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]

You can achieve this with four different solutions. There are slight differences between those. More precisely you can:

• insert a new singleton dimension with torch.Tensor.unqueeze:

  >>> x.unsqueeze(-1) # grad_fn=<UnsqueezeBackward0>
  

• use fancy indexing to add a new dimension which is identical:

  >>> x[..., None] # grad_fn=<UnsqueezeBackward0>
  

• or similarly with torch.Tensor.view:

  >>> x.view(*x.shape, 1) # grad_fn=<ViewBackward0>
  

• add a new dimension with torch.Tensor.reshape:

  >>> x.reshape(*x.shape, 1) # grad_fn=<ReshapeAliasBackward0>
  

I have added the backward gradient function name as a line comment next to each method. You can see how indexing and unsqueezing are the same, while view and reshape rely on two different methods.

All three methods: indexing, unsqueeze, and view will return a view of the tensor while reshape can return a copy of the tensor if needed (i.e. when the data is not contiguous).

You can read more about the differences between torch.view and torch.reshape on this thread.

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

Disclaimer: I don't what unbind() is doing.

However, regarding view(), the important bit is that in PyTorch all standard tensors are stored in row-major order (C-contiguous format) by default.