Im getting this error when passing the input data to the Linear (Fully Connected Layer) in PyTorch:
matrices expected, got 4D, 2D tensors
I fully understand the problem since the input data has a shape (N,C,H,W) (from a Convolutional+MaxPool layer) where:
- N: Data Samples
- C: Channels of the data
- H,W: Height and Width
Nevertheless I was expecting PyTorch to do the “reshaping” of the data form:
- [ N , D1,…Dn] –> [ N, D] where D = D1*D2*….Dn
I try to reshape the Variable.data, but I’ve read that this approach is not recommended since the gradients will conserve the previous shape, and that in general you should not mutate a Variable.data shape.
I am pretty sure there is a simple solution that goes along with the framework, but i haven’t find it.
Is there a good solution for this?
PD: The Fully connected layer has as input size the value C * H * W
Advertisement
Answer
After reading some Examples I found the solution. here is how you do it without messing up the forward/backward pass flow:
(_, C, H, W) = x.data.size() x = x.view( -1 , C * H * W)