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

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full and full_like in PyTorch

#python #pytorch #full #fulllike

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full() can create the 0D or more D tensor of zero or more elements as shown below:

*Memos:

  • full() can be used with torch but not with a tensor.
  • The 1st argument is size(Required-Type:tuple of int, list of int or size()).
  • The 2nd argument with torch is fill_value(Required-Type:int, float, complex or bool). *The 0D tensor of an element(int, float, complex or bool) also works.
  • There is dtype argument with torch(Optional-Default:None-Type:dtype): *Memos:
  • There is device argument with torch(Optional-Default:None-Type:str, int or device()): *Memos:
  • There is requires_grad argument with torch(Optional-Default:False-Type:bool): *Memos:
    • requires_grad= must be used.
    • My post explains requires_grad argument.
  • There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
    • out= must be used.
    • My post explains out argument. 
import torch torch.full(size=(), fill_value=5) torch.full(size=(), fill_value=torch.tensor(5)) torch.full(size=torch.tensor(8).size(), fill_value=5) # tensor(5)  torch.full(size=(3,), fill_value=5) torch.full(size=(3,), fill_value=torch.tensor(5)) torch.full(size=torch.tensor([8, 3, 6]).size(), fill_value=5) # tensor([5, 5, 5])  torch.full(size=(3, 2), fill_value=5) torch.full(size=torch.tensor([[8, 3], [6, 0], [2, 9]]).size(), fill_value=5) # tensor([[5, 5], [5, 5], [5, 5]])  torch.full(size=(3, 2, 4), fill_value=5) # tensor([[[5, 5, 5, 5], [5, 5, 5, 5]], # [[5, 5, 5, 5], [5, 5, 5, 5]], # [[5, 5, 5, 5], [5, 5, 5, 5]]])  torch.full(size=(3, 2, 4), fill_value=5.) # tensor([[[5., 5., 5., 5.], [5., 5., 5., 5.]], # [[5., 5., 5., 5.], [5., 5., 5., 5.]], # [[5., 5., 5., 5.], [5., 5., 5., 5.]]])  torch.full(size=(3, 2, 4), fill_value=5.+6.j) # tensor([[[5.+6.j, 5.+6.j, 5.+6.j, 5.+6.j], # [5.+6.j, 5.+6.j, 5.+6.j, 5.+6.j]], # [[5.+6.j, 5.+6.j, 5.+6.j, 5.+6.j], # [5.+6.j, 5.+6.j, 5.+6.j, 5.+6.j]], # [[5.+6.j, 5.+6.j, 5.+6.j, 5.+6.j], # [5.+6.j, 5.+6.j, 5.+6.j, 5.+6.j]]])  torch.full(size=(3, 2, 4), fill_value=True) # tensor([[[True, True, True, True], # [True, True, True, True]], # [[True, True, True, True], # [True, True, True, True]], # [[True, True, True, True], # [True, True, True, True]]])
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full_like() can get the 0D or more D tensor of the zero or more elements replaced with zero or more elements as shown below:

*Memos:

  • full_like() can be used with torch but not with a tensor.
  • The 1st argument with torch is input(Required-Type:tensor of int, float, complex or bool).
  • The 2nd argument with torch is fill_value(Required-Type:int, float, complex or bool). *The 0D tensor of an element(int, float, complex or bool) also works.
  • There is dtype argument with torch(Optional-Default:None-Type:dtype): *Memos:
    • If it's None, it's inferred from input.
    • dtype= must be used.
    • My post explains dtype argument.
  • There is device argument with torch(Optional-Default:None-Type:str, int or device()): *Memos:
    • If it's None, it's inferred from input.
    • device= must be used.
    • My post explains device argument. 
import torch my_tensor = torch.tensor(7) torch.full_like(input=my_tensor, fill_value=5) torch.full_like(input=my_tensor, fill_value=torch.tensor(5)) # tensor(5)  my_tensor = torch.tensor([7, 4, 5]) torch.full_like(input=my_tensor, fill_value=5) # tensor([5, 5, 5])  my_tensor = torch.tensor([[7, 4, 5], [2, 8, 3]]) torch.full_like(input=my_tensor, fill_value=5) # tensor([[5, 5, 5], [5, 5, 5]])  my_tensor = torch.tensor([[[7, 4, 5], [2, 8, 3]], [[6, 0, 1], [5, 9, 4]]]) torch.full_like(input=my_tensor, fill_value=5) # tensor([[[5, 5, 5], [5, 5, 5]], # [[5, 5, 5], [5, 5, 5]]])  my_tensor = torch.tensor([[[7., 4., 5.], [2., 8., 3.]], [[6., 0., 1.], [5., 9., 4.]]]) torch.full_like(input=my_tensor, fill_value=5.) # tensor([[[5., 5., 5.], [5., 5., 5.]], # [[5., 5., 5.], [5., 5., 5.]]])  my_tensor = torch.tensor([[[7.+4.j, 4.+2.j, 5.+3.j], [2.+5.j, 8.+1.j, 3.+9.j]], [[6.+9.j, 0.+3.j, 1.+8.j], [5.+3.j, 9.+4.j, 4.+6.j]]]) torch.full_like(input=my_tensor, fill_value=5.+3.j) # tensor([[[5.+3.j, 5.+3.j, 5.+3.j], # [5.+3.j, 5.+3.j, 5.+3.j]], # [[5.+3.j, 5.+3.j, 5.+3.j], # [5.+3.j, 5.+3.j, 5.+3.j]]])  my_tensor = torch.tensor([[[True, False, True], [False, True, False]], [[True, False, True], [False, True, False]]]) torch.full_like(input=my_tensor, fill_value=True) # tensor([[[True, True, True], # [True, True, True]], # [[True, True, True], # [True, True, True]]])
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