(depth_est, depth_gt, mask, weight)
| 154 | |
| 155 | return loss |
| 156 | def regression_loss(depth_est, depth_gt, mask, weight): |
| 157 | loss = F.smooth_l1_loss(depth_est[mask], depth_gt[mask], reduction='none') |
| 158 | loss = (loss* weight[mask]).mean() |
| 159 | return loss |
| 160 | |
| 161 | def binary_cross_entropy_with_logits(input, target, weight=None, size_average=None, |
| 162 | reduce=False, reduction='elementwise_mean', pos_weight=None,mask=None): |