loss/label_smoothing.py

import torch.nn as nn
import torch.nn.functional as F

class LabelSmoothingCrossEntropy(nn.Module):
    def __init__(self, eps=0.1, reduction="mean",ignore_index=-100):
        super(LabelSmoothingCrossEntropy, self).__init__()
        self.eps = eps
        self.reduction = reduction
        self.ignore_index = ignore_index

    def forward(self, output, target):
        c = output.size()[-1]
        log_preds = F.log_softmax(output, dim=-1)
        if self.reduction=="sum":
            loss = -log_preds.sum()
        else:
            loss = -log_preds.sum(dim=-1)
            if self.reduction=="mean":
                loss = loss.mean()
        return loss*self.eps/c + (1-self.eps) * F.nll_loss(log_preds, target, reduction=self.reduction,
                                                           ignore_index=self.ignore_index)