Open Chinartist opened 5 months ago
def get_gt_loss(self, inputs, scale, outputs, disp, depth_gt, mask):
singel_scale_total_loss = 0 if self.opt.volume_depth: if self.opt.l1_voxel != 'No': density_center = outputs[('density_center', 0)] label_true = torch.ones_like(density_center, requires_grad=False) all_empty = outputs[('all_empty', 0)] label_false = torch.zeros_like(all_empty, requires_grad=False) if 'l1' in self.opt.l1_voxel: surface_loss_true = F.l1_loss(density_center, label_true, size_average=True) surface_loss_false = F.l1_loss(all_empty, label_false, size_average=True) total_grid_loss = self.opt.empty_w * surface_loss_false + surface_loss_true elif 'ce' in self.opt.l1_voxel: label = torch.cat((label_true, label_false)) pred = torch.cat((density_center, all_empty)) total_grid_loss = self.criterion(pred, label) if self.local_rank == 0 and scale == 0: print('ce loss:', total_grid_loss)
this is a part of the experiment, we discuss the binary classification loss, please check the paper for details.
def get_gt_loss(self, inputs, scale, outputs, disp, depth_gt, mask):