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方法1：window10的任务管理器显示的gpu利用率数据有可能是错的，存在bug,
博主就被这个数据误导了。所以，首先从nvidia-smi或其他软件（如，Gpu-Z等）看gpu真实的利用率。
方法2、第一种情况排除后，就要考虑模型太小，batch-size太小。将改变模型结构，或将batch-size调大。
方法3
、如果第二种情况的gpu利用率还是很低，而cpu利用率很高，那瓶颈应该是数据加载的问题，gpu处理太快，cpu经常处于传输数据的过程。所以，启动多线程加载数据，num_worker可以设置2,4，
8， 16，如果你的内存空间够大，那就再设置pin_memory=True。这会让数据锁页，不允许将数据放到虚拟内存中，省掉了一点数据传输时间。
torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size,
shuffle=True, num_workers=4, pin_memory=True)
方法4、如果设置num_workers不为0后出现boken pipe问题， 就将主程序包含进下面的代码
if __name__ == '__main__':
例如下面的示例：
如果还不行，就设置num_workers=0
方法5、如果是固体硬盘，数据加载的速度很快，num_workers设为0，1， 2，
4就好，不要太大。自己可以尝试看那个比较快。设置太大，cpu对各个子线程的数据合并所花时间太多，会导致数据加载速度不升反降。
方法6、如果还是有问题，尝试重启电脑看看能否解决问题。
