sunrgbd-3d-10class.py

dataset_type = 'SUNRGBDDataset'
data_root = 'data/sunrgbd/'
class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser',
               'night_stand', 'bookshelf', 'bathtub')
train_pipeline = [
    dict(
        type='LoadPointsFromFile',
        shift_height=True,
        load_dim=6,
        use_dim=[0, 1, 2]),
    dict(type='LoadAnnotations3D'),
    dict(type='IndoorFlipData', flip_ratio_yz=0.5),
    dict(
        type='IndoorGlobalRotScale',
        shift_height=True,
        rot_range=[-1 / 6, 1 / 6],
        scale_range=[0.85, 1.15]),
    dict(type='IndoorPointSample', num_points=20000),
    dict(type='DefaultFormatBundle3D', class_names=class_names),
    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
    dict(
        type='LoadPointsFromFile',
        shift_height=True,
        load_dim=6,
        use_dim=[0, 1, 2]),
    dict(type='IndoorPointSample', num_points=20000),
    dict(type='DefaultFormatBundle3D', class_names=class_names),
    dict(type='Collect3D', keys=['points'])
]

data = dict(
    samples_per_gpu=16,
    workers_per_gpu=4,
    train=dict(
        type='RepeatDataset',
        times=5,
        dataset=dict(
            type=dataset_type,
            data_root=data_root,
            ann_file=data_root + 'sunrgbd_infos_train.pkl',
            pipeline=train_pipeline,
            classes=class_names,
            filter_empty_gt=False)),
    val=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file=data_root + 'sunrgbd_infos_val.pkl',
        pipeline=test_pipeline,
        classes=class_names,
        test_mode=True),
    test=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file=data_root + 'sunrgbd_infos_val.pkl',
        pipeline=test_pipeline,
        classes=class_names,
        test_mode=True))