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.
2.1 KiB
ONNX Inference Tests
Task: AZ-161_test_onnx_inference Name: ONNX Inference Tests Description: Implement 4 tests for ONNX model loading, inference execution, postprocessing, and CPU latency Complexity: 3 points Dependencies: AZ-152_test_infrastructure Component: Blackbox Tests Jira: AZ-161 Epic: AZ-151
Problem
The ONNX inference engine loads a model, runs detection on images, and postprocesses results. Tests must verify the full pipeline works on CPU (smoke test — no precision validation).
Outcome
- 4 passing pytest tests
- Blackbox tests in
tests/test_onnx_inference.py - Performance test in
tests/performance/test_inference_perf.py
Scope
Included
- BT-INF-01: Model loads successfully (no exception, valid engine)
- BT-INF-02: Inference returns output (array shape [batch, N, 6+])
- BT-INF-03: Postprocessing returns valid detections (x,y,w,h ∈ [0,1], cls ∈ [0,79], conf ∈ [0,1])
- PT-INF-01: ONNX inference latency (single image ≤ 10s on CPU)
Excluded
- TensorRT inference (requires NVIDIA GPU)
- Detection precision/recall validation (smoke-only per user decision)
Acceptance Criteria
AC-1: Model loads Given azaion.onnx bytes When OnnxEngine(model_bytes) is constructed Then no exception; engine has valid input_shape and batch_size
AC-2: Inference output Given ONNX engine + 1 preprocessed image When engine.run(input_blob) is called Then returns list of numpy arrays; first array has shape [batch, N, 6+]
AC-3: Valid detections Given ONNX engine output from real image When Inference.postprocess() is called Then returns list of Detection objects; each has x,y,w,h ∈ [0,1], cls ∈ [0,79], confidence ∈ [0,1]
AC-4: CPU latency Given 1 preprocessed image + ONNX model When single inference runs Then completes within 10 seconds
Constraints
- Uses onnxruntime (CPU) not onnxruntime-gpu
- ONNX model is 77MB, loaded once (session fixture)
- Image preprocessing must match model input size (1280×1280)
- Performance test marked:
@pytest.mark.performance - This is a smoke test — validates structure, not detection accuracy