jetson nx上部署paddleseg框架下biesenetV2算法训练自己的数据集产生的模型，推理速度太慢.预测程序用的是paddleseg中的C++部署示例，

[huangwan-jiayi]

这是我参考的例程， 这是我的推理时间： 以下是全部代码：

#include #include #include #include #include #include <time.h> #include <gflags/gflags.h> #include <glog/logging.h> using namespace std;

#include "paddle/include/paddle_inference_api.h" #include "yaml-cpp/yaml.h" #include "opencv2/core.hpp" #include "opencv2/imgproc.hpp" #include "opencv2/highgui.hpp"

DEFINE_string(model_dir, "", "Directory of the inference model. " "It constains deploy.yaml and infer models"); DEFINE_string(img_path, "", "Path of the test image."); DEFINE_bool(use_cpu, false, "Wether use CPU. Default: use GPU."); DEFINE_bool(use_trt, false, "Wether enable TensorRT when use GPU. Defualt: false."); DEFINE_bool(use_mkldnn, false, "Wether enable MKLDNN when use CPU. Defualt: false."); DEFINE_string(save_dir, "", "Directory of the output image.");

typedef struct YamlConfig { std::string model_file; std::string params_file; bool is_normalize; }YamlConfig;

YamlConfig load_yaml(const std::string& yaml_path) { YAML::Node node = YAML::LoadFile(yaml_path); std::string model_file = node["Deploy"]["model"].asstd::string(); std::string params_file = node["Deploy"]["params"].asstd::string(); bool is_normalize = false; if (node["Deploy"]["transforms"] && node["Deploy"]["transforms"][0]["type"].asstd::string() == "Normalize") { is_normalize = true; }

YamlConfig yaml_config = {model_file, params_file, is_normalize}; return yaml_config; }

std::shared_ptr<paddle_infer::Predictor> create_predictor(const YamlConfig& yaml_config) { std::string& model_dir = FLAGS_model_dir;

paddle_infer::Config infer_config; infer_config.SetModel(model_dir + "/" + yaml_config.model_file, model_dir + "/" + yaml_config.params_file); infer_config.EnableMemoryOptim();

if (FLAGS_use_cpu) { LOG(INFO) << "Use CPU"; if (FLAGS_use_mkldnn) { // TODO(jc): fix the bug //infer_config.EnableMKLDNN(); infer_config.SetCpuMathLibraryNumThreads(5); } } else { LOG(INFO) << "Use GPU"; infer_config.EnableUseGpu(500, 0); if (FLAGS_use_trt) { infer_config.EnableTensorRtEngine(1 << 30, 1, 1, paddle_infer::PrecisionType::kFloat32, false, false); } }

auto predictor = paddle_infer::CreatePredictor(infer_config); return predictor; }

void hwc_img_2_chw_data(const cv::Mat& hwc_img, float* data) { int rows = hwc_img.rows; int cols = hwc_img.cols; int chs = hwc_img.channels(); for (int i = 0; i < chs; ++i) { cv::extractChannel(hwc_img, cv::Mat(rows, cols, CV_32FC1, data + i * rows * cols), i); } }

cv::Mat read_process_image(bool is_normalize) { cv::Mat img = cv::imread(FLAGS_img_path, cv::IMREAD_COLOR); cv::cvtColor(img, img, cv::COLOR_BGR2RGB); if (is_normalize) { img.convertTo(img, CV_32F, 1.0 / 255, 0); img = (img - 0.5) / 0.5; } return img; }

int main(int argc, char *argv[]) { google::ParseCommandLineFlags(&argc, &argv, true); if (FLAGS_model_dir == "") { LOG(FATAL) << "The model_dir should not be empty."; }

// Load yaml std::string yaml_path = FLAGS_model_dir + "/deploy.yaml"; YamlConfig yaml_config = load_yaml(yaml_path);

// Prepare data cv::Mat img = read_process_image(yaml_config.is_normalize); int rows = img.rows; int cols = img.cols; int chs = img.channels(); std::vector input_data(1 * 3 * 1080 *1920, 0.0f); hwc_img_2_chw_data(img, input_data.data());

// Create predictor auto predictor = create_predictor(yaml_config);

// Set input auto input_names = predictor->GetInputNames(); auto input_t = predictor->GetInputHandle(input_names[0]); std::vector input_shape = {1, 3, 1080, 1920}; input_t->Reshape(input_shape); input_t->CopyFromCpu(input_data.data());

// Run clock_t start,end; start=clock(); predictor->Run(); end=clock(); cout << "The run time is:" << (double)(end-start)/CLOCKS_PER_SEC << "s" << endl; // Get output auto output_names = predictor->GetOutputNames(); auto output_t = predictor->GetOutputHandle(output_names[0]); std::vector output_shape = output_t->shape(); // n * h * w int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1, std::multiplies()); std::vector<int64_t> out_data(out_num); output_t->CopyToCpu(out_data.data());

// Get pseudo image std::vector<uint8_t> out_data_u8(out_num); for (int i = 0; i < out_num; i++) { out_data_u8[i] = static_cast<uint8_t>(out_data[i]); } cv::Mat out_gray_img(output_shape[1], output_shape[2], CV_8UC1, out_data_u8.data()); cv::Mat out_eq_img; cv::equalizeHist(out_gray_img, out_eq_img); cv::imwrite("out_img.jpg", out_eq_img);

LOG(INFO) << "Finish"; } paddle 版本2.1.2： nx 环境： 输入图像大小为1080*1920。

[funny000]

请问你这个问题解决了吗