
| 113 |
-
(95, 95, 175),
|
| 114 |
-
(95, 95, 215),
|
| 115 |
-
(95, 95, 255),
|
| 116 |
-
(95, 135, 0),
|
| 117 |
-
(95, 135, 95),
|
| 118 |
-
(95, 135, 135),
|
| 119 |
-
(95, 135, 175),
|
| 120 |
-
(95, 135, 215),
|
| 121 |
-
(95, 135, 255),
|
| 122 |
-
(95, 175, 0),
|
| 123 |
-
(95, 175, 95),
|
| 124 |
-
(95, 175, 135),
|
| 125 |
-
(95, 175, 175),
|
| 126 |
-
(95, 175, 215),
|
| 127 |
-
(95, 175, 255),
|
| 128 |
-
(95, 215, 0),
|
| 129 |
-
(95, 215, 95),
|
| 130 |
-
(95, 215, 135),
|
| 131 |
-
(95, 215, 175),
|
| 132 |
-
(95, 215, 215),
|
| 133 |
-
(95, 215, 255),
|
| 134 |
-
(95, 255, 0),
|
| 135 |
-
(95, 255, 95),
|
| 136 |
-
(95, 255, 135),
|
| 137 |
-
(95, 255, 175),
|
| 138 |
-
(95, 255, 215),
|
| 139 |
-
(95, 255, 255),
|
| 140 |
-
(135, 0, 0),
|
| 141 |
-
(135, 0, 95),
|
| 142 |
-
(135, 0, 135),
|
| 143 |
-
(135, 0, 175),
|
| 144 |
-
(135, 0, 215),
|
| 145 |
-
(135, 0, 255),
|
| 146 |
-
(135, 95, 0),
|
| 147 |
-
(135, 95, 95),
|
| 148 |
-
(135, 95, 135),
|
| 149 |
-
(135, 95, 175),
|
| 150 |
-
(135, 95, 215),
|
| 151 |
-
(135, 95, 255),
|
| 152 |
-
(135, 135, 0),
|
| 153 |
-
(135, 135, 95),
|
| 154 |
-
(135, 135, 135),
|
| 155 |
-
(135, 135, 175),
|
| 156 |
-
(135, 135, 215),
|
| 157 |
-
(135, 135, 255),
|
| 158 |
-
(135, 175, 0),
|
| 159 |
-
(135, 175, 95),
|
| 160 |
-
(135, 175, 135),
|
| 161 |
-
(135, 175, 175),
|
| 162 |
-
(135, 175, 215),
|
| 163 |
-
(135, 175, 255),
|
| 164 |
-
(135, 215, 0),
|
| 165 |
-
(135, 215, 95),
|
| 166 |
-
(135, 215, 135),
|
| 167 |
-
(135, 215, 175),
|
| 168 |
-
(135, 215, 215),
|
| 169 |
-
(135, 215, 255),
|
| 170 |
-
(135, 255, 0),
|
| 171 |
-
(135, 255, 95),
|
| 172 |
-
(135, 255, 135),
|
| 173 |
-
(135, 255, 175),
|
| 174 |
-
(135, 255, 215),
|
| 175 |
-
(135, 255, 255),
|
| 176 |
-
(175, 0, 0),
|
| 177 |
-
(175, 0, 95),
|
| 178 |
-
(175, 0, 135),
|
| 179 |
-
(175, 0, 175),
|
| 180 |
-
(175, 0, 215),
|
| 181 |
-
(175, 0, 255),
|
| 182 |
-
(175, 95, 0),
|
| 183 |
-
(175, 95, 95),
|
| 184 |
-
(175, 95, 135),
|
| 185 |
-
(175, 95, 175),
|
| 186 |
-
(175, 95, 215),
|
| 187 |
-
(175, 95, 255),
|
| 188 |
-
(175, 135, 0),
|
| 189 |
-
(175, 135, 95),
|
| 190 |
-
(175, 135, 135),
|
| 191 |
-
(175, 135, 175),
|
| 192 |
-
(175, 135, 215),
|
| 193 |
-
(175, 135, 255),
|
| 194 |
-
(175, 175, 0),
|
| 195 |
-
(175, 175, 95),
|
| 196 |
-
(175, 175, 135),
|
| 197 |
-
(175, 175, 175),
|
| 198 |
-
(175, 175, 215),
|
| 199 |
-
(175, 175, 255),
|
| 200 |
-
(175, 215, 0),
|
| 201 |
-
(175, 215, 95),
|
| 202 |
-
(175, 215, 135),
|
| 203 |
-
(175, 215, 175),
|
| 204 |
-
(175, 215, 215),
|
| 205 |
-
(175, 215, 255),
|
| 206 |
-
(175, 255, 0),
|
| 207 |
-
(175, 255, 95),
|
| 208 |
-
(175, 255, 135),
|
| 209 |
-
(175, 255, 175),
|
| 210 |
-
(175, 255, 215),
|
| 211 |
-
(175, 255, 255),
|
| 212 |
-
(215, 0, 0),
|
| 213 |
-
(215, 0, 95),
|
| 214 |
-
(215, 0, 135),
|
| 215 |
-
(215, 0, 175),
|
| 216 |
-
(215, 0, 215),
|
| 217 |
-
(215, 0, 255),
|
| 218 |
-
(215, 95, 0),
|
| 219 |
-
(215, 95, 95),
|
| 220 |
-
(215, 95, 135),
|
| 221 |
-
(215, 95, 175),
|
| 222 |
-
(215, 95, 215),
|
| 223 |
-
(215, 95, 255),
|
| 224 |
-
(215, 135, 0),
|
| 225 |
-
(215, 135, 95),
|
| 226 |
-
(215, 135, 135),
|
| 227 |
-
(215, 135, 175),
|
| 228 |
-
(215, 135, 215),
|
| 229 |
-
(215, 135, 255),
|
| 230 |
-
(215, 175, 0),
|
| 231 |
-
(215, 175, 95),
|
| 232 |
-
(215, 175, 135),
|
| 233 |
-
(215, 175, 175),
|
| 234 |
-
(215, 175, 215),
|
| 235 |
-
(215, 175, 255),
|
| 236 |
-
(215, 215, 0),
|
| 237 |
-
(215, 215, 95),
|
| 238 |
-
(215, 215, 135),
|
| 239 |
-
(215, 215, 175),
|
| 240 |
-
(215, 215, 215),
|
| 241 |
-
(215, 215, 255),
|
| 242 |
-
(215, 255, 0),
|
| 243 |
-
(215, 255, 95),
|
| 244 |
-
(215, 255, 135),
|
| 245 |
-
(215, 255, 175),
|
| 246 |
-
(215, 255, 215),
|
| 247 |
-
(215, 255, 255),
|
| 248 |
-
(255, 0, 0),
|
| 249 |
-
(255, 0, 95),
|
| 250 |
-
(255, 0, 135),
|
| 251 |
-
(255, 0, 175),
|
| 252 |
-
(255, 0, 215),
|
| 253 |
-
(255, 0, 255),
|
| 254 |
-
(255, 95, 0),
|
| 255 |
-
(255, 95, 95),
|
| 256 |
-
(255, 95, 135),
|
| 257 |
-
(255, 95, 175),
|
| 258 |
-
(255, 95, 215),
|
| 259 |
-
(255, 95, 255),
|
| 260 |
-
(255, 135, 0),
|
| 261 |
-
(255, 135, 95),
|
| 262 |
-
(255, 135, 135),
|
| 263 |
-
(255, 135, 175),
|
| 264 |
-
(255, 135, 215),
|
| 265 |
-
(255, 135, 255),
|
| 266 |
-
(255, 175, 0),
|
| 267 |
-
(255, 175, 95),
|
| 268 |
-
(255, 175, 135),
|
| 269 |
-
(255, 175, 175),
|
| 270 |
-
(255, 175, 215),
|
| 271 |
-
(255, 175, 255),
|
| 272 |
-
(255, 215, 0),
|
| 273 |
-
(255, 215, 95),
|
| 274 |
-
(255, 215, 135),
|
| 275 |
-
(255, 215, 175),
|
| 276 |
-
(255, 215, 215),
|
| 277 |
-
(255, 215, 255),
|
| 278 |
-
(255, 255, 0),
|
| 279 |
-
(255, 255, 95),
|
| 280 |
-
(255, 255, 135),
|
| 281 |
-
(255, 255, 175),
|
| 282 |
-
(255, 255, 215),
|
| 283 |
-
(255, 255, 255),
|
| 284 |
-
(8, 8, 8),
|
| 285 |
-
(18, 18, 18),
|
| 286 |
-
(28, 28, 28),
|
| 287 |
-
(38, 38, 38),
|
| 288 |
-
(48, 48, 48),
|
| 289 |
-
(58, 58, 58),
|
| 290 |
-
(68, 68, 68),
|
| 291 |
-
(78, 78, 78),
|
| 292 |
-
(88, 88, 88),
|
| 293 |
-
(98, 98, 98),
|
| 294 |
-
(108, 108, 108),
|
| 295 |
-
(118, 118, 118),
|
| 296 |
-
(128, 128, 128),
|
| 297 |
-
(138, 138, 138),
|
| 298 |
-
(148, 148, 148),
|
| 299 |
-
(158, 158, 158),
|
| 300 |
-
(168, 168, 168),
|
| 301 |
-
(178, 178, 178),
|
| 302 |
-
(188, 188, 188),
|
| 303 |
-
(198, 198, 198),
|
| 304 |
-
(208, 208, 208),
|
| 305 |
-
(218, 218, 218),
|
| 306 |
-
(228, 228, 228),
|
| 307 |
-
(238, 238, 238),
|
| 308 |
-
]
|
| 309 |
-
)
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|
spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_model_e2e.py
DELETED
|
@@ -1,150 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
import unittest
|
| 5 |
-
import torch
|
| 6 |
-
|
| 7 |
-
import detectron2.model_zoo as model_zoo
|
| 8 |
-
from detectron2.config import get_cfg
|
| 9 |
-
from detectron2.modeling import build_model
|
| 10 |
-
from detectron2.structures import BitMasks, Boxes, ImageList, Instances
|
| 11 |
-
