faster-particles 0.2.1

Point Proposal Network for particles images and related tools.

faster-particles

This package includes the following:

• Toydata generator
• LArCV data interface (2D and 3D)
• Pixel Proposal Network implementation using Tensorflow

Installation

Dependencies

You must install larcv2 and its own dependencies (ROOT, OpenCV, Numpy) in order to use LArCV data interface. To install larcv2:

git clone https://github.com/DeepLearnPhysics/larcv2.git
cd larcv2
source configure.sh
make

Install

Then install faster-particles with Pip:

pip install faster-particles

Alternatively, you can also clone the source:

git clone https://github.com/Temigo/faster-particles.git
cd faster-particles

Usage

The following assumes you installed with pip. If you cloned the source, make sure you are in the root directory and replace ppn with python faster_particles/bin/ppn.py.

To use toydata rather than LArCV data in the following sections, use option --toydata. LArCV data files can be specified with --data option. They can use regex, e.g. ppn_p[01]*.root.

Training

The program output is divided between:

• Output directory: with all the weights
• Log directory: to store all Tensorflow logs (and visualize them with Tensorboard)
• Display directory: stores regular snapshots taken during training of PPN1 and PPN2 proposals compared to ground truth.

To train PPN on 1000 steps use:

ppn train -o output/dir/ -l log/dir/ -d display/dir -n ppn -m 1000 --data path/to/data

To train the base network (currently only VGG available) on track/shower classification task use:

ppn train -o output/dir/ -l log/dir/ -d display/dir -n base -m 1000

To train on 3D data, use the argument -3d and don't forget to specify the image size with -N argument (e.g. 192 for a compression factor of 4, see larcvdata_generator.py for more details).

Inference

To run inference with a minimal score of 0.5 for predicted points:

ppn demo weights_file.ckpt -d display/dir/ -ms 0.5

The display directory will contain snapshots of the results.

More options are available through ppn train -h and ppn demo -h respectively.

Authors

K.Terao, J.W. Park, L.Domine