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Project: chainer-gqn (GitHub Link)

• chainer-gqn-master
  • trainer
    • optimizer.py
    • dataframe.py
    • scheduler.py
    • __init__.py
    • meter.py
  • LICENSE
  • gqn
    • nn
      • representation.py
      • generator.py
      • __init__.py
      • inference.py
    • preprocessing.py
    • data
      • iterator.py
      • sampler.py
      • subset.py
      • __init__.py
      • dataset.py
    • __init__.py
    • json.py
  • model.py
  • experiments
    • rooms_ring_camera
      • observation.py
      • functions.py
    • rooms_free_camera
      • ring_camera.py
      • functions.py
    • shepard_metzler
      • predictions.py
      • observation.py
      • functions.py
  • hyperparams.py
  • train.py
  • README.md
  • test.py
  • train_mn.py
  • .gitignore

:construction: Work in Progress :construction:

Neural scene representation and rendering

https://deepmind.com/blog/neural-scene-representation-and-rendering/

Requirements

• Python 3
• h5py
• Chainer
  • pip3 install chainer
• CuPy
  • pip3 install cupy-cuda100 for CUDA 10.0
  • pip3 install cupy-cuda91 for CUDA 9.1

Also you need the followings for visualization:

• ffmpeg
  • sudo apt install ffmpeg
• imagemagick
  • sudo apt install imagemagick

Current training progress

Network Architecture

Dataset

deepmind/gqn-datasets

Datasets used to train GQN in the paper are available to download.

https://github.com/deepmind/gqn-datasets

You need to convert .tfrecord files to HDF5 .h5 format before starting training.

https://github.com/musyoku/gqn-datasets-translator

gqn-dataset-renderer

I am working on a OpenGL/CUDA renderer for rendering GQN dataset.

https://github.com/musyoku/gqn-dataset-renderer

• Shepard-Metzler

• Rooms

• MNIST Dice