mmdetection3d
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open-mmlab
[Bug] Meet different errors when Training MVXNet
Prerequisite
- [X] I have searched Issues and Discussions but cannot get the expected help.
- [X] I have read the FAQ documentation but cannot get the expected help.
- [X] The bug has not been fixed in the latest version (dev) or latest version (1.x).
Task
I'm using the official example scripts/configs for the officially supported tasks/models/datasets.
Branch
master branch https://github.com/open-mmlab/mmdetection3d
Environment
sys.platform: linux Python: 3.8.13 (default, Oct 21 2022, 23:50:54) [GCC 11.2.0] CUDA available: True GPU 0,1,2,3: NVIDIA GeForce RTX 3090 CUDA_HOME: /usr NVCC: Cuda compilation tools, release 11.5, V11.5.119 GCC: gcc (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 PyTorch: 1.10.1 PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.3
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
- CuDNN 8.2
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,
TorchVision: 0.11.2 OpenCV: 4.6.0 MMCV: 1.6.2 MMCV Compiler: GCC 9.3 MMCV CUDA Compiler: 11.3 MMDetection: 2.25.3 MMSegmentation: 0.29.0 MMDetection3D: 1.0.0rc4+9556958
Reproduces the problem - code sample
Here is the mvx_config which I used.
_base_ = ['../_base_/schedules/cosine.py', '../_base_/default_runtime.py']
# model settings
voxel_size = [0.05, 0.05, 0.1]
point_cloud_range = [0, -40, -3, 70.4, 40, 1]
model = dict(
type='DynamicMVXFasterRCNN',
img_backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='caffe'),
img_neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
pts_voxel_layer=dict(
max_num_points=-1,
point_cloud_range=point_cloud_range,
voxel_size=voxel_size,
max_voxels=(-1, -1),
),
pts_voxel_encoder=dict(
type='DynamicVFE',
in_channels=4,
feat_channels=[64, 64],
with_distance=False,
voxel_size=voxel_size,
with_cluster_center=True,
with_voxel_center=True,
point_cloud_range=point_cloud_range,
fusion_layer=dict(
type='PointFusion',
img_channels=256,
pts_channels=64,
mid_channels=128,
out_channels=128,
img_levels=[0, 1, 2, 3, 4],
align_corners=False,
activate_out=True,
fuse_out=False)),
pts_middle_encoder=dict(
type='SparseEncoder',
in_channels=128,
sparse_shape=[41, 1600, 1408],
order=('conv', 'norm', 'act')),
pts_backbone=dict(
type='SECOND',
in_channels=256,
layer_nums=[5, 5],
layer_strides=[1, 2],
out_channels=[128, 256]),
pts_neck=dict(
type='SECONDFPN',
in_channels=[128, 256],
upsample_strides=[1, 2],
out_channels=[256, 256]),
pts_bbox_head=dict(
type='Anchor3DHead',
num_classes=3,
in_channels=512,
feat_channels=512,
use_direction_classifier=True,
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),
assigner_per_size=True,
diff_rad_by_sin=True,
assign_per_class=True,
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=2.0),
loss_dir=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
# model training and testing settings
train_cfg=dict(
pts=dict(
assigner=[
dict( # for Pedestrian
type='MaxIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.35,
neg_iou_thr=0.2,
min_pos_iou=0.2,
ignore_iof_thr=-1),
dict( # for Cyclist
type='MaxIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.35,
neg_iou_thr=0.2,
min_pos_iou=0.2,
ignore_iof_thr=-1),
dict( # for Car
type='MaxIoUAssigner',
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)),
test_cfg=dict(
pts=dict(
use_rotate_nms=True,
nms_across_levels=False,
nms_thr=0.01,
score_thr=0.1,
min_bbox_size=0,
nms_pre=100,
max_num=50)))
# dataset settings
dataset_type = 'KittiDataset'
data_root = '/data/hhz/kitti/'
class_names = ["Pedestrian", "Cyclist", "Car"]
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
input_modality = dict(use_lidar=True, use_camera=True)
train_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
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=[0.95, 1.05],
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='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),
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='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points', 'img'])
])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
dict(type='LoadImageFromFile'),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points', 'img'])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
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_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True,
box_type_3d='LiDAR'))
# Training settings
optimizer = dict(weight_decay=0.01)
# max_norm=10 is better for SECOND
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
evaluation = dict(interval=1, pipeline=eval_pipeline)
# You may need to download the model first is the network is unstable
# load_from = 'https://download.openmmlab.com/mmdetection3d/pretrain_models/mvx_faster_rcnn_detectron2-caffe_20e_coco-pretrain_gt-sample_kitti-3-class_moderate-79.3_20200207-a4a6a3c7.pth' # noqa
load_from = 'pre_trained/mvx_faster_rcnn_detectron2-caffe_20e_coco-pretrain_gt-sample_kitti-3-class_moderate-79.3_20200207-a4a6a3c7.pth'
And I didn't change tools/train.py.
