Hey, I am creating a DeepFake in Pytorch. My model has following architecture:
x := input-image
x -> encoder -> if: label_of_x == 0: -> decoder1, elif label_of_x == 1: -> decoder2
Its a conditional flow in which all the images pass through the same encoder but go through different decoders dependent on their class. Until now I used batchsize = 1 because I dont know if/how I can split the batch into two smaller batches (each containing all the input-images of the same class) and then merge them together for the backpropagation. Is this possible? And if so: How?
I really appreciate any help!