如何在 Torch 张量中交换两行?

问题描述

var = [[0, 1, -4, 8],[2, -3, 2, 1],[5, -8, 7, 1]]var = torch.Tensor(var)
这里，var 是一个 3 x 4 (2d) 张量.如何交换第一行和第二行以获得以下二维张量?
2, -3, 2, 10, 1, -4, 85, -8, 7, 1
 解决方案 
其他答案 不起作用，因为有些维度在复制之前被覆盖:
<预><代码>>>>var = [[0, 1, -4, 8],[2, -3, 2, 1],[5, -8, 7, 1]]>>>x = Torch.tensor(var)>>>index = torch.LongTensor([1, 0, 2])>>>x[索引] = x>>>X张量([[ 0, 1, -4, 8],[ 0, 1, -4, 8],[ 5, -8, 7, 1]])
对我来说，创建一个新的张量(具有单独的底层存储)来保存结果就足够了:
<预><代码>>>>x = Torch.tensor(var)>>>index = torch.LongTensor([1, 0, 2])>>>y = torch.zeros_like(x)>>>y[索引] = x

或者，您可以使用 index_copy_(遵循 discuss.pytorch.org)，尽管目前我认为这两种方式都没有优势.

var = [[0, 1, -4, 8],
       [2, -3, 2, 1],
       [5, -8, 7, 1]]

var = torch.Tensor(var)

Here, var is a 3 x 4 (2d) tensor. How the first and second row can be swapped to get the following 2d tensor?

2, -3, 2, 1 
0, 1, -4, 8
5, -8, 7, 1

The other answer does not work, as some dimensions get overwritten before they are copied:

>>> var = [[0, 1, -4, 8],
       [2, -3, 2, 1],
       [5, -8, 7, 1]]
>>> x = torch.tensor(var)
>>> index = torch.LongTensor([1, 0, 2])
>>> x[index] = x
>>> x
tensor([[ 0,  1, -4,  8],
        [ 0,  1, -4,  8],
        [ 5, -8,  7,  1]])

For me, it suffices to create a new tensor (with separate underlying storage) to hold the result:

>>> x = torch.tensor(var)
>>> index = torch.LongTensor([1, 0, 2])
>>> y = torch.zeros_like(x)
>>> y[index] = x

Alternatively, you can use index_copy_ (following this explanation in discuss.pytorch.org), although I don't see an advantage for either way at the moment.

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