example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_resnet_v2.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 Resnet V2 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
  28 import tensorflow as tf
  29
  30 slim = tf.contrib.slim
  31
  32
  33 def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
  34   """Builds the 35x35 resnet block."""
  35   with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
  36     with tf.variable_scope('Branch_0'):
  37       tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1')
  38     with tf.variable_scope('Branch_1'):
  39       tower_conv1_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
  40       tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3')
  41     with tf.variable_scope('Branch_2'):
  42       tower_conv2_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
  43       tower_conv2_1 = slim.conv2d(tower_conv2_0, 48, 3, scope='Conv2d_0b_3x3')
  44       tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope='Conv2d_0c_3x3')
  45     mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_1, tower_conv2_2])
  46     up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
  47                      activation_fn=None, scope='Conv2d_1x1')
  48     net += scale * up
  49     if activation_fn:
  50       net = activation_fn(net)
  51   return net
  52
  53
  54 def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
  55   """Builds the 17x17 resnet block."""
  56   with tf.variable_scope(scope, 'Block17', [net], reuse=reuse):
  57     with tf.variable_scope('Branch_0'):
  58       tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
  59     with tf.variable_scope('Branch_1'):
  60       tower_conv1_0 = slim.conv2d(net, 128, 1, scope='Conv2d_0a_1x1')
  61       tower_conv1_1 = slim.conv2d(tower_conv1_0, 160, [1, 7],
  62                                   scope='Conv2d_0b_1x7')
  63       tower_conv1_2 = slim.conv2d(tower_conv1_1, 192, [7, 1],
  64                                   scope='Conv2d_0c_7x1')
  65     mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
  66     up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
  67                      activation_fn=None, scope='Conv2d_1x1')
  68     net += scale * up
  69     if activation_fn:
  70       net = activation_fn(net)
  71   return net
  72
  73
  74 def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
  75   """Builds the 8x8 resnet block."""
  76   with tf.variable_scope(scope, 'Block8', [net], reuse=reuse):
  77     with tf.variable_scope('Branch_0'):
  78       tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
  79     with tf.variable_scope('Branch_1'):
  80       tower_conv1_0 = slim.conv2d(net, 192, 1, scope='Conv2d_0a_1x1')
  81       tower_conv1_1 = slim.conv2d(tower_conv1_0, 224, [1, 3],
  82                                   scope='Conv2d_0b_1x3')
  83       tower_conv1_2 = slim.conv2d(tower_conv1_1, 256, [3, 1],
  84                                   scope='Conv2d_0c_3x1')
  85     mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
  86     up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
  87                      activation_fn=None, scope='Conv2d_1x1')
  88     net += scale * up
  89     if activation_fn:
  90       net = activation_fn(net)
  91   return net
  92
  93
  94 def inception_resnet_v2_base(inputs,
  95                              final_endpoint='Conv2d_7b_1x1',
  96                              output_stride=16,
  97                              align_feature_maps=False,
  98                              scope=None):
  99   """Inception model from  http://arxiv.org/abs/1602.07261.
 100
 101   Constructs an Inception Resnet v2 network from inputs to the given final
 102   endpoint. This method can construct the network up to the final inception
 103   block Conv2d_7b_1x1.
 104
 105   Args:
 106     inputs: a tensor of size [batch_size, height, width, channels].
 107     final_endpoint: specifies the endpoint to construct the network up to. It
 108       can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
 109       'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
 110       'Mixed_5b', 'Mixed_6a', 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
 111     output_stride: A scalar that specifies the requested ratio of input to
 112       output spatial resolution. Only supports 8 and 16.
 113     align_feature_maps: When true, changes all the VALID paddings in the network
 114       to SAME padding so that the feature maps are aligned.
 115     scope: Optional variable_scope.
 116
 117   Returns:
 118     tensor_out: output tensor corresponding to the final_endpoint.
 119     end_points: a set of activations for external use, for example summaries or
 120                 losses.
