272 lines
9.1 KiB
Python
272 lines
9.1 KiB
Python
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# Copyright 2016 Google Inc. 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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"""Abstract class for sequence generators.
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Provides a uniform interface for interacting with generators for any model.
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"""
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import abc
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import os
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import tempfile
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# internal imports
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import tensorflow as tf
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from magenta.protobuf import generator_pb2
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class SequenceGeneratorException(Exception):
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"""Generic exception for sequence generation errors."""
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pass
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class BaseSequenceGenerator(object):
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"""Abstract class for generators."""
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__metaclass__ = abc.ABCMeta
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def __init__(self, details, checkpoint, bundle):
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"""Constructs a BaseSequenceGenerator.
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Args:
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details: A generator_pb2.GeneratorDetails for this generator.
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checkpoint: Where to look for the most recent model checkpoint. Either a
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directory to be used with tf.train.latest_checkpoint or the path to a
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single checkpoint file. Or None if a bundle should be used.
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bundle: A generator_pb2.GeneratorBundle object that contains both a
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checkpoint and a metagraph. Or None if a checkpoint should be used.
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Raises:
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SequenceGeneratorException: if neither checkpoint nor bundle is set.
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"""
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self._details = details
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self._checkpoint = checkpoint
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self._bundle = bundle
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if self._checkpoint is None and self._bundle is None:
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raise SequenceGeneratorException(
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'Either checkpoint or bundle must be set')
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if self._checkpoint is not None and self._bundle is not None:
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raise SequenceGeneratorException(
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'Checkpoint and bundle cannot both be set')
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if self._bundle:
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if self._bundle.generator_details.id != self._details.id:
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raise SequenceGeneratorException(
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'Generator id in bundle (%s) does not match this generator\'s id '
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'(%s)' % (self._bundle.generator_details.id, self._details.id))
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self._initialized = False
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@property
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def details(self):
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"""Returns a GeneratorDetails description of this generator."""
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return self._details
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@property
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def bundle_details(self):
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"""Returns the BundleDetails or None if checkpoint was used."""
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if self._bundle is None:
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return None
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return self._bundle.bundle_details
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@abc.abstractmethod
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def _initialize_with_checkpoint(self, checkpoint_file):
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"""Implementation for building the TF graph given a checkpoint file.
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Args:
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checkpoint_file: The path to the checkpoint file that should be used.
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"""
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pass
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@abc.abstractmethod
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def _initialize_with_checkpoint_and_metagraph(self, checkpoint_file,
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metagraph_file):
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"""Implementation for building the TF graph with a checkpoint and metagraph.
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The implementation should not expect the checkpoint_file and metagraph_file
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to be available after the method returns.
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Args:
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checkpoint_file: The path to the checkpoint file that should be used.
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metagraph_file: The path to the metagraph file that should be used.
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"""
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pass
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@abc.abstractmethod
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def _close(self):
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"""Implementation for closing the TF session."""
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pass
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@abc.abstractmethod
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def _generate(self, generate_sequence_request):
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"""Implementation for sequence generation based on request.
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The implementation can assume that _initialize has been called before this
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method is called.
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Args:
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generate_sequence_request: The request for generating a sequence
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Returns:
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A GenerateSequenceResponse proto.
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"""
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pass
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@abc.abstractmethod
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def _write_checkpoint_with_metagraph(self, checkpoint_filename):
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"""Implementation for writing the checkpoint and metagraph.
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Saver should be initialized with sharded=False, and save should be called
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with: meta_graph_suffix='meta', write_meta_graph=True.
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Args:
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checkpoint_filename: Path to the checkpoint file. Should be passed as the
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save_path argument to Saver.save.
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"""
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pass
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def initialize(self):
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"""Builds the TF graph and loads the checkpoint.
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If the graph has already been initialized, this is a no-op.
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Raises:
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SequenceGeneratorException: If the checkpoint cannot be found.
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"""
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if self._initialized:
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return
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# Either self._checkpoint or self._bundle should be set.
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# This is enforced by the constructor.
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if self._checkpoint is not None:
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if not tf.gfile.Exists(self._checkpoint):
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raise SequenceGeneratorException(
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'Checkpoint path does not exist: %s' % (self._checkpoint))
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checkpoint_file = self._checkpoint
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# If this is a directory, try to determine the latest checkpoint in it.
