Python tensorflow.python.keras.backend.image_data_format() Examples

def __init__(self, rate, data_format=None, **kwargs):
    super(SpatialDropout3D, self).__init__(rate, **kwargs)
    if data_format is None:
      data_format = K.image_data_format()
    if data_format not in {'channels_last', 'channels_first'}:
      raise ValueError('data_format must be in '
                       '{"channels_last", "channels_first"}')
    self.data_format = data_format
    self.input_spec = InputSpec(ndim=5) 

def preprocess_input(x, data_format=None):
    """Preprocesses a tensor encoding a batch of images.

    # Arguments
        x: input Numpy tensor, 4D.
        data_format: data format of the image tensor.

    # Returns
        Preprocessed tensor.
    """
    if data_format is None:
        data_format = K.image_data_format()
    assert data_format in {'channels_last', 'channels_first'}

    if data_format == 'channels_first':
        if x.ndim == 3:
            # 'RGB'->'BGR'
            x = x[::-1, ...]
            # Zero-center by mean pixel
            x[0, :, :] -= 103.939
            x[1, :, :] -= 116.779
            x[2, :, :] -= 123.68
        else:
            x = x[:, ::-1, ...]
            x[:, 0, :, :] -= 103.939
            x[:, 1, :, :] -= 116.779
            x[:, 2, :, :] -= 123.68
    else:
        # 'RGB'->'BGR'
        x = x[..., ::-1]
        # Zero-center by mean pixel
        x[..., 0] -= 103.939
        x[..., 1] -= 116.779
        x[..., 2] -= 123.68
    return x 

def preprocess_input(x, data_format=None):
    """Preprocesses a tensor encoding a batch of images.

    # Arguments
        x: input Numpy tensor, 4D.
        data_format: data format of the image tensor.

    # Returns
        Preprocessed tensor.
    """
    if data_format is None:
        data_format = K.image_data_format()
    assert data_format in {'channels_last', 'channels_first'}

    if data_format == 'channels_first':
        if x.ndim == 3:
            # 'RGB'->'BGR'
            x = x[::-1, ...]
            # Zero-center by mean pixel
            x[0, :, :] -= 103.939
            x[1, :, :] -= 116.779
            x[2, :, :] -= 123.68
        else:
            x = x[:, ::-1, ...]
            x[:, 0, :, :] -= 103.939
            x[:, 1, :, :] -= 116.779
            x[:, 2, :, :] -= 123.68
    else:
        # 'RGB'->'BGR'
        x = x[..., ::-1]
        # Zero-center by mean pixel
        x[..., 0] -= 103.939
        x[..., 1] -= 116.779
        x[..., 2] -= 123.68

    x *= 0.017 # scale values

    return x 

def __conv_block(ip, nb_filter, bottleneck=False, dropout_rate=None, weight_decay=1e-4):
    ''' Apply BatchNorm, Relu, 3x3 Conv2D, optional bottleneck block and dropout
    Args:
        ip: Input keras tensor
        nb_filter: number of filters
        bottleneck: add bottleneck block
        dropout_rate: dropout rate
        weight_decay: weight decay factor
    Returns: keras tensor with batch_norm, relu and convolution2d added (optional bottleneck)
    '''
    concat_axis = 1 if K.image_data_format() == 'channels_first' else -1

    x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(ip)
    x = Activation('relu')(x)

    if bottleneck:
        inter_channel = nb_filter * 4  # Obtained from https://github.com/liuzhuang13/DenseNet/blob/master/densenet.lua

        x = Conv2D(inter_channel, (1, 1), kernel_initializer='he_normal', padding='same', use_bias=False,
                   kernel_regularizer=l2(weight_decay))(x)
        x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(x)
        x = Activation('relu')(x)

    x = Conv2D(nb_filter, (3, 3), kernel_initializer='he_normal', padding='same', use_bias=False)(x)
    if dropout_rate:
        x = Dropout(dropout_rate)(x)

    return x 

def __dense_block(x, nb_layers, nb_filter, growth_rate, bottleneck=False, dropout_rate=None, weight_decay=1e-4,
                  grow_nb_filters=True, return_concat_list=False):
    ''' Build a dense_block where the output of each conv_block is fed to subsequent ones
    Args:
        x: keras tensor
        nb_layers: the number of layers of conv_block to append to the model.
        nb_filter: number of filters
        growth_rate: growth rate
        bottleneck: bottleneck block
        dropout_rate: dropout rate
        weight_decay: weight decay factor
        grow_nb_filters: flag to decide to allow number of filters to grow
        return_concat_list: return the list of feature maps along with the actual output
    Returns: keras tensor with nb_layers of conv_block appended
    '''
    concat_axis = 1 if K.image_data_format() == 'channels_first' else -1

