Metadata-Version: 1.0
Name: vegans
Version: 0.1.0
Summary: A library to easily train various existing GANs in PyTorch.
Home-page: https://github.com/unit8co/vegans/
Author: Unit8
Author-email: julien@unit8.co
License: MIT
Description: # VeGANs
        
        A library providing various existing GANs in PyTorch.
        
        This library targets mainly GAN users, who want to use existing GAN training techniques with their own generators/discriminators.
        However researchers may also find the GAN base class useful for quicker implementation of new GAN training techniques.
        
        The focus is on simplicity and providing reasonable defaults.
        
        ## How to install
        `pip install vegans`
        
        ## How to use
        The basic idea is that the user provides discriminator and generator networks, and the library takes care of training them in a selected GAN setting:
        ```
        from vegans import WGAN
        from vegans.utils import plot_losses, plot_image_samples
        
        # Create your critic and generator
        netD = Discriminator().to(device)
        netG = Generator().to(device)
        
        # Build a Wasserstein GAN
        gan = WGAN(netG, netD, dataloader, ngpu=1, nr_epochs=20)
        
        # train it
        gan.train()
        
        # vizualise results
        img_list, D_losses, G_losses = gan.get_training_results()
        plot_losses(G_losses, D_losses)
        plot_image_samples(img_list, 50)
        ```
        
        Currently the best way to learn more about how to use VeGANs is to have a look at the example [notebooks](https://github.com/unit8co/vegans).
        
        ## Contribute
        PRs and suggestions are welcome.
        
        ## Credits
        Some of the code has been inspired by some existing GAN implementations:
        * https://github.com/eriklindernoren/PyTorch-GAN
        * https://github.com/martinarjovsky/WassersteinGAN
        * https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html
        
Keywords: gan gans pytorch generative adversarial networks
Platform: UNKNOWN
