- 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.
