DeiT with Collaborative Attention

This repo contains the code to use collaborative attention with Vision Transformer. Refer to the paper's repository for more information.

Citation

If you find this code useful, please cite the paper:

@misc{cordonnier2020multihead,
    title={Multi-Head Attention: Collaborate Instead of Concatenate},
    author={Jean-Baptiste Cordonnier and Andreas Loukas and Martin Jaggi},
    year={2020},
    eprint={2006.16362},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

This repository contains PyTorch evaluation code, training code and pretrained models for DeiT (Data-Efficient Image Transformers).

They obtain competitive tradeoffs in terms of speed / precision:

For details see Training data-efficient image transformers & distillation through attention by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles and Hervé Jégou.

If you use this code for a paper please cite:

@article{touvron2020deit,
  title={Training data-efficient image transformers & distillation through attention},
  author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Herv\'e J\'egou},
  journal={arXiv preprint arXiv:2012.12877},
  year={2020}
}

Model Zoo

We provide baseline DeiT models pretrained on ImageNet 2012.

name	acc@1	acc@5	#params	url
DeiT-tiny	72.2	91.1	5M	model
DeiT-small	79.9	95.0	22M	model
DeiT-base	81.8	95.6	86M	model

The models are also available via torch hub. Before using it, make sure you have the pytorch-image-models package timm==0.3.2 by Ross Wightman installed. Note that our work relies of the augmentations proposed in this library. In particular, the RandAugment and RandErasing augmentations that we invoke are the improved versions from the timm library, which already led the timm authors to report up to 79.35% top-1 accuracy with Imagenet training for their best model, i.e., an improvement of about +1.5% compared to prior art.

To load DeiT-base with pretrained weights on ImageNet simply do:

import torch
# check you have the right version of timm
import timm
assert timm.__version__ == "0.3.2"

# now load it with torchhub
model = torch.hub.load('facebookresearch/deit:main', 'deit_base_patch16_224', pretrained=True)

Usage

First, clone the repository locally:

git clone https://github.com/facebookresearch/deit.git

Then, install PyTorch 1.7.0+ and torchvision 0.8.1+ and pytorch-image-models 0.3.2:

conda install -c pytorch pytorch torchvision
pip install timm==0.3.2

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 DeiT-base on ImageNet val with a single GPU run:

python main.py --eval --resume https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth --data-path /path/to/imagenet

This should give

* Acc@1 81.846 Acc@5 95.594 loss 0.820

For Deit-small, run:

python main.py --eval --resume https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth --model deit_small_patch16_224 --data-path /path/to/imagenet

giving

* Acc@1 79.854 Acc@5 94.968 loss 0.881

Note that Deit-small is not the same model as in Timm.

And for Deit-tiny:

python main.py --eval --resume https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth --model deit_tiny_patch16_224 --data-path /path/to/imagenet

which should give

* Acc@1 72.202 Acc@5 91.124 loss 1.219

Training

To train DeiT-small and Deit-tiny on ImageNet on a single node with 4 gpus for 300 epochs run:

DeiT-small

python -m torch.distributed.launch --nproc_per_node=4 --use_env main.py --model deit_small_patch16_224 --batch-size 256 --data-path /path/to/imagenet --output_dir /path/to/save

DeiT-tiny

python -m torch.distributed.launch --nproc_per_node=4 --use_env main.py --model deit_tiny_patch16_224 --batch-size 256 --data-path /path/to/imagenet --output_dir /path/to/save

Multinode training

Distributed training is available via Slurm and submitit:

pip install submitit

To train DeiT-base model on ImageNet on 2 nodes with 8 gpus each for 300 epochs:

python run_with_submitit.py --model deit_base_patch16_224 --data-path /path/to/imagenet

License

This repository is released under the Apache 2.0 license as found in the LICENSE file.

Contributing

We actively welcome your pull requests! Please see CONTRIBUTING.md and CODE_OF_CONDUCT.md for more info.

jbcdnr/deit-collaborative-attention