Source code for flambe.metric.loss.nll_loss

from typing import Optional

import torch
import torch.nn.functional as F

from flambe.metric.metric import Metric

[docs]class MultiLabelNLLLoss(Metric): def __init__(self, weight: Optional[torch.Tensor] = None, ignore_index: Optional[int] = None, reduction: str = 'mean') -> None: """Initialize the MultiLabelNLLLoss. Parameters ---------- weight : Optional[torch.Tensor] A manual rescaling weight given to each class. If given, has to be a Tensor of size N, where N is the number of classes. ignore_index : Optional[int], optional Specifies a target value that is ignored and does not contribute to the input gradient. When size_average is True, the loss is averaged over non-ignored targets. reduction : str, optional Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the output will be averaged 'sum': the output will be summed.\ """ super().__init__() self.weight = weight self.ignore_index = ignore_index self.reduction = reduction
[docs] def __str__(self) -> str: """Return the name of the Metric (for use in logging).""" return 'MultiLabelNLLLoss' if self.weight is None \ else 'WeightedMultiLabelNLLLoss'
[docs] def compute(self, pred: torch.Tensor, target: torch.Tensor) \ -> torch.Tensor: """Computes the Negative log likelihood loss for multilabel. Parameters ---------- pred: torch.Tensor input logits of shape (B x N) target: torch.LontTensor target tensor of shape (B x N) Returns ------- loss: torch.float Multi label negative log likelihood loss, of shape (B) """ if self.ignore_index is not None: target[:, self.ignore_index] = 0 if self.weight is None: self.weight = torch.ones(pred.size(1)).to(pred) norm_target = F.normalize(target.float(), p=1, dim=1) loss = - (self.weight * norm_target * pred).sum(dim=1) if self.reduction == 'mean': loss = loss.mean() elif self.reduction == 'sum': loss = loss.sum() elif self.reduction is not None: raise ValueError("Unknown reduction: {self.reduction}") return loss