Jetson Xavier NX使用Yolov5+DeepStream+TensorRT实现CSI摄像头的目标识别及采坑记录
文章目录
- 先补充一下: 查看版本信息
- 非法指令 (核心已转储)
- PyTorch和torchvision版本之间的对应关系
- PyTorch and torchvision报错: RuntimeError: Couldn't load custom C++ ops.
- Deepstream运行出错
- 运行LD_PRELOAD=./libmyplugins.so deepstream-app -c deepstream_app_config_yoloV5.txt指令报错
- 安装Deepstream 5.0
- dpkg: 处理归档 /var/cache/apt/archives/deepstream-5.0_5.0.1-1_arm64.deb (–unpack)时出错：
- 参考文章: Jetson nano从烧录系统到DeepStream+TensorRT+yolov5检测CSI摄像头视频
- 我主要的安装过程都是按照这一篇文章进行的, 这一篇文章写得很详细, 而且还配有详细的图片介绍, 基本上按照文章一步步来就可以实现想要的功能了
- 注意: 复制此文的语句时, 由于B站的复制会格外带上作者和文章链接, 最好首先按
F12->F1, 选择禁用JavaScript, 这样复制的时候就只有完整的命令了!- 对了, 一定要注意版本之间的对应关系, 否则安装时可能会出错❗
- 后面再写一些可能会出现的错误的情况以及对应的解决办法:
先补充一下: 查看版本信息
1.查看jetpack版本
jtop
显示页面后, 再按数字6可以查看jetpack、CUDA、cuDNN、OpenCV、TensorRT等的版本
2.查看cuda版本
nvcc -V
release 10.2, V10.2.89
3.查看cuDNN版本：
dpkg -l libcudnn8
8.0.0.180-1
4.查看opencv版本：
dpkg -l libopencv
4.1.1-2-gd5a
python3 -c "import cv2; print(cv2.__version__)"
3.3.1 注意: 如果版本为4.5.3, 则后面的运行可能会出错
python -c "import cv2; print(cv2.__version__)"
3.3.1
查看opencv3及以下版本:
pkg-config --modversion opencv
3.3.1
查看opencv4版本:
pkg-config --modversion opencv4
4.1.1
5.TensorRT版本：
dpkg -l tensorrt
7.1.3.0-1
6.操作系统版本：
lsb_release -i -r
Ubuntu 18.04
7.查看python版本
python -V
Python 2.7.17
python3 -V
Python 3.6.9
8.查看numpy版本
python -c "import numpy; print(numpy.__version__)"
1.16.6
python3 -c "import numpy; print(numpy.__version__)"
1.19.3
9.查看numpy的位置
python3 -c "import numpy; print(numpy.__file__)"
/home/deng/.local/lib/python3.6/site-packages/numpy/init.py
python -c "import numpy; print(numpy.__file__)"
/home/deng/.local/lib/python2.7/site-packages/numpy/init.pyc
10.查看pytorch版本
python3 -c "import torch; print(torch.__version__)"
1.8.0
11.查看torchvision版本
python3 -c "import torchvision; print(torchvision.__version__)"
0.9.0
12.查看deepstream版本
deepstream-app --version-all
deepstream-app version 5.0.0
DeepStreamSDK 5.0.0
CUDA Driver Version: 10.2
CUDA Runtime Version: 10.2
TensorRT Version: 7.1
cuDNN Version: 8.0
libNVWarp360 Version: 2.0.1d3
非法指令 (核心已转储)
- 问题描述: 在python3命令行中, 语句import numpy报错(见下图), 且由于numpy出错, 使用cv2、torch库也会报同样的错误
- 参考文章: https://blog.csdn.net/qq_43711697/article/details/119081630
这一篇文章已经讲得很清楚了, 是numpy的版本问题- 解决步骤: 要删除掉位于
/usr/lib/python3/dist-packages/的版本为1.13.3的numpy, 并将/home/username（这里指本机的名字）/.local/lib/python3.6/site-packages/版本号为1.19.5的numpy替换成1.19.3版本
补充一些命令语句:
python3 -m pip uninstall numpy # 卸载掉最新版的numpy
pip install numpy # 安装最新版的numpy
pip unintall numpy # 卸载掉最新版的numpy
pip install numpy==1.19.3 # 安装1.19.3版本
sudo rm -rf 文件夹名 # 删除该文件夹
PyTorch和torchvision版本之间的对应关系
官网: https://github.com/pytorch/vision#installation
| torch | torchvision | python |
|---|---|---|
| 1.9.0 | 0.10.0 | >=3.6 |
| 1.8.1 | 0.9.1 | >=3.6 |
| 1.8.0 | 0.9.0 | >=3.6 |
| 1.7.1 | 0.8.2 | >=3.6 |
| 1.7.0 | 0.8.1 | >=3.6 |
| 1.7.0 | 0.8.0 | >=3.6 |
| 1.6.0 | 0.7.0 | >=3.6 |
| 1.5.1 | 0.6.1 | >=3.5 |
| 1.5.0 | 0.6.0 | >=3.5 |
| 1.4.0 | 0.5.0 | ==2.7, >=3.5, <=3.8 |
| 1.3.1 | 0.4.2 | ==2.7, >=3.5, <=3.7 |
| 1.3.0 | 0.4.1 | ==2.7, >=3.5, <=3.7 |
| 1.2.0 | 0.4.0 | ==2.7, >=3.5, <=3.7 |
| 1.1.0 | 0.3.0 | ==2.7, >=3.5, <=3.7 |
| <=1.0.1 | 0.2.2 | ==2.7, >=3.5, <=3.7 |
PyTorch and torchvision报错: RuntimeError: Couldn’t load custom C++ ops.
