add test_evaluate_detection_batch

test/metrics/test_detection.py

@@ -39,6 +39,7 @@ for class_id, count in zip(*np.unique(PREDICTIONS[:, 5], return_counts=True)):
     IDEAL_RESULT[class_id, class_id] = count
 
 classes = np.arange(80)
+num_classes = len(classes)
 
 
 @pytest.mark.parametrize(
@@ -77,8 +78,40 @@ def test_from_detections(
         assert np.array_equal(result.matrix, expected_result)
 
 
-def test_evaluate_detection_batch():
-    ...
+@pytest.mark.parametrize(
+    "predictions, targets, num_classes, conf_threshold, iou_threshold, expected_result, exception",
+    [
+        (
+            CERTAIN_DETECTIONS,
+            DETECTIONS,
+            num_classes,
+            0.3,
+            0.5,
+            IDEAL_RESULT,
+            DoesNotRaise(),
+        )
+    ],
+)
+def test_evaluate_detection_batch(
+    predictions,
+    targets,
+    num_classes,
+    conf_threshold,
+    iou_threshold,
+    expected_result: Optional[np.ndarray],
+    exception: Exception,
+):
+    with exception:
+        result = ConfusionMatrix._evaluate_detection_batch(
+            true_detections=targets,
+            pred_detections=predictions,
+            num_classes=num_classes,
+            conf_threshold=conf_threshold,
+            iou_threshold=iou_threshold,
+        )
+
+        assert result.diagonal().sum() == result.sum()
+        assert np.array_equal(result, expected_result)
 
 
 def test_drop_extra_matches():
@@ -87,3 +120,7 @@ def test_drop_extra_matches():
 
 def test_benchmark():
     ...
+
+
+def test_from_matrix():
+    ...