南靖企业网站建设公司,wordpress文艺主题,广东做网站哪家公司好,模板建站代理【2023-2-22】FastDeploy 安装编译教程 该测试 FastDeploy CPU版本。 1. fastDeploy库编译
1.1 官方预编译库下载
预编译库下载安装
1.2 自定义CPU版本库编译
官方编译FastDeploy教程
CMakeGUI VS 2019 IDE编译FastDeploy
本人编译教程 CMAKE_CONFIGURATION_TYPES 属性设…【2023-2-22】FastDeploy 安装编译教程 该测试 FastDeploy CPU版本。 1. fastDeploy库编译
1.1 官方预编译库下载
预编译库下载安装
1.2 自定义CPU版本库编译
官方编译FastDeploy教程
CMakeGUI VS 2019 IDE编译FastDeploy
本人编译教程 CMAKE_CONFIGURATION_TYPES 属性设置为Release 请不要勾选WITH_GPU和ENABLE_TRT_BACKEND 开启ENABLE_PADDLE_BACKEND ENABLE_OPENVINO_BACKEND ENABLE_VISION 指定CMAKE_INSTALL_PREFIX 安装路径 生成fastdeploy.sln解决方案文件选择Release版本生成编译点击INSTALL-右键点击生成将编译好的SDK安装到先前指定的目录步骤⑤。
1.3 自定义GPU版本库编译
官方编译FastDeploy教程
CMakeGUI VS 2019 IDE编译FastDeploy
本人编译教程 CMAKE_CONFIGURATION_TYPES 属性设置为Release 勾选WITH_GPU和ENABLE_TRT_BACKEND 开启ENABLE_ORT_BACKEND ENABLE_PADDLE_BACKEND ENABLE_TRT_BACKEND ENABLE_OPENVINO_BACKEND ENABLE_VISION ENABLE_TEXT 设置CUDA TensorRT路径
CUDA_DIRECTORY : C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2
TRT_DRECTORY : D:/Program Files/TensorRT-8.4.3.1指定CMAKE_INSTALL_PREFIX 安装路径 生成fastdeploy.sln解决方案文件选择Release版本生成编译点击INSTALL-右键点击生成将编译好的SDK安装到先前指定的目录步骤⑤。
1.4 样例测试
picodet_l_320_coco_lcnet 模型下载
/** Description: * Version: 0.0.1* Author: chccc* Date: 2023-02-19 00:19:22* LastEditors: chccc* LastEditTime: 2023-02-20 17:23:21* FilePath: \cpp\infer.cpp*/
#include iostream
#include fastdeploy/vision.hvoid PicoDetGpuInfer(const std::string model_dir, const std::string image_file)
{std::string model_file picodet_l_320_coco_lcnet/model.pdmodel;std::string params_file picodet_l_320_coco_lcnet/model.pdiparams;std::string config_file picodet_l_320_coco_lcnet/infer_cfg.yml;;auto option fastdeploy::RuntimeOption();option.UseGpu(0);fastdeploy::vision::detection::PicoDet model fastdeploy::vision::detection::PicoDet(model_file, params_file, config_file, option);std::couttypeid(model).name()std::endl;if (!model.Initialized()) {printf([%s][%d] Error: fastdeploy::vision::detection::PicoDet initialized failed !\n, __func__, __LINE__);return;}auto im cv::imread(image_file);auto im_bak im.clone();auto start std::chrono::system_clock::now();fastdeploy::vision::DetectionResult result;if (!model.Predict(im, result)) {printf([%s][%d] Error: Failed to predict !\n, __func__, __LINE__);return;}//std::cout res.Str() std::endl;auto end std::chrono::system_clock::now();auto duration std::chrono::duration_caststd::chrono::microseconds(end - start);double costTime double(duration.count()) * std::chrono::microseconds::period::num / std::chrono::microseconds::period::den;//printf([%s][%d] model.Predict success, cost time: %lf s \n, __func__, __LINE__, costTime);std::cout std::endl;float score_thereshold 0.8;int line_size 2;float font_size 1;auto vis_image fastdeploy::vision::Visualize::VisDetection(im_bak, result, score_thereshold, line_size, font_size);std::string vis_image_path ./images/vis_result.jpg;cv::imwrite(vis_image_path, vis_image);printf([%s][%d] Visualized result saved in %s !\n, __func__, __LINE__, vis_image_path.c_str());
}int main()
{std::cout TEST std::endl;std::string model_dir ./models/;;std::string image_file ./images/1.jpg;//计时auto start std::chrono::system_clock::now();PicoDetGpuInfer(model_dir, image_file);//计时auto end std::chrono::system_clock::now();auto duration std::chrono::duration_caststd::chrono::microseconds(end - start);double costTime double(duration.count()) * std::chrono::microseconds::period::num / std::chrono::microseconds::period::den;printf([%s][%d] Model infer success, cost time: %lf s \n, __func__, __LINE__, costTime);std::cout Finished std::endl;
}
