tensorflow/python/keras/applications/mobilenet_v2.py - platform/external/tensorflow - Git at Google

 # 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.
 # ==============================================================================
 # pylint: disable=invalid-name
 """MobileNet v2 models for Keras.

 MobileNetV2 is a general architecture and can be used for multiple use cases.
 Depending on the use case, it can use different input layer size and
 different width factors. This allows different width models to reduce
 the number of multiply-adds and thereby
 reduce inference cost on mobile devices.

 MobileNetV2 is very similar to the original MobileNet,
 except that it uses inverted residual blocks with
 bottlenecking features. It has a drastically lower
 parameter count than the original MobileNet.
 MobileNets support any input size greater
 than 32 x 32, with larger image sizes
 offering better performance.

 The number of parameters and number of multiply-adds
 can be modified by using the `alpha` parameter,
 which increases/decreases the number of filters in each layer.
 By altering the image size and `alpha` parameter,
 all 22 models from the paper can be built, with ImageNet weights provided.

 The paper demonstrates the performance of MobileNets using `alpha` values of
 1.0 (also called 100 % MobileNet), 0.35, 0.5, 0.75, 1.0, 1.3, and 1.4
 For each of these `alpha` values, weights for 5 different input image sizes
 are provided (224, 192, 160, 128, and 96).

 The following table describes the performance of
 MobileNet on various input sizes:
 ------------------------------------------------------------------------
 MACs stands for Multiply Adds
  Classification Checkpoint|MACs (M)|Parameters (M)|Top 1 Accuracy|Top 5 Accuracy
 --------------------------|------------|---------------|---------|----|---------
 | [mobilenet_v2_1.4_224]  | 582 | 6.06 |          75.0 | 92.5 |
 | [mobilenet_v2_1.3_224]  | 509 | 5.34 |          74.4 | 92.1 |
 | [mobilenet_v2_1.0_224]  | 300 | 3.47 |          71.8 | 91.0 |
 | [mobilenet_v2_1.0_192]  | 221 | 3.47 |          70.7 | 90.1 |
 | [mobilenet_v2_1.0_160]  | 154 | 3.47 |          68.8 | 89.0 |
 | [mobilenet_v2_1.0_128]  | 99  | 3.47 |          65.3 | 86.9 |
 | [mobilenet_v2_1.0_96]   | 56  | 3.47 |          60.3 | 83.2 |
 | [mobilenet_v2_0.75_224] | 209 | 2.61 |          69.8 | 89.6 |
 | [mobilenet_v2_0.75_192] | 153 | 2.61 |          68.7 | 88.9 |
 | [mobilenet_v2_0.75_160] | 107 | 2.61 |          66.4 | 87.3 |
 | [mobilenet_v2_0.75_128] | 69  | 2.61 |          63.2 | 85.3 |
 | [mobilenet_v2_0.75_96]  | 39  | 2.61 |          58.8 | 81.6 |
 | [mobilenet_v2_0.5_224]  | 97  | 1.95 |          65.4 | 86.4 |
 | [mobilenet_v2_0.5_192]  | 71  | 1.95 |          63.9 | 85.4 |
 | [mobilenet_v2_0.5_160]  | 50  | 1.95 |          61.0 | 83.2 |
 | [mobilenet_v2_0.5_128]  | 32  | 1.95 |          57.7 | 80.8 |
 | [mobilenet_v2_0.5_96]   | 18  | 1.95 |          51.2 | 75.8 |
 | [mobilenet_v2_0.35_224] | 59  | 1.66 |          60.3 | 82.9 |
 | [mobilenet_v2_0.35_192] | 43  | 1.66 |          58.2 | 81.2 |
 | [mobilenet_v2_0.35_160] | 30  | 1.66 |          55.7 | 79.1 |
 | [mobilenet_v2_0.35_128] | 20  | 1.66 |          50.8 | 75.0 |
 | [mobilenet_v2_0.35_96]  | 11  | 1.66 |          45.5 | 70.4 |

 """
 from __future__ import absolute_import
 from __future__ import division
 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.utils import data_utils
 from tensorflow.python.keras.utils import layer_utils
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util.tf_export import keras_export


 BASE_WEIGHT_PATH = ('https://storage.googleapis.com/tensorflow/'
                     'keras-applications/mobilenet_v2/')


 @keras_export('keras.applications.mobilenet_v2.MobileNetV2',
               'keras.applications.MobileNetV2')
 def MobileNetV2(input_shape=None,
                 alpha=1.0,
                 include_top=True,
                 weights='imagenet',
                 input_tensor=None,
                 pooling=None,
                 classes=1000,
                 **kwargs):
   """Instantiates the MobileNetV2 architecture.

