BUGFIX: support NDHWC input in sliding_window_inference and DiceMetric

[mattlin1124]

What

Move ensure_channel_first to monai/utils/tensor_utils.py and re-export from inferers/utils.py for backward compatibility.

Update MeanDice channel normalization logic:

Use num_classes as authoritative hint when provided.

Without num_classes: apply heuristic first, then y-informed fallback.

Fail fast on ambiguous shapes (dim1 == dim-1 > 1).

Detect and raise on inconsistent inputs (label-map vs one-hot) when num_classes is missing.

Add unit tests covering:

NHWC with H==W==C and num_classes=None → expect ValueError.

NHWC with num_classes=C → matches NCHW baseline.

Label-map y_pred vs one-hot y → passes with num_classes=C, raises without.

Update docstrings to clarify channel-last support and explicit error conditions.

Why

Decouple metrics from inferers by placing generic tensor-layout helpers under utils.

Prevent silent NHWC/NCHW misinterpretation in DiceMetric.

Provide explicit errors and guidance when inputs are ambiguous or inconsistent.

How verified

Local checks: pre-commit run -a, mypy monai passed.

New tests in tests/metrics/test_dice_layouts.py cover ambiguity, parity with specified num_classes, and label-map vs one-hot cases.

CI expected to cover full platform matrix (Ubuntu/Windows/macOS, GPU backends).

Summary by CodeRabbit

• New Features
  • Added seamless support for channel-last 5D inputs (N, D, H, W, C) with C = 1, 3, or 4 in sliding window inference and Mean Dice metric. Inputs are automatically converted to channel-first (N, C, D, H, W), eliminating the need for manual permutation.
  • Works transparently with no changes to public APIs. Behavior remains unchanged for other input shapes, ensuring backward compatibility and consistent results.

[coderabbitai[bot]]

Walkthrough

Adds automatic handling of 5D channel-last tensors (N, D, H, W, C with C in {1, 3, 4}) by permuting to channel-first (N, C, D, H, W) in sliding window inference and mean Dice computation. No public signatures changed; existing logic runs after reordering.

Changes

Cohort / File(s)	Summary of Changes
Inference utilities monai/inferers/utils.py	Detect 5D channel-last inputs (C in {1,3,4}) in sliding_window_inference and permute to channel-first before windowing; behavior unchanged otherwise.
Metrics monai/metrics/meandice.py	In _compute_tensor, detect 5D channel-last y_pred/y (C in {1,3,4}) and permute to channel-first; maintain contiguity; rest of Dice logic unchanged.

Sequence Diagram(s)

sequenceDiagram
  autonumber
  participant Caller
  participant Inference as sliding_window_inference
  Caller->>Inference: input (N, D, H, W, C)
  alt 5D and C in {1,3,4}
    Inference->>Inference: Permute to (N, C, D, H, W)
  else
    Note over Inference: Use input as-is
  end
  Inference->>Inference: Windowing & aggregation
  Inference-->>Caller: output

sequenceDiagram
  autonumber
  participant Trainer as Metric caller
  participant Dice as _compute_tensor
  Trainer->>Dice: y_pred, y
  alt 5D and last dim C in {1,3,4}
    Dice->>Dice: Permute y_pred/y to (N, C, D, H, W)
  else
    Note over Dice: Use tensors as-is
  end
  Dice->>Dice: Compute Dice as before
  Dice-->>Trainer: dice score(s)

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10–15 minutes

Poem

I hop through dims with tidy cheer,
From NDHWC to channels near—
A twist, a turn, a quick permute,
Now windows slide and metrics compute.
Ears up, tail flick—jobs aligned,
Carrots for code that’s well-defined! 🥕

✨ Finishing Touches

• [ ] 📝 Generate Docstrings

🧪 Generate unit tests

• [ ] Create PR with unit tests
• [ ] Post copyable unit tests in a comment

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