Task Heads

salt.models.TaskBase

Bases: torch.nn.Module, abc.ABC

Task head base class.

Tasks wrap a dense network, a loss, a label, and a weight.

Parameters:

Name	Type	Description	Default
name	str	Arbitrary name of the task, used for logging and inference.	required
input_name	str	Which type of object is input to the task e.g. jet/track/flow.	required
dense_config	dict	Keyword arguments for salt.models.Dense, the dense network producing the task outputs.	required
loss	torch.nn.Module	Loss function applied to the dense network outputs.	required
weight	float	Weight in the overall loss.	1.0

Source code in salt/models/task.py

def __init__(
    self,
    name: str,
    input_name: str,
    dense_config: dict,
    loss: nn.Module,
    weight: float = 1.0,
):
    """Task head base class.

    Tasks wrap a dense network, a loss, a label, and a weight.

    Parameters
    ----------
    name : str
        Arbitrary name of the task, used for logging and inference.
    input_name : str
        Which type of object is input to the task e.g. jet/track/flow.
    dense_config : dict
        Keyword arguments for [`salt.models.Dense`][salt.models.Dense],
        the dense network producing the task outputs.
    loss : nn.Module
        Loss function applied to the dense network outputs.
    weight : float
        Weight in the overall loss.
    """
    super().__init__()

    self.name = name
    self.input_name = input_name
    self.net = Dense(**dense_config)
    self.loss = loss
    self.weight = weight

salt.models.ClassificationTask

Bases: salt.models.task.TaskBase

Classification task.

Parameters:

Name	Type	Description	Default
label	str	Label name for the task	required
class_names	list[str] | None	List of class names, ordered by output index. If not specified attempt to automatically determine these from the label name.	None
label_map	collections.abc.Mapping | None	Remap integer labels for training (e.g. 0,4,5 -> 0,1,2).	None
sample_weight	str | None	Name of a per sample weighting to apply in the loss function.	None
use_class_dict	bool	If True, read class weights for the loss from the class_dict file.	False
**kwargs		Keyword arguments for salt.models.TaskBase.	{}

Source code in salt/models/task.py

def __init__(
    self,
    label: str,
    class_names: list[str] | None = None,
    label_map: Mapping | None = None,
    sample_weight: str | None = None,
    use_class_dict: bool = False,
    **kwargs,
):
    """Classification task.

    Parameters
    ----------
    label : str
        Label name for the task
    class_names : list[str] | None, optional
        List of class names, ordered by output index. If not specified attempt to
        automatically determine these from the label name.
    label_map : Mapping | None, optional
        Remap integer labels for training (e.g. 0,4,5 -> 0,1,2).
    sample_weight : str | None, optional
        Name of a per sample weighting to apply in the loss function.
    use_class_dict : bool, optional
        If True, read class weights for the loss from the class_dict file.
    **kwargs
        Keyword arguments for [`salt.models.TaskBase`][salt.models.TaskBase].
    """
    super().__init__(**kwargs)
    self.label = label
    self.class_names = class_names
    self.label_map = label_map
    if self.label_map is not None and self.class_names is None:
        raise ValueError("Specify class names when using label_map.")
    if hasattr(self.loss, "ignore_index"):
        self.loss.ignore_index = -1
    self.sample_weight = sample_weight
    if self.sample_weight is not None:
        assert (
            self.loss.reduction == "none"
        ), "Sample weights only supported for reduction='none'"
    if self.class_names is None:
        self.class_names = CLASS_NAMES[self.label]
    if len(self.class_names) != self.net.output_size:
        raise ValueError(
            f"{self.name}: "
            f"Number of outputs ({self.net.output_size}) does not match "
            f"number of class names ({len(self.class_names)}). Class names: {self.class_names}"
        )
    self.use_class_dict = use_class_dict

salt.models.RegressionTaskBase

Bases: salt.models.task.TaskBase, abc.ABC

Base class for regression tasks.

