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

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

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.

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

Datasets
Models
Training
Testing
Results
Citation
Acknowledgements

Datasets

Used training and testing sets can be downloaded as follows:

Training Set	Testing Set	Visual Results
DIV2K (800 training images) + Flickr2K (2650 images) [complete training dataset DF2K]	Set5 + Set14 + BSD100 + Urban100 + Manga109 [complete testing dataset download]	here

Download training and testing datasets and put them into the corresponding folders of datasets/ and restormer/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	Google Drive
DAT	14.80M	275.75	Urban100	27.87	0.8343	Google Drive	Google Drive
DAT-2	11.21M	216.93	Urban100	27.86	0.8341	Google Drive	Google Drive
DAT-light	573K	49.69	Urban100	26.64	0.8033	Google Drive	Google Drive

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 expan

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.

ChaoXiang661 / DAT