# Copyright 2018 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.

# ==============================================================================

# pylint: disable=unidiomatic-typecheck

"""Prototype decorator for defining legacy-graph-mode functions."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

from tensorflow.python.eager import function

from tensorflow.python.framework import func_graph

from tensorflow.python.ops import variable_scope

class VariableHolder(object):

"""Holds variables for a python function."""

def __init__(self, fn):

self._fn = fn

self._variables = []

def variable_creator_scope(self, next_creator, **kwargs):

v = next_creator(**kwargs)

self._variables.append(v)

return v

def __call__(self, *args, **kwargs):

with variable_scope.variable_creator_scope(self.variable_creator_scope):

return self._fn(*args, **kwargs)

def wrap_function(fn, signature, name=None):

"""Wraps the TF 1.x function fn into a graph function.

The python function `fn` will be called once with symbolic arguments specified

in the `signature`, traced, and turned into a graph function. Any variables

created by `fn` will be owned by the object returned by `wrap_function`. The

resulting graph function can be called with tensors which match the

signature.

```python

def f(x, do_add):

v = tf.Variable(5.0)

if do_add:

op = v.assign_add(x)

else:

op = v.assign_sub(x)

with tf.control_dependencies([op]):

return v.read_value()

f_add = tf.compat.v1.wrap_function(f, [tf.TensorSpec((), tf.float32), True])

assert float(f_add(1.0)) == 6.0

assert float(f_add(1.0)) == 7.0

# Can call tf.compat.v1.wrap_function again to get a new trace, a new set

# of variables, and possibly different non-template arguments.

f_sub= tf.compat.v1.wrap_function(f, [tf.TensorSpec((), tf.float32), False])

assert float(f_sub(1.0)) == 4.0

assert float(f_sub(1.0)) == 3.0

```

Args:

fn: python function to be wrapped

signature: the placeholder and python arguments to be passed to the

wrapped function

name: Optional. The name of the function.

Returns:

the wrapped graph function.

"""

holder = VariableHolder(fn)

fn = function.Function(

func_graph.func_graph_from_py_func(

name,

holder,

args=None, kwargs=None, signature=signature,

add_control_dependencies=False),

signature=signature)

fn._variable_holder = holder

return fn