Package: gan-pytorch
GAN-PyTorchUpdate (Feb 16, 2020)Now you can install this library directly using pip!$pip3install--upgradegan_pytorchUpdate (January 29, 2020)The mnist and fmnist models are now available. Their usage is identical to the other models:fromgan_pytorchimportGeneratormodel=Generator.from_pretrained('g-mnist')OverviewThis repository contains an op-for-op PyTorch reimplementation ofGenerative Adversarial Networks.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 Generative Adversarial NetworksModel DescriptionInstallationUsageLoad pretrained modelsExample: Extended datasetExample: VisualContributingAbout Generative Adversarial NetworksIf you're new to GANs, here's an abstract straight from the paper:We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.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:$pip3installgan_pytorchInstall from source:$gitclonehttps://github.com/lornatang/Generative-Adversarial-Networks
$cdGenerative-Adversarial-Networks
$pip3install-rrequirements.txt
$pip3install-e.UsageLoading pretrained modelsLoad an Generative-Adversarial-Networks:fromgan_pytorchimportGeneratormodel=Generator.from_name("g-mnist")Load a pretrained Generative-Adversarial-Networks:fromgan_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.utilsasvutilsfromgan_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().cpu(),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://127.0.0.1:10000/.
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!