x = x.detach()
时间: 2024-06-01 18:10:46 浏览: 25
This line of code detaches the tensor x from its computational graph. This means that any future operations performed on x will not be tracked by autograd, which is the PyTorch module responsible for calculating gradients and enabling automatic differentiation.
Detaching a tensor can be useful in cases where you want to use the tensor's value for some computation, but don't want the gradients to flow back through that computation. This can be especially useful when working with pre-trained models or when implementing certain types of optimization algorithms, such as those that require fixed weights.
Note that detaching a tensor does not modify the original tensor - it simply returns a new tensor that is detached from the computational graph.