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21 lines
841 B
21 lines
841 B
1 year ago
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import torch.nn as nn
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import torch.nn.functional as F
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class LabelSmoothingCrossEntropy(nn.Module):
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def __init__(self, eps=0.1, reduction='mean',ignore_index=-100):
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super(LabelSmoothingCrossEntropy, self).__init__()
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self.eps = eps
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self.reduction = reduction
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self.ignore_index = ignore_index
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def forward(self, output, target):
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c = output.size()[-1]
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log_preds = F.log_softmax(output, dim=-1)
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if self.reduction=='sum':
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loss = -log_preds.sum()
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else:
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loss = -log_preds.sum(dim=-1)
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if self.reduction=='mean':
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loss = loss.mean()
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return loss*self.eps/c + (1-self.eps) * F.nll_loss(log_preds, target, reduction=self.reduction,
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ignore_index=self.ignore_index)
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