Rename LabelSmoothedCrossEntropy to LabelSmoothedNLLLoss

fairseq/criterions/label_smoothed_cross_entropy.py

@@ -14,7 +14,7 @@ import torch.nn.functional as F
 from .fairseq_criterion import FairseqCriterion
 
 
-class LabelSmoothedCrossEntropy(torch.autograd.Function):
+class LabelSmoothedNLLLoss(torch.autograd.Function):
 
     @staticmethod
     def forward(ctx, input, target, eps, padding_idx, weights):
@@ -59,7 +59,7 @@ class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):
         net_output = model(**sample['net_input'])
         input = F.log_softmax(net_output.view(-1, net_output.size(-1)))
         target = sample['target'].view(-1)
-        loss = LabelSmoothedCrossEntropy.apply(input, target, self.eps, self.padding_idx, self.weights)
+        loss = LabelSmoothedNLLLoss.apply(input, target, self.eps, self.padding_idx, self.weights)
         sample_size = sample['target'].size(0) if self.args.sentence_avg else sample['ntokens']
         logging_output = {
             'loss': loss.data[0],

tests/test_label_smoothing.py

@@ -8,7 +8,7 @@
 
 import torch
 import unittest
-from fairseq.criterions.label_smoothed_cross_entropy import LabelSmoothedCrossEntropy
+from fairseq.criterions.label_smoothed_cross_entropy import LabelSmoothedNLLLoss
 from torch.autograd import Variable, gradcheck
 
 
@@ -21,7 +21,7 @@ class TestLabelSmoothing(unittest.TestCase):
         input = Variable(torch.randn(3, 5), requires_grad=True)
         idx = torch.rand(3) * 4
         target = Variable(idx.long())
-        criterion = LabelSmoothedCrossEntropy()
+        criterion = LabelSmoothedNLLLoss()
         self.assertTrue(gradcheck(
             lambda x, y: criterion.apply(x, y, 0.1, 2, None), (input, target)
         ))