"""
.. image:: https://img.shields.io/badge/classification-yes-brightgreen?style=flat-square
:alt: classification badge
.. image:: https://img.shields.io/badge/segmentation-yes-brightgreen?style=flat-square
:alt: classification badge
.. autoclass:: pytorch_ood.detector.MaxLogit
:members:
:exclude-members: fit, fit_features
"""
from typing import TypeVar
from torch import Tensor
from torch.nn import Module
from ..api import Detector, ModelNotSetException
Self = TypeVar("Self")
[docs]
class MaxLogit(Detector):
"""
Implements the Max Logit Method for OOD Detection as proposed in
*Scaling Out-of-Distribution Detection for Real-World Settings*.
.. math:: - \\max_y f_y(x)
where :math:`f_y(x)` indicates the :math:`y^{th}` logits value predicted by :math:`f`.
:see Paper:
`ArXiv <https://.org/abs/1911.11132>`__
"""
def __init__(self, model: Module):
"""
:param t: temperature value T. Default is 1.
"""
super(MaxLogit, self).__init__()
self.model = model
[docs]
def predict(self, x: Tensor) -> Tensor:
"""
:param x: model inputs
"""
if self.model is None:
raise ModelNotSetException
return self.score(self.model(x))
[docs]
def predict_features(self, logits: Tensor) -> Tensor:
"""
:param logits: logits as given by the model
"""
return MaxLogit.score(logits)
def fit(self: Self, *args, **kwargs) -> Self:
"""
Not required
"""
return self
def fit_features(self: Self, *args, **kwargs) -> Self:
"""
Not required
"""
return self
[docs]
@staticmethod
def score(logits: Tensor) -> Tensor:
"""
:param logits: logits for samples
"""
return -logits.max(dim=1).values