Pytorch implementation for Stylization-Based Architecture for Fast Deep Exemplar Colorization

The project is not just for ‘Stylization-Based Architecture for Fast Deep Exemplar Colorization’. We have also improved the colorization network for better visual results or different usages. We will continue to do the experiments for new ideas, organize the code and upload the weight files for people who is interested in it. Welcome to join us to maintain the project together.

Install Dependencies

The code is written in Python 3.5 using the main following libraries:

python >=3.5,PyTorch>=0.4
Requirements: opencv-python,tensorboardX,visdom
Platforms: Ubuntu16.04,cuda-9.0

Data

Following the paper, training: download the coco dataset for transfer sub-net and he ImageNet dataset for colorization sub-net respectively. The test images in the paper comes from other colorization tasks or style transfer projects.

Architecture

Follow the folder structure given below.

├── dataset
│   └── Coco
│   └── Imagenet
├── checkpoints
│   └── 02_22_13_48
│   └── 02_25_15_33
│   └── siggraph_latest_net_G.pth
│   └── update_siggraph.pth
├── logs
├── options
│   └──base_options.py
│   └──train_options.py
├── models
│   └── network.py
│   └── RDBN.py
│   └── siggraph.py
│   └── siggraph_sample.py
├── transfer_subnet
│   └── consistencyChecker
│   └── checkpoints
│   └── video_checkpoints
│   └── segmentation
│   		└── ...
│   		└── ...
│   └── utils
│   		└── core.py
│   		└── io.py
│   		└── photo_adin.py
│   └── outputs
│   └── ade20k_semantic_rel.npy
│   └── compare_model.py
│   └── video_dataset.py
│   └── dataset.py
│   └── utilities.py
│   └── flowlib.py
│   └── make_consistencyChecker_script.py
│   └── make_video2image_script.py
│   └── wrap_xiaoke.py
│   └── xiaokemodel.py
│   └── xiaoketransfer.py
│   └── xiaoketransfer2.py
├── util
│   ├── get_data.py
│   ├── html.py
│   ├── image_pool.py
│   ├── util.py
│   └── visualizer.py
├── train.py
├── test.py
├── README.md

Video

We modified the transfer sub-net and transfer the style(artistic style, photo realistic style) on the image to the video by using optical flow to solve the consistency problem.

Contact

If you find any problems , please feel free to contact me (936214756@qq.com). A brief self-introduction is required.

Acknowledgments

Our code architecture is inspired by richzhang

bbaaii / Colorization