From ec1effdb69d33c947f30a5155c5cc4104c07a87e Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Tue, 16 Apr 2019 10:46:39 -0700 Subject: [PATCH] Apply tf1->tf2 name replaces to doc-strings and comments in tensorflow. No code changes, only doc-strings and comments. PiperOrigin-RevId: 243837271 --- tensorflow/python/saved_model/builder_impl.py | 14 +++++++------- tensorflow/python/saved_model/loader.py | 10 +++++----- tensorflow/python/saved_model/loader_impl.py | 12 ++++++------ .../python/saved_model/model_utils/export_utils.py | 2 +- tensorflow/python/saved_model/save.py | 6 +++--- tensorflow/python/saved_model/simple_save.py | 2 +- 6 files changed, 23 insertions(+), 23 deletions(-) diff --git a/tensorflow/python/saved_model/builder_impl.py b/tensorflow/python/saved_model/builder_impl.py index 37af428dcb9..b932e1bc6e0 100644 --- a/tensorflow/python/saved_model/builder_impl.py +++ b/tensorflow/python/saved_model/builder_impl.py @@ -64,9 +64,9 @@ class _SavedModelBuilder(object): Typical usage for the `SavedModelBuilder`: ```python ... - builder = tf.saved_model.Builder(export_dir) + builder = tf.compat.v1.saved_model.Builder(export_dir) - with tf.Session(graph=tf.Graph()) as sess: + with tf.compat.v1.Session(graph=tf.Graph()) as sess: ... builder.add_meta_graph_and_variables(sess, ["foo-tag"], @@ -74,7 +74,7 @@ class _SavedModelBuilder(object): assets_list=foo_assets) ... - with tf.Session(graph=tf.Graph()) as sess: + with tf.compat.v1.Session(graph=tf.Graph()) as sess: ... builder.add_meta_graph(["bar-tag", "baz-tag"]) ... @@ -252,8 +252,8 @@ class _SavedModelBuilder(object): train_op: Op or group of opts that trains the model when run. This will not be run automatically when the graph is loaded, instead saved in a SignatureDef accessible through the exported MetaGraph. - saver: An instance of tf.train.Saver that will be used to export the - metagraph. If None, a sharded Saver that restores all variables will + saver: An instance of tf.compat.v1.train.Saver that will be used to export + the metagraph. If None, a sharded Saver that restores all variables will be used. Raises: @@ -332,7 +332,7 @@ class _SavedModelBuilder(object): strip_default_attrs: Boolean. If `True`, default-valued attributes will be removed from the NodeDefs. For a detailed guide, see [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes). - saver: An instance of tf.train.Saver that will be used to export the + saver: An instance of tf.compat.v1.train.Saver that will be used to export the metagraph and save variables. If None, a sharded Saver that restores all variables will be used. @@ -441,7 +441,7 @@ class SavedModelBuilder(_SavedModelBuilder): Args: assets_collection_to_add: The collection where the asset paths are setup. """ - # Add assets to the collection with key `constants.ASSETS_KEY`, in the + # Add assets to the collection with key `saved_model.ASSETS_KEY`, in the # graph. asset_filename_map = _maybe_save_assets(_add_asset_to_collection, assets_collection_to_add) diff --git a/tensorflow/python/saved_model/loader.py b/tensorflow/python/saved_model/loader.py index 334298c232e..f33c70cbabe 100644 --- a/tensorflow/python/saved_model/loader.py +++ b/tensorflow/python/saved_model/loader.py @@ -33,9 +33,9 @@ Typical usage: ```python ... -builder = tf.saved_model.builder.SavedModelBuilder(export_dir) +builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(export_dir) -with tf.Session(graph=tf.Graph()) as sess: +with tf.compat.v1.Session(graph=tf.Graph()) as sess: ... builder.add_meta_graph_and_variables(sess, ["foo-tag"], @@ -43,7 +43,7 @@ with tf.Session(graph=tf.Graph()) as sess: assets_collection=foo_assets) ... -with tf.Session(graph=tf.Graph()) as sess: +with tf.compat.v1.Session(graph=tf.Graph()) as sess: ... builder.add_meta_graph(["bar-tag", "baz-tag"], assets_collection=bar_baz_assets) @@ -52,8 +52,8 @@ with tf.Session(graph=tf.Graph()) as sess: builder.save() ... -with tf.Session(graph=tf.Graph()) as sess: - tf.saved_model.loader.load(sess, ["foo-tag"], export_dir) +with tf.compat.v1.Session(graph=tf.Graph()) as sess: + tf.compat.v1.saved_model.loader.load(sess, ["foo-tag"], export_dir) ... ``` diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py