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");
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,92 @@
|
||||
//! Error types for the neural network crate.
|
||||
|
||||
use thiserror::Error;
|
||||
|
||||
/// Result type alias for neural network operations
|
||||
pub type NnResult<T> = Result<T, NnError>;
|
||||
|
||||
/// Neural network errors
|
||||
#[derive(Error, Debug)]
|
||||
pub enum NnError {
|
||||
/// Configuration validation error
|
||||
#[error("Configuration error: {0}")]
|
||||
Config(String),
|
||||
|
||||
/// Model loading error
|
||||
#[error("Failed to load model: {0}")]
|
||||
ModelLoad(String),
|
||||
|
||||
/// Inference error
|
||||
#[error("Inference failed: {0}")]
|
||||
Inference(String),
|
||||
|
||||
/// Shape mismatch error
|
||||
#[error("Shape mismatch: expected {expected:?}, got {actual:?}")]
|
||||
ShapeMismatch {
|
||||
/// Expected shape
|
||||
expected: Vec<usize>,
|
||||
/// Actual shape
|
||||
actual: Vec<usize>,
|
||||
},
|
||||
|
||||
/// Invalid input error
|
||||
#[error("Invalid input: {0}")]
|
||||
InvalidInput(String),
|
||||
|
||||
/// Backend not available
|
||||
#[error("Backend not available: {0}")]
|
||||
BackendUnavailable(String),
|
||||
|
||||
/// ONNX Runtime error
|
||||
#[cfg(feature = "onnx")]
|
||||
#[error("ONNX Runtime error: {0}")]
|
||||
OnnxRuntime(#[from] ort::Error),
|
||||
|
||||
/// IO error
|
||||
#[error("IO error: {0}")]
|
||||
Io(#[from] std::io::Error),
|
||||
|
||||
/// Serialization error
|
||||
#[error("Serialization error: {0}")]
|
||||
Serialization(#[from] serde_json::Error),
|
||||
|
||||
/// Tensor operation error
|
||||
#[error("Tensor operation error: {0}")]
|
||||
TensorOp(String),
|
||||
|
||||
/// Unsupported operation
|
||||
#[error("Unsupported operation: {0}")]
|
||||
Unsupported(String),
|
||||
}
|
||||
|
||||
impl NnError {
|
||||
/// Create a configuration error
|
||||
pub fn config<S: Into<String>>(msg: S) -> Self {
|
||||
NnError::Config(msg.into())
|
||||
}
|
||||
|
||||
/// Create a model load error
|
||||
pub fn model_load<S: Into<String>>(msg: S) -> Self {
|
||||
NnError::ModelLoad(msg.into())
|
||||
}
|
||||
|
||||
/// Create an inference error
|
||||
pub fn inference<S: Into<String>>(msg: S) -> Self {
|
||||
NnError::Inference(msg.into())
|
||||
}
|
||||
|
||||
/// Create a shape mismatch error
|
||||
pub fn shape_mismatch(expected: Vec<usize>, actual: Vec<usize>) -> Self {
|
||||
NnError::ShapeMismatch { expected, actual }
|
||||
}
|
||||
|
||||
/// Create an invalid input error
|
||||
pub fn invalid_input<S: Into<String>>(msg: S) -> Self {
|
||||
NnError::InvalidInput(msg.into())
|
||||
}
|
||||
|
||||
/// Create a tensor operation error
|
||||
pub fn tensor_op<S: Into<String>>(msg: S) -> Self {
|
||||
NnError::TensorOp(msg.into())
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,569 @@
|
||||
//! Inference engine abstraction for neural network backends.
|
||||
//!
|
||||
//! This module provides a unified interface for running inference across
|
||||
//! different backends (ONNX Runtime, tch-rs, Candle).
|
||||
|
||||
use crate::densepose::{DensePoseConfig, DensePoseOutput};
|
||||
use crate::error::{NnError, NnResult};
|
||||
use crate::tensor::{Tensor, TensorShape};
|
||||
use crate::translator::TranslatorConfig;
|
||||
use serde::{Deserialize, Serialize};
|
||||
use std::collections::HashMap;
|
||||
use std::sync::Arc;
|
||||
use tokio::sync::RwLock;
|
||||
use tracing::{debug, info, instrument};
|
||||
|
||||
/// Options for inference execution
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct InferenceOptions {
|
||||
/// Batch size for inference
|
||||
#[serde(default = "default_batch_size")]
|
||||
pub batch_size: usize,
|
||||
/// Whether to use GPU acceleration
|
||||
#[serde(default)]
|
||||
pub use_gpu: bool,
|
||||
/// GPU device ID (if using GPU)
|
||||
#[serde(default)]
|
||||
pub gpu_device_id: usize,
|
||||
/// Number of CPU threads for inference
|
||||
#[serde(default = "default_num_threads")]
|
||||
pub num_threads: usize,
|
||||
/// Enable model optimization/fusion
|
||||
#[serde(default = "default_optimize")]
|
||||
pub optimize: bool,
|
||||
/// Memory limit in bytes (0 = unlimited)
