example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v4.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 definition of the Inception V4 architecture.
  16
  17 As described in http://arxiv.org/abs/1602.07261.
  18
  19   Inception-v4, Inception-ResNet and the Impact of Residual Connections
  20     on Learning
  21   Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
  22 """
  23 from __future__ import absolute_import
  24 from __future__ import division
  25 from __future__ import print_function
  26
  27 import tensorflow as tf
  28
  29 from nets import inception_utils
  30
  31 slim = tf.contrib.slim
  32
  33
  34 def block_inception_a(inputs, scope=None, reuse=None):
  35   """Builds Inception-A block for Inception v4 network."""
  36   # By default use stride=1 and SAME padding
  37   with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
  38                       stride=1, padding='SAME'):
  39     with tf.variable_scope(scope, 'BlockInceptionA', [inputs], reuse=reuse):
  40       with tf.variable_scope('Branch_0'):
  41         branch_0 = slim.conv2d(inputs, 96, [1, 1], scope='Conv2d_0a_1x1')
  42       with tf.variable_scope('Branch_1'):
  43         branch_1 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1')
  44         branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3')
  45       with tf.variable_scope('Branch_2'):
  46         branch_2 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1')
  47         branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
  48         branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
  49       with tf.variable_scope('Branch_3'):
  50         branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
  51         branch_3 = slim.conv2d(branch_3, 96, [1, 1], scope='Conv2d_0b_1x1')
  52       return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
  53
  54
  55 def block_reduction_a(inputs, scope=None, reuse=None):
  56   """Builds Reduction-A block for Inception v4 network."""
  57   # By default use stride=1 and SAME padding
  58   with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
  59                       stride=1, padding='SAME'):
  60     with tf.variable_scope(scope, 'BlockReductionA', [inputs], reuse=reuse):
  61       with tf.variable_scope('Branch_0'):
  62         branch_0 = slim.conv2d(inputs, 384, [3, 3], stride=2, padding='VALID',
  63                                scope='Conv2d_1a_3x3')
  64       with tf.variable_scope('Branch_1'):
  65         branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
  66         branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3')
  67         branch_1 = slim.conv2d(branch_1, 256, [3, 3], stride=2,
  68                                padding='VALID', scope='Conv2d_1a_3x3')
  69       with tf.variable_scope('Branch_2'):
  70         branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
  71                                    scope='MaxPool_1a_3x3')
  72       return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
  73
  74
  75 def block_inception_b(inputs, scope=None, reuse=None):
  76   """Builds Inception-B block for Inception v4 network."""
  77   # By default use stride=1 and SAME padding
  78   with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
  79                       stride=1, padding='SAME'):
  80     with tf.variable_scope(scope, 'BlockInceptionB', [inputs], reuse=reuse):
  81       with tf.variable_scope('Branch_0'):
  82         branch_0 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
  83       with tf.variable_scope('Branch_1'):
  84         branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
  85         branch_1 = slim.conv2d(branch_1, 224, [1, 7], scope='Conv2d_0b_1x7')
  86         branch_1 = slim.conv2d(branch_1, 256, [7, 1], scope='Conv2d_0c_7x1')
  87       with tf.variable_scope('Branch_2'):
  88         branch_2 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
  89         branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0b_7x1')
  90         branch_2 = slim.conv2d(branch_2, 224, [1, 7], scope='Conv2d_0c_1x7')
  91         branch_2 = slim.conv2d(branch_2, 224, [7, 1], scope='Conv2d_0d_7x1')
  92         branch_2 = slim.conv2d(branch_2, 256, [1, 7], scope='Conv2d_0e_1x7')
  93       with tf.variable_scope('Branch_3'):
  94         branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
  95         branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
  96       return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
  97
  98
  99 def block_reduction_b(inputs, scope=None, reuse=None):
 100   """Builds Reduction-B block for Inception v4 network."""
 101   # By default use stride=1 and SAME padding
 102   with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
 103                       stride=1, padding='SAME'):
 104     with tf.variable_scope(scope, 'BlockReductionB', [inputs], reuse=reuse):
 105       with tf.variable_scope('Branch_0'):
 106         branch_0 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1')
 107         branch_0 = slim.conv2d(branch_0, 192, [3, 3], stride=2,
 108                                padding='VALID', scope='Conv2d_1a_3x3')
 109       with tf.variable_scope('Branch_1'):
 110         branch_1 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1')
 111         branch_1 = slim.conv2d(branch_1, 256, [1, 7], scope='Conv2d_0b_1x7')
 112         branch_1 = slim.conv2d(branch_1, 320, [7, 1], scope='Conv2d_0c_7x1')
 113         branch_1 = slim.conv2d(branch_1, 320, [3, 3], stride=2,
 114                                padding='VALID', scope='Conv2d_1a_3x3')
 115       with tf.variable_scope('Branch_2'):
 116         branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID',
 117                                    scope='MaxPool_1a_3x3')
 118       return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
 119
 120
 121 def block_inception_c(inputs, scope=None, reuse=None):
 122   """Builds Inception-C block for Inception v4 network."""
