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Commit b70ecdb5 authored by wuyuefeng's avatar wuyuefeng Committed by zhangwenwei
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Indoor boxstructure

parent 9fcfd78f
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Showing with 240 additions and 337 deletions
mmdet3d/ops/roiaware_pool3d/points_in_boxes.py
View file @ b70ecdb5
...@@ -62,7 +62,7 @@ def points_in_boxes_batch(points, boxes): ...@@ -62,7 +62,7 @@ def points_in_boxes_batch(points, boxes):
points (torch.Tensor): [B, M, 3], [x, y, z] in LiDAR coordinate points (torch.Tensor): [B, M, 3], [x, y, z] in LiDAR coordinate
boxes (torch.Tensor): [B, T, 7], boxes (torch.Tensor): [B, T, 7],
num_valid_boxes <= T, [x, y, z, w, l, h, ry] in LiDAR coordinate, num_valid_boxes <= T, [x, y, z, w, l, h, ry] in LiDAR coordinate,
(x, y, z) is the bottom center (x, y, z) is the bottom center.
Returns: Returns:
box_idxs_of_pts (torch.Tensor): (B, M, T), default background = 0 box_idxs_of_pts (torch.Tensor): (B, M, T), default background = 0
......
tests/test_box3d.py
View file @ b70ecdb5
...@@ -33,7 +33,7 @@ def test_lidar_boxes3d(): ...@@ -33,7 +33,7 @@ def test_lidar_boxes3d():
]], ]],
dtype=np.float32) dtype=np.float32)
bottom_center_box = LiDARInstance3DBoxes( bottom_center_box = LiDARInstance3DBoxes(
gravity_center_box, origin=[0.5, 0.5, 0.5]) gravity_center_box, origin=(0.5, 0.5, 0.5))
expected_tensor = torch.tensor( expected_tensor = torch.tensor(
[[ [[
-5.24223238e+00, 4.00209696e+01, -4.76429619e-01, 2.06200000e+00, -5.24223238e+00, 4.00209696e+01, -4.76429619e-01, 2.06200000e+00,
......
tests/test_dataset/test_indoor_eval.py
View file @ b70ecdb5
...@@ -5,58 +5,58 @@ from mmdet3d.core.evaluation.indoor_eval import average_precision, indoor_eval ...@@ -5,58 +5,58 @@ from mmdet3d.core.evaluation.indoor_eval import average_precision, indoor_eval
def test_indoor_eval(): def test_indoor_eval():
from mmdet3d.core.bbox.structures import DepthInstance3DBoxes, Box3DMode
det_infos = [{ det_infos = [{
'labels_3d': 'labels_3d':
torch.Tensor([4, 4, 3, 17, 2]), torch.tensor([0, 1, 2, 2, 0, 3, 1, 2, 3, 2]),
'boxes_3d': 'boxes_3d':
torch.Tensor([[ DepthInstance3DBoxes(
2.8734498, -0.187645, -0.02600911, 0.6761766, 0.56542563, torch.tensor([[
0.5953976, 0. -2.4089e-03, -3.3174e+00, 4.9438e-01, 2.1668e+00, 2.8431e-01,
1.6506e+00, 0.0000e+00
], ],
[ [
0.4031701, -3.2346897, 0.07118589, 0.73209894, -3.4269e-01, -2.7565e+00, 2.8144e-02, 6.8554e-01,
0.8711227, 0.5148243, 0. 9.6854e-01, 6.1755e-01, 0.0000e+00
], ],
[ [
-1.274147, -2.351935, 0.07428858, 1.4534658, -3.8320e+00, -1.0646e+00, 1.7074e-01, 2.4981e-01,
2.563081, 0.8587492, 0. 4.4708e-01, 6.2538e-01, 0.0000e+00
], ],
[ [
3.2214177, 0.7899204, 0.03836718, 0.05321002, 4.1073e-01, 3.3757e+00, 3.4311e-01, 8.0617e-01,
1.2607929, 0.1411697, 0. 2.8679e-01, 1.6060e+00, 0.0000e+00
], ],
[ [
-1.6804854, 2.399011, -0.13099639, 0.5608963, 6.1199e-01, -3.1041e+00, 4.1873e-01, 1.2310e+00,
0.5052759, 0.6770297, 0. 4.0162e-01, 1.7303e+00, 0.0000e+00
]]), ],
'scores_3d': [
torch.Tensor([0.9980684, 0.9747082, 0.9709939, 0.9482147, 0.84311247]) -5.9877e-01, -2.6011e+00, 1.1148e+00, 1.5704e-01,
}, { 7.5957e-01, 9.6930e-01, 0.0000e+00
'labels_3d':
torch.Tensor([17.0, 17.0, 3.0, 4.0, 17.0]),
'boxes_3d':
torch.Tensor([[
3.2112048e+00, 5.6918913e-01, -8.6143613e-04, 1.1942449e-01,
1.2988183e+00, 1.9952521e-01, 0.0000000e+00
], ],
[ [
3.248133, 0.4324184, 0.20038621, 0.17225507, 2.7462e-01, -3.0088e+00, 6.5231e-02, 8.1208e-01,
1.2736976, 0.32598814, 0. 4.1861e-01, 3.7339e-01, 0.0000e+00
], ],
[ [
-1.2793612, -2.3155289, 0.15598366, 1.2822601, -1.4704e+00, -2.0024e+00, 2.7479e-01, 1.7888e+00,
2.2253945, 0.8361754, 0. 1.0566e+00, 1.3704e+00, 0.0000e+00
], ],
[ [
2.8716104, -0.26416883, -0.04933786, 0.8190681, 8.2727e-02, -3.1160e+00, 2.5690e-01, 1.4054e+00,
0.60294986, 0.5769499, 0. 2.0772e-01, 9.6792e-01, 0.0000e+00
], ],
[ [
-2.2109854, 0.19445783, -0.01614259, 0.40659013, 2.6896e+00, 1.9881e+00, 1.1566e+00, 9.9885e-02,
0.35370222, 0.3290567, 0. 3.5713e-01, 4.5638e-01, 0.0000e+00
]]), ]]),
origin=(0.5, 0.5, 0)),
'scores_3d': 'scores_3d':
torch.Tensor([0.9965866, 0.99507546, 0.9916463, 0.9702634, 0.95803124]) torch.tensor([
1.7516e-05, 1.0167e-06, 8.4486e-07, 7.1048e-02, 6.4274e-05,
1.5003e-07, 5.8102e-06, 1.9399e-08, 5.3126e-07, 1.8630e-09
])
}] }]
label2cat = { label2cat = {
...@@ -64,168 +64,67 @@ def test_indoor_eval(): ...@@ -64,168 +64,67 @@ def test_indoor_eval():
1: 'bed', 1: 'bed',
2: 'chair', 2: 'chair',
3: 'sofa', 3: 'sofa',
4: 'table',
5: 'door',
6: 'window',
7: 'bookshelf',
8: 'picture',
9: 'counter',
10: 'desk',
11: 'curtain',
12: 'refrigerator',
13: 'showercurtrain',
14: 'toilet',
15: 'sink',
16: 'bathtub',
17: 'garbagebin'
} }
gt_annos = [{ gt_annos = [{
'gt_num': 'gt_num':
12, 10,
'gt_boxes_upright_depth':
