[Bug] RuntimeError: /io/build/temp.linux-x86_64-cpython-37/spconv/build/core_cc/src/csrc/sparse/all/SpconvOps/SpconvOps_get_indice_pairs.cc(65) not implemented for CPU ONLY build.

[zwl8979]

Prerequisite

• I have searched Issues and Discussions but cannot get the expected help.
• I have read the FAQ documentation but cannot get the expected help.
• 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

CUDA_HOME: /usr/local/cuda-10.1/
NVCC: Cuda compilation tools, release 10.1, V10.1.10
GCC: gcc (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609
PyTorch: 1.8.0+cu101
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 v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• NNPACK is enabled
• CPU capability usage: AVX2
• CUDA Runtime 10.1
• 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_70,code=sm_70
• CuDNN 7.6.3
• Magma 2.5.2
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.1, CUDNN_VERSION=7.6.3, 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 -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=1.8.0, 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: 0.9.0+cu101
OpenCV: 4.6.0
MMCV: 1.6.0
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.1
MMDetection: 2.25.3
MMSegmentation: 0.29.1
MMDetection3D: 1.0.0rc5+
spconv2.0: True

<<< conda initialize <<<

export CUDA_HOME=/usr/local/cuda-10.1/
export PATH=$PATH:/usr/local/cuda-10.1/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.1/lib64
export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/cuda-10.1/lib64

#cuda
export PATH=$PATH:/usr/local/cuda/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/cuda/lib64

Reproduces the problem - code sample

2022-11-27 23:17:22,114 - mmdet - INFO - workflow: [('train', 1)], max: 40 epochs
2022-11-27 23:17:22,114 - mmdet - INFO - Checkpoints will be saved to /home/dell/zhangweili/mmdetection3d-master/tools/work_dirs/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class by HardDiskBackend.
2022-11-27 23:17:22.798964: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/cv2/../../lib64:/usr/local/cuda-9.0/lib64
2022-11-27 23:17:22.799090: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/cv2/../../lib64:/usr/local/cuda-9.0/lib64
File "/home/dell/zhangweili/mmdetection3d-master/mmdet3d/apis/train.py", line 319, in train_detector
runner.run(data_loaders, cfg.workflow) # 启动
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 136, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 53, in train
self.run_iter(data_batch, train_mode=True, **kwargs)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 32, in run_iter
**kwargs)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/mmcv/parallel/data_parallel.py", line 77, in train_step
return self.module.train_step(*inputs[0], **kwargs[0])
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/mmdet/models/detectors/base.py", line 248, in train_step
losses = self(**data)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 116, in new_func
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/spconv/pytorch/conv.py", line 384, in forward
raise e
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/spconv/pytorch/conv.py", line 375, in forward
self.transposed)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/spconv/pytorch/ops.py", line 162, in get_indice_pairs
stream)
RuntimeError: /io/build/temp.linux-x86_64-cpython-37/spconv/build/core_cc/src/csrc/sparse/all/SpconvOps/SpconvOps_get_indice_pairs.cc(65)
not implemented for CPU ONLY build.

