as_dataloader: Creates a dataloader from its input

In mlverse/torchlight: Higher Level 'API' for 'torch'

Creates a dataloader from its input

Description

as_dataloader is used internally by luz to convert input data and valid_data as passed to fit.luz_module_generator() to a torch::dataloader

Usage

as_dataloader(x, ...)

## S3 method for class 'dataset'
as_dataloader(x, ..., batch_size = 32)

## S3 method for class 'iterable_dataset'
as_dataloader(x, ..., batch_size = 32)

## S3 method for class 'list'
as_dataloader(x, ...)

## S3 method for class 'dataloader'
as_dataloader(x, ...)

## S3 method for class 'matrix'
as_dataloader(x, ...)

## S3 method for class 'numeric'
as_dataloader(x, ...)

## S3 method for class 'array'
as_dataloader(x, ...)

## S3 method for class 'torch_tensor'
as_dataloader(x, ...)

Arguments

x	the input object.
...	Passed to torch::dataloader().
batch_size	(int, optional): how many samples per batch to load (default: 1).

Details

as_dataloader methods should have sensible defaults for batch_size, parallel workers, etc.

It allows users to quickly experiment with fit.luz_module_generator() by not requiring to create a torch::dataset and a torch::dataloader in simple experiments.

Methods (by class)

• as_dataloader(dataset): Converts a torch::dataset() to a torch::dataloader().

• as_dataloader(iterable_dataset): Converts a torch::iterable_dataset() into a torch::dataloader()

• as_dataloader(list): Converts a list of tensors or arrays with the same size in the first dimension to a torch::dataloader()

• as_dataloader(dataloader): Returns the same dataloader

• as_dataloader(matrix): Converts the matrix to a dataloader

• as_dataloader(numeric): Converts the numeric vector to a dataloader

• as_dataloader(array): Converts the array to a dataloader

• as_dataloader(torch_tensor): Converts the tensor to a dataloader

Overriding

You can implement your own as_dataloader S3 method if you want your data structure to be automatically supported by luz's fit.luz_module_generator(). The method must satisfy the following conditions:

• The method should return a torch::dataloader().

• The only required argument is x. You have good default for all other arguments.

It's better to avoid implementing as_dataloader methods for common S3 classes like data.frames. In this case, its better to assign a different class to the inputs and implement as_dataloader for it.

mlverse/torchlight documentation built on Sept. 19, 2024, 11:22 p.m.