A clean and readable Pytorch implementation of CycleGAN (https://arxiv.org/abs/1703.10593)
Code is intended to work with
Python 3.6.x, it hasn't been tested with previous versions
Follow the instructions in pytorch.org for your current setup
To plot loss graphs and draw images in a nice web browser view
pip3 install visdom
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:
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)
./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):
./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):
This project is licensed under the GPL v3 License - see the LICENSE.md file for details
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.