np.array([[
2.54621506, -0.89397144, 0.54144311, 2.90430856, 1.78370309,
0.93826824
],
[
3.36553669, 0.31014189, 0.38758934, 1.2504847,
0.71281439, 0.3908577
],
[
0.17272574, 2.90289116, 0.27966365, 0.56292468,
0.8512187, 0.4987641
],
[
2.39521956, 1.67557895, 0.40407273, 1.23511314,
0.49469376, 0.62720448
],
[
-2.41815996, -1.69104958, 0.22304082, 0.55816364,
0.48154473, 0.66580439
],
[
-0.18044823, 2.9227581, 0.24480903, 0.36165208,
0.44468427, 0.53103662
],
[
-2.44398379, -2.1610918, 0.23631772, 0.52229881,
0.63388562, 0.66596919
],
[
-2.01452827, -2.9558928, 0.8139953, 1.61732554,
0.60224247, 1.79295814
],
[
-0.61519569, 3.24365234, 1.24335742, 2.11988783,
0.26006722, 1.77748263
],
[
-2.64330673, 0.59929442, 1.59422684, 0.07352924,
0.28620502, 0.35408139
],
[
-0.58128822, 3.23699641, 0.06050609, 1.94151425,
0.16413498, 0.20168215
],
[
0.15343043, 2.24693251, 0.22470728, 0.49632657,
0.47379827, 0.43063563
]]),
'class':
np.array([3, 4, 4, 17, 2, 2, 2, 7, 11, 8, 17, 2])
}, {
'gt_num':
12,
'gt_boxes_upright_depth': 'gt_boxes_upright_depth':
np.array([[ np.array([[
3.48649406, 0.24238291, 0.48358256, 1.34014034, 0.72744983, -2.4089e-03, -3.3174e+00, 4.9438e-01, 2.1668e+00, 2.8431e-01,
0.40819243 1.6506e+00, 0.0000e+00
], ],
[ [
-0.50371504, 3.25293231, 1.25988698, 2.12330937, -3.4269e-01, -2.7565e+00, 2.8144e-02, 6.8554e-01,
0.27563906, 1.80230701 9.6854e-01, 6.1755e-01, 0.0000e+00
], ],
[ [
2.58820581, -0.99452347, 0.57732373, 2.94801593, -3.8320e+00, -1.0646e+00, 1.7074e-01, 2.4981e-01,
1.67463434, 0.88743341 4.4708e-01, 6.2538e-01, 0.0000e+00
], ],
[ [
-1.9116497, -2.88811016, 0.70502496, 1.62386703, 4.1073e-01, 3.3757e+00, 3.4311e-01, 8.0617e-01,
0.60732293, 1.5857985 2.8679e-01, 1.6060e+00, 0.0000e+00
], ],
[ [
-2.55324745, 0.6909315, 1.59045517, 0.07264495, 6.1199e-01, -3.1041e+00, 4.1873e-01, 1.2310e+00,
0.32018459, 0.3506999 4.0162e-01, 1.7303e+00, 0.0000e+00
], ],
[ [
-2.3436017, -2.1659112, 0.254318, 0.5333302, 0.56154585, -5.9877e-01, -2.6011e+00, 1.1148e+00, 1.5704e-01,
0.64904487 7.5957e-01, 9.6930e-01, 0.0000e+00
], ],
[ [
-2.32046795, -1.6880455, 0.26138437, 0.5586133, 2.7462e-01, -3.0088e+00, 6.5231e-02, 8.1208e-01,
0.59743834, 0.6378752 4.1861e-01, 3.7339e-01, 0.0000e+00
], ],
[ [
-0.46495372, 3.22126102, 0.03188983, 1.92557108, -1.4704e+00, -2.0024e+00, 2.7479e-01, 1.7888e+00,
0.15160203, 0.24680007 1.0566e+00, 1.3704e+00, 0.0000e+00
], ],
[ [
0.28087699, 2.88433838, 0.2495866, 0.57001019, 8.2727e-02, -3.1160e+00, 2.5690e-01, 1.4054e+00,
0.85177159, 0.5689255 2.0772e-01, 9.6792e-01, 0.0000e+00
], ],
[ [
-0.05292395, 2.90586925, 0.23064148, 0.39113954, 2.6896e+00, 1.9881e+00, 1.1566e+00, 9.9885e-02,
0.43746281, 0.52981442 3.5713e-01, 4.5638e-01, 0.0000e+00
],
[
0.25537968, 2.25156307, 0.24932587, 0.48192862,
0.51398182, 0.38040417
],
[
2.60432816, 1.62303996, 0.42025632, 1.23775268,
0.51761389, 0.66034317
]]), ]]),
'class': 'class':
np.array([4, 11, 3, 7, 8, 2, 2, 17, 4, 2, 2, 17]) np.array([0, 1, 2, 0, 0, 3, 1, 3, 3, 2])
}] }]
ret_value = indoor_eval(gt_annos, det_infos, [0.25, 0.5], label2cat) ret_value = indoor_eval(
garbagebin_AP_25 = ret_value['garbagebin_AP_0.25'] gt_annos,
sofa_AP_25 = ret_value['sofa_AP_0.25'] det_infos, [0.25, 0.5],
table_AP_25 = ret_value['table_AP_0.25'] label2cat,
chair_AP_25 = ret_value['chair_AP_0.25'] box_type_3d=DepthInstance3DBoxes,
mAP_25 = ret_value['mAP_0.25'] box_mode_3d=Box3DMode.DEPTH)
garbagebin_rec_25 = ret_value['garbagebin_rec_0.25']
sofa_rec_25 = ret_value['sofa_rec_0.25'] assert abs(ret_value['cabinet_AP_0.25'] - 0.666667) < 1e-3
table_rec_25 = ret_value['table_rec_0.25'] assert abs(ret_value['bed_AP_0.25'] - 1.0) < 1e-3
chair_rec_25 = ret_value['chair_rec_0.25'] assert abs(ret_value['chair_AP_0.25'] - 0.5) < 1e-3
mAR_25 = ret_value['mAR_0.25'] assert abs(ret_value['mAP_0.25'] - 0.708333) < 1e-3
sofa_AP_50 = ret_value['sofa_AP_0.50'] assert abs(ret_value['mAR_0.25'] - 0.833333) < 1e-3
table_AP_50 = ret_value['table_AP_0.50']
chair_AP_50 = ret_value['chair_AP_0.50']
mAP_50 = ret_value['mAP_0.50']
sofa_rec_50 = ret_value['sofa_rec_0.50']
table_rec_50 = ret_value['table_rec_0.50']
chair_rec_50 = ret_value['chair_rec_0.50']
mAR_50 = ret_value['mAR_0.50']
assert garbagebin_AP_25 == 0.25
assert sofa_AP_25 == 1.0
assert table_AP_25 == 0.75
assert chair_AP_25 == 0.125
assert abs(mAP_25 - 0.303571) < 0.001
assert garbagebin_rec_25 == 0.25
assert sofa_rec_25 == 1.0
assert table_rec_25 == 0.75
assert chair_rec_25 == 0.125
assert abs(mAR_25 - 0.303571) < 0.001
assert sofa_AP_50 == 0.25
assert abs(table_AP_50 - 0.416667) < 0.001
assert chair_AP_50 == 0.125
assert abs(mAP_50 - 0.113095) < 0.001
assert sofa_rec_50 == 0.5
assert table_rec_50 == 0.5
assert chair_rec_50 == 0.125
assert abs(mAR_50 - 0.160714) < 0.001
def test_average_precision(): def test_average_precision():
......
