5844 improve metatensor slicing (#5845)

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

Fixes #5844

- improves the error message when slicing data with inconsistent
metadata
- fixes an indexing case:
```
x = MetaTensor(np.zeros((10, 3, 4)))
x[slice(1, 0)]  # should return zero length data
```

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [x] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [x] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [x] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

monai/data/meta_tensor.py

@@ -235,11 +235,19 @@ def update_meta(rets: Sequence, func, args, kwargs) -> Sequence:
                         # respectively. Don't need to do anything with the metadata.
                         if batch_idx not in (slice(None, None, None), Ellipsis, None) and idx == 0:
                             ret_meta = decollate_batch(args[0], detach=False)[batch_idx]
-                            if isinstance(ret_meta, list):  # e.g. batch[0:2], re-collate
-                                ret_meta = list_data_collate(ret_meta)
-                            else:  # e.g. `batch[0]` or `batch[0, 1]`, batch index is an integer
+                            if isinstance(ret_meta, list) and ret_meta:  # e.g. batch[0:2], re-collate
+                                try:
+                                    ret_meta = list_data_collate(ret_meta)
+                                except (TypeError, ValueError, RuntimeError, IndexError) as e:
+                                    raise ValueError(
+                                        "Inconsistent batched metadata dicts when slicing a batch of MetaTensors, "
+                                        "please convert it into a torch Tensor using `x.as_tensor()` or "
+                                        "a numpy array using `x.array`."
+                                    ) from e
+                            elif isinstance(ret_meta, MetaObj):  # e.g. `batch[0]` or `batch[0, 1]`, batch_idx is int
                                 ret_meta.is_batch = False
-                            ret.__dict__ = ret_meta.__dict__.copy()
+                            if hasattr(ret_meta, "__dict__"):
+                                ret.__dict__ = ret_meta.__dict__.copy()
                     # `unbind` is used for `next(iter(batch))`. Also for `decollate_batch`.
                     # But we only want to split the batch if the `unbind` is along the 0th
                     # dimension.

tests/test_meta_tensor.py

@@ -406,6 +406,13 @@ def test_indexing(self):
         for _d in d:
             self.check_meta(_d, data)
 
+    def test_slicing(self):
+        x = MetaTensor(np.zeros((10, 3, 4)))
+        self.assertEqual(x[slice(4, 1)].shape[0], 0)
+        x.is_batch = True
+        with self.assertRaises(ValueError):
+            x[slice(0, 8)]
+
     @parameterized.expand(DTYPES)
     @SkipIfBeforePyTorchVersion((1, 8))
     def test_decollate(self, dtype):