【数据结构与算法】PyTorch中permute与contiguous对tensor结构进行变换

contiguous()——把tensor变成在内存中连续分布的形式

需要变成连续分布的情况：

contiguous：view只能用在contiguous的variable上。如果在view之前用了transpose, permute等，需要用contiguous()来返回一个contiguous copy。

import torch
import numpy as nn

def main():
    
    myArr = np.array([[[1,2,3],
                      [4,5,6]]])
    print(myArr.shape)
    print(myArr.size)
    myTsr = torch.tensor(myArr)
    print(myTsr)
    print(myTsr.shape)
    myTsr2 = myTsr.permute(2,0,1)
    print(myTsr2)
    print(myTsr2.shape)
    myTsr3 = myTsr2.contiguous().view(-1,2)
    print(myTsr3)
    print(myTsr3.shape)

if __name__ == '__main__':
    main()

运行结果：

(1, 2, 3)
6
tensor([[[1, 2, 3],
         [4, 5, 6]]], dtype=torch.int32)
torch.Size([1, 2, 3])
tensor([[[1, 4]],

        [[2, 5]],

        [[3, 6]]], dtype=torch.int32)
torch.Size([3, 1, 2])
tensor([[1, 4],
        [2, 5],
        [3, 6]], dtype=torch.int32)
torch.Size([3, 2])

标签：__,tensor,contiguous,torch,PyTorch,print,myArr
来源： https://blog.csdn.net/bigFatCat_Tom/article/details/97170387