*Memos:
- My post explains select().
- My post explains masked_select().
index_select() can get the 0D or more D tensor of the zero or more elements selected with zero or more indices, not removing one dimension from the 0D or more D tensor of zero or more elements as shown below:
*Memos:regularization
-
index_select()can be used with torch or a tensor. - The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,float,complexorbool). *It must be the 0D or more D tensor of zero or more elements. - The 2nd argument with
torchor the 1st argument with a tensor isdim(Required-Type:int). - The 3rd argument with
torchor the 2nd argument with a tensor isindex(Required-Type:tensorofint). *It must be the 0D or 1D tensor of zero or more integers. - There is
outargument withtorch(Optional-Default:None-Type:tensor): *Memos:-
out=must be used. - My post explains
outargument.
-
import torch my_tensor = torch.tensor([8, -3, 0, 1, 5, -2, -1, 4]) torch.index_select(input=my_tensor, dim=0, index=torch.tensor(4)) my_tensor.index_select(dim=0, index=torch.tensor(4)) torch.index_select(input=my_tensor, dim=-1, index=torch.tensor(4)) # tensor([5]) torch.index_select(input=my_tensor, dim=0, index=torch.tensor([5, 2, 0, 7])) torch.index_select(input=my_tensor, dim=-1, index=torch.tensor([5, 2, 0, 7])) # tensor([-2, 0, 8, 4]) my_tensor = torch.tensor([[8, -3, 0, 1], [5, -2, -1, 4]]) torch.index_select(input=my_tensor, dim=0, index=torch.tensor(1)) torch.index_select(input=my_tensor, dim=0, index=torch.tensor([1])) torch.index_select(input=my_tensor, dim=-2, index=torch.tensor(1)) torch.index_select(input=my_tensor, dim=-2, index=torch.tensor([1])) # tensor([[5, -2, -1, 4]]) torch.index_select(input=my_tensor, dim=0, index=torch.tensor([1, 0, 0, 1])) torch.index_select(input=my_tensor, dim=-2, index=torch.tensor([1, 0, 0, 1])) # tensor([[5, -2, -1, 4], # [8, -3, 0, 1], # [8, -3, 0, 1], # [5, -2, -1, 4]]) torch.index_select(input=my_tensor, dim=1, index=torch.tensor([3, 1, 2])) torch.index_select(input=my_tensor, dim=-1, index=torch.tensor([3, 1, 2])) # tensor([[1, -3, 0], # [4, -2, -1]]) my_tensor = torch.tensor([[[8, -3], [0, 1]], [[5, -2], [-1, 4]]]) torch.index_select(input=my_tensor, dim=2, index=torch.tensor(1)) torch.index_select(input=my_tensor, dim=2, index=torch.tensor([1])) torch.index_select(input=my_tensor, dim=-1, index=torch.tensor(1)) torch.index_select(input=my_tensor, dim=-1, index=torch.tensor([1])) # tensor([[[-3], [1]], # [[-2], [4]]]) my_tensor = torch.tensor([[[8., -3.], [0., 1.]], [[5., -2.], [-1., 4.]]]) torch.index_select(input=my_tensor, dim=2, index=torch.tensor(1)) # tensor([[[-3.], [1.]], # [[-2.], [4.]]]) my_tensor = torch.tensor([[[8.+0.j, -3.+0.j], [0.+0.j, 1.+0.j]], [[5.+0.j, -2.+0.j], [-1.+0.j, 4.+0.j]]]) torch.index_select(input=my_tensor, dim=2, index=torch.tensor(1)) # tensor([[[-3.+0.j], [1.+0.j]], # [[-2.+0.j], [4.+0.j]]]) my_tensor = torch.tensor([[[True, False], [True, False]], [[False, True], [False, True]]]) torch.index_select(input=my_tensor, dim=2, index=torch.tensor(1)) # tensor([[[False], [False]], # [[True], [True]]]) 
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