example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat.py

   1 # Copyright 2016 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 """Contains the model definition for the OverFeat network.
  16
  17 The definition for the network was obtained from:
  18   OverFeat: Integrated Recognition, Localization and Detection using
  19   Convolutional Networks
  20   Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and
  21   Yann LeCun, 2014
  22   http://arxiv.org/abs/1312.6229
  23
  24 Usage:
  25   with slim.arg_scope(overfeat.overfeat_arg_scope()):
  26     outputs, end_points = overfeat.overfeat(inputs)
  27
  28 @@overfeat
  29 """
  30 from __future__ import absolute_import
  31 from __future__ import division
  32 from __future__ import print_function
  33
  34 import tensorflow as tf
  35
  36 slim = tf.contrib.slim
  37 trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
  38
  39
  40 def overfeat_arg_scope(weight_decay=0.0005):
  41   with slim.arg_scope([slim.conv2d, slim.fully_connected],
  42                       activation_fn=tf.nn.relu,
  43                       weights_regularizer=slim.l2_regularizer(weight_decay),
  44                       biases_initializer=tf.zeros_initializer()):
  45     with slim.arg_scope([slim.conv2d], padding='SAME'):
  46       with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc:
  47         return arg_sc
  48
  49
  50 def overfeat(inputs,
  51              num_classes=1000,
  52              is_training=True,
  53              dropout_keep_prob=0.5,
  54              spatial_squeeze=True,
  55              scope='overfeat'):
  56   """Contains the model definition for the OverFeat network.
  57
  58   The definition for the network was obtained from:
  59     OverFeat: Integrated Recognition, Localization and Detection using
  60     Convolutional Networks
  61     Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and
  62     Yann LeCun, 2014
  63     http://arxiv.org/abs/1312.6229
  64
  65   Note: All the fully_connected layers have been transformed to conv2d layers.
  66         To use in classification mode, resize input to 231x231. To use in fully
  67         convolutional mode, set spatial_squeeze to false.
  68
  69   Args:
  70     inputs: a tensor of size [batch_size, height, width, channels].
  71     num_classes: number of predicted classes.
  72     is_training: whether or not the model is being trained.
  73     dropout_keep_prob: the probability that activations are kept in the dropout
  74       layers during training.
  75     spatial_squeeze: whether or not should squeeze the spatial dimensions of the
  76       outputs. Useful to remove unnecessary dimensions for classification.
  77     scope: Optional scope for the variables.
  78
  79   Returns:
  80     the last op containing the log predictions and end_points dict.
  81
  82   """
  83   with tf.variable_scope(scope, 'overfeat', [inputs]) as sc:
  84     end_points_collection = sc.name + '_end_points'
  85     # Collect outputs for conv2d, fully_connected and max_pool2d
  86     with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
  87                         outputs_collections=end_points_collection):
  88       net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID',
  89                         scope='conv1')
  90       net = slim.max_pool2d(net, [2, 2], scope='pool1')
  91       net = slim.conv2d(net, 256, [5, 5], padding='VALID', scope='conv2')
  92       net = slim.max_pool2d(net, [2, 2], scope='pool2')
  93       net = slim.conv2d(net, 512, [3, 3], scope='conv3')
  94       net = slim.conv2d(net, 1024, [3, 3], scope='conv4')
  95       net = slim.conv2d(net, 1024, [3, 3], scope='conv5')
  96       net = slim.max_pool2d(net, [2, 2], scope='pool5')
  97       with slim.arg_scope([slim.conv2d],
  98                           weights_initializer=trunc_normal(0.005),
  99                           biases_initializer=tf.constant_initializer(0.1)):
 100         # Use conv2d instead of fully_connected layers.
 101         net = slim.conv2d(net, 3072, [6, 6], padding='VALID', scope='fc6')
 102         net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
 103                            scope='dropout6')
 104         net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
 105         net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
 106                            scope='dropout7')
 107         net = slim.conv2d(net, num_classes, [1, 1],
 108                           activation_fn=None,
 109                           normalizer_fn=None,
 110                           biases_initializer=tf.zeros_initializer(),
 111                           scope='fc8')
 112       # Convert end_points_collection into a end_point dict.
 113       end_points = slim.utils.convert_collection_to_dict(end_points_collection)
 114       if spatial_squeeze:
 115         net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
 116         end_points[sc.name + '/fc8'] = net
 117       return net, end_points
 118 overfeat.default_image_size = 231