tests/test_dataset/test_scannet_dataset.py
View file @ b70ecdb5
...@@ -67,13 +67,12 @@ def test_getitem(): ...@@ -67,13 +67,12 @@ def test_getitem():
[-1.332374, 1.474838, -0.04405887, -0.00887359], [-1.332374, 1.474838, -0.04405887, -0.00887359],
[2.1336637, -1.3265059, -0.02880373, 0.00638155], [2.1336637, -1.3265059, -0.02880373, 0.00638155],
[0.43895668, -3.0259454, 1.5560012, 1.5911865]]) [0.43895668, -3.0259454, 1.5560012, 1.5911865]])
expected_gt_bboxes_3d = np.array([ expected_gt_bboxes_3d = torch.tensor(
[-1.5005362, -3.512584, 1.8565295, 1.7457027, 0.24149807, 0.57235193], [[-1.5005, -3.5126, 1.5704, 1.7457, 0.2415, 0.5724, 0.0000],
[-2.8848705, 3.4961755, 1.5268247, 0.66170084, 0.17433672, 0.67153597], [-2.8849, 3.4962, 1.1911, 0.6617, 0.1743, 0.6715, 0.0000],
[-1.1585636, -2.192365, 0.61649567, 0.5557011, 2.5375574, 1.2144762], [-1.1586, -2.1924, 0.0093, 0.5557, 2.5376, 1.2145, 0.0000],
[-2.930457, -2.4856408, 0.9722377, 0.6270478, 1.8461524, 0.28697443], [-2.9305, -2.4856, 0.8288, 0.6270, 1.8462, 0.2870, 0.0000],
[3.3114715, -0.00476722, 1.0712197, 0.46191898, 3.8605113, 2.1603441] [3.3115, -0.0048, -0.0090, 0.4619, 3.8605, 2.1603, 0.0000]])
])
expected_gt_labels = np.array([ expected_gt_labels = np.array([
6, 6, 4, 9, 11, 11, 10, 0, 15, 17, 17, 17, 3, 12, 4, 4, 14, 1, 0, 0, 0, 6, 6, 4, 9, 11, 11, 10, 0, 15, 17, 17, 17, 3, 12, 4, 4, 14, 1, 0, 0, 0,
0, 0, 0, 5, 5, 5 0, 0, 0, 5, 5, 5
...@@ -84,8 +83,8 @@ def test_getitem(): ...@@ -84,8 +83,8 @@ def test_getitem():
assert scannet_dataset.CLASSES == class_names assert scannet_dataset.CLASSES == class_names
assert np.allclose(points, expected_points) assert np.allclose(points, expected_points)
assert gt_bboxes_3d[:5].shape == (5, 6) assert gt_bboxes_3d.tensor[:5].shape == (5, 7)
assert np.allclose(gt_bboxes_3d[:5], expected_gt_bboxes_3d) assert torch.allclose(gt_bboxes_3d.tensor[:5], expected_gt_bboxes_3d, 1e-2)
assert np.all(gt_labels.numpy() == expected_gt_labels) assert np.all(gt_labels.numpy() == expected_gt_labels)
assert np.all(pts_semantic_mask.numpy() == expected_pts_semantic_mask) assert np.all(pts_semantic_mask.numpy() == expected_pts_semantic_mask)
assert np.all(pts_instance_mask.numpy() == expected_pts_instance_mask) assert np.all(pts_instance_mask.numpy() == expected_pts_instance_mask)
...@@ -114,42 +113,51 @@ def test_getitem(): ...@@ -114,42 +113,51 @@ def test_getitem():
def test_evaluate(): def test_evaluate():
from mmdet3d.core.bbox.structures import DepthInstance3DBoxes
root_path = './tests/data/scannet' root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl' ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetDataset(root_path, ann_file) scannet_dataset = ScanNetDataset(root_path, ann_file)
results = [] results = []
pred_boxes = dict() pred_boxes = dict()
pred_boxes['boxes_3d'] = torch.Tensor( pred_boxes['boxes_3d'] = DepthInstance3DBoxes(
[[ torch.tensor([[
3.52074146e+00, -1.48129511e+00, 1.57035351e+00, 2.31956959e-01, 1.4813e+00, 3.5207e+00, 1.5704e+00, 1.7445e+00, 2.3196e-01,
1.74445975e+00, 5.72351933e-01, 0 5.7235e-01, 0.0000e+00
],
[
2.9040e+00, -3.4803e+00, 1.1911e+00, 6.6078e-01,
1.7072e-01, 6.7154e-01, 0.0000e+00
],
[
1.1466e+00, 2.1987e+00, 9.2576e-03, 5.4184e-01,
2.5346e+00, 1.2145e+00, 0.0000e+00
],
[
2.9168e+00, 2.5016e+00, 8.2875e-01, 6.1697e-01,
1.8428e+00, 2.8697e-01, 0.0000e+00
], ],
[ [
-3.48033905e+00, -2.90395617e+00, 1.19105673e+00, 1.70723915e-01, -3.3114e+00, -1.3351e-02, -8.9524e-03, 4.4082e-01,
6.60776615e-01, 6.71535969e-01, 0 3.8582e+00, 2.1603e+00, 0.0000e+00
], ],
[ [
2.19867110e+00, -1.14655101e+00, 9.25755501e-03, 2.53463078e+00, -2.0135e+00, -3.4857e+00, 9.3848e-01, 1.9911e+00,
5.41841269e-01, 1.21447623e+00, 0 2.1603e-01, 1.2767e+00, 0.0000e+00
], ],
[ [
2.50163722, -2.91681337, 0.82875049, 1.84280431, 0.61697435, -2.1945e+00, -3.1402e+00, -3.8165e-02, 1.4801e+00,
0.28697443, 0 6.8676e-01, 1.0586e+00, 0.0000e+00
], ],
[ [
-0.01335114, 3.3114481, -0.00895238, 3.85815716, 0.44081616, -2.7553e+00, 2.4055e+00, -2.9972e-02, 1.4764e+00,
2.16034412, 0 1.4927e+00, 2.3380e+00, 0.0000e+00
