error in ''/mmdet3d/core/bbox/structures/lidar_box3d.py"

[liyang0522]

i have run the 3DSSD code , but error occured like this, i dont know the reason , can anyone help?

[liyang0522]

my 3DSSD config like this, i dont konw why the asseritonerror occured, thank you for helping me！ @ZwwWayne @yinchimaoliang @encore-zhou

dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Car']
point_cloud_range = [0, -40, -5, 70, 40, 3]
input_modality = dict(use_lidar=True, use_camera=True)
img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
file_client_args = dict(backend='disk')
train_pipeline = [
dict(
type='LoadPointsFromFile',coord_type='LIDAR',load_dim=4,use_dim=4,
file_client_args=file_client_args),
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations3D',with_bbox_3d=True,with_label_3d=True,
file_client_args=file_client_args),
dict(
type='Resize',
img_scale=[(640, 192), (2560, 768)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.78539816, 0.78539816],
scale_ratio_range=[0.95, 1.05],
translation_std=[0.2, 0.2, 0.2]),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='BackgroundPointsFilter', bbox_enlarge_range=(0.5, 2.0, 0.5)),
dict(type='IndoorPointSample', num_points=16384),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
file_client_args=file_client_args),
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1280, 384),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='Resize', multiscale_mode='value', keep_ratio=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='IndoorPointSample', num_points=16384),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points','img'])
])
]

data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_train.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=train_pipeline,
modality=input_modality,
classes=class_names,
test_mode=False,
box_type_3d='LiDAR')),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True,
box_type_3d='LiDAR'),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_test.pkl',
split='testing',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True,
box_type_3d='LiDAR'))

evaluation = dict(interval=2)

[ZwwWayne]

It seems that the boxes are empty in your case. You might need to check your code and implementation.

[encore-zhou]

Hi, could you tell me where you modify the config file? @liyang0522

[liyang0522]

@encore-zhou Sorry for the late reply. In 3DSSD, the only input is lidar point cloud , i try to input the image information and remove gt aug ,so i changed the config file like this:

base = [
'../base/models/3dssd.py', '../base/datasets/kitti-3d-car.py',
'../base/default_runtime.py'
]

model = dict(
type='SSD3DNet2',
backbone=dict(
type='PointNet2SAMSG',
LI_FUSION_cfg=dict(
ENABLED=True,
IMG_FEATURES_CHANNEL=128,
ADD_Image_Attention=True,
IMG_CHANNELS=(3, 64, 128, 256),
POINT_CHANNELS=[128, 384, 768],
DeConv_Reduce=[16, 16, 16],
DeConv_Kernels=[2, 4, 8],
DeConv_Strides=[2, 4, 8]
)),
bbox_head=dict(
num_classes=1,
bbox_coder=dict(
type='AnchorFreeBBoxCoder', num_dir_bins=12, with_rot=True)
)
)

dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Car']
point_cloud_range = [0, -40, -5, 70, 40, 3]

input_modality = dict(use_lidar=True, use_camera=True)
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)

file_client_args = dict(backend='disk')

train_pipeline = [
dict(
type='LoadPointsFromFile',coord_type='LIDAR',load_dim=4,use_dim=4,
file_client_args=file_client_args),
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations3D',with_bbox_3d=True,with_label_3d=True,
file_client_args=file_client_args),
dict(
type='Resize',
img_scale=[(640, 192), (2560, 768)],
multiscale_mode='range',
keep_ratio=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.78539816, 0.78539816],
scale_ratio_range=[0.9, 1.1],
translation_std=[0.2, 0.2, 0.2]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(
type='ObjectNoise',
num_try=100,
translation_std=[1.0, 1.0, 0],
global_rot_range=[0.0, 0.0],
rot_range=[-1.0471975511965976, 1.0471975511965976]),

dict(type='BackgroundPointsFilter', bbox_enlarge_range=(0.5, 2.0, 0.5)),
dict(type='IndoorPointSample', num_points=16384),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d'])
]

test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
file_client_args=file_client_args),
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1280, 384),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='Resize', multiscale_mode='value', keep_ratio=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='IndoorPointSample', num_points=16384),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points','img'])
])
]

data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_train.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=train_pipeline,
modality=input_modality,
classes=class_names,
test_mode=False)),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_test.pkl',
split='testing',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True))

evaluation = dict(interval=2)

lr = 0.002 # max learning rate
optimizer = dict(type='AdamW', lr=lr, weight_decay=0)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(policy='step', warmup=None, step=[80, 120])

total_epochs = 150

log_config = dict(
interval=30,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])

[encore-zhou]

Fixed in #258