all and any in PyTorch

#python #pytorch #all #any

all() can check if all the elements of a 0D or more D tensor are True, getting the 0D or more D tensor of zero or more elements as shown below:

*Memos:

all() 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).
The 2nd argument with torch or the 1st argument with a tensor is dim(Optional-Type:int, tuple of int or list of int).
The 3rd argument with torch or the 2nd argument with a tensor is keepdim(Optional-Default:False-Type:bool). *My post explains keepdim argument.
There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
- out= must be used.
- My post explains out argument.
An empty tensor returns a True of a 1D or more D tensor or an empty 1D or more D tensor.

import torch

my_tensor = torch.tensor(True)

torch.all(input=my_tensor)
my_tensor.all()
torch.all(input=my_tensor, dim=0)
torch.all(input=my_tensor, dim=-1)
torch.all(input=my_tensor, dim=(0,))
torch.all(input=my_tensor, dim=(-1,))
# tensor(True)

my_tensor = torch.tensor([True, False, True, False])

torch.all(input=my_tensor)
torch.all(input=my_tensor, dim=0)
torch.all(input=my_tensor, dim=-1)
torch.all(input=my_tensor, dim=(0,))
torch.all(input=my_tensor, dim=(-1,))
# tensor(False)

my_tensor = torch.tensor([[True, False, True, False],
                          [True, False, True, False]])
torch.all(input=my_tensor)
torch.all(input=my_tensor, dim=(0, 1))
torch.all(input=my_tensor, dim=(0, -1))
torch.all(input=my_tensor, dim=(1, 0))
torch.all(input=my_tensor, dim=(1, -2))
torch.all(input=my_tensor, dim=(-1, 0))
torch.all(input=my_tensor, dim=(-1, -2))
torch.all(input=my_tensor, dim=(-2, 1))
torch.all(input=my_tensor, dim=(-2, -1))
# tensor(False)

torch.all(input=my_tensor, dim=0)
torch.all(input=my_tensor, dim=(0,))
torch.all(input=my_tensor, dim=-2)
# tensor([True, False, True, False])

torch.all(input=my_tensor, dim=1)
torch.all(input=my_tensor, dim=-1)
torch.all(input=my_tensor, dim=(-1,))
# tensor([False, False])

my_tensor = torch.tensor([[0, 1, 2, 3],
                          [4, 5, 6, 7]])
torch.all(input=my_tensor)
# tensor(False)

my_tensor = torch.tensor([[0., 1., 2., 3.],
                          [4., 5., 6., 7.]])
torch.all(input=my_tensor)
# tensor(False)

my_tensor = torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j],
                          [4.+0.j, 5.+0.j, 6.+0.j, 7.+0.j]])
torch.all(input=my_tensor)
# tensor(False)

my_tensor = torch.tensor([[]])

torch.all(input=my_tensor)
# tensor(True)

torch.all(input=my_tensor, dim=0)
torch.all(input=my_tensor, dim=-2)
# tensor([], dtype=torch.bool)

torch.all(input=my_tensor, dim=1)
torch.all(input=my_tensor, dim=-1)
# tensor([True])

any() can check if any elements of a 0D or more D tensor are True, getting the 0D or more D tensor of zero or more elements as shown below:

*Memos:

any() 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).
The 2nd argument with torch or the 1st argument with a tensor is dim(Optional-Type:int, tuple of int or list of int).
The 3rd argument with torch or the 2nd argument with a tensor is keepdim(Optional-Default:False-Type:bool). *My post explains keepdim argument.
There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
- out= must be used.
- My post explains out argument.
An empty tensor returns a False of a 1D or more D tensor or an empty 1D or more D tensor.

import torch

my_tensor = torch.tensor(True)

torch.any(input=my_tensor)
my_tensor.any()
torch.any(input=my_tensor, dim=0)
torch.any(input=my_tensor, dim=-1)
torch.any(input=my_tensor, dim=(0,))
torch.any(input=my_tensor, dim=(-1,))
# tensor(True)

torch.any(input=my_tensor, dim=0, keepdim=True)
# tensor(True)

my_tensor = torch.tensor([True, False, True, False])

torch.any(input=my_tensor)
torch.any(input=my_tensor, dim=0)
torch.any(input=my_tensor, dim=-1)
torch.any(input=my_tensor, dim=(0,))
torch.any(input=my_tensor, dim=(-1,))
# tensor(True)

torch.any(input=my_tensor, dim=0, keepdim=True)
# tensor([True])

my_tensor = torch.tensor([[True, False, True, False],
                          [True, False, True, False]])
torch.any(input=my_tensor)
torch.any(input=my_tensor, dim=(0, 1))
torch.any(input=my_tensor, dim=(0, -1))
torch.any(input=my_tensor, dim=(1, 0))
torch.any(input=my_tensor, dim=(1, -2))
torch.any(input=my_tensor, dim=(-1, 0))
torch.any(input=my_tensor, dim=(-1, -2))
torch.any(input=my_tensor, dim=(-2, 1))
torch.any(input=my_tensor, dim=(-2, -1))
# tensor(True)

torch.any(input=my_tensor, dim=0)
torch.any(input=my_tensor, dim=(0,))
torch.any(input=my_tensor, dim=-2)
# tensor([True, False, True, False])

torch.any(input=my_tensor, dim=1)
torch.any(input=my_tensor, dim=-1)
torch.any(input=my_tensor, dim=(-1,))
# tensor([True, True])

torch.any(input=my_tensor, dim=0, keepdim=True)
# tensor([[True, False, True, False]])

my_tensor = torch.tensor([[0, 1, 2, 3],
                          [4, 5, 6, 7]])
torch.any(input=my_tensor)
# tensor(True)

my_tensor = torch.tensor([[0., 1., 2., 3.],
                          [4., 5., 6., 7.]])
torch.any(input=my_tensor)
# tensor(True)

my_tensor = torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j],
                          [4.+0.j, 5.+0.j, 6.+0.j, 7.+0.j]])
torch.any(input=my_tensor)
# tensor(True)

my_tensor = torch.tensor([[]])

torch.any(input=my_tensor)
# tensor(False)

torch.any(input=my_tensor, dim=0)
torch.any(input=my_tensor, dim=-2)
# tensor([], dtype=torch.bool)

torch.any(input=my_tensor, dim=1)
torch.any(input=my_tensor, dim=-1)
# tensor([False])

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all and any in PyTorch

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