private async Task LoadModelAsync()
{
Debug.Write("LoadModelBegin | ");
Debug.Write("LoadModel Lock | ");
_binding?.Clear();
_session?.Dispose();
StorageFile modelFile = await StorageFile.GetFileFromApplicationUriAsync(new Uri($"ms-appx:///Assets/{_appModel.ModelSource}.onnx"));
_learningModel = await LearningModel.LoadFromStorageFileAsync(modelFile);
_inferenceDeviceSelected = UseGpu ? LearningModelDeviceKind.DirectX : LearningModelDeviceKind.Cpu;
// Lock so can't create a new session or binding while also being disposed
lock (_processLock)
{
_session = new LearningModelSession(_learningModel, new LearningModelDevice(_inferenceDeviceSelected));
_binding = new LearningModelBinding(_session);
}
debugModelIO();
_inputImageDescription = _learningModel.InputFeatures.ToList().First().Name;
_outputImageDescription = _learningModel.OutputFeatures.ToList().First().Name;
Debug.Write("LoadModel Unlock\n");
}
public async Task <Output> Evaluate(Input input)
{
binding.Clear();
binding.Bind(inputParameterName, input.image);
var result = await session.EvaluateAsync(binding, "0");
var output = new Output();
output.grid = result.Outputs[outputParameterName] as TensorFloat;
return(output);
}
private async Task <List <float> > EvaluateFrame(VideoFrame frame)
{
_binding.Clear();
_binding.Bind("input_1:0", frame);
var results = await _session.EvaluateAsync(_binding, "");
TensorFloat result = results.Outputs["Identity:0"] as TensorFloat;
var shape = result.Shape;
var data = result.GetAsVectorView();
return(data.ToList <float>());
}
internal async Task <List <DetectionResult> > EvaluateFrame(VideoFrame frame)
{
_binding.Clear();
_binding.Bind("input_1:0", frame);
var results = await _session.EvaluateAsync(_binding, "");
TensorFloat result = results.Outputs["Identity:0"] as TensorFloat;
var shape = result.Shape;
var data = result.GetAsVectorView();
var detections = ParseResult(data.ToList <float>().ToArray());
Comparer cp = new Comparer();
detections.Sort(cp);
return(NMS(detections));
}
