部署环境：VS2019+cuda10.2+cmake3.17.0

v2.0.0参考  兼容并包的PaddleX-Inference部署方式

按照官方步骤基于PaddleInference的推理-Windows环境编译下载paddleX和cuda10.2版本的paddle_inference.zip，将PaddleX中的cpp拷贝出来并新建build_out文件夹

 将cpp文件夹下的cmakeList.txt修改为下图，仅对下图中的几行进行修改，其它不变：

 完全按照下图选择进行cmake，不能多也不能少，不然会有麻烦

 open Project可以看到如下几项

将其中的model_infer.cpp更改如下：

// Copyright (c) 2021 PaddlePaddle Authors. 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.#include <gflags/gflags.h>
#include <string>
#include <vector>
#include<opencv2/opencv.hpp>
#include<iostream>
#include "model_deploy/common/include/paddle_deploy.h"using namespace cv;
using namespace std;//DEFINE_string(model_filename, "", "Path of det inference model");
//DEFINE_string(params_filename, "", "Path of det inference params");
//DEFINE_string(cfg_file, "", "Path of yaml file");
//DEFINE_string(model_type, "", "model type");
//DEFINE_string(image, "", "Path of test image file");
//DEFINE_bool(use_gpu, false, "Infering with GPU or CPU");
//DEFINE_int32(gpu_id, 0, "GPU card id");
// 这里的struct是为了在自己的项目中接收推理结果，因为推理结果是
//vector<PaddleDeploy::Result>类型，我们自己的项目中没有
struct result_local
{std::vector<uint8_t> data;// the shape of maskstd::vector<float> score;
};
//为了导出dll。
#define DLLEXPORT extern "C" _declspec(dllexport)string model_type = "seg";
PaddleDeploy::Model* model;
//设置接口
DLLEXPORT void Init(string model_filename, string cfg_file, string params_filename, int gpu_id)
{// create modelmodel = PaddleDeploy::CreateModel(model_type);// model initmodel->Init(cfg_file);// inference engine initPaddleDeploy::PaddleEngineConfig engine_config;engine_config.model_filename = model_filename;engine_config.params_filename = params_filename;engine_config.use_gpu = true;engine_config.gpu_id = gpu_id;model->PaddleEngineInit(engine_config);
}DLLEXPORT  void Predict(vector<Mat> imgs, vector<result_local>& results_back)
{cout << "enter Predict       imgs.size():   " << imgs.size() << endl;vector<cv::Mat> imgs_local;if (imgs.size() == 0){cout << "nothing pic in";}for (int i = 0; i < imgs.size(); i++){imgs_local.push_back(std::move(imgs[i]));}// predictstd::vector<PaddleDeploy::Result> results;model->Predict(imgs_local, &results, 1);for (int i = 0; i < results.size(); i++){result_local temp;vector<uint8_t> data = results[i].seg_result->label_map.data;vector<float> score = results[i].seg_result->score_map.data;temp.data = data;temp.score = score;results_back.push_back(temp);}
}//int main(int argc, char** argv) {
//  // Parsing command-line
//  google::ParseCommandLineFlags(&argc, &argv, true);
//
//  // create model
//  PaddleDeploy::Model* model = PaddleDeploy::CreateModel(FLAGS_model_type);
//
//  // model init
//  model->Init(FLAGS_cfg_file);
//
//  // inference engine init
//  PaddleDeploy::PaddleEngineConfig engine_config;
//  engine_config.model_filename = FLAGS_model_filename;
//  engine_config.params_filename = FLAGS_params_filename;
//  engine_config.use_gpu = FLAGS_use_gpu;
//  engine_config.gpu_id = FLAGS_gpu_id;
//  model->PaddleEngineInit(engine_config);
//
//  // prepare data
//  std::vector<cv::Mat> imgs;
//  imgs.push_back(std::move(cv::imread(FLAGS_image)));
//
//  // predict
//  std::vector<PaddleDeploy::Result> results;
//  model->Predict(imgs, &results, 1);
//
//  std::cout << results[0] << std::endl;
//  delete model;
//  return 0;
//}

