Fix type consistency in memory cache and correct test assertions

- Store tensor-converted data in memory cache to match disk cache types
- Fix test_automatic_hybrid_caching assertions to account for
  _InplaceXform (from coderabbit)

Author: aymuos15

monai/data/dataset.py

@@ -435,14 +435,16 @@ def _cachecheck(self, item_transformed):
             if self.in_memory:
                 self._memory_cache[cache_key] = _item_transformed
             return _item_transformed
+        # Convert to tensor for disk storage (and memory cache consistency)
+        _item_converted = convert_to_tensor(_item_transformed, convert_numeric=False, track_meta=self.track_meta)
         try:
             # NOTE: Writing to a temporary directory and then using a nearly atomic rename operation
             #       to make the cache more robust to manual killing of parent process
             #       which may leave partially written cache files in an incomplete state
             with tempfile.TemporaryDirectory() as tmpdirname:
                 temp_hash_file = Path(tmpdirname) / hashfile.name
                 torch.save(
-                    obj=convert_to_tensor(_item_transformed, convert_numeric=False, track_meta=self.track_meta),
+                    obj=_item_converted,
                     f=temp_hash_file,
                     pickle_module=look_up_option(self.pickle_module, SUPPORTED_PICKLE_MOD),
                     pickle_protocol=self.pickle_protocol,
@@ -457,7 +459,7 @@ def _cachecheck(self, item_transformed):
         except PermissionError:  # project-monai/monai issue #3613
             pass
         if self.in_memory:
-            self._memory_cache[cache_key] = _item_transformed
+            self._memory_cache[cache_key] = _item_converted
         return _item_transformed
 
     def _transform(self, index: int):

tests/data/test_persistentdataset.py

@@ -291,9 +291,13 @@ def test_automatic_hybrid_caching(self):
             # Verify: ALL samples now in RAM again (automatic rebuild from disk)
             self.assertEqual(ds2.memory_cache_size, 5)
 
-            # Verify: Results are correct
+            # Verify: Results are correct (transformed by _InplaceXform)
             for i, result in enumerate(results):
-                self.assertEqual(result, [list(range(i))])
+                if i == 0:
+                    expected = [[1]]  # empty list -> append 1
+                else:
+                    expected = [[np.pi] + list(range(1, i))]  # data[0] = 0 + np.pi
+                self.assertEqual(result, expected)
 
             # === Verify RAM cache provides fast repeated access ===
             # Accessing same items again should hit RAM cache (same objects)