Package: wgan-pytorch
WassersteinGAN-PyTorchUpdate (Feb 21, 2020)The mnist and fmnist models are now available. Their usage is identical to the other models:fromwgan_pytorchimportGeneratormodel=Generator.from_pretrained('g-mnist')OverviewThis repository contains an op-for-op PyTorch reimplementation ofWasserstein GAN.The goal of this implementation is to be simple, highly extensible, and easy to integrate into your own projects. This implementation is a work in progress -- new features are currently being implemented.At the moment, you can easily:Load pretrained Generate modelsUse Generate models for extended datasetUpcoming features: In the next few days, you will be able to:Quickly finetune an Generate on your own datasetExport Generate models for productionTable of contentsAbout Wasserstein GANModel DescriptionInstallationUsageLoad pretrained modelsExample: Extended datasetExample: VisualContributingAbout Wasserstein GANIf you're new to Wasserstein GAN, here's an abstract straight from the paper:We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to other distances between distributions.Model DescriptionWe have two networks, G (Generator) and D (Discriminator).The Generator is a network for generating images. It receives a random noise z and generates images from this noise, which is called G(z).Discriminator is a discriminant network that discriminates whether an image is real. The input is x, x is a picture, and the output is D of x is the probability that x is a real picture, and if it's 1, it's 100% real, and if it's 0, it's not real.InstallationInstall from pypi:pipinstallwgan_pytorchInstall from source:gitclonehttps://github.com/Lornatang/WassersteinGAN-PyTorch.gitcdWassersteinGAN-PyTorch
pipinstall-e.UsageLoading pretrained modelsLoad an Wasserstein GAN:fromwgan_pytorchimportGeneratormodel=Generator.from_name("g-mnist")Load a pretrained Wasserstein GAN:fromwgan_pytorchimportGeneratormodel=Generator.from_pretrained("g-mnist")Example: Extended datasetAs mentioned in the example, if you load the pre-trained weights of the MNIST dataset, it will create a newimgsdirectory and generate 64 random images in theimgsdirectory.importosimporttorchimporttorchvision.utilsasvutilsfromwgan_pytorchimportGeneratordevice=torch.device("cuda:0"iftorch.cuda.is_available()else"cpu")model=Generator.from_pretrained("g-mnist")model.to(device)# switch to evaluate modemodel.eval()try:os.makedirs("./imgs")exceptOSError:passwithtorch.no_grad():foriinrange(64):noise=torch.randn(64,100,device=device)fake=model(noise)vutils.save_image(fake.detach(),f"./imgs/fake_{i:04d}.png",normalize=True)print("The fake image has been generated!")Example: Visualcd $REPO$/framework
sh start.shThen open the browser and type in the browser addresshttp://127.0.0.1:10002/.
Enjoy it.ContributingIf you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.I look forward to seeing what the community does with these models!