iGAN | Interactive Image Generation via Generative Adversarial | Machine Learning library

 by   junyanz Python Version: Current License: MIT

kandi X-RAY | iGAN Summary

kandi X-RAY | iGAN Summary

iGAN is a Python library typically used in Telecommunications, Media, Advertising, Marketing, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. iGAN has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However iGAN build file is not available. You can download it from GitHub.

iGAN (aka. interactive GAN) is the author's implementation of interactive image generation interface described in: "Generative Visual Manipulation on the Natural Image Manifold" Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alexei A. Efros In European Conference on Computer Vision (ECCV) 2016.

            kandi-support Support

              iGAN has a medium active ecosystem.
              It has 3937 star(s) with 601 fork(s). There are 177 watchers for this library.
              It had no major release in the last 6 months.
              There are 14 open issues and 15 have been closed. On average issues are closed in 147 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of iGAN is current.

            kandi-Quality Quality

              iGAN has 0 bugs and 0 code smells.

            kandi-Security Security

              iGAN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              iGAN code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              iGAN is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              iGAN releases are not available. You will need to build from source code and install.
              iGAN has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions, examples and code snippets are available.
              iGAN saves you 1542 person hours of effort in developing the same functionality from scratch.
              It has 3434 lines of code, 294 functions and 42 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed iGAN and discovered the below as its top functions. This is intended to give you an instant insight into iGAN implemented functionality, and help decide if they suit your requirements.
            • Draw the image
            • Get an image
            • Shadow image
            • Draws a painter
            • Define the inverse of the model
            • Calculate thehog function
            • Test for discretization
            • Batch norm norm
            • Main function
            • Compute the inverse of the optimization
            • Wrapper function for inference
            • Multi - layer batchnorm
            • Parse the command line arguments
            • Predict batchnorm
            • Mouse press event
            • Mouse release event handler
            • Predict the input tensor
            • Discrete discriminator
            • Generate random samples
            • Compute reconstruction loss on test images
            • Loads the model parameters
            • Calculate the inverse of the optimization
            • Update the message text
            • Load images
            • Reimplement Qt method
            • Update the image
            • Generate batchnorm
            Get all kandi verified functions for this library.

            iGAN Key Features

            No Key Features are available at this moment for iGAN.

            iGAN Examples and Code Snippets

            CycleGAN and pix2pix in PyTorch,Training/test Details,pix2pix datasets
            Pythondot img1Lines of Code : 2dot img1License : Non-SPDX (NOASSERTION)
            copy iconCopy
            bash ./datasets/download_pix2pix_dataset.sh dataset_name
            python datasets/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/data

            Community Discussions


            Visual Studio 2017 v15.3 doesn't copy nlog.config
            Asked 2017-Sep-27 at 23:25

            I just updated to Visual Studio 2017 v15.3 and Core 2.0 SDK.

            I'm working with Igans Sakalauskas' Net Core Knockout project, it was built with Core 1.1 in VS 2017.


            I've left EnableDefaultContentItemsto the default of true and removed the Content Include statements from the .csproj file in the WebApplication1.Web project.

            He is using nlog and there is a nlog.config in the root of the project. The project builds successfully but throws a file not found error when ran. It is looking for the nlog.config in the WebApplication1.Web\bin\Debug\netcoreapp1.1 folder. If I manually copy the file the project runs and all the tests pass.

            What I cannot get to work is for VS to copy the nlog.config when the project builds.

            If I add



            Answered 2017-Sep-27 at 23:25

            This is nothing to do with duplicate content items.

            Revert to the recommended approach to handling duplicate content errors in Visual Studio 2017; this is what you started with:

            I've left EnableDefaultContentItems to the default of true and removed the Content Include statements from the .csproj file in the WebApplication1.Web project.

            Now add this to your .csproj file:

            Source https://stackoverflow.com/questions/46458319

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install iGAN

            Install the python libraries. (See Requirements).
            Download the code from GitHub:
            Download the model. (See Model Zoo for details):
            Run the python script:


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            gh repo clone junyanz/iGAN

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