path: root/tensorflow/contrib/graph_editor/select.py

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Various ways of selecting operations and tensors in a graph."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import re

from six import iteritems
from six import string_types

from tensorflow.contrib.graph_editor import util
from tensorflow.python.framework import ops as tf_ops

__all__ = [
    "can_be_regex",
    "make_regex",
    "filter_ts",
    "filter_ts_from_regex",
    "filter_ops",
    "filter_ops_from_regex",
    "get_name_scope_ops",
    "check_cios",
    "get_ops_ios",
    "compute_boundary_ts",
    "get_within_boundary_ops",
    "get_forward_walk_ops",
    "get_backward_walk_ops",
    "get_walks_intersection_ops",
    "get_walks_union_ops",
    "select_ops",
    "select_ts",
    "select_ops_and_ts",
]

_RE_TYPE = type(re.compile(""))


def can_be_regex(obj):
  """Return True if obj can be turned into a regular expression."""
  return isinstance(obj, string_types + (_RE_TYPE,))


def make_regex(obj):
  """Return a compiled regular expression.

  Args:
    obj: a string or a regular expression.
  Returns:
    A compiled regular expression.
  Raises:
    ValueError: if obj could not be converted to a regular expression.
  """
  if not can_be_regex(obj):
    raise ValueError("Expected a string or a regex, got: {}".format(type(obj)))

  if isinstance(obj, string_types):
    return re.compile(obj)
  else:
    return obj


def _get_input_ts(ops):
  """Compute the list of unique input tensors of all the op in ops.

  Args:
    ops: an object convertible to a list of `tf.Operation`.
  Returns:
    The list of unique input tensors of all the op in ops.
  Raises:
    TypeError: if ops cannot be converted to a list of `tf.Operation`.
  """
  ops = util.make_list_of_op(ops)
  ts = []
  ts_set = set()
  for op in ops:
    for t in op.inputs:
      if t not in ts_set:
        ts.append(t)
        ts_set.add(t)
  return ts


def _get_output_ts(ops):
  """Compute the list of unique output tensors of all the op in ops.

  Args:
    ops: an object convertible to a list of tf.Operation.
  Returns:
    The list of unique output tensors of all the op in ops.
  Raises:
    TypeError: if ops cannot be converted to a list of tf.Operation.
  """
  ops = util.make_list_of_op(ops)
  ts = []
  for op in ops:
    ts += op.outputs
  return ts


def filter_ts(ops, positive_filter):
  """Get all the tensors which are input or output of an op in ops.

  Args:
    ops: an object convertible to a list of `tf.Operation`.
    positive_filter: a function deciding whether to keep a tensor or not.
      If `True`, all the tensors are returned.
  Returns:
    A list of `tf.Tensor`.
  Raises:
    TypeError: if ops cannot be converted to a list of `tf.Operation`.
  """
  ops = util.make_list_of_op(ops)
  ts = _get_input_ts(ops)
  util.concatenate_unique(ts, _get_output_ts(ops))
  if positive_filter is not True:
    ts = [t for t in ts if positive_filter(t)]
  return ts


def filter_ts_from_regex(ops, regex):
  r"""Get all the tensors linked to ops that match the given regex.

  Args:
    ops: an object convertible to a list of tf.Operation.
    regex: a regular expression matching the tensors' name.
      For example, "^foo(/.*)?:\d+$" will match all the tensors in the "foo"
      scope.
  Returns:
    A list of tf.Tensor.
  Raises:
    TypeError: if ops cannot be converted to a list of tf.Operation.
  """
  ops = util.make_list_of_op(ops)
  regex_obj = make_regex(regex)
  return filter_ts(ops, positive_filter=lambda op: regex_obj.search(op.name))


def filter_ops(ops, positive_filter):
  """Get the ops passing the given filter.