   Reference paper:
   - [MobileNetV2: Inverted Residuals and Linear Bottlenecks]
   (https://arxiv.org/abs/1801.04381) (CVPR 2018)

   Optionally loads weights pre-trained on ImageNet.

   Arguments:
     input_shape: Optional shape tuple, to be specified if you would
       like to use a model with an input image resolution that is not
       (224, 224, 3).
       It should have exactly 3 inputs channels (224, 224, 3).
       You can also omit this option if you would like
       to infer input_shape from an input_tensor.
       If you choose to include both input_tensor and input_shape then
       input_shape will be used if they match, if the shapes
       do not match then we will throw an error.
       E.g. `(160, 160, 3)` would be one valid value.
     alpha: Float between 0 and 1. controls the width of the network.
       This is known as the width multiplier in the MobileNetV2 paper,
       but the name is kept for consistency with `applications.MobileNetV1`
       model in Keras.
       - If `alpha` < 1.0, proportionally decreases the number
           of filters in each layer.
       - If `alpha` > 1.0, proportionally increases the number
           of filters in each layer.
       - If `alpha` = 1, default number of filters from the paper
           are used at each layer.
     include_top: Boolean, whether to include the fully-connected
       layer at the top of the network. Defaults to `True`.
     weights: String, one of `None` (random initialization),
       'imagenet' (pre-training on ImageNet),
       or the path to the weights file to be loaded.
     input_tensor: Optional Keras tensor (i.e. output of
       `layers.Input()`)
       to use as image input for the model.
     pooling: String, optional pooling mode for feature extraction
       when `include_top` is `False`.
       - `None` means that the output of the model
           will be the 4D tensor output of the
           last convolutional block.
       - `avg` means that global average pooling
           will be applied to the output of the
           last convolutional block, and thus
           the output of the model will be a
           2D tensor.
       - `max` means that global max pooling will
           be applied.
     classes: Integer, optional number of classes to classify images
       into, only to be specified if `include_top` is True, and
       if no `weights` argument is specified.
     **kwargs: For backwards compatibility only.

   Returns:
     A `keras.Model` instance.

   Raises:
     ValueError: in case of invalid argument for `weights`,
       or invalid input shape or invalid alpha, rows when
       weights='imagenet'
   """
   if 'layers' in kwargs:
     global layers
     layers = kwargs.pop('layers')
   if kwargs:
     raise ValueError('Unknown argument(s): %s' % (kwargs,))
   if not (weights in {'imagenet', None} or os.path.exists(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '
                      'or the path to the weights file to be loaded.')

   if weights == 'imagenet' and include_top and classes != 1000:
     raise ValueError('If using `weights` as `"imagenet"` with `include_top` '
                      'as true, `classes` should be 1000')

   # Determine proper input shape and default size.
   # If both input_shape and input_tensor are used, they should match
   if input_shape is not None and input_tensor is not None:
     try:
       is_input_t_tensor = backend.is_keras_tensor(input_tensor)
     except ValueError:
       try:
         is_input_t_tensor = backend.is_keras_tensor(
             layer_utils.get_source_inputs(input_tensor))
       except ValueError:
         raise ValueError('input_tensor: ', input_tensor,
                          'is not type input_tensor')
     if is_input_t_tensor:
       if backend.image_data_format == 'channels_first':
         if backend.int_shape(input_tensor)[1] != input_shape[1]:
           raise ValueError('input_shape: ', input_shape, 'and input_tensor: ',
                            input_tensor,
                            'do not meet the same shape requirements')
       else:
         if backend.int_shape(input_tensor)[2] != input_shape[1]:
           raise ValueError('input_shape: ', input_shape, 'and input_tensor: ',
                            input_tensor,
                            'do not meet the same shape requirements')
     else:
       raise ValueError('input_tensor specified: ', input_tensor,
                        'is not a keras tensor')

   # If input_shape is None, infer shape from input_tensor
   if input_shape is None and input_tensor is not None:

     try:
       backend.is_keras_tensor(input_tensor)
     except ValueError:
       raise ValueError('input_tensor: ', input_tensor, 'is type: ',
                        type(input_tensor), 'which is not a valid type')

     if input_shape is None and not backend.is_keras_tensor(input_tensor):
       default_size = 224
     elif input_shape is None and backend.is_keras_tensor(input_tensor):
       if backend.image_data_format() == 'channels_first':
         rows = backend.int_shape(input_tensor)[2]
         cols = backend.int_shape(input_tensor)[3]
       else:
         rows = backend.int_shape(input_tensor)[1]
         cols = backend.int_shape(input_tensor)[2]

       if rows == cols and rows in [96, 128, 160, 192, 224]:
         default_size = rows
       else:
         default_size = 224

