*Memos:
- My post explains movedim().
- My post explains transpose() and t().
- My post explains adjoint(), mH and mT.
- My post explains reshape() and view().
permute() 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 or a tensor. - The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,float,complexorbool). - The 2nd argument with
torch(Type:tupleofintorlistofint) or the 1st or more arguments with a tensor(Type:int,tupleofintorlistofint) aredims(Required). *Each number must be unique. - You must set the same number of dimensions as the input tensor.
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]]])
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