  Args:
    ops: an object convertible to a list of tf.Operation.
    positive_filter: a function deciding where to keep an operation or not.
      If True, all the operations are returned.
  Returns:
    A list of selected tf.Operation.
  Raises:
    TypeError: if ops cannot be converted to a list of tf.Operation.
  """
  ops = util.make_list_of_op(ops)
  if positive_filter is not True:  # pylint: disable=g-explicit-bool-comparison
    ops = [op for op in ops if positive_filter(op)]
  return ops


def filter_ops_from_regex(ops, regex):
  """Get all the operations that match the given regex.

  Args:
    ops: an object convertible to a list of `tf.Operation`.
    regex: a regular expression matching the operation's name.
      For example, `"^foo(/.*)?$"` will match all the operations in the "foo"
      scope.
  Returns:
    A list of `tf.Operation`.
  Raises:
    TypeError: if ops cannot be converted to a list of `tf.Operation`.
  """
  ops = util.make_list_of_op(ops)
  regex_obj = make_regex(regex)
  return filter_ops(ops, lambda op: regex_obj.search(op.name))


def get_name_scope_ops(ops, scope):
  """Get all the operations under the given scope path.

  Args:
    ops: an object convertible to a list of tf.Operation.
    scope: a scope path.
  Returns:
    A list of tf.Operation.
  Raises:
    TypeError: if ops cannot be converted to a list of tf.Operation.
  """
  if scope and scope[-1] == "/":
    scope = scope[:-1]
  return filter_ops_from_regex(ops, "^{}(/.*)?$".format(scope))


def check_cios(control_inputs=False, control_outputs=None, control_ios=None):
  """Do various check on control_inputs and control_outputs.

  Args:
    control_inputs: A boolean indicating whether control inputs are enabled.
    control_outputs: An instance of util.ControlOutputs or None. If not None,
      control outputs are enabled.
    control_ios:  An instance of util.ControlOutputs or None. If not None, both
      control inputs and control outputs are enabled. This is equivalent to set
      control_inputs to True and control_outputs to the util.ControlOutputs
      instance.
  Returns:
    A tuple `(control_inputs, control_outputs)` where:
      `control_inputs` is a boolean indicating whether to use control inputs.
      `control_outputs` is an instance of util.ControlOutputs or None
  Raises:
    ValueError: if control_inputs is an instance of util.ControlOutputs but
      control_outputs is not None
    TypeError: if control_outputs is not None and is not a util.ControlOutputs.
  """
  if control_ios is not None:
    if not isinstance(control_ios, util.ControlOutputs):
      raise TypeError("Expected a util.ControlOutputs, got: {}".format(
          type(control_ios)))
    if control_outputs is not None:
      raise ValueError("control_outputs should be None when using control_ios.")
    control_inputs = True
    control_outputs = control_ios
  elif control_outputs is not None:
    if not isinstance(control_outputs, util.ControlOutputs):
      raise TypeError("Expected a util.ControlOutputs, got: {}".format(
          type(control_outputs)))

  if control_outputs is not None:
    control_outputs.update()
  return control_inputs, control_outputs


def get_ops_ios(ops, control_inputs=False, control_outputs=None,
                control_ios=None):
  """Return all the `tf.Operation` which are connected to an op in ops.

  Args:
    ops: an object convertible to a list of `tf.Operation`.
    control_inputs: A boolean indicating whether control inputs are enabled.
    control_outputs: An instance of `util.ControlOutputs` or `None`. If not
      `None`, control outputs are enabled.
    control_ios:  An instance of `util.ControlOutputs` or `None`. If not `None`,
      both control inputs and control outputs are enabled. This is equivalent to
      set `control_inputs` to `True` and `control_outputs` to the
      `util.ControlOutputs` instance.
  Returns:
    All the `tf.Operation` surrounding the given ops.
  Raises:
    TypeError: if `ops` cannot be converted to a list of `tf.Operation`.
  """
  control_inputs, control_outputs = check_cios(control_inputs, control_outputs,
                                               control_ios)
  ops = util.make_list_of_op(ops)
  res = []
  for op in ops:
    util.concatenate_unique(res, [t.op for t in op.inputs])
    for t in op.outputs:
      util.concatenate_unique(res, t.consumers())
    if control_outputs is not None:
      util.concatenate_unique(res, control_outputs.get(op))
    if control_inputs:
      util.concatenate_unique(res, op.control_inputs)
  return res


def compute_boundary_ts(ops):
  """Compute the tensors at the boundary of a set of ops.

