densenet.py 文件源码

def forward(self, x):

        # If we're not training this layer, set to eval mode so that we use
        # running batchnorm stats (both for time-saving and to avoid updating
        # said stats).
        if not self.active:
            self.eval()

        out = self.conv1(F.relu(self.bn1(x)))
        out = self.conv2(F.relu(self.bn2(out)))
        out = torch.cat((x, out), 1)

        # If we're not active, return a detached output to prevent backprop.
        if self.active: 
            return out
        else:
            return out.detach()