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Linnea May
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telemetry
2 parents f05b799 + 93e6abb commit 5f79cad

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11 files changed

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‎Samples/WinMLSamplesGallery/WinMLSamplesGallery (Package)/WinMLSamplesGallery (Package).wapproj‎

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@@ -132,7 +132,7 @@
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</ItemGroup>
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<Import Project="$(WapProjPath)\Microsoft.DesktopBridge.targets" />
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<ItemGroup>
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<PackageReference Include="Microsoft.AI.MachineLearning" Version="1.11.0" />
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<PackageReference Include="Microsoft.WindowsAppSDK" Version="1.0.0-stable" />
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<PackageReference Include="Microsoft.AI.MachineLearning" Version="1.12.1" />
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<PackageReference Include="Microsoft.WindowsAppSDK" Version="1.1.4" />
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</ItemGroup>
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</Project>

‎Samples/WinMLSamplesGallery/WinMLSamplesGallery/Pages/SampleBasePage.xaml.cs‎

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using Microsoft.UI.Xaml;
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using Microsoft.UI.Xaml.Controls;
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using Microsoft.UI.Xaml.Navigation;
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using Microsoft.AI.MachineLearning;
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using Microsoft.AI.MachineLearning.Experimental;
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using System;
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namespace WinMLSamplesGallery
@@ -68,5 +70,12 @@ protected override async void OnNavigatingFrom(NavigatingCancelEventArgs e)
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}
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public static void SetModelNameForTelemetry(String modelName, String sampleName, LearningModel model)
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{
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var telemetryModelName = modelName + "_" + sampleName;
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var experimentalModel = new LearningModelExperimental(model);
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experimentalModel.SetName(telemetryModelName);
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}
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}
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}

‎Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/AdapterSelection/AdapterSelection.xaml.cs‎

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@@ -33,6 +33,9 @@ public AdapterSelection()
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adapter_options.AddRange(adapters);
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AdapterListView.ItemsSource = adapter_options;
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// Run in background thread to avoid blocking UI on sample launch
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Task.Run(() => SetModelNameForTelemetry(device));
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}
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private void ChangeAdapter(object sender, RoutedEventArgs e)
@@ -100,5 +103,16 @@ private List<string> RemoveMicrosoftBasicRenderDriver(string[] adapters_arr)
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}
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return adapters;
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}
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// Session must be created for telemety to be sent
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private void SetModelNameForTelemetry(LearningModelDevice device)
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{
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var modelName = "squeezenet1.1-7.onnx";
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var modelPath = Path.Join(Windows.ApplicationModel.Package.Current.InstalledLocation.Path, "Models", modelName);
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var model = LearningModel.LoadFromFilePath(modelPath);
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var options = new LearningModelSessionOptions();
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SampleBasePage.SetModelNameForTelemetry("SqueezeNet", "AdapterSelection", model);
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new LearningModelSession(model, device, options);
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}
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}
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}

‎Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/Batching/Batching.xaml.cs‎

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@@ -44,6 +44,7 @@ public Batching()
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var modelName = "squeezenet1.1-7-batched.onnx";
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var modelPath = Path.Join(Windows.ApplicationModel.Package.Current.InstalledLocation.Path, "Models", modelName);
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_model = LearningModel.LoadFromFilePath(modelPath);
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SampleBasePage.SetModelNameForTelemetry("SqueezeNet", "Batching", _model);
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}
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async private void StartInference(object sender, RoutedEventArgs e)

‎Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/EncryptedModel/EncryptedModel.xaml.cs‎

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@@ -48,6 +48,7 @@ private void DecryptAndEvauluate()
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// The encrypted model (encrypted.onnx) is embedded as a resource in
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// the native binary: WinMLSamplesGalleryNative.dll.
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var inferenceModel = WinMLSamplesGalleryNative.EncryptedModels.LoadEncryptedResource(DecryptionKey.Password);
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SampleBasePage.SetModelNameForTelemetry("Encrypted", "EncryptedModel", inferenceModel);
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var postProcessingModel = TensorizationModels.SoftMaxThenTopK(10);
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// Update the status

‎Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/ImageClassifier/ImageClassifier.xaml.cs‎

