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331292a992
* feat(mmdet3d): test pointpillars and centerpoint on ort, openvino and trt passed * fix(centerpoint): mvx_two_stage input error * fix(review): remove mode decorator * fix(mmdet3d): review advices * fix(regression): update mmdet3d.yml and test ort/openvino passed * unittest(mmdet3d): fix * fix(unittest): fix * fix(mmdet3d): unittest * fix(mmdet3d): unittest * fix(CI): remove mmcv.Config * fix(mmdet3d): unittest * fix(mmdet3d): support torch1.12 * fix(CI): use bigger point cloud file * improvement(mmdet3d): align backend outputs with torch * fix(mmdet3d): remove useless * style(mmdet3d): format code * style(mmdet3d): remove useless * fix(mmdet3d): sync vis_task * unittest(mmdet3d): add test * docs(mmdet3d): add docstring * unittest(ci): add unittest data * fix(mmdet3d): review advices * feat(mmdet3d): convert fail * style(mmdet3d): docstring * style(mmdet3d): docstring
134 lines
4.5 KiB
Python
134 lines
4.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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# dataset settings
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dataset_type = 'KittiDataset'
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data_root = 'tests/test_codebase/test_mmdet3d/data/kitti'
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class_names = ['Pedestrian', 'Cyclist', 'Car']
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point_cloud_range = [0, -40, -3, 70.4, 40, 1]
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input_modality = dict(use_lidar=True, use_camera=False)
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metainfo = dict(CLASSES=class_names)
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db_sampler = dict(
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data_root=data_root,
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info_path=data_root + '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(Car=5, Pedestrian=10, Cyclist=10)),
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classes=class_names,
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sample_groups=dict(Car=12, Pedestrian=6, Cyclist=6),
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points_loader=dict(
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type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4))
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train_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, # x, y, z, intensity
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use_dim=4),
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dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
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dict(type='ObjectSample', db_sampler=db_sampler),
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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(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(
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type='Pack3DDetInputs',
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keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
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]
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test_pipeline = [
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dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
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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., 1.],
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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', point_cloud_range=point_cloud_range)
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]),
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dict(type='Pack3DDetInputs', keys=['points'])
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]
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# construct a pipeline for data and gt loading in show function
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# please keep its loading function consistent with test_pipeline (e.g. client)
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eval_pipeline = [
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dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
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dict(type='Pack3DDetInputs', keys=['points'])
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]
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train_dataloader = dict(
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batch_size=6,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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dataset=dict(
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type='RepeatDataset',
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times=2,
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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ann_file='kitti_infos_train.pkl',
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data_prefix=dict(pts='training/velodyne_reduced'),
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pipeline=train_pipeline,
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modality=input_modality,
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test_mode=False,
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metainfo=metainfo,
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# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
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# and box_type_3d='Depth' in sunrgbd and scannet dataset.
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box_type_3d='LiDAR')))
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val_dataloader = dict(
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batch_size=1,
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num_workers=1,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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data_prefix=dict(pts='training/velodyne_reduced'),
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ann_file='kitti_infos_val.pkl',
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pipeline=test_pipeline,
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modality=input_modality,
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test_mode=True,
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metainfo=metainfo,
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box_type_3d='LiDAR'))
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test_dataloader = dict(
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batch_size=1,
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num_workers=1,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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data_prefix=dict(pts='training/velodyne_reduced'),
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ann_file='kitti_infos_val.pkl',
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pipeline=test_pipeline,
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modality=input_modality,
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test_mode=True,
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metainfo=metainfo,
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box_type_3d='LiDAR'))
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val_evaluator = dict(
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type='KittiMetric',
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ann_file=data_root + 'kitti_infos_val.pkl',
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metric='bbox')
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test_evaluator = val_evaluator
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vis_backends = [dict(type='LocalVisBackend')]
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visualizer = dict(
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type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
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