Reproduces the problem - command or script
When I trained the mvxnet on 1 GPU, I used such command as follows.
python3 tools/train.py configs/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py
When I trained the mvxnet on multiple GPUs, I used such command as follows.
./tools/dist_train.sh configs/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py 4 --work-dir my_checkpoints
Reproduces the problem - error message
When I trained it on a GPU, I got the error report.
2022-10-30 10:36:52,288 - mmdet - INFO - workflow: [('train', 1)], max: 40 epochs
2022-10-30 10:36:52,288 - mmdet - INFO - Checkpoints will be saved to /home/hhz/code/detection/mmdetection3d/work_dirs/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class by HardDiskBackend.
Traceback (most recent call last):
File "tools/train.py", line 262, in <module>
main()
File "tools/train.py", line 251, in main
train_model(
File "/home/hhz/code/detection/mmdetection3d/mmdet3d/apis/train.py", line 344, in train_model
train_detector(
File "/home/hhz/code/detection/mmdetection3d/mmdet3d/apis/train.py", line 319, in train_detector
runner.run(data_loaders, cfg.workflow)
File "/home/hhz/anaconda3/envs/openmmlab/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 136, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/hhz/anaconda3/envs/openmmlab/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 49, in train
for i, data_batch in enumerate(self.data_loader):
File "/home/hhz/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 359, in __iter__
return self._get_iterator()
File "/home/hhz/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 305, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "/home/hhz/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 944, in __init__
self._reset(loader, first_iter=True)
File "/home/hhz/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 975, in _reset
self._try_put_index()
File "/home/hhz/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1209, in _try_put_index
index = self._next_index()
File "/home/hhz/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 512, in _next_index
return next(self._sampler_iter) # may raise StopIteration
File "/home/hhz/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/utils/data/sampler.py", line 229, in __iter__
for idx in self.sampler:
File "/home/hhz/anaconda3/envs/openmmlab/lib/python3.8/site-packages/mmdet/datasets/samplers/group_sampler.py", line 36, in __iter__
indices = np.concatenate(indices)
File "<__array_function__ internals>", line 180, in concatenate
ValueError: need at least one array to concatenate
When I trained it on multiple GPUs, I got a strange result in the validation.
[>>>>>>>>>>>>>>>>>>>>>>>>> ] 3766/3769, 5699.5 task/s, elapsed: 1s, ETA: 0s
[>>>>>>>>>>>>>>>>>>>>>>>>> ] 3767/3769, 5699.5 task/s, elapsed: 1s, ETA: 0s
[>>>>>>>>>>>>>>>>>>>>>>>>> ] 3768/3769, 5699.6 task/s, elapsed: 1s, ETA: 0s
[>>>>>>>>>>>>>>>>>>>>>>>>>>] 3769/3769, 5699.6 task/s, elapsed: 1s, ETA: 0s
Result is saved to /tmp/tmpj3mtxq1k/resultspts_bbox.pkl.
OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.
