Why MultiLabelMarginLoss take torch.long has arguments?
From understanding MultiLabelMarginLoss take not labels but one hot encoding labels so we can use multiclass multilabels prediction.
Why MultiLabelMarginLoss arguments shoud be long? It’s compose of only 0 and 1.
I have the following error if I try with uint8 RuntimeError: Expected object of scalar type Long but got scalar type Byte for argument #2 'target'
The target tensor is not expected to contain one hot encoding. from the doc:
The criterion only considers a contiguous block of non-negative targets that starts at the front.
This allows for different samples to have variable amounts of target classes
It should contain label values and be padded by -1s. For example if you have 3 labels and sample 0 is 0, 1 and sample 1 is 2 and sample 2 is 0,1,2:
label = [[0, 1, -1],
[2, -1, -1],
[0, 1, 2]]
This is a old design choice (the cpu code is >7years old and is part of the first commit on github, so it’s most certainly older than that), I’m not sure why this was made though.
Wow! I didn’t understand at all that
thank you so much!
Do you have any recommendation for multiclass multilabels classification?
I don’t know if there is better loss function or anything that can help