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autonomousvision / differentiable_volumetric_rendering

This repository contains the code for the CVPR 2020 paper "Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision"
http://www.cvlibs.net/publications/Niemeyer2020CVPR.pdf
MIT License
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Training Batch-size and time #2

Closed JiejiangWu closed 4 years ago

JiejiangWu commented 4 years ago

Hi, Exciting works and fast open-source!
A little question: The batch-size is set to 64 in code, and in publication, it's 8. So I want to make sure that is the model trained with 8 x V100 GPU? Also, could you please tell me about how much time it takes during the whole training? Looking forward to your reply. :)

m-niemeyer commented 4 years ago

Hi @JiejiangWu , Thanks a lot for your message and interest!

Here an excerpt from our paper: "We train on a single NVIDIA V100GPU with a batch size of 64 images with1024 random pixels each."

So in the single-view reconstruction experiments, we train with a batch size of 64, and per object, we sample 1024 pixels. We train on a single V100 GPU (we did not perform multi-GPU training). We let the the big models train for quite a long time (2 - 5 weeks). These numbers do not apply to the multi-view reconstruction experiments.

If you want to train on smaller GPUs or with less time, you can use a decoder with a smaller hidden dimension, e.g. 128, by adding

model:
  decoder_kwargs:
    hidden_size: 128

to your config file.

I hope this helps. Good luck with your research!

JiejiangWu commented 4 years ago

Oh, sorry! I messed up the batch-size in the paper. You are right. Your reply solves my concerns. I think this issue can be closed.