Question

我有张量t

我想做到

我尝试使用new = torch.tensor（[0。0. 0. 0.]）张量进行四次堆叠，但这没有用。

t = torch.arange(8).reshape(1,4,2).float()
print(t)
new=torch.tensor([[0., 0., 0.,0.]])
print(new)
r = torch.stack([t,new])  # invalid argument 0: Tensors must have same number of dimensions: got 4 and 3
new=torch.tensor([[[0., 0., 0.,0.]]])
print(new)
r = torch.stack([t,new])  # invalid argument 0: Sizes of tensors must match except in dimension 0.

我也试过猫，那也不行。

Answer 1

最好先初始化所需形状的数组，然后将数据添加到适当的索引处。

import torch

t = torch.arange(8).reshape(1,4,2).float()
x = torch.zeros((1, t.shape[1]+2, t.shape[2]+2))
x[:, 1:-1, 1:-1] = t

print(x)

另一方面，如果您只想在张量上填充零（而不仅仅是在某处添加额外的零），则可以使用torch.nn.functional.pad：

import torch

t = torch.arange(8).reshape(1, 4, 2).float()
x = torch.nn.functional.pad(t, (1, 1, 1, 1))

print(x)

Answer 2

可以使用torch.cat连接张量并获得所需的张量。这是一个完整的示例：

# input tensor
In [98]: at = torch.arange(1, 9).reshape(-1, 2).float()
In [99]: at 
Out[99]: 
tensor([[1., 2.],
        [3., 4.],
        [5., 6.],
        [7., 8.]])

# columns to be padded
In [100]: col_zeros = torch.zeros(at.shape[0]).reshape(-1, 1)      
In [101]: col_zeros   
Out[101]: 
tensor([[0.],
        [0.],
        [0.],
        [0.]])

# rows to be padded
In [102]: row_zeros = torch.zeros(at.shape[1]+2).reshape(1, -1)   
In [103]: row_zeros 
Out[103]: tensor([[0., 0., 0., 0.]])

让我们为列添加第一个填充垫：

# the order in the list of tensors matter.
# since we want a zero column on both sides, we place the input tensor in the middle
# and pad the `col_zeros` on both sides (i.e. along dimension=1)
In [104]: col_padded = torch.cat([col_zeros, at, col_zeros], dim=1) 
In [105]: col_padded     
Out[105]: 
tensor([[0., 1., 2., 0.],
        [0., 3., 4., 0.],
        [0., 5., 6., 0.],
        [0., 7., 8., 0.]])

接下来，让我们填充行：

# here we pad the `row_zeros` on the upper and lower sides (i.e. along dimension=0)
# placing the already `col_padded` tensor in the middle of the list of tensors
In [106]: final_padded = torch.cat([row_zeros, col_padded, row_zeros], dim=0)  
In [107]: final_padded      
Out[107]: 
tensor([[0., 0., 0., 0.],
        [0., 1., 2., 0.],
        [0., 3., 4., 0.],
        [0., 5., 6., 0.],
        [0., 7., 8., 0.],
        [0., 0., 0., 0.]])

在两个维度上将零填充到张量

2 个答案: