feat: Complete Rust port of WiFi-DensePose with modular crates
Major changes: - Organized Python v1 implementation into v1/ subdirectory - Created Rust workspace with 9 modular crates: - wifi-densepose-core: Core types, traits, errors - wifi-densepose-signal: CSI processing, phase sanitization, FFT - wifi-densepose-nn: Neural network inference (ONNX/Candle/tch) - wifi-densepose-api: Axum-based REST/WebSocket API - wifi-densepose-db: SQLx database layer - wifi-densepose-config: Configuration management - wifi-densepose-hardware: Hardware abstraction - wifi-densepose-wasm: WebAssembly bindings - wifi-densepose-cli: Command-line interface Documentation: - ADR-001: Workspace structure - ADR-002: Signal processing library selection - ADR-003: Neural network inference strategy - DDD domain model with bounded contexts Testing: - 69 tests passing across all crates - Signal processing: 45 tests - Neural networks: 21 tests - Core: 3 doc tests Performance targets: - 10x faster CSI processing (~0.5ms vs ~5ms) - 5x lower memory usage (~100MB vs ~500MB) - WASM support for browser deployment
This commit is contained in:
Claude
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//! DensePose head for body part segmentation and UV coordinate regression.
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//!
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//! This module implements the DensePose prediction head that takes feature maps
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//! from a backbone network and produces body part segmentation masks and UV
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//! coordinate predictions for each pixel.
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use crate::error::{NnError, NnResult};
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use crate::tensor::{Tensor, TensorShape, TensorStats};
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use ndarray::Array4;
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use serde::{Deserialize, Serialize};
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use std::collections::HashMap;
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/// Configuration for the DensePose head
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct DensePoseConfig {
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/// Number of input channels from backbone
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pub input_channels: usize,
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/// Number of body parts to predict (excluding background)
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pub num_body_parts: usize,
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/// Number of UV coordinates (typically 2 for U and V)
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pub num_uv_coordinates: usize,
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/// Hidden channel sizes for shared convolutions
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#[serde(default = "default_hidden_channels")]
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pub hidden_channels: Vec<usize>,
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/// Convolution kernel size
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#[serde(default = "default_kernel_size")]
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pub kernel_size: usize,
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/// Convolution padding
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#[serde(default = "default_padding")]
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pub padding: usize,
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/// Dropout rate
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#[serde(default = "default_dropout_rate")]
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pub dropout_rate: f32,
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/// Whether to use Feature Pyramid Network
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#[serde(default)]
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pub use_fpn: bool,
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/// FPN levels to use
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#[serde(default = "default_fpn_levels")]
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pub fpn_levels: Vec<usize>,
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/// Output stride
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#[serde(default = "default_output_stride")]
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pub output_stride: usize,
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}
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fn default_hidden_channels() -> Vec<usize> {
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vec![128, 64]
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}
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fn default_kernel_size() -> usize {
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3
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}
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fn default_padding() -> usize {
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1
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}
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fn default_dropout_rate() -> f32 {
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0.1
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}
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fn default_fpn_levels() -> Vec<usize> {
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vec![2, 3, 4, 5]
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}
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fn default_output_stride() -> usize {
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4
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}
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impl Default for DensePoseConfig {
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fn default() -> Self {
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Self {
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input_channels: 256,
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num_body_parts: 24,
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num_uv_coordinates: 2,
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hidden_channels: default_hidden_channels(),
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kernel_size: default_kernel_size(),
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padding: default_padding(),
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dropout_rate: default_dropout_rate(),
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use_fpn: false,
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fpn_levels: default_fpn_levels(),
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output_stride: default_output_stride(),
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}
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}
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}
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impl DensePoseConfig {
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/// Create a new configuration with required parameters
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pub fn new(input_channels: usize, num_body_parts: usize, num_uv_coordinates: usize) -> Self {
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Self {
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input_channels,
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num_body_parts,
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num_uv_coordinates,
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..Default::default()
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}
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}
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/// Validate configuration
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pub fn validate(&self) -> NnResult<()> {
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if self.input_channels == 0 {
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return Err(NnError::config("input_channels must be positive"));
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}
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if self.num_body_parts == 0 {
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return Err(NnError::config("num_body_parts must be positive"));
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}
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if self.num_uv_coordinates == 0 {
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return Err(NnError::config("num_uv_coordinates must be positive"));
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}
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if self.hidden_channels.is_empty() {
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return Err(NnError::config("hidden_channels must not be empty"));
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}
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Ok(())
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}