|
||||
#[serde(default)]
|
||||
pub memory_limit: usize,
|
||||
/// Enable profiling
|
||||
#[serde(default)]
|
||||
pub profiling: bool,
|
||||
}
|
||||
|
||||
fn default_batch_size() -> usize {
|
||||
1
|
||||
}
|
||||
|
||||
fn default_num_threads() -> usize {
|
||||
4
|
||||
}
|
||||
|
||||
fn default_optimize() -> bool {
|
||||
true
|
||||
}
|
||||
|
||||
impl Default for InferenceOptions {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
batch_size: default_batch_size(),
|
||||
use_gpu: false,
|
||||
gpu_device_id: 0,
|
||||
num_threads: default_num_threads(),
|
||||
optimize: default_optimize(),
|
||||
memory_limit: 0,
|
||||
profiling: false,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl InferenceOptions {
|
||||
/// Create options for CPU inference
|
||||
pub fn cpu() -> Self {
|
||||
Self::default()
|
||||
}
|
||||
|
||||
/// Create options for GPU inference
|
||||
pub fn gpu(device_id: usize) -> Self {
|
||||
Self {
|
||||
use_gpu: true,
|
||||
gpu_device_id: device_id,
|
||||
..Default::default()
|
||||
}
|
||||
}
|
||||
|
||||
/// Set batch size
|
||||
pub fn with_batch_size(mut self, batch_size: usize) -> Self {
|
||||
self.batch_size = batch_size;
|
||||
self
|
||||
}
|
||||
|
||||
/// Set number of threads
|
||||
pub fn with_threads(mut self, num_threads: usize) -> Self {
|
||||
self.num_threads = num_threads;
|
||||
self
|
||||
}
|
||||
}
|
||||
|
||||
/// Backend trait for different inference engines
|
||||
pub trait Backend: Send + Sync {
|
||||
/// Get the backend name
|
||||
fn name(&self) -> &str;
|
||||
|
||||
/// Check if the backend is available
|
||||
fn is_available(&self) -> bool;
|
||||
|
||||
/// Get input names
|
||||
fn input_names(&self) -> Vec<String>;
|
||||
|
||||
/// Get output names
|
||||
fn output_names(&self) -> Vec<String>;
|
||||
|
||||
/// Get input shape for a given input name
|
||||
fn input_shape(&self, name: &str) -> Option<TensorShape>;
|
||||
|
||||
/// Get output shape for a given output name
|
||||
fn output_shape(&self, name: &str) -> Option<TensorShape>;
|
||||
|
||||
/// Run inference
|
||||
fn run(&self, inputs: HashMap<String, Tensor>) -> NnResult<HashMap<String, Tensor>>;
|
||||
|
||||
/// Run inference on a single input
|
||||
fn run_single(&self, input: &Tensor) -> NnResult<Tensor> {
|
||||
let input_names = self.input_names();
|
||||
let output_names = self.output_names();
|
||||
|
||||
if input_names.is_empty() {
|
||||
return Err(NnError::inference("No input names defined"));
|
||||
}
|
||||
if output_names.is_empty() {
|
||||
return Err(NnError::inference("No output names defined"));
|
||||
}
|
||||
|
||||
let mut inputs = HashMap::new();
|
||||
inputs.insert(input_names[0].clone(), input.clone());
|
||||
|
||||
let outputs = self.run(inputs)?;
|
||||
outputs
|
||||
.into_iter()
|
||||
.next()
|
||||
.map(|(_, v)| v)
|
||||
.ok_or_else(|| NnError::inference("No outputs returned"))
|
||||
}
|
||||
|
||||
/// Warm up the model (optional pre-run for optimization)
|
||||
fn warmup(&self) -> NnResult<()> {
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Get memory usage in bytes
|
||||
fn memory_usage(&self) -> usize {
|
||||
0
|
||||
}
|
||||
}
|
||||
|
||||
/// Mock backend for testing
|
||||
#[derive(Debug)]
|
||||
pub struct MockBackend {
|
||||
name: String,
|
||||
input_shapes: HashMap<String, TensorShape>,
|
||||
output_shapes: HashMap<String, TensorShape>,
|
||||
}
|
||||
|
||||
impl MockBackend {
|
||||
/// Create a new mock backend
|
||||
pub fn new(name: impl Into<String>) -> Self {
|
||||
Self {
|
||||
name: name.into(),
|
||||
input_shapes: HashMap::new(),
|
||||
output_shapes: HashMap::new(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Add an input definition
|
||||
pub fn with_input(mut self, name: impl Into<String>, shape: TensorShape) -> Self {
|
||||
self.input_shapes.insert(name.into(), shape);
|
||||
self
|
||||
}
|
||||
|
||||
/// Add an output definition
|
||||
pub fn with_output(mut self, name: impl Into<String>, shape: TensorShape) -> Self {
|
||||
self.output_shapes.insert(name.into(), shape);
|
||||
self
|
||||
}
|
||||
}
|
||||
|
||||
impl Backend for MockBackend {
|
||||
fn name(&self) -> &str {
