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CHAN for Image-to-Image Translation, IEEE TIP, 2021

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# CHAN for Image-to-Image Translation

> `PyTorch` implementations of **CHAN** for Image-to-Image Translation.
>
> Fei Gao, Xingxin Xu, Jun Yu, Meimei Shang, Xiang Li, and Dacheng Tao, Complementary, Heterogeneous and Adversarial Networks for Image-to-Image Translation, *IEEE Transactions on Image Processing*, 2021. (Accepted)

![](https://raw.githubusercontent.com/fei-aiart/chan/master/imgs/chan_logo0.png)

### Our Proposed Framework
#### Generator
![](https://raw.githubusercontent.com/fei-aiart/chan/master/imgs/chan.jpg)
#### Discriminator (Multi-layer Integration Discriminator, MID)
![](https://raw.githubusercontent.com/fei-aiart/chan/master/imgs/discriminator.png)
### Generated Examples 
![](https://raw.githubusercontent.com/fei-aiart/chan/master/imgs/fig_comp.jpg)

![](https://raw.githubusercontent.com/fei-aiart/chan/master/imgs/fig_celeb.jpg)

![](https://raw.githubusercontent.com/fei-aiart/chan/master/imgs/fig_lightpose.jpg)

### Prerequisites

- Linux or similar environment
- Python 2.7
- NVIDIA GPU + CUDA CuDNN

### Getting Started

#### installation
- clone this repo:
```shell script
git clone https://github.com/fei-hdu/chan.git
cd chan
```
- install PyTorch 0.4+
#### Quick Start
- Download a dataset
    - [CUFS](http://mmlab.ie.cuhk.edu.hk/archive/facesketch.html) splits of training and testing: [files](https://drive.google.com/open?id=1TYQ5O9q4ZMiuBimPMqqj7O_SyHoijwmn)
    - CityScapes, Facades, Google Map, Edge2Shoes, Edge2Handbags: [Pix2Pix bash](https://github.com/phillipi/pix2pix/blob/master/datasets/download_dataset.sh)

<!--

- We use fine-tuning to train our model. For example, on the CUFS
    - first you need train on Pix2Pix to get a coarse model or you can Download a pre-trained model(pre-trained with [Pix2Pix](https://github.com/jehovahxu/pix2pix-pytorch)) in  [here](https://drive.google.com/open?id=1dfe2pqAUD4T6fD9vO38SM2HCYJSe9Nxj)

-->

- Train:
    ```shell script
    python train.py --dataroot {dataset path} --datalist {datalist path} --pre_netG {coarse model path} --gpuid {your gpu ids}  
    ```

- Test:
    ```shell script
    python test.py --dataroot {dataset path} --datalist {datalist path} --pre_netG {final model path} --gpuid {your gpu ids}
    ```
#### Apply our pre-trained model
A *face photo-to-sketch* model pre-trained on the CUSF dataset: [Google Drive](https://drive.google.com/open?id=173H7FdwG7UOMM1G-4sm7zKUBu0yFypul)

The pre-trained model need to be save at `./checkpoint`

Then you can test this imodel.

### Results
Our final results can be downloaded [here](https://drive.google.com/open?id=1AzmHkcGa21h6Mngdc7sEIH1hOR6GrBTS)

Our Quantitative performance in a variety of image-to-image translation tasks are shown below. We assign a *score* of +1, 0, or -1 to the best, mediate, and worst model according to each performance index. For each method, the total score on each dataset and that across all these datasets, are reported. 
![](https://raw.githubusercontent.com/fei-aiart/chan/master/imgs/result_comp.jpg)

### Training/Test Tips
Best practice for training and testing your models.
Feel free to ask any questions about coding. **Xingxin Xu**, [jehovahxu@gmail.com](jehovahxu@gmail.com)<p>

### Citation
If you find this useful for your research, please cite our paper as:

> Fei Gao, Xingxin Xu, Jun Yu, Meimei Shang, Xiang Li, and Dacheng Tao, Complementary, Heterogeneous and Adversarial Networks for Image-to-Image Translation, *IEEE Transactions on Image Processing*, 2021. (Accepted)

```latex
@article{gao2021chan,
	title = {Complementary, Heterogeneous and Adversarial Networks for Image-to-Image Translation},
	author = {Fei Gao and Xingxin Xu and Jun Yu and Meimei Shang and Xiang Li and Dacheng Tao},
	journal = {IEEE Transactions on Image Processing},
	year = {2021},
	url = {https://github.com/fei-hdu/chan},
}
```

### Acknowledgments
Our code is inspired by [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)

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