private (IEnumerable <string>, IReadOnlyList <float>) Classify(WinMLSamplesGalleryNative.OpenCVImage image)
{
long start, stop;
PerformanceMetricsMonitor.ClearLog();
start = HighResolutionClock.UtcNow();
object input = image.AsTensor();
stop = HighResolutionClock.UtcNow();
var pixelAccessDuration = HighResolutionClock.DurationInMs(start, stop);
// Tensorize
start = HighResolutionClock.UtcNow();
object tensorizedOutput = input;
var tensorizationResults = Evaluate(_tensorizationSession, input);
tensorizedOutput = tensorizationResults.Outputs.First().Value;
stop = HighResolutionClock.UtcNow();
var tensorizeDuration = HighResolutionClock.DurationInMs(start, stop);
// Inference
start = HighResolutionClock.UtcNow();
var inferenceResults = Evaluate(_inferenceSession, tensorizedOutput);
var inferenceOutput = inferenceResults.Outputs.First().Value;
stop = HighResolutionClock.UtcNow();
var inferenceDuration = HighResolutionClock.DurationInMs(start, stop);
// PostProcess
start = HighResolutionClock.UtcNow();
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]);
stop = HighResolutionClock.UtcNow();
var postProcessDuration = HighResolutionClock.DurationInMs(start, stop);
PerformanceMetricsMonitor.Log("Pixel Access (CPU)", pixelAccessDuration);
PerformanceMetricsMonitor.Log("Tensorize", tensorizeDuration);
PerformanceMetricsMonitor.Log("Pre-process", 0);
PerformanceMetricsMonitor.Log("Inference", inferenceDuration);
PerformanceMetricsMonitor.Log("Post-process", postProcessDuration);
return(labels, probabilities);
}
private void TryPerformInference(bool reloadImages = true)
{
if (CurrentImagePath != null)
{
if (reloadImages)
{
Original = WinMLSamplesGalleryNative.OpenCVImage.CreateFromPath(CurrentImagePath);
Noisy = WinMLSamplesGalleryNative.OpenCVImage.AddSaltAndPepperNoise(Original);
Denoised = WinMLSamplesGalleryNative.OpenCVImage.DenoiseMedianBlur(Noisy);
var baseImageBitmap = Original.AsSoftwareBitmap();
RenderingHelpers.BindSoftwareBitmapToImageControl(InputImage, baseImageBitmap);
RenderingHelpers.BindSoftwareBitmapToImageControl(NoisyImage, Noisy.AsSoftwareBitmap());
RenderingHelpers.BindSoftwareBitmapToImageControl(DenoisedImage, Denoised.AsSoftwareBitmap());
var tensorizationModel = TensorizationModels.CastResizeAndTranspose11(Height, Width, 1, 3, baseImageBitmap.PixelHeight, baseImageBitmap.PixelWidth, "nearest");
_tensorizationSession = CreateLearningModelSession(tensorizationModel, SelectedDeviceKind);
}
WinMLSamplesGalleryNative.OpenCVImage classificationImage = null;
switch (InferenceChoice)
{
case ClassifyChoice.Original:
classificationImage = Original;
break;
case ClassifyChoice.Noisy:
classificationImage = Noisy;
break;
case ClassifyChoice.Denoised:
classificationImage = Denoised;
break;
}
// Classify
var(labels, probabilities) = Classify(classificationImage);
// Render the classification and probabilities
RenderInferenceResults(labels, probabilities);
}
}
