code: # 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.
  # 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.
  
  import warnings
  from typing import Union
  
  import numpy as np
  import torch
  
  from monai.metrics.utils import *
  from monai.utils import MetricReduction
  
  
  class SurfaceDistanceMetric:
      """
      Compute Surface Distance between two tensors. It can support both multi-classes and multi-labels tasks.
      It supports both symmetric and asymmetric surface distance calculation.
      Input `y_pred` (BNHW[D] where N is number of classes) is compared with ground truth `y` (BNHW[D]).
      `y_preds` is expected to have binarized predictions and `y` should be in one-hot format.
      You can use suitable transforms in ``monai.transforms.post`` first to achieve binarized values.
  
      Args:
          include_background: whether to skip distance computation on the first channel of
              the predicted output. Defaults to ``False``.
          symmetric: whether to calculate the symmetric average surface distance between
              `seg_pred` and `seg_gt`. Defaults to ``False``.
          distance_metric: : [``"euclidean"``, ``"chessboard"``, ``"taxicab"``]
              the metric used to compute surface distance. Defaults to ``"euclidean"``.
          reduction: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
              ``"mean_channel"``, ``"sum_channel"``}
              Define the mode to reduce computation result of 1 batch data. Defaults to ``"mean"``.
  
      """
  
      def __init__(
          self,
          include_background: bool = False,
          symmetric: bool = False,
          distance_metric: str = "euclidean",
          reduction: Union[MetricReduction, str] = MetricReduction.MEAN,
      ) -> None:
          super().__init__()
          self.include_background = include_background
          self.distance_metric = distance_metric
          self.symmetric = symmetric
          self.reduction = reduction
  
      def __call__(self, y_pred: torch.Tensor, y: torch.Tensor):
          """
          Args:
              y_pred: input data to compute, typical segmentation model output.
                  It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values
                  should be binarized.
              y: ground truth to compute the distance. It must be one-hot format and first dim is batch.
                  The values should be binarized.
  
          Raises:
              ValueError: when `y` is not a binarized tensor.
              ValueError: when `y_pred` has less than three dimensions.
          """
          if not torch.all(y_pred.byte() == y_pred):
              warnings.warn("y_pred is not a binarized tensor here!")
          if not torch.all(y.byte() == y):
              raise ValueError("y should be a binarized tensor.")
          dims = y_pred.ndimension()
          if dims < 3:
              raise ValueError("y_pred should have at least three dimensions.")
          # compute (BxC) for each channel for each batch
          f = compute_average_surface_distance(
              y_pred=y_pred,
              y=y,
              include_background=self.include_background,
              symmetric=self.symmetric,
              distance_metric=self.distance_metric,
          )
  
          # do metric reduction
          f, not_nans = do_metric_reduction(f, self.reduction)
          return f, not_nans
  
  
  def compute_average_surface_distance(
      y_pred: Union[np.ndarray, torch.Tensor],
      y: Union[np.ndarray, torch.Tensor],
      include_background: bool = False,
      symmetric: bool = False,
      distance_metric: str = "euclidean",
  ):
      """
      This function is used to compute the Average Surface Distance from `y_pred` to `y`
      under the default setting.
      In addition, if sets ``symmetric = True``, the average symmetric surface distance between
      these two inputs will be returned.
  
      Args:
          y_pred: input data to compute, typical segmentation model output.
              It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values
              should be binarized.
          y: ground truth to compute mean the distance. It must be one-hot format and first dim is batch.
              The values should be binarized.
          include_background: whether to skip distance computation on the first channel of
              the predicted output. Defaults to ``False``.
          symmetric: whether to calculate the symmetric average surface distance between
              `seg_pred` and `seg_gt`. Defaults to ``False``.
          distance_metric: : [``"euclidean"``, ``"chessboard"``, ``"taxicab"``]
              the metric used to compute surface distance. Defaults to ``"euclidean"``.
      """
  
      if not include_background:
          y_pred, y = ignore_background(
              y_pred=y_pred,
              y=y,
          )
  
      y = y.float()
      y_pred = y_pred.float()
  
      if y.shape != y_pred.shape:
          raise ValueError("y_pred and y should have same shapes.")
  
      batch_size, n_class = y_pred.shape[:2]
      asd = np.empty((batch_size, n_class))
  
      for b, c in np.ndindex(batch_size, n_class):
          (edges_pred, edges_gt) = get_mask_edges(y_pred[b, c], y[b, c])
          surface_distance = get_surface_distance(edges_pred, edges_gt, distance_metric=distance_metric)
          if surface_distance.shape == (0,):
              avg_surface_distance = np.nan
          else:
              avg_surface_distance = surface_distance.mean()
          if not symmetric:
              asd[b, c] = avg_surface_distance
          else:
              surface_distance_2 = get_surface_distance(edges_gt, edges_pred, distance_metric=distance_metric)
              if surface_distance_2.shape == (0,):
                  avg_surface_distance_2 = np.nan
              else:
                  avg_surface_distance_2 = surface_distance_2.mean()
              asd[b, c] = np.mean((avg_surface_distance, avg_surface_distance_2))
  
      return torch.from_numpy(asd)
  
apis: numpy.mean torch.from_numpy numpy.empty numpy.ndindex