fix: support decollate for numpy scalars

[arthurdjn]

Description

This PR supports numpy scalars (e.g. in the form of np.array(1) ) in the decollate_batch function (fix issue #8471).

Types of changes

• 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).
• New tests added to cover the changes.
• Integration tests passed locally by running ./runtests.sh -f -u --net --coverage.
• Quick tests passed locally by running ./runtests.sh --quick --unittests --disttests.
• In-line docstrings updated.
• Documentation updated, tested make html command in the docs/ folder.

[KumoLiu]

Thanks for the pr! Do you think it might be beneficial to convert the array into a tensor? This way, the data could be handled more consistently.

[arthurdjn]

We could, I think it does not matter for my use cases. As long as the function handles numpy scalars in the form of an array it is good for me!

I will add this change and convert it as a tensor there (L629) if you prefer :)

[KumoLiu]

Thanks for the quick fix!
May I ask the reason for only convert to tensor when batch.ndim == 0 here?

[arthurdjn]

I noticed a different behavior when using the decollate_batch function on torch tensors vs numpy arrays (see discussion #8472) so I don't want to convert numpy arrays to torch tensors as it will introduce some breaking changes

This PR only address the issue #8471 as I think it was not expected and should be supported (?).

[ericspod]

Could we consider a more complete solution? The issue it seems is that 0-d arrays are iterable but can't be iterated over. We already check for non-iterable things in decollate_batch here. Can we modeify this to correctly pick up when the batch is a 0-d array and just return it in that case? Or return its contents?

[arthurdjn]

Thanks for the feedback. The initial PR was:

if isinstance(batch, (float, int, str, bytes)) or (
    type(batch).__module__ == "numpy" and not isinstance(batch, Iterable)
):
    return batch
if isinstance(batch, np.ndarray) and batch.ndim == 0:
    return batch.item() if detach else batch
# rest ...

Is this something that you find more complete?

Note

I refactored the PR to convert from numpy array to torch tensor as suggested by @KumoLiu.

[ericspod]

Is this something that you find more complete?

What I had in mind was more of the following change:

...
    if batch is None or isinstance(batch, (float, int, str, bytes)):
        return batch
    if getattr(batch, "ndim", -1) == 0:  # assumes only Numpy objects and Pytorch tensors have ndim
        return batch.item() if detach else batch
    if isinstance(batch, torch.Tensor):
        if detach:
            batch = batch.detach()
# REMOVE
#      if batch.ndim == 0:
#          return batch.item() if detach else batch
...

[arthurdjn]

Thanks! I will update the PR to include these changes.

[coderabbitai]

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Walkthrough

Adds a pre-check in monai/data/utils.py::decollate_batch for 0‑dimensional inputs: if the incoming batch has ndim == 0 and exposes an item() method, the function returns batch.item() when detach=True, otherwise returns the original 0‑d array (short‑circuits before the torch.Tensor branch). No other control flow or logic changes and no public API or exports are modified.

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🎯 1 (Trivial) | ⏱️ ~2 minutes

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[coderabbitai]

Actionable comments posted: 0

🧹 Nitpick comments (3)

monai/data/utils.py (3)

628-635: Prefer generic 0-D detection early; drop special-case conversion.

Handle any object with ndim==0 up-front, then the torch branch can skip its own scalar check. This reduces duplication and avoids unnecessary conversion for NumPy scalars when detach=False.

Apply this diff (and remove the torch scalar early-return below):

@@
-    if isinstance(batch, (float, int, str, bytes)) or (
-        type(batch).__module__ == "numpy" and not isinstance(batch, Iterable)
-    ):
-        return batch
-    if isinstance(batch, np.ndarray) and batch.ndim == 0:
-        batch = torch.from_numpy(batch)
+    if isinstance(batch, (float, int, str, bytes)) or (
+        type(batch).__module__ == "numpy" and not isinstance(batch, Iterable)
+    ):
+        return batch
+    # generic 0-D objects (NumPy/Torch/others exposing ndim): treat as scalars
+    if getattr(batch, "ndim", -1) == 0:
+        return batch.item() if detach else batch
@@
-    if isinstance(batch, torch.Tensor):
-        if detach:
-            batch = batch.detach()
-        if batch.ndim == 0:
-            return batch.item() if detach else batch
+    if isinstance(batch, torch.Tensor):
+        if detach:
+            batch = batch.detach()
+        # ndim==0 handled above

---

628-630: Graceful fallback for unsupported NumPy dtypes.

torch.from_numpy will fail for some dtypes (e.g., datetime64, object). Consider catching and falling back to .item() or returning the ndarray unchanged.

-    if isinstance(batch, np.ndarray) and batch.ndim == 0:
-        batch = torch.from_numpy(batch)
+    if isinstance(batch, np.ndarray) and batch.ndim == 0:
+        try:
+            batch = torch.from_numpy(batch)
+        except (TypeError, ValueError):
+            return batch.item() if detach else batch

---

614-621: Doc/test touch-up for 0-D NumPy arrays.

Please note 0-D NumPy array behavior in the decollate_batch docstring and add unit tests for:

• np.array(1) with detach=True/False
• Nested structures containing 0-D arrays
• Edge dtype (e.g., bool, float32)

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🔇 Additional comments (1)

monai/data/utils.py (1)

628-635: Good fix: avoids 0-D ndarray iteration error and aligns with tensor path.

Converting 0-D NumPy arrays to torch first prevents the TypeError raised by iterating 0-D arrays and lets the existing scalar-tensor handling (.item() when detach=True) kick in. Looks correct.