dtourolleandClaude Opus 5
d57e489e91
sae_audio exposes the shipped signature to Python. It compiles audio_signature.cpp directly against FFmpeg rather than linking sae_gallery: the signature needs no model, no OpenCV and no HDF5, so a module that dragged those in would make `import sae_audio` depend on a GPU-capable build of a path that is pure CPU DSP. The point of binding rather than porting is that a fingerprint is only useful if every implementation agrees byte for byte. A numpy port would be a third implementation, and the one nobody checks against the golden vector. VR-014 then recovers a known trim from real film audio rather than from the synthetic tone: 40 random in-cap offsets, every one recovered to the nearest frame, worst error 46 ms against a 500 ms budget — and 46 ms is the quantisation floor, not a result, since offsets land on whole 92.88 ms frames. The soft spot is tier labelling rather than accuracy. Sub-frame misalignment drags the score down (0.94-0.99 near a frame boundary, 0.69-0.73 at half a frame), demoting 27 of 40 correct alignments to `loose`. Allowing +/-1 frame of slack in the score fixes it: all 40 back to `audio` at min 0.906, false matches unmoved at 0.12-0.16, for 81 ms of the budget. The module stops at the producer's edge. Sliding one signature against another is the consumer's algorithm (server SPEC §3, and the jRay plugin implements it), so a caller writing that slide in numpy is not duplicating anything this repo owns. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> TRACES: IR-004, IR-005 | VR-014 | UT-105, UT-106, UT-107, UT-108 | SR-003
366 lines
18 KiB
CMake
366 lines
18 KiB
CMake
cmake_minimum_required(VERSION 3.21)
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project(scene_actor_extraction VERSION 0.1.0 LANGUAGES CXX)
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set(CMAKE_CXX_STANDARD 20)
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set(CMAKE_CXX_STANDARD_REQUIRED ON)
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set(CMAKE_CXX_EXTENSIONS OFF)
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# ── Dependencies ──────────────────────────────────────────────────────────────
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# KPN++ (pipeline backbone)
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set(KPN_BUILD_TESTS OFF CACHE BOOL "" FORCE)
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set(KPN_BUILD_PYTHON OFF CACHE BOOL "" FORCE)
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set(KPN_BUILD_EXAMPLES OFF CACHE BOOL "" FORCE)
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option(SAE_WEB_DEBUG "Enable KPN web debug UI (localhost:9090)" OFF)
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if(SAE_WEB_DEBUG)
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set(KPN_WEB_DEBUG ON CACHE BOOL "" FORCE)
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endif()
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add_subdirectory(external/KPN)
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# OpenCV (video decode, image ops, DNN inference, face detection)
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# Accept 4 or 5: the APIs used here are stable across both, and distros have
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# begun shipping 5.x as the default (Arch/CachyOS). find_package's version
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# argument is a minimum, but OpenCV's config rejects a 5.x install when 4 is
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# requested, so probe for 5 first and fall back to 4.
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find_package(OpenCV 5 QUIET COMPONENTS
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core imgproc imgcodecs videoio dnn objdetect highgui)
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if(NOT OpenCV_FOUND)
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find_package(OpenCV 4 REQUIRED COMPONENTS
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core imgproc imgcodecs videoio dnn objdetect highgui)
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endif()
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message(STATUS "OpenCV: ${OpenCV_VERSION}")
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# ── Model paths ───────────────────────────────────────────────────────────────
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# Defined early so the backend object libraries below can embed it.
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set(SAE_MODELS_DIR "${CMAKE_SOURCE_DIR}/models"
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CACHE PATH "Directory containing ONNX model files")
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# ── Backend selection ─────────────────────────────────────────────────────────
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# Two independent compile-time axes. The core application is agnostic to both:
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# only the matching backend .cpp (in src/backends/) is compiled, and the backend
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# headers (onnxruntime / NvInfer.h / cublas / rocblas) never reach core TUs.
