Source code for kornia.losses.focal

from typing import Optional

import torch
import torch.nn as nn
import torch.nn.functional as F

from kornia.utils import one_hot


# based on:
# https://github.com/zhezh/focalloss/blob/master/focalloss.py

[docs]def focal_loss( input: torch.Tensor, target: torch.Tensor, alpha: float, gamma: float = 2.0, reduction: str = 'none', eps: float = 1e-8) -> torch.Tensor: r"""Function that computes Focal loss. See :class:`~kornia.losses.FocalLoss` for details. """ if not torch.is_tensor(input): raise TypeError("Input type is not a torch.Tensor. Got {}" .format(type(input))) if not len(input.shape) >= 2: raise ValueError("Invalid input shape, we expect BxCx*. Got: {}" .format(input.shape)) if input.size(0) != target.size(0): raise ValueError('Expected input batch_size ({}) to match target batch_size ({}).' .format(input.size(0), target.size(0))) n = input.size(0) out_size = (n,) + input.size()[2:] if target.size()[1:] != input.size()[2:]: raise ValueError('Expected target size {}, got {}'.format( out_size, target.size())) if not input.device == target.device: raise ValueError( "input and target must be in the same device. Got: {} and {}" .format( input.device, target.device)) # compute softmax over the classes axis input_soft: torch.Tensor = F.softmax(input, dim=1) + eps # create the labels one hot tensor target_one_hot: torch.Tensor = one_hot( target, num_classes=input.shape[1], device=input.device, dtype=input.dtype) # compute the actual focal loss weight = torch.pow(-input_soft + 1., gamma) focal = -alpha * weight * torch.log(input_soft) loss_tmp = torch.sum(target_one_hot * focal, dim=1) if reduction == 'none': loss = loss_tmp elif reduction == 'mean': loss = torch.mean(loss_tmp) elif reduction == 'sum': loss = torch.sum(loss_tmp) else: raise NotImplementedError("Invalid reduction mode: {}" .format(reduction)) return loss
[docs]class FocalLoss(nn.Module): r"""Criterion that computes Focal loss. According to [1], the Focal loss is computed as follows: .. math:: \text{FL}(p_t) = -\alpha_t (1 - p_t)^{\gamma} \, \text{log}(p_t) where: - :math:`p_t` is the model's estimated probability for each class. Arguments: alpha (float): Weighting factor :math:`\alpha \in [0, 1]`. gamma (float): Focusing parameter :math:`\gamma >= 0`. reduction (str, optional): Specifies the reduction to apply to the output: ‘none’ | ‘mean’ | ‘sum’. ‘none’: no reduction will be applied, ‘mean’: the sum of the output will be divided by the number of elements in the output, ‘sum’: the output will be summed. Default: ‘none’. Shape: - Input: :math:`(N, C, *)` where C = number of classes. - Target: :math:`(N, *)` where each value is :math:`0 ≤ targets[i] ≤ C−1`. Examples: >>> N = 5 # num_classes >>> kwargs = {"alpha": 0.5, "gamma": 2.0, "reduction": 'mean'} >>> loss = kornia.losses.FocalLoss(**kwargs) >>> input = torch.randn(1, N, 3, 5, requires_grad=True) >>> target = torch.empty(1, 3, 5, dtype=torch.long).random_(N) >>> output = loss(input, target) >>> output.backward() References: [1] https://arxiv.org/abs/1708.02002 """ def __init__(self, alpha: float, gamma: float = 2.0, reduction: str = 'none') -> None: super(FocalLoss, self).__init__() self.alpha: float = alpha self.gamma: float = gamma self.reduction: str = reduction self.eps: float = 1e-6 def forward( # type: ignore self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: return focal_loss(input, target, self.alpha, self.gamma, self.reduction, self.eps)