Function build_weight_init

deeplabcut/modelzoo/weight_initialization.py:23–115

Builds the WeightInitialization from a SuperAnimal model for a project. Args: cfg: The project's configuration, or the path to the project configuration file. super_animal: The SuperAnimal model with which to initialize weights. model_name: The type of the model architec

(
    cfg: dict | str | Path,
    super_animal: str,
    model_name: str,
    detector_name: str | None,
    with_decoder: bool = False,
    memory_replay: bool = False,
    customized_pose_checkpoint: str | Path | None = None,
    customized_detector_checkpoint: str | Path | None = None,
)

def build_weight_init(
    cfg: dict | str | Path,
    super_animal: str,
    model_name: str,
    detector_name: str | None,
    with_decoder: bool = False,
    memory_replay: bool = False,
    customized_pose_checkpoint: str | Path | None = None,
    customized_detector_checkpoint: str | Path | None = None,
) -> WeightInitialization:
    """Builds the WeightInitialization from a SuperAnimal model for a project.

    Args:
        cfg: The project's configuration, or the path to the project configuration file.
        super_animal: The SuperAnimal model with which to initialize weights.
        model_name: The type of the model architecture for which to load the weights.
        detector_name: The type of detector architecture for which to load the weights.
        with_decoder: Whether to load the decoder weights as well. If this is true,
            a conversion table must be specified for the given SuperAnimal in the
            project configuration file. See
            ``deeplabcut.modelzoo.utils.create_conversion_table`` to create a
            conversion table.
        memory_replay: Only when ``with_decoder=True``. Whether to train the model
            with memory replay, so that it predicts all SuperAnimal bodyparts.
        customized_pose_checkpoint: A customized SuperAnimal pose checkpoint, as an
            alternative to the Hugging Face one
        customized_detector_checkpoint: A customized SuperAnimal detector checkpoint, as
            an alternative to the Hugging Face one

    To build a WeightInitialization instance for a project using the conversion table
    specified in the project configuration file, use:

        ```
        from pathlib import Path
        from deeplabcut.utils.auxiliaryfunctions import read_config
        from deeplabcut.modelzoo import build_weight_init

        project_cfg = read_config("/path/to/my/project/config.yaml")
        super_animal = "superanimal_quadruped"
        weight_init = build_weight_init(
            cfg=project_cfg,
            super_animal="superanimal_quadruped",
            model_name="hrnet_w32",
            detector_name="fasterrcnn_resnet50_fpn_v2",
            with_decoder=True,
            memory_replay=False,
        )
        ```

    Returns:
        The built WeightInitialization.
    """
    if super_animal == "superanimal_humanbody":
        raise NotImplementedError(
            "Weight Initialization, Transfer-Learning and Finetuning is currently not supported for"
            "superanimal_humanbody"
        )