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# Copyright 2016 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.
# ==============================================================================
"""Utilities for using generic resources."""
# pylint: disable=g-bad-name
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.util import tf_should_use
_Resource = collections.namedtuple("_Resource",
["handle", "create", "is_initialized"])
def register_resource(handle, create_op, is_initialized_op, is_shared=True):
"""Registers a resource into the appropriate collections.
This makes the resource findable in either the shared or local resources
collection.
Args:
handle: op which returns a handle for the resource.
create_op: op which initializes the resource.
is_initialized_op: op which returns a scalar boolean tensor of whether
the resource has been initialized.
is_shared: if True, the resource gets added to the shared resource
collection; otherwise it gets added to the local resource collection.
"""
resource = _Resource(handle, create_op, is_initialized_op)
if is_shared:
ops.add_to_collection(ops.GraphKeys.RESOURCES, resource)
else:
ops.add_to_collection(ops.GraphKeys.LOCAL_RESOURCES, resource)
def shared_resources():
"""Returns resources visible to all tasks in the cluster."""
return ops.get_collection(ops.GraphKeys.RESOURCES)
def local_resources():
"""Returns resources intended to be local to this session."""
return ops.get_collection(ops.GraphKeys.LOCAL_RESOURCES)
def report_uninitialized_resources(resource_list=None,
name="report_uninitialized_resources"):
"""Returns the names of all uninitialized resources in resource_list.
If the returned tensor is empty then all resources have been initialized.
Args:
resource_list: resources to check. If None, will use shared_resources() +
local_resources().
name: name for the resource-checking op.
Returns:
Tensor containing names of the handles of all resources which have not
yet been initialized.
"""
if resource_list is None:
resource_list = shared_resources() + local_resources()
with ops.name_scope(name):
# Run all operations on CPU
with ops.device("/cpu:0"):
if not resource_list:
# Return an empty tensor so we only need to check for returned tensor
# size being 0 as an indication of model ready.
return array_ops.constant([], dtype=dtypes.string)
# Get a 1-D boolean tensor listing whether each resource is initialized.
variables_mask = math_ops.logical_not(
array_ops.stack([r.is_initialized for r in resource_list]))
# Get a 1-D string tensor containing all the resource names.
variable_names_tensor = array_ops.constant(
[s.handle.name for s in resource_list])
# Return a 1-D tensor containing all the names of uninitialized resources.
return array_ops.boolean_mask(variable_names_tensor, variables_mask)
@tf_should_use.should_use_result
def initialize_resources(resource_list, name="init"):
"""Initializes the resources in the given list.
Args:
resource_list: list of resources to initialize.
name: name of the initialization op.
Returns:
op responsible for initializing all resources.
"""
if resource_list:
return control_flow_ops.group(*[r.create for r in resource_list], name=name)
return control_flow_ops.no_op(name=name)
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