 121
 122   Raises:
 123     ValueError: if final_endpoint is not set to one of the predefined values,
 124       or if the output_stride is not 8 or 16, or if the output_stride is 8 and
 125       we request an end point after 'PreAuxLogits'.
 126   """
 127   if output_stride != 8 and output_stride != 16:
 128     raise ValueError('output_stride must be 8 or 16.')
 129
 130   padding = 'SAME' if align_feature_maps else 'VALID'
 131
 132   end_points = {}
 133
 134   def add_and_check_final(name, net):
 135     end_points[name] = net
 136     return name == final_endpoint
 137
 138   with tf.variable_scope(scope, 'InceptionResnetV2', [inputs]):
 139     with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
 140                         stride=1, padding='SAME'):
 141       # 149 x 149 x 32
 142       net = slim.conv2d(inputs, 32, 3, stride=2, padding=padding,
 143                         scope='Conv2d_1a_3x3')
 144       if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points
 145
 146       # 147 x 147 x 32
 147       net = slim.conv2d(net, 32, 3, padding=padding,
 148                         scope='Conv2d_2a_3x3')
 149       if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points
 150       # 147 x 147 x 64
 151       net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3')
 152       if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points
 153       # 73 x 73 x 64
 154       net = slim.max_pool2d(net, 3, stride=2, padding=padding,
 155                             scope='MaxPool_3a_3x3')
 156       if add_and_check_final('MaxPool_3a_3x3', net): return net, end_points
 157       # 73 x 73 x 80
 158       net = slim.conv2d(net, 80, 1, padding=padding,
 159                         scope='Conv2d_3b_1x1')
 160       if add_and_check_final('Conv2d_3b_1x1', net): return net, end_points
 161       # 71 x 71 x 192
 162       net = slim.conv2d(net, 192, 3, padding=padding,
 163                         scope='Conv2d_4a_3x3')
 164       if add_and_check_final('Conv2d_4a_3x3', net): return net, end_points
 165       # 35 x 35 x 192
 166       net = slim.max_pool2d(net, 3, stride=2, padding=padding,
 167                             scope='MaxPool_5a_3x3')
 168       if add_and_check_final('MaxPool_5a_3x3', net): return net, end_points
 169
 170       # 35 x 35 x 320
 171       with tf.variable_scope('Mixed_5b'):
 172         with tf.variable_scope('Branch_0'):
 173           tower_conv = slim.conv2d(net, 96, 1, scope='Conv2d_1x1')
 174         with tf.variable_scope('Branch_1'):
 175           tower_conv1_0 = slim.conv2d(net, 48, 1, scope='Conv2d_0a_1x1')
 176           tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5,
 177                                       scope='Conv2d_0b_5x5')
 178         with tf.variable_scope('Branch_2'):
 179           tower_conv2_0 = slim.conv2d(net, 64, 1, scope='Conv2d_0a_1x1')
 180           tower_conv2_1 = slim.conv2d(tower_conv2_0, 96, 3,
 181                                       scope='Conv2d_0b_3x3')
 182           tower_conv2_2 = slim.conv2d(tower_conv2_1, 96, 3,
 183                                       scope='Conv2d_0c_3x3')
 184         with tf.variable_scope('Branch_3'):
 185           tower_pool = slim.avg_pool2d(net, 3, stride=1, padding='SAME',
 186                                        scope='AvgPool_0a_3x3')
 187           tower_pool_1 = slim.conv2d(tower_pool, 64, 1,
 188                                      scope='Conv2d_0b_1x1')
 189         net = tf.concat(
 190             [tower_conv, tower_conv1_1, tower_conv2_2, tower_pool_1], 3)
 191
 192       if add_and_check_final('Mixed_5b', net): return net, end_points
 193       # TODO(alemi): Register intermediate endpoints
 194       net = slim.repeat(net, 10, block35, scale=0.17)
 195
 196       # 17 x 17 x 1088 if output_stride == 8,
 197       # 33 x 33 x 1088 if output_stride == 16
 198       use_atrous = output_stride == 8
 199
 200       with tf.variable_scope('Mixed_6a'):
 201         with tf.variable_scope('Branch_0'):
