"""
"""
import torch.nn
import torch.nn.functional as F
from ..utils import is_unknown
[docs]
class BackgroundClassLoss(torch.nn.Module):
"""
The idea of the background-class is that OOD samples are mapped to an individual class during training.
This implementation uses the normal cross-entropy, but handles remapping of the background class labels
to positive target labels.
Thus, when the target labels are :math:`\\lbrace 0, 1, 2, ..., N - 1 \\rbrace`
we will remap all entries with target label :math:`<0` to :math:`N`.
The networks output layer has to include :math:`N+1` outputs, so logits are
in the shape :math:`B \\times (N + 1)`.
"""
def __init__(self, n_classes: int, reduction: str = "mean"):
"""
:param n_classes: number of classes :math:`N` (not counting background class)
:param reduction: can be one of ``none``, ``mean``, ``sum``
"""
super(BackgroundClassLoss, self).__init__()
self.num_classes = n_classes
self.reduction = reduction
[docs]
def forward(self, logits: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:
"""
:param logits: class logits
:param targets: target labels
:return: Cross-Entropy for remapped samples
"""
if (targets >= self.num_classes).any():
raise ValueError(f"Target label to large: {targets.max()}")
unknown = is_unknown(targets)
if unknown.any():
targets[unknown] = self.num_classes
return F.cross_entropy(logits, targets, reduction=self.reduction)