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.
2.3 KiB
Augmentation Performance, Resilience & Resource Tests
Task: AZ-154_test_augmentation_nonfunc Name: Augmentation Non-Functional Tests Description: Implement performance, resilience, and resource limit tests for augmentation — throughput, parallel speedup, error handling, output bounds Complexity: 2 points Dependencies: AZ-152_test_infrastructure Component: Blackbox Tests Jira: AZ-154 Epic: AZ-151
Problem
Augmentation must perform within time thresholds, handle corrupted/missing inputs gracefully, and respect output count bounds.
Outcome
- 6 passing pytest tests across performance and resilience categories
- Performance tests in
tests/performance/test_augmentation_perf.py - Resilience and resource limit tests in
tests/test_augmentation.pywith markers
Scope
Included
- PT-AUG-01: Augmentation throughput (10 images ≤ 60s)
- PT-AUG-02: Parallel augmentation speedup (≥ 1.5× faster)
- RT-AUG-01: Handles corrupted image gracefully
- RT-AUG-02: Handles missing label file
- RT-AUG-03: Transform failure produces fewer variants (no crash)
- RL-AUG-01: Output count bounded to exactly 8
Excluded
- Blackbox functional tests (separate task 02)
Acceptance Criteria
AC-1: Throughput Given 10 images from fixture dataset When augment_annotations() runs Then completes within 60 seconds
AC-2: Parallel speedup Given 10 images from fixture dataset When run with ThreadPoolExecutor vs sequential Then parallel is ≥ 1.5× faster
AC-3: Corrupted image Given 1 valid + 1 corrupted image (truncated JPEG) When augment_annotations() runs Then valid image produces 8 outputs, corrupted skipped, no crash
AC-4: Missing label Given 1 image with no matching label file When augment_annotation() runs on it Then exception caught per-thread, pipeline continues
AC-5: Transform failure Given 1 image + label with extremely narrow bbox When augment_inner() runs Then 1-8 ImageLabel objects returned, no crash
AC-6: Output count bounded Given 1 image When augment_inner() runs Then exactly 8 outputs returned (never more)
Constraints
- Performance tests require pytest markers:
@pytest.mark.performance - Resilience tests marked:
@pytest.mark.resilience - Resource limit tests marked:
@pytest.mark.resource_limit - Performance thresholds are generous (CPU-bound, no GPU requirement)