Function Differences with torch.bartlett_window

torch.bartlett_window

torch.bartlett_window(
    window_length,
    periodic=True,
    *,
    dtype=None,
    layout=torch.strided,
    device=None,
    requires_grad=False
) -> Tensor

For more information, see torch.bartlett_window.

mindspore.ops.bartlett_window

mindspore.ops.bartlett_window(
    window_length,
    periodic=True,
    dtype=mstype.float32
) -> Tensor

For more information, see mindspore.ops.bartlett_window.

Differences

PyTorch: Returns a bartlett window with the same size as window_length. The periodic parameter determines whether the returned window will remove the last duplicate value of the symmetric window.

MindSpore: MindSpore API basically implements the same function as PyTorch, and the precision varies slightly.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameters	Parameter 1	window_length	window_length	An int in PyTorch and a Tensor in MindSpore
	Parameter 2	periodic	periodic	-
	Parameter 3	dtype	dtype	-
	Parameter 4	layout	-	Not involved
	Parameter 5	device	-	Not involved
	Parameter 6	requires_grad	-	MindSpore does not have this parameter and supports reverse derivation by default

Code Example 1

# PyTorch
import torch

torch_output = torch.bartlett_window(5, periodic=True)
print(torch_output.numpy())
#[0.         0.4        0.8        0.79999995 0.39999998]

# MindSpore
import mindspore
from mindspore import Tensor

window_length = Tensor(5, mindspore.int32)
ms_output = mindspore.ops.bartlett_window(window_length, periodic=True)
print(ms_output.asnumpy())
#[0.  0.4 0.8 0.8 0.4]