我就废话不多说了，直接上代码吧！

import numpy as npimport torchimport torch.nn as nnimport 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 < 0 or self.smooth > 1.0: raise ValueError('smooth value should be in [0,1]') def forward(self, input, target): logit = F.softmax(input, dim=1) 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

以上这篇Pytorch 实现focal_loss 多类别和二分类示例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。