使用 Pytorch 如何定义具有索引和相应值的张量

Question

Problem

I have a list of indices and a list of values like so:

i = torch.tensor([[2, 2, 1], [2, 0, 2]])
v = torch.tensor([1, 2, 3])

I want to define a ( 3x3 for the example) matrix which contains the values v at the indices i ( 1 at position (2,2) , 2 at position (2, 0) and 3 at position (1,2) ):

tensor([[0, 0, 0],
        [0, 0, 3],
        [2, 0, 1]])

What I have tried

I can do it using a trick, with torch.sparse and .to_dense() but I feel that it's not the "pytorchic" way to do that nor the most efficient:

f = torch.sparse.FloatTensor(indices, values, torch.Size([3, 3]))
print(f.to_dense())

Any idea for a better solution? Ideally I would appreciate a solution at least as fast than the one provided above. Of course this was just an example, no particular structure in tensors i and v are assumed (neither for the dimension).

Answer 1

There is an alternative, as below:

import torch

i = torch.tensor([[2, 2, 1], [2, 0, 2]])
v = torch.tensor([1, 2, 3], dtype=torch.float)   # enforcing same data-type

target = torch.zeros([3,3], dtype=torch.float)   # enforcing same data-type
target.index_put_(tuple([k for k in i]), v)

print(target)

The target tensor will be as follows:

tensor([[0., 0., 0.],
        [0., 0., 3.],
        [2., 0., 1.]])

This medium.com blog article provides a comprehensive list of all index functions for PyTorch Tensors.