 202           tower_conv = slim.conv2d(net, 384, 3, stride=1 if use_atrous else 2,
 203                                    padding=padding,
 204                                    scope='Conv2d_1a_3x3')
 205         with tf.variable_scope('Branch_1'):
 206           tower_conv1_0 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
 207           tower_conv1_1 = slim.conv2d(tower_conv1_0, 256, 3,
 208                                       scope='Conv2d_0b_3x3')
 209           tower_conv1_2 = slim.conv2d(tower_conv1_1, 384, 3,
 210                                       stride=1 if use_atrous else 2,
 211                                       padding=padding,
 212                                       scope='Conv2d_1a_3x3')
 213         with tf.variable_scope('Branch_2'):
 214           tower_pool = slim.max_pool2d(net, 3, stride=1 if use_atrous else 2,
 215                                        padding=padding,
 216                                        scope='MaxPool_1a_3x3')
 217         net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3)
 218
 219       if add_and_check_final('Mixed_6a', net): return net, end_points
 220
 221       # TODO(alemi): register intermediate endpoints
 222       with slim.arg_scope([slim.conv2d], rate=2 if use_atrous else 1):
 223         net = slim.repeat(net, 20, block17, scale=0.10)
 224       if add_and_check_final('PreAuxLogits', net): return net, end_points
 225
 226       if output_stride == 8:
 227         # TODO(gpapan): Properly support output_stride for the rest of the net.
 228         raise ValueError('output_stride==8 is only supported up to the '
 229                          'PreAuxlogits end_point for now.')
 230
 231       # 8 x 8 x 2080
 232       with tf.variable_scope('Mixed_7a'):
 233         with tf.variable_scope('Branch_0'):
 234           tower_conv = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
 235           tower_conv_1 = slim.conv2d(tower_conv, 384, 3, stride=2,
 236                                      padding=padding,
 237                                      scope='Conv2d_1a_3x3')
 238         with tf.variable_scope('Branch_1'):
 239           tower_conv1 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
 240           tower_conv1_1 = slim.conv2d(tower_conv1, 288, 3, stride=2,
 241                                       padding=padding,
 242                                       scope='Conv2d_1a_3x3')
 243         with tf.variable_scope('Branch_2'):
 244           tower_conv2 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
 245           tower_conv2_1 = slim.conv2d(tower_conv2, 288, 3,
 246                                       scope='Conv2d_0b_3x3')
 247           tower_conv2_2 = slim.conv2d(tower_conv2_1, 320, 3, stride=2,
 248                                       padding=padding,
 249                                       scope='Conv2d_1a_3x3')
 250         with tf.variable_scope('Branch_3'):
 251           tower_pool = slim.max_pool2d(net, 3, stride=2,
 252                                        padding=padding,
 253                                        scope='MaxPool_1a_3x3')
 254         net = tf.concat(
 255             [tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool], 3)
 256
 257       if add_and_check_final('Mixed_7a', net): return net, end_points
 258
 259       # TODO(alemi): register intermediate endpoints
 260       net = slim.repeat(net, 9, block8, scale=0.20)
 261       net = block8(net, activation_fn=None)
 262
 263       # 8 x 8 x 1536
 264       net = slim.conv2d(net, 1536, 1, scope='Conv2d_7b_1x1')
 265       if add_and_check_final('Conv2d_7b_1x1', net): return net, end_points
 266
 267     raise ValueError('final_endpoint (%s) not recognized', final_endpoint)
 268
 269
 270 def inception_resnet_v2(inputs, num_classes=1001, is_training=True,
 271                         dropout_keep_prob=0.8,#0.8
 272                         reuse=None,
 273                         scope='InceptionResnetV2',
 274                         create_aux_logits=True):
 275   """Creates the Inception Resnet V2 model.
