TensorFlow提供允许变量共享的模板
2018-10-28 16:58 更新
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""提供允许变量共享的模板.""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import traceback
from tensorflow.python.framework import ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util.deprecation import deprecated
__all__ = ["make_template"]
def make_template(name_, func_, create_scope_now_=False, unique_name_=None,
custom_getter_=None, **kwargs):
"""Given an arbitrary function, wrap it so that it does variable sharing.
This wraps `func_` in a Template and partially evaluates it. Templates are
functions that create variables the first time they are called and reuse them
thereafter. In order for `func_` to be compatible with a `Template` it must
have the following properties:
* The function should create all trainable variables and any variables that
should be reused by calling `tf.get_variable`. If a trainable variable is
created using `tf.Variable`, then a ValueError will be thrown. Variables
that are intended to be locals can be created by specifying
`tf.Variable(..., trainable=false)`.
* The function may use variable scopes and other templates internally to
create and reuse variables, but it shouldn't use `tf.global_variables` to
capture variables that are defined outside of the scope of the function.
* Internal scopes and variable names should not depend on any arguments that
are not supplied to `make_template`. In general you will get a ValueError
telling you that you are trying to reuse a variable that doesn't exist
if you make a mistake.
In the following example, both `z` and `w` will be scaled by the same `y`. It
is important to note that if we didn't assign `scalar_name` and used a
different name for z and w that a `ValueError` would be thrown because it
couldn't reuse the variable.
```python
def my_op(x, scalar_name):
var1 = tf.get_variable(scalar_name,
shape=[],
initializer=tf.constant_initializer(1))
return x * var1
scale_by_y = tf.make_template('scale_by_y', my_op, scalar_name='y')
z = scale_by_y(input1)
w = scale_by_y(input2)
```
As a safe-guard, the returned function will raise a `ValueError` after the
first call if trainable variables are created by calling `tf.Variable`.
If all of these are true, then 2 properties are enforced by the template:
1. Calling the same template multiple times will share all non-local
variables.
2. Two different templates are guaranteed to be unique, unless you reenter the
same variable scope as the initial definition of a template and redefine
it. An examples of this exception:
```python
def my_op(x, scalar_name):
var1 = tf.get_variable(scalar_name,
shape=[],
initializer=tf.constant_initializer(1))
return x * var1
with tf.variable_scope('scope') as vs:
scale_by_y = tf.make_template('scale_by_y', my_op, scalar_name='y')
z = scale_by_y(input1)
w = scale_by_y(input2)
# Creates a template that reuses the variables above.
with tf.variable_scope(vs, reuse=True):
scale_by_y2 = tf.make_template('scale_by_y', my_op, scalar_name='y')
z2 = scale_by_y2(input1)
w2 = scale_by_y2(input2)
```
Depending on the value of `create_scope_now_`, the full variable scope may be
captured either at the time of first call or at the time of construction. If
this option is set to True, then all Tensors created by repeated calls to the
template will have an extra trailing _N+1 to their name, as the first time the
scope is entered in the Template constructor no Tensors are created.
Note: `name_`, `func_` and `create_scope_now_` have a trailing underscore to
reduce the likelihood of collisions with kwargs.
Args:
name_: A name for the scope created by this template. If necessary, the name
will be made unique by appending `_N` to the name.
func_: The function to wrap.
create_scope_now_: Boolean controlling whether the scope should be created
when the template is constructed or when the template is called. Default
is False, meaning the scope is created when the template is called.
unique_name_: When used, it overrides name_ and is not made unique. If a
template of the same scope/unique_name already exists and reuse is false,
an error is raised. Defaults to None.
custom_getter_: Optional custom getter for variables used in `func_`. See
the @{tf.get_variable} `custom_getter` documentation for
more information.
**kwargs: Keyword arguments to apply to `func_`.
Returns:
A function to encapsulate a set of variables which should be created once
and reused. An enclosing scope will created, either where `make_template`
is called, or wherever the result is called, depending on the value of
`create_scope_now_`. Regardless of the value, the first time the template
is called it will enter the scope with no reuse, and call `func_` to create
variables, which are guaranteed to be unique. All subsequent calls will
re-enter the scope and reuse those variables.
Raises:
ValueError: if the name is None.
"""
if kwargs:
func_ = functools.partial(func_, **kwargs)
return Template(
name_, func_, create_scope_now=create_scope_now_,
unique_name=unique_name_, custom_getter=custom_getter_)
def _skip_common_stack_elements(stacktrace, base_case):
"""Skips items that the target stacktrace shares with the base stacktrace."""
for i, (trace, base) in enumerate(zip(stacktrace, base_case)):
if trace != base:
return stacktrace[i:]
return stacktrace[-1:]
class Template(object):
"""Wrap a function to aid in variable sharing.
