altametris.sara.yolo.exporter

YOLO exporter implementation.

Provides export functionality for YOLO models to various formats (ONNX, TensorRT, CoreML, etc.) with validation.

Example

>>> exporter = YoloExporter(model_path="weights/best.pt")
>>> onnx_path = exporter.export(format="onnx", imgsz=640)
>>> exporter.validate_export(onnx_path)

Attributes

logger

Classes

YoloExporter

YOLO exporter implementing BaseExporter interface.

Module Contents

altametris.sara.yolo.exporter.logger
class altametris.sara.yolo.exporter.YoloExporter(model_path: str | pathlib.Path, output_dir: str | pathlib.Path | None = None)

Bases: altametris.sara.core.base_exporter.BaseExporter

YOLO exporter implementing BaseExporter interface.

Supports export to multiple formats via Ultralytics backend.

Parameters:

  • model_path – Path to YOLO model weights

  • output_dir – Output directory for exports

Example

>>> exporter = YoloExporter(model_path="yolo11x.pt")
>>> onnx_path = exporter.export(format="onnx", imgsz=640)
>>> trt_path = exporter.export(format="tensorrt", imgsz=640, half=True)
SUPPORTED_FORMATS = ['onnx', 'torchscript', 'tensorrt', 'coreml', 'tflite', 'engine']
export(format: str = 'onnx', imgsz: int | tuple[int, int] = 640, half: bool = False, dynamic: bool = False, simplify: bool = True, **kwargs: Any) → pathlib.Path

Export YOLO model to specified format.

Parameters:

  • format – Export format

  • imgsz – Input image size (int or (height, width))

  • half – Use FP16 precision

  • dynamic – Dynamic axes for ONNX

  • simplify – Simplify ONNX model

  • **kwargs – Additional export arguments

Returns:

Path to exported model

Raises:

ExportError – If export fails

Example

>>> # Export to ONNX
>>> exporter.export(format="onnx", imgsz=640, half=False)
>>>
>>> # Export to TensorRT
>>> exporter.export(format="tensorrt", imgsz=640, half=True)
validate_export(export_path: pathlib.Path, test_image: str | pathlib.Path | None = None, **kwargs: Any) → bool

Validate exported model.

Parameters:

  • export_path – Path to exported model

  • test_image – Optional test image for validation

  • **kwargs – Additional validation arguments

Returns:

True if validation passes

Raises:

ExportError – If validation fails

Example

>>> onnx_path = exporter.export(format="onnx")
>>> exporter.validate_export(onnx_path, test_image="test.jpg")
export_to_tensorrt(imgsz: int | tuple[int, int] = 640, half: bool = True, workspace: float = 4.0, **kwargs: Any) → pathlib.Path

Export to TensorRT with optimized settings.

Parameters:

  • imgsz – Input image size

  • half – Use FP16 precision

  • workspace – TensorRT workspace size (GB)

  • **kwargs – Additional TensorRT arguments

Returns:

Path to TensorRT engine

Example

>>> trt_path = exporter.export_to_tensorrt(imgsz=640, half=True)
export_to_onnx(imgsz: int | tuple[int, int] = 640, dynamic: bool = True, simplify: bool = True, **kwargs: Any) → pathlib.Path

Export to ONNX with optimized settings.

Parameters:

  • imgsz – Input image size

  • dynamic – Dynamic axes for variable input size

  • simplify – Simplify ONNX graph

  • **kwargs – Additional ONNX arguments

Returns:

Path to ONNX model

Example

>>> onnx_path = exporter.export_to_onnx(imgsz=640, dynamic=True)
__repr__() → str

String representation.