(inputs, depth, depth_bottleneck, stride, rate=1,
               outputs_collections=None, scope=None)
Bottleneck residual unit variant with BN before convolutions.

  This is the full preactivation residual unit variant proposed in [2]. See
  Fig. 1(b) of [2] for its definition. Note that we use here the bottleneck
  variant which has an extra bottleneck layer.

  When putting together two consecutive ResNet blocks that use this unit, one
  should use stride = 2 in the last unit of the first block.

  Args:
    inputs: A tensor of size [batch, height, width, channels].
    depth: The depth of the ResNet unit output.
    depth_bottleneck: The depth of the bottleneck layers.
    stride: The ResNet unit's stride. Determines the amount of downsampling of
      the units output compared to its input.
    rate: An integer, rate for atrous convolution.
    outputs_collections: Collection to add the ResNet unit output.
    scope: Optional variable_scope.

  Returns:
    The ResNet unit's output.
Bottleneck residual unit variant with BN before convolutions.
Bottleneck residual unit variant with BN before convolutions .
def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
               outputs_collections=None, scope=None):
  """Bottleneck residual unit variant with BN before convolutions.

  This is the full preactivation residual unit variant proposed in [2]. See
  Fig. 1(b) of [2] for its definition. Note that we use here the bottleneck
  variant which has an extra bottleneck layer.

  When putting together two consecutive ResNet blocks that use this unit, one
  should use stride = 2 in the last unit of the first block.

  Args:
    inputs: A tensor of size [batch, height, width, channels].
    depth: The depth of the ResNet unit output.
    depth_bottleneck: The depth of the bottleneck layers.
    stride: The ResNet unit's stride. Determines the amount of downsampling of
      the units output compared to its input.
    rate: An integer, rate for atrous convolution.
    outputs_collections: Collection to add the ResNet unit output.
    scope: Optional variable_scope.

  Returns:
    The ResNet unit's output.
  """
  with tf.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc:
    depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
    preact = slim.batch_norm(inputs, activation_fn=tf.nn.relu, scope='preact')
    if depth == depth_in:
      shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
    else:
      shortcut = slim.conv2d(preact, depth, [1, 1], stride=stride,
                             normalizer_fn=None, activation_fn=None,
                             scope='shortcut')

    residual = slim.conv2d(preact, depth_bottleneck, [1, 1], stride=1,
                           scope='conv1')
    residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
                                        rate=rate, scope='conv2')
    residual = slim.conv2d(residual, depth, [1, 1], stride=1,
                           normalizer_fn=None, activation_fn=None,
                           scope='conv3')

    output = shortcut + residual

    return slim.utils.collect_named_outputs(outputs_collections,
                                            sc.name,
                                            output)
def bottleneck ( inputs , depth , depth_bottleneck , stride , rate = 1 , outputs_collections = None , scope = None ) : with tf . variable_scope ( scope , 'bottleneck_v2' , [ inputs ] ) as sc : depth_in = slim . utils . last_dimension ( inputs . get_shape ( ) , min_rank = 4 ) preact = slim . batch_norm ( inputs , activation_fn = tf . nn . relu , scope = 'preact' ) if depth == depth_in : shortcut = resnet_utils . subsample ( inputs , stride , 'shortcut' ) else : shortcut = slim . conv2d ( preact , depth , [ 1 , 1 ] , stride = stride , normalizer_fn = None , activation_fn = None , scope = 'shortcut' ) residual = slim . conv2d ( preact , depth_bottleneck , [ 1 , 1 ] , stride = 1 , scope = 'conv1' ) residual = resnet_utils . conv2d_same ( residual , depth_bottleneck , 3 , stride , rate = rate , scope = 'conv2' ) residual = slim . conv2d ( residual , depth , [ 1 , 1 ] , stride = 1 , normalizer_fn = None , activation_fn = None , scope = 'conv3' ) output = shortcut + residual return slim . utils . collect_named_outputs ( outputs_collections , sc . name , output )