(inputs,
              blocks,
              num_classes=None,
              is_training=True,
              global_pool=True,
              output_stride=None,
              include_root_block=True,
              spatial_squeeze=True,
              reuse=None,
              scope=None)
Generator for v2 (preactivation) ResNet models.

  This function generates a family of ResNet v2 models. See the resnet_v2_*()
  methods for specific model instantiations, obtained by selecting different
  block instantiations that produce ResNets of various depths.

  Training for image classification on Imagenet is usually done with [224, 224]
  inputs, resulting in [7, 7] feature maps at the output of the last ResNet
  block for the ResNets defined in [1] that have nominal stride equal to 32.
  However, for dense prediction tasks we advise that one uses inputs with
  spatial dimensions that are multiples of 32 plus 1, e.g., [321, 321]. In
  this case the feature maps at the ResNet output will have spatial shape
  [(height - 1) / output_stride + 1, (width - 1) / output_stride + 1]
  and corners exactly aligned with the input image corners, which greatly
  facilitates alignment of the features to the image. Using as input [225, 225]
  images results in [8, 8] feature maps at the output of the last ResNet block.

  For dense prediction tasks, the ResNet needs to run in fully-convolutional
  (FCN) mode and global_pool needs to be set to False. The ResNets in [1, 2] all
  have nominal stride equal to 32 and a good choice in FCN mode is to use
  output_stride=16 in order to increase the density of the computed features at
  small computational and memory overhead, cf. http://arxiv.org/abs/1606.00915.

  Args:
    inputs: A tensor of size [batch, height_in, width_in, channels].
    blocks: A list of length equal to the number of ResNet blocks. Each element
      is a resnet_utils.Block object describing the units in the block.
    num_classes: Number of predicted classes for classification tasks.
      If 0 or None, we return the features before the logit layer.
    is_training: whether batch_norm layers are in training mode.
    global_pool: If True, we perform global average pooling before computing the
      logits. Set to True for image classification, False for dense prediction.
    output_stride: If None, then the output will be computed at the nominal
      network stride. If output_stride is not None, it specifies the requested
      ratio of input to output spatial resolution.
    include_root_block: If True, include the initial convolution followed by
      max-pooling, if False excludes it. If excluded, `inputs` should be the
      results of an activation-less convolution.
    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
        of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
        To use this parameter, the input images must be smaller than 300x300
        pixels, in which case the output logit layer does not contain spatial
        information and can be removed.
    reuse: whether or not the network and its variables should be reused. To be
      able to reuse 'scope' must be given.
    scope: Optional variable_scope.


  Returns:
    net: A rank-4 tensor of size [batch, height_out, width_out, channels_out].
      If global_pool is False, then height_out and width_out are reduced by a
      factor of output_stride compared to the respective height_in and width_in,
      else both height_out and width_out equal one. If num_classes is 0 or None,
      then net is the output of the last ResNet block, potentially after global
      average pooling. If num_classes is a non-zero integer, net contains the
      pre-softmax activations.
    end_points: A dictionary from components of the network to the corresponding
      activation.

  Raises:
    ValueError: If the target output_stride is not valid.
Generator for v2 (preactivation) ResNet models.
Generator for v2 ( preactivation ) ResNet models .
def resnet_v2(inputs,
              blocks,
              num_classes=None,
              is_training=True,
              global_pool=True,
              output_stride=None,
              include_root_block=True,
              spatial_squeeze=True,
              reuse=None,
              scope=None):
  """Generator for v2 (preactivation) ResNet models.

  This function generates a family of ResNet v2 models. See the resnet_v2_*()
  methods for specific model instantiations, obtained by selecting different
  block instantiations that produce ResNets of various depths.

  Training for image classification on Imagenet is usually done with [224, 224]
  inputs, resulting in [7, 7] feature maps at the output of the last ResNet
  block for the ResNets defined in [1] that have nominal stride equal to 32.
  However, for dense prediction tasks we advise that one uses inputs with
  spatial dimensions that are multiples of 32 plus 1, e.g., [321, 321]. In
  this case the feature maps at the ResNet output will have spatial shape
  [(height - 1) / output_stride + 1, (width - 1) / output_stride + 1]
  and corners exactly aligned with the input image corners, which greatly
  facilitates alignment of the features to the image. Using as input [225, 225]
  images results in [8, 8] feature maps at the output of the last ResNet block.

  For dense prediction tasks, the ResNet needs to run in fully-convolutional
  (FCN) mode and global_pool needs to be set to False. The ResNets in [1, 2] all
  have nominal stride equal to 32 and a good choice in FCN mode is to use
  output_stride=16 in order to increase the density of the computed features at
  small computational and memory overhead, cf. http://arxiv.org/abs/1606.00915.

  Args:
    inputs: A tensor of size [batch, height_in, width_in, channels].
    blocks: A list of length equal to the number of ResNet blocks. Each element
      is a resnet_utils.Block object describing the units in the block.
    num_classes: Number of predicted classes for classification tasks.
      If 0 or None, we return the features before the logit layer.
    is_training: whether batch_norm layers are in training mode.
    global_pool: If True, we perform global average pooling before computing the
      logits. Set to True for image classification, False for dense prediction.
    output_stride: If None, then the output will be computed at the nominal
      network stride. If output_stride is not None, it specifies the requested
      ratio of input to output spatial resolution.
    include_root_block: If True, include the initial convolution followed by
      max-pooling, if False excludes it. If excluded, `inputs` should be the
      results of an activation-less convolution.
    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
        of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
        To use this parameter, the input images must be smaller than 300x300
        pixels, in which case the output logit layer does not contain spatial
        information and can be removed.
    reuse: whether or not the network and its variables should be reused. To be
      able to reuse 'scope' must be given.
    scope: Optional variable_scope.


  Returns:
    net: A rank-4 tensor of size [batch, height_out, width_out, channels_out].
      If global_pool is False, then height_out and width_out are reduced by a
      factor of output_stride compared to the respective height_in and width_in,
      else both height_out and width_out equal one. If num_classes is 0 or None,
      then net is the output of the last ResNet block, potentially after global
      average pooling. If num_classes is a non-zero integer, net contains the
      pre-softmax activations.
    end_points: A dictionary from components of the network to the corresponding
      activation.

  Raises:
    ValueError: If the target output_stride is not valid.
  """
  with tf.variable_scope(scope, 'resnet_v2', [inputs], reuse=reuse) as sc:
    end_points_collection = sc.original_name_scope + '_end_points'
    with slim.arg_scope([slim.conv2d, bottleneck,
                         resnet_utils.stack_blocks_dense],
                        outputs_collections=end_points_collection):
      with slim.arg_scope([slim.batch_norm], is_training=is_training):
        net = inputs
        if include_root_block:
          if output_stride is not None:
            if output_stride % 4 != 0:
              raise ValueError('The output_stride needs to be a multiple of 4.')
            output_stride /= 4
          # We do not include batch normalization or activation functions in
          # conv1 because the first ResNet unit will perform these. Cf.
          # Appendix of [2].
          with slim.arg_scope([slim.conv2d],
                              activation_fn=None, normalizer_fn=None):
            net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
          net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')
        net = resnet_utils.stack_blocks_dense(net, blocks, output_stride)
        # This is needed because the pre-activation variant does not have batch
        # normalization or activation functions in the residual unit output. See
        # Appendix of [2].
        net = slim.batch_norm(net, activation_fn=tf.nn.relu, scope='postnorm')
        # Convert end_points_collection into a dictionary of end_points.
        end_points = slim.utils.convert_collection_to_dict(
            end_points_collection)

        if global_pool:
          # Global average pooling.
          net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True)
          end_points['global_pool'] = net
        if num_classes:
          net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
                            normalizer_fn=None, scope='logits')
          end_points[sc.name + '/logits'] = net
          if spatial_squeeze:
            net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
            end_points[sc.name + '/spatial_squeeze'] = net
          end_points['predictions'] = slim.softmax(net, scope='predictions')
        return net, end_points
def resnet_v2 ( inputs , blocks , num_classes = None , is_training = True , global_pool = True , output_stride = None , include_root_block = True , spatial_squeeze = True , reuse = None , scope = None ) : with tf . variable_scope ( scope , 'resnet_v2' , [ inputs ] , reuse = reuse ) as sc : end_points_collection = sc . original_name_scope + '_end_points' with slim . arg_scope ( [ slim . conv2d , bottleneck , resnet_utils . stack_blocks_dense ] , outputs_collections = end_points_collection ) : with slim . arg_scope ( [ slim . batch_norm ] , is_training = is_training ) : net = inputs if include_root_block : if output_stride is not None : if output_stride % 4 != 0 : raise ValueError ( 'The output_stride needs to be a multiple of 4.' ) output_stride /= 4 # We do not include batch normalization or activation functions in # conv1 because the first ResNet unit will perform these. Cf. # Appendix of [2]. with slim . arg_scope ( [ slim . conv2d ] , activation_fn = None , normalizer_fn = None ) : net = resnet_utils . conv2d_same ( net , 64 , 7 , stride = 2 , scope = 'conv1' ) net = slim . max_pool2d ( net , [ 3 , 3 ] , stride = 2 , scope = 'pool1' ) net = resnet_utils . stack_blocks_dense ( net , blocks , output_stride ) # This is needed because the pre-activation variant does not have batch # normalization or activation functions in the residual unit output. See # Appendix of [2]. net = slim . batch_norm ( net , activation_fn = tf . nn . relu , scope = 'postnorm' ) # Convert end_points_collection into a dictionary of end_points. end_points = slim . utils . convert_collection_to_dict ( end_points_collection ) if global_pool : # Global average pooling. net = tf . reduce_mean ( net , [ 1 , 2 ] , name = 'pool5' , keep_dims = True ) end_points [ 'global_pool' ] = net if num_classes : net = slim . conv2d ( net , num_classes , [ 1 , 1 ] , activation_fn = None , normalizer_fn = None , scope = 'logits' ) end_points [ sc . name + '/logits' ] = net if spatial_squeeze : net = tf . squeeze ( net , [ 1 , 2 ] , name = 'SpatialSqueeze' ) end_points [ sc . name + '/spatial_squeeze' ] = net end_points [ 'predictions' ] = slim . softmax ( net , scope = 'predictions' ) return net , end_points