fix(MetaTensor): astype with torch dtype now returns MetaTensor preserving metadata

When calling MetaTensor.astype() with a torch dtype (e.g. torch.int32),
the result was a plain torch.Tensor, silently losing all metadata (affine,
spacing, applied transforms, etc.). The root cause was that out_type was
hardcoded to torch.Tensor instead of the actual type of self.

Fix by using type(self) as out_type when a torch dtype is requested, so
that convert_data_type() receives output_type=MetaTensor, sets
track_meta=True, and preserves metadata through the dtype cast.

The analyzer module already annotated the result of astype(torch.int16)
as MetaTensor, relying on this contract.

Updated test to assert the result is an instance of MetaTensor and that
the metadata key is preserved after the cast.

Closes #8202

Signed-off-by: Oleksandr Sanin <alexaaander.sanin@gmail.com>

Author: claude

monai/data/meta_tensor.py

@@ -442,7 +442,8 @@ def astype(self, dtype, device=None, *_args, **_kwargs):
             _kwargs: additional kwargs (currently unused).
 
         Returns:
-            data array instance
+            ``MetaTensor`` when a torch dtype is given (metadata is preserved),
+            or ``np.ndarray`` when a numpy dtype is given.
         """
         if isinstance(dtype, str):
             mod_str, *dtype = dtype.split(".", 1)
@@ -453,7 +454,7 @@ def astype(self, dtype, device=None, *_args, **_kwargs):
 
         out_type: type[torch.Tensor] | type[np.ndarray] | None
         if mod_str == "torch":
-            out_type = torch.Tensor
+            out_type = type(self)
         elif mod_str in ("numpy", "np"):
             out_type = np.ndarray
         else:

tests/data/meta_tensor/test_meta_tensor.py

@@ -434,8 +434,12 @@ def test_astype(self):
         for np_types in ("float32", "np.float32", "numpy.float32", np.float32, float, "int", np.uint16):
             self.assertIsInstance(t.astype(np_types), np.ndarray)
         for pt_types in ("torch.float", torch.float, "torch.float64"):
-            self.assertIsInstance(t.astype(pt_types), torch.Tensor)
-        self.assertIsInstance(t.astype("torch.float", device="cpu"), torch.Tensor)
+            result = t.astype(pt_types)
+            self.assertIsInstance(result, MetaTensor)
+            self.assertEqual(result.meta.get("fname"), "filename")
+        result = t.astype("torch.float", device="cpu")
+        self.assertIsInstance(result, MetaTensor)
+        self.assertEqual(result.meta.get("fname"), "filename")
 
     def test_transforms(self):
         key = "im"