LVT_Lite Vision Transformer (CVPR 2022)

Introduction

This repository contains the official implementation of Lite Vision Transformer with Enhanced Self-Attention.

Lite Vision Transformer (LVT).

Despite the impressive representation capacity of vision transformer models, current light-weight vision transformer models still suffer from inconsistent and incorrect dense predictions at local regions. We suspect that the power of their self-attention mechanism is limited in shallower and thinner networks. We propose Lite Vision Transformer (LVT), a novel light-weight transformer network with two enhanced self-attention mechanisms to improve the model performances for mobile deployment. For the low-level features, we introduce Convolutional Self-Attention (CSA). Unlike previous approaches of merging convolution and self-attention, CSA introduces local self-attention into the convolution within a kernel of size 3×3 to enrich low-level features in the first stage of LVT. For the high-level features, we propose Recursive Atrous Self-Attention (RASA), which utilizes the multi-scale context when calculating the similarity map and a recursive mechanism to increase the representation capability with marginal extra parameter cost. The superiority of LVT is demonstrated on ImageNet recognition, ADE20K semantic segmentation, and COCO panoptic segmentation.

Main Results

• ImageNet classification

• ADE20K semantic segmentation

• COCO panoptic segmentation

1. (512): The short side of input is rescaled to 512 with the aspect ratio unchanged.

2. (1333x800): The input is rescaled such that the maximum length does not exceed 1333 and the minimum length 800. The aspect ratio is kept.

The FPS is calculated on 2000 images on a single NVIDIA V100 GPU.

Classification Usage

The training and testing commands can be issued in LVT/classification.

Installation

Please refer to Requirements to prepare the environment.

Training:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
./distributed_train.sh 8 /PATH_TO_IMAGENET \
  --model lvt_cls --img-size 224 \
  -b 128 --lr 1.6e-3 --drop-path 0.1 --apex-amp \
  --token-label \
  --token-label-size 7 \
  --token-label-data /home/code-base/data/label_top5_train_nfnet \
  --cfg ./configs/lvt_imagenet.py \
  --output ./work_dirs/

Testing:

• Before testing, please download the checkpoint and put them in ./checkpoints/.

python validate.py \
/PATH_TO_IMAGENET  \
  --model lvt_cls \
  --checkpoint checkpoints/lvt_imagenet.pth.tar \
  --no-test-pool \
  --apex-amp \
  --img-size 224 \
  -b 128 \
  --cfg ./configs/lvt_imagenet.py

Sementic Segmentation Usage

The training and testing commands can be issued in LVT/segmentation.

Installation

Please refer to Installation to prepare the environment.

Training:

• Before training, please download the model pre-trained on ImageNet and put them in ./pretrained/.

PORT=29501 \
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
./tools/dist_train.sh \
configs/lvt/panoptic_fpn_lvt_fpn_3x_coco.py \
8 \

Testing:

• Before testing, please download the trained checkpoint and put them in ./checkpoints/.

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
bash tools/dist_test.sh \
configs/lvt/panoptic_fpn_lvt_fpn_3x_coco.py \
checkpoints/panoptic_fpn_lvt_fpn_coco.pth \
8 \
  --eval PQ

Panopotic Segmentation Usage

The training and testing commands can be issued in LVT/detection.

Installation

Please refer to Installation to prepare the environment.

Training:

• Before training, please download the model pre-trained on ImageNet and put them in ./pretrained/.

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
./tools/dist_train.sh \
local_configs/lvt/segformer.lvt.512x512.ade.160k.py \
8

Testing:

• Before testing, please download the checkpoint and put them in ./checkpoints/.

python tools/test.py \
local_configs/lvt/segformer.lvt.512x512.ade.160k.py \
./checkpoints/segformer_lvt_ade20k.pth \
  --eval mIoU

Citing LVT

If you find LVT is helpful in your project, please consider citing our paper.

@article{yang2021lite,
  title={Lite Vision Transformer with Enhanced Self-Attention},
  author={Yang, Chenglin and Wang, Yilin and Zhang, Jianming and Zhang, He and Wei, Zijun and and Yuille, Alan},
  journal={arXiv preprint arXiv:2112.10809},
  year={2021}
}

License

Please see the license file, if you have further questions contact Yilin Wang

Credits

Image classification code is based on VOLO. Semantic segmentation code is based on SegFormer. Panoptic segmentation code is based on mmdetection. Part of modules are based on PVT and Dynamic-convolution-Pytorch.