Merge main into feat/hailo-patchcore-e2e - #167
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…, localization, Autopilot support, and testing fixes (#166) ## 🔗 Related Issue Fixes # ## 📝 Description This PR adds **EfficientAD support to AnomaVision** and integrates it into the existing anomaly-detection pipeline alongside PaDiM and PatchCore. ### 🚀 EfficientAD * Added EfficientAD as a supported anomaly-detection algorithm. * Integrated EfficientAD with the existing algorithm abstraction used by PaDiM/PatchCore. * Reused the existing AnomaVision data loading and preprocessing pipeline. * Added batched training/inference to avoid unnecessary repeated computation. * Optimized teacher feature extraction and removed unnecessary inference-time computation. * Added calibrated anomaly thresholds based on **normal training images only**. * Added anomaly-map generation for pixel-level localization. * Reused the existing post-processing and visualization pipeline for: * anomaly heatmaps * localization masks * contours * bounding boxes * overlay visualizations ### 🔌 Deployment & Export * Added EfficientAD support to the existing model/export workflow. * Maintained compatibility with the existing ONNX-based detection flow. * Preserved the existing PaDiM/PatchCore inference architecture rather than introducing a separate EfficientAD pipeline. * Added EfficientAD support to **Production Autopilot**. * Autopilot can compare PaDiM, PatchCore, and EfficientAD candidates. * EfficientAD calibration data is preserved with the model artifact. ### 📊 Production Autopilot * Restored and preserved the rich `production_autopilot_report.html` dashboard. * Added EfficientAD to candidate comparison and model selection. * Report includes: * Image AUROC * Pixel AUROC * median latency * P95 latency * anomaly coverage * normal false-positive localization rate * threshold * selected model * deployment artifact * localization health * reproducibility information * Restored the existing timing/performance summary in the detection pipeline. ### 🐛 Fixes * Fixed EfficientAD training failure caused by PyTorch inference-mode tensors being used in autograd. * Fixed JSON serialization of NumPy/PyTorch scalar metrics in the Autopilot deployment manifest. * Restored Autopilot report/test compatibility. * Preserved existing helper APIs such as `_format_metric`. * Fixed report metric naming/capitalization expected by the test suite. ## 🔄 Type of Change * [x] 🐛 Bug fix (non-breaking change which fixes an issue) * [x] 🚀 New feature (non-breaking change which adds functionality) * [ ] 💥 Breaking change (fix or feature that would cause existing functionality to not work as expected) * [ ] 📖 Documentation update * [ ] 🏗️ Infrastructure / CI/CD update ## 🧪 Hardware & Matrix Testing **I have successfully built and tested this code using `uv` on:** * [x] `anomavision[cpu]` (Standard/Edge) * [ ] `anomavision[cu121]` (CUDA 12.1) * [ ] `anomavision[cu124]` (CUDA 12.4) * [ ] `anomavision[cu118]` (CUDA 11.8) **Host OS used for testing:** * [ ] Linux / Ubuntu * [x] Windows (Native or WSL2) * [ ] macOS ## 🧪 Validation * [x] Existing Autopilot unit tests updated/fixed. * [x] EfficientAD-specific integration changes covered. * [x] Autopilot report generation validated. * [x] Deployment manifest generation validated. * [ ] Full `uv run pytest` suite verified on all supported environments. * [ ] Bottle dataset benchmark completed for PaDiM vs PatchCore vs EfficientAD. * [ ] Full ONNX/OpenVINO/TensorRT/Hailo hardware validation completed. ## ✅ Developer Checklist * [x] My code follows the core style guidelines of this project (Ruff/Black formatting). * [ ] I have run `uv run pytest` and all unit tests pass locally. * [ ] **Lockfile Guard:** If I added or modified a dependency in `pyproject.toml`, I have run `uv lock --python 3.10` and committed the updated `uv.lock` file. * [x] I have added tests that prove my fix is effective or that my feature works. * [x] I have updated the documentation accordingly (if applicable). ## 📸 Screenshots / Visual Proof The Production Autopilot report now provides a rich HTML dashboard comparing PaDiM, PatchCore, and EfficientAD, including localization and deployment metrics. Recommended screenshots: * `production_autopilot_report.html` * EfficientAD anomaly heatmap * EfficientAD localization mask/overlay * PaDiM vs PatchCore vs EfficientAD comparison * Autopilot model-selection summary
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Sync the Hailo PatchCore E2E branch with the latest
main. Keep the existing Hailo-specific changes and avoid unrelated modifications.