   # If input_shape is None and no input_tensor
   elif input_shape is None:
     default_size = 224

   # If input_shape is not None, assume default size
   else:
     if backend.image_data_format() == 'channels_first':
       rows = input_shape[1]
       cols = input_shape[2]
     else:
       rows = input_shape[0]
       cols = input_shape[1]

     if rows == cols and rows in [96, 128, 160, 192, 224]:
       default_size = rows
     else:
       default_size = 224

   input_shape = imagenet_utils.obtain_input_shape(
       input_shape,
       default_size=default_size,
       min_size=32,
       data_format=backend.image_data_format(),
       require_flatten=include_top,
       weights=weights)

   if backend.image_data_format() == 'channels_last':
     row_axis, col_axis = (0, 1)
   else:
     row_axis, col_axis = (1, 2)
   rows = input_shape[row_axis]
   cols = input_shape[col_axis]

   if weights == 'imagenet':
     if alpha not in [0.35, 0.50, 0.75, 1.0, 1.3, 1.4]:
       raise ValueError('If imagenet weights are being loaded, '
                        'alpha can be one of `0.35`, `0.50`, `0.75`, '
                        '`1.0`, `1.3` or `1.4` only.')

     if rows != cols or rows not in [96, 128, 160, 192, 224]:
       rows = 224
       logging.warning('`input_shape` is undefined or non-square, '
                       'or `rows` is not in [96, 128, 160, 192, 224].'
                       ' Weights for input shape (224, 224) will be'
                       ' loaded as the default.')

   if input_tensor is None:
     img_input = layers.Input(shape=input_shape)
   else:
     if not backend.is_keras_tensor(input_tensor):
       img_input = layers.Input(tensor=input_tensor, shape=input_shape)
     else:
       img_input = input_tensor

   channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1

   first_block_filters = _make_divisible(32 * alpha, 8)
   x = layers.ZeroPadding2D(
       padding=imagenet_utils.correct_pad(img_input, 3),
       name='Conv1_pad')(img_input)
   x = layers.Conv2D(
       first_block_filters,
       kernel_size=3,
       strides=(2, 2),
       padding='valid',
       use_bias=False,
       name='Conv1')(
           x)
   x = layers.BatchNormalization(
       axis=channel_axis, epsilon=1e-3, momentum=0.999, name='bn_Conv1')(
           x)
   x = layers.ReLU(6., name='Conv1_relu')(x)

   x = _inverted_res_block(
       x, filters=16, alpha=alpha, stride=1, expansion=1, block_id=0)

   x = _inverted_res_block(
       x, filters=24, alpha=alpha, stride=2, expansion=6, block_id=1)
   x = _inverted_res_block(
       x, filters=24, alpha=alpha, stride=1, expansion=6, block_id=2)

   x = _inverted_res_block(
       x, filters=32, alpha=alpha, stride=2, expansion=6, block_id=3)
   x = _inverted_res_block(
       x, filters=32, alpha=alpha, stride=1, expansion=6, block_id=4)
   x = _inverted_res_block(
       x, filters=32, alpha=alpha, stride=1, expansion=6, block_id=5)

   x = _inverted_res_block(
       x, filters=64, alpha=alpha, stride=2, expansion=6, block_id=6)
   x = _inverted_res_block(
       x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=7)
   x = _inverted_res_block(
       x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=8)
   x = _inverted_res_block(
       x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=9)

   x = _inverted_res_block(
       x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=10)
   x = _inverted_res_block(
       x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=11)
   x = _inverted_res_block(
       x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=12)

   x = _inverted_res_block(
       x, filters=160, alpha=alpha, stride=2, expansion=6, block_id=13)
   x = _inverted_res_block(
       x, filters=160, alpha=alpha, stride=1, expansion=6, block_id=14)
   x = _inverted_res_block(
       x, filters=160, alpha=alpha, stride=1, expansion=6, block_id=15)

   x = _inverted_res_block(
       x, filters=320, alpha=alpha, stride=1, expansion=6, block_id=16)

