DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification

Related tags

Deep Learningcomputer-visiondeep-learningimage-classificationvision-transformers
Overview

DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification

Created by Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh

This repository contains PyTorch implementation for DynamicViT.

We introduce a dynamic token sparsification framework to prune redundant tokens in vision transformers progressively and dynamically based on the input:

intro

Our code is based on pytorch-image-models, DeiT and LV-ViT

[Project Page] [arXiv]

Model Zoo

We provide our DynamicViT models pretrained on ImageNet:

name arch rho [email protected] [email protected] FLOPs url
DynamicViT-256/0.7 deit_256 0.7 76.532 93.118 1.3G Google Drive / Tsinghua Cloud
DynamicViT-384/0.7 deit_small 0.7 79.316 94.676 2.9G Google Drive / Tsinghua Cloud
DynamicViT-LV-S/0.5 lvvit_s 0.5 81.970 95.756 3.7G Google Drive / Tsinghua Cloud
DynamicViT-LV-S/0.7 lvvit_s 0.7 83.076 96.252 4.6G Google Drive / Tsinghua Cloud
DynamicViT-LV-M/0.7 lvvit_m 0.7 83.816 96.584 8.5G Google Drive / Tsinghua Cloud

Usage

Requirements

  • torch>=1.7.0
  • torchvision>=0.8.1
  • timm==0.4.5

Data preparation: download and extract ImageNet images from http://image-net.org/. The directory structure should be

│ILSVRC2012/
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......

Model preparation: download pre-trained DeiT and LV-ViT models for training DynamicViT:

sh download_pretrain.sh

Demo

We provide a Jupyter notebook where you can run the visualization of DynamicViT.

To run the demo, you need to install matplotlib.

demo

Evaluation

To evaluate a pre-trained DynamicViT model on ImageNet val with a single GPU, run:

python infer.py --data-path /path/to/ILSVRC2012/ --arch arch_name --model-path /path/to/model --base_rate 0.7

Training

To train DynamicViT models on ImageNet, run:

DeiT-small

python -m torch.distributed.launch --nproc_per_node=8 --use_env main_dynamic_vit.py  --output_dir logs/dynamic-vit_deit-small --arch deit_small --input-size 224 --batch-size 96 --data-path /path/to/ILSVRC2012/ --epochs 30 --dist-eval --distill --base_rate 0.7

LV-ViT-S

python -m torch.distributed.launch --nproc_per_node=8 --use_env main_dynamic_vit.py  --output_dir logs/dynamic-vit_lvvit-s --arch lvvit_s --input-size 224 --batch-size 64 --data-path /path/to/ILSVRC2012/ --epochs 30 --dist-eval --distill --base_rate 0.7

LV-ViT-M

python -m torch.distributed.launch --nproc_per_node=8 --use_env main_dynamic_vit.py  --output_dir logs/dynamic-vit_lvvit-m --arch lvvit_m --input-size 224 --batch-size 48 --data-path /path/to/ILSVRC2012/ --epochs 30 --dist-eval --distill --base_rate 0.7

You can train models with different keeping ratio by adjusting base_rate. DynamicViT can also achieve comparable performance with only 15 epochs training (around 0.1% lower accuracy).

License

MIT License

Citation

If you find our work useful in your research, please consider citing:

@article{rao2021dynamicvit,
  title={DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification},
  author={Rao, Yongming and Zhao, Wenliang and Liu, Benlin and Lu, Jiwen and Zhou, Jie and Hsieh, Cho-Jui},
  journal={arXiv preprint arXiv:2106.02034},
  year={2021}
}
Owner
Yongming Rao
Yongming Rao
GitHub Repository https://dynamicvit.ivg-research.xyz/
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System

Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System Authors: Yixuan Su, Lei Shu, Elman Mansimov, Arshit Gupta, Deng Cai, Yi-An Lai

Amazon Web Services - Labs 123 Dec 23, 2022
Expressive Power of Invariant and Equivaraint Graph Neural Networks (ICLR 2021)

Expressive Power of Invariant and Equivaraint Graph Neural Networks In this repository, we show how to use powerful GNN (2-FGNN) to solve a graph alig

Marc Lelarge 36 Dec 12, 2022
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning. CVPR 2018

Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning Tensorflow code and models for the paper: Large Scale Fine-Grained Categ

Yin Cui 187 Oct 01, 2022
Time Dependent DFT in Tamm-Dancoff Approximation

Density Function Theory Program - kspy-tddft(tda) This is an implementation of Time-Dependent Density Functional Theory(TDDFT) using the Tamm-Dancoff

Peter Borthwick 2 Nov 17, 2022
This package implements THOR: Transformer with Stochastic Experts.

