confusion matrix

mindspore/nn/metrics/__init__.py

@@ -36,6 +36,7 @@ from .bleu_score import BleuScore
 from .cosine_similarity import CosineSimilarity
 from .occlusion_sensitivity import OcclusionSensitivity
 from .perplexity import Perplexity
+from .confusion_matrix import ConfusionMatrixMetric, ConfusionMatrix
 
 __all__ = [
     "names",
@@ -61,6 +62,8 @@ __all__ = [
     "MeanSurfaceDistance",
     "RootMeanSquareDistance",
     "Perplexity",
+    "ConfusionMatrix",
+    "ConfusionMatrixMetric",
 ]
 
 __factory__ = {
@@ -85,6 +88,8 @@ __factory__ = {
     'mean_surface_distance': MeanSurfaceDistance,
     'root_mean_square_distance': RootMeanSquareDistance,
     'perplexity': Perplexity,
+    'confusion_matrix': ConfusionMatrix,
+    'confusion_matrix_metric': ConfusionMatrixMetric,
 }
 
 

mindspore/nn/metrics/dice.py

@@ -21,19 +21,19 @@ from .metric import Metric
 class Dice(Metric):
     r"""
     The Dice coefficient is a set similarity metric. It is used to calculate the similarity between two samples. The
-        value of the Dice coefficient is 1 when the segmentation result is the best and 0 when the segmentation result
-        is the worst. The Dice coefficient indicates the ratio of the area between two objects to the total area.
-        The function is shown as follows:
+    value of the Dice coefficient is 1 when the segmentation result is the best and 0 when the segmentation result
+    is the worst. The Dice coefficient indicates the ratio of the area between two objects to the total area.
+    The function is shown as follows:
 
-        .. math::
+    .. math::
             dice = \frac{2 * (pred \bigcap true)}{pred \bigcup true}
 
-        Args:
-            smooth (float): A term added to the denominator to improve numerical stability. Should be greater than 0.
-                            Default: 1e-5.
-            threshold (float): A threshold, which is used to compare with the input tensor. Default: 0.5.
+    Args:
+        smooth (float): A term added to the denominator to improve numerical stability. Should be greater than 0.
+                        Default: 1e-5.
+        threshold (float): A threshold, which is used to compare with the input tensor. Default: 0.5.
 
-        Examples:
+    Examples:
         >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
         >>> y = Tensor(np.array([[0, 1], [1, 0], [0, 1]]))
         >>> metric = Dice(smooth=1e-5, threshold=0.5)

tests/ut/python/metrics/test_confusion_matrix.py

@@ -0,0 +1,73 @@
+# 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_confusion_matrix"""
+import numpy as np
+import pytest
+from mindspore import Tensor
+from mindspore.nn.metrics import ConfusionMatrix
+
+
+def test_confusion_matrix():
+    """test_confusion_matrix"""
+    x = Tensor(np.array([1, 0, 1, 0]))
+    y = Tensor(np.array([1, 0, 0, 1]))
+    metric = ConfusionMatrix(num_classes=2)
+    metric.clear()
+    metric.update(x, y)
+    output = metric.eval()
+
+    assert np.allclose(output, np.array([[1, 1], [1, 1]]))
+
+
+def test_confusion_matrix_update_len():
+    x = Tensor(np.array([[0.2, 0.5, 0.7], [0.3, 0.1, 0.2], [0.9, 0.6, 0.5]]))
+    metric = ConfusionMatrix(num_classes=2)
+    metric.clear()
+
+    with pytest.raises(ValueError):
+        metric.update(x)
+
+
+def test_confusion_matrix_update_dim():
+    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 = ConfusionMatrix(num_classes=2)
+    metric.clear()
+
+    with pytest.raises(ValueError):
+        metric.update(x, y)
+
+
+def test_confusion_matrix_init_num_classes():
+    with pytest.raises(TypeError):
+        ConfusionMatrix(num_classes='1')
+
+
+def test_confusion_matrix_init_normalize_value():
+    with pytest.raises(ValueError):
+        ConfusionMatrix(num_classes=2, normalize="wwe")
+
+
+def test_confusion_matrix_init_threshold():
+    with pytest.raises(TypeError):
+        ConfusionMatrix(num_classes=2, normalize='no_norm', threshold=1)
+
+
+def test_confusion_matrix_runtime():
+    metric = ConfusionMatrix(num_classes=2)
+    metric.clear()
+
+    with pytest.raises(RuntimeError):
+        metric.eval()

