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【推理引擎】从源码看ONNXRuntime的执行流程

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目录 前言 准备工作 构造 InferenceSession 对象 初始化 让模型 Run 总结 前言 在上一篇博客中:【推理引擎】ONNXRuntime 的架构设计,主要从文档上对ONNXRuntime的执行流程进行了梳理,但是想

目录
  • 前言
  • 准备工作
  • 构造 InferenceSession 对象 & 初始化
  • 让模型 Run
  • 总结

前言

在上一篇博客中:【推理引擎】ONNXRuntime 的架构设计,主要从文档上对ONNXRuntime的执行流程进行了梳理,但是想要深入理解,还需从源码角度进行分析。

本文以目标检测模型NanoDet作为分析的基础,部分代码主要参考:超轻量级NanoDet MNN/TNN/NCNN/ONNXRuntime C++工程记录 - DefTruth的文章 - 知乎,在此表示感谢!

准备工作

OrtHandlerBase 是用来操控 ONNXRuntime 的基类,各种网络模型都可以通过继承该类进而拥有 ONNXRuntime 的使用权限,比如 NanoDet;同时,NanoDet还可以扩展独属于自己的方法和成员变量,以方便推理前后的预处理和后处理工作。

构造NanoDet对象时,会自动调用OrtHandlerBase的构造方法,在构造方法内部会首先初始化一些必要的成员变量(Ort::EnvOrt::SessionOptions),这两个变量主要用于初始化 Ort::Session:

ort_env = Ort::Env(ORT_LOGGING_LEVEL_ERROR, log_id);

Ort::SessionOptions session_options;
session_options.SetIntraOpNumThreads(num_threads);
session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
session_options.SetLogSeverityLevel(4);

ort_session = new Ort::Session(ort_env, onnx_model_path, session_options);

构造 InferenceSession 对象 & 初始化

在构造 Ort::Session 对象的过程中,会调用ONNXRuntime -> onnxruntime_cxx_inline.h 中的API:

// include/onnxruntime/core/session/onnxruntime_cxx_inline.h
inline Session::Session(Env& env, const ORTCHAR_T* model_path, const SessionOptions& options) {
  ThrowOnError(GetApi().CreateSession(env, model_path, options, &p_));
}

GetApi() 是在 onnxruntime_cxx_api.h 中定义的:

// include/onnxruntime/core/session/onnxruntime_cxx_api.h

// This returns a reference to the OrtApi interface in use
inline const OrtApi& GetApi() { return *Global<void>::api_; }

// 其中 Global 的定义如下:
template <typename T>
struct Global {
  static const OrtApi* api_;
};

这里面主要定义了静态常量指针OrtApi*OrtApi是在 onnxruntime_c_api.h 中定义的:

// include/onnxruntime/core/session/onnxruntime_c_api.h

// All C API functions are defined inside this structure as pointers to functions.
// Call OrtApiBase::GetApi to get a pointer to it
struct OrtApi;
typedef struct OrtApi OrtApi;

struct OrtApi{
  ...
  // 以 CreateSession 为例:
  ORT_API2_STATUS(CreateSession, _In_ const OrtEnv* env, _In_ const ORTCHAR_T* model_path,
                  _In_ const OrtSessionOptions* options, _Outptr_ OrtSession** out);

  // 展开ORT_API2_STATUS宏:
  // _Check_return_ _Ret_maybenull_ OrtStatusPtr(ORT_API_CALL* CreateSession)(const OrtEnv* env, 
  //                                                                          const char* model_path, 
  //                                                                          const OrtSessionOptions* options, 
  //                                                                          OrtSession** out) NO_EXCEPTION ORT_MUST_USE_RESULT;
  
  ...
}

相应地,在 onnxruntime_c_api.cc 文件中定义了 CreateSesssion 的实现:

// onnxruntime/core/session/onnxruntime_c_api.cc

ORT_API_STATUS_IMPL(OrtApis::CreateSession, _In_ const OrtEnv* env, _In_ const ORTCHAR_T* model_path,
                    _In_ const OrtSessionOptions* options, _Outptr_ OrtSession** out) {
  API_IMPL_BEGIN
  std::unique_ptr<onnxruntime::InferenceSession> sess;
  OrtStatus* status = nullptr;
  *out = nullptr;

  ORT_TRY {
    ORT_API_RETURN_IF_ERROR(CreateSessionAndLoadModel(options, env, model_path, nullptr, 0, sess));
    ORT_API_RETURN_IF_ERROR(InitializeSession(options, sess));

    *out = reinterpret_cast<OrtSession*>(sess.release());
  }
  ORT_CATCH(const std::exception& e) {
    ORT_HANDLE_EXCEPTION([&]() {
      status = OrtApis::CreateStatus(ORT_FAIL, e.what());
    });
  }

  return status;
  API_IMPL_END
}

到此,我们已经定位到CreateSession的具体实现内容,可以发现它主要由两个部分组成:CreateSessionAndLoadModelInitializeSession,接下来分析这两个函数。

CreateSessionAndLoadModel 的名字就可以看出,这个函数主要负责创建 Session,以及加载模型:

// onnxruntime/core/session/onnxruntime_c_api.cc

// provider either model_path, or modal_data + model_data_length.
// 也就是说,共有两种方式用来读取模型:一种是根据ONNX模型路径;另一种时从模型数据缓冲(Model data buffer)中读取,并且需要指定模型大小(Model data buffer size)
static ORT_STATUS_PTR CreateSessionAndLoadModel(_In_ const OrtSessionOptions* options,
                                                _In_ const OrtEnv* env,
                                                _In_opt_z_ const ORTCHAR_T* model_path,
                                                _In_opt_ const void* model_data,
                                                size_t model_data_length,
                                                std::unique_ptr<onnxruntime::InferenceSession>& sess) {
  // quick check here to decide load path. InferenceSession will provide error message for invalid values.
  const Env& os_env = Env::Default();  // OS environment (注意:OS environment != ORT environment)
  bool load_config_from_model =
      os_env.GetEnvironmentVar(inference_session_utils::kOrtLoadConfigFromModelEnvVar) == "1";
  
  // 创建 InferenceSession
  if (load_config_from_model) {
    if (model_path != nullptr) {
      sess = std::make_unique<onnxruntime::InferenceSession>(
          options == nullptr ? onnxruntime::SessionOptions() : options->value,
          env->GetEnvironment(),
          model_path);
    } else {
      sess = std::make_unique<onnxruntime::InferenceSession>(
          options == nullptr ? onnxruntime::SessionOptions() : options->value,
          env->GetEnvironment(),
          model_data, static_cast<int>(model_data_length));
    }
  } else {
    sess = std::make_unique<onnxruntime::InferenceSession>(
        options == nullptr ? onnxruntime::SessionOptions() : options->value,
        env->GetEnvironment());
  }

  // Add custom domains
  if (options && !options->custom_op_domains_.empty()) {
    ORT_API_RETURN_IF_STATUS_NOT_OK(sess->AddCustomOpDomains(options->custom_op_domains_));
  }

  // Finish load
  if (load_config_from_model) {
    ORT_API_RETURN_IF_STATUS_NOT_OK(sess->Load());
  } else {
    if (model_path != nullptr) {
      ORT_API_RETURN_IF_STATUS_NOT_OK(sess->Load(model_path));
    } else {
      ORT_API_RETURN_IF_STATUS_NOT_OK(sess->Load(model_data, static_cast<int>(model_data_length)));
    }
  }

  return nullptr;
}

接下来深入到 sess->load() 中,这里面经历了多层重载函数,最终目标是为InferenceSession的成员变量model_(ClassType: std::shared_ptronnxruntime::Model)赋值:

// onnxruntime/core/session/onnxruntime_c_api.cc

common::Status InferenceSession::Load(const std::string& model_uri) {
  std::string model_type = session_options_.config_options.GetConfigOrDefault(kOrtSessionOptionsConfigLoadModelFormat, "");
  bool has_explicit_type = !model_type.empty();
  
  // 判断是否为 ORT 类型的 Model
  if ((has_explicit_type && model_type == "ORT") ||
      (!has_explicit_type && fbs::utils::IsOrtFormatModel(model_uri))) {
    return LoadOrtModel(model_uri);
  }

  if (is_model_proto_parsed_) {
    return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL,
                           "ModelProto corresponding to the model to be loaded has already been parsed. "
                           "Invoke Load().");
  }

  return Load<char>(model_uri);
}

template <typename T>
common::Status InferenceSession::Load(const std::basic_string<T>& model_uri) {
  model_location_ = ToWideString(model_uri);
  // 这里定义了一个 lambda 函数
  auto loader = [this](std::shared_ptr<onnxruntime::Model>& model) {
    LoadInterOp(model_location_, interop_domains_, [&](const char* msg) { LOGS(*session_logger_, WARNING) << msg; });
    for (const auto& domain : interop_domains_) {
      ORT_RETURN_IF_ERROR(AddCustomOpDomains({domain.get()}));
    }
    return onnxruntime::Model::Load(model_location_, model, HasLocalSchema() ? &custom_schema_registries_ : nullptr,
                                    *session_logger_);
  };

  common::Status st = Load(loader, "model_loading_uri");

  return Status::OK();
}

common::Status InferenceSession::Load(std::function<common::Status(std::shared_ptr<Model>&)> loader,
                                      const std::string& event_name) {
  std::lock_guard<onnxruntime::OrtMutex> l(session_mutex_);
  // 关键代码
  std::shared_ptr<onnxruntime::Model> p_tmp_model;
  status = loader(p_tmp_model);
  model_ = p_tmp_model;
  status = DoPostLoadProcessing(*model_);
  
  is_model_loaded_ = true;
  return status;
}

需要注意的是,onnxruntime::Model 不同于 onnxruntime::Graph,Graph 只是 Model 的一个成员变量,Model 中还包含其它基础信息,比如 version、domain、author 和 license 等内容。

在创建完 InferenceSession 后,需要进行初始化操作(InitializeSession):

// onnxruntime/core/session/onnxruntime_c_api.cc

static ORT_STATUS_PTR InitializeSession(_In_ const OrtSessionOptions* options,
                                        _In_ std::unique_ptr<::onnxruntime::InferenceSession>& sess,
                                        _Inout_opt_ OrtPrepackedWeightsContainer* prepacked_weights_container = nullptr) {
  // 创建 Providers
  std::vector<std::unique_ptr<IExecutionProvider>> provider_list;
  if (options) {
    for (auto& factory : options->provider_factories) {
      auto provider = factory->CreateProvider();
      provider_list.push_back(std::move(provider));
    }
  }

  // 注册 Providers 到 InferenceSession 中
  for (auto& provider : provider_list) {
    if (provider) {
      ORT_API_RETURN_IF_STATUS_NOT_OK(sess->RegisterExecutionProvider(std::move(provider)));
    }
  }

  if (prepacked_weights_container != nullptr) {
    ORT_API_RETURN_IF_STATUS_NOT_OK(sess->AddPrePackedWeightsContainer(
        reinterpret_cast<PrepackedWeightsContainer*>(prepacked_weights_container)));
  }
  
  // 初始化 InferenceSession
  ORT_API_RETURN_IF_STATUS_NOT_OK(sess->Initialize());

  return nullptr;
}

接下来,深入到 InferenceSession 的 Initialize() 函数中,这个函数水很深,需要分为几个小的模块来分析。

// onnxruntime/core/session/inference_session.cc

common::Status InferenceSession::Initialize() {
  ...

  bool have_cpu_ep = false;
  
  // 这里使用 {} 可以提前释放 session_mutex_,不必等到退出Initialize函数才释放,可提升效率
  {   
    std::lock_guard<onnxruntime::OrtMutex> initial_guard(session_mutex_);
    // 判断模型是否已被加载
    if (!is_model_loaded_) {    
      LOGS(*session_logger_, ERROR) << "Model was not loaded";
      return common::Status(common::ONNXRUNTIME, common::FAIL, "Model was not loaded.");
    }

    if (is_inited_) {  // 判断是否已经初始化,如果已经初始化就可以直接退出Initialize函数了
      LOGS(*session_logger_, INFO) << "Session has already been initialized.";
      return common::Status::OK();
    }
    
    // 判断是否已经设置 CPU EP 来兜底,如果忘记设置,后面会自动添加
    have_cpu_ep = execution_providers_.Get(onnxruntime::kCpuExecutionProvider) != nullptr;
  }

  if (!have_cpu_ep) {
    LOGS(*session_logger_, INFO) << "Adding default CPU execution provider.";
    CPUExecutionProviderInfo epi{session_options_.enable_cpu_mem_arena};
    auto p_cpu_exec_provider = std::make_unique<CPUExecutionProvider>(epi);
    ORT_RETURN_IF_ERROR_SESSIONID_(RegisterExecutionProvider(std::move(p_cpu_exec_provider)));
  }
  ...
}

以上代码确保了 EPs(复数,多个EP,hhh) 已被正常设置(主要是CPU已经被用作兜底),接下来从 Ort 环境中读取共享的分配器(shared allocators),并更新 EPs:

// onnxruntime/core/session/inference_session.cc

common::Status InferenceSession::Initialize() {
  ...

  std::string use_env_allocators = session_options_.config_options.GetConfigOrDefault(kOrtSessionOptionsConfigUseEnvAllocators,
                                                                                      "0");
  if (use_env_allocators == "1") {
    LOGS(*session_logger_, INFO) << "This session will use the allocator registered with the environment.";
    UpdateProvidersWithSharedAllocators();    // 更新 EPs
  }

  ...

