c++ - 如何在libtorch中将形状（n，k）的张量与形状（k）的张量堆叠？

Question

torch::stack接受 ac10::TensorList并且在给出相同形状的张量时工作得很好。但是，当您尝试发送先前torch::stack编辑的张量的输出时，它会失败并导致内存访问冲突。

更具体地说，假设我们有 3 个形状为 4 的张量，例如：

torch::Tensor x1 = torch::randn({4});
torch::Tensor x2 = torch::randn({4});
torch::Tensor x3 = torch::randn({4});
torch::Tensor y = torch::randn({4});

第一轮堆叠是微不足道的：

torch::Tensor stacked_xs = torch::stack({x1,x2,x3});

但是，尝试这样做：

torch::Tensor stacked_result = torch::stack({y, stacked_xs});

将失败。我希望获得与np.vstackPython 中相同的行为，这是允许并有效的。我该怎么办？

Answer 1

y您可以向with添加维度torch::unsqueeze。然后与cat(not stack，与 numpy 不同，但结果将是您所要求的) 连接：

torch::Tensor x1 = torch::randn({4});
torch::Tensor x2 = torch::randn({4});
torch::Tensor x3 = torch::randn({4});
torch::Tensor y = torch::randn({4});

torch::Tensor stacked_xs = torch::stack({x1,x2,x3});
torch::Tensor stacked_result = torch::cat({y.unsqueeze(0), stacked_xs});

也可以根据您的喜好展平您的第一个堆栈然后对其进行重塑：

torch::Tensor stacked_xs = torch::stack({x1,x2,x3});
torch::Tensor stacked_result = torch::cat({y, stacked_xs.view({-1}}).view({4,4});