(inputs,
                             final_endpoint='Conv2d_7b_1x1',
                             output_stride=16,
                             align_feature_maps=False,
                             scope=None)
Inception model from  http://arxiv.org/abs/1602.07261.

  Constructs an Inception Resnet v2 network from inputs to the given final
  endpoint. This method can construct the network up to the final inception
  block Conv2d_7b_1x1.

  Args:
    inputs: a tensor of size [batch_size, height, width, channels].
    final_endpoint: specifies the endpoint to construct the network up to. It
      can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
      'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
      'Mixed_5b', 'Mixed_6a', 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
    output_stride: A scalar that specifies the requested ratio of input to
      output spatial resolution. Only supports 8 and 16.
    align_feature_maps: When true, changes all the VALID paddings in the network
      to SAME padding so that the feature maps are aligned.
    scope: Optional variable_scope.

  Returns:
    tensor_out: output tensor corresponding to the final_endpoint.
    end_points: a set of activations for external use, for example summaries or
                losses.

  Raises:
    ValueError: if final_endpoint is not set to one of the predefined values,
      or if the output_stride is not 8 or 16, or if the output_stride is 8 and
      we request an end point after 'PreAuxLogits'.
Inception model from  http://arxiv.org/abs/1602.07261.
Inception model from http : // arxiv . org / abs / 1602 . 07261 .
def inception_resnet_v2_base(inputs,
                             final_endpoint='Conv2d_7b_1x1',
                             output_stride=16,
                             align_feature_maps=False,
                             scope=None):
  """Inception model from  http://arxiv.org/abs/1602.07261.

  Constructs an Inception Resnet v2 network from inputs to the given final
  endpoint. This method can construct the network up to the final inception
  block Conv2d_7b_1x1.

  Args:
    inputs: a tensor of size [batch_size, height, width, channels].
    final_endpoint: specifies the endpoint to construct the network up to. It
      can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
      'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
      'Mixed_5b', 'Mixed_6a', 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
    output_stride: A scalar that specifies the requested ratio of input to
      output spatial resolution. Only supports 8 and 16.
    align_feature_maps: When true, changes all the VALID paddings in the network
      to SAME padding so that the feature maps are aligned.
    scope: Optional variable_scope.

  Returns:
    tensor_out: output tensor corresponding to the final_endpoint.
    end_points: a set of activations for external use, for example summaries or
                losses.

  Raises:
    ValueError: if final_endpoint is not set to one of the predefined values,
      or if the output_stride is not 8 or 16, or if the output_stride is 8 and
      we request an end point after 'PreAuxLogits'.
  """
  if output_stride != 8 and output_stride != 16:
    raise ValueError('output_stride must be 8 or 16.')

  padding = 'SAME' if align_feature_maps else 'VALID'

  end_points = {}

  def add_and_check_final(name, net):
    end_points[name] = net
    return name == final_endpoint

  with tf.variable_scope(scope, 'InceptionResnetV2', [inputs]):
    with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                        stride=1, padding='SAME'):
      # 149 x 149 x 32
      net = slim.conv2d(inputs, 32, 3, stride=2, padding=padding,
                        scope='Conv2d_1a_3x3')
      if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points

      # 147 x 147 x 32
      net = slim.conv2d(net, 32, 3, padding=padding,
                        scope='Conv2d_2a_3x3')
      if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points
      # 147 x 147 x 64
      net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3')
      if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points
      # 73 x 73 x 64
      net = slim.max_pool2d(net, 3, stride=2, padding=padding,
                            scope='MaxPool_3a_3x3')
      if add_and_check_final('MaxPool_3a_3x3', net): return net, end_points
      # 73 x 73 x 80
      net = slim.conv2d(net, 80, 1, padding=padding,
                        scope='Conv2d_3b_1x1')
      if add_and_check_final('Conv2d_3b_1x1', net): return net, end_points
      # 71 x 71 x 192
      net = slim.conv2d(net, 192, 3, padding=padding,
                        scope='Conv2d_4a_3x3')
      if add_and_check_final('Conv2d_4a_3x3', net): return net, end_points
      # 35 x 35 x 192
      net = slim.max_pool2d(net, 3, stride=2, padding=padding,
                            scope='MaxPool_5a_3x3')
      if add_and_check_final('MaxPool_5a_3x3', net): return net, end_points

