def get_targets(sampling_results, gt_bboxes, gt_labels, rcnn_train_cfg):
pos_proposals = [res.pos_bboxes for res in sampling_results]
neg_proposals = [res.neg_bboxes for res in sampling_results]
pos_gt_labels = [res.pos_gt_labels for res in sampling_results]
cls_reg_targets = bbox_target(pos_proposals, neg_proposals,
pos_gt_labels, rcnn_train_cfg)
return cls_reg_targets
def test_bbox_target():
pos_bboxes = torch.tensor([[0.072, 0.47, 0.84, 0.898],
[0.23, 0.215, 0.781, 0.534],
[0.195, 0.128, 0.643, 0.944],
[0.236, 0.189, 0.689, 0.74]])
neg_bboxes = torch.tensor([[0.375, 0.371, 0.726, 0.804],
[0.024, 0.398, 0.776, 0.719]])
pos_gt_labels = torch.tensor([[0., 0., 1., 0.], [0., 0., 0., 1.],
[0., 1., 0., 0.], [0., 1., 0., 0.]])
cfg = abstractproperty()
cfg.pos_weight = 0.8
labels, label_weights = bbox_target([pos_bboxes], [neg_bboxes],
[pos_gt_labels], cfg)
assert torch.all(
torch.isclose(
labels,
torch.tensor([[0., 0., 1., 0.], [0., 0., 0., 1.], [0., 1., 0., 0.],
[0., 1., 0., 0.], [0., 0., 0., 0.], [0., 0., 0.,
0.]])))
assert torch.all(
torch.isclose(label_weights, torch.tensor([0.8] * 4 + [1.0] * 2)))