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#
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# SAE_INFERENCE_BACKEND ORT → SCRFD + ArcFace via ONNX Runtime (.onnx models)
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# TRT → SCRFD + ArcFace via raw TensorRT (.engine files)
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# SAE_GEMM_BACKEND ROCM → gallery similarity GEMM via rocBLAS / HIP
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# CUDA → gallery similarity GEMM via cuBLAS / CUDA
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# CPU → portable reference GEMM (no GPU; CI / testing)
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set(SAE_INFERENCE_BACKEND "ORT" CACHE STRING "Inference backend: ORT | TRT")
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set(SAE_GEMM_BACKEND "ROCM" CACHE STRING "Gallery GEMM backend: ROCM | CUDA | CPU")
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set_property(CACHE SAE_INFERENCE_BACKEND PROPERTY STRINGS ORT TRT)
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set_property(CACHE SAE_GEMM_BACKEND PROPERTY STRINGS ROCM CUDA CPU)
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# Enable the ORT TensorRT/CUDA execution providers inside the ORT inference
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# backend (only meaningful when ORT was built with the TensorRT EP). Off by
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# default so ROCm/CPU builds don't reference unavailable EPs.
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option(SAE_ORT_TRT_EP "ORT backend: enable TensorRT/CUDA execution providers" OFF)
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# Back-compat: a legacy -DSAE_WITH_TRT=ON/OFF seeds the new vars (ON⇒TRT+CUDA,
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# OFF⇒ORT+ROCM) unless the user set them explicitly.
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if(DEFINED SAE_WITH_TRT)
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if(SAE_WITH_TRT)
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set(SAE_INFERENCE_BACKEND "TRT" CACHE STRING "" FORCE)
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set(SAE_GEMM_BACKEND "CUDA" CACHE STRING "" FORCE)
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else()
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set(SAE_INFERENCE_BACKEND "ORT" CACHE STRING "" FORCE)
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set(SAE_GEMM_BACKEND "ROCM" CACHE STRING "" FORCE)
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endif()
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message(STATUS "SAE_WITH_TRT=${SAE_WITH_TRT} (legacy) → "
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"SAE_INFERENCE_BACKEND=${SAE_INFERENCE_BACKEND} "
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"SAE_GEMM_BACKEND=${SAE_GEMM_BACKEND}")
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endif()
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if(NOT SAE_INFERENCE_BACKEND MATCHES "^(ORT|TRT)$")
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message(FATAL_ERROR "SAE_INFERENCE_BACKEND must be ORT or TRT (got '${SAE_INFERENCE_BACKEND}')")
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endif()
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if(NOT SAE_GEMM_BACKEND MATCHES "^(ROCM|CUDA|CPU)$")
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message(FATAL_ERROR "SAE_GEMM_BACKEND must be ROCM, CUDA or CPU (got '${SAE_GEMM_BACKEND}')")
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endif()
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# CUDA runtime is needed by both TRT inference and CUDA GEMM — find it once.
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function(sae_find_cudart)
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if(TARGET cudart_dep)
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return()
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endif()
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find_library(CUDART_LIB cudart
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HINTS /opt/cuda/lib64 /usr/local/cuda/lib64 /usr/lib)
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find_path(CUDART_INCLUDE cuda_runtime_api.h
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HINTS /opt/cuda/targets/x86_64-linux/include /opt/cuda/include
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/usr/local/cuda/include /usr/include)
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if(NOT (CUDART_LIB AND CUDART_INCLUDE))
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message(FATAL_ERROR "CUDA runtime not found (cudart=${CUDART_LIB} headers=${CUDART_INCLUDE}).")
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endif()
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add_library(cudart_dep INTERFACE)
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target_include_directories(cudart_dep INTERFACE "${CUDART_INCLUDE}")
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target_link_libraries(cudart_dep INTERFACE "${CUDART_LIB}")
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set_property(GLOBAL PROPERTY sae_cudart_found TRUE)
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endfunction()
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# ── Inference backend dependency: builds the `inference_backend` object lib ────
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if(SAE_INFERENCE_BACKEND STREQUAL "ORT")
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find_library(ORT_LIB onnxruntime REQUIRED
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HINTS /usr/lib64/rocm/lib /usr/lib /usr/local/lib)
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find_path(ORT_INCLUDE onnxruntime_cxx_api.h
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PATH_SUFFIXES onnxruntime
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HINTS /usr/lib64/rocm/include/onnxruntime /usr/include/onnxruntime /usr/local/include/onnxruntime
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/usr/lib64/rocm/include /usr/include /usr/local/include
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REQUIRED)
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# The include directive is <onnxruntime/onnxruntime_cxx_api.h>, so we need the
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# parent of the onnxruntime/ subdirectory on the include path.