|
||||
&self.name
|
||||
}
|
||||
|
||||
fn is_available(&self) -> bool {
|
||||
true
|
||||
}
|
||||
|
||||
fn input_names(&self) -> Vec<String> {
|
||||
self.input_shapes.keys().cloned().collect()
|
||||
}
|
||||
|
||||
fn output_names(&self) -> Vec<String> {
|
||||
self.output_shapes.keys().cloned().collect()
|
||||
}
|
||||
|
||||
fn input_shape(&self, name: &str) -> Option<TensorShape> {
|
||||
self.input_shapes.get(name).cloned()
|
||||
}
|
||||
|
||||
fn output_shape(&self, name: &str) -> Option<TensorShape> {
|
||||
self.output_shapes.get(name).cloned()
|
||||
}
|
||||
|
||||
fn run(&self, inputs: HashMap<String, Tensor>) -> NnResult<HashMap<String, Tensor>> {
|
||||
let mut outputs = HashMap::new();
|
||||
|
||||
for (name, shape) in &self.output_shapes {
|
||||
let dims: Vec<usize> = shape.dims().to_vec();
|
||||
if dims.len() == 4 {
|
||||
outputs.insert(
|
||||
name.clone(),
|
||||
Tensor::zeros_4d([dims[0], dims[1], dims[2], dims[3]]),
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
Ok(outputs)
|
||||
}
|
||||
}
|
||||
|
||||
/// Unified inference engine that supports multiple backends
|
||||
pub struct InferenceEngine<B: Backend> {
|
||||
backend: B,
|
||||
options: InferenceOptions,
|
||||
/// Inference statistics
|
||||
stats: Arc<RwLock<InferenceStats>>,
|
||||
}
|
||||
|
||||
/// Statistics for inference performance
|
||||
#[derive(Debug, Default, Clone)]
|
||||
pub struct InferenceStats {
|
||||
/// Total number of inferences
|
||||
pub total_inferences: u64,
|
||||
/// Total inference time in milliseconds
|
||||
pub total_time_ms: f64,
|
||||
/// Average inference time
|
||||
pub avg_time_ms: f64,
|
||||
/// Min inference time
|
||||
pub min_time_ms: f64,
|
||||
/// Max inference time
|
||||
pub max_time_ms: f64,
|
||||
/// Last inference time
|
||||
pub last_time_ms: f64,
|
||||
}
|
||||
|
||||
impl InferenceStats {
|
||||
/// Record a new inference timing
|
||||
pub fn record(&mut self, time_ms: f64) {
|
||||
self.total_inferences += 1;
|
||||
self.total_time_ms += time_ms;
|
||||
self.last_time_ms = time_ms;
|
||||
self.avg_time_ms = self.total_time_ms / self.total_inferences as f64;
|
||||
|
||||
if self.total_inferences == 1 {
|
||||
self.min_time_ms = time_ms;
|
||||
self.max_time_ms = time_ms;
|
||||
} else {
|
||||
self.min_time_ms = self.min_time_ms.min(time_ms);
|
||||
self.max_time_ms = self.max_time_ms.max(time_ms);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<B: Backend> InferenceEngine<B> {
|
||||
/// Create a new inference engine with a backend
|
||||
pub fn new(backend: B, options: InferenceOptions) -> Self {
|
||||
Self {
|
||||
backend,
|
||||
options,
|
||||
stats: Arc::new(RwLock::new(InferenceStats::default())),
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the backend
|
||||
pub fn backend(&self) -> &B {
|
||||
&self.backend
|
||||
}
|
||||
|
||||
/// Get the options
|
||||
pub fn options(&self) -> &InferenceOptions {
|
||||
&self.options
|
||||
}
|
||||
|
||||
/// Check if GPU is being used
|
||||
pub fn uses_gpu(&self) -> bool {
|
||||
self.options.use_gpu && self.backend.is_available()
|
||||
}
|
||||
|
||||
/// Warm up the engine
|
||||
pub fn warmup(&self) -> NnResult<()> {
|
||||
info!("Warming up inference engine: {}", self.backend.name());
|
||||
self.backend.warmup()
|
||||
}
|
||||
|
||||
/// Run inference on a single input
|
||||
#[instrument(skip(self, input))]
|
||||
pub fn infer(&self, input: &Tensor) -> NnResult<Tensor> {
|
||||
let start = std::time::Instant::now();
|
||||
|
||||
let result = self.backend.run_single(input)?;
|
||||
|
||||
let elapsed_ms = start.elapsed().as_secs_f64() * 1000.0;
|
||||
debug!(elapsed_ms = %elapsed_ms, "Inference completed");
|
||||
|
||||
// Update stats asynchronously (best effort)
|
||||
let stats = self.stats.clone();
|
||||
tokio::spawn(async move {
|
||||
let mut stats = stats.write().await;
|
||||
stats.record(elapsed_ms);
|
||||
});
|
||||
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
/// Run inference with named inputs
|
||||
#[instrument(skip(self, inputs))]
|
||||
pub fn infer_named(&self, inputs: HashMap<String, Tensor>) -> NnResult<HashMap<String, Tensor>> {
|
||||
