Permute elements of a tensor along a dimension

Hi,

I am trying to permute the feature map dimension in a tensor. As a very simplified case,

If I have a tensor of size (5, 4, 3, 6)
I want to rearrange the above tensor along its dimension 1 (i.e. 4) from 0,1,2,3 to 0,2,1,3

One possible way I found was to do a index_select followed by cat. But, in case of a larger tensor, a lot of intermediate tensors will have to be created.

Is there a better way to achieve this?

Regards
Nabarun

I think this should do it: input[:, perm] where perm is a LongTensor that containes the permutation.

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OK, so this is what I tried:

>>> n = np.arange(480).reshape((5,4,4,6))
>>> a = torch.from_numpy(n)
>>> perm = torch.LongTensor([0,2,1,3])
>>> a[:, perm, :, :]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: indexing a tensor with an object of type LongTensor. The only supported types are integers, slices, numpy scalars and torch.LongTensor or torch.ByteTensor as the only argument.

Shouldn’t perm be a torch.LongTensor? Am I missing something here?

It will work, but you need to remove the last :, :. They don’t do anything, and for the moment we require LongTensors to appear as the last elements in the index.

Getting the same error even when I remove the trailing :, :.

I just realized the error states: torch.LongTensor or torch.ByteTensor as the only argument at the very end. So, I guess input[:, perm] will not work.

I can probably do a dimension permute to bring the required dimension to dim0 and do the permutation and dimension permute back to original order.

Seems like a hacky way to do it though.

Ah yes, you’re right. We should have full indexing implemented soon.

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Just a FYI: this is now working. However, I would be interested in a case where the permutation is different for each row (first axis)… any cool advanced indexing trick that would do it?

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This answer is a bit late but I stumbled upon this thread while trying to create a permutation matrix from permutation indexes so I’ll just share what I found works.

permutation_indices = ...
size = permutation_indices.size(0)
permutation_matrix = torch.zeros(size, size)
permutation_matrix[torch.arange(size).long(), permutation_indices] = 1

Not quite what I (or the OP) was looking for but thanks for the effort!

Also, you can achieve the same you’re doing by torch.eye(permutation_indices.size(0))[permutation_indices]

Oh yea thats an even simpler approach. Why didnt think of that. Thanks!

Yeah as above, permutations can be achieved via matrix multiplication. [a b] * [0 1; 1 0] => [b a], for example.

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I do like this:

n = np.arange(480).reshape((5,4,4,6))
a = torch.from_numpy(n)
perm = a.permute((3,0,1,2))

Regarding how to permute vectors in PyTorch: there seems to be a function pytorch.permute(), but I can’t find any documentation for it, and when I try it doesn’t seem to work as I might expect (seems to be a no-op).

The function is documented in torch.Tensor.permute. Could you post some code where you think this might be a no-op?
Here is a small example using it:

x = torch.randn(1, 2, 3)
print(x.shape)
> torch.Size([1, 2, 3])
x = x.permute(1, 2, 0)
> torch.Size([2, 3, 1])

print(x.shape)

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Oh, I see. I guess i must have found the wrong version of the documentation when I searched online. I was giving a second arg which was a LongTensor that I was expecting it to use for indexing, but which I now see was ignored.

x = torch.randn(3,3)

x

tensor([[-1.4109, -0.0597, -0.8855],

[-0.6355, -0.6556, -1.9610],

[ 1.0115, 1.5676, 0.8374]])

x.permute((0,1), torch.LongTensor([2, 1, 0]))

gives the same as x.

I see now that what I was trying to do, you are supposed to do using indexing operators.