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To offer a better metric aggregation I discussed a potential way to go with @SkafteNicki .
We agreed on the following:
aggregated
reset
in pseudo code the metric workflow will now be:
interm_outputs = do_some_first_calc(batch) broadcasted_interm = broadcast(interm_outputs) persistent_state.extend(broadcasted_interm) return aggregate(broadcasted_interm)
cc @PyTorchLightning/core-contributors
The text was updated successfully, but these errors were encountered:
justusschock
SkafteNicki
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🚀 Feature
To offer a better metric aggregation I discussed a potential way to go with @SkafteNicki .
We agreed on the following:
aggregated
-property that aggregates the metric over batches.reset
function to metrics to reset the internal persistent aggregation state (can be called by user whenever they want; usually on epoch end)in pseudo code the metric workflow will now be:
cc @PyTorchLightning/core-contributors
The text was updated successfully, but these errors were encountered: