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_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_getitem():
np.random.seed(0)
root_path = './tests/data/sunrgbd'
ann_file = './tests/data/sunrgbd/sunrgbd_infos.pkl'
class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk',
'dresser', 'night_stand', 'bookshelf', 'bathtub')
pipelines = [
dict(type='LoadPointsFromFile',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2]),
dict(type='LoadAnnotations3D'),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
),
dict(type='GlobalRotScaleTrans',
rot_range=[-0.523599, 0.523599],
scale_ratio_range=[0.85, 1.15],
shift_height=True),
dict(type='IndoorPointSample', num_points=5),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'],
meta_keys=[
'file_name', 'pcd_horizontal_flip', 'sample_idx',
'pcd_scale_factor', 'pcd_rotation'
]),
]
sunrgbd_dataset = SUNRGBDDataset(root_path, ann_file, pipelines)
data = sunrgbd_dataset[0]
points = data['points']._data
gt_bboxes_3d = data['gt_bboxes_3d']._data
gt_labels_3d = data['gt_labels_3d']._data
file_name = data['img_metas']._data['file_name']
pcd_horizontal_flip = data['img_metas']._data['pcd_horizontal_flip']
pcd_scale_factor = data['img_metas']._data['pcd_scale_factor']
pcd_rotation = data['img_metas']._data['pcd_rotation']
sample_idx = data['img_metas']._data['sample_idx']
pcd_rotation_expected = np.array([[0.99889565, 0.04698427, 0.],
[-0.04698427, 0.99889565, 0.],
[0., 0., 1.]])
assert file_name == './tests/data/sunrgbd/points/000001.bin'
assert pcd_horizontal_flip is False
assert abs(pcd_scale_factor - 0.9770964398016714) < 1e-5
assert np.allclose(pcd_rotation, pcd_rotation_expected, 1e-3)
assert sample_idx == 1
expected_points = torch.tensor([[-0.9904, 1.2596, 0.1105, 0.0905],
[-0.9948, 1.2758, 0.0437, 0.0238],
[-0.9866, 1.2641, 0.0504, 0.0304],
[-0.9915, 1.2586, 0.1265, 0.1065],
[-0.9890, 1.2561, 0.1216, 0.1017]])
expected_gt_bboxes_3d = torch.tensor(
[[0.8308, 4.1168, -1.2035, 2.2493, 1.8444, 1.9245, 1.6486],
[2.3002, 4.8149, -1.2442, 0.5718, 0.8629, 0.9510, 1.6030],
[-1.1477, 1.8090, -1.1725, 0.6965, 1.5273, 2.0563, 0.0552]])
expected_gt_labels = np.array([0, 7, 6])
original_classes = sunrgbd_dataset.CLASSES
assert torch.allclose(points, expected_points, 1e-2)
assert torch.allclose(gt_bboxes_3d.tensor, expected_gt_bboxes_3d, 1e-3)
assert np.all(gt_labels_3d.numpy() == expected_gt_labels)
assert original_classes == class_names
SUNRGBD_dataset = SUNRGBDDataset(root_path,
ann_file,
pipeline=None,
classes=['bed', 'table'])
assert SUNRGBD_dataset.CLASSES != original_classes
assert SUNRGBD_dataset.CLASSES == ['bed', 'table']
SUNRGBD_dataset = SUNRGBDDataset(root_path,
ann_file,
pipeline=None,
classes=('bed', 'table'))
assert SUNRGBD_dataset.CLASSES != original_classes
assert SUNRGBD_dataset.CLASSES == ('bed', 'table')
import tempfile
tmp_file = tempfile.NamedTemporaryFile()
with open(tmp_file.name, 'w') as f:
f.write('bed\ntable\n')
SUNRGBD_dataset = SUNRGBDDataset(root_path,
ann_file,
pipeline=None,
classes=tmp_file.name)
assert SUNRGBD_dataset.CLASSES != original_classes
assert SUNRGBD_dataset.CLASSES == ['bed', 'table']
def test_getitem():
np.random.seed(0)
root_path, ann_file, class_names, pipelines, modality = \
_generate_sunrgbd_dataset_config()
sunrgbd_dataset = SUNRGBDDataset(root_path,
ann_file,
pipelines,
modality=modality)
data = sunrgbd_dataset[0]
points = data['points']._data
gt_bboxes_3d = data['gt_bboxes_3d']._data
gt_labels_3d = data['gt_labels_3d']._data
file_name = data['img_metas']._data['file_name']
pcd_horizontal_flip = data['img_metas']._data['pcd_horizontal_flip']
pcd_scale_factor = data['img_metas']._data['pcd_scale_factor']
pcd_rotation = data['img_metas']._data['pcd_rotation']
sample_idx = data['img_metas']._data['sample_idx']
pcd_rotation_expected = np.array([[0.99889565, 0.04698427, 0.],
[-0.04698427, 0.99889565, 0.],
[0., 0., 1.]])
