Indexing a multi-dimensional tensor with a tensor in PyTorch

Question

I have the following code:

a = torch.randint(0,10,[3,3,3,3])
b = torch.LongTensor([1,1,1,1])

I have a multi-dimensional index b and want to use it to select a single cell in a. If b wasn't a tensor, I could do:

a[1,1,1,1]

Which returns the correct cell, but:

a[b]

Doesn't work, because it just selects a[1] four times.

How can I do this? Thanks

Answer 1

You can split b into 4 using chunk, and then use the chunked b to index the specific element you want:

>> a = torch.arange(3*3*3*3).view(3,3,3,3)
>> b = torch.LongTensor([[1,1,1,1], [2,2,2,2], [0, 0, 0, 0]]).t()
>> a[b.chunk(chunks=4, dim=0)]   # here's the trick!
Out[24]: tensor([[40, 80,  0]])

What's nice about it is that it can be easily generalized to any dimension of a, you just need to make number of chucks equal the dimension of a.

Answer 2

A more elegant (and simpler) solution might be to simply cast b as a tuple:

a[tuple(b)]
Out[10]: tensor(5.)

I was curious to see how this works with "regular" numpy, and found a related article explaining this quite well here.