from typing import Union
from numbers import Number
import torch
import numpy as np
__all__ = ["slice_axis", "is_scalar"]
[docs]def slice_axis(data: torch.Tensor, axis: int, begin: int, end: int) -> torch.Tensor:
"""
Examples:
>>> import torch
>>> x = torch.tensor([
>>> [ 1., 2., 3., 4.],
>>> [ 5., 6., 7., 8.],
>>> [ 9., 10., 11., 12.]
>>> ])
>>>
>>> slice_axis(x, axis=0, begin=1, end=3)
>>> # [[ 5., 6., 7., 8.],
>>> # [ 9., 10., 11., 12.]]
>>>
>>> slice_axis(x, axis=1, begin=0, end=2)
>>> # [[ 1., 2.],
>>> # [ 5., 6.],
>>> # [ 9., 10.]]
>>>
>>> slice_axis(x, axis=1, begin=-3, end=-1)
>>> # [[ 2., 3.],
>>> # [ 6., 7.],
>>> # [ 10., 11.]]
References:
- `Deep Graph Library \
<https://github.com/dmlc/dgl/blob/f25bc176d0365234ebb051d5069edff24ad2de4d/python/dgl/backend/pytorch/tensor.py#L159-L160>`_
- `mxnet.ndarray.slice_axis \
<https://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.slice_axis.html#mxnet-ndarray-slice-axis>`_
Args:
data: Source input
axis: Axis along which to be sliced
begin: The beginning index along the axis to be sliced
end: The ending index along the axis to be sliced
Returns:
output - the output of this function.
"""
if begin < 0:
begin = data.shape[axis] + begin
if end < 0:
end = data.shape[axis] + end
return torch.narrow(data, axis, begin, end - begin)
[docs]def is_scalar(data: Union[Number, Union[np.ndarray, torch.Tensor]]) -> bool:
"""
Returns True if the type of ``data`` is a scalar type.
Args:
data (Union[Number, Union[np.ndarray, torch.Tensor]]): Numerical value
Returns:
True if ``data`` is a scalar type, False if it is not.
Examples:
>>> a = torch.tensor([1.0])
>>> is_scalar(a) # True
>>> a = torch.tensor(1.0)
>>> is_scalar(a) # True
>>> a = torch.tensor([1, 2, 3])
>>> is_scalar(a) # False
>>> a = 1.0
>>> is_scalar(a) # True
"""
if isinstance(data, Number):
return True
else:
if len(data.shape) == 0:
return True
else:
try:
_ = data.item()
except ValueError:
return False
else:
return True