Dynamic Token Normalization Improves Vision Transformers

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Deep LearningDTN
Overview

Dynamic Token Normalization Improves Vision Transformers

This is the PyTorch implementation of the paper Dynamic Token Normalization Improves Vision Transfromers. Codea and Models will be available soon.

Dynamic Token Normalization

We design a novel normalization method, termed Dynamic Token Normalization (DTN), which inherits the advantages from LayerNorm and InstanceNorm. DTN can be seamlessly plugged into various transformer models, consistenly improving the performance.

Comparisons of top-1 accuracies on the validation set of ImageNet, by using ViT trained with LN and DTN.

Model Top-1 Top-5
ViT-T*-LN 72.3 91.4
ViT-T*-DTN 73.2 91.7
ViT-S*-LN 80.6 95.2
ViT-S*-DTN 81.7 95.8
ViT-B*-LN 81.7 95.8
ViT-B*-DTN 82.5 96.1

Getting Started

  • Install PyTorch
  • Clone the repo: 
    git clone https://github.com/dtn-anonymous/DTN.git

Requirements

conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.1 -c pytorch
  • Install timm==0.3.2:
pip install timm==0.3.2

Data Preparation

  • Download the ImageNet dataset which should contain train and val directionary and the txt file for correspondings between images and labels.

Training a model from scratch

An example to train our DTN is given in DTN/scripts/train.sh. To train ViT-S* with our DTN,

cd DTN/scripts   
sh train.sh layer vit_norm_s_star configs/ViT/vit.yaml

Number of GPUs and configuration file to use can be modified in train.sh

Owner
Wenqi Shao
Wenqi Shao
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