Make layers used in Keras Applications aware of calls to enable/disable_v2_behavior.

PiperOrigin-RevId: 303807515
Change-Id: I4bca246dd446def8cde73b09c106d3a0cbad3264

tensorflow/python/keras/applications/densenet.py

@@ -26,9 +26,9 @@ from __future__ import print_function
 import os
 
 from tensorflow.python.keras import backend
-from tensorflow.python.keras import layers
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
+from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
 from tensorflow.python.util.tf_export import keras_export
@@ -52,6 +52,8 @@ DENSENET201_WEIGHT_PATH_NO_TOP = (
     BASE_WEIGTHS_PATH +
     'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5')
 
+layers = VersionAwareLayers()
+
 
 def dense_block(x, blocks, name):
   """A dense block.
@@ -133,8 +135,7 @@ def DenseNet(
     input_shape=None,
     pooling=None,
     classes=1000,
-    classifier_activation='softmax',
+    classifier_activation='softmax'):
-):
   """Instantiates the DenseNet architecture.
 
   Reference paper:
@@ -358,37 +359,12 @@ def DenseNet201(include_top=True,
 
 @keras_export('keras.applications.densenet.preprocess_input')
 def preprocess_input(x, data_format=None):
-  """Preprocesses a numpy array encoding a batch of images.
-
-  Arguments
-    x: A 4D numpy array consists of RGB values within [0, 255].
-
-  Returns
-    Preprocessed array.
-
-  Raises
-    ValueError: In case of unknown `data_format` argument.
-  """
   return imagenet_utils.preprocess_input(
       x, data_format=data_format, mode='torch')
 
 
 @keras_export('keras.applications.densenet.decode_predictions')
 def decode_predictions(preds, top=5):
-  """Decodes the prediction result from the model.
-
-  Arguments
-    preds: Numpy tensor encoding a batch of predictions.
-    top: Integer, how many top-guesses to return.
-
-  Returns
-    A list of lists of top class prediction tuples
-    `(class_name, class_description, score)`.
-    One list of tuples per sample in batch input.
-
-  Raises
-    ValueError: In case of invalid shape of the `preds` array (must be 2D).
-  """
   return imagenet_utils.decode_predictions(preds, top=top)
 
 
@@ -405,7 +381,7 @@ DOC = """
   Optionally loads weights pre-trained on ImageNet.
   Note that the data format convention used by the model is
   the one specified in your Keras config at `~/.keras/keras.json`.
-  
+
   Arguments:
     include_top: whether to include the fully-connected
       layer at the top of the network.

tensorflow/python/keras/applications/efficientnet.py

@@ -28,9 +28,9 @@ import math
 import os
 
 from tensorflow.python.keras import backend
-from tensorflow.python.keras import layers
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
+from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
 from tensorflow.python.util.tf_export import keras_export
@@ -140,6 +140,8 @@ DENSE_KERNEL_INITIALIZER = {
     }
 }
 
+layers = VersionAwareLayers()
+
 
 def EfficientNet(
     width_coefficient,
@@ -157,8 +159,7 @@ def EfficientNet(
     input_shape=None,
     pooling=None,
     classes=1000,
-    classifier_activation='softmax',
+    classifier_activation='softmax'):
-):
   """Instantiates the EfficientNet architecture using given scaling coefficients.
 
   Reference paper:
@@ -664,18 +665,7 @@ def preprocess_input(x, data_format=None):  # pylint: disable=unused-argument
 
 @keras_export('keras.applications.efficientnet.decode_predictions')
 def decode_predictions(preds, top=5):
-  """Decodes the prediction result from the model.
-
-  Arguments
-    preds: Numpy tensor encoding a batch of predictions.
-    top: Integer, how many top-guesses to return.
-
-  Returns
-    A list of lists of top class prediction tuples
-    `(class_name, class_description, score)`.
-    One list of tuples per sample in batch input.
-
-  Raises
-    ValueError: In case of invalid shape of the `preds` array (must be 2D).
-  """
   return imagenet_utils.decode_predictions(preds, top=top)
+
+
+decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__

tensorflow/python/keras/applications/inception_resnet_v2.py

@@ -28,9 +28,9 @@ from __future__ import print_function
 import os
 
 from tensorflow.python.keras import backend
-from tensorflow.python.keras import layers
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
+from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
 from tensorflow.python.util.tf_export import keras_export
@@ -38,6 +38,7 @@ from tensorflow.python.util.tf_export import keras_export
 
