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Implementation of "AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition"

License: MIT License

Python 100.00%

adaptformer's Introduction

AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition

Project Page | arXiv

This is a PyTorch implementation of the paper AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition.

Shoufa Chen¹*, Chongjian Ge¹*, Zhan Tong², Jiangliu Wang^2,3, Yibing Song², Jue Wang², Ping Luo¹
¹The University of Hong Kong, ²Tencent AI Lab, ³The Chinese University of Hong Kong
*denotes equal contribution

Catalog

Video code
Image code

Usage

Install

Tesla V100 (32G): CUDA 10.1 + PyTorch 1.6.0 + torchvision 0.7.0
timm 0.4.8
einops
easydict

Data Preparation

See DATASET.md.

Training

Start

# video
OMP_NUM_THREADS=1 python3 -m torch.distributed.launch \
    --nproc_per_node=8 --nnodes=8 \
    --node_rank=$1 --master_addr=$2 --master_port=22234 \
    --use_env main_video.py \
    --finetune /path/to/pre_trained/checkpoints \
    --output_dir /path/to/output \
    --batch_size 16 --epochs 90 --blr 0.1 --weight_decay 0.0 --dist_eval \
    --data_path /path/to/SSV2 --data_set SSV2 \
    --ffn_adapt

on each of 8 nodes. --master_addr is set as the ip of the node 0. and --node_rank is 0, 1, ..., 7 for each node.

# image
python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main_image.py \
    --batch_size 128 --cls_token \
    --finetune /path/to/pre_trained/mae_pretrain_vit_b.pth \
    --dist_eval --data_path /path/to/data \
    --output_dir /path/to/output  \
    --drop_path 0.0  --blr 0.1 \
    --dataset cifar100 --ffn_adapt

To obtain the pre-trained checkpoint, see PRETRAIN.md.

Acknowledgement

The project is based on MAE, VideoMAE, timm, and MAM. Thanks for their awesome works.

Citation

@article{chen2022adaptformer,
      title={AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition},
      author={Chen, Shoufa and Ge, Chongjian and Tong, Zhan and Wang, Jiangliu and Song, Yibing and Wang, Jue and Luo, Ping},
      journal={arXiv preprint arXiv:2205.13535},
      year={2022}
}