let start = std::time::Instant::now();
|
||||
|
||||
let result = self.backend.run(inputs)?;
|
||||
|
||||
let elapsed_ms = start.elapsed().as_secs_f64() * 1000.0;
|
||||
debug!(elapsed_ms = %elapsed_ms, "Named inference completed");
|
||||
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
/// Run batched inference
|
||||
pub fn infer_batch(&self, inputs: &[Tensor]) -> NnResult<Vec<Tensor>> {
|
||||
inputs.iter().map(|input| self.infer(input)).collect()
|
||||
}
|
||||
|
||||
/// Get inference statistics
|
||||
pub async fn stats(&self) -> InferenceStats {
|
||||
self.stats.read().await.clone()
|
||||
}
|
||||
|
||||
/// Reset statistics
|
||||
pub async fn reset_stats(&self) {
|
||||
let mut stats = self.stats.write().await;
|
||||
*stats = InferenceStats::default();
|
||||
}
|
||||
|
||||
/// Get memory usage
|
||||
pub fn memory_usage(&self) -> usize {
|
||||
self.backend.memory_usage()
|
||||
}
|
||||
}
|
||||
|
||||
/// Combined pipeline for WiFi-DensePose inference
|
||||
pub struct WiFiDensePosePipeline<B: Backend> {
|
||||
/// Modality translator backend
|
||||
translator_backend: B,
|
||||
/// DensePose backend
|
||||
densepose_backend: B,
|
||||
/// Translator configuration
|
||||
translator_config: TranslatorConfig,
|
||||
/// DensePose configuration
|
||||
densepose_config: DensePoseConfig,
|
||||
/// Inference options
|
||||
options: InferenceOptions,
|
||||
}
|
||||
|
||||
impl<B: Backend> WiFiDensePosePipeline<B> {
|
||||
/// Create a new pipeline
|
||||
pub fn new(
|
||||
translator_backend: B,
|
||||
densepose_backend: B,
|
||||
translator_config: TranslatorConfig,
|
||||
densepose_config: DensePoseConfig,
|
||||
options: InferenceOptions,
|
||||
) -> Self {
|
||||
Self {
|
||||
translator_backend,
|
||||
densepose_backend,
|
||||
translator_config,
|
||||
densepose_config,
|
||||
options,
|
||||
}
|
||||
}
|
||||
|
||||
/// Run the full pipeline: CSI -> Visual Features -> DensePose
|
||||
#[instrument(skip(self, csi_input))]
|
||||
pub fn run(&self, csi_input: &Tensor) -> NnResult<DensePoseOutput> {
|
||||
// Step 1: Translate CSI to visual features
|
||||
let visual_features = self.translator_backend.run_single(csi_input)?;
|
||||
|
||||
// Step 2: Run DensePose on visual features
|
||||
let mut inputs = HashMap::new();
|
||||
inputs.insert("features".to_string(), visual_features);
|
||||
|
||||
let outputs = self.densepose_backend.run(inputs)?;
|
||||
|
||||
// Extract outputs
|
||||
let segmentation = outputs
|
||||
.get("segmentation")
|
||||
.cloned()
|
||||
.ok_or_else(|| NnError::inference("Missing segmentation output"))?;
|
||||
|
||||
let uv_coordinates = outputs
|
||||
.get("uv_coordinates")
|
||||
.cloned()
|
||||
.ok_or_else(|| NnError::inference("Missing uv_coordinates output"))?;
|
||||
|
||||
Ok(DensePoseOutput {
|
||||
segmentation,
|
||||
uv_coordinates,
|
||||
confidence: None,
|
||||
})
|
||||
}
|
||||
|
||||
/// Get translator config
|
||||
pub fn translator_config(&self) -> &TranslatorConfig {
|
||||
&self.translator_config
|
||||
}
|
||||
|
||||
/// Get DensePose config
|
||||
pub fn densepose_config(&self) -> &DensePoseConfig {
|
||||
&self.densepose_config
|
||||
}
|
||||
}
|
||||
|
||||
/// Builder for creating inference engines
|
||||
pub struct EngineBuilder {
|
||||
options: InferenceOptions,
|
||||
model_path: Option<String>,
|
||||
}
|
||||
|
||||
impl EngineBuilder {
|
||||
/// Create a new builder
|
||||
pub fn new() -> Self {
|
||||
Self {
|
||||
options: InferenceOptions::default(),
|
||||
model_path: None,
|
||||
}
|
||||
}
|
||||
|
||||
/// Set inference options
|
||||
pub fn options(mut self, options: InferenceOptions) -> Self {
|
||||
self.options = options;
|
||||
self
|
||||
}
|
||||
|
||||
/// Set model path
|
||||
pub fn model_path(mut self, path: impl Into<String>) -> Self {
|
||||
self.model_path = Some(path.into());
|
||||
self
|
||||
}
|
||||
|
||||
/// Use GPU
|
||||
pub fn gpu(mut self, device_id: usize) -> Self {
|
||||
self.options.use_gpu = true;
|
||||
self.options.gpu_device_id = device_id;
|
||||
self
|
||||
}
|
||||
|
||||
/// Use CPU
|
||||
pub fn cpu(mut self) -> Self {
|
||||
self.options.use_gpu = false;
|
||||
self
|
||||
}
|
||||
|
||||
/// Set batch size
