Supposed I have a CNN with conv layers and fully connected layers. Since image tensors in pyTorch is C * H * W, do we need to permute the dimensions of the feature maps after the final conv layer?
I’m thinking this might not be necessary since we have fully connected layers and the neurons in those layers will learn to pick the correct tensor values from the convolution feature maps during training.
But I could be wrong. Please advice.