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get_filename_component(ORT_INCLUDE_PARENT "${ORT_INCLUDE}" DIRECTORY)
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if(NOT EXISTS "${ORT_INCLUDE_PARENT}/onnxruntime")
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set(ORT_INCLUDE_PARENT "${ORT_INCLUDE}")
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endif()
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message(STATUS "Inference backend: ORT (${ORT_LIB} headers: ${ORT_INCLUDE_PARENT})")
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add_library(inference_backend OBJECT src/backends/ort_backend.cpp)
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set_target_properties(inference_backend PROPERTIES POSITION_INDEPENDENT_CODE ON)
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target_include_directories(inference_backend PRIVATE src "${ORT_INCLUDE_PARENT}")
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target_link_libraries(inference_backend PRIVATE ${OpenCV_LIBS} "${ORT_LIB}")
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target_compile_definitions(inference_backend PRIVATE
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SAE_MODELS_DIR="${SAE_MODELS_DIR}"
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$<$<BOOL:${SAE_ORT_TRT_EP}>:SAE_ORT_WITH_TRT_EP>)
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else() # TRT
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find_library(NVINFER_LIB nvinfer
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HINTS /usr/lib /usr/local/lib /opt/tensorrt/lib)
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find_path(NVINFER_INCLUDE NvInfer.h
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HINTS /usr/include /usr/local/include /opt/tensorrt/include)
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if(NOT (NVINFER_LIB AND NVINFER_INCLUDE))
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message(FATAL_ERROR
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"TensorRT not found (nvinfer=${NVINFER_LIB} headers=${NVINFER_INCLUDE}). "
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"Pass -DSAE_INFERENCE_BACKEND=ORT to load .onnx models without TensorRT.")
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endif()
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sae_find_cudart()
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message(STATUS "Inference backend: TRT (${NVINFER_LIB})")
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add_library(inference_backend OBJECT src/backends/trt_backend.cpp)
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set_target_properties(inference_backend PROPERTIES POSITION_INDEPENDENT_CODE ON)
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target_include_directories(inference_backend PRIVATE src "${NVINFER_INCLUDE}")
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target_link_libraries(inference_backend PRIVATE
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${OpenCV_LIBS} "${NVINFER_LIB}" cudart_dep)
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target_compile_definitions(inference_backend PRIVATE
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SAE_MODELS_DIR="${SAE_MODELS_DIR}")
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endif()
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# ── GEMM backend dependency: builds the `gemm_backend` object lib ──────────────
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if(SAE_GEMM_BACKEND STREQUAL "CPU")
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# Portable reference GEMM: no GPU libraries, no headers. Used for CI and as
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# the correctness oracle for the CUDA/ROCm backends.
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message(STATUS "GEMM backend: CPU (portable reference, no GPU)")
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add_library(gemm_backend OBJECT src/backends/gemm_backend.cpp)
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set_target_properties(gemm_backend PROPERTIES POSITION_INDEPENDENT_CODE ON)
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target_include_directories(gemm_backend PRIVATE src)
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target_compile_definitions(gemm_backend PRIVATE SAE_GEMM_CPU)
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# AR-026/AR-027: back the CPU path with OpenBLAS when present. Optional, so
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# the build gains no hard dependency — but without it the fallback is a
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# scalar loop, which does not hold up against a library-scale gallery, and
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# the CPU path is exactly what CI (no GPU) and the cpu builder image use.
