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.]

相关问题

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

This error occurs when you try to call the `numpy()` method on a PyTorch tensor that requires gradient computation. To fix this, you can use the `detach()` method to create a new tensor that does not require gradient computation and then call the `numpy()` method on the detached tensor. Here's an example:

import torch

# create a tensor and set requires_grad=True
x = torch.ones(2, 2, requires_grad=True)

# perform some operations on the tensor
y = x + 2
z = y * y * 3
out = z.mean()

# call the numpy() method on the tensor
# this will raise an error
# np_array = out.numpy()

# detach the tensor and call the numpy() method on the detached tensor
np_array = out.detach().numpy()

print(np_array)

In this example, we create a tensor `x` with `requires_grad=True`, perform some operations on it, and compute a scalar value `out`. When we try to call the `numpy()` method on `out`, we get the error message. To fix this, we detach `out` using the `detach()` method and then call the `numpy()` method on the detached tensor.

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

This error occurs when you try to call the numpy method on a PyTorch tensor that requires gradient computation. To fix this error, you need to detach the tensor from the computation graph before calling the numpy method. You can do this by calling the detach() method on the tensor before calling numpy().

For example:

import torch

# create a tensor
x = torch.tensor([1, 2, 3], requires_grad=True)

# detach the tensor from the computation graph
x_np = x.detach().numpy()

# print the numpy array
print(x_np)

In the code above, we first create a tensor and set the requires_grad flag to True, indicating that we want to compute gradients with respect to this tensor. We then detach the tensor from the computation graph by calling the detach() method on it. Finally, we call the numpy method on the detached tensor to get the corresponding numpy array.