mirror of
https://github.com/ruvnet/RuView
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rUv
c604ca1150
Closes the remaining doable items from the 2026-05-11 training-pipeline audit: #6 (CSI format default = 56-sc / 1 NIC) + #7 (multi-band 168-sc mesh not in config): new `TrainingConfig::for_subcarriers(native, target)` plus named presets `mmfi()` (114→56), `ht40_192()` (≈192-sc ESP32 HT40 → 56) and `multiband_168()` (168-sc ADR-078 multi-band mesh → 56). Non-MM-Fi CSI shapes are now first-class instead of requiring manual `native_subcarriers` / `num_subcarriers` overrides; the field docs list the supported source counts and the multi-NIC mapping (a 2–3-node mesh currently rides on `n_rx` until a dedicated node dimension lands). Model input width stays `num_subcarriers`; the presets only vary the resampling input. #4 (proof.rs uses synthetic data): reframed — a deterministic proof *must* use a reproducible source, so `verify-training` correctly stays on `SyntheticCsiDataset`. The real gap was that nothing exercised the on-disk `MmFiDataset` path. New `tests/test_real_loader.rs` writes synthetic CSI to `.npy` files in the `MmFiDataset::discover` layout, loads it back, and checks the resulting `CsiSample` — covering the no-interp case, the subcarrier-interpolation branch, and the empty-root case. Adds `ndarray` / `ndarray-npy` as dev-deps for the fixture writing. cargo check + cargo test -p wifi-densepose-train --no-default-features: clean, all existing tests green, 3 new loader tests + the updated config doctest pass. Purely additive — no model-shape change, no tch-module change.
97 lines
2.6 KiB
TOML
97 lines
2.6 KiB
TOML
[package]
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name = "wifi-densepose-train"
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version = "0.3.0"
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edition = "2021"
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authors = ["rUv <ruv@ruv.net>", "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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repository = "https://github.com/ruvnet/wifi-densepose"
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documentation = "https://docs.rs/wifi-densepose-train"
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keywords = ["wifi", "training", "pose-estimation", "deep-learning"]
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categories = ["science", "computer-vision"]
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readme = "README.md"
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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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required-features = ["tch-backend"]
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[features]
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default = []
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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 = { version = "0.3.0", path = "../wifi-densepose-signal", default-features = false }
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wifi-densepose-nn = { version = "0.3.0", path = "../wifi-densepose-nn" }
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# Core
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thiserror.workspace = true
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anyhow.workspace = true
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serde = { workspace = true, features = ["derive"] }
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serde_json.workspace = true
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# Tensor / math
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ndarray.workspace = true
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num-complex.workspace = true
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num-traits.workspace = true
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# PyTorch bindings (optional — only enabled by `tch-backend` feature)
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tch = { workspace = true, optional = true }
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# Graph algorithms (min-cut for optimal keypoint assignment)
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petgraph.workspace = true
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# ruvector integration (subpolynomial min-cut, sparse solvers, temporal compression, attention)
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ruvector-mincut = { workspace = true }
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ruvector-attn-mincut = { workspace = true }
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ruvector-temporal-tensor = { workspace = true }
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ruvector-solver = { workspace = true }
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ruvector-attention = { workspace = true }
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# Data loading
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ndarray-npy.workspace = true
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memmap2 = "0.9"
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walkdir.workspace = true
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# Serialization
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csv.workspace = true
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toml = "0.8"
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# Logging / progress
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tracing.workspace = true
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tracing-subscriber.workspace = true
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indicatif.workspace = true
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# Async (subset of features needed by training pipeline)
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tokio = { workspace = true, features = ["rt", "rt-multi-thread", "macros", "fs"] }
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# Crypto (for proof hash)
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sha2.workspace = true
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# CLI
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clap.workspace = true
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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.workspace = true
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proptest.workspace = true
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tempfile = "3.10"
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approx = "0.5"
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# Used by tests/test_real_loader.rs to write .npy fixtures that exercise the
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# real MmFiDataset disk-loading path (the deterministic proof uses the
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# in-memory SyntheticCsiDataset, which bypasses .npy parsing).
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ndarray.workspace = true
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ndarray-npy.workspace = true
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[[bench]]
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name = "training_bench"
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harness = false
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