#!/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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:param int size: the number of class
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:param int padding_idx: ignored class id
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:param float smoothing: smoothing rate (0.0 means the conventional CE)
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:param bool normalize_length: normalize loss by sequence length if True
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:param torch.nn.Module criterion: loss function to be smoothed
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"""
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def __init__(
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self,
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size,
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padding_idx,
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smoothing,
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normalize_length=False,
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criterion=nn.KLDivLoss(reduction="none"),
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):
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"""Construct an LabelSmoothingLoss object."""
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super(LabelSmoothingLoss, self).__init__()
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self.criterion = criterion
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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.true_dist = None
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self.normalize_length = normalize_length
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def forward(self, x, target):
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"""Compute loss between x and target.
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:param torch.Tensor x: prediction (batch, seqlen, class)
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:param torch.Tensor target:
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target signal masked with self.padding_id (batch, seqlen)
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:return: scalar float value
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:rtype torch.Tensor
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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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with torch.no_grad():
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true_dist = x.clone()
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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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