]]) ]]))
pred_boxes['labels_3d'] = torch.Tensor([6, 6, 4, 9, 11]) pred_boxes['labels_3d'] = torch.tensor([6, 6, 4, 9, 11, 11])
pred_boxes['scores_3d'] = torch.Tensor([0.5, 1.0, 1.0, 1.0, 1.0]) pred_boxes['scores_3d'] = torch.tensor([0.5, 1.0, 1.0, 1.0, 1.0, 0.5])
results.append(pred_boxes) results.append(pred_boxes)
metric = [0.25, 0.5] metric = [0.25, 0.5]
ret_dict = scannet_dataset.evaluate(results, metric) ret_dict = scannet_dataset.evaluate(results, metric)
table_average_precision_25 = ret_dict['table_AP_0.25'] assert abs(ret_dict['table_AP_0.25'] - 0.3333) < 0.01
window_average_precision_25 = ret_dict['window_AP_0.25'] assert abs(ret_dict['window_AP_0.25'] - 1.0) < 0.01
counter_average_precision_25 = ret_dict['counter_AP_0.25'] assert abs(ret_dict['counter_AP_0.25'] - 1.0) < 0.01
curtain_average_precision_25 = ret_dict['curtain_AP_0.25'] assert abs(ret_dict['curtain_AP_0.25'] - 1.0) < 0.01
assert abs(table_average_precision_25 - 0.3333) < 0.01
assert abs(window_average_precision_25 - 1) < 0.01
assert abs(counter_average_precision_25 - 1) < 0.01
assert abs(curtain_average_precision_25 - 0.5) < 0.01
tests/test_dataset/test_sunrgbd_dataset.py
View file @ b70ecdb5
...@@ -57,25 +57,15 @@ def test_getitem(): ...@@ -57,25 +57,15 @@ def test_getitem():
[0.6464, 1.5635, 0.0826, 0.0616], [0.6464, 1.5635, 0.0826, 0.0616],
[0.6453, 1.5603, 0.0849, 0.0638], [0.6453, 1.5603, 0.0849, 0.0638],
[0.6488, 1.5786, 0.0461, 0.0251]]) [0.6488, 1.5786, 0.0461, 0.0251]])
expected_gt_bboxes_3d = np.array([[ expected_gt_bboxes_3d = torch.tensor(
-2.012483, 3.9473376, -0.25446942, 2.3730404, 1.9457763, 2.0303352, [[-2.0125, 3.9473, -1.2696, 2.3730, 1.9458, 2.0303, 1.2206],
1.2205974 [-3.7037, 4.2396, -1.3126, 0.6032, 0.9104, 1.0033, 1.2663],
], [0.6529, 2.1638, -1.2370, 0.7348, 1.6113, 2.1694, 2.8140]])
[
-3.7036808, 4.2396426, -0.81091917,
0.6032123, 0.91040343, 1.003341,
1.2662518
],
[
0.6528646, 2.1638472, -0.15228128,
0.7347852, 1.6113238, 2.1694272,
2.81404
]])
expected_gt_labels = np.array([0, 7, 6]) expected_gt_labels = np.array([0, 7, 6])
original_classes = sunrgbd_dataset.CLASSES original_classes = sunrgbd_dataset.CLASSES
assert np.allclose(points, expected_points, 1e-2) assert np.allclose(points, expected_points, 1e-2)
assert np.allclose(gt_bboxes_3d, expected_gt_bboxes_3d) assert np.allclose(gt_bboxes_3d.tensor, expected_gt_bboxes_3d, 1e-3)
assert np.all(gt_labels_3d.numpy() == expected_gt_labels) assert np.all(gt_labels_3d.numpy() == expected_gt_labels)
assert original_classes == class_names assert original_classes == class_names
...@@ -101,23 +91,19 @@ def test_getitem(): ...@@ -101,23 +91,19 @@ def test_getitem():
def test_evaluate(): def test_evaluate():
from mmdet3d.core.bbox.structures import DepthInstance3DBoxes
root_path = './tests/data/sunrgbd' root_path = './tests/data/sunrgbd'
ann_file = './tests/data/sunrgbd/sunrgbd_infos.pkl' ann_file = './tests/data/sunrgbd/sunrgbd_infos.pkl'
sunrgbd_dataset = SUNRGBDDataset(root_path, ann_file) sunrgbd_dataset = SUNRGBDDataset(root_path, ann_file)
results = [] results = []
pred_boxes = dict() pred_boxes = dict()
pred_boxes['boxes_3d'] = torch.Tensor( pred_boxes['boxes_3d'] = DepthInstance3DBoxes(
[[ torch.tensor(
4.168696, -1.047307, -1.231666, 1.887584, 2.30207, 1.969614, [[1.0473, 4.1687, -1.2317, 2.3021, 1.8876, 1.9696, 1.6956],
1.69564944 [2.5831, 4.8117, -1.2733, 0.5852, 0.8832, 0.9733, 1.6500],
], [-1.0864, 1.9045, -1.2000, 0.7128, 1.5631, 2.1045, 0.1022]]))
[ pred_boxes['labels_3d'] = torch.tensor([0, 7, 6])
4.811675, -2.583086, -1.273334, 0.883176, 0.585172, 0.973334, pred_boxes['scores_3d'] = torch.tensor([0.5, 1.0, 1.0])
1.64999513
], [1.904545, 1.086364, -1.2, 1.563134, 0.71281, 2.104546,
0.1022069]])
pred_boxes['labels_3d'] = torch.Tensor([0, 7, 6])
pred_boxes['scores_3d'] = torch.Tensor([0.5, 1.0, 1.0])
results.append(pred_boxes) results.append(pred_boxes)
metric = [0.25, 0.5] metric = [0.25, 0.5]
ap_dict = sunrgbd_dataset.evaluate(results, metric) ap_dict = sunrgbd_dataset.evaluate(results, metric)
......
