Set out with out argument functions PyTorch

This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)

You can set out with the functions which have out argument as shown below:

*Memos:

I selected some popular keepdim argument functions such as arange(), rand() add(), mean(), median(), min(), max(), all(), any() and matmul().
out(Optional, tensor) can output a tensor. *Sometimes, out(Optional, tuple(tensor, tensor) or list(tensor, tensor)).
Basically, out= is needed.
Sometimes, out needs to be used with dim.

arange(). *My post explains arange():

import torch

torch.arange(start=5, end=15, step=4)
# tensor([5, 9, 13])

my_tensor = torch.tensor([0, 1, 2])

torch.arange(start=5, end=15, step=4, out=my_tensor)
# tensor([5, 9, 13])

tensor1 = torch.tensor([0, 1, 2])

tensor2 = torch.arange(start=5, end=15, step=4, out=tensor1)

tensor1, tensor2
# (tensor([5, 9, 13]), tensor([5, 9, 13]))

rand(). *My post explains rand():

import torch

tensor1 = torch.tensor([0., 1., 2.])

tensor2 = torch.rand(size=(3,), out=tensor1)

tensor1, tensor2
# (tensor([0.3379, 0.9394, 0.5509]), tensor([0.3379, 0.9394, 0.5509]))

add(). *My post explains add():

import torch

tensor1 = torch.tensor([1, 2, 3])
tensor2 = torch.tensor([4, 5, 6])
tensor3 = torch.tensor([7, 8, 9])

tensor4 = torch.add(input=tensor1, other=tensor2, out=tensor3)

tensor1, tensor2, tensor3, tensor4
# (tensor([1, 2, 3]), tensor([4, 5, 6]), tensor([5, 7, 9]), tensor([5, 7, 9]))

mean(). *My post explains mean():

import torch

tensor1 = torch.tensor([5., 4., 7., 7.])
tensor2 = torch.tensor(9.)

tensor3 = torch.mean(input=tensor1, dim=0, out=tensor2)

tensor1, tensor2, tensor3
# (tensor([5., 4., 7., 7.]), tensor(5.7500), tensor(5.7500))

median(). *My post explains median():

import torch

tensor1 = torch.tensor([5., 4., 7., 7.])
tensor2 = torch.tensor(9.)
tensor3 = torch.tensor(6)

tensor4 = torch.median(input=tensor1, dim=0, out=(tensor2, tensor3))

tensor1, tensor2, tensor3, tensor4
# (tensor([5., 4., 7., 7.]),
#  tensor(5.),
#  tensor(0),
#  torch.return_types.median_out(
#  values=tensor(5.),
#  indices=tensor(0)))

min(). *My post explains min():

import torch

tensor1 = torch.tensor([5, 4, 7, 7])
tensor2 = torch.tensor(9)
tensor3 = torch.tensor(6)

tensor4 = torch.min(input=tensor1, dim=0, out=(tensor2, tensor3))

tensor1, tensor2, tensor3, tensor4
# (tensor([5, 4, 7, 7]),
#  tensor(4),
#  tensor(1),
#  torch.return_types.min_out(
#  values=tensor(4),
#  indices=tensor(1)))

max(). *My post explains max():

import torch

tensor1 = torch.tensor([5, 4, 7, 7])
tensor2 = torch.tensor(9)
tensor3 = torch.tensor(6)

tensor4 = torch.max(input=tensor1, dim=0, out=(tensor2, tensor3))

tensor1, tensor2, tensor3, tensor4
# (tensor([5, 4, 7, 7]),
#  tensor(7),
#  tensor(2),
#  torch.return_types.max_out(
#  values=tensor(7),
#  indices=tensor(2)))

all(). *My post explains all():

import torch

tensor1 = torch.tensor([True, False, True, False])
tensor2 = torch.tensor(True)

tensor3 = torch.all(input=tensor1, out=tensor2)
tensor3 = torch.all(input=tensor1, dim=0, out=tensor2)

tensor1, tensor2, tensor3
# (tensor([True, False, True, False]), tensor(False), tensor(False))

any(). *My post explains any():

import torch

tensor1 = torch.tensor([True, False, True, False])
tensor2 = torch.tensor(True)

tensor3 = torch.any(input=tensor1, out=tensor2)
tensor3 = torch.any(input=tensor1, dim=0, out=tensor2)

tensor1, tensor2, tensor3
# (tensor([True, False, True, False]), tensor(True), tensor(True))

matmul(). *My post explains matmul():

import torch

tensor1 = torch.tensor([2, -5, 4])
tensor2 = torch.tensor([3, 6, -1])
tensor3 = torch.tensor(7)

tensor4 = torch.matmul(input=tensor1, other=tensor2, out=tensor3)

tensor1, tensor2, tensor3, tensor4
# (tensor([2, -5, 4]), tensor([3, 6, -1]), tensor(-28), tensor(-28))

This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)

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