For the 1st point, yeah, I can imagine.
But because I’ve seen papers where they use different sizes of images as input, they explicitly say that they don’t resize, but they don’t mension how they handle the different sizes in the end.
So I imagined, since I can use this network with different sizes 1 by 1, just not in a batch, that perhaps since that’s possible, there would be a way to make a list-like kind of batch instead of a torch tensor.
You know what I mean?
Certainly it’s impossible with tensors, but perhaps there’s another way?
On the 2nd point, for this list-like batch, I don’t think there will be any fundamental change since predicting with different size images on this network already works, so long as you do it 1 picture at a time. Just, instead of iterating on the 1st dimension of N x C x H x W tensor, you iterate over the length of the list with C x H_i x W_i tensors as elements of the list
(Now if a picture is too small for the current architecture, ofc that will throw an error)