  This function looks at all the tensors connected to the given ops (in/out)
  and classify them into three categories:
  1) input tensors: tensors whose generating operation is not in ops.
  2) output tensors: tensors whose consumer operations are not in ops
  3) inside tensors: tensors which are neither input nor output tensors.

  Note that a tensor can be both an inside tensor and an output tensor if it is
  consumed by operations both outside and inside of `ops`.

  Args:
    ops: an object convertible to a list of tf.Operation.
  Returns:
    A tuple `(outside_input_ts, outside_output_ts, inside_ts)` where:
      `outside_input_ts` is a Python list of input tensors;
      `outside_output_ts` is a python list of output tensors;
      `inside_ts` is a python list of inside tensors.
    Since a tensor can be both an inside tensor and an output tensor,
    `outside_output_ts` and `inside_ts` might intersect.
  Raises:
    TypeError: if ops cannot be converted to a list of tf.Operation.
  """
  ops = util.make_list_of_op(ops)
  input_ts = _get_input_ts(ops)
  output_ts = _get_output_ts(ops)
  output_ts_set = frozenset(output_ts)
  ops_set = frozenset(ops)

  # Compute inside tensors.
  inside_ts = []
  only_inside_ts = []
  for t in input_ts:
    # Skip if the input tensor is not also an output tensor.
    if t not in output_ts_set:
      continue
    # Mark as "inside".
    inside_ts.append(t)
    # Mark as "only inside" if the tensor is not both inside and output.
    consumers = frozenset(t.consumers())
    if consumers - ops_set:
      continue
    only_inside_ts.append(t)

  inside_ts_set = frozenset(inside_ts)
  only_inside_ts_set = frozenset(only_inside_ts)
  outside_output_ts = [t for t in output_ts if t not in only_inside_ts_set]
  outside_input_ts = [t for t in input_ts if t not in inside_ts_set]
  return outside_input_ts, outside_output_ts, inside_ts


def get_within_boundary_ops(ops,
                            seed_ops,
                            boundary_ops=(),
                            inclusive=True,
                            control_inputs=False,
                            control_outputs=None,
                            control_ios=None):
  """Return all the `tf.Operation` within the given boundary.

  Args:
    ops: an object convertible to a list of `tf.Operation`. those ops define the
      set in which to perform the operation (if a `tf.Graph` is given, it
      will be converted to the list of all its operations).
    seed_ops: the operations from which to start expanding.
    boundary_ops: the ops forming the boundary.
    inclusive: if `True`, the result will also include the boundary ops.
    control_inputs: A boolean indicating whether control inputs are enabled.
    control_outputs: An instance of `util.ControlOutputs` or `None`. If not
      `None`, control outputs are enabled.
    control_ios:  An instance of `util.ControlOutputs` or `None`. If not
      `None`, both control inputs and control outputs are enabled. This is
      equivalent to set control_inputs to True and control_outputs to
      the `util.ControlOutputs` instance.
  Returns:
    All the `tf.Operation` surrounding the given ops.
  Raises:
    TypeError: if `ops` or `seed_ops` cannot be converted to a list of
      `tf.Operation`.
    ValueError: if the boundary is intersecting with the seeds.
  """
  control_inputs, control_outputs = check_cios(control_inputs, control_outputs,
                                               control_ios)
  ops = util.make_list_of_op(ops)
  seed_ops = util.make_list_of_op(seed_ops, allow_graph=False)
  boundary_ops = set(util.make_list_of_op(boundary_ops))
  res = set(seed_ops)
  if boundary_ops & res:
    raise ValueError("Boundary is intersecting with the seeds.")
  wave = set(seed_ops)
  while wave:
    new_wave = set()
    ops_io = get_ops_ios(wave, control_inputs, control_outputs)
    for op in ops_io:
      if op in res:
        continue
      if op in boundary_ops:
        if inclusive:
          res.add(op)
      else:
        new_wave.add(op)
    res.update(new_wave)
    wave = new_wave
  return [op for op in ops if op in res]


def get_forward_walk_ops(seed_ops,
                         inclusive=True,
                         within_ops=None,
                         within_ops_fn=None,
                         stop_at_ts=(),
                         control_outputs=None):
  """Do a forward graph walk and return all the visited ops.

  Args:
    seed_ops: an iterable of operations from which the forward graph
      walk starts. If a list of tensors is given instead, the seed_ops are set
      to be the consumers of those tensors.
    inclusive: if True the given seed_ops are also part of the resulting set.
    within_ops: an iterable of `tf.Operation` within which the search is
      restricted. If `within_ops` is `None`, the search is performed within
      the whole graph.
    within_ops_fn: if provided, a function on ops that should return True iff
      the op is within the graph traversal. This can be used along within_ops,
      in which case an op is within if it is also in within_ops.
    stop_at_ts: an iterable of tensors at which the graph walk stops.
    control_outputs: a `util.ControlOutputs` instance or None.
      If not `None`, it will be used while walking the graph forward.
  Returns:
    A Python set of all the `tf.Operation` ahead of `seed_ops`.
  Raises:
    TypeError: if `seed_ops` or `within_ops` cannot be converted to a list of
      `tf.Operation`.
  """
  _, control_outputs = check_cios(False, control_outputs)
  if not util.is_iterable(seed_ops):
    seed_ops = [seed_ops]
  if not seed_ops:
    return []
  if isinstance(seed_ops[0], tf_ops.Tensor):
    ts = util.make_list_of_t(seed_ops, allow_graph=False)
    seed_ops = util.get_consuming_ops(ts)
  else:
    seed_ops = util.make_list_of_op(seed_ops, allow_graph=False)

  seed_ops = frozenset(seed_ops)
  stop_at_ts = frozenset(util.make_list_of_t(stop_at_ts))
  if within_ops:
    within_ops = util.make_list_of_op(within_ops, allow_graph=False)
    within_ops = frozenset(within_ops)
    seed_ops &= within_ops

  def is_within(op):
    return (within_ops is None or op in within_ops) and (
        within_ops_fn is None or within_ops_fn(op))

  result = list(seed_ops)
  wave = set(seed_ops)
  while wave:
    new_wave = set()
    for op in wave:
      for new_t in op.outputs:
        if new_t in stop_at_ts:
          continue
        for new_op in new_t.consumers():
          if new_op not in result and is_within(new_op):
            new_wave.add(new_op)
      if control_outputs is not None:
        for new_op in control_outputs.get(op):
          if new_op not in result and is_within(new_op):
            new_wave.add(new_op)
    util.concatenate_unique(result, new_wave)
    wave = new_wave
  if not inclusive:
    result = [op for op in result if op not in seed_ops]
  return result


def get_backward_walk_ops(seed_ops,
                          inclusive=True,
                          within_ops=None,
                          within_ops_fn=None,
                          stop_at_ts=(),
                          control_inputs=False):
  """Do a backward graph walk and return all the visited ops.

  Args:
    seed_ops: an iterable of operations from which the backward graph
      walk starts. If a list of tensors is given instead, the seed_ops are set
      to be the generators of those tensors.
    inclusive: if True the given seed_ops are also part of the resulting set.
    within_ops: an iterable of `tf.Operation` within which the search is
      restricted. If `within_ops` is `None`, the search is performed within
      the whole graph.
    within_ops_fn: if provided, a function on ops that should return True iff
      the op is within the graph traversal. This can be used along within_ops,
      in which case an op is within if it is also in within_ops.
    stop_at_ts: an iterable of tensors at which the graph walk stops.
    control_inputs: if True, control inputs will be used while moving backward.
  Returns:
    A Python set of all the `tf.Operation` behind `seed_ops`.
  Raises:
    TypeError: if `seed_ops` or `within_ops` cannot be converted to a list of
      `tf.Operation`.
  """
  if not util.is_iterable(seed_ops):
    seed_ops = [seed_ops]
  if not seed_ops:
    return []
  if isinstance(seed_ops[0], tf_ops.Tensor):
    ts = util.make_list_of_t(seed_ops, allow_graph=False)
    seed_ops = util.get_generating_ops(ts)
  else:
    seed_ops = util.make_list_of_op(seed_ops, allow_graph=False)