Reproduces the problem - command or script

run directly train.py

configs/mvxnet/dv_mvx-fp_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py

Reproduces the problem - error message

2022-11-27 23:17:22,114 - mmdet - INFO - workflow: [('train', 1)], max: 40 epochs
2022-11-27 23:17:22,114 - mmdet - INFO - Checkpoints will be saved to /home/dell/zhangweili/mmdetection3d-master/tools/work_dirs/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class by HardDiskBackend.
2022-11-27 23:17:22.798964: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/cv2/../../lib64:/usr/local/cuda-9.0/lib64
2022-11-27 23:17:22.799090: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/cv2/../../lib64:/usr/local/cuda-9.0/lib64
2022-11-27 23:17:22.799101: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
/home/dell/zhangweili/mmdetection3d-master/mmdet3d/models/fusion_layers/coord_transform.py:35: 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).
if 'pcd_rotation' in img_meta else torch.eye(
SPCONV_DEBUG_SAVE_PATH not found, you can specify SPCONV_DEBUG_SAVE_PATH as debug data save path to save debug data which can be attached in a issue.
[Exception|native_pair]indices=torch.Size([28776, 4]),bs=2,ss=[41, 1600, 1408],algo=ConvAlgo.Native,ksize=[3, 3, 3],stride=[1, 1, 1],padding=[1, 1, 1],dilation=[1, 1, 1],subm=True,transpose=False
Traceback (most recent call last):
File "/home/dell/zhangweili/mmdetection3d-master/tools/train.py", line 262, in
main()
File "/home/dell/zhangweili/mmdetection3d-master/tools/train.py", line 258, in main
meta=meta)
File "/home/dell/zhangweili/mmdetection3d-master/mmdet3d/apis/train.py", line 351, in train_model
meta=meta)
File "/home/dell/zhangweili/mmdetection3d-master/mmdet3d/apis/train.py", line 319, in train_detector
runner.run(data_loaders, cfg.workflow) # 启动
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 136, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 53, in train
self.run_iter(data_batch, train_mode=True, **kwargs)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 32, in run_iter
**kwargs)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/mmcv/parallel/data_parallel.py", line 77, in train_step
return self.module.train_step(*inputs[0], **kwargs[0])
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/mmdet/models/detectors/base.py", line 248, in train_step
losses = self(**data)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 116, in new_func
return old_func(*args, **kwargs)
File "/home/dell/zhangweili/mmdetection3d-master/mmdet3d/models/detectors/base.py", line 60, in forward
return self.forward_train(**kwargs)
File "/home/dell/zhangweili/mmdetection3d-master/mmdet3d/models/detectors/mvx_two_stage.py", line 274, in forward_train
points, img=img, img_metas=img_metas)
File "/home/dell/zhangweili/mmdetection3d-master/mmdet3d/models/detectors/mvx_two_stage.py", line 208, in extract_feat
pts_feats = self.extract_pts_feat(points, img_feats, img_metas)
File "/home/dell/zhangweili/mmdetection3d-master/mmdet3d/models/detectors/mvx_faster_rcnn.py", line 57, in extract_pts_feat
x = self.pts_middle_encoder(voxel_features, feature_coors, batch_size)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, inFile "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 116, in new_func
return old_func(*args, **kwargs)
File "/home/dell/zhangweili/mmdetection3d-master/mmdet3d/models/middle_encoders/sparse_encoder.py", line 123, in forward
x = self.conv_input(input_sp_tensor)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/spconv/pytorch/modules.py", line 137, in forward
input = module(input)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/spconv/pytorch/conv.py", line 384, in forward
raise e
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/spconv/pytorch/conv.py", line 375, in forward
self.transposed)
File "/home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/spconv/pytorch/ops.py", line 162, in get_indice_pairs
stream)
RuntimeError: /io/build/temp.linux-x86_64-cpython-37/spconv/build/core_cc/src/csrc/sparse/all/SpconvOps/SpconvOps_get_indice_pairs.cc(65)
not implemented for CPU ONLY build.

Additional information

Hope it can help us solve or suggest, thank you！

[JingweiZhang12]

Maybe you install the CPU-only spconv. Please install the spconv with CUDA. https://github.com/traveller59/spconv

[zwl8979]

Ok, thank you, because my cuda version is 10.1.105, the latest spconv2.x supports duda10.2 at least, so I changed the original spconv version to 1.2.1. solved！
cuda10.2/11.x refer to: https://github.com/traveller59/spconv
pip uninstall spconv
git clone -b v1.2.1 https://github.com/traveller59/spconv.git
python setup.py bdist_wheel
cd dist
pip install spconv-1.2.1-cp37-cp37m-linux_x86_64.whl

[zwl8979]

After I replace it with spconv1.2.1, what effect will spconv display as False, and the result of training with pre-training weights is 0！！！
TorchVision: 0.9.0+cu101
OpenCV: 4.6.0
MMCV: 1.6.0
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.1
MMDetection: 2.25.3
MMSegmentation: 0.29.1
MMDetection3D: 1.0.0rc5+
spconv2.0: False

