Source code for monai.handlers.hausdorff_distance

# Copyright 2020 - 2021 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 typing import Callable, Optional, Sequence

import torch

from monai.metrics import HausdorffDistanceMetric
from monai.utils import MetricReduction, exact_version, optional_import

NotComputableError, _ = optional_import("ignite.exceptions", "0.4.2", exact_version, "NotComputableError")
Metric, _ = optional_import("ignite.metrics", "0.4.2", exact_version, "Metric")
reinit__is_reduced, _ = optional_import("ignite.metrics.metric", "0.4.2", exact_version, "reinit__is_reduced")
sync_all_reduce, _ = optional_import("ignite.metrics.metric", "0.4.2", exact_version, "sync_all_reduce")

[docs]class HausdorffDistance(Metric): # type: ignore[valid-type, misc] # due to optional_import """ Computes Hausdorff distance from full size Tensor and collects average over batch, class-channels, iterations. """ def __init__( self, include_background: bool = False, distance_metric: str = "euclidean", percentile: Optional[float] = None, directed: bool = False, output_transform: Callable = lambda x: x, device: Optional[torch.device] = None, ) -> None: """ Args: include_background: whether to include distance computation on the first channel of the predicted output. Defaults to ``False``. distance_metric: : [``"euclidean"``, ``"chessboard"``, ``"taxicab"``] the metric used to compute surface distance. Defaults to ``"euclidean"``. percentile: an optional float number between 0 and 100. If specified, the corresponding percentile of the Hausdorff Distance rather than the maximum result will be achieved. Defaults to ``None``. directed: whether to calculate directed Hausdorff distance. Defaults to ``False``. output_transform: transform the ignite.engine.state.output into [y_pred, y] pair. device: device specification in case of distributed computation usage. """ super().__init__(output_transform, device=device) self.hd = HausdorffDistanceMetric( include_background=include_background, distance_metric=distance_metric, percentile=percentile, directed=directed, reduction=MetricReduction.MEAN, ) self._sum = 0.0 self._num_examples = 0
[docs] @reinit__is_reduced def reset(self) -> None: self._sum = 0.0 self._num_examples = 0
[docs] @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: """ Args: output: sequence with contents [y_pred, y]. Raises: ValueError: When ``output`` length is not 2. The metric can only support y_pred and y. """ if len(output) != 2: raise ValueError(f"output must have length 2, got {len(output)}.") y_pred, y = output score, not_nans = self.hd(y_pred, y) not_nans = int(not_nans.item()) # add all items in current batch self._sum += score.item() * not_nans self._num_examples += not_nans
[docs] @sync_all_reduce("_sum", "_num_examples") def compute(self) -> float: """ Raises: NotComputableError: When ``compute`` is called before an ``update`` occurs. """ if self._num_examples == 0: raise NotComputableError("HausdorffDistance must have at least one example before it can be computed.") return self._sum / self._num_examples