Implementation of Deformable Attention in Pytorch from the paper "Vision Transformer with Deformable Attention"

Related tags

Deep Learningdeep-learningartificial-intelligenceattention-mechanism
Overview

Deformable Attention

Implementation of Deformable Attention from this paper in Pytorch, which appears to be an improvement to what was proposed in DETR. The relative positional embedding has also been modified for better extrapolation, using the Continuous Positional Embedding proposed in SwinV2.

Install

$ pip install deformable-attention

Usage

import torch
from deformable_attention import DeformableAttention

attn = DeformableAttention(
    dim = 512,                   # feature dimensions
    dim_head = 64,               # dimension per head
    heads = 8,                   # attention heads
    dropout = 0.,                # dropout
    downsample_factor = 4,       # downsample factor (r in paper)
    offset_scale = 4,            # scale of offset, maximum offset
    offset_groups = None,        # number of offset groups, should be multiple of heads
    offset_kernel_size = 6,      # offset kernel size
)

x = torch.randn(1, 512, 64, 64)
attn(x) # (1, 512, 64, 64)

3d deformable attention

import torch
from deformable_attention import DeformableAttention3D

attn = DeformableAttention3D(
    dim = 512,                          # feature dimensions
    dim_head = 64,                      # dimension per head
    heads = 8,                          # attention heads
    dropout = 0.,                       # dropout
    downsample_factor = (2, 8, 8),      # downsample factor (r in paper)
    offset_scale = (2, 8, 8),           # scale of offset, maximum offset
    offset_kernel_size = (4, 10, 10),   # offset kernel size
)

x = torch.randn(1, 512, 10, 32, 32) # (batch, dimension, frames, height, width)
attn(x) # (1, 512, 10, 32, 32)

1d deformable attention for good measure

import torch
from deformable_attention import DeformableAttention1D

attn = DeformableAttention1D(
    dim = 128,
    downsample_factor = 4,
    offset_scale = 2,
    offset_kernel_size = 6
)

x = torch.randn(1, 128, 512)
attn(x) # (1, 128, 512)

Citation

@misc{xia2022vision,
    title   = {Vision Transformer with Deformable Attention}, 
    author  = {Zhuofan Xia and Xuran Pan and Shiji Song and Li Erran Li and Gao Huang},
    year    = {2022},
    eprint  = {2201.00520},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}
@misc{liu2021swin,
    title   = {Swin Transformer V2: Scaling Up Capacity and Resolution},
    author  = {Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo},
    year    = {2021},
    eprint  = {2111.09883},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}
You might also like...
Some code of the implements of Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network

3D-GMPDCNN Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network PyTorch implementation of "Geological Modeling Usin

null 5 Nov 21, 2022
MoCoPnet - Deformable 3D Convolution for Video Super-Resolution
MoCoPnet - Deformable 3D Convolution for Video Super-Resolution

Deformable 3D Convolution for Video Super-Resolution Pytorch implementation of l

Xinyi Ying 28 Dec 15, 2022
3D2Unet: 3D Deformable Unet for Low-Light Video Enhancement (PRCV2021)

3DDUNET This is the code for 3D2Unet: 3D Deformable Unet for Low-Light Video Enhancement (PRCV2021) Conference Paper Link Dataset We use SMOID dataset

null 1 Jan 7, 2022
Implementation of the 😇 Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones
Implementation of the 😇 Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones

HaloNet - Pytorch Implementation of the Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones. This re

Phil Wang 189 Nov 22, 2022
Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(n²) Memory"

Memory Efficient Attention Pytorch Implementation of a memory efficient multi-head attention as proposed in the paper, Self-attention Does Not Need O(

Phil Wang 180 Jan 5, 2023
Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image classification, in Pytorch
Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image classification, in Pytorch

Transformer in Transformer Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image c

Phil Wang 272 Dec 23, 2022
Official Pytorch Implementation of Relational Self-Attention: What's Missing in Attention for Video Understanding
Official Pytorch Implementation of Relational Self-Attention: What's Missing in Attention for Video Understanding

Relational Self-Attention: What's Missing in Attention for Video Understanding This repository is the official implementation of "Relational Self-Atte

mandos 43 Dec 7, 2022
The official pytorch implementation of our paper "Is Space-Time Attention All You Need for Video Understanding?"

TimeSformer This is an official pytorch implementation of Is Space-Time Attention All You Need for Video Understanding?. In this repository, we provid

Facebook Research 1k Dec 31, 2022
An implementation demo of the ICLR 2021 paper Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks in PyTorch.

Neural Attention Distillation This is an implementation demo of the ICLR 2021 paper Neural Attention Distillation: Erasing Backdoor Triggers from Deep

Yige-Li 84 Jan 4, 2023
Comments
  • The relationship between 'dim' and 'inner_dim'

    The relationship between 'dim' and 'inner_dim'

    Hi, I have a question about DeformableAttention module,

    I calculated the output volumes of the forward processes step by step, According to my calculation, the code works only when 'dim' and 'inner_dim' is same.

    Is there any reason why you implement it this way?

