PyTorch 中 tensor.permute 和 tensor.view 的区别?

问题描述

tensor.permute() 和 tensor.view() 有什么区别?

他们似乎在做同样的事情.

They seem to do the same thing.

推荐答案

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输入

In [12]: aten = torch.tensor([[1, 2, 3], [4, 5, 6]])

In [13]: aten
Out[13]:
tensor([[ 1,  2,  3],
        [ 4,  5,  6]])

In [14]: aten.shape
Out[14]: torch.Size([2, 3])

---

torch.view() 将张量重塑为不同但兼容的形状.例如，我们的输入张量 aten 的形状为 (2, 3).这可以视为形状的张量 (6, 1), (1, 6) 等，

---

torch.view() reshapes the tensor to a different but compatible shape. For example, our input tensor aten has the shape (2, 3). This can be viewed as tensors of shapes (6, 1), (1, 6) etc.,

# reshaping (or viewing) 2x3 matrix as a column vector of shape 6x1
In [15]: aten.view(6, -1)
Out[15]:
tensor([[ 1],
        [ 2],
        [ 3],
        [ 4],
        [ 5],
        [ 6]])

In [16]: aten.view(6, -1).shape
Out[16]: torch.Size([6, 1])

或者，它也可以被重塑或查看为形状 (1, 6) 的行向量，如:

Alternatively, it can also be reshaped or viewed as a row vector of shape (1, 6) as in:

In [19]: aten.view(-1, 6)
Out[19]: tensor([[ 1,  2,  3,  4,  5,  6]])

In [20]: aten.view(-1, 6).shape
Out[20]: torch.Size([1, 6])

---

而 tensor.permute() 仅用于交换轴.下面的例子会让事情变得更清楚:

---

Whereas tensor.permute() is only used to swap the axes. The below example will make things clear:

In [39]: aten
Out[39]:
tensor([[ 1,  2,  3],
        [ 4,  5,  6]])

In [40]: aten.shape
Out[40]: torch.Size([2, 3])

# swapping the axes/dimensions 0 and 1
In [41]: aten.permute(1, 0)
Out[41]:
tensor([[ 1,  4],
        [ 2,  5],
        [ 3,  6]])

# since we permute the axes/dims, the shape changed from (2, 3) => (3, 2)
In [42]: aten.permute(1, 0).shape
Out[42]: torch.Size([3, 2])

你也可以使用负索引来做同样的事情:

You can also use negative indexing to do the same thing as in:

In [45]: aten.permute(-1, 0)
Out[45]:
tensor([[ 1,  4],
        [ 2,  5],
        [ 3,  6]])

In [46]: aten.permute(-1, 0).shape
Out[46]: torch.Size([3, 2])

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