python - Why is my TF Slim Metric slow? -


i'm computing auc score pr curve using 2 different methods:

  • a) own version based on code here.
  • b) using official tf.metrics.auc function defined here.

using both functions in unit test shows official implementation bit faster own (one call takes 3 instead of 5 seconds).

but if start using official version in evaluation routine makes run super slow. here timing:

  • without computing, auc score batch needs 16.703 seconds.
  • computing auc score own version takes 31.572 seconds (per batch).
  • computing auc score tf.metrics takes 53.525 seconds (per batch).

can me figure out why? here how code looks overall:

# setup of neural network...  # either: computation of auc score version 1 auc, update_op = tf.metrics.auc(label, prediction, weights, name) dict_metrics[name] = (auc, update_op) dict_metrics["stream/" + name] = slim.metrics.streaming_mean(auc)  # or: computation of auc score version 2 auc = compute_my_own_auc_score(label, prediction, weights) dict_metrics[name] = slim.metrics.streaming_mean(auc)  # adding auc summary... # computation of few other metrics...  # getting list of metrics , updates names_to_values, names_to_updates = slim.metrics.aggregate_metric_map(dict_metrics)  # calling tf.slim evaluate slim.evaluation.evaluation_loop(eval_op=list(names_to_updates.values()), ...)

i don't how slim deals update operations, maybe have use eval_ops in different way?


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