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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmcv.transforms.base import BaseTransform
from mmengine.registry import TRANSFORMS
from mmengine.structures import InstanceData
from mmdet3d.datasets import LyftDataset
from mmdet3d.structures import Det3DDataSample, LiDARInstance3DBoxes
def _generate_nus_dataset_config():
data_root = 'tests/data/lyft'
ann_file = 'lyft_infos.pkl'
classes = [
'car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle',
'motorcycle', 'bicycle', 'pedestrian', 'animal'
]
if 'Identity' not in TRANSFORMS:
@TRANSFORMS.register_module()
class Identity(BaseTransform):
def transform(self, info):
packed_input = dict(data_samples=Det3DDataSample())
if 'ann_info' in info:
packed_input[
'data_samples'].gt_instances_3d = InstanceData()
packed_input[
'data_samples'].gt_instances_3d.labels_3d = info[
'ann_info']['gt_labels_3d']
return packed_input
pipeline = [
dict(type='Identity'),
]
modality = dict(use_lidar=True, use_camera=False)
data_prefix = dict(pts='lidar', img='', sweeps='sweeps/LIDAR_TOP')
return data_root, ann_file, classes, data_prefix, pipeline, modality
def test_getitem():
np.random.seed(0)
data_root, ann_file, classes, data_prefix, pipeline, modality = \
_generate_nus_dataset_config()
lyft_dataset = LyftDataset(
data_root,
ann_file,
data_prefix=data_prefix,
pipeline=pipeline,
metainfo=dict(classes=classes),
modality=modality)
lyft_dataset.prepare_data(0)
input_dict = lyft_dataset.get_data_info(0)
# assert the the path should contains data_prefix and data_root
assert data_prefix['pts'] in input_dict['lidar_points']['lidar_path']
assert data_root in input_dict['lidar_points']['lidar_path']
ann_info = lyft_dataset.parse_ann_info(input_dict)
# assert the keys in ann_info and the type
assert 'gt_labels_3d' in ann_info
assert ann_info['gt_labels_3d'].dtype == np.int64
assert len(ann_info['gt_labels_3d']) == 3
assert 'gt_bboxes_3d' in ann_info
assert isinstance(ann_info['gt_bboxes_3d'], LiDARInstance3DBoxes)
assert len(lyft_dataset.metainfo['classes']) == 9
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