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, 'vgg': vgg_preprocessing, 'vgg_a': vgg_preprocessing, 'vgg_16': vgg_preprocessing, 'vgg_19': vgg_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 , 'vgg' : vgg_preprocessing , 'vgg_a' : vgg_preprocessing , 'vgg_16' : vgg_preprocessing , 'vgg_19' : vgg_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
returns preprocessing_fn .