The ONNX session was built with intra_threads(1) unconditionally. That's
correct for DirectML (compute is on the GPU), but on the CPU execution
provider it pinned all matmul/conv work to a single core — ~1.78s/image,
~14s for an 8-image batch.
Derive the intra-op thread count from available_parallelism() for the CPU EP
(leaving 2 logical cores free for the UI and a possible concurrent scan;
tagging is the lowest-priority worker, so heavier workers are idle when it
runs). DirectML/Auto keep a single thread. Measured ~2.7x on a representative
machine (14s -> 5.3s per 8-image batch); sublinear because swinv2 inference is
memory-bandwidth-bound. The selected thread count is logged at load.
LyAhn
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