我就废话不多说了，直接上代码吧！
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
|
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# 支持多分类和二分类
class FocalLoss(nn.Module):
"""
This is a implementation of Focal Loss with smooth label cross entropy supported which is proposed in
'Focal Loss for Dense Object Detection. (https://arxiv.org/abs/1708.02002)'
Focal_Loss= -1*alpha*(1-pt)^gamma*log(pt)
:param num_class:
:param alpha: (tensor) 3D or 4D the scalar factor for this criterion
:param gamma: (float,double) gamma > 0 reduces the relative loss for well-classified examples (p>0.5) putting more
focus on hard misclassified example
:param smooth: (float,double) smooth value when cross entropy
:param balance_index: (int) balance class index, should be specific when alpha is float
:param size_average: (bool, optional) By default, the losses are averaged over each loss element in the batch.
"""
def __init__(self, num_class, alpha=None, gamma=2, balance_index=-1, smooth=None, size_average=True):
super(FocalLoss, self).__init__()
self.num_class = num_class
self.alpha = alpha
self.gamma = gamma
self.smooth = smooth
self.size_average = size_average
if self.alpha is None:
self.alpha = torch.ones(self.num_class, 1)
elif isinstance(self.alpha, (list, np.ndarray)):
assert len(self.alpha) == self.num_class
self.alpha = torch.FloatTensor(alpha).view(self.num_class, 1)
self.alpha = self.alpha / self.alpha.sum()
elif isinstance(self.alpha, float):
alpha = torch.ones(self.num_class, 1)
alpha = alpha * (1 - self.alpha)
alpha[balance_index] = self.alpha
self.alpha = alpha
else:
raise TypeError('Not support alpha type')
if self.smooth is not None:
if self.smooth < 0 or self.smooth > 1.0:
raise ValueError('smooth value should be in [0,1]')
def forward(self, input, target):
logit = F.softmax(input, dim=1)
if logit.dim() > 2:
# N,C,d1,d2 -> N,C,m (m=d1*d2*...)
logit = logit.view(logit.size(0), logit.size(1), -1)
logit = logit.permute(0, 2, 1).contiguous()
logit = logit.view(-1, logit.size(-1))
target = target.view(-1, 1)
# N = input.size(0)
# alpha = torch.ones(N, self.num_class)
# alpha = alpha * (1 - self.alpha)
# alpha = alpha.scatter_(1, target.long(), self.alpha)
epsilon = 1e-10
alpha = self.alpha
if alpha.device != input.device:
alpha = alpha.to(input.device)
idx = target.cpu().long()
one_hot_key = torch.FloatTensor(target.size(0), self.num_class).zero_()
one_hot_key = one_hot_key.scatter_(1, idx, 1)
if one_hot_key.device != logit.device:
one_hot_key = one_hot_key.to(logit.device)
if self.smooth:
one_hot_key = torch.clamp(
one_hot_key, self.smooth, 1.0 - self.smooth)
pt = (one_hot_key * logit).sum(1) + epsilon
logpt = pt.log()
gamma = self.gamma
alpha = alpha[idx]
loss = -1 * alpha * torch.pow((1 - pt), gamma) * logpt
if self.size_average:
loss = loss.mean()
else:
loss = loss.sum()
return loss
class BCEFocalLoss(torch.nn.Module):
"""
二分类的Focalloss alpha 固定
"""
def __init__(self, gamma=2, alpha=0.25, reduction='elementwise_mean'):
super().__init__()
self.gamma = gamma
self.alpha = alpha
self.reduction = reduction
def forward(self, _input, target):
pt = torch.sigmoid(_input)
alpha = self.alpha
loss = - alpha * (1 - pt) ** self.gamma * target * torch.log(pt) - \
(1 - alpha) * pt ** self.gamma * (1 - target) * torch.log(1 - pt)
if self.reduction == 'elementwise_mean':
loss = torch.mean(loss)
elif self.reduction == 'sum':
loss = torch.sum(loss)
return loss
|
以上这篇Pytorch 实现focal_loss 多类别和二分类示例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/qq_33278884/article/details/91572173