""" CommandLine: pytest tests/test_utils/test_anchor.py xdoctest tests/test_utils/test_anchor.py zero """ import torch def test_standard_anchor_generator(): from mmdet.core.anchor import build_anchor_generator anchor_generator_cfg = dict( type='AnchorGenerator', scales=[8], ratios=[0.5, 1.0, 2.0], strides=[4, 8]) anchor_generator = build_anchor_generator(anchor_generator_cfg) assert anchor_generator is not None def test_strides(): from mmdet.core import AnchorGenerator # Square strides self = AnchorGenerator([10], [1.], [1.], [10]) anchors = self.grid_anchors([(2, 2)], device='cpu') expected_anchors = torch.tensor([[-5., -5., 5., 5.], [5., -5., 15., 5.], [-5., 5., 5., 15.], [5., 5., 15., 15.]]) assert torch.equal(anchors[0], expected_anchors) # Different strides in x and y direction self = AnchorGenerator([(10, 20)], [1.], [1.], [10]) anchors = self.grid_anchors([(2, 2)], device='cpu') expected_anchors = torch.tensor([[-5., -5., 5., 5.], [5., -5., 15., 5.], [-5., 15., 5., 25.], [5., 15., 15., 25.]]) assert torch.equal(anchors[0], expected_anchors) def test_ssd_anchor_generator(): from mmdet.core.anchor import build_anchor_generator if torch.cuda.is_available(): device = 'cuda' else: device = 'cpu' anchor_generator_cfg = dict( type='SSDAnchorGenerator', scale_major=False, input_size=300, basesize_ratio_range=(0.15, 0.9), strides=[8, 16, 32, 64, 100, 300], ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]) featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)] anchor_generator = build_anchor_generator(anchor_generator_cfg) # check base anchors expected_base_anchors = [ torch.Tensor([[-6.5000, -6.5000, 14.5000, 14.5000], [-11.3704, -11.3704, 19.3704, 19.3704], [-10.8492, -3.4246, 18.8492, 11.4246], [-3.4246, -10.8492, 11.4246, 18.8492]]), torch.Tensor([[-14.5000, -14.5000, 30.5000, 30.5000], [-25.3729, -25.3729, 41.3729, 41.3729], [-23.8198, -7.9099, 39.8198, 23.9099], [-7.9099, -23.8198, 23.9099, 39.8198], [-30.9711, -4.9904, 46.9711, 20.9904], [-4.9904, -30.9711, 20.9904, 46.9711]]), torch.Tensor([[-33.5000, -33.5000, 65.5000, 65.5000], [-45.5366, -45.5366, 77.5366, 77.5366], [-54.0036, -19.0018, 86.0036, 51.0018], [-19.0018, -54.0036, 51.0018, 86.0036], [-69.7365, -12.5788, 101.7365, 44.5788], [-12.5788, -69.7365, 44.5788, 101.7365]]), torch.Tensor([[-44.5000, -44.5000, 108.5000, 108.5000], [-56.9817, -56.9817, 120.9817, 120.9817], [-76.1873, -22.0937, 140.1873, 86.0937], [-22.0937, -76.1873, 86.0937, 140.1873], [-100.5019, -12.1673, 164.5019, 76.1673], [-12.1673, -100.5019, 76.1673, 164.5019]]), torch.Tensor([[-53.5000, -53.5000, 153.5000, 153.5000], [-66.2185, -66.2185, 166.2185, 166.2185], [-96.3711, -23.1855, 196.3711, 123.1855], [-23.1855, -96.3711, 123.1855, 196.3711]]), torch.Tensor([[19.5000, 19.5000, 280.5000, 280.5000], [6.6342, 6.6342, 293.3658, 293.3658], [-34.5549, 57.7226, 334.5549, 242.2774], [57.7226, -34.5549, 242.2774, 334.5549]]), ] base_anchors = anchor_generator.base_anchors for i, base_anchor in enumerate(base_anchors): assert base_anchor.allclose(expected_base_anchors[i]) # check valid flags expected_valid_pixels = [5776, 2166, 600, 150, 36, 4] multi_level_valid_flags = anchor_generator.valid_flags( featmap_sizes, (300, 300), device) for i, single_level_valid_flag in enumerate(multi_level_valid_flags): assert single_level_valid_flag.sum() == expected_valid_pixels[i] # check number of base anchors for each level assert