Skip to content
master
Go to file
Code

Files

Permalink
Failed to load latest commit information.

README.md

python pytorch report

CoCosNet

Pytorch Implementation of the paper "Cross-domain Correspondence Learning for Exemplar-based Image Translation" (CVPR 2020 oral).

teaser

Update:

20200525: Training code for deepfashion complete. Due to the memory limitations, I employed the following conversions:

  • Disable the non-local layer, as the memory cost is infeasible on common hardware. If the original paper is telling the truth that the non-lacal layer works on (128-128-256) tensors, then each attention matrix would contain 128^4 elements (which takes 1GB).
  • Shrink the correspondence map size from 64 to 32, leading to 4x memory save on dense correspondence matrices.
  • Shrink the base number of filters from 64 to 16.

The truncated model barely fits in a 12GB GTX Titan X card, but the performance would not be the same.

Environment

  • Ubuntu/CentOS
  • Pytorch 1.0+
  • opencv-python
  • tqdm

TODO list

  • Prepare dataset
  • Implement the network
  • Implement the loss functions
  • Implement the trainer
  • Training on DeepFashion
  • Adjust network architecture to satisfy a single 16 GB GPU.
  • Training for other tasks

Dataset Preparation

DeepFashion

Just follow the routine in the PATN repo

Pretrained Model

The pretrained model for human pose transfer task: TO BE RELEASED

Training

run python train.py.

Citations

If you find this repo useful for your research, don't forget to cite the original paper:

@article{Zhang2020CrossdomainCL,
  title={Cross-domain Correspondence Learning for Exemplar-based Image Translation},
  author={Pan Zhang and Bo Zhang and Dong Chen and Lu Yuan and Fang Wen},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.05571}
}

Acknowledgement

TODO.

About

Pytorch Implementation of the paper ["Cross-domain Correspondence Learning for Exemplar-based Image Translation"](https://panzhang0212.github.io/CoCosNet)

Resources

License

Releases

No releases published

Packages

No packages published

Languages

You can’t perform that action at this time.