mirror of
https://github.com/azaion/detections.git
synced 2026-04-22 08:36:31 +00:00
Oleksandr Bezdieniezhnykh
8ce40a9385
- Introduced `AIAvailabilityStatus` class to manage the availability status of AI models, including methods for setting status and logging messages. - Added `AIRecognitionConfig` class to encapsulate configuration parameters for AI recognition, with a static method for creating instances from dictionaries. - Implemented enums for AI availability states to enhance clarity and maintainability. - Updated related Cython files to support the new classes and ensure proper type handling. These changes aim to improve the structure and functionality of the AI model management system, facilitating better status tracking and configuration handling.
55 lines
4.3 KiB
Markdown
55 lines
4.3 KiB
Markdown
# Autopilot State
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## Current Step
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flow: existing-code
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step: 7
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name: Refactor
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status: completed
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sub_step: done
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retry_count: 0
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## Completed Steps
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| Step | Name | Completed | Key Outcome |
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|------|------|-----------|-------------|
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| 1 | Document | 2026-03-21 | 10 modules, 4 components, full _docs/ generated from existing codebase |
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| 2 | Test Spec | 2026-03-21 | 39 test scenarios (16 positive, 8 negative, 11 non-functional), 85% total coverage, 5 artifacts produced |
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| 3 | Code Testability Rev. | 2026-03-29 | Engine factory refactoring completed: polymorphic EngineClass pattern (TensorRT/CoreML/ONNX) with auto-detection. Hardcoded values aligned with Docker compose. |
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| 4 | Decompose Tests | 2026-03-23 | 11 tasks (AZ-138..AZ-148), 35 complexity points, 3 batches. Phase 3 test data gate PASSED: 39/39 scenarios validated, 12 data files provided. |
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| 5 | Implement Tests | 2026-03-23 | 11 tasks implemented across 4 batches, 38 tests (2 skipped), all code reviews PASS_WITH_WARNINGS. Commits: 5418bd7, a469579, 861d4f0, f0e3737. |
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| 6 | Run Tests | 2026-03-30 | 23 passed, 0 failed, 0 skipped, 0 errors in 11.93s. Fixed: Cython __reduce_cython__ (clean rebuild), missing Pillow dep, relative MEDIA_DIR paths. Removed 14 dead/unreachable tests. Updated test-run skill to treat skips as blocking gate. |
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| 7 | Refactor | 2026-03-31 | Engine-centric dynamic batch refactoring. Moved source to src/. Engine pipeline redesign: preprocess/postprocess/process_frames in base InferenceEngine, dynamic batching per engine (CoreML=1, TensorRT=GPU-calculated, ONNX=config). Fixed: video partial batch flush, image accumulation data loss, frame-is-None crash. Removed detect_single_image (POST /detect delegates to run_detect). Dead code: removed msgpack, serialize methods, unused constants/fields. Configurable classes.json + log paths, HTTP timeouts. 28 e2e tests pass. |
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## Key Decisions
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- User chose to document existing codebase before proceeding
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- Component breakdown: 4 components (Domain, Inference Engines, Inference Pipeline, API)
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- Verification: 4 legacy issues found and documented (unused serialize/from_msgpack, orphaned queue declarations)
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- Input data coverage approved at ~90% (Phase 1a)
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- Test coverage approved at 85% (21/22 AC, 13/18 restrictions) with all gaps justified
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- User chose refactor path (decompose tests → implement tests → refactor)
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- Integration Tests Epic: AZ-137
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- Test Infrastructure: AZ-138 (5 pts)
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- 10 integration test tasks decomposed: AZ-139 through AZ-148 (30 pts)
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- Total: 11 tasks, 35 complexity points, 3 batches
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- Phase 3 (Test Data Validation Gate) PASSED: 39/39 scenarios have data, 85% coverage, 0 tests removed
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- Test data: 6 images, 3 videos, 1 ONNX model, 1 classes.json provided by user
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- User confirmed dependency table and test data gate
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- Jira MCP auth skipped — tickets not transitioned to In Testing
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- Test run: removed 14 dead/unreachable tests (explicit @skip + runtime always-skip), added .c to .gitignore
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- User chose to refactor (option A) — clean up legacy dead code
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- User requested: move code to src/, thorough re-analysis, exhaustive refactoring list
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- Refactoring round: 01-code-cleanup, automatic mode, 15 changes identified
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- User feedback: analyze logical flow contradictions, not just static code. Updated refactor skill Phase 1 with logical flow analysis.
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- User chose: split scope — engine refactoring as Step 7, architecture shift (streaming, DB config, media storage, Jetson) as Step 8
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- User chose: remove detect_single_image, POST /detect delegates to run_detect
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- GPU memory fraction: 80% for inference, 20% buffer (Jetson 40% deferred to Step 8)
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## Last Session
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date: 2026-03-31
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ended_at: Step 7 complete — all 11 todos done, 28 e2e tests pass
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reason: Refactoring complete
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notes: Engine-centric dynamic batch refactoring implemented. Source moved to src/. InferenceEngine base class now owns preprocess/postprocess/process_frames with per-engine max_batch_size. CoreML overrides preprocess (direct PIL, no blob reversal) and postprocess. TensorRT calculates max_batch_size from GPU memory (80% fraction) with optimization profiles for dynamic batch. All logical flow bugs fixed (LF-01 through LF-09). Dead code removed (msgpack, serialize, unused constants). POST /detect unified through run_detect. Next: Step 8 (architecture shift — streaming media, DB-backed config, media storage, Jetson support).
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## Blockers
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- none
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