(name, is_training=False)
return preprocessing_fn
Returns preprocessing_fn(image, height, width, **kwargs).

  Args:
    name: The name of the preprocessing function.
    is_training: `True` if the model is being used for training and `False`
      otherwise.

  Returns:
    preprocessing_fn: A function that preprocessing a single image (pre-batch).
      It has the following signature:
        image = preprocessing_fn(image, output_height, output_width, ...).

  Raises:
    ValueError: If Preprocessing `name` is not recognized.
Returns preprocessing_fn(image, height, width, **kwargs).
Returns preprocessing_fn ( image height width ** kwargs ) .
def get_preprocessing(name, is_training=False):
  """Returns preprocessing_fn(image, height, width, **kwargs).

  Args:
    name: The name of the preprocessing function.
    is_training: `True` if the model is being used for training and `False`
      otherwise.

  Returns:
    preprocessing_fn: A function that preprocessing a single image (pre-batch).
      It has the following signature:
        image = preprocessing_fn(image, output_height, output_width, ...).

  Raises:
    ValueError: If Preprocessing `name` is not recognized.
  """
  preprocessing_fn_map = {
      'cifarnet': cifarnet_preprocessing,
      'inception': inception_preprocessing,
      'inception_v1': inception_preprocessing,
      'inception_v2': inception_preprocessing,
      'inception_v3': inception_preprocessing,
      'inception_v4': inception_preprocessing,
      'inception_resnet_v2': inception_preprocessing,
      'lenet': lenet_preprocessing,
      'resnet_v1_50': vgg_preprocessing,
      'resnet_v1_101': vgg_preprocessing,
      'resnet_v1_152': vgg_preprocessing,
      'resnet_v2_50': vgg_preprocessing,
      'resnet_v2_101': vgg_preprocessing,
      'resnet_v2_152': vgg_preprocessing,
      'vgg': vgg_preprocessing,
      'vgg_a': vgg_preprocessing,
      'vgg_16': vgg_preprocessing,
      'vgg_19': vgg_preprocessing,
      'mobilenet': mobilenet_preprocessing,
      'mobilenetdet': mobilenetdet_preprocessing
  }

  if name not in preprocessing_fn_map:
    raise ValueError('Preprocessing name [%s] was not recognized' % name)

  def preprocessing_fn(image, output_height, output_width, **kwargs):
    return preprocessing_fn_map[name].preprocess_image(
        image, output_height, output_width, is_training=is_training, **kwargs)

  return preprocessing_fn
def get_preprocessing ( name , is_training = False ) : preprocessing_fn_map = { 'cifarnet' : cifarnet_preprocessing , 'inception' : inception_preprocessing , 'inception_v1' : inception_preprocessing , 'inception_v2' : inception_preprocessing , 'inception_v3' : inception_preprocessing , 'inception_v4' : inception_preprocessing , 'inception_resnet_v2' : inception_preprocessing , 'lenet' : lenet_preprocessing , 'resnet_v1_50' : vgg_preprocessing , 'resnet_v1_101' : vgg_preprocessing , 'resnet_v1_152' : vgg_preprocessing , 'resnet_v2_50' : vgg_preprocessing , 'resnet_v2_101' : vgg_preprocessing , 'resnet_v2_152' : vgg_preprocessing , 'vgg' : vgg_preprocessing , 'vgg_a' : vgg_preprocessing , 'vgg_16' : vgg_preprocessing , 'vgg_19' : vgg_preprocessing , 'mobilenet' : mobilenet_preprocessing , 'mobilenetdet' : mobilenetdet_preprocessing } if name not in preprocessing_fn_map : raise ValueError ( 'Preprocessing name [%s] was not recognized' % name ) def preprocessing_fn ( image , output_height , output_width , * * kwargs ) : return preprocessing_fn_map [ name ] . preprocess_image ( image , output_height , output_width , is_training = is_training , * * kwargs ) return preprocessing_fn