# Model Registry Component

## Detailed Description
The **Model Registry** is a centralized manager for all deep learning models (SuperPoint, LightGlue, AnyLoc, LiteSAM). It abstracts the loading mechanism, supporting both **TensorRT** (for production/GPU) and **PyTorch/ONNX** (for fallback/CPU/Sandbox).

It implements the "Factory" pattern, delivering initialized and ready-to-infer model wrappers to the Layer components. It also manages GPU resource allocation (e.g., memory growth).

## API Methods

### `load_model`
- **Input:** `model_name: str` (e.g., "superpoint"), `backend: str` ("tensorrt" | "pytorch" | "auto")
- **Output:** `ModelWrapper`
- **Description:** Loads the specified model weights.
    - If `backend="auto"`, attempts TensorRT first; if fails (or no GPU), falls back to PyTorch.
    - Returns a wrapper object that exposes a uniform `infer()` method regardless of backend.
- **Test Cases:**
    - Load "superpoint", backend="pytorch" -> Success.
    - Load invalid name -> Error.
    - Load "tensorrt" on CPU machine -> Fallback or Error (depending on strictness).

### `unload_model`
- **Input:** `model_name: str`
- **Output:** `void`
- **Description:** Frees GPU/RAM resources associated with the model.
- **Test Cases:**
    - Unload loaded model -> Memory released.

### `list_available_models`
- **Input:** `void`
- **Output:** `List[str]`
- **Description:** Returns list of models registered and available on disk.

## Integration Tests
- **Load All:** Iterate through all required models and verify they load successfully in the sandbox environment (likely PyTorch mode).

## Non-functional Tests
- **Warmup Time:** Measure time from `load_model` to first successful inference.
- **Switching Overhead:** Measure latency if models need to be swapped in/out of VRAM (though ideally all stay loaded).