Skip to content

Latest commit

 

History

History
92 lines (75 loc) · 6.67 KB

README.md

File metadata and controls

92 lines (75 loc) · 6.67 KB
Raw

On the Connection between Local Attention and Dynamic Depth-wise Convolution (ICLR 2022 spotlight) arxiv

This is the official PyTorch implementation of our paper. We simply replace local self attention by (dynamic) depth-wise convolution with lower computational cost. The performance is on par with the Swin Transformer.

Besides, the main contribution of our paper is the theorical and detailed comparison between depth-wise convolution and local self attention from three aspects: sparse connectivity, weight sharing and dynamic weight. By this paper, we want community to rethinking the local self attention and depth-wise convolution, and the basic model architeture designing rules.

Codes and models for object detection and semantic segmentation are avaliable in Detection and Segmentation.

Reference

@inproceedings{han2021connection,
  title={On the Connection between Local Attention and Dynamic Depth-wise Convolution},
  author={Han, Qi and Fan, Zejia and Dai, Qi and Sun, Lei and Cheng, Ming-Ming and Liu, Jiaying and Wang, Jingdong},
  booktitle={International Conference on Learning Representations},
  year={2022}
}

1. Requirements

torch>=1.5.0, torchvision, timm, pyyaml; apex-amp

data perpare: ImageNet dataset with the following structure:

│imagenet/
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......

2. Trainning

For tiny model, we train with batch-size 128 on 8 GPUs. When trainning base model, we use batch-size 64 on 16 GPUs with OpenMPI to keep the total batch-size unchanged. (With the same trainning setting, the base model couldn't train with AMP due to the anomalous gradient values.)

Please change the data path in sh scripts first.

For tiny model:

bash scripts/run_dwnet_tiny_patch4_window7_224.sh 
bash scripts/run_dynamic_dwnet_tiny_patch4_window7_224.sh

For base model, use multi node with OpenMPI:

bash scripts/run_dwnet_base_patch4_window7_224.sh 
bash scripts/run_dynamic_dwnet_base_patch4_window7_224.sh

3. Evaluation

python -m torch.distributed.launch --nproc_per_node 1 --master_port 12345 main.py --cfg configs/change_to_config_file --resume /path/to/model --data-path /path/to/imagenet --eval

4. Models

Models are provided by training on ImageNet with resolution 224.

Model #params FLOPs Top1 Acc Download
DWNet-tiny 24M 3.8G 81.2 github
dynamic DWNet-tiny 51M 3.8G 81.8 github
DWNet-base 74M 12.9G 83.2 github
dynamic dwnet-base 162M 13.0G 83.2 github

Detection (see Detection for details):

Backbone Pretrain Lr Schd box mAP mask mAP #params FLOPs config model
DWNet-T ImageNet-1K 3x 49.9 43.4 82M 730G config github
DWNet-B ImageNet-1K 3x 51.0 44.1 132M 924G config github
dynamic DWNet-T ImageNet-1K 3x 50.5 43.7 108M 730G config github
dynamic DWNet-B ImageNet-1K 3x 51.2 44.4 219M 924G config github

Segmentation (see Segmentation for details):

Backbone Pretrain Lr Schd mIoU #params FLOPs config model
DWNet-T ImageNet-1K 160K 45.5 56M 928G config github
DWNet-B ImageNet-1K 160K 48.3 108M 1129G config github
dynamic DWNet-T ImageNet-1K 160K 45.7 83M 928G config github
dynamic DWNet-B ImageNet-1K 160K 48.0 195M 1129G config github

LICENSE

This repo is under the MIT license. Some codes are borrow from Swin Transformer.