回归问题的焦点损失？

Question

我有一个训练组的回归问题，可以认为这是不平衡的。因此，我想创建一个加权损失函数，该损失函数重视艰难和简单的例子的损失贡献，而硬性示例具有更大的贡献。

我知道这是可能的加权损失类型，因为它在使用焦点损失时实现了。

我的问题是，是否可以使用L1丢失和线性输出层转换基于回归问题的焦点损失？

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.cuda.amp as amp


##

class FocalLoss(nn.Module):

    def __init__(self,
                 alpha=0.25,
                 gamma=2,
                 reduction='mean',):
        super(FocalLoss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.reduction = reduction
        self.crit = nn.BCEWithLogitsLoss(reduction='none')

    def forward(self, logits, label):
        '''
        Usage is same as nn.BCEWithLogits:
            >>> criteria = FocalLoss()
            >>> logits = torch.randn(8, 19, 384, 384)
            >>> lbs = torch.randint(0, 2, (8, 19, 384, 384)).float()
            >>> loss = criteria(logits, lbs)
        '''
        probs = torch.sigmoid(logits)
        coeff = torch.abs(label - probs).pow(self.gamma).neg()
        log_probs = torch.where(logits >= 0,
                F.softplus(logits, -1, 50),
                logits - F.softplus(logits, 1, 50))
        log_1_probs = torch.where(logits >= 0,
                -logits + F.softplus(logits, -1, 50),
                -F.softplus(logits, 1, 50))
        loss = label * self.alpha * log_probs + (1. - label) * (1. - self.alpha) * log_1_probs
        loss = loss * coeff

        if self.reduction == 'mean':
            loss = loss.mean()
        if self.reduction == 'sum':
            loss = loss.sum()
        return loss