public Interpreter(byte[] modelData, InterpreterOptions options)
{
modelDataHandle = GCHandle.Alloc(modelData, GCHandleType.Pinned);
IntPtr modelDataPtr = modelDataHandle.AddrOfPinnedObject();
model = TfLiteModelCreate(modelDataPtr, modelData.Length);
if (model == IntPtr.Zero) throw new Exception("Failed to create TensorFlowLite Model");
this.options = options ?? new InterpreterOptions();
interpreter = TfLiteInterpreterCreate(model, options.nativePtr);
if (interpreter == IntPtr.Zero) throw new Exception("Failed to create TensorFlowLite Interpreter");
}
public SmartReply(string modelPath, String[] responses)
{
this.responses = responses;
// No GPU
var options = new InterpreterOptions()
{
threads = 2
};
interpreter = new Interpreter(FileUtil.LoadFile(modelPath), options);
interpreter.LogIOInfo();
// interpreter.AllocateTensors();
}
public Bert(string modelPath, string vocabText)
{
var options = new InterpreterOptions()
{
threads = 2,
};
interpreter = new Interpreter(FileUtil.LoadFile(modelPath), options);
interpreter.LogIOInfo();
inputs0 = new int[MAX_SEQ_LENTH];
inputs1 = new int[MAX_SEQ_LENTH];
inputs2 = new int[MAX_SEQ_LENTH];
outputs0 = new float[MAX_SEQ_LENTH];
outputs1 = new float[MAX_SEQ_LENTH];
interpreter.AllocateTensors();
vocabularyTable = LoadVocabularies(vocabText);
}
public TextClassification(string modelPath, string vocabularyText)
{
vocabulary = BuildVocabulary(vocabularyText);
// NO GPU
var options = new InterpreterOptions()
{
threads = 2
};
interpreter = new Interpreter(FileUtil.LoadFile(modelPath), options);
var inputInfo = interpreter.GetInputTensorInfo(0);
var outputInfo = interpreter.GetOutputTensorInfo(0);
inputs = new float[inputInfo.shape[1]];
outputs = new float[outputInfo.shape[1]];
interpreter.ResizeInputTensor(0, inputInfo.shape);
interpreter.AllocateTensors();
}