 然后选择model_infer右键设为启动项，点击生成

在VS中新建项目

#include<iostream>
#include<opencv2/opencv.hpp>
#include<Windows.h>
using namespace std;
using namespace cv;struct result_now
{std::vector<uint8_t> data;// the shape of maskstd::vector<float> score;
};
typedef void(*ptrSub_Init)(string model_filename, string cfg_file, string params_filename, int gpu_id);
typedef void(*ptrSub_predict)(vector<cv::Mat> imgs, vector<result_now>& results_back);
HMODULE hMod = LoadLibrary("model_infer.dll");
typedef void(*ptrSub_batch_Init)(string model_filename, string cfg_file, string params_filename, int gpu_id,bool use_trt);
typedef void(*ptrSub_batch_predict)(std::vector<std::string> image_paths, vector<result_now>& results_back, int batch_size);
HMODULE hMod_batch = LoadLibrary("batch_infer.dll");
//这部分是用于接收model_infer的结果
void normal_infer()
{string imgs_path = "D:/VS_projects/test/";string cfg_file = "D:/PaddleSeg_projects/PaddleX_DLL/test/deploy.yaml";string params_filename = "D:/PaddleSeg_projects/PaddleX_DLL/test/model.pdiparams";string model_filename = "D:/PaddleSeg_projects/PaddleX_DLL/test/model.pdmodel";vector<string> img_names;glob(imgs_path, img_names);vector<Mat> imgs;for (int i = 0; i < img_names.size(); i++){Mat img = imread(img_names[i]);imgs.push_back(img);}vector<result_now> results;if (hMod != NULL){ptrSub_Init Init = (ptrSub_Init)GetProcAddress(hMod, "Init");ptrSub_predict Predict = (ptrSub_predict)GetProcAddress(hMod, "Predict");if (Init != NULL){Init(model_filename, cfg_file, params_filename, 0);}if (Predict != NULL){Predict(imgs, results);}}std::cout << results.size() << endl;std::vector<uint8_t> data = results[0].data;for (int i = 0; i < imgs[0].rows; i++){for (int j = 0; j < imgs[0].cols; j++){if (data[j + imgs[0].cols * i] == 0){continue;}imgs[0].at<Vec3b>(i, j)[0] = data[j + imgs[0].cols * i] * 100;imgs[0].at<Vec3b>(i, j)[1] = data[j + imgs[0].cols * i] * 100;imgs[0].at<Vec3b>(i, j)[2] = data[j + imgs[0].cols * i] * 100;}}std::vector<uint8_t> data1 = results[1].data;for (int i = 0; i < imgs[1].rows; i++){for (int j = 0; j < imgs[1].cols; j++){if (data1[j + imgs[1].cols * i] == 0){continue;}imgs[1].at<Vec3b>(i, j)[0] = data1[j + imgs[1].cols * i] * 100;imgs[1].at<Vec3b>(i, j)[1] = data1[j + imgs[1].cols * i] * 100;imgs[1].at<Vec3b>(i, j)[2] = data1[j + imgs[1].cols * i] * 100;}}namedWindow("re", WINDOW_AUTOSIZE);imshow("re", imgs[0]);namedWindow("re2", WINDOW_AUTOSIZE);imshow("re2", imgs[1]);waitKey(0);
}
//这部分用于接收batch_infer的结果
void batch_infer()
{string imgs_path = "D:/VS_projects/test/";string cfg_file = "D:/PaddleSeg_projects/PaddleX_DLL/test/deploy.yaml";string params_filename = "D:/PaddleSeg_projects/PaddleX_DLL/test/model.pdiparams";string model_filename = "D:/PaddleSeg_projects/PaddleX_DLL/test/model.pdmodel";vector<std::string> img_names;glob(imgs_path, img_names);vector<Mat> imgs;for (int i = 0; i < img_names.size(); i++){Mat img = imread(img_names[i]);imgs.push_back(img);}vector<result_now> results;if (hMod_batch != NULL){ptrSub_batch_Init Init = (ptrSub_batch_Init)GetProcAddress(hMod_batch, "Init");ptrSub_batch_predict Predict = (ptrSub_batch_predict)GetProcAddress(hMod_batch, "Predict");if (Init != NULL){Init(model_filename, cfg_file, params_filename,0,false);}if (Predict != NULL){Predict(img_names, results,1);}}std::cout << "local result size       "<<results.size() << endl;std::vector<uint8_t> data = results[0].data;for (int i = 0; i < imgs[0].rows; i++){for (int j = 0; j < imgs[0].cols; j++){if (data[j + imgs[0].cols * i] == 0){continue;}imgs[0].at<Vec3b>(i, j)[0] = data[j + imgs[0].cols * i] * 100;imgs[0].at<Vec3b>(i, j)[1] = data[j + imgs[0].cols * i] * 100;imgs[0].at<Vec3b>(i, j)[2] = data[j + imgs[0].cols * i] * 100;}}std::vector<uint8_t> data1 = results[1].data;for (int i = 0; i < imgs[1].rows; i++){for (int j = 0; j < imgs[1].cols; j++){if (data1[j + imgs[1].cols * i] == 0){continue;}imgs[1].at<Vec3b>(i, j)[0] = data1[j + imgs[1].cols * i] * 100;imgs[1].at<Vec3b>(i, j)[1] = data1[j + imgs[1].cols * i] * 100;imgs[1].at<Vec3b>(i, j)[2] = data1[j + imgs[1].cols * i] * 100;}}namedWindow("re", WINDOW_AUTOSIZE);imshow("re", imgs[0]);namedWindow("re2", WINDOW_AUTOSIZE);imshow("re2", imgs[1]);waitKey(0);
}
int main()
{//normal_infer();batch_infer();
}

