"""
.. 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:
:inherited-members:
:show-inheritance:
:exclude-members: fit, fit_logits
"""
from typing import Optional, TypeVar
from torch import Tensor
from torch.nn import Module
from ..api import LogitsDetector
Self = TypeVar("Self")
[docs]
class MaxLogit(LogitsDetector):
"""
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://arxiv.org/abs/1911.11132>`__
"""
def __init__(self, model: Optional[Module]):
"""
:param model: neural network to use. Can be ``None`` when using
``predict_logits(...)`` directly.
"""
super(MaxLogit, self).__init__()
self.model = model
def predict_logits(self, logits: Tensor) -> Tensor:
"""
:param logits: logits as given by the model
"""
return MaxLogit.score(logits)
[docs]
@staticmethod
def score(logits: Tensor) -> Tensor:
"""
:param logits: logits for samples
"""
return -logits.max(dim=1).values