tensorrt的下载安装cuda=11.1，Ubuntu20.04

一、tensorrt的下载安装cuda=11.1，Ubuntu20.04

TensorRT 8.6 GA for Ubuntu 20.04 and CUDA 11.0, 11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7 and 11.8 DEB local repo Package

1. tensorrt下载地址：

Log in | NVIDIA Developer

下载TensorRT-8.4.0.6.Linux.x86_64-gnu.cuda-11.6.cudnn8.3.tar.gz

# TensorRT=8.4.0.6(Ubuntu20.04_cuda11.1)

2. 解压压缩包，配置环境变量

tar xzvf TensorRT-8.4.0.6.Linux.x86_64-gnu.cuda-11.6.cudnn8.3.tar.gz

mv TensorRT-8.4.0.6 /usr/local/

sudo gedit ~/.bashrc

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/TensorRT-8.4.0.6/lib

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/TensorRT-8.4.0.6/include

3. 安装TensorRT

cd /usr/local/TensorRT-8.4.0.6/

cd python

# 选择对应的Python版本

pip install tensorrt-8.4.0.6-cp37-none-linux_x86_64.whl

4. 安装graphsurgeon

cd ../graphsurgeon

pip install graphsurgeon-0.4.5-py2.py3-none-any.whl

5. 安装uff(TensorRT与TensorFlow配合试用时)

cd ../uff

pip install uff-0.6.9-py2.py3-none-any.whl

6. 将tensorrt的库和头文件复制到系统路径

cd ..

sudo cp -r ./lib/* /usr/lib

sudo cp -r ./include/* /usr/include

7. 安装测试

python  # 进入Python环境

import tensorrt as trt

print("TensorRT version:", trt.__version__)

二、Yolov5的部署：

所有的操作在Ubuntu系统中进行
深度学习模型部署途径：
方法1:pt --> onnx/torchscript/wts -–> openvino/tensorrt --> 树莓派/jetson(linux)  
方法2:pt --> onnx/torchscipt --> ncnn --> 安卓端(android studio)  
方法3:pt --> onnx/torchscript --> ML --> ios端(xcode)  

1、文件准备：
tensorrtx，下载6.2/最新版本（感谢wangxinyu大神无私分享）: https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v6.0

2、生成wts文件：
 

#进入tensorrt里面，找到yolov5点进去，复制gen_wts.py文件，粘贴到你的yolov5项目里，运行这个命令（best.pt自己训练模型）

python gen_wts.py -w {WEIGHTS_PATH} -o {OUTPUT_DIR}

该命令将从 {WEIGHTS_PATH} 中读取训练好的网络权重，并将其转换为 .wts 格式，并将转换后的结果输出到 {OUTPUT_DIR} 中

（#在当前目录生成.wts文件，把生成的wts文件拷贝到tensorrt/yolov5下面）

3、生成部署引擎
进入到tensorrtx/yolov5文件中，
想改配置的可以直接在这两个文件里改，作者的注释写的很清楚了：
tensorrtx/yolov5/yololayer.h
tensorrtx/yolov5/yolov5.cpp

mkdir build
cd build
cmake ..
make -j12#电脑不求行的直接make不要-12
#sudo ./yolov5 -s [.wts] [.engine] [s/m/l/x/s6/m6/l6/x6 or c/c6 gd gw]
sudo ./yolov5 -s ../best.wts best.engine s
#这样就生成你的tensorrt .engine文件了

4、端侧部署模型测试图片

sudo ./yolov5 -d best.engine ../samples
# sudo ./yolov5 -d [.engine文件] [待检测图片的文件夹]
# 在build文件夹下输出检测图片

Python测试：

# 参数设置：阈值设置
CONF_THRESH = 0.5
IOU_THRESHOLD = 0.4
# 必要文件：
PLUGIN_LIBRARY = "build/libmyplugins.so"  #自定义插件文件
engine_file_path = "build/best.engine"  #模型引擎文件