assert file_name == './tests/data/sunrgbd/points/000001.bin'
assert pcd_horizontal_flip is False
assert abs(pcd_scale_factor - 0.9770964398016714) < 1e-5
assert np.allclose(pcd_rotation, pcd_rotation_expected, 1e-3)
assert sample_idx == 1
expected_points = torch.tensor([[-0.9904, 1.2596, 0.1105, 0.0905],
[-0.9948, 1.2758, 0.0437, 0.0238],
[-0.9866, 1.2641, 0.0504, 0.0304],
[-0.9915, 1.2586, 0.1265, 0.1065],
[-0.9890, 1.2561, 0.1216, 0.1017]])
expected_gt_bboxes_3d = torch.tensor(
[[0.8308, 4.1168, -1.2035, 2.2493, 1.8444, 1.9245, 1.6486],
[2.3002, 4.8149, -1.2442, 0.5718, 0.8629, 0.9510, 1.6030],
[-1.1477, 1.8090, -1.1725, 0.6965, 1.5273, 2.0563, 0.0552]])
expected_gt_labels = np.array([0, 7, 6])
original_classes = sunrgbd_dataset.CLASSES
assert torch.allclose(points, expected_points, 1e-2)
assert torch.allclose(gt_bboxes_3d.tensor, expected_gt_bboxes_3d, 1e-3)
assert np.all(gt_labels_3d.numpy() == expected_gt_labels)
assert original_classes == class_names
SUNRGBD_dataset = SUNRGBDDataset(root_path,
ann_file,
pipeline=None,
classes=['bed', 'table'])
assert SUNRGBD_dataset.CLASSES != original_classes
assert SUNRGBD_dataset.CLASSES == ['bed', 'table']
SUNRGBD_dataset = SUNRGBDDataset(root_path,
ann_file,
pipeline=None,
classes=('bed', 'table'))
assert SUNRGBD_dataset.CLASSES != original_classes
assert SUNRGBD_dataset.CLASSES == ('bed', 'table')
import tempfile
tmp_file = tempfile.NamedTemporaryFile()
with open(tmp_file.name, 'w') as f:
f.write('bed\ntable\n')
SUNRGBD_dataset = SUNRGBDDataset(root_path,
ann_file,
pipeline=None,
classes=tmp_file.name)
assert SUNRGBD_dataset.CLASSES != original_classes
assert SUNRGBD_dataset.CLASSES == ['bed', 'table']
# test multi-modality SUN RGB-D dataset
np.random.seed(0)
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)
data = sunrgbd_dataset[0]
points = data['points']._data
gt_bboxes_3d = data['gt_bboxes_3d']._data
gt_labels_3d = data['gt_labels_3d']._data
img = data['img']._data
depth2img = data['img_metas']._data['depth2img']
expected_rt_mat = np.array([[0.97959, 0.012593, -0.20061],
[0.012593, 0.99223, 0.12377],
[0.20061, -0.12377, 0.97182]])
expected_k_mat = np.array([[529.5, 0., 0.], [0., 529.5, 0.],
[365., 265., 1.]])
rt_mat = np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]
]) @ expected_rt_mat.transpose(1, 0)
expected_depth2img = expected_k_mat @ rt_mat
assert torch.allclose(points, expected_points, 1e-2)
assert torch.allclose(gt_bboxes_3d.tensor, expected_gt_bboxes_3d, 1e-3)
assert np.all(gt_labels_3d.numpy() == expected_gt_labels)
assert img.shape[:] == (3, 608, 832)
assert np.allclose(depth2img, expected_depth2img)
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()