Make layers used in Keras Applications aware of calls to enable/disable_v2_behavior.
PiperOrigin-RevId: 303807515 Change-Id: I4bca246dd446def8cde73b09c106d3a0cbad3264
This commit is contained in:
Francois Chollet
TensorFlower Gardener
parent
433d40514b
commit
30f753a95f
@ -26,9 +26,9 @@ from __future__ import print_function
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import os
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from tensorflow.python.keras import backend
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from tensorflow.python.keras import layers
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from tensorflow.python.keras.applications import imagenet_utils
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from tensorflow.python.keras.engine import training
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from tensorflow.python.keras.layers import VersionAwareLayers
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from tensorflow.python.keras.utils import data_utils
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from tensorflow.python.keras.utils import layer_utils
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from tensorflow.python.util.tf_export import keras_export
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@ -52,6 +52,8 @@ DENSENET201_WEIGHT_PATH_NO_TOP = (
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BASE_WEIGTHS_PATH +
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'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5')
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layers = VersionAwareLayers()
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def dense_block(x, blocks, name):
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"""A dense block.
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@ -133,8 +135,7 @@ def DenseNet(
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input_shape=None,
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pooling=None,
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classes=1000,
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classifier_activation='softmax',
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):
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classifier_activation='softmax'):
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"""Instantiates the DenseNet architecture.
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Reference paper:
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@ -358,37 +359,12 @@ def DenseNet201(include_top=True,
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@keras_export('keras.applications.densenet.preprocess_input')
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def preprocess_input(x, data_format=None):
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"""Preprocesses a numpy array encoding a batch of images.
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Arguments
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x: A 4D numpy array consists of RGB values within [0, 255].
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Returns
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Preprocessed array.
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Raises
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ValueError: In case of unknown `data_format` argument.
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"""
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return imagenet_utils.preprocess_input(
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x, data_format=data_format, mode='torch')
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@keras_export('keras.applications.densenet.decode_predictions')
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def decode_predictions(preds, top=5):
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"""Decodes the prediction result from the model.
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Arguments
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preds: Numpy tensor encoding a batch of predictions.
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top: Integer, how many top-guesses to return.
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Returns
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A list of lists of top class prediction tuples
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`(class_name, class_description, score)`.
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One list of tuples per sample in batch input.
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Raises
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ValueError: In case of invalid shape of the `preds` array (must be 2D).
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"""
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return imagenet_utils.decode_predictions(preds, top=top)
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@ -28,9 +28,9 @@ import math
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import os
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from tensorflow.python.keras import backend
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from tensorflow.python.keras import layers
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from tensorflow.python.keras.applications import imagenet_utils
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from tensorflow.python.keras.engine import training
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from tensorflow.python.keras.layers import VersionAwareLayers
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from tensorflow.python.keras.utils import data_utils
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from tensorflow.python.keras.utils import layer_utils
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from tensorflow.python.util.tf_export import keras_export
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@ -140,6 +140,8 @@ DENSE_KERNEL_INITIALIZER = {
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}
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}
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layers = VersionAwareLayers()
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def EfficientNet(
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width_coefficient,
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@ -157,8 +159,7 @@ def EfficientNet(
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input_shape=None,
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pooling=None,
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classes=1000,
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classifier_activation='softmax',
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):
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classifier_activation='softmax'):
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"""Instantiates the EfficientNet architecture using given scaling coefficients.
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Reference paper:
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@ -664,18 +665,7 @@ def preprocess_input(x, data_format=None): # pylint: disable=unused-argument
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@keras_export('keras.applications.efficientnet.decode_predictions')
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def decode_predictions(preds, top=5):
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"""Decodes the prediction result from the model.
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Arguments
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preds: Numpy tensor encoding a batch of predictions.
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top: Integer, how many top-guesses to return.
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Returns
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A list of lists of top class prediction tuples
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`(class_name, class_description, score)`.
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One list of tuples per sample in batch input.
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Raises
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ValueError: In case of invalid shape of the `preds` array (must be 2D).