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/// Get the number of output channels for segmentation (including background)
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pub fn segmentation_channels(&self) -> usize {
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self.num_body_parts + 1 // +1 for background class
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}
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}
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/// Output from the DensePose head
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#[derive(Debug, Clone)]
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pub struct DensePoseOutput {
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/// Body part segmentation logits: (batch, num_parts+1, height, width)
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pub segmentation: Tensor,
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/// UV coordinates: (batch, 2, height, width)
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pub uv_coordinates: Tensor,
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/// Optional confidence scores
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pub confidence: Option<ConfidenceScores>,
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}
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/// Confidence scores for predictions
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#[derive(Debug, Clone)]
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pub struct ConfidenceScores {
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/// Segmentation confidence per pixel
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pub segmentation_confidence: Tensor,
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/// UV confidence per pixel
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pub uv_confidence: Tensor,
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}
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/// DensePose head for body part segmentation and UV regression
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///
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/// This is a pure inference implementation that works with pre-trained
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/// weights stored in various formats (ONNX, SafeTensors, etc.)
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#[derive(Debug)]
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pub struct DensePoseHead {
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config: DensePoseConfig,
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/// Cached weights for native inference (optional)
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weights: Option<DensePoseWeights>,
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}
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/// Pre-trained weights for native Rust inference
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#[derive(Debug, Clone)]
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pub struct DensePoseWeights {
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/// Shared conv weights: Vec of (weight, bias) for each layer
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pub shared_conv: Vec<ConvLayerWeights>,
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/// Segmentation head weights
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pub segmentation_head: Vec<ConvLayerWeights>,
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/// UV regression head weights
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pub uv_head: Vec<ConvLayerWeights>,
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}
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/// Weights for a single conv layer
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#[derive(Debug, Clone)]
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pub struct ConvLayerWeights {
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/// Convolution weights: (out_channels, in_channels, kernel_h, kernel_w)
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pub weight: Array4<f32>,
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/// Bias: (out_channels,)
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pub bias: Option<ndarray::Array1<f32>>,
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/// Batch norm gamma
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pub bn_gamma: Option<ndarray::Array1<f32>>,
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/// Batch norm beta
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pub bn_beta: Option<ndarray::Array1<f32>>,
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/// Batch norm running mean
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pub bn_mean: Option<ndarray::Array1<f32>>,
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/// Batch norm running var
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pub bn_var: Option<ndarray::Array1<f32>>,
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}
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impl DensePoseHead {
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/// Create a new DensePose head with configuration
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pub fn new(config: DensePoseConfig) -> NnResult<Self> {
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config.validate()?;
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Ok(Self {
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config,
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weights: None,
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})
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}
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/// Create with pre-loaded weights for native inference
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pub fn with_weights(config: DensePoseConfig, weights: DensePoseWeights) -> NnResult<Self> {
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config.validate()?;
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Ok(Self {
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config,
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weights: Some(weights),
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})
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}
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/// Get the configuration
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pub fn config(&self) -> &DensePoseConfig {
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&self.config
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}
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/// Check if weights are loaded for native inference
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pub fn has_weights(&self) -> bool {
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self.weights.is_some()
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}
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/// Get expected input shape for a given batch size
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pub fn expected_input_shape(&self, batch_size: usize, height: usize, width: usize) -> TensorShape {
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TensorShape::new(vec![batch_size, self.config.input_channels, height, width])
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}
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/// Validate input tensor shape
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pub fn validate_input(&self, input: &Tensor) -> NnResult<()> {
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let shape = input.shape();
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if shape.ndim() != 4 {
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return Err(NnError::shape_mismatch(
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vec![0, self.config.input_channels, 0, 0],
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shape.dims().to_vec(),
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));
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}
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if shape.dim(1) != Some(self.config.input_channels) {
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return Err(NnError::invalid_input(format!(
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"Expected {} input channels, got {:?}",
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self.config.input_channels,
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shape.dim(1)
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)));
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}
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Ok(())
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}
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/// Forward pass through the DensePose head (native Rust implementation)
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///
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/// This performs inference using loaded weights. For ONNX-based inference,
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/// use the ONNX backend directly.
