Hello,
I am trying to understand how does nn.Conv2d interpret batch_size>1?
My data set is like this (batch_size=64, networks=2, channels=8, H=40, W=40).
Currently I’ve used a for-loop to split up the two networks and then execute each network independently, one after another.
Hence, I have inserted this into nn.Conv2d:
For Network 1: (batch_size=64, channels=8, H=40, W=40).
For Network 2: (batch_size=64, channels=8, H=40, W=40).
Is this a sound and correct approach in terms of defining the dimensions for the input into nn.Conv2d?
Does nn.Conv2d ensure that each batch is processed independently in the forward pass? I suppose no group convolution is needed for this?
Indeed, is there a way to parallelize the two networks and get rid of the for-loop?
Thank you.