计算机视觉中常用的注意力机制

本文最后更新于:2022年7月4日 上午

注意力机制(Attention)是深度学习中常用的tricks,可以在模型原有的基础上直接插入,进一步增强你模型的性能。本文记录常用 Attention 方法与 Pytorch 实现。

概述

注意力机制起初是作为自然语言处理中的工作Attention Is All You Need被大家所熟知,从而也引发了一系列的XX is All You Need的论文命题,SENET-Squeeze-and-Excitation Networks是注意力机制在计算机视觉中应用的早期工作之一,并获得了2017年imagenet, 同时也是最后一届Imagenet比赛的冠军,后面就又出现了各种各样的注意力机制,应用在计算机视觉的任务中。

论文 arxiv 镜像

如果大家遇到论文下载比较慢, 推荐使用中科院的 arxiv 镜像: http://xxx.itp.ac.cn, 国内网络能流畅访问
简单直接的方法是, 把要访问 arxiv 链接中的域名从 https://arxiv.org 换成 http://xxx.itp.ac.cn

比如: 从 https://arxiv.org/abs/1901.07249 改为 http://xxx.itp.ac.cn/abs/1901.07249

注意力

SeNet: Squeeze-and-Excitation Attention

  • Pytorch代码
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import numpy as np
import torch
from torch import nn
from torch.nn import init


class SEAttention(nn.Module):

def __init__(self, channel=512, reduction=16):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel, bias=False),
nn.Sigmoid()
)

def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.001)
if m.bias is not None:
init.constant_(m.bias, 0)

def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y.expand_as(x)


if __name__ == '__main__':
input = torch.randn(50, 512, 7, 7)
se = SEAttention(channel=512, reduction=8)
output = se(input)
print(output.shape)

CBAM: Convolutional Block Attention Module

  • Pytorch代码
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import numpy as np
import torch
from torch import nn
from torch.nn import init


class ChannelAttention(nn.Module):
def __init__(self, channel, reduction=16):
super().__init__()
self.maxpool = nn.AdaptiveMaxPool2d(1)
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.se = nn.Sequential(
nn.Conv2d(channel, channel // reduction, 1, bias=False),
nn.ReLU(),
nn.Conv2d(channel // reduction, channel, 1, bias=False)
)
self.sigmoid = nn.Sigmoid()

def forward(self, x):
max_result = self.maxpool(x)
avg_result = self.avgpool(x)
max_out = self.se(max_result)
avg_out = self.se(avg_result)
output = self.sigmoid(max_out + avg_out)
return output


class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super().__init__()
self.conv = nn.Conv2d(2, 1, kernel_size=kernel_size, padding=kernel_size // 2)
self.sigmoid = nn.Sigmoid()

def forward(self, x):
max_result, _ = torch.max(x, dim=1, keepdim=True)
avg_result = torch.mean(x, dim=1, keepdim=True)
result = torch.cat([max_result, avg_result], 1)
output = self.conv(result)
output = self.sigmoid(output)
return output


class CBAMBlock(nn.Module):

def __init__(self, channel=512, reduction=16, kernel_size=49):
super().__init__()
self.ca = ChannelAttention(channel=channel, reduction=reduction)
self.sa = SpatialAttention(kernel_size=kernel_size)

def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.001)
if m.bias is not None:
init.constant_(m.bias, 0)

def forward(self, x):
b, c, _, _ = x.size()
residual = x
out = x * self.ca(x)
out = out * self.sa(out)
return out + residual


if __name__ == '__main__':
input = torch.randn(50, 512, 7, 7)
kernel_size = input.shape[2]
cbam = CBAMBlock(channel=512, reduction=16, kernel_size=kernel_size)
output = cbam(input)
print(output.shape)


