Fix #8366: Add strict shape validation to sliding_window_inference

[mattlin1124]

Description

This PR addresses issue #8366 by implementing strict shape validation in sliding_window_inference.

Per the feedback from maintainers (@ericspod), implicit guessing (heuristics) for channel-last data has been avoided. Instead, this PR ensures that:

1. The input tensor explicitly matches the expected dimensions based on roi_size (e.g., must be 5D for 3D roi_size).
2. Validation is skipped if roi_size is an integer (broadcasting), preventing regressions in existing 1D/broadcasting tests.
3. A clear ValueError is raised if dimensions do not match, guiding users to handle channel-last data upstream using EnsureChannelFirst or EnsureChannelFirstd.

Status

• Code changes implemented in monai/inferers/utils.py
• New unit tests added in tests/inferers/test_sliding_window_inference.py
• No changes to .gitignore

Types of changes

• Bug fix (non-breaking change which fixes an issue)

[coderabbitai]

Walkthrough

Docstring in sliding_window_inference clarified that roi_size can be a scalar or per-dimension sequence, and that inputs must be channel-first with a batch dimension (NCHW / NCDHW); it advises converting NHWC/NDHWC upstream. A Raises entry was added documenting a ValueError when input spatial dimensions do not match the roi_size length. The implementation now performs strict shape validation when roi_size is a Sequence: it checks that the input tensor's number of spatial dimensions equals len(roi_size) and raises a descriptive ValueError (including guidance about channel-first data) if not. The buffered flag initialization was moved to occur after this validation. A new test test_strict_shape_validation was added to tests/inferers/test_sliding_window_inference.py; the diff shows this test insertion duplicated. Public API and exported entities are unchanged.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

• Verify the dimensionality check and exact ValueError text match expectations.
• Confirm moving buffer initialization after validation has no unintended side effects.
• Remove duplicate test_strict_shape_validation insertion in the test file if present.
• Check for linting or import issues introduced by the changes.

Pre-merge checks and finishing touches

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 50.00% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	Title clearly summarizes the main change: adding strict shape validation to sliding_window_inference to fix issue #8366.
Description check	✅ Passed	Description covers objectives, implementation details, status checklist, and change type; however, missing 'Fixes #' section header and incomplete types-of-changes checkboxes.

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• Post copyable unit tests in a comment

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Actionable comments posted: 2

🧹 Nitpick comments (2)

monai/inferers/utils.py (1)

177-177: Duplicate calculation of num_spatial_dims.

num_spatial_dims is computed on line 177 and again on line 197. Remove the duplicate.

Apply this diff:

     """
     num_spatial_dims = len(inputs.shape) - 2
 
     # Only perform strict shape validation if roi_size is a sequence (explicit dimensions).
     # If roi_size is an integer, it is broadcast to all dimensions, so we cannot
     # infer the expected dimensionality to enforce a strict check here.
     if not isinstance(roi_size, int):
         roi_dims = len(roi_size)
         if num_spatial_dims != roi_dims:
             raise ValueError(
                 f"inputs must have {roi_dims + 2} dimensions for {roi_dims}D roi_size "
                 f"(Batch, Channel, {', '.join(['Spatial'] * roi_dims)}), "
                 f"but got inputs shape {inputs.shape}.\n"
                 "If you have channel-last data (e.g. B, D, H, W, C), please use "
                 "monai.transforms.EnsureChannelFirst or EnsureChannelFirstd upstream."
             )
     # -----------------------------------------------------------------
     # ---- Strict validation: do NOT guess or permute layouts ----
     if isinstance(inputs, torch.Tensor):
         assert_channel_first(inputs, "inputs")
     buffered = buffer_steps is not None and buffer_steps > 0
-    num_spatial_dims = len(inputs.shape) - 2
     if buffered:

Also applies to: 197-197

tests/inferers/test_sliding_window_inference.py (1)

375-394: Consider testing edge cases.

