STT-tensorflow/tensorflow/python/saved_model/method_name_updater.py
Zhenyu Tan 79b52dbdc9 Internal Cleanup for docstring.
PiperOrigin-RevId: 331778134
Change-Id: Ib79e0562b3fc03456b6bb690766c779981e023fe
2020-09-15 08:56:28 -07:00

150 lines
6.0 KiB
Python

# Copyright 2020 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.
# ==============================================================================
"""SignatureDef method name utility functions.
Utility functions for manipulating signature_def.method_names.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from tensorflow.python.lib.io import file_io
from tensorflow.python.platform import tf_logging
from tensorflow.python.saved_model import constants
from tensorflow.python.saved_model import loader_impl as loader
from tensorflow.python.util import compat
from tensorflow.python.util.tf_export import tf_export
# TODO(jdchung): Consider integrated this into the saved_model_cli so that users
# could do this from the command line directly.
@tf_export(v1=["saved_model.signature_def_utils.MethodNameUpdater"])
class MethodNameUpdater(object):
"""Updates the method name(s) of the SavedModel stored in the given path.
The `MethodNameUpdater` class provides the functionality to update the method
name field in the signature_defs of the given SavedModel. For example, it
can be used to replace the `predict` `method_name` to `regress`.
Typical usages of the `MethodNameUpdater`
```python
...
updater = tf.compat.v1.saved_model.signature_def_utils.MethodNameUpdater(
export_dir)
# Update all signature_defs with key "foo" in all meta graph defs.
updater.replace_method_name(signature_key="foo", method_name="regress")
# Update a single signature_def with key "bar" in the meta graph def with
# tags ["serve"]
updater.replace_method_name(signature_key="bar", method_name="classify",
tags="serve")
updater.save(new_export_dir)
```
Note: This function will only be available through the v1 compatibility
library as tf.compat.v1.saved_model.builder.MethodNameUpdater.
"""
def __init__(self, export_dir):
"""Creates an MethodNameUpdater object.
Args:
export_dir: Directory containing the SavedModel files.
Raises:
IOError: If the saved model file does not exist, or cannot be successfully
parsed.
"""
self._export_dir = export_dir
self._saved_model = loader.parse_saved_model(export_dir)
def replace_method_name(self, signature_key, method_name, tags=None):
"""Replaces the method_name in the specified signature_def.
This will match and replace multiple sig defs iff tags is None (i.e when
multiple `MetaGraph`s have a signature_def with the same key).
If tags is not None, this will only replace a single signature_def in the
`MetaGraph` with matching tags.
Args:
signature_key: Key of the signature_def to be updated.
method_name: new method_name to replace the existing one.
tags: A tag or sequence of tags identifying the `MetaGraph` to update. If
None, all meta graphs will be updated.
Raises:
ValueError: if signature_key or method_name are not defined or
if no metagraphs were found with the associated tags or
if no meta graph has a signature_def that matches signature_key.
"""
if not signature_key:
raise ValueError("signature_key must be defined.")
if not method_name:
raise ValueError("method_name must be defined.")
if (tags is not None and not isinstance(tags, list)):
tags = [tags]
found_match = False
for meta_graph_def in self._saved_model.meta_graphs:
if tags is None or set(tags) == set(meta_graph_def.meta_info_def.tags):
if signature_key not in meta_graph_def.signature_def:
raise ValueError(
"MetaGraphDef associated with tags " + str(tags) +
" does not have a signature_def with key: " + signature_key +
". This means either you specified the wrong signature key or "
"forgot to put the signature_def with the corresponding key in "
"your SavedModel.")
meta_graph_def.signature_def[signature_key].method_name = method_name
found_match = True
if not found_match:
raise ValueError(
"MetaGraphDef associated with tags " + str(tags) +
" could not be found in SavedModel. This means either you specified "
"the invalid tags your SavedModel does not have a MetaGraph with "
"the specified tags")
def save(self, new_export_dir=None):
"""Saves the updated `SavedModel`.
Args:
new_export_dir: Path where the updated `SavedModel` will be saved. If
None, the input `SavedModel` will be overriden with the updates.
Raises:
errors.OpError: If there are errors during the file save operation.
"""
is_input_text_proto = file_io.file_exists(os.path.join(
compat.as_bytes(self._export_dir),
compat.as_bytes(constants.SAVED_MODEL_FILENAME_PBTXT)))
if not new_export_dir:
new_export_dir = self._export_dir
if is_input_text_proto:
# TODO(jdchung): Add a util for the path creation below.
path = os.path.join(
compat.as_bytes(new_export_dir),
compat.as_bytes(constants.SAVED_MODEL_FILENAME_PBTXT))
file_io.write_string_to_file(path, str(self._saved_model))
else:
path = os.path.join(
compat.as_bytes(new_export_dir),
compat.as_bytes(constants.SAVED_MODEL_FILENAME_PB))
file_io.write_string_to_file(
path, self._saved_model.SerializeToString(deterministic=True))
tf_logging.info("SavedModel written to: %s", compat.as_text(path))