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}
}

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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

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Image-Scaling Attacks and Defenses

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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

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