| | |
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| | | |
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| | | |
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| | | class SequenceBinaryCrossEntropy(nn.Module): |
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| | | def __init__( |
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| | | self, |
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| | | normalize_length=False, |
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| | | criterion=nn.BCEWithLogitsLoss(reduction="none") |
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| | | ): |
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| | | def __init__(self, normalize_length=False, criterion=nn.BCEWithLogitsLoss(reduction="none")): |
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| | | super().__init__() |
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| | | self.normalize_length = normalize_length |
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| | | self.criterion = criterion |
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| | |
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| | | size, |
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| | | padding_idx, |
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| | | normalize_length=False, |
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| | | criterion=nn.NLLLoss(reduction='none'), |
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| | | criterion=nn.NLLLoss(reduction="none"), |
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| | | ): |
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| | | """Construct an NllLoss object.""" |
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| | | super(NllLoss, self).__init__() |
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| | |
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| | | ignore = target == self.padding_idx # (B,) |
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| | | total = len(target) - ignore.sum().item() |
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| | | target = target.masked_fill(ignore, 0) # avoid -1 index |
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| | | kl = self.criterion(x , target) |
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| | | kl = self.criterion(x, target) |
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| | | denom = total if self.normalize_length else batch_size |
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| | | return kl.masked_fill(ignore, 0).sum() / denom |
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