Function Differences with torch.median

The following mapping relationships can be found in this file.

torch.median

torch.median(input, dim=-1, keepdim=False, *, out=None) -> Tensor

For more information, see torch.median.

mindspore.ops.median

mindspore.ops.median(input, axis=-1, keepdims=False) -> Tensor

For more information, see mindspore.ops.median.

Differences

PyTorch: Output the median and index of input according to the specified dim. keepdim controls whether the output and input have the same dimension. Return the median of all elements when the input has only input, or the median and index of the specified dimension when the input contains dim. out can get the output.

MindSpore: Output the median and index of input according to the specified axis. The keepdims function is identical to PyTorch. Unlike Pytorch, MindSpore returns the median and index in the specified dimension, regardless of whether the input contains axis or not. MindSpore does not have out parameter.

Code Example

# PyTorch
import torch

input = torch.tensor([[1, 2.5, 3, 1], [2.5, 3, 2, 1]], dtype=torch.float32)
print(torch.median(input))
# tensor(2.)
print(torch.median(input, dim=1, keepdim=True))
# torch.return_types.median(
# values=tensor([[1.],
#         [2.]]),
# indices=tensor([[3],
#         [2]]))

# MindSpore
import mindspore

x = mindspore.Tensor([[1, 2.5, 3, 1], [2.5, 3, 2, 1]], dtype=mindspore.float32)
print(mindspore.ops.median(x, axis=1, keepdims=True))
# (Tensor(shape=[2, 1], dtype=Float32, value=
# [[ 1.00000000e+00],
#  [ 2.00000000e+00]]), Tensor(shape=[2, 1], dtype=Int64, value=
# [[3],
#  [2]]))