深度学习之用CelebA_Spoof数据集搭建一个活体检测-训练好的模型用MNN来推理

一、模型转换准备

首先确保已完成PyTorch到ONNX的转换：深度学习之用CelebA_Spoof数据集搭建活体检测系统：模型验证与测试。这里有将PyTorch到ONNX格式的模型转换。

二、ONNX转MNN

使用MNN转换工具进行格式转换：具体的编译过程可以参考MNN的官方代码。MNN是一个轻量级的深度神经网络引擎，支持深度学习的推理与训练。适用于服务器/个人电脑/手机/嵌入式各类设备。

./MNNConvert -f ONNX --modelFile live_spoof.onnx --MNNModel live_spoof.mnn

三、C++推理工程搭建

工程结构

mnn_inference/
├── CMakeLists.txt
├── include/
│   ├── InferenceInit.h
│   └── LiveSpoofDetector.h
├── src/
│   ├── InferenceInit.cpp
│   ├── LiveSpoofDetector.cpp
│   └── CMakeLists.txt
└── third_party/MNN/

根目录下的 CMakeLists.txt

cmake_minimum_required(VERSION 3.12)
project(MNNInference)

# 设置C++标准
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CXX_STANDARD_REQUIRED ON)

# 查找OpenCV
find_package(OpenCV REQUIRED)

# 包含第三方库MNN
set(MNN_DIR ${CMAKE_SOURCE_DIR}/third_party/MNN)
include_directories(${MNN_DIR}/include)

# 添加子目录
add_subdirectory(src)

# 主可执行文件
add_executable(mnn_inference_main 
    src/main.cpp
)

# 链接库
target_link_libraries(mnn_inference_main
    PRIVATE
    inference_lib
    ${MNN_DIR}/lib/libMNN.so
    ${OpenCV_LIBS}
)

# 安装规则
install(TARGETS mnn_inference_main
    RUNTIME DESTINATION bin
)

src目录下的 CMakeLists.txt

# 添加库
add_library(inference_lib STATIC
    InferenceInit.cpp
    LiveSpoofDetector.cpp
)

# 包含目录
target_include_directories(inference_lib
    PUBLIC
    ${CMAKE_SOURCE_DIR}/include
    ${MNN_DIR}/include
    ${OpenCV_INCLUDE_DIRS}
)

# 编译选项
target_compile_options(inference_lib
    PRIVATE
    -Wall
    -O3
)

核心实现代码

将MNN读取导入模型和一些mnn_session进行预处理的公共部分抽取出来，以后可以更换不同的模型，只需要给出特定的预处理。

// InferenceInit.h
#ifndef MNN_CORE_MNN_HANDLER_H
#define MNN_CORE_MNN_HANDLER_H
#include "MNN/Interpreter.hpp"
#include "MNN/MNNDefine.h"
#include "MNN/Tensor.hpp"
#include "MNN/ImageProcess.hpp"

#include <iostream>
#include "opencv2/opencv.hpp"
#endif
#include "mylog.h"
#define LITEMNN_DEBUG
namespace mnncore
{
  class  BasicMNNHandler
  {
  protected:
    std::shared_ptr<MNN::Interpreter> mnn_interpreter;
    MNN::Session *mnn_session = nullptr;
    MNN::Tensor *input_tensor = nullptr; // assume single input.
    MNN::ScheduleConfig schedule_config;
    std::shared_ptr<MNN::CV::ImageProcess> pretreat; // init at subclass
    const char *log_id = nullptr;
    const char *mnn_path = nullptr;
    const char *mnn_model_data = nullptr;
    //int mnn_model_size = 0;

  protected:
    const int num_threads; // initialize at runtime.
    int input_batch;
    int input_channel;
    int input_height;
    int input_width;
    int dimension_type;
    int num_outputs = 1;

  protected:
    explicit BasicMNNHandler(const std::string &_mnn_path, int _num_threads = 1);
    int initialize_handler();
    std::string turnHeadDataToString(std::string headData);

    virtual ~BasicMNNHandler();

    // un-copyable
  protected:
    BasicMNNHandler(const BasicMNNHandler &) = delete; //
    BasicMNNHandler(BasicMNNHandler &&) = delete; //
    BasicMNNHandler &operator=(const BasicMNNHandler &) = delete; //
    BasicMNNHandler &operator=(BasicMNNHandler &&) = delete; //

  protected:
    virtual void transform(const cv::Mat &mat) = 0; // ? needed ?
  private:
    void print_debug_string();
  };
}

// InferenceInit.cpp
#include "mnn/core/InferenceInit.h"
namespace mnncore
{
   
   
BasicMNNHandler::BasicMNNHandler(
    const std::string &_mnn_path, int _num_threads) :
    log_id(_mnn_path.data()), mnn_path(_mnn_path.data()),num_threads(_num_threads)
{
   
   
  //initialize_handler();
}

int BasicMNNHandler::initialize_handler()
{
   
   
   std::cout<<"load  Model from file: " << mnn_path << "\n";
   mnn_interpreter = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(mnn_path));
   myLog(ERROR_, "mnn_interpreter createFromFile done!");
  
  if (nullptr == mnn_interpreter) {
   
   
		std::cout << "load centerface failed." << std::endl;
		return -1;
	}
  // 2. init schedule_config
  schedule_config.numThread = (int) num_threads;
  MNN::BackendConfig backend_config;
  backend_config.precision = MNN::BackendConfig::Precision_Low; // default Precision_High
  backend_config.memory = MNN::BackendConfig::Memory_Low;
	backend_config.power = MNN::BackendConfig::Power_Low;
  
  schedule_config.backendConfig = &backend_config;