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1 先看看官方中英文doc：
1.1 permute(dims)
将tensor的维度换位。
Permute(dims)将tensor的维度换位。参数：参数是一系列的整数，代表原来张量的维度。比如三维就有0，1，2这些dimension。例：import torchimport numpy as npa=np.array(1,2,3,4,5,6)unpermuted=torch.tensor(a)print(unpermuted.siz.
参数： - __dims__ (int .*) - 换位顺序
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1.2 permute(*dims) → Tensor
Permute the dimensions of this tensor.
Parameters： *dims (int.) – The desired ordering of dimensions
Example：
2 pytorch permute的使用 https://odfootgoti1980.mystrikingly.com/blog/nr-2003-full-game-download.
permute函数功能还是比较简单的，下面主要介绍几个细节点：
2.1 transpose与permute的异同
Tensor.permute(a,b,c,d, .)：permute函数可以对任意高维矩阵进行转置，但没有 torch.permute() 这个调用方式， 只能 Tensor.permute()：
torch.transpose(Tensor, a,b)：transpose只能操作2D矩阵的转置，有两种调用方式；
另：连续使用transpose也可实现permute的效果：
从以上操作中可知，permute相当于可以同时操作于tensor的若干维度，transpose只能同时作用于tensor的两个维度；
2.2 permute函数与contiguous、view函数之关联
contiguous：view只能作用在contiguous的variable上，如果在view之前调用了transpose、permute等，就需要调用contiguous()来返回一个contiguous copy； Mac 10 5 8 update download.
一种可能的解释是：有些tensor并不是占用一整块内存，而是由不同的数据块组成，而tensor的view()操作依赖于内存是整块的，这时只需要执行contiguous()这个函数，把tensor变成在内存中连续分布的形式；
判断ternsor是否为contiguous，可以调用torch.Tensor.is_contiguous()函数:
另：在pytorch的最新版本0.4版本中，增加了torch.reshape()，与 numpy.reshape() 的功能类似，大致相当于 tensor.contiguous().view()，这样就省去了对tensor做view()变换前，调用contiguous()的麻烦；
Sqlpro studio 1 0 455 download free. 3 permute与view函数功能demo Textlab 1 2 3.
利用函数 permute(2,0,1) 可以把 Tensor([[[1,2,3],[4,5,6]]]) 转换成： Mitti 1 2 2.
如果使用view(1,3,2) 可以得到：
5 参考
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