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| # Copyright (c) OpenMMLab. All rights reserved. | |
| """ | |
| CommandLine: | |
| pytest tests/test_utils/test_anchor.py | |
| xdoctest tests/test_utils/test_anchor.py zero | |
| """ | |
| import pytest | |
| import torch | |
| def test_standard_points_generator(): | |
| from mmdet.core.anchor import build_prior_generator | |
| # teat init | |
| anchor_generator_cfg = dict( | |
| type='MlvlPointGenerator', strides=[4, 8], offset=0) | |
| anchor_generator = build_prior_generator(anchor_generator_cfg) | |
| assert anchor_generator is not None | |
| assert anchor_generator.num_base_priors == [1, 1] | |
| # test_stride | |
| from mmdet.core.anchor import MlvlPointGenerator | |
| # Square strides | |
| mlvl_points = MlvlPointGenerator(strides=[4, 10], offset=0) | |
| mlvl_points_half_stride_generator = MlvlPointGenerator( | |
| strides=[4, 10], offset=0.5) | |
| assert mlvl_points.num_levels == 2 | |
| # assert self.num_levels == len(featmap_sizes) | |
| with pytest.raises(AssertionError): | |
| mlvl_points.grid_priors(featmap_sizes=[(2, 2)], device='cpu') | |
| priors = mlvl_points.grid_priors( | |
| featmap_sizes=[(2, 2), (4, 8)], device='cpu') | |
| priors_with_stride = mlvl_points.grid_priors( | |
| featmap_sizes=[(2, 2), (4, 8)], with_stride=True, device='cpu') | |
| assert len(priors) == 2 | |
| # assert last dimension is (coord_x, coord_y, stride_w, stride_h). | |
| assert priors_with_stride[0].size(1) == 4 | |
| assert priors_with_stride[0][0][2] == 4 | |
| assert priors_with_stride[0][0][3] == 4 | |
| assert priors_with_stride[1][0][2] == 10 | |
| assert priors_with_stride[1][0][3] == 10 | |
| stride_4_feat_2_2 = priors[0] | |
| assert (stride_4_feat_2_2[1] - stride_4_feat_2_2[0]).sum() == 4 | |
| assert stride_4_feat_2_2.size(0) == 4 | |
| assert stride_4_feat_2_2.size(1) == 2 | |
| stride_10_feat_4_8 = priors[1] | |
| assert (stride_10_feat_4_8[1] - stride_10_feat_4_8[0]).sum() == 10 | |
| assert stride_10_feat_4_8.size(0) == 4 * 8 | |
| assert stride_10_feat_4_8.size(1) == 2 | |
| # assert the offset of 0.5 * stride | |
| priors_half_offset = mlvl_points_half_stride_generator.grid_priors( | |
| featmap_sizes=[(2, 2), (4, 8)], device='cpu') | |
| assert (priors_half_offset[0][0] - priors[0][0]).sum() == 4 * 0.5 * 2 | |
| assert (priors_half_offset[1][0] - priors[1][0]).sum() == 10 * 0.5 * 2 | |
| if torch.cuda.is_available(): | |
| anchor_generator_cfg = dict( | |
| type='MlvlPointGenerator', strides=[4, 8], offset=0) | |
| anchor_generator = build_prior_generator(anchor_generator_cfg) | |
| assert anchor_generator is not None | |
| # Square strides | |
| mlvl_points = MlvlPointGenerator(strides=[4, 10], offset=0) | |
| mlvl_points_half_stride_generator = MlvlPointGenerator( | |
| strides=[4, 10], offset=0.5) | |
| assert mlvl_points.num_levels == 2 | |
| # assert self.num_levels == len(featmap_sizes) | |
| with pytest.raises(AssertionError): | |
| mlvl_points.grid_priors(featmap_sizes=[(2, 2)], device='cuda') | |
| priors = mlvl_points.grid_priors( | |
| featmap_sizes=[(2, 2), (4, 8)], device='cuda') | |
| priors_with_stride = mlvl_points.grid_priors( | |
| featmap_sizes=[(2, 2), (4, 8)], with_stride=True, device='cuda') | |
| assert len(priors) == 2 | |
| # assert last dimension is (coord_x, coord_y, stride_w, stride_h). | |
| assert priors_with_stride[0].size(1) == 4 | |
| assert priors_with_stride[0][0][2] == 4 | |
| assert priors_with_stride[0][0][3] == 4 | |
