torch_brain.dataset

• Dataset
  • Dataset
  • DatasetIndex
• NestedDataset
  • NestedDataset
  • NestedSpikingDataset
• Mixins
  • SpikingDatasetMixin
  • CalciumImagingDatasetMixin

Overview

Base classes to ease creation of PyTorch datasets for your data.

• The Dataset class is inherited by all datasets. These handle opening and accessing single datasets.

• The NestedDataset class is for opening and accessing multiple datasets through a unified interface.

• Mixin classes are provided to add modality-specific functionalities to the Dataset classes.

Dataset

torch_brain’s Dataset class (and its sub-classes) allow you to sample time-slices of your data. This is a major deviation from the standard torch.utils.data.Dataset, which is indexed by integers. To achieve arbitrary time-slice based access, our Dataset class is indexed by three things:

1. The recording id from which you want the slice,

2. Start time of the slice, and

3. End time of the slice

These are put into a DatasetIndex object, which is then used to index the Dataset. Since different machine learning applications require different ways of sampling, we provide a collection of samplers which are responsible for creating these DatasetIndex objects.

NestedDataset

The Dataset class is designed to operate on a single dataset. However, many modern ML methods perform training over multiple datasets. For this, we provide NestedDataset that allows users to open and index through multple datasets.