【nvidia jetson xavier】Deepstream 自定义检测Yolo v5模型吞吐量测试
Deepstream 自定义检测Yolo v5模型吞吐量测试
主要参考：
https://zhuanlan.zhihu.com/p/365191541
在步骤六已经在tensorrtx/yolov5/build目录下编译生成best.engine文件，接下来将原始文件car/JPEGImages/复制到对应文件夹内，这里我放置在dataset/文件夹下。
在build文件夹内测试代码：
./yolov5 -d yolov5s.engine ../../dataset/car/JPEGImages/
输出结果直接保存在build文件夹内。
使用python代码测试
4. optional, load and run the tensorrt model in python// install python-tensorrt, pycuda, etc.
// ensure the yolov5s.engine and libmyplugins.so have been built
//运行yolov5_trt.py对照片进行检测
python yolov5_trt.py
测试前需要安装pycuda包：
pip3 install pycuda
报错error: cuda.h: No such file or directory aarch64-linux-gnu-gcc failed
参考：https://blog.csdn.net/weixin_40533355/article/details/81199105
尝试：
pip3 install pycuda
仍然出错error: cuda.h: No such file or directory
参考：https://cloud-atlas.readthedocs.io/zh_CN/latest/machine_learning/jetson/develop/jetson_pycuda.html
尝试：
在 /etc/profile 中添加:
export CUDA_HOME=/usr/local/cuda
export CPATH=${CUDA_HOME}/include:${CPATH}
export LIBRARY_PATH=${CUDA_HOME}/lib64:$LIBRARY_PATH
仍然失败error: cuda.h: No such file or directory
参考：https://forums.developer.nvidia.com/t/pycuda-installation-failure-on-jetson-nano/77152
尝试：
在 /etc/profile 中添加:
export CPATH=$CPATH:/usr/local/cuda-10.2/targets/aarch64-linux/include
export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/cuda-10.2/targets/aarch64-linux/lib
仍然失败error: cuda.h: No such file or directory
参考：https://blog.csdn.net/baobei0112/article/details/101224056
尝试：
sudo su
export PATH=/usr/local/cuda-10.2/bin:/usr/local/cuda/bin:$PATH // 即 cuda 的路径这里在root 模式下再包含一遍即可
pip3 install pycuda
安装成功，退出root模式，python yolov5_trt.py运行成功。
尝试视频检测：
Compile nvdsinfer_custom_impl_Yolo
- Run command
sudo chmod -R 777 /opt/nvidia/deepstream/deepstream-5.1/sources/
- Donwload my external/yolov5-5.0 folder and move files to created yolo folder
- Compile lib
- x86 platform
cd /opt/nvidia/deepstream/deepstream-5.1/sources/yolo
CUDA_VER=11.1 make -C nvdsinfer_custom_impl_Yolo
- Jetson platform
cd /opt/nvidia/deepstream/deepstream-5.1/sources/yolo
CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
修改labels.txt为一个类 car
将测试用的car.mp4放在samples streams下面，然后修改deepstream_app_config.txt中的视频位置。
Testing model
Use my edited deepstream_app_config.txt and config_infer_primary.txt files available in my external/yolov5-5.0 folder
Run command
deepstream-app -c deepstream_app_config.txt
吞吐量测试结果：
测试数据：20190729-113131 south port 12.6km.mp4
| batchsize | FPS(Avg) |
|---|---|
| 1 | 20 |
| 2 | 19.5 |
| 4 | 21 |
| 8 | 17 |
参考操作步骤：
1.修改tensorrtx/yolov5/yolov5.cpp中的BATCH_SIZE
2.Build tensorrtx/yolov5
cd tensorrtx/yolov5
mkdir build（删除build里的内容）
cd build
cmake ..
make
3.Move generated yolov5s.wts file to tensorrtx/yolov5 folder (example for YOLOv5s)
cp yolov5/yolov5s.wts tensorrtx/yolov5/build/yolov5s.wts
4.Convert to TensorRT model (yolov5s.engine file will be generated in tensorrtx/yolov5/build folder)
sudo ./yolov5 -s best.wts best_BS2.engine m
5.删除nvdsinfer_custom_impl_Yolo中之前生成的三个文件，复制best_BS2.engine到/opt/nvidia/deepstream/deepstream-5.1/sources/yolov5文件夹
修改config_infer_primary.txt文件
-
model-engine-file=best_BS2.engine
-
num-detected-classes=1
修改deepstream_app_config.txt文件
- batch-size=2
cd /opt/nvidia/deepstream/deepstream-5.1/sources/yolov5
export CUDA_VER=10.2
make -C nvdsinfer_custom_impl_Yolo
修改labels.txt为一个类 car
将测试用的car.mp4放在samples streams下面，然后修改deepstream_app_config.txt中的视频位置。
Testing model
Use my edited deepstream_app_config.txt and config_infer_primary.txt files available in my external/yolov5-5.0 folder
Run command
deepstream-app -c deepstream_app_config.txt