pytorch tensor.contiguous()

在 PyTorch 中，当一个张量不是按照内存中连续的方式存储时，它被称为非连续性张量。在这种情况下，我们可以使用 `tensor.contiguous()` 方法来返回一个连续的张量。

具体来说，当一个张量的内存不是按照连续方式存储时，这个张量的元素在内存中的存储位置可能是不连续的。这种情况下，我们无法直接对该张量进行一些操作，比如进行矩阵乘法等操作。因此，我们需要调用 `contiguous()` 方法来将该张量复制到一个连续的内存块中，使得元素在内存中的存储位置变得连续。这样，我们就可以对它进行操作了。

需要注意的是，`contiguous()` 方法会返回一个新的张量，而不是修改原有的张量。因此，如果需要修改原有的张量，需要将返回值再次赋值给原变量。

相关问题

tensor.contiguous()

The `contiguous()` method in PyTorch is used to return a tensor with same data but a different memory layout. When a tensor is created, it may not be stored contiguously in memory. This means that the elements of the tensor might not be stored in a contiguous block of memory. If a tensor is not contiguous, it can cause performance issues when performing certain operations.

The `contiguous()` method returns a new tensor with the same data but stored contiguously in memory. This new tensor has the same values as the original tensor, but its memory layout is different.

For example, consider the following code:

import torch

x = torch.randn(3, 4)
print(x)
print(x.is_contiguous())

y = x.t()
print(y)
print(y.is_contiguous())

z = y.contiguous()
print(z)
print(z.is_contiguous())

The output of this code will be:

tensor([[-0.4677,  0.7443, -2.3223,  0.2032],
        [-0.8475,  0.1588, -0.8358, -0.3137],
        [-0.8270, -0.1226, -0.7252, -1.2620]])
True

tensor([[-0.4677, -0.8475, -0.8270],
        [ 0.7443,  0.1588, -0.1226],
        [-2.3223, -0.8358, -0.7252],
        [ 0.2032, -0.3137, -1.2620]])
False

tensor([[-0.4677, -0.8475, -0.8270],
        [ 0.7443,  0.1588, -0.1226],
        [-2.3223, -0.8358, -0.7252],
        [ 0.2032, -0.3137, -1.2620]])
True

Here, we first create a tensor `x` of shape (3, 4) and check if it is contiguous using the `is_contiguous()` method. The output is `True`.

We then transpose `x` to create a new tensor `y` using the `t()` method. We check if `y` is contiguous using `is_contiguous()`. The output is `False`, indicating that `y` is not contiguous.

To make `y` contiguous, we use the `contiguous()` method to create a new tensor `z`. We check if `z` is contiguous using `is_contiguous()`. The output is `True`, indicating that `z` is contiguous.

In summary, the `contiguous()` method is used to ensure that a tensor is stored contiguously in memory, which can improve performance for certain operations.

.contiguous()

.contiguous()是PyTorch中的一个方法，用于检查张量是否是连续的（contiguous）。\[1\]当一个张量是连续的时候，它的元素在内存中是按照一定的顺序排列的，这样可以提高计算效率。而当一个张量不是连续的时候，它的元素在内存中的排列可能是不连续的，这会导致计算效率下降。\[2\]

你可以使用torch.contiguous()方法来检查一个张量是否是连续的。如果一个张量是连续的，该方法会返回True，否则返回False。例如，对于张量a和b，你可以使用以下代码来检查它们是否是连续的：

print(a.is_contiguous()) # True
print(b.is_contiguous()) # False

你也可以参考PyTorch官方文档中关于.contiguous()方法的详细说明：\[3\]
#### 引用[.reference_title]
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