Jetson Nano TensorRT C++加速 YOLOV5，集成进qt项目中_qt c++部署tensorrt

tensorrtx C++版本的yolov5 tensorRT加速。 感谢开源大佬的无私奉献。

环境前提：搭建好YOLOv5所需环境，Jetson Nano自带了tensorRT环境。

进入正题，首先是tensorrtx 项目的使用。

简单翻译一下仓库readme。

第一步生成后续需要使用到的wts文件。准备好tensorrtx yolov5代码，yolov5官方代码，以及模型权（以官方yolov5s.pt为例)，将tensorrtx代码中的gen_wts.py文件复制到yolov5代码文件夹下。使用

python3 gen_wts.py -w yolov5s.pt -o yolov5s.wts

1
2

生成.wts文件，注意jetson nano中python是使用的python2.x版本，现在应该都是3.x版本的，所以用python3而不是python。

第二步，使用cmake构建tensorrtx项目并使用。

在tensorrtx yolov5 代码下创建build文件夹，并将第一步生成的wts文件复制到这个文件夹下。进入build文件夹输入以下命令，构建项目

cmake ..
make
1
2

生成可执行文件

使用以下命令生成tensorRT引擎文件

sudo ./yolov5 -s yolov5s.wts yolov5s.engine s
1

先准备一下测试用的图片，这里使用yolov5的图片，将tensorrtx yolov5中的samples删除，重新创建一个samples文件夹，并将yolov5以下路径的图片复制到samples文件夹下

(发现这里的cp命令错了，cp到tensorrt的根目录下了，注意复制到samples文件下）

使用可执行文件来使用tensorrtx生成的engine文件（记得回到build目录）

sudo ./yolov5 -d yolov5s.engine ../samples
1

模型没有预热，第一张照片耗时久很正常。

将其集成进qt项目中

首先是qt pro文件，以下是jetson nano环境下的配置文件。简单说明一下，除了opencv,qt,libtorch和tensorrt所需要的库和头文件，还有一个很重要的部分是需要编译.cuda文件，tensorrtx中部分函数是使用cuda进行显卡加速的,因此项目需要配置cuda文件的编译库。

#-------------------------------------------------
#
# Project created by QtCreator 2022-04-06T13:51:43
#
#-------------------------------------------------

QT       += core gui

greaterThan(QT_MAJOR_VERSION, 4): QT += widgets

TARGET = guiTensorRT
TEMPLATE = app

# The following define makes your compiler emit warnings if you use
# any feature of Qt which has been marked as deprecated (the exact warnings
# depend on your compiler). Please consult the documentation of the
# deprecated API in order to know how to port your code away from it.
DEFINES += QT_DEPRECAT/D_WARNINGS

# In order to do so, uncomment the following line.
# You can also select to disable deprecated APIs only up to a certain version of Qt.
#DEFINES += QT_DISABLE_DEPRECATED_BEFORE=0x060000    # disables all the APIs deprecated before Qt 6.0.0


SOURCES += \
        main.cpp \
        gui.cpp \
        yolov5tr.cpp \
        common.cpp

HEADERS += \
        gui.h \
        yolov5tr.h \
        common.h

# opencv
INCLUDEPATH += \
        /usr/include/opencv4/opencv2 \
        /usr/include/opencv4 \

LIBS += /usr/lib/aarch64-linux-gnu/libopencv* \


# libtorch
#INCLUDEPATH += \
#        /home/nvidia/.local/lib/python3.6/site-packages/torch/include/torch/csrc/api/include \
#        /home/nvidia/.local/lib/python3.6/site-packages/torch/include \

#LIBS += \
#        /home/nvidia/.local/lib/python3.6/site-packages/torch/lib/*.so \
#        -L/home/nvidia/.local/lib/python3.6/site-packages/torch/lib \
#        -Wl,--no-as-needed -ltorch_cuda # force to link torch_cuda


INCLUDEPATH += \
        /usr/local/cuda-10.2/targets/aarch64-linux/include/ \
        /usr/local/cuda-10.2/include/ \
        /usr/include/aarch64-linux-gnu \
        /usr/src/tensorrt/samples/common/

LIBS += \
        /usr/local/cuda-10.2/targets/aarch64-linux/lib/*.so \
        /usr/local/cuda-10.2/lib64/*.so \
        /usr/lib/aarch64-linux-gnu/*.so # cannot link opencvlib twice



#tensorRT
INCLUDEPATH += \
        /home/nvidia/Desktop/yolov5-tensorRT

LIBS += \
        /home/nvidia/Desktop/yolov5-tensorRT/build/*.so \
        -L/home/nvidia/Desktop/yolov5-tensorRT/build/

#cuda
# yolov5 tensorrt dir
HEADERS += \
    /home/nvidia/Desktop/yolov5-tensorRT/preprocess.h \

