dtourolle
5e46f52ad2
`kpn::ChannelDataSize<T>` is what a channel reports as bytes pushed, and its primary template returns `sizeof(T)`. It was never specialised in this repo — only in a KPN example — so every message type reported its header size. Each of them is a few vectors and a `cv::Mat` header owning megabytes on the heap, so a message carrying a full decoded frame was reported at roughly 200 bytes against 5.9 MB at 1080p. Four orders of magnitude. That is not a cosmetic stat. It is the one instrument for choosing channel capacities against a memory ceiling — the open half of AR-004 — and anyone who read the MB/s column to size a channel was reading fiction. The gap could not be measured with the tool that exists to measure it. Every message embeds `Frame source`, so this is not confined to the crop-carrying channels: the full decoded image rides the whole chain, and the byte figure now says so. Two things worth stating about what the number means. `cv::Mat` is reference-counted, so one frame referenced from several messages is counted once per reference — an upper bound on distinct bytes, and the right bound for "what would this channel keep alive if nothing else held it", which is the question a capacity answers. And an eof sentinel carries no image, so it costs only its header and is not charged for one. Declared against a forward declaration of the primary template rather than by including <kpn/channel.hpp>, so the message definitions keep no dependency on the framework carrying them, and any translation unit that can see these types also sees their sizes — which is what stops one channel being instantiated with the default while another gets the specialisation. Verified in both directions: with the specialisations removed, three of the five cases fail, `SceneAnnotation` reporting 40 bytes against the 37,632 its single 112x112 crop occupies. 145/145 with them. No behaviour change — this only corrects what is reported. Choosing capacities against the corrected numbers is the next commit. TRACES: AR-004 | SR-002
87 lines
3.7 KiB
CMake
87 lines
3.7 KiB
CMake
# ── Unit tests ────────────────────────────────────────────────────────────────
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# Pure, GPU-free, model-free tests. The similarity tests compile the GEMM backend
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# directly with SAE_GEMM_CPU so the suite builds and runs on a machine without a
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# GPU regardless of the main build's SAE_GEMM_BACKEND selection.
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find_package(Catch2 3 QUIET)
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if(NOT Catch2_FOUND)
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include(FetchContent)
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FetchContent_Declare(
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Catch2
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GIT_REPOSITORY https://github.com/catchorg/Catch2.git
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GIT_TAG v3.5.3
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)
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FetchContent_MakeAvailable(Catch2)
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endif()
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add_executable(sae_tests
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test_similarity.cpp
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test_calibration.cpp
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test_gallery_store.cpp
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test_face_utils.cpp
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test_track_gallery.cpp
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test_face_tracker.cpp
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test_track_registry.cpp
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test_face_detector_node.cpp
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test_scene_detector_node.cpp
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test_replay_fixtures.cpp
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test_embedding_dump.cpp
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test_audio_signature.cpp
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test_benchmark.cpp
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test_channel_bytes.cpp
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${CMAKE_SOURCE_DIR}/src/backends/gemm_backend.cpp
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${CMAKE_SOURCE_DIR}/src/gallery/gallery_store.cpp
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${CMAKE_SOURCE_DIR}/src/audio_signature.cpp
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${CMAKE_SOURCE_DIR}/src/gallery/embedder_stamp.cpp
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)
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target_include_directories(sae_tests PRIVATE ${CMAKE_SOURCE_DIR}/src)
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# SAE_GEMM_CPU: build the CPU reference GEMM regardless of the main backend.
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# SAE_MODELS_DIR: config.hpp (pulled in by track_gallery.hpp) bakes model paths.
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# SAE_TEST_FIXTURES_DIR: the audio golden vector is read from the source tree,
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# not copied, so the file the plugin repo shares is the file under test.
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# AR-026/AR-027: exercise the same kernel CI actually runs. Without this the
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# suite compiles the scalar fallback while the CPU builder image links OpenBLAS,
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# so the tested path and the shipped path would differ.
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find_package(PkgConfig QUIET)
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if(PkgConfig_FOUND)
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pkg_check_modules(OPENBLAS_T QUIET openblas)
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endif()
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if(OPENBLAS_T_FOUND)
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target_include_directories(sae_tests PRIVATE ${OPENBLAS_T_INCLUDE_DIRS})
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target_link_libraries(sae_tests PRIVATE ${OPENBLAS_T_LINK_LIBRARIES})
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elseif(NOT SAE_ALLOW_SCALAR_GEMM)
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# Same rule as the CPU backend itself: testing the scalar loop while the
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# shipped CPU path is OpenBLAS means the suite is not evidence about the
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# kernel that runs.
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message(FATAL_ERROR
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"OpenBLAS not found, and the unit tests compile the CPU GEMM kernel "
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"(AR-026). Install openblas-devel, or pass -DSAE_ALLOW_SCALAR_GEMM=ON "
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"to test the scalar fallback deliberately.")
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endif()
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target_compile_definitions(sae_tests PRIVATE
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$<$<BOOL:${OPENBLAS_T_FOUND}>:SAE_GEMM_CBLAS>
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SAE_GEMM_CPU
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SAE_MODELS_DIR="${SAE_MODELS_DIR}"
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SAE_TEST_FIXTURES_DIR="${CMAKE_CURRENT_SOURCE_DIR}/fixtures")
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# gallery_store.cpp + gallery_calibration.hpp use nlohmann/json and HDF5
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# (galleries are HDF5-native, see src/gallery/gallery_store.cpp); face_utils.hpp
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# and the calibration GEMM pull in OpenCV (calib3d/imgproc/core) via types.hpp.
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# ffmpeg_libs: audio_signature.cpp decodes the golden fixture (avformat/avcodec/
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# avutil/swresample). Still GPU-free — the audio path is pure CPU.
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target_link_libraries(sae_tests PRIVATE
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Catch2::Catch2WithMain
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nlohmann_json::nlohmann_json
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# VR-015: test_benchmark.cpp includes src/benchmark.hpp, which reads KPN's
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# diagnostics structs. Header-only — no KPN network is constructed here, so
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# the cost attribution stays testable on CI's GPU-free N100.
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kpn
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ffmpeg_libs
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${OpenCV_LIBS}
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${HDF5_CXX_LIBRARIES})
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target_include_directories(sae_tests PRIVATE ${HDF5_INCLUDE_DIRS})
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include(CTest)
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include(Catch)
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catch_discover_tests(sae_tests)
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