如何提高图像预处理速度？

Question

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

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”包括随机裁剪和颜色饱和度。“数据集”在CPU下进行。我将批大小设置为32，图像大小设置为640*640*3。实验结果表明，GPU的utils约为34%，这意味着图像预处理时间过长。有什么办法提高速度吗？你知道吗

谢谢！你知道吗