一文掌握torch.squeeze() 和torch.unsqueeze()的用法

torch.squeeze() 这个函数主要对数据的维度进行压缩，去掉维数为1的的维度，默认是将a中所有为1的维度删掉。也可以通过dim指定位置，删掉指定位置的维数为1的维度。

torch.unsqueeze()这个函数主要是对数据维度进行扩充。需要通过dim指定位置，给指定位置加上维数为1的维度。

我自己test的代码：

import torch

x = torch.zeros(3,2,4,1,2,1)# dimension of 3*2*4*1*2
print(x.size())             # torch.Size([3, 2, 4, 1, 2, 1])
print(x.shape)

y = torch.squeeze(x)        # Returns a tensor with all the dimensions of input of size 1 removed.
print(y.size())             # torch.Size([3, 2, 4, 2])
print(y.shape)

z = torch.unsqueeze(y,dim=0)# Add a dimension of 1 in the 0th position
print(z.size())             # torch.Size([1, 3, 2, 4, 2])
print(z.shape)

z = torch.unsqueeze(y,dim=1)# Add a dimension of 1 in the 1st position
print(z.size())             # torch.Size([3, 1, 2, 4, 2])
print(z.shape)

z = torch.unsqueeze(y,dim=2)# Add a dimension of 1 in the 2nd position
print(z.size())             # torch.Size([3, 2, 1, 4, 2])
print(z.shape)

y = torch.squeeze(x,dim=0)  # remove the 0th position of 1 (no 1)
print('dim=0', y.size())    # torch.Size([3, 2, 4, 1, 2, 1])
print('dim=0', y.shape)

y = torch.squeeze(x, dim=1)  # remove the 1st position of 1 (no 1)
print('dim=1', y.size())     # torch.Size([3, 2, 4, 1, 2, 1])
print('dim=1', y.shape)

y = torch.squeeze(x, dim=2)  # remove the 2nd position of 1 (no 1)
print('dim=2', y.size())     # torch.Size([3, 2, 4, 1, 2])
print('dim=2', y.shape)

y = torch.squeeze(x, dim=3)  # remove the 3rd position of 1 (yes)
print('dim=3', y.size())     # torch.Size([3, 2, 4, 2])
print('dim=3', y.shape)

y = torch.squeeze(x, dim=4)  # remove the 4th position of 1 (no 1)
print('dim=4', y.size())     # torch.Size([3, 2, 4, 1, 2, 1])
print('dim=4', y.shape)

y = torch.squeeze(x, dim=5)  # remove the 5th position of 1 (yes)
print('dim=5', y.size())     # torch.Size([3, 2, 4, 1, 2])
print('dim=5', y.shape)

y = torch.squeeze(x, dim=6)  # RuntimeError: Dimension out of range (expected to be in range of [-6, 5], but got 6)
print('dim=6', y.size())
print('dim=6', y.shape)