TensorFlow如何定义脚本语言运算符

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""脚本语言运算符.请参阅@ {$ python / script_ops}指南.
@@ py_func
""
# pylint: disable=g-bad-name
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import threading

import numpy as np
import six

from tensorflow.python import pywrap_tensorflow
from tensorflow.python.framework import function
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._lock = threading.Lock()
    self._unique_id = 0  # GUARDED_BY(self._lock)
    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, dtype=None):
    """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.
    Additionally, we convert unicode strings to (byte-)strings for Python3
    compatibility.
    Args:
      value: Value to convert to a numpy array.
      dtype: (Optional.) Desired NumPy type for the returned value.
    Returns:
      A numpy array.
    """
    result = np.asarray(value, dtype=dtype, order="C")
    if result.dtype.char == "S" and result is not value:
      return np.asarray(value, order="C", dtype=object)
    elif result.dtype.char == "U" and result is not value:
      value = np.vectorize(lambda x: x.encode())(value)
      return np.asarray(value, order="C", dtype=object)
    elif result.dtype.char == "U":
      return result.astype(np.bytes_)
    else:
      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)
    # Strings seem to lead to a memory leak here if they're not wrapped in a
    # list.
    if isinstance(ret, six.binary_type):
      ret = [ret]
    # 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."""
    with self._lock:
      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, wrap this function as an
  operation in a TensorFlow graph. The following snippet constructs a simple
  TensorFlow graph that invokes the `np.sinh()` NumPy function as a operation
  in the graph:
  ```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 = tf.py_func(my_func, [inp], tf.float32)
  ```
  **N.B.** The `tf.py_func()` operation has the following known limitations:
  * The body of the function (i.e. `func`) will not be serialized in a
    `GraphDef`. Therefore, you should not use this function if you need to
    serialize your model and restore it in a different environment.
  * The operation must run in the same address space as the Python program
    that calls `tf.py_func()`. If you are using distributed TensorFlow, you
    must run a `tf.train.Server` in the same process as the program that calls
    `tf.py_func()` and you must pin the created operation to a device in that
    server (e.g. using `with tf.device():`).
  Args:
    func: A Python function, which accepts a list of NumPy `ndarray` objects
      having element types that match the corresponding `tf.Tensor` objects
      in `inp`, and returns a list of `ndarray` objects (or a single `ndarray`)
      having element types that match the corresponding values in `Tout`.
    inp: A list of `Tensor` objects.
    Tout: A list or tuple of tensorflow data types or a single tensorflow data
      type if there is only one, indicating what `func` returns.
    stateful: (Boolean.) If True, the function should be considered stateful.
      If a function is stateless, when given the same input it will return the
      same output and have no observable side effects. Optimizations such as
      common subexpression elimination are only performed on stateless
      operations.
    name: A name for the operation (optional).
  Returns:
    A list of `Tensor` or a single `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.
  g = ops.get_default_graph()

  # pylint: disable=protected-access
  while isinstance(g, function._FuncGraph):
    # If the py_func was declared inside a _FuncGraph, its lifetime should be
    # bound to that of the outer graph instead.
    g = g._outer_graph

  cleanup = CleanupFunc(token)

  # 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)
  # pylint: enable=protected-access

  if isinstance(Tout, (list, tuple)):
    is_list_or_tuple = True
  else:
    Tout = [Tout]
    is_list_or_tuple = False
  # pylint: disable=protected-access
  if stateful:
    result = gen_script_ops._py_func(
        input=inp, token=token, Tout=Tout, name=name)
  else:
    result = gen_script_ops._py_func_stateless(
        input=inp, token=token, Tout=Tout, name=name)
  # pylint: enable=protected-access
  return result if is_list_or_tuple else result[0]


ops.NotDifferentiable("PyFunc")
ops.NotDifferentiable("PyFuncStateless")