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

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isinf, isposinf and isneginf in PyTorch

#python #pytorch #isinf #isposinf

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*Memos:

isinf() can check if the zero or more elements of a 0D or more D tensor are infinity, getting the 0D or more D tensor of zero or more boolean values as shown below:

*Memos:

  • isinf() 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([8, 5., torch.nan, torch.inf, 3.+0.j, 3.+7.j, complex(torch.nan, torch.inf), True]) torch.isinf(input=my_tensor) my_tensor.isinf() # tensor([False, False, False, True, False, False, True, False])  my_tensor = torch.tensor([[8, 5., torch.nan, torch.inf], [3.+0.j, 3.+7.j, complex(torch.nan, torch.inf), True]]) torch.isinf(input=my_tensor) # tensor([[False, False, False, True], # [False, False, True, False]])  my_tensor = torch.tensor([[[8, 5.], [torch.nan, torch.inf]], [[3.+0.j, 3.+7.j], [complex(torch.nan, torch.inf), True]]]) torch.isinf(input=my_tensor) # tensor([[[False, False], [False, True]], # [[False, False], [True, False]]])
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isposinf() can if check the zero or more elements of a 0D or more D tensor are positive infinity, getting the 0D or more D tensor of zero or more boolean values as shown below:

*Memos:

  • isposinf() can be used with torch or a tensor.
  • The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float or bool).
  • 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([8, 5., torch.nan, torch.inf, 3., 3.7, -torch.inf, True]) torch.isposinf(input=my_tensor) my_tensor.isposinf() # tensor([False, False, False, True, False, False, False, False])  my_tensor = torch.tensor([[8, 5., torch.nan, torch.inf], [3., 3.7, -torch.inf, True]]) torch.isposinf(input=my_tensor) # tensor([[False, False, False, True], # [False, False, False, False]])  my_tensor = torch.tensor([[[8, 5.], [torch.nan, torch.inf]], [[3., 3.7], [-torch.inf, True]]]) torch.isposinf(input=my_tensor) # tensor([[[False, False], [False, True]], # [[False, False], [False, False]]])
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isneginf() can if check the zero or more elements of a 0D or more D tensor are negative infinity, getting the 0D or more D tensor of zero or more boolean values as shown below:

*Memos:

  • isneginf() can be used with torch or a tensor.
  • The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float or bool).
  • 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([8, 5., torch.nan, torch.inf, 3., 3.7, -torch.inf, True]) torch.isneginf(input=my_tensor) my_tensor.isneginf() # tensor([False, False, False, False, False, False, True, False])  my_tensor = torch.tensor([[8, 5., torch.nan, torch.inf], [3., 3.7, -torch.inf, True]]) torch.isneginf(input=my_tensor) # tensor([[False, False, False, False], # [False, False, True, False]])  my_tensor = torch.tensor([[[8, 5.], [torch.nan, torch.inf]], [[3., 3.7], [-torch.inf, True]]]) torch.isneginf(input=my_tensor) # tensor([[[False, False], [False, False]], # [[False, False], [True, False]]])
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