private async Task EvaluateVideoFrameAsync(VideoFrame frame)
{
if (frame != null)
{
try
{
_stopwatch.Restart();
OnnxModelInput inputData = new OnnxModelInput();
inputData.Data = frame;
var output = await _model.EvaluateAsync(inputData);
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);
}
}
}
private async Task EvaluateVideoFrameAsync(VideoFrame frame)
{
if (frame != null)
{
try
{
_stopwatch.Restart();
OnnxModelInput inputData = new OnnxModelInput();
inputData.data = frame;
var results = await _model.EvaluateAsync(inputData);
var loss = results.loss.ToList().OrderBy(x => - (x.Value));
var labels = results.classLabel;
_stopwatch.Stop();
var lossStr = string.Join(", ", loss.Select(l => l.Key + " " + (l.Value * 100.0f).ToString("#0.00") + "%"));
string message = $"Evaluation took {_stopwatch.ElapsedMilliseconds}ms to execute, Predictions: {lossStr}.";
Debug.WriteLine(message);
await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => StatusBlock.Text = message);
}
catch (Exception ex)
{
Debug.WriteLine($"error: {ex.Message}");
await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => StatusBlock.Text = $"error: {ex.Message}");
}
await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => ButtonRun.IsEnabled = true);
}
}
public async Task <ModelOutput> EvaluateAsync(OnnxModelInput input)
{
var output = new ModelOutput();
var binding = new LearningModelBinding(_session);
binding.Bind("data", input.Data);
binding.Bind("model_outputs0", output.Model_outputs0);
var evalResult = await _session.EvaluateAsync(binding, "0");
return(output);
}
private async Task EvaluateVideoFrameAsync(VideoFrame frame)
{
if (frame != null)
{
try
{
_stopwatch.Restart();
var inputData = new OnnxModelInput {
Data = frame
};
var output = await _model.EvaluateAsync(inputData);
_stopwatch.Stop();
// display Shape values
Debug.WriteLine($"Shape values: {output.Model_outputs0.Shape.Count}");
for (var i = 0; i < output.Model_outputs0.Shape.Count; i++)
{
Debug.WriteLine($"Index {i} Shape value: {output.Model_outputs0.Shape[i]}");
}
//// display full set of values
//// 45 * 13 * 13 = 7604 values
//var vars = output.Model_outputs0.GetAsVectorView();
//for (var i = 0; i < vars.Count; i++)
//{
// Debug.WriteLine($"Index {i} value: {vars[i]}");
//}
//var product = output.Model_outputs0.GetAsVectorView()[0];
//var evalResult = string.Join(", ", product);
//string message = $"Evaluation took {_stopwatch.ElapsedMilliseconds}ms to execute, Predictions: {evalResult}.";
//Debug.WriteLine(message);
var message = $"Evaluation took {_stopwatch.ElapsedMilliseconds}ms to execute, Predictions: ";
var ra = new ResultsAnalyzer();
var analRes = ra.Postprocess(output.Model_outputs0);
foreach (var predictionModel in analRes)
{
var pred = $" {predictionModel.TagName} {(predictionModel.Probability * 100.0f):#0.00} % /-/-/";
message += pred;
}
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);
}
}
}
public async Task <ModelOutput> EvaluateAsync(OnnxModelInput input)
{
var output = new ModelOutput();
var binding = new LearningModelBinding(_session);
binding.Bind("data", input.Data);
binding.Bind("classLabel", output.ClassLabel);
binding.Bind("loss", output.Loss);
LearningModelEvaluationResult evalResult = await _session.EvaluateAsync(binding, "0");
return(output);
}
public async Task <OnnxModelOutput> EvaluateAsync(OnnxModelInput input)
{
OnnxModelOutput output = new OnnxModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data", input.data);
binding.Bind("classLabel", output.classLabel);
binding.Bind("loss", output.loss);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <OnnxModelOutput> EvaluateAsync(OnnxModelInput input)
{
binding.Bind("data", input.data);
var result = await session.EvaluateAsync(binding, string.Empty);
OnnxModelOutput output = new OnnxModelOutput();
output.classLabel = (result.Outputs["classLabel"] as TensorString).GetAsVectorView();
var predictions = result.Outputs["loss"] as IList <IDictionary <string, float> >;
output.loss = predictions[0];
return(output);
}
