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