118 lines
4.2 KiB
Python
118 lines
4.2 KiB
Python
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Utilities for using generic resources."""
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# pylint: disable=g-bad-name
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.util import tf_should_use
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_Resource = collections.namedtuple("_Resource",
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["handle", "create", "is_initialized"])
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def register_resource(handle, create_op, is_initialized_op, is_shared=True):
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"""Registers a resource into the appropriate collections.
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This makes the resource findable in either the shared or local resources
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collection.
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Args:
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handle: op which returns a handle for the resource.
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create_op: op which initializes the resource.
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is_initialized_op: op which returns a scalar boolean tensor of whether
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the resource has been initialized.
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is_shared: if True, the resource gets added to the shared resource
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collection; otherwise it gets added to the local resource collection.
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"""
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resource = _Resource(handle, create_op, is_initialized_op)
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if is_shared:
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ops.add_to_collection(ops.GraphKeys.RESOURCES, resource)
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else:
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ops.add_to_collection(ops.GraphKeys.LOCAL_RESOURCES, resource)
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def shared_resources():
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"""Returns resources visible to all tasks in the cluster."""
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return ops.get_collection(ops.GraphKeys.RESOURCES)
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def local_resources():
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"""Returns resources intended to be local to this session."""
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return ops.get_collection(ops.GraphKeys.LOCAL_RESOURCES)
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def report_uninitialized_resources(resource_list=None,
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name="report_uninitialized_resources"):
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"""Returns the names of all uninitialized resources in resource_list.
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If the returned tensor is empty then all resources have been initialized.
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Args:
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resource_list: resources to check. If None, will use shared_resources() +
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local_resources().
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name: name for the resource-checking op.
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Returns:
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Tensor containing names of the handles of all resources which have not
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yet been initialized.
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"""
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if resource_list is None:
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resource_list = shared_resources() + local_resources()
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with ops.name_scope(name):
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# Run all operations on CPU
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with ops.device("/cpu:0"):
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if not resource_list:
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# Return an empty tensor so we only need to check for returned tensor
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# size being 0 as an indication of model ready.
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return array_ops.constant([], dtype=dtypes.string)
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# Get a 1-D boolean tensor listing whether each resource is initialized.
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variables_mask = math_ops.logical_not(
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array_ops.stack([r.is_initialized for r in resource_list]))
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# Get a 1-D string tensor containing all the resource names.
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variable_names_tensor = array_ops.constant(
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[s.handle.name for s in resource_list])
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# Return a 1-D tensor containing all the names of uninitialized resources.
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return array_ops.boolean_mask(variable_names_tensor, variables_mask)
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@tf_should_use.should_use_result
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def initialize_resources(resource_list, name="init"):
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"""Initializes the resources in the given list.
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Args:
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resource_list: list of resources to initialize.
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name: name of the initialization op.
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Returns:
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op responsible for initializing all resources.
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"""
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if resource_list:
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return control_flow_ops.group(*[r.create for r in resource_list], name=name)
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return control_flow_ops.no_op(name=name)
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