Large Margin aware Focal (LMF) 损失函数是 Focal 损失函数和 LDAM损失函数加权的线性组合，帮我用pytorch代码实现LMF损失函数

好的，下面是用PyTorch实现LMF损失函数的代码：

import torch
import torch.nn as nn


class LMF_Loss(nn.Module):
    def __init__(self, gamma=2, alpha=None, beta=None):
        super(LMF_Loss, self).__init__()
        self.gamma = gamma
        self.alpha = alpha
        self.beta = beta

    def forward(self, input, target):
        ce_loss = nn.functional.cross_entropy(input, target, reduction='none')
        pt = torch.exp(-ce_loss)
        focal_loss = (1 - pt) ** self.gamma * ce_loss

        if self.alpha is not None and self.beta is not None:
            # get unique classes
            classes = torch.unique(target)

            # calculate class weights
            N = len(target)
            n_classes = len(classes)
            weights = torch.zeros(n_classes).to(target.device)

            for c in classes:
                samples_per_class = (target == c).sum()
                if samples_per_class == 0:
                    continue
                weights[c] = (N - samples_per_class) / (N * (n_classes - 1))

            alpha_factor = torch.ones_like(target).to(target.device)
            beta_factor = torch.ones_like(target).to(target.device)

            for c in classes:
                alpha_factor[target == c] *= self.alpha[weights[c] > 0]
                beta_factor[target == c] *= self.beta[weights[c] > 0]

            weighted_focal_loss = alpha_factor * beta_factor * focal_loss
            loss = weighted_focal_loss.mean()
        else:
            loss = focal_loss.mean()

        return loss

其中，`gamma`为Focal损失函数中的超参数，`alpha`和`beta`为LDAM损失函数中的权重，若不使用LDAM，则置为`None`。

使用方法如下：

criterion = LMF_Loss(gamma=2, alpha=None, beta=None)
loss = criterion(output, target)