feat: YoloPosePostprocess plugin (graphsurgeon insertion)#321
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feat: YoloPosePostprocess plugin (graphsurgeon insertion)#321triple-mu wants to merge 4 commits into
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Raw uint8 image -> NCHW float input via a CUDA kernel (letterbox + RGB + /255), staged through a pinned host buffer for a fast async H2D. Opt-in, default-off; the CPU path is unchanged. CMake enables CUDA only when nvcc is found. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Opt-in custom plugin (libyolov8_plugins.so, BUILD_PLUGINS=ON) fusing YOLOv8 detection decode + NMS + top-k as an alternative to EfficientNMS_TRT; consumes the decoded boxes+scores and emits num_dets/bboxes/scores/labels. Plugin .so loaded via --plugin-lib / $YOLOV8_PLUGIN_LIB (Python ctypes + C++ dlopen); detect.cpp auto-detects raw vs End2End/plugin engines. export-det.py --plugin. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds the seg plugin (decode+NMS, gathers 32 mask coeffs; proto matmul stays on host) and extracts shared helpers into nms_common.cuh. NMS is parallel (EfficientNMS-style: one thread per candidate marks suppression vs higher-scoring overlaps; single-thread compaction gathers top-k) — det refactored onto the shared parallel suppression too. PostSeg.plugin + export-seg.py --plugin; engine.py finds the NMS node by attribute name anywhere in the graph; segment.cpp auto-detects. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Pose plugin attached to the raw ultralytics export via onnx_graphsurgeon (export-pose.py): the transposed head [1,A,C] is fed to YoloPosePostprocess, which does parallel NMS + top-k and gathers 17 keypoints in-engine. pose.cpp auto-detects plugin outputs by binding name. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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YoloPosePostprocess 插件（graphsurgeon 插桩，并行 NMS，自包含）
pose 走 ultralytics raw 导出（无 in-graph 解码模块），故
export-pose.py用 onnx_graphsurgeon 把插件节点接到已解码的 raw 输出：转置后的[1,A,C]喂给YoloPosePostprocess，在引擎内做并行 NMS + top-k 并 gather 17 个关键点。pose.cpp按 binding 名自动识别。建立了 obb 复用的 graphsurgeon 范式。验证：bus.jpg 同 4 目标、kps_sum 差 <0.02%；TRT 8.6/10.16/11.0 编译。
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