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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.
# ==============================================================================
"""Utilities for exporting TensorFlow symbols to the API.
Exporting a function or a class:
To export a function or a class use tf_export decorator. For e.g.:
```python
@tf_export('foo', 'bar.foo')
def foo(...):
...
```
If a function is assigned to a variable, you can export it by calling
tf_export explicitly. For e.g.:
```python
foo = get_foo(...)
tf_export('foo', 'bar.foo')(foo)
```
Exporting a constant
```python
foo = 1
tf_export("consts.foo").export_constant(__name__, 'foo')
```
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import functools
import sys
from tensorflow.python.util import tf_decorator
ESTIMATOR_API_NAME = 'estimator'
TENSORFLOW_API_NAME = 'tensorflow'
_Attributes = collections.namedtuple(
'ExportedApiAttributes', ['names', 'constants'])
# Attribute values must be unique to each API.
API_ATTRS = {
TENSORFLOW_API_NAME: _Attributes(
'_tf_api_names',
'_tf_api_constants'),
ESTIMATOR_API_NAME: _Attributes(
'_estimator_api_names',
'_estimator_api_constants')
}
API_ATTRS_V1 = {
TENSORFLOW_API_NAME: _Attributes(
'_tf_api_names_v1',
'_tf_api_constants_v1'),
ESTIMATOR_API_NAME: _Attributes(
'_estimator_api_names_v1',
'_estimator_api_constants_v1')
}
class SymbolAlreadyExposedError(Exception):
"""Raised when adding API names to symbol that already has API names."""
pass
def get_canonical_name_for_symbol(symbol, api_name=TENSORFLOW_API_NAME):
"""Get canonical name for the API symbol.
Canonical name is the first non-deprecated endpoint name.
Args:
symbol: API function or class.
api_name: API name (tensorflow or estimator).
Returns:
Canonical name for the API symbol (for e.g. initializers.zeros) if
canonical name could be determined. Otherwise, returns None.
"""
if not hasattr(symbol, '__dict__'):
return None
api_names_attr = API_ATTRS[api_name].names
_, undecorated_symbol = tf_decorator.unwrap(symbol)
if api_names_attr not in undecorated_symbol.__dict__:
return None
api_names = getattr(undecorated_symbol, api_names_attr)
# TODO(annarev): may be add a separate deprecated attribute
# for estimator names.
deprecated_api_names = undecorated_symbol.__dict__.get(
'_tf_deprecated_api_names', [])
return get_canonical_name(api_names, deprecated_api_names)
def get_canonical_name(api_names, deprecated_api_names):
"""Get first non-deprecated endpoint name.
Args:
api_names: API names iterable.
deprecated_api_names: Deprecated API names iterable.
Returns:
Canonical name if there is at least one non-deprecated endpoint.
Otherwise returns None.
"""
return next(
(name for name in api_names if name not in deprecated_api_names),
None)
class api_export(object): # pylint: disable=invalid-name
"""Provides ways to export symbols to the TensorFlow API."""
def __init__(self, *args, **kwargs):
"""Export under the names *args (first one is considered canonical).
Args:
*args: API names in dot delimited format.
**kwargs: Optional keyed arguments.
v1: Names for the TensorFlow V1 API. If not set, we will use V2 API
names both for TensorFlow V1 and V2 APIs.
overrides: List of symbols that this is overriding
(those overrided api exports will be removed). Note: passing overrides
has no effect on exporting a constant.
api_name: Name of the API you want to generate (e.g. `tensorflow` or
`estimator`). Default is `tensorflow`.
"""
self._names = args
self._names_v1 = kwargs.get('v1', args)
self._api_name = kwargs.get('api_name', TENSORFLOW_API_NAME)
self._overrides = kwargs.get('overrides', [])
def __call__(self, func):
"""Calls this decorator.
Args:
func: decorated symbol (function or class).
Returns:
The input function with _tf_api_names attribute set.
Raises:
SymbolAlreadyExposedError: Raised when a symbol already has API names
and kwarg `allow_multiple_exports` not set.
"""
api_names_attr = API_ATTRS[self._api_name].names
api_names_attr_v1 = API_ATTRS_V1[self._api_name].names
# Undecorate overridden names
for f in self._overrides:
_, undecorated_f = tf_decorator.unwrap(f)
delattr(undecorated_f, api_names_attr)
delattr(undecorated_f, api_names_attr_v1)
_, undecorated_func = tf_decorator.unwrap(func)
self.set_attr(undecorated_func, api_names_attr, self._names)
self.set_attr(undecorated_func, api_names_attr_v1, self._names_v1)
return func
def set_attr(self, func, api_names_attr, names):
# Check for an existing api. We check if attribute name is in
# __dict__ instead of using hasattr to verify that subclasses have
# their own _tf_api_names as opposed to just inheriting it.
if api_names_attr in func.__dict__:
raise SymbolAlreadyExposedError(
'Symbol %s is already exposed as %s.' %
(func.__name__, getattr(func, api_names_attr))) # pylint: disable=protected-access
setattr(func, api_names_attr, names)
def export_constant(self, module_name, name):
"""Store export information for constants/string literals.
Export information is stored in the module where constants/string literals
are defined.
e.g.
```python
foo = 1
bar = 2
tf_export("consts.foo").export_constant(__name__, 'foo')
tf_export("consts.bar").export_constant(__name__, 'bar')
```
Args:
module_name: (string) Name of the module to store constant at.
name: (string) Current constant name.
"""
module = sys.modules[module_name]
api_constants_attr = API_ATTRS[self._api_name].constants
api_constants_attr_v1 = API_ATTRS_V1[self._api_name].constants
if not hasattr(module, api_constants_attr):
setattr(module, api_constants_attr, [])
# pylint: disable=protected-access
getattr(module, api_constants_attr).append(
(self._names, name))
if not hasattr(module, api_constants_attr_v1):
setattr(module, api_constants_attr_v1, [])
getattr(module, api_constants_attr_v1).append(
(self._names_v1, name))
tf_export = functools.partial(api_export, api_name=TENSORFLOW_API_NAME)
estimator_export = functools.partial(tf_export, api_name=ESTIMATOR_API_NAME)
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