2022-11-28 23:59:39,479 - mmdet - INFO - Set random seed to 0, deterministic: False
/home/dell/zhangweili/mmdetection3d-master/mmdet3d/models/dense_heads/anchor3d_head.py:85: UserWarning: dir_offset and dir_limit_offset will be depressed and be incorporated into box coder in the future
'dir_offset and dir_limit_offset will be depressed and be '
2022-11-28 23:59:39,900 - mmdet - INFO - initialize SECOND with init_cfg {'type': 'Kaiming', 'layer': 'Conv2d'}
2022-11-28 23:59:39,984 - mmdet - INFO - initialize SECONDFPN with init_cfg [{'type': 'Kaiming', 'layer': 'ConvTranspose2d'}, {'type': 'Constant', 'layer': 'NaiveSyncBatchNorm2d', 'val': 1.0}]
2022-11-28 23:59:39,989 - mmdet - INFO - initialize Anchor3DHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01, 'override': {'type': 'Normal', 'name': 'conv_cls', 'std': 0.01, 'bias_prob': 0.01}}
2022-11-28 23:59:39,991 - mmdet - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': 'Conv2d'}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]
2022-11-28 23:59:40,300 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,302 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,303 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,304 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,306 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,307 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,308 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,311 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,312 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,313 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,314 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,316 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,317 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,322 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,327 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,330 - mmdet - INFO - initialize Bottleneck with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm3'}}
2022-11-28 23:59:40,343 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2022-11-28 23:59:40,382 - mmdet - INFO - Model:
DynamicMVXFasterRCNN(
(pts_voxel_layer): Voxelization(voxel_size=[0.05, 0.05, 0.1], point_cloud_range=[0, -40, -3, 70.4, 40, 1], max_num_points=-1, max_voxels=(-1, -1), deterministic=True)
(pts_voxel_encoder): DynamicVFE(
(scatter): DynamicScatter(voxel_size=[0.05, 0.05, 0.1], point_cloud_range=[0, -40, -3, 70.4, 40, 1], average_points=True)
(vfe_layers): ModuleList(
.....
init_cfg={'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
)
2022-11-28 23:59:49,050 - mmdet - INFO - load checkpoint from local path: /home/dell/zhangweili/mmdetection3d-master/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class_20210831_060805-83442923.pth
2022-11-28 23:59:49,220 - mmdet - INFO - Start running, host: dell@dell-PowerEdge-R740, work_dir: /home/dell/zhangweili/mmdetection3d-master/tools/tools/work_dirs/my_dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class
2022-11-28 23:59:49,220 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(NORMAL ) CheckpointHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook

before_train_epoch:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(LOW ) IterTimerHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook

before_train_iter:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(LOW ) IterTimerHook
(LOW ) EvalHook

after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(NORMAL ) CheckpointHook
(LOW ) IterTimerHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook

after_train_epoch:
(NORMAL ) CheckpointHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook

before_val_epoch:
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook

before_val_iter:
(LOW ) IterTimerHook

after_val_iter:
(LOW ) IterTimerHook

after_val_epoch:
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook

after_run:
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook

2022-11-28 23:59:49,220 - mmdet - INFO - workflow: [('train', 1)], max: 20 epochs
2022-11-28 23:59:49,221 - mmdet - INFO - Checkpoints will be saved to /home/dell/zhangweili/mmdetection3d-master/tools/tools/work_dirs/my_dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class by HardDiskBackend.
2022-11-28 23:59:50.060441: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/cv2/../../lib64:/usr/local/cuda-9.0/lib64
2022-11-28 23:59:50.060558: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/dell/anaconda3/envs/mmdet3d/lib/python3.7/site-packages/cv2/../../lib64:/usr/local/cuda-9.0/lib64
2022-11-28 23:59:50.060569: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
/home/dell/zhangweili/mmdetection3d-master/mmdet3d/models/fusion_layers/coord_transform.py:35: 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).
if 'pcd_rotation' in img_meta else torch.eye(
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 9/9, 5.2 task/s, elapsed: 2s, ETA: 0s
Converting prediction to KITTI format
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 9/9, 366.0 task/s, elapsed: 0s, ETA: 0s
Result is saved to /tmp/tmpe2hzt4vs/resultspts_bbox.pkl.
2022-11-29 00:00:20,843 - 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
aos AP11:0.00, 0.00, 0.00
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
aos AP11:0.00, 0.00, 0.00
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
aos AP11:0.00, 0.00, 0.00
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
aos AP11:0.00, 0.00, 0.00
Car [email protected], 0.70, 0.70:
bbox AP11:3.0303, 3.0303, 3.0303
bev AP11:0.0000, 0.0000, 0.0000
3d AP11:0.0000, 0.0000, 0.0000
aos AP11:3.03, 3.03, 3.03
Car [email protected], 0.50, 0.50:
bbox AP11:3.0303, 3.0303, 3.0303
bev AP11:0.0000, 0.0000, 0.0000
3d AP11:0.0000, 0.0000, 0.0000
aos AP11:3.03, 3.03, 3.03

Overall AP11@easy, moderate, hard:
bbox AP11:1.0101, 1.0101, 1.0101
bev AP11:0.0000, 0.0000, 0.0000
3d AP11:0.0000, 0.0000, 0.0000
aos AP11:1.01, 1.01, 1.01

----------- 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
aos AP40:0.00, 0.00, 0.00
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
aos AP40:0.00, 0.00, 0.00
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
aos AP40:0.00, 0.00, 0.00
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
aos AP40:0.00, 0.00, 0.00
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
aos AP40:0.00, 0.00, 0.00
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
aos AP40:0.00, 0.00, 0.00

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
aos AP40:0.00, 0.00, 0.00

2022-11-29 00:00:20,844 - mmdet - INFO - Exp name: my_dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py
2022-11-29 00:00:20,844 - mmdet - INFO - Epoch(val) [1][9] 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: 3.0303, 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: 3.0303, 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: 3.0303, 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: 3.0303, 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: 3.0303, 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: 3.0303, 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: 1.0101, 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: 1.0101, 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: 1.0101, 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
/home/dell/zhangweili/mmdetection3d-master/mmdet3d/models/fusion_layers/coord_transform.py:35: 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).
if 'pcd_rotation' in img_meta else torch.eye(
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 9/9, 5.2 task/s, elapsed: 2s, ETA: 0s
Converting prediction to KITTI format
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 9/9, 391.3 task/s, elapsed: 0s, ETA: 0s
Result is saved to /tmp/tmpu2avay8u/resultspts_bbox.pkl.

[VVsssssk]

Hi, we have support spconv1.x in mmcv, so you don't need to install spconv1.x. And can you use default mmcv's spconv to try again?

[VVsssssk]

And I suggest you check your env by PointPillars model.

[zwl8979]

@VVsssssk @JingweiZhang12

1. May I use when building a virtual environment:

conda activate -n mmdtection3d python=3.7
cuda=11.1 cudnn=8005 pytorch=0.10.0 torchvision=0.11.0
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install -r requirements/mminstall.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

