---
title: 运行报错
description: "**表1 运行报错**"
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# 运行报错


**表1 运行报错**

| 报错信息 | 解决措施 |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
| 运行报错：RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is\_available() is False. If you are running on a CPU\-only machine, please use torch.load with map\_location=torch.device(‘cpu’) to map your storages to the CPU. | 一般在报错代码行加入参数map\_location=torch.device(‘cpu’)即可规避此问题。 |  |  |  |  |
| 运行报错：Unsupport data type: at::ScalarType::Double. | 在报错代码行前添加数据类型转换语句即可规避此问题。 如报错代码行为pos = label.data.eq(1).nonzero(as\_tuple =False).squeeze().npu()不支持数据类型，在代码上一行加上label = label.cpu().float().npu()进行数据类型转换。 |  |  |  |  |
| 运行报错：IndexError: invalid index of a 0\-dim tensor. Use tensor.item() in Python or tensor.item<T>() in C++ to convert a 0\-dim tensor to a number. | 遇到类似错误直接将代码中.data[0]改成.item()。 例如将： 1 M = (d\_loss\_real + torch.abs(diff)).data[0] 改为： 1 M = (d\_loss\_real + torch.abs(diff)).item() | 1 | M = (d\_loss\_real + torch.abs(diff)).data[0] | 1 | M = (d\_loss\_real + torch.abs(diff)).item() |
| 1 | M = (d\_loss\_real + torch.abs(diff)).data[0] |  |  |  |  |
| 1 | M = (d\_loss\_real + torch.abs(diff)).item() |  |  |  |  |
| 运行报错：Could not run ‘aten::empty\_with\_format’ with arguments from the ‘CPUTensorId’ backend. ‘aten::empty\_with\_format’ is only available for these backend "CUDA、NPU". | 需要将Tensor放到NPU上，类似input = input.npu()。 |  |  |  |  |
| 运行报错：options.device().type() == DeviceType::NPU INTERNAL ASSERT FAILED xxx: | 需要将Tensor放到NPU上，类似input = input.npu()。 |  |  |  |  |
| 运行报错：Attempting to deserialize object on a CUDA device but torch.cuda.is\_available() is False. | 此错误一般是torch.load()接口导致的，需要加关键字参数map\_location，如map\_location='npu’或map\_location=‘cpu’。 |  |  |  |  |
| 运行报错：RuntimeError: Incoming model is an instance of torch.nn.parallel.DistributedDataParallel. Parallel wrappers should only be applied to the model(s) AFTER. | 此错误是由于“torch.nn.parallel.DistributedDataParallel”接口在“apex.amp.initial”接口之前调用导致的，需要手动将“torch.nn.parallel.DistributedDataParallel”接口的调用移到“apex.amp.initial”接口调用之后即可。 |  |  |  |  |