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- # Copyright (c) OpenMMLab. All rights reserved.
- """Tests the Assigner objects.
-
- CommandLine:
- pytest tests/test_utils/test_assigner.py
- xdoctest tests/test_utils/test_assigner.py zero
- """
- import torch
-
- from mmdet.core.bbox.assigners import (ApproxMaxIoUAssigner,
- CenterRegionAssigner, HungarianAssigner,
- MaxIoUAssigner, PointAssigner,
- UniformAssigner)
-
-
- 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_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_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_with_empty_boxes():
- """Test corner case where a network might predict no boxes."""
- self = MaxIoUAssigner(
- pos_iou_thr=0.5,
- neg_iou_thr=0.5,
- )
- bboxes = torch.empty((0, 4))
- gt_bboxes = torch.FloatTensor([
- [0, 0, 10, 9],
- [0, 10, 10, 19],
- ])
- gt_labels = torch.LongTensor([2, 3])
-
- # Test with gt_labels
- assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels)
- 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)
- assert len(assign_result.gt_inds) == 0
- assert assign_result.labels is None
-
-
- 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_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_point_assigner():
- self = PointAssigner()
- points = torch.FloatTensor([ # [x, y, stride]
- [0, 0, 1],
- [10, 10, 1],
- [5, 5, 1],
- [32, 32, 1],
- ])
- gt_bboxes = torch.FloatTensor([
- [0, 0, 10, 9],
- [0, 10, 10, 19],
- ])
- assign_result = self.assign(points, gt_bboxes)
- expected_gt_inds = torch.LongTensor([1, 2, 1, 0])
- assert torch.all(assign_result.gt_inds == expected_gt_inds)
-
-
- def test_point_assigner_with_empty_gt():
- """Test corner case where an image might have no true detections."""
- self = PointAssigner()
- points = torch.FloatTensor([ # [x, y, stride]
- [0, 0, 1],
- [10, 10, 1],
- [5, 5, 1],
- [32, 32, 1],
- ])
- gt_bboxes = torch.FloatTensor([])
- assign_result = self.assign(points, gt_bboxes)
-
- expected_gt_inds = torch.LongTensor([0, 0, 0, 0])
- assert torch.all(assign_result.gt_inds == expected_gt_inds)
-
-
- def test_point_assigner_with_empty_boxes_and_gt():
- """Test corner case where an image might predict no points and no gt."""
- self = PointAssigner()
- points = torch.FloatTensor([])
- gt_bboxes = torch.FloatTensor([])
- assign_result = self.assign(points, gt_bboxes)
- assert len(assign_result.gt_inds) == 0
-
-
- def test_approx_iou_assigner():
- self = ApproxMaxIoUAssigner(
- 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],
- ])
- approxs_per_octave = 1
- approxs = bboxes
- squares = bboxes
- assign_result = self.assign(approxs, squares, approxs_per_octave,
- gt_bboxes)
-
- expected_gt_inds = torch.LongTensor([1, 0, 2, 0])
- assert torch.all(assign_result.gt_inds == expected_gt_inds)
-
-
- def test_approx_iou_assigner_with_empty_gt():
- """Test corner case where an image might have no true detections."""
- self = ApproxMaxIoUAssigner(
- 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([])
- approxs_per_octave = 1
- approxs = bboxes
- squares = bboxes
- assign_result = self.assign(approxs, squares, approxs_per_octave,
- gt_bboxes)
-
- expected_gt_inds = torch.LongTensor([0, 0, 0, 0])
- assert torch.all(assign_result.gt_inds == expected_gt_inds)
-
-
- def test_approx_iou_assigner_with_empty_boxes():
- """Test corner case where an network might predict no boxes."""
- self = ApproxMaxIoUAssigner(
- pos_iou_thr=0.5,
- neg_iou_thr=0.5,
- )
- bboxes = torch.empty((0, 4))
- gt_bboxes = torch.FloatTensor([
- [0, 0, 10, 9],
- [0, 10, 10, 19],
- ])
- approxs_per_octave = 1
- approxs = bboxes
- squares = bboxes
- assign_result = self.assign(approxs, squares, approxs_per_octave,
- gt_bboxes)
- assert len(assign_result.gt_inds) == 0
-
-
- def test_approx_iou_assigner_with_empty_boxes_and_gt():
- """Test corner case where an network might predict no boxes and no gt."""