   # no alpha applied to last conv as stated in the paper:
   # if the width multiplier is greater than 1 we
   # increase the number of output channels
   if alpha > 1.0:
     last_block_filters = _make_divisible(1280 * alpha, 8)
   else:
     last_block_filters = 1280

   x = layers.Conv2D(
       last_block_filters, kernel_size=1, use_bias=False, name='Conv_1')(
           x)
   x = layers.BatchNormalization(
       axis=channel_axis, epsilon=1e-3, momentum=0.999, name='Conv_1_bn')(
           x)
   x = layers.ReLU(6., name='out_relu')(x)

   if include_top:
     x = layers.GlobalAveragePooling2D()(x)
     x = layers.Dense(
         classes, activation='softmax', use_bias=True, name='Logits')(
             x)
   else:
     if pooling == 'avg':
       x = layers.GlobalAveragePooling2D()(x)
     elif pooling == 'max':
       x = layers.GlobalMaxPooling2D()(x)

   # Ensure that the model takes into account
   # any potential predecessors of `input_tensor`.
   if input_tensor is not None:
     inputs = layer_utils.get_source_inputs(input_tensor)
   else:
     inputs = img_input

   # Create model.
   model = training.Model(inputs, x, name='mobilenetv2_%0.2f_%s' % (alpha, rows))

   # Load weights.
   if weights == 'imagenet':
     if include_top:
       model_name = ('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_' +
                     str(alpha) + '_' + str(rows) + '.h5')
       weight_path = BASE_WEIGHT_PATH + model_name
       weights_path = data_utils.get_file(
           model_name, weight_path, cache_subdir='models')
     else:
       model_name = ('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_' +
                     str(alpha) + '_' + str(rows) + '_no_top' + '.h5')
       weight_path = BASE_WEIGHT_PATH + model_name
       weights_path = data_utils.get_file(
           model_name, weight_path, cache_subdir='models')
     model.load_weights(weights_path)
   elif weights is not None:
     model.load_weights(weights)

   return model


 def _inverted_res_block(inputs, expansion, stride, alpha, filters, block_id):
   """Inverted ResNet block."""
   channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1

   in_channels = backend.int_shape(inputs)[channel_axis]
   pointwise_conv_filters = int(filters * alpha)
   pointwise_filters = _make_divisible(pointwise_conv_filters, 8)
   x = inputs
   prefix = 'block_{}_'.format(block_id)

   if block_id:
     # Expand
     x = layers.Conv2D(
         expansion * in_channels,
         kernel_size=1,
         padding='same',
         use_bias=False,
         activation=None,
         name=prefix + 'expand')(
             x)
     x = layers.BatchNormalization(
         axis=channel_axis,
         epsilon=1e-3,
         momentum=0.999,
         name=prefix + 'expand_BN')(
             x)
     x = layers.ReLU(6., name=prefix + 'expand_relu')(x)
   else:
     prefix = 'expanded_conv_'

   # Depthwise
   if stride == 2:
     x = layers.ZeroPadding2D(
         padding=imagenet_utils.correct_pad(x, 3),
         name=prefix + 'pad')(x)
   x = layers.DepthwiseConv2D(
       kernel_size=3,
       strides=stride,
       activation=None,
       use_bias=False,
       padding='same' if stride == 1 else 'valid',
       name=prefix + 'depthwise')(
           x)
   x = layers.BatchNormalization(
       axis=channel_axis,
       epsilon=1e-3,
       momentum=0.999,
       name=prefix + 'depthwise_BN')(
           x)

   x = layers.ReLU(6., name=prefix + 'depthwise_relu')(x)

   # Project
   x = layers.Conv2D(
       pointwise_filters,
       kernel_size=1,
       padding='same',
       use_bias=False,
       activation=None,
       name=prefix + 'project')(
           x)
   x = layers.BatchNormalization(
       axis=channel_axis,
       epsilon=1e-3,
       momentum=0.999,
       name=prefix + 'project_BN')(
           x)

   if in_channels == pointwise_filters and stride == 1:
     return layers.Add(name=prefix + 'add')([inputs, x])
   return x


 def _make_divisible(v, divisor, min_value=None):
   if min_value is None:
     min_value = divisor
   new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
   # Make sure that round down does not go down by more than 10%.
   if new_v < 0.9 * v:
     new_v += divisor
   return new_v


 @keras_export('keras.applications.mobilenet_v2.preprocess_input')
 def preprocess_input(x, data_format=None):
   """Preprocesses the input (encoding a batch of images) for the model."""
   return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')


 @keras_export('keras.applications.mobilenet_v2.decode_predictions')
 def decode_predictions(preds, top=5):
   """Decodes the prediction result from the model."""
   return imagenet_utils.decode_predictions(preds, top=top)
	# 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.
	# ==============================================================================
	# pylint: disable=invalid-name
	"""MobileNet v2 models for Keras.

