(image, output_height, output_width, resize_side)
return _mean_image_subtraction(image, [_R_MEAN, _G_MEAN, _B_MEAN])
Preprocesses the given image for evaluation.

  Args:
    image: A `Tensor` representing an image of arbitrary size.
    output_height: The height of the image after preprocessing.
    output_width: The width of the image after preprocessing.
    resize_side: The smallest side of the image for aspect-preserving resizing.

  Returns:
    A preprocessed image.
Preprocesses the given image for evaluation.
Preprocesses the given image for evaluation .
def preprocess_for_eval(image, output_height, output_width, resize_side):
  """Preprocesses the given image for evaluation.

  Args:
    image: A `Tensor` representing an image of arbitrary size.
    output_height: The height of the image after preprocessing.
    output_width: The width of the image after preprocessing.
    resize_side: The smallest side of the image for aspect-preserving resizing.

  Returns:
    A preprocessed image.
  """
  image = _aspect_preserving_resize(image, resize_side)
  image = _central_crop([image], output_height, output_width)[0]
  image.set_shape([output_height, output_width, 3])
  image = tf.to_float(image)
  return _mean_image_subtraction(image, [_R_MEAN, _G_MEAN, _B_MEAN])
def preprocess_for_eval ( image , output_height , output_width , resize_side ) : image = _aspect_preserving_resize ( image , resize_side ) image = _central_crop ( [ image ] , output_height , output_width ) [ 0 ] image . set_shape ( [ output_height , output_width , 3 ] ) image = tf . to_float ( image ) return _mean_image_subtraction ( image , [ _R_MEAN , _G_MEAN , _B_MEAN ] )