Hire-Wave-MLP.pytorch

Implementation of Hire-MLP: Vision MLP via Hierarchical Rearrangement and An Image Patch is a Wave: Phase-Aware Vision MLP

Results and Models

Hire-MLP on ImageNet-1K Classification

Usage

Install

• PyTorch (1.7.0)
• torchvision (0.8.1)
• timm (0.3.2)
• torchprofile
• mmcv (v1.3.0)
• mmdetection (v2.11)
• mmsegmentation (v0.11)

Data preparation

Download and extract ImageNet train and val images from http://image-net.org/. The directory structure is:

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

Training

Training Hire-MLP

To train Hire-MLP-Tiny on ImageNet-1K on a single node with 8 gpus:

python -m torch.distributed.launch --nproc_per_node=8 train.py --data-path /your_path_to/imagenet/ --output_dir /your_path_to/output/ --model hire_mlp_tiny --batch-size 256 --apex-amp --input-size 224 --drop-path 0.0 --epochs 300 --test_freq 50 --test_epoch 260 --warmup-epochs 20 --warmup-lr 1e-6 --no-model-ema

To train Hire-MLP-Base on ImageNet-1K on a single node with 8 gpus:

python -m torch.distributed.launch --nproc_per_node=8 train.py --data-path /your_path_to/imagenet/ --output_dir /your_path_to/output/ --model hire_mlp_base --batch-size 128 --apex-amp --input-size 224 --drop-path 0.2 --epochs 300 --test_freq 50 --test_epoch 260 --warmup-epochs 20 --warmup-lr 1e-6 --no-model-ema

Training Wave-MLP

On a single node with 8 gpus, you can train the Wave-MLP family on ImageNet-1K as follows :

WaveMLP_T_dw:

python -m torch.distributed.launch --nproc_per_node 8 --nnodes=1 --node_rank=0 train_wave.py /your_path_to/imagenet/ --output /your_path_to/output/ --model WaveMLP_T_dw --sched cosine --epochs 300 --opt adamw -j 8 --warmup-lr 1e-6 --mixup .8 --cutmix 1.0 --model-ema --model-ema-decay 0.99996 --aa rand-m9-mstd0.5-inc1 --color-jitter 0.4 --warmup-epochs 5 --opt-eps 1e-8 --repeated-aug --remode pixel --reprob 0.25 --amp --lr 1e-3 --weight-decay .05 --drop 0 --drop-path 0.1 -b 128

WaveMLP_T:

python -m torch.distributed.launch --nproc_per_node 8 --nnodes=1 --node_rank=0 train_wave.py /your_path_to/imagenet/ --output /your_path_to/output/ --model WaveMLP_T --sched cosine --epochs 300 --opt adamw -j 8 --warmup-lr 1e-6 --mixup .8 --cutmix 1.0 --model-ema --model-ema-decay 0.99996 --aa rand-m9-mstd0.5-inc1 --color-jitter 0.4 --warmup-epochs 5 --opt-eps 1e-8 --repeated-aug --remode pixel --reprob 0.25 --amp --lr 1e-3 --weight-decay .05 --drop 0 --drop-path 0.1 -b 128

WaveMLP_S:

python -m torch.distributed.launch --nproc_per_node 8 --nnodes=1 --node_rank=0 train_wave.py /your_path_to/imagenet/ --output /your_path_to/output/ --model WaveMLP_S --sched cosine --epochs 300 --opt adamw -j 8 --warmup-lr 1e-6 --mixup .8 --cutmix 1.0 --model-ema --model-ema-decay 0.99996 --aa rand-m9-mstd0.5-inc1 --color-jitter 0.4 --warmup-epochs 5 --opt-eps 1e-8 --repeated-aug --remode pixel --reprob 0.25 --amp --lr 1e-3 --weight-decay .05 --drop 0 --drop-path 0.1 -b 128

WaveMLP_M:

python -m torch.distributed.launch --nproc_per_node 8 --nnodes=1 --node_rank=0 train_wave.py /your_path_to/imagenet/ --output /your_path_to/output/ --model WaveMLP_M --sched cosine --epochs 300 --opt adamw -j 8 --warmup-lr 1e-6 --mixup .8 --cutmix 1.0 --model-ema --model-ema-decay 0.99996 --aa rand-m9-mstd0.5-inc1 --color-jitter 0.4 --warmup-epochs 5 --opt-eps 1e-8 --repeated-aug --remode pixel --reprob 0.25 --amp --lr 1e-3 --weight-decay .05 --drop 0 --drop-path 0.1 -b 128

Evaluation

To evaluate a pre-trained Hire-MLP-Tiny on ImageNet validation set with a single GPU:

python -m torch.distributed.launch --nproc_per_node=1 train.py --data-path /your_path_to/imagenet/ --output_dir /your_path_to/output/ --batch-size 256 --input-size 224 --model hire_mlp_tiny --apex-amp --no-model-ema --resume /your_path_to/hire_mlp_tiny.pth --eval

Acknowledgement

This repo is based on DeiT, pytorch-image-models, MMDetection, MMSegmentation, Swin Transformer, CycleMLP and AS-MLP.

Citation

If you find this project useful in your research, please consider cite:

@article{guo2021hire,
  title={Hire-mlp: Vision mlp via hierarchical rearrangement},
  author={Guo, Jianyuan and Tang, Yehui and Han, Kai and Chen, Xinghao and Wu, Han and Xu, Chao and Xu, Chang and Wang, Yunhe},
  journal={arXiv preprint arXiv:2108.13341},
  year={2021}
}

@article{tang2021image,
  title={An Image Patch is a Wave: Phase-Aware Vision MLP},
  author={Tang, Yehui and Han, Kai and Guo, Jianyuan and Xu, Chang and Li, Yanxi and Xu, Chao and Wang, Yunhe},
  journal={arXiv preprint arXiv:2111.12294},
  year={2021}
}

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