Function graph_replace

tensorflow/contrib/graph_editor/transform.py:709–752

Create a new graph which compute the targets from the replaced Tensors. Args: target_ts: a single tf.Tensor or an iterable of tf.Tensor. replacement_ts: dictionary mapping from original tensors to replaced tensors dst_scope: the destination scope. src_scope: the source scope.

(target_ts, replacement_ts, dst_scope="",
                  src_scope="", reuse_dst_scope=False)

def graph_replace(target_ts, replacement_ts, dst_scope="",
                  src_scope="", reuse_dst_scope=False):
  """Create a new graph which compute the targets from the replaced Tensors.

  Args:
    target_ts: a single tf.Tensor or an iterable of tf.Tensor.
    replacement_ts: dictionary mapping from original tensors to replaced tensors
    dst_scope: the destination scope.
    src_scope: the source scope.
    reuse_dst_scope: if True the dst_scope is re-used if it already exists.
      Otherwise, the scope is given a unique name based on the one given
      by appending an underscore followed by a digit (default).
  Returns:
    A single tf.Tensor or a list of target tf.Tensor, depending on
    the type of the input argument `target_ts`.
    The returned tensors are recomputed using the tensors from replacement_ts.
  Raises:
    ValueError: if the targets are not connected to replacement_ts.
  """
  # Identify operations in the graph that will change.
  # Start forward walk at Tensors that will be replaced, and
  # backward walk at the target output Tensors.
  flatten_target_ts = util.flatten_tree(target_ts)
  # Construct the forward control dependencies edges so that
  # the get_walks_intersection_ops can also traverse the
  # control dependencies.
  graph = util.get_unique_graph(flatten_target_ts, check_types=(tf_ops.Tensor))
  control_ios = util.ControlOutputs(graph)
  ops = select.get_walks_intersection_ops(
      list(replacement_ts), flatten_target_ts, control_ios=control_ios)
  if not ops:
    raise ValueError("Targets and replacements are not connected!")

  # Complete ops to avoid malformed control flow.
  # TODO(fkp): Consider moving this function deeper (in the transformer?).
  _add_control_flow_ops(ops, control_ios)

  # Create a copy of the relevant subgraph
  unused_sgv_, info = copy_with_input_replacements(
      ops, replacement_ts, None, dst_scope, src_scope, reuse_dst_scope)
  # Return the transformed targets but keep the original if the transformed
  # counterpart cannot be found
  missing_fn = lambda original_t: original_t
  return info.transformed(target_ts, missing_fn)