Revert "Warn users when saving SavedModel with metadata."

This reverts commit 0ed710fb76.

tensorflow/core/protobuf/saved_object_graph.proto

@@ -76,9 +76,6 @@ message SavedUserObject {
   string identifier = 1;
   // Version information from the producer of this SavedUserObject.
   VersionDef version = 2;
-  // Deprecated! At the time of deprecation, Keras was the only user of this
-  // field, and its saving and loading code will be updated shortly.
-  // Please save your application-specific metadata to separate file
   // Initialization-related metadata.
   string metadata = 3;
 }

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

@@ -26,6 +26,7 @@ from __future__ import print_function
 
 import os
 import shutil
+import sys
 
 from absl.testing import parameterized
 import numpy as np
@@ -410,16 +411,14 @@ class TestSavedModelFormatAllModes(keras_parameterized.TestCase):
     self.evaluate(variables.variables_initializer(model.variables))
     saved_model_dir = self._save_model_dir()
 
-    # TODO(kathywu): Re-enable this check after removing the tf.saved_model.save
-    # metadata warning.
-    # with self.captureWritesToStream(sys.stderr) as captured_logs:
-    model.save(saved_model_dir, save_format='tf')
-    loaded = keras_load.load(saved_model_dir)
+    with self.captureWritesToStream(sys.stderr) as captured_logs:
+      model.save(saved_model_dir, save_format='tf')
+      loaded = keras_load.load(saved_model_dir)
 
     # Assert that saving does not log deprecation warnings
     # (even if it needs to set learning phase for compat reasons)
-    # if context.executing_eagerly():
-    #   self.assertNotIn('deprecated', captured_logs.contents())
+    if context.executing_eagerly():
+      self.assertNotIn('deprecated', captured_logs.contents())
 
     input_arr = array_ops.constant([[11], [12], [13]], dtype=dtypes.float32)
     input_arr2 = array_ops.constant([[14], [15], [16]], dtype=dtypes.float32)

tensorflow/python/saved_model/BUILD

@@ -349,7 +349,6 @@ py_strict_library(
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:lib",
-        "//tensorflow/python:platform",
         "//tensorflow/python:resource_variable_ops",
         "//tensorflow/python:tensor_util",
         "//tensorflow/python:tf_export",

tensorflow/python/saved_model/save.py

@@ -43,7 +43,6 @@ from tensorflow.python.lib.io import file_io
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import resource_variable_ops
-from tensorflow.python.platform import tf_logging
 from tensorflow.python.saved_model import builder_impl
 from tensorflow.python.saved_model import constants
 from tensorflow.python.saved_model import function_serialization
@@ -751,17 +750,14 @@ def _serialize_object_graph(saveable_view, asset_file_def_index):
     if serialized is not None:
       proto.concrete_functions[name].CopyFrom(serialized)
 
-  saved_object_metadata = False
   for obj, obj_proto in zip(saveable_view.nodes, proto.nodes):
-    has_saved_object_metadata = _write_object_proto(
-        obj, obj_proto, asset_file_def_index, saveable_view.function_name_map)
-    saved_object_metadata = saved_object_metadata or has_saved_object_metadata
-  return proto, saved_object_metadata
+    _write_object_proto(obj, obj_proto, asset_file_def_index,
+                        saveable_view.function_name_map)
+  return proto
 
 
 def _write_object_proto(obj, proto, asset_file_def_index, function_name_map):
   """Saves an object into SavedObject proto."""
-  has_saved_object_metadata = False  # The metadata field will be deprecated.
   if isinstance(obj, tracking.Asset):
     proto.asset.SetInParent()
     proto.asset.asset_file_def_index = asset_file_def_index[obj]
@@ -797,14 +793,11 @@ def _write_object_proto(obj, proto, asset_file_def_index, function_name_map):
     if registered_type_proto is None:
       # Fallback for types with no matching registration
       # pylint:disable=protected-access
-      metadata = obj._tracking_metadata
-      if metadata:
-        has_saved_object_metadata = True
       registered_type_proto = saved_object_graph_pb2.SavedUserObject(
           identifier=obj._object_identifier,
           version=versions_pb2.VersionDef(
               producer=1, min_consumer=1, bad_consumers=[]),
-          metadata=metadata)
+          metadata=obj._tracking_metadata)
       # pylint:enable=protected-access
     proto.user_object.CopyFrom(registered_type_proto)
 
@@ -817,7 +810,6 @@ def _write_object_proto(obj, proto, asset_file_def_index, function_name_map):
   # documentation.
   if hasattr(obj, "_write_object_proto"):
     obj._write_object_proto(proto, options)  # pylint: disable=protected-access
-  return has_saved_object_metadata
 
 
 def _export_debug_info(exported_graph, export_dir):
@@ -1015,7 +1007,8 @@ def save(obj, export_dir, signatures=None, options=None):
         instances with input signatures or concrete functions. Keys of such a
         dictionary may be arbitrary strings, but will typically be from the
         `tf.saved_model.signature_constants` module.
-    options: `tf.saved_model.SaveOptions` object for configuring save options.
+    options: Optional, `tf.saved_model.SaveOptions` object that specifies
+      options for saving.
 
   Raises:
     ValueError: If `obj` is not trackable.
@@ -1166,27 +1159,11 @@ def _build_meta_graph_impl(obj,
       for fdef in func._stateless_fn._function_cache.all_values():  # pylint: disable=protected-access
         function_aliases[fdef.name] = alias
 
-  object_graph_proto, saved_object_metadata = _serialize_object_graph(
-      saveable_view, asset_info.asset_index)
+  object_graph_proto = _serialize_object_graph(saveable_view,
+                                               asset_info.asset_index)
   meta_graph_def.object_graph_def.CopyFrom(object_graph_proto)
 
-  if saved_object_metadata:
-    tf_logging.warn(
-        'FOR KERAS USERS: The object that you are saving contains one or more '
-        'Keras models or layers. If you are loading the SavedModel with '
-        '`tf.keras.models.load_model`, continue reading (otherwise, you may '
-        'ignore the following instructions). Please change your code to save '
-        'with `tf.keras.models.save_model` or `model.save`, and confirm that '
-        'the file "keras.metadata" exists in the export directory. In the '
-        'future, Keras will only load the SavedModels that have this file. In '
-        'other words, `tf.saved_model.save` will no longer write SavedModels '
-        'that can be recovered as Keras models (this will apply in TF 2.5).'
-        '\n\nFOR DEVS: If you are overwriting _tracking_metadata in your class,'
-        ' this property has been used to save metadata in the SavedModel. The '
-        'metadta field will be deprecated soon, so please move the metadata to '
-        'a different file.')
-
-  return (meta_graph_def, exported_graph, object_saver, asset_info)
+  return meta_graph_def, exported_graph, object_saver, asset_info
 
 
 def _build_meta_graph(obj,