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Merge pull request #363 from ruvnet/feat/adr-079-camera-ground-truth
feat: camera ground-truth training pipeline with ruvector optimizations (ADR-079)
2 parents b5e924c + 35903a3 commit 8dddbf9

37 files changed

Lines changed: 6361 additions & 30 deletions

‎docs/adr/ADR-079-camera-ground-truth-training.md‎

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‎rust-port/wifi-densepose-rs/crates/wifi-densepose-nn/src/inference.rs‎

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@@ -330,9 +330,36 @@ impl<B: Backend> InferenceEngine<B> {
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Ok(result)
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}
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/// Run batched inference
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/// Run batched inference.
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///
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/// Stacks all inputs along a new batch dimension, runs a single
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/// backend call, then splits the output back into individual tensors.
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/// Falls back to sequential inference if stack/split fails.
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pub fn infer_batch(&self, inputs: &[Tensor]) -> NnResult<Vec<Tensor>> {
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inputs.iter().map(|input| self.infer(input)).collect()
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if inputs.is_empty() {
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return Ok(Vec::new());
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}
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if inputs.len() == 1 {
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return Ok(vec![self.infer(&inputs[0])?]);
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}
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// Try batched path: stack -> single call -> split
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match Tensor::stack(inputs) {
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Ok(batched_input) => {
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let n = inputs.len();
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let batched_output = self.backend.run_single(&batched_input)?;
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match batched_output.split(n) {
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Ok(outputs) => Ok(outputs),
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Err(_) => {
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// Fallback: sequential
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inputs.iter().map(|input| self.infer(input)).collect()
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}
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}
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}
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Err(_) => {
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// Fallback: sequential if shapes are incompatible
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inputs.iter().map(|input| self.infer(input)).collect()
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}
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}
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}
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/// Get inference statistics

‎rust-port/wifi-densepose-rs/crates/wifi-densepose-nn/src/tensor.rs‎

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@@ -304,6 +304,74 @@ impl Tensor {
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}
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}
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/// Stack multiple tensors along a new batch dimension (dim 0).
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///
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/// All tensors must have the same shape. The result has one extra
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/// leading dimension equal to `tensors.len()`.
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pub fn stack(tensors: &[Tensor]) -> NnResult<Tensor> {
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if tensors.is_empty() {
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return Err(NnError::tensor_op("Cannot stack zero tensors"));
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}
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let first_shape = tensors[0].shape();
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for (i, t) in tensors.iter().enumerate().skip(1) {
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if t.shape() != first_shape {
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return Err(NnError::tensor_op(&format!(
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"Shape mismatch at index {i}: expected {first_shape}, got {}",
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t.shape()
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)));
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}
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}
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let mut all_data: Vec<f32> = Vec::with_capacity(tensors.len() * first_shape.numel());
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for t in tensors {
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let data = t.to_vec()?;
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all_data.extend_from_slice(&data);
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}
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let mut new_dims = vec![tensors.len()];
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new_dims.extend_from_slice(first_shape.dims());
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let arr = ndarray::ArrayD::from_shape_vec(
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ndarray::IxDyn(&new_dims),
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all_data,
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)
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.map_err(|e| NnError::tensor_op(&format!("Stack reshape failed: {e}")))?;
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Ok(Tensor::FloatND(arr))
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}
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/// Split a tensor along dim 0 into `n` sub-tensors.
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///
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/// The first dimension must be evenly divisible by `n`.
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pub fn split(self, n: usize) -> NnResult<Vec<Tensor>> {
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if n == 0 {
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return Err(NnError::tensor_op("Cannot split into 0 pieces"));
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}
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let shape = self.shape();
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let batch = shape.dim(0).ok_or_else(|| NnError::tensor_op("Tensor has no dimensions"))?;
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if batch % n != 0 {
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return Err(NnError::tensor_op(&format!(
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"Batch dim {batch} not divisible by {n}"
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)));
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}
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let chunk_size = batch / n;
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let data = self.to_vec()?;
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let elem_per_sample = shape.numel() / batch;
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let sub_dims: Vec<usize> = {
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let mut d = shape.dims().to_vec();
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d[0] = chunk_size;
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d
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};
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let mut result = Vec::with_capacity(n);
362+
for i in 0..n {
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let start = i * chunk_size * elem_per_sample;
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let end = start + chunk_size * elem_per_sample;
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let arr = ndarray::ArrayD::from_shape_vec(
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ndarray::IxDyn(&sub_dims),
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data[start..end].to_vec(),
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)
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.map_err(|e| NnError::tensor_op(&format!("Split reshape failed: {e}")))?;
370+
result.push(Tensor::FloatND(arr));
371+
}
372+
Ok(result)
373+
}
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307375
/// Compute standard deviation
308376
pub fn std(&self) -> NnResult<f32> {
309377
match self {

