Source code for pina.loss.loss_interface
"""Module for the Loss Interface."""
from abc import ABCMeta, abstractmethod
from torch.nn.modules.loss import _Loss
import torch
[docs]
class LossInterface(_Loss, metaclass=ABCMeta):
"""
Abstract base class for all losses. All classes defining a loss function
should inherit from this interface.
"""
def __init__(self, reduction="mean"):
"""
Initialization of the :class:`LossInterface` class.
:param str reduction: The reduction method for the loss.
Available options: ``none``, ``mean``, ``sum``.
If ``none``, no reduction is applied. If ``mean``, the sum of the
loss values is divided by the number of values. If ``sum``, the loss
values are summed. Default is ``mean``.
"""
super().__init__(reduction=reduction, size_average=None, reduce=None)
[docs]
@abstractmethod
def forward(self, input, target):
"""
Forward method of the loss function.
:param torch.Tensor input: Input tensor from real data.
:param torch.Tensor target: Model tensor output.
"""
def _reduction(self, loss):
"""
Apply the reduction to the loss.
:param torch.Tensor loss: The tensor containing the pointwise losses.
:raises ValueError: If the reduction method is not valid.
:return: Reduced loss.
:rtype: torch.Tensor
"""
if self.reduction == "none":
ret = loss
elif self.reduction == "mean":
ret = torch.mean(loss, keepdim=True, dim=-1)
elif self.reduction == "sum":
ret = torch.sum(loss, keepdim=True, dim=-1)
else:
raise ValueError(self.reduction + " is not valid")
return ret