残差加se块pytorch实现

class Residual(nn.Module):

    def __init__(self,in_channels,out_channels,use_1x1conv=False,stride=1):

        super(Residual,self).__init__()

        self.conv1=nn.Conv2d(in_channels,out_channels,kernel_size=3,padding=1,stride=stride)        

        self.conv2=nn.Conv2d(out_channels,out_channels,kernel_size=3,padding=1)

        if use_1x1conv:

            self.conv3=nn.Conv2d(in_channels,out_channels,kernel_size=1,stride=stride)

        else:

            self.conv3=None

        self.bn1=nn.BatchNorm2d(out_channels)

        self.bn2=nn.BatchNorm2d(out_channels)

        self.avg_pool=nn.AdaptiveAvgPool2d(1)

        self.fc=nn.Sequential(nn.Linear(out_channels,out_channels,bias=False),

                              nn.ReLU(inplace=True),

                              nn.Linear(out_channels,out_channels,bias=False),

                              nn.Sigmoid())

    def forward(self,X):        

        Y=F.relu(self.bn1(self.conv1(X)))

        Y=self.bn2(self.conv2(Y))

        if self.conv3:

            X=self.conv3(X)

        b,c,_,_=Y.size()

        y=self.avg_pool(Y).view(b,c)

        y=self.fc(y).view(b,c,1,1)

        Y=y.expand_as(Y)*Y

        return F.relu(Y+X)