def test_iou_piecewise_sampler():
assigner = MaxIoUAssigner(pos_iou_thr=0.55,
neg_iou_thr=0.55,
min_pos_iou=0.55,
ignore_iof_thr=-1,
iou_calculator=dict(type='BboxOverlaps3D',
coordinate='lidar'))
bboxes = torch.tensor(
[[32, 32, 16, 8, 38, 42, -0.3], [32, 32, 16, 8, 38, 42, -0.3],
[32, 32, 16, 8, 38, 42, -0.3], [32, 32, 16, 8, 38, 42, -0.3],
[0, 0, 0, 10, 10, 10, 0.2], [10, 10, 10, 20, 20, 15, 0.6],
[5, 5, 5, 15, 15, 15, 0.7], [5, 5, 5, 15, 15, 15, 0.7],
[5, 5, 5, 15, 15, 15, 0.7], [32, 32, 16, 8, 38, 42, -0.3],
[32, 32, 16, 8, 38, 42, -0.3], [32, 32, 16, 8, 38, 42, -0.3]],
dtype=torch.float32).cuda()
gt_bboxes = torch.tensor(
[[0, 0, 0, 10, 10, 9, 0.2], [5, 10, 10, 20, 20, 15, 0.6]],
dtype=torch.float32).cuda()
gt_labels = torch.tensor([1, 1], dtype=torch.int64).cuda()
assign_result = assigner.assign(bboxes, gt_bboxes, gt_labels=gt_labels)
sampler = IoUNegPiecewiseSampler(num=10,
pos_fraction=0.55,
neg_piece_fractions=[0.8, 0.2],
neg_iou_piece_thrs=[0.55, 0.1],
neg_pos_ub=-1,
add_gt_as_proposals=False)
sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels)
assert sample_result.pos_inds == 4
assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)
def test_max_iou_assigner_with_ignore():
self = MaxIoUAssigner(
pos_iou_thr=0.5,
neg_iou_thr=0.5,
ignore_iof_thr=0.5,
ignore_wrt_candidates=False,
)
bboxes = torch.FloatTensor([
[0, 0, 10, 10],
[10, 10, 20, 20],
[5, 5, 15, 15],
[30, 32, 40, 42],
])
gt_bboxes = torch.FloatTensor([
[0, 0, 10, 9],
[0, 10, 10, 19],
])
gt_bboxes_ignore = torch.Tensor([
[30, 30, 40, 40],
])
assign_result = self.assign(
bboxes, gt_bboxes, gt_bboxes_ignore=gt_bboxes_ignore)
expected_gt_inds = torch.LongTensor([1, 0, 2, -1])
assert torch.all(assign_result.gt_inds == expected_gt_inds)
def test_max_iou_assigner_with_empty_boxes_and_ignore():
"""Test corner case where an network might predict no boxes and
ignore_iof_thr is on."""
self = MaxIoUAssigner(
pos_iou_thr=0.5,
neg_iou_thr=0.5,
ignore_iof_thr=0.5,
)
bboxes = torch.empty((0, 4))
gt_bboxes = torch.FloatTensor([
[0, 0, 10, 9],
[0, 10, 10, 19],
])
gt_bboxes_ignore = torch.Tensor([
[30, 30, 40, 40],
])
gt_labels = torch.LongTensor([2, 3])
# Test with gt_labels
assign_result = self.assign(
bboxes,
gt_bboxes,
gt_labels=gt_labels,
gt_bboxes_ignore=gt_bboxes_ignore)
assert len(assign_result.gt_inds) == 0
assert tuple(assign_result.labels.shape) == (0, )
# Test without gt_labels
assign_result = self.assign(
bboxes, gt_bboxes, gt_labels=None, gt_bboxes_ignore=gt_bboxes_ignore)
assert len(assign_result.gt_inds) == 0
assert assign_result.labels is None
def test_max_iou_assigner_with_empty_boxes_and_gt():
"""Test corner case where an network might predict no boxes and no gt."""
self = MaxIoUAssigner(
pos_iou_thr=0.5,
neg_iou_thr=0.5,
)
bboxes = torch.empty((0, 4))
gt_bboxes = torch.empty((0, 4))
assign_result = self.assign(bboxes, gt_bboxes)
assert len(assign_result.gt_inds) == 0
def test_max_iou_assigner_with_empty_gt():
"""Test corner case where an image might have no true detections."""
self = MaxIoUAssigner(
pos_iou_thr=0.5,
neg_iou_thr=0.5,
)
bboxes = torch.FloatTensor([
[0, 0, 10, 10],
[10, 10, 20, 20],
[5, 5, 15, 15],
[32, 32, 38, 42],
])
gt_bboxes = torch.FloatTensor(size=(0, 4))
assign_result = self.assign(bboxes, gt_bboxes)
expected_gt_inds = torch.LongTensor([0, 0, 0, 0])
assert torch.all(assign_result.gt_inds == expected_gt_inds)
def test_max_iou_assigner():
self = MaxIoUAssigner(
pos_iou_thr=0.5,
neg_iou_thr=0.5,
)
bboxes = torch.FloatTensor([
[0, 0, 10, 10],
[10, 10, 20, 20],
[5, 5, 15, 15],
[32, 32, 38, 42],
])
gt_bboxes = torch.FloatTensor([
[0, 0, 10, 9],
[0, 10, 10, 19],
])
gt_labels = torch.LongTensor([2, 3])
assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels)
assert len(assign_result.gt_inds) == 4
assert len(assign_result.labels) == 4
expected_gt_inds = torch.LongTensor([1, 0, 2, 0])
assert torch.all(assign_result.gt_inds == expected_gt_inds)
