import torch
import torch.nn as nn
import torch.nn.functional as F
[docs]class NaiveCrossEntropyLoss(nn.Module):
def __init__(self, size_average=True):
super().__init__()
self.size_average = size_average
[docs] def forward(self, input, target):
assert input.size() == target.size()
input = F.log_softmax(input)
loss = -torch.sum(input * target)
loss = loss / input.size()[0] if self.size_average else loss
return loss
[docs]class SymmetricCrossEntropyLoss(nn.Module):
[docs] def __init__(self, alpha=1.0, beta=1.0):
"""
Symmetric Cross Entropy
paper : https://arxiv.org/abs/1908.06112
Args:
alpha(float):
corresponds to overfitting issue of CE
beta(float):
corresponds to flexible exploration on the robustness of RCE
"""
super(SymmetricCrossEntropyLoss, self).__init__()
self.alpha = alpha
self.beta = beta
[docs] def forward(self, input, target):
"""
Args:
input: shape = [batch_size; num_classes]
target: shape = [batch_size]
values of a vector correspond to class index
"""
num_classes = input.shape[1]
target_one_hot = F.one_hot(target, num_classes).float()
assert target_one_hot.shape == input.shape
input = torch.clamp(input, min=1e-7, max=1.0)
target_one_hot = torch.clamp(target_one_hot, min=1e-4, max=1.0)
cross_entropy = (-torch.sum(target_one_hot * torch.log(input),
dim=1)).mean()
reverse_cross_entropy = (
-torch.sum(input * torch.log(target_one_hot), dim=1)
).mean()
loss = self.alpha * cross_entropy + self.beta * reverse_cross_entropy
return loss
[docs]class MaskCrossEntropyLoss(torch.nn.CrossEntropyLoss):
def __init__(
self,
*args,
target_name: str = "targets",
mask_name: str = "mask",
**kwargs
):
super().__init__(*args, **kwargs)
self.target_name = target_name
self.mask_name = mask_name
self.reduction = "none"
[docs] def forward(self, input, target_mask):
target = target_mask[self.target_name]
mask = target_mask[self.mask_name]
loss = super().forward(input, target)
loss = torch.mean(loss[mask == 1])
return loss
__all__ = [
"MaskCrossEntropyLoss",
"SymmetricCrossEntropyLoss",
"NaiveCrossEntropyLoss",
]