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Oleksandr Bezdieniezhnykh 142c6c4de8 Refactor constants management to use Pydantic BaseModel for configuration

- 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.

2026-03-27 18:18:30 +02:00

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Test Environment

Runtime Requirements

Requirement	Specification
Python	3.10+
OS	Linux or macOS (POSIX filesystem paths)
GPU	Optional — ONNX inference falls back to CPUExecutionProvider
Disk	Temp directory for fixture data (~500MB for augmentation output)
Network	Not required (all tests are offline)

Execution Modes

Tests MUST be runnable in two ways:

1. Local (no Docker) — primary mode

Run directly on the host machine. Required for macOS development where Docker has GPU/performance limitations.

scripts/run-tests-local.sh

2. Docker — CI/portable mode

Run inside a container for reproducible CI environments (Linux-based CI runners).

docker compose -f docker-compose.test.yml up --build --abort-on-container-exit

Both modes run the same pytest suite; the only difference is the runtime environment.

Dependencies

All test dependencies are a subset of the production requirements.txt plus pytest:

Package	Purpose
pytest	Test runner
albumentations	Augmentation tests
opencv-python-headless	Image I/O (headless — no GUI)
numpy	Array operations
onnxruntime	ONNX inference (CPU fallback)
cryptography	Encryption tests
msgpack	Annotation queue message tests
PyYAML	Config/YAML generation tests

Fixture Data

Fixture	Location	Size
100 annotated images	`_docs/00_problem/input_data/dataset/images/`	~50MB
100 YOLO labels	`_docs/00_problem/input_data/dataset/labels/`	~10KB
ONNX model	`_docs/00_problem/input_data/azaion.onnx`	81MB
Class definitions	`classes.json` (project root)	2KB

Test Isolation

Each test creates a temporary directory (via tmp_path pytest fixture) for filesystem operations
No tests modify the actual /azaion/ directory structure
No tests require running external services (RabbitMQ, Azaion API, S3 CDN)
Constants paths are patched/overridden to point to temp directories during tests

Excluded (Require External Services)

Component	Service Required	Reason for Exclusion
API upload/download	Azaion REST API	No mock server; real API has auth
CDN upload/download	S3-compatible CDN	No mock S3; real CDN has credentials
Queue consumption	RabbitMQ Streams	No mock broker; rstream requires live connection
TensorRT inference	NVIDIA GPU + TensorRT	Hardware-specific; cannot run in CI without GPU

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