diff --git a/.github/workflows/robot-vision-ci.yml b/.github/workflows/robot-vision-ci.yml new file mode 100644 index 0000000..2d88ba9 --- /dev/null +++ b/.github/workflows/robot-vision-ci.yml @@ -0,0 +1,37 @@ +name: robot-vision-ci + +on: + push: + paths: ["RobotVisionPlatform/**", ".github/workflows/robot-vision-*.yml"] + pull_request: + paths: ["RobotVisionPlatform/**", ".github/workflows/robot-vision-*.yml"] + +jobs: + device: + runs-on: ubuntu-24.04 + defaults: + run: + working-directory: RobotVisionPlatform + steps: + - uses: actions/checkout@v4 + - name: Configure + run: cmake -S device -B device/build -G Ninja -DCMAKE_BUILD_TYPE=Release -DRV_ENABLE_BOOST_HTTP=OFF + - name: Build + run: cmake --build device/build --parallel + - name: Test + run: ctest --test-dir device/build --output-on-failure + - name: Test model tooling + run: python3 -m unittest discover -s device/tools/model/tests -v + + server: + runs-on: ubuntu-24.04 + defaults: + run: + working-directory: RobotVisionPlatform + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-dotnet@v4 + with: + dotnet-version: "10.0.x" + - run: dotnet build server/RobotVision.Server.slnx --configuration Release + - run: dotnet run --project server/tests/RobotVision.Server.Tests --configuration Release diff --git a/.github/workflows/robot-vision-release-device.yml b/.github/workflows/robot-vision-release-device.yml new file mode 100644 index 0000000..6568fd5 --- /dev/null +++ b/.github/workflows/robot-vision-release-device.yml @@ -0,0 +1,25 @@ +name: robot-vision-release-device + +on: + workflow_dispatch: + push: + tags: ["robot-vision-v*"] + +jobs: + jetson-arm64: + # Native Jetson build avoids shipping an incompatible TensorRT engine/runtime. + runs-on: [self-hosted, linux, arm64, jetson] + defaults: + run: + working-directory: RobotVisionPlatform + steps: + - uses: actions/checkout@v4 + - run: cmake -S device -B device/build -G Ninja -DCMAKE_BUILD_TYPE=Release + - run: cmake --build device/build --parallel + - run: ctest --test-dir device/build --output-on-failure + - run: cmake --install device/build --prefix device/stage + - uses: actions/upload-artifact@v4 + with: + name: robot-vision-device-linux-arm64 + path: RobotVisionPlatform/device/stage + diff --git a/RobotVisionPlatform/.gitattributes b/RobotVisionPlatform/.gitattributes new file mode 100644 index 0000000..afba686 --- /dev/null +++ b/RobotVisionPlatform/.gitattributes @@ -0,0 +1,5 @@ +*.sh text eol=lf +*.yml text eol=lf +*.yaml text eol=lf +*.service text eol=lf +*.proto text eol=lf diff --git a/RobotVisionPlatform/.gitignore b/RobotVisionPlatform/.gitignore new file mode 100644 index 0000000..b835185 --- /dev/null +++ b/RobotVisionPlatform/.gitignore @@ -0,0 +1,15 @@ +device/build*/ +device/stage/ +**/bin/ +**/obj/ +.vs/ +.idea/ +*.user +*.log +.env +.venv/ +__pycache__/ +secrets/ +**/*.engine +**/*.plan +device/artifacts/ diff --git a/RobotVisionPlatform/README.md b/RobotVisionPlatform/README.md new file mode 100644 index 0000000..faf6df1 --- /dev/null +++ b/RobotVisionPlatform/README.md @@ -0,0 +1,142 @@ +# Robot Vision Platform + +Jetson Orin Nano Super에서 비전 추론을 수행하고, 서버에서 장치·영상·이벤트를 관제하며, 모델을 학습·배포·롤백하는 단일 저장소(monorepo)입니다. + +> 현재 단계는 **MVP 기반 골격**입니다. 합성 카메라/탐지기로 전체 파이프라인과 서버 수집을 먼저 검증하고, 실제 CSI/USB 카메라·TensorRT·WebRTC 구현을 어댑터로 교체합니다. + +## 처음 보는 분은 여기부터 + +로봇 비전이나 Jetson이 처음이라면 전체 문서를 한 번에 이해할 필요가 없습니다. + +1. [초보자 시작 가이드](docs/getting-started-for-beginners.md)를 따라 서버를 실행합니다. +2. 브라우저에서 장치가 표시되는 것을 확인합니다. +3. [용어집](docs/glossary.md)에서 낯선 단어만 찾아봅니다. +4. 이후 관심 분야에 따라 `tips`, `device`, `server` 문서로 이동합니다. + +### 지금 바로 할 수 있는 것 + +- .NET 서버 실행과 health check +- 예제 탐지 이벤트 전송 +- 웹 화면에서 장치 ID, 모델 버전, 탐지 결과 확인 +- 합성 카메라를 사용하는 C++20 파이프라인 빌드와 테스트 + +### 아직 구현되지 않은 것 + +- Jetson CSI/USB 카메라의 실제 프레임 입력 +- TensorRT 모델의 실제 객체 탐지 +- WebRTC 실시간 영상 재생 +- gRPC/mTLS 장치 연결과 자동 모델 업데이트 +- VLM/VLA 분석과 로봇 동작 명령 + +이 기능들은 오류가 아니라 아래 TODO에 따라 구현할 다음 단계입니다. + +## 목표 아키텍처 + +```text +Camera -> bounded queue -> TensorRT/DeepStream -> overlay/encoder -> WebRTC (video) + | | + +-> gRPC events/status --+--> ASP.NET Core -> SignalR -> MAUI/Web UI + ^ | + +------ commands --------+ + +Training -> ONNX -> TensorRT engine build on target -> signed model registry -> staged OTA/rollback +``` + +영상과 제어 경로를 분리합니다. H.264/H.265 하드웨어 인코딩 영상은 WebRTC(저지연) 또는 RTSP/SRT(불안정한 WAN)로 보내고, 작은 구조화 데이터와 명령은 protobuf/gRPC로 보냅니다. 서버는 ASP.NET Core, 관제 앱은 UI 공유가 쉬운 .NET MAUI Blazor Hybrid를 기준으로 합니다. + +## 저장소 구조 + +```text +RobotVisionPlatform/ +├── docs/ # 통합 설계·운영·초기 릴리스 가이드 +├── tips/ # 학습 기초와 실전 가이드 +├── device/ # Jetson에서 실행되는 C++20 프로젝트 +├── server/ # ASP.NET Core 수집 서버와 MAUI 관제 앱 +├── shared/proto/ # 장치/서버 공용 protobuf 계약 +├── reference/ # 함수·타입·명령을 이름으로 찾는 개발 사전 +├── deploy/ # Compose, systemd, 배포 설정 +├── scripts/ # 개발·검증 스크립트 +└── ../.github/workflows/ # GitHub가 인식하는 저장소 루트 CI/릴리스 자동화 +``` + +## 단계별 TODO + +### Phase 0 — 기반과 계약 + +- [x] 모노레포 폴더와 문서 체계 생성 +- [x] 장치 등록·상태·탐지 이벤트용 protobuf 초안 정의 +- [x] C++20 파이프라인 코어와 서버 수집 API 골격 생성 +- [x] 기본 CI, Docker Compose, systemd 배포 파일 생성 +- [ ] 장치 PKI(mTLS), 인증서 발급/회전 정책 확정 +- [ ] 카메라별 해상도·FPS·지연·보존 기간 SLO 확정 + +### Phase 1 — 단일 장치 MVP + +- [ ] JetPack/DeepStream 호환표를 실제 장치 이미지에 고정 +- [ ] CSI 또는 USB 카메라 GStreamer source 어댑터 구현 +- [ ] 기준 모델을 ONNX로 내보내고 장치에서 TensorRT engine 생성 +- [ ] 실제 탐지 메타데이터를 gRPC `Connect` 스트림으로 전송 +- [ ] NVENC H.264 + WebRTC 송출 및 서버 signaling 구현 +- [ ] 오프라인 store-and-forward와 재접속 검증 + +### Phase 2 — 관제와 운영 + +- [ ] MAUI 대시보드에 WebRTC 플레이어와 탐지 오버레이 연결 +- [ ] PostgreSQL/TimescaleDB 이벤트 저장소와 S3 호환 클립 저장소 연결 +- [ ] OpenTelemetry metrics/logs/traces 및 장치 health alert 추가 +- [ ] 원격 설정, 재시작, 로그 묶음 수집 명령 추가 +- [ ] 네트워크 손실·재부팅·전원 차단 chaos test 자동화 + +### Phase 3 — 모델 수명주기 + +- [ ] 데이터셋 버전 관리(DVC 또는 lakeFS)와 라벨 검수 흐름 확정 +- [ ] 학습/평가 파이프라인과 모델 승인 게이트 구축 +- [ ] 모델 manifest, SHA-256, 서명, 호환성 검증 추가 +- [ ] canary → cohort → fleet 단계 배포와 자동 롤백 구현 +- [ ] drift/오탐/미탐 샘플링 및 active-learning 루프 구현 + +### Phase 4 — VLM/VLA 확장 + +- [ ] 이벤트 기반 keyframe/clip 추출(상시 VLM 실행 금지) +- [ ] 서버 GPU의 VLM batch 분석과 구조화 결과 스키마 정의 +- [ ] VLA 명령은 정책 엔진·허용 목록·human-in-the-loop를 통과하도록 구현 +- [ ] 안전 정지 및 명령 감사 로그 검증 + +## 빠른 시작 + +필수 도구는 CMake 3.22+, C++20 컴파일러, .NET 10 SDK, Docker입니다. + +```powershell +./scripts/build.ps1 +./scripts/test.ps1 +dotnet run --project server/src/RobotVision.Server.Api +``` + +명령은 `RobotVisionPlatform` 폴더에서 실행합니다. 자세한 준비물과 예상 결과는 [초보자 시작 가이드](docs/getting-started-for-beginners.md)에 있습니다. + +서버 실행 후 `http://localhost:5080`, health check는 `/health`, 장치 목록은 `/api/devices`입니다. 장치 데모는 별도 터미널에서 실행합니다. Linux/Ninja는 `device/build/robot_vision_device`, Visual Studio generator는 보통 `device/build/Release/robot_vision_device.exe`에 생성됩니다. Boost HTTP adapter를 빌드했다면 `--server-host 127.0.0.1 --server-port 5080`을 더해 MVP end-to-end 전송을 확인할 수 있습니다. + +## 설계 문서 + +- [시스템 아키텍처](docs/architecture.md) +- [초기 릴리스 가이드](docs/initial-release-guide.md) +- [Jetson 준비 및 배포](docs/jetson-deployment.md) +- [통신과 영상 전송](docs/streaming-and-protocols.md) +- [모델 수명주기](docs/model-lifecycle.md) +- [보안 및 운영](docs/security-and-operations.md) +- [학습 팁 인덱스](tips/README.md) +- [초보자 시작 가이드](docs/getting-started-for-beginners.md) +- [용어집](docs/glossary.md) +- [문제 해결](docs/troubleshooting.md) +- [ONNX/TensorRT 모델 배포 가이드](device/guides/README.md) +- [함수·타입·명령 Reference 사전](reference/README.md) + +## 기술 기준 + +- Edge: C++20, CMake, `std::jthread`/`std::stop_token`, 선택형 Boost, GStreamer/DeepStream/TensorRT +- Control plane: protobuf + gRPC bidirectional streaming, TLS/mTLS +- Media plane: WebRTC + H.264 우선, RTSP/SRT 대안, keyframe JPEG는 디버그용 +- Server: .NET 10, ASP.NET Core, SignalR, MAUI Blazor Hybrid +- Observability: OpenTelemetry, Prometheus 호환 metrics, 구조화 로그 + +버전 숫자는 예시보다 실제 JetPack–DeepStream 호환표를 우선합니다. 자세한 결정 근거와 공식 링크는 각 문서에 있습니다. diff --git a/RobotVisionPlatform/deploy/compose/docker-compose.yml b/RobotVisionPlatform/deploy/compose/docker-compose.yml new file mode 100644 index 0000000..40ce015 --- /dev/null +++ b/RobotVisionPlatform/deploy/compose/docker-compose.yml @@ -0,0 +1,10 @@ +services: + api: + build: + context: ../.. + dockerfile: server/src/RobotVision.Server.Api/Dockerfile + ports: + - "5080:5080" + environment: + ASPNETCORE_ENVIRONMENT: Production + restart: unless-stopped diff --git a/RobotVisionPlatform/deploy/systemd/robot-vision-device.service b/RobotVisionPlatform/deploy/systemd/robot-vision-device.service new file mode 100644 index 0000000..35a3a6b --- /dev/null +++ b/RobotVisionPlatform/deploy/systemd/robot-vision-device.service @@ -0,0 +1,24 @@ +[Unit] +Description=Robot Vision Edge Agent +After=network-online.target +Wants=network-online.target + +[Service] +Type=simple +User=robotvision +Group=robotvision +SupplementaryGroups=video render +ExecStart=/opt/robot-vision/bin/robot_vision_device --device-id jetson-dev-001 +Restart=on-failure +RestartSec=3 +NoNewPrivileges=true +PrivateTmp=true +ProtectSystem=strict +ProtectHome=true +StateDirectory=robot-vision +LogsDirectory=robot-vision +DeviceAllow=/dev/video0 rw +EnvironmentFile=-/etc/robot-vision/environment + +[Install] +WantedBy=multi-user.target diff --git a/RobotVisionPlatform/device/CMakeLists.txt b/RobotVisionPlatform/device/CMakeLists.txt new file mode 100644 index 0000000..4500356 --- /dev/null +++ b/RobotVisionPlatform/device/CMakeLists.txt @@ -0,0 +1,47 @@ +cmake_minimum_required(VERSION 3.22) +project(robot_vision_device VERSION 0.1.0 LANGUAGES CXX) + +option(RV_BUILD_TESTS "Build device unit tests" ON) +option(RV_ENABLE_BOOST_HTTP "Enable Boost.Beast HTTP event sink when Boost is available" ON) + +# rv_core contains portable pipeline logic; hardware/network adapters are optional sources. +add_library(rv_core + src/pipeline.cpp + src/synthetic_adapters.cpp +) +target_include_directories(rv_core PUBLIC include) +target_compile_features(rv_core PUBLIC cxx_std_20) +set_target_properties(rv_core PROPERTIES CXX_EXTENSIONS OFF) + +if(MSVC) + target_compile_options(rv_core PRIVATE /W4 /permissive-) +else() + target_compile_options(rv_core PRIVATE -Wall -Wextra -Wpedantic) +endif() + +if(RV_ENABLE_BOOST_HTTP) + # Keep the core build usable when Boost is absent, but add the HTTP smoke-test sink when found. + find_package(Boost 1.74 QUIET COMPONENTS system) + if(Boost_FOUND) + target_sources(rv_core PRIVATE src/http_event_sink.cpp) + target_link_libraries(rv_core PUBLIC Boost::system) + target_compile_definitions(rv_core PUBLIC RV_HAS_BOOST_HTTP=1) + else() + message(STATUS "Boost.System not found; HTTP sink disabled") + endif() +endif() + +add_executable(robot_vision_device src/main.cpp) +target_link_libraries(robot_vision_device PRIVATE rv_core) + +include(GNUInstallDirs) +install(TARGETS robot_vision_device RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}) +install(FILES config/device.example.toml DESTINATION ${CMAKE_INSTALL_SYSCONFDIR}/robot-vision) + +if(RV_BUILD_TESTS) + # CTest runs this dependency-free executable on developer PCs and CI. + enable_testing() + add_executable(rv_core_tests tests/core_tests.cpp) + target_link_libraries(rv_core_tests PRIVATE rv_core) + add_test(NAME rv_core_tests COMMAND rv_core_tests) +endif() diff --git a/RobotVisionPlatform/device/README.md b/RobotVisionPlatform/device/README.md new file mode 100644 index 0000000..53e7830 --- /dev/null +++ b/RobotVisionPlatform/device/README.md @@ -0,0 +1,31 @@ +# Device Agent + +C++20 기반 edge 파이프라인입니다. 기본 빌드는 외부 SDK 없이 합성 입력으로 실행됩니다. + +```bash +cmake -S . -B build -DCMAKE_BUILD_TYPE=Release +cmake --build build --parallel +ctest --test-dir build --output-on-failure +./build/robot_vision_device --device-id jetson-dev-001 +``` + +Boost.System을 포함해 빌드했다면 실행 중인 MVP 서버로 바로 전송할 수 있습니다. + +```bash +./build/robot_vision_device --device-id jetson-dev-001 --server-host 127.0.0.1 --server-port 5080 +``` + +실제 장치 구현 순서는 `ICamera`의 GStreamer/NvArgus adapter, `IDetector`의 TensorRT/DeepStream adapter, `IEventSink`의 gRPC/mTLS adapter, `IVideoPublisher`의 WebRTC adapter입니다. 코어가 CPU `pixels`를 정의하지만 Jetson adapter에서는 NVMM/CUDA handle을 별도 frame payload로 확장해 zero-copy를 유지합니다. + +Boost.System이 발견되면 Boost.Asio/Beast HTTP sink가 함께 컴파일됩니다. 이 sink는 서버 계약 smoke test용이며 장기 운영 경로는 공용 proto의 bidi gRPC입니다. + +## 학습 모델을 장치에 넣기 + +모델 배포는 `PyTorch → ONNX → ONNX Runtime 검증 → Jetson TensorRT engine → bundle 배포` 순서로 진행합니다. 처음에는 [Device 모델 배포 가이드](guides/README.md)를 읽으세요. + +- ONNX 검사·실행 도구: `tools/model/` +- TensorRT 변환 스크립트: `tools/model/build_tensorrt_engine.sh` +- DeepStream 설정 예제: `config/deepstream/config_infer_primary.example.txt` +- 모델 bundle 예제: `models/example/` + +현재 `DemoDetector`는 합성 결과를 만드는 테스트 구현입니다. 실제 TensorRT/DeepStream detector를 `IDetector`에 연결하는 작업은 ONNX와 전·후처리 parity가 확인된 뒤 진행합니다. diff --git a/RobotVisionPlatform/device/config/deepstream/config_infer_primary.example.txt b/RobotVisionPlatform/device/config/deepstream/config_infer_primary.example.txt new file mode 100644 index 0000000..d807a54 --- /dev/null +++ b/RobotVisionPlatform/device/config/deepstream/config_infer_primary.example.txt @@ -0,0 +1,26 @@ +# Copy this file into a model version directory and replace every path/value. +[property] +gpu-id=0 +onnx-file=/opt/robot-vision/models/example-detector/0.1.0/model.onnx +model-engine-file=/opt/robot-vision/models/example-detector/0.1.0/model.fp16.engine +labelfile-path=/opt/robot-vision/models/example-detector/0.1.0/labels.txt +batch-size=1 +network-mode=2 +network-type=0 +num-detected-classes=2 +gie-unique-id=1 +interval=0 +model-color-format=0 +net-scale-factor=0.00392156862745098 +maintain-aspect-ratio=1 +symmetric-padding=1 +cluster-mode=2 + +# Most detectors require a model-specific output parser. Uncomment after building it. +# custom-lib-path=/opt/robot-vision/lib/libnvdsinfer_custom_robotvision.so +# parse-bbox-func-name=NvDsInferParseRobotVision + +[class-attrs-all] +pre-cluster-threshold=0.50 +nms-iou-threshold=0.45 +topk=300 diff --git a/RobotVisionPlatform/device/config/device.example.toml b/RobotVisionPlatform/device/config/device.example.toml new file mode 100644 index 0000000..cc694c5 --- /dev/null +++ b/RobotVisionPlatform/device/config/device.example.toml @@ -0,0 +1,30 @@ +[device] +id = "jetson-dev-001" +site = "lab-a" + +[camera] +kind = "synthetic" # synthetic | v4l2 | nvargus +uri = "/dev/video0" +width = 1280 +height = 720 +fps = 30 + +[inference] +backend = "demo" # demo | tensorrt | deepstream +model_manifest = "/opt/robot-vision/models/current/manifest.json" +confidence = 0.50 +queue_capacity = 2 + +[server] +control_uri = "https://vision.example.internal:7443" +webrtc_signaling_uri = "wss://vision.example.internal/webrtc" +ca_certificate = "/etc/robot-vision/pki/ca.pem" +client_certificate = "/etc/robot-vision/pki/device.pem" +client_key = "/etc/robot-vision/pki/device-key.pem" + +[video] +enabled = false +codec = "h264" +bitrate_kbps = 2500 +keyframe_interval = 30 + diff --git a/RobotVisionPlatform/device/guides/README.md b/RobotVisionPlatform/device/guides/README.md new file mode 100644 index 0000000..54b0f75 --- /dev/null +++ b/RobotVisionPlatform/device/guides/README.md @@ -0,0 +1,31 @@ +# Device 모델 배포 가이드 + +학습한 모델을 Jetson 장치에 넣을 때는 다음 순서로 진행합니다. + +```text +PyTorch/학습 도구 + -> ONNX export + -> ONNX 구조·연산 검증 + -> PC의 ONNX Runtime 기준 결과 저장 + -> Jetson에서 TensorRT engine 생성 + -> 실제 영상으로 결과·속도·온도 검증 + -> model bundle 배포 +``` + +처음에는 아래 문서를 순서대로 읽습니다. + +1. [ONNX 실전 흐름](onnx-workflow.md) +2. [Jetson TensorRT 변환](tensorrt-on-jetson.md) +3. [모델 bundle과 manifest](model-bundle.md) +4. 여러 카메라를 처리할 때 [DeepStream custom model](deepstream-custom-model.md) + +## 어떤 실행 방식을 선택할까? + +| 상황 | 권장 방식 | 이유 | +|---|---|---| +| PC에서 ONNX가 정상인지 확인 | ONNX Runtime CPU | 설치와 재현이 간단함 | +| 단일 카메라, 특수 전·후처리 | 직접 TensorRT C++ adapter | 파이프라인을 세밀하게 제어 가능 | +| 여러 카메라, tracker/OSD/encode | DeepStream `nvinfer` | NVIDIA 영상 plugin을 조합하기 쉬움 | +| TensorRT가 지원하지 않는 일부 ONNX 연산 | ONNX Runtime TensorRT EP | 지원 node는 TensorRT, 나머지는 CUDA/CPU fallback 가능 | + +TensorRT와 DeepStream 중 하나가 항상 정답은 아닙니다. 첫 모델은 두 방식의 정확도, FPS, 지연, 메모리를 실제 Jetson에서 비교한 후 선택합니다. diff --git a/RobotVisionPlatform/device/guides/deepstream-custom-model.md b/RobotVisionPlatform/device/guides/deepstream-custom-model.md new file mode 100644 index 0000000..31de025 --- /dev/null +++ b/RobotVisionPlatform/device/guides/deepstream-custom-model.md @@ -0,0 +1,33 @@ +# DeepStream에 custom ONNX 모델 연결 + +DeepStream은 GStreamer 위에서 카메라 입력, batch, TensorRT 추론, tracker, 화면 표시, 하드웨어 인코딩을 연결합니다. 