CUDA_SOURCES  += \
    /home/nvidia/Desktop/yolov5-tensorRT/preprocess.cu

CUDA_SDK = "/usr/local/cuda-10.2/"            # Path to cuda SDK install
CUDA_DIR = "/usr/local/cuda-10.2/"            # Path to cuda toolkit install

# DO NOT EDIT BEYOND THIS UNLESS YOU KNOW WHAT YOU ARE DOING....

SYSTEM_NAME = ubuntu         # Depending on your system either 'Win32', 'x64', or 'Win64'
SYSTEM_TYPE = 64            # '32' or '64', depending on your system
CUDA_ARCH = sm_53           # Type of CUDA architecture, for example 'compute_10', 'compute_11', 'sm_10' 'sm_50'
                            # https://blog.csdn.net/lb1244206405/article/details/90718040
NVCC_OPTIONS = --use_fast_math


# include paths
INCLUDEPATH += $$CUDA_DIR/include

# library directories
QMAKE_LIBDIR += $$CUDA_DIR/lib64/

CUDA_OBJECTS_DIR = ./


# Add the necessary libraries
CUDA_LIBS = -lcuda -lcudart

# The following makes sure all path names (which often include spaces) are put between quotation marks
CUDA_INC = $$join(INCLUDEPATH,'" -I"','-I"','"')
#LIBS += $$join(CUDA_LIBS,'.so ', '', '.so')
LIBS += $$CUDA_LIBS

# Configuration of the Cuda compiler
CONFIG(debug, debug|release) {
    # Debug mode
    cuda_d.input = CUDA_SOURCES
    cuda_d.output = $$CUDA_OBJECTS_DIR/${QMAKE_FILE_BASE}.o
    cuda_d.commands = $$CUDA_DIR/bin/nvcc -D_DEBUG $$NVCC_OPTIONS $$CUDA_INC $$NVCC_LIBS --machine $$SYSTEM_TYPE -arch=$$CUDA_ARCH -c -o ${QMAKE_FILE_OUT} ${QMAKE_FILE_NAME}
    cuda_d.dependency_type = TYPE_C
    QMAKE_EXTRA_COMPILERS += cuda_d
}
else {
    # Release mode
    cuda.input = CUDA_SOURCES
    cuda.output = $$CUDA_OBJECTS_DIR/${QMAKE_FILE_BASE}.o
    cuda.commands = $$CUDA_DIR/bin/nvcc $$NVCC_OPTIONS $$CUDA_INC $$NVCC_LIBS --machine $$SYSTEM_TYPE -arch=$$CUDA_ARCH -c -o ${QMAKE_FILE_OUT} ${QMAKE_FILE_NAME}
    cuda.dependency_type = TYPE_C
    QMAKE_EXTRA_COMPILERS += cuda
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129

然后我根据tensorrtx的yolov5.cpp封装了一个yolov5类，方便项目中使用。加载模型后一直保留在内存中，而无需每次推理图片都重新加载一遍模型。
yolov5tr.h

#ifndef YOLOV5TR_H
#define YOLOV5TR_H

#include <iostream>
#include <chrono>
#include <cmath>
#include "cuda_utils.h"
#include "logging.h"

#include "yololayer.h"
#include "utils.h"
#include "calibrator.h"
#include "preprocess.h"

#include "common.h"

using namespace cv;



class yolov5TR
{
private:
    void doInference(IExecutionContext& context, cudaStream_t& stream, void **buffers, float* output, int batchSize);
    ICudaEngine* engine = nullptr;
    IExecutionContext* context = nullptr;
    IRuntime* runtime = nullptr;
    float* buffers[2];
    uint8_t* img_host = nullptr;
    uint8_t* img_device = nullptr;
    cudaStream_t stream;
    int inputIndex;
    int outputIndex;
    std::string engineName;
public:
    yolov5TR();
    ~yolov5TR();
    yolov5TR(std::string engine_name);
    std::vector<Yolo::Detection> DoInference(Mat img,clock_t*allTime);

};

#endif // YOLOV5TR_H

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44

#include "yolov5tr.h"


#define USE_FP16  // set USE_INT8 or USE_FP16 or USE_FP32
#define DEVICE 0  // GPU id
#define NMS_THRESH 0.5
#define CONF_THRESH 0.1
#define BATCH_SIZE 1
#define MAX_IMAGE_INPUT_SIZE_THRESH 3000 * 3000 // ensure it exceed the maximum size in the input images !

