Differences with torch.var

The following mapping relationships can be found in this file.

PyTorch APIs	MindSpore APIs
torch.var	mindspore.ops.var
torch.Tensor.var	mindspore.Tensor.var

torch.var

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

For more information, see torch.var.

mindspore.ops.var

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

For more information, see mindspore.ops.var.

Differences

PyTorch: Output the variance of the Tensor in each dimension, or the variance 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 of the Tensor in each dimension, or the variance 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(input, dim=2, unbiased=True, keepdim=True))
# tensor([[[73.6667],
#          [18.0000]]])

# MindSpore
import mindspore as ms

input = ms.Tensor([[[9, 7, 4, -10],
                    [-9, -2, 1, -2]]], ms.float32)
print(ms.ops.var(input, axis=2, ddof=True, keepdims=True))
# [[[73.666664]
#   [17.999998]]]