2. When we set up the environment for testing, this error still popped up:

**(mmdetection3d) ubuntu@ubuntu-ThinkStation-P920:~/zhangweili/mmdetection3d-master$ python demo/pcd_demo.py demo/data/kitti/kitti_000008.bin configs/second/hv_second_secfpn_6x8_80e_kitti-3d-car.py checkpoints/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238-393f000c.pth --show
/home/ubuntu/zhangweili/mmdetection3d-master/mmdet3d/models/dense_heads/anchor3d_head.py:85: UserWarning: dir_offset and dir_limit_offset will be depressed and be incorporated into box coder in the future
'dir_offset and dir_limit_offset will be depressed and be '
load checkpoint from local path: checkpoints/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238-393f000c.pth
[Exception|native_pair]indices=torch.Size([13092, 4]),bs=1,ss=[41, 1600, 1408],algo=ConvAlgo.Native,ksize=[3, 3, 3],stride=[1, 1, 1],padding=[1, 1, 1],dilation=[1, 1, 1],subm=True,transpose=False
SPCONV_DEBUG_SAVE_PATH not found, you can specify SPCONV_DEBUG_SAVE_PATH as debug data save path to save debug data which can be attached in a issue.
Traceback (most recent call last):
File "demo/pcd_demo.py", line 44, in
main()
File "demo/pcd_demo.py", line 31, in main
result, data = inference_detector(model, args.pcd)
File "/home/ubuntu/zhangweili/mmdetection3d-master/mmdet3d/apis/inference.py", line 151, in inference_detector
result = model(return_loss=False, rescale=True, **data)
File "/home/ubuntu/anaconda3/envs/mmdetection3d/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu/anaconda3/envs/mmdetection3d/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 116, in new_func
return old_func(*args, **kwargs)
File "/home/ubuntu/zhangweili/mmdetection3d-master/mmdet3d/models/detectors/base.py", line 62, in forward
return self.forward_test(**kwargs)
File "/home/ubuntu/zhangweili/mmdetection3d-master/mmdet3d/models/detectors/base.py", line 43, in forward_test
return self.simple_test(points[0], img_metas[0], img[0], **kwargs)
File "/home/ubuntu/zhangweili/mmdetection3d-master/mmdet3d/models/detectors/voxelnet.py", line 100, in simple_test
x = self.extract_feat(points, img_metas)
File "/home/ubuntu/zhangweili/mmdetection3d-master/mmdet3d/models/detectors/voxelnet.py", line 45, in extract_feat
x = self.middle_encoder(voxel_features, coors, batch_size)
File "/home/ubuntu/anaconda3/envs/mmdetection3d/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu/anaconda3/envs/mmdetection3d/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 116, in new_func
return old_func(*args, **kwargs)
File "/home/ubuntu/zhangweili/mmdetection3d-master/mmdet3d/models/middle_encoders/sparse_encoder.py", line 123, in forward
x = self.conv_input(input_sp_tensor)
File "/home/ubuntu/anaconda3/envs/mmdetection3d/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu/anaconda3/envs/mmdetection3d/lib/python3.7/site-packages/spconv/pytorch/modules.py", line 137, in forward
input = module(input)
File "/home/ubuntu/anaconda3/envs/mmdetection3d/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ubuntu/anaconda3/envs/mmdetection3d/lib/python3.7/site-packages/spconv/pytorch/conv.py", line 384, in forward
raise e
File "/home/ubuntu/anaconda3/envs/mmdetection3d/lib/python3.7/site-packages/spconv/pytorch/conv.py", line 375, in forward
self.transposed)
File "/home/ubuntu/anaconda3/envs/mmdetection3d/lib/python3.7/site-packages/spconv/pytorch/ops.py", line 162, in get_indice_pairs
stream)
RuntimeError: /io/build/temp.linux-x86_64-cpython-37/spconv/build/core_cc/src/csrc/sparse/all/SpconvOps/SpconvOps_get_indice_pairs.cc(65)
not implemented for CPU ONLY build.

May I ask how to solve this problem? Is the reason for this problem that all dependencies cannot be installed directly?
thanks！

3. conda list :
   _# packages in environment at /home/ubuntu/anaconda3/envs/mmdetection3d:

Name Version Build Channel

_libgcc_mutex 0.1 main
openmp_mutex 5.1 1_gnu
absl-py 1.3.0 pypi_0 pypi
addict 2.4.0 pypi_0 pypi
anyio 3.6.2 pypi_0 pypi
argon2-cffi 21.3.0 pypi_0 pypi
argon2-cffi-bindings 21.2.0 pypi_0 pypi
astor 0.8.1 pypi_0 pypi
asynctest 0.13.0 pypi_0 pypi
attrs 22.2.0 pypi_0 pypi
backcall 0.2.0 pypi_0 pypi
beautifulsoup4 4.11.1 pypi_0 pypi
black 22.12.0 pypi_0 pypi
bleach 5.0.1 pypi_0 pypi
ca-certificates 2022.10.11 h06a4308_0
cachetools 4.2.4 pypi_0 pypi
ccimport 0.4.2 pypi_0 pypi
certifi 2022.12.7 py37h06a4308_0
cffi 1.15.1 pypi_0 pypi
charset-normalizer 2.1.1 pypi_0 pypi
click 8.1.3 pypi_0 pypi
codecov 2.1.12 pypi_0 pypi
colorama 0.4.6 pypi_0 pypi
configargparse 1.5.3 pypi_0 pypi
coverage 7.0.3 pypi_0 pypi
cumm 0.3.7 pypi_0 pypi
cycler 0.11.0 pypi_0 pypi
dash 2.7.1 pypi_0 pypi
dash-core-components 2.0.0 pypi_0 pypi
dash-html-components 2.0.0 pypi_0 pypi
dash-table 5.0.0 pypi_0 pypi
debugpy 1.6.4 pypi_0 pypi
decorator 5.1.1 pypi_0 pypi
defusedxml 0.7.1 pypi_0 pypi
descartes 1.1.0 pypi_0 pypi
entrypoints 0.4 pypi_0 pypi
exceptiongroup 1.1.0 pypi_0 pypi
fastjsonschema 2.16.2 pypi_0 pypi
fire 0.5.0 pypi_0 pypi
flake8 5.0.4 pypi_0 pypi
flask 2.2.2 pypi_0 pypi
fonttools 4.38.0 pypi_0 pypi
gast 0.2.2 pypi_0 pypi
google-auth 1.35.0 pypi_0 pypi
google-auth-oauthlib 0.4.6 pypi_0 pypi
google-pasta 0.2.0 pypi_0 pypi
grpcio 1.51.1 pypi_0 pypi
h5py 3.7.0 pypi_0 pypi
idna 3.4 pypi_0 pypi
imageio 2.23.0 pypi_0 pypi
importlib-metadata 4.2.0 pypi_0 pypi
importlib-resources 5.10.2 pypi_0 pypi
iniconfig 1.1.1 pypi_0 pypi
interrogate 1.5.0 pypi_0 pypi
ipykernel 6.16.2 pypi_0 pypi
ipython 7.34.0 pypi_0 pypi
ipython-genutils 0.2.0 pypi_0 pypi
ipywidgets 8.0.4 pypi_0 pypi
isort 5.11.4 pypi_0 pypi
itsdangerous 2.1.2 pypi_0 pypi
jedi 0.18.2 pypi_0 pypi
jinja2 3.1.2 pypi_0 pypi
joblib 1.2.0 pypi_0 pypi
jsonschema 4.17.3 pypi_0 pypi
jupyter 1.0.0 pypi_0 pypi
jupyter-client 7.4.8 pypi_0 pypi
jupyter-console 6.4.4 pypi_0 pypi
jupyter-core 4.12.0 pypi_0 pypi
jupyter-server 1.23.4 pypi_0 pypi
jupyterlab-pygments 0.2.2 pypi_0 pypi
jupyterlab-widgets 3.0.5 pypi_0 pypi
keras-applications 1.0.8 pypi_0 pypi
keras-preprocessing 1.1.2 pypi_0 pypi
kiwisolver 1.4.4 pypi_0 pypi
kwarray 0.6.7 pypi_0 pypi
lark 1.1.5 pypi_0 pypi
ld_impl_linux-64 2.38 h1181459_1
libffi 3.4.2 h6a678d5_6
libgcc-ng 11.2.0 h1234567_1
libgomp 11.2.0 h1234567_1
libstdcxx-ng 11.2.0 h1234567_1
llvmlite 0.36.0 pypi_0 pypi
lyft-dataset-sdk 0.0.8 pypi_0 pypi