|
||||
pub fn batch_size(mut self, size: usize) -> Self {
|
||||
self.options.batch_size = size;
|
||||
self
|
||||
}
|
||||
|
||||
/// Set number of threads
|
||||
pub fn threads(mut self, n: usize) -> Self {
|
||||
self.options.num_threads = n;
|
||||
self
|
||||
}
|
||||
|
||||
/// Build with a mock backend (for testing)
|
||||
pub fn build_mock(self) -> InferenceEngine<MockBackend> {
|
||||
let backend = MockBackend::new("mock")
|
||||
.with_input("input".to_string(), TensorShape::new(vec![1, 256, 64, 64]))
|
||||
.with_output("output".to_string(), TensorShape::new(vec![1, 256, 64, 64]));
|
||||
|
||||
InferenceEngine::new(backend, self.options)
|
||||
}
|
||||
|
||||
/// Build with ONNX backend
|
||||
#[cfg(feature = "onnx")]
|
||||
pub fn build_onnx(self) -> NnResult<InferenceEngine<crate::onnx::OnnxBackend>> {
|
||||
let model_path = self
|
||||
.model_path
|
||||
.ok_or_else(|| NnError::config("Model path required for ONNX backend"))?;
|
||||
|
||||
let backend = crate::onnx::OnnxBackend::from_file(&model_path)?;
|
||||
Ok(InferenceEngine::new(backend, self.options))
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for EngineBuilder {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_inference_options() {
|
||||
let opts = InferenceOptions::cpu().with_batch_size(4).with_threads(8);
|
||||
assert_eq!(opts.batch_size, 4);
|
||||
assert_eq!(opts.num_threads, 8);
|
||||
assert!(!opts.use_gpu);
|
||||
|
||||
let gpu_opts = InferenceOptions::gpu(0);
|
||||
assert!(gpu_opts.use_gpu);
|
||||
assert_eq!(gpu_opts.gpu_device_id, 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_mock_backend() {
|
||||
let backend = MockBackend::new("test")
|
||||
.with_input("input", TensorShape::new(vec![1, 3, 224, 224]))
|
||||
.with_output("output", TensorShape::new(vec![1, 1000]));
|
||||
|
||||
assert_eq!(backend.name(), "test");
|
||||
assert!(backend.is_available());
|
||||
assert_eq!(backend.input_names(), vec!["input".to_string()]);
|
||||
assert_eq!(backend.output_names(), vec!["output".to_string()]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_engine_builder() {
|
||||
let engine = EngineBuilder::new()
|
||||
.cpu()
|
||||
.batch_size(2)
|
||||
.threads(4)
|
||||
.build_mock();
|
||||
|
||||
assert_eq!(engine.options().batch_size, 2);
|
||||
assert_eq!(engine.options().num_threads, 4);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_inference_stats() {
|
||||
let mut stats = InferenceStats::default();
|
||||
stats.record(10.0);
|
||||
stats.record(20.0);
|
||||
stats.record(15.0);
|
||||
|
||||
assert_eq!(stats.total_inferences, 3);
|
||||
assert_eq!(stats.min_time_ms, 10.0);
|
||||
assert_eq!(stats.max_time_ms, 20.0);
|
||||
assert_eq!(stats.avg_time_ms, 15.0);
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_inference_engine() {
|
||||
let engine = EngineBuilder::new().build_mock();
|
||||
|
||||
let input = Tensor::zeros_4d([1, 256, 64, 64]);
|
||||
let output = engine.infer(&input).unwrap();
|
||||
|
||||
assert_eq!(output.shape().dims(), &[1, 256, 64, 64]);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,71 @@
|
||||
//! # WiFi-DensePose Neural Network Crate
|
||||
//!
|
||||
//! This crate provides neural network inference capabilities for the WiFi-DensePose
|
||||
//! pose estimation system. It supports multiple backends including ONNX Runtime,
|
||||
//! tch-rs (PyTorch), and Candle for flexible deployment.
|
||||
//!
|
||||
//! ## Features
|
||||
//!
|
||||
//! - **DensePose Head**: Body part segmentation and UV coordinate regression
|
||||
//! - **Modality Translator**: CSI to visual feature space translation
|
||||
//! - **Multi-Backend Support**: ONNX, PyTorch (tch), and Candle backends
|
||||
//! - **Inference Optimization**: Batching, GPU acceleration, and model caching
|
||||
//!
|
||||
//! ## Example
|
||||
//!
|
||||
//! ```rust,ignore
|
||||
//! use wifi_densepose_nn::{InferenceEngine, DensePoseConfig, OnnxBackend};
|
||||
//!
|
||||
//! // Create inference engine with ONNX backend
|
||||
//! let config = DensePoseConfig::default();
|
||||
//! let backend = OnnxBackend::from_file("model.onnx")?;
|
||||
//! let engine = InferenceEngine::new(backend, config)?;
|
||||
//!
