Focal 损失函数代码如下：def focal_loss(input_values, gamma): """Computes the focal loss""" p = torch.exp(-input_values) loss = (1 - p) ** gamma * input_values return loss.mean() class FocalLoss(nn.Module): def init(self, weight=None, gamma=0.): super(FocalLoss, self).init() assert gamma >= 0 self.gamma = gamma self.weight = weight def forward(self, input, target): return focal_loss(F.cross_entropy(input, target, reduction='none', weight=self.weight), self.gamma) LDAM损失函数代码如下：class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s self.weight = weight def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index.scatter_(1, target.data.view(-1, 1), 1) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1)) batch_m = batch_m.view((-1, 1)) x_m = x - batch_m output = torch.where(index, x_m, x) return F.cross_entropy(self.s*output, target, weight=self.weight) 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损失函数并在模型中使用

LMF损失函数的代码如下：

class LMF_Loss(nn.Module):
    def __init__(self, cls_num_list, max_m=0.5, weight=None, s=30, gamma=2.0, alpha=0.25):
        super(LMF_Loss, self).__init__()
        self.focal_loss = FocalLoss(weight=weight, gamma=gamma)
        self.ldam_loss = LDAMLoss(cls_num_list=cls_num_list, max_m=max_m, weight=weight, s=s)
        self.alpha = alpha

    def forward(self, x, target):
        focal_loss = self.focal_loss(x, target)
        ldam_loss = self.ldam_loss(x, target)
        output = self.alpha * ldam_loss + (1 - self.alpha) * focal_loss
        return output

在模型中使用时，可以将LMF_Loss作为损失函数进行指定。

例如，如果你的模型是一个分类模型，可以这样定义：

model = MyModel()
criterion = LMF_Loss(cls_num_list=[class0_num, class1_num, class2_num], max_m=0.5, weight=class_weights, s=30, gamma=2.0, alpha=0.25)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

for epoch in range(num_epochs):
    # Forward pass
    outputs = model(inputs)
    loss = criterion(outputs, labels)

    # Backward and optimize
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

其中，`cls_num_list`是一个列表，包含每个类别的样本数量，`max_m`是LDAM损失函数中的超参数，`weight`是样本权重，`s`是LDAM损失函数中的超参数，`gamma`是Focal损失函数中的超参数，`alpha`是LMF损失函数中的超参数，控制两个损失函数的加权。