- jai release.jai builds the release build, then stages release/mcap-VERSION-windows-x64/ with
mcap.exe and its symbols, onnxruntime (CUDA provider), FFmpeg, the assets, the OCR, label and
segmentation models (each checked against its models.config), NVIDIA's CUDA 13 runtime, all of
cuDNN 9 and NVRTC, a README and third-party notices; zips it with Windows' tar; prints its
SHA-256. - no_cuda leaves the CUDA runtime out; - no_build packages bin/ as it is.
- cuDNN is copied whole: it loads sub-libraries by name (9.26's cudnn_engines_tensor_ir64_9.dll),
and one missing fails every run on the GPU.
- Checked by running the package with no CUDA or cuDNN on PATH: OCR, labels and segmentation on
CUDA, video import through its FFmpeg.
- release/ is ignored.
- segmentation-plan.html: the plan for panoptic segmentation of images, per library folder, stored
in .mcap chunks (no file version bump), in six phases.
- tools/export_mask2former.py: exports facebook/mask2former-swin-base-coco-panoptic to ONNX (opset 17,
dynamic batch, 384 x 384 input) and checks it against PyTorch: on 25 images, all 180 segments
identical (IoU 1.000). Writes the class list with names for people ("sky-other-merged" is "sky"),
and PyTorch's own segmentations for segment-probe -check.
- modules/Segment: runs the model with onnxruntime (CUDA, else the CPU) and makes segments of its
outputs as transformers' post_process_panoptic_segmentation does: a segment id map (longest side at
most 1024) and each segment's class, score, area and bounds. Matches PyTorch on 99.8% of pixels,
every segment scored 0.6 or more found. 57 ms an image at batch 4 on an RTX 5080 laptop.
- tools/segment_probe.jai (jai first.jai - segment_probe): segments image files; -out draws them,
-check compares with PyTorch, -dump-input writes the model's input.
- modules/onnxruntime: ort_run_float_outputs, for a model with several outputs.
- first.jai copies the model to bin/segment-models. The model itself is not in git
(modules/Segment/models, 462 MB); tools/export_mask2former.py makes it.
