Awesome

Dual Contrastive Learning Adversarial Generative Networks (DCLGAN)

We provide our PyTorch implementation of DCLGAN, which is a simple yet powerful model for unsupervised Image-to-image translation. Compared to CycleGAN, DCLGAN performs geometry changes with more realistic results. Compared to CUT, DCLGAN is usually more robust and achieves better performance. A viriant, SimDCL (Similarity DCLGAN) also avoids mode collapse using a new similarity loss.

DCLGAN is a general model performing all kinds of Image-to-Image translation tasks. It achieves SOTA performances in most tasks that we have tested.

Dual Contrastive Learning for Unsupervised Image-to-Image Translation<br> Junlin Han, Mehrdad Shoeiby, Lars Petersson, Mohammad Ali Armin<br> DATA61-CSIRO and Australian National University<br> In NTIRE, CVPRW 2021.

Our pipeline is quite straightforward. The main idea is a dual setting with two encoders to capture the variability in two distinctive domains. <img src='imgs/dclgan.png' align="right" width=950>

Example Results

Unpaired Image-to-Image Translation

Qualitative results:

Quantitative results:

More visual results:

Prerequisites

Python 3.6 or above.

For packages, see requirements.txt.

Getting started

Clone this repo:

git clone https://github.com/JunlinHan/DCLGAN.git

Install PyTorch 1.6 or above and other dependencies (e.g., torchvision, visdom, dominate, gputil).

For pip users, please type the command pip install -r requirements.txt.

For Conda users, you can create a new Conda environment using conda env create -f environment.yml.

DCLGAN and SimDCL Training and Test

Download the grumpifycat dataset

bash ./datasets/download_cut_dataset.sh grumpifycat

The dataset is downloaded and unzipped at ./datasets/grumpifycat/.

To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097.

Train the DCL model:

python train.py --dataroot ./datasets/grumpifycat --name grumpycat_DCL

Or train the SimDCL model:

python train.py --dataroot ./datasets/grumpifycat --name grumpycat_SimDCL --model simdcl

We also support CUT:

python train.py --dataroot ./datasets/grumpifycat --name grumpycat_cut --model cut

and fastCUT:

python train.py --dataroot ./datasets/grumpifycat --name grumpycat_fastcut --model fastcut

and CycleGAN:

python train.py --dataroot ./datasets/grumpifycat --name grumpycat_cyclegan --model cycle_gan

The checkpoints will be stored at ./checkpoints/grumpycat_DCL/.

Test the DCL model:

python test.py --dataroot ./datasets/grumpifycat --name grumpycat_DCL

The test results will be saved to an html file here: ./results/grumpycat_DCL/latest_test/.

DCLGAN, SimDCL, CUT and CycleGAN

DCLGAN is a more robust unsupervised image-to-image translation model compared to previous models. Our performance is usually better than CUT & CycleGAN.

SIMDCL is a different version, it was designed to solve mode collpase. We recommend using it for small-scale, unbalanced dataset.

Datasets

Download CUT/CycleGAN/pix2pix datasets and learn how to create your own datasets.

Or download it here: https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/.

When preparing the CityScape dataset, please use Pillow=5.0.0 to run prepare_dataset.py for consistency.

Apply a pre-trained DCL model and evaluate

We provide our pre-trained DCLGAN models for:

Cat <-> Dog : https://drive.google.com/file/d/1-0SICLeoySDG0q2k1yeJEI2QJvEL-DRG/view?usp=sharing

Horse <-> Zebra: https://drive.google.com/file/d/16oPsXaP3RgGargJS0JO1K-vWBz42n5lf/view?usp=sharing

CityScapes: https://drive.google.com/file/d/1ZiLAhYG647ipaVXyZdBCsGeiHgBmME6X/view?usp=sharing

Download the pre-tained model, unzip it and put it inside ./checkpoints (You may need to create checkpoints folder by yourself if you didn't run the training code).

Example usage: Download the dataset of Horse2Zebra and test the model using:

python test.py --dataroot ./datasets/horse2zebra --name horse2zebra_dcl

For FID score, use pytorch-fid.

Test the FID for Horse-> Zebra:

python -m pytorch_fid ./results/horse2zebra_dcl/test_latest/images/fake_B ./results/horse2zebra_dcl/test_latest/images/real_B

and Zorse-> Hebra:

python -m pytorch_fid ./results/horse2zebra_dcl/test_latest/images/fake_A ./results/horse2zebra_dcl/test_latest/images/real_A

Citation

If you use our code or our results, please consider citing our paper. Thanks in advance!

@inproceedings{han2021dcl,
  title={Dual Contrastive Learning for Unsupervised Image-to-Image Translation},
  author={Junlin Han and Mehrdad Shoeiby and Lars Petersson and Mohammad Ali Armin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year={2021}
}

If you use something included in CUT, you may also CUT.

@inproceedings{park2020cut,
  title={Contrastive Learning for Unpaired Image-to-Image Translation},
  author={Taesung Park and Alexei A. Efros and Richard Zhang and Jun-Yan Zhu},
  booktitle={European Conference on Computer Vision},
  year={2020}
}

Contact

junlinhcv@gmail.com

Acknowledgments

Our code is developed based on pytorch-CycleGAN-and-pix2pix and CUT. We thank the awesome work provided by CycleGAN and CUT. We thank pytorch-fid for FID computation. Great thanks to the anonymous reviewers, from both the main CVPR conference and NTIRE. They provided invaluable feedbacks and suggestions.