| assert priors_with_stride[1][0][2] == 10 | |
| assert priors_with_stride[1][0][3] == 10 | |
| stride_4_feat_2_2 = priors[0] | |
| assert (stride_4_feat_2_2[1] - stride_4_feat_2_2[0]).sum() == 4 | |
| assert stride_4_feat_2_2.size(0) == 4 | |
| assert stride_4_feat_2_2.size(1) == 2 | |
| stride_10_feat_4_8 = priors[1] | |
| assert (stride_10_feat_4_8[1] - stride_10_feat_4_8[0]).sum() == 10 | |
| assert stride_10_feat_4_8.size(0) == 4 * 8 | |
| assert stride_10_feat_4_8.size(1) == 2 | |
| # assert the offset of 0.5 * stride | |
| priors_half_offset = mlvl_points_half_stride_generator.grid_priors( | |
| featmap_sizes=[(2, 2), (4, 8)], device='cuda') | |
| assert (priors_half_offset[0][0] - priors[0][0]).sum() == 4 * 0.5 * 2 | |
| assert (priors_half_offset[1][0] - priors[1][0]).sum() == 10 * 0.5 * 2 | |
| def test_sparse_prior(): | |
| from mmdet.core.anchor import MlvlPointGenerator | |
| mlvl_points = MlvlPointGenerator(strides=[4, 10], offset=0) | |
| prior_indexs = torch.Tensor([0, 2, 4, 5, 6, 9]).long() | |
| featmap_sizes = [(3, 5), (6, 4)] | |
| grid_anchors = mlvl_points.grid_priors( | |
| featmap_sizes=featmap_sizes, with_stride=False, device='cpu') | |
| sparse_prior = mlvl_points.sparse_priors( | |
| prior_idxs=prior_indexs, | |
| featmap_size=featmap_sizes[0], | |
| level_idx=0, | |
| device='cpu') | |
| assert not sparse_prior.is_cuda | |
| assert (sparse_prior == grid_anchors[0][prior_indexs]).all() | |
| sparse_prior = mlvl_points.sparse_priors( | |
| prior_idxs=prior_indexs, | |
| featmap_size=featmap_sizes[1], | |
| level_idx=1, | |
| device='cpu') | |
| assert (sparse_prior == grid_anchors[1][prior_indexs]).all() | |
| from mmdet.core.anchor import AnchorGenerator | |
| mlvl_anchors = AnchorGenerator( | |
| strides=[16, 32], ratios=[1.], scales=[1.], base_sizes=[4, 8]) | |
| prior_indexs = torch.Tensor([0, 2, 4, 5, 6, 9]).long() | |
| featmap_sizes = [(3, 5), (6, 4)] | |
| grid_anchors = mlvl_anchors.grid_priors( | |
| featmap_sizes=featmap_sizes, device='cpu') | |
| sparse_prior = mlvl_anchors.sparse_priors( | |
| prior_idxs=prior_indexs, | |
| featmap_size=featmap_sizes[0], | |
| level_idx=0, | |
| device='cpu') | |
| assert (sparse_prior == grid_anchors[0][prior_indexs]).all() | |
| sparse_prior = mlvl_anchors.sparse_priors( | |
| prior_idxs=prior_indexs, | |
| featmap_size=featmap_sizes[1], | |
| level_idx=1, | |
| device='cpu') | |
| assert (sparse_prior == grid_anchors[1][prior_indexs]).all() | |
| # for ssd | |
| from mmdet.core.anchor.anchor_generator import SSDAnchorGenerator | |
| featmap_sizes = [(38, 38), (19, 19), (10, 10)] | |
| anchor_generator = SSDAnchorGenerator( | |
| scale_major=False, | |
| input_size=300, | |
| basesize_ratio_range=(0.15, 0.9), | |
| strides=[8, 16, 32], | |
| ratios=[[2], [2, 3], [2, 3]]) | |
| ssd_anchors = anchor_generator.grid_anchors(featmap_sizes, device='cpu') | |
| for i in range(len(featmap_sizes)): | |
| sparse_ssd_anchors = anchor_generator.sparse_priors( | |
| prior_idxs=prior_indexs, | |
| level_idx=i, | |
| featmap_size=featmap_sizes[i], | |
| device='cpu') | |
| assert (sparse_ssd_anchors == ssd_anchors[i][prior_indexs]).all() | |
| # for yolo | |
| from mmdet.core.anchor.anchor_generator import YOLOAnchorGenerator | |
| featmap_sizes = [(38, 38), (19, 19), (10, 10)] | |
| anchor_generator = 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)], | |
| ]) | |
| yolo_anchors = anchor_generator.grid_anchors(featmap_sizes, device='cpu') | |
| for i in range(len(featmap_sizes)): | |