카메라가 여러 대이거나 tracker와 video encode가 중요하면 직접 모든 단계를 구현하는 것보다 유리합니다. + +## 필요한 파일 + +- `model.onnx`: ONNX 모델 +- `labels.txt`: 클래스 번호 순서와 같은 label 목록 +- `config_infer_primary.txt`: `nvinfer` 설정 +- detector output이 표준 형식이 아니면 bounding-box parser 공유 라이브러리 + +예제는 `device/config/deepstream/config_infer_primary.example.txt`에 있습니다. + +```ini +onnx-file=/opt/robot-vision/models/person-detector/0.1.0/model.onnx +model-engine-file=/opt/robot-vision/models/person-detector/0.1.0/model.fp16.engine +labelfile-path=/opt/robot-vision/models/person-detector/0.1.0/labels.txt +network-mode=2 +``` + +`network-mode=2`는 FP16입니다. detector는 output tensor를 box/class/confidence로 바꾸는 parser가 필요할 수 있습니다. 모델별 parser 함수명과 `custom-lib-path`를 설정하지 않으면 engine 생성에는 성공해도 탐지 결과가 나오지 않을 수 있습니다. + +## 적용 순서 + +1. `trtexec`로 ONNX parsing과 단독 추론이 되는지 확인합니다. +2. DeepStream `nvinfer`만 연결해 tensor/output을 확인합니다. +3. model-specific parser와 NMS 결과를 기준 구현과 비교합니다. +4. tracker를 추가합니다. +5. OSD와 NVENC/WebRTC 출력은 마지막에 추가합니다. + +여러 문제를 한 번에 연결하면 모델 문제와 영상 pipeline 문제를 구분하기 어렵습니다. + +공식 자료: [DeepStream custom model guide](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_using_custom_model.html) diff --git a/RobotVisionPlatform/device/guides/model-bundle.md b/RobotVisionPlatform/device/guides/model-bundle.md new file mode 100644 index 0000000..d16df2d --- /dev/null +++ b/RobotVisionPlatform/device/guides/model-bundle.md @@ -0,0 +1,30 @@ +# 모델 bundle과 배포 규칙 + +모델 한 버전은 아래 파일을 함께 배포합니다. + +```text +person-detector/0.1.0/ +├── model.onnx +├── model.fp16.engine # 대상 Jetson에서 생성, Git에는 저장하지 않음 +├── labels.txt +├── manifest.json +├── preprocessing.json # 필요하면 별도 상세 설정 +└── calibration.cache # INT8일 때만 +``` + +## manifest가 필요한 이유 + +`model.onnx`만 보면 RGB/BGR, resize 방식, normalization, label 순서, threshold를 알 수 없습니다. 서로 다른 설정으로 같은 모델 파일을 실행하면 조용히 잘못된 결과가 생길 수 있습니다. manifest는 실행 코드가 시작 전에 이 조건을 검증할 수 있게 합니다. + +예제: `device/models/example/manifest.json` + +배포 과정: + +1. 서버가 manifest와 서명을 제공합니다. +2. 장치는 임시 version 폴더에 파일을 받습니다. +3. 각 SHA-256과 호환 조건을 검사합니다. +4. ONNX를 검사하고 TensorRT engine을 생성하거나 검증합니다. +5. smoke test를 통과하면 `current` symlink를 새 version으로 atomic switch합니다. +6. health guardrail 위반 시 이전 symlink로 rollback합니다. + +engine은 Git에 commit하지 않습니다. ONNX도 크기가 크면 Git LFS나 모델 registry/object storage에서 관리하고 저장소에는 manifest만 둡니다. diff --git a/RobotVisionPlatform/device/guides/onnx-workflow.md b/RobotVisionPlatform/device/guides/onnx-workflow.md new file mode 100644 index 0000000..7cf28cf --- /dev/null +++ b/RobotVisionPlatform/device/guides/onnx-workflow.md @@ -0,0 +1,99 @@ +# ONNX 실전 흐름 + +## ONNX가 하는 일 + +ONNX는 PyTorch 같은 학습 도구에서 만든 모델을 다른 추론 도구로 옮기기 위한 공통 모델 형식입니다. 이 프로젝트에서는 다음 세 가지 목적으로 사용합니다. + +1. 학습 코드와 Jetson 실행 코드를 분리합니다. +2. 모델의 입력 이름·크기·자료형과 출력 구조를 자동 검사합니다. +3. ONNX Runtime 결과를 TensorRT 결과와 비교하는 기준으로 사용합니다. + +ONNX 파일만으로는 전처리, label 순서, confidence threshold, NMS 방식이 모두 표현되지 않을 수 있습니다. 따라서 `model.onnx`, `labels.txt`, `manifest.json`을 항상 하나의 bundle로 관리합니다. + +## 1. Python 환경 준비 + +PC에서 실행합니다. 프로젝트가 사용하는 패키지만 격리하기 위해 `uv`를 권장합니다. + +```bash +cd device/tools/model +uv sync +``` + +일반 Python 환경이라면 다음도 가능합니다. + +```bash +python -m venv .venv +# Windows: .venv\Scripts\activate +# Linux: source .venv/bin/activate +python -m pip install -e . +``` + +## 2. 학습 모델 export + +Ultralytics YOLO 계열 예시: + +```bash +uv sync --extra export +python export_ultralytics.py \ + --weights runs/detect/train/weights/best.pt \ + --output artifacts/person-detector/model.onnx \ + --image-size 640 \ + --opset 17 +``` + +`ultralytics`는 export할 때만 필요한 선택 dependency라서 기본 `uv sync`에는 설치되지 않습니다. + +`--dynamic`은 입력 크기나 batch가 실제로 바뀌어야 할 때만 사용합니다. 고정된 `1x3x640x640` 입력은 TensorRT engine 생성과 메모리 예측이 더 단순합니다. + +export 옵션은 모델 라이브러리와 TensorRT 버전에 따라 달라집니다. opset 숫자를 무조건 최신으로 올리기보다 대상 Jetson의 TensorRT parser에서 읽히는지 확인합니다. + +## 3. ONNX 구조 검사 + +```bash +python inspect_onnx.py artifacts/person-detector/model.onnx \ + --save-inferred artifacts/person-detector/model.inferred.onnx +``` + +이 명령은 ONNX checker를 실행하고 입력·출력 이름, shape, type, opset을 출력합니다. `-1`, `?`, 문자로 표시되는 차원은 실행 시 결정되는 dynamic dimension입니다. + +## 4. ONNX Runtime 추론 검증 + +```bash +python validate_onnx.py artifacts/person-detector/model.onnx \ + --provider cpu \ + --shape images=1,3,640,640 \ + --runs 10 +``` + +출력 shape, 최소/최대/평균, warm-up 이후 평균 시간이 표시됩니다. 무작위 입력은 실행 가능 여부만 검사합니다. 정확도를 검증하려면 학습 전처리를 거친 `.npy` 입력과 기대 출력을 별도 회귀 테스트로 만들어야 합니다. + +TensorRT Execution Provider가 설치된 Jetson/서버에서는 다음처럼 확인할 수 있습니다. + +```bash +python validate_onnx.py model.onnx --provider tensorrt --shape images=1,3,640,640 +``` + +provider 우선순위는 TensorRT → CUDA → CPU입니다. 일부 node가 fallback되면 성능이 예상보다 낮을 수 있으므로 verbose log나 profile로 실제 할당을 확인합니다. + +## 5. manifest 생성 + +```bash +python create_manifest.py \ + --model model.onnx \ + --model-id person-detector \ + --version 0.1.0 \ + --labels labels.txt \ + --input-name images \ + --input-shape 1,3,640,640 \ + --color RGB \ + --output manifest.json +``` + +manifest의 SHA-256은 서버와 장치가 다운로드 파일이 손상되거나 바뀌지 않았는지 확인할 때 사용합니다. 보안 배포에서는 checksum과 별도로 manifest 전자서명이 필요합니다. + +## 공식 참고 + +- [ONNX 개요](https://onnx.ai/onnx/intro/) +- [ONNX Runtime Execution Providers](https://onnxruntime.ai/docs/execution-providers/) +- [ONNX Runtime TensorRT EP](https://onnxruntime.ai/docs/execution-providers/TensorRT-ExecutionProvider.html) +- [TensorRT ONNX opset guide](https://docs.nvidia.com/deeplearning/tensorrt/latest/reference/onnx-opset-guide.html) diff --git a/RobotVisionPlatform/device/guides/tensorrt-on-jetson.md b/RobotVisionPlatform/device/guides/tensorrt-on-jetson.md new file mode 100644 index 0000000..754768c --- /dev/null +++ b/RobotVisionPlatform/device/guides/tensorrt-on-jetson.md @@ -0,0 +1,62 @@ +# Jetson에서 TensorRT engine 만들기 + +## 왜 Jetson에서 만드는가? + +TensorRT engine(`.engine`, `.plan`)은 TensorRT 버전, GPU compute capability, builder 설정에 영향을 받습니다. PC에서 만든 engine을 그대로 복사하기보다 범용 ONNX를 배포하고 대상과 동일한 JetPack/TensorRT 환경에서 engine을 생성하는 방식을 기본값으로 사용합니다. + +## 1. 설치 확인 + +```bash +dpkg-query -W nvidia-jetpack +trtexec --version +``` + +JetPack이 설치되어도 `trtexec`가 PATH에 없으면 `/usr/src/tensorrt/bin/trtexec`를 확인합니다. + +## 2. FP16 engine 생성 + +고정 shape 모델: + +```bash +./device/tools/model/build_tensorrt_engine.sh \ + --onnx /opt/robot-vision/models/person-detector/0.1.0/model.onnx \ + --engine /opt/robot-vision/models/person-detector/0.1.0/model.fp16.engine \ + --input images \ + --shape 1x3x640x640 +``` + +dynamic shape 모델: + +```bash +./device/tools/model/build_tensorrt_engine.sh \ + --onnx model.onnx --engine model.fp16.engine --input images \ + --min-shape 1x3x320x320 \ + --opt-shape 1x3x640x640 \ + --max-shape 4x3x1280x1280 +``` + +`opt-shape`은 가장 자주 사용할 크기로 지정합니다. 최대 범위를 불필요하게 크게 잡으면 engine 생성 시간과 메모리 사용량이 늘 수 있습니다. + +## 3. benchmark 읽는 법 + +스크립트는 engine 생성 후 `trtexec --loadEngine` benchmark를 실행합니다. 확인할 값: + +- GPU Compute Time: GPU가 추론 계산에 사용한 시간 +- Host Latency: 입력 준비와 enqueue 등을 포함한 host 관점 시간 +- Throughput: 초당 처리 횟수 +- percentile: 평균뿐 아니라 p95/p99 지연 확인 + +`trtexec` 결과는 모델 단독 성능입니다. 최종 승인은 카메라 캡처, 전처리, 후처리, 인코딩을 모두 포함한 device pipeline의 end-to-end latency로 합니다. + +## 4. INT8은 나중에 적용 + +FP16을 먼저 정확도 기준선으로 만듭니다. INT8은 실제 운영 장면을 대표하는 calibration dataset, calibration cache 관리, class별 정확도 회귀 검사가 준비된 뒤 사용합니다. 단순히 `--int8`만 추가한 결과를 production에 배포하지 않습니다. + +## 5. 실패할 때 + +- `Unsupported operator`: exporter/opset을 확인하거나 TensorRT plugin/custom layer를 검토 +- dynamic input 오류: 입력 tensor 이름과 min/opt/max profile 확인 +- engine load 실패: 다른 JetPack/TensorRT/GPU에서 생성된 engine인지 확인 +- 속도가 느림: CPU fallback, 입력 복사, power mode, thermal throttling을 함께 확인 + +공식 자료: [TensorRT ONNX deployment quick start](https://docs.nvidia.com/deeplearning/tensorrt/latest/getting-started/quick-start-onnx-deployment.html), [TensorRT dynamic shapes](https://docs.nvidia.com/deeplearning/tensorrt/latest/inference-library/work-with-dynamic-shapes.html) diff --git a/RobotVisionPlatform/device/include/rv/bounded_queue.hpp b/RobotVisionPlatform/device/include/rv/bounded_queue.hpp new file mode 100644 index 0000000..46328cc --- /dev/null +++ b/RobotVisionPlatform/device/include/rv/bounded_queue.hpp @@ -0,0 +1,72 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include + +namespace rv { + +/// 실시간 처리용 크기 제한 producer-consumer queue입니다. +/// +/// queue가 가득 차면 producer를 막지 않고 가장 오래된 항목을 제거합니다. +/// 이 정책은 모든 프레임 보존보다 최신 화면의 낮은 지연이 중요한 경우에 적합합니다. +template +class LatestQueue { + public: + /// @param capacity 보관할 최대 항목 수입니다. 0은 안전하게 1로 보정됩니다. + explicit LatestQueue(std::size_t capacity) : capacity_(capacity == 0 ? 1 : capacity) {} + + /// 값을 queue 뒤에 추가합니다. 가득 찼으면 가장 오래된 값을 먼저 버립니다. + /// @return queue가 열려 있어 추가됐으면 true, Close() 이후면 false입니다. + bool Push(T value) { + std::scoped_lock lock(mutex_); + if (closed_) return false; + if (items_.size() == capacity_) { + items_.pop_front(); + ++dropped_; + } + items_.push_back(std::move(value)); + ready_.notify_one(); + return true; + } + + /// 값이 생기거나 queue가 닫히거나 stop 요청이 올 때까지 기다립니다. + /// @return 값이 있으면 이동 반환하고, 정상 종료 조건이면 nullopt를 반환합니다. + std::optional Pop(std::stop_token stop) { + std::unique_lock lock(mutex_); + // condition_variable_any의 C++20 overload는 stop 요청도 wake-up 조건으로 처리합니다. + ready_.wait(lock, stop, [this] { return closed_ || !items_.empty(); }); + if (items_.empty()) return std::nullopt; + T value = std::move(items_.front()); + items_.pop_front(); + return value; + } + + /// 이후 Push를 거부하고 대기 중인 모든 consumer를 깨웁니다. + void Close() { + std::scoped_lock lock(mutex_); + closed_ = true; + ready_.notify_all(); + } + + /// queue 포화로 제거된 항목의 누적 수를 thread-safe하게 반환합니다. + [[nodiscard]] std::uint64_t dropped() const { + std::scoped_lock lock(mutex_); + return dropped_; + } + + private: + const std::size_t capacity_; + mutable std::mutex mutex_; + std::condition_variable_any ready_; + std::deque items_; + std::uint64_t dropped_{}; + bool closed_{}; +}; + +} // namespace rv diff --git a/RobotVisionPlatform/device/include/rv/interfaces.hpp b/RobotVisionPlatform/device/include/rv/interfaces.hpp new file mode 100644 index 0000000..f59d853 --- /dev/null +++ b/RobotVisionPlatform/device/include/rv/interfaces.hpp @@ -0,0 +1,60 @@ +#pragma once + +#include "rv/types.hpp" + +#include +#include +#include +#include + +namespace rv { + +/// 카메라 종류와 무관하게 파이프라인이 프레임을 읽는 계약입니다. +class ICamera { + public: + virtual ~ICamera() = default; + + /// 다음 프레임이 준비될 때까지 기다렸다 반환합니다. + /// @param stop 종료 요청을 전달하는 C++20 stop token입니다. + /// @return 캡처된 한 프레임입니다. + /// @throws std::exception 카메라 연결이나 decode가 실패한 경우입니다. + virtual Frame Read(std::stop_token stop) = 0; +}; + +/// TensorRT, DeepStream, test detector가 공통으로 구현할 추론 계약입니다. +class IDetector { + public: + virtual ~IDetector() = default; + + /// 프레임 하나를 동기적으로 추론합니다. 호출이 끝날 때까지 frame은 유효합니다. + virtual std::vector Infer(const Frame& frame) = 0; + + /// event와 metric에 기록할 모델 식별자/버전을 반환합니다. + [[nodiscard]] virtual std::string Version() const = 0; +}; + +/// 탐지 이벤트의 최종 목적지를 추상화한 계약입니다. +class IEventSink { + public: + virtual ~IEventSink() = default; + + /// 이벤트를 전송합니다. + /// @return 목적지가 이벤트를 받았으면 true, 재시도가 필요하면 false입니다. + virtual bool Publish(const DetectionEvent& event) = 0; +}; + +/// 실제 카메라 없이 pipeline을 시험하는 일정 FPS의 synthetic camera를 만듭니다. +std::unique_ptr MakeSyntheticCamera(int width, int height, int fps); +/// 세 프레임마다 예제 객체를 반환하는 test detector를 만듭니다. +std::unique_ptr MakeDemoDetector(); +/// 탐지 개수와 모델 정보를 stdout에 쓰는 sink를 만듭니다. +std::unique_ptr MakeConsoleEventSink(); + +#ifdef RV_HAS_BOOST_HTTP +/// Boost.Beast로 JSON detection event를 HTTP POST하는 MVP sink를 만듭니다. +/// production에서는 연결 재사용, TLS, disk spool이 있는 gRPC adapter로 교체합니다. +std::unique_ptr MakeHttpEventSink(std::string host, std::string port, + std::string target); +#endif + +} // namespace rv diff --git a/RobotVisionPlatform/device/include/rv/pipeline.hpp b/RobotVisionPlatform/device/include/rv/pipeline.hpp new file mode 100644 index 0000000..32769c3 --- /dev/null +++ b/RobotVisionPlatform/device/include/rv/pipeline.hpp @@ -0,0 +1,70 @@ +#pragma once + +#include "rv/bounded_queue.hpp" +#include "rv/interfaces.hpp" + +#include +#include +#include +#include +#include + +namespace rv { + +/// Pipeline의 장치 식별자와 실시간 처리 정책입니다. +struct PipelineOptions { + std::string device_id{"jetson-dev-001"}; + /// 작게 유지할수록 처리량보다 최신 프레임과 낮은 지연을 우선합니다. + std::size_t frame_queue_capacity{2}; + /// 향후 health heartbeat worker가 사용할 전송 주기입니다. + std::chrono::milliseconds heartbeat_interval{5000}; +}; + +/// camera, detector, event sink의 수명주기와 worker thread를 관리합니다. +/// +/// 사용 예: +/// @code +/// Pipeline pipeline(options, MakeSyntheticCamera(640, 480, 30), +/// MakeDemoDetector(), MakeConsoleEventSink()); +/// pipeline.Start(); +/// // ... application work ... +/// pipeline.Stop(); +/// @endcode +class Pipeline { + public: + /// adapter 소유권을 Pipeline로 이전합니다. null adapter를 전달하면 안 됩니다. + Pipeline(PipelineOptions options, std::unique_ptr camera, + std::unique_ptr detector, std::unique_ptr sink); + /// 실행 중이면 먼저 Stop()하여 worker가 adapter보다 먼저 종료되게 합니다. + ~Pipeline(); + + // Worker와 adapter는 단일 소유이므로 Pipeline 복사를 금지합니다. + Pipeline(const Pipeline&) = delete; + Pipeline& operator=(const Pipeline&) = delete; + + /// capture/inference worker를 시작합니다. 이미 시작됐으면 아무 작업도 하지 않습니다. + void Start(); + /// 두 worker에 종료를 요청하고 join이 끝날 때까지 기다립니다. + void Stop(); + /// lock-free atomic counter와 queue drop 수를 한 시점의 값으로 반환합니다. + [[nodiscard]] PipelineStats Stats() const; + + private: + /// 카메라를 읽어 latest-frame queue에 넣는 producer loop입니다. + void CaptureLoop(std::stop_token stop); + /// queue에서 프레임을 꺼내 추론하고 event sink에 보내는 consumer loop입니다. + void InferenceLoop(std::stop_token stop); + + PipelineOptions options_; + std::unique_ptr camera_; + std::unique_ptr detector_; + std::unique_ptr sink_; + LatestQueue frames_; + std::jthread capture_thread_; + std::jthread inference_thread_; + std::atomic_uint64_t captured_{}; + std::atomic_uint64_t inferred_{}; + std::atomic_uint64_t published_{}; +}; + +} // namespace rv diff --git a/RobotVisionPlatform/device/include/rv/types.hpp b/RobotVisionPlatform/device/include/rv/types.hpp new file mode 100644 index 0000000..717410e --- /dev/null +++ b/RobotVisionPlatform/device/include/rv/types.hpp @@ -0,0 +1,62 @@ +#pragma once + +#include +#include +#include +#include + +namespace rv { + +/// 시스템 간 timestamp에 사용하는 실제 시각 clock입니다. +/// 성능 측정에는 시간이 역행할 수 없는 steady_clock을 별도로 사용해야 합니다. +using Clock = std::chrono::system_clock; + +/// 카메라가 한 번 촬영한 영상 프레임과 메타데이터입니다. +struct Frame { + /// 장치 프로세스 안에서 단조 증가하는 프레임 번호입니다. + std::uint64_t sequence{}; + /// 카메라 adapter가 프레임 획득을 완료한 UTC 기준 시각입니다. + Clock::time_point captured_at{}; + /// pixel buffer의 가로/세로 크기입니다. + int width{}; + int height{}; + /// MVP의 CPU BGR byte 배열입니다. Jetson adapter에서는 복사 비용을 피하기 위해 + /// 이 필드 대신 NVMM/CUDA buffer handle을 소유하는 payload로 확장합니다. + std::vector pixels; // MVP: CPU BGR. Jetson adapter uses an NVMM handle. +}; + +/// 정규화 좌표로 표현한 객체 하나의 탐지 결과입니다. +/// x/y는 좌상단이며 모든 좌표 값은 원본 크기와 무관하게 0.0~1.0을 사용합니다. +struct Detection { + /// labels.txt와 같은 순서를 사용하는 사람이 읽을 수 있는 class 이름입니다. + std::string label; + /// 모델이 계산한 신뢰도입니다. 보통 0.0~1.0 범위입니다. + float confidence{}; + float x{}; + float y{}; + float width{}; + float height{}; +}; + +/// 한 프레임의 탐지 결과를 서버로 전송하기 위한 묶음입니다. +struct DetectionEvent { + std::string device_id; + std::uint64_t sequence{}; + Clock::time_point captured_at{}; + std::string model_version; + std::vector detections; +}; + +/// 파이프라인 상태를 외부에서 읽을 수 있는 누적 counter snapshot입니다. +struct PipelineStats { + /// 카메라에서 성공적으로 받은 전체 프레임 수입니다. + std::uint64_t captured{}; + /// detector에 전달한 전체 프레임 수입니다. + std::uint64_t inferred{}; + /// sink가 성공으로 응답한 전체 이벤트 수입니다. + std::uint64_t published{}; + /// queue가 가득 차 오래된 프레임을 제거한 횟수입니다. + std::uint64_t dropped{}; +}; + +} // namespace rv diff --git a/RobotVisionPlatform/device/models/example/labels.txt b/RobotVisionPlatform/device/models/example/labels.txt new file mode 100644 index 0000000..981c24b --- /dev/null +++ b/RobotVisionPlatform/device/models/example/labels.txt @@ -0,0 +1,2 @@ +person +forklift diff --git a/RobotVisionPlatform/device/models/example/manifest.json b/RobotVisionPlatform/device/models/example/manifest.json new file mode 100644 index 0000000..fc9ece7 --- /dev/null +++ b/RobotVisionPlatform/device/models/example/manifest.json @@ -0,0 +1,28 @@ +{ + "schemaVersion": 1, + "modelId": "example-detector", + "version": "0.1.0", + "artifact": { + "file": "model.onnx", + "format": "onnx", + "sha256": "replace-with-create-manifest-output" + }, + "input": { + "name": "images", + "shape": [1, 3, 640, 640], + "layout": "NCHW", + "color": "RGB", + "dataType": "float32", + "scale": 0.00392156862745098 + }, + "labels": ["person", "forklift"], + "postprocessing": { + "kind": "model-specific", + "confidenceThreshold": 0.5, + "nmsIouThreshold": 0.45 + }, + "runtime": { + "preferred": "tensorrt-fp16", + "engineBuildLocation": "target-device" + } +} diff --git a/RobotVisionPlatform/device/src/http_event_sink.cpp b/RobotVisionPlatform/device/src/http_event_sink.cpp new file mode 100644 index 0000000..f4e394f --- /dev/null +++ b/RobotVisionPlatform/device/src/http_event_sink.cpp @@ -0,0 +1,97 @@ +#include "rv/interfaces.hpp" + +#include +#include +#include +#include + +#include +#include +#include + +namespace rv { +namespace { +namespace beast = boost::beast; +namespace http = beast::http; +namespace net = boost::asio; +using tcp = net::ip::tcp; + +std::string Escape(std::string_view value) { + // 이 MVP JSON writer가 사용하는 문자열 필드에서 최소한의 escaping을 합니다. + // production에서는 검증된 JSON serializer를 사용해야 합니다. + std::string result; + for (unsigned char ch : value) { + switch (ch) { + case '"': result += "\\\""; break; + case '\\': result += "\\\\"; break; + case '\n': result += "\\n"; break; + case '\r': result += "\\r"; break; + case '\t': result += "\\t"; break; + default: + if (ch < 0x20) continue; // drop other control characters + result.push_back(static_cast(ch)); + } + } + return result; +} + +class HttpEventSink final : public IEventSink { + public: + HttpEventSink(std::string host, std::string port, std::string target) + : host_(std::move(host)), port_(std::move(port)), target_(std::move(target)) {} + + bool Publish(const DetectionEvent& event) override { + try { + // MVP는 호출마다 연결합니다. production 구현은 keep-alive/HTTP2/gRPC를 재사용합니다. + net::io_context context; + tcp::resolver resolver(context); + beast::tcp_stream stream(context); + stream.expires_after(std::chrono::seconds(3)); + stream.connect(resolver.resolve(host_, port_)); + + // DetectionEvent를 서버의 DetectionEventDto camelCase JSON 계약으로 변환합니다. + std::ostringstream body; + body << "{\"deviceId\":\"" << Escape(event.device_id) << "\",\"sequence\":" + << event.sequence << ",\"modelVersion\":\"" << Escape(event.model_version) + << "\",\"detections\":["; + for (std::size_t index = 0; index < event.detections.size(); ++index) { + const auto& item = event.detections[index]; + if (index) body << ','; + body << "{\"label\":\"" << Escape(item.label) << "\",\"confidence\":" + << item.confidence << ",\"x\":" << item.x << ",\"y\":" << item.y + << ",\"width\":" << item.width << ",\"height\":" << item.height << '}'; + } + body << "]}"; + + http::request request{http::verb::post, target_, 11}; + request.set(http::field::host, host_); + request.set(http::field::user_agent, "robot-vision-device/0.1"); + request.set(http::field::content_type, "application/json"); + request.body() = body.str(); + request.prepare_payload(); + http::write(stream, request); + beast::flat_buffer buffer; + http::response response; + http::read(stream, buffer, response); + beast::error_code ignored; + stream.socket().shutdown(tcp::socket::shutdown_both, ignored); + // 2xx만 성공으로 계산하여 pipeline의 published counter 의미를 일관되게 유지합니다. + return response.result_int() >= 200 && response.result_int() < 300; + } catch (...) { + return false; // Production adapter adds bounded disk spool + retry/backoff. + } + } + + private: + std::string host_; + std::string port_; + std::string target_; +}; +} // namespace + +std::unique_ptr MakeHttpEventSink(std::string host, std::string port, + std::string target) { + // unique_ptr로 반환해 sink 수명과 정리를 Pipeline 한 곳에서 관리합니다. + return std::make_unique(std::move(host), std::move(port), std::move(target)); +} +} // namespace rv diff --git a/RobotVisionPlatform/device/src/main.cpp b/RobotVisionPlatform/device/src/main.cpp new file mode 100644 index 0000000..6fd8054 --- /dev/null +++ b/RobotVisionPlatform/device/src/main.cpp @@ -0,0 +1,60 @@ +#include "rv/pipeline.hpp" + +#include +#include +#include +#include +#include +#include +#include + +namespace { +// signal handler에서는 async-signal-safe한 sig_atomic_t 값만 바꿉니다. +volatile std::sig_atomic_t running = 1; +void HandleSignal(int) { running = 0; } +} // namespace + +int main(int argc, char** argv) { + // systemd SIGTERM과 터미널 Ctrl+C(SIGINT)를 같은 정상 종료 경로로 연결합니다. + std::signal(SIGINT, HandleSignal); + std::signal(SIGTERM, HandleSignal); + + std::string device_id = "jetson-dev-001"; + std::string server_host; + std::string server_port = "5080"; + // 현재 CLI는 --option value 쌍만 받는 작은 parser입니다. + for (int index = 1; index + 1 < argc; index += 2) { + const std::string_view option = argv[index]; + if (option == "--device-id") device_id = argv[index + 1]; + if (option == "--server-host") server_host = argv[index + 1]; + if (option == "--server-port") server_port = argv[index + 1]; + } + + std::unique_ptr sink; +#ifdef RV_HAS_BOOST_HTTP + // Boost가 빌드에 포함되고 server host가 있을 때만 HTTP sink를 선택합니다. + if (!server_host.empty()) { + sink = rv::MakeHttpEventSink(server_host, server_port, "/api/events/detections"); + } +#endif + if (!sink) { + if (!server_host.empty()) { + std::cerr << "HTTP sink unavailable; rebuild with Boost.System installed\n"; + } + sink = rv::MakeConsoleEventSink(); + } + + // adapter를 생성해 Pipeline에 소유권을 넘깁니다. 실제 Jetson에서는 이 세 factory를 교체합니다. + rv::Pipeline pipeline({.device_id = device_id, .frame_queue_capacity = 2}, + rv::MakeSyntheticCamera(1280, 720, 15), rv::MakeDemoDetector(), + std::move(sink)); + pipeline.Start(); + std::cout << "robot-vision device started; Ctrl+C to stop\n"; + // main thread는 signal만 감시하고 실제 작업은 jthread worker가 담당합니다. + while (running) std::this_thread::sleep_for(std::chrono::milliseconds(200)); + pipeline.Stop(); + const auto stats = pipeline.Stats(); + std::cout << "captured=" << stats.captured << " inferred=" << stats.inferred + << " published=" << stats.published << " dropped=" << stats.dropped << '\n'; + return 0; +} diff --git a/RobotVisionPlatform/device/src/pipeline.cpp b/RobotVisionPlatform/device/src/pipeline.cpp new file mode 100644 index 0000000..9062ffd --- /dev/null +++ b/RobotVisionPlatform/device/src/pipeline.cpp @@ -0,0 +1,82 @@ +#include "rv/pipeline.hpp" + +#include +#include +#include + +namespace rv { + +Pipeline::Pipeline(PipelineOptions options, std::unique_ptr camera, + std::unique_ptr detector, std::unique_ptr sink) + : options_(std::move(options)), + camera_(std::move(camera)), + detector_(std::move(detector)), + sink_(std::move(sink)), + frames_(options_.frame_queue_capacity) {} + +Pipeline::~Pipeline() { Stop(); } + +void Pipeline::Start() { + // Start를 두 번 호출해 worker가 중복 생성되는 것을 방지합니다. + if (capture_thread_.joinable() || inference_thread_.joinable()) return; + // std::jthread는 callable에 stop_token을 자동으로 전달합니다. + capture_thread_ = std::jthread([this](std::stop_token stop) { CaptureLoop(stop); }); + inference_thread_ = std::jthread([this](std::stop_token stop) { InferenceLoop(stop); }); +} + +void Pipeline::Stop() { + // 먼저 종료 의사를 전달한 뒤 queue를 닫아 Pop에서 기다리는 consumer도 깨웁니다. + capture_thread_.request_stop(); + inference_thread_.request_stop(); + frames_.Close(); + // join은 worker가 adapter를 더 이상 사용하지 않을 때까지 기다려 수명 문제를 막습니다. + if (capture_thread_.joinable()) capture_thread_.join(); + if (inference_thread_.joinable()) inference_thread_.join(); +} + +PipelineStats Pipeline::Stats() const { + // 서로 다른 worker가 counter를 수정하므로 atomic load로 data race를 피합니다. + return {.captured = captured_.load(), + .inferred = inferred_.load(), + .published = published_.load(), + .dropped = frames_.dropped()}; +} + +void Pipeline::CaptureLoop(std::stop_token stop) { + try { + while (!stop.stop_requested()) { + // 실제 adapter는 여기서 V4L2/NvArgus/GStreamer 프레임을 기다립니다. + auto frame = camera_->Read(stop); + if (stop.stop_requested()) break; + ++captured_; + if (!frames_.Push(std::move(frame))) break; + } + } catch (const std::exception& error) { + std::cerr << "capture_error: " << error.what() << "\n"; + } + frames_.Close(); +} + +void Pipeline::InferenceLoop(std::stop_token stop) { + try { + while (!stop.stop_requested()) { + // Pop은 새 프레임, queue close, stop 요청 중 하나가 발생할 때 깨어납니다. + auto frame = frames_.Pop(stop); + if (!frame) break; + auto detections = detector_->Infer(*frame); + ++inferred_; + // model version과 원본 sequence를 함께 보내 서버가 결과의 출처를 추적하게 합니다. + DetectionEvent event{.device_id = options_.device_id, + .sequence = frame->sequence, + .captured_at = frame->captured_at, + .model_version = detector_->Version(), + .detections = std::move(detections)}; + // false는 전송 실패를 의미합니다. production sink는 자체 retry/spool 정책을 가져야 합니다. + if (sink_->Publish(event)) ++published_; + } + } catch (const std::exception& error) { + std::cerr << "inference_error: " << error.what() << "\n"; + } +} + +} // namespace rv diff --git a/RobotVisionPlatform/device/src/synthetic_adapters.cpp b/RobotVisionPlatform/device/src/synthetic_adapters.cpp new file mode 100644 index 0000000..b4705a3 --- /dev/null +++ b/RobotVisionPlatform/device/src/synthetic_adapters.cpp @@ -0,0 +1,74 @@ +#include "rv/interfaces.hpp" + +#include +#include +#include + +namespace rv { +namespace { + +class SyntheticCamera final : public ICamera { + public: + /// width/height/fps만 흉내 내는 개발용 카메라입니다. + SyntheticCamera(int width, int height, int fps) + : width_(width), height_(height), interval_(1000 / (fps <= 0 ? 1 : fps)) {} + + Frame Read(std::stop_token stop) override { + // 실제 카메라의 frame interval을 모사해 worker가 무한 속도로 돌지 않게 합니다. + std::this_thread::sleep_for(interval_); + if (stop.stop_requested()) return {}; + Frame frame{.sequence = ++sequence_, + .captured_at = Clock::now(), + .width = width_, + .height = height_, + .pixels = {}}; + // synthetic mode는 pixel을 사용하지 않으므로 큰 영상 배열 할당을 생략합니다. + return frame; + } + + private: + int width_; + int height_; + std::chrono::milliseconds interval_; + std::uint64_t sequence_{}; +}; + +class DemoDetector final : public IDetector { + public: + std::vector Infer(const Frame& frame) override { + // 테스트에서 빈 결과와 탐지 결과를 모두 경험하도록 3의 배수 프레임만 탐지합니다. + if (frame.sequence % 3 != 0) return {}; + const auto phase = static_cast(frame.sequence % 100) / 100.0F; + return {{.label = "demo-object", + .confidence = 0.91F, + .x = phase, + .y = 0.2F, + .width = 0.2F, + .height = 0.3F}}; + } + std::string Version() const override { return "demo-detector/0.1.0"; } +}; + +class ConsoleEventSink final : public IEventSink { + public: + bool Publish(const DetectionEvent& event) override { + // stdout 출력이 성공했다고 간주하므로 pipeline smoke test에서 network가 필요 없습니다. + std::cout << "device=" << event.device_id << " sequence=" << event.sequence + << " model=" << event.model_version + << " detections=" << event.detections.size() << '\n'; + return true; + } +}; + +} // namespace + +std::unique_ptr MakeSyntheticCamera(int width, int height, int fps) { + // factory 함수는 concrete type을 감춰 main이 interface에만 의존하게 합니다. + return std::make_unique(width, height, fps); +} +std::unique_ptr MakeDemoDetector() { return std::make_unique(); } +std::unique_ptr MakeConsoleEventSink() { + return std::make_unique(); +} + +} // namespace rv diff --git a/RobotVisionPlatform/device/tests/core_tests.cpp b/RobotVisionPlatform/device/tests/core_tests.cpp new file mode 100644 index 0000000..e508859 --- /dev/null +++ b/RobotVisionPlatform/device/tests/core_tests.cpp @@ -0,0 +1,29 @@ +#include "rv/bounded_queue.hpp" +#include "rv/pipeline.hpp" + +#include +#include +#include + +int main() { + // 용량 2에 세 값을 넣으면 가장 오래된 1이 제거되어야 합니다. + rv::LatestQueue queue(2); + assert(queue.Push(1)); + assert(queue.Push(2)); + assert(queue.Push(3)); + assert(queue.dropped() == 1); + std::stop_source stop; + assert(queue.Pop(stop.get_token()).value() == 2); + + // 외부 카메라나 서버 없이 adapter 조합과 thread 종료를 smoke test합니다. + rv::Pipeline pipeline({.device_id = "test", .frame_queue_capacity = 2}, + rv::MakeSyntheticCamera(32, 32, 100), rv::MakeDemoDetector(), + rv::MakeConsoleEventSink()); + pipeline.Start(); + std::this_thread::sleep_for(std::chrono::milliseconds(80)); + pipeline.Stop(); + const auto stats = pipeline.Stats(); + assert(stats.captured > 0); + assert(stats.inferred > 0); + return 0; +} diff --git a/RobotVisionPlatform/device/tools/model/README.md b/RobotVisionPlatform/device/tools/model/README.md new file mode 100644 index 0000000..30e1f35 --- /dev/null +++ b/RobotVisionPlatform/device/tools/model/README.md @@ -0,0 +1,20 @@ +# Model Tools + +`device/guides/onnx-workflow.md`를 자동화하는 작은 CLI 도구 모음입니다. + +```bash +uv sync +uv run python inspect_onnx.py model.onnx +uv run python validate_onnx.py model.onnx --shape images=1,3,640,640 +uv run python create_manifest.py --help +``` + +| 파일 | 역할 | +|---|---| +| `inspect_onnx.py` | ONNX checker, shape inference, 입출력 계약 출력 | +| `validate_onnx.py` | CPU/CUDA/TensorRT EP 추론과 출력 통계·평균 시간 측정 | +| `create_manifest.py` | artifact hash와 전처리 계약을 포함한 manifest 생성 | +| `export_ultralytics.py` | Ultralytics checkpoint를 ONNX bundle 경로로 export | +| `build_tensorrt_engine.sh` | Jetson에서 `trtexec` FP16 engine 생성과 benchmark | + +Ultralytics export 도구까지 설치하려면 `uv sync --extra export`를 사용합니다. 실제 모델과 생성된 TensorRT engine은 이 도구 폴더에 저장하지 말고 version별 model bundle 또는 외부 model registry에 저장합니다. diff --git a/RobotVisionPlatform/device/tools/model/build_tensorrt_engine.sh b/RobotVisionPlatform/device/tools/model/build_tensorrt_engine.sh new file mode 100755 index 0000000..212900d --- /dev/null +++ b/RobotVisionPlatform/device/tools/model/build_tensorrt_engine.sh @@ -0,0 +1,82 @@ +#!/usr/bin/env bash +# Build and benchmark a target-specific FP16 TensorRT engine from an ONNX model. +# Run this on the deployment Jetson so the engine matches its TensorRT/GPU versions. +set -euo pipefail + +# Arguments are kept separate from trtexec options to avoid accidental shell expansion. +onnx_path="" +engine_path="" +input_name="" +fixed_shape="" +min_shape="" +opt_shape="" +max_shape="" +trtexec_bin="${TRTEXEC_BIN:-trtexec}" + +usage() { + # Print the two supported modes: one fixed shape or a dynamic min/opt/max profile. + echo "Usage: $0 --onnx MODEL --engine ENGINE --input NAME [--shape 1x3x640x640 | --min-shape ... --opt-shape ... --max-shape ...]" +} + +# Parse `--name value` pairs. Unknown options fail instead of being forwarded silently. +while [[ $# -gt 0 ]]; do + case "$1" in + --onnx) onnx_path="$2"; shift 2 ;; + --engine) engine_path="$2"; shift 2 ;; + --input) input_name="$2"; shift 2 ;; + --shape) fixed_shape="$2"; shift 2 ;; + --min-shape) min_shape="$2"; shift 2 ;; + --opt-shape) opt_shape="$2"; shift 2 ;; + --max-shape) max_shape="$2"; shift 2 ;; + --trtexec) trtexec_bin="$2"; shift 2 ;; + -h|--help) usage; exit 0 ;; + *) echo "Unknown argument: $1" >&2; usage >&2; exit 2 ;; + esac +done + +if [[ -z "$onnx_path" || -z "$engine_path" || -z "$input_name" ]]; then + usage >&2 + exit 2 +fi +if [[ ! -f "$onnx_path" ]]; then + echo "ONNX model not found: $onnx_path" >&2 + exit 2 +fi +if ! command -v "$trtexec_bin" >/dev/null 2>&1; then + echo "trtexec not found. Set TRTEXEC_BIN or pass --trtexec /usr/src/tensorrt/bin/trtexec" >&2 + exit 2 +fi + +mkdir -p "$(dirname "$engine_path")" +build_shape_args=() +run_shape_args=() +# Dynamic profiles are builder options; benchmarking uses the most common opt shape. +if [[ -n "$fixed_shape" ]]; then + build_shape_args+=("--shapes=${input_name}:${fixed_shape}") + run_shape_args+=("--shapes=${input_name}:${fixed_shape}") +elif [[ -n "$min_shape" && -n "$opt_shape" && -n "$max_shape" ]]; then + build_shape_args+=("--minShapes=${input_name}:${min_shape}") + build_shape_args+=("--optShapes=${input_name}:${opt_shape}") + build_shape_args+=("--maxShapes=${input_name}:${max_shape}") + run_shape_args+=("--shapes=${input_name}:${opt_shape}") +else + echo "Provide --shape or all of --min-shape, --opt-shape, --max-shape" >&2 + exit 2 +fi + +# First pass parses ONNX and serializes the optimized engine without benchmarking. +"$trtexec_bin" \ + "--onnx=$onnx_path" \ + "--saveEngine=$engine_path" \ + --fp16 \ + --skipInference \ + "${build_shape_args[@]}" + +# Second pass loads exactly that engine, warms it up, and measures steady-state latency. +"$trtexec_bin" \ + "--loadEngine=$engine_path" \ + --warmUp=1000 \ + --duration=10 \ + "${run_shape_args[@]}" + +echo "TensorRT engine ready: $engine_path" diff --git a/RobotVisionPlatform/device/tools/model/create_manifest.py b/RobotVisionPlatform/device/tools/model/create_manifest.py new file mode 100644 index 0000000..c10fbfb --- /dev/null +++ b/RobotVisionPlatform/device/tools/model/create_manifest.py @@ -0,0 +1,68 @@ +#!/usr/bin/env python3 +"""Create a deterministic model manifest with SHA-256 and preprocessing metadata.