[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

pytorch_lightning/trainer/connectors/logger_connector/result.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from collections.abc import Generator, Mapping, Sequence
+from collections.abc import Generator
 from dataclasses import dataclass
 from typing import Any, Callable, Dict, Iterable, Optional, Tuple, Union
 
@@ -387,20 +387,14 @@ def get_metrics(self, on_step: bool) -> Dict[str, Dict[str, torch.Tensor]]:
             value = apply_to_collection(result_metric, ResultMetric, fn, include_none=False)
 
             # detect if the value is None. This can be nested.
-            is_empty = True
+            is_none = False
 
-            def is_empty_fn(v):
-                nonlocal is_empty
-                # update is_empty if any value is not None.
-                if v is not None:
-                    is_empty = False
+            def any_none(_):
+                nonlocal is_none
+                is_none = True
 
-            # apply detection.
-            # TODO(@tchaton): need to find a way to support NamedTuple
-            apply_to_collection(value, object, is_empty_fn, wrong_dtype=(Mapping, Sequence))
-
-            # skip is the value was actually empty.
-            if is_empty:
+            apply_to_collection(value, type(None), any_none)
+            if is_none:
                 continue
 
             # extract metadata

tests/core/test_metric_result_integration.py

@@ -194,12 +194,8 @@ def test_result_collection_restoration():
             cumulative_sum += i
 
             result.log('training_step', 'a', metric_a, on_step=True, on_epoch=True, metric_attribute="metric_a")
-            result.log(
-                'training_step', 'b', metric_b, on_step=False, on_epoch=True, metric_attribute="metric_b"
-            )
-            result.log(
-                'training_step', 'c', metric_c, on_step=True, on_epoch=False, metric_attribute="metric_c"
-            )
+            result.log('training_step', 'b', metric_b, on_step=False, on_epoch=True, metric_attribute="metric_b")
+            result.log('training_step', 'c', metric_c, on_step=True, on_epoch=False, metric_attribute="metric_c")
             result.log('training_step', 'a_1', a, on_step=True, on_epoch=True)
             result.log('training_step', 'b_1', b, on_step=False, on_epoch=True)
             result.log('training_step', 'c_1', {'1': c, '2': c}, on_step=True, on_epoch=False)
@@ -237,9 +233,7 @@ def test_result_collection_restoration():
             else:
                 assert epoch_log[k] == 1
 
-        result.log(
-            'train_epoch_end', 'a', metric_a, on_step=False, on_epoch=True, metric_attribute="metric_a_end"
-        )
+        result.log('train_epoch_end', 'a', metric_a, on_step=False, on_epoch=True, metric_attribute="metric_a_end")
 
         _result.reset()
         result.reset()