2022-10-29 23:07:30,019 - mmdet - INFO - Results of pts_bbox:
----------- AP11 Results ------------
Pedestrian [email protected], 0.50, 0.50:
bbox AP11:0.0000, 0.0000, 0.0000
bev AP11:0.0000, 0.0000, 0.0000
3d AP11:0.0000, 0.0000, 0.0000
Pedestrian [email protected], 0.25, 0.25:
bbox AP11:0.0000, 0.0000, 0.0000
bev AP11:0.0000, 0.0000, 0.0000
3d AP11:0.0000, 0.0000, 0.0000
Cyclist [email protected], 0.50, 0.50:
bbox AP11:0.0000, 0.0000, 0.0000
bev AP11:0.0000, 0.0000, 0.0000
3d AP11:0.0000, 0.0000, 0.0000
Cyclist [email protected], 0.25, 0.25:
bbox AP11:0.0000, 0.0000, 0.0000
bev AP11:0.0000, 0.0000, 0.0000
3d AP11:0.0000, 0.0000, 0.0000
Car [email protected], 0.70, 0.70:
bbox AP11:0.0000, 0.0000, 0.0000
bev AP11:0.0000, 0.0000, 0.0000
3d AP11:0.0000, 0.0000, 0.0000
Car [email protected], 0.50, 0.50:
bbox AP11:0.0000, 0.0000, 0.0000
bev AP11:0.0000, 0.0000, 0.0000
3d AP11:0.0000, 0.0000, 0.0000
Overall AP11@easy, moderate, hard:
bbox AP11:0.0000, 0.0000, 0.0000
bev AP11:0.0000, 0.0000, 0.0000
3d AP11:0.0000, 0.0000, 0.0000
----------- AP40 Results ------------
Pedestrian [email protected], 0.50, 0.50:
bbox AP40:0.0000, 0.0000, 0.0000
bev AP40:0.0000, 0.0000, 0.0000
3d AP40:0.0000, 0.0000, 0.0000
Pedestrian [email protected], 0.25, 0.25:
bbox AP40:0.0000, 0.0000, 0.0000
bev AP40:0.0000, 0.0000, 0.0000
3d AP40:0.0000, 0.0000, 0.0000
Cyclist [email protected], 0.50, 0.50:
bbox AP40:0.0000, 0.0000, 0.0000
bev AP40:0.0000, 0.0000, 0.0000
3d AP40:0.0000, 0.0000, 0.0000
Cyclist [email protected], 0.25, 0.25:
bbox AP40:0.0000, 0.0000, 0.0000
bev AP40:0.0000, 0.0000, 0.0000
3d AP40:0.0000, 0.0000, 0.0000
Car [email protected], 0.70, 0.70:
bbox AP40:0.0000, 0.0000, 0.0000
bev AP40:0.0000, 0.0000, 0.0000
3d AP40:0.0000, 0.0000, 0.0000
Car [email protected], 0.50, 0.50:
bbox AP40:0.0000, 0.0000, 0.0000
bev AP40:0.0000, 0.0000, 0.0000
3d AP40:0.0000, 0.0000, 0.0000
Overall AP40@easy, moderate, hard:
bbox AP40:0.0000, 0.0000, 0.0000
bev AP40:0.0000, 0.0000, 0.0000
3d AP40:0.0000, 0.0000, 0.0000
2022-10-29 23:07:30,026 - mmdet - INFO - Epoch(val) [1][943] pts_bbox/KITTI/Pedestrian_3D_AP11_easy_strict: 0.0000, pts_bbox/KITTI/Pedestrian_BEV_AP11_easy_strict: 0.0000, pts_bbox/KITTI/Pedestrian_2D_AP11_easy_strict: 0.0000, pts_bbox/KITTI/Pedestrian_3D_AP11_moderate_strict: 0.0000, pts_bbox/KITTI/Pedestrian_BEV_AP11_moderate_strict: 0.0000, pts_bbox/KITTI/Pedestrian_2D_AP11_moderate_strict: 0.0000, pts_bbox/KITTI/Pedestrian_3D_AP11_hard_strict: 0.0000, pts_bbox/KITTI/Pedestrian_BEV_AP11_hard_strict: 