 123   # By default use stride=1 and SAME padding
 124   with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
 125                       stride=1, padding='SAME'):
 126     with tf.variable_scope(scope, 'BlockInceptionC', [inputs], reuse=reuse):
 127       with tf.variable_scope('Branch_0'):
 128         branch_0 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1')
 129       with tf.variable_scope('Branch_1'):
 130         branch_1 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
 131         branch_1 = tf.concat(axis=3, values=[
 132             slim.conv2d(branch_1, 256, [1, 3], scope='Conv2d_0b_1x3'),
 133             slim.conv2d(branch_1, 256, [3, 1], scope='Conv2d_0c_3x1')])
 134       with tf.variable_scope('Branch_2'):
 135         branch_2 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1')
 136         branch_2 = slim.conv2d(branch_2, 448, [3, 1], scope='Conv2d_0b_3x1')
 137         branch_2 = slim.conv2d(branch_2, 512, [1, 3], scope='Conv2d_0c_1x3')
 138         branch_2 = tf.concat(axis=3, values=[
 139             slim.conv2d(branch_2, 256, [1, 3], scope='Conv2d_0d_1x3'),
 140             slim.conv2d(branch_2, 256, [3, 1], scope='Conv2d_0e_3x1')])
 141       with tf.variable_scope('Branch_3'):
 142         branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3')
 143         branch_3 = slim.conv2d(branch_3, 256, [1, 1], scope='Conv2d_0b_1x1')
 144       return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
 145
 146
 147 def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
 148   """Creates the Inception V4 network up to the given final endpoint.
 149
 150   Args:
 151     inputs: a 4-D tensor of size [batch_size, height, width, 3].
 152     final_endpoint: specifies the endpoint to construct the network up to.
 153       It can be one of [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
 154       'Mixed_3a', 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
 155       'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e',
 156       'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c',
 157       'Mixed_7d']
 158     scope: Optional variable_scope.
 159
 160   Returns:
 161     logits: the logits outputs of the model.
 162     end_points: the set of end_points from the inception model.
 163
 164   Raises:
 165     ValueError: if final_endpoint is not set to one of the predefined values,
 166   """
 167   end_points = {}
 168
 169   def add_and_check_final(name, net):
 170     end_points[name] = net
 171     return name == final_endpoint
 172
 173   with tf.variable_scope(scope, 'InceptionV4', [inputs]):
 174     with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
 175                         stride=1, padding='SAME'):
 176       # 299 x 299 x 3
 177       net = slim.conv2d(inputs, 32, [3, 3], stride=2,
 178                         padding='VALID', scope='Conv2d_1a_3x3')
 179       if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points
 180       # 149 x 149 x 32
 181       net = slim.conv2d(net, 32, [3, 3], padding='VALID',
 182                         scope='Conv2d_2a_3x3')
 183       if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points
 184       # 147 x 147 x 32
 185       net = slim.conv2d(net, 64, [3, 3], scope='Conv2d_2b_3x3')
 186       if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points
 187       # 147 x 147 x 64
 188       with tf.variable_scope('Mixed_3a'):
 189         with tf.variable_scope('Branch_0'):
 190           branch_0 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
 191                                      scope='MaxPool_0a_3x3')
 192         with tf.variable_scope('Branch_1'):
 193           branch_1 = slim.conv2d(net, 96, [3, 3], stride=2, padding='VALID',
 194                                  scope='Conv2d_0a_3x3')
 195         net = tf.concat(axis=3, values=[branch_0, branch_1])
 196         if add_and_check_final('Mixed_3a', net): return net, end_points
 197
 198       # 73 x 73 x 160
 199       with tf.variable_scope('Mixed_4a'):
 200         with tf.variable_scope('Branch_0'):
 201           branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
 202           branch_0 = slim.conv2d(branch_0, 96, [3, 3], padding='VALID',
 203                                  scope='Conv2d_1a_3x3')
 204         with tf.variable_scope('Branch_1'):
 205           branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
 206           branch_1 = slim.conv2d(branch_1, 64, [1, 7], scope='Conv2d_0b_1x7')
 207           branch_1 = slim.conv2d(branch_1, 64, [7, 1], scope='Conv2d_0c_7x1')
 208           branch_1 = slim.conv2d(branch_1, 96, [3, 3], padding='VALID',
 209                                  scope='Conv2d_1a_3x3')
 210         net = tf.concat(axis=3, values=[branch_0, branch_1])
 211         if add_and_check_final('Mixed_4a', net): return net, end_points
 212
 213       # 71 x 71 x 192
 214       with tf.variable_scope('Mixed_5a'):
 215         with tf.variable_scope('Branch_0'):
 216           branch_0 = slim.conv2d(net, 192, [3, 3], stride=2, padding='VALID',
