tensorflow/python/keras/saving/saved_model/save.py

# Copyright 2018 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.
# ==============================================================================
"""Keras SavedModel serialization."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

from tensorflow.core.framework import versions_pb2
from tensorflow.python.distribute import distribution_strategy_context
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.protobuf import saved_metadata_pb2
from tensorflow.python.keras.saving import saving_utils
from tensorflow.python.keras.saving.saved_model import constants
from tensorflow.python.keras.saving.saved_model import save_impl
from tensorflow.python.keras.saving.saved_model import utils
from tensorflow.python.keras.utils.generic_utils import LazyLoader
from tensorflow.python.keras.utils.io_utils import ask_to_proceed_with_overwrite
from tensorflow.python.platform import gfile
from tensorflow.python.saved_model import save as save_lib


# To avoid circular dependencies between keras/engine and keras/saving,
# code in keras/saving must delay imports.

base_layer = LazyLoader(
    "base_layer", globals(),
    "tensorflow.python.keras.engine.base_layer")
training_lib = LazyLoader(
    "training_lib", globals(),
    "tensorflow.python.keras.engine.training")


def save(model, filepath, overwrite, include_optimizer, signatures=None,
         options=None, save_traces=True):
  """Saves a model as a SavedModel to the filepath.

  Args:
    model: Keras model instance to be saved.
    filepath: String path to save the model.
    overwrite: whether to overwrite the existing filepath.
    include_optimizer: If True, save the model's optimizer state.
    signatures: Signatures to save with the SavedModel. Applicable to the 'tf'
      format only. Please see the `signatures` argument in `tf.saved_model.save`
      for details.
    options: (only applies to SavedModel format) `tf.saved_model.SaveOptions`
      object that specifies options for saving to SavedModel.
    save_traces: (only applies to SavedModel format) When enabled, the
      SavedModel will store the function traces for each layer. This
      can be disabled, so that only the configs of each layer are stored.
      Defaults to `True`. Disabling this will decrease serialization time
      and reduce file size, but it requires that all custom layers/models
      implement a `get_config()` method.

  Raises:
    ValueError: if the model's inputs have not been defined.
  """
  # If file exists and should not be overwritten.
  if not overwrite and os.path.exists(filepath):
    proceed = ask_to_proceed_with_overwrite(filepath)
    if not proceed:
      return

  if save_traces:
    if save_impl.should_skip_serialization(model):
      saving_utils.raise_model_input_error(model)

  if not include_optimizer:
    orig_optimizer = model.optimizer
    model.optimizer = None

  # Trace all functions and signatures with `training=0` instead of using an
  # already-set learning phase placeholder.
  # This is needed for compatibility reasons until learning phase setting
  # is removed from the public apis.
  with K.deprecated_internal_learning_phase_scope(0):
    # When saving a model involving batch norm layer within a strategy scope,
    # the replica context is not available when calling `add_update()`, and thus
    # we use the default replica context here.
    with distribution_strategy_context._get_default_replica_context():  # pylint: disable=protected-access
      with utils.keras_option_scope(save_traces):
        saved_nodes, node_paths = save_lib.save_and_return_nodes(
            model, filepath, signatures, options)

    # Save all metadata to a separate file in the SavedModel directory.
    metadata = generate_keras_metadata(saved_nodes, node_paths)

  with gfile.GFile(
      os.path.join(filepath, constants.SAVED_METADATA_PATH), "wb") as w:
    w.write(metadata.SerializeToString(deterministic=True))

  if not include_optimizer:
    model.optimizer = orig_optimizer


def generate_keras_metadata(saved_nodes, node_paths):
  """Constructs a KerasMetadata proto with the metadata of each keras object."""
  metadata = saved_metadata_pb2.SavedMetadata()

  for node_id, node in enumerate(saved_nodes):
    if isinstance(node, base_layer.Layer):
      path = node_paths[node]
      if not path:
        node_path = "root"
      else:
        node_path = "root.{}".format(
            ".".join([ref.name for ref in path]))

      metadata.nodes.add(
          node_id=node_id,
          node_path=node_path,
          version=versions_pb2.VersionDef(
              producer=1, min_consumer=1, bad_consumers=[]),
          identifier=node._object_identifier,  # pylint: disable=protected-access
          metadata=node._tracking_metadata)  # pylint: disable=protected-access

  return metadata