| import torch | |
| from mmdet.core import build_assigner, build_sampler | |
| def _dummy_bbox_sampling(proposal_list, gt_bboxes, gt_labels): | |
| """Create sample results that can be passed to BBoxHead.get_targets.""" | |
| num_imgs = 1 | |
| feat = torch.rand(1, 1, 3, 3) | |
| assign_config = dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.5, | |
| neg_iou_thr=0.5, | |
| min_pos_iou=0.5, | |
| ignore_iof_thr=-1) | |
| sampler_config = dict( | |
| type='RandomSampler', | |
| num=512, | |
| pos_fraction=0.25, | |
| neg_pos_ub=-1, | |
| add_gt_as_proposals=True) | |
| bbox_assigner = build_assigner(assign_config) | |
| bbox_sampler = build_sampler(sampler_config) | |
| gt_bboxes_ignore = [None for _ in range(num_imgs)] | |
| sampling_results = [] | |
| for i in range(num_imgs): | |
| assign_result = bbox_assigner.assign(proposal_list[i], gt_bboxes[i], | |
| gt_bboxes_ignore[i], gt_labels[i]) | |
| sampling_result = bbox_sampler.sample( | |
| assign_result, | |
| proposal_list[i], | |
| gt_bboxes[i], | |
| gt_labels[i], | |
| feats=feat) | |
| sampling_results.append(sampling_result) | |
| return sampling_results | |