Dual Aggregation Transformer for Image Super-Resolution

Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, Xiaokang Yang, and Fisher Yu, "Dual Aggregation Transformer for Image Super-Resolution", ICCV, 2023

[paper] [arXiv] [supplementary material] [visual results] [pretrained models]

🔥🔥🔥 News

• 2023-09-17: The chaiNNer and the neosr add DAT support. Additional trained DAT models are available in OpenMMLab (#11). Thank Phhofm!
• 2023-07-16: This repo is released.
• 2023-07-14: DAT is accepted at ICCV 2023. 🎉🎉🎉

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Abstract: Transformer has recently gained considerable popularity in low-level vision tasks, including image super-resolution (SR). These networks utilize self-attention along different dimensions, spatial or channel, and achieve impressive performance. This inspires us to combine the two dimensions in Transformer for a more powerful representation capability. Based on the above idea, we propose a novel Transformer model, Dual Aggregation Transformer (DAT), for image SR. Our DAT aggregates features across spatial and channel dimensions, in the inter-block and intra-block dual manner. Specifically, we alternately apply spatial and channel self-attention in consecutive Transformer blocks. The alternate strategy enables DAT to capture the global context and realize inter-block feature aggregation. Furthermore, we propose the adaptive interaction module (AIM) and the spatial-gate feed-forward network (SGFN) to achieve intra-block feature aggregation. AIM complements two self-attention mechanisms from corresponding dimensions. Meanwhile, SGFN introduces additional non-linear spatial information in the feed-forward network. Extensive experiments show that our DAT surpasses current methods.

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HR	LR	SwinIR	CAT	DAT (ours)

Dependencies

• Python 3.8
• PyTorch 1.8.0
• NVIDIA GPU + CUDA

# Clone the github repo and go to the default directory 'DAT'.
git clone https://github.com/zhengchen1999/DAT.git
conda create -n DAT python=3.8
conda activate DAT
pip install -r requirements.txt
python setup.py develop

Contents

1. Datasets
2. Models
3. Training
4. Testing
5. Results
6. Citation
7. Acknowledgements

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Datasets

Used training and testing sets can be downloaded as follows:

Training Set	Testing Set	Visual Results
DIV2K (800 training images, 100 validation images) + Flickr2K (2650 images) [complete training dataset DF2K: Google Drive / Baidu Disk]	Set5 + Set14 + BSD100 + Urban100 + Manga109 [complete testing dataset: Google Drive / Baidu Disk]	Google Drive / Baidu Disk

Download training and testing datasets and put them into the corresponding folders of datasets/. See datasets for the detail of the directory structure.

Models

Method	Params	FLOPs (G)	Dataset	PSNR (dB)	SSIM	Model Zoo	Visual Results
DAT-S	11.21M	203.34	Urban100	27.68	0.8300	Google Drive / Baidu Disk	Google Drive / Baidu Disk
DAT	14.80M	275.75	Urban100	27.87	0.8343	Google Drive / Baidu Disk	Google Drive / Baidu Disk
DAT-2	11.21M	216.93	Urban100	27.86	0.8341	Google Drive / Baidu Disk	Google Drive / Baidu Disk
DAT-light	573K	49.69	Urban100	26.64	0.8033	Google Drive / Baidu Disk	Google Drive / Baidu Disk

The performance is reported on Urban100 (x4). DAT-S, DAT, DAT-2: output size of FLOPs is 3×512×512. DAT-light: output size of FLOPs is 3×1280×720.

Training

• Download training (DF2K, already processed) and testing (Set5, Set14, BSD100, Urban100, Manga109, already processed) datasets, place them in datasets/.

• Run the following scripts. The training configuration is in options/train/.

