Oleksandr Bezdieniezhnykh
cbf370c765
- Changed the directory structure for task specifications to include a dedicated `todo/` folder within `_docs/02_tasks/` for tasks ready for implementation. - Updated references in various skills and documentation to reflect the new task lifecycle, including changes in the `implementer` and `decompose` skills. - Enhanced the README and flow documentation to clarify the new task organization and its implications for the implementation process. These updates improve task management clarity and streamline the implementation workflow.
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