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

Last update: Nov 22, 2022

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

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

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].

122 Dec 12, 2022

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

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

298 Dec 10, 2022

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

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-

2k Jan 2, 2023

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

43 Dec 7, 2022

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

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

15 Dec 4, 2022

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

Related tags

Overview

HaloNet - Pytorch

Install

Usage

Citations

You might also like...

The official implementation of ELSA: Enhanced Local Self-Attention for Vision Transformer

Unofficial PyTorch reimplementation of the paper Swin Transformer V2: Scaling Up Capacity and Resolution

Official code for "Focal Self-attention for Local-Global Interactions in Vision Transformers"

A PyTorch implementation of " EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks."

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

Implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork.

Official Pytorch Implementation of Relational Self-Attention: What's Missing in Attention for Video Understanding

Image-Scaling Attacks and Defenses

For auto aligning, cropping, and scaling HR and LR images for training image based neural networks

Releases(0.0.4)

0.0.4(Mar 24, 2021)

0.0.3(Mar 24, 2021)

0.0.2(Mar 24, 2021)

0.0.1(Mar 24, 2021)

Owner

Phil Wang

Official PyTorch implementation of Learning Intra-Batch Connections for Deep Metric Learning (ICML 2021) published at International Conference on Machine Learning

A collection of resources and papers on Diffusion Models, a darkhorse in the field of Generative Models

YOLOv5 detection interface - PyQt5 implementation

Gym for multi-agent reinforcement learning

[CVPR 2022] Unsupervised Image-to-Image Translation with Generative Prior

Official codebase for running the small, filtered-data GLIDE model from GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

Code for CVPR2021 paper 'Where and What? Examining Interpretable Disentangled Representations'.

Text Generation by Learning from Demonstrations

SPTAG: A library for fast approximate nearest neighbor search

Rotation-Only Bundle Adjustment

PyTorch-Multi-Style-Transfer - Neural Style and MSG-Net

PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks

MVFNet: Multi-View Fusion Network for Efficient Video Recognition (AAAI 2021)

Code to produce syntactic representations that can be used to study syntax processing in the human brain

Official PyTorch implementation of "ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows"

Pre-training of Graph Augmented Transformers for Medication Recommendation

Much faster than SORT(Simple Online and Realtime Tracking), a little worse than SORT

DiffStride: Learning strides in convolutional neural networks

Python scripts for performing lane detection using the LSTR model in ONNX

Secure Distributed Training at Scale