LVAE Prediction

Module containing pytorch implementations for obtaining predictions from an LVAE.

`lvae_predict_mmse_tiled_batch(model, likelihood_obj, input, mmse_count)`

Generate the MMSE (minimum mean squared error) prediction, for a given input.

This is calculated from the mean of multiple single sample predictions.

Parameters:

Name	Type	Description	Default
`model`	`LadderVAE`	Trained LVAE model.	required
`likelihood_obj`	`LikelihoodModule`	Instance of a likelihood class.	required
`input`	`torch.tensor \| tuple of (torch.tensor, Any, ...)`	Input to generate prediction for. This can include auxilary inputs such as `TileInformation`, but the model input is always the first item of the tuple. Expected shape of the model input is (S, C, Y, X).	required
`mmse_count`	`int`	Number of samples to generate to calculate MMSE (minimum mean squared error).	required

Returns:

Type Description

tuple of (tuple of (torch.Tensor[Any], Any, ...))

A tuple of 3 elements. The first element contains the MMSE prediction, the second contains the standard deviation of the samples used to create the MMSE prediction. Finally the last element contains the log-variance of the likelihood, this will be None if likelihood.predict_logvar is None. Any auxillary data included in the input will also be include with all of the MMSE prediction, the standard deviation, and the log-variance.

`lvae_predict_single_sample(model, likelihood_obj, input)`

Generate a single sample prediction from an LVAE model, for a given input.

Parameters:

Name	Type	Description	Default
`model`	`LadderVAE`	Trained LVAE model.	required
`likelihood_obj`	`LikelihoodModule`	Instance of a likelihood class.	required
`input`	`tensor`	Input to generate prediction for. Expected shape is (S, C, Y, X).	required

Returns:

Type	Description
`tuple of (torch.tensor, optional torch.tensor)`	The first element is the sample prediction, and the second element is the log-variance. The log-variance will be None if `model.predict_logvar is None`.

`lvae_predict_tiled_batch(model, likelihood_obj, input)`

Generate a single sample prediction from an LVAE model, for a given input.

Parameters:

Name	Type	Description	Default
`model`	`LadderVAE`	Trained LVAE model.	required
`likelihood_obj`	`LikelihoodModule`	Instance of a likelihood class.	required
`input`	`torch.tensor \| tuple of (torch.tensor, Any, ...)`	Input to generate prediction for. This can include auxilary inputs such as `TileInformation`, but the model input is always the first item of the tuple. Expected shape of the model input is (S, C, Y, X).	required

Returns:

Type	Description
`tuple of ((torch.tensor, Any, ...), optional tuple of (torch.tensor, Any, ...))`	The first element is the sample prediction, and the second element is the log-variance. The log-variance will be None if `model.predict_logvar is None`. Any auxillary data included in the input will also be include with both the sample prediction and the log-variance.