7092f83b34
Add license, authors, repository, documentation, keywords, categories, and readme fields to all crate Cargo.toml files. Add crate-level README files for documentation. Co-Authored-By: claude-flow <ruv@ruv.net>
100 lines
4.6 KiB
Markdown
100 lines
4.6 KiB
Markdown
# wifi-densepose-train
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[](https://crates.io/crates/wifi-densepose-train)
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[](https://docs.rs/wifi-densepose-train)
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[](LICENSE)
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Complete training pipeline for WiFi-DensePose, integrated with all five ruvector crates.
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## Overview
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`wifi-densepose-train` provides everything needed to train the WiFi-to-DensePose model: dataset
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loading, subcarrier interpolation, loss functions, evaluation metrics, and the training loop
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orchestrator. It supports both the MM-Fi dataset (NeurIPS 2023) and deterministic synthetic data
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for reproducible experiments.
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Without the `tch-backend` feature the crate still provides the dataset, configuration, and
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subcarrier interpolation APIs needed for data preprocessing and proof verification.
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## Features
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- **MM-Fi dataset loader** -- Reads the MM-Fi multimodal dataset (NeurIPS 2023) from disk with
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memory-mapped `.npy` files.
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- **Synthetic dataset** -- Deterministic, fixed-seed CSI generation for unit tests and proofs.
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- **Subcarrier interpolation** -- 114 -> 56 subcarrier compression via `ruvector-solver` sparse
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interpolation with variance-based selection.
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- **Loss functions** (`tch-backend`) -- Pose estimation losses including MSE, OKS, and combined
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multi-task loss.
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- **Metrics** (`tch-backend`) -- PCKh, OKS-AP, and per-keypoint evaluation with
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`ruvector-mincut`-based person matching.
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- **Training orchestrator** (`tch-backend`) -- Full training loop with learning rate scheduling,
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gradient clipping, checkpointing, and reproducible proofs.
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- **All 5 ruvector crates** -- `ruvector-mincut`, `ruvector-attn-mincut`,
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`ruvector-temporal-tensor`, `ruvector-solver`, and `ruvector-attention` integrated across
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dataset loading, metrics, and model attention.
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### Feature flags
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| Flag | Default | Description |
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|---------------|---------|----------------------------------------|
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| `tch-backend` | no | Enable PyTorch training via `tch-rs` |
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| `cuda` | no | CUDA GPU acceleration (implies `tch`) |
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### Binaries
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| Binary | Description |
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|--------------------|------------------------------------------|
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| `train` | Main training entry point |
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| `verify-training` | Proof verification (requires `tch-backend`) |
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## Quick Start
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```rust
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use wifi_densepose_train::config::TrainingConfig;
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use wifi_densepose_train::dataset::{SyntheticCsiDataset, SyntheticConfig, CsiDataset};
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// Build and validate config
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let config = TrainingConfig::default();
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config.validate().expect("config is valid");
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// Create a synthetic dataset (deterministic, fixed-seed)
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let syn_cfg = SyntheticConfig::default();
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let dataset = SyntheticCsiDataset::new(200, syn_cfg);
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// Load one sample
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let sample = dataset.get(0).unwrap();
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println!("amplitude shape: {:?}", sample.amplitude.shape());
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```
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## Architecture
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```text
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wifi-densepose-train/src/
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lib.rs -- Re-exports, VERSION
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config.rs -- TrainingConfig, hyperparameters, validation
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dataset.rs -- CsiDataset trait, MmFiDataset, SyntheticCsiDataset, DataLoader
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error.rs -- TrainError, ConfigError, DatasetError, SubcarrierError
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subcarrier.rs -- interpolate_subcarriers (114->56), variance-based selection
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losses.rs -- (tch) MSE, OKS, multi-task loss [feature-gated]
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metrics.rs -- (tch) PCKh, OKS-AP, person matching [feature-gated]
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model.rs -- (tch) Model definition with attention [feature-gated]
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proof.rs -- (tch) Deterministic training proofs [feature-gated]
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trainer.rs -- (tch) Training loop orchestrator [feature-gated]
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```
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## Related Crates
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| Crate | Role |
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|-------|------|
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| [`wifi-densepose-signal`](../wifi-densepose-signal) | Signal preprocessing consumed by dataset loaders |
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| [`wifi-densepose-nn`](../wifi-densepose-nn) | Inference engine that loads trained models |
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| [`ruvector-mincut`](https://crates.io/crates/ruvector-mincut) | Person matching in metrics |
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| [`ruvector-attn-mincut`](https://crates.io/crates/ruvector-attn-mincut) | Attention-weighted graph cuts |
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| [`ruvector-temporal-tensor`](https://crates.io/crates/ruvector-temporal-tensor) | Compressed CSI buffering in datasets |
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| [`ruvector-solver`](https://crates.io/crates/ruvector-solver) | Sparse subcarrier interpolation |
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| [`ruvector-attention`](https://crates.io/crates/ruvector-attention) | Spatial attention in model |
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## License
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MIT OR Apache-2.0
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