--- /dev/null
+# Copyright 2016 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.
+# ==============================================================================
+"""Contains model definitions for versions of the Oxford VGG network.
+
+These model definitions were introduced in the following technical report:
+
+ Very Deep Convolutional Networks For Large-Scale Image Recognition
+ Karen Simonyan and Andrew Zisserman
+ arXiv technical report, 2015
+ PDF: http://arxiv.org/pdf/1409.1556.pdf
+ ILSVRC 2014 Slides: http://www.robots.ox.ac.uk/~karen/pdf/ILSVRC_2014.pdf
+ CC-BY-4.0
+
+More information can be obtained from the VGG website:
+www.robots.ox.ac.uk/~vgg/research/very_deep/
+
+Usage:
+ with slim.arg_scope(vgg.vgg_arg_scope()):
+ outputs, end_points = vgg.vgg_a(inputs)
+
+ with slim.arg_scope(vgg.vgg_arg_scope()):
+ outputs, end_points = vgg.vgg_16(inputs)
+
+@@vgg_a
+@@vgg_16
+@@vgg_19
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def vgg_arg_scope(weight_decay=0.0005):
+ """Defines the VGG arg scope.
+
+ Args:
+ weight_decay: The l2 regularization coefficient.
+
+ Returns:
+ An arg_scope.
+ """
+ with slim.arg_scope([slim.conv2d, slim.fully_connected],
+ activation_fn=tf.nn.relu,
+ weights_regularizer=slim.l2_regularizer(weight_decay),
+ biases_initializer=tf.zeros_initializer()):
+ with slim.arg_scope([slim.conv2d], padding='SAME') as arg_sc:
+ return arg_sc
+
+
+def vgg_a(inputs,
+ num_classes=1000,
+ is_training=True,
+ dropout_keep_prob=0.5,
+ spatial_squeeze=True,
+ scope='vgg_a',
+ fc_conv_padding='VALID'):
+ """Oxford Net VGG 11-Layers version A Example.
+
+ Note: All the fully_connected layers have been transformed to conv2d layers.
+ To use in classification mode, resize input to 224x224.
+
+ Args:
+ inputs: a tensor of size [batch_size, height, width, channels].
+ num_classes: number of predicted classes.
+ is_training: whether or not the model is being trained.
+ dropout_keep_prob: the probability that activations are kept in the dropout
+ layers during training.
+ spatial_squeeze: whether or not should squeeze the spatial dimensions of the
+ outputs. Useful to remove unnecessary dimensions for classification.
+ scope: Optional scope for the variables.
+ fc_conv_padding: the type of padding to use for the fully connected layer
+ that is implemented as a convolutional layer. Use 'SAME' padding if you
+ are applying the network in a fully convolutional manner and want to
+ get a prediction map downsampled by a factor of 32 as an output. Otherwise,
+ the output prediction map will be (input / 32) - 6 in case of 'VALID' padding.
+
+ Returns:
+ the last op containing the log predictions and end_points dict.
+ """
+ with tf.variable_scope(scope, 'vgg_a', [inputs]) as sc:
+ end_points_collection = sc.name + '_end_points'
+ # Collect outputs for conv2d, fully_connected and max_pool2d.
+ with slim.arg_scope([slim.conv2d, slim.max_pool2d],
+ outputs_collections=end_points_collection):
+ net = slim.repeat(inputs, 1, slim.conv2d, 64, [3, 3], scope='conv1')
+ net = slim.max_pool2d(net, [2, 2], scope='pool1')
+ net = slim.repeat(net, 1, slim.conv2d, 128, [3, 3], scope='conv2')
+ net = slim.max_pool2d(net, [2, 2], scope='pool2')
+ net = slim.repeat(net, 2, slim.conv2d, 256, [3, 3], scope='conv3')
+ net = slim.max_pool2d(net, [2, 2], scope='pool3')
+ net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv4')
+ net = slim.max_pool2d(net, [2, 2], scope='pool4')
+ net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv5')
+ net = slim.max_pool2d(net, [2, 2], scope='pool5')
+ # Use conv2d instead of fully_connected layers.
+ net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
+ net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+ scope='dropout6')
+ net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
+ net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+ scope='dropout7')
+ net = slim.conv2d(net, num_classes, [1, 1],
+ activation_fn=None,
+ normalizer_fn=None,
+ scope='fc8')
+ # Convert end_points_collection into a end_point dict.
+ end_points = slim.utils.convert_collection_to_dict(end_points_collection)
+ if spatial_squeeze:
+ net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
+ end_points[sc.name + '/fc8'] = net
+ return net, end_points
+vgg_a.default_image_size = 224
+
+
+def vgg_16(inputs,
+ num_classes=1000,
+ is_training=True,
+ dropout_keep_prob=0.5,
+ spatial_squeeze=True,
+ scope='vgg_16',
+ fc_conv_padding='VALID'):
+ """Oxford Net VGG 16-Layers version D Example.
+
+ Note: All the fully_connected layers have been transformed to conv2d layers.
+ To use in classification mode, resize input to 224x224.
+
+ Args:
+ inputs: a tensor of size [batch_size, height, width, channels].
