def test_labeled_synchronized_scene_dataset(self):
"""Test synchronized scene dataset"""
expected_camera_fields = set([
'rgb',
'timestamp',
'datum_name',
'pose',
'intrinsics',
'extrinsics',
'bounding_box_2d',
'bounding_box_3d',
'depth',
'datum_type',
])
expected_lidar_fields = set([
'point_cloud',
'timestamp',
'datum_name',
'pose',
'extrinsics',
'bounding_box_2d',
'bounding_box_3d',
'extra_channels',
'datum_type',
])
expected_metadata_fields = set([
'scene_index', 'sample_index_in_scene', 'log_id', 'timestamp',
'scene_name', 'scene_description'
])
# Initialize synchronized dataset with 2 datums
scenes_dataset_json = os.path.join(self.DGP_TEST_DATASET_DIR,
"test_scene",
"scene_dataset_v1.0.json")
dataset = SynchronizedSceneDataset(
scenes_dataset_json,
split='train',
datum_names=['LIDAR', 'CAMERA_01'],
forward_context=1,
backward_context=1,
generate_depth_from_datum='LIDAR',
requested_annotations=("bounding_box_2d", "bounding_box_3d"))
# There are only 3 samples in the train and val split.
# With a forward and backward context of 1 each, the number of
# items in the dataset with the desired context frames is 1.
assert len(dataset) == 2
# Iterate through labeled dataset and check expected fields
assert dataset.calibration_table is not None
for idx, item in enumerate(dataset):
# Context size is 3 (forward + backward + reference)
assert_true(len(item) == 3)
# Check both datum and time-dimensions for expected fields
im_size = None
for t_item in item:
# Two selected datums
assert_true(len(t_item) == 2)
for datum in t_item:
if datum['datum_name'] == 'LIDAR':
# LIDAR should have point_cloud set
assert_true(set(datum.keys()) == expected_lidar_fields)
assert_true(isinstance(datum, OrderedDict))
elif datum['datum_name'].startswith('CAMERA_'):
# CAMERA_01 should have intrinsics/extrinsics set
assert_true(isinstance(datum, OrderedDict))
assert_true(datum['intrinsics'].shape == (3, 3))
assert_true(isinstance(datum['extrinsics'], Pose))
assert_true(isinstance(datum['pose'], Pose))
# Check image sizes for context frames
assert_true(
set(datum.keys()) == expected_camera_fields)
if im_size is None:
im_size = datum['rgb'].size
assert_true(datum['rgb'].size == im_size)
else:
raise RuntimeError('Unexpected datum_name {}'.format(
datum['datum_name']))
# Retrieve metadata about dataset item at index=idx
metadata = dataset.get_scene_metadata(idx)
assert_true(metadata.keys() == expected_metadata_fields)
def test_labeled_synchronized_scene_dataset(self):
"""Test synchronized scene dataset"""
expected_camera_fields = set([
'rgb', 'timestamp', 'datum_name', 'pose', 'intrinsics',
'extrinsics', 'bounding_box_2d', 'bounding_box_3d', 'class_ids',
'instance_ids', 'depth'
])
expected_lidar_fields = set([
'point_cloud', 'timestamp', 'datum_name', 'pose', 'extrinsics',
'bounding_box_3d', 'class_ids', 'instance_ids', 'extra_channels'
])
expected_metadata_fields = set([
'scene_index', 'sample_index_in_scene', 'log_id', 'timestamp',
'scene_name', 'scene_description'
])
# Initialize synchronized dataset with 2 datums
scenes_dataset_json = os.path.join(self.DGP_TEST_DATASET_DIR,
"test_scene",
"scene_dataset_v1.0.json")
dataset = SynchronizedSceneDataset(
scenes_dataset_json,
split='train',
forward_context=1,
backward_context=1,
generate_depth_from_datum='LIDAR',
requested_annotations=("bounding_box_2d", "bounding_box_3d"))
dataset.select_datums(['LIDAR', 'CAMERA_01'])
dataset.prefetch()
# There are only 3 samples in the train and val split.
# With a forward and backward context of 1 each, the number of
# items in the dataset with the desired context frames is 1.
assert len(dataset) == 2
# Iterate through labeled dataset and check expected fields
assert dataset.calibration_table is not None
for idx, item in enumerate(dataset):
# Context size is 3 (forward + backward + reference)
assert_true(len(item) == 3)
# Two selected datums
for t_item in item:
assert_true(len(t_item) == 2)
# LIDAR should have point_cloud set
for t_item in item:
assert_true(set(t_item[0].keys()) == expected_lidar_fields)
assert_true(isinstance(t_item[0], OrderedDict))
# CAMERA_01 should have intrinsics/extrinsics set
im_size = None
for t_item in item:
assert_true(isinstance(t_item[1], OrderedDict))
assert_true(t_item[1]['intrinsics'].shape == (3, 3))
assert_true(isinstance(t_item[1]['extrinsics'], Pose))
assert_true(isinstance(t_item[1]['pose'], Pose))
# Check image sizes for context frames
assert_true(set(t_item[1].keys()) == expected_camera_fields)
if im_size is None:
im_size = t_item[1]['rgb'].size
assert_true(t_item[1]['rgb'].size == im_size)
# Retrieve metadata about dataset item at index=idx
metadata = dataset.get_scene_metadata(idx)
assert_true(metadata.keys() == expected_metadata_fields)
# Make sure you cannot select unavailable datums
with assert_raises(AssertionError) as _:
dataset.select_datums(['FAKE_LIDAR_NAME'])