what does dim=-1 or -2 mean in torch.sum()?

Question

let me take a 2D matrix as example:

mat = torch.arange(9).view(3, -1)

tensor([[0, 1, 2],
        [3, 4, 5],
        [6, 7, 8]])

torch.sum(mat, dim=-2)

tensor([ 9, 12, 15])

I find the result of torch.sum(mat, dim=-2) is equal to torch.sum(mat, dim=0) and dim=-1 equal to dim=1. My question is how to understand the negative dimension here. What if the input matrix has 3 or more dimensions?

Does this answer your question? [What is a dimensional range of \[-1,0\] in Pytorch?](https://stackoverflow.com/questions/59704538/what-is-a-dimensional-range-of-1-0-in-pytorch) — Shai, Jan 12 '20 at 14:09

cristian hantig · Answer 1 · 2021-05-31T11:02:17.750

28

A tensor has multiple dimensions, ordered as in the following figure. There is a forward and backward indexing. Forward indexing uses positive integers, backward indexing uses negative integers.

Example:

-1 will be the last one, in our case it will be dim=2

-2 will be dim=1

-3 will be dim=0

edited May 31 '21 at 11:02

answered Jan 12 '20 at 10:52

cristian hantig

1,110
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Why is there a dimension **zero**? Does this refer to collections starting from zero? – Reactgular Jan 12 '20 at 14:05
9

It is common to start counting starting from zero instead of 1 – Simdi Jan 14 '20 at 18:48

Simdi · Accepted Answer · 2021-01-03T12:44:12.463

12

The minus essentially means you go backwards through the dimensions. Let A be a n-dimensional matrix. Then dim=n-1=-1, dim=n-2=-2, ..., dim=1=-(n-1), dim=0=-n. See the numpy doc for more information, as pytorch is heavily based on numpy.

edited Jan 03 '21 at 12:44

answered Jan 12 '20 at 10:28

Simdi

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what does dim=-1 or -2 mean in torch.sum()?

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