ActiveMLP (AAAI 2023)
This repo is the official implementation of "ActiveMLP: An MLP-like Architecture with Active Token Mixer", by Guoqiang Wei*, Zhizheng Zhang*, Cuiling Lan, Yan Lu and Zhibo Chen.
ActiveMLP is a general MLP-like visual backbone, which is applicable to image classification, object detection and semantic segmentation tasks. The core operator, Active Token Mixer (ATM
), actively incorporates contextual information from other tokens in the global scope. It adaptively predicts where to capture useful contexts and learns how to fuse the captured contexts with the origianl information at channel levels.
The ActiveMLP variants achieve 79.7% ~ 84.8%
acc@top1 with the models scaled from 15M ~ 76M
on ImageNet-1K. It also shows the superiority on downstream dense prediction tasks. ActiveMLP-Large
achieves 51.1% mIoU
with UperNet on ADE20K semantic segmentation dataset.
Image classification on ImageNet-1K
name | size | acc@1 | #params | FLOPs | download |
---|---|---|---|---|---|
Active-xT | 224 |
79.7 | 15M | 2.2G | model / log |
Active-T | 224 |
82.0 | 27M | 4.0G | model / log |
Active-S | 224 |
83.1 | 39M | 6.9G | model / log |
Active-B | 224 |
83.5 | 52M | 10.1G | model |
Active-L | 224 |
83.8 | 76M | 12.4G | model |
Active-L |
384 |
84.8 | 76M | 36.4G | model |
Usage
The following guideline of ActiveMLP is for image classification, the guideline for semantic segmentation can be found here.
Install
- Clone this repo:
git clone https://github.com/microsoft/ActiveMLP.git
cd ActiveMLP
- Install
pytorch
following the official guideline, we usepytorch==1.7.1
withcuda==11.1
andcudnn8
. - Install other packages with:
pip install -r requirements.txt
Data preparation
Download the standard ImageNet-1K dataset from http://image-net.org, and construct the data like:
ImageNet_Root
├── train
│ ├── n01440764
│ │ ├── n01440764_10026.JPEG
│ │ ├── n01440764_10027.JPEG
│ │ ├── ...
│ ├── ...
├── val
├── n02093754
│ ├── ILSVRC2012_val_00000832.JPEG
│ ├── ILSVRC2012_val_00003267.JPEG
│ ├── ...
├── ...
Evaluation
To evaluate a pre-trained ActiveMLP
on ImageNet val, run with:
python -m torch.distributed.launch --nproc_per_node <num-gpus> \
--use_env main.py \
--data-path <path-to-imagenet> \
--model <activemlp-model> \
--resume <checkpoint.pth> \
--eval --dist-eval
For example, to evaluate the ActiveMLP-Tiny
with two GPUs distributedly:
python -m torch.distributed.launch --nproc_per_node 2 \
--use_env main.py \
--data-path <path-to-imagenet> \
--model ActiveTiny \
--resume activemlp_tiny.pth \
--eval --dist-eval
This should give:
[ema] accuracy on 50000 test images: 81.990% acc@1 | 95.930% acc@5
Training
To train an ActiveMLP
on ImageNet from scratch, run with:
python -m torch.distributed.launch --nproc_per_node <num-gpus> \
--use_env main.py \
--batch-size <batch-szie> \
--data-path <path-to-imagenet> \
--model <activemlp-model> [other options]
For example, train the ActiveMLP-Tiny
with 1024
batch size on 8 GPUs, run with:
python -m torch.distributed.launch --nproc_per_node 8 \
--use_env main.py \
--batch-size 128 \
--data-path <path-to-imagenet> \
--model ActivexTiny \
--drop-path 0.1 \
--output-dir active_xtiny_output
Throughput
To evaluate the throughput, run with:
python -m torch.distributed.launch --nproc_per_node 1 \
--use_env main.py --batch-size 64 \
--data-path <path-to-imagenet> \
--model ActivexTiny \
--throughput
Citing
@article{wei2022activemlp,
title={ActiveMLP: An MLP-like Architecture with Active Token Mixer},
author={Wei, Guoqiang and Zhang, Zhizheng and Lan, Cuiling and Lu, Yan and Chen, Zhibo},
journal={arXiv preprint arXiv:2203.06108},
year={2022}
}
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.
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.