	MobileNetV2 is a general architecture and can be used for multiple use cases.
	Depending on the use case, it can use different input layer size and
	different width factors. This allows different width models to reduce
	the number of multiply-adds and thereby
	reduce inference cost on mobile devices.

	MobileNetV2 is very similar to the original MobileNet,
	except that it uses inverted residual blocks with
	bottlenecking features. It has a drastically lower
	parameter count than the original MobileNet.
	MobileNets support any input size greater
	than 32 x 32, with larger image sizes
	offering better performance.

	The number of parameters and number of multiply-adds
	can be modified by using the `alpha` parameter,
	which increases/decreases the number of filters in each layer.
	By altering the image size and `alpha` parameter,
	all 22 models from the paper can be built, with ImageNet weights provided.

	The paper demonstrates the performance of MobileNets using `alpha` values of
	1.0 (also called 100 % MobileNet), 0.35, 0.5, 0.75, 1.0, 1.3, and 1.4
	For each of these `alpha` values, weights for 5 different input image sizes
	are provided (224, 192, 160, 128, and 96).

	The following table describes the performance of
	MobileNet on various input sizes:
	------------------------------------------------------------------------
	MACs stands for Multiply Adds
	Classification Checkpoint\|MACs (M)\|Parameters (M)\|Top 1 Accuracy\|Top 5 Accuracy
	--------------------------\|------------\|---------------\|---------\|----\|---------
	\| [mobilenet_v2_1.4_224] \| 582 \| 6.06 \| 75.0 \| 92.5 \|
	\| [mobilenet_v2_1.3_224] \| 509 \| 5.34 \| 74.4 \| 92.1 \|
	\| [mobilenet_v2_1.0_224] \| 300 \| 3.47 \| 71.8 \| 91.0 \|
	\| [mobilenet_v2_1.0_192] \| 221 \| 3.47 \| 70.7 \| 90.1 \|
	\| [mobilenet_v2_1.0_160] \| 154 \| 3.47 \| 68.8 \| 89.0 \|
	\| [mobilenet_v2_1.0_128] \| 99 \| 3.47 \| 65.3 \| 86.9 \|
	\| [mobilenet_v2_1.0_96] \| 56 \| 3.47 \| 60.3 \| 83.2 \|
	\| [mobilenet_v2_0.75_224] \| 209 \| 2.61 \| 69.8 \| 89.6 \|
	\| [mobilenet_v2_0.75_192] \| 153 \| 2.61 \| 68.7 \| 88.9 \|
	\| [mobilenet_v2_0.75_160] \| 107 \| 2.61 \| 66.4 \| 87.3 \|
	\| [mobilenet_v2_0.75_128] \| 69 \| 2.61 \| 63.2 \| 85.3 \|
	\| [mobilenet_v2_0.75_96] \| 39 \| 2.61 \| 58.8 \| 81.6 \|
	\| [mobilenet_v2_0.5_224] \| 97 \| 1.95 \| 65.4 \| 86.4 \|
	\| [mobilenet_v2_0.5_192] \| 71 \| 1.95 \| 63.9 \| 85.4 \|
	\| [mobilenet_v2_0.5_160] \| 50 \| 1.95 \| 61.0 \| 83.2 \|
	\| [mobilenet_v2_0.5_128] \| 32 \| 1.95 \| 57.7 \| 80.8 \|
	\| [mobilenet_v2_0.5_96] \| 18 \| 1.95 \| 51.2 \| 75.8 \|
	\| [mobilenet_v2_0.35_224] \| 59 \| 1.66 \| 60.3 \| 82.9 \|
	\| [mobilenet_v2_0.35_192] \| 43 \| 1.66 \| 58.2 \| 81.2 \|
	\| [mobilenet_v2_0.35_160] \| 30 \| 1.66 \| 55.7 \| 79.1 \|
	\| [mobilenet_v2_0.35_128] \| 20 \| 1.66 \| 50.8 \| 75.0 \|
	\| [mobilenet_v2_0.35_96] \| 11 \| 1.66 \| 45.5 \| 70.4 \|

	"""
	from __future__ import absolute_import
	from __future__ import division
	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.utils import data_utils
	from tensorflow.python.keras.utils import layer_utils
	from tensorflow.python.platform import tf_logging as logging
	from tensorflow.python.util.tf_export import keras_export


	BASE_WEIGHT_PATH = ('https://storage.googleapis.com/tensorflow/'
	'keras-applications/mobilenet_v2/')


	@keras_export('keras.applications.mobilenet_v2.MobileNetV2',
	'keras.applications.MobileNetV2')
	def MobileNetV2(input_shape=None,
	alpha=1.0,
	include_top=True,
	weights='imagenet',
	input_tensor=None,
	pooling=None,
	classes=1000,
	**kwargs):
	"""Instantiates the MobileNetV2 architecture.

