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"""
SoftCrossEntropy loss Script ver： May 17th 19:00
update
SoftlabelCrossEntropy loss for soft-label based augmentations
fixme 好像说reduction='sum' 有问题？
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
# define SoftlabelCrossEntropy loss for soft-label based augmentations
def SoftCrossEntropy(input, target, reduction='sum'): # reduction='sum' fixme 好像说有问题？查一下warning
log_likelihood = -F.log_softmax(input, dim=1)
batch = input.shape[0]
if reduction == 'average':
loss = torch.sum(torch.mul(log_likelihood, target)) / batch
else:
loss = torch.sum(torch.mul(log_likelihood, target))
return loss
class SoftlabelCrossEntropy(nn.modules.loss._Loss):
__constants__ = ['reduction']
def __init__(self, reduction: str = 'sum') -> None:
super(SoftlabelCrossEntropy, self).__init__(reduction)
def forward(self, input: Tensor, target: Tensor) -> Tensor:
return SoftCrossEntropy(input, target, reduction=self.reduction)
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