public async Task <CustomNetworkOutput> EvaluateAsync(CustomNetworkInput input)
{
// Ensure the input and output fields are bound to the correct
// layer names in the onnx model
binding.Bind("input_2", input.features);
var result = await session.EvaluateAsync(binding, "0");
var output = new CustomNetworkOutput();
output.prediction = result.Outputs["Identity"] as TensorFloat;
return(output);
}
public async Task <NetworkResult> EvaluateVideoFrameAsync(
VideoFrame inputFrame)
{
// Sometimes on HL RS4 the D3D surface returned is null, so simply skip those frames
if (_customNetworkModel == null || inputFrame == null || (inputFrame.Direct3DSurface == null && inputFrame.SoftwareBitmap == null))
{
UnityEngine.Debug.Log("EvaluateVideoFrameAsync: No detection, null frame or model not initialized.");
return(new NetworkResult("None", 0f, 0));;
}
// Cache the input video frame to network input
_customNetworkInput.features = ImageFeatureValue.CreateFromVideoFrame(inputFrame);
// Perform network model inference using the input data tensor, cache output and time operation
var stopwatch = Stopwatch.StartNew();
_customNetworkOutput = await _customNetworkModel.EvaluateAsync(_customNetworkInput);
stopwatch.Stop();
// Convert prediction to datatype
var outVec = _customNetworkOutput.prediction.GetAsVectorView().ToList();
// LINQ query to check for highest probability digit
if (outVec.Max() > DetectionThreshold)
{
// Get the index of max probability value
var maxProb = outVec.Max();
var maxIndex = outVec.IndexOf(maxProb);
UnityEngine.Debug.Log($"EvaluateVideoFrameAsync: Prediction [{_labels[maxIndex]}] time: [{stopwatch.ElapsedMilliseconds} ms]");
// Return the detections
return(new NetworkResult(_labels[maxIndex], maxProb, stopwatch.ElapsedMilliseconds));
}
else
{
return(new NetworkResult("No prediction exceeded probability threshold.", 0f, stopwatch.ElapsedMilliseconds));;
}
}