      # 35 x 35 x 320
      with tf.variable_scope('Mixed_5b'):
        with tf.variable_scope('Branch_0'):
          tower_conv = slim.conv2d(net, 96, 1, scope='Conv2d_1x1')
        with tf.variable_scope('Branch_1'):
          tower_conv1_0 = slim.conv2d(net, 48, 1, scope='Conv2d_0a_1x1')
          tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5,
                                      scope='Conv2d_0b_5x5')
        with tf.variable_scope('Branch_2'):
          tower_conv2_0 = slim.conv2d(net, 64, 1, scope='Conv2d_0a_1x1')
          tower_conv2_1 = slim.conv2d(tower_conv2_0, 96, 3,
                                      scope='Conv2d_0b_3x3')
          tower_conv2_2 = slim.conv2d(tower_conv2_1, 96, 3,
                                      scope='Conv2d_0c_3x3')
        with tf.variable_scope('Branch_3'):
          tower_pool = slim.avg_pool2d(net, 3, stride=1, padding='SAME',
                                       scope='AvgPool_0a_3x3')
          tower_pool_1 = slim.conv2d(tower_pool, 64, 1,
                                     scope='Conv2d_0b_1x1')
        net = tf.concat(
            [tower_conv, tower_conv1_1, tower_conv2_2, tower_pool_1], 3)

      if add_and_check_final('Mixed_5b', net): return net, end_points
      # TODO(alemi): Register intermediate endpoints
      net = slim.repeat(net, 10, block35, scale=0.17)

      # 17 x 17 x 1088 if output_stride == 8,
      # 33 x 33 x 1088 if output_stride == 16
      use_atrous = output_stride == 8

      with tf.variable_scope('Mixed_6a'):
        with tf.variable_scope('Branch_0'):
          tower_conv = slim.conv2d(net, 384, 3, stride=1 if use_atrous else 2,
                                   padding=padding,
                                   scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_1'):
          tower_conv1_0 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
          tower_conv1_1 = slim.conv2d(tower_conv1_0, 256, 3,
                                      scope='Conv2d_0b_3x3')
          tower_conv1_2 = slim.conv2d(tower_conv1_1, 384, 3,
                                      stride=1 if use_atrous else 2,
                                      padding=padding,
                                      scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_2'):
          tower_pool = slim.max_pool2d(net, 3, stride=1 if use_atrous else 2,
                                       padding=padding,
                                       scope='MaxPool_1a_3x3')
        net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3)

      if add_and_check_final('Mixed_6a', net): return net, end_points

      # TODO(alemi): register intermediate endpoints
      with slim.arg_scope([slim.conv2d], rate=2 if use_atrous else 1):
        net = slim.repeat(net, 20, block17, scale=0.10)
      if add_and_check_final('PreAuxLogits', net): return net, end_points

      if output_stride == 8:
        # TODO(gpapan): Properly support output_stride for the rest of the net.
        raise ValueError('output_stride==8 is only supported up to the '
                         'PreAuxlogits end_point for now.')

      # 8 x 8 x 2080
      with tf.variable_scope('Mixed_7a'):
        with tf.variable_scope('Branch_0'):
          tower_conv = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
          tower_conv_1 = slim.conv2d(tower_conv, 384, 3, stride=2,
                                     padding=padding,
                                     scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_1'):
          tower_conv1 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
          tower_conv1_1 = slim.conv2d(tower_conv1, 288, 3, stride=2,
                                      padding=padding,
                                      scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_2'):
          tower_conv2 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
          tower_conv2_1 = slim.conv2d(tower_conv2, 288, 3,
                                      scope='Conv2d_0b_3x3')
          tower_conv2_2 = slim.conv2d(tower_conv2_1, 320, 3, stride=2,
                                      padding=padding,
                                      scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_3'):
          tower_pool = slim.max_pool2d(net, 3, stride=2,
                                       padding=padding,
                                       scope='MaxPool_1a_3x3')
        net = tf.concat(
            [tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool], 3)

      if add_and_check_final('Mixed_7a', net): return net, end_points

      # TODO(alemi): register intermediate endpoints
      net = slim.repeat(net, 9, block8, scale=0.20)
      net = block8(net, activation_fn=None)

