PyTorch-trained, ONNX-exported all-conv FCN (per-pixel Vx,Vy,s) run in Java via
ONNX Runtime 1.20.0 (CPU EP). CuasDnnInfer loads the model with a location resolver
(local path / scp user@host:path / http(s) -> ~/.cache/c5p_dnn/, fetching the
model.onnx + external-data .data pair) and runs a float[N][H][W] patch to raw
det/vel/off output. Verified bit-exact vs PyTorch (max abs diff 2.9e-6) via a fixed
test vector. New config param curt_dnn_model (empty default) selects the model,
mirroring the tile_processor_gpu kernel-source default/override scheme. CPU first
(.224 has no cuDNN; 82k net is microseconds/patch); GPU (CUDA/TensorRT EP) and the
CuasDetectRT integration are the next phase.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>