Skip to content

Latest commit

 

History

History
97 lines (60 loc) · 5.01 KB

README.md

File metadata and controls

97 lines (60 loc) · 5.01 KB
Raw

🌈 pytorch-gqn

Do you want to learn unsupervised representations of 3D scenes? Boy do I have the kick for you 🕍

Watch a video: https://vimeo.com/345774866

This is an implementation of Neural Scene Representation and Rendering (Eslami et al 2018).

The authors are all pretty cool: S. M. Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S. Morcos, Marta Garnelo

Summary: If you haven't read the blog post already, you should! It's super cool, and will help you get a head start on what's here.

And before you commit to reading the rest of this document, please know that there exist other implementations of GQN out there! You've got choices!

Faq: Or, A non-expert's guide to HAVING FUN with this repo ;)

This repository might look a bit intimidating, but I'm always here, and I'll try to make this README informative!

Steps:

  1. Getting the training data (to replicate the networks in the original paper)
  2. Training your dragon neural nets
  3. How to tell that it works (and exploring what it can do)

Steps 1 and 2 are optional (and more straightforward? Since the authors did all the hard work for us already by telling us how the net is built and so forth). I've trained a GQN for ~40k gradient steps (see /notes for more details), so there's checkpoints that you can use out of the box! You can skip to (3)

1. Getting training data

You should probably start by deciding what dataset to use. I used shepard_metzler_5_parts, but there are others: here. Personally I'd like to see one trained on rooms_ring_camera_with_object_rotations, but that's just me 😊

The scripts:

  • download_data.sh can help you download the data with gsutil, and put the data in a directory
  • data/tf2torch.py will convert the .tfrecord files to a LOT of .pt.gz files, each one being one particular example. Other implementations (see wohlert) will do this differently (e.g. by making one big file instead of a lot of little ones)

Great! Now we have training data. When I was writing the various bits, I wrote the tests

  • test_datasets.py will see if you have the ShepardMetzler dataset ready-to-go
  • test_building_blocks.py will see if your neural net is OK
  • test_gqn.py will run one step of training (load the data, load the model, etc). If this passes, you should be ready to scale to full training!

2. Running the training/evaluation loop

python run-gqn.py --data_parallel=True --batch_size=144 --workers=2

This will save backups every 1000 steps, and log changes to Tensorboard.

Note to self (and everyone): Add some tips about logging/keeping track of stuff during training!

fast forward a few hours, or days: Awesome! we have a checkpoint, and the images on Tensorboard look pretty good. What now?

3. You've got a GQN! Now what?

What can a trained model do?

Almost anything, you just have to code stuff. 🌼You can:

  • delete half the weights?
  • watch the activations as an image passes through it
  • compare the representations for different scenes
  • make it rotate!!

Check out the /fun folder for my first attempts. Lots more to be done here, so please submit (many) a pull request with suggestions!

FAQ

If I don't want to train my own model, can I grab the checkpoints that you have?

Sure, right here.

Why do the images in fun/Interactive.ipynb suck?

I only trained it for about 40k gradient steps (each one from a mini-batch of 36 scenes). I think the paper had like... 2 million gradient steps? The 5k Gradient Steps More Fund

Help, something happened!

IS IT A MURDERFISH?!! ....... if not, I made a help document, see if that helps?

Contributing

Need lots of help on this! If you're a researcher, graphic designer, student, garden cat, aspiring engineer, auto mech, you name it: chances are, you spot something that you like/don't like/want this to do/can fix! Open a pull request!

Some ideas:

  • I want this to be more accessible. This could take the form of visualizations, code comments, tutorial/guides, chat group? Hmm.
  • If anyone is interested in starting a joint fund for people to learn and train AI models, I'm down.
  • Train the model (even 1 epoch helps!) and contribute the checkpoints to the world!
  • Analysis is laaaacking. Add more things to the fun folder!
  • Experiment with different domains!

License

It's yours :) You wrote it. Now take good care of it