Mapping 3D tensor according to a rank tensor

Question

I have a tensor called rank which is of shape 2×3 as follows:

tensor([[ 0, 1,  2],
        [ 2,  0,  1])

I want to construct a 2X3x3 matrix where the inner matrix is populated initially to all zeros using torch.zeros(2,3,3). I want to update the last dimension to value 1 for last dimension indices corresponding to the values in rank tensor. using indices given in rank.

Final output :

    tensor([
           tensor([[1, 0,  0],
                   [0, 1, 0],
                   [0, 0, 1]],
           tensor([[[0, 0, 1],
                   [1, 0, 0],
                   [0, 1,  0]]
           ])

The value 1 is populated according to the rank given in the rank tensor. How can I do this in in pytorch and python.

Asked By: Mayank

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Source

Answer 1

Given:

i=torch.tensor([[ 0, 1, 2],
                [ 2, 0, 1]])

x=torch.tensor([[[1, 0,  0],
                 [0, 1, 0],
                 [0, 0, 1]],

                [[0, 0, 1],
                 [1, 0, 0],
                 [0, 1,  0]]])

You can perform this operation using torch.scatter_:

>>> torch.zeros(2,3,3).scatter_(2, i[:,:,None].expand_as(x), value=1)

Answered By: Ivan

Mapping 3D tensor according to a rank tensor

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