nus-seg.py

# For nuScenes we usually do 16-class segmentation.
# For labels_map we follow the uniform format of MMDetection & MMSegmentation
# i.e. we consider the unlabeled class as the last one, which is different
# from the original implementation of some methods e.g. Cylinder3D.

dataset_type = 'NuScenesSegDataset'
data_root = 'data/nuscenes/'
class_names = [
    'barrier', 'bicycle', 'bus', 'car', 'construction_vehicle', 'motorcycle',
    'pedestrian', 'traffic_cone', 'trailer', 'truck', 'driveable_surface',
    'other_flat', 'sidewalk', 'terrain', 'manmade', 'vegetation'
]
labels_map = {
    0: 16,
    1: 16,
    2: 6,
    3: 6,
    4: 6,
    5: 16,
    6: 6,
    7: 16,
    8: 16,
    9: 0,
    10: 16,
    11: 16,
    12: 7,
    13: 16,
    14: 1,
    15: 2,
    16: 2,
    17: 3,
    18: 4,
    19: 16,
    20: 16,
    21: 5,
    22: 8,
    23: 9,
    24: 10,
    25: 11,
    26: 12,
    27: 13,
    28: 14,
    29: 16,
    30: 15,
    31: 16
}

metainfo = dict(
    classes=class_names, seg_label_mapping=labels_map, max_label=31)

input_modality = dict(use_lidar=True, use_camera=False)
data_prefix = dict(
    pts='samples/LIDAR_TOP',
    img='',
    pts_semantic_mask='lidarseg/v1.0-trainval')

# 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 1.1.0
# backend_args = dict(
#     backend='petrel',
#     path_mapping=dict({
#         './data/': 's3://openmmlab/datasets/detection3d/',
#          'data/': 's3://openmmlab/datasets/detection3d/'
#      }))
backend_args = None

train_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=4,
        backend_args=backend_args),
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=False,
        with_label_3d=False,
        with_seg_3d=True,
        seg_3d_dtype='np.uint8',
        backend_args=backend_args),
    dict(type='PointSegClassMapping'),
    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],
        translation_std=[0.1, 0.1, 0.1]),
    dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask'])
]
test_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=4,
        backend_args=backend_args),
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=False,
        with_label_3d=False,
        with_seg_3d=True,
        seg_3d_dtype='np.uint8',
        backend_args=backend_args),
    dict(type='PointSegClassMapping'),
    dict(type='Pack3DDetInputs', keys=['points'])
]
tta_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=4,
        backend_args=backend_args),
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=False,
        with_label_3d=False,
        with_seg_3d=True,
        seg_3d_dtype='np.uint8',
        backend_args=backend_args),
    dict(type='PointSegClassMapping'),
    dict(
        type='TestTimeAug',
        transforms=[[
            dict(
                type='RandomFlip3D',
                sync_2d=False,
                flip_ratio_bev_horizontal=0.,
                flip_ratio_bev_vertical=0.),
            dict(
                type='RandomFlip3D',
                sync_2d=False,
                flip_ratio_bev_horizontal=0.,
                flip_ratio_bev_vertical=1.),
            dict(
                type='RandomFlip3D',
                sync_2d=False,
                flip_ratio_bev_horizontal=1.,
                flip_ratio_bev_vertical=0.),
            dict(
                type='RandomFlip3D',
                sync_2d=False,
                flip_ratio_bev_horizontal=1.,
                flip_ratio_bev_vertical=1.)
        ],
                    [
                        dict(
                            type='GlobalRotScaleTrans',
                            rot_range=[pcd_rotate_range, pcd_rotate_range],
                            scale_ratio_range=[
                                pcd_scale_factor, pcd_scale_factor
                            ],
                            translation_std=[0, 0, 0])
                        for pcd_rotate_range in [-0.78539816, 0.0, 0.78539816]
                        for pcd_scale_factor in [0.95, 1.0, 1.05]
                    ], [dict(type='Pack3DDetInputs', keys=['points'])]])
]

train_dataloader = dict(
    batch_size=2,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='nuscenes_infos_train.pkl',
        data_prefix=data_prefix,
        pipeline=train_pipeline,
        metainfo=metainfo,
        modality=input_modality,
        ignore_index=16,
        backend_args=backend_args))
val_dataloader = dict(
    batch_size=1,
    num_workers=1,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='nuscenes_infos_val.pkl',
        data_prefix=data_prefix,
        pipeline=test_pipeline,
        metainfo=metainfo,
        modality=input_modality,
        ignore_index=16,
        test_mode=True,
        backend_args=backend_args))
test_dataloader = val_dataloader

val_evaluator = dict(type='SegMetric')
test_evaluator = val_evaluator

vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')

tta_model = dict(type='Seg3DTTAModel')