|
||||
//! // Run inference
|
||||
//! let input = ndarray::Array4::zeros((1, 256, 64, 64));
|
||||
//! let output = engine.infer(&input)?;
|
||||
//! ```
|
||||
|
||||
#![warn(missing_docs)]
|
||||
#![warn(rustdoc::missing_doc_code_examples)]
|
||||
#![deny(unsafe_code)]
|
||||
|
||||
pub mod densepose;
|
||||
pub mod error;
|
||||
pub mod inference;
|
||||
#[cfg(feature = "onnx")]
|
||||
pub mod onnx;
|
||||
pub mod tensor;
|
||||
pub mod translator;
|
||||
|
||||
// Re-exports for convenience
|
||||
pub use densepose::{DensePoseConfig, DensePoseHead, DensePoseOutput};
|
||||
pub use error::{NnError, NnResult};
|
||||
pub use inference::{Backend, InferenceEngine, InferenceOptions};
|
||||
#[cfg(feature = "onnx")]
|
||||
pub use onnx::{OnnxBackend, OnnxSession};
|
||||
pub use tensor::{Tensor, TensorShape};
|
||||
pub use translator::{ModalityTranslator, TranslatorConfig, TranslatorOutput};
|
||||
|
||||
/// Prelude module for convenient imports
|
||||
pub mod prelude {
|
||||
pub use crate::densepose::{DensePoseConfig, DensePoseHead, DensePoseOutput};
|
||||
pub use crate::error::{NnError, NnResult};
|
||||
pub use crate::inference::{Backend, InferenceEngine, InferenceOptions};
|
||||
#[cfg(feature = "onnx")]
|
||||
pub use crate::onnx::{OnnxBackend, OnnxSession};
|
||||
pub use crate::tensor::{Tensor, TensorShape};
|
||||
pub use crate::translator::{ModalityTranslator, TranslatorConfig, TranslatorOutput};
|
||||
}
|
||||
|
||||
/// Version information
|
||||
pub const VERSION: &str = env!("CARGO_PKG_VERSION");
|
||||
|
||||
/// Number of body parts in DensePose model (standard configuration)
|
||||
pub const NUM_BODY_PARTS: usize = 24;
|
||||
|
||||
/// Number of UV coordinates (U and V)
|
||||
pub const NUM_UV_COORDINATES: usize = 2;
|
||||
|
||||
/// Default hidden channel sizes for networks
|
||||
pub const DEFAULT_HIDDEN_CHANNELS: &[usize] = &[256, 128, 64];
|
||||
463
rust-port/wifi-densepose-rs/crates/wifi-densepose-nn/src/onnx.rs
Normal file
463
rust-port/wifi-densepose-rs/crates/wifi-densepose-nn/src/onnx.rs
Normal file
@@ -0,0 +1,463 @@
|
||||
//! ONNX Runtime backend for neural network inference.
|
||||
//!
|
||||
//! This module provides ONNX model loading and execution using the `ort` crate.
|
||||
//! It supports CPU and GPU (CUDA/TensorRT) execution providers.
|
||||
|
||||
use crate::error::{NnError, NnResult};
|
||||
use crate::inference::{Backend, InferenceOptions};
|
||||
use crate::tensor::{Tensor, TensorShape};
|
||||
use ort::session::Session;
|
||||
use std::collections::HashMap;
|
||||
use std::path::Path;
|
||||
use std::sync::Arc;
|
||||
use tracing::info;
|
||||
|
||||
/// ONNX Runtime session wrapper
|
||||
pub struct OnnxSession {
|
||||
session: Session,
|
||||
input_names: Vec<String>,
|
||||
output_names: Vec<String>,
|
||||
input_shapes: HashMap<String, TensorShape>,
|
||||
output_shapes: HashMap<String, TensorShape>,
|
||||
}
|
||||
|
||||
impl std::fmt::Debug for OnnxSession {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
||||
f.debug_struct("OnnxSession")
|
||||
.field("input_names", &self.input_names)
|
||||
.field("output_names", &self.output_names)
|
||||
.field("input_shapes", &self.input_shapes)
|
||||
.field("output_shapes", &self.output_shapes)
|
||||
.finish()
|
||||
}
|
||||
}
|
||||
|
||||
impl OnnxSession {
|
||||
/// Create a new ONNX session from a file
|
||||
pub fn from_file<P: AsRef<Path>>(path: P, _options: &InferenceOptions) -> NnResult<Self> {
|
||||
let path = path.as_ref();
|
||||
info!(?path, "Loading ONNX model");
|
||||
|
||||
// Build session using ort 2.0 API
|
||||
let session = Session::builder()
|
||||
.map_err(|e| NnError::model_load(format!("Failed to create session builder: {}", e)))?
|
||||
.commit_from_file(path)
|
||||
.map_err(|e| NnError::model_load(format!("Failed to load model: {}", e)))?;
|
||||
|
||||
// Extract metadata using ort 2.0 API
|
||||
let input_names: Vec<String> = session
|
||||
.inputs()
|
||||
.iter()
|
||||
.map(|input| input.name().to_string())
|
||||
.collect();
|
||||
|
||||
let output_names: Vec<String> = session
|
||||
.outputs()
|
||||
.iter()
|
||||
.map(|output| output.name().to_string())
|
||||
.collect();
|
||||
|
||||
// For now, leave shapes empty - they can be populated when needed
|
||||
let input_shapes = HashMap::new();
|
||||
let output_shapes = HashMap::new();
|
||||
|
||||
info!(
|
||||
inputs = ?input_names,
|
||||
outputs = ?output_names,
|
||||
"ONNX model loaded successfully"
|
||||
);
|
||||
|
||||
Ok(Self {
|
||||
session,
|
||||
input_names,
|
||||
output_names,
|
||||
input_shapes,
|
||||
output_shapes,
|
||||
})
|
||||
}
|
||||
|
||||
/// Create from in-memory bytes
|
||||
pub fn from_bytes(bytes: &[u8], _options: &InferenceOptions) -> NnResult<Self> {
|
||||
info!("Loading ONNX model from bytes");
|
||||
|
||||
let session = Session::builder()
|
||||
.map_err(|e| NnError::model_load(format!("Failed to create session builder: {}", e)))?
