Awesome

Pytorch-CycleGAN

A clean and readable Pytorch implementation of CycleGAN (https://arxiv.org/abs/1703.10593)

Prerequisites

Code is intended to work with Python 3.6.x, it hasn't been tested with previous versions

PyTorch & torchvision

Follow the instructions in pytorch.org for your current setup

Visdom

To plot loss graphs and draw images in a nice web browser view

pip3 install visdom

Training

1. Setup the dataset

First, you will need to download and setup a dataset. The easiest way is to use one of the already existing datasets on UC Berkeley's repository:

./download_dataset <dataset_name>

Valid <dataset_name> are: apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, cityscapes, facades, iphone2dslr_flower, ae_photos

Alternatively you can build your own dataset by setting up the following directory structure:

.
├── datasets                   
|   ├── <dataset_name>         # i.e. brucewayne2batman
|   |   ├── train              # Training
|   |   |   ├── A              # Contains domain A images (i.e. Bruce Wayne)
|   |   |   └── B              # Contains domain B images (i.e. Batman)
|   |   └── test               # Testing
|   |   |   ├── A              # Contains domain A images (i.e. Bruce Wayne)
|   |   |   └── B              # Contains domain B images (i.e. Batman)

2. Train!

./train --dataroot datasets/<dataset_name>/ --cuda

This command will start a training session using the images under the dataroot/train directory with the hyperparameters that showed best results according to CycleGAN authors. You are free to change those hyperparameters, see ./train --help for a description of those.

Both generators and discriminators weights will be saved under the output directory.

If you don't own a GPU remove the --cuda option, although I advise you to get one!

You can also view the training progress as well as live output images by running python3 -m visdom in another terminal and opening http://localhost:8097/ in your favourite web browser. This should generate training loss progress as shown below (default params, horse2zebra dataset):

Generator loss Discriminator loss Generator GAN loss Generator identity loss Generator cycle loss

Testing

./test --dataroot datasets/<dataset_name>/ --cuda

This command will take the images under the dataroot/test directory, run them through the generators and save the output under the output/A and output/B directories. As with train, some parameters like the weights to load, can be tweaked, see ./test --help for more information.

Examples of the generated outputs (default params, horse2zebra dataset):

Real horse Fake zebra Real zebra Fake horse

License

This project is licensed under the GPL v3 License - see the LICENSE.md file for details

Acknowledgments

Code is basically a cleaner and less obscured implementation of pytorch-CycleGAN-and-pix2pix. All credit goes to the authors of CycleGAN, Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.