Function extract_maps

deeplabcut/compat.py:1594–1686

Extracts the scoremap, locref, partaffinityfields (if available). Returns a dictionary indexed by: trainingsetfraction, snapshotindex, and imageindex for those keys, each item contains: (image, scmap, locref, paf, bpt_names, partaffinity_graph, imagename, True/False if this image was in

(
    config,
    shuffle: int = 0,
    trainingsetindex: int = 0,
    gputouse: int | None = None,
    device: str | None = None,
    rescale: bool = False,
    Indices: list[int] | None = None,
    modelprefix: str = "",
    engine: Engine | None = None,
)

def extract_maps(
    config,
    shuffle: int = 0,
    trainingsetindex: int = 0,
    gputouse: int | None = None,
    device: str | None = None,
    rescale: bool = False,
    Indices: list[int] | None = None,
    modelprefix: str = "",
    engine: Engine | None = None,
):
    """Extracts the scoremap, locref, partaffinityfields (if available).

    Returns a dictionary indexed by: trainingsetfraction, snapshotindex, and imageindex
    for those keys, each item contains: (image, scmap, locref, paf, bpt_names,
    partaffinity_graph, imagename, True/False if this image was in trainingset).

    ----------
    config : string
        Full path of the config.yaml file as a string.

    shuffle: integer
        integers specifying shuffle index of the training dataset. The default is 0.

    trainingsetindex: int, optional
        Integer specifying which TrainingsetFraction to use. By default the first (note
        that TrainingFraction is a list in config.yaml). This variable can also be set
        to "all".

    gputouse: int or None, optional, default=None
        For the TensorFlow engine (for the PyTorch engine see ``device``). Specifies
        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

    device: str or None, optional, default=None
        The CUDA device to use for training. If None, the device will be taken from the
        ``pytorch_config.yaml`` file. Examples: {"cpu", "cuda", "cuda:0", "cuda:1"}. See
        https://pytorch.org/docs/stable/notes/cuda.html for more information.

    rescale: bool, default False
        Evaluate the model at the 'global_scale' variable
        (as set in the test/pose_config.yaml file for a particular project).
        I.e. every image will be resized according to that scale and prediction
        will be compared to the resized ground truth.
        The error will be reported in pixels at rescaled to the *original* size.
        I.e. For a [200,200] pixel image evaluated at global_scale=.5, the predictions are calculated
        on [100,100] pixel images, compared to 1/2*ground truth and this error is then multiplied by 2!.
        The evaluation images are also shown for the original size!

    engine: Engine, optional, default = None.
        The default behavior loads the engine for the shuffle from the metadata. You can
        overwrite this by passing the engine as an argument, but this should generally
        not be done.

    Examples
    --------
    If you want to extract the data for image 0 and 103 (of the training set) for model trained with shuffle 0.
    >>> deeplabcut.extract_maps(configfile,0,Indices=[0,103])