The code for Global and Local Alignment Networks for Unpaired Image-to-Image Translation arxiv
Framework:
visualization results:
This code was tested with Pytorch 1.7.0, CUDA 10.2, and Python 3.7
- Install Pytoch 1.7.0, torchvision, and other dependencies from http://pytorch.org
- Install python libraries visdom and dominate for visualization
pip install visdom dominate
- Clone this repo:
git clone https://github.com/ygjwd12345/GLANet.git
cd GLANet
Please refer to the original CUT and CycleGAN to download datasets and learn how to create your own datasets.
sh ./datasets/download_cyclegan_dataset.sh a2b
Available datasets are: apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, facades, iphone2dslr_flower, ae_photos
sh ./datasets/download_pix2pix_dataset.sh xx
Available datasets are night2day, edges2handbags, edges2shoes, facades, maps
The Cityscapes dataset can be downloaded from https://cityscapes-dataset.com. After that, use the script ./datasets/prepare_cityscapes_dataset.py to prepare the dataset.
- Train the single-modal I2I translation model. Please check run.sh. For instance:
python train.py \
--dataroot ./datasets/summer2winter \
--name summer2winter \
--model sc \
--gpu_ids 0 \
--lambda_spatial 10 \
--lambda_gradient 0 \
--attn_layers 4,7,9 \
--loss_mode cos \
--gan_mode lsgan \
--display_port 8093 \
--direction BtoA \
--patch_size 64
- Test the FID score for all training epochs, please also check run.sh. For instance:
python test_fid.py \
--dataroot ./datasets/horse2zebra \
--checkpoints_dir ./checkpoints \
--name horse2zebra \
--gpu_ids 0 \
--model sc \
--num_test 0
- Test the KID, cityscape score, D&C, LPIPS, please check run_dc_lpips.sh in evaluations folder. For instance:
python PerceptualSimilarity/lpips_2dirs.py -d0 /data2/gyang/TAGAN/results/summer2winter-F64-mixer/test_350/images/real_B -d1 /data2/gyang/TAGAN/results/summer2winter-F64-mixer/test_350/images/fake_B -o ./example_dists.txt --use_gpu
python3 segment.py test -d ./datasets/cityscapes -c 19 --arch drn_d_22 \
--pretrained ./drn_d_22_cityscapes.pth --phase val --batch-size 1
Our code is developed based on FSeSim and unguided. We also thank pytorch-fid for FID computation, LPIPS for diversity score, and D&C for density and coverage evaluation.
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