Name: aminmax(), amin() and amax() in PyTorch
Rating: 1.1 (1614 reviews)
Author: hyperkai

*Memos:

My post explains min() and max().
My post explains minimum() and maximum().
My post explains fmin() and fmax().
My post explains argmin() and argmax().
My post explains kthvalue() and topk().
My post explains cummin() and cummax().

aminmax() can get two of the 0D or more D tensors of zero or more minimum and maximum elements from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

aminmax() 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 dim argument with torch or a tensor(Optional-Type:int): *Memos:
- Setting dim can get zero or more 1st minimum and maximum elements.
- You must use dim=.
There is keepdim argument with torch or a tensor(Optional-Default:False-Type:bool): *Memos:
- You must use keepdim=.
- My post explains keepdim argument.
There is out argument with torch(Optional-Default:None-Type:tuple(tensor, tensor) or list(tensor, tensor)): *Memos:
- out= must be used.
- My post explains out argument.
The 0D tensor of one complex number with dim=0 or dim=-1 works.
An empty 2D or more D input tensor or tensor doesn't work if not setting dim.
An empty 1D input tensor or tensor doesn't work even if setting dim.

import torch

my_tensor = torch.tensor([[5, 4, 7, 7],
                          [6, 5, 3, 5],
                          [3, 8, 9, 3]])
torch.aminmax(input=my_tensor)
my_tensor.aminmax()
# torch.return_types.aminmax(
# min=tensor(3),
# max=tensor(9))

torch.aminmax(input=my_tensor, dim=0)
torch.aminmax(input=my_tensor, dim=-2)
# torch.return_types.aminmax(
# min=tensor([3, 4, 3, 3]),
# max=tensor([6, 8, 9, 7]))

torch.aminmax(input=my_tensor, dim=1)
torch.aminmax(input=my_tensor, dim=-1)
# torch.return_types.aminmax(
# min=tensor([4, 3, 3]),
# max=tensor([7, 6, 9]))

my_tensor = torch.tensor([[5., 4., 7., 7.],
                          [6., 5., 3., 5.],
                          [3., 8., 9., 3.]])
torch.aminmax(input=my_tensor)
# torch.return_types.aminmax(
# min=tensor(3.),
# max=tensor(9.))

my_tensor = torch.tensor([[True, False, True, False],
                          [False, True, False, True],
                          [True, False, True, False]])
torch.aminmax(input=my_tensor)
# torch.return_types.aminmax(
# min=tensor(False),
# max=tensor(True))

my_tensor = torch.tensor(5.+7.j)

torch.aminmax(input=my_tensor, dim=0)
torch.aminmax(input=my_tensor, dim=-1)
# torch.return_types.aminmax(
# min=tensor(5.+7.j),
# max=tensor(5.+7.j))

my_tensor = torch.tensor([])
my_tensor = torch.tensor([[]])
my_tensor = torch.tensor([[[]]])

torch.aminmax(input=my_tensor) # Error

my_tensor = torch.tensor([])

torch.aminmax(input=my_tensor, dim=0) # Error

my_tensor = torch.tensor([[]])

torch.aminmax(input=my_tensor, dim=0)
# torch.return_types.aminmax(
# min=tensor([]),
# max=tensor([]))

my_tensor = torch.tensor([[[]]])

torch.aminmax(input=my_tensor, dim=0)
# torch.return_types.aminmax(
# min=tensor([], size=(1, 0)),
# max=tensor([], size=(1, 0)))

amin() can get the 0D or more D tensor of zero or more minimum elements from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

amin() 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).
The 2nd argument with torch or the 1st argument with a tensor is dim(Optional-Type:int, tuple of int or list of int). *Setting dim can get zero or more 1st minimum elements.
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 2D or more D input tensor or tensor doesn't work if not setting dim.
An empty 1D input tensor or tensor doesn't work even if setting dim.