问题描述: 使用detect.py文件进行推理时出现如下报错:
RuntimeError: Couldn’t load custom C++ ops. This can happen if your PyTorch and torchvision versions are incompatible, or if you had errors while compiling torchvision from source. For further information on the compatible versions, check https://github.com/pytorch/vision#installation for the compatibility matrix. Please check your PyTorch version with torch.version and your torchvision version with torchvision.version and verify if they are compatible, and if not please reinstall torchvision so that it matches your PyTorch install.
意思是说PyTorch和torchvision的版本不相符❗
参考文章: https://blog.csdn.net/weixin_48695448/article/details/116424783
解决方案: 由于我以前还安装了另外一个版本的torchvision, 两个版本导致冲突! 我的 torch 版本是1.8，而torchvision版本0.10.0和0.9.0, 因此, 只需卸载掉0.10.0版本的torchvision即可
pip uninstall torchvision
Deepstream运行出错
问题描述: 安装完Deepstream后, 使用
deepstream-app -c source8_1080p_dec_infer-resnet_tracker_tiled_display_fp16_nano.txt命令测试时, 报错如下:
OpenCV Error: Assertion failed (vxGetStatus((vx_reference)img) == VX_SUCCESS) in createVXImageFromCVMat, file /dvs/git/dirty/git-master_linux/multimedia/deep-learning/VisionWorksAPI/1.5/headers/NVX/nvx_opencv_interop.hpp, line 296
An exception occurred. /dvs/git/dirty/git-master_linux/multimedia/deep-learning/VisionWorksAPI/1.5/headers/NVX/nvx_opencv_interop.hpp:296: error: (-215) vxGetStatus((vx_reference)img) == VX_SUCCESS in function createVXImageFromCVMat
详细信息:
deng@deng-desktop:/opt/nvidia/deepstream/deepstream-5.1/samples/configs/deepstream-app$ deepstream-app -c source8_1080p_dec_infer-resnet_tracker_tiled_display_fp16_nano.txt
gstnvtracker: Loading low-level lib at /opt/nvidia/deepstream/deepstream-5.1/lib/libnvds_mot_klt.so
gstnvtracker: Optional NvMOT_RemoveStreams not implemented
gstnvtracker: Batch processing is OFF
gstnvtracker: Past frame output is OFF
ERROR: Deserialize engine failed because file path: /opt/nvidia/deepstream/deepstream-5.1/samples/configs/deepstream-app/…/…/models/Primary_Detector_Nano/resnet10.caffemodel_b8_gpu0_fp16.engine open error
0:00:06.643761144 32628 0x7f28002260 WARN nvinfer gstnvinfer.cpp:616:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1691> [UID = 1]: deserialize engine from file :/opt/nvidia/deepstream/deepstream-5.1/samples/configs/deepstream-app/…/…/models/Primary_Detector_Nano/resnet10.caffemodel_b8_gpu0_fp16.engine failed
0:00:06.643953340 32628 0x7f28002260 WARN nvinfer gstnvinfer.cpp:616:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1798> [UID = 1]: deserialize backend context from engine from file :/opt/nvidia/deepstream/deepstream-5.1/samples/configs/deepstream-app/…/…/models/Primary_Detector_Nano/resnet10.caffemodel_b8_gpu0_fp16.engine failed, try rebuild
0:00:06.644056799 32628 0x7f28002260 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1716> [UID = 1]: Trying to create engine from model files
INFO: [TRT]:
INFO: [TRT]: --------------- Layers running on DLA:
INFO: [TRT]:
INFO: [TRT]: --------------- Layers running on GPU:
INFO: [TRT]: conv1 + activation_1/Relu, block_1a_conv_1 + activation_2/Relu, block_1a_conv_2, block_1a_conv_shortcut + add_1 + activation_3/Relu, block_2a_conv_1 + activation_4/Relu, block_2a_conv_2, block_2a_conv_shortcut + add_2 + activation_5/Relu, block_3a_conv_1 + activation_6/Relu, block_3a_conv_2, block_3a_conv_shortcut + add_3 + activation_7/Relu, block_4a_conv_1 + activation_8/Relu, block_4a_conv_2, block_4a_conv_shortcut + add_4 + activation_9/Relu, conv2d_cov, conv2d_cov/Sigmoid, conv2d_bbox,
INFO: [TRT]: Some tactics do not have sufficient workspace memory to run. Increasing workspace size may increase performance, please check verbose output.
