Set full_state_update attribute in custom metrics (#531)

* add full_state_update attribute to metrics

* upgrade torchmetrics

* allow range of torchmetrics versions

Co-authored-by: Samet Akcay <samet.akcay@intel.com>

anomalib/utils/metrics/min_max.py

@@ -13,6 +13,8 @@
 class MinMax(Metric):
     """Track the min and max values of the observations in each batch."""
 
+    full_state_update: bool = True
+
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         self.add_state("min", torch.tensor(float("inf")), persistent=True)  # pylint: disable=not-callable

anomalib/utils/metrics/optimal_f1.py

@@ -14,6 +14,8 @@ class OptimalF1(Metric):
     predicted anomaly scores.
     """
 
+    full_state_update: bool = False
+
     def __init__(self, num_classes: int, **kwargs):
         super().__init__(**kwargs)
 

requirements/base.txt

@@ -10,7 +10,7 @@ opencv-python>=4.5.3.56
 pandas>=1.1.0
 pytorch-lightning>=1.6.0,<1.7.0
 timm==0.5.4
-torchmetrics>=0.9.0,<=0.9.1
+torchmetrics>=0.9.1,<=0.9.3
 torchvision>=0.9.1,<=0.12.0
 torchtext>=0.9.1,<=0.12.0
 wandb==0.12.17