nus-3d.py 5.53 KB
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# If point cloud range is changed, the models should also change their point
# cloud range accordingly
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point_cloud_range = [-50, -50, -5, 50, 50, 3]
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# Using calibration info convert the Lidar-coordinate point cloud range to the
# ego-coordinate point cloud range could bring a little promotion in nuScenes.
# point_cloud_range = [-50, -50.8, -5, 50, 49.2, 3]
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# For nuScenes we usually do 10-class detection
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class_names = [
    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
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metainfo = dict(classes=class_names)
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dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
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# Input modality for nuScenes dataset, this is consistent with the submission
# format which requires the information in input_modality.
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input_modality = dict(use_lidar=True, use_camera=False)
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data_prefix = dict(pts='samples/LIDAR_TOP', img='', sweeps='sweeps/LIDAR_TOP')
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# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)

# data_root = 's3://openmmlab/datasets/detection3d/nuscenes/'

# Method 2: Use backend_args, file_client_args in versions before 1.1.0rc4
# backend_args = dict(
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#     backend='petrel',
#     path_mapping=dict({
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#         './data/': 's3://openmmlab/datasets/detection3d/',
#          'data/': 's3://openmmlab/datasets/detection3d/'
#      }))
backend_args = None
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train_pipeline = [
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    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=5,
        backend_args=backend_args),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=10,
        backend_args=backend_args),
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    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
    dict(
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        type='GlobalRotScaleTrans',
        rot_range=[-0.3925, 0.3925],
        scale_ratio_range=[0.95, 1.05],
        translation_std=[0, 0, 0]),
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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),
    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
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    dict(type='ObjectNameFilter', classes=class_names),
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    dict(type='PointShuffle'),
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    dict(
        type='Pack3DDetInputs',
        keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
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]
test_pipeline = [
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    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=5,
        backend_args=backend_args),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=10,
        test_mode=True,
        backend_args=backend_args),
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    dict(
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        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(
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                type='PointsRangeFilter', point_cloud_range=point_cloud_range)
        ]),
    dict(type='Pack3DDetInputs', keys=['points'])
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]
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# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
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    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=5,
        backend_args=backend_args),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=10,
        test_mode=True,
        backend_args=backend_args),
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    dict(type='Pack3DDetInputs', keys=['points'])
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]
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train_dataloader = dict(
    batch_size=4,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
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        type=dataset_type,
        data_root=data_root,
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        ann_file='nuscenes_infos_train.pkl',
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        pipeline=train_pipeline,
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        metainfo=metainfo,
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        modality=input_modality,
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        test_mode=False,
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        data_prefix=data_prefix,
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        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
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        box_type_3d='LiDAR',
        backend_args=backend_args))
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test_dataloader = dict(
    batch_size=1,
    num_workers=1,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
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        type=dataset_type,
        data_root=data_root,
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        ann_file='nuscenes_infos_val.pkl',
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        pipeline=test_pipeline,
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        metainfo=metainfo,
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        modality=input_modality,
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        data_prefix=data_prefix,
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        test_mode=True,
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        box_type_3d='LiDAR',
        backend_args=backend_args))
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val_dataloader = dict(
    batch_size=1,
    num_workers=1,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
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        type=dataset_type,
        data_root=data_root,
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        ann_file='nuscenes_infos_val.pkl',
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        pipeline=test_pipeline,
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        metainfo=metainfo,
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        modality=input_modality,
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        test_mode=True,
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        data_prefix=data_prefix,
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        box_type_3d='LiDAR',
        backend_args=backend_args))
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val_evaluator = dict(
    type='NuScenesMetric',
    data_root=data_root,
    ann_file=data_root + 'nuscenes_infos_val.pkl',
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    metric='bbox',
    backend_args=backend_args)
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test_evaluator = val_evaluator
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vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')