BAM: Bottleneck Attention Module

  • Pytorch代码
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import numpy as np
import torch
from torch import nn
from torch.nn import init


class Flatten(nn.Module):
def forward(self, x):
return x.view(x.shape[0], -1)


class ChannelAttention(nn.Module):
def __init__(self, channel, reduction=16, num_layers=3):
super().__init__()
self.avgpool = nn.AdaptiveAvgPool2d(1)
gate_channels = [channel]
gate_channels += [channel // reduction] * num_layers
gate_channels += [channel]

self.ca = nn.Sequential()
self.ca.add_module('flatten', Flatten())
for i in range(len(gate_channels) - 2):
self.ca.add_module('fc%d' % i, nn.Linear(gate_channels[i], gate_channels[i + 1]))
self.ca.add_module('bn%d' % i, nn.BatchNorm1d(gate_channels[i + 1]))
self.ca.add_module('relu%d' % i, nn.ReLU())
self.ca.add_module('last_fc', nn.Linear(gate_channels[-2], gate_channels[-1]))

def forward(self, x):
res = self.avgpool(x)
res = self.ca(res)
res = res.unsqueeze(-1).unsqueeze(-1).expand_as(x)
return res


class SpatialAttention(nn.Module):
def __init__(self, channel, reduction=16, num_layers=3, dia_val=2):
super().__init__()
self.sa = nn.Sequential()
self.sa.add_module('conv_reduce1',
nn.Conv2d(kernel_size=1, in_channels=channel, out_channels=channel // reduction))
self.sa.add_module('bn_reduce1', nn.BatchNorm2d(channel // reduction))
self.sa.add_module('relu_reduce1', nn.ReLU())
for i in range(num_layers):
self.sa.add_module('conv_%d' % i, nn.Conv2d(kernel_size=3, in_channels=channel // reduction,
out_channels=channel // reduction, padding=1, dilation=dia_val))
self.sa.add_module('bn_%d' % i, nn.BatchNorm2d(channel // reduction))
self.sa.add_module('relu_%d' % i, nn.ReLU())
self.sa.add_module('last_conv', nn.Conv2d(channel // reduction, 1, kernel_size=1))

def forward(self, x):
res = self.sa(x)
res = res.expand_as(x)
return res


class BAMBlock(nn.Module):

def __init__(self, channel=512, reduction=16, dia_val=2):
super().__init__()
self.ca = ChannelAttention(channel=channel, reduction=reduction)
self.sa = SpatialAttention(channel=channel, reduction=reduction, dia_val=dia_val)
self.sigmoid = nn.Sigmoid()

def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.001)
if m.bias is not None:
init.constant_(m.bias, 0)

def forward(self, x):
b, c, _, _ = x.size()
sa_out = self.sa(x)
ca_out = self.ca(x)
weight = self.sigmoid(sa_out + ca_out)
out = (1 + weight) * x
return out


if __name__ == '__main__':
input = torch.randn(50, 512, 7, 7)
bam = BAMBlock(channel=512, reduction=16, dia_val=2)
output = bam(input)
print(output.shape)

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

  • Pytorch代码
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import numpy as np
import torch
from torch import nn
from torch.nn import init
from collections import OrderedDict


class ECAAttention(nn.Module):

def __init__(self, kernel_size=3):
super().__init__()
self.gap = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(kernel_size - 1) // 2)
self.sigmoid = nn.Sigmoid()

def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.001)
if m.bias is not None:
init.constant_(m.bias, 0)

def forward(self, x):
y = self.gap(x) # bs,c,1,1
y = y.squeeze(-1).permute(0, 2, 1) # bs,1,c
y = self.conv(y) # bs,1,c
y = self.sigmoid(y) # bs,1,c
y = y.permute(0, 2, 1).unsqueeze(-1) # bs,c,1,1
return x * y.expand_as(x)


if __name__ == '__main__':
input = torch.randn(50, 512, 7, 7)
eca = ECAAttention(kernel_size=3)
output = eca(input)
print(output.shape)

SA-NET: SHUFFLE ATTENTION FOR DEEP CONVOLUTIONAL NEURAL NETWORKS

  • Pytorch代码
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import numpy as np
import torch
from torch import nn
from torch.nn import init
from torch.nn.parameter import Parameter


class ShuffleAttention(nn.Module):

def __init__(self, channel=512, reduction=16, G=8):
super().__init__()
self.G = G
self.channel = channel
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.gn = nn.GroupNorm(channel // (2 * G), channel // (2 * G))
self.cweight = Parameter(torch.zeros(1, channel // (2 * G), 1, 1))
self.cbias = Parameter(torch.ones(1, channel // (2 * G), 1, 1))
self.sweight = Parameter(torch.zeros(1, channel // (2 * G), 1, 1))
self.sbias = Parameter(torch.ones(1, channel // (2 * G), 1, 1))
self.sigmoid = nn.Sigmoid()

def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.001)
if m.bias is not None:
init.constant_(m.bias, 0)

