因此,αt应该是一个向量,向量的长度等于类别的个数,用于存放各个类别的权重。 2 PyTorch多分类实现 二分类的focal loss比较简单,网上的实现也都比较多,这里不再实现了。主要想实现一下多分类的focal loss主要是因为多分类的确实要比二分类的复杂一些,而且网上的实现五花八门,很多的讲解不够详细,并且可能有错误。
PyTorch中Focal Loss的多类分类实现 1. Focal Loss的基本概念 Focal Loss是一种用于处理类别不平衡问题的损失函数,由Facebook AI Research在2017年提出。它主要用于目标检测任务,但也可以扩展到其他多类分类问题。Focal Loss通过调整标准交叉熵损失函数,使得模型更加关注难分类的样本,同时减少易分类样本对总损失的贡献。
An (unofficial) implementation of Focal Loss, as described in the RetinaNet paper, generalized to the multi-class case. - AdeelH/pytorch-multi-class-focal-loss
这是Focal loss在Pytorch中的实现。 代码语言:javascript 复制 classWeightedFocalLoss(nn.Module):"Non weighted version of Focal Loss"def__init__(self,alpha=.25,gamma=2):super(WeightedFocalLoss,self).__init__()self.alpha=torch.tensor([alpha,1-alpha]).cuda()self.gamma=gamma defforward(self,in...
这是Focal loss在Pytorch中的实现。 classWeightedFocalLoss(nn.Module): "Non weighted version of Focal Loss" def__init__(self,alpha=.25,gamma=2): super(WeightedFocalLoss,self).__init__() self.alpha=torch.tensor([alpha,1-alpha]).cuda() ...
这是Focal loss在Pytorch中的实现。 classWeightedFocalLoss(nn.Module):"Non weighted version of Focal Loss"def__init__(self, alpha=.25, gamma=2):super(WeightedFocalLoss, self).__init__()self.alpha = torch.tensor([alpha,1-alpha])....
criterion = MultiClassFocalLossWithAlpha(alpha=[0.05, 0.05, 0.1,0.2, 0.3, 0.2]) criterion.to(device) 当在状态转换的时候,模型会把自己注册过的参数字典统一移动到cuda或者cpu上,但是会遗漏self.alpha,导致 device 或者 type 不一样的 bug 做出修改 self.register_buffer("alpha", torch.tensor(alpha))#...
1. Focal Loss 1.2 Focal Loss 定义 1.3. Focal Loss 例示 1.4. Focal Loss 求导 2. SoftmaxFocalLoss 求导 Focal Loss 损失函数: 3. Pytorch 实现 FocalLoss-PyTorch 代码语言:javascript 复制 importtorchimporttorch.nnasnnimporttorch.nn.functionalasFclassFocalLoss(nn.Module):def__init__(self,alpha=0.2...
Unified Focal loss (symmetric and asymmetric) For the Pytorch implementation, please take a look at:https://github.com/oikosohn/compound-loss-pytorch Description of the Unified Focal loss The Unified Focal loss is a new compound loss function that unifies Dice-based and cross entropy-based loss...
We still focused on the performance just using our backbone (FE-HybridSN) with CE loss. In the final experiment, we compared the convergence speed of different loss functions, including cross-entropy (CE) loss, multiclass hinge (MCH) loss, and focal loss, using training samples of the same...