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"""
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return imagenet_utils.decode_predictions(preds, top=top)
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decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
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@ -28,9 +28,9 @@ from __future__ import print_function
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import os
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from tensorflow.python.keras import backend
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from tensorflow.python.keras import layers
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from tensorflow.python.keras.applications import imagenet_utils
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from tensorflow.python.keras.engine import training
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from tensorflow.python.keras.layers import VersionAwareLayers
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from tensorflow.python.keras.utils import data_utils
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from tensorflow.python.keras.utils import layer_utils
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from tensorflow.python.util.tf_export import keras_export
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@ -38,6 +38,7 @@ from tensorflow.python.util.tf_export import keras_export
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BASE_WEIGHT_URL = ('https://storage.googleapis.com/tensorflow/'
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'keras-applications/inception_resnet_v2/')
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layers = None
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@keras_export('keras.applications.inception_resnet_v2.InceptionResNetV2',
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@ -105,9 +106,11 @@ def InceptionResNetV2(include_top=True,
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ValueError: if `classifier_activation` is not `softmax` or `None` when
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using a pretrained top layer.
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"""
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if 'layers' in kwargs:
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global layers
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if 'layers' in kwargs:
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layers = kwargs.pop('layers')
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else:
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layers = VersionAwareLayers()
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if kwargs:
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raise ValueError('Unknown argument(s): %s' % (kwargs,))
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if not (weights in {'imagenet', None} or os.path.exists(weights)):
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@ -378,36 +381,11 @@ def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'):
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@keras_export('keras.applications.inception_resnet_v2.preprocess_input')
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def preprocess_input(x, data_format=None):
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"""Preprocesses a numpy array encoding a batch of images.
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Arguments
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x: A 4D numpy array consists of RGB values within [0, 255].
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Returns
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Preprocessed array.
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Raises
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ValueError: In case of unknown `data_format` argument.
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"""
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return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')
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@keras_export('keras.applications.inception_resnet_v2.decode_predictions')
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def decode_predictions(preds, top=5):
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"""Decodes the prediction result from the model.
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Arguments
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preds: Numpy tensor encoding a batch of predictions.
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top: Integer, how many top-guesses to return.
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Returns
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A list of lists of top class prediction tuples
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`(class_name, class_description, score)`.
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One list of tuples per sample in batch input.
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Raises
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ValueError: In case of invalid shape of the `preds` array (must be 2D).
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"""
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return imagenet_utils.decode_predictions(preds, top=top)
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@ -26,9 +26,9 @@ from __future__ import print_function
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import os
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from tensorflow.python.keras import backend
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from tensorflow.python.keras import layers
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from tensorflow.python.keras.applications import imagenet_utils
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from tensorflow.python.keras.engine import training
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from tensorflow.python.keras.layers import VersionAwareLayers
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from tensorflow.python.keras.utils import data_utils
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from tensorflow.python.keras.utils import layer_utils
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from tensorflow.python.util.tf_export import keras_export
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@ -41,6 +41,8 @@ WEIGHTS_PATH_NO_TOP = (
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'https://storage.googleapis.com/tensorflow/keras-applications/'
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'inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5')
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layers = VersionAwareLayers()
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@keras_export('keras.applications.inception_v3.InceptionV3',
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'keras.applications.InceptionV3')
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@ -51,8 +53,7 @@ def InceptionV3(
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input_shape=None,
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pooling=None,
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classes=1000,
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classifier_activation='softmax',
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):
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classifier_activation='softmax'):
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"""Instantiates the Inception v3 architecture.
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Reference paper:
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@ -406,36 +407,11 @@ def conv2d_bn(x,
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@keras_export('keras.applications.inception_v3.preprocess_input')
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def preprocess_input(x, data_format=None):
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"""Preprocesses a numpy array encoding a batch of images.
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Arguments
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x: A 4D numpy array consists of RGB values within [0, 255].
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Returns
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Preprocessed array.
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Raises
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ValueError: In case of unknown `data_format` argument.
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"""
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return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')
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@keras_export('keras.applications.inception_v3.decode_predictions')
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def decode_predictions(preds, top=5):
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"""Decodes the prediction result from the model.
