53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
import numpy as np
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from mmdet3d.datasets import SemanticKITTIDataset
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def test_getitem():
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np.random.seed(0)
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root_path = './tests/data/semantickitti/'
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ann_file = './tests/data/semantickitti/semantickitti_infos.pkl'
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class_names = ('unlabeled', 'car', 'bicycle', 'motorcycle', 'truck', 'bus',
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'person', 'bicyclist', 'motorcyclist', 'road', 'parking',
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'sidewalk', 'other-ground', 'building', 'fence',
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'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign')
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pipelines = [
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dict(
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type='LoadPointsFromFile',
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coord_type='LIDAR',
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shift_height=True,
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load_dim=4,
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use_dim=[0, 1, 2]),
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dict(
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type='LoadAnnotations3D',
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with_bbox_3d=False,
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with_label_3d=False,
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with_mask_3d=False,
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with_seg_3d=True,
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seg_3d_dtype=np.int32),
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dict(
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type='RandomFlip3D',
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sync_2d=False,
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flip_ratio_bev_horizontal=1.0,
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flip_ratio_bev_vertical=1.0),
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dict(
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type='GlobalRotScaleTrans',
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rot_range=[-0.087266, 0.087266],
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scale_ratio_range=[1.0, 1.0],
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shift_height=True),
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dict(type='DefaultFormatBundle3D', class_names=class_names),
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dict(
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type='Collect3D',
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keys=[
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'points',
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'pts_semantic_mask',
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],
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meta_keys=['file_name', 'sample_idx', 'pcd_rotation']),
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]
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semantickitti_dataset = SemanticKITTIDataset(root_path, ann_file,
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pipelines)
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data = semantickitti_dataset[0]
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assert data['points']._data.shape[0] == data[
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'pts_semantic_mask']._data.shape[0]
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