@staticmethod
def channel_shuffle(x, groups):
b, c, h, w = x.shape
x = x.reshape(b, groups, -1, h, w)
x = x.permute(0, 2, 1, 3, 4)

# flatten
x = x.reshape(b, -1, h, w)

return x

def forward(self, x):
b, c, h, w = x.size()
# group into subfeatures
x = x.view(b * self.G, -1, h, w) # bs*G,c//G,h,w

# channel_split
x_0, x_1 = x.chunk(2, dim=1) # bs*G,c//(2*G),h,w

# channel attention
x_channel = self.avg_pool(x_0) # bs*G,c//(2*G),1,1
x_channel = self.cweight * x_channel + self.cbias # bs*G,c//(2*G),1,1
x_channel = x_0 * self.sigmoid(x_channel)

# spatial attention
x_spatial = self.gn(x_1) # bs*G,c//(2*G),h,w
x_spatial = self.sweight * x_spatial + self.sbias # bs*G,c//(2*G),h,w
x_spatial = x_1 * self.sigmoid(x_spatial) # bs*G,c//(2*G),h,w

# concatenate along channel axis
out = torch.cat([x_channel, x_spatial], dim=1) # bs*G,c//G,h,w
out = out.contiguous().view(b, -1, h, w)

# channel shuffle
out = self.channel_shuffle(out, 2)
return out


if __name__ == '__main__':
input = torch.randn(50, 512, 7, 7)
se = ShuffleAttention(channel=512, G=8)
output = se(input)
print(output.shape)


Polarized Self-Attention: Towards High-quality Pixel-wise Regression

  • Pytorch代码
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import numpy as np
import torch
from torch import nn
from torch.nn import init


class ParallelPolarizedSelfAttention(nn.Module):

def __init__(self, channel=512):
super().__init__()
self.ch_wv = nn.Conv2d(channel, channel // 2, kernel_size=(1, 1))
self.ch_wq = nn.Conv2d(channel, 1, kernel_size=(1, 1))
self.softmax_channel = nn.Softmax(1)
self.softmax_spatial = nn.Softmax(-1)
self.ch_wz = nn.Conv2d(channel // 2, channel, kernel_size=(1, 1))
self.ln = nn.LayerNorm(channel)
self.sigmoid = nn.Sigmoid()
self.sp_wv = nn.Conv2d(channel, channel // 2, kernel_size=(1, 1))
self.sp_wq = nn.Conv2d(channel, channel // 2, kernel_size=(1, 1))
self.agp = nn.AdaptiveAvgPool2d((1, 1))

def forward(self, x):
b, c, h, w = x.size()

# Channel-only Self-Attention
channel_wv = self.ch_wv(x) # bs,c//2,h,w
channel_wq = self.ch_wq(x) # bs,1,h,w
channel_wv = channel_wv.reshape(b, c // 2, -1) # bs,c//2,h*w
channel_wq = channel_wq.reshape(b, -1, 1) # bs,h*w,1
channel_wq = self.softmax_channel(channel_wq)
channel_wz = torch.matmul(channel_wv, channel_wq).unsqueeze(-1) # bs,c//2,1,1
channel_weight = self.sigmoid(self.ln(self.ch_wz(channel_wz).reshape(b, c, 1).permute(0, 2, 1))).permute(0, 2,
1).reshape(
b, c, 1, 1) # bs,c,1,1
channel_out = channel_weight * x

