首先,明确一下loss函数的输入: 一个pred,shape为(bs, num_classes),并且未经过softmax; 一个target,shape为(bs),也就是一个向量,并且未经过one_hot编码。 通过前面的公式可以得出,我们需要在loss实现是做三件事情: 找到当前batch内每个样本对应的类别标签,然后根据预先设置好的alpha值给每个样本分配类别权重 ...
alpha=[0.0,0.1,0.2],gamma=2,num_classes=3,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, ....
这时候思路就很明显了,要想“软化”这个 loss,就得“软化”θ(x),而软化它就再容易不过,它就是 sigmoid 函数(不懂可以去看sigmoid图像)。我们有: 所以很显然,我们将θ(x)替换为σ(Kx)即可: 现在跟 Focal Loss 做个比较。 Focal Loss Kaiming 大神的 Focal Loss 形式是: 如果落实到ŷ =σ(x)这个预...
loss = self.alpha * (1 - pred_prob) ** self.gamma * ce_loss 根据需要选择损失计算方式 if self.reduction == 'mean':loss = loss.mean()elif self.reduction == 'sum':loss = loss.sum()return loss
Focal_Loss= -1*alpha*(1-pt)^gamma*log(pt) :param num_class: :param alpha: (tensor) 3D or 4D the scalar factor for this criterion :param gamma: (float,double) gamma > 0 reduces the relative loss for well-classified examples (p>0.5) putting more ...
:param size_average: (bool, optional) By default, the losses are averaged over each loss element...
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Pytorch实现focal_loss多类别和⼆分类⽰例我就废话不多说了，直接上代码吧！import numpy as np import torch import torch.nn as nn import torch.nn.functional as F # ⽀持多分类和⼆分类 class FocalLoss(nn.Module):"""This is a implementation of Focal Loss with smooth label cross entropy ...
super(FocalLoss, self).__init__() self.num_class = num_class self.alpha = alpha self.gamma = gamma self.smooth = smooth self.size_average = size_average if self.alpha is None: self.alpha = torch.ones(self.num_class, 1) elif isinstance(self.alpha, (list, np.ndarray)): ...
import torch.nn.functional as F # 支持多分类和二分类 class FocalLoss(nn.Module): """ This is a implementation of Focal Loss with smooth label cross entropy supported which is proposed in 'Focal Loss for Dense Object Detection. (htt