	Reference paper:
	- [MobileNetV2: Inverted Residuals and Linear Bottlenecks]
	(https://arxiv.org/abs/1801.04381) (CVPR 2018)

	Optionally loads weights pre-trained on ImageNet.

	Arguments:
	input_shape: Optional shape tuple, to be specified if you would
	like to use a model with an input image resolution that is not
	(224, 224, 3).
	It should have exactly 3 inputs channels (224, 224, 3).
	You can also omit this option if you would like
	to infer input_shape from an input_tensor.
	If you choose to include both input_tensor and input_shape then
	input_shape will be used if they match, if the shapes
	do not match then we will throw an error.
	E.g. `(160, 160, 3)` would be one valid value.
	alpha: Float between 0 and 1. controls the width of the network.
	This is known as the width multiplier in the MobileNetV2 paper,
	but the name is kept for consistency with `applications.MobileNetV1`
	model in Keras.
	- If `alpha` < 1.0, proportionally decreases the number
	of filters in each layer.
	- If `alpha` > 1.0, proportionally increases the number
	of filters in each layer.
	- If `alpha` = 1, default number of filters from the paper
	are used at each layer.
	include_top: Boolean, whether to include the fully-connected
	layer at the top of the network. Defaults to `True`.
	weights: String, one of `None` (random initialization),
	'imagenet' (pre-training on ImageNet),
	or the path to the weights file to be loaded.
	input_tensor: Optional Keras tensor (i.e. output of
	`layers.Input()`)
	to use as image input for the model.
	pooling: String, optional pooling mode for feature extraction
	when `include_top` is `False`.
	- `None` means that the output of the model
	will be the 4D tensor output of the
	last convolutional block.
	- `avg` means that global average pooling
	will be applied to the output of the
	last convolutional block, and thus
	the output of the model will be a
	2D tensor.
	- `max` means that global max pooling will
	be applied.
	classes: Integer, optional number of classes to classify images
	into, only to be specified if `include_top` is True, and
	if no `weights` argument is specified.
	**kwargs: For backwards compatibility only.

	Returns:
	A `keras.Model` instance.

	Raises:
	ValueError: in case of invalid argument for `weights`,
	or invalid input shape or invalid alpha, rows when
	weights='imagenet'
	"""
	if 'layers' in kwargs:
	global layers
	layers = kwargs.pop('layers')
	if kwargs:
	raise ValueError('Unknown argument(s): %s' % (kwargs,))
	if not (weights in {'imagenet', None} or os.path.exists(weights)):
	raise ValueError('The `weights` argument should be either '
	'`None` (random initialization), `imagenet` '
	'(pre-training on ImageNet), '
	'or the path to the weights file to be loaded.')

	if weights == 'imagenet' and include_top and classes != 1000:
	raise ValueError('If using `weights` as `"imagenet"` with `include_top` '
	'as true, `classes` should be 1000')

	# Determine proper input shape and default size.
	# If both input_shape and input_tensor are used, they should match
	if input_shape is not None and input_tensor is not None:
	try:
	is_input_t_tensor = backend.is_keras_tensor(input_tensor)
	except ValueError:
	try:
	is_input_t_tensor = backend.is_keras_tensor(
	layer_utils.get_source_inputs(input_tensor))
	except ValueError:
	raise ValueError('input_tensor: ', input_tensor,
	'is not type input_tensor')
	if is_input_t_tensor:
	if backend.image_data_format == 'channels_first':
	if backend.int_shape(input_tensor)[1] != input_shape[1]:
	raise ValueError('input_shape: ', input_shape, 'and input_tensor: ',
	input_tensor,
	'do not meet the same shape requirements')
	else:
	if backend.int_shape(input_tensor)[2] != input_shape[1]:
	raise ValueError('input_shape: ', input_shape, 'and input_tensor: ',
	input_tensor,
	'do not meet the same shape requirements')
	else:
	raise ValueError('input_tensor specified: ', input_tensor,
	'is not a keras tensor')

	# If input_shape is None, infer shape from input_tensor
	if input_shape is None and input_tensor is not None:

	try:
	backend.is_keras_tensor(input_tensor)
	except ValueError:
	raise ValueError('input_tensor: ', input_tensor, 'is type: ',
	type(input_tensor), 'which is not a valid type')

	if input_shape is None and not backend.is_keras_tensor(input_tensor):
	default_size = 224
	elif input_shape is None and backend.is_keras_tensor(input_tensor):
	if backend.image_data_format() == 'channels_first':
	rows = backend.int_shape(input_tensor)[2]
	cols = backend.int_shape(input_tensor)[3]
	else:
	rows = backend.int_shape(input_tensor)[1]
	cols = backend.int_shape(input_tensor)[2]

	if rows == cols and rows in [96, 128, 160, 192, 224]:
	default_size = rows
	else:
	default_size = 224