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from pathlib import Path + + +def sha256(path: Path) -> str: + """Return a streaming SHA-256 digest without loading the whole model into RAM.""" + digest = hashlib.sha256() + with path.open("rb") as source: + for chunk in iter(lambda: source.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def main() -> int: + """Validate CLI metadata and write a reproducible model bundle manifest.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--model", type=Path, required=True) + parser.add_argument("--model-id", required=True) + parser.add_argument("--version", required=True) + parser.add_argument("--labels", type=Path, required=True) + parser.add_argument("--input-name", default="images") + parser.add_argument("--input-shape", default="1,3,640,640") + parser.add_argument("--color", choices=("RGB", "BGR"), default="RGB") + parser.add_argument("--scale", type=float, default=1.0 / 255.0) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + + dimensions = [int(value) for value in args.input_shape.split(",")] + if len(dimensions) != 4 or any(value <= 0 for value in dimensions): + parser.error("--input-shape must contain four positive dimensions, e.g. 1,3,640,640") + # Empty lines are ignored, while the remaining order is preserved as the class index contract. + labels = [line.strip() for line in args.labels.read_text(encoding="utf-8").splitlines() if line.strip()] + if not labels: + parser.error("labels file is empty") + + # Keep preprocessing and postprocessing beside the artifact hash so runtime settings cannot drift. + manifest = { + "schemaVersion": 1, + "modelId": args.model_id, + "version": args.version, + "artifact": {"file": args.model.name, "format": "onnx", "sha256": sha256(args.model)}, + "input": { + "name": args.input_name, + "shape": dimensions, + "layout": "NCHW", + "color": args.color, + "dataType": "float32", + "scale": args.scale, + }, + "labels": labels, + "postprocessing": {"kind": "model-specific", "confidenceThreshold": 0.5, "nmsIouThreshold": 0.45}, + "runtime": {"preferred": "tensorrt-fp16", "engineBuildLocation": "target-device"}, + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") + print(f"manifest={args.output} sha256={manifest['artifact']['sha256']}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/RobotVisionPlatform/device/tools/model/export_ultralytics.py b/RobotVisionPlatform/device/tools/model/export_ultralytics.py new file mode 100644 index 0000000..73c2b9d --- /dev/null +++ b/RobotVisionPlatform/device/tools/model/export_ultralytics.py @@ -0,0 +1,42 @@ +#!/usr/bin/env python3 +"""Export an Ultralytics checkpoint to ONNX and copy it to a stable bundle path.""" + +from __future__ import annotations + +import argparse +import shutil +from pathlib import Path + +from ultralytics import YOLO + + +def main() -> int: + """Export a checkpoint and copy the generated ONNX file to a stable bundle path.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--weights", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--image-size", type=int, default=640) + parser.add_argument("--opset", type=int, default=17) + parser.add_argument("--dynamic", action="store_true") + parser.add_argument("--simplify", action="store_true") + args = parser.parse_args() + + # Ultralytics selects the task from checkpoint metadata and returns the generated file path. + model = YOLO(str(args.weights)) + exported = Path(model.export( + format="onnx", + imgsz=args.image_size, + opset=args.opset, + dynamic=args.dynamic, + simplify=args.simplify, + )) + args.output.parent.mkdir(parents=True, exist_ok=True) + # model.export writes beside the checkpoint by default; copy it into our versioned bundle. + if exported.resolve() != args.output.resolve(): + shutil.copy2(exported, args.output) + print(f"onnx={args.output}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/RobotVisionPlatform/device/tools/model/inspect_onnx.py b/RobotVisionPlatform/device/tools/model/inspect_onnx.py new file mode 100644 index 0000000..b8dda3d --- /dev/null +++ b/RobotVisionPlatform/device/tools/model/inspect_onnx.py @@ -0,0 +1,65 @@ +#!/usr/bin/env python3 +"""Validate an ONNX graph and print its deployment-relevant metadata.""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +import onnx +from onnx import TensorProto, checker, shape_inference + + +def tensor_shape(value: onnx.ValueInfoProto) -> str: + """Convert ONNX dimension metadata to a readable `1x3x640x640` string.""" + tensor_type = value.type.tensor_type + dimensions: list[str] = [] + for dimension in tensor_type.shape.dim: + if dimension.HasField("dim_value"): + dimensions.append(str(dimension.dim_value)) + elif dimension.HasField("dim_param"): + dimensions.append(dimension.dim_param) + else: + dimensions.append("?") + return "x".join(dimensions) + + +def describe(kind: str, values: list[onnx.ValueInfoProto]) -> None: + """Print a compact contract line for each graph input or output.""" + for value in values: + element_type = TensorProto.DataType.Name(value.type.tensor_type.elem_type) + print(f"{kind}: name={value.name} shape={tensor_shape(value)} type={element_type}") + + +def main() -> int: + """Parse CLI arguments, check the graph, infer shapes, and print metadata.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("model", type=Path) + parser.add_argument("--save-inferred", type=Path, help="Write a shape-inferred ONNX copy") + args = parser.parse_args() + + # load_external_data=True also reads large weights stored beside the .onnx file. + model = onnx.load(args.model, load_external_data=True) + # checker catches invalid node links, types, and graph structure before deployment. + checker.check_model(model) + # Shape inference fills output dimensions that can be derived from graph operators. + inferred = shape_inference.infer_shapes(model) + + print(f"model={args.model}") + print("opsets=" + ",".join(f"{item.domain or 'ai.onnx'}:{item.version}" for item in model.opset_import)) + print(f"nodes={len(model.graph.node)} initializers={len(model.graph.initializer)}") + # Initializers are weights/constants and should not be reported as runtime inputs. + initializer_names = {item.name for item in inferred.graph.initializer} + describe("input", [item for item in inferred.graph.input if item.name not in initializer_names]) + describe("output", list(inferred.graph.output)) + print("checker=ok") + + if args.save_inferred: + args.save_inferred.parent.mkdir(parents=True, exist_ok=True) + onnx.save(inferred, args.save_inferred) + print(f"shape_inferred_model={args.save_inferred}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/RobotVisionPlatform/device/tools/model/pyproject.toml b/RobotVisionPlatform/device/tools/model/pyproject.toml new file mode 100644 index 0000000..5b8fbe9 --- /dev/null +++ b/RobotVisionPlatform/device/tools/model/pyproject.toml @@ -0,0 +1,16 @@ +[project] +name = "robot-vision-model-tools" +version = "0.1.0" +description = "ONNX inspection, validation, export, and manifest tools for RobotVisionPlatform" +requires-python = ">=3.11" +dependencies = [ + "numpy>=1.26", + "onnx>=1.16", + "onnxruntime>=1.20" +] + +[project.optional-dependencies] +export = ["ultralytics>=8.3"] + +[tool.uv] +package = false diff --git a/RobotVisionPlatform/device/tools/model/tests/test_create_manifest.py b/RobotVisionPlatform/device/tools/model/tests/test_create_manifest.py new file mode 100644 index 0000000..a9f5140 --- /dev/null +++ b/RobotVisionPlatform/device/tools/model/tests/test_create_manifest.py @@ -0,0 +1,49 @@ +from __future__ import annotations + +import hashlib +import json +import subprocess +import sys +import tempfile +import unittest +from pathlib import Path + + +class CreateManifestTests(unittest.TestCase): + """Black-box tests for the dependency-free manifest CLI.""" + + def test_cli_writes_hash_labels_and_input_contract(self) -> None: + """The CLI must preserve labels/shape and hash the exact artifact bytes.""" + tool = Path(__file__).parents[1] / "create_manifest.py" + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + model = root / "model.onnx" + labels = root / "labels.txt" + output = root / "manifest.json" + model.write_bytes(b"small-test-model") + labels.write_text("person\n\nforklift\n", encoding="utf-8") + + # Run the public CLI instead of internal helpers to cover argument parsing and file output. + subprocess.run( + [ + sys.executable, str(tool), + "--model", str(model), + "--model-id", "test-detector", + "--version", "1.2.3", + "--labels", str(labels), + "--input-name", "images", + "--input-shape", "1,3,320,320", + "--output", str(output), + ], + check=True, + ) + + manifest = json.loads(output.read_text(encoding="utf-8")) + self.assertEqual(manifest["modelId"], 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and average latency.""" + +from __future__ import annotations + +import argparse +import time +from pathlib import Path + +import numpy as np +import onnxruntime as ort + + +def parse_shapes(items: list[str]) -> dict[str, tuple[int, ...]]: + """Parse repeated `name=1,3,640,640` CLI values into concrete dimensions.""" + result: dict[str, tuple[int, ...]] = {} + for item in items: + try: + name, dimensions = item.split("=", 1) + shape = tuple(int(value) for value in dimensions.split(",")) + except ValueError as error: + raise ValueError(f"Invalid --shape '{item}'; expected name=1,3,640,640") from error + if not shape or any(value <= 0 for value in shape): + raise ValueError(f"Shape values must be positive: {item}") + result[name] = shape + return result + + +def providers_for(name: str) -> list[str | tuple[str, dict[str, object]]]: + """Return ONNX Runtime providers in preferred-to-fallback priority order.""" + if name == "cpu": + return ["CPUExecutionProvider"] + if name == "cuda": + return ["CUDAExecutionProvider", "CPUExecutionProvider"] + return [ + ("TensorrtExecutionProvider", {"trt_fp16_enable": True, "trt_timing_cache_enable": True}), + "CUDAExecutionProvider", + "CPUExecutionProvider", + ] + + +def concrete_shape(name: str, model_shape: list[int | str | None], overrides: dict[str, tuple[int, ...]]) -> tuple[int, ...]: + """Resolve a static model shape or require a CLI override for dynamic inputs.""" + if name in overrides: + return overrides[name] + if any(not isinstance(value, int) or value <= 0 for value in model_shape): + raise ValueError(f"Input '{name}' is dynamic; provide --shape {name}=1,3,640,640") + return tuple(int(value) for value in model_shape) + + +def numpy_type(ort_type: str) -> np.dtype: + """Map common ONNX Runtime tensor type names to NumPy dtypes.""" + supported = { + "tensor(float)": np.dtype(np.float32), + "tensor(float16)": np.dtype(np.float16), + "tensor(uint8)": np.dtype(np.uint8), + "tensor(int64)": np.dtype(np.int64), + } + if ort_type not in supported: + raise ValueError(f"Unsupported demo input type: {ort_type}") + return supported[ort_type] + + +def main() -> int: + """Create an inference session, build inputs, warm up, and benchmark outputs.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("model", type=Path) + parser.add_argument("--provider", choices=("cpu", "cuda", "tensorrt"), default="cpu") + parser.add_argument("--shape", action="append", default=[], help="Input override, e.g. images=1,3,640,640") + parser.add_argument("--input-npy", type=Path, help="Use a .npy array for a single-input model") + parser.add_argument("--runs", type=int, default=10) + parser.add_argument("--seed", type=int, default=42) + args = parser.parse_args() + if args.runs < 1: + parser.error("--runs must be at least 1") + + # ONNX Runtime can silently fall back; fail early so GPU tests are not mistaken for CPU tests. + required_provider = { + "cpu": "CPUExecutionProvider", + "cuda": "CUDAExecutionProvider", + "tensorrt": "TensorrtExecutionProvider", + }[args.provider] + available = ort.get_available_providers() + if required_provider not in available: + raise RuntimeError( + f"Requested provider '{required_provider}' is not installed. " + f"Available providers: {', '.join(available)}" + ) + requested = providers_for(args.provider) + session = ort.InferenceSession(str(args.model), providers=requested) + print("available_providers=" + ",".join(available)) + print("active_providers=" + ",".join(session.get_providers())) + + overrides = parse_shapes(args.shape) + rng = np.random.default_rng(args.seed) + feeds: dict[str, np.ndarray] = {} + for index, item in enumerate(session.get_inputs()): + if args.input_npy and len(session.get_inputs()) == 1: + # A saved preprocessed tensor gives more meaningful parity than random input. + value = np.load(args.input_npy) + else: + shape = concrete_shape(item.name, item.shape, overrides) + dtype = numpy_type(item.type) + value = rng.random(shape).astype(dtype) if np.issubdtype(dtype, np.floating) else np.zeros(shape, dtype=dtype) + feeds[item.name] = value + print(f"input[{index}] name={item.name} shape={value.shape} dtype={value.dtype}") + + # Exclude one-time graph/session initialization effects from the measured runs. + session.run(None, feeds) # warm-up + started = time.perf_counter() + outputs: list[np.ndarray] = [] + for _ in range(args.runs): + outputs = session.run(None, feeds) + elapsed_ms = (time.perf_counter() - started) * 1000.0 / args.runs + + for index, value in enumerate(outputs): + numeric = np.asarray(value) + # Ignore NaN/Inf when calculating readable output statistics. + finite = numeric[np.isfinite(numeric)] if np.issubdtype(numeric.dtype, np.number) else np.array([]) + stats = "non-numeric-or-empty" + if finite.size: + stats = f"min={finite.min():.6g} max={finite.max():.6g} mean={finite.mean():.6g}" + print(f"output[{index}] shape={numeric.shape} dtype={numeric.dtype} {stats}") + print(f"average_inference_ms={elapsed_ms:.3f} runs={args.runs}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/RobotVisionPlatform/docs/README.md b/RobotVisionPlatform/docs/README.md new file mode 100644 index 0000000..61b1ee1 --- /dev/null +++ b/RobotVisionPlatform/docs/README.md @@ -0,0 +1,17 @@ +# 문서 인덱스 + +| 문서 | 목적 | +|---|---| +| [getting-started-for-beginners.md](getting-started-for-beginners.md) | 처음 실행하는 사람을 위한 순서별 실습 | +| [glossary.md](glossary.md) | 프로젝트에서 사용하는 용어 설명 | +| [troubleshooting.md](troubleshooting.md) | 자주 발생하는 설치·실행 문제 | +| [../reference/README.md](../reference/README.md) | 함수·타입·명령 사전과 실제 소스 위치 | +| [architecture.md](architecture.md) | 전체 구성, 경계, 데이터 흐름 | +| [initial-release-guide.md](initial-release-guide.md) | v0.1.0 빌드·설치·검증·롤백 | +| [jetson-deployment.md](jetson-deployment.md) | Orin Nano Super 준비와 서비스 운영 | +| [streaming-and-protocols.md](streaming-and-protocols.md) | gRPC/WebRTC/압축 선택 기준 | +| [model-lifecycle.md](model-lifecycle.md) | 학습부터 OTA/롤백까지 | +| [security-and-operations.md](security-and-operations.md) | 보안, 관측성, 장애 대응 | +| [adr/0001-platform-architecture.md](adr/0001-platform-architecture.md) | 주요 기술 선택 기록 | + +문서는 구현과 같은 PR에서 갱신합니다. 호환성 표와 외부 링크는 릴리스마다 재검증합니다. diff --git a/RobotVisionPlatform/docs/adr/0001-platform-architecture.md b/RobotVisionPlatform/docs/adr/0001-platform-architecture.md new file mode 100644 index 0000000..8052213 --- /dev/null +++ b/RobotVisionPlatform/docs/adr/0001-platform-architecture.md @@ -0,0 +1,13 @@ +# ADR 0001: Edge C++20 + ASP.NET Core + MAUI Hybrid + +- 상태: Accepted +- 날짜: 2026-08-17 + +## 결정 + +Jetson 실행부는 C++20과 NVIDIA 가속 스택을 사용합니다. 서버는 ASP.NET Core, 운영 UI는 웹과 네이티브 배포가 모두 가능한 MAUI Blazor Hybrid/shared Razor components를 사용합니다. gRPC는 control plane, WebRTC는 media plane, SignalR는 UI fan-out에 사용합니다. + +## 결과 + +GPU zero-copy와 하드웨어 codec을 활용하면서 서버 생산성을 유지할 수 있습니다. 반면 프로토콜과 미디어 세션이 분리되므로 연결 수명주기와 correlation을 명시적으로 관리해야 합니다. MAUI는 서버 역할을 맡지 않고 관제 클라이언트로 한정합니다. + diff --git a/RobotVisionPlatform/docs/architecture.md b/RobotVisionPlatform/docs/architecture.md new file mode 100644 index 0000000..e27e9a7 --- /dev/null +++ b/RobotVisionPlatform/docs/architecture.md @@ -0,0 +1,45 @@ +# 시스템 아키텍처 + +## 원칙 + +1. 장치는 네트워크가 끊겨도 추론과 안전 관련 작업을 계속한다. +2. 영상(media plane)과 이벤트/제어(control plane)를 분리해 장애와 대역폭을 격리한다. +3. 모든 큐는 bounded queue로 만들고, 실시간 경로에서는 오래된 프레임을 버려 지연 누적을 막는다. +4. 카메라·추론기·전송기는 인터페이스 뒤에 두어 USB/CSI, TensorRT/DeepStream, WebRTC/SRT를 교체한다. +5. 서버가 보내는 동작 명령은 정책 검사, 만료 시간, idempotency key, 감사 로그를 갖는다. + +## 구성 요소 + +- `device`: 프레임 획득, 전처리, 추론, 로컬 규칙, 인코딩, 이벤트/상태 전송, 오프라인 버퍼 +- `server/Api`: 장치 세션, ingest, 최신 상태, SignalR fan-out, 향후 저장소/VLM 작업 큐 +- `server/Dashboard`: 웹/Windows/Android 관제 UI의 공유 Razor 컴포넌트 +- `shared/proto`: wire contract의 단일 원본 +- Object storage: 이벤트 클립/스냅샷. 