(self, inverse_indices, margin_mask, unique_vox_counts, unique_vox)
| 339 | return False |
| 340 | |
| 341 | def get_margin_vox(self, inverse_indices, margin_mask, unique_vox_counts, unique_vox): |
| 342 | unique_inv_id, counts_2 = torch.unique(inverse_indices[margin_mask], dim=0, return_counts=True) |
| 343 | temp = torch.zeros(unique_vox.shape[0]).to(unique_vox.device) |
| 344 | temp[unique_inv_id.long()] = counts_2.float() |
| 345 | margin_vox = unique_vox[(temp == unique_vox_counts) * (unique_vox_counts > 10)] |
| 346 | return margin_vox |
| 347 | |
| 348 | def updownsampling_voxel(self, points, indices, counts): |
| 349 | summed_elements = torch.zeros(counts.shape[0], points.shape[-1]).cuda() |
nothing calls this directly
no outgoing calls
no test coverage detected