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README.md

SMSR

Reposity for "Learning Sparse Masks for Efficient Image Super-Resolution"

[arXiv]

Highlights

  • Locate and skip redundant computation in SR networks at a fine-grained level for efficient inference.
  • Maintain state-of-the-art performance with significant FLOPs reduction and a speedup on mobile devices.
  • Efficient implementation of sparse convolution based on original Pytorch APIs for easier migration and deployment.

Network Architecture

Implementation of Sparse Convolution

For easier migration and deployment, we use an efficient implementation of sparse convolution based on original Pytorch APIs rather than the commonly applied CUDA-based implementation. Specifically, sparse features are first extracted from the input, as shown in the following figure. Then, matrix multiplication is executed to produce the output features.

Requirements

  • Python 3.6
  • PyTorch == 1.1.0
  • numpy
  • skimage
  • imageio
  • matplotlib
  • cv2

Train

Prepare training data

  1. Download DIV2K training data (800 training + 100 validtion images) from DIV2K dataset or SNU_CVLab.

  2. Specify '--dir_data' based on the HR and LR images path. In option.py, '--ext' is set as 'sep_reset', which first convert .png to .npy. If all the training images (.png) are converted to .npy files, then set '--ext sep' to skip converting files.

For more informaiton, please refer to EDSR(PyTorch).

Begin to train

python main.py --model SMSR --save SMSR_X2 --scale 2 --patch_size 96 --batch_size 16

Test

Prepare test data

Download benchmark datasets (e.g., Set5, Set14 and other test sets) and prepare HR/LR images in testsets/benchmark following the example of testsets/benchmark/Set5.

Demo

python main.py --dir_data testsets --data_test Set5 --scale 2 --model SMSR --save SMSR_X2 --pre_train experiment/SMSR_X2/model/model_1000.pt --test_only --save_results

Results

Visualization of Sparse Masks

Citation

@Article{Wang2020Learning,
  author  = {Wang, Longguang and Dong, Xiaoyu and Wang, Yingqian and Ying, Xinyi and Lin, Zaiping and An, Wei and Guo, Yulan},
  title   = {Learning Sparse Masks for Efficient Image Super-Resolution},
  journal = {arXiv},
  year    = {2020},
}

Acknowledgements

This code is built on EDSR (PyTorch). We thank the authors for sharing the codes.

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Learning Sparse Masks for Efficient Image Super-Resolution

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