FunASR/funasr/losses/label_smoothing_loss.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# Copyright 2019 Shigeki Karita
#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

"""Label smoothing module."""

import torch
from torch import nn
from funasr.modules.nets_utils import make_pad_mask


class LabelSmoothingLoss(nn.Module):
    """Label-smoothing loss.

    :param int size: the number of class
    :param int padding_idx: ignored class id
    :param float smoothing: smoothing rate (0.0 means the conventional CE)
    :param bool normalize_length: normalize loss by sequence length if True
    :param torch.nn.Module criterion: loss function to be smoothed
    """

    def __init__(
        self,
        size,
        padding_idx,
        smoothing,
        normalize_length=False,
        criterion=nn.KLDivLoss(reduction="none"),
    ):
        """Construct an LabelSmoothingLoss object."""
        super(LabelSmoothingLoss, self).__init__()
        self.criterion = criterion
        self.padding_idx = padding_idx
        self.confidence = 1.0 - smoothing
        self.smoothing = smoothing
        self.size = size
        self.true_dist = None
        self.normalize_length = normalize_length

    def forward(self, x, target):
        """Compute loss between x and target.

        :param torch.Tensor x: prediction (batch, seqlen, class)
        :param torch.Tensor target:
            target signal masked with self.padding_id (batch, seqlen)
        :return: scalar float value
        :rtype torch.Tensor
        """
        assert x.size(2) == self.size
        batch_size = x.size(0)
        x = x.view(-1, self.size)
        target = target.view(-1)
        with torch.no_grad():
            true_dist = x.clone()
            true_dist.fill_(self.smoothing / (self.size - 1))
            ignore = target == self.padding_idx  # (B,)
            total = len(target) - ignore.sum().item()
            target = target.masked_fill(ignore, 0)  # avoid -1 index
            true_dist.scatter_(1, target.unsqueeze(1), self.confidence)
        kl = self.criterion(torch.log_softmax(x, dim=1), true_dist)
        denom = total if self.normalize_length else batch_size
        return kl.masked_fill(ignore.unsqueeze(1), 0).sum() / denom


class SequenceBinaryCrossEntropy(nn.Module):
    def __init__(
            self,
            normalize_length=False,
            criterion=nn.BCEWithLogitsLoss(reduction="none")
    ):
        super().__init__()
        self.normalize_length = normalize_length
        self.criterion = criterion

    def forward(self, pred, label, lengths):
        pad_mask = make_pad_mask(lengths, maxlen=pred.shape[1])
        loss = self.criterion(pred, label)
        denom = (~pad_mask).sum() if self.normalize_length else pred.shape[0]
        return loss.masked_fill(pad_mask, 0).sum() / denom


class NllLoss(nn.Module):
    """Nll loss.

    :param int size: the number of class
    :param int padding_idx: ignored class id
    :param bool normalize_length: normalize loss by sequence length if True
    :param torch.nn.Module criterion: loss function
    """

    def __init__(
        self,
        size,
        padding_idx,
        normalize_length=False,
        criterion=nn.NLLLoss(reduction='none'),
    ):
        """Construct an NllLoss object."""
        super(NllLoss, self).__init__()
        self.criterion = criterion
        self.padding_idx = padding_idx
        self.size = size
        self.true_dist = None
        self.normalize_length = normalize_length

    def forward(self, x, target):
        """Compute loss between x and target.

        :param torch.Tensor x: prediction (batch, seqlen, class)
        :param torch.Tensor target:
            target signal masked with self.padding_id (batch, seqlen)
        :return: scalar float value
        :rtype torch.Tensor
        """
        assert x.size(2) == self.size
        batch_size = x.size(0)
        x = x.view(-1, self.size)
        target = target.view(-1)
        with torch.no_grad():
            ignore = target == self.padding_idx  # (B,)
            total = len(target) - ignore.sum().item()
            target = target.masked_fill(ignore, 0)  # avoid -1 index
        kl = self.criterion(x , target)
        denom = total if self.normalize_length else batch_size
        return kl.masked_fill(ignore, 0).sum() / denom