Source code for kornia.losses.focal

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

from kornia.utils.one_hot 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"""Criterion that computes Focal loss. According to :cite:`lin2018focal`, 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. Args: input: logits tensor with shape :math:`(N, C, *)` where C = number of classes. target: labels tensor with shape :math:`(N, *)` where each value is :math:`0 ≤ targets[i] ≤ C−1`. alpha: Weighting factor :math:`\alpha \in [0, 1]`. gamma: Focusing parameter :math:`\gamma >= 0`. reduction: 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. eps: Scalar to enforce numerical stabiliy. Return: the computed loss. Example: >>> N = 5 # num_classes >>> input = torch.randn(1, N, 3, 5, requires_grad=True) >>> target = torch.empty(1, 3, 5, dtype=torch.long).random_(N) >>> output = focal_loss(input, target, alpha=0.5, gamma=2.0, reduction='mean') >>> output.backward() """ if not isinstance(input, torch.Tensor): raise TypeError(f"Input type is not a torch.Tensor. Got {type(input)}") if not len(input.shape) >= 2: raise ValueError(f"Invalid input shape, we expect BxCx*. Got: {input.shape}") if input.size(0) != target.size(0): raise ValueError(f'Expected input batch_size ({input.size(0)}) to match target batch_size ({target.size(0)}).') n = input.size(0) out_size = (n,) + input.size()[2:] if target.size()[1:] != input.size()[2:]: raise ValueError(f'Expected target size {out_size}, got {target.size()}') if not input.device == target.device: raise ValueError(f"input and target must be in the same device. Got: {input.device} and {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 + 1.0, 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(f"Invalid reduction mode: {reduction}") return loss
[docs]class FocalLoss(nn.Module): r"""Criterion that computes Focal loss. According to :cite:`lin2018focal`, 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. Args: alpha: Weighting factor :math:`\alpha \in [0, 1]`. gamma: Focusing parameter :math:`\gamma >= 0`. reduction: 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. eps: Scalar to enforce numerical stabiliy. Shape: - Input: :math:`(N, C, *)` where C = number of classes. - Target: :math:`(N, *)` where each value is :math:`0 ≤ targets[i] ≤ C−1`. Example: >>> N = 5 # num_classes >>> kwargs = {"alpha": 0.5, "gamma": 2.0, "reduction": 'mean'} >>> criterion = FocalLoss(**kwargs) >>> input = torch.randn(1, N, 3, 5, requires_grad=True) >>> target = torch.empty(1, 3, 5, dtype=torch.long).random_(N) >>> output = criterion(input, target) >>> output.backward() """ def __init__(self, alpha: float, gamma: float = 2.0, reduction: str = 'none', eps: float = 1e-8) -> None: super().__init__() self.alpha: float = alpha self.gamma: float = gamma self.reduction: str = reduction self.eps: float = eps def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: return focal_loss(input, target, self.alpha, self.gamma, self.reduction, self.eps)
[docs]def binary_focal_loss_with_logits( input: torch.Tensor, target: torch.Tensor, alpha: float = 0.25, gamma: float = 2.0, reduction: str = 'none', eps: float = 1e-8, ) -> torch.Tensor: r"""Function that computes Binary Focal loss. .. 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. Args: input: input data tensor with shape :math:`(N, 1, *)`. target: the target tensor with shape :math:`(N, 1, *)`. alpha: Weighting factor for the rare class :math:`\alpha \in [0, 1]`. gamma: Focusing parameter :math:`\gamma >= 0`. reduction: 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. eps: for numerically stability when dividing. Returns: the computed loss. Examples: >>> num_classes = 1 >>> kwargs = {"alpha": 0.25, "gamma": 2.0, "reduction": 'mean'} >>> logits = torch.tensor([[[[6.325]]],[[[5.26]]],[[[87.49]]]]) >>> labels = torch.tensor([[[1.]],[[1.]],[[0.]]]) >>> binary_focal_loss_with_logits(logits, labels, **kwargs) tensor(4.6052) """ if not isinstance(input, torch.Tensor): raise TypeError(f"Input type is not a torch.Tensor. Got {type(input)}") if not len(input.shape) >= 2: raise ValueError(f"Invalid input shape, we expect BxCx*. Got: {input.shape}") if input.size(0) != target.size(0): raise ValueError(f'Expected input batch_size ({input.size(0)}) to match target batch_size ({target.size(0)}).') probs = torch.sigmoid(input) target = target.unsqueeze(dim=1) loss_tmp = -alpha * torch.pow((1.0 - probs + eps), gamma) * target * torch.log(probs + eps) - ( 1 - alpha ) * torch.pow(probs + eps, gamma) * (1.0 - target) * torch.log(1.0 - probs + eps) loss_tmp = loss_tmp.squeeze(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(f"Invalid reduction mode: {reduction}") return loss
[docs]class BinaryFocalLossWithLogits(nn.Module): r"""Criterion that computes Focal loss. According to :cite:`lin2018focal`, 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. Args: alpha): Weighting factor for the rare class :math:`\alpha \in [0, 1]`. gamma: Focusing parameter :math:`\gamma >= 0`. reduction: 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. Shape: - Input: :math:`(N, 1, *)`. - Target: :math:`(N, 1, *)`. Examples: >>> N = 1 # num_classes >>> kwargs = {"alpha": 0.25, "gamma": 2.0, "reduction": 'mean'} >>> loss = BinaryFocalLossWithLogits(**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() """ def __init__(self, alpha: float, gamma: float = 2.0, reduction: str = 'none') -> None: super().__init__() self.alpha: float = alpha self.gamma: float = gamma self.reduction: str = reduction self.eps: float = 1e-8 def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: return binary_focal_loss_with_logits(input, target, self.alpha, self.gamma, self.reduction, self.eps)