private LearningModelSession CreateLearningModelSession(LearningModel model, Nullable <LearningModelDeviceKind> kind = null)
{
var device = new LearningModelDevice(kind ?? SelectedDeviceKind);
var options = new LearningModelSessionOptions()
{
CloseModelOnSessionCreation = true // Close the model to prevent extra memory usage
};
var session = new LearningModelSession(model, device, options);
return(session);
}
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);
}
private LearningModelSession CreateLearningModelSession(LearningModel model, bool closeModel = true)
{
var device = IsCpu ? cpuDevice : dmlDevice;
var options = new LearningModelSessionOptions()
{
CloseModelOnSessionCreation = closeModel, // Close the model to prevent extra memory usage
BatchSizeOverride = 0
};
var session = new LearningModelSession(model, device, options);
return(session);
}
private async Task <LearningModelSession> CreateLearningModelSession(LearningModel model, int batchSizeOverride = -1)
{
var deviceKind = DeviceComboBox.GetDeviceKind();
var device = new LearningModelDevice(deviceKind);
var options = new LearningModelSessionOptions();
if (batchSizeOverride > 0)
{
options.BatchSizeOverride = (uint)batchSizeOverride;
}
var session = new LearningModelSession(model, device, options);
return(session);
}
private LearningModelSession CreateLearningModelSession(LearningModel model)
{
var kind =
(DeviceComboBox.SelectedIndex == 0) ?
LearningModelDeviceKind.Cpu :
LearningModelDeviceKind.DirectXHighPerformance;
var device = new LearningModelDevice(kind);
var options = new LearningModelSessionOptions()
{
CloseModelOnSessionCreation = true // Close the model to prevent extra memory usage
};
var session = new LearningModelSession(model, device, options);
return(session);
}