*Memos:
repeat_interleave() can get the 1D tensor of zero or more immediately repeated elements from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
repeat_interleave()
can be used with torch or a tensor. - The 1st argument(
input
) withtorch
or using a tensor(Optional-Type:tensor
ofint
,float
,complex
orbool
). - The 2nd argument with
torch
or the 1st argument with a tensor isrepeats
(Required-Type:int
ortensor
ofint
,float
,complex
orbool
). *The tensor must be 0D or 1D. - The 3rd argument with
torch
or the 2nd argument with a tensor isdim
(Optional-Type:int
). - There is
output_size
argument withtorch
or a tensor(Optional-Default:None
-Type:int
): *Memos:- Total output size for the given axis (e.g. sum of repeats). If given, it will avoid stream synchronization needed to calculate output shape of the tensor.
-
output_size=
must be used.
import torch
my_tensor = torch.tensor([7, 4, 2])
torch.repeat_interleave(repeats=my_tensor)
# tensor([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2])
torch.repeat_interleave(input=my_tensor, repeats=my_tensor)
my_tensor.repeat_interleave(repeats=my_tensor)
torch.repeat_interleave(input=my_tensor, repeats=my_tensor, dim=0)
torch.repeat_interleave(input=my_tensor, repeats=my_tensor, dim=-1)
# tensor([7, 7, 7, 7, 7, 7, 7, 4, 4, 4, 4, 2, 2])
torch.repeat_interleave(input=my_tensor,
repeats=torch.tensor([2, 1, 4]))
torch.repeat_interleave(input=my_tensor,
repeats=torch.tensor([2, 1, 4]), dim=0)
torch.repeat_interleave(input=my_tensor,
repeats=torch.tensor([2, 1, 4]), dim=-1)
# tensor([7, 7, 4, 2, 2, 2, 2])
torch.repeat_interleave(input=my_tensor, repeats=torch.tensor(2))
torch.repeat_interleave(input=my_tensor, repeats=torch.tensor(2), dim=0)
torch.repeat_interleave(input=my_tensor, repeats=torch.tensor(2), dim=-1)
torch.repeat_interleave(input=my_tensor, repeats=torch.tensor([2]))
torch.repeat_interleave(input=my_tensor, repeats=torch.tensor([2]), dim=0)
torch.repeat_interleave(input=my_tensor, repeats=torch.tensor([2]), dim=-1)
# tensor([7, 7, 4, 4, 2, 2])
torch.repeat_interleave(input=my_tensor, repeats=0)
torch.repeat_interleave(input=my_tensor, repeats=0, dim=0)
torch.repeat_interleave(input=my_tensor, repeats=0, dim=-1)
# tensor([], dtype=torch.int64)
torch.repeat_interleave(input=my_tensor, repeats=1)
torch.repeat_interleave(input=my_tensor, repeats=1, dim=0)
torch.repeat_interleave(input=my_tensor, repeats=1, dim=-1)
# tensor([7, 4, 2])
torch.repeat_interleave(input=my_tensor, repeats=2)
torch.repeat_interleave(input=my_tensor, repeats=2, dim=0)
torch.repeat_interleave(input=my_tensor, repeats=2, dim=-1)
# tensor([7, 7, 4, 4, 2, 2])
torch.repeat_interleave(input=my_tensor, repeats=3)
torch.repeat_interleave(input=my_tensor, repeats=3, dim=0)
torch.repeat_interleave(input=my_tensor, repeats=3, dim=-1)
# tensor([7, 7, 7, 4, 4, 4, 2, 2, 2])
etc.
torch.repeat_interleave(input=my_tensor, repeats=3, dim=0, output_size=9)
# tensor([7, 7, 7, 4, 4, 4, 2, 2, 2])
my_tensor = torch.tensor([[7, 4, 2], [5, 1, 6]])
torch.repeat_interleave(input=my_tensor, repeats=1)
# tensor([7, 4, 2, 5, 1, 6])
torch.repeat_interleave(input=my_tensor, repeats=1, dim=0)
torch.repeat_interleave(input=my_tensor, repeats=1, dim=1)
torch.repeat_interleave(input=my_tensor, repeats=1, dim=-1)
torch.repeat_interleave(input=my_tensor, repeats=1, dim=-2)
# tensor([[7, 4, 2], [5, 1, 6]])
torch.repeat_interleave(input=my_tensor, repeats=2)
# tensor([7, 7, 4, 4, 2, 2, 5, 5, 1, 1, 6, 6])
torch.repeat_interleave(input=my_tensor, repeats=2, dim=0)
torch.repeat_interleave(input=my_tensor, repeats=2, dim=-2)
# tensor([[7, 4, 2], [7, 4, 2], [5, 1, 6], [5, 1, 6]])
torch.repeat_interleave(input=my_tensor, repeats=2, dim=1)
torch.repeat_interleave(input=my_tensor, repeats=2, dim=-1)
# tensor([[7, 7, 4, 4, 2, 2], [5, 5, 1, 1, 6, 6]])
torch.repeat_interleave(input=my_tensor, repeats=3)
# tensor([7, 7, 7, 4, 4, 4, 2, 2, 2, 5, 5, 5, 1, 1, 1, 6, 6, 6])
torch.repeat_interleave(input=my_tensor, repeats=3, dim=0)
torch.repeat_interleave(input=my_tensor, repeats=3, dim=-2)
# tensor([[7, 4, 2], [7, 4, 2], [7, 4, 2], [5, 1, 6], [5, 1, 6], [5, 1, 6]])
torch.repeat_interleave(input=my_tensor, repeats=3, dim=1)
torch.repeat_interleave(input=my_tensor, repeats=3, dim=-1)
# tensor([[7, 7, 7, 4, 4, 4, 2, 2, 2], [5, 5, 5, 1, 1, 1, 6, 6, 6]])
my_tensor = torch.tensor([[7., 4., 2.], [5., 1., 6.]])
torch.repeat_interleave(input=my_tensor, repeats=1)
# tensor([7., 4., 2., 5., 1., 6.])
my_tensor = torch.tensor([[7.+0.j, 4.+0.j, 2.+0.j], [5.+0.j, 1.+0.j, 6.+0.j]])
torch.repeat_interleave(input=my_tensor, repeats=1)
# tensor([7.+0.j, 4.+0.j, 2.+0.j, 5.+0.j, 1.+0.j, 6.+0.j])
my_tensor = torch.tensor([[True, False, True], [False, True, False]])
torch.repeat_interleave(input=my_tensor, repeats=1)
# tensor([True, False, True, False, True, False])
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