# 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. # ============================================================================ # """test_mean_surface_distance""" import math import numpy as np import pytest from mindspore import Tensor from mindspore.nn.metrics import get_metric_fn, MeanSurfaceDistance def test_mean_surface_distance(): """test_mean_surface_distance""" 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 = get_metric_fn('mean_surface_distance') metric.clear() metric.update(x, y, 0) distance = metric.eval() assert math.isclose(distance, 0.8047378541243649, abs_tol=0.001) def test_mean_surface_distance_update1(): x = Tensor(np.array([[0.2, 0.5, 0.7], [0.3, 0.1, 0.2], [0.9, 0.6, 0.5]])) metric = MeanSurfaceDistance() metric.clear() with pytest.raises(ValueError): metric.update(x) def test_mean_surface_distance_update2(): x = Tensor(np.array([[0.2, 0.5, 0.7], [0.3, 0.1, 0.2], [0.9, 0.6, 0.5]])) y = Tensor(np.array([1, 0])) metric = MeanSurfaceDistance() metric.clear() with pytest.raises(ValueError): metric.update(x, y) def test_mean_surface_distance_init(): with pytest.raises(ValueError): MeanSurfaceDistance(symmetric=False, distance_metric="eucli") def test_mean_surface_distance_init2(): with pytest.raises(TypeError): MeanSurfaceDistance(symmetric=1) def test_mean_surface_distance_runtime(): metric = MeanSurfaceDistance() metric.clear() with pytest.raises(RuntimeError): metric.eval()