from detectron2.utils.events import EventStorage
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
def get_model_zoo(config_path):
|
| 15 |
-
"""
|
| 16 |
-
Like model_zoo.get, but do not load any weights (even pretrained)
|
| 17 |
-
"""
|
| 18 |
-
cfg_file = model_zoo.get_config_file(config_path)
|
| 19 |
-
cfg = get_cfg()
|
| 20 |
-
cfg.merge_from_file(cfg_file)
|
| 21 |
-
if not torch.cuda.is_available():
|
| 22 |
-
cfg.MODEL.DEVICE = "cpu"
|
| 23 |
-
return build_model(cfg)
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
def create_model_input(img, inst=None):
|
| 27 |
-
if inst is not None:
|
| 28 |
-
return {"image": img, "instances": inst}
|
| 29 |
-
else:
|
| 30 |
-
return {"image": img}
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
def get_empty_instance(h, w):
|
| 34 |
-
inst = Instances((h, w))
|
| 35 |
-
inst.gt_boxes = Boxes(torch.rand(0, 4))
|
| 36 |
-
inst.gt_classes = torch.tensor([]).to(dtype=torch.int64)
|
| 37 |
-
inst.gt_masks = BitMasks(torch.rand(0, h, w))
|
| 38 |
-
return inst
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
def get_regular_bitmask_instances(h, w):
|
| 42 |
-
inst = Instances((h, w))
|
| 43 |
-
inst.gt_boxes = Boxes(torch.rand(3, 4))
|
| 44 |
-
inst.gt_boxes.tensor[:, 2:] += inst.gt_boxes.tensor[:, :2]
|
| 45 |
-
inst.gt_classes = torch.tensor([3, 4, 5]).to(dtype=torch.int64)
|
| 46 |
-
inst.gt_masks = BitMasks((torch.rand(3, h, w) > 0.5))
|
| 47 |
-
return inst
|
| 48 |
-
|
| 49 |
-
|
| 50 |
-
class ModelE2ETest(unittest.TestCase):
|
| 51 |
-
def setUp(self):
|
| 52 |
-
torch.manual_seed(43)
|
| 53 |
-
self.model = get_model_zoo(self.CONFIG_PATH)
|
| 54 |
-
|
| 55 |
-
def _test_eval(self, input_sizes):
|
| 56 |
-
inputs = [create_model_input(torch.rand(3, s[0], s[1])) for s in input_sizes]
|
| 57 |
-
self.model.eval()
|
| 58 |
-
self.model(inputs)
|
| 59 |
-
|
| 60 |
-
def _test_train(self, input_sizes, instances):
|
| 61 |
-
assert len(input_sizes) == len(instances)
|
| 62 |
-
inputs = [
|
| 63 |
-
create_model_input(torch.rand(3, s[0], s[1]), inst)
|
| 64 |
-
for s, inst in zip(input_sizes, instances)
|
| 65 |
-
]
|
| 66 |
-
self.model.train()
|
| 67 |
-
with EventStorage():
|
| 68 |
-
losses = self.model(inputs)
|
| 69 |
-
sum(losses.values()).backward()
|
| 70 |
-
del losses
|
| 71 |
-
|
| 72 |
-
def _inf_tensor(self, *shape):
|
| 73 |
-
return 1.0 / torch.zeros(*shape, device=self.model.device)
|
| 74 |
-
|
| 75 |
-
def _nan_tensor(self, *shape):
|
| 76 |
-
return torch.zeros(*shape, device=self.model.device).fill_(float("nan"))
|
| 77 |
-
|
| 78 |
-
|
| 79 |
-
class MaskRCNNE2ETest(ModelE2ETest):
|
| 80 |
-
CONFIG_PATH = "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
|
| 81 |
-
|
| 82 |
-
def test_empty_data(self):
|
| 83 |
-
instances = [get_empty_instance(200, 250), get_empty_instance(200, 249)]
|
| 84 |
-
self._test_eval([(200, 250), (200, 249)])
|
| 85 |
-
self._test_train([(200, 250), (200, 249)], instances)
|
| 86 |
-
|
| 87 |
-
def test_half_empty_data(self):