INFO: [TRT]: Detected 1 inputs and 2 output network tensors.
ERROR: Serialize engine failed because of file path: /opt/nvidia/deepstream/deepstream-5.1/samples/models/Primary_Detector_Nano/resnet10.caffemodel_b8_gpu0_fp16.engine opened error
0:00:32.702663741 32628 0x7f28002260 WARN nvinfer gstnvinfer.cpp:616:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1744> [UID = 1]: failed to serialize cude engine to file: /opt/nvidia/deepstream/deepstream-5.1/samples/models/Primary_Detector_Nano/resnet10.caffemodel_b8_gpu0_fp16.engine
INFO: [Implicit Engine Info]: layers num: 3
0 INPUT kFLOAT input_1 3x272x480
1 OUTPUT kFLOAT conv2d_bbox 16x17x30
2 OUTPUT kFLOAT conv2d_cov/Sigmoid 4x17x30
0:00:32.867504842 32628 0x7f28002260 INFO nvinfer gstnvinfer_impl.cpp:313:notifyLoadModelStatus:<primary_gie> [UID 1]: Load new model:/opt/nvidia/deepstream/deepstream-5.1/samples/configs/deepstream-app/config_infer_primary_nano.txt sucessfully
Runtime commands:
h: Print this help
q: Quit
p: Pause
r: Resume
NOTE: To expand a source in the 2D tiled display and view object details, left-click on the source.
To go back to the tiled display, right-click anywhere on the window.
**PERF: FPS 0 (Avg) FPS 1 (Avg) FPS 2 (Avg) FPS 3 (Avg) FPS 4 (Avg) FPS 5 (Avg) FPS 6 (Avg) FPS 7 (Avg)
**PERF: 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00)
** INFO: <bus_callback:181>: Pipeline ready
Opening in BLOCKING MODE
Opening in BLOCKING MODE
Opening in BLOCKING MODE
Opening in BLOCKING MODE
Opening in BLOCKING MODE
Opening in BLOCKING MODE
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Opening in BLOCKING MODE
NvMMLiteOpen : Block : BlockType = 261
NvMMLiteOpen : Block : BlockType = 261
NvMMLiteOpen : Block : BlockType = 261
NvMMLiteOpen : Block : BlockType = 261
NvMMLiteOpen : Block : BlockType = 261
NvMMLiteOpen : Block : BlockType = 261
NvMMLiteOpen : Block : BlockType = 261
NvMMLiteOpen : Block : BlockType = 261
NVMEDIA: Reading vendor.tegra.display-size : status: 6
NvMMLiteBlockCreate : Block : BlockType = 261
NVMEDIA: Reading vendor.tegra.display-size : status: 6
NVMEDIA: Reading vendor.tegra.display-size : status: 6
NVMEDIA: Reading vendor.tegra.display-size : status: 6
NVMEDIA: Reading vendor.tegra.display-size : status: 6
NvMMLiteBlockCreate : Block : BlockType = 261
NVMEDIA: Reading vendor.tegra.display-size : status: 6
NvMMLiteBlockCreate : Block : BlockType = 261
NvMMLiteBlockCreate : Block : BlockType = 261
NvMMLiteBlockCreate : Block : BlockType = 261
NvMMLiteBlockCreate : Block : BlockType = 261
NVMEDIA: Reading vendor.tegra.display-size : status: 6
NVMEDIA: Reading vendor.tegra.display-size : status: 6
NvMMLiteBlockCreate : Block : BlockType = 261
NvMMLiteBlockCreate : Block : BlockType = 261
** INFO: <bus_callback:167>: Pipeline running
ERROR: [TRT]: …/rtSafe/cuda/caskConvolutionRunner.cpp (317) - Cuda Error in allocateContextResources: 700 (an illegal memory access was encountered)
