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1# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7#     http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15# pylint: disable=invalid-name
16"""VGG16 model for Keras."""
17from __future__ import absolute_import
18from __future__ import division
19from __future__ import print_function
20
21import os
22
23from tensorflow.python.keras import backend
24from tensorflow.python.keras import layers
25from tensorflow.python.keras.applications import imagenet_utils
26from tensorflow.python.keras.engine import training
27from tensorflow.python.keras.utils import data_utils
28from tensorflow.python.keras.utils import layer_utils
29from tensorflow.python.util.tf_export import keras_export
30
31
32WEIGHTS_PATH = ('https://storage.googleapis.com/tensorflow/keras-applications/'
33                'vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5')
34WEIGHTS_PATH_NO_TOP = ('https://storage.googleapis.com/tensorflow/'
35                       'keras-applications/vgg16/'
36                       'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5')
37
38
39@keras_export('keras.applications.vgg16.VGG16', 'keras.applications.VGG16')
40def VGG16(include_top=True,
41          weights='imagenet',
42          input_tensor=None,
43          input_shape=None,
44          pooling=None,
45          classes=1000):
46  """Instantiates the VGG16 model.
47
48  By default, it loads weights pre-trained on ImageNet. Check 'weights' for
49  other options.
50
51  This model can be built both with 'channels_first' data format
52  (channels, height, width) or 'channels_last' data format
53  (height, width, channels).
54
55  The default input size for this model is 224x224.
56
57  Arguments:
58      include_top: whether to include the 3 fully-connected
59          layers at the top of the network.
60      weights: one of `None` (random initialization),
61            'imagenet' (pre-training on ImageNet),
62            or the path to the weights file to be loaded.
63      input_tensor: optional Keras tensor
64          (i.e. output of `layers.Input()`)
65          to use as image input for the model.
66      input_shape: optional shape tuple, only to be specified
67          if `include_top` is False (otherwise the input shape
68          has to be `(224, 224, 3)`
69          (with `channels_last` data format)
70          or `(3, 224, 224)` (with `channels_first` data format).
71          It should have exactly 3 input channels,
72          and width and height should be no smaller than 32.
73          E.g. `(200, 200, 3)` would be one valid value.
74      pooling: Optional pooling mode for feature extraction
75          when `include_top` is `False`.
76          - `None` means that the output of the model will be
77              the 4D tensor output of the
78              last convolutional block.
79          - `avg` means that global average pooling
80              will be applied to the output of the
81              last convolutional block, and thus
82              the output of the model will be a 2D tensor.
83          - `max` means that global max pooling will
84              be applied.
85      classes: optional number of classes to classify images
86          into, only to be specified if `include_top` is True, and
87          if no `weights` argument is specified.
88
89  Returns:
90      A Keras model instance.
91
92  Raises:
93      ValueError: in case of invalid argument for `weights`,
94          or invalid input shape.
95  """
96  if not (weights in {'imagenet', None} or os.path.exists(weights)):
97    raise ValueError('The `weights` argument should be either '
98                     '`None` (random initialization), `imagenet` '
99                     '(pre-training on ImageNet), '
100                     'or the path to the weights file to be loaded.')
101
102  if weights == 'imagenet' and include_top and classes != 1000:
103    raise ValueError('If using `weights` as `"imagenet"` with `include_top`'
104                     ' as true, `classes` should be 1000')
105  # Determine proper input shape
106  input_shape = imagenet_utils.obtain_input_shape(
107      input_shape,
108      default_size=224,
109      min_size=32,
110      data_format=backend.image_data_format(),
111      require_flatten=include_top,
112      weights=weights)
113
114  if input_tensor is None:
115    img_input = layers.Input(shape=input_shape)
116  else:
117    if not backend.is_keras_tensor(input_tensor):
118      img_input = layers.Input(tensor=input_tensor, shape=input_shape)
119    else:
120      img_input = input_tensor
121  # Block 1
122  x = layers.Conv2D(
123      64, (3, 3), activation='relu', padding='same', name='block1_conv1')(
124          img_input)
125  x = layers.Conv2D(
126      64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
127  x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
128
129  # Block 2
130  x = layers.Conv2D(
131      128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
132  x = layers.Conv2D(
133      128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
134  x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
135
136  # Block 3
137  x = layers.Conv2D(
138      256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
139  x = layers.Conv2D(
140      256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
141  x = layers.Conv2D(
142      256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
143  x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
144
145  # Block 4
146  x = layers.Conv2D(
147      512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
148  x = layers.Conv2D(
149      512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
150  x = layers.Conv2D(
151      512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
152  x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
153
154  # Block 5
155  x = layers.Conv2D(
156      512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
157  x = layers.Conv2D(
158      512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
159  x = layers.Conv2D(
160      512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
161  x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
162
163  if include_top:
164    # Classification block
165    x = layers.Flatten(name='flatten')(x)
166    x = layers.Dense(4096, activation='relu', name='fc1')(x)
167    x = layers.Dense(4096, activation='relu', name='fc2')(x)
168    x = layers.Dense(classes, activation='softmax', name='predictions')(x)
169  else:
170    if pooling == 'avg':
171      x = layers.GlobalAveragePooling2D()(x)
172    elif pooling == 'max':
173      x = layers.GlobalMaxPooling2D()(x)
174
175  # Ensure that the model takes into account
176  # any potential predecessors of `input_tensor`.
177  if input_tensor is not None:
178    inputs = layer_utils.get_source_inputs(input_tensor)
179  else:
180    inputs = img_input
181  # Create model.
182  model = training.Model(inputs, x, name='vgg16')
183
184  # Load weights.
185  if weights == 'imagenet':
186    if include_top:
187      weights_path = data_utils.get_file(
188          'vgg16_weights_tf_dim_ordering_tf_kernels.h5',
189          WEIGHTS_PATH,
190          cache_subdir='models',
191          file_hash='64373286793e3c8b2b4e3219cbf3544b')
192    else:
193      weights_path = data_utils.get_file(
194          'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',
195          WEIGHTS_PATH_NO_TOP,
196          cache_subdir='models',
197          file_hash='6d6bbae143d832006294945121d1f1fc')
198    model.load_weights(weights_path)
199  elif weights is not None:
200    model.load_weights(weights)
201
202  return model
203
204
205@keras_export('keras.applications.vgg16.preprocess_input')
206def preprocess_input(x, data_format=None):
207  """Preprocesses the input (encoding a batch of images) to the VGG16 model."""
208  return imagenet_utils.preprocess_input(
209      x, data_format=data_format, mode='caffe')
210
211
212@keras_export('keras.applications.vgg16.decode_predictions')
213def decode_predictions(preds, top=5):
214  """Decodes the prediction result from the VGG16 model."""
215  return imagenet_utils.decode_predictions(preds, top=top)
216