Merge branch 'dev' into check_env

Author: ericspod

monai/data/meta_tensor.py

@@ -495,7 +495,8 @@ 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)
@@ -506,7 +507,7 @@ 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

@@ -435,8 +435,12 @@ 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"