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find_package(PkgConfig QUIET)
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if(PkgConfig_FOUND)
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pkg_check_modules(OPENBLAS QUIET openblas)
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endif()
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if(OPENBLAS_FOUND)
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message(STATUS "GEMM backend: CPU + OpenBLAS ${OPENBLAS_VERSION}")
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target_compile_definitions(gemm_backend PRIVATE SAE_GEMM_CBLAS)
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target_include_directories(gemm_backend PRIVATE ${OPENBLAS_INCLUDE_DIRS})
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target_link_libraries(gemm_backend PRIVATE ${OPENBLAS_LINK_LIBRARIES})
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else()
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message(WARNING "GEMM backend: CPU scalar fallback — OpenBLAS not found. "
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"Correct, but slow on a large gallery (AR-027).")
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endif()
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elseif(SAE_GEMM_BACKEND STREQUAL "CUDA")
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find_library(CUBLAS_LIB cublas
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HINTS /opt/cuda/targets/x86_64-linux/lib /opt/cuda/lib64
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/usr/local/cuda/lib64 /usr/lib)
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if(NOT CUBLAS_LIB)
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message(FATAL_ERROR "cuBLAS not found (cublas=${CUBLAS_LIB}).")
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endif()
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sae_find_cudart()
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message(STATUS "GEMM backend: CUDA (${CUBLAS_LIB})")
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add_library(gemm_backend OBJECT src/backends/gemm_backend.cpp)
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set_target_properties(gemm_backend PROPERTIES POSITION_INDEPENDENT_CODE ON)
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target_include_directories(gemm_backend PRIVATE src)
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target_link_libraries(gemm_backend PRIVATE "${CUBLAS_LIB}" cudart_dep)
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target_compile_definitions(gemm_backend PRIVATE SAE_GEMM_CUDA)
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else() # ROCM
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find_library(ROCBLAS_LIB rocblas
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HINTS /usr/lib64/rocm/lib /usr/lib64 /usr/local/lib)
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find_path(ROCBLAS_INCLUDE rocblas/rocblas.h
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HINTS /usr/lib64/rocm/include /usr/include /usr/local/include)
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find_library(HIP_LIB amdhip64
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HINTS /usr/lib64/rocm/lib /usr/lib64 /usr/local/lib)
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find_path(HIP_INCLUDE hip/hip_runtime_api.h
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HINTS /usr/lib64/rocm/include /usr/include /usr/local/include)
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if(NOT (ROCBLAS_LIB AND ROCBLAS_INCLUDE AND HIP_LIB AND HIP_INCLUDE))
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message(FATAL_ERROR
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"rocBLAS or HIP runtime not found "
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"(rocblas=${ROCBLAS_LIB} headers=${ROCBLAS_INCLUDE} "
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"hip=${HIP_LIB} headers=${HIP_INCLUDE}). "
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"Install rocblas-devel and hip-devel (or pass -DSAE_GEMM_BACKEND=CUDA).")
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endif()
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message(STATUS "GEMM backend: ROCM (${ROCBLAS_LIB})")
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add_library(gemm_backend OBJECT src/backends/gemm_backend.cpp)
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set_target_properties(gemm_backend PROPERTIES POSITION_INDEPENDENT_CODE ON)
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target_include_directories(gemm_backend PRIVATE src "${ROCBLAS_INCLUDE}" "${HIP_INCLUDE}")
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target_link_libraries(gemm_backend PRIVATE "${ROCBLAS_LIB}" "${HIP_LIB}")
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# HIP headers require the platform to be declared explicitly when compiled with g++.
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target_compile_definitions(gemm_backend PRIVATE SAE_GEMM_ROCM __HIP_PLATFORM_AMD__)
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endif()
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# FFmpeg (hwaccel video decode: CUDA/VAAPI, runtime-detected + swscale colour
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# conversion). Hwaccel support is built into libavcodec/libavutil; no extra
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# libraries are needed here.
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# libswresample is the audio side of the same dependency — downmix + resample
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# for the audio signature (IR-004, src/audio_signature.cpp). Not a new project
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# dependency: it ships with the libav* set already required above.