tests/test_pipeline/test_indoor_augment.py
View file @ b70ecdb5
import numpy as np import numpy as np
import torch
from mmdet3d.core.bbox import DepthInstance3DBoxes
from mmdet3d.datasets.pipelines import IndoorFlipData, IndoorGlobalRotScale from mmdet3d.datasets.pipelines import IndoorFlipData, IndoorGlobalRotScale
...@@ -10,15 +12,15 @@ def test_indoor_flip_data(): ...@@ -10,15 +12,15 @@ def test_indoor_flip_data():
sunrgbd_results['points'] = np.array( sunrgbd_results['points'] = np.array(
[[1.02828765e+00, 3.65790772e+00, 1.97294697e-01, 1.61959505e+00], [[1.02828765e+00, 3.65790772e+00, 1.97294697e-01, 1.61959505e+00],
[-3.95979017e-01, 1.05465031e+00, -7.49204338e-01, 6.73096001e-01]]) [-3.95979017e-01, 1.05465031e+00, -7.49204338e-01, 6.73096001e-01]])
sunrgbd_results['gt_bboxes_3d'] = np.array([[ sunrgbd_results['gt_bboxes_3d'] = DepthInstance3DBoxes(
0.213684, 1.036364, -0.982323, 0.61541, 0.572574, 0.872728, 3.07028526 np.array([[
0.213684, 1.036364, -0.982323, 0.61541, 0.572574, 0.872728,
3.07028526
], ],
[ [
-0.449953, 1.395455, -0.449953, 1.395455, -1.027778, 1.500956, 1.637298,
-1.027778, 1.500956, 0.636364, -1.58242359
1.637298, 0.636364, ]]))
-1.58242359
]])
sunrgbd_results = sunrgbd_indoor_flip_data(sunrgbd_results) sunrgbd_results = sunrgbd_indoor_flip_data(sunrgbd_results)
sunrgbd_points = sunrgbd_results['points'] sunrgbd_points = sunrgbd_results['points']
sunrgbd_gt_bboxes_3d = sunrgbd_results['gt_bboxes_3d'] sunrgbd_gt_bboxes_3d = sunrgbd_results['gt_bboxes_3d']
...@@ -26,13 +28,12 @@ def test_indoor_flip_data(): ...@@ -26,13 +28,12 @@ def test_indoor_flip_data():
expected_sunrgbd_points = np.array( expected_sunrgbd_points = np.array(
[[-1.02828765, 3.65790772, 0.1972947, 1.61959505], [[-1.02828765, 3.65790772, 0.1972947, 1.61959505],
[0.39597902, 1.05465031, -0.74920434, 0.673096]]) [0.39597902, 1.05465031, -0.74920434, 0.673096]])
expected_sunrgbd_gt_bboxes_3d = np.array([[ expected_sunrgbd_gt_bboxes_3d = torch.tensor(
-0.213684, 1.036364, -0.982323, 0.61541, 0.572574, 0.872728, 0.07130739 [[-0.2137, 1.0364, -0.9823, 0.6154, 0.5726, 0.8727, 0.0713],
], [ [0.4500, 1.3955, -1.0278, 1.5010, 1.6373, 0.6364, 4.7240]])
0.449953, 1.395455, -1.027778, 1.500956, 1.637298, 0.636364, 4.72401624
]])
assert np.allclose(sunrgbd_points, expected_sunrgbd_points) assert np.allclose(sunrgbd_points, expected_sunrgbd_points)
assert np.allclose(sunrgbd_gt_bboxes_3d, expected_sunrgbd_gt_bboxes_3d) assert torch.allclose(sunrgbd_gt_bboxes_3d.tensor,
expected_sunrgbd_gt_bboxes_3d, 1e-3)
np.random.seed(0) np.random.seed(0)
scannet_indoor_flip_data = IndoorFlipData(1, 1) scannet_indoor_flip_data = IndoorFlipData(1, 1)
...@@ -40,11 +41,17 @@ def test_indoor_flip_data(): ...@@ -40,11 +41,17 @@ def test_indoor_flip_data():
scannet_results['points'] = np.array( scannet_results['points'] = np.array(
[[1.6110241e+00, -1.6903955e-01, 5.8115810e-01, 5.9897250e-01], [[1.6110241e+00, -1.6903955e-01, 5.8115810e-01, 5.9897250e-01],
[1.3978075e+00, 4.2035791e-01, 3.8729519e-01, 4.0510958e-01]]) [1.3978075e+00, 4.2035791e-01, 3.8729519e-01, 4.0510958e-01]])
scannet_results['gt_bboxes_3d'] = np.array([[ scannet_results['gt_bboxes_3d'] = DepthInstance3DBoxes(
0.55903838, 0.48201692, 0.65688646, 0.65370704, 0.60029864, 0.5163464 np.array([[
], [ 0.55903838, 0.48201692, 0.65688646, 0.65370704, 0.60029864,
-0.03226406, 1.70392646, 0.60348618, 0.65165804, 0.72084366, 0.64667457 0.5163464
]]) ],
[
-0.03226406, 1.70392646, 0.60348618, 0.65165804,
0.72084366, 0.64667457
]]),
box_dim=6,
with_yaw=False)
scannet_results = scannet_indoor_flip_data(scannet_results) scannet_results = scannet_indoor_flip_data(scannet_results)
scannet_points = scannet_results['points'] scannet_points = scannet_results['points']
scannet_gt_bboxes_3d = scannet_results['gt_bboxes_3d'] scannet_gt_bboxes_3d = scannet_results['gt_bboxes_3d']
...@@ -52,13 +59,12 @@ def test_indoor_flip_data(): ...@@ -52,13 +59,12 @@ def test_indoor_flip_data():
expected_scannet_points = np.array( expected_scannet_points = np.array(
[[-1.6110241, 0.16903955, 0.5811581, 0.5989725], [[-1.6110241, 0.16903955, 0.5811581, 0.5989725],
[-1.3978075, -0.42035791, 0.38729519, 0.40510958]]) [-1.3978075, -0.42035791, 0.38729519, 0.40510958]])
expected_scannet_gt_bboxes_3d = np.array([[ expected_scannet_gt_bboxes_3d = torch.tensor(
-0.55903838, -0.48201692, 0.65688646, 0.65370704, 0.60029864, 0.5163464 [[-0.5590, -0.4820, 0.6569, 0.6537, 0.6003, 0.5163, 0.0000],
], [ [0.0323, -1.7039, 0.6035, 0.6517, 0.7208, 0.6467, 0.0000]])
0.03226406, -1.70392646, 0.60348618, 0.65165804, 0.72084366, 0.64667457
]])
assert np.allclose(scannet_points, expected_scannet_points) assert np.allclose(scannet_points, expected_scannet_points)
assert np.allclose(scannet_gt_bboxes_3d, expected_scannet_gt_bboxes_3d) assert torch.allclose(scannet_gt_bboxes_3d.tensor,