 276
 277   Args:
 278     inputs: a 4-D tensor of size [batch_size, height, width, 3].
 279     num_classes: number of predicted classes.
 280     is_training: whether is training or not.
 281     dropout_keep_prob: float, the fraction to keep before final layer.
 282     reuse: whether or not the network and its variables should be reused. To be
 283       able to reuse 'scope' must be given.
 284     scope: Optional variable_scope.
 285     create_aux_logits: Whether to include the auxilliary logits.
 286
 287   Returns:
 288     logits: the logits outputs of the model.
 289     end_points: the set of end_points from the inception model.
 290   """
 291   end_points = {}
 292
 293   with tf.variable_scope(scope, 'InceptionResnetV2', [inputs, num_classes],
 294                          reuse=reuse) as scope:
 295     with slim.arg_scope([slim.batch_norm, slim.dropout],
 296                         is_training=is_training):
 297
 298       net, end_points = inception_resnet_v2_base(inputs, scope=scope)
 299
 300       if create_aux_logits:
 301         with tf.variable_scope('AuxLogits'):
 302           aux = end_points['PreAuxLogits']
 303           aux = slim.avg_pool2d(aux, 5, stride=3, padding='VALID',
 304                                 scope='Conv2d_1a_3x3')
 305           aux = slim.conv2d(aux, 128, 1, scope='Conv2d_1b_1x1')
 306           aux = slim.conv2d(aux, 768, aux.get_shape()[1:3],
 307                             padding='VALID', scope='Conv2d_2a_5x5')
 308           aux = slim.flatten(aux)
 309           aux = slim.fully_connected(aux, num_classes, activation_fn=None,
 310                                      scope='Logits')
 311           end_points['AuxLogits'] = aux
 312
 313       with tf.variable_scope('Logits'):
 314         net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID',
 315                               scope='AvgPool_1a_8x8')
 316         net = slim.flatten(net)
 317
 318         net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
 319                            scope='Dropout')
 320
 321         end_points['PreLogitsFlatten'] = net
 322         # end_points['yjr_feature'] = tf.squeeze(net, axis=0)
 323
 324         logits = slim.fully_connected(net, num_classes, activation_fn=None,
 325                                       scope='Logits')
 326         end_points['Logits'] = logits
 327         end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions')
 328
 329     return logits, end_points
 330 inception_resnet_v2.default_image_size = 299
 331
 332
 333 def inception_resnet_v2_arg_scope(weight_decay=0.00004,
 334                                   batch_norm_decay=0.9997,
 335                                   batch_norm_epsilon=0.001):
 336   """Yields the scope with the default parameters for inception_resnet_v2.
 337
 338   Args:
 339     weight_decay: the weight decay for weights variables.
 340     batch_norm_decay: decay for the moving average of batch_norm momentums.
 341     batch_norm_epsilon: small float added to variance to avoid dividing by zero.
 342
 343   Returns:
 344     a arg_scope with the parameters needed for inception_resnet_v2.
 345   """
 346   # Set weight_decay for weights in conv2d and fully_connected layers.
 347   with slim.arg_scope([slim.conv2d, slim.fully_connected],
 348                       weights_regularizer=slim.l2_regularizer(weight_decay),
 349                       biases_regularizer=slim.l2_regularizer(weight_decay)):
 350
 351     batch_norm_params = {
 352         'decay': batch_norm_decay,
 353         'epsilon': batch_norm_epsilon,
 354     }
 355     # Set activation_fn and parameters for batch_norm.
 356     with slim.arg_scope([slim.conv2d], activation_fn=tf.nn.relu,
 357                         normalizer_fn=slim.batch_norm,
 358                         normalizer_params=batch_norm_params) as scope:
 359       return scope