def run()

in tensorflow_examples/lite/model_maker/core/task/model_util.py [0:0]

• 29 lines of code
• 10 McCabe index (conditional complexity)

  def run(self, input_tensors):
    """Runs inference with the TFLite model.

    Args:
      input_tensors: List / Dict of the input tensors of the TFLite model. The
        order should be the same as the keras model if it's a list. It also
        accepts tensor directly if the model has only 1 input.

    Returns:
      List of the output tensors for multi-output models, otherwise just
        the output tensor. The order should be the same as the keras model.
    """

    if not isinstance(input_tensors, list) and \
       not isinstance(input_tensors, tuple) and \
       not isinstance(input_tensors, dict):
      input_tensors = [input_tensors]

    interpreter = self.interpreter

    # Reshape inputs
    for i, input_detail in enumerate(self.input_details):
      input_tensor = _get_input_tensor(input_tensors, self.input_details, i)
      interpreter.resize_tensor_input(input_detail['index'], input_tensor.shape)
    interpreter.allocate_tensors()

    # Feed input to the interpreter
    for i, input_detail in enumerate(self.input_details):
      input_tensor = _get_input_tensor(input_tensors, self.input_details, i)
      if input_detail['quantization'] != (DEFAULT_SCALE, DEFAULT_ZERO_POINT):
        # Quantize the input
        scale, zero_point = input_detail['quantization']
        input_tensor = input_tensor / scale + zero_point
        input_tensor = np.array(input_tensor, dtype=input_detail['dtype'])
      interpreter.set_tensor(input_detail['index'], input_tensor)

    interpreter.invoke()

    output_tensors = []
    for output_detail in self.output_details:
      output_tensor = interpreter.get_tensor(output_detail['index'])
      if output_detail['quantization'] != (DEFAULT_SCALE, DEFAULT_ZERO_POINT):
        # Dequantize the output
        scale, zero_point = output_detail['quantization']
        output_tensor = output_tensor.astype(np.float32)
        output_tensor = (output_tensor - zero_point) * scale
      output_tensors.append(output_tensor)

    if len(output_tensors) == 1:
      return output_tensors[0]
    return output_tensors