def test_evaluate():
if not torch.cuda.is_available():
pytest.skip()
from mmdet3d.core.bbox.structures import DepthInstance3DBoxes
root_path = './tests/data/sunrgbd'
ann_file = './tests/data/sunrgbd/sunrgbd_infos.pkl'
sunrgbd_dataset = SUNRGBDDataset(root_path, ann_file)
results = []
pred_boxes = dict()
pred_boxes['boxes_3d'] = DepthInstance3DBoxes(
torch.tensor(
[[1.0473, 4.1687, -1.2317, 2.3021, 1.8876, 1.9696, 1.6956],
[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])
pred_boxes['scores_3d'] = torch.tensor([0.5, 1.0, 1.0])
results.append(pred_boxes)
metric = [0.25, 0.5]
ap_dict = sunrgbd_dataset.evaluate(results, metric)
bed_precision_25 = ap_dict['bed_AP_0.25']
dresser_precision_25 = ap_dict['dresser_AP_0.25']
night_stand_precision_25 = ap_dict['night_stand_AP_0.25']
assert abs(bed_precision_25 - 1) < 0.01
assert abs(dresser_precision_25 - 1) < 0.01
assert abs(night_stand_precision_25 - 1) < 0.01
def test_show():
import mmcv
import tempfile
from os import path as osp
from mmdet3d.core.bbox import DepthInstance3DBoxes
temp_dir = tempfile.mkdtemp()
root_path = './tests/data/sunrgbd'
ann_file = './tests/data/sunrgbd/sunrgbd_infos.pkl'
sunrgbd_dataset = SUNRGBDDataset(root_path, ann_file)
boxes_3d = DepthInstance3DBoxes(
torch.tensor(
[[1.1500, 4.2614, -1.0669, 1.3219, 2.1593, 1.0267, 1.6473],
[-0.9583, 2.1916, -1.0881, 0.6213, 1.3022, 1.6275, -3.0720],
[2.5697, 4.8152, -1.1157, 0.5421, 0.7019, 0.7896, 1.6712],
[0.7283, 2.5448, -1.0356, 0.7691, 0.9056, 0.5771, 1.7121],
[-0.9860, 3.2413, -1.2349, 0.5110, 0.9940, 1.1245, 0.3295]]))
scores_3d = torch.tensor(
[1.5280e-01, 1.6682e-03, 6.2811e-04, 1.2860e-03, 9.4229e-06])
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]
sunrgbd_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, '000001', '000001_points.obj')
gt_file_path = osp.join(temp_dir, '000001', '000001_gt.ply')
pred_file_path = osp.join(temp_dir, '000001', '000001_pred.ply')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
def test_indoor_eval_less_classes():
if not torch.cuda.is_available():
pytest.skip()
from mmdet3d.core.bbox.structures import Box3DMode, DepthInstance3DBoxes
det_infos = [{
'labels_3d':
torch.tensor([0]),
'boxes_3d':
DepthInstance3DBoxes(torch.tensor([[1., 1., 1., 1., 1., 1., 1.]])),
'scores_3d':
torch.tensor([.5])
}, {
'labels_3d':
torch.tensor([1]),
'boxes_3d':
DepthInstance3DBoxes(torch.tensor([[1., 1., 1., 1., 1., 1., 1.]])),
'scores_3d':
torch.tensor([.5])
}]
label2cat = {0: 'cabinet', 1: 'bed', 2: 'chair'}
gt_annos = [{
'gt_num':
2,
'gt_boxes_upright_depth':
np.array([[0., 0., 0., 1., 1., 1., 1.], [1., 1., 1., 1., 1., 1., 1.]]),
'class':
np.array([2, 0])
}, {
'gt_num':
1,
'gt_boxes_upright_depth':
np.array([
[1., 1., 1., 1., 1., 1., 1.],
]),
'class':
np.array([1])
}]
ret_value = indoor_eval(gt_annos,
det_infos, [0.25, 0.5],
label2cat,
box_type_3d=DepthInstance3DBoxes,
box_mode_3d=Box3DMode.DEPTH)
assert np.isclose(ret_value['mAP_0.25'], 0.666667)