 217                                  scope='Conv2d_1a_3x3')
 218         with tf.variable_scope('Branch_1'):
 219           branch_1 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
 220                                      scope='MaxPool_1a_3x3')
 221         net = tf.concat(axis=3, values=[branch_0, branch_1])
 222         if add_and_check_final('Mixed_5a', net): return net, end_points
 223
 224       # 35 x 35 x 384
 225       # 4 x Inception-A blocks
 226       for idx in range(4):
 227         block_scope = 'Mixed_5' + chr(ord('b') + idx)
 228         net = block_inception_a(net, block_scope)
 229         if add_and_check_final(block_scope, net): return net, end_points
 230
 231       # 35 x 35 x 384
 232       # Reduction-A block
 233       net = block_reduction_a(net, 'Mixed_6a')
 234       if add_and_check_final('Mixed_6a', net): return net, end_points
 235
 236       # 17 x 17 x 1024
 237       # 7 x Inception-B blocks
 238       for idx in range(7):
 239         block_scope = 'Mixed_6' + chr(ord('b') + idx)
 240         net = block_inception_b(net, block_scope)
 241         if add_and_check_final(block_scope, net): return net, end_points
 242
 243       # 17 x 17 x 1024
 244       # Reduction-B block
 245       net = block_reduction_b(net, 'Mixed_7a')
 246       if add_and_check_final('Mixed_7a', net): return net, end_points
 247
 248       # 8 x 8 x 1536
 249       # 3 x Inception-C blocks
 250       for idx in range(3):
 251         block_scope = 'Mixed_7' + chr(ord('b') + idx)
 252         net = block_inception_c(net, block_scope)
 253         if add_and_check_final(block_scope, net): return net, end_points
 254   raise ValueError('Unknown final endpoint %s' % final_endpoint)
 255
 256
 257 def inception_v4(inputs, num_classes=1001, is_training=True,
 258                  dropout_keep_prob=0.8,
 259                  reuse=None,
 260                  scope='InceptionV4',
 261                  create_aux_logits=True):
 262   """Creates the Inception V4 model.
 263
 264   Args:
 265     inputs: a 4-D tensor of size [batch_size, height, width, 3].
 266     num_classes: number of predicted classes.
 267     is_training: whether is training or not.
 268     dropout_keep_prob: float, the fraction to keep before final layer.
 269     reuse: whether or not the network and its variables should be reused. To be
 270       able to reuse 'scope' must be given.
 271     scope: Optional variable_scope.
 272     create_aux_logits: Whether to include the auxiliary logits.
 273
 274   Returns:
 275     logits: the logits outputs of the model.
 276     end_points: the set of end_points from the inception model.
 277   """
 278   end_points = {}
 279   with tf.variable_scope(scope, 'InceptionV4', [inputs], reuse=reuse) as scope:
 280     with slim.arg_scope([slim.batch_norm, slim.dropout],
 281                         is_training=is_training):
 282       net, end_points = inception_v4_base(inputs, scope=scope)
 283
 284       with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
 285                           stride=1, padding='SAME'):
 286         # Auxiliary Head logits
 287         if create_aux_logits:
 288           with tf.variable_scope('AuxLogits'):
 289             # 17 x 17 x 1024
 290             aux_logits = end_points['Mixed_6h']
 291             aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3,
 292                                          padding='VALID',
 293                                          scope='AvgPool_1a_5x5')
 294             aux_logits = slim.conv2d(aux_logits, 128, [1, 1],
 295                                      scope='Conv2d_1b_1x1')
 296             aux_logits = slim.conv2d(aux_logits, 768,
 297                                      aux_logits.get_shape()[1:3],
 298                                      padding='VALID', scope='Conv2d_2a')
 299             aux_logits = slim.flatten(aux_logits)
 300             aux_logits = slim.fully_connected(aux_logits, num_classes,
 301                                               activation_fn=None,
 302                                               scope='Aux_logits')
 303             end_points['AuxLogits'] = aux_logits
 304
 305         # Final pooling and prediction
 306         with tf.variable_scope('Logits'):
 307           # 8 x 8 x 1536
 308           net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID',
 309                                 scope='AvgPool_1a')
 310           # 1 x 1 x 1536
 311           net = slim.dropout(net, dropout_keep_prob, scope='Dropout_1b')
 312           net = slim.flatten(net, scope='PreLogitsFlatten')
 313           end_points['PreLogitsFlatten'] = net
 314           # 1536
 315           logits = slim.fully_connected(net, num_classes, activation_fn=None,
 316                                         scope='Logits')
 317           end_points['Logits'] = logits
 318           end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions')
 319     return logits, end_points
 320 inception_v4.default_image_size = 299
 321
 322
 323 inception_v4_arg_scope = inception_utils.inception_arg_scope