5、视频检测：

#include <iostream>
#include <chrono>
#include <cmath>
#include "cuda_utils.h"
#include "logging.h"
#include "common.hpp"
#include "utils.h"
#include "calibrator.h"

#define USE_FP16  // set USE_INT8 or USE_FP16 or USE_FP32
#define DEVICE 0  // GPU id
#define NMS_THRESH 0.4
#define CONF_THRESH 0.5
#define BATCH_SIZE 1

// 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* INPUT_BLOB_NAME = "data";
const char* OUTPUT_BLOB_NAME = "prob";
static Logger gLogger;

char *my_classes[]={ "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
         "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
         "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
         "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard","surfboard",
         "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
         "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
         "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
         "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
         "hair drier", "toothbrush" };

static int get_width(int x, float gw, int divisor = 8) {
    //return math.ceil(x / divisor) * divisor
    if (int(x * gw) % divisor == 0) {
        return int(x * gw);
    }
    return (int(x * gw / divisor) + 1) * divisor;
}

static int get_depth(int x, float gd) {
    if (x == 1) {
        return 1;
    } else {
        return round(x * gd) > 1 ? round(x * gd) : 1;
    }
}

ICudaEngine* build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) {
    INetworkDefinition* network = builder->createNetworkV2(0U);

    // Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
    ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
    assert(data);

    std::map<std::string, Weights> weightMap = loadWeights(wts_name);

    /* ------ yolov5 backbone------ */
    auto focus0 = focus(network, weightMap, *data, 3, get_width(64, gw), 3, "model.0");
    auto conv1 = convBlock(network, weightMap, *focus0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
    auto bottleneck_CSP2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2");
    auto conv3 = convBlock(network, weightMap, *bottleneck_CSP2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");
    auto bottleneck_csp4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(9, gd), true, 1, 0.5, "model.4");
    auto conv5 = convBlock(network, weightMap, *bottleneck_csp4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");
    auto bottleneck_csp6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6");
    auto conv7 = convBlock(network, weightMap, *bottleneck_csp6->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.7");
    auto spp8 = SPP(network, weightMap, *conv7->getOutput(0), get_width(1024, gw), get_width(1024, gw), 5, 9, 13, "model.8");

    /* ------ yolov5 head ------ */
    auto bottleneck_csp9 = C3(network, weightMap, *spp8->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.9");
    auto conv10 = convBlock(network, weightMap, *bottleneck_csp9->getOutput(0), get_width(512, gw), 1, 1, 1, "model.10");

    auto upsample11 = network->addResize(*conv10->getOutput(0));
    assert(upsample11);
    upsample11->setResizeMode(ResizeMode::kNEAREST);
    upsample11->setOutputDimensions(bottleneck_csp6->getOutput(0)->getDimensions());

    ITensor* inputTensors12[] = { upsample11->getOutput(0), bottleneck_csp6->getOutput(0) };
    auto cat12 = network->addConcatenation(inputTensors12, 2);
    auto bottleneck_csp13 = C3(network, weightMap, *cat12->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.13");
    auto conv14 = convBlock(network, weightMap, *bottleneck_csp13->getOutput(0), get_width(256, gw), 1, 1, 1, "model.14");

    auto upsample15 = network->addResize(*conv14->getOutput(0));
    assert(upsample15);
    upsample15->setResizeMode(ResizeMode::kNEAREST);
    upsample15->setOutputDimensions(bottleneck_csp4->getOutput(0)->getDimensions());

    ITensor* inputTensors16[] = { upsample15->getOutput(0), bottleneck_csp4->getOutput(0) };
    auto cat16 = network->addConcatenation(inputTensors16, 2);

    auto bottleneck_csp17 = C3(network, weightMap, *cat16->getOutput(0), get_width(512, gw), get_width(256, gw), get_depth(3, gd), false, 1, 0.5, "model.17");

