pytorch代码实现训练损失函数使用Focal Loss 损失函数

好的，以下是使用Pytorch实现Focal Loss损失函数的代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class FocalLoss(nn.Module):
    def __init__(self, gamma=2, alpha=None, size_average=True):
        super(FocalLoss, self).__init__()
        self.gamma = gamma
        self.alpha = alpha
        if isinstance(alpha, (float, int)): self.alpha = torch.Tensor([alpha, 1 - alpha])
        if isinstance(alpha, list): self.alpha = torch.Tensor(alpha)
        self.size_average = size_average

    def forward(self, input, target):
        if input.dim() > 2:
            input = input.view(input.size(0), input.size(1), -1)  # N,C,H,W => N,C,H*W
            input = input.transpose(1, 2)  # N,C,H*W => N,H*W,C
            input = input.contiguous().view(-1, input.size(2))  # N,H*W,C => N*H*W,C
        target = target.view(-1, 1)

        logpt = F.log_softmax(input)
        logpt = logpt.gather(1, target)
        logpt = logpt.view(-1)
        pt = logpt.data.exp()

        if self.alpha is not None:
            if self.alpha.type() != input.data.type():
                self.alpha = self.alpha.type_as(input.data)
            at = self.alpha.gather(0, target.data.view(-1))
            logpt = logpt * at

        loss = -1 * (1 - pt) ** self.gamma * logpt
        if self.size_average: return loss.mean()
        else: return loss.sum()

其中，`gamma`是超参数，控制着难易样本的权重，一般默认为2；`alpha`是类别权重系数，可以为None、float、int或list类型；`size_average`控制是否对每个样本的损失求平均，默认为True。

使用时，只需在训练代码中调用该损失函数即可：

loss_fn = FocalLoss(gamma=2, alpha=[0.25, 0.75])
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(num_epochs):
    for images, labels in train_loader:
        images = images.to(device)
        labels = labels.to(device)

        outputs = model(images)
        loss = loss_fn(outputs, labels)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()