Source code for holocron.nn.modules.loss

# Copyright (C) 2019-2022, François-Guillaume Fernandez.

# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0> for full license details.

from typing import Any, List, Optional, Union

import torch
import torch.nn as nn
from torch import Tensor

from .. import functional as F

__all__ = [
    "FocalLoss",
    "MultiLabelCrossEntropy",
    "ComplementCrossEntropy",
    "ClassBalancedWrapper",
    "MutualChannelLoss",
    "DiceLoss",
    "PolyLoss",
]


class _Loss(nn.Module):
    def __init__(
        self,
        weight: Optional[Union[float, List[float], Tensor]] = None,
        ignore_index: int = -100,
        reduction: str = "mean",
    ) -> None:
        super().__init__()
        # Cast class weights if possible
        self.weight: Optional[Tensor]
        if isinstance(weight, (float, int)):
            self.register_buffer("weight", torch.Tensor([weight, 1 - weight]))
        elif isinstance(weight, list):
            self.register_buffer("weight", torch.Tensor(weight))
        elif isinstance(weight, Tensor):
            self.register_buffer("weight", weight)
        else:
            self.weight = None
        self.ignore_index = ignore_index
        # Set the reduction method
        if reduction not in ["none", "mean", "sum"]:
            raise NotImplementedError("argument reduction received an incorrect input")
        self.reduction = reduction


