# Copyright (c) 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.
from __future__ import annotations
import warnings
from collections.abc import Callable
import torch
from torch.nn.modules.loss import _Loss
from monai.networks import one_hot
from monai.utils import LossReduction
[docs]
class TverskyLoss(_Loss):
"""
Compute the Tversky loss defined in:
Sadegh et al. (2017) Tversky loss function for image segmentation
using 3D fully convolutional deep networks. (https://arxiv.org/abs/1706.05721)
Adapted from:
https://github.com/NifTK/NiftyNet/blob/v0.6.0/niftynet/layer/loss_segmentation.py#L631
"""
[docs]
def __init__(
self,
include_background: bool = True,
to_onehot_y: bool = False,
sigmoid: bool = False,
softmax: bool = False,
other_act: Callable | None = None,
alpha: float = 0.5,
beta: float = 0.5,
reduction: LossReduction | str = LossReduction.MEAN,
smooth_nr: float = 1e-5,
smooth_dr: float = 1e-5,
batch: bool = False,
) -> 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.
sigmoid: If True, apply a sigmoid function to the prediction.
softmax: If True, apply a softmax function to the prediction.
other_act: if don't want to use `sigmoid` or `softmax`, use other callable function to execute
other activation layers, Defaults to ``None``. for example:
`other_act = torch.tanh`.
alpha: weight of false positives
beta: weight of false negatives
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.
smooth_nr: a small constant added to the numerator to avoid zero.
smooth_dr: a small constant added to the denominator to avoid nan.
batch: whether to sum the intersection and union areas over the batch dimension before the dividing.
Defaults to False, a Dice loss value is computed independently from each item in the batch
before any `reduction`.
Raises:
TypeError: When ``other_act`` is not an ``Optional[Callable]``.
ValueError: When more than 1 of [``sigmoid=True``, ``softmax=True``, ``other_act is not None``].
Incompatible values.
"""
super().__init__(reduction=LossReduction(reduction).value)
if other_act is not None and not callable(other_act):
raise TypeError(f"other_act must be None or callable but is {type(other_act).__name__}.")
if int(sigmoid) + int(softmax) + int(other_act is not None) > 1:
raise ValueError("Incompatible values: more than 1 of [sigmoid=True, softmax=True, other_act is not None].")
self.include_background = include_background
self.to_onehot_y = to_onehot_y
self.sigmoid = sigmoid
self.softmax = softmax
self.other_act = other_act
self.alpha = alpha
self.beta = beta
self.smooth_nr = float(smooth_nr)
self.smooth_dr = float(smooth_dr)
self.batch = batch
[docs]
def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""
Args:
input: the shape should be BNH[WD].
target: the shape should be BNH[WD].
Raises:
ValueError: When ``self.reduction`` is not one of ["mean", "sum", "none"].
"""
if self.sigmoid:
input = torch.sigmoid(input)
n_pred_ch = input.shape[1]
if self.softmax:
if n_pred_ch == 1:
warnings.warn("single channel prediction, `softmax=True` ignored.")
else:
input = torch.softmax(input, 1)
if self.other_act is not None:
input = self.other_act(input)
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 AssertionError(f"ground truth has differing shape ({target.shape}) from input ({input.shape})")
p0 = input
p1 = 1 - p0
g0 = target
g1 = 1 - g0
# reducing only spatial dimensions (not batch nor channels)
reduce_axis: list[int] = torch.arange(2, len(input.shape)).tolist()
if self.batch:
# reducing spatial dimensions and batch
reduce_axis = [0] + reduce_axis
tp = torch.sum(p0 * g0, reduce_axis)
fp = self.alpha * torch.sum(p0 * g1, reduce_axis)
fn = self.beta * torch.sum(p1 * g0, reduce_axis)
numerator = tp + self.smooth_nr
denominator = tp + fp + fn + self.smooth_dr
score: torch.Tensor = 1.0 - numerator / denominator
if self.reduction == LossReduction.SUM.value:
return torch.sum(score) # sum over the batch and channel dims
if self.reduction == LossReduction.NONE.value:
return score # returns [N, num_classes] losses
if self.reduction == LossReduction.MEAN.value:
return torch.mean(score)
raise ValueError(f'Unsupported reduction: {self.reduction}, available options are ["mean", "sum", "none"].')