Hi guys I’m implementing a custom pooling function where there is a trainable tensor. So far I’m using Variable to wrap the tensor and requires_grad=True. However, it seems the that the variable is not changing as the model trains, it keeps stuck at the init value of 3. What am I doing wrong?

Here is the code:

class GeneralizedMeanPooling(nn.Module):
def __init__(self):
super(GeneralizedMeanPooling, self).__init__()
self.p = torch.autograd.Variable(torch.cuda.FloatTensor([3]),
requires_grad=True)
def forward(self, x):
B, C, H, W = x.shape
x = x.view((B, C, -1))
x = torch.sum(x ** self.p.expand_as(x), dim=2) / (H * W)
x = x ** (1 / self.p)
return x

Variables don’t exist anymore so you don’t need them, you can do torch.cuda.FloatTensor([3], requires_grad=True) directly.
In nn.Module, for something to be recognized as a parameter (and thus be returned by mod.parameters()), it needs to be of type nn.Parameter(). So you should do:

I can see it going to nan if p goes toward zero.
I am not sure about all the derivatives for the pow operation. Is it defined for every value of p? For every value of 1/p?

I see a few log in there that could be problematic as well.
Also you can activate the anomaly detection mode to know exactly which op created the nan first !

You can find here the definition of the backward for the pow operator.
And the function for the backward towards p is here. As you can see, it takes the log of the input. So I expect that if x contains any 0, then you will get a nan here. You can check that with: print(x.eq(0).sum()).