Homework 1 (Color-Transfer and Texture-Transfer)

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

Assign

1. 20% (Training cycleGAN)
2. 10% (Inference cycleGAN in personal image)
3. 20% (Compare with other method)
4. 30% (Assistant)
5. 20% (Mutual evaluation)

reference: Super fast color transfer between images

Getting Started

Please first install Anaconda and create an Anaconda environment using the environment.yml file.

conda env create -f environment.yml

After you create the environment, activate it.

source activate hw1

Our current implementation only supports GPU so you need a GPU and need to have CUDA installed on your machine.

Training

1. Download dataset

mkdir datasets
bash ./download_dataset.sh <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. apple2orange
|   |   ├── trainA             # Contains domain A images (i.e. apple)
|   |   ├── trainB             # Contains domain B images (i.e. orange) 
|   |   ├── testA              # Testing
|   |   └── testB              # Testing

2. Train

python train.py --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.

Both generators and discriminators weights will be saved ./output/<dataset_name>/ the output directory.

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

Testing

The pre-trained file is on Google drive. Download the file and save it on ./output/<dataset_name>/netG_A2B.pth and ./output/<dataset_name>/netG_B2A.pth.

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

This command will take the images under the dataroot/testA/ and dataroot/testB/ directory, run them through the generators and save the output under the ./output/<dataset_name>/ directories.

Examples of the generated outputs (default params) apple2orange, summer2winter_yosemite, horse2zebra dataset:

Acknowledgments

Code is modified by PyTorch-CycleGAN. All credit goes to the authors of CycleGAN, Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.