원본 상시 업로드는 기본값이 아님 +- PostgreSQL: 장치·모델·배포·이벤트 메타데이터 + +## 장치 파이프라인 + +```text +Capture (NVMM zero-copy) + -> latest-frame queue + -> batching/preprocess + -> TensorRT inference + -> local rule engine + +-> protobuf event queue -> retrying gRPC transport + +-> OSD -> NVENC -> WebRTC/SRT +``` + +코어는 합성 구현으로 테스트할 수 있습니다. 실제 Jetson adapter는 DeepStream 9.x의 C/C++ 플러그인 또는 GStreamer 앱 파이프라인으로 연결합니다. CUDA/NVMM 버퍼를 CPU로 복사하지 않는 것이 핵심입니다. + +## 서버 처리 + +수집 handler는 무거운 VLM을 직접 실행하지 않습니다. 이벤트를 검증·저장하고 bounded channel/메시지 브로커에 넣은 뒤 즉시 응답합니다. 별도 worker가 keyframe/짧은 clip만 VLM에 batch로 전달합니다. 실시간 객체 추적은 edge에서, 의미 해석은 server에서 수행하는 계층형 추론입니다. + +## 확장 지점 + +- `ICamera`: Synthetic → V4L2/GStreamer/NvArgus +- `IDetector`: Noop → ONNX Runtime → TensorRT/DeepStream +- `IEventSink`: stdout → gRPC/mTLS → broker gateway +- `IVideoPublisher`: disabled → WebRTC → SRT/RTSP +- `IAction`: log only → GPIO/ROS 2/PLC adapter + diff --git a/RobotVisionPlatform/docs/getting-started-for-beginners.md b/RobotVisionPlatform/docs/getting-started-for-beginners.md new file mode 100644 index 0000000..b35e25a --- /dev/null +++ b/RobotVisionPlatform/docs/getting-started-for-beginners.md @@ -0,0 +1,103 @@ +# 초보자 시작 가이드 + +이 실습의 목표는 실제 카메라 없이도 다음 흐름을 눈으로 확인하는 것입니다. + +```text +예제 탐지 데이터 전송 -> 서버가 수신 -> 브라우저에 장치와 탐지 결과 표시 +``` + +처음에는 Jetson이 없어도 됩니다. Windows 개발 PC만으로 서버 부분을 실행할 수 있습니다. + +## 1. 준비물 + +필수: + +- Git +- [.NET 10 SDK](https://dotnet.microsoft.com/download/dotnet/10.0) +- PowerShell 7 또는 Windows PowerShell + +C++ 장치 프로그램까지 빌드하려면 다음 중 하나가 추가로 필요합니다. + +- Windows: Visual Studio 2022 Build Tools의 **Desktop development with C++** workload +- Ubuntu/Jetson: `build-essential`, CMake 3.22 이상, Ninja + +Docker와 MAUI는 첫 실습에는 필요하지 않습니다. + +## 2. 프로젝트 폴더로 이동 + +저장소를 받은 뒤 PowerShell에서 실행합니다. + +```powershell +cd RobotVisionPlatform +dotnet --version +``` + +`10.`으로 시작하는 버전이 보이면 준비가 된 것입니다. + +## 3. 서버 빌드와 테스트 + +```powershell +./scripts/build.ps1 +./scripts/test.ps1 +``` + +`빌드했습니다`, `오류 0개`, `Server core tests passed`가 표시되면 성공입니다. C++ compiler가 없다는 경고는 서버만 실습할 때는 무시해도 됩니다. + +## 4. 서버 실행 + +```powershell +dotnet run --project server/src/RobotVision.Server.Api +``` + +터미널이 실행 상태로 유지되는 것이 정상입니다. 브라우저에서 아래 주소를 엽니다. + +- 관제 화면: +- 상태 확인: + +처음에는 장치가 없다는 메시지가 표시됩니다. + +## 5. 예제 장치 데이터 전송 + +서버 터미널은 그대로 두고 새 PowerShell 창을 열어 같은 프로젝트 폴더로 이동한 뒤 실행합니다. + +```powershell +./scripts/send-demo-event.ps1 +``` + +다시 관제 화면을 보면 `beginner-demo-001` 장치와 `person` 탐지 결과가 나타납니다. 화면은 2초마다 새로고침됩니다. + +서버를 종료하려면 서버가 실행 중인 터미널에서 `Ctrl+C`를 누릅니다. 서버를 재시작하면 현재 장치 목록이 사라지는 것이 정상입니다. 아직 데이터베이스를 연결하지 않은 MVP이기 때문입니다. + +## 6. C++ 장치 프로그램 실행 + +Windows에서는 **Developer PowerShell for Visual Studio**를 열어 다음 명령을 사용합니다. + +```powershell +cmake -S device -B device/build +cmake --build device/build --config Release +ctest --test-dir device/build -C Release --output-on-failure +./device/build/Release/robot_vision_device.exe --device-id my-first-device +``` + +Ubuntu 또는 Jetson에서는 다음과 같습니다. + +```bash +cmake -S device -B device/build -G Ninja -DCMAKE_BUILD_TYPE=Release +cmake --build device/build +ctest --test-dir device/build --output-on-failure +./device/build/robot_vision_device --device-id my-first-device +``` + +현재 C++ 프로그램은 실제 카메라 대신 일정한 속도로 가짜 프레임을 만듭니다. 세 프레임마다 `demo-object` 하나를 탐지한 것처럼 출력합니다. 실제 카메라와 TensorRT 연결은 README의 Phase 1 작업입니다. + +## 다음 학습 순서 + +1. [비전 학습 기초](../tips/fundamentals/vision-training-basics.md) +2. [데이터셋 만들기](../tips/data/dataset-engineering.md) +3. [Fine-tuning 실습 순서](../tips/training/fine-tuning-playbook.md) +4. [전체 아키텍처](architecture.md) +5. [Jetson 배포](jetson-deployment.md) +6. [ONNX/TensorRT 모델 배포](../device/guides/README.md) + +문제가 생기면 [문제 해결 문서](troubleshooting.md)를 먼저 확인합니다. +코드에서 모르는 함수나 타입은 [Reference 사전](../reference/README.md)에서 이름으로 찾을 수 있습니다. diff --git a/RobotVisionPlatform/docs/glossary.md b/RobotVisionPlatform/docs/glossary.md new file mode 100644 index 0000000..621f0eb --- /dev/null +++ b/RobotVisionPlatform/docs/glossary.md @@ -0,0 +1,32 @@ +# 용어집 + +| 용어 | 쉬운 설명 | +|---|---| +| Edge / Device | 카메라 가까이에 설치되어 영상을 직접 처리하는 Jetson 장치 | +| Server | 여러 장치의 상태와 결과를 모아 저장하고 화면에 보여주는 프로그램 | +| Model | 이미지에서 사람이나 물체를 찾도록 학습된 파일과 계산 규칙 | +| Training | 정답이 표시된 데이터로 모델을 학습하는 과정 | +| Fine-tuning | 이미 학습된 모델을 우리 데이터에 맞게 추가 학습하는 과정 | +| Inference | 학습이 끝난 모델에 새 이미지를 넣어 결과를 얻는 과정 | +| Frame | 영상 한 장면을 구성하는 한 개의 이미지 | +| FPS | 1초에 처리하는 프레임 수. 높을수록 영상이 더 부드러움 | +| Latency | 카메라 촬영부터 결과 표시까지 걸린 시간 | +| Queue | 처리할 프레임이나 이벤트가 잠시 기다리는 줄 | +| Bounded queue | 최대 크기를 제한한 대기 줄. 메모리와 지연이 무한히 늘어나는 것을 방지함 | +| Drop | 실시간성을 유지하기 위해 처리하지 못한 오래된 프레임을 버리는 것 | +| ONNX | 서로 다른 학습·추론 도구 사이에서 모델을 전달하기 위한 공통 형식 | +| TensorRT | NVIDIA GPU에서 모델 추론을 빠르게 실행하도록 최적화하는 SDK | +| DeepStream | NVIDIA GPU 기반 영상 입력·추론·추적·인코딩 파이프라인 SDK | +| NVMM / zero-copy | 영상 데이터를 CPU 메모리로 계속 복사하지 않고 GPU 쪽에서 전달하는 방식 | +| H.264 / H.265 | 영상 크기와 네트워크 사용량을 줄이는 압축 형식 | +| WebRTC | 브라우저와 앱에 낮은 지연으로 영상·음성을 전달하는 기술 | +| gRPC | 장치와 서버가 정해진 메시지 형식으로 빠르게 통신하는 기술 | +| protobuf | gRPC 메시지의 필드와 타입을 정의하는 형식 | +| SignalR | 서버가 웹/앱 화면에 변경 사항을 실시간으로 알려주는 .NET 기술 | +| VLM | 이미지·영상과 문장을 함께 이해하는 Vision-Language Model | +| VLA | 시각·언어 정보로 로봇 행동까지 결정하는 Vision-Language-Action 모델 | +| mTLS | 장치와 서버가 서로 인증서를 확인하는 양방향 암호화 연결 | +| OTA | 현장 장치를 직접 방문하지 않고 네트워크로 업데이트하는 방식 | +| Canary | 전체 장치보다 먼저 소수 장치에 새 버전을 배포해 확인하는 방식 | +| Rollback | 문제가 발생했을 때 이전 정상 버전으로 되돌리는 것 | + diff --git a/RobotVisionPlatform/docs/initial-release-guide.md b/RobotVisionPlatform/docs/initial-release-guide.md new file mode 100644 index 0000000..df2308b --- /dev/null +++ b/RobotVisionPlatform/docs/initial-release-guide.md @@ -0,0 +1,63 @@ +# 초기 릴리스 가이드 (v0.1.0) + +v0.1.0의 목적은 **합성 장치 → 이벤트 수집 서버 → 웹 관제 화면**의 end-to-end 계약 검증입니다. 실제 카메라 영상/WebRTC/TensorRT는 다음 마일스톤입니다. + +## 사전 조건 + +- 개발 PC: CMake 3.22+, C++20 compiler, .NET 10 SDK, Docker(선택) +- Jetson: JetPack/펌웨어 확인, 19V 정격 전원, 충분한 냉각, 유선 LAN 우선 +- 배포 환경: 장치 ID, 서버 주소, 시간 동기화, 인증서 발급 계획 + +## 1. 검증 빌드 + +```bash +./scripts/build.sh +./scripts/test.sh +``` + +Windows PowerShell에서는 `./scripts/build.ps1`, `./scripts/test.ps1`을 사용합니다. C++ compiler가 없는 PC에서는 .NET만 검증되고 CI Linux job이 C++ 빌드를 보완합니다. + +## 2. 서버 기동 + +```bash +docker compose -f deploy/compose/docker-compose.yml up --build +curl http://localhost:5080/health +``` + +또는 `dotnet run --project server/src/RobotVision.Server.Api`로 실행합니다. + +## 3. 장치 smoke test + +현재 기본 agent는 stdout sink를 사용합니다. + +```bash +./device/build/robot_vision_device --device-id jetson-001 +``` + +서버 계약은 아래 이벤트로 검증합니다. + +```bash +curl -X POST http://localhost:5080/api/events/detections \ + -H 'content-type: application/json' \ + -d '{"deviceId":"jetson-001","sequence":1,"modelVersion":"demo/0.1.0","detections":[]}' +curl http://localhost:5080/api/devices/jetson-001 +``` + +## 4. 릴리스 산출물 + +- `robot_vision_device` arm64 binary 또는 `.deb` +- server container image(digest로 고정) +- model bundle + manifest + signature +- `device.toml` schema/version과 migration note +- SBOM, checksum, release notes, JetPack/DeepStream 호환표 + +tag는 SemVer를 사용합니다. 장치 binary, 서버, 모델은 서로 다른 버전을 가지며 release manifest에서 검증된 조합을 선언합니다. + +## 5. 단계 배포와 롤백 + +1. lab device 1대에 설치하고 health/FPS/온도/이벤트를 30분 확인 +2. canary cohort에서 24시간 관찰 +3. cohort를 점진 확대 +4. crash loop, latency/temperature/drop guardrail 위반 시 이전 symlink와 systemd service로 즉시 rollback + +현재 골격은 자동 OTA를 구현하지 않았습니다. 인증·서명·atomic switch가 완료되기 전 원격 덮어쓰기는 금지합니다. diff --git a/RobotVisionPlatform/docs/jetson-deployment.md b/RobotVisionPlatform/docs/jetson-deployment.md new file mode 100644 index 0000000..ff43e9b --- /dev/null +++ b/RobotVisionPlatform/docs/jetson-deployment.md @@ -0,0 +1,49 @@ +# Jetson Orin Nano Super 준비와 배포 + +대상은 **NVIDIA Jetson Orin Nano Super Developer Kit**입니다. Developer Kit은 개발/검증용이며 양산 제품은 production module과 carrier/thermal/power 설계를 별도로 검토해야 합니다. + +## 1. OS와 펌웨어 + +1. [공식 시작 가이드](https://developer.nvidia.com/embedded/learn/get-started-jetson-orin-nano-devkit)에서 JetPack 6.x 호환 펌웨어 여부를 먼저 확인합니다. 구형 출하 펌웨어는 6.x SD 이미지와 바로 호환되지 않을 수 있습니다. +2. SDK Manager 또는 공식 SD 이미지를 사용합니다. NVMe를 운영 저장소로 권장합니다. +3. `cat /etc/nv_tegra_release`, `dpkg-query -W nvidia-jetpack`, `nvpmodel -q` 결과를 릴리스 기록에 첨부합니다. +4. Super Mode는 충분한 전원과 냉각을 전제로 설정하고 30분 이상 thermal soak test를 수행합니다. + +JetPack, CUDA, cuDNN, TensorRT, DeepStream은 독립적으로 최신 버전을 섞지 않습니다. [DeepStream release notes](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Release_notes.html)의 platform compatibility를 기준으로 한 세트를 고정합니다. + +## 2. 빌드 + +```bash +sudo apt-get update +sudo apt-get install -y build-essential cmake ninja-build libboost-system-dev +cmake -S device -B device/build -G Ninja -DCMAKE_BUILD_TYPE=Release +cmake --build device/build +ctest --test-dir device/build --output-on-failure +cmake --install device/build --prefix device/stage +``` + +실제 adapter를 추가할 때 JetPack이 제공하는 GStreamer, CUDA, TensorRT 개발 패키지를 사용합니다. 컨테이너를 쓴다면 Jetson용 L4T/DeepStream base image와 NVIDIA Container Runtime의 호환성을 고정합니다. + +## 3. 설치 + +```bash +sudo install -d -o robotvision -g robotvision /opt/robot-vision/bin /etc/robot-vision +sudo install -m 0755 device/stage/bin/robot_vision_device /opt/robot-vision/bin/ +sudo install -m 0640 device/config/device.example.toml /etc/robot-vision/device.toml +sudo install -m 0644 deploy/systemd/robot-vision-device.service /etc/systemd/system/ +sudo systemctl daemon-reload +sudo systemctl enable --now robot-vision-device +``` + +운영에서는 `DynamicUser`보다 카메라/video 그룹과 인증서 권한을 가진 고정 전용 사용자를 생성합니다. unit의 `ExecStart`는 현재 CLI와 일치하며 TOML parser가 연결되기 전까지 `--device-id`만 사용합니다. + +## 4. 확인 + +```bash +systemctl status robot-vision-device +journalctl -u robot-vision-device -f +tegrastats +``` + +카메라 분리, 서버 차단, Wi-Fi 전환, 재부팅, 온도 상승 상태를 검증합니다. OTA 전에는 이전 binary/model symlink를 유지해 한 명령으로 rollback할 수 있어야 합니다. + diff --git a/RobotVisionPlatform/docs/model-lifecycle.md b/RobotVisionPlatform/docs/model-lifecycle.md new file mode 100644 index 0000000..6297845 --- /dev/null +++ b/RobotVisionPlatform/docs/model-lifecycle.md @@ -0,0 +1,30 @@ +# 모델 수명주기 + +## 흐름 + +1. 수집: 개인정보 마스킹, scene/device metadata, 중복 제거 후 샘플만 보존 +2. 라벨: 클래스 정의서와 edge-case bucket을 먼저 고정 +3. 분할: 같은 영상/장소가 train과 validation에 섞이지 않도록 group split +4. 학습: pretrained detector fine-tuning, augmentation ablation, 실험 추적 +5. 평가: mAP 외에 class별 precision/recall, latency, memory, thermal throttling 측정 +6. export: ONNX 검증 후 target Jetson에서 TensorRT engine 생성 +7. 승인: manifest와 artifact hash/signature 검증 +8. 배포: canary 1대 → cohort → fleet, health/accuracy guardrail 위반 시 rollback +9. 개선: uncertainty/오탐/미탐 후보를 active-learning queue로 회수 + +TensorRT engine은 GPU/JetPack/TensorRT 버전에 민감할 수 있으므로 범용 ONNX와 target별 engine을 구분하고 manifest에 호환 조건을 기록합니다. 모델 파일만 교체하지 말고 labels, preprocessing, postprocessing, calibration cache를 하나의 불변 bundle로 취급합니다. + +예시 manifest: + +```json +{ + "modelId": "forklift-detector", + "version": "0.1.0", + "format": "onnx", + "sha256": "replace-me", + "input": { "width": 640, "height": 640, "color": "RGB" }, + "labels": ["person", "forklift"], + "minimumRuntime": { "jetpack": "6.x", "tensorrt": "verify-on-target" } +} +``` + diff --git a/RobotVisionPlatform/docs/security-and-operations.md b/RobotVisionPlatform/docs/security-and-operations.md new file mode 100644 index 0000000..946590f --- /dev/null +++ b/RobotVisionPlatform/docs/security-and-operations.md @@ -0,0 +1,13 @@ +# 보안 및 운영 + +- 장치별 인증서와 mTLS를 사용하고 bootstrap token은 1회용으로 제한합니다. +- private key와 Wi-Fi 비밀번호는 이미지·Git·평문 TOML에 넣지 않습니다. +- 모델/설정 bundle은 서명과 SHA-256을 확인한 뒤 staging 경로에 내려받고 atomic switch합니다. +- 명령은 allow-list, TTL, idempotency key, issuer, audit event를 요구합니다. +- 카메라 영상은 최소 수집·최소 보존·접근 감사 원칙을 적용하고 얼굴/번호판 정책을 별도로 정합니다. +- root가 아닌 systemd 사용자, read-only root filesystem, 필요한 device/capability만 허용합니다. + +관측 항목은 capture/inference/publish FPS, end-to-end latency p50/p95/p99, queue drops, reconnect count, temperature, power mode, disk, active model hash입니다. 로그·metric·trace에는 같은 `device_id`와 `correlation_id`를 넣습니다. + +장애 시 장치는 지수 backoff+jitter로 재접속하고 최근 이벤트를 용량 제한 spool에 보관합니다. 디스크가 차면 원본 프레임보다 오래된 비중요 이벤트를 먼저 제거합니다. 서버 장애가 장치 로컬 동작을 중단시키면 안 됩니다. + diff --git a/RobotVisionPlatform/docs/streaming-and-protocols.md b/RobotVisionPlatform/docs/streaming-and-protocols.md new file mode 100644 index 0000000..a94f6c4 --- /dev/null +++ b/RobotVisionPlatform/docs/streaming-and-protocols.md @@ -0,0 +1,24 @@ +# 통신과 영상 전송 + +## 권장 조합 + +| 데이터 | 기본 | 대안 | 이유 | +|---|---|---|---| +| live video | WebRTC + H.264 | SRT/RTSP | 브라우저/MAUI 저지연 재생, congestion control | +| detection/health | gRPC bidirectional stream | MQTT | typed contract, 장치 명령을 같은 세션에서 전달 | +| dashboard update | SignalR | SSE | .NET 클라이언트와 웹 fan-out | +| clip upload | HTTPS object upload | gRPC chunk | 재시도와 대용량 분리 | + +protobuf 자체가 작은 수치 이벤트에 효율적이므로 이미 압축된 JPEG/H.264를 gzip으로 다시 압축하지 않습니다. gRPC message compression은 큰 반복 텍스트에만 측정 후 적용합니다. 영상은 NVENC에서 H.264 low-latency preset을 사용하고, 해상도/FPS/bitrate를 네트워크 상태에 맞춰 조절합니다. + +`shared/proto/vision/v1/device.proto`의 `Connect`는 장치가 시작하는 장기 bidi stream입니다. heartbeat, detection batch, model state를 보내고 서버는 config/model/action 명령을 돌려줍니다. 메시지에 `device_id`, `sequence`, UTC timestamp, schema version을 두어 재연결과 중복을 처리합니다. + +WebRTC signaling은 인증된 HTTPS/SignalR endpoint로 추가하고, NAT 환경에서는 STUN/TURN을 둡니다. LAN 전용 MVP라도 TLS와 장치 신원은 생략하지 않습니다. + +## 공식 참고 + +- [NVIDIA Jetson WebRTC hardware acceleration](https://docs.nvidia.com/jetson/archives/r36.3/DeveloperGuide/SD/HardwareAccelerationInTheWebrtcFramework.html) +- [gRPC C++ basics](https://grpc.io/docs/languages/cpp/basics/) +- [ASP.NET Core gRPC services](https://learn.microsoft.com/aspnet/core/grpc/services?view=aspnetcore-10.0) +- [ASP.NET Core SignalR](https://learn.microsoft.com/aspnet/core/signalr/introduction) + diff --git a/RobotVisionPlatform/docs/troubleshooting.md b/RobotVisionPlatform/docs/troubleshooting.md new file mode 100644 index 0000000..8694d07 --- /dev/null +++ b/RobotVisionPlatform/docs/troubleshooting.md @@ -0,0 +1,50 @@ +# 문제 해결 + +## `dotnet` 명령을 찾을 수 없음 + +.NET 10 **SDK**를 설치하고 새 터미널을 엽니다. Runtime만 설치하면 빌드할 수 없습니다. `dotnet --info`로 SDK 목록을 확인합니다. + +## C++ compiler를 찾을 수 없음 + +Windows에서는 Visual Studio Build Tools와 **Desktop development with C++** workload를 설치한 뒤 일반 PowerShell이 아니라 **Developer PowerShell for Visual Studio**에서 실행합니다. + +Ubuntu/Jetson: + +```bash +sudo apt-get update +sudo apt-get install -y build-essential cmake ninja-build +``` + +## CMake가 `nmake`를 찾지 못함 + +일반 PowerShell에서 MSVC 환경 변수가 설정되지 않았을 때 발생합니다. Developer PowerShell을 사용하거나 Ninja를 설치한 뒤 `-G Ninja`를 지정합니다. + +## 5080 포트를 이미 사용 중임 + +기존 서버를 `Ctrl+C`로 종료합니다. 다른 포트를 사용하려면 다음과 같이 실행합니다. + +```powershell +dotnet run --project server/src/RobotVision.Server.Api --urls http://localhost:5090 +``` + +이 경우 데모 스크립트에도 포트를 전달합니다. + +```powershell +./scripts/send-demo-event.ps1 -ServerUrl http://localhost:5090 +``` + +## 관제 화면에 장치가 표시되지 않음 + +1. 가 열리는지 확인합니다. +2. 데모 스크립트 결과가 `202`인지 확인합니다. +3. 관제 화면을 새로고침하고 2초 기다립니다. +4. 서버 재시작 후라면 데모 이벤트를 다시 보냅니다. + +## MAUI 프로젝트가 빌드되지 않음 + +MAUI는 첫 실습과 서버 실행에 필요하지 않습니다. 필요한 경우 Visual Studio의 MAUI workload 또는 `dotnet workload install maui`를 설치합니다. 기본 solution은 MAUI를 제외하므로 일반 서버 CI에는 영향을 주지 않습니다. + +## Jetson에서 Super Mode가 보이지 않음 + +구형 출하 펌웨어가 JetPack 6.x와 호환되지 않을 수 있습니다. 임의 패키지 설치보다 [NVIDIA 공식 시작 가이드](https://developer.nvidia.com/embedded/learn/get-started-jetson-orin-nano-devkit)의 펌웨어 확인 절차를 먼저 따릅니다. + diff --git a/RobotVisionPlatform/reference/README.md b/RobotVisionPlatform/reference/README.md new file mode 100644 index 0000000..0b1d2da --- /dev/null +++ b/RobotVisionPlatform/reference/README.md @@ -0,0 +1,37 @@ +# Robot Vision Reference + +프로젝트에서 보이는 함수·타입·명령을 이름으로 찾아보는 사전입니다. 