markdown 3.3.5 pypi_0 pypi
markupsafe 2.1.1 pypi_0 pypi
matplotlib 3.5.3 pypi_0 pypi
matplotlib-inline 0.1.6 pypi_0 pypi
mccabe 0.7.0 pypi_0 pypi
mistune 2.0.4 pypi_0 pypi
mmcls 0.25.0 pypi_0 pypi
mmcv-full 1.6.0 pypi_0 pypi
mmdet 2.26.0 pypi_0 pypi
mmdet3d 1.0.0rc6 dev_0
mmsegmentation 0.29.1 pypi_0 pypi
mypy-extensions 0.4.3 pypi_0 pypi
nbclassic 0.4.8 pypi_0 pypi
nbclient 0.7.2 pypi_0 pypi
nbconvert 7.2.7 pypi_0 pypi
nbformat 5.5.0 pypi_0 pypi
ncurses 6.3 h5eee18b_3
nest-asyncio 1.5.6 pypi_0 pypi
networkx 2.2 pypi_0 pypi
ninja 1.11.1 pypi_0 pypi
notebook 6.5.2 pypi_0 pypi
notebook-shim 0.2.2 pypi_0 pypi
numba 0.53.0 pypi_0 pypi
numpy 1.21.6 pypi_0 pypi
nuscenes-devkit 1.1.9 pypi_0 pypi
oauthlib 3.2.2 pypi_0 pypi
open3d 0.16.0 pypi_0 pypi
opencv-python 4.7.0.68 pypi_0 pypi
openssl 1.1.1s h7f8727e_0
opt-einsum 3.3.0 pypi_0 pypi
packaging 22.0 pypi_0 pypi
pandas 1.3.5 pypi_0 pypi
pandocfilters 1.5.0 pypi_0 pypi
parso 0.8.3 pypi_0 pypi
pathspec 0.10.3 pypi_0 pypi
pccm 0.4.4 pypi_0 pypi
pexpect 4.8.0 pypi_0 pypi
pickleshare 0.7.5 pypi_0 pypi
pillow 9.4.0 pypi_0 pypi
pip 22.3.1 py37h06a4308_0
pkgutil-resolve-name 1.3.10 pypi_0 pypi
platformdirs 2.6.2 pypi_0 pypi
plotly 5.11.0 pypi_0 pypi
pluggy 1.0.0 pypi_0 pypi
plyfile 0.7.4 pypi_0 pypi
portalocker 2.6.0 pypi_0 pypi
prettytable 3.5.0 pypi_0 pypi
prometheus-client 0.15.0 pypi_0 pypi
prompt-toolkit 3.0.36 pypi_0 pypi
protobuf 4.21.12 pypi_0 pypi
psutil 5.9.4 pypi_0 pypi
ptyprocess 0.7.0 pypi_0 pypi
py 1.11.0 pypi_0 pypi
pyasn1 0.4.8 pypi_0 pypi
pyasn1-modules 0.2.8 pypi_0 pypi
pybind11 2.10.3 pypi_0 pypi
pycocotools 2.0.6 pypi_0 pypi
pycodestyle 2.9.1 pypi_0 pypi
pycparser 2.21 pypi_0 pypi
pyflakes 2.5.0 pypi_0 pypi
pygments 2.14.0 pypi_0 pypi
pyparsing 3.0.9 pypi_0 pypi
pyquaternion 0.9.9 pypi_0 pypi
pyrsistent 0.19.3 pypi_0 pypi
pytest 7.2.0 pypi_0 pypi
pytest-cov 4.0.0 pypi_0 pypi
pytest-runner 6.0.0 pypi_0 pypi
python 3.7.15 h7a1cb2a_1
python-dateutil 2.8.2 pypi_0 pypi
pytz 2022.7 pypi_0 pypi
pywavelets 1.3.0 pypi_0 pypi
pyyaml 6.0 pypi_0 pypi
pyzmq 24.0.1 pypi_0 pypi
qtconsole 5.4.0 pypi_0 pypi
qtpy 2.3.0 pypi_0 pypi
readline 8.2 h5eee18b_0
requests 2.28.1 pypi_0 pypi
requests-oauthlib 1.3.1 pypi_0 pypi
rsa 4.9 pypi_0 pypi
scikit-image 0.19.3 pypi_0 pypi
scikit-learn 1.0.2 pypi_0 pypi
scipy 1.4.1 pypi_0 pypi
send2trash 1.8.0 pypi_0 pypi
setuptools 65.5.0 py37h06a4308_0
shapely 2.0.0 pypi_0 pypi
six 1.16.0 pypi_0 pypi
sniffio 1.3.0 pypi_0 pypi
soupsieve 2.3.2.post1 pypi_0 pypi