anchor_generator.num_base_anchors == [4, 6, 6, 6, 4, 4] # check anchor generation anchors = anchor_generator.grid_anchors(featmap_sizes, device) assert len(anchors) == 6 def test_anchor_generator_with_tuples(): from mmdet.core.anchor import build_anchor_generator if torch.cuda.is_available(): device = 'cuda' else: device = 'cpu' anchor_generator_cfg = dict( type='SSDAnchorGenerator', scale_major=False, input_size=300, basesize_ratio_range=(0.15, 0.9), strides=[8, 16, 32, 64, 100, 300], ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]) featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)] anchor_generator = build_anchor_generator(anchor_generator_cfg) anchors = anchor_generator.grid_anchors(featmap_sizes, device) anchor_generator_cfg_tuples = dict( type='SSDAnchorGenerator', scale_major=False, input_size=300, basesize_ratio_range=(0.15, 0.9), strides=[(8, 8), (16, 16), (32, 32), (64, 64), (100, 100), (300, 300)], ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]) anchor_generator_tuples = build_anchor_generator( anchor_generator_cfg_tuples) anchors_tuples = anchor_generator_tuples.grid_anchors( featmap_sizes, device) for anchor, anchor_tuples in zip(anchors, anchors_tuples): assert torch.equal(anchor, anchor_tuples) def test_yolo_anchor_generator(): from mmdet.core.anchor import build_anchor_generator if torch.cuda.is_available(): device = 'cuda' else: device = 'cpu' anchor_generator_cfg = dict( type='YOLOAnchorGenerator', strides=[32, 16, 8], base_sizes=[ [(116, 90), (156, 198), (373, 326)], [(30, 61), (62, 45), (59, 119)], [(10, 13), (16, 30), (33, 23)], ]) featmap_sizes = [(14, 18), (28, 36), (56, 72)] anchor_generator = build_anchor_generator(anchor_generator_cfg) # check base anchors expected_base_anchors = [ torch.Tensor([[-42.0000, -29.0000, 74.0000, 61.0000], [-62.0000, -83.0000, 94.0000, 115.0000], [-170.5000, -147.0000, 202.5000, 179.0000]]), torch.Tensor([[-7.0000, -22.5000, 23.0000, 38.5000], [-23.0000, -14.5000, 39.0000, 30.5000], [-21.5000, -51.5000, 37.5000, 67.5000]]), torch.Tensor([[-1.0000, -2.5000, 9.0000, 10.5000], [-4.0000, -11.0000, 12.0000, 19.0000], [-12.5000, -7.5000, 20.5000, 15.5000]]) ] base_anchors = anchor_generator.base_anchors for i, base_anchor in enumerate(base_anchors): assert base_anchor.allclose(expected_base_anchors[i]) # check number of base anchors for each level assert anchor_generator.num_base_anchors == [3, 3, 3] # check anchor generation anchors = anchor_generator.grid_anchors(featmap_sizes, device) assert len(anchors) == 3 def test_retina_anchor(): from mmdet.models import build_head if torch.cuda.is_available(): device = 'cuda' else: device = 'cpu' # head configs modified from # configs/nas_fpn/retinanet_r50_fpn_crop640_50e.py bbox_head = dict( type='RetinaSepBNHead', num_classes=4, num_ins=5, in_channels=4, stacked_convs=1, feat_channels=4, anchor_generator=dict( type='AnchorGenerator', octave_base_scale=4, scales_per_octave=3, ratios=[0.5, 1.0, 2.0], strides=[8, 16, 32, 64, 128]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[.0, .0, .0, .0], target_stds=[1.0, 1.0, 1.0, 1.0])) retina_head = build_head(bbox_head) assert retina_head.anchor_generator is not None # use the featmap sizes in NASFPN setting to test retina head featmap_sizes = [(80, 80), (40, 40), (20, 20), (10, 10), (5, 5)] # check base anchors expected_base_anchors = [ torch.Tensor([[-22.6274, -11.3137, 22.6274, 11.3137], [-28.5088, -14.2544, 28.5088, 