Source code for mindspore.nn.metrics.root_mean_square_surface_distance

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"""RootMeanSquareSurfaceDistance."""
from __future__ import absolute_import

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 RootMeanSquareDistance(Metric): r""" Computes the Root Mean Square Surface Distance from `y_pred` to `y` under the default setting. 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 Root Mean Square Surface Distance from set(B) to set(A) is: .. math:: RmsSurDis(B \rightarrow A) = \sqrt{\frac{\sum_{s_{B} \in S(B)}^{} {\text{dis}^2 \left ( s_{B}, S(A) \right )} }{\left | S(B) \right |}} Where the \|\|\*\|\| denotes a distance measure. \|\*\| denotes the number of elements. The Root Mean Square Surface Distance from set(B) to set(A) and from set(A) to set(B) is: .. math:: RmsSurDis(A \leftrightarrow B) = \sqrt{\frac{\sum_{s_{A} \in S(A)}^{} {\text{dis} \left ( s_{A}, S(B) \right ) ^{2}} + \sum_{s_{B} \in S(B)}^{} {\text{dis} \left ( s_{B}, S(A) \right ) ^{2}}}{\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 symmetric average root mean square distance between y_pred and y. If False, only calculates :math:`RmsSurDis(y_{pred}, y)` surface distance, otherwise, the mean of distance from `y_pred` to `y` and from `y` to `y_pred`, i.e. :math:`RmsSurDis({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.RootMeanSquareDistance(symmetric=False, distance_metric="euclidean") >>> metric.clear() >>> metric.update(x, y, 0) >>> root_mean_square_distance = metric.eval() >>> print(root_mean_square_distance) 1.0000000000000002 """ def __init__(self, symmetric=False, distance_metric="euclidean"): super(RootMeanSquareDistance, 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._y_pred_edges = None self._is_update = None self._y_edges = None 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 not np.any(y_edges): dis = np.full(y_edges.shape, np.inf) else: 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) return dis[y_pred_edges]
[文档] def clear(self): """Clears the internal evaluation result.""" self._y_pred_edges = 0 self._y_edges = 0 self._is_update = False
[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 `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 'RootMeanSquareDistance.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 TypeError("For 'RootMeanSquareDistance.update', the label index (input[2]) must be int or float, " "but got label index type: {}.".format(type(label_idx))) if label_idx not in y_pred and label_idx not in y: raise ValueError("For 'RootMeanSquareDistance.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("For 'RootMeanSquareDistance.update', the size of predicted value (input[0]) " "and true value (input[1]) should be greater than 0, in addition to that, " "predicted value and true value should have the same shape, " "but got predicted value size: {}, shape: {}, true value size: {}, shape: {}. " .format(y_pred.size, y_pred.shape, y.size, 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 Root Mean Square Distance. Returns: numpy.float64, root mean square surface distance. 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.") residual_mean_square_distance = self._get_surface_distance(self._y_pred_edges, self._y_edges) if residual_mean_square_distance.shape == (0,): return np.inf rms_surface_distance = (residual_mean_square_distance**2).mean() if not self.symmetric: return rms_surface_distance contrary_residual_mean_square_distance = self._get_surface_distance(self._y_edges, self._y_pred_edges) if contrary_residual_mean_square_distance.shape == (0,): return np.inf contrary_rms_surface_distance = (contrary_residual_mean_square_distance**2).mean() rms_distance = np.sqrt(np.mean((rms_surface_distance, contrary_rms_surface_distance))) return rms_distance