ThomasDelteil commented on a change in pull request #9583: use nd for accuracy
calculation
URL: https://github.com/apache/incubator-mxnet/pull/9583#discussion_r175615075
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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:
Thanks yes that's my understanding. However I think it should be left to the
user to decide when to block, since it depends highly on their GPU and model
size (like every 100 batches or every epoch). Also is there a reason why the
accuracy is stored on the CPU rather than on specific context? My measures
showed great improvements when storing the accuracy on GPU. Maybe if you don't
mind we can continue the discussion there:
https://github.com/apache/incubator-mxnet/issues/9571
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