同样是图像分割的c++ inference,已经实现过libtorch和ncnn框架的c++推理,今天实现一下onnxruntime的c++推理。
使用的其他库和工具:
代码如下
#include
#include
#include
#include
#include "img_seg.h"
#pragma warning(disable:4996)
using namespace std;
float sigmoid(float x) {
return 1.0 / (1.0 + exp(-x));
}
int main(int argc, char* argv[]) {
Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "test");
Ort::SessionOptions session_options;
Ort::Session session_{ nullptr };
session_options.SetIntraOpNumThreads(1);
session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_EXTENDED);
#ifdef _WIN32
const wchar_t* model_path = L"BiSeNet.onnx";
#else
const char* model_path = "BiSeNet.onnx";
#endif
//Ort::Session session(env, model_path, session_options);
session_ = Ort::Session(env, model_path, session_options);
cout << "session created sucess" << endl;
// print model input layer (node names, types, shape etc.)
Ort::AllocatorWithDefaultOptions allocator;
//输入输出节点数量和名称
size_t num_input_nodes = session_.GetInputCount();
size_t num_output_nodes = session_.GetOutputCount();
Ort::AllocatorWithDefaultOptions ort_alloc;
std::vector<const char*>input_node_names;
std::vector<const char*>output_node_names;
std::vector<int64_t> input_node_dims;
std::vector<int64_t> output_node_dims;
for (int i = 0; i < num_input_nodes; i++)
{
auto input_node_name = session_.GetInputName(i, allocator);
input_node_names.push_back(input_node_name);
Ort::TypeInfo type_info = session_.GetInputTypeInfo(i);
auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
ONNXTensorElementDataType type = tensor_info.GetElementType();
input_node_dims = tensor_info.GetShape();
}
for (int i = 0; i < num_output_nodes; i++)
{
auto output_node_name = session_.GetOutputName(i, allocator);
output_node_names.push_back(output_node_name);
Ort::TypeInfo type_info = session_.GetOutputTypeInfo(i);
auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
output_node_dims = tensor_info.GetShape();
}
cv::Mat image = cv::imread("0166096.jpg");
cv::resize(image, image, { 512, 512 }, 0.0, 0.0, cv::INTER_CUBIC);//调整大小到512*512
cv::imshow("image", image); //打印原图片
cv::waitKey();
cv::cvtColor(image, image, cv::COLOR_BGR2RGB); //BRG格式转化为RGB格式
//input_node_dims[0] = 1;
float* floatarr = nullptr;
int input_tensor_size = image.cols * image.rows * 3;
std::size_t counter = 0;//std::vector空间一次性分配完成,避免过多的数据copy
std::vector<float>input_data(input_tensor_size);
std::vector<float>output_data;
// 此处内置了减均值0.5,比方差0.5
for (unsigned k = 0; k < 3; k++)
{
for (unsigned i = 0; i < image.rows; i++)
{
for (unsigned j = 0; j < image.cols; j++)
{
input_data[counter++] = (static_cast<float>(image.at<cv::Vec3b>(i, j)[k]) / 255.0 - 0.5)/0.5;
}
}
}
auto memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
Ort::Value input_tensor = Ort::Value::CreateTensor<float>(memory_info, input_data.data(), input_tensor_size, input_node_dims.data(), 4);
assert(input_tensor.IsTensor());
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());
floatarr = output_tensors.front().GetTensorMutableData<float>();
int64_t output_tensor_size = 1;
for (auto& it : output_node_dims)
{
output_tensor_size *= it;
}
std::vector<float>results(output_tensor_size);
for (unsigned i = 0; i < output_tensor_size; i++)
{
results[i] = floatarr[i];
//results[i] = sigmoid(floatarr[i]); // 使用sigmoid函数
}
cv::Mat output_tensor(results);
output_tensor = output_tensor.reshape(1, { 512,512 })*255.0;
//cv::threshold(output_tensor, output_tensor, 20, 255, cv::THRESH_BINARY_INV);
cv::imshow("result", output_tensor); //打印结果
cv::waitKey(0);
return 0;
}
说明,在使用
后来使用sigmoid也可以实现正常效果了,代码如下:
主要不同的操作是使用了sigmoid,并且后面不需要×255.0。
#include
#include
#include
#include
#include "img_seg.h"
#pragma warning(disable:4996)
using namespace std;
float sigmoid(float x) {
return 1.0 / (1.0 + exp(-x));
}
int main(int argc, char* argv[]) {
//记录程序运行时间
auto start_time = clock();
Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "test");
Ort::SessionOptions session_options;
Ort::Session session_{ nullptr };
session_options.SetIntraOpNumThreads(2);
session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_EXTENDED);
#ifdef _WIN32
const wchar_t* model_path = L"BiSeNet.onnx";
#else
const char* model_path = "BiSeNet.onnx";
#endif
//Ort::Session session(env, model_path, session_options);
session_ = Ort::Session(env, model_path, session_options);
cout << "session created sucess" << endl;
// print model input layer (node names, types, shape etc.)
Ort::AllocatorWithDefaultOptions allocator;
//输入输出节点数量和名称
size_t num_input_nodes = session_.GetInputCount();
size_t num_output_nodes = session_.GetOutputCount();
Ort::AllocatorWithDefaultOptions ort_alloc;
std::vector<const char*>input_node_names;
std::vector<const char*>output_node_names;
std::vector<int64_t> input_node_dims;
std::vector<int64_t> output_node_dims;
for (int i = 0; i < num_input_nodes; i++)
{
auto input_node_name = session_.GetInputName(i, allocator);
input_node_names.push_back(input_node_name);
Ort::TypeInfo type_info = session_.GetInputTypeInfo(i);
auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
ONNXTensorElementDataType type = tensor_info.GetElementType();
input_node_dims = tensor_info.GetShape();
}
for (int i = 0; i < num_output_nodes; i++)
{
auto output_node_name = session_.GetOutputName(i, allocator);
output_node_names.push_back(output_node_name);
Ort::TypeInfo type_info = session_.GetOutputTypeInfo(i);
auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
output_node_dims = tensor_info.GetShape();
}
cv::Mat image = cv::imread("0166096.jpg");
cv::resize(image, image, { 512, 512 }, 0.0, 0.0, cv::INTER_CUBIC);//调整大小到512*512
//cv::imshow("image", image); //打印原图片
//cv::waitKey();
cv::cvtColor(image, image, cv::COLOR_BGR2RGB); //BRG格式转化为RGB格式
//input_node_dims[0] = 1;
float* floatarr = nullptr;
int input_tensor_size = image.cols * image.rows * 3;
std::size_t counter = 0;//std::vector空间一次性分配完成,避免过多的数据copy
std::vector<float>input_data(input_tensor_size);
std::vector<float>output_data;
// 此处内置了减均值0.5,比方差0.5
for (unsigned k = 0; k < 3; k++)
{
for (unsigned i = 0; i < image.rows; i++)
{
for (unsigned j = 0; j < image.cols; j++)
{
input_data[counter++] = (static_cast<float>(image.at<cv::Vec3b>(i, j)[k]) / 255.0 - 0.5)/0.5;
}
}
}
auto memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
Ort::Value input_tensor = Ort::Value::CreateTensor<float>(memory_info, input_data.data(), input_tensor_size, input_node_dims.data(), 4);
assert(input_tensor.IsTensor());
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());
floatarr = output_tensors.front().GetTensorMutableData<float>();
int64_t output_tensor_size = 1;
for (auto& it : output_node_dims)
{
output_tensor_size *= it;
}
std::vector<float>results(output_tensor_size);
for (unsigned i = 0; i < output_tensor_size; i++)
{
//results[i] = floatarr[i];
results[i] = sigmoid(floatarr[i]);
}
// 需要sigmoid函数
cv::Mat output_tensor(results);
output_tensor = output_tensor.reshape(1, { 512,512 });//*255.0
//cv::threshold(output_tensor, output_tensor, 20, 255, cv::THRESH_BINARY_INV);
cv::imshow("result", output_tensor); //打印结果
cv::waitKey(0);
auto end_time = clock();
printf("Proceed exit after %.2f seconds\n", static_cast<float>(end_time - start_time) / CLOCKS_PER_SEC);
printf("Done!\n");
return 0;
}
#include
#include
#include
#include
#include
#include "img_seg.h"
#pragma warning(disable:4996)
using namespace std;
float sigmoid(float x) {
return 1.0 / (1.0 + exp(-x));
}
int main(int argc, char* argv[]) {
//记录程序运行时间
auto start_time = clock();
Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "test");
Ort::SessionOptions session_options;
Ort::Session session_{ nullptr };
session_options.SetIntraOpNumThreads(2);
session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_EXTENDED);
#ifdef _WIN32
const wchar_t* model_path = L"BiSeNet.onnx";
#else
const char* model_path = "BiSeNet.onnx";
#endif
//Ort::Session session(env, model_path, session_options);
session_ = Ort::Session(env, model_path, session_options);
cout << "session created sucess" << endl;
// print model input layer (node names, types, shape etc.)
Ort::AllocatorWithDefaultOptions allocator;
//输入输出节点数量和名称
size_t num_input_nodes = session_.GetInputCount();
size_t num_output_nodes = session_.GetOutputCount();
Ort::AllocatorWithDefaultOptions ort_alloc;
std::vector<const char*>input_node_names;
std::vector<const char*>output_node_names;
std::vector<int64_t> input_node_dims;
std::vector<int64_t> output_node_dims;
for (int i = 0; i < num_input_nodes; i++)
{
auto input_node_name = session_.GetInputName(i, allocator);
input_node_names.push_back(input_node_name);
Ort::TypeInfo type_info = session_.GetInputTypeInfo(i);
auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
ONNXTensorElementDataType type = tensor_info.GetElementType();
input_node_dims = tensor_info.GetShape();
}
for (int i = 0; i < num_output_nodes; i++)
{
auto output_node_name = session_.GetOutputName(i, allocator);
output_node_names.push_back(output_node_name);
Ort::TypeInfo type_info = session_.GetOutputTypeInfo(i);
auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
output_node_dims = tensor_info.GetShape();
}
cv::Mat image = cv::imread("0166096.jpg");
cv::resize(image, image, { 512, 512 }, 0.0, 0.0, cv::INTER_CUBIC);//调整大小到512*512
//cv::imshow("image", image); //打印原图片
//cv::waitKey();
cv::cvtColor(image, image, cv::COLOR_BGR2RGB); //BRG格式转化为RGB格式
//input_node_dims[0] = 1;
float* floatarr = nullptr;
int input_tensor_size = image.cols * image.rows * 3;
std::size_t counter = 0;//std::vector空间一次性分配完成,避免过多的数据copy
std::vector<float>input_data(input_tensor_size);
std::vector<float>output_data;
// 此处内置了减均值0.5,比方差0.5
for (unsigned k = 0; k < 3; k++)
{
for (unsigned i = 0; i < image.rows; i++)
{
for (unsigned j = 0; j < image.cols; j++)
{
input_data[counter++] = (static_cast<float>(image.at<cv::Vec3b>(i, j)[k]) / 255.0 - 0.5)/0.5;
}
}
}
auto memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
Ort::Value input_tensor = Ort::Value::CreateTensor<float>(memory_info, input_data.data(), input_tensor_size, input_node_dims.data(), 4);
assert(input_tensor.IsTensor());
std::chrono::steady_clock::time_point begin =
std::chrono::steady_clock::now();
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());
floatarr = output_tensors.front().GetTensorMutableData<float>();
std::chrono::steady_clock::time_point end =
std::chrono::steady_clock::now();
std::cout << "Minimum Inference Latency: "
<< std::chrono::duration_cast<std::chrono::milliseconds>(end - begin).count() / static_cast<float>(1)
<< " ms" << std::endl;
int64_t output_tensor_size = 1;
for (auto& it : output_node_dims)
{
output_tensor_size *= it;
}
std::vector<float>results(output_tensor_size);
for (unsigned i = 0; i < output_tensor_size; i++)
{
results[i] = floatarr[i];
//results[i] = sigmoid(floatarr[i]);
}
// 需要sigmoid函数
cv::Mat output_tensor(results);
output_tensor = output_tensor.reshape(1, { 512,512 });//*255.0
//cv::threshold(output_tensor, output_tensor, 20, 255, cv::THRESH_BINARY_INV);
auto end_time = clock();
printf("Proceed exit after %.2f seconds\n", static_cast<float>(end_time - start_time) / CLOCKS_PER_SEC);
printf("Done!\n");
cv::imshow("result", output_tensor); //打印结果
cv::waitKey(0);
return 0;
}