dtourolleandClaude Opus 5
61e487fbee
Tier T2 — composition rather than units. The registry tests construct awkward states directly; these feed the pieces real 480x360 footage with the cuts, gaps and crowded frames that synthetic input does not produce. Six cases: - fixture integrity: exact frame and face counts, contiguous face_offset, and the embedder identity each dump carries (GR-004). The counts are asserted exactly rather than approximately, which was impossible before AR-004 — what a lossy run dropped depended on timing. - determinism: replaying a fixture twice gives identical track ids and windows. This is the property the whole fixture strategy rests on; without it every golden output derived from a fixture is unreliable and the CI replay tier is worthless. - every face is assigned a track, and flush leaves nothing open — a track still live at EOF is a window that never reaches the output. - windows are well-formed and inside the clip. A window ends at the last sighting, so it can never extend past the footage that produced it. - a longer extinction window yields fewer, longer tracks. On the sparse fixture (140 faces over 385 frames) that is the difference the constant actually makes: absorbing a gap versus splitting a window. - the cut-heavy fixture still contains cuts. This guards the corpus, not the code: a regeneration that produced cut-free fixtures would leave the association tests passing while silently testing nothing. Driving the functors directly rather than through a KPN network is deliberate — no threads, no channels, no scheduling, so the same input gives the same output. Suite: 86 cases, 6106 assertions. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> TRACES: AR-004, AR-012, AR-013, VR-001, VR-002 | SR-002
57 lines
2.3 KiB
CMake
57 lines
2.3 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_replay_fixtures.cpp
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test_audio_signature.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 ${CMAKE_SOURCE_DIR}/src)
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# SAE_GEMM_CPU: build the CPU reference GEMM regardless of the main backend.
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# SAE_MODELS_DIR: config.hpp (pulled in by track_gallery.hpp) bakes model paths.
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# SAE_TEST_FIXTURES_DIR: the audio golden vector is read from the source tree,
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# not copied, so the file the plugin repo shares is the file under test.
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target_compile_definitions(sae_tests PRIVATE
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SAE_GEMM_CPU
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SAE_MODELS_DIR="${SAE_MODELS_DIR}"
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SAE_TEST_FIXTURES_DIR="${CMAKE_CURRENT_SOURCE_DIR}/fixtures")
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# gallery_store.cpp + gallery_calibration.hpp use nlohmann/json and HDF5
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# (galleries are HDF5-native, see src/gallery/gallery_store.cpp); face_utils.hpp
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# and the calibration GEMM pull in OpenCV (calib3d/imgproc/core) via types.hpp.
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# ffmpeg_libs: audio_signature.cpp decodes the golden fixture (avformat/avcodec/
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# avutil/swresample). Still GPU-free — the audio path is pure CPU.
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target_link_libraries(sae_tests PRIVATE
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Catch2::Catch2WithMain
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nlohmann_json::nlohmann_json
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ffmpeg_libs
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${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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