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_show():
import mmcv
import tempfile
from os import path as osp
from mmdet3d.core.bbox import DepthInstance3DBoxes
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetDataset(root_path, ann_file)
boxes_3d = DepthInstance3DBoxes(
torch.tensor([[
-2.4053e+00, 9.2295e-01, 8.0661e-02, 2.4054e+00, 2.1468e+00,
8.5990e-01, 0.0000e+00
],
[
-1.9341e+00, -2.0741e+00, 3.0698e-03, 3.2206e-01,
2.5322e-01, 3.5144e-01, 0.0000e+00
],
[
-3.6908e+00, 8.0684e-03, 2.6201e-01, 4.1515e-01,
7.6489e-01, 5.3585e-01, 0.0000e+00
],
[
2.6332e+00, 8.5143e-01, -4.9964e-03, 3.0367e-01,
1.3448e+00, 1.8329e+00, 0.0000e+00
],
[
2.0221e-02, 2.6153e+00, 1.5109e-02, 7.3335e-01,
1.0429e+00, 1.0251e+00, 0.0000e+00
]]))
scores_3d = torch.tensor(
[1.2058e-04, 2.3012e-03, 6.2324e-06, 6.6139e-06, 6.7965e-05])
labels_3d = torch.tensor([0, 0, 0, 0, 0])
result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d)
results = [result]
scannet_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
def test_getitem():
np.random.seed(0)
root_path = './tests/data/scannet/'
ann_file = './tests/data/scannet/scannet_infos.pkl'
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')
pipelines = [
dict(type='LoadPointsFromFile',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2]),
dict(type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
with_mask_3d=True,
with_seg_3d=True),
dict(type='IndoorPointSample', num_points=5),
dict(type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=1.0,
flip_ratio_bev_vertical=1.0),
dict(type='GlobalRotScaleTrans',
rot_range=[-0.087266, 0.087266],
scale_ratio_range=[1.0, 1.0],
shift_height=True),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D',
keys=[
'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
'pts_instance_mask'
],
meta_keys=['file_name', 'sample_idx', 'pcd_rotation']),
]
scannet_dataset = ScanNetDataset(root_path, ann_file, pipelines)
data = scannet_dataset[0]
points = data['points']._data
gt_bboxes_3d = data['gt_bboxes_3d']._data
gt_labels = data['gt_labels_3d']._data
pts_semantic_mask = data['pts_semantic_mask']._data
pts_instance_mask = data['pts_instance_mask']._data
file_name = data['img_metas']._data['file_name']
pcd_rotation = data['img_metas']._data['pcd_rotation']
sample_idx = data['img_metas']._data['sample_idx']
expected_rotation = np.array([[0.99654, 0.08311407, 0.],
[-0.08311407, 0.99654, 0.], [0., 0., 1.]])
assert file_name == './tests/data/scannet/points/scene0000_00.bin'
assert np.allclose(pcd_rotation, expected_rotation, 1e-3)
assert sample_idx == 'scene0000_00'
expected_points = torch.tensor([[-2.7231, -2.2068, 2.3543, 2.3895],
[-0.4065, -3.4857, 2.1330, 2.1682],
[-1.4578, 1.3510, -0.0441, -0.0089],
[2.2428, -1.1323, -0.0288, 0.0064],
[0.7052, -2.9752, 1.5560, 1.5912]])
expected_gt_bboxes_3d = torch.tensor(
[[-1.1835, -3.6317, 1.5704, 1.7577, 0.3761, 0.5724, 0.0000],
[-3.1832, 3.2269, 1.1911, 0.6727, 0.2251, 0.6715, 0.0000],
[-0.9598, -2.2864, 0.0093, 0.7506, 2.5709, 1.2145, 0.0000],
[-2.6988, -2.7354, 0.8288, 0.7680, 1.8877, 0.2870, 0.0000],
[3.2989, 0.2885, -0.0090, 0.7600, 3.8814, 2.1603, 0.0000]])
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,
0, 0, 0, 5, 5, 5
])
expected_pts_semantic_mask = np.array([3, 1, 2, 2, 15])
expected_pts_instance_mask = np.array([44, 22, 10, 10, 57])
original_classes = scannet_dataset.CLASSES
assert scannet_dataset.CLASSES == class_names
assert torch.allclose(points, expected_points, 1e-2)
assert gt_bboxes_3d.tensor[:5].shape == (5, 7)
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(pts_semantic_mask.numpy() == expected_pts_semantic_mask)
assert np.all(pts_instance_mask.numpy() == expected_pts_instance_mask)
assert original_classes == class_names
scannet_dataset = ScanNetDataset(root_path,
ann_file,
pipeline=None,
classes=['cabinet', 'bed'])
assert scannet_dataset.CLASSES != original_classes
assert scannet_dataset.CLASSES == ['cabinet', 'bed']
scannet_dataset = ScanNetDataset(root_path,
ann_file,
pipeline=None,
classes=('cabinet', 'bed'))
assert scannet_dataset.CLASSES != original_classes
assert scannet_dataset.CLASSES == ('cabinet', 'bed')
# Test load classes from file
import tempfile
tmp_file = tempfile.NamedTemporaryFile()
with open(tmp_file.name, 'w') as f:
f.write('cabinet\nbed\n')
scannet_dataset = ScanNetDataset(root_path,
ann_file,
pipeline=None,
classes=tmp_file.name)
assert scannet_dataset.CLASSES != original_classes
assert scannet_dataset.CLASSES == ['cabinet', 'bed']
def test_show():
import mmcv
import tempfile
from os import path as osp
from mmdet3d.core.bbox import DepthInstance3DBoxes
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetDataset(root_path, ann_file)
boxes_3d = DepthInstance3DBoxes(
torch.tensor([[
-2.4053e+00, 9.2295e-01, 8.0661e-02, 2.4054e+00, 2.1468e+00,
8.5990e-01, 0.0000e+00
],
[
-1.9341e+00, -2.0741e+00, 3.0698e-03, 3.2206e-01,
2.5322e-01, 3.5144e-01, 0.0000e+00
],
[
-3.6908e+00, 8.0684e-03, 2.6201e-01, 4.1515e-01,
7.6489e-01, 5.3585e-01, 0.0000e+00
],
[
2.6332e+00, 8.5143e-01, -4.9964e-03, 3.0367e-01,
1.3448e+00, 1.8329e+00, 0.0000e+00
],
[
2.0221e-02, 2.6153e+00, 1.5109e-02, 7.3335e-01,
1.0429e+00, 1.0251e+00, 0.0000e+00
]]))
scores_3d = torch.tensor(
[1.2058e-04, 2.3012e-03, 6.2324e-06, 6.6139e-06, 6.7965e-05])
labels_3d = torch.tensor([0, 0, 0, 0, 0])
result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d)
results = [result]
scannet_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
# show function 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='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
scannet_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
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