I want to do a conv1D operation but on an image. Let’s say the data is of size (1,1,H,W). Treat this as a data of size (1,1,H) W times, and do Conv1D(1,1,k) on each and stacking the outputs back. Basically, it is equivalent to doing a Conv2D with kernel size = (k,W), however, with each column of the kernel equal. It is like doing np.tile(kernel,(1,W)) on the regular Conv1D kernel and then using it on the image data.
How can I do this efficiently?