centerpoint_voxel0075_second_secfpn_head-dcn-circlenms_8xb4-flip-tta-cyclic-20e_nus-3d.py

_base_ = './centerpoint_voxel0075_second_secfpn_' \
         'head-dcn-circlenms_8xb4_cyclic-20e_nus-3d.py'

point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
# 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 = [-54, -54.8, -5.0, 54, 53.2, 3.0]
file_client_args = dict(backend='disk')
class_names = [
    'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
    'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]

test_pipeline = [
    dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=9,
        use_dim=[0, 1, 2, 3, 4],
        pad_empty_sweeps=True,
        remove_close=True),
    dict(
        type='MultiScaleFlipAug3D',
        img_scale=(1333, 800),
        pts_scale_ratio=1,
        # Add double-flip augmentation
        flip=True,
        pcd_horizontal_flip=True,
        pcd_vertical_flip=True,
        transforms=[
            dict(
                type='GlobalRotScaleTrans',
                rot_range=[0, 0],
                scale_ratio_range=[1., 1.],
                translation_std=[0, 0, 0]),
            dict(type='RandomFlip3D', sync_2d=False),
            dict(
                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
        ]),
    dict(type='Pack3DDetInputs', keys=['points'])
]

data = dict(
    val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline))