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Add TF models slim nets
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Aurélien Geron
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nets/vgg.py
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244
nets/vgg.py
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Contains model definitions for versions of the Oxford VGG network.
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These model definitions were introduced in the following technical report:
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Very Deep Convolutional Networks For Large-Scale Image Recognition
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Karen Simonyan and Andrew Zisserman
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arXiv technical report, 2015
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PDF: http://arxiv.org/pdf/1409.1556.pdf
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ILSVRC 2014 Slides: http://www.robots.ox.ac.uk/~karen/pdf/ILSVRC_2014.pdf
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CC-BY-4.0
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More information can be obtained from the VGG website:
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www.robots.ox.ac.uk/~vgg/research/very_deep/
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Usage:
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with slim.arg_scope(vgg.vgg_arg_scope()):
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outputs, end_points = vgg.vgg_a(inputs)
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with slim.arg_scope(vgg.vgg_arg_scope()):
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outputs, end_points = vgg.vgg_16(inputs)
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@@vgg_a
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@@vgg_16
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@@vgg_19
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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slim = tf.contrib.slim
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def vgg_arg_scope(weight_decay=0.0005):
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"""Defines the VGG arg scope.
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Args:
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weight_decay: The l2 regularization coefficient.
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Returns:
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An arg_scope.
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"""
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with slim.arg_scope([slim.conv2d, slim.fully_connected],
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activation_fn=tf.nn.relu,
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weights_regularizer=slim.l2_regularizer(weight_decay),
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biases_initializer=tf.zeros_initializer):
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with slim.arg_scope([slim.conv2d], padding='SAME') as arg_sc:
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return arg_sc
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def vgg_a(inputs,
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num_classes=1000,
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is_training=True,
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dropout_keep_prob=0.5,
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spatial_squeeze=True,
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scope='vgg_a'):
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"""Oxford Net VGG 11-Layers version A Example.
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Note: All the fully_connected layers have been transformed to conv2d layers.
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To use in classification mode, resize input to 224x224.
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Args:
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inputs: a tensor of size [batch_size, height, width, channels].
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num_classes: number of predicted classes.
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is_training: whether or not the model is being trained.
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dropout_keep_prob: the probability that activations are kept in the dropout
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layers during training.
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spatial_squeeze: whether or not should squeeze the spatial dimensions of the
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outputs. Useful to remove unnecessary dimensions for classification.
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scope: Optional scope for the variables.
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Returns:
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the last op containing the log predictions and end_points dict.
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"""
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with tf.variable_scope(scope, 'vgg_a', [inputs]) as sc:
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end_points_collection = sc.name + '_end_points'
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# Collect outputs for conv2d, fully_connected and max_pool2d.
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with slim.arg_scope([slim.conv2d, slim.max_pool2d],
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outputs_collections=end_points_collection):
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net = slim.repeat(inputs, 1, slim.conv2d, 64, [3, 3], scope='conv1')
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net = slim.max_pool2d(net, [2, 2], scope='pool1')
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net = slim.repeat(net, 1, slim.conv2d, 128, [3, 3], scope='conv2')
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net = slim.max_pool2d(net, [2, 2], scope='pool2')
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net = slim.repeat(net, 2, slim.conv2d, 256, [3, 3], scope='conv3')
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net = slim.max_pool2d(net, [2, 2], scope='pool3')
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net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv4')
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net = slim.max_pool2d(net, [2, 2], scope='pool4')
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net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv5')
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net = slim.max_pool2d(net, [2, 2], scope='pool5')
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# Use conv2d instead of fully_connected layers.
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net = slim.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')
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net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
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scope='dropout6')
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net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
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net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
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scope='dropout7')
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net = slim.conv2d(net, num_classes, [1, 1],
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activation_fn=None,
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normalizer_fn=None,
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scope='fc8')
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# Convert end_points_collection into a end_point dict.
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end_points = dict(tf.get_collection(end_points_collection))
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if spatial_squeeze:
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net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
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end_points[sc.name + '/fc8'] = net
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return net, end_points
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vgg_a.default_image_size = 224
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def vgg_16(inputs,
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num_classes=1000,
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is_training=True,
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dropout_keep_prob=0.5,
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spatial_squeeze=True,
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scope='vgg_16'):
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"""Oxford Net VGG 16-Layers version D Example.
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Note: All the fully_connected layers have been transformed to conv2d layers.
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To use in classification mode, resize input to 224x224.
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Args:
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inputs: a tensor of size [batch_size, height, width, channels].
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num_classes: number of predicted classes.
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is_training: whether or not the model is being trained.
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dropout_keep_prob: the probability that activations are kept in the dropout
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layers during training.
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spatial_squeeze: whether or not should squeeze the spatial dimensions of the
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outputs. Useful to remove unnecessary dimensions for classification.
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scope: Optional scope for the variables.
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Returns:
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the last op containing the log predictions and end_points dict.
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"""
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with tf.variable_scope(scope, 'vgg_16', [inputs]) as sc:
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end_points_collection = sc.name + '_end_points'
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# Collect outputs for conv2d, fully_connected and max_pool2d.
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with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
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outputs_collections=end_points_collection):
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net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
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net = slim.max_pool2d(net, [2, 2], scope='pool1')
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net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
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net = slim.max_pool2d(net, [2, 2], scope='pool2')
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net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
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net = slim.max_pool2d(net, [2, 2], scope='pool3')
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net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
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net = slim.max_pool2d(net, [2, 2], scope='pool4')
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net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
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net = slim.max_pool2d(net, [2, 2], scope='pool5')
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# Use conv2d instead of fully_connected layers.
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net = slim.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')
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net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
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scope='dropout6')
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net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
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net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
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scope='dropout7')
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net = slim.conv2d(net, num_classes, [1, 1],
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activation_fn=None,
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normalizer_fn=None,
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scope='fc8')
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# Convert end_points_collection into a end_point dict.
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end_points = dict(tf.get_collection(end_points_collection))
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if spatial_squeeze:
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net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
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end_points[sc.name + '/fc8'] = net
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return net, end_points
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vgg_16.default_image_size = 224
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def vgg_19(inputs,
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num_classes=1000,
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is_training=True,
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dropout_keep_prob=0.5,
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spatial_squeeze=True,
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scope='vgg_19'):
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"""Oxford Net VGG 19-Layers version E Example.
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Note: All the fully_connected layers have been transformed to conv2d layers.
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To use in classification mode, resize input to 224x224.
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Args:
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inputs: a tensor of size [batch_size, height, width, channels].
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num_classes: number of predicted classes.
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is_training: whether or not the model is being trained.
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dropout_keep_prob: the probability that activations are kept in the dropout
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layers during training.
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spatial_squeeze: whether or not should squeeze the spatial dimensions of the
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outputs. Useful to remove unnecessary dimensions for classification.
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scope: Optional scope for the variables.
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Returns:
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the last op containing the log predictions and end_points dict.
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"""
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with tf.variable_scope(scope, 'vgg_19', [inputs]) as sc:
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end_points_collection = sc.name + '_end_points'
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# Collect outputs for conv2d, fully_connected and max_pool2d.
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with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
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outputs_collections=end_points_collection):
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net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
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net = slim.max_pool2d(net, [2, 2], scope='pool1')
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net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
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net = slim.max_pool2d(net, [2, 2], scope='pool2')
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net = slim.repeat(net, 4, slim.conv2d, 256, [3, 3], scope='conv3')
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net = slim.max_pool2d(net, [2, 2], scope='pool3')
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net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv4')
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net = slim.max_pool2d(net, [2, 2], scope='pool4')
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net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv5')
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net = slim.max_pool2d(net, [2, 2], scope='pool5')
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# Use conv2d instead of fully_connected layers.
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net = slim.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')
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net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
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scope='dropout6')
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net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
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net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
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scope='dropout7')
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net = slim.conv2d(net, num_classes, [1, 1],
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activation_fn=None,
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normalizer_fn=None,
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scope='fc8')
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# Convert end_points_collection into a end_point dict.
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end_points = dict(tf.get_collection(end_points_collection))
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if spatial_squeeze:
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net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
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end_points[sc.name + '/fc8'] = net
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return net, end_points
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vgg_19.default_image_size = 224
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# Alias
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vgg_d = vgg_16
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vgg_e = vgg_19
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