Implementation of the šŸ˜‡ Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones

Related tags

Deep Learning deep-learning artificial-intelligence vision attention-mechanism
Overview

HaloNet - Pytorch

Implementation of the Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones. This repository will only house the attention layer and not much more.

Install

$ pip install halonet-pytorch

Usage

import torch
from halonet_pytorch import HaloAttention

attn = HaloAttention(
    dim = 512,         # dimension of feature map
    block_size = 8,    # neighborhood block size (feature map must be divisible by this)
    halo_size = 4,     # halo size (block receptive field)
    dim_head = 64,     # dimension of each head
    heads = 4          # number of attention heads
).cuda()

fmap = torch.randn(1, 512, 32, 32).cuda()
attn(fmap) # (1, 512, 32, 32)

Citations

@misc{vaswani2021scaling,
    title   = {Scaling Local Self-Attention For Parameter Efficient Visual Backbones}, 
    author  = {Ashish Vaswani and Prajit Ramachandran and Aravind Srinivas and Niki Parmar and Blake Hechtman and Jonathon Shlens},
    year    = {2021},
    eprint  = {2103.12731},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}
You might also like...
The official implementation of ELSA: Enhanced Local Self-Attention for Vision Transformer
The official implementation of ELSA: Enhanced Local Self-Attention for Vision Transformer

ELSA: Enhanced Local Self-Attention for Vision Transformer By Jingkai Zhou, Pich

DamoCV 87 Dec 19, 2022
Unofficial PyTorch reimplementation of the paper Swin Transformer V2: Scaling Up Capacity and Resolution

PyTorch reimplementation of the paper Swin Transformer V2: Scaling Up Capacity and Resolution [arXiv 2021].

Christoph Reich 122 Dec 12, 2022
Official code for
Official code for "Focal Self-attention for Local-Global Interactions in Vision Transformers"

Focal Transformer This is the official implementation of our Focal Transformer -- "Focal Self-attention for Local-Global Interactions in Vision Transf

Microsoft 486 Dec 20, 2022
A PyTorch implementation of " EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks."

EfficientNet A PyTorch implementation of EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. [arxiv] [Official TF Repo] Implemen

AhnDW 298 Dec 10, 2022
Official pytorch implementation of
Official pytorch implementation of "Scaling-up Disentanglement for Image Translation", ICCV 2021.

Official pytorch implementation of "Scaling-up Disentanglement for Image Translation", ICCV 2021.

Aviv Gabbay 41 Nov 29, 2022
Implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork.

YOLOv4-large This is the implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork. YOLOv4-CSP YOLOv4-tiny YOLOv4-

Kin-Yiu, Wong 2k Jan 2, 2023
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
Image-Scaling Attacks and Defenses
Image-Scaling Attacks and Defenses

Image-Scaling Attacks & Defenses This repository belongs to our publication: Erwin Quiring, David Klein, Daniel Arp, Martin Johns and Konrad Rieck. Ad

Erwin Quiring 163 Nov 21, 2022
For auto aligning, cropping, and scaling HR and LR images for training image based neural networks

ImgAlign For auto aligning, cropping, and scaling HR and LR images for training image based neural networks Usage Make sure OpenCV is installed, 'pip

null 15 Dec 4, 2022
Releases(0.0.4)
Owner
Phil Wang
Working with Attention. It's all we need.
Phil Wang
GitHub
Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(nĀ²) 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
Facilitates implementing deep neural-network backbones, data augmentations

Introduction Nowadays, the training of Deep Learning models is fragmented and unified. When AI engineers face up with one specific task, the common wa

null 40 Dec 29, 2022
Implementation of "The Power of Scale for Parameter-Efficient Prompt Tuning"

Prompt-Tuning Implementation of "The Power of Scale for Parameter-Efficient Prompt Tuning" Currently, we support the following huggigface models: Bart

Andrew Zeng 36 Dec 19, 2022
Official implementation of NeurIPS 2021 paper "Contextual Similarity Aggregation with Self-attention for Visual Re-ranking"

CSA: Contextual Similarity Aggregation with Self-attention for Visual Re-ranking PyTorch training code for CSA (Contextual Similarity Aggregation). We

Hui Wu 19 Oct 21, 2022
In this project we use both Resnet and Self-attention layer for cat, dog and flower classification.

cdf_att_classification classes = {0: 'cat', 1: 'dog', 2: 'flower'} In this project we use both Resnet and Self-attention layer for cdf-Classification.

null 3 Nov 23, 2022
Compositional and Parameter-Efficient Representations for Large Knowledge Graphs

NodePiece - Compositional and Parameter-Efficient Representations for Large Knowledge Graphs NodePiece is a "tokenizer" for reducing entity vocabulary

Michael Galkin 107 Jan 4, 2023
The Power of Scale for Parameter-Efficient Prompt Tuning

The Power of Scale for Parameter-Efficient Prompt Tuning Implementation of soft embeddings from https://arxiv.org/abs/2104.08691v1 using Pytorch and H

Kip Parker 208 Dec 30, 2022
Code for T-Few from "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning"

T-Few This repository contains the official code for the paper: "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learni

null 220 Dec 31, 2022
Pytorch implementation of the paper "Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization"

Pytorch implementation of the paper "Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization"

Dongkyu Lee 4 Sep 18, 2022
šŸ€ Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.ā­ā­ā­

?? Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.ā­ā­ā­

xmu-xiaoma66 7.7k Jan 5, 2023