"""
.. 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.WeightedEBO
:members:
:exclude-members: fit, fit_features
"""
from typing import TypeVar
import torch
import torch.nn.functional as F
from torch import Tensor
from ..api import Detector, ModelNotSetException
Self = TypeVar("Self")
[docs]
class WeightedEBO(Detector):
"""
Implements the Weighted Energy Based Score of *VOS: Learning what you don’t know by virtual outlier synthesis*.
This methods calculates the energy for a vector of logits and the weights (you only get from the training with :class:`pytorch_ood.loss.VOSRegLoss`).
This value can be used as outlier score.
.. math::
E(x) = - \\log{\\sum_i w_{i} e^{f_i(x)}}
where :math:`f_i(x)` indicates the :math:`i^{th}` logit value predicted by :math:`f` and :math:`w` indicates the weights.
Example Code:
.. code :: python
weights = torch.nn.Linear(num_classes, 1))
detector = WeightedEBO(model, weights)
scores = detector(images)
:see Paper:
`ArXiv <https://arxiv.org/pdf/2202.01197.pdf>`__
:see Implementation:
`GitHub <https://github.com/deeplearning-wisc/vos/>`__
"""
def fit(self: Self, *args, **kwargs) -> Self:
"""
Not required.
"""
return self
def fit_features(self: Self, *args, **kwargs) -> Self:
"""
Not required.
"""
return self
def __init__(self, model: torch.nn.Module, weights: torch.nn.Linear):
"""
:param model: neural network to use, is assumed to output features
:param weights: neural network layer, with num_classes inputs.
"""
super(WeightedEBO, self).__init__()
self.model = model
self.weights = weights
[docs]
def predict(self, x: torch.Tensor) -> torch.Tensor:
"""
Calculate weighted energy for inputs
:param x: input tensor, will be passed through model
:return: Weighted Energy score
"""
if self.model is None:
raise ModelNotSetException
return self.score(self.model(x), self.weights)
[docs]
def predict_features(self, logits: Tensor) -> Tensor:
"""
:param logits: logits given by your model
"""
return self.score(logits, self.weights)
[docs]
@staticmethod
def score(logits: torch.Tensor, weights: torch.nn.Module) -> torch.Tensor:
"""
:param logits: logits of input
:param weights: weights as torch.nn.module
"""
if len(logits.shape) == 2:
conf = torch.log(
torch.sum((F.relu(weights.weight) * torch.exp(logits)), dim=1, keepdim=False)
)
return -conf
elif len(logits.shape) == 4:
# Permutation depends on shape of logits
tmp_scores_ = logits.permute(0, 2, 3, 1)
conf = torch.log(
torch.sum(
(F.relu(weights.weight) * torch.exp(tmp_scores_)),
dim=3,
keepdim=False,
)
)
return -conf
else:
raise ValueError(f"Unsupported input shape: {logits.shape}")