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