 BASE_WEIGHT_URL = ('https://storage.googleapis.com/tensorflow/'
                    'keras-applications/inception_resnet_v2/')
+layers = None
 
 
 @keras_export('keras.applications.inception_resnet_v2.InceptionResNetV2',
@@ -105,9 +106,11 @@ def InceptionResNetV2(include_top=True,
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
+  global layers
   if 'layers' in kwargs:
-    global layers
     layers = kwargs.pop('layers')
+  else:
+    layers = VersionAwareLayers()
   if kwargs:
     raise ValueError('Unknown argument(s): %s' % (kwargs,))
   if not (weights in {'imagenet', None} or os.path.exists(weights)):
@@ -378,36 +381,11 @@ def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'):
 
 @keras_export('keras.applications.inception_resnet_v2.preprocess_input')
 def preprocess_input(x, data_format=None):
-  """Preprocesses a numpy array encoding a batch of images.
-
-  Arguments
-    x: A 4D numpy array consists of RGB values within [0, 255].
-
-  Returns
-    Preprocessed array.
-
-  Raises
-    ValueError: In case of unknown `data_format` argument.
-  """
   return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')
 
 
 @keras_export('keras.applications.inception_resnet_v2.decode_predictions')
 def decode_predictions(preds, top=5):
-  """Decodes the prediction result from the model.
-
-  Arguments
-    preds: Numpy tensor encoding a batch of predictions.
-    top: Integer, how many top-guesses to return.
-
-  Returns
-    A list of lists of top class prediction tuples
-    `(class_name, class_description, score)`.
-    One list of tuples per sample in batch input.
-
-  Raises
-    ValueError: In case of invalid shape of the `preds` array (must be 2D).
-  """
   return imagenet_utils.decode_predictions(preds, top=top)
 
 

tensorflow/python/keras/applications/inception_v3.py

@@ -26,9 +26,9 @@ from __future__ import print_function
 import os
 
 from tensorflow.python.keras import backend
-from tensorflow.python.keras import layers
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
+from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
 from tensorflow.python.util.tf_export import keras_export
@@ -41,6 +41,8 @@ WEIGHTS_PATH_NO_TOP = (
     'https://storage.googleapis.com/tensorflow/keras-applications/'
     'inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5')
 
+layers = VersionAwareLayers()
+
 
 @keras_export('keras.applications.inception_v3.InceptionV3',
               'keras.applications.InceptionV3')
@@ -51,8 +53,7 @@ def InceptionV3(
     input_shape=None,
     pooling=None,
     classes=1000,
-    classifier_activation='softmax',
+    classifier_activation='softmax'):
-):
   """Instantiates the Inception v3 architecture.
 
   Reference paper:
@@ -406,36 +407,11 @@ def conv2d_bn(x,
 
 @keras_export('keras.applications.inception_v3.preprocess_input')
 def preprocess_input(x, data_format=None):
-  """Preprocesses a numpy array encoding a batch of images.
-
-  Arguments
-    x: A 4D numpy array consists of RGB values within [0, 255].
-
-  Returns
-    Preprocessed array.
-
-  Raises
-    ValueError: In case of unknown `data_format` argument.
-  """
   return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')
 
 
 @keras_export('keras.applications.inception_v3.decode_predictions')
 def decode_predictions(preds, top=5):
-  """Decodes the prediction result from the model.
-
-  Arguments
-    preds: Numpy tensor encoding a batch of predictions.
-    top: Integer, how many top-guesses to return.
-
-  Returns
-    A list of lists of top class prediction tuples
-    `(class_name, class_description, score)`.
-    One list of tuples per sample in batch input.
-
-  Raises
-    ValueError: In case of invalid shape of the `preds` array (must be 2D).
-  """
   return imagenet_utils.decode_predictions(preds, top=top)
 
