Function find_all_hinted_output_nodes

tensorflow/lite/python/op_hint.py:1226–1258

Find all Ophints output nodes in the graph. This is used to get all the output nodes those are ophinted, it is important for operation like convert_variables_to_constants keep all ophints structure. Note: only one of session or graph_def should be used, not both. Why this can be useful? Som

(session=None, graph_def=None)

def find_all_hinted_output_nodes(session=None, graph_def=None):
  """Find all Ophints output nodes in the graph.

  This is used to get all the output nodes those are ophinted, it is important
  for operation like convert_variables_to_constants keep all ophints structure.
  Note: only one of session or graph_def should be used, not both.
  Why this can be useful? Some TensorFlow ops (e.g. bidirectional rnn), can
  generate multiple outputs for unfused subgraph. If not all output nodes are
  consumed, graph optimization can potentially drop the unused nodes and cause
  ophints in an invalid states (due to missing ophinted output nodes). So it's
  important for us to find all those hinted output nodes and make sure they're
  not discarded away.

  Args:
    session: A TensorFlow session that contains the graph to convert.
    graph_def: A graph def that we should convert.

  Returns:
    A list of OpHints output nodes.
  Raises:
    ValueError: If both session and graph_def are provided.
  """
  if session is not None and graph_def is not None:
    raise ValueError("Provide only one of session and graph_def.")
  hinted_outputs_nodes = []
  if session is not None:
    hints = _find_all_hints_in_nodes(session.graph_def.node)
  elif graph_def is not None:
    hints = _find_all_hints_in_nodes(graph_def.node)
  for hint in _six.itervalues(hints):
    _, output_nodes = hint.flattened_inputs_and_outputs()
    hinted_outputs_nodes.extend(output_nodes)
  return hinted_outputs_nodes


@_tf_export(v1=["lite.experimental.convert_op_hints_to_stubs"])