9bbe95648c
Full implementation of Project AETHER — Contrastive CSI Embedding Model. ## Phases Delivered 1. ProjectionHead (64→128→128) + L2 normalization 2. CsiAugmenter (5 physically-motivated augmentations) 3. InfoNCE contrastive loss + SimCLR pretraining 4. FingerprintIndex (4 index types: env, activity, temporal, person) 5. RVF SEG_EMBED (0x0C) + CLI integration 6. Cross-modal alignment (PoseEncoder + InfoNCE) 7. Deep RuVector: MicroLoRA, EWC++, drift detection, hard-negative mining, SEG_LORA ## Stats - 276 tests passing (191 lib + 51 bin + 16 rvf + 18 vitals) - 3,342 additions across 8 files - Zero unsafe/unwrap/panic/todo stubs - ~55KB INT8 model for ESP32 edge deployment Also fixes deprecated GitHub Actions (v3→v4) and adds feat/* branch CI triggers. Closes #50
62 lines
1.5 KiB
TOML
62 lines
1.5 KiB
TOML
[package]
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name = "wifi-densepose-nn"
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version.workspace = true
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edition.workspace = true
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authors.workspace = true
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license.workspace = true
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repository.workspace = true
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documentation.workspace = true
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keywords = ["neural-network", "onnx", "inference", "densepose", "deep-learning"]
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categories = ["science", "computer-vision"]
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description = "Neural network inference for WiFi-DensePose pose estimation"
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readme = "README.md"
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[features]
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default = ["onnx"]
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onnx = ["ort"]
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tch-backend = ["tch"]
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candle-backend = ["candle-core", "candle-nn"]
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cuda = ["onnx"]
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tensorrt = ["onnx"]
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all-backends = ["onnx", "tch-backend", "candle-backend"]
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[dependencies]
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# Core utilities
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thiserror.workspace = true
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anyhow.workspace = true
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serde.workspace = true
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serde_json.workspace = true
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tracing.workspace = true
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# Tensor operations
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ndarray.workspace = true
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num-traits.workspace = true
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# ONNX Runtime (default)
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ort = { workspace = true, optional = true }
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# PyTorch backend (optional)
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tch = { workspace = true, optional = true }
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# Candle backend (optional)
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candle-core = { workspace = true, optional = true }
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candle-nn = { workspace = true, optional = true }
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# Async runtime
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tokio = { workspace = true, features = ["sync", "rt"] }
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# Additional utilities
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parking_lot = "0.12"
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once_cell = "1.19"
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memmap2 = "0.9"
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[dev-dependencies]
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criterion.workspace = true
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proptest.workspace = true
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tokio = { workspace = true, features = ["rt-multi-thread", "macros"] }
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tempfile = "3.10"
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[[bench]]
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name = "inference_bench"
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harness = false
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