iccv2021 / autoformer

This is the implementation for ICCV submission AutoFormer: Searching Transformers for Visual Recognition.

Environment Setup

To set up the enviroment you can easily run the following command:

git clone https://github.com/ICCV2021/Autoformer.git
cd Autoformer
conda create -n Autoformer python=3.6
conda activate Autoformer
pip install -r requirements.txt

Data Preparation

You need to first download the ImageNet-2012 to the folder ./data/imagenet and move the validation set to the subfolder ./data/imagenet/val. To move the validation set, you cloud use the following script: https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh

The directory structure is the standard layout as following.

/path/to/imagenet/
  train/
    class1/
      img1.jpeg
    class2/
      img2.jpeg
  val/
    class1/
      img3.jpeg
    class/2
      img4.jpeg

Model Zoo

For evaluation, we provide the checkpoints of our models in Google Drive.

After downloading the models, you can do the evaluation following the description in Quick Start - Test).

Model download links:

Model	Params.	Top-1 Acc. %	Top-5 Acc. %	Link
AutoFormer-T	5.8M	75.3	92.7	Google Drive
AutoFormer-S	22.9M	81.7	95.7	Google Drive
AutoFormer-B	53.7M	82.4	95.7	Google Drive

Quick Start

We provide test code of AutoFormer as follows.

Test

To test our trained models, you need to put the downloaded model in /PATH/TO/CHECKPOINT. After that you could use the following command to test the model (Please change your config file/model checkpoint according to different models. Here we use the AutoFormer-B as an example).

python -m torch.distributed.launch --nproc_per_node=8 --use_env supernet_train.py --data-path /PATH/TO/IMAGENT --gp \
--change_qk --relative_position --mode retrain --dist-eval --cfg ./experiments/subnet/AutoFormer-B.yaml --resume /PATH/TO/CHECKPOINT --eval 

Train & Search

We will release the training and search code upon acceptance of the paper.

Performance

Left: Top-1 accuracy on ImageNet. Our method achieves very competitive performance, being superior to the recent DeiT and ViT. Right: 1000 random sampled good architectures in the supernet-S. The supernet trained under our strategy allows subnets to be well optimized.