Replace pluggable components to create a proper Configurable hierarchy.

Summary: This large diff rewrites a significant portion of Implicitron’s config hierarchy. The new hierarchy, and some of the default implementation classes, are as follows:

Experiment
    data_source: ImplicitronDataSource
        dataset_map_provider
        data_loader_map_provider
    model_factory: ImplicitronModelFactory
        model: GenericModel
    optimizer_factory: ImplicitronOptimizerFactory
    training_loop: ImplicitronTrainingLoop
        evaluator: ImplicitronEvaluator

Experiment (used to be ExperimentConfig) is now a top-level Configurable and contains as members mainly (mostly new) high-level factory Configurables.
Experiment’s job is to run factories, do some accelerate setup and then pass the results to the main training loop.
ImplicitronOptimizerFactory and ImplicitronModelFactory are new high-level factories that create the optimizer, scheduler, model, and stats objects.
TrainingLoop is a new configurable that runs the main training loop and the inner train-validate step.
Evaluator is a new configurable that TrainingLoop uses to run validation/test steps.
GenericModel is not the only model choice anymore. Instead, ImplicitronModelBase (by default instantiated with GenericModel) is a member of Experiment and can be easily replaced by a custom implementation by the user.

All the new Configurables are children of ReplaceableBase, and can be easily replaced with custom implementations.

In addition, I added support for the exponential LR schedule, updated the config files and the test, as well as added a config file that reproduces NERF results and a test to run the repro experiment.

Reviewed By: bottler

Differential Revision: D37723227

fbshipit-source-id: b36bee880d6aa53efdd2abfaae4489d8ab1e8a27

Introduction

PyTorch3D provides efficient, reusable components for 3D Computer Vision research with PyTorch.

Key features include:

Data structure for storing and manipulating triangle meshes
Efficient operations on triangle meshes (projective transformations, graph convolution, sampling, loss functions)
A differentiable mesh renderer

PyTorch3D is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data. For this reason, all operators in PyTorch3D:

Are implemented using PyTorch tensors
Can handle minibatches of hetereogenous data
Can be differentiated
Can utilize GPUs for acceleration

Within FAIR, PyTorch3D has been used to power research projects such as Mesh R-CNN.

Installation

For detailed instructions refer to INSTALL.md.

License

PyTorch3D is released under the BSD License.

Tutorials

Get started with PyTorch3D by trying one of the tutorial notebooks.


Deform a sphere mesh to dolphin	Bundle adjustment


Render textured meshes	Camera position optimization


Render textured pointclouds	Fit a mesh with texture


Render DensePose data	Load & Render ShapeNet data


Fit Textured Volume	Fit A Simple Neural Radiance Field

Documentation

Learn more about the API by reading the PyTorch3D documentation.

We also have deep dive notes on several API components:

Overview Video

We have created a short (~14 min) video tutorial providing an overview of the PyTorch3D codebase including several code examples. Click on the image below to watch the video on YouTube:

Development

We welcome new contributions to PyTorch3D and we will be actively maintaining this library! Please refer to CONTRIBUTING.md for full instructions on how to run the code, tests and linter, and submit your pull requests.

Development and Compatibility

main branch: actively developed, without any guarantee, Anything can be broken at any time
- REMARK: this includes nightly builds which are built from main
- HINT: the commit history can help locate regressions or changes
backward-compatibility between releases: no guarantee. Best efforts to communicate breaking changes and facilitate migration of code or data (incl. models).

Contributors

PyTorch3D is written and maintained by the Facebook AI Research Computer Vision Team.

In alphabetical order:

Amitav Baruah
Steve Branson
Luya Gao
Georgia Gkioxari
Taylor Gordon
Justin Johnson
Patrick Labatut
Christoph Lassner
Wan-Yen Lo
David Novotny
Nikhila Ravi
Jeremy Reizenstein
Dave Schnizlein
Roman Shapovalov
Olivia Wiles

Citation

If you find PyTorch3D useful in your research, please cite our tech report:

@article{ravi2020pytorch3d,
    author = {Nikhila Ravi and Jeremy Reizenstein and David Novotny and Taylor Gordon
                  and Wan-Yen Lo and Justin Johnson and Georgia Gkioxari},
    title = {Accelerating 3D Deep Learning with PyTorch3D},
    journal = {arXiv:2007.08501},
    year = {2020},
}

If you are using the pulsar backend for sphere-rendering (the PulsarPointRenderer or pytorch3d.renderer.points.pulsar.Renderer), please cite the tech report:

@article{lassner2020pulsar,
    author = {Christoph Lassner and Michael Zollh\"ofer},
    title = {Pulsar: Efficient Sphere-based Neural Rendering},
    journal = {arXiv:2004.07484},
    year = {2020},
}

News

Please see below for a timeline of the codebase updates in reverse chronological order. We are sharing updates on the releases as well as research projects which are built with PyTorch3D. The changelogs for the releases are available under Releases, and the builds can be installed using conda as per the instructions in INSTALL.md.

[Dec 16th 2021]: PyTorch3D v0.6.1 released

[Oct 6th 2021]: PyTorch3D v0.6.0 released

[Aug 5th 2021]: PyTorch3D v0.5.0 released

[Feb 9th 2021]: PyTorch3D v0.4.0 released with support for implicit functions, volume rendering and a reimplementation of NeRF.

[November 2nd 2020]: PyTorch3D v0.3.0 released, integrating the pulsar backend.

[Aug 28th 2020]: PyTorch3D v0.2.5 released

[July 17th 2020]: PyTorch3D tech report published on ArXiv: https://arxiv.org/abs/2007.08501

[April 24th 2020]: PyTorch3D v0.2.0 released

[March 25th 2020]: SynSin codebase released using PyTorch3D: https://github.com/facebookresearch/synsin

[March 8th 2020]: PyTorch3D v0.1.1 bug fix release

[Jan 23rd 2020]: PyTorch3D v0.1.0 released. Mesh R-CNN codebase released: https://github.com/facebookresearch/meshrcnn