Official Pytorch implementation of "Patch-wise Graph Contrastive Learning for Image Translation" (AAAI 2024)

Chanyong Jung, Gihyun Kwon, Jong Chul Ye

Link: https://arxiv.org/abs/2312.08223

• We provide the pretrained model: horse-to-zebra
• For the training from scratch (ex. horse2zebra dataset):

python train.py --name [folder-name] --dataroot [path-to-data] \
--lambda_GNN 0.1 --num_hop 2 --gnn_idt --nonzero_th 0.1 \
--pooling_num 1 --pooling_ratio '1,0.5' --down_scale 4 \
--gpu_ids 0

• For evaluation:

python test.py --dataroot [path-to-dataset] --name [experiment-name] \
--CUT_mode CUT --phase test --epoch [epoch-for-test] --num_test [test-size]

python -m pytorch_fid [path-to-output] [path-to-input]

Cite

@article{jung2023patch,
  title={Patch-wise Graph Contrastive Learning for Image Translation},
  author={Jung, Chanyong and Kwon, Gihyun and Ye, Jong Chul},
  journal={arXiv preprint arXiv:2312.08223},
  year={2023}
}

Acknowledgement

Our source code is based on the official implementation of HnegSRC and CUT.