CADyQ : Content-Aware Dynamic Quantization for Image Super Resolution

This respository is the official implementation of our ECCV2022 paper.

The overview of the proposed quantization framework CADyQ for SR network, which we illustrate with a residual block based backbone. For each given patch and each layer, our CADyQ module introduces a light-weight bit selector that dynamically selects the bit-width and its corresponding quantization function $Q_{b^{k}}$ among the candidate quantization functions with distinct bit-widths. The bit selector is conditioned on the estimated quantization sensitivity (the average gradient magnitude ${|\nabla{}|}$ of the given patch and the standard deviation ${\sigma}$ of the layer feature). Qconv denotes the convolution layer of the quantized features and weights.

Our implementation is based on EDSR(PyTorch) and PAMS(PyTorch).

Conda Environment setting

conda env create -f environment.yml --name CADyQ
conda activate CADyQ

Dependencies

• kornia (pip install kornia)
• Python 3.6
• PyTorch == 1.1.0
• coloredlogs >= 14.0
• scikit-image

Datasets

• For training, we use DIV2K datasets.

• For testing, we use benchmark datasets and Test2K,4K.8K. Test8K contains the images (index 1401-1500) from DIV8K. Test2K/4K contain the images (index 1201-1300/1301-1400) from DIV8K which are downsampled to 2K and 4K resolution.

  # for training
  DIV2K 

  # for testing
  benchmark
  Test2K
  Test4K
  Test8K

How to train CADyQ

sh train_carn_cadyq.sh
sh train_idn_cadyq.sh
sh train_edsrbaseline_cadyq.sh
sh train_srresnet_cadyq.sh

Model weights for stduent and teacher model to start training from can be accessed from Google Drive.

How to test CADyQ

sh test_cadyq_patch.sh # for patch-wise inference
sh test_cadyq_image.sh # for image-wise inference

• One example of the inference command

CUDA_VISIBLE_DEVICES=0 python3 main.py \
--test_only --cadyq --search_space 4+6+8 --scale 4 --k_bits 8 \
--model CARN --n_feats 64 --n_resblocks 9 --group 1 --multi_scale \
--student_weights dir/for/our/pretrained_model \
--data_test Urban100 --dir_data dir/for/datasets \

• Our pretrained model can be accessed from Google Drive.

Citation

If you found our implementation useful, please consider citing our paper:

@article{hong2022cadyq,
  title={CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution},
  author={Hong, Cheeun and Baik, Sungyong and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu},
  journal={arXiv preprint arXiv:2207.10345},
  year={2022}
}

Contact

Email : cheeun914@snu.ac.kr