Function evaluate_network

deeplabcut/compat.py:382–573

Evaluates the network. Evaluates the network based on the saved models at different stages of the training network. The evaluation results are stored in the .h5 and .csv file under the subdirectory 'evaluation_results'. Change the snapshotindex parameter in the config file to 'all'

(
    config: str | Path,
    shuffles: Sequence[int] = (1,),
    trainingsetindex: int | str = 0,
    plotting: bool | str = False,
    show_errors: bool = True,
    comparison_bodyparts: str | list[str] = "all",
    gputouse: str | None = None,
    rescale: bool = False,
    modelprefix: str = "",
    per_keypoint_evaluation: bool = False,
    snapshots_to_evaluate: list[str] | None = None,
    pcutoff: float | list[float] | dict[str, float] | None = None,
    engine: Engine | None = None,
    **torch_kwargs,
)

@renamed_parameter(old="comparisonbodyparts", new="comparison_bodyparts", since="3.0.0")
@renamed_parameter(old="Shuffles", new="shuffles", since="3.0.0")
def evaluate_network(
    config: str | Path,
    shuffles: Sequence[int] = (1,),
    trainingsetindex: int | str = 0,
    plotting: bool | str = False,
    show_errors: bool = True,
    comparison_bodyparts: str | list[str] = "all",
    gputouse: str | None = None,
    rescale: bool = False,
    modelprefix: str = "",
    per_keypoint_evaluation: bool = False,
    snapshots_to_evaluate: list[str] | None = None,
    pcutoff: float | list[float] | dict[str, float] | None = None,
    engine: Engine | None = None,
    **torch_kwargs,
):
    """Evaluates the network.

    Evaluates the network based on the saved models at different stages of the training
    network. The evaluation results are stored in the .h5 and .csv file under the
    subdirectory 'evaluation_results'. Change the snapshotindex parameter in the config
    file to 'all' in order to evaluate all the saved models.

    Parameters
    ----------
    config : string
        Full path of the config.yaml file.

    shuffles: sequence of int, optional, default=[1]
        List of integers specifying the shuffle indices of the training dataset.

    trainingsetindex: int or str, optional, default=0
        Integer specifying which "TrainingsetFraction" to use.
        Note that "TrainingFraction" is a list in config.yaml. This variable can also
        be set to "all".

    plotting: bool or str, optional, default=False
        Plots the predictions on the train and test images.
        If provided it must be either ``True``, ``False``, ``"bodypart"``, or
        ``"individual"``. Setting to ``True`` defaults as ``"bodypart"`` for
        multi-animal projects.
        If a detector is used, the predicted bounding boxes will also be plotted.

    show_errors: bool, optional, default=True
        Display train and test errors.

    comparison_bodyparts: str or list, optional, default="all"
        The average error will be computed for those body parts only.
        The provided list has to be a subset of the defined body parts.

    gputouse: int or None, optional, default=None
        Indicates the GPU to use (see number in ``nvidia-smi``). If you do not have a
        GPU put `None``.
        See: https://nvidia.custhelp.com/app/answers/detail/a_id/3751/~/useful-nvidia-smi-queries

    rescale: bool, optional, default=False
        Evaluate the model at the ``'global_scale'`` variable (as set in the
        ``pose_config.yaml`` file for a particular project). I.e. every image will be