layers.py 文件源码

def forward(self, x):
        """
        Compute the forward pass of the composite transformation H(x),
        where x is the concatenation of the current and all preceding
        feature maps.
        """
        if self.bottleneck:
            out = self.conv1(F.relu(self.bn1(x)))
            if self.p > 0:
                out = F.dropout(out, p=self.p, training=self.training)
            out = self.conv2(F.relu(self.bn2(out)))
            if self.p > 0:
                out = F.dropout(out, p=self.p, training=self.training)
        else:
            out = self.conv2(F.relu(self.bn2(x)))
            if self.p > 0:
                out = F.dropout(out, p=self.p, training=self.training)  
        return torch.cat((x, out), 1)