Pytorch 实现focal_loss 多类别和二分类示例


Posted in Python onJanuary 14, 2020

我就废话不多说了,直接上代码吧!

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三水点靠木。

Python 相关文章推荐
浅谈python多线程和队列管理shell程序
Aug 04 Python
理解python中生成器用法
Dec 20 Python
浅谈python日志的配置文件路径问题
Apr 28 Python
influx+grafana自定义python采集数据和一些坑的总结
Sep 17 Python
Python线性拟合实现函数与用法示例
Dec 13 Python
Apache,wsgi,django 程序部署配置方法详解
Jul 01 Python
基于pytorch 预训练的词向量用法详解
Jan 06 Python
Python 写了个新型冠状病毒疫情传播模拟程序
Feb 14 Python
Python3.6 + TensorFlow 安装配置图文教程(Windows 64 bit)
Feb 24 Python
使用python计算三角形的斜边例子
Apr 15 Python
Python爬虫实现vip电影下载的示例代码
Apr 20 Python
python lambda 表达式形式分析
Apr 03 Python
Python实现钉钉订阅消息功能
Jan 14 #Python
Python Tensor FLow简单使用方法实例详解
Jan 14 #Python
Python利用全连接神经网络求解MNIST问题详解
Jan 14 #Python
基于pytorch的lstm参数使用详解
Jan 14 #Python
Python利用逻辑回归模型解决MNIST手写数字识别问题详解
Jan 14 #Python
np.random.seed() 的使用详解
Jan 14 #Python
下载与当前Chrome对应的chromedriver.exe(用于python+selenium)
Jan 14 #Python
You might like
用PHP调用数据库的存贮过程
2006/10/09 PHP
PHP 批量更新网页内容实现代码
2010/01/05 PHP
php采用session实现防止页面重复刷新
2015/12/24 PHP
php实现无限级分类查询(递归、非递归)
2016/03/10 PHP
laravel框架使用极光推送消息操作示例
2020/02/15 PHP
sina的lightbox效果。
2007/01/09 Javascript
JS时间选择器 兼容IE6,7,8,9
2012/06/26 Javascript
javascript游戏开发之《三国志曹操传》零部件开发(一)让静态人物动起来
2013/01/23 Javascript
javascript-简单的日历实现及Date对象语法介绍(附图)
2013/05/30 Javascript
onbeforeunload与onunload事件异同点总结
2013/06/24 Javascript
window.location.href中url中数据量太大时的解决方法
2013/12/23 Javascript
js实例属性和原型属性示例详解
2014/11/23 Javascript
jQuery中nextAll()方法用法实例
2015/01/07 Javascript
跟我学习javascript的for循环和for...in循环
2015/11/18 Javascript
基于jQuery实现复选框是否选中进行答题提示
2015/12/10 Javascript
原生js实现图片轮播特效
2015/12/18 Javascript
原生js的数组除重复简单实例
2016/05/24 Javascript
JavaScript队列、优先队列与循环队列
2016/11/14 Javascript
easyUI实现类似搜索框关键词自动提示功能示例代码
2016/12/27 Javascript
js 输入框 正则表达式(菜鸟必看教程)
2017/02/19 Javascript
div中文字内容溢出常见的解决方法
2017/03/16 Javascript
vue通过滚动行为实现从列表到详情,返回列表原位置的方法
2018/08/31 Javascript
jQuery实现的模仿雨滴下落动画效果
2018/12/11 jQuery
python通过cookie模拟已登录状态的初步研究
2016/11/09 Python
linux环境下python中MySQLdb模块的安装方法
2017/06/16 Python
Ubuntu16.04/树莓派Python3+opencv配置教程(分享)
2018/04/02 Python
pandas按照列的值排序(某一列或者多列)
2020/12/13 Python
美国最大的在线水培用品商店:GrowersHouse.com
2018/08/14 全球购物
某公司C#程序员面试题笔试题
2014/05/26 面试题
经贸日语专业个人求职信
2013/12/13 职场文书
标准毕业生自荐信
2014/06/24 职场文书
医德医风自我评价
2014/09/19 职场文书
群众路线教育实践活动学习笔记
2014/11/05 职场文书
晚会主持人开场白台词
2015/05/28 职场文书
2016年秋季运动会加油稿
2015/12/21 职场文书
Python Pandas模块实现数据的统计分析的方法
2021/06/24 Python