YOLOv5-6.1添加注意力机制（SE、CBAM、ECA、CA）

目录
0. 添加方法1. SE1.1 SE1.2 C3-SE
2. CBAM2.1 CBAM2.2 C3-CBAM
3. ECA3.1 ECA3.2 C3-ECA
4. CA4.1 CA4.2 C3-CA

0. 添加方法
主要步骤： （1）在models/common.py中注册注意力模块 （2）在models/yolo.py中的parse_model函数中添加注意力模块 （3）修改配置文件yolov5s.yaml （4）运行yolo.py进行验证 各个注意力机制模块的添加方法类似，各注意力模块的修改参照SE。 本文添加注意力完整代码：https://github.com/double-vin/yolov5_attention
1. SE
Squeeze-and-Excitation Networks https://github.com/hujie-frank/SENet
1.1 SE
在models/common.py中注册SE模块
class SE(nn.Module):
def __init__(self, c1, c2, ratio=16):
super(SE, self).__init__()
#c*1*1
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.l1 = nn.Linear(c1, c1 // ratio, bias=False)
self.relu = nn.ReLU(inplace=True)
self.l2 = nn.Linear(c1 // ratio, c1, bias=False)
self.sig = nn.Sigmoid()

def forward(self, x):
b, c, _, _ = x.size()
y = self.avgpool(x).view(b, c)
y = self.l1(y)
y = self.relu(y)
y = self.l2(y)
y = self.sig(y)
y = y.view(b, c, 1, 1)
return x * y.expand_as(x)

在models/yolo.py中的parse_model函数中添加SE模块 修改配置文件yolov5s.yaml。 添加注意力的两种方法：一是在backbone的最后一层添加注意力；二是将backbone中的C3全部替换。 这里使用第一种，第二种见下文中的C3SE 注意：SE添加至第9层，第9层之后所有的编号都要+1，则： 1>两个Concat的from系数分别由[-1, 14]，[-1, 10]改为[-1, 15]，[-1, 11] 2>Detect的from系数由[17, 20, 23]改为[18,21,24] 验证：运行yolo.py
1.2 C3-SE
在models/common.py中注册C3SE模块：
class SEBottleneck(nn.Module):
# Standard bottleneck
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, ratio=16): # ch_in, ch_out, shortcut, groups, expansion
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_, c2, 3, 1, g=g)
self.add = shortcut and c1 == c2
# self.se=SE(c1,c2,ratio)
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.l1 = nn.Linear(c1, c1 // ratio, bias=False)
self.relu = nn.ReLU(inplace=True)
self.l2 = nn.Linear(c1 // ratio, c1, bias=False)
self.sig = nn.Sigmoid()

def forward(self, x):
x1 = self.cv2(self.cv1(x))
b, c, _, _ = x.size()
y = self.avgpool(x1).view(b, c)
y = self.l1(y)
y = self.relu(y)
y = self.l2(y)
y = self.sig(y)
y = y.view(b, c, 1, 1)
out = x1 * y.expand_as(x1)

# out=self.se(x1)*x1
return x + out if self.add else out

class C3SE(C3):
# C3 module with SEBottleneck()
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
super().__init__(c1, c2, n, shortcut, g, e)
c_ = int(c2 * e) # hidden channels
self.m = nn.Sequential(*(SEBottleneck(c_, c_, shortcut) for _ in range(n)))

在models/yolo.py中的parse_model函数中添加C3SE模块 修改配置文件yolov5s.yaml。 验证：运行yolo.py
2. CBAM
《CBAM: Convolutional Block Attention Module》
2.1 CBAM
class ChannelAttention(nn.Module):
def __init__(self, in_planes, ratio=16):
super(ChannelAttention, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.f1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False)
self.relu = nn.ReLU()
self.f2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False)
self.sigmoid = nn.Sigmoid()

def forward(self, x):
avg_out = self.f2(self.relu(self.f1(self.avg_pool(x))))
max_out = self.f2(self.relu(self.f1(self.max_pool(x))))
out = self.sigmoid(avg_out + max_out)
return out

class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
# (特征图的大小-算子的size+2*padding)/步长+1
self.conv = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
self.sigmoid = nn.Sigmoid()

def forward(self, x):
# 1*h*w
avg_out = torch.mean(x, dim=1, keepdim=True)
max_out, _ = torch.max(x, dim=1, keepdim=True)
x = torch.cat([avg_out, max_out], dim=1)
#2*h*w
x = self.conv(x)
#1*h*w
return self.sigmoid(x)

class CBAM(nn.Module):
# CSP Bottleneck with 3 convolutions
def __init__(self, c1, c2, ratio=16, kernel_size=7): # ch_in, ch_out, number, shortcut, groups, expansion
super(CBAM, self).__init__()
self.channel_attention = ChannelAttention(c1, ratio)
self.spatial_attention = SpatialAttention(kernel_size)

def forward(self, x):
out = self.channel_attention(x) * x
# c*h*w
# c*h*w * 1*h*w
out = self.spatial_attention(out) * out
return out

2.2 C3-CBAM
class CBAMBottleneck(nn.Module):
# Standard bottleneck
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5,ratio=16,kernel_size=7): # ch_in, ch_out, shortcut, groups, expansion
super(CBAMBottleneck,self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_, c2, 3, 1, g=g)
self.add = shortcut and c1 == c2
self.channel_attention = ChannelAttention(c2, ratio)
self.spatial_attention = SpatialAttention(kernel_size)
#self.cbam=CBAM(c1,c2,ratio,kernel_size)

def forward(self, x):
x1 = self.cv2(self.cv1(x))
out = self.channel_attention(x1) * x1
# print('outchannels:{}'.format(out.shape))
out = self.spatial_attention(out) * out
return x + out if self.add else out

class C3CBAM(C3):
# C3 module with CBAMBottleneck()
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
super().__init__(c1, c2, n, shortcut, g, e)
c_ = int(c2 * e) # hidden channels
self.m = nn.Sequential(*(CBAMBottleneck(c_, c_, shortcut) for _ in range(n)))

3. ECA
《ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks》 https://github.com/BangguWu/ECANet
3.1 ECA
class ECA(nn.Module):
"""Constructs a ECA module.
Args:
channel: Number of channels of the input feature map
k_size: Adaptive selection of kernel size
"""

def __init__(self, c1, c2, k_size=3):
super(ECA, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size – 1) // 2, bias=False)
self.sigmoid = nn.Sigmoid()

def forward(self, x):
# feature descriptor on the global spatial information
y = self.avg_pool(x)

# print(y.shape,y.squeeze(-1).shape,y.squeeze(-1).transpose(-1, -2).shape)
# Two different branches of ECA module
# 50*C*1*1
# 50*C*1
# 50*1*C
y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)

# Multi-scale information fusion
y = self.sigmoid(y)

return x * y.expand_as(x)

3.2 C3-ECA
class ECABottleneck(nn.Module):
# Standard bottleneck
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, ratio=16, k_size=3): # ch_in, ch_out, shortcut, groups, expansion
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_, c2, 3, 1, g=g)
self.add = shortcut and c1 == c2
# self.eca=ECA(c1,c2)
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size – 1) // 2, bias=False)
self.sigmoid = nn.Sigmoid()

def forward(self, x):
x1 = self.cv2(self.cv1(x))
# out=self.eca(x1)*x1
y = self.avg_pool(x1)
y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
y = self.sigmoid(y)
out = x1 * y.expand_as(x1)

return x + out if self.add else out

class C3ECA(C3):
# C3 module with ECABottleneck()
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
super().__init__(c1, c2, n, shortcut, g, e)
c_ = int(c2 * e) # hidden channels
self.m = nn.Sequential(*(ECABottleneck(c_, c_, shortcut) for _ in range(n)))

4. CA
Coordinate Attention for Efficient Mobile Network Design https://github.com/Andrew-Qibin/CoordAttention
4.1 CA
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()
# c*1*W
x_h = self.pool_h(x)
# c*H*1
# C*1*h
x_w = self.pool_w(x).permute(0, 1, 3, 2)
y = torch.cat([x_h, x_w], dim=2)
# C*1*(h+w)
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

4.2 C3-CA
class CABottleneck(nn.Module):
# Standard bottleneck
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, ratio=32): # ch_in, ch_out, shortcut, groups, expansion
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_, c2, 3, 1, g=g)
self.add = shortcut and c1 == c2
# self.ca=CoordAtt(c1,c2,ratio)
self.pool_h = nn.AdaptiveAvgPool2d((None, 1))
self.pool_w = nn.AdaptiveAvgPool2d((1, None))
mip = max(8, c1 // ratio)
self.conv1 = nn.Conv2d(c1, mip, kernel_size=1, stride=1, padding=0)
self.bn1 = nn.BatchNorm2d(mip)
self.act = h_swish()
self.conv_h = nn.Conv2d(mip, c2, kernel_size=1, stride=1, padding=0)
self.conv_w = nn.Conv2d(mip, c2, kernel_size=1, stride=1, padding=0)

def forward(self, x):
x1=self.cv2(self.cv1(x))
n, c, h, w = x.size()
# c*1*W
x_h = self.pool_h(x1)
# c*H*1
# C*1*h
x_w = self.pool_w(x1).permute(0, 1, 3, 2)
y = torch.cat([x_h, x_w], dim=2)
# C*1*(h+w)
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 = x1 * a_w * a_h

# out=self.ca(x1)*x1
return x + out if self.add else out

class C3CA(C3):
# C3 module with CABottleneck()
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
super().__init__(c1, c2, n, shortcut, g, e)
c_ = int(c2 * e) # hidden channels
self.m = nn.Sequential(*(CABottleneck(c_, c_,shortcut) for _ in range(n)))

Tips：添加注意力的位置不局限，可以尝试各种排列组合 参考： 多种注意力介绍 添加注意力视频讲解 添加CBAM来源：June vinvin