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DynaMixer: A Vision MLP Architecture with Dynamic Mixing[arxiv]

This is a Pytorch implementation of our paper DynaMixer, ICML 2022.

Comparison with Recent MLP-like Models

Model	Sub-model	Parameters	Top 1 Acc.
Cycle-MLP	T	28M	81.3
ViP	Small/7	25M	81.5
Hire-MLP	S	33M	82.1
DynaMixer	S	26M	82.7
Cycle-MLP	S	50M	82.9
ViP	Medium/7	55M	82.7
Hire-MLP	B	58M	83.2
DynaMixer	M	57M	83.7
Cycle-MLP	B	88M	83.4
ViP	Large/7	88M	83.2
Hire-MLP	L	96M	83.8
DynaMixer	L	97M	84.3

Requirements

Environment

torch==1.9.0
torchvision>=0.10.0
pyyaml
timm==0.4.12
fvcore
apex if you use 'apex amp'

Data

data prepare: ImageNet with the following folder structure, you can extract imagenet by this script.

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

Training

Command line for training on 8 GPUs (V100)

train dynamixer_s:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./distributed_train.sh 8 /path/to/imagenet --model dynamixer_s -b 256 -j 8 --opt adamw --epochs 300 --sched cosine --apex-amp --img-size 224 --drop-path 0.1 --lr 2e-3 --weight-decay 0.05 --remode pixel --reprob 0.25 --aa rand-m9-mstd0.5-inc1 --smoothing 0.1 --mixup 0.8 --cutmix 1.0 --warmup-lr 1e-6 --warmup-epochs 20

train dynamixer_m:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./distributed_train.sh 8 /path/to/imagenet --model dynamixer_m -b 128 -j 8 --opt adamw --epochs 300 --sched cosine --apex-amp --img-size 224 --drop-path 0.1 --lr 2e-3 --weight-decay 0.05 --remode pixel --reprob 0.25 --aa rand-m9-mstd0.5-inc1 --smoothing 0.1 --mixup 0.8 --cutmix 1.0 --warmup-lr 1e-6 --warmup-epochs 20

train dynamixer_l:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./distributed_train.sh 8 /path/to/imagenet --model dynamixer_l -b 64 -j 8 --opt adamw --epochs 300 --sched cosine --apex-amp --img-size 224 --drop-path 0.3 --lr 2e-3 --weight-decay 0.05 --remode pixel --reprob 0.25 --aa rand-m9-mstd0.5-inc1 --smoothing 0.1 --mixup 0.8 --cutmix 1.0 --warmup-lr 1e-6 --warmup-epochs 20

Reference

You may want to cite:

@inproceedings{wang2022dynamixer,
  title={Dynamixer: a vision MLP architecture with dynamic mixing},
  author={Wang, Ziyu and Jiang, Wenhao and Zhu, Yiming M and Yuan, Li and Song, Yibing and Liu, Wei},
  booktitle={International Conference on Machine Learning},
  pages={22691--22701},
  year={2022},
  organization={PMLR}
}

Acknowledgement

The code is based on following repos:
https://github.com/Andrew-Qibin/VisionPermutator
https://github.com/ShoufaChen/CycleMLP.
Thanks for their wonderful works.

License

Dynamixer is released under MIT License.