 

tensorflow/python/keras/applications/mobilenet.py

@@ -67,9 +67,9 @@ from __future__ import print_function
 import os
 
 from tensorflow.python.keras import backend
-from tensorflow.python.keras import layers
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
+from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
 from tensorflow.python.platform import tf_logging as logging
@@ -77,6 +77,7 @@ from tensorflow.python.util.tf_export import keras_export
 
 BASE_WEIGHT_PATH = ('https://storage.googleapis.com/tensorflow/'
                     'keras-applications/mobilenet/')
+layers = None
 
 
 @keras_export('keras.applications.mobilenet.MobileNet',
@@ -155,9 +156,11 @@ def MobileNet(input_shape=None,
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
+  global layers
   if 'layers' in kwargs:
-    global layers
     layers = kwargs.pop('layers')
+  else:
+    layers = VersionAwareLayers()
   if kwargs:
     raise ValueError('Unknown argument(s): %s' % (kwargs,))
   if not (weights in {'imagenet', None} or os.path.exists(weights)):
@@ -439,36 +442,11 @@ def _depthwise_conv_block(inputs,
 
 @keras_export('keras.applications.mobilenet.preprocess_input')
 def preprocess_input(x, data_format=None):
-  """Preprocesses a numpy array encoding a batch of images.
-
-  Arguments
-    x: A 4D numpy array consists of RGB values within [0, 255].
-
-  Returns
-    Preprocessed array.
-
-  Raises
-    ValueError: In case of unknown `data_format` argument.
-  """
   return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')
 
 
 @keras_export('keras.applications.mobilenet.decode_predictions')
 def decode_predictions(preds, top=5):
-  """Decodes the prediction result from the model.
-
-  Arguments
-    preds: Numpy tensor encoding a batch of predictions.
-    top: Integer, how many top-guesses to return.
-
-  Returns
-    A list of lists of top class prediction tuples
-    `(class_name, class_description, score)`.
-    One list of tuples per sample in batch input.
-
-  Raises
-    ValueError: In case of invalid shape of the `preds` array (must be 2D).
-  """
   return imagenet_utils.decode_predictions(preds, top=top)
 
 

tensorflow/python/keras/applications/mobilenet_v2.py

@@ -80,9 +80,9 @@ from __future__ import print_function
 import os
 
 from tensorflow.python.keras import backend
-from tensorflow.python.keras import layers
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
+from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
 from tensorflow.python.platform import tf_logging as logging
@@ -90,6 +90,7 @@ from tensorflow.python.util.tf_export import keras_export
 
 BASE_WEIGHT_PATH = ('https://storage.googleapis.com/tensorflow/'
                     'keras-applications/mobilenet_v2/')
+layers = None
 
 
 @keras_export('keras.applications.mobilenet_v2.MobileNetV2',
@@ -173,9 +174,11 @@ def MobileNetV2(input_shape=None,
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
+  global layers
   if 'layers' in kwargs:
-    global layers
     layers = kwargs.pop('layers')
+  else:
+    layers = VersionAwareLayers()
   if kwargs:
     raise ValueError('Unknown argument(s): %s' % (kwargs,))
   if not (weights in {'imagenet', None} or os.path.exists(weights)):

tensorflow/python/keras/applications/nasnet.py

@@ -44,9 +44,9 @@ from __future__ import print_function
 import os
 
 from tensorflow.python.keras import backend
-from tensorflow.python.keras import layers
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
+from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
 from tensorflow.python.platform import tf_logging as logging
@@ -60,6 +60,8 @@ NASNET_MOBILE_WEIGHT_PATH_NO_TOP = BASE_WEIGHTS_PATH + 'NASNet-mobile-no-top.h5'
 NASNET_LARGE_WEIGHT_PATH = BASE_WEIGHTS_PATH + 'NASNet-large.h5'
 NASNET_LARGE_WEIGHT_PATH_NO_TOP = BASE_WEIGHTS_PATH + 'NASNet-large-no-top.h5'
 
+layers = VersionAwareLayers()
+
 
 def NASNet(
     input_shape=None,
@@ -74,8 +76,7 @@ def NASNet(
     pooling=None,
     classes=1000,
     default_size=None,
-    classifier_activation='softmax',
+    classifier_activation='softmax'):
-):
   """Instantiates a NASNet model.
 
