| import torch | |
| import torch.nn as nn | |
| class RegularizationLoss(nn.Module): | |
| """ | |
| Enforce losses on pixels without any gts. | |
| """ | |
| def __init__(self, loss_weight=0.1, data_type=['sfm', 'stereo', 'lidar'], **kwargs): | |
| super(RegularizationLoss, self).__init__() | |
| self.loss_weight = loss_weight | |
| self.data_type = data_type | |
| self.eps = 1e-6 | |
| def forward(self, prediction, target, mask=None, **kwargs): | |
| pred_wo_gt = prediction[~mask] | |
| #loss = - torch.sum(pred_wo_gt) / (pred_wo_gt.numel() + 1e-8) | |
| loss = 1/ (torch.sum(pred_wo_gt) / (pred_wo_gt.numel() + self.eps)) | |
| return loss * self.loss_weight |