Differences with torch.var_mean

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torch.var_mean

torch.var_mean(input, dim, unbiased=True, keepdim=False, *, out=None)

For more information, see torch.var_mean.

mindspore.ops.var_mean

mindspore.ops.var_mean(input, axis=None, ddof=0, keepdims=False)

For more information, see mindspore.ops.var_mean.

Differences

PyTorch: Output the variance and mean value of the Tensor in each dimension, or the variance and mean value of the specified dimension according to dim. If unbiased is True, use Bessel for correction; if False, use bias estimation to calculate the variance. keepdim controls whether the output and input dimensions are the same.

MindSpore: Output the variance and mean value of the Tensor in each dimension, or the variance and mean value of the specified dimension according to axis. If ddof is a boolean, it has the same effect as unbiased; if ddof is an integer, the divisor used in the calculation is N-ddof, where N denotes the number of elements. keepdim controls whether the output and the input have the same dimensionality.

Categories

Subcategories

PyTorch

MindSpore

Differences

Parameters

Parameter 1

input

input

Same function, different parameter names

Parameter 2

dim

axis

Same function, different parameter names

Parameter 3

unbiased

ddof

ddof is the same as unbiased when it is a boolean value

Parameter 4

keepdim

keepdims

Same function, different parameter names

Parameter 5

out

-

MindSpore does not have this parameter

Code Example

# PyTorch
import torch

input = torch.tensor([[[9, 7, 4, -10],
                       [-9, -2, 1, -2]]], dtype=torch.float32)
print(torch.var_mean(input, dim=2, unbiased=True, keepdim=True))
# (tensor([[[73.6667],
#          [18.0000]]]), tensor([[[ 2.5000],
#          [-3.0000]]]))

# MindSpore
import mindspore as ms

input = ms.Tensor([[[9, 7, 4, -10],
                    [-9, -2, 1, -2]]], ms.float32)
print(ms.ops.var_mean(input, axis=2, ddof=True, keepdims=True))
# (Tensor(shape=[1, 2, 1], dtype=Float32, value=
# [[[ 7.36666641e+01],
#   [ 1.79999981e+01]]]), Tensor(shape=[1, 2, 1], dtype=Float32, value=
# [[[ 2.50000000e+00],
#   [-3.00000000e+00]]]))