ERROR: [TRT]: FAILED_EXECUTION: std::exception
ERROR: Failed to enqueue inference batch
ERROR: Infer context enqueue buffer failed, nvinfer error:NVDSINFER_TENSORRT_ERROR
0:00:33.827139022 32628 0x15e41a30 WARN nvinfer gstnvinfer.cpp:1225:gst_nvinfer_input_queue_loop:<primary_gie> error: Failed to queue input batch for inferencing
ERROR from primary_gie: Failed to queue input batch for inferencing
Debug info: /dvs/git/dirty/git-master_linux/deepstream/sdk/src/gst-plugins/gst-nvinfer/gstnvinfer.cpp(1225): gst_nvinfer_input_queue_loop (): /GstPipeline:pipeline/GstBin:primary_gie_bin/GstNvInfer:primary_gie
Quitting
vxSetImmediateModeTarget returned 0xfffffff4
KLT Tracker Init
OpenCV Error: Assertion failed (vxGetStatus((vx_reference)img) == VX_SUCCESS) in createVXImageFromCVMat, file /dvs/git/dirty/git-master_linux/multimedia/deep-learning/VisionWorksAPI/1.5/headers/NVX/nvx_opencv_interop.hpp, line 296
An exception occurred. /dvs/git/dirty/git-master_linux/multimedia/deep-learning/VisionWorksAPI/1.5/headers/NVX/nvx_opencv_interop.hpp:296: error: (-215) vxGetStatus((vx_reference)img) == VX_SUCCESS in function createVXImageFromCVMat
OpenCV Error: Assertion failed (vxGetStatus((vx_reference)img) == VX_SUCCESS) in createVXImageFromCVMat, file /dvs/git/dirty/git-master_linux/multimedia/deep-learning/VisionWorksAPI/1.5/headers/NVX/nvx_opencv_interop.hpp, line 296
An exception occurred. /dvs/git/dirty/git-master_linux/multimedia/deep-learning/VisionWorksAPI/1.5/headers/NVX/nvx_opencv_interop.hpp:296: error: (-215) vxGetStatus((vx_reference)img) == VX_SUCCESS in function createVXImageFromCVMat
一开始, 由下面这一个报错, 我认为是缺少了一个engine文件, 可能是安装出错的原因, 就一直到网上去找这个文件的问题, 但仍然没有解决。。。 (下面有解决方案❗)
ERROR: Deserialize engine failed because file path: /opt/nvidia/deepstream/deepstream-5.1/samples/configs/deepstream-app/…/…/models/Primary_Detector_Nano/resnet10.caffemodel_b8_gpu0_fp16.engine open error
于是, 我干脆先跳过测试Deepstream这一步, 直接进行Yolov5检测CSI摄像头那一步, 安装没有出现问题, 但是运行指令
LD_PRELOAD=./libmyplugins.so deepstream-app -c deepstream_app_config_yoloV5.txt报错
运行LD_PRELOAD=./libmyplugins.so deepstream-app -c deepstream_app_config_yoloV5.txt指令报错
报错如下:
0:00:05.440110343 14335 0x3aa54800 ERROR nvinfer gstnvinfer.cpp:1111:get_converted_buffer:<primary_gie> cudaMemset2DAsync failed with error cudaErrorInvalidValue while converting buffer
0:00:05.440207400 14335 0x3aa54800 WARN nvinfer gstnvinfer.cpp:1372:gst_nvinfer_process_full_frame:<primary_gie> error: Buffer conversion failed
ERROR from primary_gie: Buffer conversion failed
详细信息:
deng@deng-desktop:~/Yolov5-in-Deepstream-5.0/Deepstream 5.0$ LD_PRELOAD=./libmyplugins.so deepstream-app -c deepstream_app_config_yoloV5.txt
Using winsys: x11
0:00:04.112944064 14335 0x3b176350 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1702> [UID = 1]: deserialized trt engine from :/home/deng/Yolov5-in-Deepstream-5.0/Deepstream 5.0/yolov5s.engine
INFO: [Implicit Engine Info]: layers num: 2
