torch.unsqueeze()函数理解

torch.unsqueeze(input, dim)  使用时等同于  input.unsqueeze(dim)

torch.unsqueeze()函数起到升维的作用，dim等于几表示在第几维度加一，比如原来x的size=([4])，x.unsqueeze(0)之后就变成了size=([1, 4])，而x.unsqueeze(1)之后就变成了size=([4, 1])，注意dim∈[-input.dim() – 1, input.dim() + 1]
例如

1. 输入一维张量，即input.dim()=1

# 输入：
x = torch.tensor([1, 2, 3, 4])  # x.dim()=1
print(x)
print(x.shape)
y = x.unsqueeze(0)
print(y)
print(y.shape)  # 此时y.dim()=2
z = x.unsqueeze(1)
print(z)
print(z.shape)  # 此时z.dim()=2

# 输出：
tensor([1, 2, 3, 4])
torch.Size([4])
tensor([[1, 2, 3, 4]])
torch.Size([1, 4])
tensor([[1],
        [2],
        [3],
        [4]])
torch.Size([4, 1])

2. 输入二维张量，即input.dim()=2

# 输入：
x = torch.tensor([[1, 2, 3], [4, 5, 6]])  # x.dim()=2
print(x)
print(x.shape)
y = x.unsqueeze(0)
print(y)
print(y.shape)  # 此时y.dim()=3
z = x.unsqueeze(1)
print(z)
print(z.shape)  # 此时z.dim()=3

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

        [[4, 5, 6]]])
torch.Size([2, 1, 3])

3. 输入四维张量，即input.dim()=4

# 输入：
x = torch.tensor([[[[1, 2, 3], 
                    [4, 5, 6]],
                [[0, 2, 1], 
                 [1, 5, 2]]],
                  
                [[[1, 2, 3], 
                  [4, 5, 6]],
                [[0, 2, 1], 
                 [1, 5, 2]]]])
print(x)
print(x.shape)

y2 = x.unsqueeze(2)
print(y2)
print(y2.shape)

y3 = x.unsqueeze(3)
print(y3)
print(y3.shape)

# 输出：
tensor([[[[1, 2, 3],
          [4, 5, 6]],

         [[0, 2, 1],
          [1, 5, 2]]],


        [[[1, 2, 3],
          [4, 5, 6]],

         [[0, 2, 1],
          [1, 5, 2]]]])
torch.Size([2, 2, 2, 3])
tensor([[[[[1, 2, 3],
           [4, 5, 6]]],


         [[[0, 2, 1],
           [1, 5, 2]]]],



        [[[[1, 2, 3],
           [4, 5, 6]]],


         [[[0, 2, 1],
           [1, 5, 2]]]]])
torch.Size([2, 2, 1, 2, 3])
tensor([[[[[1, 2, 3]],

          [[4, 5, 6]]],


         [[[0, 2, 1]],

          [[1, 5, 2]]]],



        [[[[1, 2, 3]],

          [[4, 5, 6]]],


         [[[0, 2, 1]],

          [[1, 5, 2]]]]])
torch.Size([2, 2, 2, 1, 3])