Fix the bug in F1AdaptiveThreshold which occurs only when there are no anomalous images in a validation set

src/anomalib/metrics/threshold/f1_adaptive_threshold.py

@@ -6,6 +6,7 @@
 import logging
 
 import torch
+from torchmetrics.utilities.data import dim_zero_cat
 
 from anomalib.metrics.precision_recall_curve import BinaryPrecisionRecallCurve
 
@@ -69,6 +70,23 @@ def compute(self) -> torch.Tensor:
             )
             logging.warning(msg)
 
+            self.value = torch.max(dim_zero_cat(self.preds))
+
+            return self.value
+
+        if not any(0 in batch for batch in self.target):
+            msg = (
+                "The validation set does not contain any normal images. As a result, the adaptive threshold will "
+                "take the value of the lowest anomaly score observed in the anomalous validation images, which may "
+                "lead to poor predictions. For a more reliable adaptive threshold computation, please add some normal "
+                "images to the validation set."
+            )
+            logging.warning(msg)
+
+            self.value = torch.min(dim_zero_cat(self.preds))
+
+            return self.value
+
         precision, recall, thresholds = super().compute()
         f1_score = (2 * precision * recall) / (precision + recall + 1e-10)
         if thresholds.dim() == 0:

tests/unit/metrics/test_adaptive_threshold.py

@@ -18,6 +18,8 @@
     [
         (torch.Tensor([0, 0, 0, 1, 1]), torch.Tensor([2.3, 1.6, 2.6, 7.9, 3.3]), 3.3),  # standard case
         (torch.Tensor([1, 0, 0, 0]), torch.Tensor([4, 3, 2, 1]), 4),  # 100% recall for all thresholds
+        (torch.Tensor([1, 1, 1, 1]), torch.Tensor([4, 3, 2, 1]), 1),  # use minimum value when all images are anomalous
+        (torch.Tensor([0, 0, 0, 0]), torch.Tensor([4, 3, 2, 1]), 4),  # use maximum value when all images are normal
     ],
 )
 def test_adaptive_threshold(labels: torch.Tensor, preds: torch.Tensor, target_threshold: int | float) -> None: