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- _base_ = './hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py'
-
- model = dict(
- pts_backbone=dict(
- _delete_=True,
- type='NoStemRegNet',
- arch='regnetx_3.2gf',
- init_cfg=dict(
- type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf'),
- out_indices=(1, 2, 3),
- frozen_stages=-1,
- strides=(1, 2, 2, 2),
- base_channels=64,
- stem_channels=64,
- norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
- norm_eval=False,
- style='pytorch'),
- pts_neck=dict(in_channels=[192, 432, 1008]))
-
- # If point cloud range is changed, the models should also change their point
- # cloud range accordingly
- point_cloud_range = [-50, -50, -5, 50, 50, 3]
- # For nuScenes we usually do 10-class detection
- class_names = [
- 'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
- 'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
- ]
- file_client_args = dict(backend='disk')
- # Uncomment the following if use ceph or other file clients.
- # See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
- # for more details.
- # file_client_args = dict(
- # backend='petrel',
- # path_mapping=dict({
- # './data/nuscenes/': 's3://nuscenes/nuscenes/',
- # 'data/nuscenes/': 's3://nuscenes/nuscenes/'
- # }))
- train_pipeline = [
- dict(
- type='LoadPointsFromFile',
- coord_type='LIDAR',
- load_dim=5,
- use_dim=5,
- file_client_args=file_client_args),
- dict(
- type='LoadPointsFromMultiSweeps',
- sweeps_num=10,
- file_client_args=file_client_args),
- dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
- dict(
- type='GlobalRotScaleTrans',
- rot_range=[-0.7854, 0.7854],
- scale_ratio_range=[0.9, 1.1],
- translation_std=[0.2, 0.2, 0.2]),
- dict(
- type='RandomFlip3D',
- 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='DefaultFormatBundle3D', class_names=class_names),
- dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
- ]
-
- data = dict(train=dict(pipeline=train_pipeline))
- lr_config = dict(step=[28, 34])
- runner = dict(max_epochs=36)
- evaluation = dict(interval=36)
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