lightning_pose.losses

lightning_pose.losses.factory Module

High-level loss class that orchestrates the individual losses.

Classes

LossFactory

Factory object that contains an object for each specified loss.

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`	Parent class for all losses.
`HeatmapLoss`	Parent class for different heatmap losses (MSE, Wasserstein, etc).
`HeatmapMSELoss`	MSE loss between heatmaps.
`HeatmapKLLoss`	Kullback-Leibler loss between heatmaps.
`HeatmapJSLoss`	Kullback-Leibler loss between heatmaps.
`PCALoss`	Penalize predictions that fall outside a low-dimensional subspace.
`TemporalLoss`	Penalize temporal differences for each target.
`TemporalHeatmapLoss`	Penalize temporal differences for each heatmap.
`UnimodalLoss`	Encourage heatmaps to be unimodal using various measures.
`RegressionMSELoss`	MSE loss between ground truth and predicted coordinates.
`RegressionRMSELoss`	Root MSE loss between ground truth and predicted coordinates.
`PairwiseProjectionsLoss`	Penalize projections from each pair of cameras into 3D world space.