    // yolo layer 0
    IConvolutionLayer* det0 = network->addConvolutionNd(*bottleneck_csp17->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.0.weight"], weightMap["model.24.m.0.bias"]);
    auto conv18 = convBlock(network, weightMap, *bottleneck_csp17->getOutput(0), get_width(256, gw), 3, 2, 1, "model.18");
    ITensor* inputTensors19[] = { conv18->getOutput(0), conv14->getOutput(0) };
    auto cat19 = network->addConcatenation(inputTensors19, 2);
    auto bottleneck_csp20 = C3(network, weightMap, *cat19->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.20");
    //yolo layer 1
    IConvolutionLayer* det1 = network->addConvolutionNd(*bottleneck_csp20->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.1.weight"], weightMap["model.24.m.1.bias"]);
    auto conv21 = convBlock(network, weightMap, *bottleneck_csp20->getOutput(0), get_width(512, gw), 3, 2, 1, "model.21");
    ITensor* inputTensors22[] = { conv21->getOutput(0), conv10->getOutput(0) };
    auto cat22 = network->addConcatenation(inputTensors22, 2);
    auto bottleneck_csp23 = C3(network, weightMap, *cat22->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.23");
    IConvolutionLayer* det2 = network->addConvolutionNd(*bottleneck_csp23->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.2.weight"], weightMap["model.24.m.2.bias"]);

    auto yolo = addYoLoLayer(network, weightMap, "model.24", std::vector<IConvolutionLayer*>{det0, det1, det2});
    yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
    network->markOutput(*yolo->getOutput(0));

    // Build engine
    builder->setMaxBatchSize(maxBatchSize);
    config->setMaxWorkspaceSize(16 * (1 << 20));  // 16MB
#if defined(USE_FP16)
    config->setFlag(BuilderFlag::kFP16);
#elif defined(USE_INT8)
    std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;
    assert(builder->platformHasFastInt8());
    config->setFlag(BuilderFlag::kINT8);
    Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, INPUT_W, INPUT_H, "./coco_calib/", "int8calib.table", INPUT_BLOB_NAME);
    config->setInt8Calibrator(calibrator);
#endif

    std::cout << "Building engine, please wait for a while..." << std::endl;
    ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
    std::cout << "Build engine successfully!" << std::endl;

    // Don't need the network any more
    network->destroy();

    // Release host memory
    for (auto& mem : weightMap)
    {
        free((void*)(mem.second.values));
    }

    return engine;
}

ICudaEngine* build_engine_p6(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) {
    INetworkDefinition* network = builder->createNetworkV2(0U);

    // Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
    ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
    assert(data);

    std::map<std::string, Weights> weightMap = loadWeights(wts_name);

    /* ------ yolov5 backbone------ */
    auto focus0 = focus(network, weightMap, *data, 3, get_width(64, gw), 3, "model.0");
    auto conv1 = convBlock(network, weightMap, *focus0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
    auto c3_2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2");
    auto conv3 = convBlock(network, weightMap, *c3_2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");
    auto c3_4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(9, gd), true, 1, 0.5, "model.4");
    auto conv5 = convBlock(network, weightMap, *c3_4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");
    auto c3_6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6");
    auto conv7 = convBlock(network, weightMap, *c3_6->getOutput(0), get_width(768, gw), 3, 2, 1, "model.7");
    auto c3_8 = C3(network, weightMap, *conv7->getOutput(0), get_width(768, gw), get_width(768, gw), get_depth(3, gd), true, 1, 0.5, "model.8");
    auto conv9 = convBlock(network, weightMap, *c3_8->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.9");
    auto spp10 = SPP(network, weightMap, *conv9->getOutput(0), get_width(1024, gw), get_width(1024, gw), 3, 5, 7, "model.10");
    auto c3_11 = C3(network, weightMap, *spp10->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.11");