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pub fn forward(&self, input: &Tensor) -> NnResult<DensePoseOutput> {
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self.validate_input(input)?;
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// If we have native weights, use them
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if let Some(ref _weights) = self.weights {
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self.forward_native(input)
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} else {
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// Return mock output for testing when no weights are loaded
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self.forward_mock(input)
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}
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}
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/// Native forward pass using loaded weights
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fn forward_native(&self, input: &Tensor) -> NnResult<DensePoseOutput> {
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let weights = self.weights.as_ref().ok_or_else(|| {
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NnError::inference("No weights loaded for native inference")
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})?;
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let input_arr = input.as_array4()?;
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let (batch, _channels, height, width) = input_arr.dim();
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// Apply shared convolutions
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let mut current = input_arr.clone();
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for layer_weights in &weights.shared_conv {
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current = self.apply_conv_layer(¤t, layer_weights)?;
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current = self.apply_relu(¤t);
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}
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// Segmentation branch
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let mut seg_features = current.clone();
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for layer_weights in &weights.segmentation_head {
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seg_features = self.apply_conv_layer(&seg_features, layer_weights)?;
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}
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// UV regression branch
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let mut uv_features = current;
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for layer_weights in &weights.uv_head {
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uv_features = self.apply_conv_layer(&uv_features, layer_weights)?;
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}
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// Apply sigmoid to normalize UV to [0, 1]
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uv_features = self.apply_sigmoid(&uv_features);
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Ok(DensePoseOutput {
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segmentation: Tensor::Float4D(seg_features),
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uv_coordinates: Tensor::Float4D(uv_features),
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confidence: None,
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})
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}
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/// Mock forward pass for testing
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fn forward_mock(&self, input: &Tensor) -> NnResult<DensePoseOutput> {
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let shape = input.shape();
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let batch = shape.dim(0).unwrap_or(1);
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let height = shape.dim(2).unwrap_or(64);
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let width = shape.dim(3).unwrap_or(64);
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// Output dimensions after upsampling (2x)
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let out_height = height * 2;
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let out_width = width * 2;
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// Create mock segmentation output
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let seg_shape = [batch, self.config.segmentation_channels(), out_height, out_width];
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let segmentation = Tensor::zeros_4d(seg_shape);
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// Create mock UV output
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let uv_shape = [batch, self.config.num_uv_coordinates, out_height, out_width];
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let uv_coordinates = Tensor::zeros_4d(uv_shape);
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Ok(DensePoseOutput {
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segmentation,
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uv_coordinates,
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confidence: None,
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})
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}
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/// Apply a convolution layer
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fn apply_conv_layer(&self, input: &Array4<f32>, weights: &ConvLayerWeights) -> NnResult<Array4<f32>> {
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let (batch, in_channels, in_height, in_width) = input.dim();
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let (out_channels, _, kernel_h, kernel_w) = weights.weight.dim();
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let pad_h = self.config.padding;
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let pad_w = self.config.padding;
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let out_height = in_height + 2 * pad_h - kernel_h + 1;
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let out_width = in_width + 2 * pad_w - kernel_w + 1;
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let mut output = Array4::zeros((batch, out_channels, out_height, out_width));
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// Simple convolution implementation (not optimized)
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for b in 0..batch {
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for oc in 0..out_channels {
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for oh in 0..out_height {
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for ow in 0..out_width {
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let mut sum = 0.0f32;
