Rearrange the logging a bit - log everything in one place after each …

…epoch
stanfordnlp · Jan 16, 2025 · 6587190 · 6587190
1 parent 10099c1
commit 6587190
Showing 1 changed file with 14 additions and 15 deletions.
diff --git a/stanza/models/constituency/parser_training.py b/stanza/models/constituency/parser_training.py
@@ -425,10 +425,22 @@ def iterate_training(args, trainer, train_trees, train_sequences, transitions, d
             trainer.save(args['save_name'], save_optimizer=False)
         if epoch_stats.nans > 0:
             tlogger.warning("Had to ignore %d batches with NaN", epoch_stats.nans)
+        # TODO: refactor the logging?
+        total_correct = sum(v for _, v in epoch_stats.transitions_correct.items())
+        correct_transitions_str = "\n  ".join(["%s: %d" % (x, epoch_stats.transitions_correct[x]) for x in epoch_stats.transitions_correct])
+        tlogger.info("Transitions correct: %d\n  %s", total_correct, correct_transitions_str)
+        total_incorrect = sum(v for _, v in epoch_stats.transitions_incorrect.items())
+        incorrect_transitions_str = "\n  ".join(["%s: %d" % (x, epoch_stats.transitions_incorrect[x]) for x in epoch_stats.transitions_incorrect])
+        tlogger.info("Transitions incorrect: %d\n  %s", total_incorrect, incorrect_transitions_str)
+        if len(epoch_stats.repairs_used) > 0:
+            tlogger.info("Oracle repairs:\n  %s", "\n  ".join("%s (%s): %d" % (x.name, x.value, y) for x, y in epoch_stats.repairs_used.most_common()))
+        if epoch_stats.fake_transitions_used > 0:
+            tlogger.info("Fake transitions used: %d", epoch_stats.fake_transitions_used)
+
         stats_log_lines = [
             "Epoch %d finished" % trainer.epochs_trained,
-            "Transitions correct: %s" % epoch_stats.transitions_correct,
-            "Transitions incorrect: %s" % epoch_stats.transitions_incorrect,
+            "Transitions correct: %d" % total_correct,
+            "Transitions incorrect: %s" % total_incorrect,
             "Total loss for epoch: %.5f" % epoch_stats.epoch_loss,
             "Dev score      (%5d): %8f" % (trainer.epochs_trained, f1),
             "Best dev score (%5d): %8f" % (trainer.best_epoch, trainer.best_f1)
@@ -549,19 +561,6 @@ def train_model_one_epoch(epoch, trainer, transition_tensors, process_outputs, m
         optimizer.zero_grad()
         epoch_stats = epoch_stats + batch_stats
 
-
-    # TODO: refactor the logging?
-    total_correct = sum(v for _, v in epoch_stats.transitions_correct.items())
-    correct_transitions_str = "\n  ".join(["%s: %d" % (x, epoch_stats.transitions_correct[x]) for x in epoch_stats.transitions_correct])
-    tlogger.info("Transitions correct: %d\n  %s", total_correct, correct_transitions_str)
-    total_incorrect = sum(v for _, v in epoch_stats.transitions_incorrect.items())
-    incorrect_transitions_str = "\n  ".join(["%s: %d" % (x, epoch_stats.transitions_incorrect[x]) for x in epoch_stats.transitions_incorrect])
-    tlogger.info("Transitions incorrect: %d\n  %s", total_incorrect, incorrect_transitions_str)
-    if len(epoch_stats.repairs_used) > 0:
-        tlogger.info("Oracle repairs:\n  %s", "\n  ".join("%s (%s): %d" % (x.name, x.value, y) for x, y in epoch_stats.repairs_used.most_common()))
-    if epoch_stats.fake_transitions_used > 0:
-        tlogger.info("Fake transitions used: %d", epoch_stats.fake_transitions_used)
-
     return epoch_stats
 
 def train_model_one_batch(epoch, batch_idx, model, training_batch, transition_tensors, process_outputs, model_loss_function, oracle, args):