// stuff we know about the network and the input/output blobs
static const int INPUT_H = Yolo::INPUT_H;
static const int INPUT_W = Yolo::INPUT_W;
static const int CLASS_NUM = Yolo::CLASS_NUM;
static const int OUTPUT_SIZE = Yolo::MAX_OUTPUT_BBOX_COUNT * sizeof(Yolo::Detection) / sizeof(float) + 1;  // we assume the yololayer outputs no more than MAX_OUTPUT_BBOX_COUNT boxes that conf >= 0.1
const char* YOLOv5_INPUT_BLOB_NAME = "data";
const char* YOLOv5_OUTPUT_BLOB_NAME = "prob";

class Logger : public ILogger
{
    void log(Severity severity, const char* msg) noexcept override
    {
        // suppress info-level messages
        if (severity <= Severity::kWARNING)
            std::cout << msg << std::endl;
    }
};
static Logger gLogger;

yolov5TR::~yolov5TR(){
    // Release stream and buffers
    cudaStreamDestroy(stream);
    CUDA_CHECK(cudaFree(img_device));
    CUDA_CHECK(cudaFreeHost(img_host));
    CUDA_CHECK(cudaFree(buffers[inputIndex]));
    CUDA_CHECK(cudaFree(buffers[outputIndex]));

    // Destroy the engine
    context->destroy();
    engine->destroy();
    runtime->destroy();
    std::cout<<"deleted"<<std::endl;
}

yolov5TR::yolov5TR(std::string engine_name){
   engineName = engine_name;
   std::ifstream fin(engine_name);
   std::string cached_engine = "";
   while (fin.peek() != EOF){
       std::stringstream buffer;
       buffer << fin.rdbuf();
       cached_engine.append(buffer.str());
   }
   fin.close();
   runtime = createInferRuntime(gLogger);
   assert(runtime != nullptr);
   this->engine = runtime->deserializeCudaEngine(cached_engine.data(), cached_engine.size());
   assert(engine != nullptr);
   context = engine->createExecutionContext();
   assert(context != nullptr);

   assert(engine->getNbBindings() == 2);
   inputIndex = engine->getBindingIndex(YOLOv5_INPUT_BLOB_NAME);
   outputIndex = engine->getBindingIndex(YOLOv5_OUTPUT_BLOB_NAME);
   assert(inputIndex == 0);
   assert(outputIndex == 1);

   //Create GPU buffers on device
   CUDA_CHECK(cudaMalloc((void**)&buffers[inputIndex], BATCH_SIZE * 3 * INPUT_H * INPUT_W * sizeof(float)));
   CUDA_CHECK(cudaMalloc((void**)&buffers[outputIndex], BATCH_SIZE * OUTPUT_SIZE * sizeof(float)));

//     Create stream
   CUDA_CHECK(cudaStreamCreate(&stream));

   CUDA_CHECK(cudaMallocHost((void**)&img_host, MAX_IMAGE_INPUT_SIZE_THRESH * 3));
   CUDA_CHECK(cudaMalloc((void**)&img_device, MAX_IMAGE_INPUT_SIZE_THRESH * 3));



}

void yolov5TR::doInference(IExecutionContext& context, cudaStream_t& stream, void **buffers, float* output, int batchSize) {
    // infer on the batch asynchronously, and DMA output back to host
    context.enqueue(batchSize, buffers, stream, nullptr);
    CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
    cudaStreamSynchronize(stream);
}

std::vector<Yolo::Detection> yolov5TR::DoInference(Mat img,clock_t*allTime){
    float* buffer_idx = (float*)buffers[inputIndex];
    size_t  size_image = img.cols * img.rows * 3;

    //copy data to pinned memory
    memcpy(img_host,img.data,size_image);
    //copy data to device memory
    CUDA_CHECK(cudaMemcpyAsync(img_device,img_host,size_image,cudaMemcpyHostToDevice,stream));
    preprocess_kernel_img(img_device, img.cols, img.rows, buffer_idx, INPUT_W, INPUT_H, stream);

    // Run inference
    auto start = std::chrono::system_clock::now();
    static float prob[OUTPUT_SIZE];
    doInference(*context, stream, (void**)buffers, prob, BATCH_SIZE);
    auto end = std::chrono::system_clock::now();
    std::cout << "inference time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
    *allTime = *allTime + std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count();
    std::vector<std::vector<Yolo::Detection>> batch_res(1);

    auto& res1 = batch_res[0];
    nms(res1, prob, CONF_THRESH, NMS_THRESH);

    auto& res = batch_res[0];
    for (size_t j = 0; j < res.size(); j++) {
        cv::Rect r = get_rect(img, res[j].bbox);
        cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2);
        cv::putText(img, std::to_string((int)res[j].class_id), cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2);
    }

    return res;

}


1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122

本来还有qt部分的代码可以演示的，但是还没有整理完，而且目前手头上暂时没有usb相机，暂且作罢，先将精华部分整理出来。

附录

关于python的tensorrt加速，实际上yolov5官方提供了export.py进行导出。直接使用官方代码就可以导出，并且推理代码也是支持tensorrt进行推理的。甚至backbone魔改后的版本，也是可以进行导出和使用的。