DosGAN-PyTorch

PyTorch Implementation of Exploring Explicit Domain Supervision for Latent Space Disentanglement in Unpaired Image-to-Image Translation.

Dependency:

Python 2.7

PyTorch 0.4.0

Usage:

Multiple identity translation

1. Downloading Facescrub dataset following http://www.vintage.winklerbros.net/facescrub.html, and save it to root_dir.

2. Splitting training and testing sets into train_dir and val_dir:

   $ python split2train_val.py root_dir train_dir val_dir

3. Train a classifier for domain feature extraction and save it to dosgan_cls:

   $ python main_dosgan.py --mode cls --model_dir dosgan_cls --train_data_path train_dir --test_data_path val_dir

4. Train DosGAN:

   $ python main_dosgan.py --mode train --model_dir dosgan --cls_save_dir dosgan_cls/models --train_data_path train_dir --test_data_path val_dir

5. Train DosGAN-c:

   $ python main_dosgan.py --mode train --model_dir dosgan_c --cls_save_dir dosgan_cls/models --non_conditional false --train_data_path train_dir --test_data_path val_dir

6. Test DosGAN:

   $ python main_dosgan.py --mode test --model_dir dosgan_c --cls_save_dir dosgan_cls/models --train_data_path train_dir --test_data_path val_dir

7. Test DosGAN-c:

   $ python main_dosgan.py --mode test --model_dir dosgan_c --cls_save_dir dosgan_cls/models --non_conditional false --train_data_path train_dir --test_data_path val_dir

Other mutliple domain translation

1. For other kinds of dataset, you can place train set and test set like:

   data
   ├── YOUR_DATASET_train_dir
       ├── damain1
       |   ├── 1.jpg
       |   ├── 2.jpg
       |   └── ...
       ├── domain2
       |   ├── 1.jpg
       |   ├── 2.jpg
       |   └── ...
       ├── domain3
       |   ├── 1.jpg
       |   ├── 2.jpg
       |   └── ...
       ...
   
   data
   ├── YOUR_DATASET_val_dir
       ├── damain1
       |   ├── 1.jpg
       |   ├── 2.jpg
       |   └── ...
       ├── domain2
       |   ├── 1.jpg
       |   ├── 2.jpg
       |   └── ...
       ├── domain3
       |   ├── 1.jpg
       |   ├── 2.jpg
       |   └── ...
       ...
   

2. Giving multiple season translation for example (season dataset). Train a classifier for season domain feature extraction and save it to dosgan_season_cls:

   $ python main_dosgan.py --mode cls --model_dir dosgan_season_cls --ft_num 64 --c_dim 4 --image_size 256 --train_data_path season_train_dir --test_data_path season_val_dir

3. Train DosGAN for multiple season translation:

   $ python main_dosgan.py --mode train --model_dir dosgan_season --cls_save_dir dosgan_season_cls/models --ft_num 64 --c_dim 4 --image_size 256 --lambda_fs 0.15 --num_iters 300000 --train_data_path season_train_dir --test_data_path season_val_dir

Results:

1. Multiple identity translation

# Results of DosGAN:

# Results of DosGAN-c:

2. Multiple season translation: