OnnxRuntime – 模型部署笔记3，总结OnnxRuntime模型推理流程
1 OnnxRuntime模型推理流程
1.1 OnnxRuntime模型推理流程总体简介
OnnxRuntime模型推理的整个流程主要可以分为2个阶段，分别是初始化阶段、模型推理阶段。
初始化阶段需要依次执行以下任务：
- 设置env
- 设置session option，此阶段可以设置session的相关选项，可用来是否启用CUDA、TensorRT等加速器
- 设置session，根据onnx模型构建session
模型推理阶段需要依次执行以下任务：
- 输入数据预处理，比如图片数据NHWC->NCHW，归一化，，减均值，除方差等
-
设置session的输入Tensor
-
执行session进行模型推理
-
获取输出Tensor
-
输出数据后处理
整体的思维导图如下：
1.2 详细解读OnnxRuntime模型推理流程
我们以OnnxRuntime在Github上的官方示例：https://github.com/microsoft/onnxruntime-inference-examples/blob/main/c_cxx/squeezenet/main.cpp 为参考依次详细解读使用OnnxRuntime进行模型推理的每一步。
源代码如下：
// Copyright(c) Microsoft Corporation.All rights reserved.
// Licensed under the MIT License.
//
#include <assert.h>
#include <onnxruntime_cxx_api.h>
#include <iostream>
#include <vector>
#ifdef HAVE_TENSORRT_PROVIDER_FACTORY_H
#include <tensorrt_provider_factory.h>
#include <tensorrt_provider_options.h>
std::unique_ptr<OrtTensorRTProviderOptionsV2> get_default_trt_provider_options() {
auto tensorrt_options = std::make_unique<OrtTensorRTProviderOptionsV2>();
tensorrt_options->device_id = 0;
tensorrt_options->has_user_compute_stream = 0;
tensorrt_options->user_compute_stream = nullptr;
tensorrt_options->trt_max_partition_iterations = 1000;
tensorrt_options->trt_min_subgraph_size = 1;
tensorrt_options->trt_max_workspace_size = 1 << 30;
tensorrt_options->trt_fp16_enable = false;
tensorrt_options->trt_int8_enable = false;
tensorrt_options->trt_int8_calibration_table_name = "";
tensorrt_options->trt_int8_use_native_calibration_table = false;
tensorrt_options->trt_dla_enable = false;
tensorrt_options->trt_dla_core = 0;
tensorrt_options->trt_dump_subgraphs = false;
tensorrt_options->trt_engine_cache_enable = false;
tensorrt_options->trt_engine_cache_path = "";
tensorrt_options->trt_engine_decryption_enable = false;
tensorrt_options->trt_engine_decryption_lib_path = "";
tensorrt_options->trt_force_sequential_engine_build = false;
return tensorrt_options;
}
#endif
void run_ort_trt() {
Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "test");
const auto& api = Ort::GetApi();
OrtTensorRTProviderOptionsV2* tensorrt_options;
Ort::SessionOptions session_options;
session_options.SetIntraOpNumThreads(1);
session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_EXTENDED);
#ifdef _WIN32
const wchar_t* model_path = L"squeezenet.onnx";
#else
const char* model_path = "squeezenet.onnx";
#endif
//*****************************************************************************************
// It's not suggested to directly new OrtTensorRTProviderOptionsV2 to get provider options
//*****************************************************************************************
//
// auto tensorrt_options = get_default_trt_provider_options();
// session_options.AppendExecutionProvider_TensorRT_V2(*tensorrt_options.get());
//**************************************************************************************************************************
// It's suggested to use CreateTensorRTProviderOptions() to get provider options
// since ORT takes care of valid options for you
//**************************************************************************************************************************
Ort::ThrowOnError(api.CreateTensorRTProviderOptions(&tensorrt_options));
std::unique_ptr<OrtTensorRTProviderOptionsV2, decltype(api.ReleaseTensorRTProviderOptions)> rel_trt_options(
tensorrt_options, api.ReleaseTensorRTProviderOptions);
Ort::ThrowOnError(api.SessionOptionsAppendExecutionProvider_TensorRT_V2(static_cast<OrtSessionOptions*>(session_options),
rel_trt_options.get()));
std::cout << "Running ORT TRT EP with default provider options" << std::endl;
Ort::Session session(env, model_path, session_options);
//*************************************************************************
// print model input layer (node names, types, shape etc.)
Ort::AllocatorWithDefaultOptions allocator;
// print number of model input nodes
const size_t num_input_nodes = session.GetInputCount();
std::vector<Ort::AllocatedStringPtr> input_names_ptr;
std::vector<const char*> input_node_names;
input_names_ptr.reserve(num_input_nodes);
input_node_names.reserve(num_input_nodes);
std::vector<int64_t> input_node_dims; // simplify... this model has only 1 input node {1, 3, 224, 224}.
// Otherwise need vector<vector<>>
std::cout << "Number of inputs = " << num_input_nodes << std::endl;
// iterate over all input nodes
for (size_t i = 0; i < num_input_nodes; i++) {
// print input node names
auto input_name = session.GetInputNameAllocated(i, allocator);
std::cout << "Input " << i << " : name =" << input_name.get() << std::endl;
input_node_names.push_back(input_name.get());
input_names_ptr.push_back(std::move(input_name));
// print input node types
auto type_info = session.GetInputTypeInfo(i);
auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
ONNXTensorElementDataType type = tensor_info.GetElementType();
std::cout << "Input " << i << " : type = " << type << std::endl;
// print input shapes/dims
input_node_dims = tensor_info.GetShape();
std::cout << "Input " << i << " : num_dims = " << input_node_dims.size() << '\n';
for (size_t j = 0; j < input_node_dims.size(); j++) {
std::cout << "Input " << i << " : dim[" << j << "] =" << input_node_dims[j] << '\n';
}
std::cout << std::flush;
}
constexpr size_t input_tensor_size = 224 * 224 * 3; // simplify ... using known dim values to calculate size
// use OrtGetTensorShapeElementCount() to get official size!
std::vector<float> input_tensor_values(input_tensor_size);
std::vector<const char*> output_node_names = {"softmaxout_1"};
// initialize input data with values in [0.0, 1.0]
for (unsigned int i = 0; i < input_tensor_size; i++) input_tensor_values[i] = (float)i / (input_tensor_size + 1);
// create input tensor object from data values
auto memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
auto input_tensor = Ort::Value::CreateTensor<float>(memory_info, input_tensor_values.data(), input_tensor_size,
input_node_dims.data(), 4);
assert(input_tensor.IsTensor());
// score model & input tensor, get back output tensor
auto output_tensors =
session.Run(Ort::RunOptions{nullptr}, input_node_names.data(), &input_tensor, 1, output_node_names.data(), 1);
assert(output_tensors.size() == 1 && output_tensors.front().IsTensor());
// Get pointer to output tensor float values
float* floatarr = output_tensors.front().GetTensorMutableData<float>();
assert(abs(floatarr[0] - 0.000045) < 1e-6);
// score the model, and print scores for first 5 classes
for (int i = 0; i < 5; i++) {
std::cout << "Score for class [" << i << "] = " << floatarr[i] << '\n';
}
std::cout << std::flush;
// Results should be as below...
// Score for class[0] = 0.000045
// Score for class[1] = 0.003846
// Score for class[2] = 0.000125
// Score for class[3] = 0.001180
// Score for class[4] = 0.001317
std::cout << "Done!" << std::endl;
}
int main(int /*argc*/, char*[]) {
run_ort_trt();
return 0;
}
接下来我们根据上述代码依次解读每一个步骤。
1 设置env
使用以下代码设置env
Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "test");
2 设置session option
这里的加速器使用的TensorRT
OrtTensorRTProviderOptionsV2* tensorrt_options;
Ort::SessionOptions session_options;
session_options.SetIntraOpNumThreads(1);
session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_EXTENDED);
Ort::ThrowOnError(api.CreateTensorRTProviderOptions(&tensorrt_options));
std::unique_ptr<OrtTensorRTProviderOptionsV2, decltype(api.ReleaseTensorRTProviderOptions)> rel_trt_options(tensorrt_options, api.ReleaseTensorRTProviderOptions);
Ort::ThrowOnError(api.SessionOptionsAppendExecutionProvider_TensorRT_V2(static_cast<OrtSessionOptions*>(session_options),rel_trt_options.get()));
3 设置session，加载onnx模型
Ort::Session session(env, model_path, session_options);
4 输入数据预处理
constexpr size_t input_tensor_size = 224 * 224 * 3;
std::vector<float> input_tensor_values(input_tensor_size);
for (unsigned int i = 0; i < input_tensor_size; i++) input_tensor_values[i] = (float)i / (input_tensor_size + 1);
5 设置session的输入Tensor
auto memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
std::vector<const char*> output_node_names = {"softmaxout_1"};
auto input_tensor = Ort::Value::CreateTensor<float>(memory_info, input_tensor_values.data(), input_tensor_size,input_node_dims.data(), 4);
assert(input_tensor.IsTensor());
6 执行推理，获取输出Tensor
std::vector<const char*> output_node_names = {"softmaxout_1"};
auto output_tensors =
session.Run(Ort::RunOptions{nullptr}, input_node_names.data(), &input_tensor, 1, output_node_names.data(), 1);
assert(output_tensors.size() == 1 && output_tensors.front().IsTensor());
7 输出结果后处理
float* floatarr = output_tensors.front().GetTensorMutableData<float>();
assert(abs(floatarr[0] - 0.000045) < 1e-6);
for (int i = 0; i < 5; i++) {
std::cout << "Score for class [" << i << "] = " << floatarr[i] << '\n';
}
本文作者：StubbornHuang
版权声明：本文为站长原创文章，如果转载请注明原文链接！
原文标题：OnnxRuntime – 模型部署笔记3，总结OnnxRuntime模型推理流程
原文链接：https://www.stubbornhuang.com/2563/
发布于：2023年03月24日 10:34:23
修改于：2023年06月21日 16:52:38
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