‎rust-port/wifi-densepose-rs/crates/wifi-densepose-sensing-server/src/cli.rs‎

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//! CLI argument definitions and early-exit mode handlers.
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use std::path::PathBuf;
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use clap::Parser;
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/// CLI arguments for the sensing server.
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#[derive(Parser, Debug)]
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#[command(name = "sensing-server", about = "WiFi-DensePose sensing server")]
9+
pub struct Args {
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/// HTTP port for UI and REST API
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#[arg(long, default_value = "8080")]
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pub http_port: u16,
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/// WebSocket port for sensing stream
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#[arg(long, default_value = "8765")]
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pub ws_port: u16,
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/// UDP port for ESP32 CSI frames
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#[arg(long, default_value = "5005")]
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pub udp_port: u16,
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/// Path to UI static files
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#[arg(long, default_value = "../../ui")]
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pub ui_path: PathBuf,
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/// Tick interval in milliseconds (default 100 ms = 10 fps for smooth pose animation)
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#[arg(long, default_value = "100")]
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pub tick_ms: u64,
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/// Bind address (default 127.0.0.1; set to 0.0.0.0 for network access)
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#[arg(long, default_value = "127.0.0.1", env = "SENSING_BIND_ADDR")]
32+
pub bind_addr: String,
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/// Data source: auto, wifi, esp32, simulate
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#[arg(long, default_value = "auto")]
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pub source: String,
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/// Run vital sign detection benchmark (1000 frames) and exit
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#[arg(long)]
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pub benchmark: bool,
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/// Load model config from an RVF container at startup
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#[arg(long, value_name = "PATH")]
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pub load_rvf: Option<PathBuf>,
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/// Save current model state as an RVF container on shutdown
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#[arg(long, value_name = "PATH")]
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pub save_rvf: Option<PathBuf>,
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/// Load a trained .rvf model for inference
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#[arg(long, value_name = "PATH")]
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pub model: Option<PathBuf>,
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/// Enable progressive loading (Layer A instant start)
55+
#[arg(long)]
56+
pub progressive: bool,
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58+
/// Export an RVF container package and exit (no server)
59+
#[arg(long, value_name = "PATH")]
60+
pub export_rvf: Option<PathBuf>,
61+
62+
/// Run training mode (train a model and exit)
63+
#[arg(long)]
64+
pub train: bool,
65+
66+
/// Path to dataset directory (MM-Fi or Wi-Pose)
67+
#[arg(long, value_name = "PATH")]
68+
pub dataset: Option<PathBuf>,
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70+
/// Dataset type: "mmfi" or "wipose"
71+
#[arg(long, value_name = "TYPE", default_value = "mmfi")]
72+
pub dataset_type: String,
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/// Number of training epochs
75+
#[arg(long, default_value = "100")]
76+
pub epochs: usize,
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/// Directory for training checkpoints
79+
#[arg(long, value_name = "DIR")]
80+
pub checkpoint_dir: Option<PathBuf>,
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/// Run self-supervised contrastive pretraining (ADR-024)
83+
#[arg(long)]
84+
pub pretrain: bool,
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/// Number of pretraining epochs (default 50)
87+
#[arg(long, default_value = "50")]
88+
pub pretrain_epochs: usize,
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/// Extract embeddings mode: load model and extract CSI embeddings
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#[arg(long)]
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pub embed: bool,
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/// Build fingerprint index from embeddings (env|activity|temporal|person)
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#[arg(long, value_name = "TYPE")]
96+
pub build_index: Option<String>,
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98+
/// Node positions for multistatic fusion (format: "x,y,z;x,y,z;...")
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#[arg(long, env = "SENSING_NODE_POSITIONS")]
100+
pub node_positions: Option<String>,
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102+
/// Start field model calibration on boot (empty room required)
103+
#[arg(long)]
104+
pub calibrate: bool,
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}

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