private void DecryptAndEvauluate()
{
// Load the encrypted model.
// The encrypted model (encrypted.onnx) is embedded as a resource in
// the native binary: WinMLSamplesGalleryNative.dll.
var inferenceModel = WinMLSamplesGalleryNative.EncryptedModels.LoadEncryptedResource(DecryptionKey.Password);
var postProcessingModel = TensorizationModels.SoftMaxThenTopK(10);
// Update the status
var isModelDecrypted = inferenceModel != null;
UpdateStatus(isModelDecrypted);
// If loading the decrypted model failed (ie: due to an invalid key/password),
// then skip performing evaluate.
if (!isModelDecrypted)
{
return;
}
// Draw the image to classify in the Image control
var decoder = ImageHelper.CreateBitmapDecoderFromPath("ms-appx:///InputData/hummingbird.jpg");
// Create sessions
var device = new LearningModelDevice(LearningModelDeviceKind.Cpu);
var options = new LearningModelSessionOptions()
{
CloseModelOnSessionCreation = true // Close the model to prevent extra memory usage
};
var inferenceSession = new LearningModelSession(inferenceModel, device, options);
var postProcessingSession = new LearningModelSession(postProcessingModel, device, options);
// Classify the current image
var softwareBitmap = decoder.GetSoftwareBitmapAsync().GetAwaiter().GetResult();
var input = VideoFrame.CreateWithSoftwareBitmap(softwareBitmap);
// Inference
var inferenceResults = Evaluate(inferenceSession, input);
var inferenceOutput = inferenceResults.Outputs.First().Value;
// PostProcess
var postProcessedOutputs = Evaluate(postProcessingSession, inferenceOutput);
var topKValues = (TensorFloat)postProcessedOutputs.Outputs["TopKValues"];
var topKIndices = (TensorInt64Bit)postProcessedOutputs.Outputs["TopKIndices"];
// Return results
var probabilities = topKValues.GetAsVectorView();
var indices = topKIndices.GetAsVectorView();
var labels = indices.Select((index) => ClassificationLabels.ImageNet[index]);
// Render the classification and probabilities
RenderInferenceResults(labels, probabilities);
}
public OpenCVInterop()
{
this.InitializeComponent();
CurrentImagePath = null;
InferenceChoice = ClassifyChoice.Denoised;
// Load inference session
var modelName = "squeezenet1.1-7.onnx";
var modelPath = Path.Join(Windows.ApplicationModel.Package.Current.InstalledLocation.Path, "Models", modelName);
var model = LearningModel.LoadFromFilePath(modelPath);
_inferenceSession = CreateLearningModelSession(model);
// Load post processing session
_postProcessingSession = CreateLearningModelSession(TensorizationModels.SoftMaxThenTopK(TopK));
BasicGridView.SelectedIndex = 0;
}
public ImageSharpInterop()
{
this.InitializeComponent();
var tensorizationModel = TensorizationModels.BasicTensorization(Height, Width, BatchSize, Channels, Height, Width, "nearest");
_tensorizationSession = CreateLearningModelSession(tensorizationModel, SelectedDeviceKind);
var inferenceModelName = "squeezenet1.1-7.onnx";
var inferenceModelPath = Path.Join(Windows.ApplicationModel.Package.Current.InstalledLocation.Path, "Models", inferenceModelName);
var inferenceModel = LearningModel.LoadFromFilePath(inferenceModelPath);
_inferenceSession = CreateLearningModelSession(inferenceModel);
var postProcessingModel = TensorizationModels.SoftMaxThenTopK(TopK);
_postProcessingSession = CreateLearningModelSession(postProcessingModel);
BasicGridView.SelectedIndex = 0;
}
