public async Task <modelOutput> EvaluateAsync(modelInput input)
{
binding.Bind("data", input.data);
var result = await session.EvaluateAsync(binding, "0");
var output = new modelOutput();
output.model_outputs0 = result.Outputs["model_outputs0"] as TensorFloat;
return(output);
}
private async Task EvaluateVideoFrameAsync(VideoFrame frame)
{
if (frame != null)
{
try
{
_stopwatch.Restart();
//OnnxModelInput inputData = new OnnxModelInput();
modelInput inputData = new modelInput();
inputData.data = ImageFeatureValue.CreateFromVideoFrame(frame);
var output = await _model.EvaluateAsync(inputData);
var product = output.model_outputs0.GetAsVectorView()[0];
//var loss = output.Loss[0][product];
_stopwatch.Stop();
var lossStr = string.Join(", ", product + " ");
string message = $"Evaluation took {_stopwatch.ElapsedMilliseconds}ms to execute, Predictions: {lossStr}.";
//var product = output.ClassLabel.GetAsVectorView()[0];
//var loss = output.Loss[0][product];
//_stopwatch.Stop();
//var lossStr = string.Join(", ", product + " " + (loss * 100.0f).ToString("#0.00") + "%");
// string message = $"Evaluation took {_stopwatch.ElapsedMilliseconds}ms to execute, Predictions: {lossStr}.";
Debug.WriteLine(message);
await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => StatusBlock.Text = message);
}
catch (Exception ex)
{
var err_message = $"error: {ex.Message}";
Debug.WriteLine(err_message);
await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => StatusBlock.Text = err_message);
}
}
}
