from __future__ import absolute_import
from __future__ import division
import torch
import torch.nn as nn
[docs]class CrossEntropyLoss(nn.Module):
r"""Cross entropy loss with label smoothing regularizer.
Reference:
Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
With label smoothing, the label :math:`y` for a class is computed by
.. math::
\begin{equation}
(1 - \epsilon) \times y + \frac{\epsilon}{K},
\end{equation}
where :math:`K` denotes the number of classes and :math:`\epsilon` is a weight. When
:math:`\epsilon = 0`, the loss function reduces to the normal cross entropy.
Args:
num_classes (int): number of classes.
epsilon (float, optional): weight. Default is 0.1.
use_gpu (bool, optional): whether to use gpu devices. Default is True.
label_smooth (bool, optional): whether to apply label smoothing. Default is True.
"""
def __init__(self, num_classes, epsilon=0.1, use_gpu=True, label_smooth=True):
super(CrossEntropyLoss, self).__init__()
self.num_classes = num_classes
self.epsilon = epsilon if label_smooth else 0
self.use_gpu = use_gpu
self.logsoftmax = nn.LogSoftmax(dim=1)
[docs] def forward(self, inputs, targets):
"""
Args:
inputs (torch.Tensor): prediction matrix (before softmax) with
shape (batch_size, num_classes).
targets (torch.LongTensor): ground truth labels with shape (batch_size).
Each position contains the label index.
"""
log_probs = self.logsoftmax(inputs)
targets = torch.zeros(log_probs.size()).scatter_(1, targets.unsqueeze(1).data.cpu(), 1)
if self.use_gpu: targets = targets.cuda()
targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
return (- targets * log_probs).mean(0).sum()