    x_list = [x]

    for i in range(nb_layers):
        cb = __conv_block(x, growth_rate, bottleneck, dropout_rate, weight_decay)
        x_list.append(cb)

        x = concatenate([x, cb], axis=concat_axis)

        if grow_nb_filters:
            nb_filter += growth_rate

    if return_concat_list:
        return x, nb_filter, x_list
    else:
        return x, nb_filter 

def preprocess_input(x, data_format=None):
    """Preprocesses a tensor encoding a batch of images.

    # Arguments
        x: input Numpy tensor, 4D.
        data_format: data format of the image tensor.

    # Returns
        Preprocessed tensor.
    """
    if data_format is None:
        data_format = K.image_data_format()
    assert data_format in {'channels_last', 'channels_first'}

    if data_format == 'channels_first':
        if x.ndim == 3:
            # 'RGB'->'BGR'
            x = x[::-1, ...]
            # Zero-center by mean pixel
            x[0, :, :] -= 103.939
            x[1, :, :] -= 116.779
            x[2, :, :] -= 123.68
        else:
            x = x[:, ::-1, ...]
            x[:, 0, :, :] -= 103.939
            x[:, 1, :, :] -= 116.779
            x[:, 2, :, :] -= 123.68
    else:
        # 'RGB'->'BGR'
        x = x[..., ::-1]
        # Zero-center by mean pixel
        x[..., 0] -= 103.939
        x[..., 1] -= 116.779
        x[..., 2] -= 123.68
    return x 

def conv2d_bn(x,
              filters,
              num_row,
              num_col,
              padding='same',
              strides=(1, 1),
              name=None):
    """Utility function to apply conv + BN.

    # Arguments
        x: input tensor.
        filters: filters in `Conv2D`.
        num_row: height of the convolution kernel.
        num_col: width of the convolution kernel.
        padding: padding mode in `Conv2D`.
        strides: strides in `Conv2D`.
        name: name of the ops; will become `name + '_conv'`
            for the convolution and `name + '_bn'` for the
            batch norm layer.

    # Returns
        Output tensor after applying `Conv2D` and `BatchNormalization`.
    """
    if name is not None:
        bn_name = name + '_bn'
        conv_name = name + '_conv'
    else:
        bn_name = None
        conv_name = None
    if K.image_data_format() == 'channels_first':
        bn_axis = 1
    else:
        bn_axis = 3
    x = Conv2D(
        filters, (num_row, num_col),
        strides=strides,
        padding=padding,
        use_bias=False,
        name=conv_name)(x)
    x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)
    x = Activation('relu', name=name)(x)
    return x 

def identity_block(input_tensor, kernel_size, filters, stage, block):
  """The identity block is the block that has no 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

  # 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),
                    kernel_initializer='he_normal',
                    kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    bias_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    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,
                    padding='same',
                    kernel_initializer='he_normal',
                    kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    bias_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    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),
                    kernel_initializer='he_normal',
                    kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    bias_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    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)

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

def identity_block(input_tensor, kernel_size, filters, stage, block):
  """The identity block is the block that has no conv layer at shortcut.

  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

  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=regularizers.l2(L2_WEIGHT_DECAY),
                    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, use_bias=False,
                    padding='same',
                    kernel_initializer='he_normal',
                    kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    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=regularizers.l2(L2_WEIGHT_DECAY),
                    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)

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

def identity_building_block(input_tensor,
                            kernel_size,
                            filters,
                            stage,
                            block,
                            training=None):
  """The identity block is the block that has no 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
    training: Only used if training keras model with Estimator.  In other
      scenarios it is handled automatically.

  Returns:
    Output tensor for the block.
  """
  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,
                             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)

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

def identity_block(input_tensor, kernel_size, filters, stage, block):
  """The identity block is the block that has no 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

  # 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=regularizers.l2(L2_WEIGHT_DECAY),
                    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,
                    padding='same', use_bias=False,
                    kernel_initializer='he_normal',
                    kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    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=regularizers.l2(L2_WEIGHT_DECAY),
                    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)

