Based on the DeepJetCore framework (https://github.com/DL4Jets/DeepJetCore) [CMS-AN-17-126] for HGCal reconstruction purposes.
The framework consists of two parts:
- HGCal ntuple converter (Dependencies: root5.3/6)
- DNN training/evaluation (Dependencies: DeepJetCore and all therein).
The DeepJetCore framework and the DeepHGCal framework should be checked out to the same parent directory. Before usage, always set up the environment by sourcing XXX_env.sh
The experiments are usually conducted in three steps:
- Training
- Testing (dumping of inference result somewhere on disk)
- Plotting and anlysis
python bin/train/train_file.py path/to/config.ini config_name
python bin/train/train_file.py path/to/config.ini config_name --test True
python bin/plot/plot_file.py path/to/config.ini config_name
For clustering, the plot_file can be plot_inference_clustering.py
It will plot the resolution histogram as well as output mean and variance of resolution on stdout.