0.0000, pts_bbox/KITTI/Pedestrian_2D_AP11_hard_strict: 0.0000, pts_bbox/KITTI/Pedestrian_3D_AP11_easy_loose: 0.0000, pts_bbox/KITTI/Pedestrian_BEV_AP11_easy_loose: 0.0000, pts_bbox/KITTI/Pedestrian_2D_AP11_easy_loose: 0.0000, pts_bbox/KITTI/Pedestrian_3D_AP11_moderate_loose: 0.0000, pts_bbox/KITTI/Pedestrian_BEV_AP11_moderate_loose: 0.0000, pts_bbox/KITTI/Pedestrian_2D_AP11_moderate_loose: 0.0000, pts_bbox/KITTI/Pedestrian_3D_AP11_hard_loose: 0.0000, pts_bbox/KITTI/Pedestrian_BEV_AP11_hard_loose: 0.0000, pts_bbox/KITTI/Pedestrian_2D_AP11_hard_loose: 0.0000, pts_bbox/KITTI/Cyclist_3D_AP11_easy_strict: 0.0000, pts_bbox/KITTI/Cyclist_BEV_AP11_easy_strict: 0.0000, pts_bbox/KITTI/Cyclist_2D_AP11_easy_strict: 0.0000, pts_bbox/KITTI/Cyclist_3D_AP11_moderate_strict: 0.0000, pts_bbox/KITTI/Cyclist_BEV_AP11_moderate_strict: 0.0000, pts_bbox/KITTI/Cyclist_2D_AP11_moderate_strict: 0.0000, pts_bbox/KITTI/Cyclist_3D_AP11_hard_strict: 0.0000, pts_bbox/KITTI/Cyclist_BEV_AP11_hard_strict: 0.0000, pts_bbox/KITTI/Cyclist_2D_AP11_hard_strict: 0.0000, pts_bbox/KITTI/Cyclist_3D_AP11_easy_loose: 0.0000, pts_bbox/KITTI/Cyclist_BEV_AP11_easy_loose: 0.0000, pts_bbox/KITTI/Cyclist_2D_AP11_easy_loose: 0.0000, pts_bbox/KITTI/Cyclist_3D_AP11_moderate_loose: 0.0000, pts_bbox/KITTI/Cyclist_BEV_AP11_moderate_loose: 0.0000, pts_bbox/KITTI/Cyclist_2D_AP11_moderate_loose: 0.0000, pts_bbox/KITTI/Cyclist_3D_AP11_hard_loose: 0.0000, pts_bbox/KITTI/Cyclist_BEV_AP11_hard_loose: 0.0000, pts_bbox/KITTI/Cyclist_2D_AP11_hard_loose: 0.0000, pts_bbox/KITTI/Car_3D_AP11_easy_strict: 0.0000, pts_bbox/KITTI/Car_BEV_AP11_easy_strict: 0.0000, pts_bbox/KITTI/Car_2D_AP11_easy_strict: 0.0000, pts_bbox/KITTI/Car_3D_AP11_moderate_strict: 0.0000, pts_bbox/KITTI/Car_BEV_AP11_moderate_strict: 0.0000, pts_bbox/KITTI/Car_2D_AP11_moderate_strict: 0.0000, pts_bbox/KITTI/Car_3D_AP11_hard_strict: 0.0000, pts_bbox/KITTI/Car_BEV_AP11_hard_strict: 0.0000, pts_bbox/KITTI/Car_2D_AP11_hard_strict: 0.0000, pts_bbox/KITTI/Car_3D_AP11_easy_loose: 0.0000, pts_bbox/KITTI/Car_BEV_AP11_easy_loose: 0.0000, pts_bbox/KITTI/Car_2D_AP11_easy_loose: 0.0000, pts_bbox/KITTI/Car_3D_AP11_moderate_loose: 0.0000, pts_bbox/KITTI/Car_BEV_AP11_moderate_loose: 0.0000, pts_bbox/KITTI/Car_2D_AP11_moderate_loose: 0.0000, pts_bbox/KITTI/Car_3D_AP11_hard_loose: 0.0000, pts_bbox/KITTI/Car_BEV_AP11_hard_loose: 0.0000, pts_bbox/KITTI/Car_2D_AP11_hard_loose: 0.0000, pts_bbox/KITTI/Overall_3D_AP11_easy: 0.0000, pts_bbox/KITTI/Overall_BEV_AP11_easy: 0.0000, pts_bbox/KITTI/Overall_2D_AP11_easy: 