  The constant introduction of standardized benchmarks in the literature has
helped accelerating the recent advances in meta-learning research. They offer a
way to get a fair comparison between different algorithms, and the wide range
of datasets available allows full control over the complexity of this
evaluation. However, for a large majority of code available online, the data
pipeline is often specific to one dataset, and testing on another dataset
requires significant rework. We introduce Torchmeta, a library built on top of
PyTorch that enables seamless and consistent evaluation of meta-learning
algorithms on multiple datasets, by providing data-loaders for most of the
standard benchmarks in few-shot classification and regression, with a new
meta-dataset abstraction. It also features some extensions for PyTorch to
simplify the development of models compatible with meta-learning algorithms.
The code is available here: https://github.com/tristandeleu/pytorch-meta
