Replace the 14 subpackages with 13 flat modules (6.7k -> 2.7k lines),
the Hydra config groups with typed dataclasses and one self-contained
YAML file per experiment (configs/), and the docs with docs/usage.md and
docs/design.md. All capabilities stay: GamGam, ttH CP, Delphes, binary
and multiclass tasks, transformer, fine-tuning from Pretrained_GNN, DDP,
torch.compile/CUDA graphs/bfloat16, predict and ONNX export.
Behavior: prepared caches, pretrained-model logits and two CPU training
epochs are bit-identical to the previous code on the fixtures and a 60k
event GamGam subset. Energies are now computed with one vectorized cosh
(<= 2.4e-7 relative difference on 969 of the full GamGam objects). The
ttH and Delphes configs now use Number % 10 as the fold (the raw event
index selected only the first 10 events), and use the paper recipe
(64/4/4, Adam 1e-4, decay 0.99). The ROOT score writer handles jagged
trees. Prediction files store source file and entry instead of sample
ids, and checkpo…
2 days ago
feat: replace DGL with shared packed events and native PyTorch graphs
- validation: use pinned public Hugging Face fixture (528346c0a2dcf57bc6f47e0e929113ae0f5ec47a)
- data: resolve training sources from Hugging Face (02920c2576f35911f7068c0379c06585ae9c8b57)
