Properly handle empty metric_names passed to Trainer._filter_metrics

composer/core/evaluator.py

@@ -86,11 +86,10 @@ def __init__(
         self.label = label
         self.dataloader = ensure_data_spec(dataloader)
 
-        self.metric_names = []
         if metric_names is not None:
             if not isinstance(metric_names, list):
                 raise ValueError(f'``metric_names`` should be a list of strings, not a {type(metric_names)}')
-            self.metric_names = metric_names
+        self.metric_names = metric_names
 
         self.subset_num_batches = subset_num_batches
         self._eval_interval = None

composer/trainer/trainer.py

@@ -106,14 +106,13 @@ def _get_default_scheduler_frequency(schedulers: Optional[Union[Scheduler, Seque
 def _filter_metrics(metrics: Dict[str, Metric], metric_names: Optional[List[str]]) -> Dict[str, Metric]:
     """Filter the metrics based on the given metric_names as regex strings (e.g. 'Accuracy', 'f1' for 'BinaryF1Score', 'Top-.' for 'Top-1 Accuracy' and 'Top-2 Accuracy', etc). If no metric_names are provided, all metrics will be returned."""
     metrics = deepcopy(metrics)
-    if not metric_names:
+    if metric_names is None:
         return metrics
-    else:
-        filtered_metrics = {}
-        for name, metric in metrics.items():
-            if any(re.match(f'.*{metric_name}.*', name, re.IGNORECASE) for metric_name in metric_names):
-                filtered_metrics[name] = metric
-        return filtered_metrics
+    filtered_metrics = {}
+    for name, metric in metrics.items():
+        if any(re.match(f'.*{metric_name}.*', name, re.IGNORECASE) for metric_name in metric_names):
+            filtered_metrics[name] = metric
+    return filtered_metrics
 
 
 def _validate_precision(precision: Precision, device: Device):

tests/trainer/test_trainer.py

@@ -167,6 +167,46 @@ def test_compile_unsupported_torch_version_exception(self, caplog, model: Compos
                         auto_log_hparams=True,
                         compile_config={})
 
+    def test_eval_metrics(self):
+        model = SimpleModel()
+        train_dataloader = DataLoader(RandomClassificationDataset(size=1), batch_size=1)
+        all_metrics = model.get_metrics(is_train=False)
+
+        # Test default eval metrics
+        trainer = Trainer(
+            model=model,
+            train_dataloader=train_dataloader,
+            eval_dataloader=Evaluator(label='eval',
+                                      dataloader=DataLoader(RandomClassificationDataset(size=1), batch_size=1)),
+        )
+
+        assert trainer.state.eval_metrics['eval'] == all_metrics
+
+        # Test empty eval metrics
+        trainer = Trainer(
+            model=model,
+            train_dataloader=train_dataloader,
+            eval_dataloader=Evaluator(label='eval',
+                                      dataloader=DataLoader(RandomClassificationDataset(size=1), batch_size=1),
+                                      metric_names=[]),
+        )
+
+        assert trainer.state.eval_metrics['eval'] == {}
+
+        # Test selected eval metrics
+        single_metric = next(iter(all_metrics))
+        trainer = Trainer(
+            model=model,
+            train_dataloader=train_dataloader,
+            eval_dataloader=Evaluator(label='eval',
+                                      dataloader=DataLoader(RandomClassificationDataset(size=1), batch_size=1),
+                                      metric_names=[single_metric]),
+        )
+
+        eval_metric_names = trainer.state.eval_metrics['eval'].keys()
+        assert len(eval_metric_names) == 1
+        assert next(iter(eval_metric_names)) == single_metric
+
 
 def _assert_optimizer_is_on_device(optimizer: torch.optim.Optimizer):
     for state in optimizer.state.values():