public void SetProperties(IPropertySet configuration)
{
this.configuration = configuration;
var modelFile = StorageFile.GetFileFromApplicationUriAsync(new Uri($"ms-appx:///segm_video_effect_uwp/Assets/{_kModelFileName}")).GetAwaiter().GetResult();
Debug.WriteLine(modelFile);
_learningModel = LearningModelPreview.LoadModelFromStorageFileAsync(modelFile).GetAwaiter().GetResult();
InferencingOptionsPreview options = _learningModel.InferencingOptions;
options.PreferredDeviceKind = LearningModelDeviceKindPreview.LearningDeviceGpu;
//options.IsTracingEnabled = true;
_learningModel.InferencingOptions = options;
fpsTimer = new Timer(1000);
fpsTimer.Elapsed += this.OnTimedEvent;
fpsTimer.AutoReset = true;
fpsTimer.Start();
fpsLabelOffset = new Vector2(10, 10);
fpsLabelColor = Color.FromArgb(255, 255, 0, 0);
}
/// <summary>
/// Load the labels and model and initialize WinML
/// </summary>
/// <returns></returns>
private async Task LoadModelAsync()
{
_evaluationLock.Wait();
{
_binding = null;
_model = null;
_isReadyForEval = false;
try
{
// Load Model
StorageFile modelFile = await StorageFile.GetFileFromApplicationUriAsync(new Uri($"ms-appx:///Assets/{_kModelFileName}.onnx"));
_model = await LearningModelPreview.LoadModelFromStorageFileAsync(modelFile);
// Hardcoding to use GPU
InferencingOptionsPreview options = _model.InferencingOptions;
options.PreferredDeviceKind = _useGPU ? LearningModelDeviceKindPreview.LearningDeviceGpu : LearningModelDeviceKindPreview.LearningDeviceCpu;
_model.InferencingOptions = options;
// Debugging logic to see the input and output of ther model
List <ILearningModelVariableDescriptorPreview> inputFeatures = _model.Description.InputFeatures.ToList();
List <ILearningModelVariableDescriptorPreview> outputFeatures = _model.Description.OutputFeatures.ToList();
var metadata = _model.Description.Metadata;
foreach (var md in metadata)
{
Debug.WriteLine($"{md.Key} | {md.Value}");
}
_inputImageDescription =
inputFeatures.FirstOrDefault(feature => feature.ModelFeatureKind == LearningModelFeatureKindPreview.Image)
as ImageVariableDescriptorPreview;
_outputImageDescription =
outputFeatures.FirstOrDefault(feature => feature.ModelFeatureKind == LearningModelFeatureKindPreview.Image)
as ImageVariableDescriptorPreview;
_isReadyForEval = true;
}
catch (Exception ex)
{
NotifyUser($"error: {ex.Message}", NotifyType.ErrorMessage);
Debug.WriteLine($"error: {ex.Message}");
}
}
_evaluationLock.Release();
}
// Loads the ML model file and sets
private async Task LoadModelAsync()
{
if (model != null)
{
return;
}
Debug.WriteLine($"Loading Model");
try
{
// Load Model
var modelFile = await StorageFile.GetFileFromApplicationUriAsync(ModelUri);
Debug.WriteLine($"Model file discovered at: {modelFile.Path}");
model = await LearningModelPreview.LoadModelFromStorageFileAsync(modelFile);
Debug.WriteLine($"LearningModelPReview object instantiated: {modelFile.Path}");
options = model.InferencingOptions;
options.PreferredDeviceKind = LearningModelDeviceKindPreview.LearningDeviceGpu;
model.InferencingOptions = options;
// Retrieve model input and output variable descriptions (we already know the model takes an image in and outputs a tensor)
var inputFeatures = model.Description.InputFeatures.ToList();
Debug.WriteLine($"{inputFeatures.Count} Input Features");
var outputFeatures = model.Description.OutputFeatures.ToList();
Debug.WriteLine($"{inputFeatures.Count} Output Features");
inputImageDescription = inputFeatures.FirstOrDefault(feature => feature.ModelFeatureKind == LearningModelFeatureKindPreview.Image)
as ImageVariableDescriptorPreview;
outputTensorDescription = outputFeatures.FirstOrDefault(feature => feature.ModelFeatureKind == LearningModelFeatureKindPreview.Tensor)
as TensorVariableDescriptorPreview;
binding = new LearningModelBindingPreview(model); // Create bindings for the input and output buffer
outputArray = new List <float>(); // R4 WinML does needs the output pre-allocated for multi-dimensional tensors
outputArray.AddRange(new float[21125]); // Total size of TinyYOLO output
if (inputImageDescription != null && outputTensorDescription != null)
{
binding.Bind(inputImageDescription.Name, evaluatableVideoFrame);
binding.Bind(outputTensorDescription.Name, outputArray);
modelBindingComplete = true;
}
else
{
modelBindingComplete = false;
}
}
catch (Exception ex)
{
Debug.WriteLine($"Error loading model: {ex.Message}");
model = null;
modelBindingComplete = false;
}
}
