PETRIC: PET Rapid Image reconstruction Challenge

Participating

The organisers will provide GPU-enabled cloud runners which have access to larger private datasets for evaluation. To gain access, you must register. The organisers will then create a private team submission repository for you.

Layout

The organisers will import your submitted algorithm from main.py and then run & evaluate it. Please create this file! See the example main_*.py files for inspiration.

SIRF, CIL, and CUDA are already installed (using synerbi/sirf). Additional dependencies may be specified via apt.txt, environment.yml, and/or requirements.txt.

• (required) main.py: must define a class Submission(cil.optimisation.algorithms.Algorithm) and a list of submission_callbacks
• apt.txt: passed to apt install
• environment.yml: passed to conda install
• requirements.txt: passed to pip install

You can also find some example notebooks here which should help you with your development:

• https://github.com/SyneRBI/SIRF-Contribs/blob/master/src/notebooks/BSREM_illustration.ipynb

Organiser setup

The organisers will execute (after downloading https://petric.tomography.stfc.ac.uk/data/ to /path/to/data):

docker run --rm -it -v /path/to/data:/mnt/share/petric:ro -v .:/workdir -w /workdir --gpus all synerbi/sirf:edge-gpu /bin/bash
# ... or ideally synerbi/sirf:latest-gpu after the next SIRF release!
pip install git+https://github.com/TomographicImaging/Hackathon-000-Stochastic-QualityMetrics
# ... conda/pip/apt install environment.yml/requirements.txt/apt.txt
python petric.py

[!TIP] petric.py will effectively execute:

from main import Submission, submission_callbacks  # your submission
from petric import data, metrics  # our data & evaluation
assert issubclass(Submission, cil.optimisation.algorithms.Algorithm)
Submission(data).run(numpy.inf, callbacks=metrics + submission_callbacks)

[!WARNING] To avoid timing out (5 min runtime), please disable any debugging/plotting code before submitting! This includes removing any progress/logging from submission_callbacks.

• data to test/train your Algorithms is available at https://petric.tomography.stfc.ac.uk/data/ and is likely to grow (more info to follow soon)
  • fewer datasets will be used by the organisers to provide a temporary leaderboard
• metrics are calculated by class QualityMetrics within petric.py

Any modifications to petric.py are ignored.