stack() in PyTorch
Tags: pytorch, stack, tensor, function
Published: 2024-07-14 03:09:37

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*Memos:
- [My post](https://dev.to/hyperkai/hstack-and-columnstack-in-pytorch-2mfb) explains [hstack()](https://pytorch.org/docs/stable/generated/torch.hstack.html) and [column_stack()](https://pytorch.org/docs/stable/generated/torch.column_stack.html).
- [My post](https://dev.to/hyperkai/vstack-and-dstack-in-pytorch-58ml) explains [vstack()](https://pytorch.org/docs/stable/generated/torch.vstack.html) and [dstack()](https://pytorch.org/docs/stable/generated/torch.dstack.html).
- [My post](https://dev.to/hyperkai/cat-in-pytorch-4jea) explains [cat()](https://pytorch.org/docs/stable/generated/torch.cat.html).

[stack()](https://pytorch.org/docs/stable/generated/torch.stack.html) can get the 1D or more D stacked tensor of zero or more elements from the one or more 0D or more D tensors of zero or more elements as shown below: 

*Memos:
- `stack()` can be used with [torch](https://pytorch.org/docs/stable/torch.html) but not with a tensor.
- The 1st argument with `torch` is `tensors`(Required-Type:`tuple` or `list` of `tensor` of `int`, `float`, `complex` or `bool`). *The size of tensors must be the same.
- The 2nd argument with `torch` is `dim`(Optional-Default:`0`-Type:`int`).
- There is `out` argument with `torch`(Optional-Type:`tensor`):
  *Memos:
  - `out=` must be used.
  - [My post](https://dev.to/hyperkai/set-out-with-out-argument-functions-pytorch-3ee) explains `out` argument.
- `tensors+1D` tensor is returned.

```python
import torch

tensor1 = torch.tensor(2)
tensor2 = torch.tensor(7)
tensor3 = torch.tensor(4)

torch.stack(tensors=(tensor1, tensor2, tensor3))
torch.stack(tensors=(tensor1, tensor2, tensor3), dim=0)
torch.stack(tensors=(tensor1, tensor2, tensor3), dim=-1)
# tensor([2, 7, 4])

tensor1 = torch.tensor([2, 7, 4])
tensor2 = torch.tensor([8, 3, 2])
tensor3 = torch.tensor([5, 0, 8])

torch.stack(tensors=(tensor1, tensor2, tensor3))
torch.stack(tensors=(tensor1, tensor2, tensor3), dim=0)
torch.stack(tensors=(tensor1, tensor2, tensor3), dim=-2)
# tensor([[2, 7, 4], [8, 3, 2], [5, 0, 8]])

torch.stack(tensors=(tensor1, tensor2, tensor3), dim=1)
torch.stack(tensors=(tensor1, tensor2, tensor3), dim=-1)
# tensor([[2, 8, 5], [7, 3, 0], [4, 2, 8]])

tensor1 = torch.tensor([[2, 7, 4], [8, 3, 2]])
tensor2 = torch.tensor([[5, 0, 8], [3, 6, 1]])
tensor3 = torch.tensor([[9, 4, 7], [1, 0, 5]])

torch.stack(tensors=(tensor1, tensor2, tensor3))
torch.stack(tensors=(tensor1, tensor2, tensor3), dim=0)
torch.stack(tensors=(tensor1, tensor2, tensor3), dim=-3)
# tensor([[[2, 7, 4], [8, 3, 2]],
#         [[5, 0, 8], [3, 6, 1]],
#         [[9, 4, 7], [1, 0, 5]]])

torch.stack(tensors=(tensor1, tensor2, tensor3), dim=1)
torch.stack(tensors=(tensor1, tensor2, tensor3), dim=-2)
# tensor([[[2, 7, 4], [5, 0, 8], [9, 4, 7]],
#         [[8, 3, 2], [3, 6, 1], [1, 0, 5]]])

torch.stack(tensors=(tensor1, tensor2, tensor3), dim=-1)
torch.stack(tensors=(tensor1, tensor2, tensor3), dim=2)
# tensor([[[2, 5, 9], [7, 0, 4], [4, 8, 7]],
#         [[8, 3, 1], [3, 6, 0], [2, 1, 5]]])

tensor1 = torch.tensor([[2., 7., 4.], [8., 3., 2.]])
tensor2 = torch.tensor([[5., 0., 8.], [3., 6., 1.]])
tensor3 = torch.tensor([[9., 4., 7.], [1., 0., 5.]])

torch.stack(tensors=(tensor1, tensor2, tensor3))
# tensor([[[2., 7., 4.], [8., 3., 2.]],
#         [[5., 0., 8.], [3., 6., 1.]],
#         [[9., 4., 7.], [1., 0., 5.]]])

tensor1 = torch.tensor([[2.+0.j, 7.+0.j, 4.+0.j],
                        [8.+0.j, 3.+0.j, 2.+0.j]])
tensor2 = torch.tensor([[5.+0.j, 0.+0.j, 8.+0.j],
                        [3.+0.j, 6.+0.j, 1.+0.j]])
tensor3 = torch.tensor([[9.+0.j, 4.+0.j, 7.+0.j],
                        [1.+0.j, 0.+0.j, 5.+0.j]])
torch.stack(tensors=(tensor1, tensor2, tensor3))
# tensor([[[2.+0.j, 7.+0.j, 4.+0.j],
#          [8.+0.j, 3.+0.j, 2.+0.j]],
#         [[5.+0.j, 0.+0.j, 8.+0.j],
#          [3.+0.j, 6.+0.j, 1.+0.j]],
#         [[9.+0.j, 4.+0.j, 7.+0.j],
#          [1.+0.j, 0.+0.j, 5.+0.j]]])

tensor1 = torch.tensor([[True, False, True], [False, True, False]])
tensor2 = torch.tensor([[False, True, False], [True, False, True]])
tensor3 = torch.tensor([[True, False, True], [False, True, False]])

torch.stack(tensors=(tensor1, tensor2, tensor3))
# tensor([[[True, False, True], [False, True, False]],
#         [[False, True, False], [True, False, True]],
#         [[True, False, True], [False, True, False]]])
```