   Reference paper:
@@ -785,36 +786,11 @@ def _reduction_a_cell(ip, p, filters, block_id=None):
 
 @keras_export('keras.applications.nasnet.preprocess_input')
 def preprocess_input(x, data_format=None):
-  """Preprocesses a numpy array encoding a batch of images.
-
-  Arguments
-    x: A 4D numpy array consists of RGB values within [0, 255].
-
-  Returns
-    Preprocessed array.
-
-  Raises
-    ValueError: In case of unknown `data_format` argument.
-  """
   return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')
 
 
 @keras_export('keras.applications.nasnet.decode_predictions')
 def decode_predictions(preds, top=5):
-  """Decodes the prediction result from the model.
-
-  Arguments
-    preds: Numpy tensor encoding a batch of predictions.
-    top: Integer, how many top-guesses to return.
-
-  Returns
-    A list of lists of top class prediction tuples
-    `(class_name, class_description, score)`.
-    One list of tuples per sample in batch input.
-
-  Raises
-    ValueError: In case of invalid shape of the `preds` array (must be 2D).
-  """
   return imagenet_utils.decode_predictions(preds, top=top)
 
 

tensorflow/python/keras/applications/resnet.py

@@ -26,9 +26,9 @@ from __future__ import print_function
 import os
 
 from tensorflow.python.keras import backend
-from tensorflow.python.keras import layers
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
+from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
 from tensorflow.python.util.tf_export import keras_export
@@ -55,6 +55,8 @@ WEIGHTS_HASHES = {
         ('34fb605428fcc7aa4d62f44404c11509', '0f678c91647380debd923963594981b3')
 }
 
+layers = None
+
 
 def ResNet(stack_fn,
            preact,
@@ -129,9 +131,11 @@ def ResNet(stack_fn,
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
+  global layers
   if 'layers' in kwargs:
-    global layers
     layers = kwargs.pop('layers')
+  else:
+    layers = VersionAwareLayers()
   if kwargs:
     raise ValueError('Unknown argument(s): %s' % (kwargs,))
   if not (weights in {'imagenet', None} or os.path.exists(weights)):
@@ -517,17 +521,6 @@ def ResNet152(include_top=True,
 @keras_export('keras.applications.resnet50.preprocess_input',
               'keras.applications.resnet.preprocess_input')
 def preprocess_input(x, data_format=None):
-  """Preprocesses a numpy array encoding a batch of images.
-
-  Arguments
-    x: A 4D numpy array consists of RGB values within [0, 255].
-
-  Returns
-    Preprocessed array.
-
-  Raises
-    ValueError: In case of unknown `data_format` argument.
-  """
   return imagenet_utils.preprocess_input(
       x, data_format=data_format, mode='caffe')
 
@@ -535,20 +528,6 @@ def preprocess_input(x, data_format=None):
 @keras_export('keras.applications.resnet50.decode_predictions',
               'keras.applications.resnet.decode_predictions')
 def decode_predictions(preds, top=5):
-  """Decodes the prediction result from the model.
-
-  Arguments
-    preds: Numpy tensor encoding a batch of predictions.
-    top: Integer, how many top-guesses to return.
-
-  Returns
-    A list of lists of top class prediction tuples
-    `(class_name, class_description, score)`.
-    One list of tuples per sample in batch input.
-
-  Raises
-    ValueError: In case of invalid shape of the `preds` array (must be 2D).
-  """
   return imagenet_utils.decode_predictions(preds, top=top)
 
 
@@ -565,7 +544,7 @@ DOC = """
   Optionally loads weights pre-trained on ImageNet.
   Note that the data format convention used by the model is
   the one specified in your Keras config at `~/.keras/keras.json`.
-  
+
   Arguments:
     include_top: whether to include the fully-connected
       layer at the top of the network.

tensorflow/python/keras/applications/resnet_v2.py

@@ -37,8 +37,7 @@ def ResNet50V2(
     input_shape=None,
     pooling=None,
     classes=1000,
-    classifier_activation='softmax',
+    classifier_activation='softmax'):
-):
   """Instantiates the ResNet50V2 architecture."""
   def stack_fn(x):
     x = resnet.stack2(x, 64, 3, name='conv2')
@@ -57,8 +56,7 @@ def ResNet50V2(
       input_shape,
       pooling,
       classes,
-      classifier_activation=classifier_activation,
+      classifier_activation=classifier_activation)
-  )
 
