Pytorch view vs unsqueeze when adding a dummy dimension?

Question

I have a pytorch tensor that has shape [n1, n2, n3]. I need to make the shape [n1, n2, n3, 1].

So I know I can use either unsqueeze or view. Is there a difference in what each one would do in this case?

Answer 1

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.