Function copy_op_handler

tensorflow/contrib/graph_editor/transform.py:131–191

Copy a `tf.Operation`. Args: info: Transform._TmpInfo instance. op: the `tf.Operation` to be copied. new_inputs: The new inputs for this op. copy_shape: also copy the shape of the tensor nodedef_fn: If provided, a function that will be run on the NodeDef and should retur

(info, op, new_inputs, copy_shape=False, nodedef_fn=None)

def copy_op_handler(info, op, new_inputs, copy_shape=False, nodedef_fn=None):
  """Copy a `tf.Operation`.

  Args:
    info: Transform._TmpInfo instance.
    op: the `tf.Operation` to be copied.
    new_inputs: The new inputs for this op.
    copy_shape: also copy the shape of the tensor
    nodedef_fn: If provided, a function that will be run on the NodeDef
      and should return a mutated NodeDef before a new Operation is created.
      This is useful as certain features cannot be set on the Operation and
      must be modified in NodeDef.

  Returns:
    A `(op, op_outputs)` tuple containing the transformed op and its outputs.
  """
  # The `new_inputs` was added to this function. For compatibility reason,
  # let's raise an error if `new_inputs` is a boolean.
  if isinstance(new_inputs, bool):
    raise TypeError("the `new_inputs` argument must be an iterable.")

  # pylint: disable=protected-access

  # Clone the node def:
  node_def_ = deepcopy(op.node_def)

  # Transform name:
  name_ = info.new_name(op.name)
  name_ = info.graph_.unique_name(name_)
  node_def_.name = name_

  # Mutate NodeDef if requested:
  if nodedef_fn is not None:
    node_def_ = nodedef_fn(node_def_)

  # Copy the other inputs needed for initialization
  output_types_ = op._output_types[:]
  input_types_ = op._input_types[:]

  # Make a copy of the op_def too.
  # Its unique to every _type_ of Operation.
  op_def_ = deepcopy(op.op_def)

  # Initialize a new Operation instance
  op_ = tf_ops.Operation(node_def_, info.graph_, new_inputs, output_types_,
                         [], input_types_, None, op_def_)

  # copy the shape over
  if copy_shape:
    for t, t_ in zip(op.outputs, op_.outputs):
      t_.set_shape(t.get_shape())

  # Original op cannot be finalised here yet. Because some ops require this
  # attribute to exist, we will create a dummy original_op first and then
  # later finalise it with the actual original_op when all the ops have
  # been copied.
  # TODO(fkp): Stop worrying about _original_op and remove this code?
  if op._original_op: