artist.optim.regularizers

Classes

`Regularizer`	Initialize the base regularizer.
`SmoothnessRegularizer`	Initialize the regularizer.
`IdealSurfaceRegularizer`	Initialize the regularizer.

Module Contents

class artist.optim.regularizers.Regularizer(reduction_dimensions: tuple[int, Ellipsis])

Initialize the base regularizer.

Parameters

reduction_dimensionstuple[int, …]: The dimensions along which to reduce the regularization term.

reduction_dimensions

abstractmethod __call__(current_control_points: torch.Tensor, original_control_points: torch.Tensor, device: torch.device | None = None, **kwargs: Any) → torch.Tensor

Compute the regularization.

Parameters

current_control_pointstorch.Tensor: The current control points. Shape is [number_of_heliostats, number_of_facets_per_surface, number_of_control_points_u_direction, number_of_control_points_v_direction, 3].
original_control_pointstorch.Tensor: The current control points. Shape is [number_of_heliostats, number_of_facets_per_surface, number_of_control_points_u_direction, number_of_control_points_v_direction, 3].
devicetorch.device | None: The device on which to perform computations or load tensors and models (default is None). If None, ARTIST will automatically select the most appropriate device (CUDA or CPU) based on availability and OS.
**kwargsAny: Keyword arguments.

Raises

NotImplementedError: This abstract method must be overridden.

class artist.optim.regularizers.SmoothnessRegularizer(reduction_dimensions: tuple[int, Ellipsis])

Bases: Regularizer

Initialize the regularizer.

Parameters

reduction_dimensionstuple[int, …]: Dimensions along which to reduce the loss.

__call__(current_control_points: torch.Tensor, original_control_points: torch.Tensor, device: torch.device | None = None, **kwargs: Any) → torch.Tensor

Compute the Laplacian regularization loss.

The loss measures how much each control-point displacement differs from the average of its four immediate neighbors, thereby penalizing localized, non-smooth deformations.

Parameters

current_control_pointstorch.Tensor: The current control points. Shape is [number_of_heliostats, number_of_facets_per_surface, number_of_control_points_u_direction, number_of_control_points_v_direction, 3].
original_control_pointstorch.Tensor: The original control points. Shape is [number_of_heliostats, number_of_facets_per_surface, number_of_control_points_u_direction, number_of_control_points_v_direction, 3].
devicetorch.device | None: The device on which to perform computations or load tensors and models (default is None). If None, ARTIST will automatically select the most appropriate device (CUDA or CPU) based on availability and OS.

Returns

torch.Tensor: Laplacian regularization loss per surface.

class artist.optim.regularizers.IdealSurfaceRegularizer(reduction_dimensions: tuple[int, Ellipsis])

Bases: Regularizer

Initialize the regularizer.

Parameters

reduction_dimensionstuple[int, …]: Dimensions along which to reduce the loss.

__call__(current_control_points: torch.Tensor, original_control_points: torch.Tensor, device: torch.device | None = None, **kwargs: Any) → torch.Tensor

Compute the L2 loss between current control points and original control points.

Parameters

current_control_pointstorch.Tensor: The current control points. Shape is [number_of_heliostats, number_of_facets_per_surface, number_of_control_points_u_direction, number_of_control_points_v_direction, 3].
original_control_pointstorch.Tensor: The current control points. Shape is [number_of_heliostats, number_of_facets_per_surface, number_of_control_points_u_direction, number_of_control_points_v_direction, 3].
devicetorch.device | None: The device on which to perform computations or load tensors and models (default is None). If None, ARTIST will automatically select the most appropriate device (CUDA or CPU) based on availability and OS.

Returns

torch.Tensor: L2 deviation loss per surface.