[GitHub] sxjscience commented on a change in pull request #9583: use nd for accuracy calculation

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

sxjscience commented on a change in pull request #9583: use nd for accuracy calculation
URL: https://github.com/apache/incubator-mxnet/pull/9583#discussion_r175607170
 
 

 ##########
 File path: python/mxnet/metric.py
 ##########
 @@ -380,23 +380,27 @@ def update(self, labels, preds):
         Parameters
         ----------
         labels : list of `NDArray`
-            The labels of the data.
+            The labels of the data with class indices as values, one per sample.
 
         preds : list of `NDArray`
-            Predicted values.
+            Prediction values for samples. Each prediction value can either be the class index,
+            or a vector of likelihoods for all classes.
         """
         check_label_shapes(labels, preds)
 
         for label, pred_label in zip(labels, preds):
             if pred_label.shape != label.shape:
                 pred_label = ndarray.argmax(pred_label, axis=self.axis)
-            pred_label = pred_label.asnumpy().astype('int32')
-            label = label.asnumpy().astype('int32')
+            pred_label = pred_label.astype('int32')
+            label = label.astype('int32')
 
             check_label_shapes(label, pred_label)
 
-            self.sum_metric += (pred_label.flat == label.flat).sum()
-            self.num_inst += len(pred_label.flat)
+            if pred_label.context != label.context:
+                pred_label = pred_label.as_in_context(label.context)
+
+            self.sum_metric += (pred_label.flatten() == label.flatten()).sum().asscalar()
 
 Review comment:
   However, completely using the non-blocking logic will cause some other problems. To be more specific, the allocated NDArrays cannot be reused and will finally cause an OOM. We should use `asscalar()` to avoid this.

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