template<typename Dtype>void SmoothL1LossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>&top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>&bottom) {// after forwards, diff_ holds w_in * (b0 -b1) int count=diff_.count(); //调用反向smoothloss,diff_.gpu_data(...
SmoothL1Loss的初步学习理解 主要参考了这一篇和这一篇博客 另外附上L1损失函数代码: def_smooth_l1_loss(x,t,in_weight,sigma):#sigma=1sigma2=sigma**2diff=in_weight*(x-t)abs_diff=diff.abs()flag=(abs_diff.data<(1./sigma2)).float()y=(flag*(sigma2/2.)*(diff**2)+(1-flag)*(abs_...
1、F.smooth_l1_loss import torch import torch.nn.functional as F # 自己设计的smooth_l1_loss def smooth_l1_loss(a, b): loss_part1 = torch.abs(a - b) loss_part2 = loss_part1 ** 2 loss_part2 = loss_part2 * 0.50 loss2 = torch.where(loss_part1 >= 1, loss_part1 - 0.5,...