import torch

my_tensor = torch.tensor([[5, 4, 7, 7],
                          [6, 5, 3, 5],
                          [3, 8, 9, 3]])
torch.amin(input=my_tensor)
my_tensor.amin()
torch.amin(input=my_tensor, dim=(0, 1))
torch.amin(input=my_tensor, dim=(0, -1))
torch.amin(input=my_tensor, dim=(1, 0))
torch.amin(input=my_tensor, dim=(1, -2))
torch.amin(input=my_tensor, dim=(-1, 0))
torch.amin(input=my_tensor, dim=(-1, -2))
torch.amin(input=my_tensor, dim=(-2, 1))
torch.amin(input=my_tensor, dim=(-2, -1))
# tensor(3)

torch.amin(input=my_tensor, dim=0)
torch.amin(input=my_tensor, dim=-2)
torch.amin(input=my_tensor, dim=(0,))
torch.amin(input=my_tensor, dim=(-2,))
# tensor([3, 4, 3, 3])

torch.amin(input=my_tensor, dim=1)
torch.amin(input=my_tensor, dim=-1)
torch.amin(input=my_tensor, dim=(1,))
torch.amin(input=my_tensor, dim=(-1,))
# tensor([4, 3, 3])

my_tensor = torch.tensor([[5., 4., 7., 7.],
                          [6., 5., 3., 5.],
                          [3., 8., 9., 3.]])
torch.amin(input=my_tensor)
# tensor(3.)

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

my_tensor = torch.tensor([])
my_tensor = torch.tensor([[]])
my_tensor = torch.tensor([[[]]])

torch.amin(input=my_tensor) # Error

my_tensor = torch.tensor([])

torch.amin(input=my_tensor, dim=0) # Error

my_tensor = torch.tensor([[]])

torch.amin(input=my_tensor, dim=0)
# tensor([])

my_tensor = torch.tensor([[[]]])

torch.amin(input=my_tensor, dim=0)
# tensor([], size=(1, 0))

amax() can get the 0D or more D tensor of zero or more maximum elements from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

amax() 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).
The 2nd argument with torch or the 1st argument with a tensor is dim(Optional-Type:int, tuple of int or list of int). *Setting dim can get zero or more 1st maximum elements.
The 3rd argument with torch or the 2nd argument 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 2D or more D input tensor or tensor doesn't work if not setting dim.
An empty 1D input tensor or tensor doesn't work even if setting dim.

import torch

my_tensor = torch.tensor([[5, 4, 7, 7],
                          [6, 5, 3, 5],
                          [3, 8, 9, 3]])
torch.amax(input=my_tensor)
my_tensor.amax()
torch.amax(input=my_tensor, dim=(0, 1))
torch.amax(input=my_tensor, dim=(0, -1))
torch.amax(input=my_tensor, dim=(1, 0))
torch.amax(input=my_tensor, dim=(1, -2))
torch.amax(input=my_tensor, dim=(-1, 0))
torch.amax(input=my_tensor, dim=(-1, -2))
torch.amax(input=my_tensor, dim=(-2, 1))
torch.amax(input=my_tensor, dim=(-2, -1))
# tensor(9)

torch.amax(input=my_tensor, dim=0)
torch.amax(input=my_tensor, dim=-2)
torch.amax(input=my_tensor, dim=(0,))
torch.amax(input=my_tensor, dim=(-2,))
# tensor([6, 8, 9, 7])

torch.amax(input=my_tensor, dim=1)
torch.amax(input=my_tensor, dim=-1)
torch.amax(input=my_tensor, dim=(1,))
torch.amax(input=my_tensor, dim=(-1,))
# tensor([7, 6, 9])

my_tensor = torch.tensor([[5., 4., 7., 7.],
                          [6., 5., 3., 5.],
                          [3., 8., 9., 3.]])
torch.amax(input=my_tensor)
# tensor(9.)

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

my_tensor = torch.tensor([])
my_tensor = torch.tensor([[]])
my_tensor = torch.tensor([[[]]])

torch.amax(input=my_tensor) # Error

my_tensor = torch.tensor([])

torch.amax(input=my_tensor, dim=0) # Error

my_tensor = torch.tensor([[]])

torch.amax(input=my_tensor, dim=0)
# tensor([])

my_tensor = torch.tensor([[[]]])

torch.amax(input=my_tensor, dim=0)
# tensor([], size=(1, 0))