Remove the __init__ content for keras/saving.

All the direct dependencies has been changed to use explicit import, rather than rely on the __init__ shortcut.

PiperOrigin-RevId: 274001008

tensorflow/python/keras/api/BUILD

@@ -14,6 +14,9 @@ keras_packages = [
     "tensorflow.python",
     "tensorflow.python.keras",
     "tensorflow.python.keras.activations",
+    "tensorflow.python.keras.saving.model_config",
+    "tensorflow.python.keras.saving.save",
+    "tensorflow.python.keras.saving.saved_model_experimental",
     "tensorflow.python.keras.utils.data_utils",
     "tensorflow.python.keras.utils.generic_utils",
     "tensorflow.python.keras.utils.io_utils",

tensorflow/python/keras/engine/network.py

@@ -39,12 +39,13 @@ from tensorflow.python.framework import func_graph
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.keras import backend
-from tensorflow.python.keras import saving
 from tensorflow.python.keras.engine import base_layer
 from tensorflow.python.keras.engine import base_layer_utils
 from tensorflow.python.keras.engine import input_layer as input_layer_module
 from tensorflow.python.keras.engine import node as node_module
 from tensorflow.python.keras.engine import training_utils
+from tensorflow.python.keras.saving import hdf5_format
+from tensorflow.python.keras.saving import save
 from tensorflow.python.keras.saving.saved_model import network_serialization
 from tensorflow.python.keras.utils import generic_utils
 from tensorflow.python.keras.utils import layer_utils
@@ -982,8 +983,8 @@ class Network(base_layer.Layer):
     model = load_model('my_model.h5')
     ```
     """
-    saving.save_model(self, filepath, overwrite, include_optimizer, save_format,
+    save.save_model(self, filepath, overwrite, include_optimizer, save_format,
-                      signatures, options)
+                    signatures, options)
 
   def save_weights(self, filepath, overwrite=True, save_format=None):
     """Saves all layer weights.
@@ -1082,7 +1083,7 @@ class Network(base_layer.Layer):
         return
     if save_format == 'h5':
       with h5py.File(filepath, 'w') as f:
-        saving.save_weights_to_hdf5_group(f, self.layers)
+        hdf5_format.save_weights_to_hdf5_group(f, self.layers)
     else:
       if context.executing_eagerly():
         session = None
@@ -1194,10 +1195,10 @@ class Network(base_layer.Layer):
       if 'layer_names' not in f.attrs and 'model_weights' in f:
         f = f['model_weights']
       if by_name:
-        saving.load_weights_from_hdf5_group_by_name(
+        hdf5_format.load_weights_from_hdf5_group_by_name(
             f, self.layers, skip_mismatch=skip_mismatch)
       else:
-        saving.load_weights_from_hdf5_group(f, self.layers)
+        hdf5_format.load_weights_from_hdf5_group(f, self.layers)
 
   def _updated_config(self):
     """Util shared between different serialization methods.

tensorflow/python/keras/layers/cudnn_recurrent_test.py

@@ -469,7 +469,7 @@ class CuDNNV1OnlyTest(keras_parameterized.TestCase):
 
     def assert_not_compatible(src, dest, message):
       with self.assertRaises(ValueError) as ex:
-        keras.saving.preprocess_weights_for_loading(
+        keras.saving.hdf5_format.preprocess_weights_for_loading(
             dest,
             get_layer_weights(src))
       self.assertIn(message, str(ex.exception))

tensorflow/python/keras/layers/preprocessing/text_vectorization_test.py

@@ -31,9 +31,9 @@ from tensorflow.python import keras
 from tensorflow.python.eager import context
 from tensorflow.python.framework import dtypes
 from tensorflow.python.keras import keras_parameterized
-from tensorflow.python.keras import saving
 from tensorflow.python.keras import testing_utils
 from tensorflow.python.keras.layers.preprocessing import preprocessing_test_utils
+from tensorflow.python.keras.saving import saved_model_experimental as saving
 from tensorflow.python.keras.utils.generic_utils import CustomObjectScope
 from tensorflow.python.platform import test
 

tensorflow/python/keras/layers/tensorflow_op_layer_test.py

@@ -29,9 +29,9 @@ from tensorflow.python.eager import def_function
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import ops
 from tensorflow.python.keras import keras_parameterized
-from tensorflow.python.keras import saving
 from tensorflow.python.keras import testing_utils
 from tensorflow.python.keras.optimizer_v2 import adam
+from tensorflow.python.keras.saving import model_config
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gen_nn_ops
 from tensorflow.python.ops import math_ops
@@ -332,7 +332,7 @@ class AutoLambdaTest(keras_parameterized.TestCase):
   def test_json_serialization(self):
     inputs = keras.Input(shape=(4,), dtype='uint8')
     outputs = math_ops.cast(inputs, 'float32') / 4.
-    model = saving.model_from_json(keras.Model(inputs, outputs).to_json())
+    model = model_config.model_from_json(keras.Model(inputs, outputs).to_json())
     self.assertAllEqual(
         self.evaluate(model(np.array([0, 64, 128, 192], np.uint8))),
         [0., 16., 32., 48.])