14.2544], [-35.9188, -17.9594, 35.9188, 17.9594], [-16.0000, -16.0000, 16.0000, 16.0000], [-20.1587, -20.1587, 20.1587, 20.1587], [-25.3984, -25.3984, 25.3984, 25.3984], [-11.3137, -22.6274, 11.3137, 22.6274], [-14.2544, -28.5088, 14.2544, 28.5088], [-17.9594, -35.9188, 17.9594, 35.9188]]), torch.Tensor([[-45.2548, -22.6274, 45.2548, 22.6274], [-57.0175, -28.5088, 57.0175, 28.5088], [-71.8376, -35.9188, 71.8376, 35.9188], [-32.0000, -32.0000, 32.0000, 32.0000], [-40.3175, -40.3175, 40.3175, 40.3175], [-50.7968, -50.7968, 50.7968, 50.7968], [-22.6274, -45.2548, 22.6274, 45.2548], [-28.5088, -57.0175, 28.5088, 57.0175], [-35.9188, -71.8376, 35.9188, 71.8376]]), torch.Tensor([[-90.5097, -45.2548, 90.5097, 45.2548], [-114.0350, -57.0175, 114.0350, 57.0175], [-143.6751, -71.8376, 143.6751, 71.8376], [-64.0000, -64.0000, 64.0000, 64.0000], [-80.6349, -80.6349, 80.6349, 80.6349], [-101.5937, -101.5937, 101.5937, 101.5937], [-45.2548, -90.5097, 45.2548, 90.5097], [-57.0175, -114.0350, 57.0175, 114.0350], [-71.8376, -143.6751, 71.8376, 143.6751]]), torch.Tensor([[-181.0193, -90.5097, 181.0193, 90.5097], [-228.0701, -114.0350, 228.0701, 114.0350], [-287.3503, -143.6751, 287.3503, 143.6751], [-128.0000, -128.0000, 128.0000, 128.0000], [-161.2699, -161.2699, 161.2699, 161.2699], [-203.1873, -203.1873, 203.1873, 203.1873], [-90.5097, -181.0193, 90.5097, 181.0193], [-114.0350, -228.0701, 114.0350, 228.0701], [-143.6751, -287.3503, 143.6751, 287.3503]]), torch.Tensor([[-362.0387, -181.0193, 362.0387, 181.0193], [-456.1401, -228.0701, 456.1401, 228.0701], [-574.7006, -287.3503, 574.7006, 287.3503], [-256.0000, -256.0000, 256.0000, 256.0000], [-322.5398, -322.5398, 322.5398, 322.5398], [-406.3747, -406.3747, 406.3747, 406.3747], [-181.0193, -362.0387, 181.0193, 362.0387], [-228.0701, -456.1401, 228.0701, 456.1401], [-287.3503, -574.7006, 287.3503, 574.7006]]) ] base_anchors = retina_head.anchor_generator.base_anchors for i, base_anchor in enumerate(base_anchors): assert base_anchor.allclose(expected_base_anchors[i]) # check valid flags expected_valid_pixels = [57600, 14400, 3600, 900, 225] multi_level_valid_flags = retina_head.anchor_generator.valid_flags( featmap_sizes, (640, 640), device) for i, single_level_valid_flag in enumerate(multi_level_valid_flags): assert single_level_valid_flag.sum() == expected_valid_pixels[i] # check number of base anchors for each level assert retina_head.anchor_generator.num_base_anchors == [9, 9, 9, 9, 9] # check anchor generation anchors = retina_head.anchor_generator.grid_anchors(featmap_sizes, device) assert len(anchors) == 5 def test_guided_anchor(): from mmdet.models import build_head if torch.cuda.is_available(): device = 'cuda' else: device = 'cpu' # head configs modified from # configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py bbox_head = dict( type='GARetinaHead', num_classes=8, in_channels=4, stacked_convs=1, feat_channels=4, approx_anchor_generator=dict( type='AnchorGenerator', octave_base_scale=4, scales_per_octave=3, ratios=[0.5, 1.0, 2.0], strides=[8, 16, 32, 64, 128]), square_anchor_generator=dict( type='AnchorGenerator', ratios=[1.0], scales=[4], strides=[8, 16, 32, 64, 128])) ga_retina_head = build_head(bbox_head) assert ga_retina_head.approx_anchor_generator is not None # use the featmap sizes in NASFPN setting to test ga_retina_head featmap_sizes = [(100, 152), (50, 76), (25, 38), (13, 19), (7, 10)] # check base anchors expected_approxs = [ torch.Tensor([[-22.6274, -11.3137, 22.6274, 11.3137], [-28.5088, -14.2544, 28.5088, 14.2544], [-35.9188, -17.9594, 35.9188, 17.9594], [-16.0000, -16.0000, 16.0000, 16.0000], [-20.1587, -20.1587, 20.1587, 20.1587], [-25.3984, -25.3984, 25.3984, 25.3984], [-11.3137, -22.6274, 11.3137, 22.6274], [-14.2544, -28.5088, 14.2544, 28.5088], [-17.9594, -35.9188, 17.9594, 35.9188]]), torch.Tensor([[-45.2548, -22.6274, 45.2548, 22.6274], [-57.0175, -28.5088, 57.0175, 28.5088], [-71.8376, -35.9188, 71.8376, 35.9188], [-32.0000, -32.0000, 32.0000, 32.0000], [-40.3175, -40.3175, 40.3175, 40.3175], [-50.7968, -50.7968, 50.7968, 50.7968], [-22.6274, -45.2548, 22.6274, 45.2548], [-28.5088, -57.0175, 28.5088, 57.0175], [-35.9188, -71.8376, 35.9188, 71.8376]]), torch.Tensor([[-90.5097, -45.2548, 90.5097, 45.2548], [-114.0350, -57.0175, 114.0350, 57.0175], [-143.6751, -71.8376, 143.6751, 71.8376], [-64.0000, -64.0000, 64.0000, 64.0000], [-80.6349, -80.6349, 80.6349, 80.6349], [-101.5937, -101.5937, 101.5937, 101.5937], [-45.2548, -90.5097, 45.2548, 90.5097], [-57.0175, -114.0350, 57.0175, 114.0350], [-71.8376, -143.6751, 71.8376, 143.6751]]), torch.Tensor([[-181.0193, -90.5097, 181.0193, 90.5097], [-228.0701, -114.0350, 228.0701, 114.0350], [-287.3503, -143.6751, 287.3503, 143.6751], [-128.0000, -128.0000, 128.0000, 128.0000], [-161.2699, -161.2699, 161.2699, 161.2699], [-203.1873, -203.1873, 203.1873, 203.1873], [-90.5097, -181.0193, 90.5097, 181.0193], [-114.0350, -228.0701, 114.0350, 228.0701], [-143.6751, -287.3503, 143.6751, 287.3503]]), torch.Tensor([[-362.0387, -181.0193, 362.0387, 181.0193], [-456.1401, -228.0701, 456.1401, 228.0701], [-574.7006, -287.3503, 574.7006, 287.3503], [-256.0000, -256.0000, 256.0000, 256.0000], [-322.5398, -322.5398, 322.5398, 322.5398], [-406.3747, -406.3747, 406.3747, 406.3747], [-181.0193, -362.0387, 181.0193, 362.0387], [-228.0701, -456.1401, 228.0701, 456.1401], [-287.3503, -574.7006, 287.3503, 574.7006]]) ] approxs = ga_retina_head.approx_anchor_generator.base_anchors for i, base_anchor in enumerate(approxs): assert base_anchor.allclose(expected_approxs[i]) # check valid flags expected_valid_pixels = [136800, 34200, 8550, 2223, 630] multi_level_valid_flags = ga_retina_head.approx_anchor_generator \ .valid_flags(featmap_sizes, (800, 1216), device) for i, single_level_valid_flag in enumerate(multi_level_valid_flags): assert single_level_valid_flag.sum() == expected_valid_pixels[i] # check number of base anchors for each level assert ga_retina_head.approx_anchor_generator.num_base_anchors == [ 9, 9, 9, 9, 9 ] # check approx generation squares = ga_retina_head.square_anchor_generator.grid_anchors( featmap_sizes, device) assert len(squares) == 5 expected_squares = [ torch.Tensor([[-16., -16., 16., 16.]]), torch.Tensor([[-32., -32., 32., 32]]), torch.Tensor([[-64., -64., 64., 64.]]), torch.Tensor([[-128., -128., 128., 128.]]), torch.Tensor([[-256., -256., 256., 256.]]) ] squares = ga_retina_head.square_anchor_generator.base_anchors for i, base_anchor in enumerate(squares): assert base_anchor.allclose(expected_squares[i]) # square_anchor_generator does not check valid flags # check number of base anchors for each level assert (ga_retina_head.square_anchor_generator.num_base_anchors == [ 1, 1, 1, 1, 1 ]) # check square generation anchors = ga_retina_head.square_anchor_generator.grid_anchors( featmap_sizes, device) assert len(anchors) == 5