Dice loss 与CE loss组合代码

以下是将Dice loss和CE loss组合的代码：

import torch.nn.functional as F

class DiceCELoss(nn.Module):
    def __init__(self, weight=None, size_average=True):
        super(DiceCELoss, self).__init__()

    def forward(self, inputs, targets, smooth=1):
        # flatten label and prediction tensors
        inputs = inputs.view(-1)
        targets = targets.view(-1)

        # calculate dice score
        intersection = (inputs * targets).sum()
        dice = (2. * intersection + smooth) / (inputs.sum() + targets.sum() + smooth)

        # calculate cross entropy loss
        ce_loss = F.cross_entropy(inputs, targets)

        # combine dice score and cross entropy loss
        loss = 1 - dice + ce_loss

        return loss.mean()

在这个代码中，我们定义了一个新的损失函数`DiceCELoss`，它将Dice loss和CE loss结合在一起。在`forward`方法中，首先将输入和目标张量展平，然后计算Dice分数并计算交叉熵损失。最后，将Dice分数和交叉熵损失结合在一起，得到最终的损失值。