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# Copyright 2017 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.
# ==============================================================================
"""Decorator that provides a warning if the wrapped object is never used."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import sys
import traceback
import six # pylint: disable=unused-import
from tensorflow.python.platform import tf_logging
from tensorflow.python.util import tf_decorator
# pylint: enable=g-bad-import-order,g-import-not-at-top
class _TFShouldUseHelper(object):
"""Object stored in TFShouldUse-wrapped objects.
When it is deleted it will emit a warning or error if its `sate` method
has not been called by time of deletion.
"""
def __init__(self, type_, repr_, stack_frame, fatal_error_if_unsated):
self._type = type_
self._repr = repr_
self._stack_frame = stack_frame
self._fatal_error_if_unsated = fatal_error_if_unsated
self._sated = False
def sate(self):
self._sated = True
self._type = None
self._repr = None
self._stack_frame = None
self._logging_module = None
def __del__(self):
if self._sated:
return
if self._fatal_error_if_unsated:
logger = tf_logging.fatal
else:
logger = tf_logging.error
creation_stack = ''.join(
[line.rstrip() for line in traceback.format_stack(self._stack_frame)])
logger(
'==================================\n'
'Object was never used (type %s):\n%s\nIf you want to mark it as '
'used call its "mark_used()" method.\nIt was originally created '
'here:\n%s\n'
'==================================' %
(self._type, self._repr, creation_stack))
def _new__init__(self, true_value, tf_should_use_helper):
# pylint: disable=protected-access
self._tf_should_use_helper = tf_should_use_helper
self._true_value = true_value
def _new__setattr__(self, key, value):
if key in ('_tf_should_use_helper', '_true_value'):
return object.__setattr__(self, key, value)
return setattr(
object.__getattribute__(self, '_true_value'),
key, value)
def _new__getattribute__(self, key):
if key not in ('_tf_should_use_helper', '_true_value'):
object.__getattribute__(self, '_tf_should_use_helper').sate()
if key in ('_tf_should_use_helper', 'mark_used', '__setatt__'):
return object.__getattribute__(self, key)
return getattr(object.__getattribute__(self, '_true_value'), key)
def _new_mark_used(self, *args, **kwargs):
object.__getattribute__(self, '_tf_should_use_helper').sate()
try:
mu = object.__getattribute__(
object.__getattribute__(self, '_true_value'),
'mark_used')
return mu(*args, **kwargs)
except AttributeError:
pass
_WRAPPERS = dict()
def _get_wrapper(x, tf_should_use_helper):
"""Create a wrapper for object x, whose class subclasses type(x).
The wrapper will emit a warning if it is deleted without any of its
properties being accessed or methods being called.
Args:
x: The instance to wrap.
tf_should_use_helper: The object that tracks usage.
Returns:
An object wrapping `x`, of type `type(x)`.
"""
type_x = type(x)
memoized = _WRAPPERS.get(type_x, None)
if memoized:
return memoized(x, tf_should_use_helper)
tx = copy.deepcopy(type_x)
copy_tx = type(tx.__name__, tx.__bases__, dict(tx.__dict__))
copy_tx.__init__ = _new__init__
copy_tx.__getattribute__ = _new__getattribute__
copy_tx.mark_used = _new_mark_used
copy_tx.__setattr__ = _new__setattr__
_WRAPPERS[type_x] = copy_tx
return copy_tx(x, tf_should_use_helper)
def _add_should_use_warning(x, fatal_error=False):
"""Wraps object x so that if it is never used, a warning is logged.
Args:
x: Python object.
fatal_error: Python bool. If `True`, tf.logging.fatal is raised
if the returned value is never used.
Returns:
An instance of `TFShouldUseWarningWrapper` which subclasses `type(x)`
and is a very shallow wrapper for `x` which logs access into `x`.
"""
if x is None or x == []: # pylint: disable=g-explicit-bool-comparison
return x
# Extract the current frame for later use by traceback printing.
try:
raise ValueError()
except ValueError:
stack_frame = sys.exc_info()[2].tb_frame.f_back
tf_should_use_helper = _TFShouldUseHelper(
type_=type(x),
repr_=repr(x),
stack_frame=stack_frame,
fatal_error_if_unsated=fatal_error)
return _get_wrapper(x, tf_should_use_helper)
def should_use_result(fn):
"""Function wrapper that ensures the function's output is used.
If the output is not used, a `tf.logging.error` is logged.
An output is marked as used if any of its attributes are read, modified, or
updated. Examples when the output is a `Tensor` include:
- Using it in any capacity (e.g. `y = t + 0`, `sess.run(t)`)
- Accessing a property (e.g. getting `t.name` or `t.op`).
Note, certain behaviors cannot be tracked - for these the object may not
be marked as used. Examples include:
- `t != 0`. In this case, comparison is done on types / ids.
- `isinstance(t, tf.Tensor)`. Similar to above.
Args:
fn: The function to wrap.
Returns:
The wrapped function.
"""
def wrapped(*args, **kwargs):
return _add_should_use_warning(fn(*args, **kwargs))
return tf_decorator.make_decorator(
fn, wrapped, 'should_use_result',
((fn.__doc__ or '') +
('\n\n '
'**NOTE** The output of this function should be used. If it is not, '
'a warning will be logged. To mark the output as used, '
'call its .mark_used() method.')))
def must_use_result_or_fatal(fn):
"""Function wrapper that ensures the function's output is used.
If the output is not used, a `tf.logging.fatal` error is raised.
An output is marked as used if any of its attributes are read, modified, or
updated. Examples when the output is a `Tensor` include:
- Using it in any capacity (e.g. `y = t + 0`, `sess.run(t)`)
- Accessing a property (e.g. getting `t.name` or `t.op`).
Note, certain behaviors cannot be tracked - for these the object may not
be marked as used. Examples include:
- `t != 0`. In this case, comparison is done on types / ids.
- `isinstance(t, tf.Tensor)`. Similar to above.
Args:
fn: The function to wrap.
Returns:
The wrapped function.
"""
def wrapped(*args, **kwargs):
return _add_should_use_warning(fn(*args, **kwargs), fatal_error=True)
return tf_decorator.make_decorator(
fn, wrapped, 'must_use_result_or_fatal',
((fn.__doc__ or '') +
('\n\n '
'**NOTE** The output of this function must be used. If it is not, '
'a fatal error will be raised. To mark the output as used, '
'call its .mark_used() method.')))
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