File size: 5,192 Bytes
34d1f8b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
_base_ = [
'../_base_/datasets/nus-3d.py',
'../_base_/models/centerpoint_voxel01_second_secfpn_nus.py',
'../_base_/schedules/cyclic-20e.py', '../_base_/default_runtime.py'
]
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 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 = [-51.2, -52, -5.0, 51.2, 50.4, 3.0]
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
data_prefix = dict(pts='samples/LIDAR_TOP', img='', sweeps='sweeps/LIDAR_TOP')
model = dict(
data_preprocessor=dict(
voxel_layer=dict(point_cloud_range=point_cloud_range)),
pts_bbox_head=dict(bbox_coder=dict(pc_range=point_cloud_range[:2])),
# model training and testing settings
train_cfg=dict(pts=dict(point_cloud_range=point_cloud_range)),
test_cfg=dict(pts=dict(pc_range=point_cloud_range[:2])))
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
backend_args = None
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,
trailer=5,
construction_vehicle=5,
traffic_cone=5,
barrier=5,
motorcycle=5,
bicycle=5,
pedestrian=5)),
classes=class_names,
sample_groups=dict(
car=2,
truck=3,
construction_vehicle=7,
bus=4,
trailer=6,
barrier=2,
motorcycle=6,
bicycle=6,
pedestrian=2,
traffic_cone=2),
points_loader=dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=[0, 1, 2, 3, 4],
backend_args=backend_args),
backend_args=backend_args)
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=9,
use_dim=[0, 1, 2, 3, 4],
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=5,
use_dim=5,
backend_args=backend_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=9,
use_dim=[0, 1, 2, 3, 4],
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='PointsRangeFilter', point_cloud_range=point_cloud_range)
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
_delete_=True,
batch_size=4,
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,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
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)))
train_cfg = dict(val_interval=20)
|