我们以最经典的Yolov5目标检测网络为例解释一下10种主流推理部署框架的大概内容，省略模型训练的过程，只讨论模型转换、环境配置、推理部署等步骤。

Intel的OpenVINO — CPUNvidia的TensorRT — GPU/CPUOpenCV DNN Module — GPU/CPUMicrosoft ONNX Runtime — GPU/CPU腾讯 NCNN— 移动端阿里巴巴 MNN — 移动端Rockchip（瑞芯微）的RKNN — 移动端（NPU）百度PaddlePaddle（飞桨） — 服务器端/移动端华为 MindSpore — 移动、边缘和云端（深度学习框架）Google TensorFlow Lite — 移动端

Intel的OpenVINO — CPU

OpenVINO是英特尔基于自身现有的硬件平台开发的一种可以加快高性能计算机视觉和深度学习视觉应用开发速度工具套件，支持各种英特尔平台的硬件加速器上进行深度学习，并且允许直接异构执行。 支持在Windows与Linux系统，Python/C++语言。

首先，确保已安装OpenVINO工具包。可以从OpenVINO官网下载并安装。

<code>tar -xvzf l_openvino_toolkit_p_2020.3.194.tgz

cd l_openvino_toolkit_p_2020.3.194

运行图形化安装命令:sudo ./install_GUI.sh至此，OpenVINO 核心组件安装完成，接下来安装依赖包。

cd /opt/intel/openvino/install_dependencies

sudo -E ./install_openvino_dependencies.sh

安装完成后，设置环境变量：source /opt/intel/openvino/bin/setupvars.sh

接下来配置模型优化器，依次运行以下命令:

cd /opt/intel/openvino/deployment_tools/model_optimizer/install_prerequisites

sudo ./install_prerequisites.sh

上面这条命令会安装所有的深度学习框架的支持，如果只希望安装某一个框架的支持，以安装Caffe 框架支持为例，可以这么做:sudo ./install_prerequisites_caffe.sh

接下来，我们使用YOLOv5的自带工具将模型导出为ONNX格式：

python export.py --weights yolov5s.pt --img 640 --batch 1 --device cpu --simplify

再使用OpenVINO提供的Model Optimizer工具，将ONNX模型转换为OpenVINO IR格式（.xml和.bin文件）。运行以下命令：

mo --input_model yolov5s.onnx --output_dir . --data_type FP16

# python mo_onnx.py --input_model yolov5s.onnx --output_dir .

使用netron观察模型output节点，接下来就是编写模型推理代码。

模型载入

import openvino.runtime as ov

import cv2

import numpy as np

import openvino.preprocess as op

def Init():

global core

global model

global compiled_model

global infer_request

#核心创建

core = ov.Core()

#读取用YOLOv5模型转换而来的IR模型

model = core.read_model("best2.xml", "best2.bin")

#运用PPP(PrePostProcessor)对模型进行预处理

Premodel = op.PrePostProcessor(model)

Premodel.input().tensor().set_element_type(ov.Type.u8).set_layout(ov.Layout("NHWC")).set_color_format(op.ColorFormat.BGR)

Premodel.input().preprocess().convert_element_type(ov.Type.f32).convert_color(op.ColorFormat.RGB).scale(

[255., 255., 255.])

Premodel.input().model().set_layout(ov.Layout("NCHW"))

Premodel.output(0).tensor().set_element_type(ov.Type.f32)

model = Premodel.build()

compiled_model = core.compile_model(model, "CPU") #加载模型，可用CPU or GPU

infer_request = compiled_model.create_infer_request() #生成推理

图像尺寸调整

def resizeimg(image, new_shape):

old_size = image.shape[:2]

#记录新形状和原生图像矩形形状的比率

ratio = float(new_shape[-1] / max(old_size))

new_size = tuple([int(x * ratio) for x in old_size])

image = cv2.resize(image, (new_size[1], new_size[0]))

delta_w = new_shape[1] - new_size[1]

delta_h = new_shape[0] - new_size[0]

color = [100, 100, 100]

new_im = cv2.copyMakeBorder(image, 0, delta_h, 0, delta_w, cv2.BORDER_CONSTANT, value=color) #增广操作

return new_im, delta_w, delta_h

推理过程以及结果展示

#************************************#

# 推理主程序 #

def main(img,infer_request):

push =[]

img_re,dw,dh = resizeimg(img,(640,640)) #尺寸处理

input_tensor = np.expand_dims(img_re, 0) #获得输入张量

infer_request.infer({ 0: input_tensor}) #输入到推理引擎

output = infer_request.get_output_tensor(0) #获得推理结果

detections = output.data[0] #获得检测数据

boxes = []

class_ids = []

confidences = []

for prediction in detections:

confidence = prediction[4].item() #获取置信度

if confidence >= 0.6: #初步过滤，过滤掉绝大多数的无效数据

classes_scores = prediction[5:]

_, _, _, max_indx = cv2.minMaxLoc(classes_scores)

class_id = max_indx[1]

if (classes_scores[class_id] > .25):

confidences.append(confidence)

class_ids.append(class_id)

x, y, w, h = prediction[0].item(), prediction[1].item(), prediction[2].item(), prediction[3].item() #获取有效信息

xmin = x - (w / 2) #由于NMSBoxes缘故，需要从中心点得到左上角点

ymin = y - (h / 2)

box = np.array([xmin, ymin, w, h]) #记录数据

boxes.append(box)

indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.5) #NMS筛选

detections = []

for i in indexes:

j = i.item()

detections.append({ "class_index": class_ids[j], "confidence": confidences[j], "box": boxes[j]}) #储存获取的目标名称和框选位

for detection in detections:

box = detection["box"]

classId = detection["class_index"]

confidence = detection["confidence"]

if(confidence<0.88): #再次过滤

continue

else :

push.append(classId)

rx = img.shape[1] / (img_re.shape[1] - dw)

ry = img.shape[0] / (img_re.shape[0] - dh)

img_re = cv2.rectangle(img_re, (int(box[0]), int(box[1])), (int(box[0] + box[2]), int(box[1] + box[3])), (0, 255, 0), 3)

box[0] = rx * box[0] #恢复原尺寸box，如果尺寸不变可以忽略

box[1] = box[1] *ry

box[2] = rx * box[2]

box[3] = box[3] *ry

xmax = box[0] + box[2]

ymax = box[1] + box[3]

img = cv2.rectangle(img, (int(box[0]), int(box[1])), (int(xmax), int(ymax)), (0, 255, 0), 3) #绘制物体框

img = cv2.rectangle(img, (int(box[0]), int(box[1]) - 20), (int(xmax), int(box[1])), (0, 255, 0), cv2.FILLED) #绘制目标名称底色填充矩形

img = cv2.putText(img, str(label[classId])+' '+str(int(confidence*100))+'%', (int(box[0]), int(box[1]) - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0)) #绘制目标名称

cv2.imshow("d", img_re)

cv2.imshow('w',img)

cv2.waitKey(0)

主程序

#********************主程序***********************#

def MainToSolve(infer):

img = cv2.imread("boundtest.jpg") #如果需要实时，只需要将输入img变成从摄像机抓取的帧画面

main(img,infer)

#从这里开始，初始化以及推理

Init()

MainToSolve(infer_request)

Nvidia的TensorRT — GPU/CPU

TensorRT是可以在NVIDIA各种GPU硬件平台下运行的一个C++推理框架。我们利用Pytorch、TF或者其他框架训练好的模型，可以转化为TensorRT的格式，然后利用TensorRT推理引擎去运行我们这个模型，从而提升这个模型在英伟达GPU上运行的速度。速度提升的比例是比较可观的。

