window10+paddleseg+C++部署
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 mask std::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 model model = PaddleDeploy::CreateModel(model_type); // model init model->Init(cfg_file); // inference engine init PaddleDeploy::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])); } // predict std::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 mask std::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 mask std::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 model model = PaddleDeploy::CreateModel(model_type); // model init model->Init(cfg_file); // inference engine init PaddleDeploy::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) { // infer std::vector<PaddleDeploy::Result> results; for (int i = 0; i < image_paths.size(); i += batch_size) { // Read image results.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：
参考：
工业党福利：使用PaddleX高效实现指针型仪表读取系列文章
