GAN-PyTorch

Overview

This repository contains an op-for-op PyTorch reimplementation of Generative Adversarial Networks.

Table of contents

• GAN-PyTorch
  • Overview
    • Table of contents
    • Download weights
    • Test
    • Train
    • Contributing
    • Credit
      • Generative Adversarial Networks

Download weights

• Google Driver
• Baidu Driver access:llot

Test

Modify the contents of the file as follows.

1. config.py line 35 mode="train" change to model="valid";
2. config.py line 79 model_path=f"results/{exp_name}/g-last.pth" change to model_path=f"<YOUR-WEIGHTS-PATH>.pth";
3. Run python validate.py.

Train

Modify the contents of the file as follows.

1. config.py line 35 mode="valid" change to model="train";
2. Run python train.py.

If you want to load weights that you've trained before, modify the contents of the file as follows.

1. config.py line 35 mode="valid" change to model="train";
2. config.py line 51 start_epoch=0 change to start_epoch=XXX;
3. config.py line 52 resume=False change to resume=True;
4. config.py line 53 resume_d_weight="" change to resume_d_weight=<YOUR-RESUME-D-WIGHTS-PATH>;
5. config.py line 54 resume_g_weight="" change to resume_g_weight=<YOUR-RESUME-G-WIGHTS-PATH>;
6. Run python train.py.

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!

Credit

Generative Adversarial Networks

Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio

Abstract
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.

[Paper] [Authors' Implementation]

@article{adversarial,
  title={Generative Adversarial Networks},
  author={Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio},
  journal={nips},
  year={2014}
}