def test_reduction(self): self.args.label_smoothing = 0.1 crit = LabelSmoothedCrossEntropyCriterion.build_criterion( self.args, self.task) loss, _, logging_output = crit(self.model, self.sample, reduce=True) unreduced_loss, _, _ = crit(self.model, self.sample, reduce=False) self.assertAlmostEqual(loss, unreduced_loss.sum())
def test_zero_eps(self): self.args.label_smoothing = 0.0 nll_crit = CrossEntropyCriterion.build_criterion(self.args, self.task) smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion(self.args, self.task) nll_loss, nll_sample_size, nll_logging_output = nll_crit(self.model, self.sample) smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit(self.model, self.sample) self.assertAlmostEqual(nll_loss, smooth_loss)
def test_nll_loss(self): self.args.label_smoothing = 0.1 nll_crit = CrossEntropyCriterion.build_criterion(self.args, self.task) smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion(self.args, self.task) nll_loss, nll_sample_size, nll_logging_output = nll_crit(self.model, self.sample) smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit(self.model, self.sample) self.assertLess(abs(nll_loss - nll_logging_output['loss']), 1e-6) self.assertLess(abs(nll_loss - smooth_logging_output['nll_loss']), 1e-6)
def test_padding(self): self.args.label_smoothing = 0.1 crit = LabelSmoothedCrossEntropyCriterion.build_criterion(self.args, self.task) loss, _, logging_output = crit(self.model, self.sample) def get_one_no_padding(idx): # create a new sample with just a single batch item so that there's # no padding sample1 = next(test_utils.dummy_dataloader([self.data[idx]])) args1 = copy.copy(self.args) args1.probs = args1.probs[idx, :, :].unsqueeze(0) model1 = self.task.build_model(args1) loss1, _, _ = crit(model1, sample1) return loss1 loss1 = get_one_no_padding(0) loss2 = get_one_no_padding(1) self.assertAlmostEqual(loss, loss1 + loss2)