# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""RootMeanSquareSurfaceDistance."""
from scipy.ndimage import morphology
import numpy as np
from mindspore._checkparam import Validator as validator
from .metric import Metric, rearrange_inputs
[docs]class RootMeanSquareDistance(Metric):
"""
This function is used to compute the Residual Mean Square Distance from `y_pred` to `y` under the default
setting. Residual Mean Square Distance(RMS), the mean is taken from each of the points in the vector, these
residuals are squared (to remove negative signs), summed, weighted by the mean and then the square-root is taken.
Measured in mm.
Args:
distance_metric (string): The parameter of calculating Hausdorff distance supports three measurement methods,
"euclidean", "chessboard" or "taxicab". Default: "euclidean".
symmetric (bool): if calculate the symmetric average surface distance between `y_pred` and `y`. In addition,
if sets ``symmetric = True``, the average symmetric surface distance between these two inputs
will be returned. Defaults: 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()
[docs] 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 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)
surface_distance = dis[y_pred_edges]
return surface_distance
[docs] @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 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 not be int or float.
ValueError: If the value of label_idx is not in y_pred or y.
ValueError: If y_pred and y should have different shape.
"""
if len(inputs) != 3:
raise ValueError('MeanSurfaceDistance need 3 inputs (y_pred, y, label), 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("The data type of label_idx must be int or float, but got {}.".format(type(label_idx)))
if label_idx not in y_pred and label_idx not in y:
raise ValueError("The label_idx should be in y_pred or y, but {} is not.".format(label_idx))
if y_pred.size == 0 or y_pred.shape != y.shape:
raise ValueError("y_pred and y should have same shape, but got {}, {}.".format(y_pred.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
[docs] def eval(self):
"""
Calculate residual mean square surface distance.
Returns:
A float with residual 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('Call the update method before calling eval.')
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