/** Initializes a {@code Classifier}. */
protected ClassifierBase(Activity activity, Device device, int numThreads)
{
intValues = new int[getImageSizeX() * getImageSizeY()];
tfliteModel = loadModelFile(activity);
switch (device)
{
case Device.NNAPI:
tfliteOptions.SetUseNNAPI(true);
break;
case Device.GPU:
//gpuDelegate = new Xamarin.TensorFlow.Lite. GpuDelegate();
//tfliteOptions.addDelegate(gpuDelegate);
break;
case Device.CPU:
break;
}
tfliteOptions.SetNumThreads(numThreads);
tflite = new Interpreter(tfliteModel, tfliteOptions);
labels = loadLabelList(activity);
imgData =
ByteBuffer.AllocateDirect(
DIM_BATCH_SIZE
* getImageSizeX()
* getImageSizeY()
* DIM_PIXEL_SIZE
* getNumBytesPerChannel());
imgData.Order(ByteOrder.NativeOrder());
//LOGGER.d("Created a Tensorflow Lite Image Classifier.");
}
public async Task<IEnumerable<Prediction>> ClassifyAsync(byte[] bytes)
{
var mappedByteBuffer = GetModelAsMappedByteBuffer();
//var interpreter = new Xamarin.TensorFlow.Lite.Interpreter(mappedByteBuffer);
System.Console.WriteLine($"Running Tensorflow interpreter");
System.Console.WriteLine($"Tensorflow runtime version {TensorFlowLite.RuntimeVersion()}");
System.Console.WriteLine($"Tensorflow schema version {TensorFlowLite.SchemaVersion()}");
var interpreterOptions = new Interpreter.Options();
//TODO: Pass from UI?
var numThreads = 1;
interpreterOptions.SetNumThreads(numThreads);
//TODO: Look into use of GPU delegate vs NNAPI
// https://developer.android.com/ndk/guides/neuralnetworks
interpreterOptions.SetUseNNAPI(true);
interpreterOptions.SetAllowFp16PrecisionForFp32(true);
//var interpreter = new Interpreter(mappedByteBuffer);
var interpreter = new Interpreter(mappedByteBuffer, interpreterOptions);
var tensor = interpreter.GetInputTensor(0);
var shape = tensor.Shape();
var width = shape[1];
var height = shape[2];
var labels = await LoadLabelsAsync(LabelsFileName);
var byteBuffer = GetPhotoAsByteBuffer(bytes, width, height);
//var outputLocations = new float[1][] { new float[labels.Count] };
var outputLocations = new[] { new float[labels.Count] };
var outputs = Java.Lang.Object.FromArray(outputLocations);
interpreter.Run(byteBuffer, outputs);
var classificationResult = outputs.ToArray<float[]>();
var result = new List<Prediction>();
for (var i = 0; i < labels.Count; i++)
{
var label = labels[i];
result.Add(new Prediction(label, classificationResult[0][i]));
}
//TODO: Consider using this event or MediatR to return results to view model
//https://blog.duijzer.com/posts/mvvmcross_with_mediatr/
PredictionCompleted?.Invoke(this, new PredictionCompletedEventArgs(result));
return result;
}