tensorflow/python/keras/mixed_precision/experimental/keras_test.py

@@ -37,7 +37,6 @@ from tensorflow.python.keras import layers
 from tensorflow.python.keras import models
 from tensorflow.python.keras import optimizers
 from tensorflow.python.keras import regularizers
-from tensorflow.python.keras import saving
 from tensorflow.python.keras import testing_utils
 from tensorflow.python.keras.engine import base_layer
 from tensorflow.python.keras.engine import base_layer_utils
@@ -46,6 +45,7 @@ from tensorflow.python.keras.mixed_precision.experimental import loss_scale_opti
 from tensorflow.python.keras.mixed_precision.experimental import policy
 from tensorflow.python.keras.mixed_precision.experimental import test_util as mp_test_util
 from tensorflow.python.keras.optimizer_v2 import gradient_descent
+from tensorflow.python.keras.saving import save
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import variables
@@ -1135,7 +1135,7 @@ class KerasModelTest(keras_parameterized.TestCase):
     self.assertEqual(backend.get_value(loss_scale._num_good_steps), 0)
 
     # Load model weights and ensure loss scale weights are restored.
-    model = saving.load_model(save_path, custom_objects={'AddLayer': AddLayer})
+    model = save.load_model(save_path, custom_objects={'AddLayer': AddLayer})
     loss_scale = model.optimizer.loss_scale
     (weight,) = model.trainable_weights
     loaded_weight = backend.get_value(weight)

tensorflow/python/keras/models.py

@@ -22,7 +22,6 @@ from __future__ import print_function
 from tensorflow.python.keras import backend as K
 from tensorflow.python.keras import metrics as metrics_module
 from tensorflow.python.keras import optimizers
-from tensorflow.python.keras import saving
 from tensorflow.python.keras.engine import network
 from tensorflow.python.keras.engine import sequential
 from tensorflow.python.keras.engine import training
@@ -31,6 +30,8 @@ from tensorflow.python.keras.engine.base_layer import Layer
 from tensorflow.python.keras.engine.input_layer import Input
 from tensorflow.python.keras.engine.input_layer import InputLayer
 from tensorflow.python.keras.engine.network import Network
+from tensorflow.python.keras.saving import model_config
+from tensorflow.python.keras.saving import save
 from tensorflow.python.keras.utils import generic_utils
 from tensorflow.python.keras.utils.generic_utils import CustomObjectScope
 from tensorflow.python.platform import tf_logging as logging
@@ -41,11 +42,11 @@ from tensorflow.python.util.tf_export import keras_export
 # API entries importable from `keras.models`:
 Model = training.Model  # pylint: disable=invalid-name
 Sequential = sequential.Sequential  # pylint: disable=invalid-name
-save_model = saving.save_model
+save_model = save.save_model
-load_model = saving.load_model
+load_model = save.load_model
-model_from_config = saving.model_from_config
+model_from_config = model_config.model_from_config
-model_from_yaml = saving.model_from_yaml
+model_from_yaml = model_config.model_from_yaml
-model_from_json = saving.model_from_json
+model_from_json = model_config.model_from_json
 
 
 # Callable used to clone a layer with weights preserved.

tensorflow/python/keras/saving/__init__.py

@@ -1,35 +0,0 @@
-# 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.
-# ==============================================================================
-"""Utils for saving and loading Keras Models."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.python.keras.saving.hdf5_format import load_attributes_from_hdf5_group
-from tensorflow.python.keras.saving.hdf5_format import load_model_from_hdf5
-from tensorflow.python.keras.saving.hdf5_format import load_weights_from_hdf5_group
-from tensorflow.python.keras.saving.hdf5_format import load_weights_from_hdf5_group_by_name
-from tensorflow.python.keras.saving.hdf5_format import preprocess_weights_for_loading
-from tensorflow.python.keras.saving.hdf5_format import save_attributes_to_hdf5_group
-from tensorflow.python.keras.saving.hdf5_format import save_model_to_hdf5
-from tensorflow.python.keras.saving.hdf5_format import save_weights_to_hdf5_group
-from tensorflow.python.keras.saving.model_config import model_from_config
-from tensorflow.python.keras.saving.model_config import model_from_json
-from tensorflow.python.keras.saving.model_config import model_from_yaml
-from tensorflow.python.keras.saving.save import load_model
-from tensorflow.python.keras.saving.save import save_model
-from tensorflow.python.keras.saving.saved_model_experimental import export_saved_model
-from tensorflow.python.keras.saving.saved_model_experimental import load_from_saved_model
-from tensorflow.python.keras.saving.saving_utils import trace_model_call

tensorflow/python/keras/saving/saved_model_experimental.py

@@ -25,7 +25,7 @@ from tensorflow.python.framework import ops
 from tensorflow.python.keras import backend as K
 from tensorflow.python.keras import optimizers
 from tensorflow.python.keras.optimizer_v2 import optimizer_v2
-from tensorflow.python.keras.saving import model_from_json
+from tensorflow.python.keras.saving import model_config
 from tensorflow.python.keras.saving import saving_utils
 from tensorflow.python.keras.utils import mode_keys
 from tensorflow.python.lib.io import file_io
@@ -417,7 +417,8 @@ def load_from_saved_model(saved_model_path, custom_objects=None):
       compat.as_bytes(constants.ASSETS_DIRECTORY),
       compat.as_bytes(constants.SAVED_MODEL_FILENAME_JSON))
   model_json = file_io.read_file_to_string(model_json_filepath)
-  model = model_from_json(model_json, custom_objects=custom_objects)
+  model = model_config.model_from_json(
+      model_json, custom_objects=custom_objects)
 
   # restore model weights
   checkpoint_prefix = os.path.join(