Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.这是什么意思

这是PyTorch的错误提示，意思是当你在一个需要求导的Tensor上调用numpy()方法时，会报错。PyTorch中的Tensor可以进行自动微分，而numpy()方法将Tensor转化为了NumPy数组，这会破坏Tensor的计算图，从而无法进行反向传播求导。为了解决这个问题，可以使用detach()方法将Tensor从计算图中分离出来，然后再调用numpy()方法，这样就可以得到一个不需要求导的NumPy数组了。

相关问题

Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.

This error message is usually encountered when trying to convert a PyTorch tensor to a NumPy array using the `numpy()` method on a tensor that has gradients attached to it.

To resolve this error, you can use the `detach()` method to detach the tensor from the computation graph and then convert it to a NumPy array using the `numpy()` method.

Here's an example:

import torch

x = torch.ones(2, 2, requires_grad=True)
y = x + 2
z = y * y * 3
out = z.mean()

# This will raise the error:
# a = x.numpy()

# This will work:
a = x.detach().numpy()

In the above example, we create a tensor `x` with `requires_grad=True`, which means that gradients will be computed for `x` during backpropagation. We then perform some operations on `x` to obtain `out`.

If we try to convert `x` to a NumPy array using the `numpy()` method, we'll get the error message mentioned in the question. Instead, we can use `detach()` to create a new tensor that shares the same data but is detached from the computation graph, and then convert that to a NumPy array using `numpy()`.

Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.怎么办

这个错误通常发生在将需要梯度计算的 Tensor 直接转换为 numpy 数组时，可以使用 `detach()` 方法将 Tensor 从计算图中分离出来，然后再转换为 numpy 数组。例如：

import torch

x = torch.tensor([1., 2., 3.], requires_grad=True)
y = x * 2
z = y.sum()
z.backward()

# 直接转换会出错
# numpy_array = x.numpy()

# 使用 detach 方法分离出计算图后再转换
numpy_array = x.detach().numpy()
print(numpy_array)

输出结果为：

[1. 2. 3.]