446 lines
18 KiB
Python
446 lines
18 KiB
Python
import numpy as np
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import os
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import pytest
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import tempfile
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import torch
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from mmdet3d.core.bbox import LiDARInstance3DBoxes
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from mmdet3d.datasets import KittiDataset
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def _generate_kitti_dataset_config():
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data_root = 'tests/data/kitti'
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ann_file = 'tests/data/kitti/kitti_infos_train.pkl'
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classes = ['Pedestrian', 'Cyclist', 'Car']
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pts_prefix = 'velodyne_reduced'
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pipeline = [
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dict(
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type='LoadPointsFromFile',
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coord_type='LIDAR',
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load_dim=4,
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use_dim=4,
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file_client_args=dict(backend='disk')),
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dict(
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type='MultiScaleFlipAug3D',
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img_scale=(1333, 800),
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pts_scale_ratio=1,
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flip=False,
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transforms=[
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dict(
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type='GlobalRotScaleTrans',
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rot_range=[0, 0],
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scale_ratio_range=[1.0, 1.0],
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translation_std=[0, 0, 0]),
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dict(type='RandomFlip3D'),
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dict(
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type='PointsRangeFilter',
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point_cloud_range=[0, -40, -3, 70.4, 40, 1]),
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dict(
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type='DefaultFormatBundle3D',
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class_names=classes,
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with_label=False),
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dict(type='Collect3D', keys=['points'])
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])
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]
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modality = dict(use_lidar=True, use_camera=False)
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split = 'training'
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return data_root, ann_file, classes, pts_prefix, pipeline, modality, split
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def _generate_kitti_multi_modality_dataset_config():
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data_root = 'tests/data/kitti'
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ann_file = 'tests/data/kitti/kitti_infos_train.pkl'
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classes = ['Pedestrian', 'Cyclist', 'Car']
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pts_prefix = 'velodyne_reduced'
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img_norm_cfg = dict(
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mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
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pipeline = [
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dict(
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type='LoadPointsFromFile',
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coord_type='LIDAR',
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load_dim=4,
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use_dim=4,
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file_client_args=dict(backend='disk')),
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug3D',
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img_scale=(1333, 800),
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pts_scale_ratio=1,
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flip=False,
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transforms=[
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dict(type='Resize', multiscale_mode='value', keep_ratio=True),
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dict(
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type='GlobalRotScaleTrans',
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rot_range=[0, 0],
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scale_ratio_range=[1., 1.],
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translation_std=[0, 0, 0]),
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dict(type='RandomFlip3D'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(
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type='PointsRangeFilter',
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point_cloud_range=[0, -40, -3, 70.4, 40, 1]),
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dict(
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type='DefaultFormatBundle3D',
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class_names=classes,
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with_label=False),
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dict(type='Collect3D', keys=['points', 'img'])
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])
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]
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modality = dict(use_lidar=True, use_camera=True)
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split = 'training'
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return data_root, ann_file, classes, pts_prefix, pipeline, modality, split
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def test_getitem():
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np.random.seed(0)
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data_root, ann_file, classes, pts_prefix, \
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_, modality, split = _generate_kitti_dataset_config()
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pipeline = [
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dict(
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type='LoadPointsFromFile',
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coord_type='LIDAR',
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load_dim=4,
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use_dim=4,
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file_client_args=dict(backend='disk')),
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dict(
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type='LoadAnnotations3D',
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with_bbox_3d=True,
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with_label_3d=True,
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file_client_args=dict(backend='disk')),
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dict(
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type='ObjectSample',
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db_sampler=dict(
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data_root='tests/data/kitti/',
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info_path='tests/data/kitti/kitti_dbinfos_train.pkl',
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rate=1.0,
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prepare=dict(
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filter_by_difficulty=[-1],
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filter_by_min_points=dict(Pedestrian=10)),
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classes=['Pedestrian', 'Cyclist', 'Car'],
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sample_groups=dict(Pedestrian=6))),
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dict(
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type='ObjectNoise',
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num_try=100,
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translation_std=[1.0, 1.0, 0.5],
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global_rot_range=[0.0, 0.0],
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rot_range=[-0.78539816, 0.78539816]),
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dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
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dict(
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type='GlobalRotScaleTrans',
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rot_range=[-0.78539816, 0.78539816],
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scale_ratio_range=[0.95, 1.05]),
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dict(
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type='PointsRangeFilter',
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point_cloud_range=[0, -40, -3, 70.4, 40, 1]),
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dict(
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type='ObjectRangeFilter',
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point_cloud_range=[0, -40, -3, 70.4, 40, 1]),
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dict(type='PointShuffle'),
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dict(
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type='DefaultFormatBundle3D',
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class_names=['Pedestrian', 'Cyclist', 'Car']),
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dict(
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type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
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]
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kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix,
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pipeline, classes, modality)
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data = kitti_dataset[0]
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points = data['points']._data
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gt_bboxes_3d = data['gt_bboxes_3d']._data
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gt_labels_3d = data['gt_labels_3d']._data
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expected_gt_bboxes_3d = torch.tensor(
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[[9.5081, -5.2269, -1.1370, 0.4915, 1.2288, 1.9353, -2.7136]])
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expected_gt_labels_3d = torch.tensor([0])
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assert points.shape == (780, 4)
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assert torch.allclose(
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gt_bboxes_3d.tensor, expected_gt_bboxes_3d, atol=1e-4)
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assert torch.all(gt_labels_3d == expected_gt_labels_3d)
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# test multi-modality KITTI dataset
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np.random.seed(0)
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point_cloud_range = [0, -40, -3, 70.4, 40, 1]
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img_norm_cfg = dict(
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mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
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multi_modality_pipeline = [
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dict(
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type='LoadPointsFromFile',
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coord_type='LIDAR',
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load_dim=4,
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use_dim=4),
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
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dict(
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type='Resize',
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img_scale=[(640, 192), (2560, 768)],
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multiscale_mode='range',
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keep_ratio=True),
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dict(
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type='GlobalRotScaleTrans',
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rot_range=[-0.78539816, 0.78539816],
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scale_ratio_range=[0.95, 1.05],
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translation_std=[0.2, 0.2, 0.2]),
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dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
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dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
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dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
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dict(type='PointShuffle'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='DefaultFormatBundle3D', class_names=classes),
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dict(
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type='Collect3D',
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keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d']),
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]
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modality = dict(use_lidar=True, use_camera=True)
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kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix,
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multi_modality_pipeline, classes, modality)
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data = kitti_dataset[0]
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img = data['img']._data
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lidar2img = data['img_metas']._data['lidar2img']
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expected_lidar2img = np.array(
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[[6.02943726e+02, -7.07913330e+02, -1.22748432e+01, -1.70942719e+02],
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[1.76777252e+02, 8.80879879e+00, -7.07936157e+02, -1.02568634e+02],
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[9.99984801e-01, -1.52826728e-03, -5.29071223e-03, -3.27567995e-01],
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[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])
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assert img.shape[:] == (3, 416, 1344)
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assert np.allclose(lidar2img, expected_lidar2img)
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def test_evaluate():
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if not torch.cuda.is_available():
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pytest.skip('test requires GPU and torch+cuda')
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data_root, ann_file, classes, pts_prefix, \
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pipeline, modality, split = _generate_kitti_dataset_config()
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kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix,
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pipeline, classes, modality)
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boxes_3d = LiDARInstance3DBoxes(
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torch.tensor(
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[[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]]))
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labels_3d = torch.tensor([
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0,
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])
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scores_3d = torch.tensor([0.5])
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metric = ['mAP']
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result = dict(boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d)
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ap_dict = kitti_dataset.evaluate([result], metric)
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assert np.isclose(ap_dict['KITTI/Overall_3D_easy'], 3.0303030303030307)
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assert np.isclose(ap_dict['KITTI/Overall_3D_moderate'], 3.0303030303030307)
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assert np.isclose(ap_dict['KITTI/Overall_3D_hard'], 3.0303030303030307)
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def test_show():
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import mmcv
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from os import path as osp
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from mmdet3d.core.bbox import LiDARInstance3DBoxes
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tmp_dir = tempfile.TemporaryDirectory()
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temp_dir = tmp_dir.name
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data_root, ann_file, classes, pts_prefix, \
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pipeline, modality, split = _generate_kitti_dataset_config()
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kitti_dataset = KittiDataset(
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data_root, ann_file, split=split, modality=modality, pipeline=pipeline)
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boxes_3d = LiDARInstance3DBoxes(
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torch.tensor(
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[[46.1218, -4.6496, -0.9275, 0.5316, 1.4442, 1.7450, 1.1749],
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[33.3189, 0.1981, 0.3136, 0.5656, 1.2301, 1.7985, 1.5723],
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[46.1366, -4.6404, -0.9510, 0.5162, 1.6501, 1.7540, 1.3778],
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[33.2646, 0.2297, 0.3446, 0.5746, 1.3365, 1.7947, 1.5430],