    /* ------ yolov5 head ------ */
    auto conv12 = convBlock(network, weightMap, *c3_11->getOutput(0), get_width(768, gw), 1, 1, 1, "model.12");
    auto upsample13 = network->addResize(*conv12->getOutput(0));
    assert(upsample13);
    upsample13->setResizeMode(ResizeMode::kNEAREST);
    upsample13->setOutputDimensions(c3_8->getOutput(0)->getDimensions());
    ITensor* inputTensors14[] = { upsample13->getOutput(0), c3_8->getOutput(0) };
    auto cat14 = network->addConcatenation(inputTensors14, 2);
    auto c3_15 = C3(network, weightMap, *cat14->getOutput(0), get_width(1536, gw), get_width(768, gw), get_depth(3, gd), false, 1, 0.5, "model.15");

    auto conv16 = convBlock(network, weightMap, *c3_15->getOutput(0), get_width(512, gw), 1, 1, 1, "model.16");
    auto upsample17 = network->addResize(*conv16->getOutput(0));
    assert(upsample17);
    upsample17->setResizeMode(ResizeMode::kNEAREST);
    upsample17->setOutputDimensions(c3_6->getOutput(0)->getDimensions());
    ITensor* inputTensors18[] = { upsample17->getOutput(0), c3_6->getOutput(0) };
    auto cat18 = network->addConcatenation(inputTensors18, 2);
    auto c3_19 = C3(network, weightMap, *cat18->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.19");

    auto conv20 = convBlock(network, weightMap, *c3_19->getOutput(0), get_width(256, gw), 1, 1, 1, "model.20");
    auto upsample21 = network->addResize(*conv20->getOutput(0));
    assert(upsample21);
    upsample21->setResizeMode(ResizeMode::kNEAREST);
    upsample21->setOutputDimensions(c3_4->getOutput(0)->getDimensions());
    ITensor* inputTensors21[] = { upsample21->getOutput(0), c3_4->getOutput(0) };
    auto cat22 = network->addConcatenation(inputTensors21, 2);
    auto c3_23 = C3(network, weightMap, *cat22->getOutput(0), get_width(512, gw), get_width(256, gw), get_depth(3, gd), false, 1, 0.5, "model.23");

    auto conv24 = convBlock(network, weightMap, *c3_23->getOutput(0), get_width(256, gw), 3, 2, 1, "model.24");
    ITensor* inputTensors25[] = { conv24->getOutput(0), conv20->getOutput(0) };
    auto cat25 = network->addConcatenation(inputTensors25, 2);
    auto c3_26 = C3(network, weightMap, *cat25->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.26");

    auto conv27 = convBlock(network, weightMap, *c3_26->getOutput(0), get_width(512, gw), 3, 2, 1, "model.27");
    ITensor* inputTensors28[] = { conv27->getOutput(0), conv16->getOutput(0) };
    auto cat28 = network->addConcatenation(inputTensors28, 2);
    auto c3_29 = C3(network, weightMap, *cat28->getOutput(0), get_width(1536, gw), get_width(768, gw), get_depth(3, gd), false, 1, 0.5, "model.29");

    auto conv30 = convBlock(network, weightMap, *c3_29->getOutput(0), get_width(768, gw), 3, 2, 1, "model.30");
    ITensor* inputTensors31[] = { conv30->getOutput(0), conv12->getOutput(0) };
    auto cat31 = network->addConcatenation(inputTensors31, 2);
    auto c3_32 = C3(network, weightMap, *cat31->getOutput(0), get_width(2048, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.32");

    /* ------ detect ------ */
    IConvolutionLayer* det0 = network->addConvolutionNd(*c3_23->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.0.weight"], weightMap["model.33.m.0.bias"]);
    IConvolutionLayer* det1 = network->addConvolutionNd(*c3_26->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.1.weight"], weightMap["model.33.m.1.bias"]);
    IConvolutionLayer* det2 = network->addConvolutionNd(*c3_29->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.2.weight"], weightMap["model.33.m.2.bias"]);
    IConvolutionLayer* det3 = network->addConvolutionNd(*c3_32->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.3.weight"], weightMap["model.33.m.3.bias"]);

    auto yolo = addYoLoLayer(network, weightMap, "model.33", std::vector<IConvolutionLayer*>{det0, det1, det2, det3});
    yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
    network->markOutput(*yolo->getOutput(0));

