# 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