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find_package(PkgConfig REQUIRED)
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pkg_check_modules(AVFORMAT REQUIRED libavformat)
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pkg_check_modules(AVCODEC REQUIRED libavcodec)
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pkg_check_modules(AVUTIL REQUIRED libavutil)
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pkg_check_modules(SWSCALE REQUIRED libswscale)
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pkg_check_modules(SWRESAMPLE REQUIRED libswresample)
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add_library(ffmpeg_libs INTERFACE)
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target_compile_options(ffmpeg_libs INTERFACE
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${AVFORMAT_CFLAGS_OTHER} ${AVCODEC_CFLAGS_OTHER}
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${AVUTIL_CFLAGS_OTHER} ${SWSCALE_CFLAGS_OTHER}
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${SWRESAMPLE_CFLAGS_OTHER})
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target_include_directories(ffmpeg_libs INTERFACE
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${AVFORMAT_INCLUDE_DIRS} ${AVCODEC_INCLUDE_DIRS}
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${AVUTIL_INCLUDE_DIRS} ${SWSCALE_INCLUDE_DIRS}
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${SWRESAMPLE_INCLUDE_DIRS})
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target_link_libraries(ffmpeg_libs INTERFACE
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${AVFORMAT_LIBRARIES} ${AVCODEC_LIBRARIES}
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${AVUTIL_LIBRARIES} ${SWSCALE_LIBRARIES}
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${SWRESAMPLE_LIBRARIES})
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message(STATUS "FFmpeg: avformat=${AVFORMAT_VERSION} avcodec=${AVCODEC_VERSION} "
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"swresample=${SWRESAMPLE_VERSION}")
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# nlohmann/json (gallery + output serialisation)
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include(FetchContent)
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FetchContent_Declare(
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nlohmann_json
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GIT_REPOSITORY https://github.com/nlohmann/json.git
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GIT_TAG v3.11.3
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GIT_SHALLOW TRUE
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)
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FetchContent_MakeAvailable(nlohmann_json)
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# nanobind (Python bindings for the sae_embed module)
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find_package(Python 3.8 COMPONENTS Interpreter Development.Module REQUIRED)
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FetchContent_Declare(
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nanobind
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GIT_REPOSITORY https://github.com/wjakob/nanobind.git
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GIT_TAG v2.4.0
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GIT_SHALLOW TRUE
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)
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FetchContent_MakeAvailable(nanobind)
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# ── Model paths ───────────────────────────────────────────────────────────────
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set(SAE_MODELS_DIR "${CMAKE_SOURCE_DIR}/models"
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CACHE PATH "Directory containing ONNX model files")
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# ── Shared library: gallery store + compiled-in backends ──────────────────────
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# The backend object libraries carry their own ORT/TRT/CUDA/ROCm linkage and
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# headers; sae_gallery re-exports those object files so every binary that links
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# sae_gallery gets the chosen backend without ever seeing its headers.
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# HDF5 (C++) — gallery fast-load path + embedding dump. Found here so sae_gallery
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# (gallery_store.cpp) can link it; scene_analyze/dump_embeddings reuse the same vars.
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find_package(HDF5 REQUIRED COMPONENTS CXX)
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add_library(sae_gallery STATIC
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src/gallery/gallery_store.cpp
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src/gallery/gallery_builder.cpp
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src/audio_signature.cpp # IR-004 — content-derived audio signature
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src/gallery/embedder_stamp.cpp # GR-004 — gallery/embedder binding
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)
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set_target_properties(sae_gallery PROPERTIES POSITION_INDEPENDENT_CODE ON)
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target_include_directories(sae_gallery PUBLIC src ${HDF5_INCLUDE_DIRS})
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target_link_libraries(sae_gallery PUBLIC
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kpn
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${OpenCV_LIBS}
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nlohmann_json::nlohmann_json
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inference_backend
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gemm_backend
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ffmpeg_libs
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${HDF5_CXX_LIBRARIES}
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)
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target_compile_definitions(sae_gallery PUBLIC
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SAE_MODELS_DIR="${SAE_MODELS_DIR}"
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)
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# ── embed_faces — image → embedding JSON (used by gallery builder scripts) ────
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add_executable(embed_faces src/embed_faces.cpp)
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target_link_libraries(embed_faces PRIVATE sae_gallery)
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# ── sae_embed — Python module: load SCRFD+ArcFace once, embed many images ───
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nanobind_add_module(sae_embed src/python_bindings.cpp)
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target_link_libraries(sae_embed PRIVATE sae_gallery)
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# ── sae_kpn — Python module: run the real downstream nodes over dumped embeddings ─
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# Assembles face_tracker/identity_matcher/scene_tracker in a Python-driven KPN
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# network (KPN_BUILD_PYTHON is enabled per-TU inside the .cpp). Powers the
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# threshold-sweep optimizer in scripts/optimizer/.
