Source code for monai.losses.focal_loss

# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
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from typing import Optional, Union

import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _WeightedLoss

from monai.utils import LossReduction


[docs]class FocalLoss(_WeightedLoss): """ Reimplementation of the Focal Loss described in: - "Focal Loss for Dense Object Detection", T. Lin et al., ICCV 2017 - "AnatomyNet: Deep learning for fast and fully automated whole‚Äźvolume segmentation of head and neck anatomy", Zhu et al., Medical Physics 2018 """ def __init__( self, gamma: float = 2.0, weight: Optional[torch.Tensor] = None, reduction: Union[LossReduction, str] = LossReduction.MEAN, ) -> None: """ Args: gamma: value of the exponent gamma in the definition of the Focal loss. weight: weights to apply to the voxels of each class. If None no weights are applied. This corresponds to the weights `\alpha` in [1]. reduction: {``"none"``, ``"mean"``, ``"sum"``} Specifies the reduction to apply to the output. Defaults to ``"mean"``. - ``"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. Example: .. code-block:: python import torch from monai.losses import FocalLoss pred = torch.tensor([[1, 0], [0, 1], [1, 0]], dtype=torch.float32) grnd = torch.tensor([[0], [1], [0]], dtype=torch.int64) fl = FocalLoss() fl(pred, grnd) """ super(FocalLoss, self).__init__(weight=weight, reduction=LossReduction(reduction).value) self.gamma = gamma self.weight: Optional[torch.Tensor] = None
[docs] def forward(self, logits: torch.Tensor, target: torch.Tensor) -> torch.Tensor: """ Args: logits: the shape should be BCH[WD]. where C (greater than 1) is the number of classes. Softmax over the logits is integrated in this module for improved numerical stability. target: the shape should be B1H[WD] or BCH[WD]. If the target's shape is B1H[WD], the target that this loss expects should be a class index in the range [0, C-1] where C is the number of classes. Raises: ValueError: When ``target`` ndim differs from ``logits``. ValueError: When ``target`` channel is not 1 and ``target`` shape differs from ``logits``. ValueError: When ``self.reduction`` is not one of ["mean", "sum", "none"]. """ i = logits t = target if i.ndimension() != t.ndimension(): raise ValueError(f"logits and target ndim must match, got logits={i.ndimension()} target={t.ndimension()}.") if t.shape[1] != 1 and t.shape[1] != i.shape[1]: raise ValueError( "target must have one channel or have the same shape as the logits. " "If it has one channel, it should be a class index in the range [0, C-1] " f"where C is the number of classes inferred from 'logits': C={i.shape[1]}. " ) if i.shape[1] == 1: raise NotImplementedError("Single-channel predictions not supported.") # Change the shape of logits and target to # num_batch x num_class x num_voxels. if i.dim() > 2: i = i.view(i.size(0), i.size(1), -1) # N,C,H,W => N,C,H*W t = t.view(t.size(0), t.size(1), -1) # N,1,H,W => N,1,H*W or N,C,H*W else: # Compatibility with classification. i = i.unsqueeze(2) # N,C => N,C,1 t = t.unsqueeze(2) # N,1 => N,1,1 or N,C,1 # Compute the log proba (more stable numerically than softmax). logpt = F.log_softmax(i, dim=1) # N,C,H*W # Keep only log proba values of the ground truth class for each voxel. if target.shape[1] == 1: logpt = logpt.gather(1, t.long()) # N,C,H*W => N,1,H*W logpt = torch.squeeze(logpt, dim=1) # N,1,H*W => N,H*W # Get the proba pt = torch.exp(logpt) # N,H*W or N,C,H*W if self.weight is not None: self.weight = self.weight.to(i) # Convert the weight to a map in which each voxel # has the weight associated with the ground-truth label # associated with this voxel in target. at = self.weight[None, :, None] # C => 1,C,1 at = at.expand((t.size(0), -1, t.size(2))) # 1,C,1 => N,C,H*W if target.shape[1] == 1: at = at.gather(1, t.long()) # selection of the weights => N,1,H*W at = torch.squeeze(at, dim=1) # N,1,H*W => N,H*W # Multiply the log proba by their weights. logpt = logpt * at # Compute the loss mini-batch. weight = torch.pow(-pt + 1.0, self.gamma) if target.shape[1] == 1: loss = torch.mean(-weight * logpt, dim=1) # N else: loss = torch.mean(-weight * t * logpt, dim=-1) # N,C if self.reduction == LossReduction.SUM.value: return loss.sum() if self.reduction == LossReduction.NONE.value: return loss if self.reduction == LossReduction.MEAN.value: return loss.mean() raise ValueError(f'Unsupported reduction: {self.reduction}, available options are ["mean", "sum", "none"].')