wzj52501/giqa

Pytorch implementation of Generated Image Quality Assessment

GIQA: Generated Image Quality Assessment

This is the official pytorch implementation of ECCV2020 "GIQA: Generated Image Quality Assessment" （https://arxiv.org/abs/2003.08932）. The major contributors of this repository include Shuyang Gu, Jianmin Bao, Dong Chen, Fang Wen at Microsoft Research Asia.

Introduction

GIQA aims to solve the problem of quality evaluation of a single generated image. In this source, we release the code of our GMM-GIQA and KNN-GIQA which are convenient to use.

Citation

If you find our code helpful for your research, please consider citing:

@article{gu2020giqa,
  title={GIQA: Generated Image Quality Assessment},
  author={Gu, Shuyang and Bao, Jianmin and Chen, Dong and Wen, Fang},
  journal={arXiv preprint arXiv:2003.08932},
  year={2020}
} 

Getting Started

Prerequisite

• Linux.
• Pytorch 1.0.0.
• CUDA9.2 or 10.

Running code

• Download pretrained models here. We provide the LSUN-cat GMM model with PCA95 in this link, if you need more models, please contact me.

• Extract features:

  python write_act.py path/to/dataset --act_path path/to/activation --pca_rate pca_rate --pca_path path/to/pca --gpu gpu_id
  

• Get KNN-GIQA score:

  python knn_score.py path/to/test-folder --act_path path/to/activation --pca_path path/to/pca --K number/of/nearest-neighbor --output_file output/file/path --gpu gpu_id
  

• Get GMM-GIQA score:

  first build the GMM model:

  python get_gmm.py --act_path path/to/activation --kernel_number number-of-Gaussian-components --gmm_path path/to/gmm
  

  then get the GMM-GIQA score:

  python gmm_score.py path/to/test-folder --gmm_path path/to/gmm --pca_path path/to/pca --ourput_file output/file/path --gpu gpu_id
  

• For all these running bash, if we do not use PCA (such as FFHQ), just remove the pca_rate and pca_path options.

LGIQA dataset

• The LGIQA dataset contains three sub-dataset, named LGIQA-FFHQ, LGIQA-cat, LGIQA-cityscapes. You can download the cat and cityscapes sub-dataset here. For security reason, if you need LGIQA-FFHQ dataset, please contact me.

Reference

pytorch-fid