/GCP-Colorization

Official code for ICCV 2021 paper "Towards Vivid and Diverse Image Colorization with Generative Color Prior".

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GCP-Colorization (ICCV 2021)

Official code for ICCV 2021 paper "Towards Vivid and Diverse Image Colorization with Generative Color Prior".

The name of our method, GCP-Colorization, is short for Colorization with Generative Color Prior.

Installation

Core dependencies

  • CUDA >= 10.0 (test on 10.0 and 10.2)
  • gcc >= 7.3
  • pytorch >= 1.6 (test on 1.6 and 1.7.1)
  • python3 (test on 3.8)

Install DCN package

cd ops
python setup.py develop

Download the Pre-trained Models

Download the pretrained models from [Google Drive ] and put them into the assets folder. If you want to reproduce or get the results reported in our ICCV 2021 paper for academic purpose, you can check model zoo. Otherwise, you just need to use the default options.

Inference

Test in the wild images

  1. Predict ImageNet label (0-999) for these images

    1. install awesome timm, pip install timm
    2. use a SOTA classification model from timm to predict the labels 
      python predict_imagenet_label.py testcase_in_the_wild --model beit_large_patch16_512 --pretrained
      here testcase_in_the_wild folder has the images you want to test
    3. you will get the label map in assets/predicted_label_for_user_image.txt

  2. Inference colorization

    python main.py --expname inference_in_the_wild --test_folder testcase_in_the_wild DATA.FULL_RES_OUTPUT True
    options:
    --expname: the results will be saved in results/{expname}-{time} folder
    --inference_in_the_wild: contains the images you want to test
    --bs: batch size
    DATA.FULL_RES_OUTPUT: True or False. If set to True
                          the full resolution results will be saved in results/{expname}-{time}/full_resolution_results
                          batch size should be 1 if this flag is set to True
    DATA.CENTER_CROP: whether to center crop the input images.
                      This flag and DATA.FULL_RES_OUTPUT flag can not be both set to True

  3. If everything goes well, you will get the results similar as visual_results.md.

Test images from ImageNet val set

  • The most worry-free way is to make sure the images' names are consistent with the official ImageNet name. Because we provide the GT ImageNet labels in imagenet_val_label_map.txt. You can check testcase_imagenet for examples.

  • Also, the test images should better be color images rather than grayscale images. If you want to test on grayscale images, please read the read_data_from_dataiter function from base_solver.py, prepare the grayscale images following the pipeline and hack the related code.

  • Inference

    python main.py --expname inference_imagenet --test_folder testcase_imagenet DATA.FULL_RES_OUTPUT False DATA.CENTER_CROP False

  • If everything goes well, you will get the following quantitative results on the full 50,000 ImageNet validation images (yes, FID is better than the number reported in our ICCV 2021 paper)

    eval@256x256 FID↓ Colorfulness↑ ΔColorfulness↓
    w/o center crop 1.325 34.89 3.45
    w/ center crop 1.262 34.74 4.12

TODO

  • add colab demo (PR welcomed)
  • replace current DCN with compiling-free torchvision version torchvision.ops.deform_conv2d (PR welcomed)

Citation

If you find this project useful for your research, please consider citing our paper:

@inproceedings{wu2021towards,
  title={Towards vivid and diverse image colorization with generative color prior},
  author={Wu, Yanze and Wang, Xintao and Li, Yu and Zhang, Honglun and Zhao, Xun and Shan, Ying},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2021}
}

Acknowedgement

This project borrows codes from CoCosNet, DGP, and BigGAN-PyTorch. DCN code from early version of mmcv. predict_imagenet_label.py from timm. Thanks the authors for sharing their awesome projects.