Parameters:

Name	Type	Description	Default
targets	list[str] | str	Regression target(s).	required
scaler	salt.utils.scalers.RegressionTargetScaler	Functional scaler for regression targets - cannot be used with other target scaling options.	None
target_denominators	list[str] | str | None	Variables to divide regression target(s) by (i.e. for regressing a ratio). - cannot be used with other target scaling options.	None
norm_params	dict | None	Mean and std normalization parameters for each target, used for scaling. - cannot be used with other target scaling options..	None
**kwargs		Keyword arguments for salt.models.TaskBase.	{}

Source code in salt/models/task.py

def __init__(
    self,
    targets: list[str] | str,
    scaler: RegressionTargetScaler | None = None,
    target_denominators: list[str] | str | None = None,
    norm_params: dict | None = None,
    **kwargs,
):
    """Base class for regression tasks.

    Parameters
    ----------
    targets : list[str] | str
        Regression target(s).
    scaler : RegressionTargetScaler
        Functional scaler for regression targets
            - cannot be used with other target scaling options.
    target_denominators : list[str] | str | None, optional
        Variables to divide regression target(s) by (i.e. for regressing a ratio).
            - cannot be used with other target scaling options.
    norm_params : dict | None, optional
        Mean and std normalization parameters for each target, used for scaling.
            - cannot be used with other target scaling options..
    **kwargs
        Keyword arguments for [`salt.models.TaskBase`][salt.models.TaskBase].
    """
    super().__init__(**kwargs)
    self.scaler = scaler
    self.targets = listify(targets)
    self.target_denominators = listify(target_denominators)
    if norm_params:
        norm_params["mean"] = listify(norm_params["mean"])
        norm_params["std"] = listify(norm_params["std"])
    self.norm_params = norm_params

    if [scaler, target_denominators, norm_params].count(None) not in {2, 3}:
        raise ValueError("Can only use a single scaling method")

    if self.scaler:
        for target in self.targets:
            self.scaler.scale(target, torch.Tensor(1))
    if self.target_denominators and len(self.targets) != len(self.target_denominators):
        raise ValueError(
            f"{self.name}: "
            f"Number of targets ({len(self.targets)}) does not match "
            f"number of target denominators ({len(self.target_denominators)})"
        )
    if self.norm_params and len(self.norm_params["mean"]) != len(self.targets):
        raise ValueError(
            f"{self.name}: "
            f"Number of means in norm_params ({len(self.norm_params['mean'])}) does not match "
            f"number of targets ({len(self.targets)})"
        )
    if self.norm_params and len(self.norm_params["std"]) != len(self.targets):
        raise ValueError(
            f"{self.name}: "
            f"Number of stds in norm_params ({len(self.norm_params['std'])}) does not match "
            f"number of targets ({len(self.targets)})"
        )

salt.models.RegressionTask

Bases: salt.models.task.RegressionTaskBase

Regression task.

Parameters:

Name	Type	Description	Default
scaler		dummy text	None
**kwargs		Keyword arguments for salt.models.RegressionTaskBase.	{}

Source code in salt/models/task.py

def __init__(self, scaler=None, **kwargs):
    """Regression task.

    Parameters
    ----------
    scaler
        dummy text
    **kwargs
        Keyword arguments for
        [`salt.models.RegressionTaskBase`][salt.models.RegressionTaskBase].
    """
    super().__init__(**kwargs)
    if self.net.output_size != len(self.targets):
        raise ValueError(
            f"{self.name}: "
            f"Number of outputs ({self.net.output_size}) does not match "
            f"number of targets ({len(self.targets)})"
        )
    self.scaler = scaler

salt.models.GaussianRegressionTask

Bases: salt.models.task.RegressionTaskBase

Regression task that outputs a mean and variance for each target. The loss function is the negative log likelihood of a Gaussian distribution.

Parameters:

Name	Type	Description	Default
**kwargs		Keyword arguments for salt.models.RegressionTaskBase.	{}

Source code in salt/models/task.py

def __init__(self, **kwargs):
    """Regression task that outputs a mean and variance for each target.
    The loss function is the negative log likelihood of a Gaussian distribution.

    Parameters
    ----------
    **kwargs
        Keyword arguments for
        [`salt.models.RegressionTaskBase`][salt.models.RegressionTaskBase].
    """
    super().__init__(**kwargs)
    if self.net.output_size != 2 * len(self.targets):
        raise ValueError(
            f"{self.name}: "
            f"Number of targets ({len(self.targets)}) is not twice the "
            f"number of outputs ({self.net.output_size})"
        )

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Last update: November 16, 2023
Created: October 20, 2023