106 lines
3.7 KiB
Python
106 lines
3.7 KiB
Python
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# Copyright 2018 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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"""TensorBoard helper routine for TF op evaluator.
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Requires TensorFlow.
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"""
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import threading
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class PersistentOpEvaluator:
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"""Evaluate a fixed TensorFlow graph repeatedly, safely, efficiently.
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Extend this class to create a particular kind of op evaluator, like an
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image encoder. In `initialize_graph`, create an appropriate TensorFlow
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graph with placeholder inputs. In `run`, evaluate this graph and
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return its result. This class will manage a singleton graph and
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session to preserve memory usage, and will ensure that this graph and
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session do not interfere with other concurrent sessions.
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A subclass of this class offers a threadsafe, highly parallel Python
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entry point for evaluating a particular TensorFlow graph.
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Example usage:
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class FluxCapacitanceEvaluator(PersistentOpEvaluator):
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\"\"\"Compute the flux capacitance required for a system.
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Arguments:
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x: Available power input, as a `float`, in jigawatts.
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Returns:
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A `float`, in nanofarads.
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\"\"\"
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def initialize_graph(self):
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self._placeholder = tf.placeholder(some_dtype)
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self._op = some_op(self._placeholder)
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def run(self, x):
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return self._op.eval(feed_dict: {self._placeholder: x})
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evaluate_flux_capacitance = FluxCapacitanceEvaluator()
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for x in xs:
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evaluate_flux_capacitance(x)
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"""
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def __init__(self):
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super().__init__()
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self._session = None
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self._initialization_lock = threading.Lock()
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def _lazily_initialize(self):
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"""Initialize the graph and session, if this has not yet been done."""
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# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
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import tensorflow.compat.v1 as tf
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with self._initialization_lock:
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if self._session:
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return
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graph = tf.Graph()
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with graph.as_default():
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self.initialize_graph()
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# Don't reserve GPU because libpng can't run on GPU.
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config = tf.ConfigProto(device_count={"GPU": 0})
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self._session = tf.Session(graph=graph, config=config)
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def initialize_graph(self):
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"""Create the TensorFlow graph needed to compute this operation.
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This should write ops to the default graph and return `None`.
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"""
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raise NotImplementedError(
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'Subclasses must implement "initialize_graph".'
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)
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def run(self, *args, **kwargs):
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"""Evaluate the ops with the given input.
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When this function is called, the default session will have the
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graph defined by a previous call to `initialize_graph`. This
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function should evaluate any ops necessary to compute the result
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of the query for the given *args and **kwargs, likely returning
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the result of a call to `some_op.eval(...)`.
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"""
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raise NotImplementedError('Subclasses must implement "run".')
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def __call__(self, *args, **kwargs):
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self._lazily_initialize()
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with self._session.as_default():
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return self.run(*args, **kwargs)
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