 
 @keras_export('keras.applications.resnet_v2.ResNet101V2',
@@ -70,8 +68,7 @@ def ResNet101V2(
     input_shape=None,
     pooling=None,
     classes=1000,
-    classifier_activation='softmax',
+    classifier_activation='softmax'):
-):
   """Instantiates the ResNet101V2 architecture."""
   def stack_fn(x):
     x = resnet.stack2(x, 64, 3, name='conv2')
@@ -90,8 +87,7 @@ def ResNet101V2(
       input_shape,
       pooling,
       classes,
-      classifier_activation=classifier_activation,
+      classifier_activation=classifier_activation)
-  )
 
 
 @keras_export('keras.applications.resnet_v2.ResNet152V2',
@@ -103,8 +99,7 @@ def ResNet152V2(
     input_shape=None,
     pooling=None,
     classes=1000,
-    classifier_activation='softmax',
+    classifier_activation='softmax'):
-):
   """Instantiates the ResNet152V2 architecture."""
   def stack_fn(x):
     x = resnet.stack2(x, 64, 3, name='conv2')
@@ -123,43 +118,17 @@ def ResNet152V2(
       input_shape,
       pooling,
       classes,
-      classifier_activation=classifier_activation,
+      classifier_activation=classifier_activation)
-  )
 
 
 @keras_export('keras.applications.resnet_v2.preprocess_input')
 def preprocess_input(x, data_format=None):
-  """Preprocesses a numpy array encoding a batch of images.
-
-  Arguments
-    x: A 4D numpy array consists of RGB values within [0, 255].
-
-  Returns
-    Preprocessed array.
-
-  Raises
-    ValueError: In case of unknown `data_format` argument.
-  """
   return imagenet_utils.preprocess_input(
       x, data_format=data_format, mode='tf')
 
 
 @keras_export('keras.applications.resnet_v2.decode_predictions')
 def decode_predictions(preds, top=5):
-  """Decodes the prediction result from the model.
-
-  Arguments
-    preds: Numpy tensor encoding a batch of predictions.
-    top: Integer, how many top-guesses to return.
-
-  Returns
-    A list of lists of top class prediction tuples
-    `(class_name, class_description, score)`.
-    One list of tuples per sample in batch input.
-
-  Raises
-    ValueError: In case of invalid shape of the `preds` array (must be 2D).
-  """
   return imagenet_utils.decode_predictions(preds, top=top)
 
 

tensorflow/python/keras/applications/vgg16.py

@@ -26,9 +26,9 @@ from __future__ import print_function
 import os
 
 from tensorflow.python.keras import backend
-from tensorflow.python.keras import layers
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
+from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
 from tensorflow.python.util.tf_export import keras_export
@@ -40,6 +40,8 @@ WEIGHTS_PATH_NO_TOP = ('https://storage.googleapis.com/tensorflow/'
                        'keras-applications/vgg16/'
                        'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5')
 
+layers = VersionAwareLayers()
+
 
 @keras_export('keras.applications.vgg16.VGG16', 'keras.applications.VGG16')
 def VGG16(
@@ -49,8 +51,7 @@ def VGG16(
     input_shape=None,
     pooling=None,
     classes=1000,
-    classifier_activation='softmax',
+    classifier_activation='softmax'):
-):
   """Instantiates the VGG16 model.
 
