lightning_pose.losses

lightning_pose.losses.factory Module

High-level loss class that orchestrates the individual losses.

Classes

LossFactory(losses_params_dict, data_module)

Factory object that contains an object for each specified loss.

lightning_pose.losses.helpers Module

Helper functions for losses.

Functions

convert_dict_values_to_tensors(param_dict, ...)

Classes

EmpiricalEpsilon(percentile)

Find percentile value of a given loss tensor.

lightning_pose.losses.losses Module

Supervised and unsupervised losses implemented in pytorch.

The lightning pose package defines each loss as its own class; an initialized loss object, in addition to computing the loss, stores hyperparameters related to the loss (weight in the final objective funcion, epsilon-insensitivity parameter, etc.)

A separate LossFactory class (defined in lightning_pose.losses.factory) collects all losses for a given model and orchestrates their execution, logging, etc.

The general flow of each loss class is as follows: - input: predicted and ground truth data - step 0: remove ground truth samples containing nans if desired - step 1: compute loss for each batch element/keypoint/etc - step 2: epsilon-insensitivity: set loss to zero for any batch element with loss < epsilon - step 3: reduce loss (usually mean) - step 4: log values to a dict - step 5: return weighted loss

Functions

get_loss_classes()

Get a dict with all the loss classes.

Classes

Loss([data_module, epsilon, log_weight])	Parent class for all losses.
HeatmapLoss([data_module, log_weight])	Parent class for different heatmap losses (MSE, Wasserstein, etc).
HeatmapMSELoss([data_module, log_weight])	MSE loss between heatmaps.
HeatmapKLLoss([data_module, log_weight])	Kullback-Leibler loss between heatmaps.
HeatmapJSLoss([data_module, log_weight])	Kullback-Leibler loss between heatmaps.
PCALoss(loss_name[, components_to_keep, ...])	Penalize predictions that fall outside a low-dimensional subspace.
TemporalLoss([data_module, epsilon, ...])	Penalize temporal differences for each target.
TemporalHeatmapLoss(loss_name[, ...])	Penalize temporal differences for each heatmap.
UnimodalLoss(loss_name, ...[, data_module, ...])	Encourage heatmaps to be unimodal using various measures.
RegressionMSELoss([data_module, epsilon, ...])	MSE loss between ground truth and predicted coordinates.
RegressionRMSELoss([data_module, epsilon, ...])	Root MSE loss between ground truth and predicted coordinates.