0.0000, pts_bbox/KITTI/Overall_3D_AP11_moderate: 0.0000, pts_bbox/KITTI/Overall_BEV_AP11_moderate: 0.0000, pts_bbox/KITTI/Overall_2D_AP11_moderate: 0.0000, pts_bbox/KITTI/Overall_3D_AP11_hard: 0.0000, pts_bbox/KITTI/Overall_BEV_AP11_hard: 0.0000, pts_bbox/KITTI/Overall_2D_AP11_hard: 0.0000, pts_bbox/KITTI/Pedestrian_3D_AP40_easy_strict: 0.0000, pts_bbox/KITTI/Pedestrian_BEV_AP40_easy_strict: 0.0000, pts_bbox/KITTI/Pedestrian_2D_AP40_easy_strict: 0.0000, pts_bbox/KITTI/Pedestrian_3D_AP40_moderate_strict: 0.0000, pts_bbox/KITTI/Pedestrian_BEV_AP40_moderate_strict: 0.0000, pts_bbox/KITTI/Pedestrian_2D_AP40_moderate_strict: 0.0000, pts_bbox/KITTI/Pedestrian_3D_AP40_hard_strict: 0.0000, pts_bbox/KITTI/Pedestrian_BEV_AP40_hard_strict: 0.0000, pts_bbox/KITTI/Pedestrian_2D_AP40_hard_strict: 0.0000, pts_bbox/KITTI/Pedestrian_3D_AP40_easy_loose: 0.0000, pts_bbox/KITTI/Pedestrian_BEV_AP40_easy_loose: 0.0000, pts_bbox/KITTI/Pedestrian_2D_AP40_easy_loose: 0.0000, pts_bbox/KITTI/Pedestrian_3D_AP40_moderate_loose: 0.0000, pts_bbox/KITTI/Pedestrian_BEV_AP40_moderate_loose: 0.0000, pts_bbox/KITTI/Pedestrian_2D_AP40_moderate_loose: 0.0000, pts_bbox/KITTI/Pedestrian_3D_AP40_hard_loose: 0.0000, pts_bbox/KITTI/Pedestrian_BEV_AP40_hard_loose: 0.0000, pts_bbox/KITTI/Pedestrian_2D_AP40_hard_loose: 0.0000, pts_bbox/KITTI/Cyclist_3D_AP40_easy_strict: 0.0000, pts_bbox/KITTI/Cyclist_BEV_AP40_easy_strict: 0.0000, pts_bbox/KITTI/Cyclist_2D_AP40_easy_strict: 0.0000, pts_bbox/KITTI/Cyclist_3D_AP40_moderate_strict: 0.0000, pts_bbox/KITTI/Cyclist_BEV_AP40_moderate_strict: 0.0000, pts_bbox/KITTI/Cyclist_2D_AP40_moderate_strict: 0.0000, pts_bbox/KITTI/Cyclist_3D_AP40_hard_strict: 0.0000, pts_bbox/KITTI/Cyclist_BEV_AP40_hard_strict: 0.0000, pts_bbox/KITTI/Cyclist_2D_AP40_hard_strict: 0.0000, pts_bbox/KITTI/Cyclist_3D_AP40_easy_loose: 0.0000, pts_bbox/KITTI/Cyclist_BEV_AP40_easy_loose: 0.0000, pts_bbox/KITTI/Cyclist_2D_AP40_easy_loose: 0.0000, pts_bbox/KITTI/Cyclist_3D_AP40_moderate_loose: 0.0000, pts_bbox/KITTI/Cyclist_BEV_AP40_moderate_loose: 0.0000, pts_bbox/KITTI/Cyclist_2D_AP40_moderate_loose: 0.0000, pts_bbox/KITTI/Cyclist_3D_AP40_hard_loose: 0.0000, pts_bbox/KITTI/Cyclist_BEV_AP40_hard_loose: 0.0000, pts_bbox/KITTI/Cyclist_2D_AP40_hard_loose: 0.0000, pts_bbox/KITTI/Car_3D_AP40_easy_strict: 0.0000, pts_bbox/KITTI/Car_BEV_AP40_easy_strict: 0.0000, pts_bbox/KITTI/Car_2D_AP40_easy_strict: 0.0000, pts_bbox/KITTI/Car_3D_AP40_moderate_strict: 0.0000, pts_bbox/KITTI/Car_BEV_AP40_moderate_strict: 0.0000, pts_bbox/KITTI/Car_2D_AP40_moderate_strict: 