From ce54c9487f346074b0ed15c849ae7ec4bc032fc5 Mon Sep 17 00:00:00 2001 From: Shiran Yu Date: Mon, 23 Feb 2026 19:42:04 +0000 Subject: [PATCH] fix(losses): add missing docstring entries for default parameters - Add missing `alpha` parameter (default 2.0) to HausdorffDTLoss.__init__ docstring - Add "Defaults to X" notes to SoftclDiceLoss and SoftDiceclDiceLoss docstrings for `iter_`, `smooth`, and `alpha` parameters Signed-off-by: Shiran Yu --- monai/losses/cldice.py | 10 +++++----- monai/losses/hausdorff_loss.py | 1 + 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/monai/losses/cldice.py b/monai/losses/cldice.py index 406cc3825f..c877567bbc 100644 --- a/monai/losses/cldice.py +++ b/monai/losses/cldice.py @@ -126,8 +126,8 @@ class SoftclDiceLoss(_Loss): def __init__(self, iter_: int = 3, smooth: float = 1.0) -> None: """ Args: - iter_: Number of iterations for skeletonization - smooth: Smoothing parameter + iter_: Number of iterations for skeletonization. Defaults to 3. + smooth: Smoothing parameter. Defaults to 1.0. """ super().__init__() self.iter = iter_ @@ -160,9 +160,9 @@ class SoftDiceclDiceLoss(_Loss): def __init__(self, iter_: int = 3, alpha: float = 0.5, smooth: float = 1.0) -> None: """ Args: - iter_: Number of iterations for skeletonization - smooth: Smoothing parameter - alpha: Weighing factor for cldice + iter_: Number of iterations for skeletonization. Defaults to 3. + smooth: Smoothing parameter. Defaults to 1.0. + alpha: Weighing factor for cldice. Defaults to 0.5. """ super().__init__() self.iter = iter_ diff --git a/monai/losses/hausdorff_loss.py b/monai/losses/hausdorff_loss.py index c58be2d253..b75433e1da 100644 --- a/monai/losses/hausdorff_loss.py +++ b/monai/losses/hausdorff_loss.py @@ -54,6 +54,7 @@ def __init__( ) -> None: """ Args: + alpha: the exponent to transform the distance when computing the loss. Defaults to 2.0. include_background: if False, channel index 0 (background category) is excluded from the calculation. if the non-background segmentations are small compared to the total image size they can get overwhelmed by the signal from the background so excluding it in such cases helps convergence.