def conv_building_block()

in example_zoo/tensorflow/models/keras_cifar_main/official/resnet/keras/resnet_cifar_model.py [0:0]

• 54 lines of code
• 2 McCabe index (conditional complexity)

def conv_building_block(input_tensor,
                        kernel_size,
                        filters,
                        stage,
                        block,
                        strides=(2, 2),
                        training=None):
  """A block that has a conv layer at shortcut.

  Arguments:
    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 first conv layer in the block.
    training: Only used if training keras model with Estimator.  In other
      scenarios it is handled automatically.

  Returns:
    Output tensor for the block.

  Note that from stage 3,
  the first conv layer at main path is with strides=(2, 2)
  And the shortcut should have strides=(2, 2) as well
  """
  filters1, filters2 = filters
  if tf.keras.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 = tf.keras.layers.Conv2D(filters1, kernel_size, strides=strides,
                             padding='same',
                             kernel_initializer='he_normal',
                             kernel_regularizer=
                             tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
                             bias_regularizer=
                             tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
                             name=conv_name_base + '2a')(input_tensor)
  x = tf.keras.layers.BatchNormalization(axis=bn_axis,
                                         name=bn_name_base + '2a',
                                         momentum=BATCH_NORM_DECAY,
                                         epsilon=BATCH_NORM_EPSILON)(
                                             x, training=training)
  x = tf.keras.layers.Activation('relu')(x)

  x = tf.keras.layers.Conv2D(filters2, kernel_size, padding='same',
                             kernel_initializer='he_normal',
                             kernel_regularizer=
                             tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
                             bias_regularizer=
                             tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
                             name=conv_name_base + '2b')(x)
  x = tf.keras.layers.BatchNormalization(axis=bn_axis,
                                         name=bn_name_base + '2b',
                                         momentum=BATCH_NORM_DECAY,
                                         epsilon=BATCH_NORM_EPSILON)(
                                             x, training=training)

  shortcut = tf.keras.layers.Conv2D(filters2, (1, 1), strides=strides,
                                    kernel_initializer='he_normal',
                                    kernel_regularizer=
                                    tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
                                    bias_regularizer=
                                    tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
                                    name=conv_name_base + '1')(input_tensor)
  shortcut = tf.keras.layers.BatchNormalization(
      axis=bn_axis, name=bn_name_base + '1',
      momentum=BATCH_NORM_DECAY, epsilon=BATCH_NORM_EPSILON)(
          shortcut, training=training)

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