*Memos:
unflatten() can add zero or more dimensions to the 1D or more D tensor of zero or more elements, getting the 1D or more D tensor of zero or more elements as shown below:
*Memos:
-
unflatten()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
torchand the 1st argument with a tensor isdim(Required-Type:int). - The 3rd argument with
torchand the 2nd argument with a tensor issizes(Required-Type:tupleofintorlistofint). *-1infers and adjust the size. - The difference between Unflatten() and unflatten() is:
-
Unflatten()hasunflattened_sizeargument which is identical tosizesargument ofunflatten(). - Basically,
Unflatten()is used to define a model whileunflatten()is not used to define a model.
-
import torch
my_tensor = torch.tensor([7, 1, -8, 3, -6, 0])
torch.unflatten(input=my_tensor, dim=0, sizes=(6,))
my_tensor.unflatten(dim=0, sizes=(6,))
torch.unflatten(input=my_tensor, dim=0, sizes=(-1,))
torch.unflatten(input=my_tensor, dim=-1, sizes=(6,))
torch.unflatten(input=my_tensor, dim=-1, sizes=(-1,))
# tensor([7, 1, -8, 3, -6, 0])
torch.unflatten(input=my_tensor, dim=0, sizes=(1, 6))
torch.unflatten(input=my_tensor, dim=0, sizes=(-1, 6))
torch.unflatten(input=my_tensor, dim=0, sizes=(1, -1))
torch.unflatten(input=my_tensor, dim=-1, sizes=(1, 6))
torch.unflatten(input=my_tensor, dim=-1, sizes=(-1, 6))
torch.unflatten(input=my_tensor, dim=-1, sizes=(1, -1))
# tensor([[7, 1, -8, 3, -6, 0]])
torch.unflatten(input=my_tensor, dim=0, sizes=(2, 3))
torch.unflatten(input=my_tensor, dim=0, sizes=(2, -1))
torch.unflatten(input=my_tensor, dim=-1, sizes=(2, 3))
torch.unflatten(input=my_tensor, dim=-1, sizes=(2, -1))
# tensor([[7, 1, -8], [3, -6, 0]])
torch.unflatten(input=my_tensor, dim=0, sizes=(3, 2))
torch.unflatten(input=my_tensor, dim=0, sizes=(3, -1))
torch.unflatten(input=my_tensor, dim=-1, sizes=(3, 2))
torch.unflatten(input=my_tensor, dim=-1, sizes=(3, -1))
# tensor([[7, 1], [-8, 3], [-6, 0]])
torch.unflatten(input=my_tensor, dim=0, sizes=(6, 1))
torch.unflatten(input=my_tensor, dim=0, sizes=(6, -1))
torch.unflatten(input=my_tensor, dim=-1, sizes=(6, 1))
torch.unflatten(input=my_tensor, dim=-1, sizes=(6, -1))
# tensor([[7], [1], [-8], [3], [-6], [0]])
torch.unflatten(input=my_tensor, dim=0, sizes=(1, 2, 3))
torch.unflatten(input=my_tensor, dim=0, sizes=(-1, 2, 3))
torch.unflatten(input=my_tensor, dim=0, sizes=(1, -1, 3))
torch.unflatten(input=my_tensor, dim=0, sizes=(1, 2, -1))
torch.unflatten(input=my_tensor, dim=-1, sizes=(1, 2, 3))
torch.unflatten(input=my_tensor, dim=-1, sizes=(-1, 2, 3))
torch.unflatten(input=my_tensor, dim=-1, sizes=(1, -1, 3))
torch.unflatten(input=my_tensor, dim=-1, sizes=(1, 2, -1))
# tensor([[[7, 1, -8], [3, -6, 0]]])
etc.
