full and full_like in PyTorch

#python #pytorch #full #fulllike

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:
- If it's None, it's inferred from fill_value, then for floating-point numbers, get_default_dtype() is used. *My post explains get_default_dtype() and set_default_dtype().
- 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, get_default_device() is used. *My post explains get_default_device() and set_default_device().
- device= must be used.
- My post explains device argument.
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]]])

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]]])

DEV Community

full and full_like in PyTorch

Top comments (0)

Read next

Automating Server Health Checks with Python, Bash, and PowerShell

How to Automate Everyday Tasks with Python

Automating Faceless Shorts Videos for YouTube and TikTok Using OpenAI and ElevenLabs

Introducing JDK 23: Java Developer's Ultimate Upgrade with Advanced AI Enhancements