Class Interpreter

tensorflow/lite/python/interpreter.py:173–456

Interpreter interface for TensorFlow Lite Models. This makes the TensorFlow Lite interpreter accessible in Python. It is possible to use this interpreter in a multithreaded Python environment, but you must be sure to call functions of a particular instance from only one thread at a time. So

@_tf_export('lite.Interpreter')
class Interpreter(object):
  """Interpreter interface for TensorFlow Lite Models.

  This makes the TensorFlow Lite interpreter accessible in Python.
  It is possible to use this interpreter in a multithreaded Python environment,
  but you must be sure to call functions of a particular instance from only
  one thread at a time. So if you want to have 4 threads running different
  inferences simultaneously, create  an interpreter for each one as thread-local
  data. Similarly, if you are calling invoke() in one thread on a single
  interpreter but you want to use tensor() on another thread once it is done,
  you must use a synchronization primitive between the threads to ensure invoke
  has returned before calling tensor().
  """

  def __init__(self,
               model_path=None,
               model_content=None,
               experimental_delegates=None):
    """Constructor.

    Args:
      model_path: Path to TF-Lite Flatbuffer file.
      model_content: Content of model.
      experimental_delegates: Experimental. Subject to change. List of
        [TfLiteDelegate](https://www.tensorflow.org/lite/performance/delegates)
        objects returned by lite.load_delegate().

    Raises:
      ValueError: If the interpreter was unable to create.
    """
    if model_path and not model_content:
      self._interpreter = (
          _interpreter_wrapper.InterpreterWrapper_CreateWrapperCPPFromFile(
              model_path))
      if not self._interpreter:
        raise ValueError('Failed to open {}'.format(model_path))
    elif model_content and not model_path:
      # Take a reference, so the pointer remains valid.
      # Since python strings are immutable then PyString_XX functions
      # will always return the same pointer.
      self._model_content = model_content
      self._interpreter = (
          _interpreter_wrapper.InterpreterWrapper_CreateWrapperCPPFromBuffer(
              model_content))
    elif not model_path and not model_path:
      raise ValueError('`model_path` or `model_content` must be specified.')
    else:
      raise ValueError('Can\'t both provide `model_path` and `model_content`')

    # Each delegate is a wrapper that owns the delegates that have been loaded
    # as plugins. The interpreter wrapper will be using them, but we need to
    # hold them in a list so that the lifetime is preserved at least as long as
    # the interpreter wrapper.
    self._delegates = []
    if experimental_delegates:
      self._delegates = experimental_delegates
      for delegate in self._delegates:
        self._interpreter.ModifyGraphWithDelegate(