| sparse_yolo_anchors = anchor_generator.sparse_priors( | |
| prior_idxs=prior_indexs, | |
| level_idx=i, | |
| featmap_size=featmap_sizes[i], | |
| device='cpu') | |
| assert (sparse_yolo_anchors == yolo_anchors[i][prior_indexs]).all() | |
| if torch.cuda.is_available(): | |
| mlvl_points = MlvlPointGenerator(strides=[4, 10], offset=0) | |
| prior_indexs = torch.Tensor([0, 3, 4, 5, 6, 7, 1, 2, 4, 5, 6, | |
| 9]).long().cuda() | |
| featmap_sizes = [(6, 8), (6, 4)] | |
| grid_anchors = mlvl_points.grid_priors( | |
| featmap_sizes=featmap_sizes, with_stride=False, device='cuda') | |
| sparse_prior = mlvl_points.sparse_priors( | |
| prior_idxs=prior_indexs, | |
| featmap_size=featmap_sizes[0], | |
| level_idx=0, | |
| device='cuda') | |
| assert (sparse_prior == grid_anchors[0][prior_indexs]).all() | |
| sparse_prior = mlvl_points.sparse_priors( | |
| prior_idxs=prior_indexs, | |
| featmap_size=featmap_sizes[1], | |
| level_idx=1, | |
| device='cuda') | |
| assert (sparse_prior == grid_anchors[1][prior_indexs]).all() | |
| assert sparse_prior.is_cuda | |
| mlvl_anchors = AnchorGenerator( | |
| strides=[16, 32], | |
| ratios=[1., 2.5], | |
| scales=[1., 5.], | |
| base_sizes=[4, 8]) | |
| prior_indexs = torch.Tensor([4, 5, 6, 7, 0, 2, 50, 4, 5, 6, | |
| 9]).long().cuda() | |
| featmap_sizes = [(13, 5), (16, 4)] | |
| grid_anchors = mlvl_anchors.grid_priors( | |
| featmap_sizes=featmap_sizes, device='cuda') | |
| sparse_prior = mlvl_anchors.sparse_priors( | |
| prior_idxs=prior_indexs, | |
| featmap_size=featmap_sizes[0], | |
| level_idx=0, | |
| device='cuda') | |
| assert (sparse_prior == grid_anchors[0][prior_indexs]).all() | |
| sparse_prior = mlvl_anchors.sparse_priors( | |
| prior_idxs=prior_indexs, | |
| featmap_size=featmap_sizes[1], | |
| level_idx=1, | |
| device='cuda') | |
| assert (sparse_prior == grid_anchors[1][prior_indexs]).all() | |
| # for ssd | |
| from mmdet.core.anchor.anchor_generator import SSDAnchorGenerator | |
| featmap_sizes = [(38, 38), (19, 19), (10, 10)] | |
| anchor_generator = SSDAnchorGenerator( | |
| scale_major=False, | |
| input_size=300, | |
| basesize_ratio_range=(0.15, 0.9), | |
| strides=[8, 16, 32], | |
| ratios=[[2], [2, 3], [2, 3]]) | |
| ssd_anchors = anchor_generator.grid_anchors( | |
| featmap_sizes, device='cuda') | |
| for i in range(len(featmap_sizes)): | |
| sparse_ssd_anchors = anchor_generator.sparse_priors( | |
| prior_idxs=prior_indexs, | |
| level_idx=i, | |
| featmap_size=featmap_sizes[i], | |
| device='cuda') | |
| assert (sparse_ssd_anchors == ssd_anchors[i][prior_indexs]).all() | |
| # for yolo | |
| from mmdet.core.anchor.anchor_generator import YOLOAnchorGenerator | |
| featmap_sizes = [(38, 38), (19, 19), (10, 10)] | |
| anchor_generator = 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)], | |
| ]) | |
| yolo_anchors = anchor_generator.grid_anchors( | |
| featmap_sizes, device='cuda') | |
| for i in range(len(featmap_sizes)): | |
| sparse_yolo_anchors = anchor_generator.sparse_priors( | |
| prior_idxs=prior_indexs, | |
| level_idx=i, | |
| featmap_size=featmap_sizes[i], | |
| device='cuda') | |
| assert (sparse_yolo_anchors == yolo_anchors[i][prior_indexs]).all() | |
| 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.num_base_priors == \ | |
| anchor_generator.num_base_anchors | |
| assert anchor_generator.num_base_priors == [3, 3] | |
| 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' | |
| # min_sizes max_sizes must set at the same time | |
| with pytest.raises(AssertionError): | |