# Spatial-only Self-Attention
spatial_wv = self.sp_wv(x) # bs,c//2,h,w
spatial_wq = self.sp_wq(x) # bs,c//2,h,w
spatial_wq = self.agp(spatial_wq) # bs,c//2,1,1
spatial_wv = spatial_wv.reshape(b, c // 2, -1) # bs,c//2,h*w
spatial_wq = spatial_wq.permute(0, 2, 3, 1).reshape(b, 1, c // 2) # bs,1,c//2
spatial_wq = self.softmax_spatial(spatial_wq)
spatial_wz = torch.matmul(spatial_wq, spatial_wv) # bs,1,h*w
spatial_weight = self.sigmoid(spatial_wz.reshape(b, 1, h, w)) # bs,1,h,w
spatial_out = spatial_weight * x
out = spatial_out + channel_out
return out


class SequentialPolarizedSelfAttention(nn.Module):

def __init__(self, channel=512):
super().__init__()
self.ch_wv = nn.Conv2d(channel, channel // 2, kernel_size=(1, 1))
self.ch_wq = nn.Conv2d(channel, 1, kernel_size=(1, 1))
self.softmax_channel = nn.Softmax(1)
self.softmax_spatial = nn.Softmax(-1)
self.ch_wz = nn.Conv2d(channel // 2, channel, kernel_size=(1, 1))
self.ln = nn.LayerNorm(channel)
self.sigmoid = nn.Sigmoid()
self.sp_wv = nn.Conv2d(channel, channel // 2, kernel_size=(1, 1))
self.sp_wq = nn.Conv2d(channel, channel // 2, kernel_size=(1, 1))
self.agp = nn.AdaptiveAvgPool2d((1, 1))

def forward(self, x):
b, c, h, w = x.size()

# Channel-only Self-Attention
channel_wv = self.ch_wv(x) # bs,c//2,h,w
channel_wq = self.ch_wq(x) # bs,1,h,w
channel_wv = channel_wv.reshape(b, c // 2, -1) # bs,c//2,h*w
channel_wq = channel_wq.reshape(b, -1, 1) # bs,h*w,1
channel_wq = self.softmax_channel(channel_wq)
channel_wz = torch.matmul(channel_wv, channel_wq).unsqueeze(-1) # bs,c//2,1,1
channel_weight = self.sigmoid(self.ln(self.ch_wz(channel_wz).reshape(b, c, 1).permute(0, 2, 1))).permute(0, 2,
1).reshape(
b, c, 1, 1) # bs,c,1,1
channel_out = channel_weight * x

# Spatial-only Self-Attention
spatial_wv = self.sp_wv(channel_out) # bs,c//2,h,w
spatial_wq = self.sp_wq(channel_out) # bs,c//2,h,w
spatial_wq = self.agp(spatial_wq) # bs,c//2,1,1
spatial_wv = spatial_wv.reshape(b, c // 2, -1) # bs,c//2,h*w
spatial_wq = spatial_wq.permute(0, 2, 3, 1).reshape(b, 1, c // 2) # bs,1,c//2
spatial_wq = self.softmax_spatial(spatial_wq)
spatial_wz = torch.matmul(spatial_wq, spatial_wv) # bs,1,h*w
spatial_weight = self.sigmoid(spatial_wz.reshape(b, 1, h, w)) # bs,1,h,w
spatial_out = spatial_weight * channel_out
return spatial_out


if __name__ == '__main__':
input = torch.randn(1, 512, 7, 7)
psa = SequentialPolarizedSelfAttention(channel=512)
output = psa(input)
print(output.shape)


Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks

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import numpy as np
import torch
from torch import nn
from torch.nn import init


class SpatialGroupEnhance(nn.Module):

def __init__(self, groups):
super().__init__()
self.groups = groups
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.weight = nn.Parameter(torch.zeros(1, groups, 1, 1))
self.bias = nn.Parameter(torch.zeros(1, groups, 1, 1))
self.sig = nn.Sigmoid()
self.init_weights()

def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.001)
if m.bias is not None:
init.constant_(m.bias, 0)

def forward(self, x):
b, c, h, w = x.shape
x = x.view(b * self.groups, -1, h, w) # bs*g,dim//g,h,w
xn = x * self.avg_pool(x) # bs*g,dim//g,h,w
xn = xn.sum(dim=1, keepdim=True) # bs*g,1,h,w
t = xn.view(b * self.groups, -1) # bs*g,h*w

