Take a look at the repository for more information

Install

python

!git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

python

import os
os.chdir('pytorch-CycleGAN-and-pix2pix/')

python

!pip install -r requirements.txt

Datasets

Download one of the official datasets with:

bash ./datasets/download_cyclegan_dataset.sh [apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, cityscapes, facades, iphone2dslr_flower, ae_photos]

Or use your own dataset by creating the appropriate folders and adding in the images.

Create a dataset folder under /dataset for your dataset.
Create subfolders testA, testB, trainA, and trainB under your dataset's folder. Place any images you want to transform from a to b (cat2dog) in the testA folder, images you want to transform from b to a (dog2cat) in the testB folder, and do the same for the trainA and trainB folders.

python

!bash ./datasets/download_cyclegan_dataset.sh horse2zebra

Pretrained models

Download one of the official pretrained models with:

bash ./scripts/download_cyclegan_model.sh [apple2orange, orange2apple, summer2winter_yosemite, winter2summer_yosemite, horse2zebra, zebra2horse, monet2photo, style_monet, style_cezanne, style_ukiyoe, style_vangogh, sat2map, map2sat, cityscapes_photo2label, cityscapes_label2photo, facades_photo2label, facades_label2photo, iphone2dslr_flower]

Or add your own pretrained model to ./checkpoints/{NAME}_pretrained/latest_net_G.pt

python

!bash ./scripts/download_cyclegan_model.sh horse2zebra

Training

python train.py --dataroot ./datasets/horse2zebra --name horse2zebra --model cycle_gan

Change the --dataroot and --name to your own dataset's path and model's name. Use --gpu_ids 0,1,.. to train on multiple GPUs and --batch_size to change the batch size. I've found that a batch size of 16 fits onto 4 V100s and can finish training an epoch in ~90s.

Once your model has trained, copy over the last checkpoint to a format that the testing model can automatically detect:

Use cp ./checkpoints/horse2zebra/latest_net_G_A.pth ./checkpoints/horse2zebra/latest_net_G.pth if you want to transform images from class A to class B and cp ./checkpoints/horse2zebra/latest_net_G_B.pth ./checkpoints/horse2zebra/latest_net_G.pth if you want to transform images from class B to class A.

python

!python train.py --dataroot ./datasets/horse2zebra --name horse2zebra --model cycle_gan --display_id -1

Testing

python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout

Change the --dataroot and --name to be consistent with your trained model's configuration.

from https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix: The option --model test is used for generating results of CycleGAN only for one side. This option will automatically set --dataset_mode single, which only loads the images from one set. On the contrary, using --model cycle_gan requires loading and generating results in both directions, which is sometimes unnecessary. The results will be saved at ./results/. Use --results_dir {directory_path_to_save_result} to specify the results directory.

For your own experiments, you might want to specify --netG, --norm, --no_dropout to match the generator architecture of the trained model.

python

!python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout

Visualize

python

import matplotlib.pyplot as plt

img = plt.imread('./results/horse2zebra_pretrained/test_latest/images/n02381460_1010_fake.png')
plt.imshow(img)

python

import matplotlib.pyplot as plt

img = plt.imread('./results/horse2zebra_pretrained/test_latest/images/n02381460_1010_real.png')
plt.imshow(img)