Cropping in neural networks

Question

The problem I am stuck with is in cropping tensors at certain parts of the network to obtain specific dimensions.

I am trying to construct the U-net convolutional neural network. Almost all of the components of this are well covered in the MMA neural network framework; the contraction part of the network was straightforward but the expansion path is more difficult.

At several locations in the network I need to crop a layer to match that of another in a different part of the network. For example, I need to concatenate a layer with dimensions {1,568,568} with another of dimensions {1,392,392} and need to match the dimensions of the two by cropping from the first.

PaddingLayer is close but (unlike ArrayPad) does not permit negative padding values, that is, something like PaddingLayer[{-88,-88}] would do the trick but it isn't possible. ResizeLayer is not the answer because it introduces some form of interpolation.

Is there some other way of dealing with this?

Answer 1

PartLayer is close, but it can only crop the first dimension. You can use PartLayer in combination with TransposeLayer to crop tensors:

data = {Table[i + j, {i, 10}, {j, 10}]};
Dimensions[data]

{1, 10, 10}

NetChain[{TransposeLayer[3 -> 1],
   TransposeLayer[1 -> 2],
   PartLayer[2 ;; 3],
   TransposeLayer[1 -> 2],
   PartLayer[2 ;; 3],
   TransposeLayer[1 -> 3]}]@data

{{{4.}, {5.}}, {{5.}, {6.}}}