update meta tensor api

monai/data/__init__.py

@@ -46,7 +46,7 @@
     resolve_writer,
 )
 from .iterable_dataset import CSVIterableDataset, IterableDataset, ShuffleBuffer
-from .meta_obj import get_track_meta, get_track_transforms, set_track_meta, set_track_transforms
+from .meta_obj import MetaObj, get_track_meta, get_track_transforms, set_track_meta, set_track_transforms
 from .meta_tensor import MetaTensor
 from .nifti_saver import NiftiSaver
 from .nifti_writer import write_nifti

monai/data/meta_obj.py

@@ -109,7 +109,8 @@ class MetaObj:
 
     """
 
-    _meta: dict
+    def __init__(self):
+        self._meta: dict = self.get_default_meta()
 
     @staticmethod
     def flatten_meta_objs(args: Sequence[Any]) -> list[MetaObj]:

monai/data/meta_tensor.py

@@ -62,34 +62,33 @@ class MetaTensor(MetaObj, torch.Tensor):
 
     @staticmethod
     def __new__(cls, x, affine: torch.Tensor | None = None, meta: dict | None = None, *args, **kwargs) -> MetaTensor:
+        return torch.as_tensor(x, *args, **kwargs).as_subclass(cls)  # type: ignore
+
+    def __init__(self, x, affine: torch.Tensor | None = None, meta: dict | None = None) -> None:
         """
         If `meta` is given, use it. Else, if `meta` exists in the input tensor, use it.
         Else, use the default value. Similar for the affine, except this could come from
         four places.
         Priority: `affine`, `meta["affine"]`, `x.affine`, `get_default_affine`.
         """
-        out: MetaTensor = torch.as_tensor(x, *args, **kwargs).as_subclass(cls)  # type: ignore
+        super().__init__()
         # set meta
         if meta is not None:
-            out.meta = meta
+            self.meta = meta
         elif isinstance(x, MetaObj):
-            out.meta = x.meta
-        else:
-            out.meta = out.get_default_meta()
+            self.meta = x.meta
         # set the affine
         if affine is not None:
-            if "affine" in out.meta:
-                warnings.warn("Setting affine, but the applied meta contains an affine. " "This will be overwritten.")
-            out.affine = affine
-        elif "affine" in out.meta:
+            if "affine" in self.meta:
+                warnings.warn("Setting affine, but the applied meta contains an affine. This will be overwritten.")
+            self.affine = affine
+        elif "affine" in self.meta:
             pass  # nothing to do
         elif isinstance(x, MetaTensor):
-            out.affine = x.affine
+            self.affine = x.affine
         else:
-            out.affine = out.get_default_affine()
-        out.affine = out.affine.to(out.device)
-
-        return out
+            self.affine = self.get_default_affine()
+        self.affine = self.affine.to(self.device)
 
     def _copy_attr(self, attribute: str, input_objs: list[MetaObj], default_fn: Callable, deep_copy: bool) -> None:
         super()._copy_attr(attribute, input_objs, default_fn, deep_copy)
@@ -113,8 +112,8 @@ def __torch_function__(cls, func, types, args=(), kwargs=None) -> torch.Tensor:
         ret.affine = ret.affine.to(ret.device)
         return ret
 
-    def get_default_affine(self) -> torch.Tensor:
-        return torch.eye(4, device=self.device)
+    def get_default_affine(self, dtype=torch.float64) -> torch.Tensor:
+        return torch.eye(4, device=self.device, dtype=dtype)
 
     def as_tensor(self) -> torch.Tensor:
         """

tests/test_meta_tensor.py

@@ -44,7 +44,7 @@ class TestMetaTensor(unittest.TestCase):
     @staticmethod
     def get_im(shape=None, dtype=None, device=None):
         if shape is None:
-            shape = shape = (1, 10, 8)
+            shape = (1, 10, 8)
         affine = torch.randint(0, 10, (4, 4))
         meta = {"fname": rand_string()}
         t = torch.rand(shape)