def var_and_name_scope(names):
"""Creates a variable scope and a name scope.
If a variable_scope is provided, this will reenter that variable scope.
However, if none is provided then the variable scope will match the generated
part of the name scope.
Args:
names: A tuple of name_scope, variable_scope or None.
Yields:
The result of name_scope and variable_scope as a tuple.
"""
# pylint: disable=protected-access
if not names:
yield None, None
else:
name, var_scope = names
with tf.name_scope(name) as scope:
# TODO(eiderman): This is a workaround until the variable_scope updates land
# in a TF release.
old_vs = tf.get_variable_scope()
if var_scope is None:
count = len(name.split("/"))
scoped_name = "/".join(scope.split("/")[-count - 1 : -1])
full_name = (old_vs.name + "/" + scoped_name).lstrip("/")
else:
full_name = var_scope.name
vs_key = tf.get_collection(variable_scope._VARSCOPE_KEY)
try:
vs_key[0] = variable_scope._VariableScope(old_vs.reuse, name=full_name, initializer=old_vs.initializer)
vs_key[0].name_scope = scope
yield scope, vs_key[0]
finally:
vs_key[0] = old_vs
def track_model_updates(main_name, track_name, tau):
"""
Build an update op to make parameters of a tracking model follow a main model.
Call outside of the scope of both the main and tracking model.
Returns:
A group of `tf.assign` ops which require no inputs (only parameter values).
"""
updates = []
params = [var for var in tf.all_variables()
if var.name.startswith(main_name + "/")]
for param in params:
track_param_name = param.op.name.replace(main_name + "/",
track_name + "/")
with tf.variable_scope(_VariableScope(True), reuse=True):
try:
track_param = tf.get_variable(track_param_name)
except ValueError:
logging.warn("Tracking model variable %s does not exist",
track_param_name)
continue
# TODO sparse params
update_op = tf.assign(track_param,
tau * param + (1 - tau) * track_param)
updates.append(update_op)
return tf.group(*updates)
def var_and_name_scope(names):
"""Creates a variable scope and a name scope.
If a variable_scope is provided, this will reenter that variable scope.
However, if none is provided then the variable scope will match the generated
part of the name scope.
Args:
names: A tuple of name_scope, variable_scope or None.
Yields:
The result of name_scope and variable_scope as a tuple.
"""
# pylint: disable=protected-access
if not names:
yield None, None
else:
name, var_scope = names
with tf.name_scope(name) as scope:
# TODO(eiderman): This is a workaround until the variable_scope updates land
# in a TF release.
old_vs = tf.get_variable_scope()
if var_scope is None:
count = len(name.split('/'))
scoped_name = '/'.join(scope.split('/')[-count - 1:-1])
full_name = (old_vs.name + '/' + scoped_name).lstrip('/')
else:
full_name = var_scope.name
vs_key = tf.get_collection_ref(variable_scope._VARSCOPESTORE_KEY)
try:
# TODO(eiderman): Remove this hack or fix the full file.
try:
vs_key[0] = tf.VariableScope(
old_vs.reuse,
name=full_name,
initializer=old_vs.initializer,
regularizer=old_vs.regularizer,
caching_device=old_vs.caching_device)
except AttributeError:
vs_key[0] = variable_scope._VariableScope(
old_vs.reuse,
name=full_name,
initializer=old_vs.initializer)
vs_key[0].name_scope = scope
yield scope, vs_key[0]
finally:
vs_key[0] = old_vs
def match_variable(name, scope_name):
"""
Match a variable (initialize with same value) from another variable scope.
After initialization, the values of the two variables are not tied in any
way.
"""
# HACK: Using private _VariableScope API in order to be able to get an
# absolute-path to the given variable scope name (i.e., not have it treated
# as a relative path and placed under whatever variable scope might contain
# this function call)
with tf.variable_scope(_VariableScope(True, scope_name), reuse=True):
track_var = tf.get_variable(name)
# Create a dummy initializer.
initializer = lambda *args, **kwargs: track_var.initialized_value()
return tf.get_variable(name, shape=track_var.get_shape(),
initializer=initializer)
