87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2019 Shigeki Karita
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Label smoothing module."""
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import torch
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from torch import nn
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class LabelSmoothingLoss(nn.Module):
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"""Label-smoothing loss.
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In a standard CE loss, the label's data distribution is:
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[0,1,2] ->
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[
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[1.0, 0.0, 0.0],
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[0.0, 1.0, 0.0],
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[0.0, 0.0, 1.0],
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]
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In the smoothing version CE Loss,some probabilities
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are taken from the true label prob (1.0) and are divided
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among other labels.
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e.g.
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smoothing=0.1
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[0,1,2] ->
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[
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[0.9, 0.05, 0.05],
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[0.05, 0.9, 0.05],
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[0.05, 0.05, 0.9],
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]
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Args:
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size (int): the number of class
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padding_idx (int): padding class id which will be ignored for loss
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smoothing (float): smoothing rate (0.0 means the conventional CE)
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normalize_length (bool):
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normalize loss by sequence length if True
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normalize loss by batch size if False
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"""
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def __init__(self,
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size: int,
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padding_idx: int,
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smoothing: float,
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normalize_length: bool = False):
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"""Construct an LabelSmoothingLoss object."""
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super(LabelSmoothingLoss, self).__init__()
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self.criterion = nn.KLDivLoss(reduction="none")
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self.padding_idx = padding_idx
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self.confidence = 1.0 - smoothing
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self.smoothing = smoothing
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self.size = size
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self.normalize_length = normalize_length
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def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
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"""Compute loss between x and target.
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The model outputs and data labels tensors are flatten to
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(batch*seqlen, class) shape and a mask is applied to the
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padding part which should not be calculated for loss.
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Args:
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x (torch.Tensor): prediction (batch, seqlen, class)
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target (torch.Tensor):
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target signal masked with self.padding_id (batch, seqlen)
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Returns:
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loss (torch.Tensor) : The KL loss, scalar float value
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"""
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assert x.size(2) == self.size
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batch_size = x.size(0)
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x = x.view(-1, self.size)
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target = target.view(-1)
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# use zeros_like instead of torch.no_grad() for true_dist,
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# since no_grad() can not be exported by JIT
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true_dist = torch.zeros_like(x)
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true_dist.fill_(self.smoothing / (self.size - 1))
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ignore = target == self.padding_idx # (B,)
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total = len(target) - ignore.sum().item()
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target = target.masked_fill(ignore, 0) # avoid -1 index
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true_dist.scatter_(1, target.unsqueeze(1), self.confidence)
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kl = self.criterion(torch.log_softmax(x, dim=1), true_dist)
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denom = total if self.normalize_length else batch_size
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return kl.masked_fill(ignore.unsqueeze(1), 0).sum() / denom
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