# 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."""
import numpy as np
from mindspore._checkparam import Validator as validator
from .metric import Metric
[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.
Examples:
>>> 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._y_pred_edges = 0
self._is_update = False
self.distance_metric_list = ["euclidean", "chessboard", "taxicab"]
distance_metric = validator.check_value_type("distance_metric", distance_metric, [str])
validator.check_string(distance_metric, self.distance_metric_list, "distance_metric")
self.distance_metric = distance_metric
self.symmetric = validator.check_value_type("symmetric", symmetric, [bool])
self._y_edges = 0
self.clear()
[docs] def clear(self):
"""Clears the internal evaluation result."""
self._y_pred_edges = 0
self._y_edges = 0
self._is_update = False
[docs] 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.
"""
if len(inputs) != 3:
raise ValueError('RootMeanSurfaceDistance need 3 inputs (y_pred, y, label), '
'but got {}.'.format(len(inputs)))
self._y_pred_edges, self._y_edges = self._check_surface_distance_inputs(inputs)
self._is_update = True
[docs] def eval(self):
"""
Calculate residual mean square surface distance.
"""
if self._is_update is False:
raise RuntimeError('Call the update method before calling eval.')
residual_mean_square_distance = Metric._get_surface_distance(self._y_pred_edges, self._y_edges,
self.distance_metric)
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 = Metric._get_surface_distance(self._y_edges, self._y_pred_edges,
self.distance_metric)
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