tensor.contiguous()
时间: 2024-04-29 16:23:21 浏览: 16
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.