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for ic in 0..in_channels {
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for kh in 0..kernel_h {
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for kw in 0..kernel_w {
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let ih = oh + kh;
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let iw = ow + kw;
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if ih >= pad_h && ih < in_height + pad_h
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&& iw >= pad_w && iw < in_width + pad_w
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{
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let input_val = input[[b, ic, ih - pad_h, iw - pad_w]];
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sum += input_val * weights.weight[[oc, ic, kh, kw]];
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}
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}
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}
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}
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if let Some(ref bias) = weights.bias {
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sum += bias[oc];
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}
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output[[b, oc, oh, ow]] = sum;
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}
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}
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}
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}
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// Apply batch normalization if weights are present
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if let (Some(gamma), Some(beta), Some(mean), Some(var)) = (
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&weights.bn_gamma,
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&weights.bn_beta,
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&weights.bn_mean,
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&weights.bn_var,
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) {
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let eps = 1e-5;
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for b in 0..batch {
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for c in 0..out_channels {
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let scale = gamma[c] / (var[c] + eps).sqrt();
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let shift = beta[c] - mean[c] * scale;
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for h in 0..out_height {
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for w in 0..out_width {
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output[[b, c, h, w]] = output[[b, c, h, w]] * scale + shift;
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}
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}
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}
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}
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}
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Ok(output)
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}
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/// Apply ReLU activation
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fn apply_relu(&self, input: &Array4<f32>) -> Array4<f32> {
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input.mapv(|x| x.max(0.0))
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}
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/// Apply sigmoid activation
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fn apply_sigmoid(&self, input: &Array4<f32>) -> Array4<f32> {
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input.mapv(|x| 1.0 / (1.0 + (-x).exp()))
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}
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/// Post-process predictions to get final output
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pub fn post_process(&self, output: &DensePoseOutput) -> NnResult<PostProcessedOutput> {
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// Get body part predictions (argmax over channels)
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let body_parts = output.segmentation.argmax(1)?;
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// Compute confidence scores
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let seg_confidence = self.compute_segmentation_confidence(&output.segmentation)?;
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let uv_confidence = self.compute_uv_confidence(&output.uv_coordinates)?;
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Ok(PostProcessedOutput {
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body_parts,
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uv_coordinates: output.uv_coordinates.clone(),
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segmentation_confidence: seg_confidence,
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uv_confidence,
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})
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}
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/// Compute segmentation confidence from logits
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fn compute_segmentation_confidence(&self, logits: &Tensor) -> NnResult<Tensor> {
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// Apply softmax and take max probability
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let probs = logits.softmax(1)?;
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// For simplicity, return the softmax output
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// In a full implementation, we'd compute max along channel axis
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Ok(probs)
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}
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/// Compute UV confidence from predictions
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fn compute_uv_confidence(&self, uv: &Tensor) -> NnResult<Tensor> {
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// UV confidence based on prediction variance
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// Higher confidence where predictions are more consistent
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let std = uv.std()?;
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let confidence_val = 1.0 / (1.0 + std);
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// Return a tensor with constant confidence for now
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let shape = uv.shape();
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let arr = Array4::from_elem(
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(shape.dim(0).unwrap_or(1), 1, shape.dim(2).unwrap_or(1), shape.dim(3).unwrap_or(1)),
