DiscoGAN in PyTorch
PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks.
* All samples in README.md are genearted by neural network except the first image for each row.
* Network structure is slightly diffferent (here) from the author's code.
Requirements
- Python 2.7
- Pillow
- tqdm
- PyTorch
- torch-vision
Usage
First download datasets (from pix2pix) with:
$ bash ./data/download_dataset.sh dataset_name
facades
: 400 images from CMP Facades dataset.cityscapes
: 2975 images from the Cityscapes training set.maps
: 1096 training images scraped from Google Mapsedges2shoes
: 50k training images from UT Zappos50K dataset.edges2handbags
: 137K Amazon Handbag images from iGAN project.
or you can use your own dataset by placing images like:
data
βββ YOUR_DATASET_NAME
β βββ A
β | βββ xxx.jpg (name doesn't matter)
β | βββ yyy.jpg
β | βββ ...
β βββ B
β βββ zzz.jpg
β βββ www.jpg
β βββ ...
βββ download_dataset.sh
All images in each dataset should have same size like using imagemagick:
# for Ubuntu
$ sudo apt-get install imagemagick
$ mogrify -resize 256x256! -quality 100 -path YOUR_DATASET_NAME/A/*.jpg
$ mogrify -resize 256x256! -quality 100 -path YOUR_DATASET_NAME/B/*.jpg
# for Mac
$ brew install imagemagick
$ mogrify -resize 256x256! -quality 100 -path YOUR_DATASET_NAME/A/*.jpg
$ mogrify -resize 256x256! -quality 100 -path YOUR_DATASET_NAME/B/*.jpg
# for scale and center crop
$ mogrify -resize 256x256^ -gravity center -crop 256x256+0+0 -quality 100 -path ../A/*.jpg
To train a model:
$ python main.py --dataset=edges2shoes --num_gpu=1
$ python main.py --dataset=YOUR_DATASET_NAME --num_gpu=4
To test a model (use your load_path
):
$ python main.py --dataset=edges2handbags --load_path=logs/edges2handbags_2017-03-18_10-55-37 --num_gpu=0 --is_train=False
Results
1. Toy dataset
Result of samples from 2-dimensional Gaussian mixture models. IPython notebook
# iteration: 0:
# iteration: 10000:
2. Shoes2handbags dataset
# iteration: 11200:
x_A
-> G_AB(x_A)
-> G_BA(G_AB(x_A))
(shoe -> handbag -> shoe)
x_B
-> G_BA(x_B)
-> G_AB(G_BA(x_B))
(handbag -> shoe -> handbag)
x_A
-> G_AB(x_A)
-> G_BA(G_AB(x_A))
-> G_AB(G_BA(G_AB(x_A)))
-> G_BA(G_AB(G_BA(G_AB(x_A))))
-> ...
3. Edges2shoes dataset
# iteration: 9600:
x_A
-> G_AB(x_A)
-> G_BA(G_AB(x_A))
(color -> sketch -> color)
x_B
-> G_BA(x_B)
-> G_AB(G_BA(x_B))
(sketch -> color -> sketch)
x_A
-> G_AB(x_A)
-> G_BA(G_AB(x_A))
-> G_AB(G_BA(G_AB(x_A)))
-> G_BA(G_AB(G_BA(G_AB(x_A))))
-> ...
4. Edges2handbags dataset
# iteration: 9500:
x_A
-> G_AB(x_A)
-> G_BA(G_AB(x_A))
(color -> sketch -> color)
x_B
-> G_BA(x_B)
-> G_AB(G_BA(x_B))
(sketch -> color -> sketch)
x_A
-> G_AB(x_A)
-> G_BA(G_AB(x_A))
-> G_AB(G_BA(G_AB(x_A)))
-> G_BA(G_AB(G_BA(G_AB(x_A))))
-> ...
5. Cityscapes dataset
# iteration: 8350:
x_B
-> G_BA(x_B)
-> G_AB(G_BA(x_B))
(image -> segmentation -> image)
x_A
-> G_AB(x_A)
-> G_BA(G_AB(x_A))
(segmentation -> image -> segmentation)
6. Map dataset
# iteration: 22200:
x_B
-> G_BA(x_B)
-> G_AB(G_BA(x_B))
(image -> segmentation -> image)
x_A
-> G_AB(x_A)
-> G_BA(G_AB(x_A))
(segmentation -> image -> segmentation)
7. Facades dataset
Generation and reconstruction on dense segmentation dataset looks weird which are not included in the paper.
I guess a naive choice of mean square error
loss for reconstruction need some change on this dataset.
# iteration: 19450:
x_B
-> G_BA(x_B)
-> G_AB(G_BA(x_B))
(image -> segmentation -> image)
x_A
-> G_AB(x_A)
-> G_BA(G_AB(x_A))
(segmentation -> image -> segmentation)
Related works
Author
Taehoon Kim / @carpedm20