minimum and maximum in PyTorch

#python #pytorch #minimum #maximum

*Memos:

My post explains min() and max().
My post explains fmin() and fmax().
My post explains argmin() and argmax().
My post explains aminmax(), amin() and amax().
My post explains kthvalue() and topk().
My post explains cummin() and cummax().

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

*Memos:

minimum() 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 is other(Required-Type:tensor of int, float or bool).
There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
- out= must be used.
- My post explains out argument.
nan is taken if there are a number and nan.

import torch

tensor1 = torch.tensor([5., float('nan'), 4., float('nan')])
tensor2 = torch.tensor([[7., 8., float('nan'), float('nan')],
                        [-9., 2., 0., -6.]])
torch.minimum(input=tensor1, other=tensor2)
tensor1.minimum(other=tensor2)
# tensor([[5., nan, nan, nan],
#         [-9., nan, 0., nan]])

tensor1 = torch.tensor(5.)
tensor2 = torch.tensor([[[7., 8.], [float('nan'), float('nan')]],
                        [[-9., 2.], [0., -6.]]])
torch.minimum(input=tensor1, other=tensor2)
# tensor([[[5., 5.], [nan, nan]],
#         [[-9., 2.], [0., -6.]]])

tensor1 = torch.tensor(5)
tensor2 = torch.tensor([[[7, 8], [-5, -1]],
                        [[-9, 2], [0, -6]]])
torch.minimum(input=tensor1, other=tensor2)
# tensor([[[5, 5], [-5, -1]],
#         [[-9, 2], [0, -6]]])

tensor1 = torch.tensor(True)
tensor2 = torch.tensor([[[True, False], [True, False]],
                        [[False, True], [False, True]]])
torch.minimum(input=tensor1, other=tensor2)
# tensor([[[True, False], [True, False]],
#         [[False, True], [False, True]]])

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

*Memos:

maximum() 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 is other(Required-Type:tensor of int, float or bool).
There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
- out= must be used.
- My post explains out argument.
nan is taken if there are a number and nan.

import torch

tensor1 = torch.tensor([5., float('nan'), 4., float('nan')])
tensor2 = torch.tensor([[7., 8., float('nan'), float('nan')],
                        [-9., 2., 0., -6.]])
torch.maximum(input=tensor1, other=tensor2)
tensor1.maximum(other=tensor2)
# tensor([[7., nan, nan, nan],
#         [5., nan, 4., nan]])

tensor1 = torch.tensor(5.)
tensor2 = torch.tensor([[[7., 8.], [float('nan'), float('nan')]],
                        [[-9., 2.], [0., -6.]]])
torch.maximum(input=tensor1, other=tensor2)
# tensor([[[7., 8.], [nan, nan]],
#         [[5., 5.], [5., 5.]]])

tensor1 = torch.tensor(5)
tensor2 = torch.tensor([[[7, 8], [-5, -1]],
                        [[-9, 2], [0, -6]]])
torch.maximum(input=tensor1, other=tensor2)
# tensor([[[7, 8], [5, 5]],
#         [[5, 5], [5, 5]]])

tensor1 = torch.tensor(True)
tensor2 = torch.tensor([[[True, False], [True, False]],
                        [[False, True], [False, True]]])
torch.maximum(input=tensor1, other=tensor2)
# tensor([[[True, True], [True, True]],
#         [[True, True], [True, True]]])