so the way I understand it is that it moves in 3 dimensions. I get that part. So for me I would have expected a 3D conv to just move around however it wants as long as we specify the 3 dimenions to move around.
So the input tensor could be of size (A,B,C) or (A,B,C,D) or (A,B,C,D,E) or even (X1,X2,X3,…,X_100) and the 3d convolution should work as long as we specify which 3 dimensions to move around. Does what I am thinking make sense?
But instead I have the layer just explode on my face with an error. It seems its been hardcoded to expect 5 dimensional tensors no matter what. I’m fine with that (even though I might consider it an incomplete implementation) but its not clear from the docs what is going on. Specially cuz it has the paragraph of “in the simplest case…”