Spaces:
Sleeping
Sleeping
File size: 3,668 Bytes
b725c5a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
# ## Citations
# ```bibtex
# @inproceedings{yao2021wenet,
# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
# booktitle={Proc. Interspeech},
# year={2021},
# address={Brno, Czech Republic },
# organization={IEEE}
# }
# @article{zhang2022wenet,
# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
# journal={arXiv preprint arXiv:2203.15455},
# year={2022}
# }
#
"""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
|