qsketch (quantiles sketch)

This package computes quantiles of the output of arbitrary pytorch Modules called projectors over arbitrary datasets.

This package features multiprocessing capabilities which allow training deep models in an effective manner.

It is intended to be useful for research about training with sliced Wasserstein distances.

installation

qsketch depends on torchpercentile, please check https://github.com/aliutkus/torchpercentile for installation instructions.

Type pip install -e . in the root directory

Usage

Checkout the example test.py for a simple example of using torch to learn a generative model on MNIST that minimizes the Sliced Wasserstein distance.

The module comprises some key ingredients which are:

• GSW Generalized Sliced Wasserstein objects are created with a dataset as an argument, the specifications of the projections to use (either linear or arbitrary pytorch Modules), and will automatically compute the generalized sliced Wasserstein distance between a batch and this dataset. See the example.

• Sketcher objects encapsulate the application of some Module on the data, and the computation of quantiles of the corresponding outputs. A Sketcher object is initialized with a data source, which can notably be a DataStream (see below), and is directly accessed with the module of which output you want to compute the quantiles on the data:

  quantiles = sketcher[test_module]

  It is also possible to start a stream of sketching through the stream method, in which case the sketcher will start sketching processes that will fill in a queue, that can be used for training.

• DataStream objects take a Dataset and continuously fill a queue from which one can get content. This is useful for multiprocessing and asynchronous training.

• ModulesDataset is a class that takes some torch Module classname as a parameter and creates a Dataset out of it. The idea is that each sample of a ModulesDataset is an instance of the provided class, initialized with a specific random seed. This is usefull for iterating over random projections, or more generally random pytorch Modules to be applied on the data.