Intermediate Layers of AlexNet/VGG

I am trying to use PyTorch to get the outputs from intermediate layers of AlexNet/VGG:

alexnet_model = models.alexnet(pretrained=True)
modules = list((alexnet_model).children())[:-1*int(depth)]
alexnet_model = nn.Sequential(*modules)

What is odd is that I get the same output values (i.e. the same exact model) when depth=1 and depth=2, and then the same output values for depth=3 all the way to depth=10. I observe this same phenomenon for VGG too. However, I don’t observe this for ResNet, which gives me different output values (i.e. different models) for all depths [1, 10].

Any ideas about what might be going on?

list((alexnet_model).children()) will return a list of length 3 containing the first nn.Sequential block for the feature extraction, the nn.AdaptiveAvgPool2d layer, and the last nn.Sequential block used as the classifier.
If you use depth>=3, modules will be empty and you will just get back your input tensor.

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Thanks for your response! How do I get outputs of the layers within the sequential blocks? And how is this working for the ResNet architecture?

You could use forward hook as described in this example.


Thanks for the reference! Apparently, the layers within the sequential blocks (of AlexNet, VGG, etc.) don’t have names associated with them (e.g. ‘self.fc2’); how could I extract outputs from certain layers within the last sequential block using your function?

You can access a module inside an nn.Sequential block by indexing it:

model = nn.Sequential(
    nn.Conv2d(3, 6, 3, 1, 1),
    nn.Conv2d(6, 1, 3, 1, 1)
# get second conv layer
c = model[2]
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