接下来需要设定 SessionState,需要注意:SessionState 只能被 InferenceSession 修改,

// onnxruntime/core/session/inference_session.cc

common::Status InferenceSession::Initialize() {
  ...

  session_state_ = std::make_unique<SessionState>(
      model_->MainGraph(),
      execution_providers_,
      session_options_.enable_mem_pattern && session_options_.execution_mode == ExecutionMode::ORT_SEQUENTIAL,
      GetIntraOpThreadPoolToUse(),
      GetInterOpThreadPoolToUse(),
      data_transfer_mgr_,
      *session_logger_,
      session_profiler_,
      session_options_.use_deterministic_compute,
      session_options_.enable_mem_reuse,
      prepacked_weights_container_);
  
  ...
}

接下来从EPs实例中收集内核注册表(kernel registries),内核注册表分为两类:

  1. Custom execution provider type specific kernel registries. 》》 比如CUDA EP
  2. Common execution provider type specific kernel registries. 》》 比如CPU EP

这两类注册表的优先级并不相同,前者要高于后者。

// onnxruntime/core/session/inference_session.cc

common::Status InferenceSession::Initialize() {
  ...

  ORT_RETURN_IF_ERROR_SESSIONID_(kernel_registry_manager_.RegisterKernels(execution_providers_));
  
  ...
}

在 KernelRegistryManager 中注册完注册表之后,开始执行非常重要的图优化:

// onnxruntime/core/session/inference_session.cc

common::Status InferenceSession::Initialize() {
  ...

  // add predefined transformers
  // 添加预先定义的变换
  ORT_RETURN_IF_ERROR_SESSIONID_(AddPredefinedTransformers(graph_transformation_mgr_,
                                                            session_options_.graph_optimization_level,
                                                            saving_runtime_optimizations));

  // apply any transformations to the main graph and any subgraphs
  // 在主图和子图上执行所有的优化Pass,并且根据各个EP的能力对graph进行子图切分
  ORT_RETURN_IF_ERROR_SESSIONID_(TransformGraph(graph, graph_transformation_mgr_,
                                                execution_providers_, kernel_registry_manager_,
                                                insert_cast_transformer_,
                                                *session_state_,
                                                saving_ort_format));

  // now that all the transforms are done, call Resolve on the main graph. this will recurse into the subgraphs.
  // 所有的图变换都已经执行完毕,然后调用 Resolve() 函数
  ORT_RETURN_IF_ERROR_SESSIONID_(graph.Resolve());

  // Update temporary copies of metadata, input- and output definitions to the same state as the resolved graph
  ORT_RETURN_IF_ERROR_SESSIONID_(SaveModelMetadata(*model_));

  ...
}

还有一些结尾工作:

// onnxruntime/core/session/inference_session.cc

common::Status InferenceSession::Initialize() {
  ...

  ORT_RETURN_IF_ERROR_SESSIONID_(
      session_state_->FinalizeSessionState(model_location_, kernel_registry_manager_,
                                            session_options_,
                                            serialized_session_state,
                                            // need to keep the initializers if saving the optimized model
                                            !saving_model,
                                            saving_ort_format));
  
  // Resolve memory pattern flags of the main graph and subgraph session states
  ResolveMemoryPatternFlags(*session_state_);

  // 在 session 创建完成之后,分别调用各个EP的OnSessionInitializationEnd方法,这一步主要为EP提供一个机会,进行选择性地同步或者清理临时资源
  // 从而减少内存占用,确保第一次运行时足够快
  if (status.IsOK()) {
    for (auto& xp : execution_providers_) {
      auto end_status = xp->OnSessionInitializationEnd();
      if (status.IsOK()) {
        status = end_status;
      }
    }
  }
  
  return status;
}
让模型 Run

通过上一个阶段,已经成功构造出 NanoDet 对象,接下来需要输入图像,并由 NanoDet 来执行:

// 
std::vector<types::BoxF> detected_boxes;
cv::Mat img_bgr = cv::imread(test_img_path);
nanodet->detect(img_bgr, detected_boxes);

detect 函数内部:

void NanoDet::detect(const cv::Mat &mat, std::vector<types::BoxF> &detected_boxes,
                     float score_threshold, float iou_threshold,
                     unsigned int topk, unsigned int nms_type)
{
    if (mat.empty()) return;
    auto img_height = static_cast<float>(mat.rows);
    auto img_width = static_cast<float>(mat.cols);
    const int target_height = (int) input_node_dims.at(2);
    const int target_width = (int) input_node_dims.at(3);

    // 0. resize & unscale
    cv::Mat mat_rs;
    NanoScaleParams scale_params;
    this->resize_unscale(mat, mat_rs, target_height, target_width, scale_params);

    // 1. make input tensor
    Ort::Value input_tensor = this->transform(mat_rs);
    
    // 2. inference scores & boxes.
    auto output_tensors = ort_session->Run(
        Ort::RunOptions{nullptr}, input_node_names.data(),
        &input_tensor, 1, output_node_names.data(), num_outputs
    );
    // 3. rescale & exclude.
    std::vector<types::BoxF> bbox_collection;
    this->generate_bboxes(scale_params, bbox_collection, output_tensors, score_threshold, img_height, img_width);
    // 4. hard|blend|offset nms with topk.
    this->nms(bbox_collection, detected_boxes, iou_threshold, topk, nms_type);
}

其中,第 0 和 1 步是模型输入的预处理,这里不再深入介绍,想要了解可参考源码。接下来重点对第 2 步的 ort_seesion->Run() 进行深入剖析。

// include/onnxruntime/core/session/onnxruntime_cxx_inline.h

inline std::vector<Value> Session::Run(const RunOptions& run_options, const char* const* input_names, const Value* input_values, size_t input_count,
                                       const char* const* output_names, size_t output_names_count) {
  std::vector<Ort::Value> output_values;
  for (size_t i = 0; i < output_names_count; i++)
    output_values.emplace_back(nullptr);
  Run(run_options, input_names, input_values, input_count, output_names, output_values.data(), output_names_count);
  return output_values;
}

inline void Session::Run(const RunOptions& run_options, const char* const* input_names, const Value* input_values, size_t input_count,
                         const char* const* output_names, Value* output_values, size_t output_count) {
  static_assert(sizeof(Value) == sizeof(OrtValue*), "Value is really just an array of OrtValue* in memory, so we can reinterpret_cast safely");
  auto ort_input_values = reinterpret_cast<const OrtValue**>(const_cast<Value*>(input_values));
  auto ort_output_values = reinterpret_cast<OrtValue**>(output_values);
  ThrowOnError(GetApi().Run(p_, run_options, input_names, ort_input_values, input_count, output_names, output_count, ort_output_values));
}

又到了熟悉的环节,GetApi()可参考上一章节的内容,直接到 onnxruntime_c_api.cc 中查看 Run 函数对应的实现:

// onnxruntime/core/session/onnxruntime_c_api.cc

ORT_API_STATUS_IMPL(OrtApis::Run, _Inout_ OrtSession* sess, _In_opt_ const OrtRunOptions* run_options,
                    _In_reads_(input_len) const char* const* input_names,
                    _In_reads_(input_len) const OrtValue* const* input, size_t input_len,
                    _In_reads_(output_names_len) const char* const* output_names1, size_t output_names_len,
                    _Inout_updates_all_(output_names_len) OrtValue** output) {
  API_IMPL_BEGIN
  // 获取 inferencesession
  auto session = reinterpret_cast<::onnxruntime::InferenceSession*>(sess);
  const int queue_id = 0;
  
  // 模型输入:feed_names & feeds
  std::vector<std::string> feed_names(input_len);
  std::vector<OrtValue> feeds(input_len);

  for (size_t i = 0; i != input_len; ++i) {
    if (input_names[i] == nullptr || input_names[i][0] == '\0') {
      return OrtApis::CreateStatus(ORT_INVALID_ARGUMENT, "input name cannot be empty");
    }

    feed_names[i] = input_names[i];
    auto& ort_value = feeds[i] = *reinterpret_cast<const ::OrtValue*>(input[i]);

    if (ort_value.Fence()) ort_value.Fence()->BeforeUsingAsInput(onnxruntime::kCpuExecutionProvider, queue_id);
  }

  // 模型输出:output_names & fetches
  std::vector<std::string> output_names(output_names_len);
  for (size_t i = 0; i != output_names_len; ++i) {
    if (output_names1[i] == nullptr || output_names1[i][0] == '\0') {
      return OrtApis::CreateStatus(ORT_INVALID_ARGUMENT, "output name cannot be empty");
    }
    output_names[i] = output_names1[i];
  }

  std::vector<OrtValue> fetches(output_names_len);
  for (size_t i = 0; i != output_names_len; ++i) {
    if (output[i] != nullptr) {
      ::OrtValue& value = *(output[i]);
      if (value.Fence())
        value.Fence()->BeforeUsingAsOutput(onnxruntime::kCpuExecutionProvider, queue_id);
      fetches[i] = value;
    }
  }
  
  // 调用 InferenceSession 的 Run 函数,执行推理
  Status status;
  if (run_options == nullptr) {
    OrtRunOptions op;
    status = session->Run(op, feed_names, feeds, output_names, &fetches, nullptr);
  } else {
    status = session->Run(*run_options, feed_names, feeds, output_names, &fetches, nullptr);
  }
  
  // Run 结束后,将 fetches 中的内容取出放到 output 中
  if (!status.IsOK())
    return ToOrtStatus(status);
  for (size_t i = 0; i != output_names_len; ++i) {
    ::OrtValue& value = fetches[i];
    if (value.Fence())
      value.Fence()->BeforeUsingAsInput(onnxruntime::kCpuExecutionProvider, queue_id);
    if (output[i] == nullptr) {
      output[i] = new OrtValue(value);
    }
  }
  return nullptr;
  API_IMPL_END
}

进入到 InferenceSession::Run 的内部:

Status InferenceSession::Run(const RunOptions& run_options,
                             const std::vector<std::string>& feed_names, const std::vector<OrtValue>& feeds,
                             const std::vector<std::string>& output_names, std::vector<OrtValue>* p_fetches,
                             const std::vector<OrtDevice>* p_fetches_device_info) {

  std::vector<IExecutionProvider*> exec_providers_to_stop;
  exec_providers_to_stop.reserve(execution_providers_.NumProviders());

  std::vector<AllocatorPtr> arenas_to_shrink;

  // 验证输入输出,并由 FeedsFetchesManager 进行管理
  ORT_RETURN_IF_ERROR_SESSIONID_(ValidateInputs(feed_names, feeds));
  ORT_RETURN_IF_ERROR_SESSIONID_(ValidateOutputs(output_names, p_fetches));
  FeedsFetchesInfo info(feed_names, output_names, session_state_->GetOrtValueNameIdxMap());
  FeedsFetchesManager feeds_fetches_manager{std::move(info)};
  
  // current_num_runs_ 的类型是:std::atomic<int>,表示并行运行 EP 的数量
  ++current_num_runs_; 
  
  // info all execution providers InferenceSession:Run started
  for (auto& xp : execution_providers_) {
    // call OnRunStart and add to exec_providers_to_stop if successful
    auto start_func = [&xp, &exec_providers_to_stop]() {
      auto status = xp->OnRunStart();
      if (status.IsOK())
        exec_providers_to_stop.push_back(xp.get());

      return status;
    };

    ORT_CHECK_AND_SET_RETVAL(start_func());
  }
  
  if (run_options.only_execute_path_to_fetches) {
    session_state_->UpdateToBeExecutedNodes(feeds_fetches_manager.GetFeedsFetchesInfo().fetches_mlvalue_idxs);
  }

  session_state_->IncrementGraphExecutionCounter();
  
  // execute the graph
  ORT_CHECK_AND_SET_RETVAL(utils::ExecuteGraph(*session_state_, feeds_fetches_manager, feeds, *p_fetches,
                                                session_options_.execution_mode, run_options.terminate, run_logger,
                                                run_options.only_execute_path_to_fetches));

  // info all execution providers InferenceSession:Run ended
  for (auto* xp : exec_providers_to_stop) {
    auto status = xp->OnRunEnd(/*sync_stream*/ true);
    ORT_CHECK_AND_SET_RETVAL(status);
  }
  
  --current_num_runs_;
}

至此,模型已经完成推理,接下来只需处理输出内容即可,对应 nanodet->detect() 函数的 3、4 部分。

总结

本文主要介绍了InferenceSession的构造和初始化,以及模型的推理过程,可以发现其中还是蛮复杂的。由于对ONNXRuntime的源码仍然了解有限,有许多重要的部分被略过,打算接下来分别针对突破。

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