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Add config validator that maps null -> default - #3475
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🟢 Approval recommended
The change is small and targeted, removes duplicated logic, and is backed by focused unit tests for both the new DefaultIfNone behavior and related validation helpers.
Pull request overview
This PR introduces a reusable config-validation helper that normalizes explicit null values to a field’s declared default during Pydantic validation, and applies it to the PyTorch detector model config so existing YAML configs with box_score_thresh: null validate to the intended default.
Changes:
- Added
DefaultIfNone[...]annotated type (via aBeforeValidator) that maps explicitNoneinputs to “use field default”. - Replaced the bespoke
box_score_threshfield validator inDetectorModelConfigwithDefaultIfNone[Fraction]. - Added a new test module covering
DefaultIfNonebehavior plus existing validators indeeplabcut.core.config.validation.
File summaries
| File | Description |
|---|---|
deeplabcut/core/config/validation.py |
Adds DefaultIfNone annotated type using PydanticUseDefault to centralize None -> default normalization. |
deeplabcut/pose_estimation_pytorch/config/model.py |
Switches box_score_thresh to DefaultIfNone[Fraction], removing a one-off field validator. |
tests/core/config/test_validation.py |
Adds unit tests validating DefaultIfNone and other validator utilities/annotated types. |
Review details
- Files reviewed: 3/3 changed files
- Comments generated: 0
- Review effort level: Lite
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| def _use_default_if_none(value: Any) -> Any: |
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| class TestNDArrayInt: |
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Out of scope for this PR but in hindsight I find the name to be slightly confusing. No concerns on functionality at all, just the int part.
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Follow-up PR for #3470, introducing a central validator for cases when null needs to be mapped to default.