模型转换：

python export.py ---weights weights/v5lite-g.pt --batch-size 1 --imgsz 640 --include onnx --simplify

trtexec --explicitBatch --onnx=./v5lite-g.onnx --saveEngine=v5lite-g.trt --fp16

YOLOv5-Lite的TensorRT — C++版本示例

模型加载

void V5lite::LoadEngine() {

// create and load engine

std::fstream existEngine;

existEngine.open(engine_file, std::ios::in);

readTrtFile(engine_file, engine);

assert(engine != nullptr);

}

预处理输入图像

std::vector<float> V5lite::prepareImage(std::vector<cv::Mat> &vec_img) {

std::vector<float> result(BATCH_SIZE * IMAGE_WIDTH * IMAGE_HEIGHT * INPUT_CHANNEL);

float *data = result.data();

int index = 0;

for (const cv::Mat &src_img : vec_img)

{

if (!src_img.data)

continue;

float ratio = float(IMAGE_WIDTH) / float(src_img.cols) < float(IMAGE_HEIGHT) / float(src_img.rows) ? float(IMAGE_WIDTH) / float(src_img.cols) : float(IMAGE_HEIGHT) / float(src_img.rows);

cv::Mat flt_img = cv::Mat::zeros(cv::Size(IMAGE_WIDTH, IMAGE_HEIGHT), CV_8UC3);

cv::Mat rsz_img;

cv::resize(src_img, rsz_img, cv::Size(), ratio, ratio);

rsz_img.copyTo(flt_img(cv::Rect(0, 0, rsz_img.cols, rsz_img.rows)));

flt_img.convertTo(flt_img, CV_32FC3, 1.0 / 255);

//HWC TO CHW

int channelLength = IMAGE_WIDTH * IMAGE_HEIGHT;

std::vector<cv::Mat> split_img = {

cv::Mat(IMAGE_HEIGHT, IMAGE_WIDTH, CV_32FC1, data + channelLength * (index + 2)),

cv::Mat(IMAGE_HEIGHT, IMAGE_WIDTH, CV_32FC1, data + channelLength * (index + 1)),

cv::Mat(IMAGE_HEIGHT, IMAGE_WIDTH, CV_32FC1, data + channelLength * index)

};

index += 3;

cv::split(flt_img, split_img);

}

return result;

}

模型推理

bool V5lite::InferenceFolder(const std::string &folder_name) {

std::vector<std::string> sample_images = readFolder(folder_name);

//get context

assert(engine != nullptr);

context = engine->createExecutionContext();

assert(context != nullptr);

//get buffers

assert(engine->getNbBindings() == 2);

void *buffers[2];

std::vector<int64_t> bufferSize;

int nbBindings = engine->getNbBindings();

bufferSize.resize(nbBindings);

for (int i = 0; i < nbBindings; ++i) {

nvinfer1::Dims dims = engine->getBindingDimensions(i);

nvinfer1::DataType dtype = engine->getBindingDataType(i);

int64_t totalSize = volume(dims) * 1 * getElementSize(dtype);

bufferSize[i] = totalSize;

std::cout << "binding" << i << ": " << totalSize << std::endl;

cudaMalloc(&buffers[i], totalSize);

}

//get stream

cudaStream_t stream;

cudaStreamCreate(&stream);

int outSize = bufferSize[1] / sizeof(float) / BATCH_SIZE;

EngineInference(sample_images, outSize, buffers, bufferSize, stream);

// release the stream and the buffers

cudaStreamDestroy(stream);

cudaFree(buffers[0]);

cudaFree(buffers[1]);

// destroy the engine

context->destroy();

engine->destroy();

}

OpenCV DNN Module — GPU/CPU

OpenCV的DNN（Deep Neural Network）模块是一个强大的工具，允许开发者在计算机视觉应用中使用深度学习模型。该模块支持多种深度学习框架和模型格式，并提供了高效的推理能力。

安装依赖

sudo apt-get update

sudo apt-get install build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev

sudo apt-get install python3-dev python3-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev

sudo apt-get install libdc1394-22-dev

编译opencv

cd opencv

mkdir build

cd build

cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules -D WITH_CUDA=ON -D WITH_CUDNN=ON -D OPENCV_DNN_CUDA=ON ..

make -j$(nproc)

sudo make install

sudo ldconfig

示例代码

#include <vector>

#include <string>

#include <utility>

#include <opencv2/opencv.hpp>

using namespace std;

using namespace cv;

using namespace dnn;

vector< pair<dnn::Backend, dnn::Target> > backendTargetPairs = {

std::make_pair<dnn::Backend, dnn::Target>(dnn::DNN_BACKEND_OPENCV, dnn::DNN_TARGET_CPU),

std::make_pair<dnn::Backend, dnn::Target>(dnn::DNN_BACKEND_CUDA, dnn::DNN_TARGET_CUDA),

std::make_pair<dnn::Backend, dnn::Target>(dnn::DNN_BACKEND_CUDA, dnn::DNN_TARGET_CUDA_FP16),

std::make_pair<dnn::Backend, dnn::Target>(dnn::DNN_BACKEND_TIMVX, dnn::DNN_TARGET_NPU),

std::make_pair<dnn::Backend, dnn::Target>(dnn::DNN_BACKEND_CANN, dnn::DNN_TARGET_NPU) };

vector<string> labelYolox = {

"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" };

class YoloX {

private:

Net net;

string modelPath;

Size inputSize;

float confThreshold;

float nmsThreshold;

float objThreshold;

dnn::Backend backendId;

dnn::Target targetId;

int num_classes;

vector<int> strides;

Mat expandedStrides;

Mat grids;

public:

YoloX(string modPath, float confThresh = 0.35, float nmsThresh = 0.5, float objThresh = 0.5, dnn::Backend bId = DNN_BACKEND_DEFAULT, dnn::Target tId = DNN_TARGET_CPU) :

modelPath(modPath), confThreshold(confThresh),

nmsThreshold(nmsThresh), objThreshold(objThresh),

backendId(bId), targetId(tId)

{

this->num_classes = int(labelYolox.size());

this->net = readNet(modelPath);

this->inputSize = Size(640, 640);

this->strides = vector<int>{ 8, 16, 32 };

this->net.setPreferableBackend(this->backendId);

this->net.setPreferableTarget(this->targetId);

this->generateAnchors();

}

Mat preprocess(Mat img)

{

Mat blob;

Image2BlobParams paramYolox;

paramYolox.datalayout = DNN_LAYOUT_NCHW;

paramYolox.ddepth = CV_32F;

paramYolox.mean = Scalar::all(0);

paramYolox.scalefactor = Scalar::all(1);

paramYolox.size = Size(img.cols, img.rows);

paramYolox.swapRB = true;

blob = blobFromImageWithParams(img, paramYolox);

return blob;

}

Mat infer(Mat srcimg)

{

Mat inputBlob = this->preprocess(srcimg);

this->net.setInput(inputBlob);

vector<Mat> outs;

this->net.forward(outs, this->net.getUnconnectedOutLayersNames());

Mat predictions = this->postprocess(outs[0]);

return predictions;

}

Mat postprocess(Mat outputs)

{

Mat dets = outputs.reshape(0,outputs.size[1]);

Mat col01;

add(dets.colRange(0, 2), this->grids, col01);

Mat col23;

exp(dets.colRange(2, 4), col23);

vector<Mat> col = { col01, col23 };

Mat boxes;

hconcat(col, boxes);

float* ptr = this->expandedStrides.ptr<float>(0);

for (int r = 0; r < boxes.rows; r++, ptr++)

{

boxes.rowRange(r, r + 1) = *ptr * boxes.rowRange(r, r + 1);

}

// get boxes

Mat boxes_xyxy(boxes.rows, boxes.cols, CV_32FC1, Scalar(1));

Mat scores = dets.colRange(5, dets.cols).clone();

vector<float> maxScores(dets.rows);

vector<int> maxScoreIdx(dets.rows);

vector<Rect2d> boxesXYXY(dets.rows);

for (int r = 0; r < boxes_xyxy.rows; r++, ptr++)

