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# 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.
# ==============================================================================
"""## Script Language Operators.
TensorFlow provides allows you to wrap python/numpy functions as
TensorFlow operators.
"""
# pylint: disable=g-bad-name
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.framework import common_shapes
from tensorflow.python.framework import ops
from tensorflow.python.ops import gen_script_ops
class FuncRegistry(object):
"""A helper class to keep track of registered py functions.
FuncRegistry keeps a map from unique tokens (string) to python
functions, which takes numpy arrays and outputs numpy arrays.
"""
def __init__(self):
self._unique_id = 0
self._funcs = {}
def insert(self, func):
"""Registers `func` and returns a unique token for this entry."""
token = self._next_unique_token()
self._funcs[token] = func
return token
def remove(self, token):
"""Removes the registered function corresponding to `token`."""
self._funcs.pop(token, None)
@staticmethod
def _convert(value):
"""Converts an arg to numpy, avoiding dangerous string and unicode dtypes.
Numpy pads with zeros when using string and unicode dtypes if different
components of a tensor have different lengths. This is bad: ignoring the
padding is wrong for text data, and removing the padding is wrong for binary
data. To avoid this bug, we redo the conversion using an object dtype.
Args:
value: Value to convert to a numpy array.
Returns:
A numpy array.
"""
result = np.asarray(value, order="C")
if result.dtype.char in "SU" and result is not value:
return np.asarray(value, order="C", dtype=object)
return result
def __call__(self, token, args):
"""Calls the registered function for `token` with args."""
func = self._funcs[token]
if func is None:
raise ValueError("callback %s is not found" % token)
ret = func(*args)
# Ensures that we return either a single numpy array or a list of numpy
# arrays.
if isinstance(ret, (tuple, list)):
return [self._convert(x) for x in ret]
else:
return self._convert(ret)
def size(self):
"""Returns how many functions are currently registered."""
return len(self._funcs)
def _next_unique_token(self):
"""Returns a unique token."""
uid = self._unique_id
self._unique_id += 1
return "pyfunc_%d" % uid
# Global registry for py functions.
_py_funcs = FuncRegistry()
pywrap_tensorflow.InitializePyTrampoline(_py_funcs)
class CleanupFunc(object):
"""A helper class to remove a registered function from _py_funcs."""
def __init__(self, token):
self._token = token
def __del__(self):
_py_funcs.remove(self._token)
def py_func(func, inp, Tout, stateful=True, name=None):
"""Wraps a python function and uses it as a tensorflow op.
Given a python function `func`, which takes numpy arrays as its
inputs and returns numpy arrays as its outputs. E.g.,
```python
def my_func(x):
# x will be a numpy array with the contents of the placeholder below
return np.sinh(x)
inp = tf.placeholder(tf.float32, [...])
y = py_func(my_func, [inp], [tf.float32])
```
The above snippet constructs a tf graph which invokes a numpy
sinh(x) as an op in the graph.
Args:
func: A python function.
inp: A list of `Tensor`.
Tout: A list of tensorflow data types indicating what `func`
returns.
stateful: A boolean indicating whether the function should be considered
stateful or stateless. I.e. whether it, given the same input, will
return the same output and at the same time does not change state
in an observable way. Optimizations such as common subexpression
elimination are only possible when operations are stateless.
name: A name for the operation (optional).
Returns:
A list of `Tensor` which `func` computes.
"""
token = _py_funcs.insert(func)
# We tie the registered function's life-time with the current
# default graph. I.e., when the current graph is destroyed, we
# should remove its py funcs.
cleanup = CleanupFunc(token)
g = ops.get_default_graph()
# pylint: disable=protected-access
#
# TODO(zhifengc): Consider adding a Graph method to collect
# `cleanup` objects in one of its member.
if not hasattr(g, "_cleanup_py_funcs_used_in_graph"):
g._cleanup_py_funcs_used_in_graph = []
# When g is destroyed, elements in _cleanup_py_funcs_used_in_graph
# will be destroyed and their __del__ will remove the 'token' from
# the funcs registry.
g._cleanup_py_funcs_used_in_graph.append(cleanup)
if stateful:
return gen_script_ops._py_func(input=inp, token=token, Tout=Tout, name=name)
# pylint: enable=protected-access
else:
return gen_script_ops._py_func_stateless(
input=inp, token=token, Tout=Tout,
name=name)
# pylint: enable=protected-access
ops.RegisterShape("PyFunc")(common_shapes.call_cpp_shape_fn)
ops.RegisterShape("PyFuncStateless")(common_shapes.call_cpp_shape_fn)
ops.NotDifferentiable("PyFunc")
ops.NotDifferentiable("PyFuncStateless")
|
