Tensorflow：如何提高图像预处理速度？

Question

我现在正在使用tensorlfow数据集来实现SSD图像预处理方法。数据集的代码为：

dataset = dataset.prefetch(buffer_size=batch_size)

is_training = True if mode == 'Train' else False   

if is_training:    
  dataset = dataset.shuffle(buffer_size=shuffle_buffer)

# If we are training over multiple epochs before 
#evaluating, repeat the
# dataset for the appropriate number of epochs.
if num_epochs is not None:
    dataset = dataset.repeat(num_epochs)

if is_training and num_gpus and examples_per_epoch:
    total_examples = num_epochs * examples_per_epoch

total_batches = total_examples // batch_size // 
 num_gpus * num_gpus
dataset.take(total_batches * batch_size)

# Parse the raw records into images and labels. Testing 
has shown that setting
# num_parallel_batches > 1 produces no improvement in 
throughput, since
# batch_size is almost always much greater than the 
number of CPU cores.

dataset = dataset.apply(
  tf.contrib.data.map_and_batch(
      parse_record_fn,
      batch_size=batch_size,
      num_parallel_batches=1,
      drop_remainder=True))
dataset = 
dataset.prefetch(buffer_size=tf.contrib.data.AUTOTUNE)

其中“ parse_record_fn”包括随机裁切和颜色饱和度。 “数据集”在CPU下进行。我将批次大小设置为32，将图像大小设置为640 * 640 * 3。实验结果表明，GPU使用率约为34％，这意味着在图像预处理上花费了太多时间。有什么方法可以提高速度？

谢谢！