Function Differences with torch.std_mean

torch.std_mean

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

For more information, see torch.std_mean.

mindspore.ops.std_mean

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

For more information, see mindspore.ops.std_mean.

Differences

PyTorch: Output the standard deviation and mean value of the Tensor in each dimension, or the standard deviation 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 standard deviation. keepdim controls whether the output and input dimensions are the same.

MindSpore: Output the standard deviation and mean value of the Tensor in each dimension, or the standard deviation 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.

Code Example

# PyTorch
import torch

input = torch.tensor([[[9, 7, 4, -10],
                       [-9, -2, 1, -2]]], dtype=torch.float32)
print(torch.std_mean(input, dim=2, unbiased=True, keepdim=True))
# (tensor([[[8.5829],
#          [4.2426]]]), 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.std_mean(input, axis=2, ddof=True, keepdims=True))
# (Tensor(shape=[1, 2, 1], dtype=Float32, value=
# [[[ 8.58292866e+00],
#   [ 4.24264050e+00]]]), Tensor(shape=[1, 2, 1], dtype=Float32, value=
# [[[ 2.50000000e+00],
#   [-3.00000000e+00]]]))