extra_loss.py

# -*- coding: utf-8 -*-
# @Time    : 2021/7/11 12:32 下午
# @Author  : Bubble
# @FileName: extra_loss.py

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


class LabelSmoothingLoss(nn.Module):
    """
    NLL loss with label smoothing.
    """

    def __init__(self, smoothing=0.01):
        """
        Constructor for the LabelSmoothing module.
        :param smoothing: label smoothing factor
        """
        super(LabelSmoothingLoss, self).__init__()
        self.confidence = 1.0 - smoothing
        self.smoothing = smoothing

    def forward(self, x, target):
        logprobs = torch.nn.functional.log_softmax(x, dim=-1)

        nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1))
        nll_loss = nll_loss.squeeze(1)
        smooth_loss = -logprobs.mean(dim=-1)
        loss = self.confidence * nll_loss + self.smoothing * smooth_loss
        return loss.mean()


class FocalLoss(nn.Module):
    def __init__(self, alpha=0.25, gamma=2, num_classes=2, size_average=True):
        """
        focal_loss损失函数, -α(1-yi)**γ *ce_loss(xi,yi)
        步骤详细的实现了 focal_loss损失函数.
        :param alpha:   阿尔法α,类别权重.      当α是列表时,为各类别权重,当α为常数时,类别权重为[α, 1-α, 1-α, ....],常用于 目标检测算法中抑制背景类 , retainnet中设置为0.25
        :param gamma:   伽马γ,难易样本调节参数. retainnet中设置为2
        :param num_classes:     类别数量
        :param size_average:    损失计算方式,默认取均值
        """
        super(FocalLoss, self).__init__()
        self.size_average = size_average
        if isinstance(alpha, list):
            assert len(alpha) == num_classes  # α可以以list方式输入,size:[num_classes] 用于对不同类别精细地赋予权重
            #             print(" --- Focal_loss alpha = {}, 将对每一类权重进行精细化赋值 --- ".format(alpha))
            self.alpha = torch.Tensor(alpha)
        else:
            assert alpha < 1  # 如果α为一个常数,则降低第一类的影响,在目标检测中为第一类
            #             print(" --- Focal_loss alpha = {} ,将对背景类进行衰减,请在目标检测任务中使用 --- ".format(alpha))
            self.alpha = torch.zeros(num_classes)
            self.alpha[0] += alpha
            self.alpha[1:] += (1 - alpha)  # α 最终为 [ α, 1-α, 1-α, 1-α, 1-α, ...] size:[num_classes]

        self.gamma = gamma

    def forward(self, preds, labels):
        """
        focal_loss损失计算
        :param preds:   预测类别. size:[B,N,C] or [B,C]    分别对应与检测与分类任务, B 批次, N检测框数, C类别数
        :param labels:  实际类别. size:[B,N] or [B]
        :return:
        """
        # assert preds.dim()==2 and labels.dim()==1
        preds = preds.view(-1, preds.size(-1))
        self.alpha = self.alpha.to(preds.device)
        preds_logsoft = F.log_softmax(preds, dim=1)  # log_softmax
        preds_softmax = torch.exp(preds_logsoft)  # softmax

        preds_softmax = preds_softmax.gather(1, labels.view(-1, 1))  # 这部分实现nll_loss ( crossempty = log_softmax + nll )
        preds_logsoft = preds_logsoft.gather(1, labels.view(-1, 1))
        self.alpha = self.alpha.gather(0, labels.view(-1))
        loss = -torch.mul(torch.pow((1 - preds_softmax), self.gamma),
                          preds_logsoft)  # torch.pow((1-preds_softmax), self.gamma) 为focal loss中 (1-pt)**γ

        loss = torch.mul(self.alpha, loss.t())
        if self.size_average:
            loss = loss.mean()
        else:
            loss = loss.sum()
        return loss