Awesome
This repository contains Pytorch evaluation code, training code and pretrained models for the following projects:
- SPACH (A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP)
- sMLP (Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?)
- ShiftViT (When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism)
Other unofficial implementations:
- ShiftViT
- SPACH
Main Results on ImageNet with Pretrained Models
name | acc@1 | #params | FLOPs | url |
---|---|---|---|---|
SPACH-Conv-MS-XXS | 73.1 | 5M | 0.7G | github |
SPACH-Trans-MS-XXS | 65.4 | 2M | 0.5G | github |
SPACH-MLP-MS-XXS | 74.5 | 6M | 0.9G | github |
SPACH-Conv-MS-S | 81.6 | 44M | 7.2G | github |
SPACH-Trans-MS-S | 82.9 | 40M | 7.6G | github |
SPACH-MLP-MS-S | 82.1 | 46M | 8.2G | github |
SPACH-Hybrid-MS-S | 83.7 | 63M | 11.2G | github |
SPACH-Hybrid-MS-S+ | 83.9 | 63M | 12.3G | github |
sMLPNet-T | 81.9 | 24M | 5.0G | |
sMLPNet-S | 83.1 | 49M | 10.3G | github |
sMLPNet-B | 83.4 | 66M | 14.0G | github |
Shift-T / light | 79.4 | 20M | 3.0G | github |
Shift-T | 81.7 | 29M | 4.5G | github |
Shift-S / light | 81.6 | 34M | 5.7G | github |
Shift-S | 82.8 | 50M | 8.8G | github |
Usage
Install
First, clone the repo and install requirements:
git clone https://github.com/microsoft/Spach
pip install -r requirements.txt
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
class/2
img4.jpeg
Evaluation
To evaluate a pre-trained model on ImageNet val with a single GPU run:
python main.py --eval --resume <checkpoint> --model <model-name>--data-path <imagenet-path>
For example, to evaluate the SPACH-Hybrid-MS-S model, run
python main.py --eval --resume --model spach_ms_s_patch4_224_hybrid spach_ms_hybrid_s.pth --data-path <imagenet-path>
giving
* Acc@1 83.658 Acc@5 96.762 loss 0.688
You can find all supported models in models/registry.py.
Training
One can simply call the following script to run training process. Distributed training is recommended even on single GPU node.
python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --use_env main.py \
--model <model-name>
--data-path <imagenet-path>
--output_dir <output-path>
--dist-eval
Citation
@article{zhao2021battle,
title={A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP},
author={Zhao, Yucheng and Wang, Guangting and Tang, Chuanxin and Luo, Chong and Zeng, Wenjun and Zha, Zheng-Jun},
journal={arXiv preprint arXiv:2108.13002},
year={2021}
}
@article{tang2021sparse,
title={Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?},
author={Tang, Chuanxin and Zhao, Yucheng and Wang, Guangting and Luo, Chong and Xie, Wenxuan and Zeng, Wenjun},
journal={arXiv preprint arXiv:2109.05422},
year={2021}
}
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
Acknowledgement
Our code are built on top of DeiT. We test throughput following Swin Transformer