mirror of
https://github.com/azaion/ai-training.git
synced 2026-04-22 23:36:36 +00:00
Oleksandr Bezdieniezhnykh
142c6c4de8
- Replaced module-level path variables in constants.py with a structured Pydantic Config class. - Updated all relevant modules (train.py, augmentation.py, exports.py, dataset-visualiser.py, manual_run.py) to access paths through the new config structure. - Fixed bugs related to image processing and model saving. - Enhanced test infrastructure to accommodate the new configuration approach. This refactor improves code maintainability and clarity by centralizing configuration management.
34 lines
1.4 KiB
Markdown
34 lines
1.4 KiB
Markdown
# Performance Test Scenarios
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## PT-AUG-01: Augmentation throughput
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- **Input**: 10 images from fixture dataset
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- **Action**: Run `augment_annotations()`, measure wall time
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- **Expected**: Completes within 60 seconds (10 images × 8 outputs = 80 files)
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- **Traces**: Restriction: Augmentation runs continuously
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- **Note**: Threshold is generous; actual performance depends on CPU
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## PT-AUG-02: Parallel augmentation speedup
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- **Input**: 10 images from fixture dataset
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- **Action**: Run with ThreadPoolExecutor vs sequential, compare times
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- **Expected**: Parallel is ≥ 1.5× faster than sequential
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- **Traces**: AC: Parallelized per-image processing
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## PT-DSF-01: Dataset formation throughput
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- **Input**: 100 images + labels
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- **Action**: Run `form_dataset()`, measure wall time
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- **Expected**: Completes within 30 seconds
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- **Traces**: Restriction: Dataset formation before training
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## PT-ENC-01: Encryption throughput
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- **Input**: 10MB random bytes
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- **Action**: Encrypt + decrypt roundtrip, measure wall time
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- **Expected**: Completes within 5 seconds
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- **Traces**: AC: Model encryption feasible for large models
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## PT-INF-01: ONNX inference latency (single image)
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- **Input**: 1 preprocessed image + ONNX model
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- **Action**: Run single inference, measure wall time
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- **Expected**: Completes within 10 seconds on CPU (no GPU requirement for test)
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- **Traces**: AC: Inference capability
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- **Note**: Production uses GPU; CPU is slower but validates correctness
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