  # DAT-S, input=64x64, 4 GPUs
  python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/Train/train_DAT_S_x2.yml --launcher pytorch
  python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/Train/train_DAT_S_x3.yml --launcher pytorch
  python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/Train/train_DAT_S_x4.yml --launcher pytorch
  
  # DAT, input=64x64, 4 GPUs
  python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/Train/train_DAT_x2.yml --launcher pytorch
  python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/Train/train_DAT_x3.yml --launcher pytorch
  python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/Train/train_DAT_x4.yml --launcher pytorch
  
  # DAT-2, input=64x64, 4 GPUs
  python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/Train/train_DAT_2_x2.yml --launcher pytorch
  python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/Train/train_DAT_2_x3.yml --launcher pytorch
  python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/Train/train_DAT_2_x4.yml --launcher pytorch
  
  # DAT-light, input=64x64, 4 GPUs
  python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/Train/train_DAT_light_x2.yml --launcher pytorch
  python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/Train/train_DAT_light_x3.yml --launcher pytorch
  python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/Train/train_DAT_light_x4.yml --launcher pytorch

• The training experiment is in experiments/.

Testing

Test images with HR

• Download the pre-trained models and place them in experiments/pretrained_models/.

  We provide pre-trained models for image SR: DAT-S, DAT, DAT-2, and DAT-light (x2, x3, x4).

• Download testing (Set5, Set14, BSD100, Urban100, Manga109) datasets, place them in datasets/.

• Run the following scripts. The testing configuration is in options/test/ (e.g., test_DAT_x2.yml).

  Note 1: You can set use_chop: True (default: False) in YML to chop the image for testing.

  # No self-ensemble
  # DAT-S, reproduces results in Table 2 of the main paper
  python basicsr/test.py -opt options/Test/test_DAT_S_x2.yml
  python basicsr/test.py -opt options/Test/test_DAT_S_x3.yml
  python basicsr/test.py -opt options/Test/test_DAT_S_x4.yml
  
  # DAT, reproduces results in Table 2 of the main paper
  python basicsr/test.py -opt options/Test/test_DAT_x2.yml
  python basicsr/test.py -opt options/Test/test_DAT_x3.yml
  python basicsr/test.py -opt options/Test/test_DAT_x4.yml
  
  # DAT-2, reproduces results in Table 1 of the supplementary material
  python basicsr/test.py -opt options/Test/test_DAT_2_x2.yml
  python basicsr/test.py -opt options/Test/test_DAT_2_x3.yml
  python basicsr/test.py -opt options/Test/test_DAT_2_x4.yml
  
  # DAT-light, reproduces results in Table 2 of the supplementary material
  python basicsr/test.py -opt options/Test/test_DAT_light_x2.yml
  python basicsr/test.py -opt options/Test/test_DAT_light_x3.yml
  python basicsr/test.py -opt options/Test/test_DAT_light_x4.yml

• The output is in results/.

Test images without HR

• Download the pre-trained models and place them in experiments/pretrained_models/.

  We provide pre-trained models for image SR: DAT-S, DAT, and DAT-2 (x2, x3, x4).

• Put your dataset (single LR images) in datasets/single. Some test images are in this folder.

• Run the following scripts. The testing configuration is in options/test/ (e.g., test_single_x2.yml).

  Note 1: The default model is DAT. You can use other models like DAT-S by modifying the YML.

  Note 2: You can set use_chop: True (default: False) in YML to chop the image for testing.

  # Test on your dataset
  python basicsr/test.py -opt options/Test/test_single_x2.yml
  python basicsr/test.py -opt options/Test/test_single_x3.yml
  python basicsr/test.py -opt options/Test/test_single_x4.yml

• The output is in results/.

Results

We achieved state-of-the-art performance. Detailed results can be found in the paper. All visual results of DAT can be downloaded here.

Click to expand

• results in Table 2 of the main paper

• results in Table 1 of the supplementary material

• results in Table 2 of the supplementary material

• visual comparison (x4) in the main paper

• visual comparison (x4) in the supplementary material

Citation

If you find the code helpful in your research or work, please cite the following paper(s).

@inproceedings{chen2023dual,
    title={Dual Aggregation Transformer for Image Super-Resolution},
    author={Chen, Zheng and Zhang, Yulun and Gu, Jinjin and Kong, Linghe and Yang, Xiaokang and Yu, Fisher},
    booktitle={ICCV},
    year={2023}
}

Acknowledgements

This code is built on BasicSR.