dacapo.experiments.tasks.losses.loss

Classes

Loss

A class used to represent a loss function. This class is an abstract class

Module Contents

class dacapo.experiments.tasks.losses.loss.Loss

A class used to represent a loss function. This class is an abstract class that should be inherited by any loss function class.

compute(prediction, target, weight) → torch.Tensor

Function to compute the loss for the provided prediction and target, with respect to the weight.

Note

This class is abstract. Subclasses must implement the abstract methods. Once created, the values of its attributes cannot be changed.

abstract compute(prediction: torch.Tensor, target: torch.Tensor, weight: torch.Tensor | None = None) → torch.Tensor

Compute the loss for the given prediction and target. Optionally, if given, a loss weight should be considered.

All arguments are torch tensors. The return type should be a torch scalar that can be used with an optimizer, just as usual when training with torch.

Parameters:

  • prediction – The predicted tensor.

  • target – The target tensor.

  • weight – The weight tensor.

Returns:

The computed loss tensor.

Raises:

NotImplementedError – If the method is not implemented in the subclass.

Examples

>>> loss = MSELoss()
>>> prediction = torch.tensor([1.0, 2.0, 3.0])
>>> target = torch.tensor([1.0, 2.0, 3.0])
>>> weight = torch.tensor([1.0, 1.0, 1.0])
>>> loss.compute(prediction, target, weight)
tensor(0.)

Note

This method must be implemented in the subclass. It should return the computed loss tensor.