Crate ai_dataloader

The ai-dataloader crate provides a Rust implementation to the PyTorch DataLoader.

Unlike the python version where almost everything happens in runtime, ai-dataloader is built on Rust’s powerful trait system.

Highlights

• Iterable or indexable (Map style) DataLoader.
• Customizable Sampler, BatchSampler and collate_fn.
• Default collate function that will cover most of the uses cases, supporting nested type.
• Shuffling for iterable and indexable DataLoader.

Examples

Examples can be found in the examples folder.

PyTorch DataLoader function equivalents

DataLoader creation

PyTorch	ai-dataloader	Notes
DataLoader(dataset)	DataLoader::builder(dataset).build()	Create a DataLoader with default parameters
DataLoader(dataset, batch_size=2)	DataLoader::builder(dataset).batch_size(2).build()	Setup the batch size
DataLoader(dataset, shuffle=True)	DataLoader::builder(dataset).shuffle().build()	Shuffle the data
DataLoader(dataset, sampler=CustomSampler)	DataLoader::builder(dataset).sampler::<CustomSampler>().build()	Provide a custom sampler

Combined options

PyTorch	ai-dataloader
DataLoader(dataset, shuffle=True, batch_size=2, drop_last=True, collate_fn=CustomCollate)	DataLoaderBuilder::new(dataset).shuffle().batch_size(2).drop_last().collate_fn(CustomCollate).build()

DataLoader iteration

PyTorch	ai-dataloader	Notes
for text, label in data_loader:	for (text, label) in data_loader.iter()	Simple iteration

Choosing between Iterable or Indexable dataloader

You can choose Iterable DataLoader for instance if your dataset arrived from a stream and you don’t have random access into it. It’s also useful for large dataset to only load a small part at the time in the RAM. When the order mater, for instance in Reinforcement Learning, Iterable DataLoader is also a good fit.

Otherwise Indexable Dataloader (Map style in PyTorch doc) maybe be a good fit.

Both support shuffling the sample.

To choose iterable:

use ai_dataloader::iterable::DataLoader;

To choose indexable:

use ai_dataloader::indexable::DataLoader;

Re-exports

pub use indexable::sampler;

Modules

collate

Merges a list of samples to form a batch.

indexable

Indexable Dataloader.

iterable

Indexable Dataloader.

Structs

NdarrayDataset

Basic dataset than can contains two ndarray of any dimension.

Traits

Dataset

A dataset is just something that has a length and is indexable. A Vec of dataset collate output must also be collatable.

GetSample

Return a sample from the dataset at a given index.

Len

Basic trait for anything that could have a length. Even if a lot of struct have a len() method in the standard library, to my knowledge this function is not included into any standard trait.