tensorRT_Pro 项目实践
参考：https://github.com/shouxieai/tensorRT_Pro/blob/main/tutorial/README.zh-cn.md
前提：
https://www.cnblogs.com/odesey/p/17619218.html
https://www.cnblogs.com/odesey/p/17619240.html
- ubuntu20.04
- opencv4.2
- cuda11.8
- cuDNN v8.9.0 (July 11th, 2023), for CUDA 11.x
- TensorRT-8.6.1
- protobuf-3.19.4
配置 tensorRT_Pro
git clone https://github.com/shouxieai/tensorRT_Pro.git
cd tensorRT_Pro
# 适配Protobuf版本
#切换终端目录到onnx下
cd onnx
修改 make_pb.sh ：
# 请修改protoc为你要使用的版本protoc
protoc=/home/h/programs/protobuf/bin/protoc
执行生成pb文件，并自动复制。使用make_pb.sh脚本
bash make_pb.sh
修改 CMakeLists.txt：
cmake_minimum_required(VERSION 2.6)
project(pro)
option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_BUILD_TYPE Debug)
set(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/workspace)
# # 如果要支持python则设置python路径
# set(HAS_PYTHON OFF)
# set(PythonRoot "/home/h/programs/miniconda3")
# set(PythonName "python3.9")
# 如果你是不同显卡，请设置为显卡对应的号码参考这里：https://developer.nvidia.com/zh-cn/cuda-gpus#compute, 3060: 86
set(CUDA_GEN_CODE "-gencode=arch=compute_86,code=sm_86")
# 如果你的opencv找不到，可以自己指定目录
set(OpenCV_DIR "/home/h/programs/cv/build")
set(CUDA_TOOLKIT_ROOT_DIR "/home/h/programs/cuda-11.8")
set(CUDNN_DIR "/home/h/programs/cudnn-linux-x86_64-8.9.0.131_cuda11-archive")
set(TENSORRT_DIR "/home/h/programs/TensorRT-8.6.1.6")
# set(CUDA_TOOLKIT_ROOT_DIR "/data/sxai/lean/cuda-10.2")
# set(CUDNN_DIR "/data/sxai/lean/cudnn7.6.5.32-cuda10.2")
# set(TENSORRT_DIR "/data/sxai/lean/TensorRT-7.0.0.11")
# set(CUDA_TOOLKIT_ROOT_DIR "/data/sxai/lean/cuda-11.1")
# set(CUDNN_DIR "/data/sxai/lean/cudnn8.2.2.26")
# set(TENSORRT_DIR "/data/sxai/lean/TensorRT-7.2.1.6")
# 因为protobuf，需要用特定版本，所以这里指定路径
set(PROTOBUF_DIR "/home/h/programs/protobuf")
修改 .vscode/c_cpp_properties.json
{
"configurations": [
{
"name": "Linux",
"includePath": [
"${workspaceFolder}/src/**",
"/home/h/programs/protobuf/include/**",
"/home/h/programs/cv/build/**",
"/home/h/programs/cuda-11.8/include/**",
"/home/h/programs/TensorRT-8.6.1.6/include/**",
"/home/h/programs/cudnn-linux-x86_64-8.9.0.131_cuda11-archive/include/**",
"/home/h/programs/miniconda3/include/python3.9/**"
],
"defines": ["__CUDACC__", "HAS_PYTHON"],
"compilerPath": "/usr/bin/gcc",
"cStandard": "gnu11",
"cppStandard": "gnu++11",
"intelliSenseMode": "linux-gcc-x64",
"configurationProvider": "ms-vscode.makefile-tools",
"browse": {
"path": [
"${workspaceFolder}/src/**",
"/home/h/programs/protobuf/include/**",
"/home/h/programs/cv/build/**",
"/home/h/programs/cuda-11.8/include/**",
"/home/h/programs/TensorRT-8.6.1.6/include/**",
"/home/h/programs/cudnn-linux-x86_64-8.9.0.131_cuda11-archive/include/**",
"/home/h/programs/miniconda3/include/python3.9/**"
],
"limitSymbolsToIncludedHeaders": false,
"databaseFilename": ""
}
}
],
"version": 4
}
编译：
cd tensorRT_Pro
mkdir build && cd build
cmake ..
make yolo -j64
[2023-08-12 15:46:00][info][app_yolo.cpp:132]:===================== test YoloV7 FP32 yolov7 ==================================
[2023-08-12 15:46:00][warn][trt_builder.cpp:33]:NVInfer: The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
[2023-08-12 15:46:00][info][trt_infer.cpp:177]:Infer 0x7f54ec000c80 detail
[2023-08-12 15:46:00][info][trt_infer.cpp:178]: Base device: [ID 0][arch 8.6][GMEM 9.71 GB/11.76 GB]
[2023-08-12 15:46:00][warn][trt_builder.cpp:33]:NVInfer: The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
[2023-08-12 15:46:00][info][trt_infer.cpp:179]: Max Batch Size: 1
[2023-08-12 15:46:00][info][trt_infer.cpp:180]: Inputs: 1
[2023-08-12 15:46:00][info][trt_infer.cpp:184]: 0.images : shape {1 x 3 x 640 x 640}, Float32
[2023-08-12 15:46:00][info][trt_infer.cpp:187]: Outputs: 1
[2023-08-12 15:46:00][info][trt_infer.cpp:191]: 0.output : shape {1 x 25200 x 85}, Float32
[2023-08-12 15:46:00][warn][trt_builder.cpp:33]:NVInfer: The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
[2023-08-12 15:46:10][info][app_yolo.cpp:83]:yolov7.FP32.trtmodel[YoloV7] average: 14.00 ms / image, FPS: 71.42
[2023-08-12 15:46:10][info][app_yolo.cpp:109]:Save to yolov7_YoloV7_FP32_result/car.jpg, 7 object, average time 14.00 ms
[2023-08-12 15:46:10][info][app_yolo.cpp:109]:Save to yolov7_YoloV7_FP32_result/zgjr.jpg, 4 object, average time 14.00 ms
[2023-08-12 15:46:10][info][app_yolo.cpp:109]:Save to yolov7_YoloV7_FP32_result/gril.jpg, 2 object, average time 14.00 ms
[2023-08-12 15:46:10][info][app_yolo.cpp:109]:Save to yolov7_YoloV7_FP32_result/group.jpg, 18 object, average time 14.00 ms
[2023-08-12 15:46:10][info][app_yolo.cpp:109]:Save to yolov7_YoloV7_FP32_result/yq.jpg, 2 object, average time 14.00 ms
[2023-08-12 15:46:10][info][app_yolo.cpp:109]:Save to yolov7_YoloV7_FP32_result/zand.jpg, 4 object, average time 14.00 ms
[2023-08-12 15:46:10][info][yolo.cpp:289]:Engine destroy.
[100%] Built target yolo
