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In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the fam- ily of Wasserstein GANs (WGANs) is considered to be state-of-the-art due to the theoretical contributions and competitive qualitative performance

Project description

WassersteinGAN_DIV-PyTorch

Update (Feb 22, 2020)

The mnist and fmnist models are now available. Their usage is identical to the other models:

from wgandiv_pytorch import Generator
model = Generator.from_pretrained('g-mnist') 

Overview

This repository contains an op-for-op PyTorch reimplementation of Wasserstein Divergence for GANs.

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 models
  • Use Generate models for extended dataset

Upcoming features: In the next few days, you will be able to:

  • Quickly finetune an Generate on your own dataset
  • Export Generate models for production

Table of contents

  1. About Wasserstein GAN DIV
  2. Model Description
  3. Installation
  4. Usage
  5. Contributing

About Wasserstein GAN DIV

If you're new to Wasserstein GAN DIV, here's an abstract straight from the paper:

In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the fam- ily of Wasserstein GANs (WGANs) is considered to be state-of-the-art due to the theoretical contributions and competitive qualitative performance. However, it is very challenging to approximate the k-Lipschitz constraint required by the Wasserstein-1 metric (W-met). In this paper, we propose a novel Wasserstein divergence (W-div), which is a relaxed version of W-met and does not require the k-Lipschitz constraint.As a concrete application, we introduce a Wasserstein divergence objective for GANs (WGAN-div), which can faithfully approximate W-div through optimization. Under various settings, including progressive growing training, we demonstrate the stability of the proposed WGAN-div owing to its theoretical and practical advantages over WGANs. Also, we study the quantitative and visual performance of WGAN-div on standard image synthesis benchmarks, showing the superior performance of WGAN-div compared to the state-of-the-art methods.

Model Description

We 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.

Installation

Install from pypi:

pip install wgandiv_pytorch

Install from source:

git clone https://github.com/Lornatang/WassersteinGAN_DIV-PyTorch.git
cd WassersteinGAN_DIV-PyTorch
pip install -e .

Usage

Loading pretrained models

Load an Wasserstein GAN DIV:

from wgandiv_pytorch import Generator
model = Generator.from_name("g-mnist")

Load a pretrained Wasserstein GAN DIV:

from wgandiv_pytorch import Generator
model = Generator.from_pretrained("g-mnist")

Example: Extended dataset

As mentioned in the example, if you load the pre-trained weights of the MNIST dataset, it will create a new imgs directory and generate 64 random images in the imgs directory.

import os
import torch
import torchvision.utils as vutils
from wgandiv_pytorch import Generator

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

model = Generator.from_pretrained("g-mnist")
model.to(device)
# switch to evaluate mode
model.eval()

try:
    os.makedirs("./imgs")
except OSError:
    pass

with torch.no_grad():
    for i in range(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: Visual

cd $REPO$/framework
sh start.sh

Then open the browser and type in the browser address http://127.0.0.1:10004/. Enjoy it.

Contributing

If 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!

Project details


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