{

boxes_xyxy.at<float>(r, 0) = boxes.at<float>(r, 0) - boxes.at<float>(r, 2) / 2.f;

boxes_xyxy.at<float>(r, 1) = boxes.at<float>(r, 1) - boxes.at<float>(r, 3) / 2.f;

boxes_xyxy.at<float>(r, 2) = boxes.at<float>(r, 0) + boxes.at<float>(r, 2) / 2.f;

boxes_xyxy.at<float>(r, 3) = boxes.at<float>(r, 1) + boxes.at<float>(r, 3) / 2.f;

// get scores and class indices

scores.rowRange(r, r + 1) = scores.rowRange(r, r + 1) * dets.at<float>(r, 4);

double minVal, maxVal;

Point maxIdx;

minMaxLoc(scores.rowRange(r, r+1), &minVal, &maxVal, nullptr, &maxIdx);

maxScoreIdx[r] = maxIdx.x;

maxScores[r] = float(maxVal);

boxesXYXY[r].x = boxes_xyxy.at<float>(r, 0);

boxesXYXY[r].y = boxes_xyxy.at<float>(r, 1);

boxesXYXY[r].width = boxes_xyxy.at<float>(r, 2);

boxesXYXY[r].height = boxes_xyxy.at<float>(r, 3);

}

vector<int> keep;

NMSBoxesBatched(boxesXYXY, maxScores, maxScoreIdx, this->confThreshold, this->nmsThreshold, keep);

Mat candidates(int(keep.size()), 6, CV_32FC1);

int row = 0;

for (auto idx : keep)

{

boxes_xyxy.rowRange(idx, idx + 1).copyTo(candidates(Rect(0, row, 4, 1)));

candidates.at<float>(row, 4) = maxScores[idx];

candidates.at<float>(row, 5) = float(maxScoreIdx[idx]);

row++;

}

if (keep.size() == 0)

return Mat();

return candidates;

}

void generateAnchors()

{

vector< tuple<int, int, int> > nb;

int total = 0;

for (auto v : this->strides)

{

int w = this->inputSize.width / v;

int h = this->inputSize.height / v;

nb.push_back(tuple<int, int, int>(w * h, w, v));

total += w * h;

}

this->grids = Mat(total, 2, CV_32FC1);

this->expandedStrides = Mat(total, 1, CV_32FC1);

float* ptrGrids = this->grids.ptr<float>(0);

float* ptrStrides = this->expandedStrides.ptr<float>(0);

int pos = 0;

for (auto le : nb)

{

int r = get<1>(le);

for (int i = 0; i < get<0>(le); i++, pos++)

{

*ptrGrids++ = float(i % r);

*ptrGrids++ = float(i / r);

*ptrStrides++ = float((get<2>(le)));

}

}

}

};

std::string keys =

"{ help h | | Print help message. }"

"{ model m | object_detection_yolox_2022nov.onnx | Usage: Path to the model, defaults to object_detection_yolox_2022nov.onnx }"

"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}"

"{ confidence | 0.5 | Class confidence }"

"{ obj | 0.5 | Enter object threshold }"

"{ nms | 0.5 | Enter nms IOU threshold }"

"{ save s | true | Specify to save results. This flag is invalid when using camera. }"

"{ vis v | 1 | Specify to open a window for result visualization. This flag is invalid when using camera. }"

"{ backend bt | 0 | Choose one of computation backends: "

"0: (default) OpenCV implementation + CPU, "

"1: CUDA + GPU (CUDA), "

"2: CUDA + GPU (CUDA FP16), "

"3: TIM-VX + NPU, "

"4: CANN + NPU}";

pair<Mat, double> letterBox(Mat srcimg, Size targetSize = Size(640, 640))

{

Mat paddedImg(targetSize.height, targetSize.width, CV_32FC3, Scalar::all(114.0));

Mat resizeImg;

double ratio = min(targetSize.height / double(srcimg.rows), targetSize.width / double(srcimg.cols));

resize(srcimg, resizeImg, Size(int(srcimg.cols * ratio), int(srcimg.rows * ratio)), INTER_LINEAR);

resizeImg.copyTo(paddedImg(Rect(0, 0, int(srcimg.cols * ratio), int(srcimg.rows * ratio))));

return pair<Mat, double>(paddedImg, ratio);

}

Mat unLetterBox(Mat bbox, double letterboxScale)

{

return bbox / letterboxScale;

}

Mat visualize(Mat dets, Mat srcimg, double letterbox_scale, double fps = -1)

{

Mat resImg = srcimg.clone();

if (fps > 0)

putText(resImg, format("FPS: %.2f", fps), Size(10, 25), FONT_HERSHEY_SIMPLEX, 1, Scalar(0, 0, 255), 2);

for (int row = 0; row < dets.rows; row++)

{

Mat boxF = unLetterBox(dets(Rect(0, row, 4, 1)), letterbox_scale);

Mat box;

boxF.convertTo(box, CV_32S);

float score = dets.at<float>(row, 4);

int clsId = int(dets.at<float>(row, 5));

int x0 = box.at<int>(0, 0);

int y0 = box.at<int>(0, 1);

int x1 = box.at<int>(0, 2);

int y1 = box.at<int>(0, 3);

string text = format("%s : %f", labelYolox[clsId].c_str(), score * 100);

int font = FONT_HERSHEY_SIMPLEX;

int baseLine = 0;

Size txtSize = getTextSize(text, font, 0.4, 1, &baseLine);

rectangle(resImg, Point(x0, y0), Point(x1, y1), Scalar(0, 255, 0), 2);

rectangle(resImg, Point(x0, y0 + 1), Point(x0 + txtSize.width + 1, y0 + int(1.5 * txtSize.height)), Scalar(255, 255, 255), -1);

putText(resImg, text, Point(x0, y0 + txtSize.height), font, 0.4, Scalar(0, 0, 0), 1);

}

return resImg;

}

int main(int argc, char** argv)

{

CommandLineParser parser(argc, argv, keys);

parser.about("Use this script to run Yolox deep learning networks in opencv_zoo using OpenCV.");

if (parser.has("help"))

{

parser.printMessage();

return 0;

}

string model = parser.get<String>("model");

float confThreshold = parser.get<float>("confidence");

float objThreshold = parser.get<float>("obj");

float nmsThreshold = parser.get<float>("nms");

bool vis = parser.get<bool>("vis");

bool save = parser.get<bool>("save");

int backendTargetid = parser.get<int>("backend");

if (model.empty())

{

CV_Error(Error::StsError, "Model file " + model + " not found");

}

YoloX modelNet(model, confThreshold, nmsThreshold, objThreshold,

backendTargetPairs[backendTargetid].first, backendTargetPairs[backendTargetid].second);

//! [Open a video file or an image file or a camera stream]

VideoCapture cap;

if (parser.has("input"))

cap.open(samples::findFile(parser.get<String>("input")));

else

cap.open(0);

if (!cap.isOpened())

CV_Error(Error::StsError, "Cannot open video or file");

Mat frame, inputBlob;

double letterboxScale;

static const std::string kWinName = model;

int nbInference = 0;

while (waitKey(1) < 0)

{

cap >> frame;

if (frame.empty())

{

cout << "Frame is empty" << endl;

waitKey();

break;

}

pair<Mat, double> w = letterBox(frame);

inputBlob = get<0>(w);

letterboxScale = get<1>(w);

TickMeter tm;

tm.start();

Mat predictions = modelNet.infer(inputBlob);

tm.stop();

cout << "Inference time: " << tm.getTimeMilli() << " ms\n";

Mat img = visualize(predictions, frame, letterboxScale, tm.getFPS());

if (save && parser.has("input"))

{

imwrite("result.jpg", img);

}

if (vis)

{

imshow(kWinName, img);

}

}

return 0;

