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We provide the Pytorch implementation of "The Spatially-Correlative Loss for Various Image Translation Tasks". Based on the inherent self-similarity of object, we propose a new structure-preserving loss for one-sided unsupervised I2I network. The new loss will deal only with spatial relationship of repeated signal, regardless of their original absolute value.

The Spatially-Correlative Loss for Various Image Translation Tasks
Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai
NTU and Monash University
In CVPR2021

• a simple example to use the proposed loss

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 https://pytorch.org
• Install python libraries visdom and dominate for visualization

pip install visdom dominate

• Clone this repo:

git clone https://github.com/lyndonzheng/F-LSeSim
cd F-LSeSim

Please refer to the original CUT and CycleGAN to download datasets and learn how to create your own datasets.

• Train the single-modal I2I translation model:

sh ./scripts/train_sc.sh 

• Set --use_norm for cosine similarity map, the default similarity is dot-based attention score. --learned_attn, --augment for the learned self-similarity.

• To view training results and loss plots, run python -m visdom.server and copy the URL https://localhost:port.

• Training models will be saved under the checkpoints folder.

• The more training options can be found in the options folder.
  
  

• Train the single-image translation model:

sh ./scripts/train_sinsc.sh 

As the multi-modal I2I translation model was trained on MUNIT, we would not plan to merge the code to this repository. If you wish to obtain multi-modal results, please contact us at [email protected].

• Test the single-modal I2I translation model:

sh ./scripts/test_sc.sh

• Test the single-image translation model:

sh ./scripts/test_sinsc.sh

• Test the FID score for all training epochs:

sh ./scripts/test_fid.sh

Download the pre-trained models (will be released soon) using the following links and put them undercheckpoints/ directory.

• Single-modal translation model: horse2zebra, semantic2image, apple2orange
• Single-image translation model: image2monet

@inproceedings{zheng2021spatiallycorrelative,
  title={The Spatially-Correlative Loss for Various Image Translation Tasks},
  author={Zheng, Chuanxia and Cham, Tat-Jen and Cai, Jianfei},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2021}
}

Our code is developed based on CUT and CycleGAN. We also thank pytorch-fid for FID computation, LPIPS for diversity score, and D&C for density and coverage evaluation.