+ num_classes: number of predicted classes.
+ is_training: whether or not the model is being trained.
+ dropout_keep_prob: the probability that activations are kept in the dropout
+ layers during training.
+ spatial_squeeze: whether or not should squeeze the spatial dimensions of the
+ outputs. Useful to remove unnecessary dimensions for classification.
+ scope: Optional scope for the variables.
+ fc_conv_padding: the type of padding to use for the fully connected layer
+ that is implemented as a convolutional layer. Use 'SAME' padding if you
+ are applying the network in a fully convolutional manner and want to
+ get a prediction map downsampled by a factor of 32 as an output. Otherwise,
+ the output prediction map will be (input / 32) - 6 in case of 'VALID' padding.
+
+ Returns:
+ the last op containing the log predictions and end_points dict.
+ """
+ with tf.variable_scope(scope, 'vgg_16', [inputs]) as sc:
+ end_points_collection = sc.name + '_end_points'
+ # Collect outputs for conv2d, fully_connected and max_pool2d.
+ with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
+ outputs_collections=end_points_collection):
+ net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
+ net = slim.max_pool2d(net, [2, 2], scope='pool1')
+ net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
+ net = slim.max_pool2d(net, [2, 2], scope='pool2')
+ net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
+ net = slim.max_pool2d(net, [2, 2], scope='pool3')
+ net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
+ net = slim.max_pool2d(net, [2, 2], scope='pool4')
+ net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
+ net = slim.max_pool2d(net, [2, 2], scope='pool5')
+ # Use conv2d instead of fully_connected layers.
+ net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
+ net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+ scope='dropout6')
+ net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
+ net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+ scope='dropout7')
+ # yjr_feature = tf.squeeze(net)
+ net = slim.conv2d(net, num_classes, [1, 1],
+ activation_fn=None,
+ normalizer_fn=None,
+ scope='fc8')
+ # Convert end_points_collection into a end_point dict.
+ end_points = slim.utils.convert_collection_to_dict(end_points_collection)
+ if spatial_squeeze:
+ net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
+ end_points[sc.name + '/fc8'] = net
+ # end_points['yjr_feature'] = yjr_feature
+ end_points['predictions'] = slim.softmax(net, scope='predictions')
+ return net, end_points
+vgg_16.default_image_size = 224
+
+
+def vgg_19(inputs,
+ num_classes=1000,
+ is_training=True,
+ dropout_keep_prob=0.5,
+ spatial_squeeze=True,
+ scope='vgg_19',
+ fc_conv_padding='VALID'):
+ """Oxford Net VGG 19-Layers version E Example.
+
+ Note: All the fully_connected layers have been transformed to conv2d layers.
+ To use in classification mode, resize input to 224x224.
+
+ Args:
+ inputs: a tensor of size [batch_size, height, width, channels].
+ num_classes: number of predicted classes.
+ is_training: whether or not the model is being trained.
+ dropout_keep_prob: the probability that activations are kept in the dropout
+ layers during training.
+ spatial_squeeze: whether or not should squeeze the spatial dimensions of the
+ outputs. Useful to remove unnecessary dimensions for classification.
+ scope: Optional scope for the variables.
+ fc_conv_padding: the type of padding to use for the fully connected layer
+ that is implemented as a convolutional layer. Use 'SAME' padding if you
+ are applying the network in a fully convolutional manner and want to
+ get a prediction map downsampled by a factor of 32 as an output. Otherwise,
+ the output prediction map will be (input / 32) - 6 in case of 'VALID' padding.
+
+ Returns:
+ the last op containing the log predictions and end_points dict.
+ """
+ with tf.variable_scope(scope, 'vgg_19', [inputs]) as sc:
+ end_points_collection = sc.name + '_end_points'
+ # Collect outputs for conv2d, fully_connected and max_pool2d.
+ with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
+ outputs_collections=end_points_collection):
+ net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
+ net = slim.max_pool2d(net, [2, 2], scope='pool1')
+ net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
+ net = slim.max_pool2d(net, [2, 2], scope='pool2')
+ net = slim.repeat(net, 4, slim.conv2d, 256, [3, 3], scope='conv3')
+ net = slim.max_pool2d(net, [2, 2], scope='pool3')
+ net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv4')
+ net = slim.max_pool2d(net, [2, 2], scope='pool4')
+ net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv5')
+ net = slim.max_pool2d(net, [2, 2], scope='pool5')
+ # Use conv2d instead of fully_connected layers.
+ net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
+ net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+ scope='dropout6')
+ net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
+ net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+ scope='dropout7')
+ net = slim.conv2d(net, num_classes, [1, 1],
+ activation_fn=None,
+ normalizer_fn=None,
+ scope='fc8')
+ # Convert end_points_collection into a end_point dict.
+ end_points = slim.utils.convert_collection_to_dict(end_points_collection)
+ if spatial_squeeze:
+ net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
+ end_points[sc.name + '/fc8'] = net
+ return net, end_points
+vgg_19.default_image_size = 224
+
+# Alias
+vgg_d = vgg_16
+vgg_e = vgg_19
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