}

Microsoft ONNX Runtime — GPU/CPU

Microsoft 和合作伙伴社区创建了 ONNX 作为表示机器学习模型的开放标准。 许多框架（包括 TensorFlow、PyTorch、scikit-learn、Keras、Chainer、MXNet 和 MATLAB）的模型都可以导出或转换为标准 ONNX 格式。 在模型采用 ONNX 格式后，可以在各种平台和设备上运行这些模型。

示例代码

import cv2

import numpy as np

import onnxruntime as ort

import time

def plot_one_box(x, img, color=None, label=None, line_thickness=None):

"""

description: Plots one bounding box on image img,

this function comes from YoLov5 project.

param:

x: a box likes [x1,y1,x2,y2]

img: a opencv image object

color: color to draw rectangle, such as (0,255,0)

label: str

line_thickness: int

return:

no return

"""

tl = (

line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1

) # line/font thickness

color = color or [random.randint(0, 255) for _ in range(3)]

c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))

cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)

if label:

tf = max(tl - 1, 1) # font thickness

t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]

c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3

cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled

cv2.putText(

img,

label,

(c1[0], c1[1] - 2),

0,

tl / 3,

[225, 255, 255],

thickness=tf,

lineType=cv2.LINE_AA,

)

def _make_grid( nx, ny):

xv, yv = np.meshgrid(np.arange(ny), np.arange(nx))

return np.stack((xv, yv), 2).reshape((-1, 2)).astype(np.float32)

def cal_outputs(outs,nl,na,model_w,model_h,anchor_grid,stride):

row_ind = 0

grid = [np.zeros(1)] * nl

for i in range(nl):

h, w = int(model_w/ stride[i]), int(model_h / stride[i])

length = int(na * h * w)

if grid[i].shape[2:4] != (h, w):

grid[i] = _make_grid(w, h)

outs[row_ind:row_ind + length, 0:2] = (outs[row_ind:row_ind + length, 0:2] * 2. - 0.5 + np.tile(

grid[i], (na, 1))) * int(stride[i])

outs[row_ind:row_ind + length, 2:4] = (outs[row_ind:row_ind + length, 2:4] * 2) ** 2 * np.repeat(

anchor_grid[i], h * w, axis=0)

row_ind += length

return outs

def post_process_opencv(outputs,model_h,model_w,img_h,img_w,thred_nms,thred_cond):

conf = outputs[:,4].tolist()

c_x = outputs[:,0]/model_w*img_w

c_y = outputs[:,1]/model_h*img_h

w = outputs[:,2]/model_w*img_w

h = outputs[:,3]/model_h*img_h

p_cls = outputs[:,5:]

if len(p_cls.shape)==1:

p_cls = np.expand_dims(p_cls,1)

cls_id = np.argmax(p_cls,axis=1)

p_x1 = np.expand_dims(c_x-w/2,-1)

p_y1 = np.expand_dims(c_y-h/2,-1)

p_x2 = np.expand_dims(c_x+w/2,-1)

p_y2 = np.expand_dims(c_y+h/2,-1)

areas = np.concatenate((p_x1,p_y1,p_x2,p_y2),axis=-1)

areas = areas.tolist()

ids = cv2.dnn.NMSBoxes(areas,conf,thred_cond,thred_nms)

if len(ids)>0:

return np.array(areas)[ids],np.array(conf)[ids],cls_id[ids]

else:

return [],[],[]

def infer_img(img0,net,model_h,model_w,nl,na,stride,anchor_grid,thred_nms=0.4,thred_cond=0.5):

# 图像预处理

img = cv2.resize(img0, [model_w,model_h], interpolation=cv2.INTER_AREA)

img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

img = img.astype(np.float32) / 255.0

blob = np.expand_dims(np.transpose(img, (2, 0, 1)), axis=0)

# 模型推理

outs = net.run(None, { net.get_inputs()[0].name: blob})[0].squeeze(axis=0)

# 输出坐标矫正

outs = cal_outputs(outs,nl,na,model_w,model_h,anchor_grid,stride)

# 检测框计算

img_h,img_w,_ = np.shape(img0)

boxes,confs,ids = post_process_opencv(outs,model_h,model_w,img_h,img_w,thred_nms,thred_cond)

return boxes,confs,ids

if __name__ == "__main__":

# 模型加载

model_pb_path = "best.onnx"

so = ort.SessionOptions()

net = ort.InferenceSession(model_pb_path, so)

# 标签字典

dic_labels= { 0:'boat',

1:'noboat',}

# 模型参数

model_h = 320

model_w = 320

nl = 3

na = 3

stride=[8.,16.,32.]

anchors = [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]]

anchor_grid = np.asarray(anchors, dtype=np.float32).reshape(nl, -1, 2)

video = 0

cap = cv2.VideoCapture(video)

flag_det = False

while True:

success, img0 = cap.read()

if success:

if flag_det:

t1 = time.time()

det_boxes,scores,ids = infer_img(img0,net,model_h,model_w,nl,na,stride,anchor_grid,thred_nms=0.4,thred_cond=0.5)

t2 = time.time()

for box,score,id in zip(det_boxes,scores,ids):

label = '%s:%.2f'%(dic_labels[id],score)

plot_one_box(box.astype(np.int16), img0, color=(255,0,0), label=label, line_thickness=None)

str_FPS = "FPS: %.2f"%(1./(t2-t1))

cv2.putText(img0,str_FPS,(50,50),cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),3)

cv2.imshow("video",img0)

key=cv2.waitKey(1) & 0xFF

if key == ord('q'):

break

elif key & 0xFF == ord('s'):

flag_det = not flag_det

print(flag_det)

cap.release()

腾讯 NCNN— 移动端

ncnn 是腾讯优图实验室首个开源项目，是一个为手机端极致优化的高性能神经网络前向计算框架。ncnn 从设计之初深刻考虑手机端的部署和使用。 无第三方依赖，跨平台，手机端 cpu 的速度快于目前所有已知的开源框架。 基于 ncnn，开发者能够将深度学习算法轻松移植到手机端高效执行， 开发出人工智能 APP，将 AI 带到你的指尖。 ncnn 目前已在腾讯多款应用中使用，如：QQ，Qzone，微信，天天 P 图等。

Android&NCNN学习—nanodet实例

关于舰船检测项目调试记录

#include "layer.h"

#include "net.h"

#if defined(USE_NCNN_SIMPLEOCV)

#include "simpleocv.h"

#else

#include <opencv2/core/core.hpp>

#include <opencv2/highgui/highgui.hpp>

#include <opencv2/imgproc/imgproc.hpp>

#endif

#include <float.h>

#include <stdio.h>

#include <vector>

#include <sys/time.h>

// 0 : FP16

// 1 : INT8

#define USE_INT8 0

// 0 : Image

// 1 : Camera

#define USE_CAMERA 0

struct Object

{

cv::Rect_<float> rect;

int label;

float prob;

};

static inline float intersection_area(const Object& a, const Object& b)

{

cv::Rect_<float> inter = a.rect & b.rect;

return inter.area();

}

static void qsort_descent_inplace(std::vector<Object>& faceobjects, int left, int right)

{

int i = left;

int j = right;

float p = faceobjects[(left + right) / 2].prob;

while (i <= j)

{

while (faceobjects[i].prob > p)

i++;

while (faceobjects[j].prob < p)

j--;

if (i <= j)

{

// swap

std::swap(faceobjects[i], faceobjects[j]);

i++;

j--;

}

}

#pragma omp parallel sections

{

#pragma omp section

{

if (left < j) qsort_descent_inplace(faceobjects, left, j);

}

#pragma omp section

{

if (i < right) qsort_descent_inplace(faceobjects, i, right);

}

}

}

static void qsort_descent_inplace(std::vector<Object>& faceobjects)

{

if (faceobjects.empty())

return;

qsort_descent_inplace(faceobjects, 0, faceobjects.size() - 1);

}

static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold)

{

picked.clear();

const int n = faceobjects.size();

std::vector<float> areas(n);

for (int i = 0; i < n; i++)

{

areas[i] = faceobjects[i].rect.area();

