Commit e1f49695 authored by Myle Ott's avatar Myle Ott
Browse files

Rename LabelSmoothedCrossEntropy to LabelSmoothedNLLLoss

parent b1dfd39e

fairseq/criterions/label_smoothed_cross_entropy.py

+2 −2
Changes for fairseq/criterions/label_smoothed_cross_entropy.py: 2 added lines, 2 removed lines.
Original line number Diff line number Diff line
@@ -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

+2 −2
Changes for tests/test_label_smoothing.py: 2 added lines, 2 removed lines.
Original line number Diff line number Diff line
@@ -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)

        ))