STT-tensorflow/tensorflow/python/util/module_wrapper.py

240 lines
8.2 KiB
Python

# Copyright 2019 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.
# ==============================================================================
"""Provides wrapper for TensorFlow modules."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import importlib
import types
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import tf_decorator
from tensorflow.python.util import tf_inspect
from tensorflow.python.util import tf_stack
from tensorflow.tools.compatibility import all_renames_v2
_PER_MODULE_WARNING_LIMIT = 1
def get_rename_v2(name):
if name not in all_renames_v2.symbol_renames:
return None
return all_renames_v2.symbol_renames[name]
def _call_location():
# We want to get stack frame 3 frames up from current frame,
# i.e. above __getattr__, _tfmw_add_deprecation_warning,
# and _call_location calls.
stack = tf_stack.extract_stack(limit=4)
if not stack: # should never happen as we're in a function
return 'UNKNOWN'
frame = stack[0]
return '{}:{}'.format(frame.filename, frame.lineno)
def contains_deprecation_decorator(decorators):
return any(
d.decorator_name == 'deprecated' for d in decorators)
def has_deprecation_decorator(symbol):
"""Checks if given object has a deprecation decorator.
We check if deprecation decorator is in decorators as well as
whether symbol is a class whose __init__ method has a deprecation
decorator.
Args:
symbol: Python object.
Returns:
True if symbol has deprecation decorator.
"""
decorators, symbol = tf_decorator.unwrap(symbol)
if contains_deprecation_decorator(decorators):
return True
if tf_inspect.isfunction(symbol):
return False
if not tf_inspect.isclass(symbol):
return False
if not hasattr(symbol, '__init__'):
return False
init_decorators, _ = tf_decorator.unwrap(symbol.__init__)
return contains_deprecation_decorator(init_decorators)
class TFModuleWrapper(types.ModuleType):
"""Wrapper for TF modules to support deprecation messages and lazyloading."""
def __init__( # pylint: disable=super-on-old-class
self,
wrapped,
module_name,
public_apis=None,
deprecation=True,
has_lite=False): # pylint: enable=super-on-old-class
super(TFModuleWrapper, self).__init__(wrapped.__name__)
# A cache for all members which do not print deprecations (any more).
self._tfmw_attr_map = {}
self.__dict__.update(wrapped.__dict__)
# Prefix all local attributes with _tfmw_ so that we can
# handle them differently in attribute access methods.
self._tfmw_wrapped_module = wrapped
self._tfmw_module_name = module_name
self._tfmw_public_apis = public_apis
self._tfmw_print_deprecation_warnings = deprecation
self._tfmw_has_lite = has_lite
# Set __all__ so that import * work for lazy loaded modules
if self._tfmw_public_apis:
self._tfmw_wrapped_module.__all__ = list(self._tfmw_public_apis.keys())
self.__all__ = list(self._tfmw_public_apis.keys())
else:
if hasattr(self._tfmw_wrapped_module, '__all__'):
self.__all__ = self._tfmw_wrapped_module.__all__
else:
self._tfmw_wrapped_module.__all__ = [
attr for attr in dir(self._tfmw_wrapped_module)
if not attr.startswith('_')
]
self.__all__ = self._tfmw_wrapped_module.__all__
# names we already checked for deprecation
self._tfmw_deprecated_checked = set()
self._tfmw_warning_count = 0
def _tfmw_add_deprecation_warning(self, name, attr):
"""Print deprecation warning for attr with given name if necessary."""
if (self._tfmw_warning_count < _PER_MODULE_WARNING_LIMIT and
name not in self._tfmw_deprecated_checked):
self._tfmw_deprecated_checked.add(name)
if self._tfmw_module_name:
full_name = 'tf.%s.%s' % (self._tfmw_module_name, name)
else:
full_name = 'tf.%s' % name
rename = get_rename_v2(full_name)
if rename and not has_deprecation_decorator(attr):
call_location = _call_location()
# skip locations in Python source
if not call_location.startswith('<'):
logging.warning(
'From %s: The name %s is deprecated. Please use %s instead.\n',
_call_location(), full_name, rename)
self._tfmw_warning_count += 1
return True
return False
def _tfmw_import_module(self, name):
symbol_loc_info = self._tfmw_public_apis[name]
if symbol_loc_info[0]:
module = importlib.import_module(symbol_loc_info[0])
attr = getattr(module, symbol_loc_info[1])
else:
attr = importlib.import_module(symbol_loc_info[1])
setattr(self._tfmw_wrapped_module, name, attr)
self.__dict__[name] = attr
return attr
def __getattribute__(self, name): # pylint: disable=super-on-old-class
# Handle edge case where we unpickle and the object is not initialized yet
# and does not have _tfmw_attr_map attribute. Otherwise, calling
# __getattribute__ on __setstate__ will result in infinite recursion where
# we keep trying to get _tfmw_wrapped_module in __getattr__.
try:
attr_map = object.__getattribute__(self, '_tfmw_attr_map')
except AttributeError:
self._tfmw_attr_map = attr_map = {}
try:
# Use cached attrs if available
return attr_map[name]
except KeyError:
# Make sure we do not import from tensorflow/lite/__init__.py
if name == 'lite':
if self._tfmw_has_lite:
attr = self._tfmw_import_module(name)
setattr(self._tfmw_wrapped_module, 'lite', attr)
attr_map[name] = attr
return attr
# Placeholder for Google-internal contrib error
attr = super(TFModuleWrapper, self).__getattribute__(name)
# Return and cache dunders and our own members.
if name.startswith('__') or name.startswith('_tfmw_'):
attr_map[name] = attr
return attr
# Print deprecations, only cache functions after deprecation warnings have
# stopped.
if not (self._tfmw_print_deprecation_warnings and
self._tfmw_add_deprecation_warning(name, attr)):
attr_map[name] = attr
return attr
def __getattr__(self, name):
try:
attr = getattr(self._tfmw_wrapped_module, name)
except AttributeError:
# Placeholder for Google-internal contrib error
if not self._tfmw_public_apis:
raise
if name not in self._tfmw_public_apis:
raise
attr = self._tfmw_import_module(name)
if self._tfmw_print_deprecation_warnings:
self._tfmw_add_deprecation_warning(name, attr)
return attr
def __setattr__(self, arg, val): # pylint: disable=super-on-old-class
if not arg.startswith('_tfmw_'):
setattr(self._tfmw_wrapped_module, arg, val)
self.__dict__[arg] = val
if arg not in self.__all__ and arg != '__all__':
self.__all__.append(arg)
if arg in self._tfmw_attr_map:
self._tfmw_attr_map[arg] = val
super(TFModuleWrapper, self).__setattr__(arg, val)
def __dir__(self):
if self._tfmw_public_apis:
return list(
set(self._tfmw_public_apis.keys()).union(
set([
attr for attr in dir(self._tfmw_wrapped_module)
if not attr.startswith('_')
])))
else:
return dir(self._tfmw_wrapped_module)
def __delattr__(self, name): # pylint: disable=super-on-old-class
if name.startswith('_tfmw_'):
super(TFModuleWrapper, self).__delattr__(name)
else:
delattr(self._tfmw_wrapped_module, name)
def __repr__(self):
return self._tfmw_wrapped_module.__repr__()
def __reduce__(self):
return importlib.import_module, (self.__name__,)