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Mini-Swin

README.md

Mini-Swin

This repo is for MiniViT for swin transformers.

Model Zoo

Model Params. Input Top-1 Acc. % Top-5 Acc. % Download link
Mini-Swin-T 12M 224x224 81.3 95.7 model, log
Mini-Swin-S 26M 224x224 83.9 97.0 model, log
Mini-Swin-B 46M 224x224 84.5 97.3 model, log
Mini-Swin-B 47M 384x384 85.5 97.6 model, log

Usage

Create the environment:

pip install -r requirements.txt

Data Preparation

You can download the ImageNet-1K dataset from http://www.image-net.org/.

The train set and validation set should be saved as the *.tar archives:

ImageNet/
├── train.tar
└── val.tar

Our code also supports storing images as individual files as follow:

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

Training

Training Mini-Swin-Tiny

python -m torch.distributed.launch --nproc_per_node 8 main.py --cfg configs/swin_tiny_patch4_window7_224_minivit_sharenum6.yaml --data-path <data-path>  --output <output-folder> --tag mini-swin-tiny --batch-size 128 --is_sep_layernorm --is_transform_heads --is_transform_ffn --do_distill --alpha 0.0 --teacher <teacher-path> --attn_loss --hidden_loss --hidden_relation --student_layer_list 11_9_7_5_3_1 --teacher_layer_list 23_21_15_9_3_1 --hidden_weight 0.1
Training Mini-Swin-Small 
python -m torch.distributed.launch --nproc_per_node 8 main.py --cfg configs/swin_small_patch4_window7_224_minivit_sharenum2.yaml --data-path   --output  --tag mini-swin-small --batch-size 128 --is_sep_layernorm --is_transform_heads --is_transform_ffn --do_distill --alpha 0.0 --teacher  --attn_loss --hidden_loss --hidden_relation --student_layer_list 23_21_15_9_3_1 --teacher_layer_list 23_21_15_9_3_1 --hidden_weight 0.1
Training Mini-Swin-Base 
python -m torch.distributed.launch --nproc_per_node 8 main.py --cfg configs/swin_base_patch4_window7_224_minivit_sharenum2.yaml --data-path   --output  --tag mini-swin-base --batch-size 128 --is_sep_layernorm --is_transform_heads --is_transform_ffn --do_distill --alpha 0.0 --teacher  --attn_loss --hidden_loss --hidden_relation --student_layer_list 23_21_15_9_3_1 --teacher_layer_list 23_21_15_9_3_1 --hidden_weight 0.1

Finetune Mini-Swin-B with resolution 384:

python -m torch.distributed.launch --nproc_per_node 8 main.py --cfg configs/swin_base_patch4_window7_224to384_minivit_sharenum2_adamw.yaml --data-path <data-path>  --output <output-folder> --tag mini-swin-base-224to384 --batch-size 16 --accumulation-steps 2 --is_sep_layernorm --is_transform_heads --is_transform_ffn --resume <model-224-ckpt> --resume_weight_only --train_224to384

Evaluation

Run the following commands for evaluation:

Evaluate Mini-Swin-Tiny

python -m torch.distributed.launch --nproc_per_node 8 main.py --cfg configs/swin_tiny_patch4_window7_224_minivit_sharenum6.yaml --data-path <data-path> --batch-size 64 --is_sep_layernorm --is_transform_ffn --is_transform_heads --resume checkpoints/mini-swin-tiny-12m.pth --eval
Evaluate Mini-Swin-Small 
python -m torch.distributed.launch --nproc_per_node 8 main.py --cfg configs/swin_small_patch4_window7_224_minivit_sharenum2.yaml --data-path  --batch-size 64 --is_sep_layernorm --is_transform_ffn --is_transform_heads --resume checkpoints/mini-swin-small-26m.pth --eval
Evaluate Mini-Swin-Base 
python -m torch.distributed.launch --nproc_per_node 8 main.py --cfg configs/swin_base_patch4_window7_224_minivit_sharenum2.yaml --data-path  --batch-size 64 --is_sep_layernorm --is_transform_ffn --is_transform_heads --resume checkpoints/mini-swin-base-46m.pth --eval
Evaluate Mini-Swin-Base-384 
python -m torch.distributed.launch --nproc_per_node 8 main.py --cfg configs/swin_base_patch4_window7_224to384_minivit_sharenum2_adamw.yaml --data-path  --batch-size 32 --is_sep_layernorm --is_transform_ffn --is_transform_heads --resume checkpoints/mini-swin-base-224to384.pth --eval

Bibtex

If this repo is helpful for you, please consider to cite it. Thank you! :)

@InProceedings{MiniViT,
    title     = {MiniViT: Compressing Vision Transformers With Weight Multiplexing},
    author    = {Zhang, Jinnian and Peng, Houwen and Wu, Kan and Liu, Mengchen and Xiao, Bin and Fu, Jianlong and Yuan, Lu},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {12145-12154}
}

License

Our code is based on Swin Transformer. Thank you!

MIT License