DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks

Official implementation of our WACV 2022 paper.

Conda Environment setting

conda env create -f environment.yml --name DAQ
conda activate DAQ
conda install -c anaconda scikit-image

Dependencies

Python 3.6
PyTorch == 1.1.0
coloredlogs >= 14.0
scikit-image

Codes

Our implementation is based on EDSR(PyTorch).

Train

sh train_EDSR_x4.sh

Pretrained model to start training from can be accessed from Google Drive.

Test

sh test.sh edsr_baseline 2 2 4 (edsr_baseline w2a2qq4)
sh test.sh edsr_baseline 3 3 4 (edsr_baseline w3a3qq4)
sh test.sh edsr_baseline 4 4 4 (edsr_baseline w4a4qq4)
sh test.sh edsr_full 2 2 8 (edsr_full w2a2qq8)

Our pretrained model can be accessed from Google Drive.

Additional Results

Our model achieves the following performance (PSNR / SSIM) when trained for 60 epochs :

Model	Precision (w/a)	Set5	Set14	B100	Urban100
EDSR-baseline (x4)	32 / 32	32.10 / 0.894	28.58 / 0.781	27.56 / 0.736	26.04 / 0.785
EDSR-baseline-DAQ	4 / 4	31.85 / 0.887	28.38 / 0.776	27.42 / 0.732	25.73 / 0.772
EDSR-baseline-DAQ	3 / 3	31.66 / 0.884	28.19 / 0.771	27.28 / 0.728	25.40 / 0.762
EDSR-baseline-DAQ	2 / 2	31.01 / 0.871	27.89 / 0.762	27.09 / 0.719	24.88 / 0.740

Model	Precision (w/a)	Set5	Set14	B100	Urban100
RDN (x4)	32 / 32	32.24 / 0.896	28.67 / 0.784	27.63 / 0.738	26.29 / 0.792
RDN-DAQ	4 / 4	31.91 / 0.889	28.38 / 0.775	27.38 / 0.733	25.81 / 0.779
RDN-DAQ	3 / 3	31.57 / 0.883	28.18 / 0.771	27.27 / 0.728	25.47 / 0.765
RDN-DAQ	2 / 2	30.71 / 0.866	27.61 / 0.755	26.93 / 0.715	24.71 / 0.731

Model	Precision (w/a)	Set5	Set14	B100	Urban100
SRResNet (x4)	32 / 32	32.07 / 0.893	28.50 / 0.780	27.52 / 0.735	25.86 / 0.779
SRResNet-DAQ	4 / 4	31.85 / 0.889	28.41 / 0.777	27.45 / 0.732	25.70 / 0.772
SRResNet-DAQ	3 / 3	31.81 / 0.889	28.35 / 0.776	27.40 / 0.733	25.63 / 0.772
SRResNet-DAQ	2 / 2	31.57 / 0.886	28.19 / 0.773	27.30 / 0.729	25.39 / 0.765

Citation

@inproceedings{hong2022daq,
  title={DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks},
  author={Hong, Cheeun and Kim, Heewon and Baik, Sungyong and Oh, Junghun and Lee, Kyoung Mu},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={2675--2684},
  year={2022}
}

Contact

Email : cheeun914@snu.ac.kr

Cheeun/DAQ-pytorch