	# If input_shape is None and no input_tensor
	elif input_shape is None:
	default_size = 224

	# If input_shape is not None, assume default size
	else:
	if backend.image_data_format() == 'channels_first':
	rows = input_shape[1]
	cols = input_shape[2]
	else:
	rows = input_shape[0]
	cols = input_shape[1]

	if rows == cols and rows in [96, 128, 160, 192, 224]:
	default_size = rows
	else:
	default_size = 224

	input_shape = imagenet_utils.obtain_input_shape(
	input_shape,
	default_size=default_size,
	min_size=32,
	data_format=backend.image_data_format(),
	require_flatten=include_top,
	weights=weights)

	if backend.image_data_format() == 'channels_last':
	row_axis, col_axis = (0, 1)
	else:
	row_axis, col_axis = (1, 2)
	rows = input_shape[row_axis]
	cols = input_shape[col_axis]

	if weights == 'imagenet':
	if alpha not in [0.35, 0.50, 0.75, 1.0, 1.3, 1.4]:
	raise ValueError('If imagenet weights are being loaded, '
	'alpha can be one of `0.35`, `0.50`, `0.75`, '
	'`1.0`, `1.3` or `1.4` only.')

	if rows != cols or rows not in [96, 128, 160, 192, 224]:
	rows = 224
	logging.warning('`input_shape` is undefined or non-square, '
	'or `rows` is not in [96, 128, 160, 192, 224].'
	' Weights for input shape (224, 224) will be'
	' loaded as the default.')

	if input_tensor is None:
	img_input = layers.Input(shape=input_shape)
	else:
	if not backend.is_keras_tensor(input_tensor):
	img_input = layers.Input(tensor=input_tensor, shape=input_shape)
	else:
	img_input = input_tensor

	channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1

	first_block_filters = _make_divisible(32 * alpha, 8)
	x = layers.ZeroPadding2D(
	padding=imagenet_utils.correct_pad(img_input, 3),
	name='Conv1_pad')(img_input)
	x = layers.Conv2D(
	first_block_filters,
	kernel_size=3,
	strides=(2, 2),
	padding='valid',
	use_bias=False,
	name='Conv1')(
	x)
	x = layers.BatchNormalization(
	axis=channel_axis, epsilon=1e-3, momentum=0.999, name='bn_Conv1')(
	x)
	x = layers.ReLU(6., name='Conv1_relu')(x)

	x = _inverted_res_block(
	x, filters=16, alpha=alpha, stride=1, expansion=1, block_id=0)

	x = _inverted_res_block(
	x, filters=24, alpha=alpha, stride=2, expansion=6, block_id=1)
	x = _inverted_res_block(
	x, filters=24, alpha=alpha, stride=1, expansion=6, block_id=2)

	x = _inverted_res_block(
	x, filters=32, alpha=alpha, stride=2, expansion=6, block_id=3)
	x = _inverted_res_block(
	x, filters=32, alpha=alpha, stride=1, expansion=6, block_id=4)
	x = _inverted_res_block(
	x, filters=32, alpha=alpha, stride=1, expansion=6, block_id=5)

	x = _inverted_res_block(
	x, filters=64, alpha=alpha, stride=2, expansion=6, block_id=6)
	x = _inverted_res_block(
	x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=7)
	x = _inverted_res_block(
	x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=8)
	x = _inverted_res_block(
	x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=9)

	x = _inverted_res_block(
	x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=10)
	x = _inverted_res_block(
	x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=11)
	x = _inverted_res_block(
	x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=12)

	x = _inverted_res_block(
	x, filters=160, alpha=alpha, stride=2, expansion=6, block_id=13)
	x = _inverted_res_block(
	x, filters=160, alpha=alpha, stride=1, expansion=6, block_id=14)
	x = _inverted_res_block(
	x, filters=160, alpha=alpha, stride=1, expansion=6, block_id=15)

	x = _inverted_res_block(
	x, filters=320, alpha=alpha, stride=1, expansion=6, block_id=16)