 将上边生成的dll以及build_out\paddle_deploy中的dll一起放到新建的项目中，即可调用

补充：

如果调用batch_infer,则更改batch_infer.cpp的代码：

// Copyright (c) 2021 PaddlePaddle Authors. 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.#include <gflags/gflags.h>
#include <omp.h>
#include <memory>
#include <string>
#include <fstream>
#include<opencv2/opencv.hpp>
#include<iostream>
#include "model_deploy/common/include/paddle_deploy.h"
using namespace cv;
using namespace std;//DEFINE_string(model_filename, "D:/PaddleSeg_projects/PaddleX_DLL4/weights_info/model.pdmodel", "Path of det inference model");
//DEFINE_string(params_filename, "D:/PaddleSeg_projects/PaddleX_DLL4/weights_info/model.pdiparams", "Path of det inference params");
//DEFINE_string(cfg_file, "D:/PaddleSeg_projects/PaddleX_DLL4/weights_info/deploy.yaml", "Path of yaml file");
//DEFINE_string(model_type, "seg", "model type");
//DEFINE_string(image_list, "D:/VS_projects/test/in.txt", "Path of test image file");
//DEFINE_int32(batch_size, 1, "Batch size of infering");
//DEFINE_bool(use_gpu, true, "Infering with GPU or CPU");
//DEFINE_int32(gpu_id, 0, "GPU card id");
//DEFINE_bool(use_trt, false, "Infering with TensorRT");struct result_local
{std::vector<uint8_t> data;// the shape of maskstd::vector<float> score;
};#define DLLEXPORT extern "C" _declspec(dllexport)string model_type = "seg";
PaddleDeploy::Model* model;DLLEXPORT void Init(string model_filename, string cfg_file, string params_filename, int gpu_id,bool use_trt)
{// create modelmodel = PaddleDeploy::CreateModel(model_type);// model initmodel->Init(cfg_file);// inference engine initPaddleDeploy::PaddleEngineConfig engine_config;engine_config.model_filename = model_filename;engine_config.params_filename = params_filename;engine_config.use_gpu = true;engine_config.gpu_id = gpu_id;engine_config.use_trt = use_trt;if (use_trt) {engine_config.precision = 0;}model->PaddleEngineInit(engine_config);std::cout << "Paddle  Init  Sucess" << std::endl;
}DLLEXPORT  void Predict(std::vector<std::string> image_paths, vector<result_local>& results_back,int batch_size)
{// inferstd::vector<PaddleDeploy::Result> results;for (int i = 0; i < image_paths.size(); i += batch_size) {// Read imageresults.clear();int im_vec_size = std::min(static_cast<int>(image_paths.size()), i + batch_size);std::vector<cv::Mat> im_vec(im_vec_size - i);#pragma omp parallel for num_threads(im_vec_size - i)for (int j = i; j < im_vec_size; ++j) {im_vec[j - i] = std::move(cv::imread(image_paths[j], 1));}std::cout << "im_vec size       " << im_vec.size() << endl;model->Predict(im_vec, &results);std::vector<uint8_t> data = results[0].seg_result->label_map.data;cv::Mat img = cv::imread(image_paths[i]);for (int i = 0; i < img.rows; i++){for (int j = 0; j < img.cols; j++){if (data[j + img.cols * i] == 0){continue;}img.at<Vec3b>(i, j)[0] = data[j + img.cols * i] * 100;img.at<Vec3b>(i, j)[1] = data[j + img.cols * i] * 100;img.at<Vec3b>(i, j)[2] = data[j + img.cols * i] * 100;}}namedWindow("re", WINDOW_AUTOSIZE);imshow("re", img);waitKey(0);for (int i = 0; i < results.size(); i++){result_local temp;vector<uint8_t> data = results[i].seg_result->label_map.data;vector<float> score = results[i].seg_result->score_map.data;temp.data = data;temp.score = score;results_back.push_back(temp);}}
}//int main(int argc, char** argv) {
//  // Parsing command-line
//  google::ParseCommandLineFlags(&argc, &argv, true);
//
//  // create model
//  PaddleDeploy::Model* model = PaddleDeploy::CreateModel(FLAGS_model_type);
//
//  // model init
//  model->Init(FLAGS_cfg_file);
//
//  // inference engine init
//  PaddleDeploy::PaddleEngineConfig engine_config;
//  engine_config.model_filename = FLAGS_model_filename;
//  engine_config.params_filename = FLAGS_params_filename;
//  engine_config.use_gpu = FLAGS_use_gpu;
//  engine_config.gpu_id = FLAGS_gpu_id;
//  engine_config.use_trt = FLAGS_use_trt;
//  if (FLAGS_use_trt) {
//    engine_config.precision = 0;
//  }
//  model->PaddleEngineInit(engine_config);
//
//  // Mini-batch
//  std::vector<std::string> image_paths;
//  if (FLAGS_image_list != "") {
//    std::ifstream inf(FLAGS_image_list);
//    if (!inf) {
//      std::cerr << "Fail to open file " << FLAGS_image_list << std::endl;
//      return -1;
//    }
//    std::string image_path;
//    while (getline(inf, image_path)) {
//      image_paths.push_back(image_path);
//    }
//  }
//  // infer
//  std::vector<PaddleDeploy::Result> results;
//  for (int i = 0; i < image_paths.size(); i += FLAGS_batch_size) {
//    // Read image
//    results.clear();
//    int im_vec_size =
//        std::min(static_cast<int>(image_paths.size()), i + FLAGS_batch_size);
//    std::vector<cv::Mat> im_vec(im_vec_size - i);
//    #pragma omp parallel for num_threads(im_vec_size - i)
//    for (int j = i; j < im_vec_size; ++j) {
//      im_vec[j - i] = std::move(cv::imread(image_paths[j], 1));
//    }
//    std::cout << im_vec.size() << "      :::::::::" << endl;
//    model->Predict(im_vec, &results);
//
//    std::cout << i / FLAGS_batch_size << " group -----" << std::endl;
//    for (auto j = 0; j < results.size(); ++j) {
//      std::cout << "Result for sample " << j << std::endl;
//      std::cout << results[j] << std::endl;
//    }
//    std::cout << results.size() << std::endl;
//    std::vector<uint8_t> data = results[0].seg_result->label_map.data;
//    cv::Mat img = cv::imread(image_paths[i]);
//    for (int i = 0; i < img.rows; i++)
//    {
//        for (int j = 0; j < img.cols; j++)
//        {
//            if (data[j + img.cols * i] == 0)
//            {
//                continue;
//            }
//            img.at<Vec3b>(i, j)[0] = data[j + img.cols * i] * 100;
//            img.at<Vec3b>(i, j)[1] = data[j + img.cols * i] * 100;
//            img.at<Vec3b>(i, j)[2] = data[j + img.cols * i] * 100;
//        }
//    }
//    namedWindow("re", WINDOW_AUTOSIZE);
//    imshow("re", img);
//    waitKey(0);
//  }
//  delete model;
//  return 0;
//}

 

关于cuda11.0+vs2019

有如下改动：