我就废话不多说了，直接上代码吧！

import torchimport torch.nn.functional as Fimport numpy as npfrom torch.autograd import Variable'''pytorch实现focal loss的两种方式(现在讨论的是基于分割任务)在计算损失函数的过程中考虑到类别不平衡的问题，假设加上背景类别共有6个类别'''def compute_class_weights(histogram): classWeights = np.ones(6, dtype=np.float32) normHist = histogram / np.sum(histogram) for i in range(6): classWeights[i] = 1 / (np.log(1.10 + normHist[i])) return classWeightsdef focal_loss_my(input,target): ''' :param input: shape [batch_size,num_classes,H,W] 仅仅经过卷积操作后的输出，并没有经过任何激活函数的作用 :param target: shape [batch_size,H,W] :return: ''' n, c, h, w = input.size() target = target.long() input = input.transpose(1, 2).transpose(2, 3).contiguous().view(-1, c) target = target.contiguous().view(-1) number_0 = torch.sum(target == 0).item() number_1 = torch.sum(target == 1).item() number_2 = torch.sum(target == 2).item() number_3 = torch.sum(target == 3).item() number_4 = torch.sum(target == 4).item() number_5 = torch.sum(target == 5).item() frequency = torch.tensor((number_0, number_1, number_2, number_3, number_4, number_5), dtype=torch.float32) frequency = frequency.numpy() classWeights = compute_class_weights(frequency) ''' 根据当前给出的ground truth label计算出每个类别所占据的权重 ''' # weights=torch.from_numpy(classWeights).float().cuda() weights = torch.from_numpy(classWeights).float() focal_frequency = F.nll_loss(F.softmax(input, dim=1), target, reduction='none') ''' 上面一篇博文讲过 F.nll_loss(torch.log(F.softmax(inputs, dim=1)，target)的函数功能与F.cross_entropy相同 可见F.nll_loss中实现了对于target的one-hot encoding编码功能，将其编码成与input shape相同的tensor 然后与前面那一项（即F.nll_loss输入的第一项）进行 element-wise production 相当于取出了 log(p_gt)即当前样本点被分类为正确类别的概率 现在去掉取log的操作，相当于 focal_frequency shape [num_samples] 即取出ground truth类别的概率数值，并取了负号 ''' focal_frequency += 1.0#shape [num_samples] 1-P（gt_classes） focal_frequency = torch.pow(focal_frequency, 2) # torch.Size([75]) focal_frequency = focal_frequency.repeat(c, 1) ''' 进行repeat操作后，focal_frequency shape [num_classes,num_samples] ''' focal_frequency = focal_frequency.transpose(1, 0) loss = F.nll_loss(focal_frequency * (torch.log(F.softmax(input, dim=1))), target, weight=None, reduction='elementwise_mean') return lossdef focal_loss_zhihu(input, target): ''' :param input: 使用知乎上面大神给出的方案 https://zhuanlan.zhihu.com/p/28527749 :param target: :return: ''' n, c, h, w = input.size() target = target.long() inputs = input.transpose(1, 2).transpose(2, 3).contiguous().view(-1, c) target = target.contiguous().view(-1) N = inputs.size(0) C = inputs.size(1) number_0 = torch.sum(target == 0).item() number_1 = torch.sum(target == 1).item() number_2 = torch.sum(target == 2).item() number_3 = torch.sum(target == 3).item() number_4 = torch.sum(target == 4).item() number_5 = torch.sum(target == 5).item() frequency = torch.tensor((number_0, number_1, number_2, number_3, number_4, number_5), dtype=torch.float32) frequency = frequency.numpy() classWeights = compute_class_weights(frequency) weights = torch.from_numpy(classWeights).float() weights=weights[target.view(-1)]#这行代码非常重要 gamma = 2 P = F.softmax(inputs, dim=1)#shape [num_samples,num_classes] class_mask = inputs.data.new(N, C).fill_(0) class_mask = Variable(class_mask) ids = target.view(-1, 1) class_mask.scatter_(1, ids.data, 1.)#shape [num_samples,num_classes] one-hot encoding probs = (P * class_mask).sum(1).view(-1, 1)#shape [num_samples,] log_p = probs.log() print('in calculating batch_loss',weights.shape,probs.shape,log_p.shape) # batch_loss = -weights * (torch.pow((1 - probs), gamma)) * log_p batch_loss = -(torch.pow((1 - probs), gamma)) * log_p print(batch_loss.shape) loss = batch_loss.mean() return lossif __name__=='__main__': pred=torch.rand((2,6,5,5)) y=torch.from_numpy(np.random.randint(0,6,(2,5,5))) loss1=focal_loss_my(pred,y) loss2=focal_loss_zhihu(pred,y) print('loss1',loss1) print('loss2', loss2)'''in calculating batch_loss torch.Size([50]) torch.Size([50, 1]) torch.Size([50, 1])torch.Size([50, 1])loss1 tensor(1.3166)loss2 tensor(1.3166)'''

以上这篇pytorch实现focal loss的两种方式小结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。