Jetson Nano配置YOLOv5并實現(xiàn)FPS=25的實時檢測（超詳細(xì)保姆級）

一、版本說明

JetPack 4.6——2021.8
yolov5-v6.0版本
使用的為yolov5的yolov5n.pt，并利用tensorrtx進(jìn)行加速推理，在調(diào)用攝像頭實時檢測可以達(dá)到FPS=25。

二、配置CUDA

sudo gedit ~/.bashrc

在打開的文檔的末尾添加如下：

export CUDA_HOME=/usr/local/cuda-10.2
export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH
export PATH=/usr/local/cuda-10.2/bin:$PATH

保持并退出，終端執(zhí)行

source ~/.bashrc
nvcc -V	#如果配置成功可以看到CUDA的版本號

三、修改Nano板顯存

1.打開終端輸入：

sudo gedit /etc/systemd/nvzramconfig.sh

2.修改nvzramconfig.sh文件

修改mem = $((("${totalmem}"/2/"${NRDEVICES}")*1024))
為mem = $((("${totalmem}"*2/"${NRDEVICES}")*1024))

3.重啟Jetson Nano

4.終端中輸入：

free -h

可查看到swap已經(jīng)變?yōu)?.7G

四、配置Pytorch1.8

1.下載torch-1.8.0-cp36-cp36m-linux_aarch64.whl

下載地址：nvidia.box.com/shared/static/p57jwntv436lfrd78inwl7iml6p13fzh.whl
網(wǎng)盤分享：鏈接：https://pan.baidu.com/s/1tS51E3a-a-w9_OdCNraoAg
提取碼：30qr
說明：建議在電腦上下載后拷貝到Jetson Nano的文件夾下，因為該網(wǎng)站的服務(wù)器在國外，可能下載比較慢或網(wǎng)頁加載不出來，可以打開VPN進(jìn)行下載。

2.安裝所需的依賴包及pytorch

打開終端輸入：

sudo apt-get update
sudo apt-get upgrade
sudo apt-get dist-upgrade
sudo apt-get install python3-pip libopenblas-base libopenmpi-dev

因為下面用pip指令安裝時用默認(rèn)選用的國外源，所以下載比較費時間，建議更換一下國內(nèi)源，這里提供幾種源，當(dāng)使用某種國內(nèi)源pip無法下載某一包時可以嘗試切換再下載。具體步驟如下：

打開終端輸入：

mkdir ~/.pip
sudo gedit ~/.pip/pip.conf

在空白文件中輸入如下內(nèi)容保存并退出：
以下為豆瓣源

[global]
timeout=6000
index-url=https://pypi.doubanio.com/simple
trusted-host=pypi.doubanio.com

以下為阿里源

[global]
index-url=http://mirrors.aliyun.com/pypi/simple/
[install]
trusted-host=mirrors.aliyun.com

以下為清華源

[global]
index-url=https://pypi.tuna.tsinghua.edu.cn/simple/
[install]
trusted-host=https://pypi.tuna.tsinghua.edu.cn

終端輸入：

pip3 install --upgrade pip		#如果pip已是最新，可不執(zhí)行
pip3 install Cython
pip3 install numpy
pip3 install torch-1.8.0-cp36-cp36m-linux_aarch64.whl		#注意要在存放該文件下的位置打開終端并運行
sudo apt-get install libjpeg-dev zlib1g-dev libpython3-dev libavcodec-dev libavformat-dev libswscale-dev
git clone --branch v0.9.0 https://github.com/pytorch/vision torchvision  #下載torchvision，會下載一個文件夾
cd torchvision	#或者進(jìn)入到這個文件夾，右鍵打開終端
export BUILD_VERSION=0.9.0
python3 setup.py install --user	#時間較久
#驗證torch和torchvision這兩個模塊是否安裝成功
python3
import torch
print(torch.__version__)	#注意version前后都是有兩個橫杠
#如果安裝成功會打印出版本號
import torchvision
print(torchvision.__version__)
#如果安裝成功會打印出版本號

五、搭建yolov5環(huán)境

終端中輸入：

git clone https://github.com/ultralytics/yolov5.git		#因為不開VPN很容易下載出錯，建議在電腦中下載后拷貝到j(luò)etson nano中
python3 -m pip install --upgrade pip
cd yolov5	#如果是手動下載的，文件名稱為yolov5-master.zip壓縮包格式，所以要對用unzip yolov5-master.zip進(jìn)行解壓，然后再進(jìn)入到該文件夾
pip3 install -r requirements.txt		#我的問題是對matplotlib包裝不上，解決辦法，在下方。如果其他包安裝不上，可去重新執(zhí)行換源那一步，更換另一種國內(nèi)源。
python3 -m pip list		#可查看python中安裝的包
以下指令可以用來測試yolov5
python3 detect.py --source data/images/bus.jpg --weights yolov5n.pt --img 640	#圖片測試
python3 detect.py --source video.mp4 --weights yolov5n.pt --img 640	#視頻測試,需要自己準(zhǔn)備視頻
python3 detect.py --source 0 --weights yolov5n.pt --img 640	#攝像頭測試

