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fix trainer final evaluation with tstv & best-model #3363

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Oct 27, 2023
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fix trainer final evaluation with tstv & best-model #3363

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56 changes: 28 additions & 28 deletions flair/trainers/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -767,39 +767,39 @@ def train_custom(
# TensorboardLogger -> closes writer
self.dispatch("_training_finally")

# test best model if test data is present
if self.corpus.test and not train_with_test:
log_line(log)
# test best model if test data is present
if self.corpus.test and not train_with_test:
log_line(log)

self.model.eval()
self.model.eval()

if (base_path / "best-model.pt").exists():
log.info("Loading model from best epoch ...")
self.model.load_state_dict(self.model.load(base_path / "best-model.pt").state_dict())
else:
log.info("Testing using last state of model ...")

test_results = self.model.evaluate(
self.corpus.test,
gold_label_type=self.model.label_type,
mini_batch_size=eval_batch_size,
out_path=base_path / "test.tsv",
embedding_storage_mode="none",
main_evaluation_metric=main_evaluation_metric,
gold_label_dictionary=gold_label_dictionary_for_eval,
exclude_labels=exclude_labels,
return_loss=False,
)
if (base_path / "best-model.pt").exists():
log.info("Loading model from best epoch ...")
self.model.load_state_dict(self.model.load(base_path / "best-model.pt").state_dict())
else:
log.info("Testing using last state of model ...")

test_results = self.model.evaluate(
self.corpus.test,
gold_label_type=self.model.label_type,
mini_batch_size=eval_batch_size,
out_path=base_path / "test.tsv",
embedding_storage_mode="none",
main_evaluation_metric=main_evaluation_metric,
gold_label_dictionary=gold_label_dictionary_for_eval,
exclude_labels=exclude_labels,
return_loss=False,
)

log.info(test_results.detailed_results)
log_line(log)
log.info(test_results.detailed_results)
log_line(log)

# get and return the final test score of best model
self.return_values["test_score"] = test_results.main_score
# get and return the final test score of best model
self.return_values["test_score"] = test_results.main_score

else:
self.return_values["test_score"] = 0
log.info("Test data not provided setting final score to 0")
else:
self.return_values["test_score"] = 0
log.info("Test data not provided setting final score to 0")

# MetricHistoryPlugin -> stores the loss history in return_values
self.dispatch("after_training")
Expand Down