sketchgraphs_models¶

This module contains supporting code to the main sketchgraphs dataset. In particular, it contains two models based on graph representations of the data, which are geared towards the task of generation (see sketchgraphs_models.graph) and autoconstrain (see sketchgraphs_models.autoconstraint). In addition, this module contains a number of submodules to help the implementation of these models.

These models are tuned and operate on a subset of the full sketchgraphs dataset. In particular, they only handle constraints which relate at most two entities (e.g. the mirror constraint is not handled), and are trained on a subset of sketches which excludes sketches that are too large or too small. In addition, these model use a quantization strategy to model continuous parameters in the dataset, which must be pre-computed before training. To create quantization maps, see sketchgraphs.pipeline.make_quantization_statistics.

Modules

`sketchgraphs_models.autoconstraint`	This module implements the model, dataset and training procedure for the auto-constraint model.
`sketchgraphs_models.distributed_utils`	Utility functions for distributed (multi-gpu) training.
`sketchgraphs_models.graph`	This module contains the main implementation for the graph models.
`sketchgraphs_models.nn`	This module provides utilities and generic build blocks for graph neural networks.
`sketchgraphs_models.torch_extensions`	Pytorch extensions for our models.
`sketchgraphs_models.training`	Utilities for model training.