Got different results when running compiled version of torch.nn.functional.multilabel_margin_loss. Reproducer: import torch dtype = torch.float32 C = 6 N = 2 reduction = "none" #backend = "eager" # this works backend = "aot_eager" # this fails def func(x, y, reduction): result = ...
size_average=None,reduce=None,reduction='mean'):super(MultiLabelMarginLoss,self).__init__(size_average,reduce,reduction)defforward(self,input,target):returnF.multilabel_margin_loss(input,target,reduction=self.reduction)
例子: >>>loss = nn.MultiLabelMarginLoss()>>>x = torch.FloatTensor([[0.1,0.2,0.4,0.8]])>>># for target y, only consider labels 3 and 0, not after label -1>>>y = torch.LongTensor([[3,0,-1,1]])>>>loss(x, y)>>># 0.25 * ((1-(0.1-0.2)) + (1-(0.1-0.4)) + (1...
1、MultiLabelSoftMarginLoss原理MultiLabelSoftMarginLoss针对multi-labelone-versus-all(多分类,且每个样本只能属于一个类)的情形。loss的计算公式如下:其中,x是模型预测的标签,x的shape是(N,C),N表示batchsize,C是分类数;y是真实标签,shape也是(N,C),。的值域是(0,);的值域 ...
示例2: set_loss_margin # 需要导入模块: from torch import nn [as 别名]# 或者: from torch.nn importMultiLabelMarginLoss[as 别名]defset_loss_margin(self, scores, gold_mask, margin):"""Since the pytorch built-inMultiLabelMarginLossfixes the margin as 1. ...
1、可以仔细查看公式，两个Loss在BCEWithLogitsLoss的weight为1的时候是一样的 2、可以简单跑一个demo...
1、MultiLabelSoftMarginLoss原理 MultiLabelSoftMarginLoss针对multi-label one-versus-all(多分类,且每个样本只能属于一个类)的情形。 loss的计算公式如下: 其中,x是模型预测的标签,x的shape是(N,C),N表示batch size,C是分类数;y是真实标签,shape也是(N,C),。 的值域是(0,); 的值域是... ...
Multi-label Margin Loss Measuremlconfmat
Tensors and Dynamic neural networks in Python with strong GPU acceleration - MultiLabelMarginLoss with customized margin. · pytorch/pytorch@13bd125
label = torch.from_numpy(label).float() ## 通过BCEWithLogitsLoss直接计算输入值(pick) crition1 = torch.nn.BCEWithLogitsLoss() loss1 = crition1(pred, label) print(loss1) crition2 = torch.nn.MultiLabelSoftMarginLoss() loss2 = crition2(pred, label) ...