      # 8 x 8 x 1536
      net = slim.conv2d(net, 1536, 1, scope='Conv2d_7b_1x1')
      if add_and_check_final('Conv2d_7b_1x1', net): return net, end_points

    raise ValueError('final_endpoint (%s) not recognized', final_endpoint)
def inception_resnet_v2_base ( inputs , final_endpoint = 'Conv2d_7b_1x1' , output_stride = 16 , align_feature_maps = False , scope = None ) : if output_stride != 8 and output_stride != 16 : raise ValueError ( 'output_stride must be 8 or 16.' ) padding = 'SAME' if align_feature_maps else 'VALID' end_points = { } def add_and_check_final ( name , net ) : end_points [ name ] = net return name == final_endpoint with tf . variable_scope ( scope , 'InceptionResnetV2' , [ inputs ] ) : with slim . arg_scope ( [ slim . conv2d , slim . max_pool2d , slim . avg_pool2d ] , stride = 1 , padding = 'SAME' ) : # 149 x 149 x 32 net = slim . conv2d ( inputs , 32 , 3 , stride = 2 , padding = padding , scope = 'Conv2d_1a_3x3' ) if add_and_check_final ( 'Conv2d_1a_3x3' , net ) : return net , end_points # 147 x 147 x 32 net = slim . conv2d ( net , 32 , 3 , padding = padding , scope = 'Conv2d_2a_3x3' ) if add_and_check_final ( 'Conv2d_2a_3x3' , net ) : return net , end_points # 147 x 147 x 64 net = slim . conv2d ( net , 64 , 3 , scope = 'Conv2d_2b_3x3' ) if add_and_check_final ( 'Conv2d_2b_3x3' , net ) : return net , end_points # 73 x 73 x 64 net = slim . max_pool2d ( net , 3 , stride = 2 , padding = padding , scope = 'MaxPool_3a_3x3' ) if add_and_check_final ( 'MaxPool_3a_3x3' , net ) : return net , end_points # 73 x 73 x 80 net = slim . conv2d ( net , 80 , 1 , padding = padding , scope = 'Conv2d_3b_1x1' ) if add_and_check_final ( 'Conv2d_3b_1x1' , net ) : return net , end_points # 71 x 71 x 192 net = slim . conv2d ( net , 192 , 3 , padding = padding , scope = 'Conv2d_4a_3x3' ) if add_and_check_final ( 'Conv2d_4a_3x3' , net ) : return net , end_points # 35 x 35 x 192 net = slim . max_pool2d ( net , 3 , stride = 2 , padding = padding , scope = 'MaxPool_5a_3x3' ) if add_and_check_final ( 'MaxPool_5a_3x3' , net ) : return net , end_points # 35 x 35 x 320 with tf . variable_scope ( 'Mixed_5b' ) : with tf . variable_scope ( 'Branch_0' ) : tower_conv = slim . conv2d ( net , 96 , 1 , scope = 'Conv2d_1x1' ) with tf . variable_scope ( 'Branch_1' ) : tower_conv1_0 = slim . conv2d ( net , 48 , 1 , scope = 'Conv2d_0a_1x1' ) tower_conv1_1 = slim . conv2d ( tower_conv1_0 , 64 , 5 , scope = 'Conv2d_0b_5x5' ) with tf . variable_scope ( 'Branch_2' ) : tower_conv2_0 = slim . conv2d ( net , 64 , 1 , scope = 'Conv2d_0a_1x1' ) tower_conv2_1 = slim . conv2d ( tower_conv2_0 , 96 , 3 , scope = 'Conv2d_0b_3x3' ) tower_conv2_2 = slim . conv2d ( tower_conv2_1 , 96 , 3 , scope = 'Conv2d_0c_3x3' ) with tf . variable_scope ( 'Branch_3' ) : tower_pool = slim . avg_pool2d ( net , 3 , stride = 1 , padding = 'SAME' , scope = 'AvgPool_0a_3x3' ) tower_pool_1 = slim . conv2d ( tower_pool , 64 , 1 , scope = 'Conv2d_0b_1x1' ) net = tf . concat ( [ tower_conv , tower_conv1_1 , tower_conv2_2 , tower_pool_1 ] , 3 ) if add_and_check_final ( 