Current test covers dimensional mismatches. Consider adding:

• Valid 5D input (should pass)
• roi_size as int (should skip strict validation)
• MetaTensor input

Example additional test cases:

# Valid 5D input should pass
inputs_5d = torch.randn((1, 3, 16, 16, 16), device=device)
result = sliding_window_inference(inputs_5d, roi_size, sw_batch_size, predictor)
self.assertEqual(result.shape, inputs_5d.shape)

# roi_size as int should skip strict validation (preserves existing behavior)
inputs_4d_int_roi = torch.randn((1, 3, 16, 16), device=device)
result = sliding_window_inference(inputs_4d_int_roi, 8, sw_batch_size, predictor)
self.assertIsNotNone(result)

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monai/inferers/utils.py

47-47: Undefined name Optional

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🔇 Additional comments (3)

monai/inferers/utils.py (2)

173-174: LGTM!

Clear documentation of channel-first requirement with actionable guidance.

---

177-196: Strict validation logic is sound.

Correctly validates dimensionality mismatch and provides clear error guidance. The check for isinstance(roi_size, int) appropriately skips validation for broadcasted cases.

tests/inferers/test_sliding_window_inference.py (1)

375-394: Test correctly validates dimensional mismatch detection.

Both test cases properly verify that inputs with incorrect dimensions raise ValueError with the expected message.

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Actionable comments posted: 1

🧹 Nitpick comments (1)

monai/inferers/utils.py (1)

190-207: Strict roi_size vs input dimensionality check looks good; consider minor robustness tweaks.

The new block correctly enforces that a sequence roi_size with k spatial dims is only accepted for inputs with k spatial dims (k + 2 total), and the error message clearly points users to EnsureChannelFirst[d]. Two minor, optional points:

• isinstance(roi_size, int) will treat NumPy integer scalars as non-int and then call len(roi_size), which will fail; if you expect such inputs in the wild, numbers.Integral (or similar) would make this more robust.
• Ruff’s TRY003 warning on the long ValueError message could be silenced by factoring the message into a local msg variable, but that’s purely stylistic.

Otherwise the logic and placement (before padding and window setup) are sound.

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🔇 Additional comments (2)

monai/inferers/utils.py (2)

16-36: Import of Optional is correct and resolves the previous undefined-name.

Type hints below use Optional; this import is appropriate and fixes the Ruff F821 warning without side effects.

---

184-186: Docstring note clarifies channel-first requirement and is aligned with the new error messaging.

The added Note lines make the NCHW/NCDHW expectation and the EnsureChannelFirst/EnsureChannelFirstd recommendation explicit, which matches the new ValueError text below.

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♻️ Duplicate comments (1)

monai/inferers/utils.py (1)

44-90: assert_channel_first layout enforcement and allow_binary_two_channel behavior are inconsistent with intent.

Two issues here:

1. No real layout enforcement when num_classes is None.

   • For 4D/5D tensors, if num_classes is left as None (as in sliding_window_inference(inputs, ...)), this function never raises. It only returns early for non‑tensors/other ndims and otherwise performs no checks.
   • Docstring and the trailing comment (“Enforce channel-first layout …”, “we simply require channel at dim=1”) overstate what is enforced. As used in sliding_window_inference, this helper is effectively a no‑op and does not reject NHWC/NDHWC inputs; only the new roi_size/ndim check adds validation.

2. allow_binary_two_channel is a no‑op.

   • When num_classes == 2, ok = (c == num_classes) already accepts C == 2. The extra branch
     if allow_binary_two_channel and num_classes == 2: ok = ok or (c == 2)
     never expands the accepted cases, so the flag has no effect and adds API surface without behavior.

I’d suggest either simplifying the API (drop allow_binary_two_channel for now) or making both the flag and the docstring match the actual behavior. For example, if the goal is to support binary tasks that may be encoded as 1‑channel or 2‑channel, something like this would make the flag meaningful and keep the comments honest:

@@
-def assert_channel_first(
-    t: torch.Tensor,
-    name: str,
-    num_classes: Optional[int] = None,
-    allow_binary_two_channel: bool = False,
-) -> None:
+def assert_channel_first(
+    t: torch.Tensor,
+    name: str,
+    num_classes: Optional[int] = None,
+    allow_binary_two_channel: bool = False,
+) -> None:
@@
-    if num_classes is not None:
-        ok = (c == num_classes) or (num_classes == 1 and c == 1)
-        if allow_binary_two_channel and num_classes == 2:
-            ok = ok or (c == 2)
+    if num_classes is not None:
+        ok = c == num_classes
+        # For binary tasks, optionally accept either 1‑channel or C==num_classes.
+        if allow_binary_two_channel and num_classes == 2:
+            ok = ok or (c == 1)
         if not ok:
@@
-    # No guessing when num_classes is None; we simply require channel at dim=1.
-    # If callers provided NHWC/NDHWC, they must convert upstream.
+    # When num_classes is None we only check dimensionality; callers are
+    # responsible for ensuring channel-first layout (NCHW/NCDHW) upstream.

Alternatively, if you don’t need the flag yet, dropping allow_binary_two_channel and updating the doc/comment accordingly would keep the helper minimal and avoid confusion.

🧹 Nitpick comments (1)

monai/inferers/utils.py (1)

183-207: New roi_size vs ndim validation is good; consider where assert_channel_first runs and long error strings.

The strict check

• computes num_spatial_dims = len(inputs.shape) - 2,
• compares it to len(roi_size) when roi_size is a sequence, and
• raises a clear ValueError with guidance to use EnsureChannelFirst[d],

which aligns with the PR goal of explicit dimensionality validation without guessing. Skipping the check when roi_size is an int also preserves the existing 1D/broadcast behavior.

Two small follow‑ups to consider:

• Placement of assert_channel_first. Right now it runs only when inputs is a torch.Tensor, before convert_data_type. If callers ever pass numpy arrays (even though the type hint is Tensor/MetaTensor), those inputs get converted later but never go through assert_channel_first. If you want uniform enforcement, you could move the call to just after convert_data_type(...) so the check always sees a tensor.
• Long error messages (TRY003). Both the new ValueError here and the one in assert_channel_first are long multi‑line strings. If you want to satisfy Ruff’s TRY003, you could factor these into a small helper or custom exception instead of embedding the full text in the raise.

These are non‑blocking and can be deferred if you prefer.

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monai/inferers/utils.py (1)

16-16: Import of Optional is correct and resolves the undefined name.

No issues here; this matches the new type hints.

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🔇 Additional comments (3)

monai/inferers/utils.py (2)

178-179: LGTM: Clear documentation of channel-first requirement.

The updated Note appropriately directs users to convert channel-last data upstream.

---

183-199: LGTM: Strict validation correctly implemented.

The dimensionality check properly enforces that input shape matches roi_size dimensions, with appropriate skipping for integer roi_size. Error message clearly guides users to upstream conversion.

tests/inferers/test_sliding_window_inference.py (1)

375-394: Unable to complete verification due to repository access failure. Based on the provided code snippet alone, the test method test_strict_shape_validation is shown once (lines 375-394) and the test logic is sound—it correctly validates that 4D and 3D inputs raise ValueError with the expected message when a 3D roi_size (requiring 5D input) is provided. However, without access to the full test file, I cannot confirm whether duplication exists elsewhere or verify test coverage completeness. Additionally, the test method lacks a docstring, which violates the PEP8 guideline requiring docstrings for all definitions.

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Actionable comments posted: 0

🧹 Nitpick comments (1)

monai/inferers/utils.py (1)

155-156: Orphaned comment after validation block.

Comment appears after validation ends (line 153), making it unclear what it refers to. Either move it before line 141 as a section header or remove it since lines 141-143 already explain the validation approach.

Apply this diff to remove the orphaned comment:

-    # -----------------------------------------------------------------
-    # ---- Strict validation: do NOT guess or permute layouts ----
-

Or move it before the validation block:

+    # -----------------------------------------------------------------
+    # ---- Strict validation: do NOT guess or permute layouts ----
+
     # Only perform strict shape validation if roi_size is a sequence (explicit dimensions).

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🔇 Additional comments (4)

monai/inferers/utils.py (4)

37-37: LGTM.

tqdm import follows MONAI's optional_import pattern and is used on line 238.

---

135-136: LGTM.

Docstring clarifies channel-first requirement and provides clear guidance for channel-last data.

---

141-153: LGTM.

Validation logic correctly enforces dimension matching when roi_size is a sequence, skips when integer (preserving broadcasting). Verbose error message (TRY003 flag) is intentional and provides clear user guidance per PR objectives.

---

157-157: LGTM.

Moving buffered initialization after validation follows fail-fast principle - validate inputs before setup.

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monai/inferers/utils.py (1)

134-137: Document the new ValueError in the docstring (optional)

Since the function now raises a ValueError for shape mismatches, consider adding a short Raises: section to the docstring mentioning this, to stay closer to the Google-style guideline used elsewhere.

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monai/inferers/utils.py (1)

36-37: tqdm optional_import usage looks good

Module-level tqdm import via optional_import matches common MONAI patterns and keeps the dependency optional; no issues here.

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monai/inferers/utils.py (1)

140-155: Validation logic is correct and well-structured.

The strict shape validation properly enforces dimensionality requirements for sequence roi_size while preserving backward compatibility for integer/scalar values via the Integral check. The error message is clear and directs users to the appropriate transforms.

The buffered flag initialization at line 155 improves code clarity by making the condition explicit.

Optional: Static analysis flags the error message length (TRY003). You could extract it to a constant if preferred:

_SHAPE_MISMATCH_MSG = (
    "inputs must have {expected_dims} dimensions for {roi_dims}D roi_size "
    "(Batch, Channel, {spatial_dims}), but got inputs shape {actual_shape}.\n"
    "If you have channel-last data (e.g. B, D, H, W, C), please use "
    "monai.transforms.EnsureChannelFirst or EnsureChannelFirstd upstream."
)

Then use:

raise ValueError(_SHAPE_MISMATCH_MSG.format(
    expected_dims=roi_dims + 2,
    roi_dims=roi_dims,
    spatial_dims=', '.join(['Spatial'] * roi_dims),
    actual_shape=inputs.shape
))

However, the current inline approach is perfectly acceptable.

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monai/inferers/utils.py (2)

16-16: LGTM!

The Integral import is correctly placed and necessary for the robust type checking in the validation logic below.

---

135-136: LGTM!

The docstring clarification properly documents the channel-first requirement and provides clear guidance for handling channel-last data upstream.

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monai/inferers/utils.py (1)

133-136: LGTM!

Clear documentation of the channel-first requirement.

tests/inferers/test_sliding_window_inference.py (1)

375-393: Unable to verify due to repository access limitations. Manual verification required to confirm:

• Whether an inverted condition exists in sliding_window_inference function
• Whether the test assertions are correct
• Current state of validation logic

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monai/inferers/utils.py (1)

138-153: Shape validation logic is sound; consider clarifying supported roi_size types and factoring error text

The new Sequence-based check correctly enforces inputs.ndim == len(roi_size) + 2 for tuple/list/torch.Size roi sizes and skips scalar/broadcasted cases, matching the PR intent. Two minor follow‑ups:

• Ensure tests cover typical non‑tuple ROI sizes you expect in the wild (e.g., torch.Size, possibly np.ndarray) so this Sequence check aligns with caller behavior.
• To satisfy Ruff’s TRY003 and keep the function lean, you might move the long error message string into a small helper or constant.

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monai/inferers/utils.py (1)

77-136: Docstring doesn’t document new ValueError or follow full Google-style sections

The function now raises a ValueError for mismatched dimensionality, but the docstring lacks a Raises: (and Returns:) section and still uses a mixed style. Consider adding proper Args, Returns, and Raises sections that explicitly mention the dimensionality check and the channel‑first expectation.

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[ericspod]

Hi @mattlin1124 thanks for this addition, I think it looks fine as an added sanity check of the dimensions but leaves figuring out channel dimension first to the user. Does this supercede #8532? If so you can close that PR if this one completely addresses the problem. My comments here are very minor.

