2007 Enhance the unit tests of MapLabelValue transform

monai/transforms/utility/array.py

@@ -777,6 +777,9 @@ def __init__(self, orig_labels: Sequence, target_labels: Sequence, dtype: DtypeL
         """
         if len(orig_labels) != len(target_labels):
             raise ValueError("orig_labels and target_labels must have the same length.")
+        if all([o == z for o, z in zip(orig_labels, target_labels)]):
+            raise ValueError("orig_labels and target_labels are exactly the same, should be different to map.")
+
         self.orig_labels = orig_labels
         self.target_labels = target_labels
         self.dtype = dtype

tests/test_map_label_value.py

@@ -58,9 +58,26 @@
     np.array([["label0", "label2"], ["label2", "label1"]]),
 ]
 
+TEST_CASE_8 = [
+    {"orig_labels": ["label3", "label2", "label1"], "target_labels": ["label1", "label2", "label3"], "dtype": "str"},
+    np.array([["label3", "label1"], ["label1", "label2"]]),
+    np.array([["label1", "label3"], ["label3", "label2"]]),
+]
+
 
 class TestMapLabelValue(unittest.TestCase):
-    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7])
+    @parameterized.expand(
+        [
+            TEST_CASE_1,
+            TEST_CASE_2,
+            TEST_CASE_3,
+            TEST_CASE_4,
+            TEST_CASE_5,
+            TEST_CASE_6,
+            TEST_CASE_7,
+            TEST_CASE_8,
+        ]
+    )
     def test_shape(self, input_param, input_data, expected_value):
         result = MapLabelValue(**input_param)(input_data)
         np.testing.assert_equal(result, expected_value)

tests/test_sailency_inferer.py → tests/test_saliency_inferer.py

@@ -25,7 +25,7 @@
 TEST_CASE_3 = ["GradCAMpp"]
 
 
-class TestGradientClassActivationMap(unittest.TestCase):
+class TestSaliencyInferer(unittest.TestCase):
     @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
     def test_shape(self, cam_name):
         model = DenseNet(