Fix RandGridDistortiond crash when transform is skipped

monai/transforms/spatial/dictionary.py

@@ -1161,8 +1161,7 @@ def __call__(
         d = dict(data)
         first_key: Hashable = self.first_key(d)
         if first_key == ():
-            out: dict[Hashable, NdarrayOrTensor] = convert_to_tensor(d, track_meta=get_track_meta())
-            return out
+            return d
 
         self.randomize(None)
         # all the keys share the same random Affine factor
@@ -1322,8 +1321,7 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, N
         first_key: Hashable = self.first_key(d)
 
         if first_key == ():
-            out: dict[Hashable, NdarrayOrTensor] = convert_to_tensor(d, track_meta=get_track_meta())
-            return out
+            return d
 
         self.randomize(None)
         device = self.rand_2d_elastic.device
@@ -1473,8 +1471,7 @@ def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> dict[Hashable, torc
         first_key: Hashable = self.first_key(d)
 
         if first_key == ():
-            out: dict[Hashable, torch.Tensor] = convert_to_tensor(d, track_meta=get_track_meta())
-            return out
+            return d
 
         self.randomize(None)
         if isinstance(d[first_key], MetaTensor) and d[first_key].pending_operations:  # type: ignore
@@ -2134,8 +2131,7 @@ def __call__(self, data: Mapping[Hashable, torch.Tensor], lazy: bool | None = No
         d = dict(data)
         first_key: Hashable = self.first_key(d)
         if first_key == ():
-            out: dict[Hashable, torch.Tensor] = convert_to_tensor(d, track_meta=get_track_meta())
-            return out
+            return d
 
         self.randomize(None)
 
@@ -2305,13 +2301,13 @@ def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> dict[Hashable, torc
         d = dict(data)
         self.randomize(None)
         if not self._do_transform:
-            out: dict[Hashable, torch.Tensor] = convert_to_tensor(d, track_meta=get_track_meta())
-            return out
+            for key in self.key_iterator(d):
+                d[key] = convert_to_tensor(d[key], track_meta=get_track_meta())
+            return d
 
         first_key: Hashable = self.first_key(d)
         if first_key == ():
-            out = convert_to_tensor(d, track_meta=get_track_meta())
-            return out
+            return d
         if isinstance(d[first_key], MetaTensor) and d[first_key].pending_operations:  # type: ignore
             warnings.warn(f"data['{first_key}'] has pending operations, transform may return incorrect results.")
         self.rand_grid_distortion.randomize(d[first_key].shape[1:])
@@ -2633,8 +2629,7 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, N
         d = dict(data)
         first_key: Hashable = self.first_key(d)
         if first_key == ():
-            out: dict[Hashable, NdarrayOrTensor] = convert_to_tensor(d, track_meta=get_track_meta())
-            return out
+            return d
 
         self.randomize(None)
 

tests/transforms/test_rand_grid_distortiond.py

@@ -77,14 +77,28 @@
 
 
 class TestRandGridDistortiond(unittest.TestCase):
+    """Test cases for RandGridDistortiond dictionary transform."""
+
     @parameterized.expand(TESTS)
     def test_rand_grid_distortiond(self, input_param, seed, input_data, expected_val_img, expected_val_mask):
+        """Verify distortion produces expected output for image and mask keys."""
         g = RandGridDistortiond(**input_param)
         g.set_random_state(seed=seed)
         result = g(input_data)
         assert_allclose(result["img"], expected_val_img, type_test=False, rtol=1e-4, atol=1e-4)
         assert_allclose(result["mask"], expected_val_mask, type_test=False, rtol=1e-4, atol=1e-4)
 
+    def test_no_transform_with_non_tensor_metadata(self):
+        """When _do_transform is False, non-tensor values in the dict should not cause an error."""
+        img = np.indices([6, 6]).astype(np.float32)
+        data = {"img": img, "extra_info": 42, "label_name": "tumor"}
+        g = RandGridDistortiond(keys=["img"], prob=0.0)  # prob=0 ensures _do_transform is False
+        result = g(data)
+        # non-tensor metadata should pass through unchanged
+        self.assertEqual(result["extra_info"], 42)
+        self.assertEqual(result["label_name"], "tumor")
+        assert_allclose(result["img"], img, type_test=False)
+
 
 if __name__ == "__main__":
     unittest.main()