摘要：
本篇文章主要关注对openvino C++接口的调用，根据它的工作逻辑建立一个完整的demo。
openVino推理引擎工作逻辑.png
1. 模型准备
pytorch模型转为onnx格式（这个pytorch里面已经可以很方便的转换了）。安装好openvino后在命令行下输入一下命令即可转换为openvino需要的.bin 和.xml格式的模型。
python "G:\openVINO\install\openvino_2021\deployment_tools\model_optimizer\mo.py" --input_model="K:\classifier5.onnx" --output_dir="K:\\model\\5" --model_name="classifier5" --data_type=FP16
image.png
2. vs2017配置openvino+opencv
包含目录
image.png
库目录
image.png
附加依赖项
image.png
3.说明
根据openvino推理引擎的逻辑，先需要读取模型--->准备input bolb 和output bolb--->模型导入到设备上--->创建infer request，后面就可以不断的输入图像去预测了。
4.源码
#include <iostream>
#include <vector>
#include <string>
#include "opencv.hpp"
#include "inference_engine.hpp"
//read model
int readModel(std::string binPath, std::string xmlPath, InferenceEngine::CNNNetwork &model)
{
InferenceEngine::Core ie;
if (binPath.empty() || xmlPath.empty())
{
return -1;
}
try
{
model = ie.ReadNetwork(xmlPath, binPath);
}
catch (...)
{
return -1;
}
return 0;
}
int predict(InferenceEngine::CNNNetwork &model,
InferenceEngine::InferRequest &infer_request,
cv::Mat &image)
{
std::string input_name = model.getInputsInfo().begin()->first;
std::string output_name = model.getOutputsInfo().begin()->first;
InferenceEngine::Blob::Ptr input = infer_request.GetBlob(input_name);
InferenceEngine::Blob::Ptr output = infer_request.GetBlob(output_name);
cv::resize(image, image, cv::Size(input->getTensorDesc().getDims()[2],
input->getTensorDesc().getDims()[3]));
size_t channels_number = input->getTensorDesc().getDims()[1];
size_t image_size = input->getTensorDesc().getDims()[3] * input->getTensorDesc().getDims()[2];
auto input_data = input->buffer()
.as<InferenceEngine::PrecisionTrait<InferenceEngine::Precision::FP32>::value_type *>();
for (size_t pid = 0; pid < image_size; ++pid)
{
for (size_t ch = 0; ch < channels_number; ++ch)
{
input_data[ch*image_size + pid] = image.at<cv::Vec3b>(pid)[ch] / 255.0f;
}
}
infer_request.Infer();
auto output_data = output->buffer().
as<InferenceEngine::PrecisionTrait<InferenceEngine::Precision::FP32>::value_type *>();
/*for (size_t id = 0; id < 2; ++id)
{
auto result = output_data[id];
std::cout << result << "\n";
}*/
if (output_data[0] < output_data[1]) return 1;
else { return -1; }
return 0;
}
int initiateModel(std::string binPath, std::string xmlPath,
InferenceEngine::CNNNetwork &model,
InferenceEngine::InferRequest &infer_request)
{
InferenceEngine::Core ie;
int readFlag = readModel(binPath, xmlPath, model);
if (readFlag == -1)
{
//read model failed
return -1;
}
//prepare input blobs
InferenceEngine::InputInfo::Ptr input_info = model.getInputsInfo().begin()->second;;
std::string input_name = model.getInputsInfo().begin()->first;
input_info->setLayout(InferenceEngine::Layout::NCHW);
input_info->setPrecision(InferenceEngine::Precision::FP32);
//prepare output blobs
InferenceEngine::DataPtr output_info = model.getOutputsInfo().begin()->second;
std::string output_name = model.getOutputsInfo().begin()->first;
output_info->setPrecision(InferenceEngine::Precision::FP32);
//load model to device
InferenceEngine::ExecutableNetwork executable_network = ie.LoadNetwork(model, "CPU");
//create infer request
infer_request = executable_network.CreateInferRequest();
return 0;
}
int demo()
{
std::string binPath = "K:\\model\\5\\classifier5.bin";
std::string xmlPath = "K:\\model\\5\\classifier5.xml";
std::string imagePath = "K:\\imageData\\polarity\\data5\\val\\neg\\00114.bmp";
InferenceEngine::CNNNetwork model;
InferenceEngine::InferRequest infer_request;
//initinalize the model
initiateModel(binPath, xmlPath, model, infer_request);
cv::Mat image = cv::imread(imagePath);
cv::cvtColor(image, image, cv::COLOR_BGR2RGB);
if (image.empty()) {
return -1;
}
int cls = predict(model, infer_request, image);
if (cls == 1)
{
std::cout << "pos\n";
}
else if (cls == -1)
{
std::cout << "neg\n";
}
return 0;
}
int getAllImagePath(std::string path, std::vector<cv::String> &imagePathList)
{
cv::glob(path, imagePathList);
return 0;
}
int accTest()
{
std::vector<cv::String> imagePathList;
std::string binPath = "K:\\model\\5\\classifier5.bin";
std::string xmlPath = "K:\\model\\5\\classifier5.xml";
std::string imagePath = "K:\\imageData\\polarity\\data5\\val\\neg\\00114.bmp";
std::string folder = "K:\\imageData\\polarity\\data5\\val\\neg";
InferenceEngine::CNNNetwork model;
InferenceEngine::InferRequest infer_request;
//initinalize the model
initiateModel(binPath, xmlPath, model, infer_request);
getAllImagePath(folder, imagePathList);
int neg = 0;
int pos = 0;
int item;
for (auto &path : imagePathList)
{
cv::Mat image = cv::imread(path);
cv::cvtColor(image, image, cv::COLOR_BGR2RGB);
if (image.empty()) {
return -1;
}
item = predict(model, infer_request, image);
if (item == -1)
{
neg += 1;
}
else if (item == 1)
{
pos += 1;
}
}
float all = pos + neg;
printf("pos=%d, neg=%d, acc:pos:%f neg:%f\n", pos, neg, pos / all, neg / all);
return 0;
}
int speedTest()
{
std::vector<cv::String> imagePathList;
std::string binPath = "K:\\model\\5\\classifier5.bin";
std::string xmlPath = "K:\\model\\5\\classifier5.xml";
std::string imagePath = "K:\\imageData\\polarity\\data5\\val\\neg\\00114.bmp";
std::string folder = "K:\\imageData\\polarity\\data5\\val\\neg";
InferenceEngine::CNNNetwork model;
InferenceEngine::InferRequest infer_request;
//initinalize the model
initiateModel(binPath, xmlPath, model, infer_request);
cv::Mat image = cv::imread(imagePath);
cv::cvtColor(image, image, cv::COLOR_BGR2RGB);
if (image.empty()) {
return -1;
}
int num = 1000;
clock_t startTime = clock();
for (int i = 0; i < num; i++) predict(model, infer_request, image);
clock_t endTime = clock();
std::cout << "inference one image " << num << "times run time: " << double(endTime - startTime) * 1000 / CLOCKS_PER_SEC << "ms\n";
return 0;
}
int main()
{
demo();
accTest();
speedTest();
}
5.结果
image.png
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