   Reference paper:
@@ -227,37 +228,12 @@ def VGG16(
 
 @keras_export('keras.applications.vgg16.preprocess_input')
 def preprocess_input(x, data_format=None):
-  """Preprocesses a numpy array encoding a batch of images.
-
-  Arguments
-    x: A 4D numpy array consists of RGB values within [0, 255].
-
-  Returns
-    Preprocessed array.
-
-  Raises
-    ValueError: In case of unknown `data_format` argument.
-  """
   return imagenet_utils.preprocess_input(
       x, data_format=data_format, mode='caffe')
 
 
 @keras_export('keras.applications.vgg16.decode_predictions')
 def decode_predictions(preds, top=5):
-  """Decodes the prediction result from the model.
-
-  Arguments
-    preds: Numpy tensor encoding a batch of predictions.
-    top: Integer, how many top-guesses to return.
-
-  Returns
-    A list of lists of top class prediction tuples
-    `(class_name, class_description, score)`.
-    One list of tuples per sample in batch input.
-
-  Raises
-    ValueError: In case of invalid shape of the `preds` array (must be 2D).
-  """
   return imagenet_utils.decode_predictions(preds, top=top)
 
 

tensorflow/python/keras/applications/vgg19.py

@@ -26,9 +26,9 @@ from __future__ import print_function
 import os
 
 from tensorflow.python.keras import backend
-from tensorflow.python.keras import layers
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
+from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
 from tensorflow.python.util.tf_export import keras_export
@@ -40,6 +40,8 @@ WEIGHTS_PATH_NO_TOP = ('https://storage.googleapis.com/tensorflow/'
                        'keras-applications/vgg19/'
                        'vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5')
 
+layers = VersionAwareLayers()
+
 
 @keras_export('keras.applications.vgg19.VGG19', 'keras.applications.VGG19')
 def VGG19(
@@ -49,8 +51,7 @@ def VGG19(
     input_shape=None,
     pooling=None,
     classes=1000,
-    classifier_activation='softmax',
+    classifier_activation='softmax'):
-):
   """Instantiates the VGG19 architecture.
 
   Reference:
@@ -232,37 +233,12 @@ def VGG19(
 
 @keras_export('keras.applications.vgg19.preprocess_input')
 def preprocess_input(x, data_format=None):
-  """Preprocesses a numpy array encoding a batch of images.
-
-  Arguments
-    x: A 4D numpy array consists of RGB values within [0, 255].
-
-  Returns
-    Preprocessed array.
-
-  Raises
-    ValueError: In case of unknown `data_format` argument.
-  """
   return imagenet_utils.preprocess_input(
       x, data_format=data_format, mode='caffe')
 
 
 @keras_export('keras.applications.vgg19.decode_predictions')
 def decode_predictions(preds, top=5):
-  """Decodes the prediction result from the model.
-
-  Arguments
-    preds: Numpy tensor encoding a batch of predictions.
-    top: Integer, how many top-guesses to return.
-
-  Returns
-    A list of lists of top class prediction tuples
-    `(class_name, class_description, score)`.
-    One list of tuples per sample in batch input.
-
-  Raises
-    ValueError: In case of invalid shape of the `preds` array (must be 2D).
-  """
   return imagenet_utils.decode_predictions(preds, top=top)
 
 

tensorflow/python/keras/applications/xception.py

@@ -30,9 +30,9 @@ from __future__ import print_function
 import os
 
 from tensorflow.python.keras import backend
-from tensorflow.python.keras import layers
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.engine import training
+from tensorflow.python.keras.layers import VersionAwareLayers
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
 from tensorflow.python.util.tf_export import keras_export
@@ -45,6 +45,8 @@ TF_WEIGHTS_PATH_NO_TOP = (
     'https://storage.googleapis.com/tensorflow/keras-applications/'
     'xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5')
 
+layers = VersionAwareLayers()
+
 
 @keras_export('keras.applications.xception.Xception',
               'keras.applications.Xception')
@@ -55,8 +57,7 @@ def Xception(
     input_shape=None,
     pooling=None,
     classes=1000,
-    classifier_activation='softmax',
+    classifier_activation='softmax'):
-):
   """Instantiates the Xception architecture.
 
