a = torch.zeros(4, 5, 6)
a = a[:, :, None, :]
assert a.shape == (4, 5, 1, 6)

Use torch.unsqueeze(input, dim, out=None):

>>> import torch
>>> a = torch.Tensor([1, 2, 3, 4, 5])
>>> a

 1
 2
 3
 4
 5
[torch.FloatTensor of size 5]

>>> a = a.unsqueeze(0)
>>> a

 1  2  3  4  5
[torch.FloatTensor of size 1x5]

Padding does not add dimensions to a tensor but adds elements to an existing dimension. For example: Say you have a vector shaped (3,) with values [1, 2, 3] and want to multiply it by a tensor shaped (2, 3) If you just 0-pad it with 2 elements, you will get a tensor shaped (5,) with values [1, 2, 3, 0, 0], which will be no good to operate with the (2,3) tensor.

You have two options for this:

1. Repeat the tensor across a new dimension. You can use torch.repeat or the more efficient torch.expand to get the tensor

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

which you can then operate with any other (2, 3) shaped tensor.

2. The most efficient and common way to do this is to convert your (3,) tensor to a (1, 3) tensor. This you can do by using unsqueeze() method. This will add a new dimension of size 1. Now your (1, 3) tensor can operate with any tensor shaped (2, 3). I suggest you take a look at broadcast semantics for more info.

There is nn.utils.addSingletonDimension. I have found answer here https://www.bountysource.com/issues/38773074-convenience-function-to-add-a-singleton-dimension-to-a-torch-tensor

You can use expand, repeat, or repeat_interleave:

import torch

x = torch.randn((5, 1, 100, 100))
x1_3channels = x.expand(-1, 3, -1, -1)
x2_3channels = x.repeat(1, 3, 1, 1)
x3_3channels = x.repeat_interleave(3, dim=1)

print(x1_3channels.shape)  # torch.Size([5, 3, 100, 100])
print(x2_3channels.shape)  # torch.Size([5, 3, 100, 100])
print(x3_3channels.shape)  # torch.Size([5, 3, 100, 100])

Note that, as stated in the docs:

• torch.expand():

Expanding a tensor does not allocate new memory, but only creates a new view on the existing tensor where a dimension of size one is expanded to a larger size by setting the stride to 0. Any dimension of size 1 can be expanded to an arbitrary value without allocating new memory.

• torch.repeat():

Unlike expand(), this function copies the tensor’s data.

There are several ways:

torch.unsqueeze:

torch.unsqueeze(x, 0)

Using None (or np.newaxis):

x[None, ...]
# or
x[np.newaxis, ...]

reshape or view:

x.reshape(1, *x.shape)
# or
x.view(1, *x.shape)