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。
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