artist.optim.loss

Classes

`Loss`	Initialize the base loss.
`VectorLoss`	Initialize the vector loss.
`FocalSpotLoss`	Initialize the focal spot loss.
`PixelLoss`	Initialize the pixel loss.
`KLDivergenceLoss`	Initialize the Kullback-Leibler divergence loss.
`AngleLoss`	Initialize the angle loss.

Functions

mean_loss_per_heliostat(→ torch.Tensor)

Calculate the mean loss per heliostat from a loss per sample.

Module Contents

class artist.optim.loss.Loss(loss_function: torch.nn.Module)

Initialize the base loss.

Parameters

loss_functiontorch.nn.Module: A torch module implementing a loss.

loss_function

abstractmethod __call__(prediction: torch.Tensor, ground_truth: torch.Tensor, **kwargs: Any) → torch.Tensor

Compute the loss.

Parameters

predictiontorch.Tensor: The predicted values. Shape is variable.
ground_truthtorch.Tensor: The ground truth. Shape is variable.
**kwargsAny: Keyword arguments.

Raises

NotImplementedError: This abstract method must be overridden.

class artist.optim.loss.VectorLoss

Bases: Loss

Initialize the vector loss.

__call__(prediction: torch.Tensor, ground_truth: torch.Tensor, **kwargs: Any) → torch.Tensor

Compute the vector loss.

Parameters

predictiontorch.Tensor: The predicted values. Shape is [number_of_samples, ...].
ground_truthtorch.Tensor: The ground truth. Shape is [number_of_samples, ...].
**kwargsAny: Keyword arguments. reduction_dimensions is an expected keyword argument for the vector loss.

Raises

ValueError: If expected keyword arguments are not passed.

Returns

torch.Tensor: The summed MSE vector loss reduced along the specified dimensions. Shape is [number_of_samples].

class artist.optim.loss.FocalSpotLoss(scenario: artist.scenario.scenario.Scenario)

Bases: Loss

Initialize the focal spot loss.

Parameters

scenarioScenario: The scenario.

scenario

__call__(prediction: torch.Tensor, ground_truth: torch.Tensor, **kwargs: Any) → torch.Tensor

Compute the focal spot loss.

First the focal spots of the prediction are computed, then the loss is computed and reduced along the specified dimensions.

Parameters

predictiontorch.Tensor: The predicted values. Shape is [number_of_samples, bitmap_resolution_e, bitmap_resolution_u].
ground_truthtorch.Tensor: The ground truth. Shape is [number_of_samples, 4].
**kwargsAny: Keyword arguments. target_area_indices and device are expected keyword arguments for the focal spot loss.

Raises

ValueError: If expected keyword arguments are not passed.

Returns

torch.Tensor: The focal spot loss. Shape is [number_of_samples].

class artist.optim.loss.PixelLoss(scenario: artist.scenario.scenario.Scenario)

Bases: Loss

Initialize the pixel loss.

Parameters

scenarioScenario: The scenario.

scenario

__call__(prediction: torch.Tensor, ground_truth: torch.Tensor, **kwargs: Any) → torch.Tensor

Compute the pixel loss.

First the predicted bitmaps and the ground truth are normalized, then the loss is computed and reduced along the specified dimensions.

Parameters

predictiontorch.Tensor: The predicted values. Shape is [number_of_samples, bitmap_resolution_e, bitmap_resolution_u].
ground_truthtorch.Tensor: The ground truth. Shape is [number_of_samples, bitmap_resolution_e, bitmap_resolution_u].
**kwargsAny: Keyword arguments. reduction_dimensions, target_area_indices, and device are expected keyword arguments for the pixel loss.

Raises

ValueError: If expected keyword arguments are not passed.

Returns

torch.Tensor: The summed MSE pixel loss reduced along the specified dimensions. Shape is [number_of_samples].

class artist.optim.loss.KLDivergenceLoss

Bases: Loss

Initialize the Kullback-Leibler divergence loss.

__call__(prediction: torch.Tensor, ground_truth: torch.Tensor, **kwargs: Any) → torch.Tensor

Compute the Kullback-Leibler divergence loss \(D_{\mathrm{KL}}(P \parallel Q)\).

The elements in the prediction and ground truth are normalized and shifted, to be greater or equal to zero. The KL-divergence is defined by:

\[D_{\mathrm{KL}}(P \parallel Q) = \sum_{x} P(x) \log \frac{P(x)}{Q(x)},\]

where \(P\) is the ground truth distribution and \(Q\) is the approximation or prediction of \(Q\). The KL-divergence is an asymmetric function. Switching \(P\) and \(Q\) has the following effect: \(P \parallel Q\) Penalizes extra mass in the prediction where the ground truth has none. \(Q \parallel P\) Penalizes missing mass in the prediction where the ground truth has mass.

Parameters

predictiontorch.Tensor: The predicted values. Shape is [number_of_samples, bitmap_resolution_e, bitmap_resolution_u].
ground_truthtorch.Tensor: The ground truth. Shape is [number_of_samples, bitmap_resolution_e, bitmap_resolution_u].
**kwargsAny: Keyword arguments. reduction_dimensions is an expected keyword argument for the KL-divergence loss.

Raises

ValueError: If expected keyword arguments are not passed.

Returns

torch.Tensor: The summed KL-divergence loss reduced along the specified dimensions. Shape is [number_of_samples].

class artist.optim.loss.AngleLoss

Bases: Loss

Initialize the angle loss.

__call__(prediction: torch.Tensor, ground_truth: torch.Tensor, **kwargs: Any) → torch.Tensor

Compute the cosine similarity between the prediction and ground truth.

Parameters

predictiontorch.Tensor: The predicted values. Shape is [number_of_samples, 4].
ground_truthtorch.Tensor: The ground truth. Shape is [number_of_samples, 4].
**kwargsAny: Keyword arguments.

Returns

torch.Tensor: The summed loss reduced along the specified dimensions. Shape is [number_of_samples].

artist.optim.loss.mean_loss_per_heliostat(loss_per_sample: torch.Tensor, number_of_samples_per_heliostat: int) → torch.Tensor

Calculate the mean loss per heliostat from a loss per sample.

Parameters

loss_per_sampletorch.Tensor: Loss per sample. Shape is [number_of_samples].
number_of_samples_per_heliostatint: Number of samples per heliostat.

Returns

torch.Tensor: Loss per heliostat. Shape is [number_of_heliostats].