cailean
/
ofxOnnxRuntime
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

windows version

master
Yuya Hanai 3 years ago
parent
66634a6120
commit
3ebd08c73d
2 changed files with 201 additions and 156 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 18
      README.md
  2. 113
      example-onnx_mnist/src/ofApp.cpp

18
README.md
View File

@ -7,9 +7,23 @@
## Installation
- macOS
- copy `libonnxruntime.1.10.0.dylib` to `/usr/local/lib`
- Generate a project using ProjectGenerator.
- Windows
- There are two ways to install ONNX Runtime on your project.
1. Install using NuGet
- I recommend this way in general.
- Generate a project using ProjectGenerator.
- Open `sln` file.
- Right click your project on `Solution Explorer` pane, and then select `Manage NuGet Packages...`.
- From `Browse` tab, search `Microsoft.ML.OnnxRuntime` (CPU) or `Microsoft.ML.OnnxRuntime.Gpu` (GPU) and install it.
2. DLL direct download
- You can download prebuilt DLLs from (here)[https://github.com/microsoft/onnxruntime/releases].
- Unzip downloaded `onnxruntime-win-x64-(gpu-)1.10.0.zip` and locate files on `libs\onnxruntime\lib\vs\x64\` .
- Generate a project using ProjectGenerator, then all libs are linked correctly and all dlls are copied to `bin`.
## Tested environment
- MacBookPro 2018 Intel + macOS Catalina
- oF 0.11.2 + MacBookPro 2018 Intel + macOS Catalina
- oF 0.11.2 + VS2017 + Windows 10 + RTX2080Ti + CUDA 11.4
## ToDo
- check M1 Mac (should work), Windows CPU&GPU, Linux CPU&GPU
- check M1 Mac (should work), Linux CPU&GPU

113
example-onnx_mnist/src/ofApp.cpp
View File

@ -3,9 +3,8 @@
#include "ofxOnnxRuntime.h"
// code from
//https://github.com/microsoft/onnxruntime-inference-examples/blob/main/c_cxx/MNIST/MNIST.cpp
template <typename T>
static void softmax(T& input) {
// https://github.com/microsoft/onnxruntime-inference-examples/blob/main/c_cxx/MNIST/MNIST.cpp
template <typename T> static void softmax(T &input) {
float rowmax = *std::max_element(input.begin(), input.end());
std::vector<float> y(input.size());
float sum = 0.0f;
@ -18,23 +17,55 @@ static void softmax(T& input) {
}
// This is the structure to interface with the MNIST model
// After instantiation, set the input_image_ data to be the 28x28 pixel image of the number to recognize
// Then call Run() to fill in the results_ data with the probabilities of each
// result_ holds the index with highest probability (aka the number the model thinks is in the image)
// After instantiation, set the input_image_ data to be the 28x28 pixel image of
// the number to recognize Then call Run() to fill in the results_ data with the
// probabilities of each result_ holds the index with highest probability (aka
// the number the model thinks is in the image)
struct MNIST {
MNIST() {
auto memory_info = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
input_tensor_ = Ort::Value::CreateTensor<float>(memory_info, input_image_.data(), input_image_.size(), input_shape_.data(), input_shape_.size());
output_tensor_ = Ort::Value::CreateTensor<float>(memory_info, results_.data(), results_.size(), output_shape_.data(), output_shape_.size());
#ifdef _MSC_VER
Ort::SessionOptions sf;
#define USE_CUDA
#define USE_TENSORRT
#ifdef USE_CUDA
#ifdef USE_TENSORRT
sf.AppendExecutionProvider_TensorRT(OrtTensorRTProviderOptions{ 0 });
#endif
sf.AppendExecutionProvider_CUDA(OrtCUDAProviderOptions());
#endif
string path = ofToDataPath("mnist-8.onnx", true);
std::wstring widestr = std::wstring(path.begin(), path.end());
session_ = make_shared<Ort::Session>(env, widestr.c_str(), sf);
#else
// OSX
session_ = make_shared<Ort::Session>(
env, ofToDataPath("mnist-8.onnx", true).c_str(),
Ort::SessionOptions{ nullptr });
#endif
auto memory_info =
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
input_tensor_ = Ort::Value::CreateTensor<float>(
memory_info, input_image_.data(), input_image_.size(),
input_shape_.data(), input_shape_.size());
output_tensor_ = Ort::Value::CreateTensor<float>(
memory_info, results_.data(), results_.size(), output_shape_.data(),
output_shape_.size());
}
std::ptrdiff_t Run() {
const char* input_names[] = {"Input3"};
const char* output_names[] = {"Plus214_Output_0"};
const char *input_names[] = { "Input3" };
const char *output_names[] = { "Plus214_Output_0" };
session_.Run(Ort::RunOptions{nullptr}, input_names, &input_tensor_, 1, output_names, &output_tensor_, 1);
session_->Run(Ort::RunOptions{ nullptr }, input_names, &input_tensor_, 1,
output_names, &output_tensor_, 1);
softmax(results_);
result_ = std::distance(results_.begin(), std::max_element(results_.begin(), results_.end()));
result_ = std::distance(results_.begin(),
std::max_element(results_.begin(), results_.end()));
return result_;
}
@ -43,29 +74,31 @@ struct MNIST {
std::array<float, width_ * height_> input_image_{};
std::array<float, 10> results_{};
int64_t result_{0};
int64_t result_{ 0 };
private:
private:
Ort::Env env;
Ort::Session session_{env, ofToDataPath("mnist-8.onnx", true).c_str(), Ort::SessionOptions{nullptr}};
shared_ptr<Ort::Session>
session_; // {env, (const wchar_t*)ofToDataPath("mnist-8.onnx",
// true).c_str(), Ort::SessionOptions{ nullptr }};
Ort::Value input_tensor_{nullptr};
std::array<int64_t, 4> input_shape_{1, 1, width_, height_};
Ort::Value input_tensor_{ nullptr };
std::array<int64_t, 4> input_shape_{ 1, 1, width_, height_ };
Ort::Value output_tensor_{nullptr};
std::array<int64_t, 2> output_shape_{1, 10};
Ort::Value output_tensor_{ nullptr };
std::array<int64_t, 2> output_shape_{ 1, 10 };
};
class ofApp : public ofBaseApp{
class ofApp : public ofBaseApp {
shared_ptr<MNIST> mnist;
ofFbo fbo_render;
ofFbo fbo_classification;
ofFloatPixels pix;
bool prev_pressed = false;
glm::vec2 prev_pt;
public:
void setup()
{
void setup() {
ofSetVerticalSync(true);
ofSetFrameRate(60);
@ -79,10 +112,11 @@ public:
fbo_classification.allocate(28, 28, GL_R32F, 0);
pix.setFromExternalPixels(&mnist->input_image_.front(), 28, 28, 1);
mnist->Run();
}
void update()
{
void update() {
if (ofGetMousePressed()) {
auto pt = glm::vec2(ofGetMouseX(), ofGetMouseY() - 60);
fbo_render.begin();
@ -98,28 +132,30 @@ public:
fbo_classification.begin();
ofClear(0);
fbo_render.draw(0, 0, fbo_classification.getWidth(), fbo_classification.getHeight());
fbo_render.draw(0, 0, fbo_classification.getWidth(),
fbo_classification.getHeight());
fbo_classification.end();
fbo_classification.readToPixels(pix);
mnist->Run();
prev_pt = pt;
prev_pressed = true;
} else {
}
else {
prev_pressed = false;
}
}
void draw()
{
void draw() {
ofClear(128);
fbo_render.draw(0, 60);
fbo_classification.draw(0, 340);
// render result
for (int i=0; i<10; ++i) {
for (int i = 0; i < 10; ++i) {
stringstream ss;
ss << i << ":" << std::fixed << std::setprecision(3) << mnist->results_[i];
ss << i << ":" << std::fixed << std::setprecision(3)
<< mnist->results_[i];
ofDrawBitmapString(ss.str(), 300, 70 + i * 30);
ofPushStyle();
ofSetColor(0, 255, 0);
@ -134,8 +170,7 @@ public:
ofDrawBitmapStringHighlight(ss.str(), 10, 20);
}
void keyPressed(int key)
{
void keyPressed(int key) {
if (key == 'c') {
fbo_render.begin();
ofClear(0);
@ -144,27 +179,23 @@ public:
}
void keyReleased(int key) {}
void mouseMoved(int x, int y ) {}
void mouseMoved(int x, int y) {}
void mouseDragged(int x, int y, int button) {}
void mousePressed(int x, int y, int button) {
}
void mousePressed(int x, int y, int button) {}
void mouseReleased(int x, int y, int button) {}
void windowResized(int w, int h) {}
void dragEvent(ofDragInfo dragInfo) {}
void gotMessage(ofMessage msg) {}
};
//========================================================================
int main( ){
ofSetupOpenGL(640,400,OF_WINDOW); // <-------- setup the GL context
int main() {
ofSetupOpenGL(640, 400, OF_WINDOW); // <-------- setup the GL context
// this kicks off the running of my app
// can be OF_WINDOW or OF_FULLSCREEN
// pass in width and height too:
ofRunApp(new ofApp());
}

Write Preview
Loading…
Cancel
Save