|
||||
.commit_from_memory(bytes)
|
||||
.map_err(|e| NnError::model_load(format!("Failed to load model from bytes: {}", e)))?;
|
||||
|
||||
let input_names: Vec<String> = session
|
||||
.inputs()
|
||||
.iter()
|
||||
.map(|input| input.name().to_string())
|
||||
.collect();
|
||||
|
||||
let output_names: Vec<String> = session
|
||||
.outputs()
|
||||
.iter()
|
||||
.map(|output| output.name().to_string())
|
||||
.collect();
|
||||
|
||||
let input_shapes = HashMap::new();
|
||||
let output_shapes = HashMap::new();
|
||||
|
||||
Ok(Self {
|
||||
session,
|
||||
input_names,
|
||||
output_names,
|
||||
input_shapes,
|
||||
output_shapes,
|
||||
})
|
||||
}
|
||||
|
||||
/// Get input names
|
||||
pub fn input_names(&self) -> &[String] {
|
||||
&self.input_names
|
||||
}
|
||||
|
||||
/// Get output names
|
||||
pub fn output_names(&self) -> &[String] {
|
||||
&self.output_names
|
||||
}
|
||||
|
||||
/// Run inference
|
||||
pub fn run(&mut self, inputs: HashMap<String, Tensor>) -> NnResult<HashMap<String, Tensor>> {
|
||||
// Get the first input tensor
|
||||
let first_input_name = self.input_names.first()
|
||||
.ok_or_else(|| NnError::inference("No input names defined"))?;
|
||||
|
||||
let tensor = inputs
|
||||
.get(first_input_name)
|
||||
.ok_or_else(|| NnError::invalid_input(format!("Missing input: {}", first_input_name)))?;
|
||||
|
||||
let arr = tensor.as_array4()?;
|
||||
|
||||
// Get shape and data for ort tensor creation
|
||||
let shape: Vec<i64> = arr.shape().iter().map(|&d| d as i64).collect();
|
||||
let data: Vec<f32> = arr.iter().cloned().collect();
|
||||
|
||||
// Create ORT tensor from shape and data
|
||||
let ort_tensor = ort::value::Tensor::from_array((shape, data))
|
||||
.map_err(|e| NnError::tensor_op(format!("Failed to create ORT tensor: {}", e)))?;
|
||||
|
||||
// Build input map - inputs! macro returns Vec directly
|
||||
let session_inputs = ort::inputs![first_input_name.as_str() => ort_tensor];
|
||||
|
||||
// Run session
|
||||
let session_outputs = self.session
|
||||
.run(session_inputs)
|
||||
.map_err(|e| NnError::inference(format!("Inference failed: {}", e)))?;
|
||||
|
||||
// Extract outputs
|
||||
let mut result = HashMap::new();
|
||||
|
||||
for name in self.output_names.iter() {
|
||||
if let Some(output) = session_outputs.get(name.as_str()) {
|
||||
// Try to extract tensor - returns (shape, data) tuple in ort 2.0
|
||||
if let Ok((shape, data)) = output.try_extract_tensor::<f32>() {
|
||||
let dims: Vec<usize> = shape.iter().map(|&d| d as usize).collect();
|
||||
|
||||
if dims.len() == 4 {
|
||||
// Convert to 4D array
|
||||
let arr4 = ndarray::Array4::from_shape_vec(
|
||||
(dims[0], dims[1], dims[2], dims[3]),
|
||||
data.to_vec(),
|
||||
).map_err(|e| NnError::tensor_op(format!("Shape error: {}", e)))?;
|
||||
result.insert(name.clone(), Tensor::Float4D(arr4));
|
||||
} else {
|
||||
// Handle other dimensionalities
|
||||
let arr_dyn = ndarray::ArrayD::from_shape_vec(
|
||||
ndarray::IxDyn(&dims),
|
||||
data.to_vec(),
|
||||
).map_err(|e| NnError::tensor_op(format!("Shape error: {}", e)))?;
|
||||
result.insert(name.clone(), Tensor::FloatND(arr_dyn));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Ok(result)
|
||||
}
|
||||
}
|
||||
|
||||
/// ONNX Runtime backend implementation
|
||||
pub struct OnnxBackend {
|
||||
session: Arc<parking_lot::RwLock<OnnxSession>>,
|
||||
options: InferenceOptions,
|
||||
}
|
||||
|
||||
impl std::fmt::Debug for OnnxBackend {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
||||
f.debug_struct("OnnxBackend")
|
||||
.field("options", &self.options)
|
||||
.finish()
|
||||
}
|
||||
}
|
||||
|
||||
impl OnnxBackend {
|
||||
/// Create backend from file
|
||||
pub fn from_file<P: AsRef<Path>>(path: P) -> NnResult<Self> {
|
||||
let options = InferenceOptions::default();
|
||||
let session = OnnxSession::from_file(path, &options)?;
|
||||
Ok(Self {
|
||||
session: Arc::new(parking_lot::RwLock::new(session)),
|
||||
options,
|
||||
})
|
||||
}
|
||||
|
||||
/// Create backend from file with options
|
||||
pub fn from_file_with_options<P: AsRef<Path>>(path: P, options: InferenceOptions) -> NnResult<Self> {
|
||||
let session = OnnxSession::from_file(path, &options)?;
|
||||
Ok(Self {
|
||||
session: Arc::new(parking_lot::RwLock::new(session)),
|
||||
options,
|
||||
})
|
||||
}
|
||||
|
||||
/// Create backend from bytes
|
||||
pub fn from_bytes(bytes: &[u8]) -> NnResult<Self> {
|
||||