0 INPUT kFLOAT data 3x640x640
1 OUTPUT kFLOAT prob 6001x1x1
0:00:04.113348357 14335 0x3b176350 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1806> [UID = 1]: Use deserialized engine model: /home/deng/Yolov5-in-Deepstream-5.0/Deepstream 5.0/yolov5s.engine
0:00:04.298495025 14335 0x3b176350 INFO nvinfer gstnvinfer_impl.cpp:313:notifyLoadModelStatus:<primary_gie> [UID 1]: Load new model:/home/deng/Yolov5-in-Deepstream-5.0/Deepstream 5.0/config_infer_primary_yoloV5.txt sucessfully
Runtime commands:
h: Print this help
q: Quit
p: Pause
r: Resume
** INFO: <bus_callback:181>: Pipeline ready
**PERF: FPS 0 (Avg)
**PERF: 0.00 (0.00)
Opening in BLOCKING MODE
NvMMLiteOpen : Block : BlockType = 261
NVMEDIA: Reading vendor.tegra.display-size : status: 6
NvMMLiteBlockCreate : Block : BlockType = 261
** INFO: <bus_callback:167>: Pipeline running
0:00:05.440110343 14335 0x3aa54800 ERROR nvinfer gstnvinfer.cpp:1111:get_converted_buffer:<primary_gie> cudaMemset2DAsync failed with error cudaErrorInvalidValue while converting buffer
0:00:05.440207400 14335 0x3aa54800 WARN nvinfer gstnvinfer.cpp:1372:gst_nvinfer_process_full_frame:<primary_gie> error: Buffer conversion failed
ERROR from primary_gie: Buffer conversion failed
Debug info: /dvs/git/dirty/git-master_linux/deepstream/sdk/src/gst-plugins/gst-nvinfer/gstnvinfer.cpp(1372): gst_nvinfer_process_full_frame (): /GstPipeline:pipeline/GstBin:primary_gie_bin/GstNvInfer:primary_gie
Quitting
参考文章: https://forums.developer.nvidia.com/t/problem-facing-with-deepstream-app/176675
原因分析: Deepstream和jetpack版本不相符
我的Jetson Xavier NX的jetpack版本是4.4.1 [L4T 32.4.4], 按照博客安装的Deepstream版本是5.1, 两者版本不相符导致报错
问题解决:
- 由于Deepstream 5.1对应JetPack 4.5.1 [L4T 32.5.1]
- 而Deepstream 5.0对应jetpack4.4.1 [L4T 32.4.4]
- 因此, 只需要将Deepstream的版本改为5.0即可
安装Deepstream 5.0
- NVIDIA官网: https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Quickstart.html#jetson-setup
- 由于官网的版本是Deepstream 5.1, 因此可以按照下面的两篇文章来下载Deepstream 5.0
- 参考文章①: https://blog.csdn.net/weixin_38661447/article/details/107214222
- 参考文章②: https://www.cnblogs.com/cc1784380709/p/14429294.html
- 注意: 安装完依赖包和librdkafka后, 之后可能需要换源
sudo gedit /etc/apt/sources.list.d/nvidia-l4t-apt-source.list- 完成之后, 直接使用如下命令进行安装❗
sudo apt install deepstream-5.0
dpkg: 处理归档 /var/cache/apt/archives/deepstream-5.0_5.0.1-1_arm64.deb (–unpack)时出错：
上面安装deepstream-5.0的方法是没有问题的, 但是我这里出现了报错:
dpkg: 处理归档 /var/cache/apt/archives/deepstream-5.0_5.0.1-1_arm64.deb (–unpack)时出错：
正试图覆盖 /opt/nvidia/deepstream/deepstream，它同时被包含于软件包 deepstream-5.1 5.1.0-1
在处理时有错误发生：
/var/cache/apt/archives/deepstream-5.0_5.0.1-1_arm64.deb
E: Sub-process /usr/bin/dpkg returned an error code (1)
参考文章: https://blog.csdn.net/weixin_45885232/article/details/109290570
解决方案: 执行下面的命令, 强制覆盖安装deepstream-5.0即可
sudo dpkg -i --force-overwrite /var/cache/apt/archives/deepstream-5.0_5.0.1-1_arm64.deb
下一篇文章: Jetson Xavier NX使用yolov5+deepsort实现CSI摄像头的目标跟踪
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