t = t - t.mean(dim=1, keepdim=True) # bs*g,h*w
std = t.std(dim=1, keepdim=True) + 1e-5
t = t / std # bs*g,h*w
t = t.view(b, self.groups, h, w) # bs,g,h*w

t = t * self.weight + self.bias # bs,g,h*w
t = t.view(b * self.groups, 1, h, w) # bs*g,1,h*w
x = x * self.sig(t)
x = x.view(b, c, h, w)

return x


if __name__ == '__main__':
input = torch.randn(50, 512, 7, 7)
sge = SpatialGroupEnhance(groups=8)
output = sge(input)
print(output.shape)

Coordinate Attention for Efficient Mobile Network Design

主要应用在轻量级网络上,在resnet系列上效果不好。

  • Pytorch代码
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import torch
import torch.nn as nn
import torch.nn.functional as F


class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)

def forward(self, x):
return self.relu(x + 3) / 6


class h_swish(nn.Module):
def __init__(self, inplace=True):
super(h_swish, self).__init__()
self.sigmoid = h_sigmoid(inplace=inplace)

def forward(self, x):
return x * self.sigmoid(x)


class CoordAtt(nn.Module):
def __init__(self, inp, oup, reduction=32):
super(CoordAtt, self).__init__()
self.pool_h = nn.AdaptiveAvgPool2d((None, 1))
self.pool_w = nn.AdaptiveAvgPool2d((1, None))

mip = max(8, inp // reduction)

self.conv1 = nn.Conv2d(inp, mip, kernel_size=1, stride=1, padding=0)
self.bn1 = nn.BatchNorm2d(mip)
self.act = h_swish()

self.conv_h = nn.Conv2d(mip, oup, kernel_size=1, stride=1, padding=0)
self.conv_w = nn.Conv2d(mip, oup, kernel_size=1, stride=1, padding=0)

def forward(self, x):
identity = x

n, c, h, w = x.size()
x_h = self.pool_h(x)
x_w = self.pool_w(x).permute(0, 1, 3, 2)

y = torch.cat([x_h, x_w], dim=2)
y = self.conv1(y)
y = self.bn1(y)
y = self.act(y)

x_h, x_w = torch.split(y, [h, w], dim=2)
x_w = x_w.permute(0, 1, 3, 2)

a_h = self.conv_h(x_h).sigmoid()
a_w = self.conv_w(x_w).sigmoid()

out = identity * a_w * a_h

return out

Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions

计算量特别大,效果一般

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class GAM_Attention(nn.Module):
def __init__(self, in_channels, out_channels, rate=4):
super(GAM_Attention, self).__init__()

self.channel_attention = nn.Sequential(
nn.Linear(in_channels, int(in_channels / rate)),
nn.ReLU(inplace=True),
nn.Linear(int(in_channels / rate), in_channels)
)

self.spatial_attention = nn.Sequential(
nn.Conv2d(in_channels, int(in_channels / rate), kernel_size=7, padding=3),
nn.BatchNorm2d(int(in_channels / rate)),
nn.ReLU(inplace=True),
nn.Conv2d(int(in_channels / rate), out_channels, kernel_size=7, padding=3),
nn.BatchNorm2d(out_channels)
)

def forward(self, x):
# print(x)
b, c, h, w = x.shape
x_permute = x.permute(0, 2, 3, 1).view(b, -1, c)
x_att_permute = self.channel_attention(x_permute).view(b, h, w, c)
x_channel_att = x_att_permute.permute(0, 3, 1, 2)

x = x * x_channel_att

x_spatial_att = self.spatial_attention(x).sigmoid()
out = x * x_spatial_att
# print(out)

return out

更多注意力

双路注意力机制-DANET

位置注意力-CCNET

在上面的danet上改的,主要是解决计算量的问题, 通过十字交叉的结构来解决

参考资料


计算机视觉中常用的注意力机制
https://www.zywvvd.com/notes/study/deep-learning/attention/visual-attention/visual-attention/
作者
Yiwei Zhang
发布于
2022年5月24日
许可协议