windows version

README.md

@@ -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

example-onnx_mnist/src/ofApp.cpp

@@ -4,8 +4,7 @@
 
 // code from
 // https://github.com/microsoft/onnxruntime-inference-examples/blob/main/c_cxx/MNIST/MNIST.cpp
-template <typename T>
-static void softmax(T& input) {
+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" };
 
-    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_;
 	}
 
@@ -47,7 +78,9 @@ struct MNIST {
 
 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_ };
@@ -63,9 +96,9 @@ class ofApp : public ofBaseApp{
 	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();
@@ -98,19 +132,20 @@ 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);
@@ -119,7 +154,8 @@ public:
 		// render result
 		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);
@@ -147,15 +182,12 @@ public:
 	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) {}
-    
 };
 
 //========================================================================
@@ -166,5 +198,4 @@ int main( ){
 	// can be OF_WINDOW or OF_FULLSCREEN
 	// pass in width and height too:
 	ofRunApp(new ofApp());
-    
 }