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# 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 inspect
import warnings
from collections.abc import Callable
from typing import Any, Optional
import torch
from torch.nn.modules.loss import _Loss
__all__ = ["MaskedLoss"]
class MaskedLoss(_Loss):
"""
This is a wrapper class for the loss functions. It allows for additional
weighting masks to be applied to both input and target.
See Also:
- :py:class:`monai.losses.MaskedDiceLoss`
"""
def __init__(
self, loss: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] | _Loss, *loss_args: Any, **loss_kwargs: Any
) -> None:
"""
Args:
loss: loss function to be wrapped, this could be a loss class or an instance of a loss class.
loss_args: arguments to the loss function's constructor if `loss` is a class.
loss_kwargs: keyword arguments to the loss function's constructor if `loss` is a class.
"""
super().__init__()
self.loss: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = (
loss(*loss_args, **loss_kwargs) if inspect.isclass(loss) else loss
)
if not callable(self.loss):
raise ValueError("The loss function is not callable.")
def forward(self, input: torch.Tensor, target: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Args:
input: the shape should be BNH[WD].
target: the shape should be BNH[WD].
mask: the shape should be B1H[WD] or 11H[WD].
"""
if mask is None:
warnings.warn("No mask value specified for the MaskedLoss.")
return self.loss(input, target)
if input.dim() != mask.dim():
warnings.warn(f"Dim of input ({input.shape}) is different from mask ({mask.shape}).")
if input.shape[0] != mask.shape[0] and mask.shape[0] != 1:
raise ValueError(f"Batch size of mask ({mask.shape}) must be one or equal to input ({input.shape}).")
if target.dim() > 1:
if mask.shape[1] != 1:
raise ValueError(f"Mask ({mask.shape}) must have only one channel.")
if input.shape[2:] != mask.shape[2:]:
warnings.warn(f"Spatial size of input ({input.shape}) is different from mask ({mask.shape}).")
return self.loss(input * mask, target * mask)
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