처음부터 끝까지 읽기보다 편집기 검색(`Ctrl+F`)으로 필요한 항목을 찾으세요. + +## 언어별 사전 + +| 문서 | 찾을 수 있는 내용 | +|---|---| +| [C++20 장치 코드](cpp20-device-reference.md) | interface, smart pointer, move, jthread, stop_token, queue, atomic | +| [C# 서버 코드](csharp-server-reference.md) | record, DI, ConcurrentDictionary, Channel, BackgroundService, SignalR | +| [Python 모델 도구](python-model-tools-reference.md) | argparse, Path, hashing, ONNX, ONNX Runtime, NumPy | +| [스크립트·프로토콜](scripts-and-protocol-reference.md) | PowerShell, Bash, CMake, protobuf, gRPC 기본 사용법 | + +## 기능에서 역으로 찾기 + +| 하고 싶은 일 | 찾아볼 항목 | 실제 시작 파일 | +|---|---|---| +| 카메라 종류 추가 | `ICamera`, `override`, factory | [`interfaces.hpp`](../device/include/rv/interfaces.hpp) | +| TensorRT detector 추가 | `IDetector`, `unique_ptr` | [`interfaces.hpp`](../device/include/rv/interfaces.hpp) | +| 프레임 지연 제어 | `LatestQueue`, `condition_variable_any` | [`bounded_queue.hpp`](../device/include/rv/bounded_queue.hpp) | +| 장치 event 수신 | Minimal API `MapPost` | [`Program.cs`](../server/src/RobotVision.Server.Api/Program.cs) | +| 최신 장치 상태 저장 | `ConcurrentDictionary`, `AddOrUpdate` | [`DeviceRegistry.cs`](../server/src/RobotVision.Server.Api/DeviceRegistry.cs) | +| 화면에 실시간 알림 | `Channel`, `BackgroundService`, SignalR | [`DetectionFanoutWorker.cs`](../server/src/RobotVision.Server.Api/DetectionFanoutWorker.cs) | +| ONNX 입출력 확인 | `onnx.load`, `checker`, shape inference | [`inspect_onnx.py`](../device/tools/model/inspect_onnx.py) | +| ONNX 추론 | `InferenceSession`, `session.run` | [`validate_onnx.py`](../device/tools/model/validate_onnx.py) | +| TensorRT engine 생성 | `trtexec`, dynamic shape | [`build_tensorrt_engine.sh`](../device/tools/model/build_tensorrt_engine.sh) | +| 통신 필드 추가 | protobuf `message`, `oneof` | [`device.proto`](../shared/proto/vision/v1/device.proto) | + +## 표기 규칙 + +- `입력`: 함수가 받는 값 +- `반환`: 함수가 호출자에게 돌려주는 값 +- `수명`: 객체가 언제까지 유효한지 +- `thread-safe`: 여러 thread에서 동시에 사용 가능한지 +- `주의`: 이 프로젝트에서 자주 발생할 수 있는 실수 + +코드 동작의 전체 흐름은 [시스템 아키텍처](../docs/architecture.md), 모델 배포 흐름은 [Device 모델 배포 가이드](../device/guides/README.md)를 함께 참고합니다. diff --git a/RobotVisionPlatform/reference/cpp20-device-reference.md b/RobotVisionPlatform/reference/cpp20-device-reference.md new file mode 100644 index 0000000..8af6a35 --- /dev/null +++ b/RobotVisionPlatform/reference/cpp20-device-reference.md @@ -0,0 +1,186 @@ +# C++20 장치 코드 사전 + +## `class`와 interface + +`ICamera`, `IDetector`, `IEventSink`는 구현이 반드시 제공해야 할 함수만 선언하는 interface 역할입니다. + +```cpp +class IDetector { + public: + virtual ~IDetector() = default; + virtual std::vector Infer(const Frame& frame) = 0; +}; +``` + +- `virtual`: 실제 객체의 override 함수를 호출하게 합니다. +- `= 0`: 구현이 없는 pure virtual 함수라는 뜻입니다. +- virtual destructor: interface pointer로 삭제해도 concrete destructor가 실행됩니다. +- 위치: [`interfaces.hpp`](../device/include/rv/interfaces.hpp) + +새 detector는 다음 형태입니다. + +```cpp +class TensorRtDetector final : public rv::IDetector { + public: + std::vector Infer(const rv::Frame& frame) override; + std::string Version() const override { return "person-detector/1.0.0"; } +}; +``` + +`final`은 더 이상 상속하지 않음을, `override`는 base 함수와 signature가 정확히 같은지 compiler가 검사함을 뜻합니다. + +## `struct`와 aggregate initialization + +단순 데이터 묶음은 `struct`로 선언하고 지정 초기화할 수 있습니다. + +```cpp +rv::Detection detection{ + .label = "person", + .confidence = 0.92F, + .x = 0.1F, + .y = 0.2F, + .width = 0.3F, + .height = 0.5F}; +``` + +- 멤버는 선언된 순서를 지켜 지정합니다. +- `0.92F`의 `F`는 float literal임을 나타냅니다. +- 위치: [`types.hpp`](../device/include/rv/types.hpp) + +## `std::unique_ptr` + +객체 소유자가 한 곳뿐임을 나타내는 smart pointer입니다. + +```cpp +auto detector = std::make_unique(); +std::unique_ptr base = std::move(detector); +``` + +- `std::make_unique`: heap 객체 생성과 pointer wrapping을 한 번에 합니다. +- `std::move`: 소유권을 복사하지 않고 이전합니다. 이전 pointer는 비게 됩니다. +- Pipeline이 adapter 소유권을 가지므로 raw `new/delete`가 필요 없습니다. +- 위치: [`pipeline.hpp`](../device/include/rv/pipeline.hpp) + +## `const T&` + +복사 없이 읽기 전용으로 전달하는 reference입니다. + +```cpp +std::vector Infer(const Frame& frame); +``` + +- `Frame` 전체 pixel buffer를 복사하지 않습니다. +- 함수가 반환되기 전까지만 `frame`을 사용해야 합니다. +- `const`이므로 함수 내부에서 frame을 수정할 수 없습니다. + +## `std::jthread` + +C++20의 자동 join thread입니다. + +```cpp +worker = std::jthread([this](std::stop_token stop) { + WorkLoop(stop); +}); +``` + +- 생성 즉시 lambda를 새 thread에서 실행합니다. +- destructor는 종료 요청 후 join합니다. +- 프로젝트는 명시적 `Stop()`으로 종료 순서를 분명하게 합니다. +- 위치: [`pipeline.cpp`](../device/src/pipeline.cpp) + +## `std::stop_token`과 `request_stop()` + +worker에게 강제 종료 대신 협력적 종료를 요청합니다. + +```cpp +worker.request_stop(); +while (!stop.stop_requested()) { + // 한 단위 작업 +} +``` + +stop 요청은 exception이나 thread kill이 아닙니다. loop와 blocking wait가 token을 확인해야 종료됩니다. + +## `std::condition_variable_any::wait` + +queue가 비어 있을 때 CPU를 소비하는 반복 확인 대신 thread를 잠재웁니다. + +```cpp +ready.wait(lock, stop, [this] { return closed || !items.empty(); }); +``` + +- `lock`: 기다리는 동안 mutex를 풀고, 깨어날 때 다시 잡습니다. +- `stop`: 종료 요청도 wake-up 조건입니다. +- predicate: spurious wake-up이 생겨도 실제 조건을 다시 검사합니다. +- 위치: [`bounded_queue.hpp`](../device/include/rv/bounded_queue.hpp) + +## `std::scoped_lock`과 `std::unique_lock` + +```cpp +std::scoped_lock lock(mutex); // scope 끝에서 자동 unlock +std::unique_lock lock(mutex); // wait처럼 unlock/relock이 필요한 경우 +``` + +직접 `mutex.lock()`/`unlock()`을 호출하면 중간 exception이나 return에서 unlock을 잊을 수 있습니다. + +## `std::optional` + +“값이 있음”과 “정상적으로 값이 없음”을 모두 표현합니다. + +```cpp +auto value = queue.Pop(stop); +if (!value) break; +Use(*value); +``` + +이 프로젝트에서 `nullopt`는 queue close 또는 stop으로 consumer가 끝나야 함을 뜻합니다. + +## `std::atomic_uint64_t` + +여러 thread가 counter를 수정할 때 data race를 막습니다. + +```cpp +std::atomic_uint64_t captured{}; +++captured; +auto snapshot = captured.load(); +``` + +여러 필드가 반드시 같은 순간의 값이어야 한다면 atomic 여러 개만으로는 부족하고 별도 lock/snapshot 설계가 필요합니다. + +## `std::move` + +큰 buffer 또는 vector의 내부 자원을 새 객체로 이전합니다. + +```cpp +frames.Push(std::move(frame)); +event.detections = std::move(detections); +``` + +move 이후 원본은 유효하지만 내용은 보장되지 않으므로 다시 읽지 않습니다. + +## factory 함수 + +```cpp +std::unique_ptr MakeSyntheticCamera(int width, int height, int fps); +``` + +호출자가 concrete class 이름을 몰라도 interface 객체를 만들 수 있습니다. 실제 adapter로 교체해도 `main`과 `Pipeline` 변경을 줄여 줍니다. + +## `[[nodiscard]]` + +반환값을 무시하면 compiler가 경고할 수 있게 합니다. + +```cpp +[[nodiscard]] PipelineStats Stats() const; +``` + +상태나 오류 결과를 실수로 버리는 것을 방지할 때 사용합니다. + +## 종료 signal + +```cpp +volatile std::sig_atomic_t running = 1; +void HandleSignal(int) { running = 0; } +``` + +signal handler 안에서는 logging, allocation, mutex 같은 일반 함수를 호출하면 안전하지 않을 수 있습니다. handler는 flag만 바꾸고 정상 thread가 `Pipeline::Stop()`을 호출합니다. diff --git a/RobotVisionPlatform/reference/csharp-server-reference.md b/RobotVisionPlatform/reference/csharp-server-reference.md new file mode 100644 index 0000000..4b862e5 --- /dev/null +++ b/RobotVisionPlatform/reference/csharp-server-reference.md @@ -0,0 +1,157 @@ +# C# 서버 코드 사전 + +## `record` + +값 중심의 불변 데이터 계약에 사용합니다. + +```csharp +public sealed record BoundingBox(float X, float Y, float Width, float Height); +``` + +- 같은 필드 값을 가진 record끼리 값 비교가 가능합니다. +- request/response DTO와 snapshot에 적합합니다. +- `sealed`는 상속을 막아 계약을 단순하게 유지합니다. +- 위치: [`DeviceContracts.cs`](../server/src/RobotVision.Server.Contracts/DeviceContracts.cs) + +## Dependency Injection과 생성자 주입 + +ASP.NET Core가 필요한 객체를 constructor parameter로 제공합니다. + +```csharp +public sealed class Worker( + Channel channel, + ILogger logger) : BackgroundService +``` + +`Program.cs`의 `AddSingleton`, `AddHostedService` 등록을 보고 객체를 생성합니다. 코드에서 직접 `new Worker(...)`할 필요가 없습니다. + +## `AddSingleton()` + +application lifetime 동안 객체 하나를 공유합니다. + +```csharp +builder.Services.AddSingleton(); +``` + +장치 최신 상태처럼 모든 request가 같은 데이터를 봐야 할 때 적합합니다. request별 상태에는 scoped lifetime을 사용해야 합니다. + +## `ConcurrentDictionary` + +동시에 여러 request가 읽고 쓸 수 있는 thread-safe dictionary입니다. + +```csharp +private readonly ConcurrentDictionary _devices = new(); +``` + +여러 작업을 묶은 복합 불변식까지 자동으로 보호하는 것은 아닙니다. 이 프로젝트는 `AddOrUpdate` 한 번 안에서 최신 sequence를 선택합니다. + +## `AddOrUpdate` + +key가 없으면 추가하고, 있으면 현재 값을 받아 새 값을 계산합니다. + +```csharp +devices.AddOrUpdate(id, newValue, (_, current) => + sequence >= current.LastSequence ? newValue : current); +``` + +늦게 도착한 낮은 sequence event가 최신 상태를 덮어쓰지 못하게 합니다. + +## `TryGetValue` / `TryGet` + +exception 없이 값 존재 여부와 값을 함께 반환하는 패턴입니다. + +```csharp +if (registry.TryGet(deviceId, out var snapshot)) +{ + // snapshot 사용 +} +``` + +`out` parameter는 함수가 호출자 변수에 값을 채워 줍니다. + +## `Channel` + +비동기 producer-consumer queue입니다. + +```csharp +var channel = Channel.CreateBounded(1024); +channel.Writer.TryWrite(item); +await foreach (var item in channel.Reader.ReadAllAsync(token)) { } +``` + +- HTTP endpoint가 producer, `DetectionFanoutWorker`가 consumer입니다. +- bounded channel은 느린 UI 때문에 memory가 계속 늘어나는 것을 막습니다. +- `DropOldest`는 관제 화면에서 최신 상태를 우선하는 정책입니다. + +## `BackgroundService` + +ASP.NET Core application과 함께 시작·종료되는 장기 worker base class입니다. + +```csharp +protected override async Task ExecuteAsync(CancellationToken stoppingToken) +``` + +`stoppingToken`을 모든 비동기 wait/전송에 전달해야 application이 빠르게 종료됩니다. + +## `async`, `await`, `Task` + +I/O가 끝날 때까지 thread를 막지 않고 나중에 계속 실행합니다. + +```csharp +await hub.Clients.Group(group).SendAsync("detection", item, token); +``` + +`await`를 생략한 fire-and-forget 작업은 exception과 lifetime을 잃기 쉬우므로 background worker에서는 피합니다. + +## `CancellationToken` + +C++ `stop_token`과 비슷한 협력적 취소 신호입니다. + +```csharp +await operation(stoppingToken); +``` + +취소는 실패와 구분해야 하므로 worker는 `OperationCanceledException`을 일반 오류로 기록하지 않습니다. + +## Minimal API `MapGet` / `MapPost` + +route와 handler를 한 곳에 선언합니다. + +```csharp +app.MapGet("/api/devices", (DeviceRegistry registry) => Results.Ok(registry.List())); +app.MapPost("/api/events", (EventDto item) => Results.Accepted()); +``` + +parameter는 route, body 또는 DI에서 자동 binding됩니다. public API가 커지면 endpoint group과 validator로 분리합니다. + +## `Results.Ok`, `Accepted`, `BadRequest`, `NotFound` + +| 함수 | HTTP | 의미 | +|---|---:|---| +| `Results.Ok(value)` | 200 | 조회 성공 | +| `Results.Accepted(location)` | 202 | 수집했지만 후속 처리는 비동기 | +| `Results.BadRequest(value)` | 400 | client 입력 오류 | +| `Results.NotFound()` | 404 | 장치 없음 | + +## SignalR `Hub`와 group + +SignalR Hub는 서버가 연결된 client의 함수를 호출할 수 있게 합니다. + +```csharp +await Groups.AddToGroupAsync(Context.ConnectionId, "device:jetson-001"); +await hub.Clients.Group("device:jetson-001").SendAsync("detection", item); +``` + +group을 사용하면 모든 client가 아닌 특정 장치 화면에만 event를 보낼 수 있습니다. + +## `ILogger` 구조화 logging + +```csharp +logger.LogError(error, "Failed for {DeviceId}", deviceId); +``` + +문자열 보간보다 `{DeviceId}` template을 사용하면 log backend가 필드를 검색 가능한 값으로 보존합니다. + +## MAUI `MauiApp.CreateBuilder()`와 `BlazorWebView` + +`MauiApp.CreateBuilder()`는 native application, DI, logging을 구성합니다. `AddMauiBlazorWebView()`는 Razor component를 native WebView 안에서 실행할 service를 등록합니다. 현재 앱은 골격이며 SignalR/WebRTC 연결은 다음 단계입니다. diff --git a/RobotVisionPlatform/reference/python-model-tools-reference.md b/RobotVisionPlatform/reference/python-model-tools-reference.md new file mode 100644 index 0000000..3471ba5 --- /dev/null +++ b/RobotVisionPlatform/reference/python-model-tools-reference.md @@ -0,0 +1,170 @@ +# Python 모델 도구 사전 + +## `argparse.ArgumentParser` + +CLI 옵션을 선언하고 자동 도움말과 타입 변환을 제공합니다. + +```python +parser = argparse.ArgumentParser(description=__doc__) +parser.add_argument("model", type=Path) +parser.add_argument("--runs", type=int, default=10) +args = parser.parse_args() +``` + +사용자는 `python tool.py --help`로 옵션을 확인합니다. 잘못된 필수 옵션은 실행 전에 오류가 됩니다. + +## `pathlib.Path` + +운영체제와 무관하게 파일 경로를 다룹니다. + +```python +model = Path("models/model.onnx") +model.exists() +model.parent.mkdir(parents=True, exist_ok=True) +data = model.read_bytes() +``` + +문자열 경로 결합보다 Windows와 Linux separator 차이를 줄여 줍니다. + +## type hint + +```python +def sha256(path: Path) -> str: +``` + +`path`는 Path, 반환값은 str이라는 설명입니다. Python runtime이 자동 강제하지 않으므로 editor와 type checker의 도움을 받습니다. + +## docstring + +함수 첫 줄의 문자열은 함수 사용 목적을 설명합니다. + +```python +def parse_shapes(items: list[str]) -> dict[str, tuple[int, ...]]: + """Parse repeated shape options into concrete dimensions.""" +``` + +`help(parse_shapes)`나 IDE hover에서 볼 수 있습니다. + +## `hashlib.sha256` + +파일 내용의 고정 길이 fingerprint를 계산합니다. + +```python +digest = hashlib.sha256() +with path.open("rb") as source: + for chunk in iter(lambda: source.read(1024 * 1024), b""): + digest.update(chunk) +checksum = digest.hexdigest() +``` + +큰 ONNX 파일을 한 번에 RAM에 올리지 않고 1 MiB씩 읽습니다. checksum은 손상 확인용이며 배포 신뢰성에는 별도 전자서명이 필요합니다. + +## `json.dumps` + +Python dictionary를 JSON 문자열로 변환합니다. + +```python +text = json.dumps(manifest, ensure_ascii=False, indent=2) + "\n" +``` + +- `ensure_ascii=False`: 한글을 읽을 수 있게 유지합니다. +- `indent=2`: 사람이 검토하기 쉬운 formatting입니다. + +## `onnx.load` + +```python +model = onnx.load(path, load_external_data=True) +``` + +ONNX graph와 weight를 읽습니다. `load_external_data=True`는 큰 weight가 외부 파일로 분리된 모델도 함께 읽습니다. + +## `onnx.checker.check_model` + +graph 연결, operator schema, type 등 ONNX 구조가 유효한지 확인합니다. + +```python +checker.check_model(model) +``` + +통과는 정확도가 좋다는 뜻이 아니라 “ONNX 구조가 규칙에 맞다”는 뜻입니다. + +## `shape_inference.infer_shapes` + +입력과 operator 정보로 계산 가능한 중간/output shape를 채웁니다. + +```python +inferred = shape_inference.infer_shapes(model) +``` + +custom operator나 data-dependent shape는 모두 추론되지 않을 수 있습니다. + +## `onnxruntime.InferenceSession` + +ONNX graph를 지정 execution provider에서 실행할 session으로 준비합니다. + +```python +session = ort.InferenceSession( + "model.onnx", + providers=["CUDAExecutionProvider", "CPUExecutionProvider"], +) +``` + +provider는 앞에서부터 우선합니다. 프로젝트 도구는 요청한 GPU provider가 없을 때 CPU로 조용히 대체하지 않고 오류를 냅니다. + +## `session.get_inputs()` / `get_outputs()` + +모델의 runtime tensor 계약을 읽습니다. + +```python +for item in session.get_inputs(): + print(item.name, item.shape, item.type) +``` + +manifest의 input 이름·shape·type과 비교할 때 사용합니다. + +## `session.run` + +```python +outputs = session.run(None, {"images": input_array}) +``` + +- 첫 인자 `None`: 모든 output을 반환합니다. +- dictionary key: 정확한 ONNX input tensor 이름입니다. +- value: shape/type이 맞는 NumPy array입니다. + +첫 실행에는 graph 최적화와 cache 준비 시간이 포함될 수 있어 benchmark 전에 warm-up합니다. + +## NumPy `default_rng`, `astype`, `isfinite` + +```python +rng = np.random.default_rng(42) +input_array = rng.random(shape).astype(np.float32) +finite = output[np.isfinite(output)] +``` + +- 고정 seed는 같은 random input을 재현합니다. +- `astype`은 ONNX input type에 맞춥니다. +- `isfinite`는 NaN/Inf를 통계에서 분리합니다. + +random input은 실행 가능성 검사에만 적합합니다. 모델 정확도 비교에는 실제 전처리를 적용한 고정 `.npy` tensor를 사용합니다. + +## `subprocess.run(check=True)` + +테스트가 CLI를 별도 process로 실행합니다. + +```python +subprocess.run([sys.executable, str(tool), "--help"], check=True) +``` + +`check=True`이면 command 실패가 exception이 되어 test도 실패합니다. 문자열 한 줄보다 argument list가 shell quoting에 안전합니다. + +## `tempfile.TemporaryDirectory` + +테스트 전용 파일을 만들고 scope가 끝나면 자동 정리합니다. + +```python +with tempfile.TemporaryDirectory() as directory: + root = Path(directory) +``` + +실제 model directory를 오염시키지 않고 file I/O를 검증할 수 있습니다. diff --git a/RobotVisionPlatform/reference/scripts-and-protocol-reference.md b/RobotVisionPlatform/reference/scripts-and-protocol-reference.md new file mode 100644 index 0000000..2e78007 --- /dev/null +++ b/RobotVisionPlatform/reference/scripts-and-protocol-reference.md @@ -0,0 +1,137 @@ +# 스크립트·프로토콜 사전 + +## PowerShell `$ErrorActionPreference = 'Stop'` + +native가 아닌 PowerShell command 오류도 즉시 script 실패로 처리합니다. + +```powershell +$ErrorActionPreference = 'Stop' +``` + +실패한 build 이후 다음 배포 명령이 계속 실행되는 것을 막습니다. + +## PowerShell `$PSScriptRoot` + +현재 script 파일이 있는 폴더입니다. + +```powershell +$root = Split-Path -Parent $PSScriptRoot +``` + +사용자가 어느 폴더에서 script를 실행해도 프로젝트 경로를 찾을 수 있습니다. + +## `Invoke-WebRequest` + +```powershell +$response = Invoke-WebRequest -Uri $url -Method Post -ContentType 'application/json' -Body $body +``` + +HTTP 요청을 보내고 status/body/header를 반환합니다. [`send-demo-event.ps1`](../scripts/send-demo-event.ps1)은 수집 API smoke test에 사용합니다. + +## Bash `set -euo pipefail` + +```bash +set -euo pipefail +``` + +- `-e`: command 실패 시 종료 +- `-u`: 정의되지 않은 변수 사용 시 종료 +- `pipefail`: pipeline 중간 command 실패도 전체 실패 + +## Bash `[[ ... ]]`와 `case` + +```bash +while [[ $# -gt 0 ]]; do + case "$1" in + --onnx) onnx_path="$2"; shift 2 ;; + *) exit 2 ;; + esac +done +``` + +CLI option을 순서대로 읽습니다. 변수는 공백이 포함된 경로를 보호하기 위해 항상 `"$value"`로 인용합니다. + +## CMake `cmake -S`, `-B`, `--build` + +```bash +cmake -S device -B device/build -G Ninja -DCMAKE_BUILD_TYPE=Release +cmake --build device/build --parallel +``` + +- `-S`: source의 `CMakeLists.txt` 위치 +- `-B`: 생성 파일과 binary가 들어갈 build 폴더 +- `-G Ninja`: 사용할 build backend +- source tree와 build 결과를 분리합니다. + +## CTest + +```bash +ctest --test-dir device/build --output-on-failure +``` + +CMake의 `add_test`로 등록된 실행 파일을 수행합니다. `--output-on-failure`는 성공 로그는 줄이고 실패 원인을 보여 줍니다. + +## `trtexec` + +TensorRT에 포함된 model parser, engine builder, benchmark CLI입니다. + +```bash +trtexec --onnx=model.onnx --saveEngine=model.engine --fp16 --shapes=images:1x3x640x640 +trtexec --loadEngine=model.engine --shapes=images:1x3x640x640 +``` + +engine은 대상 Jetson에서 생성합니다. 자세한 설명은 [TensorRT 가이드](../device/guides/tensorrt-on-jetson.md)를 봅니다. + +## protobuf `message` + +```proto +message Detection { + string label = 1; + float confidence = 2; +} +``` + +- 각 field 번호는 wire format의 identity입니다. +- 이미 배포한 번호를 다른 의미로 재사용하면 안 됩니다. +- 이름 변경보다 번호/타입 호환성이 더 중요합니다. + +## protobuf `oneof` + +```proto +oneof payload { + Heartbeat heartbeat = 10; + DetectionBatch detections = 11; +} +``` + +한 envelope에 여러 종류 중 하나만 들어가게 합니다. 