assert np.isclose(ret_value['mAR_0.25'], 0.666667)
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, ann_file, _, pipelines, modality = \
_generate_sunrgbd_dataset_config()
sunrgbd_dataset = SUNRGBDDataset(root_path,
ann_file,
pipelines,
modality=modality)
boxes_3d = DepthInstance3DBoxes(
torch.tensor(
[[1.1500, 4.2614, -1.0669, 1.3219, 2.1593, 1.0267, 1.6473],
[-0.9583, 2.1916, -1.0881, 0.6213, 1.3022, 1.6275, -3.0720],
[2.5697, 4.8152, -1.1157, 0.5421, 0.7019, 0.7896, 1.6712],
[0.7283, 2.5448, -1.0356, 0.7691, 0.9056, 0.5771, 1.7121],
[-0.9860, 3.2413, -1.2349, 0.5110, 0.9940, 1.1245, 0.3295]]))
scores_3d = torch.tensor(
[1.5280e-01, 1.6682e-03, 6.2811e-04, 1.2860e-03, 9.4229e-06])
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]
sunrgbd_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, '000001', '000001_points.obj')
gt_file_path = osp.join(temp_dir, '000001', '000001_gt.obj')
pred_file_path = osp.join(temp_dir, '000001', '000001_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()
# test multi-modality show
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
root_path, ann_file, _, multi_modality_pipelines, modality = \
_generate_sunrgbd_multi_modality_dataset_config()
sunrgbd_dataset = SUNRGBDDataset(root_path,
ann_file,
multi_modality_pipelines,
modality=modality)
sunrgbd_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, '000001', '000001_points.obj')
gt_file_path = osp.join(temp_dir, '000001', '000001_gt.obj')
pred_file_path = osp.join(temp_dir, '000001', '000001_pred.obj')
img_file_path = osp.join(temp_dir, '000001', '000001_img.png')
img_pred_path = osp.join(temp_dir, '000001', '000001_pred.png')
img_gt_file = osp.join(temp_dir, '000001', '000001_gt.png')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
mmcv.check_file_exist(img_file_path)
mmcv.check_file_exist(img_pred_path)
mmcv.check_file_exist(img_gt_file)
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
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_evaluate():
if not torch.cuda.is_available():
pytest.skip()
from mmdet3d.core.bbox.structures import DepthInstance3DBoxes
root_path = './tests/data/s3dis'
ann_file = './tests/data/s3dis/s3dis_infos.pkl'
s3dis_dataset = S3DISDataset(root_path, ann_file)
results = []
pred_boxes = dict()
pred_boxes['boxes_3d'] = DepthInstance3DBoxes(
torch.tensor([[2.3080, 2.4175, 0.2010, 0.8820, 0.8690, 0.6970, 0.0000],
[2.4730, 0.7090, 0.2010, 0.9080, 0.9620, 0.7030, 0.0000],
[5.3235, 0.4910, 0.0740, 0.8410, 0.9020, 0.8790,
0.0000]]))
pred_boxes['labels_3d'] = torch.tensor([1, 1, 3])
pred_boxes['scores_3d'] = torch.tensor([0.5, 1.0, 1.0])
results.append(pred_boxes)
ret_dict = s3dis_dataset.evaluate(results)
assert abs(ret_dict['chair_AP_0.25'] - 0.666) < 0.01
assert abs(ret_dict['chair_AP_0.50'] - 0.666) < 0.01
assert abs(ret_dict['bookcase_AP_0.25'] - 0.5) < 0.01
assert abs(ret_dict['bookcase_AP_0.50'] - 0.5) < 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, ann_file, class_names, pipelines, modality = \
_generate_sunrgbd_dataset_config()
sunrgbd_dataset = SUNRGBDDataset(root_path,
ann_file,
pipelines,
modality=modality)
boxes_3d = DepthInstance3DBoxes(
torch.tensor(
[[1.1500, 4.2614, -1.0669, 1.3219, 2.1593, 1.0267, 1.6473],
[-0.9583, 2.1916, -1.0881, 0.6213, 1.3022, 1.6275, -3.0720],
[2.5697, 4.8152, -1.1157, 0.5421, 0.7019, 0.7896, 1.6712],
[0.7283, 2.5448, -1.0356, 0.7691, 0.9056, 0.5771, 1.7121],
[-0.9860, 3.2413, -1.2349, 0.5110, 0.9940, 1.1245, 0.3295]]))
scores_3d = torch.tensor(
[1.5280e-01, 1.6682e-03, 6.2811e-04, 1.2860e-03, 9.4229e-06])
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]
sunrgbd_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, '000001', '000001_points.obj')
gt_file_path = osp.join(temp_dir, '000001', '000001_gt.obj')
pred_file_path = osp.join(temp_dir, '000001', '000001_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()
# test show with pipeline
eval_pipeline = [
dict(type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
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
sunrgbd_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, '000001', '000001_points.obj')
gt_file_path = osp.join(temp_dir, '000001', '000001_gt.obj')
pred_file_path = osp.join(temp_dir, '000001', '000001_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()
# test multi-modality show
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
root_path, ann_file, class_names, multi_modality_pipelines, modality = \
_generate_sunrgbd_multi_modality_dataset_config()
sunrgbd_dataset = SUNRGBDDataset(root_path,
ann_file,
multi_modality_pipelines,
modality=modality)
sunrgbd_dataset.show(results, temp_dir, False, multi_modality_pipelines)
pts_file_path = osp.join(temp_dir, '000001', '000001_points.obj')
gt_file_path = osp.join(temp_dir, '000001', '000001_gt.obj')
pred_file_path = osp.join(temp_dir, '000001', '000001_pred.obj')
img_file_path = osp.join(temp_dir, '000001', '000001_img.png')
img_pred_path = osp.join(temp_dir, '000001', '000001_pred.png')
img_gt_file = osp.join(temp_dir, '000001', '000001_gt.png')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
mmcv.check_file_exist(img_file_path)
mmcv.check_file_exist(img_pred_path)
mmcv.check_file_exist(img_gt_file)
tmp_dir.cleanup()
# test multi-modality show with pipeline
eval_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2]),
dict(type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points', 'img'])
]
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
sunrgbd_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, '000001', '000001_points.obj')
gt_file_path = osp.join(temp_dir, '000001', '000001_gt.obj')
pred_file_path = osp.join(temp_dir, '000001', '000001_pred.obj')
img_file_path = osp.join(temp_dir, '000001', '000001_img.png')
img_pred_path = osp.join(temp_dir, '000001', '000001_pred.png')
