# 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.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import Optional, Sequence, Union
import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from monai.networks import one_hot
from monai.utils import LossReduction
[docs]class FocalLoss(_Loss):
"""
FocalLoss is an extension of BCEWithLogitsLoss that down-weights loss from
high confidence correct predictions.
Reimplementation of the Focal Loss (with a build-in sigmoid activation) described in:
- "Focal Loss for Dense Object Detection", T. Lin et al., ICCV 2017
- "AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy",
Zhu et al., Medical Physics 2018
Example:
>>> import torch
>>> from monai.losses import FocalLoss
>>> from torch.nn import BCEWithLogitsLoss
>>> shape = B, N, *DIMS = 2, 3, 5, 7, 11
>>> input = torch.rand(*shape)
>>> target = torch.rand(*shape)
>>> # Demonstrate equivalence to BCE when gamma=0
>>> fl_g0_criterion = FocalLoss(reduction='none', gamma=0)
>>> fl_g0_loss = fl_g0_criterion(input, target)
>>> bce_criterion = BCEWithLogitsLoss(reduction='none')
>>> bce_loss = bce_criterion(input, target)
>>> assert torch.allclose(fl_g0_loss, bce_loss)
>>> # Demonstrate "focus" by setting gamma > 0.
>>> fl_g2_criterion = FocalLoss(reduction='none', gamma=2)
>>> fl_g2_loss = fl_g2_criterion(input, target)
>>> # Mark easy and hard cases
>>> is_easy = (target > 0.7) & (input > 0.7)
>>> is_hard = (target > 0.7) & (input < 0.3)
>>> easy_loss_g0 = fl_g0_loss[is_easy].mean()
>>> hard_loss_g0 = fl_g0_loss[is_hard].mean()
>>> easy_loss_g2 = fl_g2_loss[is_easy].mean()
>>> hard_loss_g2 = fl_g2_loss[is_hard].mean()
>>> # Gamma > 0 causes the loss function to "focus" on the hard
>>> # cases. IE, easy cases are downweighted, so hard cases
>>> # receive a higher proportion of the loss.
>>> hard_to_easy_ratio_g2 = hard_loss_g2 / easy_loss_g2
>>> hard_to_easy_ratio_g0 = hard_loss_g0 / easy_loss_g0
>>> assert hard_to_easy_ratio_g2 > hard_to_easy_ratio_g0
"""
[docs] def __init__(
self,
include_background: bool = True,
to_onehot_y: bool = False,
gamma: float = 2.0,
weight: Optional[Union[Sequence[float], float, int, torch.Tensor]] = None,
reduction: Union[LossReduction, str] = LossReduction.MEAN,
) -> None:
"""
Args:
include_background: if False, channel index 0 (background category) is excluded from the calculation.
to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False.
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].
The input can be a single value (same weight for all classes), a sequence of values (the length
of the sequence should be the same as the number of classes, if not ``include_background``, the
number should not include class 0).
The value/values should be no less than 0. Defaults to None.
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:
>>> 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(to_onehot_y=True)
>>> fl(pred, grnd)
"""
super().__init__(reduction=LossReduction(reduction).value)
self.include_background = include_background
self.to_onehot_y = to_onehot_y
self.gamma = gamma
self.weight: Optional[Union[Sequence[float], float, int, torch.Tensor]] = weight
[docs] def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""
Args:
input: the shape should be BNH[WD], where N is the number of classes.
The input should be the original logits since it will be transformed by
a sigmoid in the forward function.
target: the shape should be BNH[WD] or B1H[WD], where N is the number of classes.
Raises:
ValueError: When input and target (after one hot transform if set)
have different shapes.
ValueError: When ``self.reduction`` is not one of ["mean", "sum", "none"].
ValueError: When ``self.weight`` is a sequence and the length is not equal to the
number of classes.
ValueError: When ``self.weight`` is/contains a value that is less than 0.
"""
n_pred_ch = input.shape[1]
if self.to_onehot_y:
if n_pred_ch == 1:
warnings.warn("single channel prediction, `to_onehot_y=True` ignored.")
else:
target = one_hot(target, num_classes=n_pred_ch)
if not self.include_background:
if n_pred_ch == 1:
warnings.warn("single channel prediction, `include_background=False` ignored.")
else:
# if skipping background, removing first channel
target = target[:, 1:]
input = input[:, 1:]
if target.shape != input.shape:
raise ValueError(f"ground truth has different shape ({target.shape}) from input ({input.shape})")
i = input
t = target
# Change the shape of input and target to B x N x num_voxels.
b, n = t.shape[:2]
i = i.reshape(b, n, -1)
t = t.reshape(b, n, -1)
# computing binary cross entropy with logits
# see also https://github.com/pytorch/pytorch/blob/v1.9.0/aten/src/ATen/native/Loss.cpp#L231
max_val = (-i).clamp(min=0)
ce = i - i * t + max_val + ((-max_val).exp() + (-i - max_val).exp()).log()
if self.weight is not None:
class_weight: Optional[torch.Tensor] = None
if isinstance(self.weight, (float, int)):
class_weight = torch.as_tensor([self.weight] * i.size(1))
else:
class_weight = torch.as_tensor(self.weight)
if class_weight.size(0) != i.size(1):
raise ValueError(
"the length of the weight sequence should be the same as the number of classes. "
+ "If `include_background=False`, the number should not include class 0."
)
if class_weight.min() < 0:
raise ValueError("the value/values of weights should be no less than 0.")
class_weight = class_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 = class_weight[None, :, None] # N => 1,N,1
at = at.expand((t.size(0), -1, t.size(2))) # 1,N,1 => B,N,H*W
# Multiply the log proba by their weights.
ce = ce * at
# Compute the loss mini-batch.
# (1-p_t)^gamma * log(p_t) with reduced chance of overflow
p = F.logsigmoid(-i * (t * 2.0 - 1.0))
flat_loss: torch.Tensor = (p * self.gamma).exp() * ce
# Previously there was a mean over the last dimension, which did not
# return a compatible BCE loss. To maintain backwards compatible
# behavior we have a flag that performs this extra step, disable or
# parameterize if necessary. (Or justify why the mean should be there)
average_spatial_dims = True
if self.reduction == LossReduction.SUM.value:
if average_spatial_dims:
flat_loss = flat_loss.mean(dim=-1)
loss = flat_loss.sum()
elif self.reduction == LossReduction.MEAN.value:
if average_spatial_dims:
flat_loss = flat_loss.mean(dim=-1)
loss = flat_loss.mean()
elif self.reduction == LossReduction.NONE.value:
spacetime_dims = input.shape[2:]
loss = flat_loss.reshape([b, n] + list(spacetime_dims))
else:
raise ValueError(f'Unsupported reduction: {self.reduction}, available options are ["mean", "sum", "none"].')
return loss
```