expected_scannet_gt_bboxes_3d, 1e-2)
def test_global_rot_scale(): def test_global_rot_scale():
...@@ -69,15 +75,15 @@ def test_global_rot_scale(): ...@@ -69,15 +75,15 @@ def test_global_rot_scale():
sunrgbd_results['points'] = np.array( sunrgbd_results['points'] = np.array(
[[1.02828765e+00, 3.65790772e+00, 1.97294697e-01, 1.61959505e+00], [[1.02828765e+00, 3.65790772e+00, 1.97294697e-01, 1.61959505e+00],
[-3.95979017e-01, 1.05465031e+00, -7.49204338e-01, 6.73096001e-01]]) [-3.95979017e-01, 1.05465031e+00, -7.49204338e-01, 6.73096001e-01]])
sunrgbd_results['gt_bboxes_3d'] = np.array([[ sunrgbd_results['gt_bboxes_3d'] = DepthInstance3DBoxes(
0.213684, 1.036364, -0.982323, 0.61541, 0.572574, 0.872728, 3.07028526 np.array([[
0.213684, 1.036364, -0.982323, 0.61541, 0.572574, 0.872728,
3.07028526
], ],
[ [
-0.449953, 1.395455, -0.449953, 1.395455, -1.027778, 1.500956, 1.637298,
-1.027778, 1.500956, 0.636364, -1.58242359
1.637298, 0.636364, ]]))
-1.58242359
]])
sunrgbd_results = sunrgbd_augment(sunrgbd_results) sunrgbd_results = sunrgbd_augment(sunrgbd_results)
sunrgbd_points = sunrgbd_results['points'] sunrgbd_points = sunrgbd_results['points']
...@@ -86,18 +92,12 @@ def test_global_rot_scale(): ...@@ -86,18 +92,12 @@ def test_global_rot_scale():
expected_sunrgbd_points = np.array( expected_sunrgbd_points = np.array(
[[0.89427376, 3.94489646, 0.21003141, 1.72415094], [[0.89427376, 3.94489646, 0.21003141, 1.72415094],
[-0.47835783, 1.09972989, -0.79757058, 0.71654893]]) [-0.47835783, 1.09972989, -0.79757058, 0.71654893]])
expected_sunrgbd_gt_bboxes_3d = np.array([[ expected_sunrgbd_gt_bboxes_3d = torch.tensor(
0.17080999, 1.11345031, -1.04573864, 0.65513891, 0.60953755, [[0.1708, 1.1135, -1.0457, 0.6551, 0.6095, 0.9291, 3.0192],
0.92906854, 3.01916788 [-0.5543, 1.4591, -1.0941, 1.5979, 1.7430, 0.6774, -1.6335]])
],
[
-0.55427876, 1.45912611,
-1.09412807, 1.59785293,
1.74299674, 0.67744563,
-1.63354097
]])
assert np.allclose(sunrgbd_points, expected_sunrgbd_points) assert np.allclose(sunrgbd_points, expected_sunrgbd_points)
assert np.allclose(sunrgbd_gt_bboxes_3d, expected_sunrgbd_gt_bboxes_3d) assert torch.allclose(sunrgbd_gt_bboxes_3d.tensor,
expected_sunrgbd_gt_bboxes_3d, 1e-3)
np.random.seed(0) np.random.seed(0)
scannet_augment = IndoorGlobalRotScale( scannet_augment = IndoorGlobalRotScale(
...@@ -106,11 +106,17 @@ def test_global_rot_scale(): ...@@ -106,11 +106,17 @@ def test_global_rot_scale():
scannet_results['points'] = np.array( scannet_results['points'] = np.array(
[[1.6110241e+00, -1.6903955e-01, 5.8115810e-01, 5.9897250e-01], [[1.6110241e+00, -1.6903955e-01, 5.8115810e-01, 5.9897250e-01],
[1.3978075e+00, 4.2035791e-01, 3.8729519e-01, 4.0510958e-01]]) [1.3978075e+00, 4.2035791e-01, 3.8729519e-01, 4.0510958e-01]])
scannet_results['gt_bboxes_3d'] = np.array([[ scannet_results['gt_bboxes_3d'] = DepthInstance3DBoxes(
0.55903838, 0.48201692, 0.65688646, 0.65370704, 0.60029864, 0.5163464 np.array([[
], [ 0.55903838, 0.48201692, 0.65688646, 0.65370704, 0.60029864,
-0.03226406, 1.70392646, 0.60348618, 0.65165804, 0.72084366, 0.64667457 0.5163464
]]) ],
[
-0.03226406, 1.70392646, 0.60348618, 0.65165804,
0.72084366, 0.64667457
]]),
box_dim=6,
with_yaw=False)
scannet_results = scannet_augment(scannet_results) scannet_results = scannet_augment(scannet_results)
scannet_points = scannet_results['points'] scannet_points = scannet_results['points']
scannet_gt_bboxes_3d = scannet_results['gt_bboxes_3d'] scannet_gt_bboxes_3d = scannet_results['gt_bboxes_3d']
...@@ -118,10 +124,9 @@ def test_global_rot_scale(): ...@@ -118,10 +124,9 @@ def test_global_rot_scale():
expected_scannet_points = np.array( expected_scannet_points = np.array(
[[1.61240576, -0.15530836, 0.5811581, 0.5989725], [[1.61240576, -0.15530836, 0.5811581, 0.5989725],
[1.39417555, 0.43225122, 0.38729519, 0.40510958]]) [1.39417555, 0.43225122, 0.38729519, 0.40510958]])
expected_scannet_gt_bboxes_3d = np.array([[ expected_scannet_gt_bboxes_3d = torch.tensor(
0.55491157, 0.48676213, 0.65688646, 0.65879754, 0.60584609, 0.5163464 [[0.5549, 0.4868, 0.6569, 0.6588, 0.6058, 0.5163, 0.0000],
], [ [-0.0468, 1.7036, 0.6035, 0.6578, 0.7264, 0.6467, 0.0000]])
-0.04677942, 1.70358975, 0.60348618, 0.65777559, 0.72636927, 0.64667457
]])
assert np.allclose(scannet_points, expected_scannet_points) assert np.allclose(scannet_points, expected_scannet_points)
assert np.allclose(scannet_gt_bboxes_3d, expected_scannet_gt_bboxes_3d) assert torch.allclose(scannet_gt_bboxes_3d.tensor,
expected_scannet_gt_bboxes_3d, 1e-3)
tests/test_pipeline/test_indoor_pipeline.py
View file @ b70ecdb5
...@@ -2,7 +2,9 @@ import os.path as osp ...@@ -2,7 +2,9 @@ import os.path as osp
import mmcv import mmcv
import numpy as np import numpy as np
