(self, h, w, Ly, Lx, device)
| 704 | return edge_dist |
| 705 | |
| 706 | def get_weighting(self, h, w, Ly, Lx, device): |
| 707 | weighting = self.delta_border(h, w) |
| 708 | weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], |
| 709 | self.split_input_params["clip_max_weight"], ) |
| 710 | weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) |
| 711 | |
| 712 | if self.split_input_params["tie_braker"]: |
| 713 | L_weighting = self.delta_border(Ly, Lx) |
| 714 | L_weighting = torch.clip(L_weighting, |
| 715 | self.split_input_params["clip_min_tie_weight"], |
| 716 | self.split_input_params["clip_max_tie_weight"]) |
| 717 | |
| 718 | L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) |
| 719 | weighting = weighting * L_weighting |
| 720 | return weighting |
| 721 | |
| 722 | def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code |
| 723 | """ |
no test coverage detected