pytorch如何使用Focal Loss

Focal loss 是文章 Focal Loss for Dense Object Detection 中提出对简单样本的进行decay的一种损失函数。是对标准的Cross Entropy Loss 的一种改进。 FL对于简单样本（p比较大）回应较小的loss。如论文中的图1，在p=0.6时，标准的CE然后又较大的loss，但是对于FL就有相对较小的loss回应。这样就是对简单样本的一种decay。其中alpha 是对每个类别在训练数据中的频率有关，但是下面的实现我们是基于alpha=1进行实验的。

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在PyTorch中使用Focal Loss，你可以按照以下步骤进行操作

方法一：

1、创建FocalLoss.py文件，添加一下代码

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代码修改处：

classnum 处改为你分类的数量
P = F.softmax(inputs) 改为 P = F.softmax(inputs，dim=1)

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

class FocalLoss(nn.Module):
    r"""
        This criterion is a implemenation of Focal Loss, which is proposed in 
        Focal Loss for Dense Object Detection.

            Loss(x, class) = - \alpha (1-softmax(x)[class])^gamma \log(softmax(x)[class])

        The losses are averaged across observations for each minibatch.

        Args:
            alpha(1D Tensor, Variable) : the scalar factor for this criterion
            gamma(float, double) : gamma > 0; reduces the relative loss for well-classiﬁed examples (p > .5), 
                                   putting more focus on hard, misclassiﬁed examples
            size_average(bool): By default, the losses are averaged over observations for each minibatch.
                                However, if the field size_average is set to False, the losses are
                                instead summed for each minibatch.


    """
    def __init__(self, class_num=5, alpha=None, gamma=2, size_average=True):
        super(FocalLoss, self).__init__()
        if alpha is None:
            self.alpha = Variable(torch.ones(class_num, 1))
        else:
            if isinstance(alpha, Variable):
                self.alpha = alpha
            else:
                self.alpha = Variable(alpha)
        self.gamma = gamma
        self.class_num = class_num
        self.size_average = size_average

    def forward(self, inputs, targets):
        N = inputs.size(0)
        C = inputs.size(1)
        P = F.softmax(inputs)

        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.)
        #print(class_mask)


        if inputs.is_cuda and not self.alpha.is_cuda:
            self.alpha = self.alpha.cuda()
        alpha = self.alpha[ids.data.view(-1)]

        probs = (P*class_mask).sum(1).view(-1,1)

        log_p = probs.log()
        #print('probs size= {}'.format(probs.size()))
        #print(probs)

        batch_loss = -alpha*(torch.pow((1-probs), self.gamma))*log_p 
        #print('-----bacth_loss------')
        #print(batch_loss)


        if self.size_average:
            loss = batch_loss.mean()
        else:
            loss = batch_loss.sum()
        return loss

2、在你的训练函数里加入模块

from FocalLoss import FocalLoss

loss = FocalLoss()

方法二：

首先，确保你已经导入了torch和torch.nn模块，其中torch.nn提供了各种常见的损失函数。

import torch
import torch.nn as nn

然后，定义一个自定义的Focal Loss类，继承自torch.nn.Module。在类的构造函数中，可以指定Focal Loss所需的参数，例如γ（调节因子）和权重。

class FocalLoss(nn.Module):
    def __init__(self, gamma=2, weight=None):
        super(FocalLoss, self).__init__()
        self.gamma = gamma
        self.weight = weight

    def forward(self, inputs, targets):
        ce_loss = nn.CrossEntropyLoss(weight=self.weight)(inputs, targets)  # 使用交叉熵损失函数计算基础损失
        pt = torch.exp(-ce_loss)  # 计算预测的概率
        focal_loss = (1 - pt) ** self.gamma * ce_loss  # 根据Focal Loss公式计算Focal Loss
        return focal_loss

接下来，在模型训练时，使用自定义的Focal Loss替代交叉熵损失函数即可。

# 定义模型
model = YourModel()

# 定义损失函数（使用自定义的Focal Loss）
criterion = FocalLoss(gamma=2, weight=None)

# 初始化优化器等

# 开始训练循环
for epoch in range(num_epochs):
    # 前向传播、计算损失
    outputs = model(inputs)
    loss = criterion(outputs, targets)

    # 反向传播、更新模型参数
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    # 其他操作（如打印训练日志等）