all() and any() in PyTorch

#pytorch #all #any #function

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

*Memos:

all() can be used with torch or a tensor.
The tensor of zero or more integers, floating-point numbers, complex numbers or boolean values can be used.
The 2nd argument(int, tuple of int or list of int) with torch or the 1st argument(int, tuple of int or list of int) with a tensor is dim(Optional).
The 3rd argument(bool) with torch or the 2nd argument(bool) with a tensor is keepdim(Optional-Default:False) which keeps the dimension of the input tensor.
An empty tensor returns True of a 1D or more D tensor or an empty 1D or more D tensor.

import torch

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

torch.all(my_tensor, dim=0)
torch.all(my_tensor, dim=-3)
torch.all(my_tensor, dim=(0,))
torch.all(my_tensor, dim=(-3,))
# tensor([[True, True, True],
#         [False, True, True]])

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

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

torch.all(my_tensor, dim=(0, 2))
torch.all(my_tensor, dim=(0, -1))
torch.all(my_tensor, dim=(2, 0))
torch.all(my_tensor, dim=(2, -3))
torch.all(my_tensor, dim=(-1, 0))
torch.all(my_tensor, dim=(-1, -3))
torch.all(my_tensor, dim=(-3, 2))
torch.all(my_tensor, dim=(-3, -1))
# tensor([True, False])

torch.all(my_tensor, dim=(0, 1))
torch.all(my_tensor, dim=(0, -2))
torch.all(my_tensor, dim=(1, 0))
torch.all(my_tensor, dim=(1, -3))
torch.all(my_tensor, dim=(-2, 0))
torch.all(my_tensor, dim=(-2, -3))
torch.all(my_tensor, dim=(-3, 1))
torch.all(my_tensor, dim=(-3, -2))
# tensor([False, True, True])

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

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

any() can check if any element of a 0D or more D tensor is True as shown below:

*Memos:

any() can be used with torch or a tensor.
The tensor of zero or more integers, floating-point numbers, complex numbers or boolean values can be used.
The 2nd argument(int, tuple of int or list of int) with torch or the 1st argument(int, tuple of int or list of int) with a tensor is dim(Optional).
The 3rd argument(bool) with torch or the 2nd argument(bool) with a tensor is keepdim(Optional-Default:False) which keeps the dimension of the input tensor.
An empty tensor returns False of a 1D or more D tensor or an empty 1D or more D tensor.

import torch

my_tensor = torch.tensor([[[False, False, False],
                           [0, 0, 0]],
                          [[2, 2, 2],
                           [0+7j, 0+3j, 0+0j]]])
torch.any(my_tensor)
my_tensor.any()
# tensor(True)

torch.any(my_tensor, dim=0)
torch.any(my_tensor, dim=-3)
torch.any(my_tensor, dim=(0,))
torch.any(my_tensor, dim=(-3,))
torch.any(my_tensor, dim=(0, 1))
# tensor([[True, True, True],
#         [False, False, False]])

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

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

torch.any(my_tensor, dim=(0, 2))
torch.any(my_tensor, dim=(0, -1))
torch.any(my_tensor, dim=(2, 0))
torch.any(my_tensor, dim=(2, -3))
torch.any(my_tensor, dim=(-1, 0))
torch.any(my_tensor, dim=(-1, -3))
torch.any(my_tensor, dim=(-3, 2))
torch.any(my_tensor, dim=(-3, -1))
# tensor([True, False])

torch.any(my_tensor, dim=(0, 1))
torch.any(my_tensor, dim=(0, -2))
torch.any(my_tensor, dim=(1, 0))
torch.any(my_tensor, dim=(1, -3))
torch.any(my_tensor, dim=(-2, 0))
torch.any(my_tensor, dim=(-2, -3))
torch.any(my_tensor, dim=(-3, 1))
torch.any(my_tensor, dim=(-3, -2))
# tensor([True, True, True])

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

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

DEV Community

all() and any() in PyTorch

Top comments (0)

Read next

Why Should You Care About Minimal Reproducible Examples (and how to create one)

Exploring the Power of Union and Intersection Types in TypeScript

The Hungary-Serbia railway project is a landmark project of China-Hungary jointly building the “Belt and Road”

Interview with Kareem Ebrahim, creator of TableX