permute() in PyTorch
Tags: pytorch, permute, tensor, function
Published: 2024-07-12 16:09:14

*Memos:
- [My post](https://dev.to/hyperkai/movedim-in-pytorch-5gla) explains [movedim()](https://pytorch.org/docs/stable/generated/torch.movedim.html).
- [My post](https://dev.to/hyperkai/transpose-and-t-in-pytorch-1pp2) explains [transpose()](https://pytorch.org/docs/stable/generated/torch.transpose.html) and [t()](https://pytorch.org/docs/stable/generated/torch.t.html).
- [My post](https://dev.to/hyperkai/adjoint-mh-and-mt-in-pytorch-5c04) explains [adjoint()](https://pytorch.org/docs/stable/generated/torch.adjoint.html), [mH](https://pytorch.org/docs/stable/tensors.html#torch.Tensor.mH) and [mT](https://pytorch.org/docs/stable/tensors.html#torch.Tensor.mT).
- [My post](https://dev.to/hyperkai/reshape-and-view-in-pytorch-40n5) explains [reshape()](https://pytorch.org/docs/stable/generated/torch.reshape.html) and [view()](https://pytorch.org/docs/stable/generated/torch.Tensor.view.html).

[permute()](https://pytorch.org/docs/stable/generated/torch.permute.html) can get the view of the 1D or more D tensor of zero or more elements with its dimensions permuted without losing data from the 1D or more D tensor of zero or more elements as shown below:

*Memos:
- `permute()` can be used with [torch](https://pytorch.org/docs/stable/torch.html) or a tensor.
- The 1st argument(`input`) with `torch` or using a tensor(Required-Type:`tensor` of `int`, `float`, `complex` or `bool`).
- The 2nd argument with `torch`(Type:`tuple` of `int` or `list` of `int`) or the 1st or more arguments with a tensor(Type:`int`, `tuple` of `int` or `list` of `int`) are `dims`(Required). *Each number must be unique.

```python
import torch

my_tensor = torch.tensor([[[0, 1, 2], [3, 4, 5]],
                          [[6, 7, 8], [9, 10, 11]],
                          [[12, 13, 14], [15, 16, 17]],
                          [[18, 19, 20], [21, 22, 23]]])
torch.permute(input=my_tensor, dims=(0, 1, 2))
torch.permute(input=my_tensor, dims=(-3, -2, -1))
my_tensor.permute(dims=(0, 1, 2))
my_tensor.permute(dims=(-3, -2, -1))
my_tensor.permute(0, 1, 2)
my_tensor.permute(-3, -2, -1)
# tensor([[[0, 1, 2], [3, 4, 5]],
#         [[6, 7, 8], [9, 10, 11]],
#         [[12, 13, 14], [15, 16, 17]],
#         [[18, 19, 20], [21, 22, 23]]])

torch.permute(input=my_tensor, dims=(2, 0, 1))
torch.permute(input=my_tensor, dims=(-1, -3, -2))
# tensor([[[0, 3], [6, 9], [12, 15], [18, 21]],
#         [[1, 4], [7, 10], [13, 16], [19, 22]],
#         [[2, 5], [8, 11], [14, 17], [20, 23]]])

torch.permute(input=my_tensor, dims=(1, 2, 0))
torch.permute(input=my_tensor, dims=(-2, -1, -3))
# tensor([[[0, 6, 12, 18], [1, 7, 13, 19], [2, 8, 14, 20]],
#         [[3, 9, 15, 21], [4, 10, 16, 22], [5, 11, 17, 23]]])

torch.permute(input=my_tensor, dims=(2, 1, 0))
torch.permute(input=my_tensor, dims=(-1, -2, -3))
# tensor([[[0, 6, 12, 18], [3, 9, 15, 21]],
#         [[1, 7, 13, 19], [4, 10, 16, 22]],
#         [[2, 8, 14, 20], [5, 11, 17, 23]]])

torch.permute(input=my_tensor, dims=(0, 2, 1))
torch.permute(input=my_tensor, dims=(-3, -1, -2))
# tensor([[[0, 3], [1, 4], [2, 5]],
#         [[6, 9], [7, 10], [8, 11]],
#         [[12, 15], [13, 16], [14, 17]],
#         [[18, 21], [19, 22], [20, 23]]])

torch.permute(input=my_tensor, dims=(1, 0, 2))
torch.permute(input=my_tensor, dims=(-2, -3, -1))
# tensor([[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
#         [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]]])

my_tensor = torch.tensor([[[0., 1., 2.], [3., 4., 5.]],
                          [[6., 7., 8.], [9., 10., 11.]],
                          [[12., 13., 14.], [15., 16., 17.]],
                          [[18., 19., 20.], [21., 22., 23.]]])
torch.permute(input=my_tensor, dims=(0, 1, 2))
# tensor([[[0., 1., 2.], [3., 4., 5.]],
#         [[ 6., 7., 8.], [9., 10., 11.]],
#         [[12., 13., 14.], [15., 16., 17.]],
#         [[18., 19., 20.], [21., 22., 23.]]])

my_tensor = torch.tensor([[[0.+0.j, 1.+0.j, 2.+0.j],
                           [3.+0.j, 4.+0.j, 5.+0.j]],
                          [[6.+0.j, 7.+0.j, 8.+0.j],
                           [9.+0.j, 10.+0.j, 11.+0.j]],
                          [[12.+0.j, 13.+0.j, 14.+0.j],
                           [15.+0.j, 16.+0.j, 17.+0.j]],
                          [[18.+0.j, 19.+0.j, 20.+0.j],
                           [21.+0.j, 22.+0.j, 23.+0.j]]])
torch.permute(input=my_tensor, dims=(0, 1, 2))
# tensor([[[0.+0.j, 1.+0.j, 2.+0.j],
#          [3.+0.j, 4.+0.j, 5.+0.j]],
#         [[6.+0.j, 7.+0.j, 8.+0.j],
#          [9.+0.j, 10.+0.j, 11.+0.j]],
#         [[12.+0.j, 13.+0.j, 14.+0.j],
#          [15.+0.j, 16.+0.j, 17.+0.j]],
#         [[18.+0.j, 19.+0.j, 20.+0.j],
#          [21.+0.j, 22.+0.j, 23.+0.j]]])

my_tensor = torch.tensor([[[True, False, True], [True, False, True]],
                          [[False, True, False], [False, True, False]],
                          [[True, False, True], [True, False, True]],
                          [[False, True, False], [False, True, False]]])
torch.permute(input=my_tensor, dims=(0, 1, 2))
# tensor([[[True, False, True], [True, False, True]],
#         [[False, True, False], [False, True, False]],
#         [[True, False, True], [True, False, True]],
#         [[False, True, False], [False, True, False]]])
```