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Visual Attention Emerges from Recurrent Sparse Reconstruction (ICML 2022)

Baifeng Shi, Yale Song, Neel Joshi, Trevor Darrell, Xin Wang

Codebase of our ICML'22 paper "Visual Attention Emerges from Recurrent Sparse Reconstruction".

Usage

Install PyTorch 1.7.0+ and torchvision 0.8.1+ from the official website.

requirements.txt lists all the dependencies:

pip install -r requirements.txt

In addition, please also install the magickwand library:

apt-get install libmagickwand-dev

Training

Take RVT-Ti with VARS-D for an example. We use single node with 8 gpus for training:

python -m torch.distributed.launch --nproc_per_node=8 --master_port 12345  main.py --model rvt_tiny --data-path path/to/imagenet  --output_dir output/here  --num_workers 8 --batch-size 128 --attention vars_d

We provide pretrained weights for VARS-D and VARS-SD.

To train models with different scales or different attention algorithms, please change the arguments --model and --attention.

Testing

python main.py --model rvt_tiny --data-path path/to/imagenet --eval --resume path/to/checkpoint --attention vars_d

To enable robustness evaluation, please add one of --inc_path /path/to/imagenet-c, --ina_path /path/to/imagenet-a, --inr_path /path/to/imagenet-r or --insk_path /path/to/imagenet-sketch to test ImageNet-C, ImageNet-A, ImageNet-R or ImageNet-Sketch.

If you want to test the accuracy under adversarial attackers, please add --fgsm_test or --pgd_test.

Links

This codebase is built upon the official code of "Towards Robust Vision Transformer".

Citation

If you found this code helpful, please consider citing our work:

@article{shi2022visual,
  title={Visual Attention Emerges from Recurrent Sparse Reconstruction},
  author={Shi, Baifeng and Song, Yale and Joshi, Neel and Darrell, Trevor and Wang, Xin},
  journal={arXiv preprint arXiv:2204.10962},
  year={2022}
}