sxjscience commented on a change in pull request #9583: use nd for accuracy
calculation
URL: https://github.com/apache/incubator-mxnet/pull/9583#discussion_r175613060
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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:
I think it's because the OPs are pushed in a much faster speed than the real
computation. The graph will keep expanding and the allocation OPs will be
executed to allocate new space (Even before the actual computation is
performed). We have to call a blocking operator at some point to make sure that
the current calculation in the graph has been completed. CC @piiswrong for this.
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