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nanobind_add_module(sae_kpn src/kpn_bindings.cpp)
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target_link_libraries(sae_kpn PRIVATE sae_gallery)
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# ── sae_audio — Python module: the v1 audio signature (IR-004) ────────────────
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# Compiles audio_signature.cpp directly and links only FFmpeg, rather than
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# linking sae_gallery: the signature needs no model, no OpenCV and no HDF5, and
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# a module that dragged all three in would make `import sae_audio` depend on a
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# GPU-capable build of a repo whose audio path is pure CPU DSP. tests/ compiles
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# the same source the same way, for the same reason.
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nanobind_add_module(sae_audio src/audio_bindings.cpp src/audio_signature.cpp)
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target_include_directories(sae_audio PRIVATE src)
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target_link_libraries(sae_audio PRIVATE ffmpeg_libs)
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# HDF5 already found above (before sae_gallery); vars HDF5_CXX_LIBRARIES / _INCLUDE_DIRS
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# are reused by scene_analyze / dump_embeddings below.
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# ── analyze — main analysis binary ───────────────────────────────────────────
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add_executable(scene_analyze src/main.cpp)
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target_link_libraries(scene_analyze PRIVATE sae_gallery ${HDF5_CXX_LIBRARIES})
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target_include_directories(scene_analyze PRIVATE ${HDF5_INCLUDE_DIRS})
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# ── analyze_debug — same binary with debug frame/crop output ─────────────────
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add_executable(scene_analyze_debug src/main.cpp)
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target_link_libraries(scene_analyze_debug PRIVATE sae_gallery ${HDF5_CXX_LIBRARIES})
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target_include_directories(scene_analyze_debug PRIVATE ${HDF5_INCLUDE_DIRS})
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target_compile_definitions(scene_analyze_debug PRIVATE SAE_DEBUG=1)
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# ── dump_embeddings — standalone embedding dumper, NO gallery/matcher ─────────
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# Front-half only (decode→detect→align→embed→HDF5) for the optimizer replay corpus
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# and model bake-off. Skips gallery load + calibration (~24s/run faster).
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add_executable(dump_embeddings src/dump_embeddings.cpp)
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target_link_libraries(dump_embeddings PRIVATE sae_gallery ${HDF5_CXX_LIBRARIES})
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target_include_directories(dump_embeddings PRIVATE ${HDF5_INCLUDE_DIRS})
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|
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# ── scene_preview — live annotated display while analysing ───────────────────
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add_executable(scene_preview src/scene_preview.cpp)
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|
target_link_libraries(scene_preview PRIVATE sae_gallery)
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|
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# ── build_gallery — offline gallery construction tool ────────────────────────
|
|
add_executable(build_gallery src/build_gallery.cpp)
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|
target_link_libraries(build_gallery PRIVATE sae_gallery)
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|
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|
# ── Optional: web debug UI for pipeline introspection ────────────────────────
|
|
if(SAE_WEB_DEBUG)
|
|
kpn_target_enable_web_debug(scene_analyze)
|
|
kpn_target_enable_web_debug(scene_analyze_debug)
|
|
kpn_target_enable_web_debug(scene_preview)
|
|
endif()
|
|
|
|
# ── Tests ─────────────────────────────────────────────────────────────────────
|
|
option(SAE_BUILD_TESTS "Build unit tests (GPU-free)" OFF)
|
|
if(SAE_BUILD_TESTS)
|
|
enable_testing()
|
|
add_subdirectory(tests)
|
|
endif()
|
|
|
|
message(STATUS "OpenCV ${OpenCV_VERSION} found")
|
|
message(STATUS "Models dir: ${SAE_MODELS_DIR}")
|