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fix(MetaTensor): astype with torch dtype now returns MetaTensor preserving metadata #8911

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fix(MetaTensor): astype with torch dtype now returns MetaTensor preserving metadata #8911

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5 changes: 3 additions & 2 deletions monai/data/meta_tensor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand All @@ -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:
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8 changes: 6 additions & 2 deletions tests/data/meta_tensor/test_meta_tensor.py
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Expand Up @@ -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"
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