Shortcuts

Source code for ding.torch_utils.loss.cross_entropy_loss

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Any, Optional


[docs]class LabelSmoothCELoss(nn.Module): """ Overview: Label smooth cross entropy loss. Interfaces: ``__init__``, ``forward``. """
[docs] def __init__(self, ratio: float) -> None: """ Overview: Initialize the LabelSmoothCELoss object using the given arguments. Arguments: - ratio (:obj:`float`): The ratio of label-smoothing (the value is in 0-1). If the ratio is larger, the \ extent of label smoothing is larger. """ super().__init__() self.ratio = ratio
[docs] def forward(self, logits: torch.Tensor, labels: torch.LongTensor) -> torch.Tensor: """ Overview: Calculate label smooth cross entropy loss. Arguments: - logits (:obj:`torch.Tensor`): Predicted logits. - labels (:obj:`torch.LongTensor`): Ground truth. Returns: - loss (:obj:`torch.Tensor`): Calculated loss. """ B, N = logits.shape val = float(self.ratio) / (N - 1) one_hot = torch.full_like(logits, val) one_hot.scatter_(1, labels.unsqueeze(1), 1 - val) logits = F.log_softmax(logits, dim=1) return -torch.sum(logits * (one_hot.detach())) / B
[docs]class SoftFocalLoss(nn.Module): """ Overview: Soft focal loss. Interfaces: ``__init__``, ``forward``. """
[docs] def __init__( self, gamma: int = 2, weight: Any = None, size_average: bool = True, reduce: Optional[bool] = None ) -> None: """ Overview: Initialize the SoftFocalLoss object using the given arguments. Arguments: - gamma (:obj:`int`): The extent of focus on hard samples. A smaller ``gamma`` will lead to more focus on \ easy samples, while a larger ``gamma`` will lead to more focus on hard samples. - weight (:obj:`Any`): The weight for loss of each class. - size_average (:obj:`bool`): By default, the losses are averaged over each loss element in the batch. \ Note that for some losses, there are multiple elements per sample. If the field ``size_average`` is \ set to ``False``, the losses are instead summed for each minibatch. Ignored when ``reduce`` is \ ``False``. - reduce (:obj:`Optional[bool]`): By default, the losses are averaged or summed over observations for \ each minibatch depending on size_average. When ``reduce`` is ``False``, returns a loss for each batch \ element instead and ignores ``size_average``. """ super().__init__() self.gamma = gamma self.nll_loss = torch.nn.NLLLoss2d(weight, size_average, reduce=reduce)
[docs] def forward(self, inputs: torch.Tensor, targets: torch.LongTensor) -> torch.Tensor: """ Overview: Calculate soft focal loss. Arguments: - logits (:obj:`torch.Tensor`): Predicted logits. - labels (:obj:`torch.LongTensor`): Ground truth. Returns: - loss (:obj:`torch.Tensor`): Calculated loss. """ return self.nll_loss((1 - F.softmax(inputs, 1)) ** self.gamma * F.log_softmax(inputs, 1), targets)
[docs]def build_ce_criterion(cfg: dict) -> nn.Module: """ Overview: Get a cross entropy loss instance according to given config. Arguments: - cfg (:obj:`dict`) : Config dict. It contains: - type (:obj:`str`): Type of loss function, now supports ['cross_entropy', 'label_smooth_ce', \ 'soft_focal_loss']. - kwargs (:obj:`dict`): Arguments for the corresponding loss function. Returns: - loss (:obj:`nn.Module`): loss function instance """ if cfg.type == 'cross_entropy': return nn.CrossEntropyLoss() elif cfg.type == 'label_smooth_ce': return LabelSmoothCELoss(cfg.kwargs.smooth_ratio) elif cfg.type == 'soft_focal_loss': return SoftFocalLoss() else: raise ValueError("invalid criterion type:{}".format(cfg.type))