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https://github.com/ruvnet/RuView
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ruv
7d26b15eef
Real CSI masked-autoencoder behind feature `tch-backend` (ADR-027 §2.0):
- CsiMae: dual-stream per-token amp+phase embed → fuse → residual-MLP encoder
over the visible tokens → flatten-to-latent bottleneck → learned per-position
query + broadcast latent → residual-MLP decoder → dec_amp_head / dec_ph_head
→ index_select the masked positions. (MLP-based v0; self-attention transformer
blocks are iter 3.)
- CsiMae::reconstruction_loss(pred_amp, pred_phase, tgt_amp, tgt_phase, phase_w)
= MSE(amp) + phase_w * MSE(phase).
- MaeBatch::from_windows — partition computed once from window 0 and reused
across the batch (the bottleneck fixes n_tokens), ndarray → tch conversion.
- pretrain_step(model, opt, batch) -> f64 — one Adam step, returns the loss.
- src/bin/pretrain_mae.rs — synthetic-data pre-train driver (required-features
= ["tch-backend"]); clap args for epochs/batch/samples/lr/mask-ratio/save.
- #[cfg(feature="tch-backend")] smoke test: loss halves when overfitting one
batch over 60 steps; also asserts model.n_visible/n_masked match
mask_csi_window's clamping.
v0 limits (documented in the module): fixed n_tokens; batch-shared masking;
MSE on unwrapped phase (vs a circular loss). The dev box has no LibTorch, so the
tch path is CI-verified (`--features tch-backend`), not locally. The default
`cargo test -p wifi-densepose-train --no-default-features` stays green (121 lib
tests) — the model module and the bin are both feature-gated.
Co-Authored-By: claude-flow <ruv@ruv.net>
97 lines
2.4 KiB
TOML
97 lines
2.4 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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[[bin]]
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name = "pretrain-mae"
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path = "src/bin/pretrain_mae.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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[[bench]]
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name = "training_bench"
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harness = false
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