    Best regards, Hankyu

    opened by hanq0212 4
  • TypeError: meshgrid() got an unexpected keyword argument 'indexing'

    TypeError: meshgrid() got an unexpected keyword argument 'indexing'

    @lucidrains while trying to perform import torch from deformable_attention import DeformableAttention

    attn = DeformableAttention( dim = 512, # feature dimensions dim_head = 64, # dimension per head heads = 8, # attention heads dropout = 0., # dropout downsample_factor = 4, # downsample factor (r in paper) offset_scale = 4, # scale of offset, maximum offset offset_groups = None, # number of offset groups, should be multiple of heads offset_kernel_size = 6, # offset kernel size )

    x = torch.randn(1, 512, 64, 64) attn(x)

    Got error below from the line.. Kindly help

    https://github.com/lucidrains/deformable-attention/blob/9f3c0ae35652ce54687e0db409921018bfca3310/deformable_attention/deformable_attention_2d.py#L26

    opened by ChidanandKumarKS 1
Owner
Phil Wang
Working with Attention. It's all we need
Phil Wang
GitHub Repository
This repository contains the source code for the paper Tutorial on amortized optimization for learning to optimize over continuous domains by Brandon Amos

Tutorial on Amortized Optimization This repository contains the source code for the paper Tutorial on amortized optimization for learning to optimize

Meta Research 144 Dec 26, 2022
Library of various Few-Shot Learning frameworks for text classification

FewShotText This repository contains code for the paper A Neural Few-Shot Text Classification Reality Check Environment setup # Create environment pyt

Thomas Dopierre 47 Jan 03, 2023
Official PyTorch Implementation of GAN-Supervised Dense Visual Alignment

GAN-Supervised Dense Visual Alignment — Official PyTorch Implementation Paper | Project Page | Video This repo contains training, evaluation and visua

944 Jan 07, 2023
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?

Adversrial Machine Learning Benchmarks This code belongs to the papers: Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness? Det

Adversarial Machine Learning 9 Nov 27, 2022
Global-Local Attention for Emotion Recognition

Global-Local Attention for Emotion Recognition Requirements Python 3 Install tensorflow (or tensorflow-gpu) = 2.0.0 Install some other packages pip i

Minh Nhat Le 15 Apr 21, 2022
A real-time speech emotion recognition application using Scikit-learn and gradio

Speech-Emotion-Recognition-App A real-time speech emotion recognition application using Scikit-learn and gradio. Requirements librosa==0.6.3 numpy sou

Son Tran 6 Oct 04, 2022
Image-to-image regression with uncertainty quantification in PyTorch

Image-to-image regression with uncertainty quantification in PyTorch. Take any dataset and train a model to regress images to images with rigorous, distribution-free uncertainty quantification.

Anastasios Angelopoulos 25 Dec 26, 2022
Addon and nodes for working with structural biology and molecular data in Blender.

Molecular Nodes 🧬 🔬 💻 Buy Me a Coffee to Keep Development Going! Join a Community of Blender SciVis People! What is Molecular Nodes? Molecular Node

Brady Johnston 456 Jan 08, 2023
Xview3 solution - XView3 challenge, 2nd place solution

Xview3, 2nd place solution https://iuu.xview.us/ test split aggregate score publ

Selim Seferbekov 24 Nov 23, 2022
Implementation of Graph Convolutional Networks in TensorFlow

Graph Convolutional Networks This is a TensorFlow implementation of Graph Convolutional Networks for the task of (semi-supervised) classification of n

Thomas Kipf 6.6k Dec 30, 2022
Learning Synthetic Environments and Reward Networks for Reinforcement Learning

Learning Synthetic Environments and Reward Networks for Reinforcement Learning We explore meta-learning agent-agnostic neural Synthetic Environments (

AutoML-Freiburg-Hannover 16 Sep 02, 2022
Code implementation of Data Efficient Stagewise Knowledge Distillation paper.

Data Efficient Stagewise Knowledge Distillation Table of Contents Data Efficient Stagewise Knowledge Distillation Table of Contents Requirements Image

IvLabs 112 Dec 02, 2022
Implicit Deep Adaptive Design (iDAD)

Implicit Deep Adaptive Design (iDAD) This code supports the NeurIPS paper 'Implicit Deep Adaptive Design: Policy-Based Experimental Design without Lik

Desi 12 Aug 14, 2022
Incomplete easy-to-use math solver and PDF generator.

Math Expert Let me do your work Preview preview.mp4 Introduction Math Expert is our (@salastro, @younis-tarek, @marawn-mogeb) math high school graduat

SalahDin Ahmed 22 Jul 11, 2022
Official implementation of "Watermarking Images in Self-Supervised Latent-Spaces"

🔍 Watermarking Images in Self-Supervised Latent-Spaces PyTorch implementation and pretrained models for the paper. For details, see Watermarking Imag

Meta Research 32 Dec 13, 2022
Fast, accurate and reliable software for algebraic CT reconstruction

KCT CBCT Fast, accurate and reliable software for algebraic CT reconstruction. This set of software tools includes OpenCL implementation of modern CT

Vojtěch Kulvait 4 Dec 14, 2022
WRENCH: Weak supeRvision bENCHmark

🔧 What is it? Wrench is a benchmark platform containing diverse weak supervision tasks. It also provides a common and easy framework for development

Jieyu Zhang 176 Dec 28, 2022
Python implementation of NARS (Non-Axiomatic-Reasoning-System)

Python implementation of NARS (Non-Axiomatic-Reasoning-System)

Bowen XU 11 Dec 20, 2022
A program that can analyze videos according to the weights you select

MaskMonitor A program that can analyze videos according to the weights you select 下載 訓練完的 weight檔案 執行 MaskDetection.py 內部可更改 輸入來源(鏡頭, 影片, 圖片) 以及輸出條件(人

Patrick_star 1 Nov 07, 2021
Time-series-deep-learning - Developing Deep learning LSTM, BiLSTM models, and NeuralProphet for multi-step time-series forecasting of stock price.

Stock Price Prediction Using Deep Learning Univariate Time Series Predicting stock price using historical data of a company using Neural networks for

Abdultawwab Safarji 7 Nov 27, 2022