Add support for optional conditioning in PatchInferer, SliceInferer, and SlidingWindowInferer

[FinnBehrendt]

Fixes #8220

Description

This PR adds support for optional conditioning in MONAI’s inferers, allowing models to receive auxiliary inputs for conditioning that are processed (patched, sliced) the same way as the inputs. This is particularly relevant for generative models like conditional GANs or DMs.

Example Usage:

# Given a conditioned model, inputs of shape (1, C, H, W, D) and condition of shape (1, C, H, W, D)
output = SliceInferer(...)(inputs, model, condition=cond_tensor)

Types of changes

• Extended PatchInferer, SliceInferer, and SlidingWindowInferer to optionally accept a condition tensor (passed as a kwarg).
• The condition can now be:
  • None (default)
  • A tensor of the same shape as inputs
• The inferers now slice/patch the conditions alongside the corresponding inputs and feed them to the network.
• Updated unit tests for each inferer:
  • Verified with and without conditioning

• [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.
• [x] Integration tests passed locally by running ./runtests.sh -f -u --net --coverage.
• [x] 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.

Additional extensions such as support for dense vector conditioning (e.g., (1, C, Z), with Z being the conditional dimension) could be explored in a follow-up PR if there’s interest.

[FinnBehrendt]

Thanks a lot for the suggestions! I just pushed the updated changes.

[coderabbitai[bot]]

Walkthrough

The code introduces support for an optional condition tensor to the PatchInferer, SlidingWindowInferer, and SliceInferer classes, as well as the sliding_window_inference utility. This allows conditional inference by synchronously processing the condition tensor alongside inputs and passing it to the network. Comprehensive tests are added for this new conditional mechanism.

Changes

File(s)	Change Summary
monai/inferers/inferer.py	Added optional condition argument to PatchInferer, SlidingWindowInferer, and SliceInferer methods; updated control flow to validate and propagate condition.
monai/inferers/utils.py	Updated sliding_window_inference to accept and process an optional condition tensor in sync with inputs.
tests/inferers/test_patch_inferer.py	Added new test class and cases to verify PatchInferer with condition argument.
tests/inferers/test_slice_inferer.py	Added new test class to verify SliceInferer with condition support.
tests/inferers/test_sliding_window_inference.py	Added new test class to verify sliding_window_inference and SlidingWindowInferer with condition.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant Inferer (Patch/SlidingWindow/Slice)
    participant Network

    User->>Inferer: call(inputs, network, condition=cond)
    Inferer->>Inferer: Validate shapes/types of inputs and condition
    loop For each patch/slice/window
        Inferer->>Network: network(input_patch, condition=cond_patch)
        Network-->>Inferer: output_patch
    end
    Inferer-->>User: aggregated_output

Assessment against linked issues

Objective	Addressed	Explanation
Add support for conditional-based models in SliceInferer (#8220)	✅
Ensure condition tensor is processed slice-by-slice in sync with inputs (#8220)	✅
Validate shape and type matching between condition and inputs for correct inference (#8220)	✅
Add tests verifying SliceInferer and related inferers handle condition argument correctly (#8220)	✅

Poem

A rabbit with code in its paws,
Adds "condition"—and earns applause!
Now slices and patches, in sync they go,
Through inferers, the tensors flow.
With tests that hop and outputs that gleam,
This conditional leap fulfills the dream!
🐇✨

✨ Finishing Touches

• [ ] 📝 Generate Docstrings

---

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[KumoLiu]

/build