experiments/STT-tensorflow
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Remove the __init__ content for keras/saving.

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

PiperOrigin-RevId: 274001008
This commit is contained in:
Scott Zhu 2019-10-10 11:25:09 -07:00 committed by TensorFlower Gardener
parent e258e9e31c
commit 402ccb1803
9 changed files with 26 additions and 55 deletions
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3
tensorflow/python/keras/api/BUILD
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@ -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",

11
tensorflow/python/keras/engine/network.py
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@ -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
@ -982,7 +983,7 @@ 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)
def save_weights(self, filepath, overwrite=True, save_format=None):
@ -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.

2
tensorflow/python/keras/layers/cudnn_recurrent_test.py
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@ -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))

2
tensorflow/python/keras/layers/preprocessing/text_vectorization_test.py
View File

@ -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

4
tensorflow/python/keras/layers/tensorflow_op_layer_test.py
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@ -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.])

4
tensorflow/python/keras/mixed_precision/experimental/keras_test.py
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@ -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)

13
tensorflow/python/keras/models.py
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@ -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
load_model = saving.load_model
model_from_config = saving.model_from_config
model_from_yaml = saving.model_from_yaml
model_from_json = saving.model_from_json
save_model = save.save_model
load_model = save.load_model
model_from_config = model_config.model_from_config
model_from_yaml = model_config.model_from_yaml
model_from_json = model_config.model_from_json
# Callable used to clone a layer with weights preserved.

35
tensorflow/python/keras/saving/__init__.py
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@ -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

5
tensorflow/python/keras/saving/saved_model_experimental.py
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@ -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(