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 (potentially empty) list of submission_callbacks, e.g.:

  • main_BSREM.py
  • main_ISTA.py
  • main_OSEM.py

• apt.txt: passed to apt install

• environment.yml: passed to conda install, e.g.:

  name: winning-submission
  channels: [conda-forge, nvidia]
  dependencies:
  - cupy
  - cuda-version =11.8
  - pip
  - pip:
    - git+https://github.com/MyResearchGroup/prize-winning-algos

• requirements.txt: passed to pip install, e.g.:

  cupy-cuda11x
  git+https://github.com/MyResearchGroup/prize-winning-algos

Tip

You probably should create either an environment.yml or requirements.txt file (but not both).

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 installing nvidia-docker & downloading https://petric.tomography.stfc.ac.uk/data/ to /path/to/data):

# 1. git clone & cd to your submission repository
# 2. mount `.` to container `/workdir`:
docker run --rm -it --gpus all -p 6006:6006 \
  -v /path/to/data:/mnt/share/petric:ro \
  -v .:/workdir -w /workdir synerbi/sirf:edge-gpu /bin/bash
# 3. install metrics & GPU libraries
conda install monai tensorboard tensorboardx jupytext cudatoolkit=11.8
pip uninstall torch # monai installs pytorch (CPU), so remove it
pip install tensorflow[and-cuda]==2.14  # last to support cu118
pip install torch --index-url https://download.pytorch.org/whl/cu118
pip install git+https://github.com/TomographicImaging/Hackathon-000-Stochastic-QualityMetrics
# 4. optionally, conda/pip/apt install environment.yml/requirements.txt/apt.txt
# 5. run your submission
python petric.py &
# 6. optionally, serve logs at <http://localhost:6006>
tensorboard --bind_all --port 6006 --logdir ./output

FAQ

See the wiki/Home and wiki/FAQ for more info.

Tip

petric.py will effectively execute:

from main import Submission, submission_callbacks  # your submission (`main.py`)
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 (currently 10 min runtime, will likely be increased a bit for the final evaluation after submissions close), please disable any debugging/plotting code before submitting! This includes removing any progress/logging from submission_callbacks and any debugging from Submission.__init__.

• 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 available during the submission phase, but more will be available for the final evaluation after submissions close
  • please contact us if you'd like to contribute your own public datasets!
• metrics are calculated by class QualityMetrics within petric.py
  • this does not contribute to your runtime limit
  • effectively, only Submission(data).run(np.inf, callbacks=submission_callbacks) is timed
• when using the temporary leaderboard, it is best to:
  • change Horizontal Axis to Relative
  • untick Ignore outliers in chart scaling
  • see the wiki for details

Any modifications to petric.py are ignored.