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

def __init__(self, kernel_size, filters, stage, block):
    """The identity block is the block that has no conv layer at shortcut.
      # Arguments
          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
      # Returns
          Output tensor for the block.
    """
    super().__init__(name='identity' + str(stage) + block)
    filters1, filters2, filters3 = filters
    if K.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'

    self.conv1 = layers.Conv2D(
        filters1, (1, 1),
        kernel_initializer='he_normal',
        name=conv_name_base + '2a')
    self.bn1 = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')
    self.act1 = layers.Activation('relu')

    self.conv2 = layers.Conv2D(
        filters2,
        kernel_size,
        padding='same',
        kernel_initializer='he_normal',
        name=conv_name_base + '2b')
    self.bn2 = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')
    self.act2 = layers.Activation('relu')

    self.conv3 = layers.Conv2D(
        filters3, (1, 1),
        kernel_initializer='he_normal',
        name=conv_name_base + '2c')
    self.bn3 = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')

    self.add = layers.Add()
    self.act = layers.Activation('relu')

  #pylint: disable=arguments-differ 

def __init__(self, kernel_size, filters, stage, block, strides=(2, 2)):
    """A block that has a conv layer at shortcut.
    # Arguments
        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.
    # 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
    """
    super().__init__(name='conv_block' + str(stage) + block)
    filters1, filters2, filters3 = filters
    if K.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'

    self.conv1 = layers.Conv2D(
        filters1, (1, 1),
        strides=strides,
        kernel_initializer='he_normal',
        name=conv_name_base + '2a')
    self.bn1 = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')
    self.act1 = layers.Activation('relu')

    self.conv2 = layers.Conv2D(
        filters2,
        kernel_size,
        padding='same',
        kernel_initializer='he_normal',
        name=conv_name_base + '2b')
    self.bn2 = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')
    self.act2 = layers.Activation('relu')

    self.conv3 = layers.Conv2D(
        filters3, (1, 1),
        kernel_initializer='he_normal',
        name=conv_name_base + '2c')
    self.bn3 = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')

    self.shortcut_conv = layers.Conv2D(
        filters3, (1, 1),
        strides=strides,
        kernel_initializer='he_normal',
        name=conv_name_base + '1')
    self.shortcut_bn = layers.BatchNormalization(
        axis=bn_axis, name=bn_name_base + '1')

    self.add = layers.Add()
    self.act = layers.Activation('relu')

  #pylint: disable=arguments-differ 

def identity_building_block(input_tensor,
                            kernel_size,
                            filters,
                            stage,
                            block,
                            training=None):
  """The identity block is the block that has no 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: current block label, used for generating layer names
    training: Only used if training keras model with Estimator.  In other
      scenarios it is handled automatically.

  Returns:
    Output tensor for the block.
  """
  filters1, filters2 = 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, kernel_size,
                    padding='same', use_bias=False,
                    kernel_initializer='he_normal',
                    kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    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, training=training)
  x = layers.Activation('relu')(x)

  x = layers.Conv2D(filters2, kernel_size,
                    padding='same', use_bias=False,
                    kernel_initializer='he_normal',
                    kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    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, training=training)

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

def identity_building_block(input_tensor,
                            kernel_size,
                            filters,
                            stage,
                            block,
                            training=None):
  """The identity block is the block that has no 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
    training: Only used if training keras model with Estimator.  In other
      scenarios it is handled automatically.

  Returns:
    Output tensor for the block.
  """
  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,
                             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)

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

def identity_block(input_tensor, kernel_size, filters, stage, block):
  """The identity block is the block that has no 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

  # 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=regularizers.l2(L2_WEIGHT_DECAY),
                    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,
                    padding='same', use_bias=False,
                    kernel_initializer='he_normal',
                    kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    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=regularizers.l2(L2_WEIGHT_DECAY),
                    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)

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

def identity_building_block(input_tensor,
                            kernel_size,
                            filters,
                            stage,
                            block,
                            training=None):
  """The identity block is the block that has no 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: current block label, used for generating layer names
    training: Only used if training keras model with Estimator.  In other
      scenarios it is handled automatically.

  Returns:
    Output tensor for the block.
  """
  filters1, filters2 = 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, kernel_size,
                    padding='same', use_bias=False,
                    kernel_initializer='he_normal',
                    kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    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, training=training)
  x = layers.Activation('relu')(x)

  x = layers.Conv2D(filters2, kernel_size,
                    padding='same', use_bias=False,
                    kernel_initializer='he_normal',
                    kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    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, training=training)

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

def identity_block(input_tensor, kernel_size, filters, stage, block):
  """The identity block is the block that has no 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

  # 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),
                    kernel_initializer='he_normal',
                    kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    bias_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    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,
                    padding='same',
                    kernel_initializer='he_normal',
                    kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    bias_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    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),
                    kernel_initializer='he_normal',
                    kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    bias_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    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)

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