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Signed-off-by: Kaan Çolak <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,181 @@ | ||
| _base_ = [ | ||
| '../_base_/datasets/nus-3d.py', | ||
| '../_base_/models/centerpoint_pillar02_second_secfpn_nus.py', | ||
| '../_base_/schedules/cyclic-20e.py', '../_base_/default_runtime.py' | ||
| ] | ||
|
|
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| point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0] | ||
|
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| class_names = [ | ||
| "car", | ||
| "truck", | ||
| "bus", | ||
| "bicycle", | ||
| "pedestrian", | ||
| ] | ||
|
|
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| data_prefix = dict(pts='samples/LIDAR_TOP', img='', sweeps='sweeps/LIDAR_TOP') | ||
|
|
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| out_size_factor = 1 | ||
| model = dict( | ||
| data_preprocessor=dict( | ||
| voxel_layer=dict( | ||
| point_cloud_range=point_cloud_range)), | ||
| pts_voxel_encoder=dict( | ||
| point_cloud_range=point_cloud_range, | ||
| in_channels=4, | ||
| feat_channels=[32, 32], | ||
| use_voxel_center_z=False), | ||
| pts_middle_encoder=dict( | ||
| in_channels=32), | ||
| pts_backbone=dict( | ||
| in_channels=32, | ||
| layer_strides=[1, 2, 2],), | ||
| pts_neck=dict( | ||
| upsample_strides=[1, 2, 4], ), | ||
| pts_bbox_head=dict( | ||
| tasks=[dict( | ||
| num_class=len(class_names), | ||
| class_names=class_names)], | ||
| bbox_coder=dict( | ||
| out_size_factor=out_size_factor, | ||
| pc_range=point_cloud_range[:2])), | ||
| # model training and testing settings | ||
| train_cfg=dict( | ||
| pts=dict( | ||
| point_cloud_range=point_cloud_range, | ||
| out_size_factor=out_size_factor)), | ||
| test_cfg=dict( | ||
| pts=dict( | ||
| pc_range=point_cloud_range[:2], | ||
| nms_type='circle', | ||
| out_size_factor=out_size_factor,))) | ||
|
|
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| dataset_type = 'NuScenesDataset' | ||
| data_root = 'data/nuscenes/' | ||
| backend_args = None | ||
|
|
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| point_load_dim = 5 | ||
| point_use_dim = [0, 1, 2, 4] | ||
|
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| db_sampler = dict( | ||
| data_root=data_root, | ||
| info_path=data_root + 'nuscenes_dbinfos_train.pkl', | ||
| rate=1.0, | ||
| prepare=dict( | ||
| filter_by_difficulty=[-1], | ||
| filter_by_min_points=dict( | ||
| car=5, | ||
| truck=5, | ||
| bus=5, | ||
| bicycle=5, | ||
| pedestrian=5)), | ||
| classes=class_names, | ||
| sample_groups=dict( | ||
| car=2, | ||
| truck=3, | ||
| bus=4, | ||
| bicycle=6, | ||
| pedestrian=2), | ||
| points_loader=dict( | ||
| type='LoadPointsFromFile', | ||
| coord_type='LIDAR', | ||
| load_dim=point_load_dim, | ||
| use_dim=point_use_dim, | ||
| backend_args=backend_args), | ||
| backend_args=backend_args) | ||
|
|
||
| train_pipeline = [ | ||
| dict( | ||
| type='LoadPointsFromFile', | ||
| coord_type='LIDAR', | ||
| load_dim=point_load_dim, | ||
| use_dim=5, | ||
| backend_args=backend_args), | ||
| dict( | ||
| type='LoadPointsFromMultiSweeps', | ||
| sweeps_num=9, | ||
| use_dim=point_use_dim, | ||
| pad_empty_sweeps=True, | ||
| remove_close=True, | ||
| backend_args=backend_args), | ||
| dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True), | ||
| dict(type='ObjectSample', db_sampler=db_sampler), | ||
| dict( | ||
| type='GlobalRotScaleTrans', | ||
| rot_range=[-0.3925, 0.3925], | ||
| scale_ratio_range=[0.95, 1.05], | ||
| translation_std=[0, 0, 0]), | ||
| dict( | ||
| type='RandomFlip3D', | ||
| sync_2d=False, | ||
| flip_ratio_bev_horizontal=0.5, | ||
| flip_ratio_bev_vertical=0.5), | ||
| dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range), | ||
| dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range), | ||
| dict(type='ObjectNameFilter', classes=class_names), | ||
| dict(type='PointShuffle'), | ||
| dict( | ||
| type='Pack3DDetInputs', | ||
| keys=['points', 'gt_bboxes_3d', 'gt_labels_3d']) | ||
| ] | ||
| test_pipeline = [ | ||
| dict( | ||
| type='LoadPointsFromFile', | ||
| coord_type='LIDAR', | ||
| load_dim=point_load_dim, | ||
| use_dim=5, | ||
| backend_args=backend_args), | ||
| dict( | ||
| type='LoadPointsFromMultiSweeps', | ||
| sweeps_num=9, | ||
| use_dim=point_use_dim, | ||
| pad_empty_sweeps=True, | ||
| remove_close=True, | ||
| backend_args=backend_args), | ||
| dict( | ||
| 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(type='Pack3DDetInputs', keys=['points']) | ||
| ] | ||
|
|
||
| train_dataloader = dict( | ||
| _delete_=True, | ||
| batch_size=2, | ||
| num_workers=4, | ||
| persistent_workers=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=True), | ||
| dataset=dict( | ||
| type='CBGSDataset', | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file='nuscenes_infos_train.pkl', | ||
| pipeline=train_pipeline, | ||
| metainfo=dict(classes=class_names), | ||
| test_mode=False, | ||
| data_prefix=data_prefix, | ||
| use_valid_flag=True, | ||
| box_type_3d='LiDAR', | ||
| backend_args=backend_args))) | ||
| test_dataloader = dict( | ||
| dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names))) | ||
| val_dataloader = dict( | ||
| dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names))) | ||
|
|
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| train_cfg = dict(val_interval=5) | ||
| default_hooks = dict( | ||
| checkpoint=dict( | ||
| type='CheckpointHook', | ||
| interval=1, | ||
| save_optimizer=True)) |
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