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@@ -243,6 +243,8 @@ private void InitializeWindowsMachineLearning()
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{
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var modelPath = _modelDictionary[model];
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var inferenceModel = LearningModel.LoadFromFilePath(modelPath);
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SetModelNameForTelemetry(inferenceModel);
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_inferenceSession = CreateLearningModelSession(inferenceModel);
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var preProcessor = _preProcessorDictionary[model];
@@ -444,5 +446,13 @@ private void DeviceComboBox_SelectionChanged(object sender, SelectionChangedEven
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{
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TryPerformInference();
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}
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private void SetModelNameForTelemetry(LearningModel model)
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{
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var viewModel = (ClassifierViewModel)AllModelsGrid.SelectedItem;
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var modelName = viewModel.Title;
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var sampleName = "ImageClassifier";
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SampleBasePage.SetModelNameForTelemetry(modelName, sampleName, model);
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}
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}
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}

‎Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/ImageEffects/ImageEffects.xaml.cs‎

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@@ -364,6 +364,7 @@ private LearningModelSession CreateLearningModelSession(LearningModel model, boo
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CloseModelOnSessionCreation = closeModel, // Close the model to prevent extra memory usage
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BatchSizeOverride = 0
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};
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SampleBasePage.SetModelNameForTelemetry("TensorizationModel", "ImageEffects", model);
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var session = new LearningModelSession(model, device, options);
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return session;
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}

‎Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/ImageSharpInterop/ImageSharpInterop.xaml.cs‎

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@@ -61,6 +61,7 @@ public ImageSharpInterop()
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var inferenceModelName = "squeezenet1.1-7.onnx";
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var inferenceModelPath = Path.Join(Windows.ApplicationModel.Package.Current.InstalledLocation.Path, "Models", inferenceModelName);
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var inferenceModel = LearningModel.LoadFromFilePath(inferenceModelPath);
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SampleBasePage.SetModelNameForTelemetry("SqueezeNet", "ImageSharp", inferenceModel);
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_inferenceSession = CreateLearningModelSession(inferenceModel);
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var postProcessingModel = TensorizationModels.SoftMaxThenTopK(TopK);

‎Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/StreamEffect/StreamEffect.xaml.cs‎

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using System.Threading.Tasks;
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using System.Diagnostics;
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using System.Runtime.CompilerServices;
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using Microsoft.AI.MachineLearning;
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using System.IO;
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using WinMLSamplesGalleryNative;
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using System.Runtime.InteropServices;
@@ -30,13 +31,23 @@ public sealed partial class StreamEffect : Page
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public StreamEffect()
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{
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this.InitializeComponent();
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// Run in background thread to avoid blocking UI on sample launch
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Task.Run(() => SetModelNameForTelemetry());
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demoHwnd = (IntPtr)WinMLSamplesGalleryNative.StreamEffect.CreateInferenceWindow();
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currentHwnd = GetForegroundWindow();
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modelPath = Path.Join(Windows.ApplicationModel.Package.Current.InstalledLocation.Path, "Models");
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}
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private void SetModelNameForTelemetry()
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{
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var inferenceBuilder = Microsoft.AI.MachineLearning.Experimental.LearningModelBuilder.Create(11);
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var inferenceModel = inferenceBuilder.CreateModel();
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SampleBasePage.SetModelNameForTelemetry("FCNResnet", "StreamEffect", inferenceModel);
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}
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public void CloseInferenceWindow()
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{
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// if have a windtask running and it's not complete, destroy the window

‎Samples/WinMLSamplesGallery/WinMLSamplesGallery/WinMLSamplesGallery.csproj‎

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</ItemGroup>
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<ItemGroup>
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<PackageReference Include="Microsoft.AI.MachineLearning" Version="1.11.0" />
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<PackageReference Include="Microsoft.Windows.CsWinRT" Version="1.3.5" />
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<PackageReference Include="Microsoft.WindowsAppSDK" Version="1.0.0-stable" />
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<PackageReference Include="SixLabors.ImageSharp" Version="1.0.4" />
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<PackageReference Include="Microsoft.AI.MachineLearning" Version="1.12.1" />
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<PackageReference Include="Microsoft.Windows.CsWinRT" Version="1.6.5" />
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<PackageReference Include="Microsoft.WindowsAppSDK" Version="1.1.4" />
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<PackageReference Include="SixLabors.ImageSharp" Version="2.1.3" />
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<Manifest Include="$(ApplicationManifest)" />
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</ItemGroup>
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