How to get topk's values with its indices (2D)?
I have two 3D tensor and I want to use one's top k indices get another top k.
For example for the following tensor
a = torch.tensor([[[1], [2], [3]],
                  [[4], [5], [6]]])

b = torch.tensor([[[7,1], [8,2], [9,3]],
                  [[10,4],[11,5],[12,6]]])

pytorch's topk function will give me the following.
top_tensor, indices = torch.topk(a, 2, dim=1)

# top_tensor: tensor([[[3], [2]],
#                    [[6],  [5]]])

# indices: tensor([[[2], [1]],
#                 [[2],  [1]]])

But I want to use the result of a, map to b.
# use indices to do something for b, get torch.tensor([[[8,2], [9,3]],
#                                                      [[11,5],[12,6]]])

In this case, I don't know the real values of b, so I can't use topk to b.
on the other word, I want to get a funtion foo_slice as following:
top_tensor, indices = torch.topk(a, 2, dim=1)
# top_tensor == foo_slice(a, indices)

Is there any approach to achieve this using pytorch?
Thanks!

The solution what you are looking for is here
So the code based solution to your problem is as follows
#inputs are changed in order from the above ques

a = torch.tensor([[[1], [2], [3]],
                  [[5], [6], [4]]])

b = torch.tensor([[[7,1], [8,2], [9,3]],
                  [[11,5],[12,6],[10,4]]])

top_tensor, indices = torch.topk(a, 2, dim=1)

v = [indices.view(-1,2)[i] for i in range(0,indices.shape[1])]


new_tensor = []
for i,f in enumerate(v):
      new_tensor.append(torch.index_select(b[i], 0, f))
print(new_tensor ) #[tensor([[9, 3],
                   #         [8, 2]]),
                   #tensor([[12,  6],
                   #        [11,  5]])]