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monai/inferers/utils.py (2)

133-138: Note/Raises additions align with new behavior

The channel‑first note and the ValueError entry for dimension mismatch accurately document the new validation. If you want fuller coverage later, you could also mention the pre‑existing ValueErrors (e.g., invalid overlap, buffer_dim ranges), but that’s optional.

---

141-157: Strict shape validation logic matches requirements; TRY003 fix is optional

The new block correctly:

• Computes spatial dims from inputs (len(inputs.shape) - 2).
• Applies validation only when roi_size is a Sequence, preserving integer/broadcast behavior for legacy 1D patterns.
• Raises a clear, actionable ValueError for mismatched dimensionality (e.g., 3D roi_size vs 4D input), with guidance for channel‑last layouts.

Functionally this matches the PR objective and avoids the earlier len(int) / numpy‑int pitfalls.

If you want to satisfy Ruff’s TRY003 warning, you can factor the long error message into a local variable before raising, e.g.:

-        if num_spatial_dims != roi_dims:
-            raise ValueError(
-                f"Inputs must have {roi_dims + 2} dimensions for {roi_dims}D roi_size "
-                f"(Batch, Channel, {', '.join(['Spatial'] * roi_dims)}), "
-                f"but got inputs shape {inputs.shape}.\n"
-                "If you have channel-last data (e.g. B, D, H, W, C), please use "
-                "monai.transforms.EnsureChannelFirst or EnsureChannelFirstd upstream."
-            )
+        if num_spatial_dims != roi_dims:
+            msg = (
+                f"Inputs must have {roi_dims + 2} dimensions for {roi_dims}D roi_size "
+                f"(Batch, Channel, {', '.join(['Spatial'] * roi_dims)}), "
+                f"but got inputs shape {inputs.shape}.\n"
+                "If you have channel-last data (e.g. B, D, H, W, C), please use "
+                "monai.transforms.EnsureChannelFirst or EnsureChannelFirstd upstream."
+            )
+            raise ValueError(msg)

Purely stylistic; behavior is unchanged.

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monai/inferers/utils.py (1)

77-83: roi_size docstring clarification looks good

The updated description clearly spells out allowable shapes and ties the tuple length to spatial dimensionality; this matches the new strict validation semantics. No change requested.

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monai/inferers/utils.py (2)

77-82: Docstring updates are accurate; optional extra exception docs.

The expanded roi_size description and the new Note/Raises sections correctly reflect the expected usage (scalar vs per-dimension tuple) and the new shape check for channel-first inputs. This aligns with the implementation and new tests.

If you want to fully match the guidelines for documenting raised exceptions, you could optionally mention other existing ValueError cases (e.g., invalid overlap, out-of-range buffer_dim) in the Raises section as well, but this is not required for this PR.

As per coding guidelines, being explicit about all documented exceptions is preferred but not mandatory here.

Also applies to: 134-139

---

142-158: Shape validation logic looks correct; long error message is a minor style nit.

The strict validation that runs only when roi_size is a Sequence correctly enforces num_spatial_dims == len(roi_size) and cleanly skips scalar/broadcasting cases (including non-Sequence integer-likes), which matches the stated PR goals and avoids the earlier len(int) / numpy-int pitfalls. The error message is clear and user-friendly for channel-last inputs.

Static analysis (Ruff TRY003) flags the long error message literal, so if you care about that rule you could optionally move the message into a named constant or small helper to keep the raise line shorter; functionally this is fine as is.

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[mattlin1124]

Hi @Nic-Ma @KumoLiu @ericspod,

Just a quick update: per @ericspod 's suggestion, I have closed the old PR #8532 and updated the docstrings in this PR accordingly.

However, I noticed that the CI checks (build-docs and packaging) are now failing with the same error: OSError: [Errno 28] No space left on device.

It seems the runner ran out of disk space during the installation of heavy dependencies (like NVIDIA libraries and PyTorch). This appears to be an infrastructure issue unrelated to my recent changes.

Since I don't have permission to re-run the failed jobs, could I ask for your help to trigger a re-run?

Thanks for your help!