Adding unittest skip only to test cc functions and resolving shape check bug

Signed-off-by: Vijay Vignesh Prasad Rao <vijayvigneshp02@gmail.com>

Author: VijayVignesh1

monai/metrics/meandice.py

@@ -418,11 +418,12 @@ def __call__(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor | tupl
                 y_pred = torch.sigmoid(y_pred)
             y_pred = y_pred > 0.5
 
-        if self.per_component and (len(y_pred.shape) != 5 or y_pred.shape[1] != 2):
-            raise ValueError(
-                f"per_component requires 5D binary segmentation with 2 channels (background + foreground). "
-                f"Got shape {y_pred.shape}, expected shape (B, 2, D, H, W)."
-            )
+        if self.per_component:
+            if len(y_pred.shape) != 5 or len(y.shape) != 5 or y_pred.shape[1] != 2 or y.shape[1] != 2:
+                raise ValueError(
+                    "per_component requires both y_pred and y to be 5D binary segmentations "
+                    f"with 2 channels. Got y_pred={tuple(y_pred.shape)}, y={tuple(y.shape)}."
+                )
 
         first_ch = 0 if self.include_background and not self.per_component else 1
         data = []

tests/metrics/test_compute_meandice.py

@@ -272,7 +272,6 @@
 ]
 
 
-@unittest.skipUnless(has_ndimage, "Requires scipy.ndimage.")
 class TestComputeMeanDice(unittest.TestCase):
 
     @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_9, TEST_CASE_11, TEST_CASE_12])
@@ -325,12 +324,14 @@ def test_nans_class(self, params, input_data, expected_value):
 
     # CC DiceMetric  tests
     @parameterized.expand([TEST_CASE_16])
+    @unittest.skipUnless(has_ndimage, "Requires scipy.ndimage.")
     def test_cc_dice_value(self, params, input_data, expected_value):
         dice_metric = DiceMetric(**params)
         dice_metric(**input_data)
         result = dice_metric.aggregate(reduction="none")
         np.testing.assert_allclose(result.cpu().numpy(), expected_value, atol=1e-4)
 
+    @unittest.skipUnless(has_ndimage, "Requires scipy.ndimage.")
     def test_input_dimensions(self):
         with self.assertRaises(ValueError):
             DiceMetric(per_component=True)(torch.ones([3, 3, 144, 144]), torch.ones([3, 3, 145, 145]))