import torch
from mmdet3d.core.bbox import DepthInstance3DBoxes
from mmdet3d.datasets.pipelines import Compose from mmdet3d.datasets.pipelines import Compose
...@@ -57,7 +59,8 @@ def test_scannet_pipeline(): ...@@ -57,7 +59,8 @@ def test_scannet_pipeline():
data_path, info['pts_instance_mask_path']) data_path, info['pts_instance_mask_path'])
results['ann_info']['pts_semantic_mask_path'] = osp.join( results['ann_info']['pts_semantic_mask_path'] = osp.join(
data_path, info['pts_semantic_mask_path']) data_path, info['pts_semantic_mask_path'])
results['ann_info']['gt_bboxes_3d'] = scannet_gt_bboxes_3d results['ann_info']['gt_bboxes_3d'] = DepthInstance3DBoxes(
scannet_gt_bboxes_3d, box_dim=6, with_yaw=False)
results['ann_info']['gt_labels_3d'] = scannet_gt_labels_3d results['ann_info']['gt_labels_3d'] = scannet_gt_labels_3d
results['bbox3d_fields'] = [] results['bbox3d_fields'] = []
...@@ -77,13 +80,12 @@ def test_scannet_pipeline(): ...@@ -77,13 +80,12 @@ def test_scannet_pipeline():
[-1.332374, 1.474838, -0.04405887, -0.00887359], [-1.332374, 1.474838, -0.04405887, -0.00887359],
[2.1336637, -1.3265059, -0.02880373, 0.00638155], [2.1336637, -1.3265059, -0.02880373, 0.00638155],
[0.43895668, -3.0259454, 1.5560012, 1.5911865]]) [0.43895668, -3.0259454, 1.5560012, 1.5911865]])
expected_gt_bboxes_3d = np.array([ expected_gt_bboxes_3d = torch.tensor(
[-1.5005362, -3.512584, 1.8565295, 1.7457027, 0.24149807, 0.57235193], [[-1.5005, -3.5126, 1.8565, 1.7457, 0.2415, 0.5724, 0.0000],
[-2.8848705, 3.4961755, 1.5268247, 0.66170084, 0.17433672, 0.67153597], [-2.8849, 3.4962, 1.5268, 0.6617, 0.1743, 0.6715, 0.0000],
[-1.1585636, -2.192365, 0.61649567, 0.5557011, 2.5375574, 1.2144762], [-1.1586, -2.1924, 0.6165, 0.5557, 2.5376, 1.2145, 0.0000],
[-2.930457, -2.4856408, 0.9722377, 0.6270478, 1.8461524, 0.28697443], [-2.9305, -2.4856, 0.9722, 0.6270, 1.8462, 0.2870, 0.0000],
[3.3114715, -0.00476722, 1.0712197, 0.46191898, 3.8605113, 2.1603441] [3.3115, -0.0048, 1.0712, 0.4619, 3.8605, 2.1603, 0.0000]])
])
expected_gt_labels_3d = np.array([ expected_gt_labels_3d = np.array([
6, 6, 4, 9, 11, 11, 10, 0, 15, 17, 17, 17, 3, 12, 4, 4, 14, 1, 0, 0, 0, 6, 6, 4, 9, 11, 11, 10, 0, 15, 17, 17, 17, 3, 12, 4, 4, 14, 1, 0, 0, 0,
0, 0, 0, 5, 5, 5 0, 0, 0, 5, 5, 5
...@@ -91,7 +93,8 @@ def test_scannet_pipeline(): ...@@ -91,7 +93,8 @@ def test_scannet_pipeline():
expected_pts_semantic_mask = np.array([3, 1, 2, 2, 15]) expected_pts_semantic_mask = np.array([3, 1, 2, 2, 15])
expected_pts_instance_mask = np.array([44, 22, 10, 10, 57]) expected_pts_instance_mask = np.array([44, 22, 10, 10, 57])
assert np.allclose(points, expected_points) assert np.allclose(points, expected_points)
assert np.allclose(gt_bboxes_3d[:5, :], expected_gt_bboxes_3d) assert torch.allclose(gt_bboxes_3d.tensor[:5, :], expected_gt_bboxes_3d,
1e-2)
assert np.all(gt_labels_3d.numpy() == expected_gt_labels_3d) assert np.all(gt_labels_3d.numpy() == expected_gt_labels_3d)
assert np.all(pts_semantic_mask.numpy() == expected_pts_semantic_mask) assert np.all(pts_semantic_mask.numpy() == expected_pts_semantic_mask)
assert np.all(pts_instance_mask.numpy() == expected_pts_instance_mask) assert np.all(pts_instance_mask.numpy() == expected_pts_instance_mask)
...@@ -130,12 +133,12 @@ def test_sunrgbd_pipeline(): ...@@ -130,12 +133,12 @@ def test_sunrgbd_pipeline():
np.float32) np.float32)
gt_labels_3d = info['annos']['class'].astype(np.long) gt_labels_3d = info['annos']['class'].astype(np.long)
else: else:
gt_bboxes_3d = np.zeros((1, 6), dtype=np.float32) gt_bboxes_3d = np.zeros((1, 7), dtype=np.float32)
gt_labels_3d = np.zeros((1, ), dtype=np.long) gt_labels_3d = np.zeros((1, ), dtype=np.long)
# prepare input of pipeline # prepare input of pipeline
results['ann_info'] = dict() results['ann_info'] = dict()
results['ann_info']['gt_bboxes_3d'] = gt_bboxes_3d results['ann_info']['gt_bboxes_3d'] = DepthInstance3DBoxes(gt_bboxes_3d)
results['ann_info']['gt_labels_3d'] = gt_labels_3d results['ann_info']['gt_labels_3d'] = gt_labels_3d
results['bbox3d_fields'] = [] results['bbox3d_fields'] = []
results['pts_mask_fields'] = [] results['pts_mask_fields'] = []
...@@ -150,21 +153,11 @@ def test_sunrgbd_pipeline(): ...@@ -150,21 +153,11 @@ def test_sunrgbd_pipeline():
[0.6464, 1.5635, 0.0826, 0.0616], [0.6464, 1.5635, 0.0826, 0.0616],
[0.6453, 1.5603, 0.0849, 0.0638], [0.6453, 1.5603, 0.0849, 0.0638],
[0.6488, 1.5786, 0.0461, 0.0251]]) [0.6488, 1.5786, 0.0461, 0.0251]])
expected_gt_bboxes_3d = np.array([[ expected_gt_bboxes_3d = torch.tensor(
-2.012483, 3.9473376, -0.25446942, 2.3730404, 1.9457763, 2.0303352, [[-2.0125, 3.9473, -0.2545, 2.3730, 1.9458, 2.0303, 1.2206],
1.2205974 [-3.7037, 4.2396, -0.8109, 0.6032, 0.9104, 1.0033, 1.2663],
], [0.6529, 2.1638, -0.1523, 0.7348, 1.6113, 2.1694, 2.8140]])
