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_base_ = ['./centerpoint_voxel01_second_secfpn_8xb4-cyclic-20e_nus-3d.py'] |
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voxel_size = [0.075, 0.075, 0.2] |
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point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0] |
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class_names = [ |
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'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', |
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'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' |
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] |
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data_prefix = dict(pts='samples/LIDAR_TOP', img='', sweeps='sweeps/LIDAR_TOP') |
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model = dict( |
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data_preprocessor=dict( |
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voxel_layer=dict( |
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voxel_size=voxel_size, point_cloud_range=point_cloud_range)), |
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pts_middle_encoder=dict(sparse_shape=[41, 1440, 1440]), |
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pts_bbox_head=dict( |
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bbox_coder=dict( |
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voxel_size=voxel_size[:2], pc_range=point_cloud_range[:2])), |
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train_cfg=dict( |
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pts=dict( |
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grid_size=[1440, 1440, 40], |
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voxel_size=voxel_size, |
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point_cloud_range=point_cloud_range)), |
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test_cfg=dict( |
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pts=dict(voxel_size=voxel_size[:2], pc_range=point_cloud_range[:2]))) |
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dataset_type = 'NuScenesDataset' |
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data_root = 'data/nuscenes/' |
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backend_args = None |
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db_sampler = dict( |
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data_root=data_root, |
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info_path=data_root + 'nuscenes_dbinfos_train.pkl', |
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rate=1.0, |
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prepare=dict( |
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filter_by_difficulty=[-1], |
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filter_by_min_points=dict( |
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car=5, |
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truck=5, |
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bus=5, |
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trailer=5, |
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construction_vehicle=5, |
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traffic_cone=5, |
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barrier=5, |
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motorcycle=5, |
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bicycle=5, |
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pedestrian=5)), |
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classes=class_names, |
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sample_groups=dict( |
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car=2, |
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truck=3, |
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construction_vehicle=7, |
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bus=4, |
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trailer=6, |
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barrier=2, |
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motorcycle=6, |
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bicycle=6, |
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pedestrian=2, |
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traffic_cone=2), |
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points_loader=dict( |
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type='LoadPointsFromFile', |
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coord_type='LIDAR', |
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load_dim=5, |
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use_dim=[0, 1, 2, 3, 4], |
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backend_args=backend_args), |
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backend_args=backend_args) |
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train_pipeline = [ |
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dict( |
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type='LoadPointsFromFile', |
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coord_type='LIDAR', |
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load_dim=5, |
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use_dim=5, |
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backend_args=backend_args), |
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dict( |
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type='LoadPointsFromMultiSweeps', |
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sweeps_num=9, |
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use_dim=[0, 1, 2, 3, 4], |
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pad_empty_sweeps=True, |
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remove_close=True, |
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backend_args=backend_args), |
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dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True), |
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dict(type='ObjectSample', db_sampler=db_sampler), |
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dict( |
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type='GlobalRotScaleTrans', |
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rot_range=[-0.3925, 0.3925], |
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scale_ratio_range=[0.95, 1.05], |
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translation_std=[0, 0, 0]), |
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dict( |
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type='RandomFlip3D', |
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sync_2d=False, |
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flip_ratio_bev_horizontal=0.5, |
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flip_ratio_bev_vertical=0.5), |
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dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range), |
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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( |
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type='Pack3DDetInputs', |
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keys=['points', 'gt_bboxes_3d', 'gt_labels_3d']) |
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] |
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test_pipeline = [ |
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dict( |
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type='LoadPointsFromFile', |
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coord_type='LIDAR', |
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load_dim=5, |
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use_dim=5, |
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backend_args=backend_args), |
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dict( |
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type='LoadPointsFromMultiSweeps', |
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sweeps_num=9, |
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use_dim=[0, 1, 2, 3, 4], |
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pad_empty_sweeps=True, |
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remove_close=True, |
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backend_args=backend_args), |
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dict( |
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type='MultiScaleFlipAug3D', |
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img_scale=(1333, 800), |
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pts_scale_ratio=1, |
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flip=False, |
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transforms=[ |
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dict( |
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type='GlobalRotScaleTrans', |
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rot_range=[0, 0], |
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scale_ratio_range=[1., 1.], |
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translation_std=[0, 0, 0]), |
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dict(type='RandomFlip3D'), |
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dict( |
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type='PointsRangeFilter', point_cloud_range=point_cloud_range) |
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]), |
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dict(type='Pack3DDetInputs', keys=['points']) |
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] |
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train_dataloader = dict( |
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dataset=dict( |
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dataset=dict( |
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pipeline=train_pipeline, metainfo=dict(classes=class_names)))) |
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test_dataloader = dict( |
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dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names))) |
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val_dataloader = dict( |
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dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names))) |
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