새 payload는 기존 번호를 건드리지 않고 새 번호로 추가합니다. + +## gRPC bidirectional streaming + +```proto +rpc Connect(stream DeviceEnvelope) returns (stream ServerCommand); +``` + +- request 앞 `stream`: 장치가 여러 메시지를 계속 전송 +- response 앞 `stream`: 서버도 여러 명령을 계속 반환 +- 장치가 연결을 시작하므로 방화벽/NAT 환경에서 관리하기 쉽습니다. +- 영상 binary를 이 stream에 계속 넣기보다 WebRTC media plane과 분리합니다. + +## `google.protobuf.Timestamp` + +언어별 문자열 timestamp 대신 protobuf 표준 UTC timestamp를 사용합니다. `captured_at`은 카메라 촬영 시각, `sent_at`은 envelope 전송 시각이므로 둘의 차이로 장치 내부 지연을 추정할 수 있습니다. + +## GitHub Actions job/step + +```yaml +jobs: + device: + runs-on: ubuntu-24.04 + steps: + - uses: actions/checkout@v4 + - run: cmake --build device/build +``` + +- job은 독립 runner에서 실행되는 작업 묶음입니다. +- step은 job 안에서 순서대로 실행됩니다. +- 로컬 test script와 CI command를 같은 형태로 유지하면 환경 차이를 줄일 수 있습니다. diff --git a/RobotVisionPlatform/scripts/build.ps1 b/RobotVisionPlatform/scripts/build.ps1 new file mode 100644 index 0000000..84e1472 --- /dev/null +++ b/RobotVisionPlatform/scripts/build.ps1 @@ -0,0 +1,17 @@ +$ErrorActionPreference = 'Stop' +# Resolve every path from the script location so the command works from any directory. +$root = Split-Path -Parent $PSScriptRoot + +# Build the dependency-light server solution first. +dotnet build "$root/server/RobotVision.Server.slnx" --configuration Release + +# A normal PowerShell may not expose MSVC; Developer PowerShell adds `cl` to PATH. +$compiler = Get-Command cl, g++, clang++ -ErrorAction SilentlyContinue | Select-Object -First 1 +if ($null -eq $compiler) { + Write-Warning 'C++ compiler was not found. Run from a Visual Studio Developer PowerShell or install LLVM/GCC.' + exit 0 +} + +# Disable optional Boost HTTP so the portable C++ core is always testable. +cmake -S "$root/device" -B "$root/device/build" -DRV_ENABLE_BOOST_HTTP=OFF +cmake --build "$root/device/build" --config Release --parallel diff --git a/RobotVisionPlatform/scripts/build.sh b/RobotVisionPlatform/scripts/build.sh new file mode 100755 index 0000000..1c5a7bf --- /dev/null +++ b/RobotVisionPlatform/scripts/build.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash +# Build both the .NET server and portable C++ device core on Linux/Jetson. +set -euo pipefail +# BASH_SOURCE makes paths independent of the caller's current directory. +repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +dotnet build "$repo_root/server/RobotVision.Server.slnx" --configuration Release +cmake -S "$repo_root/device" -B "$repo_root/device/build" -G Ninja -DCMAKE_BUILD_TYPE=Release -DRV_ENABLE_BOOST_HTTP=OFF +cmake --build "$repo_root/device/build" --parallel diff --git a/RobotVisionPlatform/scripts/send-demo-event.ps1 b/RobotVisionPlatform/scripts/send-demo-event.ps1 new file mode 100644 index 0000000..7f1c1c2 --- /dev/null +++ b/RobotVisionPlatform/scripts/send-demo-event.ps1 @@ -0,0 +1,26 @@ +param( + # Base URL of the locally running ASP.NET Core ingest server. + [string]$ServerUrl = 'http://localhost:5080', + # Device ID shown on the monitoring page. + [string]$DeviceId = 'beginner-demo-001' +) + +$ErrorActionPreference = 'Stop' +# PowerShell object -> JSON avoids quote escaping mistakes in a handwritten JSON string. +$body = @{ + deviceId = $DeviceId + sequence = 1 + modelVersion = 'demo/0.1.0' + detections = @( + @{ label = 'person'; confidence = 0.94; x = 0.10; y = 0.20; width = 0.30; height = 0.50 } + ) +} | ConvertTo-Json -Depth 4 + +# POST the same camelCase contract used by the C++ HTTP event sink. +$response = Invoke-WebRequest ` + -Uri "$($ServerUrl.TrimEnd('/'))/api/events/detections" ` + -Method Post ` + -ContentType 'application/json' ` + -Body $body + +Write-Host "Demo event accepted: HTTP $($response.StatusCode), device=$DeviceId" diff --git a/RobotVisionPlatform/scripts/test.ps1 b/RobotVisionPlatform/scripts/test.ps1 new file mode 100644 index 0000000..06f8392 --- /dev/null +++ b/RobotVisionPlatform/scripts/test.ps1 @@ -0,0 +1,12 @@ +$ErrorActionPreference = 'Stop' +$root = Split-Path -Parent $PSScriptRoot + +# Run fast server behavior and dependency-free model-manifest tests. +dotnet run --project "$root/server/tests/RobotVision.Server.Tests" --configuration Release +python -m unittest discover -s "$root/device/tools/model/tests" -v +# C++ tests are optional on Windows machines without a configured compiler/build tree. +if (Test-Path "$root/device/build/CTestTestfile.cmake") { + ctest --test-dir "$root/device/build" -C Release --output-on-failure +} else { + Write-Warning 'C++ build directory is unavailable; device tests were skipped.' +} diff --git a/RobotVisionPlatform/scripts/test.sh b/RobotVisionPlatform/scripts/test.sh new file mode 100755 index 0000000..0f211ca --- /dev/null +++ b/RobotVisionPlatform/scripts/test.sh @@ -0,0 +1,7 @@ +#!/usr/bin/env bash +# Run server, model-tool, and already-configured C++ tests; stop at the first failure. +set -euo pipefail +repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +dotnet run --project "$repo_root/server/tests/RobotVision.Server.Tests" --configuration Release +python3 -m unittest discover -s "$repo_root/device/tools/model/tests" -v +ctest --test-dir "$repo_root/device/build" --output-on-failure diff --git a/RobotVisionPlatform/server/Directory.Build.props b/RobotVisionPlatform/server/Directory.Build.props new file mode 100644 index 0000000..6b7a99a --- /dev/null +++ b/RobotVisionPlatform/server/Directory.Build.props @@ -0,0 +1,10 @@ + + + net10.0 + enable + enable + true + true + + + diff --git a/RobotVisionPlatform/server/README.md b/RobotVisionPlatform/server/README.md new file mode 100644 index 0000000..e216de1 --- /dev/null +++ b/RobotVisionPlatform/server/README.md @@ -0,0 +1,26 @@ +# Server and Dashboard + +서버는 ASP.NET Core 수집 API와 SignalR fan-out을 제공하고, 운영 UI는 웹 MVP와 선택형 MAUI Blazor Hybrid 앱으로 구성합니다. + +```bash +dotnet build RobotVision.Server.slnx +dotnet run --project src/RobotVision.Server.Api +``` + +테스트 이벤트: + +```bash +curl -X POST http://localhost:5080/api/events/detections \ + -H 'content-type: application/json' \ + -d '{"deviceId":"jetson-001","sequence":1,"modelVersion":"demo/1","detections":[{"label":"person","confidence":0.94,"x":0.1,"y":0.2,"width":0.3,"height":0.5}]}' +``` + +브라우저에서 `http://localhost:5080`을 열면 최신 장치 상태를 볼 수 있습니다. 데이터는 현재 in-memory이므로 production 단계에서 PostgreSQL과 object storage를 연결합니다. + +MAUI 프로젝트는 기본 solution에서 제외되어 CI에 MAUI workload를 강제하지 않습니다. 설치 후 별도로 빌드합니다. + +```bash +dotnet workload install maui +dotnet build src/RobotVision.Dashboard.Maui/RobotVision.Dashboard.Maui.csproj -f net10.0-windows10.0.19041.0 +``` + diff --git a/RobotVisionPlatform/server/RobotVision.Server.slnx b/RobotVisionPlatform/server/RobotVision.Server.slnx new file mode 100644 index 0000000..a3daf5f --- /dev/null +++ b/RobotVisionPlatform/server/RobotVision.Server.slnx @@ -0,0 +1,6 @@ + + + + + + diff --git a/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/App.xaml b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/App.xaml new file mode 100644 index 0000000..b132d65 --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/App.xaml @@ -0,0 +1,7 @@ + + + + + diff --git a/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/App.xaml.cs b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/App.xaml.cs new file mode 100644 index 0000000..5fb9fe5 --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/App.xaml.cs @@ -0,0 +1,11 @@ +namespace RobotVision.Dashboard.Maui; + +public partial class App : Application +{ + /// XAML resource를 초기화하고 첫 화면을 생성합니다. + public App() + { + InitializeComponent(); + MainPage = new MainPage(); + } +} diff --git a/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/Main.razor b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/Main.razor new file mode 100644 index 0000000..382e737 --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/Main.razor @@ -0,0 +1,4 @@ +@* This Razor component is the shared starting point for future SignalR/WebRTC monitoring UI. *@ +

Robot Vision

+

MAUI Blazor Hybrid 관제 클라이언트 골격입니다.

+

다음 단계에서 SignalR client와 WebRTC player를 이 공유 Razor UI에 연결합니다.

diff --git a/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/MainPage.xaml b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/MainPage.xaml new file mode 100644 index 0000000..f2f9e13 --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/MainPage.xaml @@ -0,0 +1,12 @@ + + + + + + + + + diff --git a/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/MainPage.xaml.cs b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/MainPage.xaml.cs new file mode 100644 index 0000000..aa09864 --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/MainPage.xaml.cs @@ -0,0 +1,8 @@ +namespace RobotVision.Dashboard.Maui; + +/// BlazorWebView를 호스팅하는 MAUI native page입니다. +public partial class MainPage : ContentPage +{ + /// MainPage.xaml의 control tree를 생성합니다. + public MainPage() => InitializeComponent(); +} diff --git a/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/MauiProgram.cs b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/MauiProgram.cs new file mode 100644 index 0000000..0d65a4d --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/MauiProgram.cs @@ -0,0 +1,20 @@ +using Microsoft.Extensions.Logging; + +namespace RobotVision.Dashboard.Maui; + +public static class MauiProgram +{ + /// MAUI application과 BlazorWebView dependency를 구성합니다. + public static MauiApp CreateMauiApp() + { + // UseMauiApp은 App 수명주기를 등록하고 DI container를 준비합니다. + var builder = MauiApp.CreateBuilder().UseMauiApp(); + // Razor component를 native WebView 안에서 실행하는 Blazor Hybrid service입니다. + builder.Services.AddMauiBlazorWebView(); +#if DEBUG + builder.Services.AddBlazorWebViewDeveloperTools(); + builder.Logging.AddDebug(); +#endif + return builder.Build(); + } +} diff --git a/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/RobotVision.Dashboard.Maui.csproj b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/RobotVision.Dashboard.Maui.csproj new file mode 100644 index 0000000..446b77f --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/RobotVision.Dashboard.Maui.csproj @@ -0,0 +1,17 @@ + + + net10.0-android;net10.0-windows10.0.19041.0 + Exe + RobotVision.Dashboard.Maui + true + true + Robot Vision + io.robotvision.dashboard + 0.1.0 + 1 + + + + + + diff --git a/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/_Imports.razor b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/_Imports.razor new file mode 100644 index 0000000..dbd903a --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/_Imports.razor @@ -0,0 +1,2 @@ +@using Microsoft.AspNetCore.Components.Web + diff --git a/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/wwwroot/index.html b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/wwwroot/index.html new file mode 100644 index 0000000..6b427dd --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Dashboard.Maui/wwwroot/index.html @@ -0,0 +1,3 @@ + +
Loading…
+ diff --git a/RobotVisionPlatform/server/src/RobotVision.Server.Api/DetectionFanoutWorker.cs b/RobotVisionPlatform/server/src/RobotVision.Server.Api/DetectionFanoutWorker.cs new file mode 100644 index 0000000..6b6d765 --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Server.Api/DetectionFanoutWorker.cs @@ -0,0 +1,36 @@ +using System.Threading.Channels; +using Microsoft.AspNetCore.SignalR; +using RobotVision.Server.Contracts; + +namespace RobotVision.Server.Api; + +/// +/// HTTP ingest와 SignalR 전송을 분리하는 background consumer입니다. +/// 수집 request가 느린 client에게 직접 묶여 대기하지 않도록 bounded channel을 읽습니다. +/// +public sealed class DetectionFanoutWorker( + Channel channel, + IHubContext hub, + ILogger logger) : BackgroundService +{ + /// application 종료 token이 취소될 때까지 channel event를 client group에 전송합니다. + protected override async Task ExecuteAsync(CancellationToken stoppingToken) + { + // ReadAllAsync는 새 item을 비동기로 기다리고 channel이 완료되면 loop를 종료합니다. + await foreach (var item in channel.Reader.ReadAllAsync(stoppingToken)) + { + try + { + // 상세 화면에는 해당 장치만, fleet 화면에는 전체 event를 보냅니다. + await hub.Clients.Group(MonitoringHub.GroupName(item.DeviceId)) + .SendAsync("detection", item, stoppingToken); + await hub.Clients.Group("fleet").SendAsync("detection", item, stoppingToken); + } + catch (Exception error) when (error is not OperationCanceledException) + { + // 하나의 전송 실패가 worker 전체를 종료하지 않도록 기록 후 다음 item을 처리합니다. + logger.LogError(error, "Failed to fan out event for {DeviceId}", item.DeviceId); + } + } + } +} diff --git a/RobotVisionPlatform/server/src/RobotVision.Server.Api/DeviceRegistry.cs b/RobotVisionPlatform/server/src/RobotVision.Server.Api/DeviceRegistry.cs new file mode 100644 index 0000000..23fa1d2 --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Server.Api/DeviceRegistry.cs @@ -0,0 +1,37 @@ +using System.Collections.Concurrent; +using RobotVision.Server.Contracts; + +namespace RobotVision.Server.Api; + +/// +/// 장치별 가장 최신 detection snapshot을 thread-safe하게 보관하는 MVP in-memory registry입니다. +/// 서버 재시작 후에도 보존하려면 이 클래스를 database repository로 교체합니다. +/// +public sealed class DeviceRegistry +{ + // 여러 HTTP request가 동시에 접근하므로 일반 Dictionary 대신 ConcurrentDictionary를 사용합니다. + private readonly ConcurrentDictionary _devices = new(StringComparer.Ordinal); + + /// 새 event가 기존 sequence 이상일 때만 장치의 최신 상태를 갱신합니다. + /// 수집 API가 검증한 detection event입니다. + public void Upsert(DetectionEventDto item) + { + var snapshot = new DeviceSnapshot( + item.DeviceId, + DateTimeOffset.UtcNow, + item.Sequence, + item.ModelVersion, + item.Detections); + // 늦게 도착한 오래된 event가 최신 화면을 되돌리지 않게 sequence를 비교합니다. + _devices.AddOrUpdate(item.DeviceId, snapshot, (_, current) => + item.Sequence >= current.LastSequence ? snapshot : current); + } + + /// 장치 ID로 정렬된 현재 snapshot 복사본을 반환합니다. + public IReadOnlyCollection List() => + _devices.Values.OrderBy(x => x.DeviceId, StringComparer.Ordinal).ToArray(); + + /// 장치 ID에 해당하는 최신 snapshot을 찾아 반환합니다. + public bool TryGet(string deviceId, out DeviceSnapshot? snapshot) => + _devices.TryGetValue(deviceId, out snapshot); +} diff --git a/RobotVisionPlatform/server/src/RobotVision.Server.Api/Dockerfile b/RobotVisionPlatform/server/src/RobotVision.Server.Api/Dockerfile new file mode 100644 index 0000000..b0e54fe --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Server.Api/Dockerfile @@ -0,0 +1,16 @@ +FROM mcr.microsoft.com/dotnet/sdk:10.0 AS build +WORKDIR /src +COPY server/Directory.Build.props server/ +COPY server/src/RobotVision.Server.Contracts/RobotVision.Server.Contracts.csproj server/src/RobotVision.Server.Contracts/ +COPY server/src/RobotVision.Server.Api/RobotVision.Server.Api.csproj server/src/RobotVision.Server.Api/ +RUN dotnet restore server/src/RobotVision.Server.Api/RobotVision.Server.Api.csproj +COPY server/src server/src +RUN dotnet publish server/src/RobotVision.Server.Api/RobotVision.Server.Api.csproj -c Release -o /app --no-restore + +FROM mcr.microsoft.com/dotnet/aspnet:10.0 +WORKDIR /app +COPY --from=build /app . +USER $APP_UID +EXPOSE 5080 +ENTRYPOINT ["dotnet", "RobotVision.Server.Api.dll"] + diff --git a/RobotVisionPlatform/server/src/RobotVision.Server.Api/MonitoringHub.cs b/RobotVisionPlatform/server/src/RobotVision.Server.Api/MonitoringHub.cs new file mode 100644 index 0000000..76c7acf --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Server.Api/MonitoringHub.cs @@ -0,0 +1,24 @@ +using Microsoft.AspNetCore.SignalR; + +namespace RobotVision.Server.Api; + +/// 웹/MAUI client가 특정 장치의 실시간 event를 구독하는 SignalR endpoint입니다. +public sealed class MonitoringHub : Hub +{ + /// 현재 연결을 지정 장치의 SignalR group에 추가합니다. + public Task WatchDevice(string deviceId) => + Groups.AddToGroupAsync(Context.ConnectionId, GroupName(deviceId)); + + /// 현재 연결을 지정 장치 group에서 제거합니다. + public Task StopWatchingDevice(string deviceId) => + Groups.RemoveFromGroupAsync(Context.ConnectionId, GroupName(deviceId)); + + /// 장치 ID를 서버 전체에서 일관된 group 이름으로 변환합니다. + /// deviceId가 비어 있거나 64자를 초과하면 발생합니다. + public static string GroupName(string deviceId) + { + if (string.IsNullOrWhiteSpace(deviceId) || deviceId.Length > 64) + throw new ArgumentException("deviceId must be 1–64 non-whitespace characters.", nameof(deviceId)); + return $"device:{deviceId}"; + } +} diff --git a/RobotVisionPlatform/server/src/RobotVision.Server.Api/Program.cs b/RobotVisionPlatform/server/src/RobotVision.Server.Api/Program.cs new file mode 100644 index 0000000..3293ce2 --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Server.Api/Program.cs @@ -0,0 +1,56 @@ +using System.Threading.Channels; +using Microsoft.AspNetCore.Http.Json; +using RobotVision.Server.Api; +using RobotVision.Server.Contracts; + +var builder = WebApplication.CreateBuilder(args); +// JSON request/response를 C# PascalCase 대신 JavaScript 친화적인 camelCase로 통일합니다. +builder.Services.Configure(options => + options.SerializerOptions.PropertyNamingPolicy = System.Text.Json.JsonNamingPolicy.CamelCase); +builder.Services.AddSignalR(); +// Registry와 channel은 application 전체에서 하나만 존재해야 하므로 singleton입니다. +builder.Services.AddSingleton(); +builder.Services.AddSingleton(Channel.CreateBounded(new BoundedChannelOptions(1024) +{ + // 순간 burst가 1024개를 넘으면 오래된 UI 알림을 버리고 최신 상태를 우선합니다. + FullMode = BoundedChannelFullMode.DropOldest, + SingleReader = true, + SingleWriter = false +})); +builder.Services.AddHostedService(); +builder.Services.AddHealthChecks(); + +var app = builder.Build(); +// wwwroot/index.html을 기본 관제 페이지로 제공합니다. +app.UseDefaultFiles(); +app.UseStaticFiles(); +app.MapHealthChecks("/health"); +app.MapHub("/hubs/monitoring"); + +// 현재 장치 목록 또는 장치 하나의 최신 snapshot을 조회하는 read API입니다. +app.MapGet("/api/devices", (DeviceRegistry registry) => Results.Ok(registry.List())); +app.MapGet("/api/devices/{deviceId}", (string deviceId, DeviceRegistry registry) => + registry.TryGet(deviceId, out var device) ? Results.Ok(device) : Results.NotFound()); + +app.MapPost("/api/events/detections", async ( + DetectionEventDto item, + DeviceRegistry registry, + Channel channel, + CancellationToken cancellationToken) => +{ + // 잘못된 ID와 비정상적으로 큰 payload를 background queue에 넣기 전에 거부합니다. + if (string.IsNullOrWhiteSpace(item.DeviceId) || item.Detections is null || item.Detections.Count > 1_000) + return Results.BadRequest(new { error = "deviceId is required; max 1000 detections" }); + + // 조회용 최신 상태를 먼저 갱신하고, 실시간 UI fan-out은 bounded channel에 위임합니다. + registry.Upsert(item); + if (!channel.Writer.TryWrite(item)) + return Results.StatusCode(StatusCodes.Status503ServiceUnavailable); + await Task.CompletedTask; + return Results.Accepted($"/api/devices/{Uri.EscapeDataString(item.DeviceId)}"); +}); + +app.Run(); + +// Exposing the generated top-level Program type lets integration-test projects reference the host. +public partial class Program; diff --git a/RobotVisionPlatform/server/src/RobotVision.Server.Api/RobotVision.Server.Api.csproj b/RobotVisionPlatform/server/src/RobotVision.Server.Api/RobotVision.Server.Api.csproj new file mode 100644 index 0000000..26a4c16 --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Server.Api/RobotVision.Server.Api.csproj @@ -0,0 +1,6 @@ + + + + + + diff --git a/RobotVisionPlatform/server/src/RobotVision.Server.Api/appsettings.json b/RobotVisionPlatform/server/src/RobotVision.Server.Api/appsettings.json new file mode 100644 index 0000000..679d9d0 --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Server.Api/appsettings.json @@ -0,0 +1,18 @@ +{ + "Logging": { + "LogLevel": { + "Default": "Information", + "Microsoft.AspNetCore": "Warning" + } + }, + "AllowedHosts": "*", + "Kestrel": { + "Endpoints": { + "Http": { + "Url": "http://0.0.0.0:5080", + "Protocols": "Http1AndHttp2" + } + } + } +} + diff --git a/RobotVisionPlatform/server/src/RobotVision.Server.Api/wwwroot/index.html b/RobotVisionPlatform/server/src/RobotVision.Server.Api/wwwroot/index.html new file mode 100644 index 0000000..0b4df30 --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Server.Api/wwwroot/index.html @@ -0,0 +1,44 @@ + + + + + + Robot Vision Monitor + + + +

Robot Vision Monitor

MVP device registry · refreshes every 2 seconds
+
장치 이벤트를 기다리는 중…
+ + + diff --git a/RobotVisionPlatform/server/src/RobotVision.Server.Contracts/DeviceContracts.cs b/RobotVisionPlatform/server/src/RobotVision.Server.Contracts/DeviceContracts.cs new file mode 100644 index 0000000..a3783b6 --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Server.Contracts/DeviceContracts.cs @@ -0,0 +1,36 @@ +namespace RobotVision.Server.Contracts; + +/// 원본 영상 크기와 무관한 0~1 정규화 좌표의 객체 영역입니다. +public sealed record BoundingBox(float X, float Y, float Width, float Height); + +/// 장치 또는 HTTP client가 서버에 보내는 객체 하나의 탐지 결과입니다. +/// 모델 label 목록에 정의된 class 이름입니다. +/// 일반적으로 0~1 범위인 모델 신뢰도입니다. +public sealed record DetectionDto( + string Label, + float Confidence, + float X, + float Y, + float Width, + float Height) +{ + /// 개별 좌표 필드를 하나의 BoundingBox value object로 반환합니다. + public BoundingBox Box => new(X, Y, Width, Height); +} + +/// 한 프레임의 탐지 결과를 수집 API로 보내는 request 계약입니다. +/// 장치별 단조 증가 번호로 중복/역순 event를 판별합니다. +public sealed record DetectionEventDto( + string DeviceId, + ulong Sequence, + string ModelVersion, + IReadOnlyList Detections, + DateTimeOffset? CapturedAt = null); + +/// 관제 화면이 읽는 장치별 최신 상태의 불변 snapshot입니다. +public sealed record DeviceSnapshot( + string DeviceId, + DateTimeOffset LastSeenAt, + ulong LastSequence, + string ModelVersion, + IReadOnlyList Detections); diff --git a/RobotVisionPlatform/server/src/RobotVision.Server.Contracts/RobotVision.Server.Contracts.csproj b/RobotVisionPlatform/server/src/RobotVision.Server.Contracts/RobotVision.Server.Contracts.csproj new file mode 100644 index 0000000..9609c61 --- /dev/null +++ b/RobotVisionPlatform/server/src/RobotVision.Server.Contracts/RobotVision.Server.Contracts.csproj @@ -0,0 +1,2 @@ + + diff --git a/RobotVisionPlatform/server/tests/RobotVision.Server.Tests/Program.cs b/RobotVisionPlatform/server/tests/RobotVision.Server.Tests/Program.cs new file mode 100644 index 0000000..aa27af7 --- /dev/null +++ b/RobotVisionPlatform/server/tests/RobotVision.Server.Tests/Program.cs @@ -0,0 +1,10 @@ +using RobotVision.Server.Api; +using RobotVision.Server.Contracts; + +var registry = new DeviceRegistry(); +// 최신 sequence 2를 먼저 넣고 오래된 sequence 1이 상태를 덮어쓰지 못하는지 확인합니다. +registry.Upsert(new DetectionEventDto("test-device", 2, "model/1", [])); +registry.Upsert(new DetectionEventDto("test-device", 1, "stale", [])); +if (!registry.TryGet("test-device", out var item) || item?.LastSequence != 2) + throw new InvalidOperationException("Registry must reject out-of-order snapshots."); +Console.WriteLine("Server core tests passed."); diff --git a/RobotVisionPlatform/server/tests/RobotVision.Server.Tests/RobotVision.Server.Tests.csproj b/RobotVisionPlatform/server/tests/RobotVision.Server.Tests/RobotVision.Server.Tests.csproj new file mode 100644 index 0000000..45fb238 --- /dev/null +++ b/RobotVisionPlatform/server/tests/RobotVision.Server.Tests/RobotVision.Server.Tests.csproj @@ -0,0 +1,8 @@ + + Exe + + + + + + diff --git a/RobotVisionPlatform/shared/proto/vision/v1/device.proto b/RobotVisionPlatform/shared/proto/vision/v1/device.proto new file mode 100644 index 0000000..58dde13 --- /dev/null +++ b/RobotVisionPlatform/shared/proto/vision/v1/device.proto @@ -0,0 +1,91 @@ +syntax = "proto3"; + +package robotvision.vision.v1; +option csharp_namespace = "RobotVision.Contracts.Grpc.V1"; + +import "google/protobuf/timestamp.proto"; + +// DeviceControl keeps one long-lived, device-initiated control-plane connection. +service DeviceControl { + // The device sends status/events while the server returns commands on the same stream. + rpc Connect(stream DeviceEnvelope) returns (stream ServerCommand); +} + +// DeviceEnvelope wraps every device-to-server payload with ordering and identity fields. +message DeviceEnvelope { + // Stable fleet identifier provisioned with the device certificate. + string device_id = 1; + // Monotonically increasing per-device number used for duplicate/order handling. + uint64 sequence = 2; + // UTC time when this envelope left the device process. + google.protobuf.Timestamp sent_at = 3; + // Exactly one payload is present. New payload types can be added with new field numbers. + oneof payload { + Heartbeat heartbeat = 10; + DetectionBatch detections = 11; + ModelState model_state = 12; + } +} + +// Heartbeat is a lightweight periodic health and performance report. +message Heartbeat { + double capture_fps = 1; + double inference_fps = 2; + double temperature_c = 3; + uint64 dropped_frames = 4; + string software_version = 5; +} + +// DetectionBatch contains all objects detected in one captured frame. +message DetectionBatch { + // Camera capture time; different from the later envelope sent_at time. + google.protobuf.Timestamp captured_at = 1; + string model_version = 2; + repeated Detection items = 3; +} + +// Detection uses normalized top-left x/y and width/height coordinates in the 0..1 range. +message Detection { + string label = 1; + float confidence = 2; + float x = 3; + float y = 4; + float width = 5; + float height = 6; + // Stable ID assigned by a tracker; zero means no tracking ID is available. + uint64 track_id = 7; +} + +// ModelState reports which signed artifact is staged, active, or failed on the device. +message ModelState { + string model_id = 1; + string version = 2; + string sha256 = 3; + string status = 4; +} + +// ServerCommand wraps one expiring, auditable server-to-device request. +message ServerCommand { + // Unique idempotency key; a reconnecting device must not execute it twice. + string command_id = 1; + // The device rejects commands received after this UTC time. + google.protobuf.Timestamp expires_at = 2; + oneof payload { + UpdateConfiguration update_configuration = 10; + DeployModel deploy_model = 11; + RequestKeyframe request_keyframe = 12; + } +} + +// RFC 7396-style JSON merge patch for fields allowed by the device policy. +message UpdateConfiguration { string json_merge_patch = 1; } + +// DeployModel points to a signed manifest rather than an unverified raw model URL. +message DeployModel { + string manifest_uri = 1; + string manifest_sha256 = 2; + string signature = 3; +} + +// RequestKeyframe asks for a diagnostic image without enabling continuous upload. +message RequestKeyframe { string reason = 1; } diff --git a/RobotVisionPlatform/tips/README.md b/RobotVisionPlatform/tips/README.md new file mode 100644 index 0000000..7980f92 --- /dev/null +++ b/RobotVisionPlatform/tips/README.md @@ -0,0 +1,13 @@ +# 모델 학습 Tips + +처음에는 [학습 기초](fundamentals/vision-training-basics.md) → [데이터셋](data/dataset-engineering.md) → [fine-tuning](training/fine-tuning-playbook.md) → [평가](evaluation/edge-evaluation.md) → [배포 최적화](deployment/jetson-optimization.md) 순서로 진행합니다. + +## 카테고리 + +- `fundamentals`: 학습·과적합·손실·transfer learning 기초 +- `data`: 수집, 라벨링, split, 증강, 개인정보 +- `training`: 실험 설계와 fine-tuning 실행 절차 +- `evaluation`: 정확도뿐 아니라 장치 지연/열/전력 평가 +- `deployment`: ONNX/TensorRT/DeepStream 최적화 +- `resources`: 공식 문서와 학습 사이트 + diff --git a/RobotVisionPlatform/tips/data/dataset-engineering.md b/RobotVisionPlatform/tips/data/dataset-engineering.md new file mode 100644 index 0000000..f45ef21 --- /dev/null +++ b/RobotVisionPlatform/tips/data/dataset-engineering.md @@ -0,0 +1,11 @@ +# 데이터셋 엔지니어링 + +- 영상의 인접 프레임을 무작위 split하지 않습니다. clip/site/device 단위 group split을 사용합니다. +- 클래스마다 정상, 가림, 역광, 야간, motion blur, 작은 물체, 빈 장면 bucket을 관리합니다. +- 라벨 정의에는 포함/제외 예시와 최소 크기, 가림 처리 규칙을 둡니다. +- 중복 프레임을 줄이고 hard negative를 의도적으로 포함합니다. +- 증강은 실제 카메라 현상을 모사할 때만 사용하고 validation에는 적용하지 않습니다. +- dataset version에 원본 checksum, 라벨 schema, split seed, license/consent를 기록합니다. + +초기에는 전체 영상을 올리지 말고 edge에서 낮은 confidence, 규칙 위반, 주기적 unbiased sample을 선정하는 것이 비용과 개인정보 측면에서 유리합니다. + diff --git a/RobotVisionPlatform/tips/deployment/jetson-optimization.md b/RobotVisionPlatform/tips/deployment/jetson-optimization.md new file mode 100644 index 0000000..ebe8388 --- /dev/null +++ b/RobotVisionPlatform/tips/deployment/jetson-optimization.md @@ -0,0 +1,12 @@ +# Jetson 최적화 + +1. PyTorch 모델을 ONNX로 export하고 ONNX Runtime 또는 Polygraphy로 출력 parity를 확인합니다. +2. TensorRT engine은 배포 대상과 동일한 JetPack/TensorRT 환경에서 생성합니다. +3. FP16 baseline을 먼저 만들고 정확도와 메모리가 필요할 때 INT8을 검토합니다. +4. GStreamer/DeepStream의 NVMM surface를 유지해 CPU 복사를 피합니다. +5. capture, preprocess, inference, encode의 latency를 각각 측정합니다. +6. queue depth를 제한하고 실시간 영상은 backlog보다 최신 프레임을 우선합니다. +7. 고정 max clock 결과만 보고 용량을 산정하지 말고 운영 power mode에서 soak test합니다. + +DeepStream은 다중 스트림, tracker, OSD, inference plugin을 결합할 때 우선 검토하고, 단일 모델과 특수 후처리가 중심이면 직접 TensorRT adapter가 더 단순할 수 있습니다. + diff --git a/RobotVisionPlatform/tips/evaluation/edge-evaluation.md b/RobotVisionPlatform/tips/evaluation/edge-evaluation.md new file mode 100644 index 0000000..18f6e9f --- /dev/null +++ b/RobotVisionPlatform/tips/evaluation/edge-evaluation.md @@ -0,0 +1,6 @@ +# Edge 평가 + +정확도 표에는 mAP50-95, class별 precision/recall, confidence threshold를 포함합니다. 시스템 표에는 capture-to-action latency p50/p95/p99, steady-state FPS, peak memory, power mode, 온도, throttling, 네트워크 사용량, drop rate를 포함합니다. + +평가 시 cold start와 warm state를 분리하고 최소 30분 이상 열 평형 상태를 관찰합니다. 랜/Wi-Fi 정상, packet loss, 서버 단절, 재접속, 디스크 부족, 카메라 분리 시나리오를 반복합니다. 모델 후보는 정확도 하나가 아니라 이 제약의 Pareto frontier에서 선택합니다. + diff --git a/RobotVisionPlatform/tips/fundamentals/vision-training-basics.md b/RobotVisionPlatform/tips/fundamentals/vision-training-basics.md new file mode 100644 index 0000000..00bbada --- /dev/null +++ b/RobotVisionPlatform/tips/fundamentals/vision-training-basics.md @@ -0,0 +1,19 @@ +# 비전 모델 학습 기초 + +모델 학습은 사진을 반복해서 보여주고 정답과의 차이를 줄이는 과정입니다. 한 번의 전체 데이터 학습을 `epoch`, 한 번에 처리하는 작은 데이터 묶음을 `batch`, 모델을 얼마나 크게 수정할지를 `learning rate`라고 합니다. + +## 먼저 정할 것 + +문제 유형(분류/탐지/분할/추적), 실제 행동으로 이어질 metric, 오탐과 미탐의 비용, 목표 FPS·전력·메모리를 먼저 적습니다. 장치 제약을 마지막에 고려하면 정확하지만 배포할 수 없는 모델이 나옵니다. + +transfer learning은 대규모 사전학습 weight에서 시작해 작은 learning rate로 task head와 backbone을 조정합니다. 초반에는 backbone freeze로 baseline을 만들고, 데이터가 충분하면 점진적으로 unfreeze합니다. train loss가 낮다는 이유만으로 채택하지 말고 촬영 장소/날짜/카메라가 겹치지 않는 holdout에서 비교합니다. + +- `weight`: 모델이 학습한 숫자 값 +- `backbone`: 이미지의 기본 특징을 찾는 앞부분 +- `head`: 특징을 이용해 클래스와 위치를 예측하는 뒷부분 +- `freeze`: 일부 weight를 바꾸지 않고 학습하는 것 +- `baseline`: 이후 실험과 비교할 가장 단순한 첫 결과 +- `loss`: 예측이 정답과 얼마나 다른지 나타내는 학습용 숫자 +- `validation/holdout`: 학습에 사용하지 않고 성능 확인에만 사용하는 데이터 + +과적합 신호는 train metric 개선과 validation metric 악화, 특정 배경에 대한 의존, confidence 과대입니다. 더 다양한 실제 데이터, weight decay, 적절한 augmentation, early stopping으로 대응하되 validation leakage를 먼저 의심합니다. diff --git a/RobotVisionPlatform/tips/resources/official-resources.md b/RobotVisionPlatform/tips/resources/official-resources.md new file mode 100644 index 0000000..cf1efb5 --- /dev/null +++ b/RobotVisionPlatform/tips/resources/official-resources.md @@ -0,0 +1,19 @@ +# 공식 참고 자료 + +- [Jetson Orin Nano Developer Kit 시작 가이드](https://developer.nvidia.com/embedded/learn/get-started-jetson-orin-nano-devkit) +- [JetPack SDK](https://developer.nvidia.com/embedded/jetpack) +- [DeepStream 개발자 가이드](https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html) +- [TensorRT 문서](https://docs.nvidia.com/deeplearning/tensorrt/latest/) +- [TAO Toolkit 문서](https://docs.nvidia.com/tao/tao-toolkit/) +- [ONNX](https://onnx.ai/onnx/intro/) +- [PyTorch transfer learning tutorial](https://docs.pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) +- [Ultralytics 모델 학습 문서](https://docs.ultralytics.com/modes/train/) +- [gRPC C++ 문서](https://grpc.io/docs/languages/cpp/) +- [ASP.NET Core gRPC](https://learn.microsoft.com/aspnet/core/grpc/) +- [.NET MAUI Blazor Hybrid](https://learn.microsoft.com/aspnet/core/blazor/hybrid/) +- [OpenTelemetry C++](https://opentelemetry.io/docs/languages/cpp/) +- [ONNX Runtime Execution Providers](https://onnxruntime.ai/docs/execution-providers/) +- [TensorRT ONNX 배포 Quick Start](https://docs.nvidia.com/deeplearning/tensorrt/latest/getting-started/quick-start-onnx-deployment.html) +- [DeepStream custom model 가이드](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_using_custom_model.html) + +블로그의 명령을 그대로 복사하기보다 JetPack–DeepStream 호환표와 각 릴리스 노트를 먼저 확인합니다. diff --git a/RobotVisionPlatform/tips/training/fine-tuning-playbook.md b/RobotVisionPlatform/tips/training/fine-tuning-playbook.md new file mode 100644 index 0000000..0555492 --- /dev/null +++ b/RobotVisionPlatform/tips/training/fine-tuning-playbook.md @@ -0,0 +1,13 @@ +# Fine-tuning 실전 플레이북 + +1. pretrained checkpoint로 재현 가능한 baseline을 만든다. +2. seed, code commit, dataset version, hyperparameter, hardware를 기록한다. +3. 20~50개 샘플을 과적합시켜 데이터/라벨/코드 경로가 정상인지 확인한다. +4. input size, batch size, learning rate를 작은 sweep으로 찾는다. +5. augmentation은 하나씩 추가해 ablation한다. +6. class별 PR curve와 운영 threshold를 선택한다. +7. ONNX export 전후 output을 동일 입력으로 비교한다. +8. 실제 Jetson에서 warm-up 후 latency/전력/온도/메모리를 측정한다. + +mixed precision은 성능에 유리하지만 작은 물체나 후처리에서 수치 오차를 확인합니다. INT8은 대표 calibration set이 필요하며 정확도 회귀를 별도 승인 조건으로 둡니다. +