Dice Loss Error

[albertnanda]

I have two part question,

1. The example given in the code bugs out i.e.

   dice_loss_for_NLP/loss/dice_loss.py

   Line 41 in 418d09d

   >>> loss = DiceLoss(with_logits=True, ohem_ratio=0.1)

   
   IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)
2. The other question is related to the implementation, say the classifier has perfectly predicted the labels, but there would be still some dice loss because of loss = 1 - ((2 * interection + self.smooth) /
   (torch.sum(torch.square(flat_input, ), -1) + torch.sum(torch.square(flat_target), -1) + self.smooth))
   smooth. Is this the expected behavior or am I missing something.

input = torch.FloatTensor([[1., .0, .0, .0],[0., 1, .0, .0]])
target = torch.LongTensor([0, 1])
loss = DiceLoss(with_logits=False,reduction=None,ohem_ratio=0.)
input.requires_grad=True
output = loss(input, target)

Output
tensor([1.9998, 1.9998], grad_fn=)

[xiaoya-li]

Hey, thanks for asking.
Response to quesiton2:
As shown in

dice_loss_for_NLP/tasks/tnews/train.py

Line 139 in 418d09d

loss_fct = DiceLoss(with_logits=True, smooth=self.args.dice_smooth, ohem_ratio=self.args.dice_ohem,

we recommend using the following setting for multi-class tasks:

$ loss_fct = DiceLoss(square_denominator=True, with_logits=False, index_label_position=True,
                        smooth=1, ohem_ratio=0, alpha=0.01, reduction="none")

• If input_probs = torch.FloatTensor([[0.9, 0.03, 0.03, 0.03],[0.03, 0.9, 0.03, 0.03]]) and target = torch.LongTensor([0, 1]), then the dice loss should be :

$ loss_fct(input_probs, target)
> tensor([0.0079, 0.0079])

• If input_probs = torch.FloatTensor([[0.9, 0.1, .0, .0],[.0, 0.9, 0.1, .0]]) and target = torch.LongTensor([0, 1]), then the dice loss should be :

$ loss_fct(input_probs, target)
> tensor([0.0151, 0.0151])

These are in line with our expectations.

[albertnanda]

@xiaoya-li : Let me try it out, also can you recommend the settings for Multi-Label classification and NER task.
NER Task:

inp = torch.FloatTensor([[[.1,.2,.3,.4]]*4,[[.5,.5,0,0]]*4]) #2 sentences, 4 words and 4 tags per word
target = torch.LongTensor([[0,1,1,2],[0,3,2,3]]) #tags 0,3, 2 sentences and 4 words

[kk19990709]

I have also met question 1, do you have any solution?@albertnanda