stack(): argument 'tensors' (position 1) must be tuple of Tensors, not numpy.ndarray

这个错误通常表示你正在尝试将一个numpy数组作为参数传递给一个期望接收张量元组的函数。在PyTorch中，张量是指由torch.Tensor类表示的多维数组对象，而numpy数组是由NumPy库表示的多维数组对象。

要解决这个问题，你需要将numpy数组转换为PyTorch张量。你可以使用`torch.from_numpy()`函数将NumPy数组转换为PyTorch张量，然后再将其传递给期望接收张量元组的函数。

例如，假设你有一个名为`x_np`的NumPy数组，并且你想将其作为参数传递给`stack()`函数。你可以使用以下代码将`x_np`转换为PyTorch张量：

import torch
x = torch.from_numpy(x_np)

然后，你可以将`x`作为参数传递给`stack()`函数：

stacked = torch.stack((x,))

注意，`torch.stack()`函数期望传入一个张量元组，因此你需要使用双括号来将`x`包装在元组中。

相关问题

TypeError: stack(): argument 'tensors' (position 1) must be tuple of Tensors, not Tensor

This error occurs when the `stack()` function in PyTorch is called with a single tensor instead of a tuple of tensors.

To resolve this error, you need to pass a tuple of tensors to the `stack()` function instead of a single tensor. For example:

import torch

# Create two tensors
tensor1 = torch.tensor([1, 2, 3])
tensor2 = torch.tensor([4, 5, 6])

# Use stack() function to stack the tensors
stacked_tensors = torch.stack((tensor1, tensor2))

print(stacked_tensors)

This will output:

tensor([[1, 2, 3],
        [4, 5, 6]])

Notice that we passed a tuple of tensors `(tensor1, tensor2)` to the `stack()` function.

RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 64 but got size 63 for tensor number 1 in the list.

This error occurs when you are trying to concatenate or stack tensors along a particular dimension, but the sizes of the tensors do not match in that dimension except for dimension 1. In other words, all dimensions except dimension 1 must have the same size.

In this specific case, you are trying to concatenate or stack a list of tensors where the size of dimension 1 in one of the tensors is 63, while the other tensors have a size of 64 in dimension 1. To fix this error, you need to make sure that all tensors in the list have the same size in all dimensions except dimension 1. You can do this by either resizing the tensor with size 63 to have a size of 64 in dimension 1, or by removing that tensor from the list if it is not needed.