def conv_block()

in tensorflow_examples/profiling/resnet_model.py [0:0]
75 lines of code
2 McCabe index (conditional complexity)

def conv_block(input_tensor,
               kernel_size,
               filters,
               stage,
               block,
               strides=(2, 2),
               use_l2_regularizer=True):
  """A block that has a conv layer at shortcut.

  Note that from stage 3,
  the second conv layer at main path is with strides=(2, 2)
  And the shortcut should have strides=(2, 2) as well

  Args:
    input_tensor: input tensor
    kernel_size: default 3, the kernel size of middle conv layer at main path
    filters: list of integers, the filters of 3 conv layer at main path
    stage: integer, current stage label, used for generating layer names
    block: 'a','b'..., current block label, used for generating layer names
    strides: Strides for the second conv layer in the block.
    use_l2_regularizer: whether to use L2 regularizer on Conv layer.

  Returns:
    Output tensor for the block.
  """
  filters1, filters2, filters3 = filters
  if backend.image_data_format() == 'channels_last':
    bn_axis = 3
  else:
    bn_axis = 1
  conv_name_base = 'res' + str(stage) + block + '_branch'
  bn_name_base = 'bn' + str(stage) + block + '_branch'

  x = layers.Conv2D(
      filters1, (1, 1),
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name=conv_name_base + '2a')(
          input_tensor)
  x = layers.BatchNormalization(
      axis=bn_axis,
      momentum=BATCH_NORM_DECAY,
      epsilon=BATCH_NORM_EPSILON,
      name=bn_name_base + '2a')(
          x)
  x = layers.Activation('relu')(x)

  x = layers.Conv2D(
      filters2,
      kernel_size,
      strides=strides,
      padding='same',
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name=conv_name_base + '2b')(
          x)
  x = layers.BatchNormalization(
      axis=bn_axis,
      momentum=BATCH_NORM_DECAY,
      epsilon=BATCH_NORM_EPSILON,
      name=bn_name_base + '2b')(
          x)
  x = layers.Activation('relu')(x)

  x = layers.Conv2D(
      filters3, (1, 1),
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name=conv_name_base + '2c')(
          x)
  x = layers.BatchNormalization(
      axis=bn_axis,
      momentum=BATCH_NORM_DECAY,
      epsilon=BATCH_NORM_EPSILON,
      name=bn_name_base + '2c')(
          x)

  shortcut = layers.Conv2D(
      filters3, (1, 1),
      strides=strides,
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name=conv_name_base + '1')(
          input_tensor)
  shortcut = layers.BatchNormalization(
      axis=bn_axis,
      momentum=BATCH_NORM_DECAY,
      epsilon=BATCH_NORM_EPSILON,
      name=bn_name_base + '1')(
          shortcut)

  x = layers.add([x, shortcut])
  x = layers.Activation('relu')(x)
  return x