spconv 2.2.6 pypi_0 pypi
sqlite 3.40.0 h5082296_0
tabulate 0.9.0 pypi_0 pypi
tenacity 8.1.0 pypi_0 pypi
tensorboard 2.1.1 pypi_0 pypi
tensorflow-estimator 2.1.0 pypi_0 pypi
tensorflow-gpu 2.1.0 pypi_0 pypi
termcolor 2.2.0 pypi_0 pypi
terminado 0.17.1 pypi_0 pypi
terminaltables 3.1.10 pypi_0 pypi
threadpoolctl 3.1.0 pypi_0 pypi
tifffile 2021.11.2 pypi_0 pypi
tinycss2 1.2.1 pypi_0 pypi
tk 8.6.12 h1ccaba5_0
toml 0.10.2 pypi_0 pypi
tomli 2.0.1 pypi_0 pypi
torch 1.10.0+cu111 pypi_0 pypi
torchaudio 0.10.0+rocm4.1 pypi_0 pypi
torchvision 0.11.0+cu111 pypi_0 pypi
tornado 6.2 pypi_0 pypi
tqdm 4.64.1 pypi_0 pypi
traitlets 5.8.0 pypi_0 pypi
trimesh 2.35.39 pypi_0 pypi
typed-ast 1.5.4 pypi_0 pypi
typing-extensions 4.4.0 pypi_0 pypi
ubelt 1.2.3 pypi_0 pypi
urllib3 1.26.13 pypi_0 pypi
waymo-open-dataset-tf-2-1-0 1.2.0 pypi_0 pypi
wcwidth 0.2.5 pypi_0 pypi
webencodings 0.5.1 pypi_0 pypi
websocket-client 1.4.2 pypi_0 pypi
werkzeug 2.2.2 pypi_0 pypi
wheel 0.37.1 pyhd3eb1b0_0
widgetsnbextension 4.0.5 pypi_0 pypi
wrapt 1.14.1 pypi_0 pypi
xdoctest 1.1.0 pypi_0 pypi
xz 5.2.8 h5eee18b_0
yapf 0.32.0 pypi_0 pypi
zipp 3.11.0 pypi_0 pypi
zlib 1.2.13 h5eee18b_0

[zwl8979]

@VVsssssk @JingweiZhang12 @
execute statement：pyhton ./mmdet3d/utils/collect_envs.py
fatal: not a git repository (or any of the parent directories): .git
sys.platform: linux
Python: 3.7.15 (default, Nov 24 2022, 21:12:53) [GCC 11.2.0]
CUDA available: True
GPU 0: NVIDIA RTX A6000
CUDA_HOME: /usr/local/cuda-11.1
NVCC: Cuda compilation tools, release 11.1, V11.1.74
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
PyTorch: 1.10.0+cu111
PyTorch compiling details: PyTorch built with:

• GCC 7.3
• C++ Version: 201402
• Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 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: AVX512
• CUDA Runtime 11.1
• 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_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
• CuDNN 8.0.5
• Magma 2.5.2
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, 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=1.10.0, 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: 0.11.0+cu111
OpenCV: 4.7.0
MMCV: 1.6.0
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 11.1
MMDetection: 2.26.0
MMSegmentation: 0.29.1
MMDetection3D: 1.0.0rc6+
spconv2.0: True
It took almost two weeks to build the light environment, please solve it, thank you!

[yyxr75]

Is it done? I came across this issue recently.