  stop_at_ts = frozenset(util.make_list_of_t(stop_at_ts))
  seed_ops = frozenset(util.make_list_of_op(seed_ops))
  if within_ops:
    within_ops = util.make_list_of_op(within_ops, allow_graph=False)
    within_ops = frozenset(within_ops)
    seed_ops &= within_ops

  def is_within(op):
    return (within_ops is None or op in within_ops) and (
        within_ops_fn is None or within_ops_fn(op))

  result = list(seed_ops)
  wave = set(seed_ops)
  while wave:
    new_wave = set()
    for op in wave:
      for new_t in op.inputs:
        if new_t in stop_at_ts:
          continue
        if new_t.op not in result and is_within(new_t.op):
          new_wave.add(new_t.op)
      if control_inputs:
        for new_op in op.control_inputs:
          if new_op not in result and is_within(new_op):
            new_wave.add(new_op)
    util.concatenate_unique(result, new_wave)
    wave = new_wave
  if not inclusive:
    result = [op for op in result if op not in seed_ops]
  return result


def get_walks_intersection_ops(forward_seed_ops,
                               backward_seed_ops,
                               forward_inclusive=True,
                               backward_inclusive=True,
                               within_ops=None,
                               within_ops_fn=None,
                               control_inputs=False,
                               control_outputs=None,
                               control_ios=None):
  """Return the intersection of a forward and a backward walk.

  Args:
    forward_seed_ops: an iterable of operations from which the forward graph
      walk starts. If a list of tensors is given instead, the seed_ops are set
      to be the consumers of those tensors.
    backward_seed_ops: an iterable of operations from which the backward graph
      walk starts. If a list of tensors is given instead, the seed_ops are set
      to be the generators of those tensors.
    forward_inclusive: if True the given forward_seed_ops are also part of the
      resulting set.
    backward_inclusive: if True the given backward_seed_ops are also part of the
      resulting set.
    within_ops: an iterable of tf.Operation within which the search is
      restricted. If within_ops is None, the search is performed within
      the whole graph.
    within_ops_fn: if provided, a function on ops that should return True iff
      the op is within the graph traversal. This can be used along within_ops,
      in which case an op is within if it is also in within_ops.
    control_inputs: A boolean indicating whether control inputs are enabled.
    control_outputs: An instance of util.ControlOutputs or None. If not None,
      control outputs are enabled.
    control_ios:  An instance of util.ControlOutputs or None. If not None, both
      control inputs and control outputs are enabled. This is equivalent to set
      control_inputs to True and control_outputs to the util.ControlOutputs
      instance.
  Returns:
    A Python set of all the tf.Operation in the intersection of a forward and a
      backward walk.
  Raises:
    TypeError: if `forward_seed_ops` or `backward_seed_ops` or `within_ops`
      cannot be converted to a list of `tf.Operation`.
  """
  control_inputs, control_outputs = check_cios(control_inputs, control_outputs,
                                               control_ios)
  forward_ops = get_forward_walk_ops(
      forward_seed_ops,
      inclusive=forward_inclusive,
      within_ops=within_ops,
      within_ops_fn=within_ops_fn,
      control_outputs=control_outputs)
  backward_ops = get_backward_walk_ops(
      backward_seed_ops,
      inclusive=backward_inclusive,
      within_ops=within_ops,
      within_ops_fn=within_ops_fn,
      control_inputs=control_inputs)
  return [op for op in forward_ops if op in backward_ops]


def get_walks_union_ops(forward_seed_ops,
                        backward_seed_ops,
                        forward_inclusive=True,
                        backward_inclusive=True,
                        within_ops=None,
                        within_ops_fn=None,
                        control_inputs=False,
                        control_outputs=None,
                        control_ios=None):
  """Return the union of a forward and a backward walk.