img_gt_file = osp.join(temp_dir, '000001', '000001_gt.png')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
mmcv.check_file_exist(img_file_path)
mmcv.check_file_exist(img_pred_path)
mmcv.check_file_exist(img_gt_file)
tmp_dir.cleanup()
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_indoor_eval():
if not torch.cuda.is_available():
pytest.skip()
from mmdet3d.core.bbox.structures import Box3DMode, DepthInstance3DBoxes
det_infos = [{
'labels_3d':
torch.tensor([0, 1, 2, 2, 0, 3, 1, 2, 3, 2]),
'boxes_3d':
DepthInstance3DBoxes(
torch.tensor([[
-2.4089e-03, -3.3174e+00, 4.9438e-01, 2.1668e+00, 2.8431e-01,
1.6506e+00, 0.0000e+00
],
[
-3.4269e-01, -2.7565e+00, 2.8144e-02, 6.8554e-01,
9.6854e-01, 6.1755e-01, 0.0000e+00
],
[
-3.8320e+00, -1.0646e+00, 1.7074e-01, 2.4981e-01,
4.4708e-01, 6.2538e-01, 0.0000e+00
],
[
4.1073e-01, 3.3757e+00, 3.4311e-01, 8.0617e-01,
2.8679e-01, 1.6060e+00, 0.0000e+00
],
[
6.1199e-01, -3.1041e+00, 4.1873e-01, 1.2310e+00,
4.0162e-01, 1.7303e+00, 0.0000e+00
],
[
-5.9877e-01, -2.6011e+00, 1.1148e+00, 1.5704e-01,
7.5957e-01, 9.6930e-01, 0.0000e+00
],
[
2.7462e-01, -3.0088e+00, 6.5231e-02, 8.1208e-01,
4.1861e-01, 3.7339e-01, 0.0000e+00
],
[
-1.4704e+00, -2.0024e+00, 2.7479e-01, 1.7888e+00,
1.0566e+00, 1.3704e+00, 0.0000e+00
],
[
8.2727e-02, -3.1160e+00, 2.5690e-01, 1.4054e+00,
2.0772e-01, 9.6792e-01, 0.0000e+00
],
[
2.6896e+00, 1.9881e+00, 1.1566e+00, 9.9885e-02,
3.5713e-01, 4.5638e-01, 0.0000e+00
]]),
origin=(0.5, 0.5, 0)),
'scores_3d':
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 = {
0: 'cabinet',
1: 'bed',
2: 'chair',
3: 'sofa',
}
gt_annos = [{
'gt_num':
10,
'gt_boxes_upright_depth':
np.array([[
-2.4089e-03, -3.3174e+00, 4.9438e-01, 2.1668e+00, 2.8431e-01,
1.6506e+00, 0.0000e+00
],
[
-3.4269e-01, -2.7565e+00, 2.8144e-02, 6.8554e-01,
9.6854e-01, 6.1755e-01, 0.0000e+00
],
[
-3.8320e+00, -1.0646e+00, 1.7074e-01, 2.4981e-01,
4.4708e-01, 6.2538e-01, 0.0000e+00
],
[
4.1073e-01, 3.3757e+00, 3.4311e-01, 8.0617e-01,
2.8679e-01, 1.6060e+00, 0.0000e+00
],
[
6.1199e-01, -3.1041e+00, 4.1873e-01, 1.2310e+00,
4.0162e-01, 1.7303e+00, 0.0000e+00
],
[
-5.9877e-01, -2.6011e+00, 1.1148e+00, 1.5704e-01,
7.5957e-01, 9.6930e-01, 0.0000e+00
],
[
2.7462e-01, -3.0088e+00, 6.5231e-02, 8.1208e-01,
4.1861e-01, 3.7339e-01, 0.0000e+00
],
[
-1.4704e+00, -2.0024e+00, 2.7479e-01, 1.7888e+00,
1.0566e+00, 1.3704e+00, 0.0000e+00
],
[
8.2727e-02, -3.1160e+00, 2.5690e-01, 1.4054e+00,
2.0772e-01, 9.6792e-01, 0.0000e+00
],
[
2.6896e+00, 1.9881e+00, 1.1566e+00, 9.9885e-02,
3.5713e-01, 4.5638e-01, 0.0000e+00
]]),
'class':
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,
box_type_3d=DepthInstance3DBoxes,
box_mode_3d=Box3DMode.DEPTH)
assert np.isclose(ret_value['cabinet_AP_0.25'], 0.666667)
assert np.isclose(ret_value['bed_AP_0.25'], 1.0)
assert np.isclose(ret_value['chair_AP_0.25'], 0.5)
assert np.isclose(ret_value['mAP_0.25'], 0.708333)
assert np.isclose(ret_value['mAR_0.25'], 0.833333)
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