.detach().cpu()
时间: 2023-12-24 10:43:56 浏览: 30
This method is used in PyTorch to detach a tensor from the current computation graph and move it to the CPU.
When a tensor is created in PyTorch, it is automatically added to the computation graph, which keeps track of all the operations that are performed on it. This allows for automatic differentiation and backpropagation during training.
However, sometimes we may want to detach a tensor from the computation graph, either to prevent backpropagation or to move it to a different device.
The `.detach()` method creates a new tensor that shares the same data as the original tensor, but is no longer part of the computation graph. The `.cpu()` method then moves this new tensor to the CPU.
For example:
```
import torch
x = torch.randn(3, 4, requires_grad=True)
y = x.detach().cpu()
print(x.is_leaf) # True
print(y.is_leaf) # True
print(y.device) # cpu
```
In this example, `x` is a tensor with requires_grad=True, so it is part of the computation graph. We then use `.detach()` to create a new tensor `y` that is not part of the graph, and `.cpu()` to move it to the CPU. The resulting tensor `y` has the same data as `x`, but cannot be used for backpropagation.