index bfabef9174d..4e3869b1aaa 100644 --- a/tensorflow/python/saved_model/loader_impl.py +++ b/tensorflow/python/saved_model/loader_impl.py @@ -353,10 +353,10 @@ class SavedModelLoader(object): """Restore SavedModel variable values into the session. Args: - sess: tf.Session to restore variable values. - saver: a tf.train.Saver object. Can be None if there are no variables in - graph. This may be the saver returned by the load_graph() function, or a - default `tf.train.Saver()`. + sess: tf.compat.v1.Session to restore variable values. + saver: a tf.compat.v1.train.Saver object. Can be None if there are no + variables in graph. This may be the saver returned by the load_graph() + function, or a default `tf.compat.v1.train.Saver()`. import_scope: Optional `string` -- if specified, prepend this string followed by '/' to all loaded tensor names. This scope is applied to tensor instances loaded into the passed session, but it is *not* written @@ -383,7 +383,7 @@ class SavedModelLoader(object): """Run initialization ops defined in the `MetaGraphDef`. Args: - sess: tf.Session to restore variable values. + sess: tf.compat.v1.Session to restore variable values. tags: a set of string tags identifying a MetaGraphDef. import_scope: Optional `string` -- if specified, prepend this string followed by '/' to all loaded tensor names. This scope is applied to @@ -404,7 +404,7 @@ class SavedModelLoader(object): """Load the MetaGraphDef graph and restore variable values into the session. Args: - sess: tf.Session to restore variable values. + sess: tf.compat.v1.Session to restore variable values. tags: a set of string tags identifying a MetaGraphDef. import_scope: Optional `string` -- if specified, prepend this string followed by '/' to all loaded tensor names. This scope is applied to diff --git a/tensorflow/python/saved_model/model_utils/export_utils.py b/tensorflow/python/saved_model/model_utils/export_utils.py index adb6bf26677..737f76edf08 100644 --- a/tensorflow/python/saved_model/model_utils/export_utils.py +++ b/tensorflow/python/saved_model/model_utils/export_utils.py @@ -79,7 +79,7 @@ def build_all_signature_defs(receiver_tensors, additional serving signatures, which may be used to feed inputs at different points within the input receiver subgraph. A typical usage is to allow feeding raw feature `Tensor`s *downstream* of the - tf.parse_example() op. Defaults to None. + tf.io.parse_example() op. Defaults to None. serving_only: boolean; if true, resulting signature defs will only include valid serving signatures. If false, all requested signatures will be returned. diff --git a/tensorflow/python/saved_model/save.py b/tensorflow/python/saved_model/save.py index ad04e17f5ab..3ad68624c93 100644 --- a/tensorflow/python/saved_model/save.py +++ b/tensorflow/python/saved_model/save.py @@ -773,9 +773,9 @@ def save(obj, export_dir, signatures=None): @compatibility(eager) Not well supported when graph building. From TensorFlow 1.x, - `tf.enable_eager_execution()` should run first. Calling tf.saved_model.save in - a loop when graph building from TensorFlow 1.x will add new save operations to - the default graph each iteration. + `tf.compat.v1.enable_eager_execution()` should run first. Calling + tf.saved_model.save in a loop when graph building from TensorFlow 1.x will + add new save operations to the default graph each iteration. May not be called from within a function body. @end_compatibility diff --git a/tensorflow/python/saved_model/simple_save.py b/tensorflow/python/saved_model/simple_save.py index 169504ec891..da51d71fd84 100644 --- a/tensorflow/python/saved_model/simple_save.py +++ b/tensorflow/python/saved_model/simple_save.py @@ -45,7 +45,7 @@ def simple_save(session, export_dir, inputs, outputs, legacy_init_op=None): Although in many cases it's not necessary to understand all of the many ways to configure a SavedModel, this method has a few practical implications: - It will be treated as a graph for inference / serving (i.e. uses the tag - `tag_constants.SERVING`) + `saved_model.SERVING`) - The SavedModel will load in TensorFlow Serving and supports the [Predict API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto).