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AS-MLP

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[ICLR'22] This is an official implementation for "AS-MLP: An Axial Shifted MLP Architecture for Vision".

// repository documentation

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# AS-MLP architecture for Image Classification

This repo is the official implementation of our ICLR2022 paper "AS-MLP: An Axial Shifted MLP Architecture for Vision" ([arXiv](https://arxiv.org/abs/2107.08391)).

## Model Zoo

### Image Classification on ImageNet-1K

| Network | Resolution | Top-1 (%) | Params | FLOPs | Throughput (image/s) | model |
|:---:|:---:|:---:|:---:| :---:| :---:|:---:|
| AS-MLP-T | 224x224 | 81.3 | 28M | 4.4G | 1047 | [onedrive](https://shanghaitecheducn-my.sharepoint.com/:u:/g/personal/liandz_shanghaitech_edu_cn/Eb2lPJI2BshEjJxf9CmRkCUBnB63Wj25-FVVc6zJvgRg6w?e=dEQd5q) |
| AS-MLP-S | 224x224 | 83.1 | 50M | 8.5G | 619 | [onedrive](https://shanghaitecheducn-my.sharepoint.com/:u:/g/personal/liandz_shanghaitech_edu_cn/EVIbw8xHKk1Aq9MBQL-cRbIBVJA_hJE-HEVNf_GVgkw2iQ?e=QIIp2e) |
| AS-MLP-B | 224x224 | 83.3 | 88M | 15.2G | 455 | [onedrive](https://shanghaitecheducn-my.sharepoint.com/:u:/g/personal/liandz_shanghaitech_edu_cn/EZVBFW_LKctLqgrnnINy88wBRtGFava9wp_65emsvVW2KQ?e=clNjuw) |

## Usage

### Install

- Clone this repo:

```bash
git clone https://github.com/svip-lab/AS-MLP
cd AS-MLP
```

- Create a conda virtual environment and activate it:

```bash
conda create -n asmlp python=3.7 -y
conda activate asmlp
```

- Install `CUDA==10.1` with `cudnn7` following
  the [official installation instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
- Install `PyTorch==1.7.1` and `torchvision==0.8.2` with `CUDA==10.1`:

```bash
conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.1 -c pytorch
```

- Install `timm==0.3.2`:

```bash
pip install timm==0.3.2
```

- Install `cupy-cuda101`:

```bash
pip install cupy-cuda101
```

- Install `Apex`:

```bash
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
```

- Install other requirements:

```bash
pip install opencv-python==4.4.0.46 termcolor==1.1.0 yacs==0.1.8
```

<!-- ### Data preparation

We use standard ImageNet dataset, you can download it from http://image-net.org/. We provide the following two ways to
load data:

- For standard folder dataset, move validation images to labeled sub-folders. The file structure should look like:
  ```bash
  $ tree data
  imagenet
  ├── train
  │   ├── class1
  │   │   ├── img1.jpeg
  │   │   ├── img2.jpeg
  │   │   └── ...
  │   ├── class2
  │   │   ├── img3.jpeg
  │   │   └── ...
  │   └── ...
  └── val
      ├── class1
      │   ├── img4.jpeg
      │   ├── img5.jpeg
      │   └── ...
      ├── class2
      │   ├── img6.jpeg
      │   └── ...
      └── ...
 
  ``` -->

### Evaluation

To evaluate a pre-trained `AS-MLP` on ImageNet val, run:

```bash
bash train_scripts/test.sh
```

### Training from scratch

To train a `AS-MLP` on ImageNet from scratch, run:

```bash
bash train_scripts/train.sh
```

You can easily reproduce our results. Enjoy!

### Throughput

To measure the throughput, run:

```bash
bash train_scripts/get_throughput.sh
```

### Citation
If this project is helpful for you, you can cite our paper:
```
@InProceedings{Lian_2021_ASMLP,
	title={AS-MLP: An Axial Shifted MLP Architecture for Vision},
	author={Lian, Dongze and Yu, Zehao and Sun, Xing and Gao, Shenghua},
	booktitle={International Conference on Learning Representations (ICLR)},
	year={2022}
}
```

### Other Links

> **Object Detection and Instance Segmentation**: See [AS-MLP for Object Detection](https://github.com/svip-lab/AS-MLP-Object-Detection).

> **Semantic Segmentation**: See [AS-MLP for Semantic Segmentation](https://github.com/svip-lab/AS-MLP-Semantic-Segmentation).

### Acknowledgement
The code is built upon [Swin-Transformer](https://github.com/microsoft/Swin-Transformer), the cuda kernel is modified from [Involution](https://github.com/d-li14/involution/blob/main/cls/mmcls/models/utils/involution_cuda.py).

AS-MLP

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