pytorch--自定义loss(-log BinaryCrossEntropy FocalLoss)

1. 负log loss；
2. binary crossentropy；
3. focal loss；

网上找到的loss写的都普遍复杂，我自己稍微写的逻辑简单一点。

if inputs.is_cuda and not self.alpha.is_cuda:
            self.alpha = self.alpha.cuda()

focal loss

focal loss仔细实践起来可以分为两种情况，一种是二分类（sigmoid**）的时候，还有一种情况就是多分类（softmax**）的时候。

二分类focal loss

class FocalLoss(nn.Module):
    """ -[alpha*y*(1-p)^gamma*log(p)+(1-alpha)(1-y)*p^gamma*log(1-p)] loss"""

    def __init__(self, gamma, alpha=None , onehot=False):
        super(FocalLoss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.onehot = onehot


    def forward(self, inputs, targets):
        """

        :param input: onehot
        :param target: 默认是onehot以后
        :return:
        """
        N = inputs.size(0)
        C = inputs.size(1)
        inputs = torch.clamp(inputs, min=0.001, max=1.0)  ##将一个张量中的数值限制在一个范围内，如限制在[0.1,1.0]范围内，可以避免一些运算错误，如预测结果q中元素可能为0
        if inputs.is_cuda and not self.alpha.is_cuda:
            self.alpha = self.alpha.cuda()    

         if not self.onehot:
            class_mask = inputs.data.new(N, C).fill_(0)
            class_mask = Variable(class_mask)
            ids = targets.view(-1, 1)
            class_mask.scatter_(1, ids.data, 1.)
            targets = class_mask

        pos_sample_loss_matrix = -targets * (torch.pow((1 - inputs), self.gamma)) * inputs.log()  ## 正样本的loss
        # mean_pos_sample_loss = pos_sample_loss_matrix.sum() / targets.sum()

        neg_sample_loss_matrix = -(targets == 0).float() * (torch.pow((inputs), self.gamma)) * (1 - inputs).log()  ## 负样本的loss
        # mean_neg_sample_loss = pos_sample_loss_matrix.sum() / targets.sum()
        if self.alpha:
            return (self.alpha * pos_sample_loss_matrix + (1 - self.alpha) * neg_sample_loss_matrix).sum() / (N * C)
        else:
            return (pos_sample_loss_matrix + neg_sample_loss_matrix).sum() / (N * C)

多分类focal loss

class FocalLoss(nn.Module):
    """ -[y*(1-p)^gamma*log(p) loss
        softmax**输入的foacl loss。
    """

    def __init__(self, gamma, onehot=False):
        super(FocalLoss, self).__init__()

        self.gamma = gamma
        self.onehot = onehot


    def forward(self, inputs, targets):
        """

        :param input: onehot
        :param target: 默认是onehot以后
        :return:
        """

        inputs = torch.clamp(inputs, min=0.001, max=1.0)  ##将一个张量中的数值限制在一个范围内，如限制在[0.1,1.0]范围内，可以避免一些运算错误，如预测结果q中元素可能为0

         if not self.onehot:
            N = inputs.size(0)
            C = inputs.size(1)
            class_mask = inputs.data.new(N, C).fill_(0)
            class_mask = Variable(class_mask)
            ids = targets.view(-1, 1)
            class_mask.scatter_(1, ids.data, 1.)
            targets = class_mask

        pos_sample_loss_matrix = -targets * (torch.pow((1 - inputs), self.gamma)) * inputs.log()  ## 正样本的loss
        # mean_pos_sample_loss = pos_sample_loss_matrix.sum() / targets.sum()

        ## 默认输出均值
        ## 这里不能直接求mean，
        # 因为整个矩阵还是原来的输入大小的，
        # 求loss应该是除以label中有目标的总数。
        
        return pos_sample_loss_matrix / targets.sum()
        

代码

• NegtiveLogLoss
• BinaryCrossEntropy

import torch
import torch.nn as nn
from torch.autograd import Variable

class NegtiveLogLoss(nn.Module):
   """ -log(p) loss"""

   def __init__(self, onehot=False):
       super(NegtiveLogLoss, self).__init__()
       self.onehot = onehot

   def forward(self, inputs, targets):
       """

       :param input: onehot
       :param target: 默认是onehot以后
       :return:
       """

       inputs = torch.clamp(inputs, min=0.001, max=1.0)  ## 将一个张量中的数值限制在一个范围内，如限制在[0.1,1.0]范围内，可以避免一些运算错误，如预测结果q中元素可能为0

       if not self.onehot:
           N = inputs.size(0)
           C = inputs.size(1)
           class_mask = inputs.data.new(N, C).fill_(0)
           class_mask = Variable(class_mask)
           ids = targets.view(-1, 1)
           class_mask.scatter_(1, ids.data, 1.)
           targets = class_mask

       loss_matrix = -targets * inputs.log()  ## 对预测的矩阵里面的每个元素做log，
       ## 然后乘以one hot的label，也就是说获得1位置的值了。
       ## 这时候还是个矩阵，还没有计算均值
       ## 默认输出均值
       return loss_matrix.sum() / targets.sum()  ## 这里不能直接求mean，
       # 因为整个矩阵还是原来的输入大小的，
       # 求loss应该是除以label中有目标的总数。



class BinaryCrossEntropy(nn.Module):
   """ -(ylog(p)+(1-y)log(1-p) loss"""

   def __init__(self, alpha=None, onehot=False):
       super(BinaryCrossEntropy, self).__init__()
       self.alpha = alpha
       self.onehot = onehot

   def forward(self, inputs, targets):
       """

       :param input: onehot
       :param target: 默认是onehot以后
       :return:
       """
       N = inputs.size(0)
       C = inputs.size(1)
       inputs = torch.clamp(inputs, min=0.001, max=1.0)  ##将一个张量中的数值限制在一个范围内，如限制在[0.1,1.0]范围内，可以避免一些运算错误，如预测结果q中元素可能为0
       
       if not self.onehot:

           class_mask = inputs.data.new(N, C).fill_(0)
           class_mask = Variable(class_mask)
           ids = targets.view(-1, 1)
           class_mask.scatter_(1, ids.data, 1.)
           targets = class_mask

       pos_sample_loss_matrix = -targets * inputs.log()  ## 正样本的loss
       # mean_pos_sample_loss = pos_sample_loss_matrix.sum() / targets.sum()
      
       neg_sample_loss_matrix = -(targets == 0).float() * (1 - inputs).log()  ## 负样本的loss
       # mean_neg_sample_loss = pos_sample_loss_matrix.sum() / targets.sum()

       if self.alpha:
           return (self.alpha*pos_sample_loss_matrix + (1-self.alpha)*neg_sample_loss_matrix).sum() / (N * C)
       else:
           return (pos_sample_loss_matrix + neg_sample_loss_matrix).sum() / (N * C)  

引用

• http://kodgv.xyz/2019/04/22/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/FocalLoss%E9%92%88%E5%AF%B9%E4%B8%8D%E5%B9%B3%E8%A1%A1%E6%95%B0%E6%8D%AE/