Templates are functions that create variables the first time they are called
and reuse them thereafter. See `make_template` for full documentation.
Note: By default, the full variable scope is captured at the time of first
call. If `create_scope_now_` is passed as True to the constructor, the full
scope will be captured there, but no variables will created until the first
call.
"""
def __init__(self, name, func, create_scope_now=False, unique_name=None,
custom_getter=None):
"""Creates a template for the given function.
Args:
name: A name for the scope created by this template. The
name will be made unique by appending `_N` to the it (see how
`tf.variable_scope` treats the `default_name` for details).
func: The function to apply each time.
create_scope_now: Whether to create the scope at Template construction
time, rather than first call. Defaults to false. Creating the scope at
construction time may be more convenient if the template is to passed
through much lower level code, and you want to be sure of the scope
name without knowing exactly where it will be first called. If set to
True, the scope will be created in the constructor, and all subsequent
times in __call__, leading to a trailing numeral being added to the
names of all created Tensors. If set to False, the scope will be created
at the first call location.
unique_name: When used, it overrides name_ and is not made unique. If a
template of the same scope/unique_name already exists and reuse is
false, an error is raised. Defaults to None.
custom_getter: optional custom getter to pass to variable_scope()
Raises:
ValueError: if the name is None.
"""
self._func = func
self._stacktrace = traceback.format_stack()[:-2]
self._name = name
self._unique_name = unique_name
self._custom_getter = custom_getter
if name is None:
raise ValueError("name cannot be None.")
if create_scope_now:
with variable_scope._pure_variable_scope( # pylint:disable=protected-access
(self._unique_name or
variable_scope._get_unique_variable_scope(self._name)), # pylint:disable=protected-access
custom_getter=self._custom_getter) as vs:
self._variable_scope = vs
else:
self._variable_scope = None
# This variable keeps track of whether the template has been called yet,
# which is not the same as whether the scope has been created.
self._variables_created = False
def _call_func(self, args, kwargs, check_for_new_variables):
try:
vars_at_start = len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES))
trainable_at_start = len(
ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES))
result = self._func(*args, **kwargs)
if check_for_new_variables:
trainable_variables = ops.get_collection(
ops.GraphKeys.TRAINABLE_VARIABLES)
# If a variable that we intend to train is created as a side effect
# of creating a template, then that is almost certainly an error.
if trainable_at_start != len(trainable_variables):
raise ValueError("Trainable variable created when calling a template "
"after the first time, perhaps you used tf.Variable "
"when you meant tf.get_variable: %s" %
(trainable_variables[trainable_at_start:],))
# Non-trainable tracking variables are a legitimate reason why a new
# variable would be created, but it is a relatively advanced use-case,
# so log it.
variables = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
if vars_at_start != len(variables):
logging.info("New variables created when calling a template after "
"the first time, perhaps you used tf.Variable when you "
"meant tf.get_variable: %s",
variables[vars_at_start:])
return result
except Exception as exc:
# Reraise the exception, but append the original definition to the
# trace.
args = exc.args
if not args:
arg0 = ""
else:
arg0 = args[0]
trace = "".join(_skip_common_stack_elements(self._stacktrace,
traceback.format_stack()))
arg0 = "%s\n\noriginally defined at:\n%s" % (arg0, trace)
new_args = [arg0]
new_args.extend(args[1:])
exc.args = tuple(new_args)
raise
def __call__(self, *args, **kwargs):
if self._variable_scope:
if self._variables_created:
# This is not the first visit to __call__, so variables have already
# been created, and we want to reuse them.
with variable_scope.variable_scope(self._variable_scope, reuse=True):
return self._call_func(args, kwargs, check_for_new_variables=True)
else:
# This is the first visit to __call__, but the scope has already been
# created in the constructor. Set _variables_created after the inner
# function is successfully called so that subsequent calls take the if
# branch above.
with variable_scope.variable_scope(self._variable_scope):
result = self._call_func(args, kwargs, check_for_new_variables=False)
self._variables_created = True
return result
else:
# The scope was not created at construction time, so create it here.
# Subsequent calls should reuse variables.
with variable_scope.variable_scope(
self._unique_name, self._name,
custom_getter=self._custom_getter) as vs:
self._variable_scope = vs
result = self._call_func(args, kwargs, check_for_new_variables=False)
self._variables_created = True
return result
@property
def variable_scope(self):
"""Returns the variable scope object created by this Template."""
return self._variable_scope
@property
@deprecated(
"2017-02-21", "The .var_scope property is deprecated. Please change your "
"code to use the .variable_scope property")
def var_scope(self):
"""Returns the variable scope object created by this Template."""
return self._variable_scope
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