explainingai-code/GANs-Pytorch

This repo implements a simple GAN with fc layers and trains it on MNIST

GAN Implementation in PyTorch

This repository implements training and inference methods of GAN with just fc layers on MNIST.

GAN Explanation & Implementation Video

Output on MNIST

Data preparation

For setting up the mnist dataset:

Follow - https://github.com/explainingai-code/Pytorch-VAE#data-preparation

The directory structure should look like this

$REPO_ROOT
    -> data
        -> train
            -> images
                -> 0
                    *.png
                -> 1
                ...
                -> 9
                    *.png
        -> test
            -> images
                -> 0
                    *.png
                ...
    -> dataset
    -> tools
        

Quickstart

• Create a new conda environment with python 3.8 then run below commands
• git clone https://github.com/explainingai-code/GANs-Pytorch.git
• cd GANs-Pytorch
• pip install -r requirements.txt
• python -m tools.train_gan for training and saving inference samples

Training on colored mnist images

• Ensure dataset is prepared according to the Data Preparation instructions
• Change the IM_CHANNELS field to 3 in train_gan.py
• Uncomment lines 56-59 in the dataset/mnist_dataset.py file

Training on custom dataset images

• Dump all *.png files(or whatever format images you have) in the path data/train/images
• Comment https://github.com/explainingai-code/GANs-Pytorch/blob/main/dataset/mnist_dataset.py#L43
• Directory structure should be following:

data
    -> train
        -> images 
            *.png           

• Change the IM_PATH field to data/train in train_gan.py
• Change the channels and image sizes accordingly

Output

Outputs will be saved every 50 steps in samples directory .

During training of GAN the following output will be saved

• Latest Model checkpoints for generator and discriminator in $REPO_ROOT directory

During inference every 50 steps the following output will be saved

• Sampled image grid for in samples/*.png

Citations

@misc{goodfellow2014generative,
      title={Generative Adversarial Networks}, 
      author={Ian J. Goodfellow and Jean Pouget-Abadie and Mehdi Mirza and Bing Xu and David Warde-Farley and Sherjil Ozair and Aaron Courville and Yoshua Bengio},
      year={2014},
      eprint={1406.2661},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}