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Rename LabelSmoothedCrossEntropy to LabelSmoothedNLLLoss
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@ -14,7 +14,7 @@ import torch.nn.functional as F
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from .fairseq_criterion import FairseqCriterion
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class LabelSmoothedCrossEntropy(torch.autograd.Function):
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class LabelSmoothedNLLLoss(torch.autograd.Function):
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@staticmethod
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def forward(ctx, input, target, eps, padding_idx, weights):
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@ -59,7 +59,7 @@ class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):
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net_output = model(**sample['net_input'])
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input = F.log_softmax(net_output.view(-1, net_output.size(-1)))
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target = sample['target'].view(-1)
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loss = LabelSmoothedCrossEntropy.apply(input, target, self.eps, self.padding_idx, self.weights)
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loss = LabelSmoothedNLLLoss.apply(input, target, self.eps, self.padding_idx, self.weights)
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sample_size = sample['target'].size(0) if self.args.sentence_avg else sample['ntokens']
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logging_output = {
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'loss': loss.data[0],
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@ -8,7 +8,7 @@
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import torch
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import unittest
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from fairseq.criterions.label_smoothed_cross_entropy import LabelSmoothedCrossEntropy
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from fairseq.criterions.label_smoothed_cross_entropy import LabelSmoothedNLLLoss
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from torch.autograd import Variable, gradcheck
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@ -21,7 +21,7 @@ class TestLabelSmoothing(unittest.TestCase):
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input = Variable(torch.randn(3, 5), requires_grad=True)
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idx = torch.rand(3) * 4
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target = Variable(idx.long())
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criterion = LabelSmoothedCrossEntropy()
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criterion = LabelSmoothedNLLLoss()
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self.assertTrue(gradcheck(
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lambda x, y: criterion.apply(x, y, 0.1, 2, None), (input, target)
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))
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