  Args:
    forward_seed_ops: an iterable of operations from which the forward graph
      walk starts. If a list of tensors is given instead, the seed_ops are set
      to be the consumers of those tensors.
    backward_seed_ops: an iterable of operations from which the backward graph
      walk starts. If a list of tensors is given instead, the seed_ops are set
      to be the generators of those tensors.
    forward_inclusive: if True the given forward_seed_ops are also part of the
      resulting set.
    backward_inclusive: if True the given backward_seed_ops are also part of the
      resulting set.
    within_ops: restrict the search within those operations. If within_ops is
      None, the search is done within the whole graph.
    within_ops_fn: if provided, a function on ops that should return True iff
      the op is within the graph traversal. This can be used along within_ops,
      in which case an op is within if it is also in within_ops.
    control_inputs: A boolean indicating whether control inputs are enabled.
    control_outputs: An instance of util.ControlOutputs or None. If not None,
      control outputs are enabled.
    control_ios:  An instance of util.ControlOutputs or None. If not None, both
      control inputs and control outputs are enabled. This is equivalent to set
      control_inputs to True and control_outputs to the util.ControlOutputs
      instance.
  Returns:
    A Python set of all the tf.Operation in the union of a forward and a
      backward walk.
  Raises:
    TypeError: if forward_seed_ops or backward_seed_ops or within_ops cannot be
      converted to a list of tf.Operation.
  """
  control_inputs, control_outputs = check_cios(control_inputs, control_outputs,
                                               control_ios)
  forward_ops = get_forward_walk_ops(
      forward_seed_ops,
      inclusive=forward_inclusive,
      within_ops=within_ops,
      within_ops_fn=within_ops_fn,
      control_outputs=control_outputs)
  backward_ops = get_backward_walk_ops(
      backward_seed_ops,
      inclusive=backward_inclusive,
      within_ops=within_ops,
      within_ops_fn=within_ops_fn,
      control_inputs=control_inputs)
  return util.concatenate_unique(forward_ops, backward_ops)


def select_ops(*args, **kwargs):
  """Helper to select operations.

  Args:
    *args: list of 1) regular expressions (compiled or not) or 2) (array of)
      `tf.Operation`. `tf.Tensor` instances are silently ignored.
    **kwargs: 'graph': `tf.Graph` in which to perform the regex query.This is
      required when using regex.
      'positive_filter': an elem if selected only if `positive_filter(elem)` is
        `True`. This is optional.
      'restrict_ops_regex': a regular expression is ignored if it doesn't start
        with the substring "(?#ops)".
  Returns:
    A list of `tf.Operation`.
  Raises:
    TypeError: if the optional keyword argument graph is not a `tf.Graph`
      or if an argument in args is not an (array of) `tf.Operation`
      or an (array of) `tf.Tensor` (silently ignored) or a string
      or a regular expression.
    ValueError: if one of the keyword arguments is unexpected or if a regular
      expression is used without passing a graph as a keyword argument.
  """
  # get keywords arguments
  graph = None
  positive_filter = None
  restrict_ops_regex = False
  for k, v in iteritems(kwargs):
    if k == "graph":
      graph = v
      if graph is not None and not isinstance(graph, tf_ops.Graph):
        raise TypeError("Expected a tf.Graph, got: {}".format(type(graph)))
    elif k == "positive_filter":
      positive_filter = v
    elif k == "restrict_ops_regex":
      restrict_ops_regex = v
    elif k == "restrict_ts_regex":
      pass
    else:
      raise ValueError("Wrong keywords argument: {}.".format(k))

  ops = []

  for arg in args:
    if can_be_regex(arg):
      if graph is None:
        raise ValueError("Use the keyword argument 'graph' to use regex.")
      regex = make_regex(arg)
      if regex.pattern.startswith("(?#ts)"):
        continue
      if restrict_ops_regex and not regex.pattern.startswith("(?#ops)"):
        continue
      ops_ = filter_ops_from_regex(graph, regex)
      for op_ in ops_:
        if op_ not in ops:
          if positive_filter is None or positive_filter(op_):
            ops.append(op_)
    else:
      ops_aux = util.make_list_of_op(arg, ignore_ts=True)
      if positive_filter is not None:
        ops_aux = [op for op in ops_aux if positive_filter(op)]
      ops_aux = [op for op in ops_aux if op not in ops]
      ops += ops_aux

  return ops


def select_ts(*args, **kwargs):
  """Helper to select tensors.

