Source code for mindspore.nn.metrics.mean_surface_distance

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"""MeanSurfaceDistance."""
from scipy.ndimage import morphology
import numpy as np
from mindspore._checkparam import Validator as validator
from .metric import Metric, rearrange_inputs


[文档]class MeanSurfaceDistance(Metric): r""" Computes the Average Surface Distance from `y_pred` to `y` under the default setting. It measures how much, on average, the surface varies between the segmentation and the GT (ground truth). Given two sets A and B, S(A) denotes the set of surface voxels of A. The shortest distance of an arbitrary voxel v to S(A) is defined as: .. math:: {\text{dis}}\left (v, S(A)\right ) = \underset{s_{A} \in S(A)}{\text{min }}\rVert v - s_{A} \rVert The Average Surface Distance from set(B) to set(A) is given by: .. math:: AvgSurDis(B \rightarrow A) = \frac{\sum_{s_{B} \in S(B)}^{} {\text{dis} \left ( s_{B}, S(A) \right )} } {\left | S(B) \right |} Where the \|\|\*\|\| denotes a distance measure. \|\*\| denotes the number of elements. The mean of surface distance from set(B) to set(A) and from set(A) to set(B) is: .. math:: MeanSurDis(A \leftrightarrow B) = \frac{\sum_{s_{A} \in S(A)}^{} {\text{dis} \left ( s_{A}, S(B) \right )} + \sum_{s_{B} \in S(B)}^{} {\text{dis} \left ( s_{B}, S(A) \right )} }{\left | S(A) \right | + \left | S(B) \right |} Args: distance_metric (string): Three measurement methods are supported: "euclidean", "chessboard" or "taxicab". Default: "euclidean". symmetric (bool): Whether to calculate the Mean Surface Distance between y_pred and y. If False, it only calculates :math:`AvgSurDis({y_pred} \rightarrow y)`, otherwise, the mean of distance from `y_pred` to `y` and from `y` to `y_pred`, i.e. :math:`MeanSurDis(y_{pred} \leftrightarrow y)`, will be returned. Default: False. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import numpy as np >>> from mindspore import nn, Tensor >>> x = Tensor(np.array([[3, 0, 1], [1, 3, 0], [1, 0, 2]])) >>> y = Tensor(np.array([[0, 2, 1], [1, 2, 1], [0, 0, 1]])) >>> metric = nn.MeanSurfaceDistance(symmetric=False, distance_metric="euclidean") >>> metric.clear() >>> metric.update(x, y, 0) >>> mean_average_distance = metric.eval() >>> print(mean_average_distance) 0.8047378541243649 """ def __init__(self, symmetric=False, distance_metric="euclidean"): super(MeanSurfaceDistance, self).__init__() self.distance_metric_list = ["euclidean", "chessboard", "taxicab"] distance_metric = validator.check_value_type("distance_metric", distance_metric, [str]) self.distance_metric = validator.check_string(distance_metric, self.distance_metric_list, "distance_metric") self.symmetric = validator.check_value_type("symmetric", symmetric, [bool]) self.clear() self._is_update = None self._y_edges = None self._y_pred_edges = None
[文档] def clear(self): """Clears the internal evaluation result.""" self._y_pred_edges = 0 self._y_edges = 0 self._is_update = False
def _get_surface_distance(self, y_pred_edges, y_edges): """ Calculate the surface distances from `y_pred_edges` to `y_edges`. Args: y_pred_edges (np.ndarray): the edge of the predictions. y_edges (np.ndarray): the edge of the ground truth. """ if not np.any(y_pred_edges): return np.array([]) if np.any(y_edges): if self.distance_metric == "euclidean": dis = morphology.distance_transform_edt(~y_edges) elif self.distance_metric in self.distance_metric_list[-2:]: dis = morphology.distance_transform_cdt(~y_edges, metric=self.distance_metric) else: dis = np.full(y_edges.shape, np.inf) return dis[y_pred_edges]
[文档] @rearrange_inputs def update(self, *inputs): """ Updates the internal evaluation result 'y_pred', 'y' and 'label_idx'. Args: inputs: Input 'y_pred', 'y' and 'label_idx'. 'y_pred' and 'y' are a Tensor, list or numpy.ndarray. 'y_pred' is the predicted binary image. 'y' is the actual binary image. 'label_idx', the data type of `label_idx` is int. Raises: ValueError: If the number of the inputs is not 3. TypeError: If the data type of label_idx is not int or float. ValueError: If the value of label_idx is not in y_pred or y. ValueError: If y_pred and y have different shapes. """ if len(inputs) != 3: raise ValueError("For 'MeanSurfaceDistance.update', it needs 3 inputs (predicted value, true value, " "label index), but got {}".format(len(inputs))) y_pred = self._convert_data(inputs[0]) y = self._convert_data(inputs[1]) label_idx = inputs[2] if not isinstance(label_idx, (int, float)): raise ValueError(f"For 'MeanSurfaceDistance.update', the label index (input[2]) must be int or float, " f"but got {type(label_idx)}.") if label_idx not in y_pred and label_idx not in y: raise ValueError("For 'MeanSurfaceDistance.update', the label index (input[2]) should be in predicted " "value (input[0]) or true value (input[1]), but {} is not.".format(label_idx)) if y_pred.size == 0 or y_pred.shape != y.shape: raise ValueError(f"For 'MeanSurfaceDistance.update', the size of predicted value (input[0]) and true " f"value (input[1]) should be greater than 0, in addition to that, predicted value and " f"true value should have the same shape, but got predicted value size: {y_pred.size}, " f"shape: {y_pred.shape}, true value size: {y.size}, shape: {y.shape}.") if y_pred.dtype != bool: y_pred = y_pred == label_idx if y.dtype != bool: y = y == label_idx self._y_pred_edges = morphology.binary_erosion(y_pred) ^ y_pred self._y_edges = morphology.binary_erosion(y) ^ y self._is_update = True
[文档] def eval(self): """ Calculate mean surface distance. Returns: numpy.float64. The mean surface distance value. Raises: RuntimeError: If the update method is not called first, an error will be reported. """ if self._is_update is False: raise RuntimeError("Please call the 'update' method before calling 'eval' method.") mean_surface_distance = self._get_surface_distance(self._y_pred_edges, self._y_edges) if mean_surface_distance.shape == (0,): return np.inf avg_surface_distance = mean_surface_distance.mean() if not self.symmetric: return avg_surface_distance contrary_mean_surface_distance = self._get_surface_distance(self._y_edges, self._y_pred_edges) if contrary_mean_surface_distance.shape == (0,): return np.inf contrary_avg_surface_distance = contrary_mean_surface_distance.mean() return np.mean((avg_surface_distance, contrary_avg_surface_distance))