I am trying to use the loss weighing for my unbalanced data set. During my search on how to provide the weight factors to a loss function I have come upon this discrepancy:
Doc for NLLlos:


weight  ( Tensor  ,  optional ) – a manual rescaling weight given to each class. If given, it has to be a Tensor of size C. Otherwise, it is treated as if having all ones.

Doc for BCEWithLogitsLoss:


weight  ( Tensor  ,  optional ) – a manual rescaling weight given to the loss of each batch element. If given, has to be a Tensor of size “nbatch”.

I am using BCEWithLogitsLoss in my model and I tried providing the loss in the form of one dimensional tensor where each entry represents the weight factor for corresponding bach sample. This gave me an error of the form:
The size of tensor a (2) must match the size of tensor b (4) at non-singleton dimension 1
type or paste code here

When my batch size was 4. Note the tensor of weight factors for a 2 class problem was of the following form:
tensor([0.2083, 0.7917, 0.7917, 0.2083])

Then I tried providing the weight factor as specified in the NLLloss docs, where each entry in a tensor is weight factor for a specific class. And if I understand correctly, in this case labels need to be in range [0,1,2,…,n] where n is the number of classes and then class weight factors are mapped to the labels by position, for example: if class is 2 weight factor is weight_tensor[2] (weight with index 2 in weight tensor) . When I implemented it like this the code worked fine. So I was thinking maybe the docs for BCEWithLogits need to be changed, since it seem the later implementation works correctly.
If this is not the case I don’t understand in what form exactly do the class weights need to be passed to the BCEWithLogits loss.
Thank you!