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.

_docs/02_document/modules/inference_tensorrt_engine.md

@@ -0,0 +1,53 @@
+# Module: inference/tensorrt_engine
+
+## Purpose
+TensorRT-based inference engine implementation. Provides GPU-accelerated inference using NVIDIA TensorRT with CUDA memory management, plus ONNX-to-TensorRT conversion.
+
+## Public Interface
+
+### TensorRTEngine (extends InferenceEngine)
+| Method | Signature | Returns | Description |
+|--------|-----------|---------|-------------|
+| `__init__` | `(model_bytes: bytes, **kwargs)` | — | Deserializes TensorRT engine from bytes, allocates CUDA memory |
+| `get_input_shape` | `() -> Tuple[int, int]` | (height, width) | Returns model input dimensions |
+| `get_batch_size` | `() -> int` | int | Returns configured batch size |
+| `run` | `(input_data: np.ndarray) -> List[np.ndarray]` | Output tensors | Runs async inference on CUDA stream |
+| `get_gpu_memory_bytes` | `(device_id=0) -> int` | GPU memory in bytes | Queries total GPU VRAM via pynvml (static) |
+| `get_engine_filename` | `(device_id=0) -> str \| None` | Filename string | Generates device-specific engine filename (static) |
+| `convert_from_onnx` | `(onnx_model: bytes) -> bytes \| None` | Serialized TensorRT plan | Converts ONNX model to TensorRT engine (static) |
+
+## Internal Logic
+- **Initialization**: Deserializes TensorRT engine, creates execution context, allocates pinned host memory and device memory for input/output tensors.
+- **Dynamic shapes**: Handles -1 (dynamic) dimensions, defaults to 1280×1280 for spatial dims, batch size from engine or constructor.
+- **Output shape**: [batch_size, 300 max detections, 6 values per detection (x1, y1, x2, y2, conf, cls)].
+- **Inference flow**: Host→Device async copy → execute_async_v3 → synchronize → Device→Host copy.
+- **ONNX conversion**: Creates TensorRT builder, parses ONNX, configures workspace (90% of GPU memory), enables FP16 if supported, builds serialized network.
+- **Engine filename**: `azaion.cc_{major}.{minor}_sm_{sm_count}.engine` — uniquely identifies engine per GPU architecture.
+
+## Dependencies
+- `inference/onnx_engine` — InferenceEngine ABC
+- `tensorrt` (external) — TensorRT runtime and builder
+- `pycuda.driver` (external) — CUDA memory management
+- `pycuda.autoinit` (external) — CUDA context auto-initialization
+- `pynvml` (external) — GPU memory query
+- `numpy`, `json`, `struct`, `re`, `subprocess`, `pathlib`, `typing` (stdlib/external)
+
+## Consumers
+start_inference
+
+## Data Models
+None.
+
+## Configuration
+None.
+
+## External Integrations
+- NVIDIA TensorRT runtime (GPU inference)
+- CUDA driver API (memory allocation, streams)
+- NVML (GPU hardware queries)
+
+## Security
+None.
+
+## Tests
+None.