YOLOv5添加注意力机制
添加注意力机制代码
首先在common.py文件中添加所需要的注意力机制代码（这里网上很多博客中都有提到，就傻瓜式复制粘贴就好了）
SE
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)
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
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
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)
在backbone最后添加注意力机制
注意：这里以SE为例，其他的相同方法修改，仅名称不同
1.修改yolo.py文件
在yolo.py中的parse_model函数中修改，将原有的代码块改为如下所示：
if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
BottleneckCSP, C3, C3TR, C3SPP, C3Ghost,SE,CBAM,CoordAtt,ECA):
2.修改yaml文件
直接在backbone最后一层C3之后添加SE模块，并将之后的连接层进行修改
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3, [1024]],
[-1, 1, SE, [1024]],
[-1, 1, SPPF, [1024, 5]], # 10
]
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, C3, [512, False]], # 14
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, C3, [256, False]], # 18 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]],
[[-1, 15], 1, Concat, [1]], # cat head P4
[-1, 3, C3, [512, False]], # 21 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]],
[[-1, 11], 1, Concat, [1]], # cat head P5
[-1, 3, C3, [1024, False]], # 24 (P5/32-large)
[[18, 21, 24], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
在C3中添加注意力机制
1.在common.py的文件中添加代码
SE
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)))
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)))
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)))
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)))
2.在yolo.py中修改代码如下所示
3.修改yaml文件（这里以CA为例，其他的相同的修改）
看个人需求，我这里只修改了backbone中的C3
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3CA, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3CA, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3CA, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3CA, [1024]],
[-1, 1, SPPF, [1024, 5]], # 9
]