DeepLearnPhysics / dynamic-gcnn

Dynamic Graph Convolutional Neural Network for 3D point cloud semantic segmentation

dgcnn

This is an implementation of 3D point cloud semantic segmentation for Dynamic Graph Convolutional Neural Network. The number of edge convolution layers, fully connected layers, and number of filters per each layer are all configurable. The implementation includes a few variations such as residual unit (edge convolution with identity mapping), with or without fully connected layers, etc.. Experimental results on DeepLearnPhysics open data set will be made available.

Requirements

• tensorflow >= v1.3
• numpy >= 1.13
• Optional requirements for IO include h5py, larcv

Help

An executable script can be found at bin/dgcnn.py. The script takes train or inference arguments. Try --help to list available arguments:

bin/dgcnn.py train --help

How to run

Below is an example of how to train the network using mydata.hdf5 data file with hdf5 format, 4 GPUs with batch size 24 and mini-batch size of 6, store snapshot every 500 iterations, print out info (loss,accuracy,etc) every 10 iterations, and store tensorboard summary every 50 iterations.

bin/dgcnn.py train --gpus 0,1,2,3 -bs 24 -mbs 6 -chks 500 -rs 10 -ss 50 -if mydata.hdf5 -io h5

See --help to find more flags and a descipriton for arguments.