[GitHub] solin319 commented on a change in pull request #8423: Re-implement segnet in MXnet

[GitBox <gi...@apache.org>]

solin319 commented on a change in pull request #8423: Re-implement segnet in MXnet
URL: https://github.com/apache/incubator-mxnet/pull/8423#discussion_r148445696
 
 

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 File path: example/segnet/common/contrib_metrics.py
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 @@ -0,0 +1,156 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+
+import numpy
+import mxnet as mx
+
+class Accuracy(mx.metric.EvalMetric):
+    """Computes accuracy classification score.
+
+    The accuracy score is defined as
+
+    .. math::
+        \\text{accuracy}(y, \\hat{y}) = \\frac{1}{n} \\sum_{i=0}^{n-1}
+        \\text{1}(\\hat{y_i} == y_i)
+
+    Parameters
+    ----------
+    axis : int, default=1
+        The axis that represents classes
+    name : str
+        Name of this metric instance for display.
+    output_names : list of str, or None
+        Name of predictions that should be used when updating with update_dict.
+        By default include all predictions.
+    label_names : list of str, or None
+        Name of labels that should be used when updating with update_dict.
+        By default include all labels.
+    ignore_label : int
+        Number of label that should not be computed.
+
+    Examples
+    --------
+    >>> predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]
+    >>> labels   = [mx.nd.array([0, 1, 1])]
+    >>> acc = mx.metric.Accuracy()
+    >>> acc.update(preds = predicts, labels = labels)
+    >>> print acc.get()
+    ('accuracy', 0.6666666666666666)
+    """
+    def __init__(self, axis=1, name='accuracy',
+                 output_names=None, label_names=None, ignore_label=-1):
+        super(Accuracy, self).__init__(
+            name, axis=axis,
+            output_names=output_names, label_names=label_names)
+        self.axis = axis
+        self.ignore_label = ignore_label
+
+    def update(self, labels, preds):
+        """Updates the internal evaluation result.
+
+        Parameters
+        ----------
+        labels : list of `NDArray`
+            The labels of the data.
+
+        preds : list of `NDArray`
+            Predicted values.
+        """
+        mx.metric.check_label_shapes(labels, preds)
+
+        for label, pred_label in zip(labels, preds):
+            if pred_label.shape != label.shape:
+                pred_label = mx.ndarray.argmax(pred_label, axis=self.axis)
+            pred_label = pred_label.asnumpy().astype('int32')
+            label = label.asnumpy().astype('int32')
+
+            mx.metric.check_label_shapes(label, pred_label)
+
+            self.sum_metric += (pred_label.flat == label.flat).sum()
 
 Review comment:
   ```
   elf.sum_metric -= len(pred_label[pred_label == self.ignore_label])
   ```
   This can solve the problem caused by ignore_label  located in pred_label.

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