Getting Intermediate Output of Self Created Sequential

vision
1

I created my own sequential that has many modules in it. I was able to create a module PrintLayer and I placed it after the output i want to save.

class PrintLayer(nn.Module):
def init(self):
super(PrintLayer, self).init()

def forward(self, x):
    print(x.shape)
    return x

It could print the shape and value of the output of a specific layer i want to observe during the forward pass, but how exactly do I save that value so I could retrieve it after the forward pass. Thanks!

2

You could store the activations in a dict using forward hooks.
I’ve created a small example in this thread.

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3

The problem is my model is embedded in a very complicated way like this.

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4

Would it be feasible to set the hook in the __init__ of some sub-module, e.g. UnetSkipConnectionBlock?
Otherwise, you would need to address your layers from top to bottom.

5

Yeah I can add stuff to the submodule, but I’m unsure how to add the hook there since each submodule is a Sequential and isn’t created like the example you have.

6

You would have to index the modules like in this example:

model = nn.Sequential(nn.Sequential(nn.Sequential(nn.Linear(10, 2))))
model[0][0][0].register_forward_hook
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7

Thanks!

And then you would pass it a function like get_activation that creates a hook and saves the value in a dict?

8

Yes, then I would use the method I’ve linked in the other post.
Note that you might want to remove the .detach in hook, if you want to backpropagate.

9

I’m still having trouble accessing the modules. When i print out the children print(list(model.netG.children())), I get

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, and then when I print model.netG.module( I had to use .module to get rid of dataparallel error), I get
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. Now when I try to index that, it says UnetGenerator doesn’t support indexing.

10

It seems some sub-modules have the name “model”.
Have a look at this small example, how to index the layers:

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc1 = nn.Sequential(nn.Linear(10, 10), nn.ReLU())
        
    def forward(self, x):
        return self.fc1(x)

model = MyModel()
print(model)
model.fc1[0] # get linear layer