   Optionally loads weights pre-trained on ImageNet.
@@ -312,36 +313,11 @@ def Xception(
 
 @keras_export('keras.applications.xception.preprocess_input')
 def preprocess_input(x, data_format=None):
-  """Preprocesses a numpy array encoding a batch of images.
-
-  Arguments
-    x: A 4D numpy array consists of RGB values within [0, 255].
-
-  Returns
-    Preprocessed array.
-
-  Raises
-    ValueError: In case of unknown `data_format` argument.
-  """
   return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')
 
 
 @keras_export('keras.applications.xception.decode_predictions')
 def decode_predictions(preds, top=5):
-  """Decodes the prediction result from the model.
-
-  Arguments
-    preds: Numpy tensor encoding a batch of predictions.
-    top: Integer, how many top-guesses to return.
-
-  Returns
-    A list of lists of top class prediction tuples
-    `(class_name, class_description, score)`.
-    One list of tuples per sample in batch input.
-
-  Raises
-    ValueError: In case of invalid shape of the `preds` array (must be 2D).
-  """
   return imagenet_utils.decode_predictions(preds, top=top)
 
 

tensorflow/python/keras/layers/__init__.py

@@ -242,9 +242,28 @@ from tensorflow.python.keras.layers.rnn_cell_wrapper_v2 import DropoutWrapper
 from tensorflow.python.keras.layers.rnn_cell_wrapper_v2 import ResidualWrapper
 
 # Serialization functions
+from tensorflow.python.keras.layers import serialization
 from tensorflow.python.keras.layers.serialization import deserialize
 from tensorflow.python.keras.layers.serialization import serialize
 
+
+class VersionAwareLayers(object):
+  """Utility to be used internally to access layers in a V1/V2-aware fashion.
+
+  When using layers within the Keras codebase, under the constraint that
+  e.g. `layers.BatchNormalization` should be the `BatchNormalization` version
+  corresponding to the current runtime (TF1 or TF2), do not simply access
+  `layers.BatchNormalization` since it would ignore e.g. an early
+  `compat.v2.disable_v2_behavior()` call. Instead, use an instance
+  of `VersionAwareLayers` (which you can use just like the `layers` module).
+  """
+
+  def __getattr__(self, name):
+    serialization.populate_deserializable_objects()
+    if name in serialization.LOCAL.ALL_OBJECTS:
+      return serialization.LOCAL.ALL_OBJECTS[name]
+    return super(VersionAwareLayers, self).__getattr__(name)
+
 del absolute_import
 del division
 del print_function

tensorflow/python/keras/layers/serialization.py

@@ -26,6 +26,7 @@ import threading
 from tensorflow.python import tf2
 from tensorflow.python.keras.engine import base_layer
 from tensorflow.python.keras.engine import input_layer
+from tensorflow.python.keras.engine import input_spec
 from tensorflow.python.keras.layers import advanced_activations
 from tensorflow.python.keras.layers import convolutional
 from tensorflow.python.keras.layers import convolutional_recurrent
@@ -128,6 +129,7 @@ def populate_deserializable_objects():
   from tensorflow.python.feature_column import sequence_feature_column as sfc  # pylint: disable=g-import-not-at-top
 
   LOCAL.ALL_OBJECTS['Input'] = input_layer.Input
+  LOCAL.ALL_OBJECTS['InputSpec'] = input_spec.InputSpec
   LOCAL.ALL_OBJECTS['Network'] = models.Network
   LOCAL.ALL_OBJECTS['Model'] = models.Model
   LOCAL.ALL_OBJECTS['Sequential'] = models.Sequential
@@ -135,6 +137,15 @@ def populate_deserializable_objects():
   LOCAL.ALL_OBJECTS['WideDeepModel'] = WideDeepModel
   LOCAL.ALL_OBJECTS['DenseFeatures'] = dense_features.DenseFeatures
   LOCAL.ALL_OBJECTS['SequenceFeatures'] = sfc.SequenceFeatures
+  # Merge layers, function versions.
+  LOCAL.ALL_OBJECTS['add'] = merge.add
+  LOCAL.ALL_OBJECTS['subtract'] = merge.subtract
+  LOCAL.ALL_OBJECTS['multiply'] = merge.multiply
+  LOCAL.ALL_OBJECTS['average'] = merge.average
+  LOCAL.ALL_OBJECTS['maximum'] = merge.maximum
+  LOCAL.ALL_OBJECTS['minimum'] = merge.minimum
+  LOCAL.ALL_OBJECTS['concatenate'] = merge.concatenate
+  LOCAL.ALL_OBJECTS['dot'] = merge.dot
 
 
 @keras_export('keras.layers.serialize')