tests/ut/python/metrics/test_confusion_matrix_metric.py

@@ -0,0 +1,94 @@
+# 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_confusion_matrix_metric"""
+import numpy as np
+import pytest
+from mindspore import Tensor
+from mindspore.nn.metrics import ConfusionMatrixMetric
+
+
+def test_confusion_matrix_metric():
+    """test_confusion_matrix_metric"""
+    metric = ConfusionMatrixMetric(skip_channel=True, metric_name="tpr", calculation_method=False)
+    metric.clear()
+    x = Tensor(np.array([[[0], [1]], [[1], [0]]]))
+    y = Tensor(np.array([[[0], [1]], [[0], [1]]]))
+    metric.update(x, y)
+
+    x = Tensor(np.array([[[0], [1]], [[1], [0]]]))
+    y = Tensor(np.array([[[0], [1]], [[1], [0]]]))
+    metric.update(x, y)
+    output = metric.eval()
+
+    assert np.allclose(output, np.array([0.75]))
+
+
+def test_confusion_matrix_metric_update_len():
+    x = Tensor(np.array([[0.2, 0.5, 0.7], [0.3, 0.1, 0.2], [0.9, 0.6, 0.5]]))
+    metric = ConfusionMatrixMetric(skip_channel=True, metric_name="ppv", calculation_method=True)
+    metric.clear()
+
+    with pytest.raises(ValueError):
+        metric.update(x)
+
+
+def test_confusion_matrix_metric_update_dim():
+    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 = ConfusionMatrixMetric(skip_channel=True, metric_name="tnr", calculation_method=True)
+    metric.clear()
+
+    with pytest.raises(ValueError):
+        metric.update(y, x)
+
+
+def test_confusion_matrix_metric_init_skip_channel():
+    with pytest.raises(TypeError):
+        ConfusionMatrixMetric(skip_channel=1)
+
+
+def test_confusion_matrix_metric_init_compute_sample():
+    with pytest.raises(TypeError):
+        ConfusionMatrixMetric(calculation_method=1)
+
+
+def test_confusion_matrix_metric_init_metric_name_type():
+    with pytest.raises(TypeError):
+        metric = ConfusionMatrixMetric(skip_channel=True, metric_name=1, calculation_method=False)
+        x = Tensor(np.array([[[0], [1]], [[1], [0]]]))
+        y = Tensor(np.array([[[0], [1]], [[1], [0]]]))
+        metric.update(x, y)
+        output = metric.eval()
+
+        assert np.allclose(output, np.array([0.75]))
+
+
+def test_confusion_matrix_metric_init_metric_name_str():
+    with pytest.raises(NotImplementedError):
+        metric = ConfusionMatrixMetric(skip_channel=True, metric_name="wwwww", calculation_method=False)
+        x = Tensor(np.array([[[0], [1]], [[1], [0]]]))
+        y = Tensor(np.array([[[0], [1]], [[1], [0]]]))
+        metric.update(x, y)
+        output = metric.eval()
+
+        assert np.allclose(output, np.array([0.75]))
+
+
+def test_confusion_matrix_metric_runtime():
+    metric = ConfusionMatrixMetric(skip_channel=True, metric_name="tnr", calculation_method=True)
+    metric.clear()
+
+    with pytest.raises(RuntimeError):
+        metric.eval()