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import torch
import torch.nn as nn
import torch.nn.functional as F
class LabelSmoothingCrossEntropy(nn.Module):
"""
NLL loss with label smoothing.
"""
def __init__(self, smoothing=0.1):
"""
Constructor for the LabelSmoothing module.
:param smoothing: label smoothing factor
"""
super(LabelSmoothingCrossEntropy, self).__init__()
assert smoothing < 1.0
self.smoothing = smoothing
self.confidence = 1. - smoothing
def forward(self, x, target):
logprobs = F.log_softmax(x, dim=-1)
nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1))
nll_loss = nll_loss.squeeze(1)
smooth_loss = -logprobs.mean(dim=-1)
loss = self.confidence * nll_loss + self.smoothing * smooth_loss
return loss.mean()
class SoftTargetCrossEntropy(nn.Module):
def __init__(self):
super(SoftTargetCrossEntropy, self).__init__()
def forward(self, x, target):
loss = torch.sum(-target * F.log_softmax(x, dim=-1), dim=-1)
return loss.mean()
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