問題1：解決matplotlib安裝不上問題
解決：下載matplotlib的whl包（下方有網(wǎng)盤分享）
問題2：在運行yolov5的detect.py文件時出現(xiàn) “Illegal instruction（core dumped）”
解決：

sudo gedit ~/.bashrc
末尾添加
export OPENBLAS_CORETYPE=ARMV8
保持關(guān)閉
source ~/.bashrc

網(wǎng)盤分享：
yolov5：鏈接：https://pan.baidu.com/s/1oGLTyUZ9TzEWO1VfxV70yw
提取碼：3ran
yolov5n.pt:鏈接：https://pan.baidu.com/s/1k-EDuIJgKc_9OYubWOhJcg
提取碼：oe0w
下載yolov5n.pt文件在https://github.com/ultralytics/yolov5中下載，如圖位置所示
matplotlib：鏈接：https://pan.baidu.com/s/19DanfBYMxKerxlDSuIF8MQ
提取碼：fp4i

六、利用tensorrtx加速推理

1.下載tensorrtx

下載地址：https://github.com/wang-xinyu/tensorrtx.git

或者

git clone https://github.com/wang-xinyu/tensorrtx.git

網(wǎng)盤分享：鏈接：https://pan.baidu.com/s/14vCw3V74bWrT_3QQ-Yk–A
提取碼：3zom

2.編譯

將下載的tensorrtx項目中的yolov5/gen_wts.py復(fù)制到上述的yolov5（注意：不是tensorrtx下的yolov5?。。。┫拢缓笤诖颂幋蜷_終端

打開終端輸入：

python3 gen_wts.py -w yolov5n.pt -o yolov5n.wts		#生成wts文件，要先把yolov5n.pt文件放在此處再去執(zhí)行
cd ~/tensorrtx/yolov5/		#如果是手動下載的名稱可能是tensorrtx-master
mkdir build
cd build
將生成的wts文件復(fù)制到build下	#手動下載的，名稱為yolov5-master
cmake ..
make -j4
sudo ./yolov5 -s yolov5n.wts yolov5n.engine n #生成engine文件
sudo ./yolov5 -d yolov5n.engine ../samples/	#測試圖片查看效果,發(fā)現(xiàn)在檢測zidane.jpg時漏檢，這時可以返回上一層文件夾找到y(tǒng)olov5.cpp中的CONF_THRESH=0.25再進(jìn)入到build中重新make -j4，再重新運行該指令即可 

3.調(diào)用USB攝像頭

參考了該文章https://blog.csdn.net/weixin_54603153/article/details/120079220

（1）在tensorrtx/yolov5下備份yolov5.cpp文件，因為如果更換模型時重新推理加速時需要用到該文件。

（2）然后對yolov5.cpp文件修改為如下內(nèi)容

修改了12行和342行

#include <iostream>
#include <chrono>
#include "cuda_utils.h"
#include "logging.h"
#include "common.hpp"
#include "utils.h"
#include "calibrator.h"
 
#define USE_FP32  // set USE_INT8 or USE_FP16 or USE_FP32
#define DEVICE 0  // GPU id
#define NMS_THRESH 0.4 //0.4
#define CONF_THRESH 0.25	//置信度，默認(rèn)值為0.5，由于效果不好修改為0.25取得了較好的效果
#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("/home/cao-yolox/yolov5/tensorrtx-master/yolov5/samples/1.mp4");	#修改為自己要檢測的視頻或者圖片，注意要寫全路徑，如果調(diào)用攝像頭，則括號內(nèi)的參數(shù)設(shè)為0，注意引號要去掉。
    //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);
                std::string jetson_fps = "Jetson Nano 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;
}
 

4.重新編譯

進(jìn)入到buid下重新make。注意只要修改了yolov5.cpp就要重新make。
執(zhí)行

sudo ./yolov5 -v yolov5n.engine	#注意要提前插好攝像頭

問題：出現(xiàn)Failed to load module “canberra-gtk-module”
解決：

sudo apt-get install libcanberra-gtk-module

5.效果

如下的測試，是在一個公用的行人檢測的視頻上進(jìn)行的，如果想用可在如下鏈接下載：
鏈接：https://pan.baidu.com/s/1HivF1OifVA8pHnGKtkXPfg
提取碼：jr7o

七、參考

1.https://www.bilibili.com/read/cv11869887?spm_id_from=333.999.0.0

2.https://blog.csdn.net/weixin_54603153/article/details/120079220

3.https://github.com/wang-xinyu/tensorrtx/blob/master/yolov5/README.md文章來源地址http://www.zghlxwxcb.cn/news/detail-643034.html

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