def test_evaluate():
if not torch.cuda.is_available():
pytest.skip()
from mmdet3d.core.bbox.structures import DepthInstance3DBoxes
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetDataset(root_path, ann_file)
results = []
pred_boxes = dict()
pred_boxes['boxes_3d'] = DepthInstance3DBoxes(
torch.tensor([[
1.4813e+00, 3.5207e+00, 1.5704e+00, 1.7445e+00, 2.3196e-01,
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
],
[
-3.3114e+00, -1.3351e-02, -8.9524e-03, 4.4082e-01,
3.8582e+00, 2.1603e+00, 0.0000e+00
],
[
-2.0135e+00, -3.4857e+00, 9.3848e-01, 1.9911e+00,
2.1603e-01, 1.2767e+00, 0.0000e+00
],
[
-2.1945e+00, -3.1402e+00, -3.8165e-02, 1.4801e+00,
6.8676e-01, 1.0586e+00, 0.0000e+00
],
[
-2.7553e+00, 2.4055e+00, -2.9972e-02, 1.4764e+00,
1.4927e+00, 2.3380e+00, 0.0000e+00
]]))
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, 0.5])
results.append(pred_boxes)
metric = [0.25, 0.5]
ret_dict = scannet_dataset.evaluate(results, metric)
assert abs(ret_dict['table_AP_0.25'] - 0.3333) < 0.01
assert abs(ret_dict['window_AP_0.25'] - 1.0) < 0.01
assert abs(ret_dict['counter_AP_0.25'] - 1.0) < 0.01
assert abs(ret_dict['curtain_AP_0.25'] - 1.0) < 0.01
def test_evaluate():
if not torch.cuda.is_available():
pytest.skip()
from mmdet3d.core.bbox.structures import DepthInstance3DBoxes
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetDataset(root_path, ann_file)
results = []
pred_boxes = dict()
pred_boxes['boxes_3d'] = DepthInstance3DBoxes(
torch.tensor([[
1.4813e+00, 3.5207e+00, 1.5704e+00, 1.7445e+00, 2.3196e-01,
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
],
[
-3.3114e+00, -1.3351e-02, -8.9524e-03, 4.4082e-01,
3.8582e+00, 2.1603e+00, 0.0000e+00
],
[
-2.0135e+00, -3.4857e+00, 9.3848e-01, 1.9911e+00,
2.1603e-01, 1.2767e+00, 0.0000e+00
],
[
-2.1945e+00, -3.1402e+00, -3.8165e-02, 1.4801e+00,
6.8676e-01, 1.0586e+00, 0.0000e+00
],
[
-2.7553e+00, 2.4055e+00, -2.9972e-02, 1.4764e+00,
1.4927e+00, 2.3380e+00, 0.0000e+00
]]))
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, 0.5])
results.append(pred_boxes)
metric = [0.25, 0.5]
ret_dict = scannet_dataset.evaluate(results, metric)
assert abs(ret_dict['table_AP_0.25'] - 0.3333) < 0.01
assert abs(ret_dict['window_AP_0.25'] - 1.0) < 0.01
assert abs(ret_dict['counter_AP_0.25'] - 1.0) < 0.01
assert abs(ret_dict['curtain_AP_0.25'] - 1.0) < 0.01
# test evaluate with pipeline
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')
eval_pipeline = [
dict(type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2]),
dict(type='GlobalAlignment', rotation_axis=2),
dict(type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
ret_dict = scannet_dataset.evaluate(results,
metric,
pipeline=eval_pipeline)
assert abs(ret_dict['table_AP_0.25'] - 0.3333) < 0.01
assert abs(ret_dict['window_AP_0.25'] - 1.0) < 0.01
assert abs(ret_dict['counter_AP_0.25'] - 1.0) < 0.01
assert abs(ret_dict['curtain_AP_0.25'] - 1.0) < 0.01