@kpar, another way to extract features layer by layer of a pre-existing neural network is to recreate a new network, adding one by one all the layers from the pre-trained network, plus some “transparent layers” that just extract features:
net = models.alexnet(pretrained=True).features`

class Feature_extractor(nn.module):
    def forward(self, input):
        self.feature = input.clone()
        return input

new_net = nn.Sequential().cuda() # the new network

target_layers = [conv_1, conv_2, conv_4] # layers you want to extract`

i = 1
for layer in list(cnn):
    if isinstance(layer,nn.Conv2d):
        name = "conv_"+str(i)
        art_net.add_module(name,layer)

        if name in target_layers:
            new_net.add_module("extractor_"+str(i),Feature_extractor())

        i+=1

    if isinstance(layer,nn.ReLU):
        name = "relu_"+str(i)
        new_net.add_module(name,layer)

    if isinstance(layer,nn.MaxPool2d):
        name = "pool_"+str(i)
        new_net.add_module(name,layer)

new_net.forward(your_image)
print new_net.extractor_3.feature