🧑💻 Optimize PatchPredictor Tests for CI runs#1096
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- Optimize `PatchPredictor` Tests for CI runs
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## develop #1096 +/- ##
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- Coverage 99.88% 99.77% -0.12%
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Files 86 86
Lines 11661 11661
Branches 1531 1531
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- Hits 11648 11635 -13
- Misses 7 17 +10
- Partials 6 9 +3 ☔ View full report in Codecov by Harness. 🚀 New features to boost your workflow:
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Pull request overview
Optimizes PatchPredictor test execution for CI by introducing lightweight in-memory tests (no downloads / minimal IO) and skipping heavier integration-style tests when running on CI.
Changes:
- Added a
FakeMiniModel+ patched dataloader fixture to exercisePatchPredictor.run()without file IO or image decoding. - Added fast unit tests for string-model initialization (without weight downloads) and for
save_predictionsoutput formats. - Marked multiple existing integration tests as CI-skipped via
running_on_ci().
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| def _extract_probabilities_from_annotation_store(db_file: str | Path) -> dict: | ||
| """Helper function to extract probabilities from Annotation Store.""" | ||
| con = sqlite3.connect(dbfile) | ||
| con = sqlite3.connect(db_file) | ||
| cur = con.cursor() | ||
| annotations_properties = list(cur.execute("SELECT properties FROM annotations")) |
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| class FakeMiniModel(ModelABC): | ||
| """Lightweight model double with a realistic TIAToolbox-style interface.""" | ||
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| class_dict = {0: "background", 1: "tumour"} # noqa: RUF012 | ||
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PatchPredictorTests for CI runs