Fix IndexError in adaptive threshold computation

anomalib/utils/metrics/adaptive_threshold.py

@@ -37,5 +37,10 @@ def compute(self) -> torch.Tensor:
 
         precision, recall, thresholds = self.precision_recall_curve.compute()
         f1_score = (2 * precision * recall) / (precision + recall + 1e-10)
-        self.value = thresholds[torch.argmax(f1_score)]
+        if thresholds.dim() == 0:
+            # special case where recall is 1.0 even for the highest threshold.
+            # In this case 'thresholds' will be scalar.
+            self.value = thresholds
+        else:
+            self.value = thresholds[torch.argmax(f1_score)]
         return self.value

tests/pre_merge/utils/metrics/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (C) 2020 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# 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.

tests/pre_merge/utils/metrics/test_adaptive_threshold.py

@@ -0,0 +1,37 @@
+"""Tests for the adaptive threshold metric."""
+
+# Copyright (C) 2020 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+import pytest
+import torch
+
+from anomalib.utils.metrics import AdaptiveThreshold
+
+
+@pytest.mark.parametrize(
+    ["labels", "preds", "target_threshold"],
+    [
+        (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
+    ],
+)
+def test_adaptive_threshold(labels, preds, target_threshold):
+    """Test if the adaptive threshold computation returns the desired value."""
+
+    adaptive_threshold = AdaptiveThreshold(default_value=0.5)
+    adaptive_threshold.update(preds, labels)
+    threshold_value = adaptive_threshold.compute()
+
+    assert threshold_value == target_threshold