File size: 4,699 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 |
_base_ = ['./centerpoint_voxel01_second_secfpn_8xb4-cyclic-20e_nus-3d.py']
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
voxel_size = [0.075, 0.075, 0.2]
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]
# 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(
voxel_size=voxel_size, point_cloud_range=point_cloud_range)),
pts_middle_encoder=dict(sparse_shape=[41, 1440, 1440]),
pts_bbox_head=dict(
bbox_coder=dict(
voxel_size=voxel_size[:2], pc_range=point_cloud_range[:2])),
train_cfg=dict(
pts=dict(
grid_size=[1440, 1440, 40],
voxel_size=voxel_size,
point_cloud_range=point_cloud_range)),
test_cfg=dict(
pts=dict(voxel_size=voxel_size[:2], 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(
dataset=dict(
dataset=dict(
pipeline=train_pipeline, metainfo=dict(classes=class_names))))
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)))
|