python|YOLOv5添加注意力机制 python|yolov5

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

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):

【python|YOLOv5添加注意力机制】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 outclass 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 outclass 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)))

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 outclass 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 outclass 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

文章图片

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 ]