torch.premute的介绍

permute进行的是置换。

permute的dim需要参数表示进行置换的维度。

import torch

import numpy as np

x=np.arange(24).reshape((2,3,4))

x = torch.tensor(x)

print(x)

y=x.permute((2, 1, 0))

print(y)

由代码可知列和批进行了置换。每一列都对应了一个新批，而批又转为了列。

输出:

tensor([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],

[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]], dtype=torch.int32)
tensor([[[ 0, 12],
[ 4, 16],
[ 8, 20]],

[[ 1, 13],
[ 5, 17],
[ 9, 21]],

[[ 2, 14],
[ 6, 18],
[10, 22]],

[[ 3, 15],
[ 7, 19],
[11, 23]]], dtype=torch.int32)

 

import torch

import numpy as np

x=np.arange(24).reshape((2,3,4))

x = torch.tensor(x)

print(x)

y=x.permute((2, 0, 1))

permute(2,0,1)操作行变列，批变行，列变批。

print(y)

tensor([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],

[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]], dtype=torch.int32)

tensor([[[ 0, 4, 8],
[12, 16, 20]],

[[ 1, 5, 9],
[13, 17, 21]],

[[ 2, 6, 10],
[14, 18, 22]],

[[ 3, 7, 11],
[15, 19, 23]]], dtype=torch.int32)