87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
#!/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
|
|
|
|
|
|
class LabelSmoothingLoss(nn.Module):
|
|
"""Label-smoothing loss.
|
|
|
|
In a standard CE loss, the label's data distribution is:
|
|
[0,1,2] ->
|
|
[
|
|
[1.0, 0.0, 0.0],
|
|
[0.0, 1.0, 0.0],
|
|
[0.0, 0.0, 1.0],
|
|
]
|
|
|
|
In the smoothing version CE Loss,some probabilities
|
|
are taken from the true label prob (1.0) and are divided
|
|
among other labels.
|
|
|
|
e.g.
|
|
smoothing=0.1
|
|
[0,1,2] ->
|
|
[
|
|
[0.9, 0.05, 0.05],
|
|
[0.05, 0.9, 0.05],
|
|
[0.05, 0.05, 0.9],
|
|
]
|
|
|
|
Args:
|
|
size (int): the number of class
|
|
padding_idx (int): padding class id which will be ignored for loss
|
|
smoothing (float): smoothing rate (0.0 means the conventional CE)
|
|
normalize_length (bool):
|
|
normalize loss by sequence length if True
|
|
normalize loss by batch size if False
|
|
"""
|
|
def __init__(self,
|
|
size: int,
|
|
padding_idx: int,
|
|
smoothing: float,
|
|
normalize_length: bool = False):
|
|
"""Construct an LabelSmoothingLoss object."""
|
|
super(LabelSmoothingLoss, self).__init__()
|
|
self.criterion = nn.KLDivLoss(reduction="none")
|
|
self.padding_idx = padding_idx
|
|
self.confidence = 1.0 - smoothing
|
|
self.smoothing = smoothing
|
|
self.size = size
|
|
self.normalize_length = normalize_length
|
|
|
|
def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
|
|
"""Compute loss between x and target.
|
|
|
|
The model outputs and data labels tensors are flatten to
|
|
(batch*seqlen, class) shape and a mask is applied to the
|
|
padding part which should not be calculated for loss.
|
|
|
|
Args:
|
|
x (torch.Tensor): prediction (batch, seqlen, class)
|
|
target (torch.Tensor):
|
|
target signal masked with self.padding_id (batch, seqlen)
|
|
Returns:
|
|
loss (torch.Tensor) : The KL loss, scalar float value
|
|
"""
|
|
assert x.size(2) == self.size
|
|
batch_size = x.size(0)
|
|
x = x.view(-1, self.size)
|
|
target = target.view(-1)
|
|
# use zeros_like instead of torch.no_grad() for true_dist,
|
|
# since no_grad() can not be exported by JIT
|
|
true_dist = torch.zeros_like(x)
|
|
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
|