def test_random_sampler():
assigner = 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],
[32, 32, 38, 42],
])
gt_bboxes = torch.FloatTensor([
[0, 0, 10, 9],
[0, 10, 10, 19],
])
gt_labels = torch.LongTensor([1, 2])
gt_bboxes_ignore = torch.Tensor([
[30, 30, 40, 40],
])
assign_result = assigner.assign(bboxes,
gt_bboxes,
gt_bboxes_ignore=gt_bboxes_ignore,
gt_labels=gt_labels)
sampler = RandomSampler(num=10,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=True)
sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels)
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],
[32, 32, 38, 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_ohem_sampler():
assigner = 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],
[32, 32, 38, 42],
])
gt_bboxes = torch.FloatTensor([
[0, 0, 10, 9],
[0, 10, 10, 19],
])
gt_labels = torch.LongTensor([1, 2])
gt_bboxes_ignore = torch.Tensor([
[30, 30, 40, 40],
])
assign_result = assigner.assign(bboxes,
gt_bboxes,
gt_bboxes_ignore=gt_bboxes_ignore,
gt_labels=gt_labels)
context = _context_for_ohem()
sampler = OHEMSampler(num=10,
pos_fraction=0.5,
context=context,
neg_pos_ub=-1,
add_gt_as_proposals=True)
feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]]
sample_result = sampler.sample(assign_result,
bboxes,
gt_bboxes,
gt_labels,
feats=feats)
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_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([])
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)