[
-3.7036808, 4.2396426, -0.81091917,
0.6032123, 0.91040343, 1.003341,
1.2662518
],
[
0.6528646, 2.1638472, -0.15228128,
0.7347852, 1.6113238, 2.1694272,
2.81404
]])
expected_gt_labels_3d = np.array([0, 7, 6]) expected_gt_labels_3d = np.array([0, 7, 6])
assert np.allclose(gt_bboxes_3d, expected_gt_bboxes_3d) assert torch.allclose(gt_bboxes_3d.tensor, expected_gt_bboxes_3d, 1e-3)
assert np.allclose(gt_labels_3d.flatten(), expected_gt_labels_3d) assert np.allclose(gt_labels_3d.flatten(), expected_gt_labels_3d)
assert np.allclose(points, expected_points, 1e-2) assert np.allclose(points, expected_points, 1e-2)
tests/test_pipeline/test_loading.py
View file @ b70ecdb5
...@@ -4,6 +4,7 @@ import mmcv ...@@ -4,6 +4,7 @@ import mmcv
import numpy as np import numpy as np
import pytest import pytest
from mmdet3d.core.bbox import DepthInstance3DBoxes
from mmdet3d.datasets.pipelines import LoadAnnotations3D, LoadPointsFromFile from mmdet3d.datasets.pipelines import LoadAnnotations3D, LoadPointsFromFile
...@@ -79,7 +80,8 @@ def test_load_annotations3D(): ...@@ -79,7 +80,8 @@ def test_load_annotations3D():
data_path, scannet_info['pts_instance_mask_path']) data_path, scannet_info['pts_instance_mask_path'])
scannet_results['ann_info']['pts_semantic_mask_path'] = osp.join( scannet_results['ann_info']['pts_semantic_mask_path'] = osp.join(
data_path, scannet_info['pts_semantic_mask_path']) data_path, scannet_info['pts_semantic_mask_path'])
scannet_results['ann_info']['gt_bboxes_3d'] = scannet_gt_bboxes_3d scannet_results['ann_info']['gt_bboxes_3d'] = DepthInstance3DBoxes(
scannet_gt_bboxes_3d, box_dim=6, with_yaw=False)
scannet_results['ann_info']['gt_labels_3d'] = scannet_gt_labels_3d scannet_results['ann_info']['gt_labels_3d'] = scannet_gt_labels_3d
scannet_results['bbox3d_fields'] = [] scannet_results['bbox3d_fields'] = []
...@@ -92,7 +94,7 @@ def test_load_annotations3D(): ...@@ -92,7 +94,7 @@ def test_load_annotations3D():
scannet_pts_instance_mask = scannet_results['pts_instance_mask'] scannet_pts_instance_mask = scannet_results['pts_instance_mask']
scannet_pts_semantic_mask = scannet_results['pts_semantic_mask'] scannet_pts_semantic_mask = scannet_results['pts_semantic_mask']
assert scannet_gt_boxes.shape == (27, 6) assert scannet_gt_boxes.tensor.shape == (27, 7)
assert scannet_gt_lbaels.shape == (27, ) assert scannet_gt_lbaels.shape == (27, )
assert scannet_pts_instance_mask.shape == (100, ) assert scannet_pts_instance_mask.shape == (100, )
assert scannet_pts_semantic_mask.shape == (100, ) assert scannet_pts_semantic_mask.shape == (100, )
tools/test.py
View file @ b70ecdb5
...@@ -9,7 +9,7 @@ from tools.fuse_conv_bn import fuse_module ...@@ -9,7 +9,7 @@ from tools.fuse_conv_bn import fuse_module
from mmdet3d.datasets import build_dataloader, build_dataset from mmdet3d.datasets import build_dataloader, build_dataset
from mmdet3d.models import build_detector from mmdet3d.models import build_detector
from mmdet.apis import multi_gpu_test, single_gpu_test from mmdet.apis import multi_gpu_test, set_random_seed, single_gpu_test
from mmdet.core import wrap_fp16_model from mmdet.core import wrap_fp16_model
...@@ -76,6 +76,11 @@ def parse_args(): ...@@ -76,6 +76,11 @@ def parse_args():
'--tmpdir', '--tmpdir',
help='tmp directory used for collecting results from multiple ' help='tmp directory used for collecting results from multiple '
'workers, available when gpu_collect is not specified') 'workers, available when gpu_collect is not specified')
parser.add_argument('--seed', type=int, default=0, help='random seed')
parser.add_argument(
'--deterministic',
action='store_true',
help='whether to set deterministic options for CUDNN backend.')
parser.add_argument( parser.add_argument(
'--options', nargs='+', action=MultipleKVAction, help='custom options') '--options', nargs='+', action=MultipleKVAction, help='custom options')
parser.add_argument( parser.add_argument(
...@@ -108,6 +113,7 @@ def main(): ...@@ -108,6 +113,7 @@ def main():
# set cudnn_benchmark # set cudnn_benchmark
if cfg.get('cudnn_benchmark', False): if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None cfg.model.pretrained = None
cfg.data.test.test_mode = True cfg.data.test.test_mode = True
...@@ -118,6 +124,10 @@ def main(): ...@@ -118,6 +124,10 @@ def main():
distributed = True distributed = True
init_dist(args.launcher, **cfg.dist_params) init_dist(args.launcher, **cfg.dist_params)
# set random seeds
if args.seed is not None:
set_random_seed(args.seed, deterministic=args.deterministic)
# build the dataloader # build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed) # TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test) dataset = build_dataset(cfg.data.test)
......
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