assert use_sigmoid is True, 'Only sigmoid focal loss supported now.' self.use_sigmoid = use_sigmoid self.gamma = gamma self.alpha = alpha self.reduction = reduction self.loss_weight = loss_weight self.activated = activated def forward(self, pred, target, weight=None, avg_factor=None, red...
use_sigmoid=True, reduction='mean', loss_weight=loss_cls_weight * (num_classes / 80 * 3 / num_det_layers)), # 修改此处实现IoU损失函数的替换 loss_bbox=dict( type='IoULoss', focal=True, iou_mode='ciou', bbox_format='xywh', eps=1e-7, reduction='mean', loss_weight=loss_bbox_...
reduction (str, optional): The method used to reduce the loss into a scalar. Defaults to 'mean'. Options are "none", "mean" and "sum". loss_weight (float, optional): Weight of loss. Defaults to 1.0. """ super(VarifocalLoss, self).__init__() assert use_sigmoid is True, \ 'On...
我将从我自己的思考角度出发,来分析这个问题,最后得到 Focal Loss,也给出我昨晚得到的类似的 loss。 硬截断 整篇文章都是从二分类问题出发,同样的思想可以用于多分类问题。二分类问题的标准 loss 是交叉熵。 其中y∈{0,1} 是真实标签,ŷ 是预测值。当然,对于二分类我们几乎都是用 sigmoid 函数激活 ŷ ...
use_sigmoid (bool, optional): Whether to the prediction is used for sigmoid or softmax. Defaults to True. gamma (float, optional): The gamma for calculating the modulating factor. Defaults to 2.0. alpha (float, optional): A balanced form for Focal Loss. ...
2.Sigmoid Focal Loss 论文中没有用一般多分类任务采取的softmax loss,而是使用了多标签分类中的sigmoid loss(即逐个判断属于每个类别的概率,不要求所有概率的和为1,一个检测框可以属于多个类别),原因是sigmoid的形式训练过程中会更稳定。因此RetinaNet分类subnet输出的通道数是 KA 而不是 (K+1)A(K为类别数,A为...
sigmoid_focal_loss( inputs, axis=- 1, alpha=0.25, gamma=2.0, start_index=0, reduction='valid', **kwargs)[source] Compute the focal loss of sigmoid cross entropy. [Lin et.al, 2017]. Examples: x = dragon.constant([[0.5, 0.5], [...
针对你遇到的错误信息 RuntimeError: sigmoid_focal_loss_forward_impl: implementation for device cuda:0 not found,我们可以从以下几个方面进行分析和解决: 1. 错误信息分析 这个错误信息表明,程序在尝试调用 sigmoid_focal_loss_forward_impl 函数时,无法在 CUDA 设备(这里是 cuda:0)上找到相应的实现。这通常意...
这条比较明显,对于logx来说,如果x过大,logx数值很大,所以对x作sigmoid(x)使得其数值范围在(0,1)之间,log(sigmoid(x))数值也在(0,1)内。 第3条的妙用 如果不设置合适的bias的话,focal loss的表现可能会不如L1或者L2 loss(自己实现过)。 如论文[3]中section 3.3及section 4.1 initialization所述,在训练刚...
use_sigmoid: loss_cls = self.loss_weight * quality_focal_loss( pred, target, score, weight, beta=self.beta, reduction=reduction, avg_factor=avg_factor) else: raise NotImplementedError return loss_cls @LOSSES.register_module class DistributionFocalLoss(nn.Module): def __init__(self, reduction...