CBAM: Convolutional Block Attention Module
PDF: https://arxiv.org/pdf/1807.06521.pdf
PyTorch代码: https://github.com/shanglianlm0525/PyTorch-Networks
1 概述
CBAM是基于卷积块的注意机制，它结合了空间注意力机制和通道注意力机制，它能显著提高图像分类和目标检测的正确率。
2 Channel Attention Module
channel attention: C×H×W ------> C×1×1
PyTorch代码:
class ChannelAttentionModule(nn.Module):
def __init__(self, channel, ratio=16):
super(ChannelAttentionModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.shared_MLP = nn.Sequential(
nn.Conv2d(channel, channel // ratio, 1, bias=False),
nn.ReLU(),
nn.Conv2d(channel // ratio, channel, 1, bias=False)
)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avgout = self.shared_MLP(self.avg_pool(x))
maxout = self.shared_MLP(self.max_pool(x))
return self.sigmoid(avgout + maxout)
3 Spatial Attention Module
spatial attention: C×H×W ------> 1×H×W
PyTorch代码:
class SpatialAttentionModule(nn.Module):
def __init__(self):
super(SpatialAttentionModule, self).__init__()
self.conv2d = nn.Conv2d(in_channels=2, out_channels=1, kernel_size=7, stride=1, padding=3)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avgout = torch.mean(x, dim=1, keepdim=True)
maxout, _ = torch.max(x, dim=1, keepdim=True)
out = torch.cat([avgout, maxout], dim=1)
out = self.sigmoid(self.conv2d(out))
return out
4 ResBlock + CBAM
PyTorch代码:
class CBAM(nn.Module):
def __init__(self, channel):
super(CBAM, self).__init__()
self.channel_attention = ChannelAttentionModule(channel)
self.spatial_attention = SpatialAttentionModule()
def forward(self, x):
out = self.channel_attention(x) * x
out = self.spatial_attention(out) * out
return out
class ResBlock_CBAM(nn.Module):
def __init__(self,in_places, places, stride=1,downsampling=False, expansion = 4):
super(ResBlock_CBAM,self).__init__()
self.expansion = expansion
self.downsampling = downsampling
self.bottleneck = nn.Sequential(
nn.Conv2d(in_channels=in_places,out_channels=places,kernel_size=1,stride=1, bias=False),
nn.BatchNorm2d(places),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=places, out_channels=places, kernel_size=3, stride=stride, padding=1, bias=False),
nn.BatchNorm2d(places),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=places, out_channels=places*self.expansion, kernel_size=1, stride=1, bias=False),
nn.BatchNorm2d(places*self.expansion),
)
self.cbam = CBAM(channel=places*self.expansion)
if self.downsampling:
self.downsample = nn.Sequential(
nn.Conv2d(in_channels=in_places, out_channels=places*self.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(places*self.expansion)
)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
residual = x
out = self.bottleneck(x)
out = self.cbam(out)
if self.downsampling:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
5 Ablation
5-1 Channel attention
使用avgpool和maxpool可以更好的降低错误率，大概有1-2%的提升，同时使用能提供更加精细的信息，有利于提升模型的表现
5-2 Spatial attention
空间注意力机制参数有avg, max组成, 此外kernel size=7时效果最好
5-3 Arrangement of the channel and spatial attention
先channel attention然后spatial attention效果(最终的CBAM模块组成) > 先spatial attention然后channel attention 效果 > 并行channel attention和spatial attention