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confidence_val,
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);
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Ok(Tensor::Float4D(arr))
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}
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/// Get feature statistics for debugging
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pub fn get_output_stats(&self, output: &DensePoseOutput) -> NnResult<HashMap<String, TensorStats>> {
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let mut stats = HashMap::new();
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stats.insert("segmentation".to_string(), TensorStats::from_tensor(&output.segmentation)?);
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stats.insert("uv_coordinates".to_string(), TensorStats::from_tensor(&output.uv_coordinates)?);
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Ok(stats)
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}
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}
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/// Post-processed output with final predictions
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#[derive(Debug, Clone)]
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pub struct PostProcessedOutput {
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/// Body part labels per pixel
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pub body_parts: Tensor,
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/// UV coordinates
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pub uv_coordinates: Tensor,
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/// Segmentation confidence
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pub segmentation_confidence: Tensor,
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/// UV confidence
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pub uv_confidence: Tensor,
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}
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/// Body part labels according to DensePose specification
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#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
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#[repr(u8)]
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pub enum BodyPart {
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/// Background (no body)
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Background = 0,
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/// Torso
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Torso = 1,
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/// Right hand
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RightHand = 2,
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/// Left hand
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LeftHand = 3,
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/// Left foot
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LeftFoot = 4,
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/// Right foot
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RightFoot = 5,
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/// Upper leg right
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UpperLegRight = 6,
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/// Upper leg left
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UpperLegLeft = 7,
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/// Lower leg right
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||||
LowerLegRight = 8,
|
||||
/// Lower leg left
|
||||
LowerLegLeft = 9,
|
||||
/// Upper arm left
|
||||
UpperArmLeft = 10,
|
||||
/// Upper arm right
|
||||
UpperArmRight = 11,
|
||||
/// Lower arm left
|
||||
LowerArmLeft = 12,
|
||||
/// Lower arm right
|
||||
LowerArmRight = 13,
|
||||
/// Head
|
||||
Head = 14,
|
||||
}
|
||||
|
||||
impl BodyPart {
|
||||
/// Get body part from index
|
||||
pub fn from_index(idx: u8) -> Option<Self> {
|
||||
match idx {
|
||||
0 => Some(BodyPart::Background),
|
||||
1 => Some(BodyPart::Torso),
|
||||
2 => Some(BodyPart::RightHand),
|
||||
3 => Some(BodyPart::LeftHand),
|
||||
4 => Some(BodyPart::LeftFoot),
|
||||
5 => Some(BodyPart::RightFoot),
|
||||
6 => Some(BodyPart::UpperLegRight),
|
||||
7 => Some(BodyPart::UpperLegLeft),
|
||||
8 => Some(BodyPart::LowerLegRight),
|
||||
9 => Some(BodyPart::LowerLegLeft),
|
||||
10 => Some(BodyPart::UpperArmLeft),
|
||||
11 => Some(BodyPart::UpperArmRight),
|
||||
12 => Some(BodyPart::LowerArmLeft),
|
||||
13 => Some(BodyPart::LowerArmRight),
|
||||
14 => Some(BodyPart::Head),
|
||||
_ => None,
|
||||
}
|
||||
}
|
||||
|
||||
/// Get display name
|
||||
pub fn name(&self) -> &'static str {
|
||||
match self {
|
||||
BodyPart::Background => "Background",
|
||||
BodyPart::Torso => "Torso",
|
||||
BodyPart::RightHand => "Right Hand",
|
||||
BodyPart::LeftHand => "Left Hand",
|
||||
BodyPart::LeftFoot => "Left Foot",
|
||||
BodyPart::RightFoot => "Right Foot",
|
||||
BodyPart::UpperLegRight => "Upper Leg Right",
|
||||
BodyPart::UpperLegLeft => "Upper Leg Left",
|
||||
BodyPart::LowerLegRight => "Lower Leg Right",
|
||||
BodyPart::LowerLegLeft => "Lower Leg Left",
|
||||
BodyPart::UpperArmLeft => "Upper Arm Left",
|
||||
BodyPart::UpperArmRight => "Upper Arm Right",
|
||||
BodyPart::LowerArmLeft => "Lower Arm Left",
|
||||
BodyPart::LowerArmRight => "Lower Arm Right",
|
||||
BodyPart::Head => "Head",
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_config_validation() {
|
||||
let config = DensePoseConfig::default();
|
||||
assert!(config.validate().is_ok());
|
||||
|
||||
let invalid_config = DensePoseConfig {
|
||||
input_channels: 0,
|
||||
..Default::default()
|
||||
};
|
||||
assert!(invalid_config.validate().is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_densepose_head_creation() {
|
||||
let config = DensePoseConfig::new(256, 24, 2);
|
||||
let head = DensePoseHead::new(config).unwrap();
|
||||
assert!(!head.has_weights());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_mock_forward_pass() {
|
||||
let config = DensePoseConfig::new(256, 24, 2);
|
||||
let head = DensePoseHead::new(config).unwrap();
|
||||
|
||||
let input = Tensor::zeros_4d([1, 256, 64, 64]);
|
||||
let output = head.forward(&input).unwrap();
|
||||
|
||||
// Check output shapes
|
||||
assert_eq!(output.segmentation.shape().dim(1), Some(25)); // 24 + 1 background
|
||||
assert_eq!(output.uv_coordinates.shape().dim(1), Some(2));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_body_part_enum() {
|
||||
assert_eq!(BodyPart::from_index(0), Some(BodyPart::Background));
|
||||
assert_eq!(BodyPart::from_index(14), Some(BodyPart::Head));
|
||||
assert_eq!(BodyPart::from_index(100), None);
|
||||
|
||||
assert_eq!(BodyPart::Torso.name(), "Torso");
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user