def cutout()

in autoaugment.py [0:0]

• 26 lines of code
• 1 McCabe index (conditional complexity)

def cutout(image, pad_size, replace=0):
  """Apply cutout (https://arxiv.org/abs/1708.04552) to image.
  This operation applies a (2*pad_size x 2*pad_size) mask of zeros to
  a random location within `img`. The pixel values filled in will be of the
  value `replace`. The located where the mask will be applied is randomly
  chosen uniformly over the whole image.
  Args:
    image: An image Tensor of type uint8.
    pad_size: Specifies how big the zero mask that will be generated is that
      is applied to the image. The mask will be of size
      (2*pad_size x 2*pad_size).
    replace: What pixel value to fill in the image in the area that has
      the cutout mask applied to it.
  Returns:
    An image Tensor that is of type uint8.
  """
  image_height = tf.shape(image)[0]
  image_width = tf.shape(image)[1]

  # Sample the center location in the image where the zero mask will be applied.
  cutout_center_height = tf.random_uniform(
      shape=[], minval=0, maxval=image_height,
      dtype=tf.int32)

  cutout_center_width = tf.random_uniform(
      shape=[], minval=0, maxval=image_width,
      dtype=tf.int32)

  lower_pad = tf.maximum(0, cutout_center_height - pad_size)
  upper_pad = tf.maximum(0, image_height - cutout_center_height - pad_size)
  left_pad = tf.maximum(0, cutout_center_width - pad_size)
  right_pad = tf.maximum(0, image_width - cutout_center_width - pad_size)

  cutout_shape = [image_height - (lower_pad + upper_pad),
                  image_width - (left_pad + right_pad)]
  padding_dims = [[lower_pad, upper_pad], [left_pad, right_pad]]
  mask = tf.pad(
      tf.zeros(cutout_shape, dtype=image.dtype),
      padding_dims, constant_values=1)
  mask = tf.expand_dims(mask, -1)
  mask = tf.tile(mask, [1, 1, 3])
  image = tf.where(
      tf.equal(mask, 0),
      tf.ones_like(image, dtype=image.dtype) * replace,
      image)
  return image