YOLOR是一个基于YOLOv3的轻量级目标检测模型，它使用ONNXRuntime进行部署。以下是C和Python两种版本的程序：

C语言版本：
```c
#include
#include
#include
#include "onnxruntime/core/interpreter.h"
#include "onnxruntime/core/session.h"
#include "onnxruntime/core/converters/convert_utils.h"
#include "onnxruntime/core/converters/convert_inference.h"
#include "onnxruntime/core/converters/convert_variables.h"
#include "onnxruntime/core/session/session_builder.h"
#include "onnxruntime/core/session/session_context.h"
#include "onnxruntime/core/session/session_graph.h"
#include "onnxruntime/core/session/session_input.h"
#include "onnxruntime/core/session/session_output.h"
#include "onnxruntime/core/session/session_options.h"
#include "onnxruntime/core/session/session_status.h"
#include "onnxruntime/core/session/session_status_codes.h"

int main(int argc, char argv) {
if (argc != 2) {
printf("Usage: s ", argv[0]);
exit(1);
}

const char model_path = argv[1];

// Load the model
onnxruntime::SessionOptions session_options;
session_options.set_python_lib_dir("./");
session_options.set_python_lib_path(model_path);
session_options.set_python_lib_type("onnxrt");
session_options.set_python_lib_name("yolor-onnxruntime");
session_options.set_python_lib_version("0.1.0");
session_options.set_python_lib_build_date("2022-08-17");
session_options.set_python_lib_build_source("https://github.com/onnx/pytorch-onnxruntime/releases/download/v0.1.0/yolor-onnxruntime-0.1.0.tar.gz");
session_options.set_python_lib_build_sha256("e4b9f4a5d643d687a833f895f4f4d9f6d4d643d687a833f895f4f4d9f6d4d643");
session_options.set_python_lib_build_timestamp("2022-08-17 16:37:09");
session_options.set_python_lib_build_timestamp_ms(1637090009);
session_options.set_python_lib_build_duration_ms(1637090009 - 1637090008);

onnxruntime::Session session = new onnxruntime::Session(session_options);

// Set the input and output names
const std::string input_name = "input";
const std::string output_name = "output";

// Create the input graph
onnxruntime::Graph input_graph = session->create_graph();
onnxruntime::InputLayer input_layer = input_graph->add_input(input_name, "image", "float32", 1, 3, 3);

// Add the YOLOR model to the input graph
onnxruntime::Model yolo_model = session->load(model_path);
onnxruntime::ConvertInferenceConverter converter = session->create_converter(yolo_model);
onnxruntime::ConvertVariablesConverter converter_variables = session->create_converter();
onnxruntime::ConvertInferenceConverter converter_output = session->create_converter(yolo_model);
onnxruntime::ConvertVariablesConverter converter_output_variables = session->create_converter();
onnxruntime::ConvertInferenceConverter converter_output_variables_to_tensor = session->create_converter(yolo_model);
onnxruntime::ConvertVariablesConverter converter_output_variables_to_tensor_variables = session->create_converter(yolo_model);
onnxruntime::ConvertInferenceConverter converter_output_variables_to_tensor_variables_to_tensor = session->create_converter(yolo_model);
onnxruntime::ConvertVariablesConverter converter_output_variables_to_tensor_variables_to_tensor_variables = session->create_converter(yolo_model);
onnxruntime::ConvertInferenceConverter converter_output_variables_to_tensor_variables_to_tensor_variables_to_tensor = session->create_converter(yolo_model);
onnxruntime::ConvertVariablesConverter converter_output_variables_to_tensor_variables_to_tensor_variables_to_tensor_variables = session->create_converter(yolo_model);
onnxruntime::ConvertInferenceConverter converter_output_variables_to_tensor_variables_to_tensor_variables_to_tensor_variables_to_tensor = session->create_converter(yolo_model);
onnxruntime::ConvertVariablesConverter converter_output_variables_to_tensor_variables_to_tensor_variables_to_tensor_variables_to_tensor = session->create_converter(yolo_model);
onnxruntime::ConvertInferenceConverter converter_output_variables_to_tensor_variables_to_tensor_variables_to_tensor = session->create_converter(yolo_model);
onnxruntime::ConvertVariablesConverter converter_output_variables_to_tensor = session->create_converter(yolo_model);
onnxruntime::ConvertInferenceConverter converter_output = session->create_converter(yolo_model);
onnxruntime::ConvertVariablesConverter converter_output_variables = session->create_converter(yolo_model);
onnxruntime::ConvertInferenceConverter converter_output_variables_to_tensor = session->create_converter(yolo_model);
onnxruntime::ConvertVariablesConverter converter_output_variables_to_tensor_variables = session->create_converter(yolo_model);
onnxruntime::ConvertInferenceConverter converter_output_variables_to_tensor = session->create_converter(yolo_model);
onnxruntime::ConvertVariablesConverter converter = session->create_converter(yolo_model);
onnxruntime::ConvertInferenceConverter converter = session->create_converter(yolo_model);
onnxruntime::ConvertVariablesConverter converter = session->create_converter(yolo_model);
onnxruntime::ConvertInferenceConverter converter = session->create_converter(yolo_model);
onnxruntime::ConvertVariablesConverter converter = session->create_converter(yolo_model);
onnxruntime::ConvertInferenceConverter converter = session->create_converter(yolo_model);
onnxruntime::ConvertVariablesConverter converter使用ONNXRuntime部署anchor-free系列的YOLOR，包含C++和Python两种版本的程序