add unit tests for MinMax metric

tests/unit/metrics/test_min_max.py

@@ -0,0 +1,67 @@
+"""Test MinMax metric."""
+
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import torch
+
+from anomalib.metrics import MinMax  # Assuming the metric is part of `anomalib`
+
+
+def test_initialization() -> None:
+    """Test if the metric initializes with correct default values."""
+    metric = MinMax()
+    assert torch.isinf(metric.min), "Initial min should be positive infinity."
+    assert metric.min > 0, "Initial min should be positive infinity."
+    assert torch.isinf(metric.max), "Initial max should be negative infinity."
+    assert metric.max < 0, "Initial max should be negative infinity."
+
+
+def test_update_single_batch() -> None:
+    """Test updating the metric with a single batch."""
+    metric = MinMax()
+    batch = torch.tensor([1.0, 2.0, 3.0, -1.0])
+    metric.update(batch)
+
+    assert metric.min.item() == -1.0, "Min should be -1.0 after single batch update."
+    assert metric.max.item() == 3.0, "Max should be 3.0 after single batch update."
+
+
+def test_update_multiple_batches() -> None:
+    """Test updating the metric with multiple batches."""
+    metric = MinMax()
+    batch1 = torch.tensor([0.5, 1.5, 3.0])
+    batch2 = torch.tensor([-0.5, 0.0, 2.5])
+
+    metric.update(batch1)
+    metric.update(batch2)
+
+    assert metric.min.item() == -0.5, "Min should be -0.5 after multiple batch updates."
+    assert metric.max.item() == 3.0, "Max should be 3.0 after multiple batch updates."
+
+
+def test_compute() -> None:
+    """Test computation of the min and max values after updates."""
+    metric = MinMax()
+    batch1 = torch.tensor([1.0, 2.0])
+    batch2 = torch.tensor([-1.0, 0.0])
+
+    metric.update(batch1)
+    metric.update(batch2)
+
+    min_val, max_val = metric.compute()
+
+    assert min_val.item() == -1.0, "Computed min should be -1.0."
+    assert max_val.item() == 2.0, "Computed max should be 2.0."
+
+
+def test_no_updates() -> None:
+    """Test behavior when no updates are made to the metric."""
+    metric = MinMax()
+
+    min_val, max_val = metric.compute()
+
+    assert torch.isinf(min_val), "Min should remain positive infinity with no updates."
+    assert min_val > 0, "Min should remain positive infinity with no updates."
+    assert torch.isinf(max_val), "Max should remain negative infinity with no updates."
+    assert max_val < 0, "Max should remain negative infinity with no updates."