deeplabcut.pose_estimation_pytorch.config.inference

Inference configuration classes for DeepLabCut pose estimation models.

Classes:

Name	Description
`AutocastConfig`	Automatic mixed precision configuration.
`CompileConfig`	Model compilation configuration for inference optimization.
`EvaluationConfig`	Configuration for evaluation metrics computation.
`InferenceConfig`	Complete inference configuration.
`MultithreadingConfig`	Multithreading configuration for inference.

AutocastConfig

Bases: DLCBaseConfig

Automatic mixed precision configuration.

Attributes:

Name	Type	Description
`enabled`	`bool`	Whether autocast is enabled
`dtype`	`bool`	Data type for autocast (float16, bfloat16)

Source code in deeplabcut/pose_estimation_pytorch/config/inference.py

class AutocastConfig(DLCBaseConfig):
    """Automatic mixed precision configuration.

    Attributes:
        enabled: Whether autocast is enabled
        dtype: Data type for autocast (float16, bfloat16)
    """

    enabled: bool = False

CompileConfig

Bases: DLCBaseConfig

Model compilation configuration for inference optimization.

Attributes:

Name	Type	Description
`enabled`	`bool`	Whether compilation is enabled
`mode`	`bool`	Compilation mode

Source code in deeplabcut/pose_estimation_pytorch/config/inference.py

class CompileConfig(DLCBaseConfig):
    """Model compilation configuration for inference optimization.

    Attributes:
        enabled: Whether compilation is enabled
        mode: Compilation mode
    """

    enabled: bool = False
    backend: str = "inductor"

EvaluationConfig

Bases: DLCBaseConfig

Configuration for evaluation metrics computation.

Attributes:

Name	Type	Description
`pcutoff`	`float \| list[float] \| dict[str, float]`	Confidence threshold for RMSE computation. Can be: - float: Single threshold for all bodyparts - list[float]: One value per bodypart (and unique bodypart if any) - dict[str, float]: Mapping bodypart names to thresholds
`comparison_bodyparts`	`Literal['all'] \| list[str] \| None`	Subset of bodyparts to compute metrics for. Can be "all", None (all bodyparts), or a list of bodypart names.
`per_keypoint_evaluation`	`bool`	Whether to compute train and test RMSE for each keypoint individually.
`force_multi_animal`	`bool`	If True, use multi-animal evaluation even if loader contains only a single animal.

Source code in deeplabcut/pose_estimation_pytorch/config/inference.py

class EvaluationConfig(DLCBaseConfig):
    """Configuration for evaluation metrics computation.

    Attributes:
        pcutoff: Confidence threshold for RMSE computation. Can be:
            - float: Single threshold for all bodyparts
            - list[float]: One value per bodypart (and unique bodypart if any)
            - dict[str, float]: Mapping bodypart names to thresholds
        comparison_bodyparts: Subset of bodyparts to compute metrics for.
            Can be "all", None (all bodyparts), or a list of bodypart names.
        per_keypoint_evaluation: Whether to compute train and test RMSE
            for each keypoint individually.
        force_multi_animal: If True, use multi-animal evaluation even if
            loader contains only a single animal.
    """

    mode: Literal["train", "test", "all"] = "all"
    pcutoff: float | list[float] | dict[str, float] = 0.6
    comparison_bodyparts: Literal["all"] | list[str] | None = "all"
    per_keypoint_evaluation: bool = False
    force_multi_animal: bool = False

InferenceConfig

Bases: DLCBaseConfig

Complete inference configuration.

Attributes:

Name	Type	Description
`multithreading`	`MultithreadingConfig`	Multithreading configuration
`compile`	`CompileConfig`	Compilation configuration
`autocast`	`AutocastConfig`	Autocast configuration
`conditions`	`dict[str, Any] \| None`	Conditions for conditional models (CTD)
`snapshot`	`int \| str \| list[int] \| None`	Snapshot(s) to use for inference
`eval`	`EvaluationConfig`	Evaluation configuration

Source code in deeplabcut/pose_estimation_pytorch/config/inference.py

class InferenceConfig(DLCBaseConfig):
    """Complete inference configuration.

    Attributes:
        multithreading: Multithreading configuration
        compile: Compilation configuration
        autocast: Autocast configuration
        conditions: Conditions for conditional models (CTD)
        snapshot: Snapshot(s) to use for inference
        eval: Evaluation configuration
    """

    multithreading: MultithreadingConfig = Field(default_factory=MultithreadingConfig)
    compile: CompileConfig = Field(default_factory=CompileConfig)
    autocast: AutocastConfig = Field(default_factory=AutocastConfig)
    conditions: dict[str, Any] | None = None
    snapshot: int | str | list[int] | None = None
    eval: EvaluationConfig = Field(default_factory=EvaluationConfig)
    output_dir: str | None = None

MultithreadingConfig

Bases: DLCBaseConfig

Multithreading configuration for inference.

Attributes:

Name	Type	Description
`enabled`	`bool`	Whether multithreading is enabled
`queue_length`	`int`	Length of the processing queue
`timeout`	`float`	Timeout for processing tasks

Source code in deeplabcut/pose_estimation_pytorch/config/inference.py

class MultithreadingConfig(DLCBaseConfig):
    """Multithreading configuration for inference.

    Attributes:
        enabled: Whether multithreading is enabled
        queue_length: Length of the processing queue
        timeout: Timeout for processing tasks
    """

    enabled: bool = True
    queue_length: int = 4
    timeout: float = 30.0