'Mixed_5b' , net ) : return net , end_points # TODO(alemi): Register intermediate endpoints net = slim . repeat ( net , 10 , block35 , scale = 0.17 ) # 17 x 17 x 1088 if output_stride == 8, # 33 x 33 x 1088 if output_stride == 16 use_atrous = output_stride == 8 with tf . variable_scope ( 'Mixed_6a' ) : with tf . variable_scope ( 'Branch_0' ) : tower_conv = slim . conv2d ( net , 384 , 3 , stride = 1 if use_atrous else 2 , padding = padding , scope = 'Conv2d_1a_3x3' ) with tf . variable_scope ( 'Branch_1' ) : tower_conv1_0 = slim . conv2d ( net , 256 , 1 , scope = 'Conv2d_0a_1x1' ) tower_conv1_1 = slim . conv2d ( tower_conv1_0 , 256 , 3 , scope = 'Conv2d_0b_3x3' ) tower_conv1_2 = slim . conv2d ( tower_conv1_1 , 384 , 3 , stride = 1 if use_atrous else 2 , padding = padding , scope = 'Conv2d_1a_3x3' ) with tf . variable_scope ( 'Branch_2' ) : tower_pool = slim . max_pool2d ( net , 3 , stride = 1 if use_atrous else 2 , padding = padding , scope = 'MaxPool_1a_3x3' ) net = tf . concat ( [ tower_conv , tower_conv1_2 , tower_pool ] , 3 ) if add_and_check_final ( 'Mixed_6a' , net ) : return net , end_points # TODO(alemi): register intermediate endpoints with slim . arg_scope ( [ slim . conv2d ] , rate = 2 if use_atrous else 1 ) : net = slim . repeat ( net , 20 , block17 , scale = 0.10 ) if add_and_check_final ( 'PreAuxLogits' , net ) : return net , end_points if output_stride == 8 : # TODO(gpapan): Properly support output_stride for the rest of the net. raise ValueError ( 'output_stride==8 is only supported up to the ' 'PreAuxlogits end_point for now.' ) # 8 x 8 x 2080 with tf . variable_scope ( 'Mixed_7a' ) : with tf . variable_scope ( 'Branch_0' ) : tower_conv = slim . conv2d ( net , 256 , 1 , scope = 'Conv2d_0a_1x1' ) tower_conv_1 = slim . conv2d ( tower_conv , 384 , 3 , stride = 2 , padding = padding , scope = 'Conv2d_1a_3x3' ) with tf . variable_scope ( 'Branch_1' ) : tower_conv1 = slim . conv2d ( net , 256 , 1 , scope = 'Conv2d_0a_1x1' ) tower_conv1_1 = slim . conv2d ( tower_conv1 , 288 , 3 , stride = 2 , padding = padding , scope = 'Conv2d_1a_3x3' ) with tf . variable_scope ( 'Branch_2' ) : tower_conv2 = slim . conv2d ( net , 256 , 1 , scope = 'Conv2d_0a_1x1' ) tower_conv2_1 = slim . conv2d ( tower_conv2 , 288 , 3 , scope = 'Conv2d_0b_3x3' ) tower_conv2_2 = slim . conv2d ( tower_conv2_1 , 320 , 3 , stride = 2 , padding = padding , scope = 'Conv2d_1a_3x3' ) with tf . variable_scope ( 'Branch_3' ) : tower_pool = slim . max_pool2d ( net , 3 , stride = 2 , padding = padding , scope = 'MaxPool_1a_3x3' ) net = tf . concat ( [ tower_conv_1 , tower_conv1_1 , tower_conv2_2 , tower_pool ] , 3 ) if add_and_check_final ( 'Mixed_7a' , net ) : return net , end_points # TODO(alemi): register intermediate endpoints net = slim . repeat ( net , 9 , block8 , scale = 0.20 ) net = block8 ( net , activation_fn = None ) # 8 x 8 x 1536 net = slim . conv2d ( net , 1536 , 1 , scope = 'Conv2d_7b_1x1' ) if add_and_check_final ( 'Conv2d_7b_1x1' , net ) : return net , end_points raise ValueError ( 'final_endpoint (%s) not recognized' , final_endpoint )