}

for (int i = 0; i < n; i++)

{

const Object& a = faceobjects[i];

int keep = 1;

for (int j = 0; j < (int)picked.size(); j++)

{

const Object& b = faceobjects[picked[j]];

// intersection over union

float inter_area = intersection_area(a, b);

float union_area = areas[i] + areas[picked[j]] - inter_area;

// float IoU = inter_area / union_area

if (inter_area / union_area > nms_threshold)

keep = 0;

}

if (keep)

picked.push_back(i);

}

}

static inline float sigmoid(float x)

{

return static_cast<float>(1.f / (1.f + exp(-x)));

}

// unsigmoid

static inline float unsigmoid(float y) {

return static_cast<float>(-1.0 * (log((1.0 / y) - 1.0)));

}

static void generate_proposals(const ncnn::Mat &anchors, int stride, const ncnn::Mat &in_pad,

const ncnn::Mat &feat_blob, float prob_threshold,

std::vector <Object> &objects) {

const int num_grid = feat_blob.h;

if (prob_threshold > 0.6)

float unsig_pro = unsigmoid(prob_threshold);

int num_grid_x;

int num_grid_y;

if (in_pad.w > in_pad.h) {

num_grid_x = in_pad.w / stride;

num_grid_y = num_grid / num_grid_x;

} else {

num_grid_y = in_pad.h / stride;

num_grid_x = num_grid / num_grid_y;

}

const int num_class = feat_blob.w - 5;

const int num_anchors = anchors.w / 2;

for (int q = 0; q < num_anchors; q++) {

const float anchor_w = anchors[q * 2];

const float anchor_h = anchors[q * 2 + 1];

const ncnn::Mat feat = feat_blob.channel(q);

for (int i = 0; i < num_grid_y; i++) {

for (int j = 0; j < num_grid_x; j++) {

const float *featptr = feat.row(i * num_grid_x + j);

// find class index with max class score

int class_index = 0;

float class_score = -FLT_MAX;

float box_score = featptr[4];

if (prob_threshold > 0.6) {

// while prob_threshold > 0.6, unsigmoid better than sigmoid

if (box_score > unsig_pro) {

for (int k = 0; k < num_class; k++) {

float score = featptr[5 + k];

if (score > class_score) {

class_index = k;

class_score = score;

}

}

float confidence = sigmoid(box_score) * sigmoid(class_score);

if (confidence >= prob_threshold) {

float dx = sigmoid(featptr[0]);

float dy = sigmoid(featptr[1]);

float dw = sigmoid(featptr[2]);

float dh = sigmoid(featptr[3]);

float pb_cx = (dx * 2.f - 0.5f + j) * stride;

float pb_cy = (dy * 2.f - 0.5f + i) * stride;

float pb_w = pow(dw * 2.f, 2) * anchor_w;

float pb_h = pow(dh * 2.f, 2) * anchor_h;

float x0 = pb_cx - pb_w * 0.5f;

float y0 = pb_cy - pb_h * 0.5f;

float x1 = pb_cx + pb_w * 0.5f;

float y1 = pb_cy + pb_h * 0.5f;

Object obj;

obj.rect.x = x0;

obj.rect.y = y0;

obj.rect.width = x1 - x0;

obj.rect.height = y1 - y0;

obj.label = class_index;

obj.prob = confidence;

objects.push_back(obj);

}

} else {

for (int k = 0; k < num_class; k++) {

float score = featptr[5 + k];

if (score > class_score) {

class_index = k;

class_score = score;

}

}

float confidence = sigmoid(box_score) * sigmoid(class_score);

if (confidence >= prob_threshold) {

float dx = sigmoid(featptr[0]);

float dy = sigmoid(featptr[1]);

float dw = sigmoid(featptr[2]);

float dh = sigmoid(featptr[3]);

float pb_cx = (dx * 2.f - 0.5f + j) * stride;

float pb_cy = (dy * 2.f - 0.5f + i) * stride;

float pb_w = pow(dw * 2.f, 2) * anchor_w;

float pb_h = pow(dh * 2.f, 2) * anchor_h;

float x0 = pb_cx - pb_w * 0.5f;

float y0 = pb_cy - pb_h * 0.5f;

float x1 = pb_cx + pb_w * 0.5f;

float y1 = pb_cy + pb_h * 0.5f;

Object obj;

obj.rect.x = x0;

obj.rect.y = y0;

obj.rect.width = x1 - x0;

obj.rect.height = y1 - y0;

obj.label = class_index;

obj.prob = confidence;

objects.push_back(obj);

}

}

}

}

}

}

}

static int detect_yolov5(const cv::Mat& bgr, std::vector<Object>& objects)

{

ncnn::Net yolov5;

#if USE_INT8

yolov5.opt.use_int8_inference=true;

#else

yolov5.opt.use_vulkan_compute = true;

yolov5.opt.use_bf16_storage = true;

#endif

// original pretrained model from https://github.com/ultralytics/yolov5

// the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models

#if USE_INT8

yolov5.load_param("v5lite-i8e.param");

yolov5.load_model("yolov5-i8e.bin");

#else

yolov5.load_param("v5lite-e.param");

yolov5.load_model("v5lite-e.bin");

#endif

const int target_size = 320;

const float prob_threshold = 0.60f;

const float nms_threshold = 0.60f;

int img_w = bgr.cols;

int img_h = bgr.rows;

// letterbox pad to multiple of 32

int w = img_w;

int h = img_h;

float scale = 1.f;

if (w > h)

{

scale = (float)target_size / w;

w = target_size;

h = h * scale;

}

else

{

scale = (float)target_size / h;

h = target_size;

w = w * scale;

}

ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, img_w, img_h, w, h);

// pad to target_size rectangle

// yolov5/utils/datasets.py letterbox

int wpad = (w + 31) / 32 * 32 - w;

int hpad = (h + 31) / 32 * 32 - h;

ncnn::Mat in_pad;

ncnn::copy_make_border(in, in_pad, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, ncnn::BORDER_CONSTANT, 114.f);

const float norm_vals[3] = { 1 / 255.f, 1 / 255.f, 1 / 255.f};

in_pad.substract_mean_normalize(0, norm_vals);

ncnn::Extractor ex = yolov5.create_extractor();

ex.input("images", in_pad);

std::vector<Object> proposals;

// stride 8

{

ncnn::Mat out;

ex.extract("output", out);

ncnn::Mat anchors(6);

anchors[0] = 10.f;

anchors[1] = 13.f;

anchors[2] = 16.f;

anchors[3] = 30.f;

anchors[4] = 33.f;

anchors[5] = 23.f;

std::vector<Object> objects8;

generate_proposals(anchors, 8, in_pad, out, prob_threshold, objects8);

proposals.insert(proposals.end(), objects8.begin(), objects8.end());

}

// stride 16

{

ncnn::Mat out;

ex.extract("1111", out);

ncnn::Mat anchors(6);

anchors[0] = 30.f;

anchors[1] = 61.f;

anchors[2] = 62.f;

anchors[3] = 45.f;

anchors[4] = 59.f;

anchors[5] = 119.f;

std::vector<Object> objects16;

generate_proposals(anchors, 16, in_pad, out, prob_threshold, objects16);

proposals.insert(proposals.end(), objects16.begin(), objects16.end());

}

// stride 32

{

ncnn::Mat out;

ex.extract("2222", out);

ncnn::Mat anchors(6);

anchors[0] = 116.f;

anchors[1] = 90.f;

anchors[2] = 156.f;

anchors[3] = 198.f;

anchors[4] = 373.f;

anchors[5] = 326.f;

std::vector<Object> objects32;

generate_proposals(anchors, 32, in_pad, out, prob_threshold, objects32);

proposals.insert(proposals.end(), objects32.begin(), objects32.end());

}

// sort all proposals by score from highest to lowest

qsort_descent_inplace(proposals);

