hausdorff_distance

mindspore/nn/metrics/__init__.py

@@ -19,6 +19,7 @@ Functions to measure the performance of the machine learning models
 on the evaluation dataset. It's used to choose the best model.
 """
 from .accuracy import Accuracy
+from .hausdorff_distance import HausdorffDistance
 from .error import MAE, MSE
 from .metric import Metric
 from .precision import Precision
@@ -33,6 +34,7 @@ __all__ = [
     "MAE", "MSE",
     "Metric",
     "Precision",
+    "HausdorffDistance",
     "Recall",
     "Fbeta",
     "F1",
@@ -49,6 +51,7 @@ __factory__ = {
     'recall': Recall,
     'F1': F1,
     'topk': TopKCategoricalAccuracy,
+    'hausdorff_distance': HausdorffDistance,
     'top_1_accuracy': Top1CategoricalAccuracy,
     'top_5_accuracy': Top5CategoricalAccuracy,
     'mae': MAE,

tests/ut/python/metrics/test_hausdorff_distance.py

@@ -0,0 +1,65 @@
+# 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_hausdorff_distance"""
+
+import math
+import numpy as np
+import pytest
+from mindspore import Tensor
+from mindspore.nn.metrics import get_metric_fn, HausdorffDistance
+
+
+def test_hausdorff_distance():
+    """test_hausdorff_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('hausdorff_distance')
+    metric.clear()
+    metric.update(x, y, 0)
+    distance = metric.eval()
+
+    assert math.isclose(distance, 1.4142135623730951, abs_tol=0.001)
+
+
+def test_hausdorff_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 = HausdorffDistance()
+    metric.clear()
+
+    with pytest.raises(ValueError):
+        metric.update(x)
+
+
+def test_hausdorff_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 = HausdorffDistance()
+    metric.clear()
+
+    with pytest.raises(ValueError):
+        metric.update(x, y)
+
+
+def test_hausdorff_distance_init():
+    with pytest.raises(ValueError):
+        HausdorffDistance(distance_metric="eucli", percentile=None, directed=False, crop=False)
+
+
+def test_hausdorff_distance_runtime():
+    metric = HausdorffDistance()
+    metric.clear()
+
+    with pytest.raises(RuntimeError):
+        metric.eval()