dacapo.experiments.tasks.losses.affinities_loss

Classes

AffinitiesLoss

A class representing a loss function that calculates the loss between affinities and local shape descriptors (LSDs).

Module Contents

class dacapo.experiments.tasks.losses.affinities_loss.AffinitiesLoss(num_affinities: int, lsds_to_affs_weight_ratio: float)

A class representing a loss function that calculates the loss between affinities and local shape descriptors (LSDs).

num_affinities: int the number of affinities

lsds_to_affs_weight_ratio: float the ratio of the weight of the loss between affinities and LSDs

compute(prediction, target, weight=None): Calculate the total loss between prediction and target.

Note

The AffinitiesLoss class is used to calculate the loss between affinities and local shape descriptors (LSDs).

num_affinities

lsds_to_affs_weight_ratio

compute(prediction, target, weight)

Method to calculate the total loss between affinities and LSDs.

Parameters:

prediction – torch.Tensor the model’s prediction
target – torch.Tensor the target values
weight – torch.Tensor the weight to apply to the loss

Returns:

torch.Tensor: the total loss between affinities and LSDs

Raises:

ValueError – if the number of affinities in the prediction and target does not match

Examples

>>> affinities_loss = AffinitiesLoss(3, 0.5)
>>> prediction = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]])
>>> target = torch.tensor([[9, 10, 11, 12], [13, 14, 15, 16]])
>>> weight = torch.tensor([[1, 1, 1, 1], [1, 1, 1, 1]])
>>> affinities_loss.compute(prediction, target, weight)
tensor(0.5)

Note

The AffinitiesLoss class is used to calculate the loss between affinities and local shape descriptors (LSDs).