|
| 88 |
-
instances = [get_empty_instance(200, 250), get_regular_bitmask_instances(200, 249)]
|
| 89 |
-
self._test_train([(200, 250), (200, 249)], instances)
|
| 90 |
-
|
| 91 |
-
def test_rpn_inf_nan_data(self):
|
| 92 |
-
self.model.eval()
|
| 93 |
-
for tensor in [self._inf_tensor, self._nan_tensor]:
|
| 94 |
-
images = ImageList(tensor(1, 3, 512, 512), [(510, 510)])
|
| 95 |
-
features = {
|
| 96 |
-
"p2": tensor(1, 256, 256, 256),
|
| 97 |
-
"p3": tensor(1, 256, 128, 128),
|
| 98 |
-
"p4": tensor(1, 256, 64, 64),
|
| 99 |
-
"p5": tensor(1, 256, 32, 32),
|
| 100 |
-
"p6": tensor(1, 256, 16, 16),
|
| 101 |
-
}
|
| 102 |
-
props, _ = self.model.proposal_generator(images, features)
|
| 103 |
-
self.assertEqual(len(props[0]), 0)
|
| 104 |
-
|
| 105 |
-
def test_roiheads_inf_nan_data(self):
|
| 106 |
-
self.model.eval()
|
| 107 |
-
for tensor in [self._inf_tensor, self._nan_tensor]:
|
| 108 |
-
images = ImageList(tensor(1, 3, 512, 512), [(510, 510)])
|
| 109 |
-
features = {
|
| 110 |
-
"p2": tensor(1, 256, 256, 256),
|
| 111 |
-
"p3": tensor(1, 256, 128, 128),
|
| 112 |
-
"p4": tensor(1, 256, 64, 64),
|
| 113 |
-
"p5": tensor(1, 256, 32, 32),
|
| 114 |
-
"p6": tensor(1, 256, 16, 16),
|
| 115 |
-
}
|
| 116 |
-
props = [Instances((510, 510))]
|
| 117 |
-
props[0].proposal_boxes = Boxes([[10, 10, 20, 20]]).to(device=self.model.device)
|
| 118 |
-
props[0].objectness_logits = torch.tensor([1.0]).reshape(1, 1)
|
| 119 |
-
det, _ = self.model.roi_heads(images, features, props)
|
| 120 |
-
self.assertEqual(len(det[0]), 0)
|
| 121 |
-
|
| 122 |
-
|
| 123 |
-
class RetinaNetE2ETest(ModelE2ETest):
|
| 124 |
-
CONFIG_PATH = "COCO-Detection/retinanet_R_50_FPN_1x.yaml"
|
| 125 |
-
|
| 126 |
-
def test_empty_data(self):
|
| 127 |
-
instances = [get_empty_instance(200, 250), get_empty_instance(200, 249)]
|
| 128 |
-
self._test_eval([(200, 250), (200, 249)])
|
| 129 |
-
self._test_train([(200, 250), (200, 249)], instances)
|
| 130 |
-
|
| 131 |
-
def test_inf_nan_data(self):
|
| 132 |
-
self.model.eval()
|
| 133 |
-
self.model.score_threshold = -999999999
|
| 134 |
-
for tensor in [self._inf_tensor, self._nan_tensor]:
|
| 135 |
-
images = ImageList(tensor(1, 3, 512, 512), [(510, 510)])
|
| 136 |
-
features = [
|
| 137 |
-
tensor(1, 256, 128, 128),
|
| 138 |
-
tensor(1, 256, 64, 64),
|
| 139 |
-
tensor(1, 256, 32, 32),
|
| 140 |
-
tensor(1, 256, 16, 16),
|
| 141 |
-
tensor(1, 256, 8, 8),
|
| 142 |
-
]
|
| 143 |
-
anchors = self.model.anchor_generator(features)
|
| 144 |
-
box_cls, box_delta = self.model.head(features)
|
| 145 |
-
box_cls = [tensor(*k.shape) for k in box_cls]
|
| 146 |
-
box_delta = [tensor(*k.shape) for k in box_delta]
|
| 147 |
-
det = self.model.inference(box_cls, box_delta, anchors, images.image_sizes)
|
| 148 |
-
# all predictions (if any) are infinite or nan
|
| 149 |
-
if len(det[0]):
|
| 150 |
-
self.assertTrue(torch.isfinite(det[0].pred_boxes.tensor).sum() == 0)
|
|
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