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| import copy | |
| import torch | |
| import numpy as np | |
| from mmdet.evaluation import INSTANCE_OFFSET | |
| INSTANCE_OFFSET_HB = 10000 | |
| def mmpan2hbpan(pred_pan_map, num_classes): | |
| pan_seg_map = - np.ones_like(pred_pan_map) | |
| for itm in np.unique(pred_pan_map): | |
| if itm >= INSTANCE_OFFSET: | |
| # cls labels (from segmentation maps) | |
| cls = itm % INSTANCE_OFFSET | |
| # id labels (from tracking maps) | |
| ins = itm // INSTANCE_OFFSET | |
| pan_seg_map[pred_pan_map == itm] = cls * INSTANCE_OFFSET_HB + ins | |
| elif itm == num_classes: | |
| pan_seg_map[pred_pan_map == itm] = num_classes * INSTANCE_OFFSET_HB | |
| else: | |
| pan_seg_map[pred_pan_map == itm] = itm * INSTANCE_OFFSET_HB | |
| assert -1 not in pan_seg_map | |
| return pan_seg_map | |
| def mmgt2hbpan(data_samples): | |
| pan_map = copy.deepcopy(data_samples.gt_sem_seg.sem_seg[0]) | |
| pan_map = pan_map * INSTANCE_OFFSET_HB | |
| gt_instances = data_samples.gt_instances | |
| for idx in range(len(gt_instances)): | |
| mask = torch.tensor(gt_instances.masks.masks[idx], dtype=torch.bool) | |
| instance_id = gt_instances.instances_ids[idx].item() | |
| pan_map[mask] = instance_id | |
| return pan_map | |