Is there any diffrence between index_select and tensor[sequence] in PyTorch?
everyone. I'm new to PyTorch. Now I'm learning the indexing of a tensor. I notice that we can indexing a tensor by tensor.index_select() and tensor[sequence].  

In [1]: x = torch.randn(3, 4)

In [2]: indices = torch.tensor([0, 2])

In [3]: x.index_select(0, indices)
Out[3]:
tensor([[ 0.2760, -0.9543, -1.0499,  0.7828],
        [ 1.3514, -1.1289,  0.5052, -0.0547]])

In [4]: x[[0,2]]
Out[4]:
tensor([[ 0.2760, -0.9543, -1.0499,  0.7828],
        [ 1.3514, -1.1289,  0.5052, -0.0547]])


I am puzzled about these two methods and look for some doc. But I failed. Can anyone can tell me are there some differences between them and what are these difference?

This looks like a remnant of old (slower) indexing.

See this pull request.

I also think you used to not be able to do binary logical indexing on tensors.

a = torch.randn((1,3,4,4))
dim = 2
indices = [0,1]


%timeit a.index_select(dim, torch.tensor(indices))


12.7 µs ± 1.28 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)


%timeit a[:,:,indices,:]


16.7 µs ± 640 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

