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https://github.com/PaddlePaddle/FastDeploy.git
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dc13eb7049
* 对RKNPU2后端进行修改，当模型为非量化模型时，不在NPU执行normalize操作，当模型为量化模型时，在NUP上执行normalize操作 * 更新RKNPU2框架，输出数据的数据类型统一返回fp32类型 * 更新scrfd，拆分disable_normalize和disable_permute * 更新scrfd代码，支持量化 * 更新scrfd python example代码 * 更新模型转换代码，支持量化模型 * 更新文档 * 按照要求修改 * 按照要求修改 * 修正模型转换文档 * 更新一下转换脚本
86 lines
2.4 KiB
C++
86 lines
2.4 KiB
C++
#include <iostream>
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#include <string>
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#include "fastdeploy/vision.h"
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void ONNXInfer(const std::string& model_dir, const std::string& image_file) {
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std::string model_file = model_dir + "/scrfd_500m_bnkps_shape640x640.onnx";
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std::string params_file;
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auto option = fastdeploy::RuntimeOption();
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option.UseCpu();
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auto format = fastdeploy::ModelFormat::ONNX;
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auto model = fastdeploy::vision::facedet::SCRFD(
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model_file, params_file, option, format);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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fastdeploy::TimeCounter tc;
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tc.Start();
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auto im = cv::imread(image_file);
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fastdeploy::vision::FaceDetectionResult res;
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if (!model.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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auto vis_im = fastdeploy::vision::Visualize::VisFaceDetection(im, res);
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tc.End();
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tc.PrintInfo("SCRFD in ONNX");
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std::cout << res.Str() << std::endl;
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cv::imwrite("infer_onnx.jpg", vis_im);
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std::cout
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<< "Visualized result saved in ./infer_onnx.jpg"
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<< std::endl;
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}
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void RKNPU2Infer(const std::string& model_dir, const std::string& image_file) {
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std::string model_file = model_dir + "/scrfd_500m_bnkps_shape640x640_rk3588.rknn";
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std::string params_file;
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auto option = fastdeploy::RuntimeOption();
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option.UseRKNPU2();
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auto format = fastdeploy::ModelFormat::RKNN;
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auto model = fastdeploy::vision::facedet::SCRFD(model_file, params_file, option, format);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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model.DisableNormalize();
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model.DisablePermute();
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fastdeploy::TimeCounter tc;
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tc.Start();
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auto im = cv::imread(image_file);
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fastdeploy::vision::FaceDetectionResult res;
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if (!model.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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auto vis_im = fastdeploy::vision::VisFaceDetection(im, res);
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tc.End();
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tc.PrintInfo("SCRFD in RKNN");
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std::cout << res.Str() << std::endl;
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cv::imwrite("infer_rknn.jpg", vis_im);
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std::cout
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<< "Visualized result saved in ./infer_rknn.jpg"
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<< std::endl;
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}
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int main(int argc, char* argv[]) {
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if (argc < 3) {
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std::cout
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<< "Usage: infer_demo path/to/model_dir path/to/image run_option, "
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"e.g ./infer_model ./picodet_model_dir ./test.jpeg"
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<< std::endl;
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return -1;
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}
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RKNPU2Infer(argv[1], argv[2]);
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ONNXInfer(argv[1], argv[2]);
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return 0;
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} |