def distorted_inputs(dataset, batch_size=None, num_preprocess_threads=None):
 if (not batch_size):
  batch_size = FLAGS.batch_size
 with tf.device('/cpu:0'):
  (images, labels) = batch_inputs(dataset, batch_size, train=True, num_preprocess_threads=num_preprocess_threads, num_readers=FLAGS.num_readers)
 return (images, labels)

def distorted_inputs ( dataset , batch_size = None , num_preprocess_threads = None ) : if ( not batch_size ) : batch_size = FLAGS . batch_size with tf . device ( '/cpu:0' ) : ( images , labels ) = batch_inputs ( dataset , batch_size , train = True , num_preprocess_threads = num_preprocess_threads , num_readers = FLAGS . num_readers ) return ( images , labels )
generate batches of distorted versions of imagenet images .