Focal Loss及代码 |
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Focal Loss及代码
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Focal Loss处理样本不平衡
前言样本不平衡的解释在交叉熵上的改进pytorch代码
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前言
《Focal Loss for Dense Object Detection》 论文地址：https://arxiv.org/pdf/1708.02002.pdf ICCV2017 ，Focal Loss虽然一开始提出是为了解决单阶段目标检测算法中的样本不平衡问题，但是照样是可以用在其它样本不平衡的任务之中。 样本不平衡的解释相比于大段文字，我更喜欢用简洁直观的图来概括：
Focal Loss就是基于上述分析，加入了两个权重而已。 乘了权重之后，容易样本所得到的loss就变得更小： 同理，多分类也是乘以这样两个系数。 对于one-hot的编码形式来说：最后都是计算这样一个结果： Focal_Loss= -1*alpha*(1-pt)^gamma*log(pt) pytorch代码 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 supported which is proposed in 'Focal Loss for Dense Object Detection. (https://arxiv.org/abs/1708.02002)' 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 focus on hard misclassified example :param smooth: (float,double) smooth value when cross entropy :param balance_index: (int) balance class index, should be specific when alpha is float :param size_average: (bool, optional) By default, the losses are averaged over each loss element in the batch. """ def __init__(self, num_class, alpha=None, gamma=2, balance_index=-1, smooth=None, size_average=True): 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)): assert len(self.alpha) == self.num_class self.alpha = torch.FloatTensor(alpha).view(self.num_class, 1) self.alpha = self.alpha / self.alpha.sum() elif isinstance(self.alpha, float): alpha = torch.ones(self.num_class, 1) alpha = alpha * (1 - self.alpha) alpha[balance_index] = self.alpha self.alpha = alpha else: raise TypeError('Not support alpha type') if self.smooth is not None: if self.smooth 1.0: raise ValueError('smooth value should be in [0,1]') def forward(self, input, target): logit = F.softmax(input, dim=1) #这里看情况选择，如果之前softmax了，后续就不用了 if logit.dim() > 2: # N,C,d1,d2 -> N,C,m (m=d1*d2*...) logit = logit.view(logit.size(0), logit.size(1), -1) logit = logit.permute(0, 2, 1).contiguous() logit = logit.view(-1, logit.size(-1)) target = target.view(-1, 1) # N = input.size(0) # alpha = torch.ones(N, self.num_class) # alpha = alpha * (1 - self.alpha) # alpha = alpha.scatter_(1, target.long(), self.alpha) epsilon = 1e-10 alpha = self.alpha if alpha.device != input.device: alpha = alpha.to(input.device) idx = target.cpu().long() one_hot_key = torch.FloatTensor(target.size(0), self.num_class).zero_() one_hot_key = one_hot_key.scatter_(1, idx, 1) if one_hot_key.device != logit.device: one_hot_key = one_hot_key.to(logit.device) if self.smooth: one_hot_key = torch.clamp( one_hot_key, self.smooth, 1.0 - self.smooth) pt = (one_hot_key * logit).sum(1) + epsilon logpt = pt.log() gamma = self.gamma alpha = alpha[idx] loss = -1 * alpha * torch.pow((1 - pt), gamma) * logpt if self.size_average: loss = loss.mean() else: loss = loss.sum() return loss class BCEFocalLoss(torch.nn.Module): """ 二分类的Focalloss alpha 固定 """ def __init__(self, gamma=2, alpha=0.25, reduction='elementwise_mean'): super().__init__() self.gamma = gamma self.alpha = alpha self.reduction = reduction def forward(self, _input, target): pt = torch.sigmoid(_input) alpha = self.alpha loss = - alpha * (1 - pt) ** self.gamma * target * torch.log(pt) - \ (1 - alpha) * pt ** self.gamma * (1 - target) * torch.log(1 - pt) if self.reduction == 'elementwise_mean': loss = torch.mean(loss) elif self.reduction == 'sum': loss = torch.sum(loss) return loss |
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