Pytorch_implementation_of_CartoonGAN_(CVPR_2018

# pytorch-CartoonGAN Pytorch implementation of CartoonGAN [1] (CVPR 2018) * Parameters without information in the paper were set arbitrarily. * I used face-cropped celebA (src) and anime (tgt) collected from the web data because I could not find the author's data. ## Tensorflow version [CartoonGAN-tensorflow](https://github.com/taki0112/CartoonGAN-Tensorflow) ## Usage ### 1.Download VGG19 [VGG19](https://download.pytorch.org/models/vgg19-dcbb9e9d.pth) ### 2.Train ``` python CartoonGAN.py --name your_project_name --src_data src_data_path --tgt_data tgt_data_path --vgg_model pre_trained_VGG19_model_path ``` ### Folder structure The following shows basic folder structure. ``` ├── data │ ├── src_data # src data (not included in this repo) │ │ ├── train │ │ └── test │ └── tgt_data # tgt data (not included in this repo) │ ├── train │ └── pair # edge-promoting results to be saved here │ ├── CartoonGAN.py # training code ├── edge_promoting.py ├── utils.py ├── networks.py └── name_results # results to be saved here ``` ## Resutls ### paper results <img src = 'assets/paper_results2.png'> <img src = 'assets/paper_results.png'> ### celebA2anime face ### Initialization phase (reconstruction) <table align='center'> <tr align='center'> <td> Input - Result (this repo) </td> </tr> <tr> <td><img src = 'assets/Initialization_phase_result1.png'> </tr> <tr> <td><img src = 'assets/Initialization_phase_result2.png'> </tr> <tr> <td><img src = 'assets/Initialization_phase_result3.png'> </tr> </table> ### Cartoonization * I got the author's results from [CaroonGAN-Test-Pytorch-Torch](https://github.com/Yijunmaverick/CartoonGAN-Test-Pytorch-Torch). <table align='center'> <tr align='center'> <td> Input - Result (this repo) </td> <td> Author's pre-trained model (Hayao) </td> <td> Author's pre-trained model (Hosoda) </td> </tr> <tr> <td><img src = 'assets/Cartoonization_result1.png'> <td><img src = 'assets/00055_Hayao.jpg' height = '200px'> <td><img src = 'assets/00055_Hosoda.jpg' height = '200px'> </tr> <tr> <td><img src = 'assets/Cartoonization_result2.png'> <td><img src = 'assets/00009_Hayao.jpg' height = '200px'> <td><img src = 'assets/00009_Hosoda.jpg' height = '200px'> </tr> <tr> <td><img src = 'assets/Cartoonization_result3.png'> <td><img src = 'assets/00010_Hayao.jpg' height = '200px'> <td><img src = 'assets/00010_Hosoda.jpg' height = '200px'> </tr> <tr> <td><img src = 'assets/Cartoonization_result4.png'> <td><img src = 'assets/00155_Hayao.jpg' height = '200px'> <td><img src = 'assets/00155_Hosoda.jpg' height = '200px'> </tr> <tr> <td><img src = 'assets/Cartoonization_result5.png'> <td><img src = 'assets/00014_Hayao.jpg' height = '200px'> <td><img src = 'assets/00014_Hosoda.jpg' height = '200px'> </tr> </table> ## Development Environment * NVIDIA GTX 1080 ti * cuda 8.0 * python 3.5.3 * pytorch 0.4.0 * torchvision 0.2.1 * opencv 3.2.0 ## Reference [1] Chen, Yang, Yu-Kun Lai, and Yong-Jin Liu. "CartoonGAN: Generative Adversarial Networks for Photo Cartoonization." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. (Full paper: http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.pdf)