runtimeerror: indices should be either on cpu or on the same device as the i
时间: 2023-09-19 09:02:20 浏览: 153
该错误是由于在PyTorch中使用的不正确的索引导致的。
在PyTorch中,当你尝试对张量进行索引操作时,索引应该在CPU上,或者与待索引的张量在相同的设备上。
为了解决这个问题,你可以考虑以下几个方面:
1.确保索引是在CPU上进行操作:首先,你可以使用`.cpu()`方法将索引转移到CPU上,以匹配张量的设备。例如,如果你的索引是`indices`,可以使用`indices.cpu()`来确保索引在CPU上。
2.确保张量和索引在相同的设备上:如果你的张量在GPU上,你需要确保索引也在相同的GPU设备上。可以使用`indices.to(device)`方法将索引移动到相同的设备上。
3.将张量和索引都移动到相同的设备上:如果你的索引在CPU上而张量在GPU上,你需要先将索引移动到GPU上,然后执行索引操作。可以使用`indices.to(device)`方法将索引移动到相同的设备上。
总之,要解决runtime error: indices should be either on cpu or on the same device as the input tensor这个问题,你需要确保索引在CPU或者与待索引的张量在相同的设备上。
相关问题
RuntimeError: indices should be either on cpu or on the same device as the indexed tensor (cpu)
This error message typically occurs when you are trying to access or manipulate a tensor on a device (such as GPU) that is different from the device on which the tensor was originally created (such as CPU).
To resolve this error, you should ensure that all tensors and variables are on the same device. You can do this by using the .to() method to move tensors and variables to the desired device before indexing or manipulating them.
For example, if you have a tensor "x" that was created on the CPU, and you want to index it using a tensor "indices" that is on the GPU, you can move "x" to the GPU using the .to() method:
```
x = x.to(device) # Move x to the desired device (e.g. GPU)
output = x[indices] # Now it is safe to index x using indices
```
Alternatively, you can move the "indices" tensor to the CPU using the same method:
```
indices = indices.to('cpu') # Move indices to the same device as x
output = x[indices] # Now it is safe to index x using indices
```
In summary, make sure all tensors and variables are on the same device before indexing or manipulating them.
在使用yolo训练的时候出现以下错误RuntimeError: indices should be either on cpu or on the same device as the indexed tensor (cpu)
这个错误通常是因为索引张量(indices)和被索引的张量(indexed tensor)不在同一个设备上。要解决这个问题,可以将索引张量(indices)移动到与被索引的张量(indexed tensor)相同的设备上,例如:
```python
# 将索引张量(indices)移动到与被索引的张量(indexed tensor)相同的设备上
indices = indices.to(device)
output = indexed_tensor[indices]
```
其中 `device` 是被索引的张量(indexed tensor)所在的设备,可以通过 `indexed_tensor.device` 获得。