my_tensor = torch.tensor([[7, 1, -8], [3, -6, 0]])
torch.unflatten(input=my_tensor, dim=0, sizes=(2,))
torch.unflatten(input=my_tensor, dim=0, sizes=(-1,))
torch.unflatten(input=my_tensor, dim=1, sizes=(3,))
torch.unflatten(input=my_tensor, dim=1, sizes=(-1,))
torch.unflatten(input=my_tensor, dim=-1, sizes=(3,))
torch.unflatten(input=my_tensor, dim=-1, sizes=(-1,))
torch.unflatten(input=my_tensor, dim=-2, sizes=(2,))
torch.unflatten(input=my_tensor, dim=-2, sizes=(-1,))
# tensor([[7, 1, -8], [3, -6, 0]])
torch.unflatten(input=my_tensor, dim=0, sizes=(1, 2))
torch.unflatten(input=my_tensor, dim=0, sizes=(-1, 2))
torch.unflatten(input=my_tensor, dim=-2, sizes=(1, 2))
torch.unflatten(input=my_tensor, dim=-2, sizes=(-1, 2))
# tensor([[[7, 1, -8], [3, -6, 0]]])
torch.unflatten(input=my_tensor, dim=0, sizes=(2, 1))
torch.unflatten(input=my_tensor, dim=0, sizes=(2, -1))
torch.unflatten(input=my_tensor, dim=1, sizes=(1, 3))
torch.unflatten(input=my_tensor, dim=1, sizes=(-1, 3))
torch.unflatten(input=my_tensor, dim=-1, sizes=(1, 3))
torch.unflatten(input=my_tensor, dim=-1, sizes=(-1, 3))
torch.unflatten(input=my_tensor, dim=-2, sizes=(2, 1))
torch.unflatten(input=my_tensor, dim=-2, sizes=(2, -1))
# tensor([[[7, 1, -8]], [[3, -6, 0]]])
torch.unflatten(input=my_tensor, dim=1, sizes=(3, 1))
torch.unflatten(input=my_tensor, dim=1, sizes=(3, -1))
torch.unflatten(input=my_tensor, dim=-1, sizes=(3, 1))
torch.unflatten(input=my_tensor, dim=-1, sizes=(3, -1))
# tensor([[[7], [1], [-8]], [[3], [-6], [0]]])
torch.unflatten(input=my_tensor, dim=0, sizes=(1, 1, 2))
torch.unflatten(input=my_tensor, dim=0, sizes=(-1, 1, 2))
torch.unflatten(input=my_tensor, dim=0, sizes=(1, -1, 2))
torch.unflatten(input=my_tensor, dim=0, sizes=(1, 1, -1))
torch.unflatten(input=my_tensor, dim=-2, sizes=(1, 1, 2))
torch.unflatten(input=my_tensor, dim=-2, sizes=(-1, 1, 2))
torch.unflatten(input=my_tensor, dim=-2, sizes=(1, -1, 2))
torch.unflatten(input=my_tensor, dim=-2, sizes=(1, 1, -1))
# tensor([[[[7, 1, -8], [3, -6, 0]]]])
torch.unflatten(input=my_tensor, dim=1, sizes=(1, 1, 3))
torch.unflatten(input=my_tensor, dim=1, sizes=(-1, 1, 3))
torch.unflatten(input=my_tensor, dim=1, sizes=(1, -1, 3))
torch.unflatten(input=my_tensor, dim=1, sizes=(1, 1, -1))
torch.unflatten(input=my_tensor, dim=-1, sizes=(1, 1, 3))
torch.unflatten(input=my_tensor, dim=-1, sizes=(-1, 1, 3))
torch.unflatten(input=my_tensor, dim=-1, sizes=(1, -1, 3))
torch.unflatten(input=my_tensor, dim=-1, sizes=(1, 1, -1))
# tensor([[[[7, 1, -8]]], [[[3, -6, 0]]]])
my_tensor = torch.tensor([[7., 1., -8.], [3., -6., 0.]])
torch.unflatten(input=my_tensor, dim=0, sizes=(2,))
# tensor([[7., 1., -8.], [3., -6., 0.]])
my_tensor = torch.tensor([[7.+0.j, 1.+0.j, -8.+0.j],
[3.+0.j, -6.+0.j, 0.+0.j]])
torch.unflatten(input=my_tensor, dim=0, sizes=(2,))
# tensor([[7.+0.j, 1.+0.j, -8.+0.j],
# [3.+0.j, -6.+0.j, 0.+0.j]])
my_tensor = torch.tensor([[True, False, True], [False, True, False]])
torch.unflatten(input=my_tensor, dim=0, sizes=(2,))
# tensor([[True, False, True], [False, True, False]])
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