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Arguments
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preds: Numpy tensor encoding a batch of predictions.
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top: Integer, how many top-guesses to return.
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Returns
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A list of lists of top class prediction tuples
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`(class_name, class_description, score)`.
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One list of tuples per sample in batch input.
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Raises
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ValueError: In case of invalid shape of the `preds` array (must be 2D).
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"""
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return imagenet_utils.decode_predictions(preds, top=top)
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@ -67,9 +67,9 @@ from __future__ import print_function
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import os
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from tensorflow.python.keras import backend
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from tensorflow.python.keras import layers
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from tensorflow.python.keras.applications import imagenet_utils
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from tensorflow.python.keras.engine import training
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from tensorflow.python.keras.layers import VersionAwareLayers
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from tensorflow.python.keras.utils import data_utils
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from tensorflow.python.keras.utils import layer_utils
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from tensorflow.python.platform import tf_logging as logging
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@ -77,6 +77,7 @@ from tensorflow.python.util.tf_export import keras_export
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BASE_WEIGHT_PATH = ('https://storage.googleapis.com/tensorflow/'
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'keras-applications/mobilenet/')
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layers = None
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@keras_export('keras.applications.mobilenet.MobileNet',
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@ -155,9 +156,11 @@ def MobileNet(input_shape=None,
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ValueError: if `classifier_activation` is not `softmax` or `None` when
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using a pretrained top layer.
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"""
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if 'layers' in kwargs:
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global layers
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if 'layers' in kwargs:
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layers = kwargs.pop('layers')
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else:
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layers = VersionAwareLayers()
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if kwargs:
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raise ValueError('Unknown argument(s): %s' % (kwargs,))
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if not (weights in {'imagenet', None} or os.path.exists(weights)):
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@ -439,36 +442,11 @@ def _depthwise_conv_block(inputs,
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@keras_export('keras.applications.mobilenet.preprocess_input')
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def preprocess_input(x, data_format=None):
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"""Preprocesses a numpy array encoding a batch of images.
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Arguments
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x: A 4D numpy array consists of RGB values within [0, 255].
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Returns
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Preprocessed array.
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Raises
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ValueError: In case of unknown `data_format` argument.
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"""
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return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')
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@keras_export('keras.applications.mobilenet.decode_predictions')
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def decode_predictions(preds, top=5):
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"""Decodes the prediction result from the model.
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Arguments
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preds: Numpy tensor encoding a batch of predictions.
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top: Integer, how many top-guesses to return.
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Returns
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A list of lists of top class prediction tuples
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`(class_name, class_description, score)`.
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One list of tuples per sample in batch input.
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Raises
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ValueError: In case of invalid shape of the `preds` array (must be 2D).
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"""
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return imagenet_utils.decode_predictions(preds, top=top)
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@ -80,9 +80,9 @@ from __future__ import print_function
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import os
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from tensorflow.python.keras import backend
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from tensorflow.python.keras import layers
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from tensorflow.python.keras.applications import imagenet_utils
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from tensorflow.python.keras.engine import training
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from tensorflow.python.keras.layers import VersionAwareLayers
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from tensorflow.python.keras.utils import data_utils
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from tensorflow.python.keras.utils import layer_utils
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from tensorflow.python.platform import tf_logging as logging
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@ -90,6 +90,7 @@ from tensorflow.python.util.tf_export import keras_export
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BASE_WEIGHT_PATH = ('https://storage.googleapis.com/tensorflow/'
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'keras-applications/mobilenet_v2/')
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layers = None
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@keras_export('keras.applications.mobilenet_v2.MobileNetV2',
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@ -173,9 +174,11 @@ def MobileNetV2(input_shape=None,
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ValueError: if `classifier_activation` is not `softmax` or `None` when
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using a pretrained top layer.
