I was wondering how to handle 2D input data while doing a 1D convolution in PyTorch. This nice answer handles three situations: https://stats.stackexchange.com/questions/292751/is-a-1d-convolution-of-size-m-with-k-channels-the-same-as-a-2d-convolution-o/292791
I am interested in the second example: I have multiple 1D vectors of the same length that I can combine into a 2D matrix as input, and I want a 1D array as output. I would like to do a 1D convolution with 1 channel, a kernelsize of n×1 and a 2D input, but it seems that this is not possible in PyTorch as the input shape of Conv1D is minibatch×in_channels×iW (implying a height of 1 instead of n).
My question is, how can I do a 1D convolution with a 2D input (aka multiple 1D arrays stacked into a matrix)?
Thanks in advance.