Pytorch之view及view_as使用详解

import numpy as np
import torch
from torch.autograd import Variable
 
x = torch.Tensor(2,2,2)
print(x)
 
y = x.view(1,8)
print(y)
 
z = x.view(-1,4) # the size -1 is inferred from other dimensions
print(z)
 
t = x.view(8)
print(t)

tensor([[[1.3712e-14, 6.4069e+02],
   [4.3066e+21, 1.1824e+22]],

  [[4.3066e+21, 6.3828e+28],
   [3.8016e-39, 0.0000e+00]]])

#x.view(1,8)生成的是[1,8]的张量
tensor([[1.3712e-14, 6.4069e+02, 4.3066e+21, 1.1824e+22, 4.3066e+21, 6.3828e+28,
   3.8016e-39, 0.0000e+00]])

#x.view(-1,4)其中-1是在4下的另一个维度的大小，也就是8/4=2，所以生成的是[2,4]的张量
tensor([[1.3712e-14, 6.4069e+02, 4.3066e+21, 1.1824e+22],
  [4.3066e+21, 6.3828e+28, 3.8016e-39, 0.0000e+00]])

x.view(8)生成的是[8,]的张量，是个数组
tensor([1.3712e-14, 6.4069e+02, 4.3066e+21, 1.1824e+22, 4.3066e+21, 6.3828e+28,
  3.8016e-39, 0.0000e+00])

self.view(tensor.size())

a = torch.Tensor(2, 4)
b = a.view_as(torch.Tensor(4, 2))
print (b)

tensor([[1.3712e-14, 6.4069e+02],
  [4.3066e+21, 1.1824e+22],
  [4.3066e+21, 6.3828e+28],
  [3.8016e-39, 0.0000e+00]])