Source code for monai.losses.multi_scale

# 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.
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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}

[docs] 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 ( """
[docs] 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
[docs] 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