- self = ApproxMaxIoUAssigner(
- pos_iou_thr=0.5,
- neg_iou_thr=0.5,
- )
- bboxes = torch.empty((0, 4))
- gt_bboxes = torch.empty((0, 4))
- approxs_per_octave = 1
- approxs = bboxes
- squares = bboxes
- assign_result = self.assign(approxs, squares, approxs_per_octave,
- gt_bboxes)
- assert len(assign_result.gt_inds) == 0
-
-
- def test_random_assign_result():
- """Test random instantiation of assign result to catch corner cases."""
- from mmdet.core.bbox.assigners.assign_result import AssignResult
- AssignResult.random()
-
- AssignResult.random(num_gts=0, num_preds=0)
- AssignResult.random(num_gts=0, num_preds=3)
- AssignResult.random(num_gts=3, num_preds=3)
- AssignResult.random(num_gts=0, num_preds=3)
- AssignResult.random(num_gts=7, num_preds=7)
- AssignResult.random(num_gts=7, num_preds=64)
- AssignResult.random(num_gts=24, num_preds=3)
-
-
- def test_center_region_assigner():
- self = CenterRegionAssigner(pos_scale=0.3, neg_scale=1)
- bboxes = torch.FloatTensor([[0, 0, 10, 10], [10, 10, 20, 20], [8, 8, 9,
- 9]])
- gt_bboxes = torch.FloatTensor([
- [0, 0, 11, 11], # match bboxes[0]
- [10, 10, 20, 20], # match bboxes[1]
- [4.5, 4.5, 5.5, 5.5], # match bboxes[0] but area is too small
- [0, 0, 10, 10], # match bboxes[1] and has a smaller area than gt[0]
- ])
- gt_labels = torch.LongTensor([2, 3, 4, 5])
- assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels)
- assert len(assign_result.gt_inds) == 3
- assert len(assign_result.labels) == 3
- expected_gt_inds = torch.LongTensor([4, 2, 0])
- assert torch.all(assign_result.gt_inds == expected_gt_inds)
- shadowed_labels = assign_result.get_extra_property('shadowed_labels')
- # [8, 8, 9, 9] in the shadowed region of [0, 0, 11, 11] (label: 2)
- assert torch.any(shadowed_labels == torch.LongTensor([[2, 2]]))
- # [8, 8, 9, 9] in the shadowed region of [0, 0, 10, 10] (label: 5)
- assert torch.any(shadowed_labels == torch.LongTensor([[2, 5]]))
- # [0, 0, 10, 10] is already assigned to [4.5, 4.5, 5.5, 5.5].
- # Therefore, [0, 0, 11, 11] (label: 2) is shadowed
- assert torch.any(shadowed_labels == torch.LongTensor([[0, 2]]))
-
-
- def test_center_region_assigner_with_ignore():
- self = CenterRegionAssigner(
- pos_scale=0.5,
- neg_scale=1,
- )
- bboxes = torch.FloatTensor([
- [0, 0, 10, 10],
- [10, 10, 20, 20],
- ])
- gt_bboxes = torch.FloatTensor([
- [0, 0, 10, 10], # match bboxes[0]
- [10, 10, 20, 20], # match bboxes[1]
- ])
- gt_bboxes_ignore = torch.FloatTensor([
- [0, 0, 10, 10], # match bboxes[0]
- ])
- gt_labels = torch.LongTensor([1, 2])
- assign_result = self.assign(
- bboxes,
- gt_bboxes,
- gt_bboxes_ignore=gt_bboxes_ignore,
- gt_labels=gt_labels)
- assert len(assign_result.gt_inds) == 2
- assert len(assign_result.labels) == 2
-
- expected_gt_inds = torch.LongTensor([-1, 2])
- assert torch.all(assign_result.gt_inds == expected_gt_inds)
-
-
- def test_center_region_assigner_with_empty_bboxes():
- self = CenterRegionAssigner(
- pos_scale=0.5,
- neg_scale=1,
- )
- bboxes = torch.empty((0, 4)).float()
- gt_bboxes = torch.FloatTensor([
- [0, 0, 10, 10], # match bboxes[0]
- [10, 10, 20, 20], # match bboxes[1]
- ])
- gt_labels = torch.LongTensor([1, 2])
- assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels)
- assert assign_result.gt_inds is None or assign_result.gt_inds.numel() == 0
- assert assign_result.labels is None or assign_result.labels.numel() == 0
-
-
- def test_center_region_assigner_with_empty_gts():
- self = CenterRegionAssigner(
- pos_scale=0.5,
- neg_scale=1,
- )
- bboxes = torch.FloatTensor([
- [0, 0, 10, 10],
- [10, 10, 20, 20],