0.0000, pts_bbox/KITTI/Car_3D_AP40_hard_strict: 0.0000, pts_bbox/KITTI/Car_BEV_AP40_hard_strict: 0.0000, pts_bbox/KITTI/Car_2D_AP40_hard_strict: 0.0000, pts_bbox/KITTI/Car_3D_AP40_easy_loose: 0.0000, pts_bbox/KITTI/Car_BEV_AP40_easy_loose: 0.0000, pts_bbox/KITTI/Car_2D_AP40_easy_loose: 0.0000, pts_bbox/KITTI/Car_3D_AP40_moderate_loose: 0.0000, pts_bbox/KITTI/Car_BEV_AP40_moderate_loose: 0.0000, pts_bbox/KITTI/Car_2D_AP40_moderate_loose: 0.0000, pts_bbox/KITTI/Car_3D_AP40_hard_loose: 0.0000, pts_bbox/KITTI/Car_BEV_AP40_hard_loose: 0.0000, pts_bbox/KITTI/Car_2D_AP40_hard_loose: 0.0000, pts_bbox/KITTI/Overall_3D_AP40_easy: 0.0000, pts_bbox/KITTI/Overall_BEV_AP40_easy: 0.0000, pts_bbox/KITTI/Overall_2D_AP40_easy: 0.0000, pts_bbox/KITTI/Overall_3D_AP40_moderate: 0.0000, pts_bbox/KITTI/Overall_BEV_AP40_moderate: 0.0000, pts_bbox/KITTI/Overall_2D_AP40_moderate: 0.0000, pts_bbox/KITTI/Overall_3D_AP40_hard: 0.0000, pts_bbox/KITTI/Overall_BEV_AP40_hard: 0.0000, pts_bbox/KITTI/Overall_2D_AP40_hard: 0.0000
2022-10-29 23:07:32,242 - mmdet - INFO - Saving checkpoint at 2 epochs
[ ] 0/3769, elapsed: 0s, ETA:/home/hhz/code/detection/mmdetection3d/mmdet3d/models/fusion_layers/coord_transform.py:34: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
torch.tensor(img_meta['pcd_rotation'], dtype=dtype, device=device)
Additional information
I used KITTI for the training. It seems a problem with mmcv. @lindahua @happynear @aditya9710 I need your help. Thanks
Hi, It seems like a Nane loss error, can you check your log to see whether you get Nane loss in training? And if you used 4 GPU, I suggest you can turn down the learning rate in training.
Ok, I'll try turning down the learning rate. But I don't understand what you mean Nane loss. Can you explain it in details? Thanks! @VVsssssk
Hello, I found another issue reporting the same problem. There may be some problems with the original mvx-net model. @VVsssssk @lindahua @atinfinity @mickeyouyou
Yeah, when I trained MVXNet find some problems too, sometimes model loss is Nane or raises OOM error, so I think maybe it's unstable? And then I turned down the model's learning rate to get a relatively normal result.
@hellohaozheng Hi, we have fixed this bug in the PR https://github.com/open-mmlab/mmdetection3d/pull/2282
你好，你解决了吗，我也遇到相同的问题，可视化出来的检测框也是完全偏离物体的
I remember I fixed this problem by adjusting the coordinate system orientation, you can try it
i have same problem can you please guide me @JingweiZhang12 @hellohaozheng @VVsssssk @CrushDory
