altametris.sara.core.base_exporter
==================================

.. py:module:: altametris.sara.core.base_exporter

.. autoapi-nested-parse::

   Base exporter class for model export.

   Provides a common interface for exporting models to different formats
   (ONNX, TensorRT, CoreML, TFLite, etc.) with validation.

   .. rubric:: Example

   >>> class MyExporter(BaseExporter):
   ...     def export(self, format="onnx", **kwargs):
   ...         # Export logic
   ...         return export_path
   ...     def validate_export(self, export_path):
   ...         # Validation logic
   ...         return True



Attributes
----------

.. autoapisummary::

   altametris.sara.core.base_exporter.logger


Classes
-------

.. autoapisummary::

   altametris.sara.core.base_exporter.BaseExporter


Module Contents
---------------

.. py:data:: logger

.. py:class:: BaseExporter(model_path: Union[str, pathlib.Path], output_dir: Optional[Union[str, pathlib.Path]] = None)

   Bases: :py:obj:`abc.ABC`


   Abstract base class for model export.

   Provides common export infrastructure:
   - Multi-format export support
   - Export validation
   - Path management
   - Format-specific configuration

   :param model_path: Path to source model
   :param output_dir: Directory for exported models

   .. rubric:: Example

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


   .. py:attribute:: SUPPORTED_FORMATS
      :value: ['onnx', 'torchscript', 'tensorrt', 'coreml', 'tflite']



   .. py:attribute:: model_path


   .. py:attribute:: output_dir


   .. py:method:: export(format: str = 'onnx', **kwargs: Any) -> pathlib.Path
      :abstractmethod:


      Export model to specified format.

      :param format: Export format (onnx, tensorrt, etc.)
      :param \*\*kwargs: Format-specific export arguments

      :returns: Path to exported model

      :raises ExportError: If export fails

      .. note:: Must be implemented by subclasses



   .. py:method:: validate_export(export_path: pathlib.Path, **kwargs: Any) -> bool
      :abstractmethod:


      Validate exported model.

      :param export_path: Path to exported model
      :param \*\*kwargs: Validation arguments

      :returns: True if validation passes

      :raises ExportError: If validation fails

      .. note:: Must be implemented by subclasses



   .. py:method:: validate_format(format: str) -> None

      Validate export format is supported.

      :param format: Format string to validate

      :raises ExportError: If format is not supported



   .. py:method:: get_export_path(format: str, suffix: Optional[str] = None) -> pathlib.Path

      Generate export file path.

      :param format: Export format
      :param suffix: Optional suffix before extension

      :returns: Path for exported model

      .. rubric:: Example

      >>> exporter.get_export_path("onnx")
      Path("weights/best.onnx")
      >>> exporter.get_export_path("onnx", suffix="_fp16")
      Path("weights/best_fp16.onnx")



   .. py:method:: cleanup_export(export_path: pathlib.Path) -> None

      Clean up temporary export files.

      :param export_path: Path to export file to remove

      .. rubric:: Example

      >>> exporter.cleanup_export(Path("weights/temp.onnx"))



   .. py:method:: export_all(formats: Optional[list[str]] = None, **kwargs: Any) -> dict[str, Optional[pathlib.Path]]

      Export model to multiple formats.

      :param formats: List of formats to export (default: all supported)
      :param \*\*kwargs: Export arguments

      :returns: Dictionary mapping format to export path (None if export failed)

      .. rubric:: Example

      >>> exports = exporter.export_all(formats=["onnx", "tensorrt"])
      >>> print(exports["onnx"])



   .. py:method:: __repr__() -> str

      String representation of exporter.