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71 lines
2.1 KiB
71 lines
2.1 KiB
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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# """test_mean_surface_distance"""
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import math
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import numpy as np
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import pytest
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from mindspore import Tensor
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from mindspore.nn.metrics import get_metric_fn, MeanSurfaceDistance
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def test_mean_surface_distance():
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"""test_mean_surface_distance"""
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x = Tensor(np.array([[3, 0, 1], [1, 3, 0], [1, 0, 2]]))
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y = Tensor(np.array([[0, 2, 1], [1, 2, 1], [0, 0, 1]]))
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metric = get_metric_fn('mean_surface_distance')
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metric.clear()
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metric.update(x, y, 0)
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distance = metric.eval()
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assert math.isclose(distance, 0.8047378541243649, abs_tol=0.001)
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def test_mean_surface_distance_update1():
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x = Tensor(np.array([[0.2, 0.5, 0.7], [0.3, 0.1, 0.2], [0.9, 0.6, 0.5]]))
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metric = MeanSurfaceDistance()
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metric.clear()
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with pytest.raises(ValueError):
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metric.update(x)
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def test_mean_surface_distance_update2():
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x = Tensor(np.array([[0.2, 0.5, 0.7], [0.3, 0.1, 0.2], [0.9, 0.6, 0.5]]))
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y = Tensor(np.array([1, 0]))
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metric = MeanSurfaceDistance()
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metric.clear()
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with pytest.raises(ValueError):
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metric.update(x, y)
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def test_mean_surface_distance_init():
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with pytest.raises(ValueError):
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MeanSurfaceDistance(symmetric=False, distance_metric="eucli")
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def test_mean_surface_distance_init2():
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with pytest.raises(TypeError):
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MeanSurfaceDistance(symmetric=1)
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def test_mean_surface_distance_runtime():
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metric = MeanSurfaceDistance()
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metric.clear()
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with pytest.raises(RuntimeError):
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metric.eval()
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