- ])
- gt_bboxes = torch.empty((0, 4)).float()
- gt_labels = torch.empty((0, )).long()
- assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels)
- assert len(assign_result.gt_inds) == 2
- expected_gt_inds = torch.LongTensor([0, 0])
- assert torch.all(assign_result.gt_inds == expected_gt_inds)
-
-
- def test_hungarian_match_assigner():
- self = HungarianAssigner()
- assert self.iou_cost.iou_mode == 'giou'
-
- # test no gt bboxes
- bbox_pred = torch.rand((10, 4))
- cls_pred = torch.rand((10, 81))
- gt_bboxes = torch.empty((0, 4)).float()
- gt_labels = torch.empty((0, )).long()
- img_meta = dict(img_shape=(10, 8, 3))
- assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels,
- img_meta)
- assert torch.all(assign_result.gt_inds == 0)
- assert torch.all(assign_result.labels == -1)
-
- # test with gt bboxes
- gt_bboxes = torch.FloatTensor([[0, 0, 5, 7], [3, 5, 7, 8]])
- gt_labels = torch.LongTensor([1, 20])
- assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels,
- img_meta)
- assert torch.all(assign_result.gt_inds > -1)
- assert (assign_result.gt_inds > 0).sum() == gt_bboxes.size(0)
- assert (assign_result.labels > -1).sum() == gt_bboxes.size(0)
-
- # test iou mode
- self = HungarianAssigner(
- iou_cost=dict(type='IoUCost', iou_mode='iou', weight=1.0))
- assert self.iou_cost.iou_mode == 'iou'
- assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels,
- img_meta)
- assert torch.all(assign_result.gt_inds > -1)
- assert (assign_result.gt_inds > 0).sum() == gt_bboxes.size(0)
- assert (assign_result.labels > -1).sum() == gt_bboxes.size(0)
-
- # test focal loss mode
- self = HungarianAssigner(
- iou_cost=dict(type='IoUCost', iou_mode='giou', weight=1.0),
- cls_cost=dict(type='FocalLossCost', weight=1.))
- assert self.iou_cost.iou_mode == 'giou'
- assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels,
- img_meta)
- assert torch.all(assign_result.gt_inds > -1)
- assert (assign_result.gt_inds > 0).sum() == gt_bboxes.size(0)
- assert (assign_result.labels > -1).sum() == gt_bboxes.size(0)
-
-
- def test_uniform_assigner():
- self = UniformAssigner(0.15, 0.7, 1)
- pred_bbox = torch.FloatTensor([
- [1, 1, 12, 8],
- [4, 4, 20, 20],
- [1, 5, 15, 15],
- [30, 5, 32, 42],
- ])
- anchor = 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(
- pred_bbox, anchor, 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_uniform_assigner_with_empty_gt():
- """Test corner case where an image might have no true detections."""
- self = UniformAssigner(0.15, 0.7, 1)
- pred_bbox = torch.FloatTensor([
- [1, 1, 12, 8],
- [4, 4, 20, 20],
- [1, 5, 15, 15],
- [30, 5, 32, 42],
- ])
- anchor = 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(pred_bbox, anchor, gt_bboxes)
-
- expected_gt_inds = torch.LongTensor([0, 0, 0, 0])
- assert torch.all(assign_result.gt_inds == expected_gt_inds)
-
-
- def test_uniform_assigner_with_empty_boxes():
- """Test corner case where a network might predict no boxes."""
- self = UniformAssigner(0.15, 0.7, 1)
- pred_bbox = torch.empty((0, 4))
- anchor = torch.empty((0, 4))
- gt_bboxes = torch.FloatTensor([
- [0, 0, 10, 9],
- [0, 10, 10, 19],
- ])
- gt_labels = torch.LongTensor([2, 3])
-
- # Test with gt_labels
- assign_result = self.assign(
- pred_bbox, anchor, gt_bboxes, gt_labels=gt_labels)
- assert len(assign_result.gt_inds) == 0
- assert tuple(assign_result.labels.shape) == (0, )
-
- # Test without gt_labels
- assign_result = self.assign(pred_bbox, anchor, gt_bboxes, gt_labels=None)
- assert len(assign_result.gt_inds) == 0
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