Source code of AAAI 2022 paper "Towards End-to-End Image Compression and Analysis with Transformers".

Towards End-to-End Image Compression and Analysis with Transformers

Source code of our AAAI 2022 paper "Towards End-to-End Image Compression and Analysis with Transformers".

Usage

The code is run with Python 3.7, Pytorch 1.8.1, Timm 0.4.9 and Compressai 1.1.4.

Data preparation

Download and extract ImageNet train and val images from http://image-net.org/. The directory structure is the standard layout for the torchvision datasets.ImageFolder, and the training and validation data is expected to be in the train folder and val folder respectively:

/path/to/imagenet/
  train/
    class1/
      img1.jpeg
    class2/
      img2.jpeg
  val/
    class1/
      img3.jpeg
    class2/
      img4.jpeg

Pretrained model

The ./pretrained_model provides the pretrained model without compression.

• Test

Please adjust --data-path and run sh test.sh:

python main.py --eval --resume ./pretrain_s/checkpoint.pth --model pretrained_model --data-path /path/to/imagenet/ --output_dir ./eval

The ./pretrain_s/checkpoint.pth can be downloaded from Baidu Netdisk, with access code aaai.

• Train

Please adjust --data-path and run sh train.sh:

python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --model pretrained_model --no-model-ema --clip-grad 1.0 --batch-size 128 --num_workers 16 --data-path /path/to/imagenet/ --output_dir ./ckp_pretrain

Full model

The ./full_model provides the full model with compression.

• Test

Please adjust --data-path and --resume, respectively. Run sh test.sh:

python main.py --eval --resume ./ckp_s_q1/checkpoint.pth --model full_model --no-pretrained --data-path /path/to/imagenet/ --output_dir ./eval

The ./ckp_s_q1/checkpoint.pth, ./ckp_s_q2/checkpoint.pth and ./ckp_s_q3/checkpoint.pth can be downloaded from Baidu Netdisk, with access code aaai.

• Train

Please download ./pretrain_s/checkpoint.pth from Baidu Netdisk with access code aaai, adjust --data-path and --quality, respectively.

Run sh train.sh:

python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --model full_model --batch-size 128 --num_workers 16 --clip-grad 1.0 --quality 1 --data-path /path/to/imagenet/ --output_dir ./ckp_full

Citation

@InProceedings{Bai2022AAAI,
  title={Towards End-to-End Image Compression and Analysis with Transformers},
  author={Bai, Yuanchao and Yang, Xu and Liu, Xianming and Jiang, Junjun and Wang, Yaowei and Ji, Xiangyang and Gao, Wen},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2022}
}