Thesis Portal: BAM: Bottleneck Attention Module
Purpose of BAM:
Add attention mechanism to the network .
The structure of BAM:
①Channel attention branch : similar to SEblock;
②Spatial attention branch : 1x1 convolution for dimension reduction, two 3x3 dilated convolutions to increase the receptive field, 1x1 convolution Output single channel weight;
③The two are connected in parallel .
import torch
import torch.nn as nn
class ChannelAttention(nn.Module): # Channel attention branch
def __init__(self, channels, r=16): # r: reduction ratio
super(ChannelAttention, self).__init__()
hidden_channels = channels // r
self.attn = nn.Sequential(
nn.AdaptiveAvgPool2d(1), # avgpool
nn.Conv2d(channels, hidden_channels, 1, 1, 0), # 1x1conv,代替全连接
nn.Conv2d(hidden_channels, channels, 1, 1, 0), # 1x1conv,代替全连接
nn.BatchNorm2d(channels) # bn
)
def forward(self, x):
return self.attn(x) # Mc(F) = BN(MLP(AvgPool(F))) = BN(W1(W0AvgPool(F)+b0)+b1),对应原文公式(3),(B,C,1,1)
class SpatialAttention(nn.Module): # Spatial attention branch
def __init__(self, channels, r=16, d=4): # r: reduction ratio; d: dilation value
super(SpatialAttention, self).__init__()
hidden_channels = channels // r
self.attn = nn.Sequential(
nn.Conv2d(channels, hidden_channels, 1, 1, 0, bias=False), # 1x1conv
# 对于kernel_size=3,stride=1的卷积,padding=dilation,保证卷积前后尺寸不变
nn.Conv2d(hidden_channels, hidden_channels, 3, 1, d, d, bias=False), # dilated conv
nn.Conv2d(hidden_channels, hidden_channels, 3, 1, d, d, bias=False), # dilated conv
nn.Conv2d(hidden_channels, 1, 1, 1, 0, bias=False), # 1x1conv,输出通道为1
nn.BatchNorm2d(1) # bn
)
def forward(self, x):
return self.attn(x) # Ms(F) = BN(f31×1(f23×3(f13×3(f01×1(F))))),对应原文公式(4),(B,1,H,W)
class BAM(nn.Module): # BAM
def __init__(self, channels, r=16, d=4):
super(BAM, self).__init__()
self.channel_attention = ChannelAttention(channels, r) # channel attention branch
self.spatial_attention = SpatialAttention(channels, r, d) # spatial attention branch
self.sigmoid = nn.Sigmoid() # sigmoid
def forward(self, x):
_, c, h, w = x.shape # b,c,h,w
channel_weights = self.channel_attention(x) # (B,C,1,1)
spatial_weights = self.spatial_attention(x) # (B,1,H,W)
# (B,C,1,1) + (B,1,H,W) -> (B,C,H,W)
weights = self.sigmoid(channel_weights + spatial_weights) # M(F) = σ(Mc(F)+Ms(F)),对应原文公式(2)
return x + x * weights # F0 = F+F⊗M(F),对应原文公式(1)