Hi,
I’m trying to understand the process of semantic segmentation and I’m having trouble at the loss function. For simple classification networks the loss function is usually a 1 dimensional tenor having size equal to the number of classes, but for semantic segmentation the target is also an image.
I have an input image of the shape: Inputs: torch.Size([1, 3, 224, 224]) which produces an output of shape: Outout: torch.Size([1, 32, 224, 224]). The target on the other hand is of the shape: Targets: torch.Size([1, 1, 360, 480]).
There is a size mismatch, does that matter?
I tried with the loss function: criterion =torch.nn.BCEWithLogitsLoss()  but with this PyCharm just stalls and crashes.
The example I am following used CrossEntropyLoss2D() as shown here: https://github.com/ycszen/pytorch-segmentation/blob/master/loss.py 64
but when I use that I get an error with a warning that NLLLoss2d has been deprecated. Furthermore, there is no 2D loss function listed here in the documentation: https://pytorch.org/docs/stable/nn.html 10
I tried resizing the target making it the same as the output but that didint work as well.
I would like to read more about the loss function for semantic segmentation but couldnt find much help. Why am I having trouble with the loss functions, is it because of the size mismatch? since they are both images, do I need to write my own class to handle this?
Many thanks for any help/guidance.