ThomasDelteil commented on a change in pull request #9583: use nd for accuracy
calculation
URL: https://github.com/apache/incubator-mxnet/pull/9583#discussion_r175592748
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File path: python/mxnet/metric.py
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@@ -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:
@szha to precise what I mean, I don't think using numpy is slower in itself,
it is just that having a blocking operation in the loop is limiting the use of
the parallelization happening in the backend and lead to GPU starvation.
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