This repo scaffolds a PyTorch project for a transformer-based foundation model over event camera data. See DESIGN.md for the design draft.

• src/ecfm: library code
• configs: YAML configs
• scripts: training and utility entrypoints
• data: placeholder for datasets
• tests: basic unit tests
• docs: extra docs

1. Create a venv and install dependencies.
2. Run a dry training loop with synthetic events.

python -m venv .venv
.venv\Scripts\Activate.ps1
pip install -e .
python scripts\train.py --config configs\small.yaml

If your default Python is 3.14, install a CUDA-enabled PyTorch build in a Python 3.12 venv (CUDA wheels are not available for 3.14).

py -3.12 -m venv .venv312
. .\.venv312\Scripts\Activate.ps1
python -m pip install --upgrade pip
pip install --upgrade --force-reinstall torch torchvision --index-url https://download.pytorch.org/whl/cu121
pip install -e .
python -c "import torch; print(torch.__version__, torch.cuda.is_available(), torch.cuda.get_device_name(0))"

If the dataset is placed at datasets/THU-EACT-50-CHL, run:

python scripts\train.py --config configs\thu_smoke.yaml