autopep8 fix

Author: sadra-barikbin

ignite/metrics/mean_average_precision.py

@@ -14,7 +14,6 @@
 
 
 class MeanAveragePrecision(_BasePrecisionRecall):
-
     _tp: Dict[int, List[torch.Tensor]]
     _fp: Dict[int, List[torch.Tensor]]
     _scores: Union[Dict[int, List[torch.Tensor]], List[torch.Tensor]]
@@ -187,7 +186,7 @@ def reset(self) -> None:
     def _check_binary_multilabel_cases(self, output: Sequence[torch.Tensor]) -> None:
         # Ignore the check in `_BaseClassification` since `y_pred` consists of probabilities here.
         _, y = output
-        if not torch.equal(y, y ** 2):
+        if not torch.equal(y, y**2):
             raise ValueError("For binary cases, y must be comprised of 0's and 1's.")
 
     def _check_type(self, output: Sequence[torch.Tensor]) -> None:

ignite/metrics/vision/object_detection_map.py

@@ -9,11 +9,7 @@ class ObjectDetectionMAP(MeanAveragePrecision):
     def __init__(
         self,
         iou_thresholds: Optional[Union[Sequence[float], torch.Tensor]] = None,
-        flavor: Optional[
-            Literal[
-                "COCO",
-            ]
-        ] = "COCO",
+        flavor: Optional[Literal["COCO",]] = "COCO",
         rec_thresholds: Optional[Union[Sequence[float], torch.Tensor]] = None,
         output_transform: Callable = lambda x: x,
         device: Union[str, torch.device] = torch.device("cpu"),

tests/ignite/metrics/test_mean_average_precision.py

@@ -156,7 +156,6 @@ def sklearn_precision_recall_curve_allowing_multiple_recalls_at_single_threshold
     ],
 )
 def test__measure_recall_and_precision(allow_multiple_recalls_at_single_threshold, sklearn_pr_rec_curve):
-
     # Classification
     m = MeanAveragePrecision(allow_multiple_recalls_at_single_threshold=allow_multiple_recalls_at_single_threshold)
 

tests/ignite/metrics/vision/test_object_detection_map.py

@@ -559,7 +559,6 @@ def pycoco_mAP(
     ]
 )
 def sample(request) -> Sample:
-
     data = coco_val2017_sample() if request.param[0] == "coco2017" else random_sample()
     if request.param[1] == "with_an_empty_pred":
         data[0][1] = {
@@ -802,7 +801,6 @@ def test_matching():
 
 
 def test_compute(sample):
-
     device = idist.device()
     metric_50 = ObjectDetectionMAP(iou_thresholds=[0.5], device=device)
     metric_75 = ObjectDetectionMAP(iou_thresholds=[0.75], device=device)