ec98e40fff
Implements the training infrastructure described in ADR-015: - config.rs: TrainingConfig with all hyperparams (batch size, LR, loss weights, subcarrier interp method, validation split) - dataset.rs: MmFiDataset (real MM-Fi .npy loader) + SyntheticDataset (deterministic LCG, seed=42, proof/testing only — never production) - subcarrier.rs: Linear/cubic interpolation 114→56 subcarriers - error.rs: Typed errors (DataNotFound, InvalidFormat, IoError) - losses.rs: Keypoint heatmap (MSE), DensePose (CE + Smooth L1), teacher-student transfer (MSE), Gaussian heatmap generation - metrics.rs: PCK@0.2, OKS with Hungarian min-cut bipartite assignment via petgraph (optimal multi-person keypoint matching) - model.rs: WiFiDensePoseModel end-to-end with tch-rs (PyTorch bindings) - trainer.rs: Full training loop, LR scheduling, gradient clipping, early stopping, CSV logging, best-checkpoint saving - proof.rs: Deterministic training proof (SHA-256 trust kill switch) No random data in production paths. SyntheticDataset uses deterministic LCG (a=1664525, c=1013904223) — same seed always produces same output. https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4
81 lines
1.7 KiB
TOML
81 lines
1.7 KiB
TOML
[package]
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name = "wifi-densepose-train"
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version = "0.1.0"
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edition = "2021"
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authors = ["WiFi-DensePose Contributors"]
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license = "MIT OR Apache-2.0"
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description = "Training pipeline for WiFi-DensePose pose estimation"
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keywords = ["wifi", "training", "pose-estimation", "deep-learning"]
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[[bin]]
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name = "train"
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path = "src/bin/train.rs"
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[[bin]]
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name = "verify-training"
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path = "src/bin/verify_training.rs"
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[features]
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default = ["tch-backend"]
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tch-backend = ["tch"]
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cuda = ["tch-backend"]
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[dependencies]
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# Internal crates
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wifi-densepose-signal = { path = "../wifi-densepose-signal" }
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wifi-densepose-nn = { path = "../wifi-densepose-nn", default-features = false }
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# Core
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thiserror = "1.0"
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anyhow = "1.0"
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serde = { version = "1.0", features = ["derive"] }
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serde_json = "1.0"
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# Tensor / math
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ndarray = { version = "0.15", features = ["serde"] }
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ndarray-linalg = { version = "0.16", features = ["openblas-static"] }
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num-complex = "0.4"
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num-traits = "0.2"
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# PyTorch bindings (training)
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tch = { version = "0.14", optional = true }
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# Graph algorithms (min-cut for optimal keypoint assignment)
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petgraph = "0.6"
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# Data loading
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ndarray-npy = "0.8"
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memmap2 = "0.9"
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walkdir = "2.4"
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# Serialization
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csv = "1.3"
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toml = "0.8"
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# Logging / progress
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tracing = "0.1"
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tracing-subscriber = { version = "0.3", features = ["env-filter"] }
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indicatif = "0.17"
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# Async
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tokio = { version = "1.35", features = ["rt", "rt-multi-thread", "macros", "fs"] }
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# Crypto (for proof hash)
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sha2 = "0.10"
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# CLI
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clap = { version = "4.4", features = ["derive"] }
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# Time
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chrono = { version = "0.4", features = ["serde"] }
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[dev-dependencies]
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criterion = { version = "0.5", features = ["html_reports"] }
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proptest = "1.4"
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tempfile = "3.10"
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approx = "0.5"
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[[bench]]
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name = "training_bench"
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harness = false
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