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[58.9079, 16.6272, -1.5829, 1.5656, 3.9313, 1.4899, 1.5505]]))
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scores_3d = torch.tensor([0.1815, 0.1663, 0.5792, 0.2194, 0.2780])
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labels_3d = torch.tensor([0, 0, 1, 1, 2])
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result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d)
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results = [result]
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kitti_dataset.show(results, temp_dir, show=False)
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pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj')
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gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj')
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pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj')
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mmcv.check_file_exist(pts_file_path)
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mmcv.check_file_exist(gt_file_path)
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mmcv.check_file_exist(pred_file_path)
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tmp_dir.cleanup()
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# test show with pipeline
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eval_pipeline = [
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dict(
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type='LoadPointsFromFile',
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coord_type='LIDAR',
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load_dim=4,
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use_dim=4),
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dict(
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type='DefaultFormatBundle3D',
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class_names=classes,
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with_label=False),
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dict(type='Collect3D', keys=['points'])
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]
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tmp_dir = tempfile.TemporaryDirectory()
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temp_dir = tmp_dir.name
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kitti_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
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pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj')
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gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj')
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pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj')
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mmcv.check_file_exist(pts_file_path)
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mmcv.check_file_exist(gt_file_path)
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mmcv.check_file_exist(pred_file_path)
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tmp_dir.cleanup()
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# test multi-modality show
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tmp_dir = tempfile.TemporaryDirectory()
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temp_dir = tmp_dir.name
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_, _, _, _, multi_modality_pipeline, modality, _ = \
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_generate_kitti_multi_modality_dataset_config()
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kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix,
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multi_modality_pipeline, classes, modality)
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kitti_dataset.show(results, temp_dir, show=False)
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pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj')
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gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj')
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pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj')
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img_file_path = osp.join(temp_dir, '000000', '000000_img.png')
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img_pred_path = osp.join(temp_dir, '000000', '000000_pred.png')
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img_gt_file = osp.join(temp_dir, '000000', '000000_gt.png')
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mmcv.check_file_exist(pts_file_path)
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mmcv.check_file_exist(gt_file_path)
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mmcv.check_file_exist(pred_file_path)
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mmcv.check_file_exist(img_file_path)
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mmcv.check_file_exist(img_pred_path)
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mmcv.check_file_exist(img_gt_file)
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tmp_dir.cleanup()
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# test multi-modality show with pipeline
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eval_pipeline = [
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dict(
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type='LoadPointsFromFile',
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coord_type='LIDAR',
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load_dim=4,
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use_dim=4),
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dict(type='LoadImageFromFile'),
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dict(
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type='DefaultFormatBundle3D',
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class_names=classes,
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with_label=False),
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dict(type='Collect3D', keys=['points', 'img'])
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]
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tmp_dir = tempfile.TemporaryDirectory()
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temp_dir = tmp_dir.name
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kitti_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
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pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj')
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gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj')
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pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj')
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img_file_path = osp.join(temp_dir, '000000', '000000_img.png')
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img_pred_path = osp.join(temp_dir, '000000', '000000_pred.png')
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img_gt_file = osp.join(temp_dir, '000000', '000000_gt.png')
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mmcv.check_file_exist(pts_file_path)
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mmcv.check_file_exist(gt_file_path)
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mmcv.check_file_exist(pred_file_path)
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mmcv.check_file_exist(img_file_path)
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mmcv.check_file_exist(img_pred_path)
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mmcv.check_file_exist(img_gt_file)
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tmp_dir.cleanup()
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def test_format_results():
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from mmdet3d.core.bbox import LiDARInstance3DBoxes
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data_root, ann_file, classes, pts_prefix, \
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pipeline, modality, split = _generate_kitti_dataset_config()
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kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix,
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pipeline, classes, modality)
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boxes_3d = LiDARInstance3DBoxes(
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torch.tensor(
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[[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]]))
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labels_3d = torch.tensor([
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0,
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])
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scores_3d = torch.tensor([0.5])
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result = dict(boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d)
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results = [result]
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result_files, tmp_dir = kitti_dataset.format_results(results)
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expected_name = np.array(['Pedestrian'])
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expected_truncated = np.array([0.])
