SPM_CycleGAN

We provide PyTorch implementations for both unpaired and paired Molecular model to SPM image translation.

The code is modified by Zhiwen Zhu. The original code comes from Jun-Yan Zhu and Taesung Park.

Note: The current software works well with PyTorch 1.4

CycleGAN: Project | Paper | Torch | Tensorflow Core Tutorial | PyTorch Colab

If you use this code for your research, please cite:

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.
Jun-Yan Zhu*, Taesung Park*, Phillip Isola, Alexei A. Efros. In ICCV 2017. (* equal contributions) [Bibtex]

Image-to-Image Translation with Conditional Adversarial Networks.
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros. In CVPR 2017. [Bibtex]

Talks and Course

CycleGAN slides: pptx | pdf

CycleGAN course assignment code and handout designed by Prof. Roger Grosse for CSC321 "Intro to Neural Networks and Machine Learning" at University of Toronto. Please contact the instructor if you would like to adopt it in your course.

Other implementations

[Tensorflow] (by Harry Yang), [Tensorflow] (by Archit Rathore), [Tensorflow] (by Van Huy), [Tensorflow] (by Xiaowei Hu), [Tensorflow2] (by Zhenliang He), [TensorLayer1.0] (by luoxier), [TensorLayer2.0] (by zsdonghao), [Chainer] (by Yanghua Jin), [Minimal PyTorch] (by yunjey), [Mxnet] (by Ldpe2G), [lasagne/Keras] (by tjwei), [Keras] (by Simon Karlsson), [OneFlow] (by Ldpe2G)

Prerequisites

Linux or macOS
Python 3
CPU or NVIDIA GPU + CUDA CuDNN

Getting Started

Installation

Clone this repo:

git clone https://github.com/gggg0034/SPM_CycleGAN
cd SPM_CycleGAN

Install PyTorch and 0.4+ and other dependencies (e.g., torchvision, visdom and dominate).
- 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.

CycleGAN train/test

Download a CycleGAN dataset (e.g. maps):

bash ./datasets/download_cyclegan_dataset.sh maps

To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097.
To log training progress and test images to W&B dashboard, set the --use_wandb flag with train and test script
Train a model:

#!./scripts/train_cyclegan.sh
python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan

To see more intermediate results, check out ./checkpoints/maps_cyclegan/web/index.html.

Test the model:

#!./scripts/test_cyclegan.sh
python test.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan

The test results will be saved to a html file here: ./results/maps_cyclegan/latest_test/index.html.

Custom Model and Dataset

If you plan to implement custom models and dataset for your new applications, we provide a dataset template and a model template as a starting point.

Citation

If you use this code for your research, please cite our papers.

@inproceedings{CycleGAN2017,
  title={Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks},
  author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
  booktitle={Computer Vision (ICCV), 2017 IEEE International Conference on},
  year={2017}
}


@inproceedings{isola2017image,
  title={Image-to-Image Translation with Conditional Adversarial Networks},
  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
  booktitle={Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on},
  year={2017}
}

Related Projects

Acknowledgments

This code is inspired by pytorch-DCGAN.