LearningModelSession.EvaluateAsync(LearningModelBinding, String) Método

Definição

Namespace:

Windows.AI.MachineLearning

Avalie de forma assíncrona o modelo de machine learning usando os valores de recurso já associados em associações.

public:
 virtual IAsyncOperation<LearningModelEvaluationResult ^> ^ EvaluateAsync(LearningModelBinding ^ bindings, Platform::String ^ correlationId) = EvaluateAsync;

/// [Windows.Foundation.Metadata.RemoteAsync]
IAsyncOperation<LearningModelEvaluationResult> EvaluateAsync(LearningModelBinding const& bindings, winrt::hstring const& correlationId);

[Windows.Foundation.Metadata.RemoteAsync]
public IAsyncOperation<LearningModelEvaluationResult> EvaluateAsync(LearningModelBinding bindings, string correlationId);

function evaluateAsync(bindings, correlationId)

Public Function EvaluateAsync (bindings As LearningModelBinding, correlationId As String) As IAsyncOperation(Of LearningModelEvaluationResult)

Parâmetros

bindings

LearningModelBinding

Os valores associados aos recursos de entrada e saída nomeados.

correlationId

String

Platform::String

winrt::hstring

Cadeia de caracteres opcional fornecida pelo usuário para conectar os resultados de saída.

Retornos

IAsyncOperation<LearningModelEvaluationResult>

Um LearningModelEvaluationResult da avaliação.

Atributos

RemoteAsyncAttribute

Exemplos

O exemplo a seguir recupera os primeiros recursos de entrada e saída do modelo, cria um quadro de saída, associa os recursos de entrada e saída e avalia o modelo.

private async Task EvaluateModelAsync(
    VideoFrame _inputFrame, 
    LearningModelSession _session, 
    IReadOnlyList<ILearningModelFeatureDescriptor> _inputFeatures, 
    IReadOnlyList<ILearningModelFeatureDescriptor> _outputFeatures,
    LearningModel _model)
{
    ImageFeatureDescriptor _inputImageDescription;
    TensorFeatureDescriptor _outputImageDescription;
    LearningModelBinding _binding = null;
    VideoFrame _outputFrame = null;
    LearningModelEvaluationResult _results;

    try
    {
        // Retrieve the first input feature which is an image
        _inputImageDescription =
            _inputFeatures.FirstOrDefault(feature => feature.Kind == LearningModelFeatureKind.Image)
            as ImageFeatureDescriptor;

        // Retrieve the first output feature which is a tensor
        _outputImageDescription =
            _outputFeatures.FirstOrDefault(feature => feature.Kind == LearningModelFeatureKind.Tensor)
            as TensorFeatureDescriptor;

        // Create output frame based on expected image width and height
        _outputFrame = new VideoFrame(
            BitmapPixelFormat.Bgra8, 
            (int)_inputImageDescription.Width, 
            (int)_inputImageDescription.Height);

        // Create binding and then bind input/output features
        _binding = new LearningModelBinding(_session);

        _binding.Bind(_inputImageDescription.Name, _inputFrame);
        _binding.Bind(_outputImageDescription.Name, _outputFrame);

        // Evaluate and get the results
        _results = await _session.EvaluateAsync(_binding, "test");
    }
    catch (Exception ex)
    {
        StatusBlock.Text = $"error: {ex.Message}";
        _model = null;
    }
}

Comentários

Windows Server

Para usar essa API no Windows Server, você deve usar o Windows Server 2019 com a Experiência desktop.

Acesso thread-safe

Essa API é thread-safe.