DeepGen

A collection of modern Deep Generative Models.

Install

pip3 install deepgen

Usage

Toy example:

import os
import torch

from deepgen.gan.gan import GAN
from torchvision import datasets
import torchvision.transforms as transforms

img_size = (1, 28, 28)
batch_size = 64

if __name__ == '__main__':
    data_loader = torch.utils.data.DataLoader(
        datasets.MNIST(
            '../data/',
            train=True,
            download=True,
            transform=transforms.Compose(
                [transforms.Resize(28), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]
            ),
        ),
        batch_size=batch_size,
        shuffle=True,
    )

    os.makedirs('images', exist_ok=True)

    model = GAN()

    print(model)
    his = model.train(data_loader=data_loader, n_epoch=5, sample_interval=10)
    print(his)

TODO:

Updating

Generative Adversarial Network (GANs)

• Vanilla GAN
• Boundary Seeking GAN
• Auxiliary Classifier GAN
• BEGAN
• BicycleGAN
• Boundary-Seeking GAN
• Conditional GAN
• Context-Conditional GAN
• Context Encoder
• Coupled GAN
• CycleGAN
• Deep Convolutional GAN
• DiscoGAN
• DRAGAN
• DualGAN
• Energy-Based GAN
• Enhanced Super-Resolution GAN
• GAN
• InfoGAN
• Least Squares GAN
• MUNIT
• Pix2Pix
• PixelDA
• Relativistic GAN
• Semi-Supervised GAN
• Softmax GAN
• StarGAN
• Super-Resolution GAN
• UNIT
• Wasserstein GAN
• Wasserstein GAN GP
• Wasserstein GAN DIV

Variational Autoencoder (VAEs)

• Vanilla VAE
• Conditional VAE
• Denoising VAE
• Adversarial Variational Bayes
• Adversarial Autoencoder