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if tf.gfile.IsDirectory(checkpoint_file):
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checkpoint_file = tf.train.latest_checkpoint(checkpoint_file)
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if checkpoint_file is None:
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raise SequenceGeneratorException(
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'No checkpoint file found in directory: %s' % self._checkpoint)
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if (not tf.gfile.Exists(checkpoint_file) or
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tf.gfile.IsDirectory(checkpoint_file)):
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raise SequenceGeneratorException(
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'Checkpoint path is not a file: %s (supplied path: %s)' % (
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checkpoint_file, self._checkpoint))
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self._initialize_with_checkpoint(checkpoint_file)
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else:
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# Write checkpoint and metagraph files to a temp dir.
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tempdir = None
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try:
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tempdir = tempfile.mkdtemp()
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checkpoint_filename = os.path.join(tempdir, 'model.ckpt')
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with tf.gfile.Open(checkpoint_filename, 'wb') as f:
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# For now, we support only 1 checkpoint file.
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# If needed, we can later change this to support sharded checkpoints.
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f.write(self._bundle.checkpoint_file[0])
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metagraph_filename = os.path.join(tempdir, 'model.ckpt.meta')
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with tf.gfile.Open(metagraph_filename, 'wb') as f:
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f.write(self._bundle.metagraph_file)
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self._initialize_with_checkpoint_and_metagraph(
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checkpoint_filename, metagraph_filename)
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finally:
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# Clean up the temp dir.
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if tempdir is not None:
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tf.gfile.DeleteRecursively(tempdir)
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self._initialized = True
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def close(self):
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"""Closes the TF session.
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If the session was already closed, this is a no-op.
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"""
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if self._initialized:
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self._close()
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self._initialized = False
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def __enter__(self):
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"""When used as a context manager, initializes the TF session."""
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self.initialize()
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return self
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def __exit__(self, *args):
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"""When used as a context manager, closes the TF session."""
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self.close()
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def generate(self, generate_sequence_request):
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"""Generates a sequence from the model based on the request.
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Also initializes the TF graph if not yet initialized.
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Args:
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generate_sequence_request: The request for generating a sequence
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Returns:
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A GenerateSequenceResponse proto.
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"""
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self.initialize()
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return self._generate(generate_sequence_request)
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def create_bundle_file(self, bundle_file, description=None):
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"""Writes a generator_pb2.GeneratorBundle file in the specified location.
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Saves the checkpoint, metagraph, and generator id in one file.
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Args:
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bundle_file: Location to write the bundle file.
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description: A short, human-readable text description of the bundle (e.g.,
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training data, hyper parameters, etc.).
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Raises:
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SequenceGeneratorException: if there is an error creating the bundle file.
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"""
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if not bundle_file:
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raise SequenceGeneratorException('Bundle file location not specified.')
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self.initialize()
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tempdir = None
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try:
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tempdir = tempfile.mkdtemp()
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checkpoint_filename = os.path.join(tempdir, 'model.ckpt')
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self._write_checkpoint_with_metagraph(checkpoint_filename)
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if not os.path.isfile(checkpoint_filename):
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raise SequenceGeneratorException(
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'Could not read checkpoint file: %s' % (checkpoint_filename))
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metagraph_filename = checkpoint_filename + '.meta'
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if not os.path.isfile(metagraph_filename):
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raise SequenceGeneratorException(
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'Could not read metagraph file: %s' % (metagraph_filename))
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bundle = generator_pb2.GeneratorBundle()
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bundle.generator_details.CopyFrom(self.details)
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if description is not None:
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bundle.bundle_details.description = description
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with tf.gfile.Open(checkpoint_filename, 'rb') as f:
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bundle.checkpoint_file.append(f.read())
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with tf.gfile.Open(metagraph_filename, 'rb') as f:
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bundle.metagraph_file = f.read()
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with tf.gfile.Open(bundle_file, 'wb') as f:
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f.write(bundle.SerializeToString())
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finally:
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if tempdir is not None:
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tf.gfile.DeleteRecursively(tempdir)
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