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"""
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if 'layers' in kwargs:
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global layers
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if 'layers' in kwargs:
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layers = kwargs.pop('layers')
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else:
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layers = VersionAwareLayers()
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if kwargs:
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raise ValueError('Unknown argument(s): %s' % (kwargs,))
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if not (weights in {'imagenet', None} or os.path.exists(weights)):
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@ -44,9 +44,9 @@ from __future__ import print_function
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import os
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from tensorflow.python.keras import backend
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from tensorflow.python.keras import layers
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from tensorflow.python.keras.applications import imagenet_utils
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from tensorflow.python.keras.engine import training
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from tensorflow.python.keras.layers import VersionAwareLayers
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from tensorflow.python.keras.utils import data_utils
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from tensorflow.python.keras.utils import layer_utils
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from tensorflow.python.platform import tf_logging as logging
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@ -60,6 +60,8 @@ NASNET_MOBILE_WEIGHT_PATH_NO_TOP = BASE_WEIGHTS_PATH + 'NASNet-mobile-no-top.h5'
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NASNET_LARGE_WEIGHT_PATH = BASE_WEIGHTS_PATH + 'NASNet-large.h5'
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NASNET_LARGE_WEIGHT_PATH_NO_TOP = BASE_WEIGHTS_PATH + 'NASNet-large-no-top.h5'
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layers = VersionAwareLayers()
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def NASNet(
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input_shape=None,
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@ -74,8 +76,7 @@ def NASNet(
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pooling=None,
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classes=1000,
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default_size=None,
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classifier_activation='softmax',
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):
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classifier_activation='softmax'):
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"""Instantiates a NASNet model.
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Reference paper:
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@ -785,36 +786,11 @@ def _reduction_a_cell(ip, p, filters, block_id=None):
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@keras_export('keras.applications.nasnet.preprocess_input')
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def preprocess_input(x, data_format=None):
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"""Preprocesses a numpy array encoding a batch of images.
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Arguments
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x: A 4D numpy array consists of RGB values within [0, 255].
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Returns
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Preprocessed array.
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Raises
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ValueError: In case of unknown `data_format` argument.
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"""
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return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')
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@keras_export('keras.applications.nasnet.decode_predictions')
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def decode_predictions(preds, top=5):
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"""Decodes the prediction result from the model.
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Arguments
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preds: Numpy tensor encoding a batch of predictions.
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top: Integer, how many top-guesses to return.
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Returns
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A list of lists of top class prediction tuples
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`(class_name, class_description, score)`.
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One list of tuples per sample in batch input.
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Raises
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ValueError: In case of invalid shape of the `preds` array (must be 2D).
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"""
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return imagenet_utils.decode_predictions(preds, top=top)
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@ -26,9 +26,9 @@ from __future__ import print_function
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import os
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from tensorflow.python.keras import backend
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from tensorflow.python.keras import layers
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from tensorflow.python.keras.applications import imagenet_utils
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from tensorflow.python.keras.engine import training
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from tensorflow.python.keras.layers import VersionAwareLayers
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from tensorflow.python.keras.utils import data_utils
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from tensorflow.python.keras.utils import layer_utils
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from tensorflow.python.util.tf_export import keras_export
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@ -55,6 +55,8 @@ WEIGHTS_HASHES = {
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('34fb605428fcc7aa4d62f44404c11509', '0f678c91647380debd923963594981b3')
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}
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layers = None
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def ResNet(stack_fn,
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preact,
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@ -129,9 +131,11 @@ def ResNet(stack_fn,
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ValueError: if `classifier_activation` is not `softmax` or `None` when
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using a pretrained top layer.
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"""
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if 'layers' in kwargs:
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global layers
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if 'layers' in kwargs:
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layers = kwargs.pop('layers')
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else:
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layers = VersionAwareLayers()
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if kwargs:
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raise ValueError('Unknown argument(s): %s' % (kwargs,))
|
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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)
|
||||
|
||||
|
||||
|
||||
@ -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)
|
||||
|
||||
|
||||
|
||||
@ -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)
|
||||
|
||||
|
||||
|
||||
@ -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)
|
||||
|
||||
|
||||
|
||||
@ -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)
|
||||
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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')
|
||||
|
||||
Loading…
Reference in New Issue
Block a user