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], [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 a 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_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_boxes_and_gt(): """Test corner case where a 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.empty(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)
def test_score_hlr_sampler_empty_pred(): assigner = MaxIoUAssigner( pos_iou_thr=0.5, neg_iou_thr=0.5, ignore_iof_thr=0.5, ignore_wrt_candidates=False, ) context = _context_for_ohem() sampler = ScoreHLRSampler( num=10, pos_fraction=0.5, context=context, neg_pos_ub=-1, add_gt_as_proposals=True) gt_bboxes_ignore = torch.Tensor([]) feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]] # empty bbox bboxes = torch.empty(0, 4) gt_bboxes = torch.FloatTensor([ [0, 0, 10, 10], [10, 10, 20, 20], [5, 5, 15, 15], [32, 32, 38, 42], ]) gt_labels = torch.LongTensor([1, 2, 2, 3]) assign_result = assigner.assign( bboxes, gt_bboxes, gt_bboxes_ignore=gt_bboxes_ignore, gt_labels=gt_labels) sample_result, _ = sampler.sample( assign_result, bboxes, gt_bboxes, gt_labels, feats=feats) assert len(sample_result.neg_inds) == 0 assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) # empty gt bboxes = torch.FloatTensor([ [0, 0, 10, 10], [10, 10, 20, 20], [5, 5, 15, 15], [32, 32, 38, 42], ]) gt_bboxes = torch.empty(0, 4) gt_labels = torch.LongTensor([]) assign_result = assigner.assign( bboxes, gt_bboxes, gt_bboxes_ignore=gt_bboxes_ignore, gt_labels=gt_labels) sample_result, _ = sampler.sample( assign_result, bboxes, gt_bboxes, gt_labels, feats=feats) assert len(sample_result.pos_inds) == 0 assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) # non-empty input 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, 10], [10, 10, 20, 20], [5, 5, 15, 15], [32, 32, 38, 42], ]) gt_labels = torch.LongTensor([1, 2, 2, 3]) assign_result = assigner.assign( bboxes, gt_bboxes, gt_bboxes_ignore=gt_bboxes_ignore, gt_labels=gt_labels) 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)