如何将张量列表转换为 torch::Tensor？

Question

I'm trying to convert the following Python code into its equivalent libtorch:

tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]],
                  [A[0, 1], A[1, 1], A[2, 1]]
                 ])

In Pytorch we could simply use torch.stack or simply use a torch.tensor() like below:

tfm = torch.tensor([[A_tensor[0,0], A_tensor[1,0],0],
                    [A_tensor[0,1], A_tensor[1,1],0]
                   ])

However, in libtorch, this doesn't hold, that is I can not simply do:

auto tfm = torch::tensor ({{A.index({0,0}), A.index({1,0}), A.index({2,0})},
                           {A.index({0,1}), A.index({1,1}), A.index({2,1})}
                         });

or even using a std::vector doesn't work. the same thing goes to torch::stack. I'm currently using three torch::stack to get this done:

auto x = torch::stack({ A.index({0,0}), A.index({1,0}), A.index({2,0}) });
auto y = torch::stack({ A.index({0,1}), A.index({1,1}), A.index({2,1}) });
tfm = torch::stack({ x,y });

So is there any better way for doing this? Can we do this using a one-liner?

Answer 1

所以 C++ libtorch 确实不允许从像 Pytorch 这样的张量列表中构造张量（据我所知），但你仍然可以使用torch::stack实现这个结果（如果你有兴趣， view 在这里实现）并view ：

auto tfm = torch::stack( {A[0][0], A[1][0], A[2][0], A[0][1], A[1][1], A[2][1]} ).view(2,3);