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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 torch
from torch.nn.modules.loss import _Loss
from monai.networks.layers import gaussian_1d, separable_filtering
from monai.utils import LossReduction
def make_gaussian_kernel(sigma: int) -> torch.Tensor:
if sigma <= 0:
raise ValueError(f"expecting positive sigma, got sigma={sigma}")
return gaussian_1d(sigma=torch.tensor(sigma), truncated=3, approx="sampled", normalize=False)
def make_cauchy_kernel(sigma: int) -> torch.Tensor:
if sigma <= 0:
raise ValueError(f"expecting positive sigma, got sigma={sigma}")
tail = int(sigma * 5)
k = torch.tensor([((x / sigma) ** 2 + 1) for x in range(-tail, tail + 1)])
k = torch.reciprocal(k)
k = k / torch.sum(k)
return k
kernel_fn_dict = {"gaussian": make_gaussian_kernel, "cauchy": make_cauchy_kernel}
class MultiScaleLoss(_Loss):
"""
This is a wrapper class.
It smooths the input and target at different scales before passing them into the wrapped loss function.
Adapted from:
DeepReg (https://github.com/DeepRegNet/DeepReg)
"""
def __init__(
self,
loss: _Loss,
scales: list | None = None,
kernel: str = "gaussian",
reduction: LossReduction | str = LossReduction.MEAN,
) -> None:
"""
Args:
loss: loss function to be wrapped
scales: list of scalars or None, if None, do not apply any scaling.
kernel: gaussian or cauchy.
"""
super().__init__(reduction=LossReduction(reduction).value)
if kernel not in kernel_fn_dict:
raise ValueError(f"got unsupported kernel type: {kernel}", "only support gaussian and cauchy")
self.kernel_fn = kernel_fn_dict[kernel]
self.loss = loss
self.scales = scales
def forward(self, y_true: torch.Tensor, y_pred: torch.Tensor) -> torch.Tensor:
if self.scales is None:
loss: torch.Tensor = self.loss(y_pred, y_true)
else:
loss_list = []
for s in self.scales:
if s == 0:
# no smoothing
loss_list.append(self.loss(y_pred, y_true))
else:
loss_list.append(
self.loss(
separable_filtering(y_pred, [self.kernel_fn(s).to(y_pred)] * (y_true.ndim - 2)),
separable_filtering(y_true, [self.kernel_fn(s).to(y_pred)] * (y_true.ndim - 2)),
)
)
loss = torch.stack(loss_list, dim=0)
if self.reduction == LossReduction.MEAN.value:
loss = torch.mean(loss) # the batch and channel average
elif self.reduction == LossReduction.SUM.value:
loss = torch.sum(loss) # sum over the batch and channel dims
elif self.reduction != LossReduction.NONE.value:
raise ValueError(f'Unsupported reduction: {self.reduction}, available options are ["mean", "sum", "none"].')
return loss
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