I have an existing CNN architecture which gets an image (size: $640 × 352$) and returns and image of the same size ($640 × 352$, via convolution and deconvolution). And I'm trying to halve the output size of the first convolution layer for better performance. The thing is: I want that the rest of the network works as before (aside from adding a $2 ×$-up-convolution at the end).
So I have an image the size of $640 × 352$. Originally a convolution with stride $ S = 2 $ and a filter kernel with size $ 7 × 7 $ is applied. Padding is zero: $ P = 0 $.
Now I want to change the convolution to return an output halve the size of the former output size of the convolution, so I just need to add an additional $2×$-deconvolution at the end, for the same result.
I played a little bit around and tried something like $ F = 8 $ and $ S = 4 $ which seems to work for the first dimension, but not for the second. It returns $640×384$.
Any ideas how to realize this?