	# no alpha applied to last conv as stated in the paper:
	# if the width multiplier is greater than 1 we
	# increase the number of output channels
	if alpha > 1.0:
	last_block_filters = _make_divisible(1280 * alpha, 8)
	else:
	last_block_filters = 1280

	x = layers.Conv2D(
	last_block_filters, kernel_size=1, use_bias=False, name='Conv_1')(
	x)
	x = layers.BatchNormalization(
	axis=channel_axis, epsilon=1e-3, momentum=0.999, name='Conv_1_bn')(
	x)
	x = layers.ReLU(6., name='out_relu')(x)

	if include_top:
	x = layers.GlobalAveragePooling2D()(x)
	x = layers.Dense(
	classes, activation='softmax', use_bias=True, name='Logits')(
	x)
	else:
	if pooling == 'avg':
	x = layers.GlobalAveragePooling2D()(x)
	elif pooling == 'max':
	x = layers.GlobalMaxPooling2D()(x)

	# Ensure that the model takes into account
	# any potential predecessors of `input_tensor`.
	if input_tensor is not None:
	inputs = layer_utils.get_source_inputs(input_tensor)
	else:
	inputs = img_input

	# Create model.
	model = training.Model(inputs, x, name='mobilenetv2_%0.2f_%s' % (alpha, rows))

	# Load weights.
	if weights == 'imagenet':
	if include_top:
	model_name = ('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_' +
	str(alpha) + '_' + str(rows) + '.h5')
	weight_path = BASE_WEIGHT_PATH + model_name
	weights_path = data_utils.get_file(
	model_name, weight_path, cache_subdir='models')
	else:
	model_name = ('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_' +
	str(alpha) + '_' + str(rows) + '_no_top' + '.h5')
	weight_path = BASE_WEIGHT_PATH + model_name
	weights_path = data_utils.get_file(
	model_name, weight_path, cache_subdir='models')
	model.load_weights(weights_path)
	elif weights is not None:
	model.load_weights(weights)

	return model


	def _inverted_res_block(inputs, expansion, stride, alpha, filters, block_id):
	"""Inverted ResNet block."""
	channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1

	in_channels = backend.int_shape(inputs)[channel_axis]
	pointwise_conv_filters = int(filters * alpha)
	pointwise_filters = _make_divisible(pointwise_conv_filters, 8)
	x = inputs
	prefix = 'block_{}_'.format(block_id)

	if block_id:
	# Expand
	x = layers.Conv2D(
	expansion * in_channels,
	kernel_size=1,
	padding='same',
	use_bias=False,
	activation=None,
	name=prefix + 'expand')(
	x)
	x = layers.BatchNormalization(
	axis=channel_axis,
	epsilon=1e-3,
	momentum=0.999,
	name=prefix + 'expand_BN')(
	x)
	x = layers.ReLU(6., name=prefix + 'expand_relu')(x)
	else:
	prefix = 'expanded_conv_'

	# Depthwise
	if stride == 2:
	x = layers.ZeroPadding2D(
	padding=imagenet_utils.correct_pad(x, 3),
	name=prefix + 'pad')(x)
	x = layers.DepthwiseConv2D(
	kernel_size=3,
	strides=stride,
	activation=None,
	use_bias=False,
	padding='same' if stride == 1 else 'valid',
	name=prefix + 'depthwise')(
	x)
	x = layers.BatchNormalization(
	axis=channel_axis,
	epsilon=1e-3,
	momentum=0.999,
	name=prefix + 'depthwise_BN')(
	x)

	x = layers.ReLU(6., name=prefix + 'depthwise_relu')(x)

	# Project
	x = layers.Conv2D(
	pointwise_filters,
	kernel_size=1,
	padding='same',
	use_bias=False,
	activation=None,
	name=prefix + 'project')(
	x)
	x = layers.BatchNormalization(
	axis=channel_axis,
	epsilon=1e-3,
	momentum=0.999,
	name=prefix + 'project_BN')(
	x)

	if in_channels == pointwise_filters and stride == 1:
	return layers.Add(name=prefix + 'add')([inputs, x])
	return x


	def _make_divisible(v, divisor, min_value=None):
	if min_value is None:
	min_value = divisor
	new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
	# Make sure that round down does not go down by more than 10%.
	if new_v < 0.9 * v:
	new_v += divisor
	return new_v


	@keras_export('keras.applications.mobilenet_v2.preprocess_input')
	def preprocess_input(x, data_format=None):
	"""Preprocesses the input (encoding a batch of images) for the model."""
	return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')


	@keras_export('keras.applications.mobilenet_v2.decode_predictions')
	def decode_predictions(preds, top=5):
	"""Decodes the prediction result from the model."""
	return imagenet_utils.decode_predictions(preds, top=top)