| anchor_generator_cfg = dict( | |
| type='SSDAnchorGenerator', | |
| scale_major=False, | |
| min_sizes=[48, 100, 150, 202, 253, 300], | |
| max_sizes=None, | |
| strides=[8, 16, 32, 64, 100, 300], | |
| ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]) | |
| build_anchor_generator(anchor_generator_cfg) | |
| # length of min_sizes max_sizes must be the same | |
| with pytest.raises(AssertionError): | |
| anchor_generator_cfg = dict( | |
| type='SSDAnchorGenerator', | |
| scale_major=False, | |
| min_sizes=[48, 100, 150, 202, 253, 300], | |
| max_sizes=[100, 150, 202, 253], | |
| strides=[8, 16, 32, 64, 100, 300], | |
| ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]) | |
| build_anchor_generator(anchor_generator_cfg) | |
| # test setting anchor size manually | |
| anchor_generator_cfg = dict( | |
| type='SSDAnchorGenerator', | |
| scale_major=False, | |
| min_sizes=[48, 100, 150, 202, 253, 304], | |
| max_sizes=[100, 150, 202, 253, 304, 320], | |
| strides=[16, 32, 64, 107, 160, 320], | |
| ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]]) | |
| featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)] | |
| anchor_generator = build_anchor_generator(anchor_generator_cfg) | |
| expected_base_anchors = [ | |
| torch.Tensor([[-16.0000, -16.0000, 32.0000, 32.0000], | |
| [-26.6410, -26.6410, 42.6410, 42.6410], | |
| [-25.9411, -8.9706, 41.9411, 24.9706], | |
| [-8.9706, -25.9411, 24.9706, 41.9411], | |
| [-33.5692, -5.8564, 49.5692, 21.8564], | |
| [-5.8564, -33.5692, 21.8564, 49.5692]]), | |
| torch.Tensor([[-34.0000, -34.0000, 66.0000, 66.0000], | |
| [-45.2372, -45.2372, 77.2372, 77.2372], | |
| [-54.7107, -19.3553, 86.7107, 51.3553], | |
| [-19.3553, -54.7107, 51.3553, 86.7107], | |
| [-70.6025, -12.8675, 102.6025, 44.8675], | |
| [-12.8675, -70.6025, 44.8675, 102.6025]]), | |
| torch.Tensor([[-43.0000, -43.0000, 107.0000, 107.0000], | |
| [-55.0345, -55.0345, 119.0345, 119.0345], | |
| [-74.0660, -21.0330, 138.0660, 85.0330], | |
| [-21.0330, -74.0660, 85.0330, 138.0660], | |
| [-97.9038, -11.3013, 161.9038, 75.3013], | |
| [-11.3013, -97.9038, 75.3013, 161.9038]]), | |
| torch.Tensor([[-47.5000, -47.5000, 154.5000, 154.5000], | |
| [-59.5332, -59.5332, 166.5332, 166.5332], | |
| [-89.3356, -17.9178, 196.3356, 124.9178], | |
| [-17.9178, -89.3356, 124.9178, 196.3356], | |
| [-121.4371, -4.8124, 228.4371, 111.8124], | |
| [-4.8124, -121.4371, 111.8124, 228.4371]]), | |
| torch.Tensor([[-46.5000, -46.5000, 206.5000, 206.5000], | |
| [-58.6651, -58.6651, 218.6651, 218.6651], | |
| [-98.8980, -9.4490, 258.8980, 169.4490], | |
| [-9.4490, -98.8980, 169.4490, 258.8980], | |
| [-139.1044, 6.9652, 299.1044, 153.0348], | |
| [6.9652, -139.1044, 153.0348, 299.1044]]), | |
| torch.Tensor([[8.0000, 8.0000, 312.0000, 312.0000], | |
| [4.0513, 4.0513, 315.9487, 315.9487], | |
| [-54.9605, 52.5198, 374.9604, 267.4802], | |
| [52.5198, -54.9605, 267.4802, 374.9604], | |
| [-103.2717, 72.2428, 423.2717, 247.7572], | |
| [72.2428, -103.2717, 247.7572, 423.2717]]) | |
| ] | |
| 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 = [2400, 600, 150, 54, 24, 6] | |
| multi_level_valid_flags = anchor_generator.valid_flags( | |
| featmap_sizes, (320, 320), 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 == [6, 6, 6, 6, 6, 6] | |
| # check anchor generation | |
| anchors = anchor_generator.grid_anchors(featmap_sizes, device) | |
| assert len(anchors) == 6 | |
| # test vgg ssd anchor setting | |
| 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 | |