[docs] class FocalLoss(_Loss): r"""Implementation of Focal Loss as described in `"Focal Loss for Dense Object Detection" <https://arxiv.org/pdf/1708.02002.pdf>`_. While the weighted cross-entropy is described by: .. math:: CE(p_t) = -\alpha_t log(p_t) where :math:`\alpha_t` is the loss weight of class :math:`t`, and :math:`p_t` is the predicted probability of class :math:`t`. the focal loss introduces a modulating factor .. math:: FL(p_t) = -\alpha_t (1 - p_t)^\gamma log(p_t) where :math:`\gamma` is a positive focusing parameter. Args: gamma (float, optional): exponent parameter of the focal loss weight (torch.Tensor[K], optional): class weight for loss computation ignore_index (int, optional): specifies target value that is ignored and do not contribute to gradient reduction (str, optional): type of reduction to apply to the final loss """ def __init__(self, gamma: float = 2.0, **kwargs: Any) -> None: super().__init__(**kwargs) self.gamma = gamma def forward(self, x: Tensor, target: Tensor) -> Tensor: return F.focal_loss(x, target, self.weight, self.ignore_index, self.reduction, self.gamma) def __repr__(self) -> str: return f"{self.__class__.__name__}(gamma={self.gamma}, reduction='{self.reduction}')"
[docs] class MultiLabelCrossEntropy(_Loss): """Implementation of the cross-entropy loss for multi-label targets Args: weight (torch.Tensor[K], optional): class weight for loss computation ignore_index (int, optional): specifies target value that is ignored and do not contribute to gradient reduction (str, optional): type of reduction to apply to the final loss """ def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) def forward(self, x: Tensor, target: Tensor) -> Tensor: return F.multilabel_cross_entropy(x, target, self.weight, self.ignore_index, self.reduction) def __repr__(self) -> str: return f"{self.__class__.__name__}(reduction='{self.reduction}')"
[docs] class ComplementCrossEntropy(_Loss): """Implements the complement cross entropy loss from `"Imbalanced Image Classification with Complement Cross Entropy" <https://arxiv.org/pdf/2009.02189.pdf>`_ Args: gamma (float, optional): smoothing factor weight (torch.Tensor[K], optional): class weight for loss computation ignore_index (int, optional): specifies target value that is ignored and do not contribute to gradient reduction (str, optional): type of reduction to apply to the final loss """ def __init__(self, gamma: float = -1, **kwargs: Any) -> None: super().__init__(**kwargs) self.gamma = gamma def forward(self, x: Tensor, target: Tensor) -> Tensor: return F.complement_cross_entropy(x, target, self.weight, self.ignore_index, self.reduction, self.gamma) def __repr__(self) -> str: return f"{self.__class__.__name__}(gamma={self.gamma}, reduction='{self.reduction}')"
[docs] class ClassBalancedWrapper(nn.Module): r"""Implementation of the class-balanced loss as described in `"Class-Balanced Loss Based on Effective Number of Samples" <https://arxiv.org/pdf/1901.05555.pdf>`_. Given a loss function :math:`\mathcal{L}`, the class-balanced loss is described by: .. math:: CB(p, y) = \frac{1 - \beta}{1 - \beta^{n_y}} \mathcal{L}(p, y) where :math:`p` is the predicted probability for class :math:`y`, :math:`n_y` is the number of training samples for class :math:`y`, and :math:`\beta` is exponential factor. Args: criterion (torch.nn.Module): loss module num_samples (torch.Tensor[K]): number of samples for each class beta (float, optional): rebalancing exponent """ def __init__(self, criterion: nn.Module, num_samples: Tensor, beta: float = 0.99) -> None: super().__init__() self.criterion = criterion self.beta = beta cb_weights = (1 - beta) / (1 - beta**num_samples) if self.criterion.weight is None: self.criterion.weight = cb_weights else: self.criterion.weight *= cb_weights.to(device=self.criterion.weight.device) def forward(self, x: Tensor, target: Tensor) -> Tensor: return self.criterion.forward(x, target) def __repr__(self) -> str: return f"{self.__class__.__name__}({self.criterion.__repr__()}, beta={self.beta})"
[docs] class MutualChannelLoss(_Loss): """Implements the mutual channel loss from `"The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification" <https://arxiv.org/pdf/2002.04264.pdf>`_. Args: weight (torch.Tensor[K], optional): class weight for loss computation ignore_index (int, optional): specifies target value that is ignored and do not contribute to gradient reduction (str, optional): type of reduction to apply to the final loss xi (in, optional): num of features per class alpha (float, optional): diversity factor """ def __init__( self, weight: Optional[Union[float, List[float], Tensor]] = None, ignore_index: int = -100, reduction: str = "mean", xi: int = 2, alpha: float = 1, ) -> None: super().__init__(weight, ignore_index, reduction) self.xi = xi self.alpha = alpha def forward(self, x: Tensor, target: Tensor) -> Tensor: return F.mutual_channel_loss(x, target, self.weight, self.ignore_index, self.reduction, self.xi, self.alpha) def __repr__(self) -> str: return f"{self.__class__.__name__}(reduction='{self.reduction}', xi={self.xi}, alpha={self.alpha})"
[docs] class DiceLoss(_Loss): """Implements the dice loss from `"V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation" <https://arxiv.org/pdf/1606.04797.pdf>`_ Args: weight (torch.Tensor[K], optional): class weight for loss computation gamma (float, optional): recall/precision control param eps (float, optional): small value added to avoid division by zero """ def __init__( self, weight: Optional[Union[float, List[float], Tensor]] = None, gamma: float = 1.0, eps: float = 1e-8, ) -> None: super().__init__(weight) self.gamma = gamma self.eps = eps def forward(self, x: Tensor, target: Tensor) -> Tensor: return F.dice_loss(x, target, self.weight, self.gamma, self.eps) def __repr__(self) -> str: return f"{self.__class__.__name__}(reduction='{self.reduction}', gamma={self.gamma}, eps={self.eps})"
[docs] class PolyLoss(_Loss): """Implements the Poly1 loss from `"PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions" <https://arxiv.org/pdf/2204.12511.pdf>`_. Args: weight (torch.Tensor[K], optional): class weight for loss computation eps (float, optional): epsilon 1 from the paper ignore_index: int = -100, reduction: str = 'mean', """ def __init__( self, *args: Any, eps: float = 2.0, **kwargs: Any, ) -> None: super().__init__(*args, **kwargs) self.eps = eps def forward(self, x: Tensor, target: Tensor) -> Tensor: return F.poly_loss(x, target, self.eps, self.weight, self.ignore_index, self.reduction) def __repr__(self) -> str: return f"{self.__class__.__name__}(eps={self.eps}, reduction='{self.reduction}')"