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# coding=utf-8
# Copyright 2021 The IDEA Authors. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from torch.nn import functional as F
class FocalLoss(torch.nn.Module):
"""Multi-class Focal loss implementation"""
def __init__(self, gamma=2, weight=None, ignore_index=-100):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.weight = weight
self.ignore_index = ignore_index
def forward(self, input, target):
"""
input: [N, C]
target: [N, ]
"""
logpt = F.log_softmax(input, dim=1)
pt = torch.exp(logpt)
logpt = (1-pt)**self.gamma * logpt
loss = F.nll_loss(logpt, target, self.weight, ignore_index=self.ignore_index)
return loss
# 交叉熵平滑滤波 防止过拟合
class LabelSmoothingCorrectionCrossEntropy(torch.nn.Module):
def __init__(self, eps=0.1, reduction='mean', ignore_index=-100):
super(LabelSmoothingCorrectionCrossEntropy, self).__init__()
self.eps = eps
self.reduction = reduction
self.ignore_index = ignore_index
def forward(self, output, target):
c = output.size()[-1]
log_preds = F.log_softmax(output, dim=-1)
if self.reduction == 'sum':
loss = -log_preds.sum()
else:
loss = -log_preds.sum(dim=-1)
if self.reduction == 'mean':
loss = loss.mean()
# task specific
labels_hat = torch.argmax(output, dim=1)
lt_sum = labels_hat + target
abs_lt_sub = abs(labels_hat - target)
correction_loss = 0
for i in range(c):
if lt_sum[i] == 0:
pass
elif lt_sum[i] == 1:
if abs_lt_sub[i] == 1:
pass
else:
correction_loss -= self.eps*(0.5945275813408382)
else:
correction_loss += self.eps*(1/0.32447699714575207)
correction_loss /= c
# print(correction_loss)
return loss*self.eps/c + (1-self.eps) * \
F.nll_loss(log_preds, target, reduction=self.reduction, ignore_index=self.ignore_index) + correction_loss
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