// apply nms with nms_threshold

std::vector<int> picked;

nms_sorted_bboxes(proposals, picked, nms_threshold);

int count = picked.size();

objects.resize(count);

for (int i = 0; i < count; i++)

{

objects[i] = proposals[picked[i]];

// adjust offset to original unpadded

float x0 = (objects[i].rect.x - (wpad / 2)) / scale;

float y0 = (objects[i].rect.y - (hpad / 2)) / scale;

float x1 = (objects[i].rect.x + objects[i].rect.width - (wpad / 2)) / scale;

float y1 = (objects[i].rect.y + objects[i].rect.height - (hpad / 2)) / scale;

// clip

x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f);

y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f);

x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f);

y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f);

objects[i].rect.x = x0;

objects[i].rect.y = y0;

objects[i].rect.width = x1 - x0;

objects[i].rect.height = y1 - y0;

}

return 0;

}

static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects)

{

static const char* class_names[] = {

"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"

};

cv::Mat image = bgr.clone();

for (size_t i = 0; i < objects.size(); i++)

{

const Object& obj = objects[i];

fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob,

obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);

cv::rectangle(image, obj.rect, cv::Scalar(0, 255, 0));

char text[256];

sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100);

int baseLine = 0;

cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);

int x = obj.rect.x;

int y = obj.rect.y - label_size.height - baseLine;

if (y < 0)

y = 0;

if (x + label_size.width > image.cols)

x = image.cols - label_size.width;

cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),

cv::Scalar(255, 255, 255), -1);

cv::putText(image, text, cv::Point(x, y + label_size.height),

cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));

}

#if USE_CAMERA

imshow("外接摄像头", image);

cv::waitKey(1);

#else

cv::imwrite("result.jpg", image);

#endif

}

#if USE_CAMERA

int main(int argc, char** argv)

{

cv::VideoCapture capture;

capture.open(0); //修改这个参数可以选择打开想要用的摄像头

cv::Mat frame;

while (true)

{

capture >> frame;

cv::Mat m = frame;

std::vector<Object> objects;

detect_yolov5(frame, objects);

draw_objects(m, objects);

if (cv::waitKey(30) >= 0)

break;

}

}

#else

int main(int argc, char** argv)

{

if (argc != 2)

{

fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]);

return -1;

}

const char* imagepath = argv[1];

struct timespec begin, end;

long time;

clock_gettime(CLOCK_MONOTONIC, &begin);

cv::Mat m = cv::imread(imagepath, 1);

if (m.empty())

{

fprintf(stderr, "cv::imread %s failed\n", imagepath);

return -1;

}

std::vector<Object> objects;

detect_yolov5(m, objects);

clock_gettime(CLOCK_MONOTONIC, &end);

time = (end.tv_sec - begin.tv_sec) + (end.tv_nsec - begin.tv_nsec);

printf(">> Time : %lf ms\n", (double)time/1000000);

draw_objects(m, objects);

return 0;

}

#endif

阿里巴巴 MNN — 移动端

MNN 是一个轻量级的深度学习端侧推理引擎，核心解决深度神经网络模型在端侧推理运行问题，涵盖深度神经网络模型的优化、转换和推理。目前，MNN已经在手淘、手猫、优酷、聚划算、UC、飞猪、千牛等 20 多个 App 中使用。

示例代码

#include <iostream>

#include <string>

#include <ctime>

#include <stdio.h>

#include <omp.h>

#include <MNN/MNNDefine.h>

#include <MNN/MNNForwardType.h>

#include <MNN/Interpreter.hpp>

#include "utils.h"

#define use_camera 0

#define mnnd 1

std::vector<BoxInfo> decode(cv::Mat &cv_mat, std::shared_ptr<MNN::Interpreter> &net, MNN::Session *session, int INPUT_SIZE)

{

std::vector<int> dims{ 1, INPUT_SIZE, INPUT_SIZE, 3};

auto nhwc_Tensor = MNN::Tensor::create<float>(dims, NULL, MNN::Tensor::TENSORFLOW);

auto nhwc_data = nhwc_Tensor->host<float>();

auto nhwc_size = nhwc_Tensor->size();

std::memcpy(nhwc_data, cv_mat.data, nhwc_size);

auto inputTensor = net->getSessionInput(session, nullptr);

inputTensor->copyFromHostTensor(nhwc_Tensor);

net->runSession(session);

MNN::Tensor *tensor_scores = net->getSessionOutput(session, "outputs");

MNN::Tensor tensor_scores_host(tensor_scores, tensor_scores->getDimensionType());

tensor_scores->copyToHostTensor(&tensor_scores_host);

auto pred_dims = tensor_scores_host.shape();

#if mnnd

const unsigned int num_proposals = pred_dims.at(1);

const unsigned int num_classes = pred_dims.at(2) - 5;

std::vector<BoxInfo> bbox_collection;

for (unsigned int i = 0; i < num_proposals; ++i)

{

const float *offset_obj_cls_ptr = tensor_scores_host.host<float>() + (i * (num_classes + 5)); // row ptr

float obj_conf = offset_obj_cls_ptr[4];

if (obj_conf < 0.5)

continue;

float cls_conf = offset_obj_cls_ptr[5];

unsigned int label = 0;

for (unsigned int j = 0; j < num_classes; ++j)

{

float tmp_conf = offset_obj_cls_ptr[j + 5];

if (tmp_conf > cls_conf)

{

cls_conf = tmp_conf;

label = j;

}

}

float conf = obj_conf * cls_conf;

if (conf < 0.50)

continue;

float cx = offset_obj_cls_ptr[0];

float cy = offset_obj_cls_ptr[1];

float w = offset_obj_cls_ptr[2];

float h = offset_obj_cls_ptr[3];

float x1 = (cx - w / 2.f);

float y1 = (cy - h / 2.f);

float x2 = (cx + w / 2.f);

float y2 = (cy + h / 2.f);

BoxInfo box;

box.x1 = std::max(0.f, x1);

box.y1 = std::max(0.f, y1);

box.x2 = std::min(x2, (float)INPUT_SIZE - 1.f);

box.y2 = std::min(y2, (float)INPUT_SIZE - 1.f);

box.score = conf;

box.label = label;

bbox_collection.push_back(box);

}

#else

const unsigned int num_proposals = pred_dims.at(0);

const unsigned int num_datainfo = pred_dims.at(1);

std::vector<BoxInfo> bbox_collection;

for (unsigned int i = 0; i < num_proposals; ++i)

{

const float *offset_obj_cls_ptr = tensor_scores_host.host<float>() + (i * num_datainfo); // row ptr

float obj_conf = offset_obj_cls_ptr[4];

if (obj_conf < 0.5)

continue;

float x1 = offset_obj_cls_ptr[0];

float y1 = offset_obj_cls_ptr[1];

float x2 = offset_obj_cls_ptr[2];

float y2 = offset_obj_cls_ptr[3];

BoxInfo box;

box.x1 = std::max(0.f, x1);

box.y1 = std::max(0.f, y1);

box.x2 = std::min(x2, (float)INPUT_SIZE - 1.f);

box.y2 = std::min(y2, (float)INPUT_SIZE - 1.f);

box.score = offset_obj_cls_ptr[4];

box.label = offset_obj_cls_ptr[5];

bbox_collection.push_back(box);

}

#endif

delete nhwc_Tensor;

return bbox_collection;

}

int main(int argc, char const *argv[])

{

std::string model_name = "../models/v5lite-e-mnnd_fp16.mnn";

std::shared_ptr<MNN::Interpreter> net = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_name.c_str()));

if (nullptr == net)

{

return 0;

}

MNN::ScheduleConfig config;

config.numThread = 4;

config.type = static_cast<MNNForwardType>(MNN_FORWARD_CPU);

MNN::BackendConfig backendConfig;

// backendConfig.precision = (MNN::BackendConfig::PrecisionMode)1;

backendConfig.precision = MNN::BackendConfig::Precision_Low_BF16;

config.backendConfig = &backendConfig;

MNN::Session *session = net->createSession(config);

std::vector<BoxInfo> bbox_collection;

cv::Mat image;

MatInfo mmat_objection;

mmat_objection.inpSize = 320;

#if use_camera

cv::VideoCapture capture;

capture.open(0);

cv::Mat frame;

while (true)

{

bbox_collection.clear();

struct timespec begin, end;

long time;

clock_gettime(CLOCK_MONOTONIC, &begin);

capture >> frame;

cv::Mat raw_image = frame;

cv::Mat pimg = preprocess(raw_image, mmat_objection);

bbox_collection = decode(pimg, net, session, mmat_objection.inpSize);

nms(bbox_collection, 0.50);

draw_box(raw_image, bbox_collection, mmat_objection);

clock_gettime(CLOCK_MONOTONIC, &end);

time = (end.tv_sec - begin.tv_sec) + (end.tv_nsec - begin.tv_nsec);

if(time > 0) printf(">> Time : %lf ms\n", (double)time / 1000000);

}

#else

for (size_t i = 0; i < 100; i++)

{

bbox_collection.clear();

struct timespec begin, end;

long time;

clock_gettime(CLOCK_MONOTONIC, &begin);

std::string image_name = "../images/000000001000.jpg";

cv::Mat raw_image = cv::imread(image_name.c_str());

cv::Mat pimg = preprocess(raw_image, mmat_objection);

bbox_collection = decode(pimg, net, session, mmat_objection.inpSize);

nms(bbox_collection, 0.50);

draw_box(raw_image, bbox_collection, mmat_objection);

clock_gettime(CLOCK_MONOTONIC, &end);

time = (end.tv_sec - begin.tv_sec) + (end.tv_nsec - begin.tv_nsec);

if(time > 0) printf(">> Time : %lf ms\n", (double)time / 1000000);

}

#endif

return 0;

}

Rockchip（瑞芯微）的RKNN — 移动端（NPU）

RKNN-Toolkit 是Rockchip为Rockchip NPU平台提供的模型转换和推理工具，Rockchip 提供了完整了模型转换 Python 工具，方便用户将自主研发的算法模型转换成 RKNN 模型，同时 Rockchip 也提供了C/C++和Python API 接口，适用于AI算法在嵌入式设备上的部署。RKNN Toolkit2 提供了模型转换、量化功能、模型推理、性能和内存评估、量化精度分析、模型加密等功能。

rknn-toolkit2 github官方库

// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved.

//

// Licensed under the Apache License, Version 2.0 (the "License");

// you may not use this file except in compliance with the License.

// You may obtain a copy of the License at

//

// http://www.apache.org/licenses/LICENSE-2.0

//

// Unless required by applicable law or agreed to in writing, software

// distributed under the License is distributed on an "AS IS" BASIS,

// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

// See the License for the specific language governing permissions and

// limitations under the License.

/*-------------------------------------------

Includes

-------------------------------------------*/

#include <dlfcn.h>

#include <stdio.h>

#include <stdlib.h>

#include <string.h>

#include <sys/time.h>

#define _BASETSD_H

#include "RgaUtils.h"

#include "postprocess.h"

#include "rknn_api.h"

#include "preprocess.h"

#define PERF_WITH_POST 1

/*-------------------------------------------

Functions

-------------------------------------------*/

static void dump_tensor_attr(rknn_tensor_attr *attr)

{

std::string shape_str = attr->n_dims < 1 ? "" : std::to_string(attr->dims[0]);

for (int i = 1; i < attr->n_dims; ++i)

{

shape_str += ", " + std::to_string(attr->dims[i]);

}

printf(" index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, w_stride = %d, size_with_stride=%d, fmt=%s, "

"type=%s, qnt_type=%s, "

"zp=%d, scale=%f\n",

attr->index, attr->name, attr->n_dims, shape_str.c_str(), attr->n_elems, attr->size, attr->w_stride,

attr->size_with_stride, get_format_string(attr->fmt), get_type_string(attr->type),

get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale);

}

double __get_us(struct timeval t) { return (t.tv_sec * 1000000 + t.tv_usec); }

static unsigned char *load_data(FILE *fp, size_t ofst, size_t sz)

{

unsigned char *data;

int ret;

data = NULL;

if (NULL == fp)

{

return NULL;

}

ret = fseek(fp, ofst, SEEK_SET);

if (ret != 0)

{

printf("blob seek failure.\n");

return NULL;

}

data = (unsigned char *)malloc(sz);

if (data == NULL)

{

printf("buffer malloc failure.\n");

return NULL;

}

ret = fread(data, 1, sz, fp);

return data;

}

static unsigned char *load_model(const char *filename, int *model_size)

{

FILE *fp;

unsigned char *data;

fp = fopen(filename, "rb");

if (NULL == fp)

{

printf("Open file %s failed.\n", filename);

return NULL;

}

fseek(fp, 0, SEEK_END);

int size = ftell(fp);

data = load_data(fp, 0, size);

fclose(fp);

*model_size = size;

return data;

}

static int saveFloat(const char *file_name, float *output, int element_size)

{

FILE *fp;

fp = fopen(file_name, "w");

for (int i = 0; i < element_size; i++)

{

fprintf(fp, "%.6f\n", output[i]);

}

fclose(fp);

return 0;

}

/*-------------------------------------------

Main Functions

-------------------------------------------*/

int main(int argc, char **argv)

{

if (argc < 3)

{

printf("Usage: %s <rknn model> <input_image_path> <resize/letterbox> <output_image_path>\n", argv[0]);

return -1;

}

int ret;

rknn_context ctx;

size_t actual_size = 0;

int img_width = 0;

int img_height = 0;

int img_channel = 0;

const float nms_threshold = NMS_THRESH; // 默认的NMS阈值

const float box_conf_threshold = BOX_THRESH; // 默认的置信度阈值

struct timeval start_time, stop_time;

char *model_name = (char *)argv[1];

char *input_path = argv[2];

std::string option = "letterbox";

std::string out_path = "./out.jpg";

if (argc >= 4)

{

option = argv[3];

}

if (argc >= 5)

{

out_path = argv[4];

}

// init rga context

rga_buffer_t src;

rga_buffer_t dst;

memset(&src, 0, sizeof(src));

memset(&dst, 0, sizeof(dst));

printf("post process config: box_conf_threshold = %.2f, nms_threshold = %.2f\n", box_conf_threshold, nms_threshold);

/* Create the neural network */

printf("Loading mode...\n");

int model_data_size = 0;

unsigned char *model_data = load_model(model_name, &model_data_size);

ret = rknn_init(&ctx, model_data, model_data_size, 0, NULL);

if (ret < 0)

{

printf("rknn_init error ret=%d\n", ret);

return -1;

}

rknn_sdk_version version;

ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &version, sizeof(rknn_sdk_version));

if (ret < 0)

{

printf("rknn_init error ret=%d\n", ret);

return -1;

}

printf("sdk version: %s driver version: %s\n", version.api_version, version.drv_version);

rknn_input_output_num io_num;

ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num));

if (ret < 0)

{

printf("rknn_init error ret=%d\n", ret);

return -1;

}

printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output);

rknn_tensor_attr input_attrs[io_num.n_input];

memset(input_attrs, 0, sizeof(input_attrs));

for (int i = 0; i < io_num.n_input; i++)

{

input_attrs[i].index = i;

ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr));

if (ret < 0)

{

printf("rknn_init error ret=%d\n", ret);

return -1;

}

dump_tensor_attr(&(input_attrs[i]));

}

rknn_tensor_attr output_attrs[io_num.n_output];

memset(output_attrs, 0, sizeof(output_attrs));

for (int i = 0; i < io_num.n_output; i++)

{

output_attrs[i].index = i;

ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr));

dump_tensor_attr(&(output_attrs[i]));

}

int channel = 3;

int width = 0;

int height = 0;

if (input_attrs[0].fmt == RKNN_TENSOR_NCHW)

{

printf("model is NCHW input fmt\n");

channel = input_attrs[0].dims[1];

height = input_attrs[0].dims[2];

width = input_attrs[0].dims[3];

}

else

{

printf("model is NHWC input fmt\n");

height = input_attrs[0].dims[1];

width = input_attrs[0].dims[2];

channel = input_attrs[0].dims[3];

}

printf("model input height=%d, width=%d, channel=%d\n", height, width, channel);

rknn_input inputs[1];

memset(inputs, 0, sizeof(inputs));

inputs[0].index = 0;

inputs[0].type = RKNN_TENSOR_UINT8;

inputs[0].size = width * height * channel;

inputs[0].fmt = RKNN_TENSOR_NHWC;

inputs[0].pass_through = 0;

// 读取图片

printf("Read %s ...\n", input_path);

cv::Mat orig_img = cv::imread(input_path, 1);

if (!orig_img.data)

{

printf("cv::imread %s fail!\n", input_path);

return -1;

}

cv::Mat img;

cv::cvtColor(orig_img, img, cv::COLOR_BGR2RGB);

img_width = img.cols;

img_height = img.rows;

printf("img width = %d, img height = %d\n", img_width, img_height);

// 指定目标大小和预处理方式,默认使用LetterBox的预处理

BOX_RECT pads;

memset(&pads, 0, sizeof(BOX_RECT));

cv::Size target_size(width, height);

cv::Mat resized_img(target_size.height, target_size.width, CV_8UC3);

// 计算缩放比例

float scale_w = (float)target_size.width / img.cols;

float scale_h = (float)target_size.height / img.rows;

if (img_width != width || img_height != height)

{

// 直接缩放采用RGA加速

if (option == "resize")

{

printf("resize image by rga\n");

ret = resize_rga(src, dst, img, resized_img, target_size);

if (ret != 0)

{

fprintf(stderr, "resize with rga error\n");

return -1;

}

// 保存预处理图片

cv::imwrite("resize_input.jpg", resized_img);

}

else if (option == "letterbox")

{

printf("resize image with letterbox\n");

float min_scale = std::min(scale_w, scale_h);

scale_w = min_scale;

scale_h = min_scale;

letterbox(img, resized_img, pads, min_scale, target_size);

// 保存预处理图片

cv::imwrite("letterbox_input.jpg", resized_img);

}

else

{

fprintf(stderr, "Invalid resize option. Use 'resize' or 'letterbox'.\n");

return -1;

}

inputs[0].buf = resized_img.data;

}

else

{

inputs[0].buf = img.data;

}

gettimeofday(&start_time, NULL);

rknn_inputs_set(ctx, io_num.n_input, inputs);

rknn_output outputs[io_num.n_output];

memset(outputs, 0, sizeof(outputs));

for (int i = 0; i < io_num.n_output; i++)

{

outputs[i].index = i;

outputs[i].want_float = 0;

}

// 执行推理

ret = rknn_run(ctx, NULL);

ret = rknn_outputs_get(ctx, io_num.n_output, outputs, NULL);

gettimeofday(&stop_time, NULL);

printf("once run use %f ms\n", (__get_us(stop_time) - __get_us(start_time)) / 1000);

// 后处理

detect_result_group_t detect_result_group;

std::vector<float> out_scales;

std::vector<int32_t> out_zps;

for (int i = 0; i < io_num.n_output; ++i)

{

out_scales.push_back(output_attrs[i].scale);

out_zps.push_back(output_attrs[i].zp);

}

post_process((int8_t *)outputs[0].buf, (int8_t *)outputs[1].buf, (int8_t *)outputs[2].buf, height, width,

box_conf_threshold, nms_threshold, pads, scale_w, scale_h, out_zps, out_scales, &detect_result_group);

// 画框和概率

char text[256];

for (int i = 0; i < detect_result_group.count; i++)

{

detect_result_t *det_result = &(detect_result_group.results[i]);

sprintf(text, "%s %.1f%%", det_result->name, det_result->prop * 100);

printf("%s @ (%d %d %d %d) %f\n", det_result->name, det_result->box.left, det_result->box.top,

det_result->box.right, det_result->box.bottom, det_result->prop);

int x1 = det_result->box.left;

int y1 = det_result->box.top;

int x2 = det_result->box.right;

int y2 = det_result->box.bottom;

rectangle(orig_img, cv::Point(x1, y1), cv::Point(x2, y2), cv::Scalar(256, 0, 0, 256), 3);

putText(orig_img, text, cv::Point(x1, y1 + 12), cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(255, 255, 255));

}

printf("save detect result to %s\n", out_path.c_str());

imwrite(out_path, orig_img);

ret = rknn_outputs_release(ctx, io_num.n_output, outputs);

// 耗时统计

int test_count = 10;

gettimeofday(&start_time, NULL);

for (int i = 0; i < test_count; ++i)

{

rknn_inputs_set(ctx, io_num.n_input, inputs);

ret = rknn_run(ctx, NULL);

ret = rknn_outputs_get(ctx, io_num.n_output, outputs, NULL);

#if PERF_WITH_POST

post_process((int8_t *)outputs[0].buf, (int8_t *)outputs[1].buf, (int8_t *)outputs[2].buf, height, width,

box_conf_threshold, nms_threshold, pads, scale_w, scale_h, out_zps, out_scales, &detect_result_group);

#endif

ret = rknn_outputs_release(ctx, io_num.n_output, outputs);

}

gettimeofday(&stop_time, NULL);

printf("loop count = %d , average run %f ms\n", test_count,

(__get_us(stop_time) - __get_us(start_time)) / 1000.0 / test_count);

deinitPostProcess();

// release

ret = rknn_destroy(ctx);

if (model_data)

{

free(model_data);

}

return 0;

}

百度PaddlePaddle（飞桨） — 服务器端/移动端

百度出品的深度学习平台飞桨（PaddlePaddle）是主流深度学习框架中一款完全国产化的产品，与Google TensorFlow、Facebook Pytorch齐名。2016 年飞桨正式开源，是国内首个全面开源开放、技术领先、功能完备的产业级深度学习平台。相比国内其他平台，飞桨是一个功能完整的深度学习平台，也是唯一成熟稳定、具备大规模推广条件的深度学习平台。

华为 MindSpore — 移动、边缘和云端（深度学习框架）

MindSpore是端边云全场景按需协同的华为自研AI计算框架，提供全场景统一API，为全场景AI的模型开发、模型运行、模型部署提供端到端能力。MindSpore采用端-边-云按需协作分布式架构、微分原生编程新范式以及AI Native新执行模式，实现更好的资源效率、安全可信，同时降低行业AI开发门槛、释放昇腾芯片算力，助力普惠AI。

Google TensorFlow Lite — 移动端

TensorFlowLite是Google在2017年5月推出的轻量级机器学习解决方案，主要针对移动端设备和嵌入式设备。针对移动端设备特点，TensorFlow Lite使用了诸多技术对内核进行了定制优化，预熔激活，量子化内核。TensorFlow Lite具有以下特征：跨平台，核心代码由C++编写，可以运行在不同平台上；轻量级，代码体积小，模型文件小，运行内存低；支持硬件加速。

参考地址 ：

【OpenCV+OPENVINO使用】openvino安装教程

自训练YOLOv5 模型使用 OpenVINO™ 优化并部署在英特尔开发套件

yolov5使用onnxruntime进行c++部署

基于树莓派4B的YOLOv5-Lite目标检测的移植与部署（含训练教程）

一个针对 OpenCV DNN 进行调整的模型动物园，具有不同平台上的基准测试。

AI平台-MNN【推理引擎】

《MindSpore基础实践》——MindSpore基础

移动端机器学习框架TensorFlow Lite简介与实践

走进国产深度学习框架——百度飞桨（paddlepaddle）