@albanD
On giving more thought if I need to do a 3D convolution with (1 x Kx x  Ky) kernel, reshaping the input for a 2D convolution would make sense right? I would probably save time and definitely save memory.
And similarly if I have kernel = 1 (used for changing the number of features/channels) then reshaping the input for 1D convolution is a better choice? (3D conv to 1D conv would save lot of memory, but dont know if reshaping would have some adverse affects in forward or backward pass)
What do you think???