  Args:
    *args: list of 1) regular expressions (compiled or not) or 2) (array of)
      `tf.Tensor`. `tf.Operation` instances are silently ignored.
    **kwargs: 'graph': `tf.Graph` in which to perform the regex query.This is
      required when using regex.
      'positive_filter': an elem if selected only if `positive_filter(elem)` is
        `True`. This is optional.
      'restrict_ts_regex': a regular expression is ignored if it doesn't start
        with the substring "(?#ts)".
  Returns:
    A list of `tf.Tensor`.
  Raises:
    TypeError: if the optional keyword argument graph is not a `tf.Graph`
      or if an argument in args is not an (array of) `tf.Tensor`
      or an (array of) `tf.Operation` (silently ignored) or a string
      or a regular expression.
    ValueError: if one of the keyword arguments is unexpected or if a regular
      expression is used without passing a graph as a keyword argument.
  """
  # get keywords arguments
  graph = None
  positive_filter = None
  restrict_ts_regex = False
  for k, v in iteritems(kwargs):
    if k == "graph":
      graph = v
      if graph is not None and not isinstance(graph, tf_ops.Graph):
        raise TypeError("Expected a tf.Graph, got {}".format(type(graph)))
    elif k == "positive_filter":
      positive_filter = v
    elif k == "restrict_ts_regex":
      restrict_ts_regex = v
    elif k == "restrict_ops_regex":
      pass
    else:
      raise ValueError("Wrong keywords argument: {}.".format(k))

  ts = []

  for arg in args:
    if can_be_regex(arg):
      if graph is None:
        raise ValueError("Use the keyword argument 'graph' to use regex.")
      regex = make_regex(arg)
      if regex.pattern.startswith("(?#ops)"):
        continue
      if restrict_ts_regex and not regex.pattern.startswith("(?#ts)"):
        continue
      ts_ = filter_ts_from_regex(graph, regex)
      for t_ in ts_:
        if t_ not in ts:
          if positive_filter is None or positive_filter(t_):
            ts.append(t_)
    else:
      ts_aux = util.make_list_of_t(arg, ignore_ops=True)
      if positive_filter is not None:
        ts_aux = [t for t in ts_aux if positive_filter(t)]
      ts_aux = [t for t in ts_aux if t not in ts]
      ts += ts_aux

  return ts


def select_ops_and_ts(*args, **kwargs):
  """Helper to select operations and tensors.

  Args:
    *args: list of 1) regular expressions (compiled or not) or 2) (array of)
      `tf.Operation` 3) (array of) tf.Tensor. Regular expressions matching
      tensors must start with the comment `"(?#ts)"`, for instance:
      `"(?#ts)^foo/.*"`.
    **kwargs: 'graph': `tf.Graph` in which to perform the regex query.This is
      required when using regex.
      'positive_filter': an elem if selected only if `positive_filter(elem)` is
        `True`. This is optional.
  Returns:
    A tuple `(ops, ts)` where:
      `ops` is a list of `tf.Operation`, and
      `ts` is a list of `tf.Tensor`
  Raises:
    TypeError: if the optional keyword argument graph is not a `tf.Graph`
      or if an argument in args is not an (array of) `tf.Tensor`
      or an (array of) `tf.Operation` or a string or a regular expression.
    ValueError: if one of the keyword arguments is unexpected or if a regular
      expression is used without passing a graph as a keyword argument.
  """
  ops = select_ops(*args, restrict_ops_regex=False, **kwargs)
  ts = select_ts(*args, restrict_ts_regex=True, **kwargs)
  return ops, ts