[Bug] Problem when training PV-RCNN with Waymo database: Error during evaluation after first epoch #3027
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Prerequisite
Task
I have modified the scripts/configs, or I'm working on my own tasks/models/datasets.
Branch
main branch https://github.com/open-mmlab/mmdetection3d
Environment
sys.platform: linux
Python: 3.8.19 (default, Mar 20 2024, 19:58:24) [GCC 11.2.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0,1: NVIDIA GeForce RTX 3090
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.2, V12.2.140
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
PyTorch: 2.3.1
PyTorch compiling details: PyTorch built with:
TorchVision: 0.18.1
OpenCV: 4.10.0
MMEngine: 0.10.4
MMDetection: 3.3.0
MMDetection3D: 1.4.0+962f093
spconv2.0: False
Reproduces the problem - code sample
base = [
'../base/datasets/waymoD5-3d-3class.py',
'../base/schedules/cyclic-40e.py', '../base/default_runtime.py'
]
voxel_size = [0.05, 0.05, 0.1]
point_cloud_range = [0, -40, -3, 70.4, 40, 1]
data_root = 'data/waymo/kitti_format/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
metainfo = dict(CLASSES=class_names)
backend_args = None
db_sampler = dict(
data_root=data_root,
info_path=data_root + 'waymo_dbinfos_train.pkl',
rate=1.0,
prepare=dict(
filter_by_difficulty=[-1],
filter_by_min_points=dict(Car=5, Pedestrian=5, Cyclist=5)),
classes=class_names,
sample_groups=dict(Car=15, Pedestrian=10, Cyclist=10),
points_loader=dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=6,
use_dim=4,
backend_args=backend_args),
backend_args=backend_args)
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=6,
use_dim=4,
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(type='ObjectSample', db_sampler=db_sampler, use_ground_plane=False),
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]),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=6,
use_dim=4,
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'])
]
model = dict(
type='PointVoxelRCNN',
data_preprocessor=dict(
type='Det3DDataPreprocessor',
voxel=True,
voxel_layer=dict(
max_num_points=5, # max_points_per_voxel
point_cloud_range=point_cloud_range,
voxel_size=voxel_size,
max_voxels=(16000, 40000))),
voxel_encoder=dict(type='HardSimpleVFE'),
middle_encoder=dict(
type='SparseEncoder',
in_channels=4,
sparse_shape=[41, 1600, 1408],
order=('conv', 'norm', 'act'),
encoder_paddings=((0, 0, 0), ((1, 1, 1), 0, 0), ((1, 1, 1), 0, 0),
((0, 1, 1), 0, 0)),
return_middle_feats=True),
points_encoder=dict(
type='VoxelSetAbstraction',
num_keypoints=2048,
fused_out_channel=128,
voxel_size=voxel_size,
point_cloud_range=point_cloud_range,
voxel_sa_cfgs_list=[
dict(
type='StackedSAModuleMSG',
in_channels=16,
scale_factor=1,
radius=(0.4, 0.8),
sample_nums=(16, 16),
mlp_channels=((16, 16), (16, 16)),
use_xyz=True),
dict(
type='StackedSAModuleMSG',
in_channels=32,
scale_factor=2,
radius=(0.8, 1.2),
sample_nums=(16, 32),
mlp_channels=((32, 32), (32, 32)),
use_xyz=True),
dict(
type='StackedSAModuleMSG',
in_channels=64,
scale_factor=4,
radius=(1.2, 2.4),
sample_nums=(16, 32),
mlp_channels=((64, 64), (64, 64)),
use_xyz=True),
dict(
type='StackedSAModuleMSG',
in_channels=64,
scale_factor=8,
radius=(2.4, 4.8),
sample_nums=(16, 32),
mlp_channels=((64, 64), (64, 64)),
use_xyz=True)
],
rawpoints_sa_cfgs=dict(
type='StackedSAModuleMSG',
in_channels=1,
radius=(0.4, 0.8),
sample_nums=(16, 16),
mlp_channels=((16, 16), (16, 16)),
use_xyz=True),
bev_feat_channel=256,
bev_scale_factor=8),
backbone=dict(
type='SECOND',
in_channels=256,
layer_nums=[5, 5],
layer_strides=[1, 2],
out_channels=[128, 256]),
neck=dict(
type='SECONDFPN',
in_channels=[128, 256],
upsample_strides=[1, 2],
out_channels=[256, 256]),
rpn_head=dict(
type='PartA2RPNHead',
num_classes=3,
in_channels=512,
feat_channels=512,
use_direction_classifier=True,
dir_offset=0.78539,
anchor_generator=dict(
type='Anchor3DRangeGenerator',
ranges=[[0, -40.0, -0.6, 70.4, 40.0, -0.6],
[0, -40.0, -0.6, 70.4, 40.0, -0.6],
[0, -40.0, -1.78, 70.4, 40.0, -1.78]],
sizes=[[0.8, 0.6, 1.73], [1.76, 0.6, 1.73], [3.9, 1.6, 1.56]],
rotations=[0, 1.57],
reshape_out=False),
diff_rad_by_sin=True,
assigner_per_size=True,
assign_per_class=True,
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
loss_cls=dict(
type='mmdet.FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(
type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=2.0),
loss_dir=dict(
type='mmdet.CrossEntropyLoss', use_sigmoid=False,
loss_weight=0.2)),
roi_head=dict(
type='PVRCNNRoiHead',
num_classes=3,
semantic_head=dict(
type='ForegroundSegmentationHead',
in_channels=640,
extra_width=0.1,
loss_seg=dict(
type='mmdet.FocalLoss',
use_sigmoid=True,
reduction='sum',
gamma=2.0,
alpha=0.25,
activated=True,
loss_weight=1.0)),
bbox_roi_extractor=dict(
type='Batch3DRoIGridExtractor',
grid_size=6,
roi_layer=dict(
type='StackedSAModuleMSG',
in_channels=128,
radius=(0.8, 1.6),
sample_nums=(16, 16),
mlp_channels=((64, 64), (64, 64)),
use_xyz=True,
pool_mod='max'),
),
bbox_head=dict(
type='PVRCNNBBoxHead',
in_channels=128,
grid_size=6,
num_classes=3,
class_agnostic=True,
shared_fc_channels=(256, 256),
reg_channels=(256, 256),
cls_channels=(256, 256),
dropout_ratio=0.3,
with_corner_loss=True,
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
loss_bbox=dict(
type='mmdet.SmoothL1Loss',
beta=1.0 / 9.0,
reduction='sum',
loss_weight=1.0),
loss_cls=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='sum',
loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=[
dict( # for Pedestrian
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.5,
neg_iou_thr=0.35,
min_pos_iou=0.35,
ignore_iof_thr=-1),
dict( # for Cyclist
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.5,
neg_iou_thr=0.35,
min_pos_iou=0.35,
ignore_iof_thr=-1),
dict( # for Car
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.45,
min_pos_iou=0.45,
ignore_iof_thr=-1)
],
allowed_border=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=9000,
nms_post=512,
max_num=512,
nms_thr=0.8,
score_thr=0,
use_rotate_nms=True),
rcnn=dict(
assigner=[
dict( # for Pedestrian
type='Max3DIoUAssigner',
iou_calculator=dict(
type='BboxOverlaps3D', coordinate='lidar'),
pos_iou_thr=0.55,
neg_iou_thr=0.55,
min_pos_iou=0.55,
ignore_iof_thr=-1),
dict( # for Cyclist
type='Max3DIoUAssigner',
iou_calculator=dict(
type='BboxOverlaps3D', coordinate='lidar'),
pos_iou_thr=0.55,
neg_iou_thr=0.55,
min_pos_iou=0.55,
ignore_iof_thr=-1),
dict( # for Car
type='Max3DIoUAssigner',
iou_calculator=dict(
type='BboxOverlaps3D', coordinate='lidar'),
pos_iou_thr=0.55,
neg_iou_thr=0.55,
min_pos_iou=0.55,
ignore_iof_thr=-1)
],
sampler=dict(
type='IoUNegPiecewiseSampler',
num=128,
pos_fraction=0.5,
neg_piece_fractions=[0.8, 0.2],
neg_iou_piece_thrs=[0.55, 0.1],
neg_pos_ub=-1,
add_gt_as_proposals=False,
return_iou=True),
cls_pos_thr=0.75,
cls_neg_thr=0.25)),
test_cfg=dict(
rpn=dict(
nms_pre=1024,
nms_post=100,
max_num=100,
nms_thr=0.7,
score_thr=0,
use_rotate_nms=True),
rcnn=dict(
use_rotate_nms=True,
use_raw_score=True,
nms_thr=0.1,
score_thr=0.1)))
train_dataloader = dict(
batch_size=2,
num_workers=2,
dataset=dict(dataset=dict(pipeline=train_pipeline, metainfo=metainfo)))
test_dataloader = dict(dataset=dict(pipeline=test_pipeline, metainfo=metainfo))
eval_dataloader = dict(dataset=dict(pipeline=test_pipeline, metainfo=metainfo))
lr = 0.001
optim_wrapper = dict(optimizer=dict(lr=lr))
param_scheduler = [
# learning rate scheduler
# During the first 16 epochs, learning rate increases from 0 to lr * 10
# during the next 24 epochs, learning rate decreases from lr * 10 to
# lr * 1e-4
dict(
type='CosineAnnealingLR',
T_max=15,
eta_min=lr * 10,
begin=0,
end=15,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=25,
eta_min=lr * 1e-4,
begin=15,
end=40,
by_epoch=True,
convert_to_iter_based=True),
# momentum scheduler
# During the first 16 epochs, momentum increases from 0 to 0.85 / 0.95
# during the next 24 epochs, momentum increases from 0.85 / 0.95 to 1
dict(
type='CosineAnnealingMomentum',
T_max=15,
eta_min=0.85 / 0.95,
begin=0,
end=15,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingMomentum',
T_max=25,
eta_min=1,
begin=15,
end=40,
by_epoch=True,
convert_to_iter_based=True)
]
Reproduces the problem - command or script
python tools/train.py configs/pv_rcnn/pv_rcnn_8xb2-80e_waymoD5-3d-3class.pyReproduces the problem - error message
Additional information
I am trying to train the PV-RCNN model with the Waymo database. I have processed the waymo database, as it says in the documentation getting the following folders: kitti_format and waymo_format.
The config file has been modified to use the waymo base file and the input channels. The training starts correctly and completes the first epoch without problems. However, when the program tries to perform the evaluation at the end of the first epoch, it fails and throws an error.
Is the error due to the conversion of the data used, or is it directly caused by the model configuration, which does not accept this database?
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