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#!/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_detach.models.transformer.utils.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]).to(pred.device)
loss = self.criterion(pred, label)
denom = (~pad_mask).sum() if self.normalize_length else pred.shape[0]
return loss.masked_fill(pad_mask.unsqueeze(-1), 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
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