1

我有一个张量 t

1 2
3 4
5 6
7 8

我想成功

0 0 0 0
0 1 2 0
0 3 4 0
0 5 6 0
0 7 8 0
0 0 0 0

我尝试用 new=torch.tensor([0. 0. 0. 0.]) 张量堆叠四次，但没有奏效。

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，那也没用。

4

2 回答 2

3

最好先初始化所需形状的数组，然后在适当的索引处添加数据。

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，如果您只想用零填充您的张量（而不仅仅是在某处添加额外的零），您可以使用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)
于 2019-09-19T09:25:50.863 回答
1

可以torch.cat用来连接张量并获得所需的张量。这是一个完整的例子：

# 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.]])

让我们先填充列：

# 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.]])
于 2019-12-05T07:11:20.533 回答