isclose¶
Documentation¶
-
treetensor.torch.
isclose
(input, other, *args, **kwargs)[source]¶ Returns a new tensor with boolean elements representing if each element of
input
is “close” to the corresponding element ofother
.Examples:
>>> import torch >>> import treetensor.torch as ttorch >>> import math >>> ttorch.isclose( ... ttorch.tensor((1., 2, 3)), ... ttorch.tensor((1 + 1e-10, 3, 4)) ... ) tensor([ True, False, False]) >>> ttorch.isclose( ... ttorch.tensor({ ... 'a': [1., 2, 3], ... 'b': {'x': [[float('inf'), 4, 1e20], ... [-math.inf, 2.2943, 9483.32]]}, ... }), ... ttorch.tensor({ ... 'a': [1 + 1e-10, 3, 4], ... 'b': {'x': [[math.inf, 6, 1e20+1], ... [-float('inf'), 2.294300000001, 9484.32]]}, ... }), ... ) <Tensor 0x7f5b3219f370> ├── a --> tensor([ True, False, False]) └── b --> <Tensor 0x7f5b3219f550> └── x --> tensor([[ True, False, True], [ True, True, False]])
Torch Version Related
This documentation is based on torch.isclose in torch v1.10.0+cu102. Its arguments’ arrangements depend on the version of pytorch you installed.
If some arguments listed here are not working properly, please check your pytorch’s version with the following command and find its documentation.
1 | python -c 'import torch;print(torch.__version__)' |
The arguments and keyword arguments supported in torch v1.10.0+cu102 is listed below.
Description From Torch v1.10.0+cu102¶
-
torch.
isclose
(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) → Tensor¶ Returns a new tensor with boolean elements representing if each element of
input
is “close” to the corresponding element ofother
. Closeness is defined as:\[\lvert \text{input} - \text{other} \rvert \leq \texttt{atol} + \texttt{rtol} \times \lvert \text{other} \rvert\]where
input
andother
are finite. Whereinput
and/orother
are nonfinite they are close if and only if they are equal, with NaNs being considered equal to each other whenequal_nan
is True.- Args:
input (Tensor): first tensor to compare other (Tensor): second tensor to compare atol (float, optional): absolute tolerance. Default: 1e-08 rtol (float, optional): relative tolerance. Default: 1e-05 equal_nan (bool, optional): if
True
, then twoNaN
s will be considered equal. Default:False
Examples:
>>> torch.isclose(torch.tensor((1., 2, 3)), torch.tensor((1 + 1e-10, 3, 4))) tensor([ True, False, False]) >>> torch.isclose(torch.tensor((float('inf'), 4)), torch.tensor((float('inf'), 6)), rtol=.5) tensor([True, True])