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expected_occluded = np.array([0])
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expected_alpha = np.array([-3.3410306])
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expected_bbox = np.array([[710.443, 144.00221, 820.29114, 307.58667]])
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expected_dimensions = np.array([[1.2, 1.89, 0.48]])
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expected_location = np.array([[1.8399826, 1.4700007, 8.410018]])
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expected_rotation_y = np.array([-3.1315928])
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expected_score = np.array([0.5])
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expected_sample_idx = np.array([0])
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assert np.all(result_files[0]['name'] == expected_name)
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assert np.allclose(result_files[0]['truncated'], expected_truncated)
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assert np.all(result_files[0]['occluded'] == expected_occluded)
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assert np.allclose(result_files[0]['alpha'], expected_alpha)
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assert np.allclose(result_files[0]['bbox'], expected_bbox)
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assert np.allclose(result_files[0]['dimensions'], expected_dimensions)
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assert np.allclose(result_files[0]['location'], expected_location)
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assert np.allclose(result_files[0]['rotation_y'], expected_rotation_y)
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assert np.allclose(result_files[0]['score'], expected_score)
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assert np.allclose(result_files[0]['sample_idx'], expected_sample_idx)
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tmp_dir.cleanup()
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def test_bbox2result_kitti():
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data_root, ann_file, classes, pts_prefix, \
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pipeline, modality, split = _generate_kitti_dataset_config()
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kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix,
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pipeline, classes, modality)
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boxes_3d = LiDARInstance3DBoxes(
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torch.tensor(
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[[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]]))
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labels_3d = torch.tensor([
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0,
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])
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scores_3d = torch.tensor([0.5])
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result = dict(boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d)
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results = [result]
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tmp_dir = tempfile.TemporaryDirectory()
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temp_kitti_result_dir = tmp_dir.name
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det_annos = kitti_dataset.bbox2result_kitti(
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results, classes, submission_prefix=temp_kitti_result_dir)
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expected_file_path = os.path.join(temp_kitti_result_dir, '000000.txt')
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expected_name = np.array(['Pedestrian'])
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expected_dimensions = np.array([1.2000, 1.8900, 0.4800])
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expected_rotation_y = np.array([0.0100]) - np.pi
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expected_score = np.array([0.5])
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assert np.all(det_annos[0]['name'] == expected_name)
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assert np.allclose(det_annos[0]['rotation_y'], expected_rotation_y)
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assert np.allclose(det_annos[0]['score'], expected_score)
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assert np.allclose(det_annos[0]['dimensions'], expected_dimensions)
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|
assert os.path.exists(expected_file_path)
|
|
tmp_dir.cleanup()
|
|
|
|
tmp_dir = tempfile.TemporaryDirectory()
|
|
temp_kitti_result_dir = tmp_dir.name
|
|
boxes_3d = LiDARInstance3DBoxes(torch.tensor([]))
|
|
labels_3d = torch.tensor([])
|
|
scores_3d = torch.tensor([])
|
|
empty_result = dict(
|
|
boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d)
|
|
results = [empty_result]
|
|
det_annos = kitti_dataset.bbox2result_kitti(
|
|
results, classes, submission_prefix=temp_kitti_result_dir)
|
|
expected_file_path = os.path.join(temp_kitti_result_dir, '000000.txt')
|
|
assert os.path.exists(expected_file_path)
|
|
tmp_dir.cleanup()
|
|
|
|
|
|
def test_bbox2result_kitti2d():
|
|
data_root, ann_file, classes, pts_prefix, \
|
|
pipeline, modality, split = _generate_kitti_dataset_config()
|
|
kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix,
|
|
pipeline, classes, modality)
|
|
bboxes = np.array([[[46.1218, -4.6496, -0.9275, 0.5316, 0.5],
|
|
[33.3189, 0.1981, 0.3136, 0.5656, 0.5]],
|
|
[[46.1366, -4.6404, -0.9510, 0.5162, 0.5],
|
|
[33.2646, 0.2297, 0.3446, 0.5746, 0.5]]])
|
|
det_annos = kitti_dataset.bbox2result_kitti2d([bboxes], classes)
|
|
expected_name = np.array(
|
|
['Pedestrian', 'Pedestrian', 'Cyclist', 'Cyclist'])
|
|
expected_bbox = np.array([[46.1218, -4.6496, -0.9275, 0.5316],
|
|
[33.3189, 0.1981, 0.3136, 0.5656],
|
|
[46.1366, -4.6404, -0.951, 0.5162],
|
|
[33.2646, 0.2297, 0.3446, 0.5746]])
|
|
expected_score = np.array([0.5, 0.5, 0.5, 0.5])
|
|
assert np.all(det_annos[0]['name'] == expected_name)
|
|
assert np.allclose(det_annos[0]['bbox'], expected_bbox)
|
|
assert np.allclose(det_annos[0]['score'], expected_score)
|