Get the metric class types. The first choice will be the metrics configured in the config file, if not found, the default metrics will be used. If no metrics is found and the eval dataset exists, the method will raise an error. Returns: The metric types.
(self)
| 644 | return train_preprocessor, eval_preprocessor |
| 645 | |
| 646 | def get_metrics(self) -> List[Union[str, Dict]]: |
| 647 | """Get the metric class types. |
| 648 | |
| 649 | The first choice will be the metrics configured in the config file, if not found, the default metrics will be |
| 650 | used. |
| 651 | If no metrics is found and the eval dataset exists, the method will raise an error. |
| 652 | |
| 653 | Returns: The metric types. |
| 654 | |
| 655 | """ |
| 656 | metrics = self.cfg.evaluation.metrics if hasattr( |
| 657 | self.cfg, 'evaluation') and hasattr(self.cfg.evaluation, |
| 658 | 'metrics') else None |
| 659 | metrics = metrics if metrics is not None else task_default_metrics.get( |
| 660 | self.cfg.task) |
| 661 | if metrics is None and self.eval_dataset is not None: |
| 662 | raise ValueError( |
| 663 | f'Metrics are needed in evaluation, please try to either ' |
| 664 | f'add metrics in configuration.json or add the default metric for {self.cfg.task}.' |
| 665 | ) |
| 666 | if isinstance(metrics, (str, Mapping)): |
| 667 | metrics = [metrics] |
| 668 | return metrics |
| 669 | |
| 670 | def set_checkpoint_file_to_hook(self, checkpoint_path, load_all_state, |
| 671 | strict): |