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import numpy as np |
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from mmcv.transforms.base import BaseTransform |
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from mmengine.registry import TRANSFORMS |
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from mmengine.structures import InstanceData |
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from mmdet3d.datasets import NuScenesDataset |
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from mmdet3d.structures import Det3DDataSample, LiDARInstance3DBoxes |
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def _generate_nus_dataset_config(): |
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data_root = 'tests/data/nuscenes' |
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ann_file = 'nus_info.pkl' |
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classes = [ |
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'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle', |
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'motorcycle', 'pedestrian', 'traffic_cone', 'barrier' |
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] |
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if 'Identity' not in TRANSFORMS: |
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@TRANSFORMS.register_module() |
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class Identity(BaseTransform): |
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def transform(self, info): |
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packed_input = dict(data_samples=Det3DDataSample()) |
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if 'ann_info' in info: |
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packed_input[ |
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'data_samples'].gt_instances_3d = InstanceData() |
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packed_input[ |
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'data_samples'].gt_instances_3d.labels_3d = info[ |
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'ann_info']['gt_labels_3d'] |
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return packed_input |
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pipeline = [ |
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dict(type='Identity'), |
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] |
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modality = dict(use_lidar=True, use_camera=True) |
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data_prefix = dict( |
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pts='samples/LIDAR_TOP', |
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img='samples/CAM_BACK_LEFT', |
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sweeps='sweeps/LIDAR_TOP') |
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return data_root, ann_file, classes, data_prefix, pipeline, modality |
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def test_getitem(): |
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np.random.seed(0) |
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data_root, ann_file, classes, data_prefix, pipeline, modality = \ |
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_generate_nus_dataset_config() |
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nus_dataset = NuScenesDataset( |
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data_root=data_root, |
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ann_file=ann_file, |
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data_prefix=data_prefix, |
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pipeline=pipeline, |
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metainfo=dict(classes=classes), |
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modality=modality) |
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nus_dataset.prepare_data(0) |
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input_dict = nus_dataset.get_data_info(0) |
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assert data_prefix['pts'] in input_dict['lidar_points']['lidar_path'] |
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assert data_root in input_dict['lidar_points']['lidar_path'] |
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for cam_id, img_info in input_dict['images'].items(): |
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if 'img_path' in img_info: |
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assert data_prefix['img'] in img_info['img_path'] |
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assert data_root in img_info['img_path'] |
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ann_info = nus_dataset.parse_ann_info(input_dict) |
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assert 'gt_labels_3d' in ann_info |
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assert ann_info['gt_labels_3d'].dtype == np.int64 |
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assert len(ann_info['gt_labels_3d']) == 37 |
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assert 'gt_bboxes_3d' in ann_info |
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assert isinstance(ann_info['gt_bboxes_3d'], LiDARInstance3DBoxes) |
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assert len(nus_dataset.metainfo['classes']) == 10 |
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assert input_dict['token'] == 'fd8420396768425eabec9bdddf7e64b6' |
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assert input_dict['timestamp'] == 1533201470.448696 |
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