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timm_fine_tuning

HuggingFace TIMM fine-tuning Algorithm.

Borrowed with permission from https://github.com/huggingface/pytorch-image-models. Copyright 2020 Ross Wightman (https://github.com/rwightman)

Classes​

TIMMFineTuning​

class TIMMFineTuning(    model_id: str,    schema: Optional[BitfountSchema] = None,    datastructure: Optional[DataStructure] = None,    image_column_name: Optional[str] = None,    target_column_name: Optional[str] = None,    labels: Optional[List[str]] = None,    args: Optional[TIMMTrainingConfig] = None,    batch_transformations: Union[List[Union[str, Dict[str, Any]]], Dict[Literal['train', 'validation'], List[Union[str, Dict[str, Any]]]], ForwardRef(None)] = None,    return_weights: bool = False,    save_path: Union[str, os.PathLike, ForwardRef(None)] = None,):

HuggingFace TIMM Fine Tuning Algorithm.

Arguments

  • ****kwargs**: Additional keyword arguments passed to the Worker side.
  • args: The training configuration.
  • batch_transformations: The batch transformations to be applied to the batches. Can be a list of strings or a list of dictionaries, which will be applied to both training and validation, or a dictionary with keys "train" and "validation" mapped to a list of strings or a list of dictionaries, specifying the batch transformations to be applied at each individual step. They are only applied if datastructure is not passed. Defaults to apply DEFAULT_IMAGE_TRANSFORMATIONS to both training and validation.
  • datastructure: The datastructure relating to the dataset to be trained on. Defaults to None.
  • image_column_name: The column name of the image column used in training. Defaults to None.
  • labels: The labels of the target column. Defaults to None.
  • model_id: The Hugging Face model ID.
  • return_weights: Whether to return the weights of the model.
  • save_path: The path to save the model to.
  • schema: The schema of the dataset to be trained on. Defaults to None.
  • target_column_name: The column name of the target column. Defaults to None.

Attributes

  • class_name: The name of the algorithm class.
  • fields_dict: A dictionary mapping all attributes that will be serialized in the class to their marshamllow field type. (e.g. fields_dict = {"class_name": fields.Str()}).
  • nested_fields: A dictionary mapping all nested attributes to a registry that contains class names mapped to the respective classes. (e.g. nested_fields = {"datastructure": datastructure.registry})

Ancestors​

Variables​

  • static nested_fields : ClassVar[Dict[str, Mapping[str, Any]]]

Methods​

create​

def create(self, role: Union[str, Role], **kwargs: Any) ‑> Any:

Create an instance representing the role specified.

modeller​

def modeller(    self, **kwargs: Any,) ‑> bitfount.federated.algorithms.hugging_face_algorithms.timm_fine_tuning._ModellerSide:

Returns the modeller side of the TIMMFineTuning algorithm.

worker​

def worker(    self, **kwargs: Any,) ‑> bitfount.federated.algorithms.hugging_face_algorithms.timm_fine_tuning._WorkerSide:

Returns the worker side of the TIMMFineTuning algorithm.