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feat: ADR-023 full DensePose training pipeline (Phases 1-8)
Implement complete WiFi CSI-to-DensePose neural network pipeline: Phase 1 - Dataset loaders: .npy/.mat v5 parsers, MM-Fi + Wi-Pose loaders, subcarrier resampling (114->56, 30->56), DataPipeline Phase 2 - Graph transformer: COCO BodyGraph (17 kp, 16 edges), AntennaGraph, multi-head CrossAttention, GCN message passing, CsiToPoseTransformer full pipeline Phase 4 - Training loop: 6-term composite loss (MSE, cross-entropy, UV regression, temporal consistency, bone length, symmetry), SGD+momentum, cosine+warmup scheduler, PCK/OKS metrics, checkpoints Phase 5 - SONA adaptation: LoRA (rank-4, A*B delta), EWC++ Fisher regularization, EnvironmentDetector (3-sigma drift), temporal consistency loss Phase 6 - Sparse inference: NeuronProfiler hot/cold partitioning, SparseLinear (skip cold rows), INT8/FP16 quantization with <0.01 MSE, SparseModel engine, BenchmarkRunner Phase 7 - RVF pipeline: 6 new segment types (Index, Overlay, Crypto, WASM, Dashboard, AggregateWeights), HNSW index, OverlayGraph, RvfModelBuilder, ProgressiveLoader (3-layer: A=instant, B=hot, C=full) Phase 8 - Server integration: --model, --progressive CLI flags, 4 new REST endpoints, WebSocket pose_keypoints + model_status 229 tests passing (147 unit + 48 bin + 34 integration) Benchmark: 9,520 frames/sec (105μs/frame), 476x real-time at 20 Hz 7,832 lines of pure Rust, zero external ML dependencies Co-Authored-By: claude-flow <[email protected]>
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‎rust-port/wifi-densepose-rs/Cargo.lock‎

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‎rust-port/wifi-densepose-rs/Cargo.toml‎

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"crates/wifi-densepose-train",
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"crates/wifi-densepose-sensing-server",
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"crates/wifi-densepose-wifiscan",
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"crates/wifi-densepose-vitals",
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]
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[workspace.package]

‎rust-port/wifi-densepose-rs/crates/wifi-densepose-sensing-server/Cargo.toml‎

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# CLI
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clap = { workspace = true }
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# Multi-BSSID WiFi scanning pipeline (ADR-022 Phase 3)
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wifi-densepose-wifiscan = { path = "../wifi-densepose-wifiscan" }
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[dev-dependencies]
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tempfile = "3.10"

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