THOR: Transformer with Stochastic Experts This PyTorch package implements Taming Sparsely Activated Transformer with Stochastic Experts. Installation

Microsoft 45 Nov 22, 2022
An efficient implementation of GPNN

Efficient-GPNN An efficient implementation of GPNN as depicted in "Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Mo

7 Apr 16, 2022
Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation

Attention Gated Networks (Image Classification & Segmentation) Pytorch implementation of attention gates used in U-Net and VGG-16 models. The framewor

Ozan Oktay 1.6k Dec 30, 2022
A Python implementation of global optimization with gaussian processes.

Bayesian Optimization Pure Python implementation of bayesian global optimization with gaussian processes. PyPI (pip): $ pip install bayesian-optimizat

fernando 6.5k Jan 02, 2023
Official Pytorch implementation of C3-GAN

Official pytorch implemenation of C3-GAN Contrastive Fine-grained Class Clustering via Generative Adversarial Networks [Paper] Authors: Yunji Kim, Jun

NAVER AI 114 Dec 02, 2022
PyTorch trainer and model for Sequence Classification

PyTorch-trainer-and-model-for-Sequence-Classification After cloning the repository, modify your training data so that the training data is a .csv file

NhanTieu 2 Dec 09, 2022
Unofficial implementation of HiFi-GAN+ from the paper "Bandwidth Extension is All You Need" by Su, et al.

HiFi-GAN+ This project is an unoffical implementation of the HiFi-GAN+ model for audio bandwidth extension, from the paper Bandwidth Extension is All

Brent M. Spell 134 Dec 30, 2022
A convolutional recurrent neural network for classifying A/B phases in EEG signals recorded for sleep analysis.

CAP-Classification-CRNN A deep learning model based on Inception modules paired with gated recurrent units (GRU) for the classification of CAP phases

Apurva R. Umredkar 2 Nov 25, 2022
Python scripts form performing stereo depth estimation using the HITNET model in ONNX.

ONNX-HITNET-Stereo-Depth-estimation Python scripts form performing stereo depth estimation using the HITNET model in ONNX. Stereo depth estimation on

Ibai Gorordo 30 Nov 08, 2022
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

TL;DR Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently. Click on the image to

4.2k Jan 01, 2023
Hierarchical Aggregation for 3D Instance Segmentation (ICCV 2021)

HAIS Hierarchical Aggregation for 3D Instance Segmentation (ICCV 2021) by Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang*. (*) Corresp

Hust Visual Learning Team 145 Jan 05, 2023
SymPy-powered, Wolfram|Alpha-like answer engine totally in your browser, without backend computation

SymPy Beta SymPy Beta is a fork of SymPy Gamma. The purpose of this project is to run a SymPy-powered, Wolfram|Alpha-like answer engine totally in you

Liumeo 25 Dec 21, 2022
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds (Local-Lip)

Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds (Local-Lip) Introduction TL;DR: We propose an efficient and trainabl

17 Dec 01, 2022
Custom IMDB Dataset is extracted between 2020-2021 and custom distilBERT model is trained for movie success probability prediction

IMDB Success Predictor Project involves Web Scraping custom IMDB data between 2020 and 2021 of 10000 movies and shows sorted by number of votes ,fine

Gautam Diwan 1 Jan 18, 2022
Python scripts performing class agnostic object localization using the Object Localization Network model in ONNX.

ONNX Object Localization Network Python scripts performing class agnostic object localization using the Object Localization Network model in ONNX. Ori

Ibai Gorordo 15 Oct 14, 2022
《Single Image Reflection Removal Beyond Linearity》(CVPR 2019)

Single-Image-Reflection-Removal-Beyond-Linearity Paper Single Image Reflection Removal Beyond Linearity. Qiang Wen, Yinjie Tan, Jing Qin, Wenxi Liu, G

Qiang Wen 51 Jun 24, 2022