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- _base_ = [
- '../_base_/models/hv_second_secfpn_waymo.py',
- '../_base_/datasets/waymoD5-3d-3class.py',
- '../_base_/schedules/schedule_2x.py',
- '../_base_/default_runtime.py',
- ]
-
- dataset_type = 'WaymoDataset'
- data_root = 'data/waymo/kitti_format/'
- class_names = ['Car', 'Pedestrian', 'Cyclist']
- point_cloud_range = [-76.8, -51.2, -2, 76.8, 51.2, 4]
- input_modality = dict(use_lidar=True, use_camera=False)
-
- db_sampler = dict(
- data_root=data_root,
- info_path=data_root + 'waymo_dbinfos_train.pkl',
- rate=1.0,
- prepare=dict(
- filter_by_difficulty=[-1],
- filter_by_min_points=dict(Car=5, Pedestrian=5, Cyclist=5)),
- classes=class_names,
- sample_groups=dict(Car=15, Pedestrian=10, Cyclist=10),
- points_loader=dict(
- type='LoadPointsFromFile', load_dim=5, use_dim=[0, 1, 2, 3, 4]))
-
- train_pipeline = [
- dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=6, use_dim=5),
- dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
- dict(type='ObjectSample', db_sampler=db_sampler),
- dict(
- type='RandomFlip3D',
- sync_2d=False,
- flip_ratio_bev_horizontal=0.5,
- flip_ratio_bev_vertical=0.5),
- dict(
- type='GlobalRotScaleTrans',
- rot_range=[-0.78539816, 0.78539816],
- scale_ratio_range=[0.95, 1.05]),
- dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
- dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
- dict(type='PointShuffle'),
- dict(type='DefaultFormatBundle3D', class_names=class_names),
- dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
- ]
-
- test_pipeline = [
- dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=6, use_dim=5),
- dict(
- type='MultiScaleFlipAug3D',
- img_scale=(1333, 800),
- pts_scale_ratio=1,
- flip=False,
- transforms=[
- dict(
- type='GlobalRotScaleTrans',
- rot_range=[0, 0],
- scale_ratio_range=[1., 1.],
- translation_std=[0, 0, 0]),
- dict(type='RandomFlip3D'),
- dict(
- type='PointsRangeFilter', point_cloud_range=point_cloud_range),
- dict(
- type='DefaultFormatBundle3D',
- class_names=class_names,
- with_label=False),
- dict(type='Collect3D', keys=['points'])
- ])
- ]
-
- data = dict(
- samples_per_gpu=4,
- workers_per_gpu=4,
- train=dict(
- type='RepeatDataset',
- times=2,
- dataset=dict(
- type=dataset_type,
- data_root=data_root,
- ann_file=data_root + 'waymo_infos_train.pkl',
- split='training',
- pipeline=train_pipeline,
- modality=input_modality,
- classes=class_names,
- test_mode=False,
- # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
- # and box_type_3d='Depth' in sunrgbd and scannet dataset.
- box_type_3d='LiDAR',
- # load one frame every five frames
- load_interval=5)),
- val=dict(
- type=dataset_type,
- data_root=data_root,
- ann_file=data_root + 'waymo_infos_val.pkl',
- split='training',
- pipeline=test_pipeline,
- modality=input_modality,
- classes=class_names,
- test_mode=True,
- box_type_3d='LiDAR'),
- test=dict(
- type=dataset_type,
- data_root=data_root,
- ann_file=data_root + 'waymo_infos_val.pkl',
- split='training',
- pipeline=test_pipeline,
- modality=input_modality,
- classes=class_names,
- test_mode=True,
- box_type_3d='LiDAR'))
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