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Super Kai (Kazuya Ito)
Super Kai (Kazuya Ito)

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argwhere and nonzero in PyTorch

#python #pytorch #argwhere #nonzero

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*Memos:

argwhere() can get the 2D tensor of the zero or more indices of non-zero elements from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

  • argwhere() can be used with torch or a tensor.
  • The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float, complex or bool). 
import torch my_tensor = torch.tensor(5) torch.argwhere(input=my_tensor) my_tensor.argwhere() # tensor([], size=(1, 0), dtype=torch.int64)  my_tensor = torch.tensor([5, 0, 4, 0, 3, 1]) torch.argwhere(input=my_tensor) # tensor([[0], [2], [4], [5]])  my_tensor = torch.tensor([5., 0., 4., 0., 3., 1]) torch.argwhere(input=my_tensor) # tensor([[0], [2], [4], [5]])  my_tensor = torch.tensor([5.+0.j, 0.+0.j, 4.+0.j, 0.+0.j, 3.+0.j, 1.+0.j]) torch.argwhere(input=my_tensor) # tensor([[0], [2], [4], [5]])  my_tensor = torch.tensor([True, False, True, False, True, False]) torch.argwhere(input=my_tensor) # tensor([[0], [2], [4]])  my_tensor = torch.tensor([[5, 0, 4], [0, 3, 1]]) torch.argwhere(input=my_tensor) # tensor([[0, 0], [0, 2], [1, 1], [1, 2]])  my_tensor = torch.tensor([[[5, 0, 4], [0, 3, 1]], [[0, 7, 0], [0, 6, 8]]]) torch.argwhere(input=my_tensor) # tensor([[0, 0, 0], # [0, 0, 2], # [0, 1, 1], # [0, 1, 2], # [1, 0, 1], # [1, 1, 1], # [1, 1, 2]])
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nonzero() can get the 2D tensor of the zero or more indices of non-zero elements or the one or more 1D tensors of the zero or more indices of non-zero elements from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

  • nonzero() can be used with torch or a tensor.
  • The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float, complex or bool).
  • There is as_tuple argument with torch or a tensor(Optional-Default:False-Type:bool): *Memos:
    • If as_tuple is True, the tuple of zero or more 1D tensors is returned.
  • There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
    • out= must be used.
    • My post explains out argument. 
import torch my_tensor = torch.tensor(5) torch.nonzero(input=my_tensor) my_tensor.nonzero() # tensor([], size=(1, 0), dtype=torch.int64)  torch.nonzero(input=my_tensor, as_tuple=True) # (tensor([0]),)  my_tensor = torch.tensor([5, 0, 4, 0, 3, 1]) torch.nonzero(input=my_tensor) my_tensor.nonzero() # tensor([[0], [2], [4], [5]])  torch.nonzero(input=my_tensor, as_tuple=True) # (tensor([0, 2, 4, 5]),)  my_tensor = torch.tensor([5., 0., 4., 0., 3., 1]) torch.nonzero(input=my_tensor) # tensor([[0], [2], [4], [5]])  my_tensor = torch.tensor([5.+0.j, 0.+0.j, 4.+0.j, 0.+0.j, 3.+0.j, 1.+0.j]) torch.nonzero(input=my_tensor) # tensor([[0], [2], [4], [5]])  my_tensor = torch.tensor([True, False, True, False, True, False]) torch.nonzero(input=my_tensor) # tensor([[0], [2], [4]])  my_tensor = torch.tensor([[5, 0, 4], [0, 3, 1]]) torch.nonzero(input=my_tensor) # tensor([[0, 0], [0, 2], [1, 1], [1, 2]])  torch.nonzero(input=my_tensor, as_tuple=True) # (tensor([0, 0, 1, 1]), # tensor([0, 2, 1, 2]))  my_tensor = torch.tensor([[[5, 0, 4], [0, 3, 1]], [[0, 7, 0], [0, 6, 8]]]) torch.nonzero(input=my_tensor) # tensor([[0, 0, 0], # [0, 0, 2], # [0, 1, 1], # [0, 1, 2], # [1, 0, 1], # [1, 1, 1], # [1, 1, 2]])  torch.nonzero(input=my_tensor, as_tuple=True) # (tensor([0, 0, 0, 0, 1, 1, 1]), # tensor([0, 0, 1, 1, 0, 1, 1]), # tensor([0, 2, 1, 2, 1, 1, 2]))
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