U-Net網(wǎng)絡模型（注意力改進版本）
這一段時間做項目用到了U-Net網(wǎng)絡模型，但是原始的U-Net網(wǎng)絡還有很大的改良空間，在卷積下采樣的過程中加入了通道注意力和空間注意力 。

常規(guī)的U-net模型如下圖：

紅色箭頭為可以添加的地方：即下采樣之間。

通道空間注意力是一個即插即用的注意力模塊（如下圖）：

代碼加入之后對于分割效果是有提升的：（代碼如下）

CBAM代碼：

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)

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

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

網(wǎng)絡模型結(jié)合之后代碼：文章來源地址http://www.zghlxwxcb.cn/news/detail-840374.html

class conv_block(nn.Module):
    def __init__(self,ch_in,ch_out):
        super(conv_block,self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(ch_in, ch_out, kernel_size=3,stride=1,padding=1,bias=True),
            nn.BatchNorm2d(ch_out),
            nn.ReLU(inplace=True),
            nn.Conv2d(ch_out, ch_out, kernel_size=3,stride=1,padding=1,bias=True),
            nn.BatchNorm2d(ch_out),
            nn.ReLU(inplace=True)
        )

    def forward(self,x):
        x = self.conv(x)
        return x

class up_conv(nn.Module):
    def __init__(self,ch_in,ch_out):
        super(up_conv,self).__init__()
        self.up = nn.Sequential(
            nn.Upsample(scale_factor=2),
            nn.Conv2d(ch_in,ch_out,kernel_size=3,stride=1,padding=1,bias=True),
		    nn.BatchNorm2d(ch_out),
			nn.ReLU(inplace=True)
        )

    def forward(self,x):
        x = self.up(x)
        return x

class U_Net_v1(nn.Module):   #添加了空間注意力和通道注意力
    def __init__(self,img_ch=3,output_ch=2):
        super(U_Net_v1,self).__init__()
        
        self.Maxpool = nn.MaxPool2d(kernel_size=2,stride=2)

        self.Conv1 = conv_block(ch_in=img_ch,ch_out=64) #64
        self.Conv2 = conv_block(ch_in=64,ch_out=128)  #64 128
        self.Conv3 = conv_block(ch_in=128,ch_out=256) #128 256
        self.Conv4 = conv_block(ch_in=256,ch_out=512) #256 512
        self.Conv5 = conv_block(ch_in=512,ch_out=1024) #512 1024

        self.cbam1 = CBAM(channel=64)
        self.cbam2 = CBAM(channel=128)
        self.cbam3 = CBAM(channel=256)
        self.cbam4 = CBAM(channel=512)

        self.Up5 = up_conv(ch_in=1024,ch_out=512)  #1024 512
        self.Up_conv5 = conv_block(ch_in=1024, ch_out=512)  

        self.Up4 = up_conv(ch_in=512,ch_out=256)  #512 256
        self.Up_conv4 = conv_block(ch_in=512, ch_out=256)  
        
        self.Up3 = up_conv(ch_in=256,ch_out=128)  #256 128
        self.Up_conv3 = conv_block(ch_in=256, ch_out=128) 
        
        self.Up2 = up_conv(ch_in=128,ch_out=64) #128 64
        self.Up_conv2 = conv_block(ch_in=128, ch_out=64)  

        self.Conv_1x1 = nn.Conv2d(64,output_ch,kernel_size=1,stride=1,padding=0)  #64


    def forward(self,x):
        # encoding path
        x1 = self.Conv1(x)
        x1 = self.cbam1(x1) + x1

        x2 = self.Maxpool(x1)
        x2 = self.Conv2(x2)
        x2 = self.cbam2(x2) + x2
        
        x3 = self.Maxpool(x2)
        x3 = self.Conv3(x3)
        x3 = self.cbam3(x3) + x3

        x4 = self.Maxpool(x3)
        x4 = self.Conv4(x4)
        x4 = self.cbam4(x4) + x4

        x5 = self.Maxpool(x4)
        x5 = self.Conv5(x5)

        # decoding + concat path
        d5 = self.Up5(x5)
        d5 = torch.cat((x4,d5),dim=1)
        
        d5 = self.Up_conv5(d5)
        
        d4 = self.Up4(d5)
        d4 = torch.cat((x3,d4),dim=1)
        d4 = self.Up_conv4(d4)

        d3 = self.Up3(d4)
        d3 = torch.cat((x2,d3),dim=1)
        d3 = self.Up_conv3(d3)

        d2 = self.Up2(d3)
        d2 = torch.cat((x1,d2),dim=1)
        d2 = self.Up_conv2(d2)

        d1 = self.Conv_1x1(d2)

        return d1

到了這里，關于U-Net網(wǎng)絡模型改進（添加通道與空間注意力機制）---親測有效，指標提升的文章就介紹完了。如果您還想了解更多內(nèi)容，請在右上角搜索TOY模板網(wǎng)以前的文章或繼續(xù)瀏覽下面的相關文章，希望大家以后多多支持TOY模板網(wǎng)！