pytorch中的torch.unsqueeze和squeeze张量维度变化问题

顾名思义：unsqueeze，扩展维度，返回一个新的张量，对输入的既定位置插入维度 1
squeeze，压缩维度，将输入张量形状中的1 去除并返回。
torch.unsqueeze(input, dim)
torch.squeeze(input, dim)

tensor (Tensor) – 输入张量
dim (int) – 插入/消除维度的索引

【pytorch中的torch.unsqueeze和squeeze张量维度变化问题】以下用一个二维张量进行举例：
压缩维度仅对（0，1）索引进行示例，（-1，-2）原理类似

import torchx = torch.Tensor([[1, 2, 3, 4],[5,6,7,8]])print('#' * 50)print(x)print(x.size())print(x.dim())##########print('#' * 50)print(torch.unsqueeze(x, 0))print(torch.unsqueeze(x, 0).size())print(torch.unsqueeze(x, 0).dim())m=torch.unsqueeze(x, 0)print(m.squeeze(0))n=m.squeeze(0)print(n.size())print(n.dim())##########print('#' * 50)print(torch.unsqueeze(x, 1))print(torch.unsqueeze(x, 1).size())print(torch.unsqueeze(x, 1).dim())a=torch.unsqueeze(x, 1)print(a.squeeze(1))b=a.squeeze(1)print(b.size())print(b.dim())##########print('#' * 50)print(torch.unsqueeze(x, -1))print(torch.unsqueeze(x, -1).size())print(torch.unsqueeze(x, 1).dim())##########print('#' * 50)print(torch.unsqueeze(x, -2))print(torch.unsqueeze(x, -2).size())print(torch.unsqueeze(x, -2).dim())

相应结果：

##################################################tensor([[1., 2., 3., 4.],[5., 6., 7., 8.]])torch.Size([2, 4])2##################################################tensor([[[1., 2., 3., 4.],[5., 6., 7., 8.]]])torch.Size([1, 2, 4])3tensor([[1., 2., 3., 4.],[5., 6., 7., 8.]])torch.Size([2, 4])2##################################################tensor([[[1., 2., 3., 4.]],[[5., 6., 7., 8.]]])torch.Size([2, 1, 4])3tensor([[1., 2., 3., 4.],[5., 6., 7., 8.]])torch.Size([2, 4])2##################################################tensor([[[1.],[2.],[3.],[4.]],[[5.],[6.],[7.],[8.]]])torch.Size([2, 4, 1])3##################################################tensor([[[1., 2., 3., 4.]],[[5., 6., 7., 8.]]])torch.Size([2, 1, 4])3