Use apply_to_collection in metrics_to_scalars

CHANGELOG.md

@@ -87,6 +87,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Changed these `Trainer` methods to be protected: `call_setup_hook`, `call_configure_sharded_model`, `pre_dispatch`, `dispatch`, `post_dispatch`, `call_teardown_hook`, `run_train`, `run_sanity_check`, `run_evaluate`, `run_evaluation`, `run_predict`, `track_output_for_epoch_end`
 
 
+- Changed `metrics_to_scalars` to work with any collection or value ([#7888](https://github.com/PyTorchLightning/pytorch-lightning/pull/7888))
+
+
 - Changed `clip_grad_norm` to use `torch.nn.utils.clip_grad_norm_` ([#7025](https://github.com/PyTorchLightning/pytorch-lightning/pull/7025))
 
 

pytorch_lightning/utilities/metrics.py

@@ -12,29 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Helper functions to operate on metric values. """
+import numbers
+from typing import Any
 
 import torch
 
+from pytorch_lightning.utilities.apply_func import apply_to_collection
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 
-def metrics_to_scalars(metrics: dict) -> dict:
-    """ Recursively walk through a dictionary of metrics and convert single-item tensors to scalar values. """
+def metrics_to_scalars(metrics: Any) -> Any:
+    """Recursively walk through a collection and convert single-item tensors to scalar values"""
 
-    # TODO: this is duplicated in MetricsHolder. should be unified
-    new_metrics = {}
-    for k, v in metrics.items():
-        if isinstance(v, torch.Tensor):
-            if v.numel() != 1:
-                raise MisconfigurationException(
-                    f"The metric `{k}` does not contain a single element"
-                    f" thus it cannot be converted to float. Found `{v}`"
-                )
-            v = v.item()
+    def to_item(value: torch.Tensor) -> numbers.Number:
+        if value.numel() != 1:
+            raise MisconfigurationException(
+                f"The metric `{value}` does not contain a single element"
+                f" thus it cannot be converted to float."
+            )
+        return value.item()
 
-        if isinstance(v, dict):
-            v = metrics_to_scalars(v)
-
-        new_metrics[k] = v
-
-    return new_metrics
+    return apply_to_collection(metrics, torch.Tensor, to_item)

tests/trainer/logging_/test_logger_connector.py

@@ -487,7 +487,7 @@ def test_step(self, *args, **kwargs):
 
     trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
 
-    match = "The metric `test` does not contain a single element"
+    match = "The metric `.*` does not contain a single element"
     with pytest.raises(MisconfigurationException, match=match):
         trainer.validate(model)
     with pytest.raises(MisconfigurationException, match=match):