    // Build engine
    builder->setMaxBatchSize(maxBatchSize);
    config->setMaxWorkspaceSize(16 * (1 << 20));  // 16MB
#if defined(USE_FP16)
    config->setFlag(BuilderFlag::kFP16);
#elif defined(USE_INT8)
    std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;
    assert(builder->platformHasFastInt8());
    config->setFlag(BuilderFlag::kINT8);
    Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, INPUT_W, INPUT_H, "./coco_calib/", "int8calib.table", INPUT_BLOB_NAME);
    config->setInt8Calibrator(calibrator);
#endif

    std::cout << "Building engine, please wait for a while..." << std::endl;
    ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
    std::cout << "Build engine successfully!" << std::endl;

    // Don't need the network any more
    network->destroy();

    // Release host memory
    for (auto& mem : weightMap)
    {
        free((void*)(mem.second.values));
    }

    return engine;
}

void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream, float& gd, float& gw, std::string& wts_name) {
    // Create builder
    IBuilder* builder = createInferBuilder(gLogger);
    IBuilderConfig* config = builder->createBuilderConfig();

    // Create model to populate the network, then set the outputs and create an engine
    ICudaEngine* engine = build_engine(maxBatchSize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
    assert(engine != nullptr);

    // Serialize the engine
    (*modelStream) = engine->serialize();

    // Close everything down
    engine->destroy();
    builder->destroy();
    config->destroy();
}

void doInference(IExecutionContext& context, cudaStream_t& stream, void **buffers, float* input, float* output, int batchSize) {
    // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
    CUDA_CHECK(cudaMemcpyAsync(buffers[0], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
    context.enqueue(batchSize, buffers, stream, nullptr);
    CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
    cudaStreamSynchronize(stream);
}

bool parse_args(int argc, char** argv, std::string& engine) {
    if (argc < 3) return false;
    if (std::string(argv[1]) == "-v" && argc == 3) {
        engine = std::string(argv[2]);
    } else {
        return false;
    }
    return true;
}

int main(int argc, char** argv) {
    cudaSetDevice(DEVICE);

    //std::string wts_name = "";
    std::string engine_name = "";
    //float gd = 0.0f, gw = 0.0f;
    //std::string img_dir;

	if(!parse_args(argc,argv,engine_name)){
		std::cerr << "arguments not right!" << std::endl;
        	std::cerr << "./yolov5 -v [.engine] // run inference with camera" << std::endl;
		return -1;
	}

	std::ifstream file(engine_name, std::ios::binary);
        if (!file.good()) {
		std::cerr<<" read "<<engine_name<<" error! "<<std::endl;
		return -1;
	}
	char *trtModelStream{ nullptr };
	size_t size = 0;
        file.seekg(0, file.end);
        size = file.tellg();
        file.seekg(0, file.beg);
        trtModelStream = new char[size];
        assert(trtModelStream);
        file.read(trtModelStream, size);
        file.close();


    // prepare input data ---------------------------
    static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W];
    //for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
    //    data[i] = 1.0;
    static float prob[BATCH_SIZE * OUTPUT_SIZE];
    IRuntime* runtime = createInferRuntime(gLogger);
    assert(runtime != nullptr);
    ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
    assert(engine != nullptr);
    IExecutionContext* context = engine->createExecutionContext();
    assert(context != nullptr);
    delete[] trtModelStream;
    assert(engine->getNbBindings() == 2);
    void* buffers[2];
    // In order to bind the buffers, we need to know the names of the input and output tensors.
    // Note that indices are guaranteed to be less than IEngine::getNbBindings()
    const int inputIndex = engine->getBindingIndex(INPUT_BLOB_NAME);
    const int outputIndex = engine->getBindingIndex(OUTPUT_BLOB_NAME);
    assert(inputIndex == 0);
    assert(outputIndex == 1);
    // Create GPU buffers on device
    CUDA_CHECK(cudaMalloc(&buffers[inputIndex], BATCH_SIZE * 3 * INPUT_H * INPUT_W * sizeof(float)));
    CUDA_CHECK(cudaMalloc(&buffers[outputIndex], BATCH_SIZE * OUTPUT_SIZE * sizeof(float)));
    // Create stream
    cudaStream_t stream;
    CUDA_CHECK(cudaStreamCreate(&stream));


	cv::VideoCapture capture(0);
    //cv::VideoCapture capture("../overpass.mp4");
    //int fourcc = cv::VideoWriter::fourcc('M','J','P','G');
    //capture.set(cv::CAP_PROP_FOURCC, fourcc);
    if(!capture.isOpened()){
        std::cout << "Error opening video stream or file" << std::endl;
        return -1;
    }

	int key;
    int fcount = 0;
    while(1)
    {
        cv::Mat frame;
        capture >> frame;
        if(frame.empty())
        {
            std::cout << "Fail to read image from camera!" << std::endl;
            break;
        }
        fcount++;
        //if (fcount < BATCH_SIZE && f + 1 != (int)file_names.size()) continue;
        for (int b = 0; b < fcount; b++) {
            //cv::Mat img = cv::imread(img_dir + "/" + file_names[f - fcount + 1 + b]);
			cv::Mat img = frame;
            if (img.empty()) continue;
            cv::Mat pr_img = preprocess_img(img, INPUT_W, INPUT_H); // letterbox BGR to RGB
            int i = 0;
            for (int row = 0; row < INPUT_H; ++row) {
                uchar* uc_pixel = pr_img.data + row * pr_img.step;
                for (int col = 0; col < INPUT_W; ++col) {
                    data[b * 3 * INPUT_H * INPUT_W + i] = (float)uc_pixel[2] / 255.0;
                    data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = (float)uc_pixel[1] / 255.0;
                    data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = (float)uc_pixel[0] / 255.0;
                    uc_pixel += 3;
                    ++i;
                }
            }
        }

        // Run inference
        auto start = std::chrono::system_clock::now();
        doInference(*context, stream, buffers, data, prob, BATCH_SIZE);
        auto end = std::chrono::system_clock::now();
        //std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
        int fps = 1000.0/std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count();
		std::vector<std::vector<Yolo::Detection>> batch_res(fcount);
        for (int b = 0; b < fcount; b++) {
            auto& res = batch_res[b];
            nms(res, &prob[b * OUTPUT_SIZE], CONF_THRESH, NMS_THRESH);
        }
        for (int b = 0; b < fcount; b++) {
            auto& res = batch_res[b];
            //std::cout << res.size() << std::endl;
            //cv::Mat img = cv::imread(img_dir + "/" + file_names[f - fcount + 1 + b]);
            for (size_t j = 0; j < res.size(); j++) {
                cv::Rect r = get_rect(frame, res[j].bbox);
                cv::rectangle(frame, r, cv::Scalar(0x27, 0xC1, 0x36), 2);
		std::string label = my_classes[(int)res[j].class_id];
                cv::putText(frame, label, cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2);
                //add FPS in the windows
//				std::string jetson_fps = "Jetson NX FPS: " + std::to_string(fps);
//				cv::putText(frame, jetson_fps, cv::Point(11,80), cv::FONT_HERSHEY_PLAIN, 3, cv::Scalar(0, 0, 255), 2, cv::LINE_AA);
			}
            //cv::imwrite("_" + file_names[f - fcount + 1 + b], img);
        }
		cv::imshow("yolov5",frame);
        key = cv::waitKey(1);
        if (key == 'q'){
            break;
        }
        fcount = 0;
    }

	capture.release();
    // Release stream and buffers
    cudaStreamDestroy(stream);
    CUDA_CHECK(cudaFree(buffers[inputIndex]));
    CUDA_CHECK(cudaFree(buffers[outputIndex]));
    // Destroy the engine
    context->destroy();
    engine->destroy();
    runtime->destroy();

    return 0;
}

用以上代码替换原来的yolov5.cpp，然后重新编译：

cd tensorrtx/yolov5/build
cmake ..
make
sudo ./yolov5 -v best.engine

参考：在ubuntu20.04上利用tensorrt部署yolov5（C++和Python接口）