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

  This is the original residual unit proposed in [1]. See Fig. 1(a) 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 after convolutions.
Bottleneck residual unit variant with BN after convolutions .
def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
               outputs_collections=None, scope=None):
  """Bottleneck residual unit variant with BN after convolutions.

  This is the original residual unit proposed in [1]. See Fig. 1(a) 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_v1', [inputs]) as sc:
    depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
    if depth == depth_in:
      shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
    else:
      shortcut = slim.conv2d(inputs, depth, [1, 1], stride=stride,
                             activation_fn=None, scope='shortcut')

    residual = slim.conv2d(inputs, 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,
                           activation_fn=None, scope='conv3')

    output = tf.nn.relu(shortcut + residual)

    return slim.utils.collect_named_outputs(outputs_collections,
                                            sc.original_name_scope,
                                            output)
def bottleneck ( inputs , depth , depth_bottleneck , stride , rate = 1 , outputs_collections = None , scope = None ) : with tf . variable_scope ( scope , 'bottleneck_v1' , [ inputs ] ) as sc : depth_in = slim . utils . last_dimension ( inputs . get_shape ( ) , min_rank = 4 ) if depth == depth_in : shortcut = resnet_utils . subsample ( inputs , stride , 'shortcut' ) else : shortcut = slim . conv2d ( inputs , depth , [ 1 , 1 ] , stride = stride , activation_fn = None , scope = 'shortcut' ) residual = slim . conv2d ( inputs , 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 , activation_fn = None , scope = 'conv3' ) output = tf . nn . relu ( shortcut + residual ) return slim . utils . collect_named_outputs ( outputs_collections , sc . original_name_scope , output )