let options = InferenceOptions::default();
|
||||
let session = OnnxSession::from_bytes(bytes, &options)?;
|
||||
Ok(Self {
|
||||
session: Arc::new(parking_lot::RwLock::new(session)),
|
||||
options,
|
||||
})
|
||||
}
|
||||
|
||||
/// Create backend from bytes with options
|
||||
pub fn from_bytes_with_options(bytes: &[u8], options: InferenceOptions) -> NnResult<Self> {
|
||||
let session = OnnxSession::from_bytes(bytes, &options)?;
|
||||
Ok(Self {
|
||||
session: Arc::new(parking_lot::RwLock::new(session)),
|
||||
options,
|
||||
})
|
||||
}
|
||||
|
||||
/// Get options
|
||||
pub fn options(&self) -> &InferenceOptions {
|
||||
&self.options
|
||||
}
|
||||
}
|
||||
|
||||
impl Backend for OnnxBackend {
|
||||
fn name(&self) -> &str {
|
||||
"onnxruntime"
|
||||
}
|
||||
|
||||
fn is_available(&self) -> bool {
|
||||
true
|
||||
}
|
||||
|
||||
fn input_names(&self) -> Vec<String> {
|
||||
self.session.read().input_names.clone()
|
||||
}
|
||||
|
||||
fn output_names(&self) -> Vec<String> {
|
||||
self.session.read().output_names.clone()
|
||||
}
|
||||
|
||||
fn input_shape(&self, name: &str) -> Option<TensorShape> {
|
||||
self.session.read().input_shapes.get(name).cloned()
|
||||
}
|
||||
|
||||
fn output_shape(&self, name: &str) -> Option<TensorShape> {
|
||||
self.session.read().output_shapes.get(name).cloned()
|
||||
}
|
||||
|
||||
fn run(&self, inputs: HashMap<String, Tensor>) -> NnResult<HashMap<String, Tensor>> {
|
||||
self.session.write().run(inputs)
|
||||
}
|
||||
|
||||
fn warmup(&self) -> NnResult<()> {
|
||||
let session = self.session.read();
|
||||
let mut dummy_inputs = HashMap::new();
|
||||
|
||||
for name in &session.input_names {
|
||||
if let Some(shape) = session.input_shapes.get(name) {
|
||||
let dims = shape.dims();
|
||||
if dims.len() == 4 {
|
||||
dummy_inputs.insert(
|
||||
name.clone(),
|
||||
Tensor::zeros_4d([dims[0], dims[1], dims[2], dims[3]]),
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
drop(session); // Release read lock before running
|
||||
|
||||
if !dummy_inputs.is_empty() {
|
||||
let _ = self.run(dummy_inputs)?;
|
||||
info!("ONNX warmup completed");
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
/// Model metadata from ONNX file
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct OnnxModelInfo {
|
||||
/// Model producer name
|
||||
pub producer_name: Option<String>,
|
||||
/// Model version
|
||||
pub model_version: Option<i64>,
|
||||
/// Domain
|
||||
pub domain: Option<String>,
|
||||
/// Description
|
||||
pub description: Option<String>,
|
||||
/// Input specifications
|
||||
pub inputs: Vec<TensorSpec>,
|
||||
/// Output specifications
|
||||
pub outputs: Vec<TensorSpec>,
|
||||
}
|
||||
|
||||
/// Tensor specification
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct TensorSpec {
|
||||
/// Name of the tensor
|
||||
pub name: String,
|
||||
/// Shape (may contain dynamic dimensions as -1)
|
||||
pub shape: Vec<i64>,
|
||||
/// Data type
|
||||
pub dtype: String,
|
||||
}
|
||||
|
||||
/// Load model info without creating a full session
|
||||
pub fn load_model_info<P: AsRef<Path>>(path: P) -> NnResult<OnnxModelInfo> {
|
||||
let session = Session::builder()
|
||||
.map_err(|e| NnError::model_load(format!("Failed to create session builder: {}", e)))?
|
||||
.commit_from_file(path.as_ref())
|
||||
.map_err(|e| NnError::model_load(format!("Failed to load model: {}", e)))?;
|
||||
|
||||
let inputs: Vec<TensorSpec> = session
|
||||
.inputs()
|
||||
.iter()
|
||||
.map(|input| {
|
||||
TensorSpec {
|
||||
name: input.name().to_string(),
|
||||
shape: vec![],
|
||||
dtype: "float32".to_string(),
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
|
||||
let outputs: Vec<TensorSpec> = session
|
||||
.outputs()
|
||||
.iter()
|
||||
.map(|output| {
|
||||
TensorSpec {
|
||||
name: output.name().to_string(),
|
||||
shape: vec![],
|
||||
dtype: "float32".to_string(),
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
|
||||
Ok(OnnxModelInfo {
|
||||
producer_name: None,
|
||||
model_version: None,
|
||||
domain: None,
|
||||
description: None,
|
||||
inputs,
|
||||
outputs,
|
||||
})
|
||||
}
|
||||
|
||||
/// Builder for ONNX backend
|
||||
pub struct OnnxBackendBuilder {
|
||||
model_path: Option<String>,
|
||||
model_bytes: Option<Vec<u8>>,
|
||||
options: InferenceOptions,
|
||||
}
|
||||
|
||||
impl OnnxBackendBuilder {
|
||||
/// Create a new builder
|
||||
pub fn new() -> Self {
|
||||
Self {
|
||||
model_path: None,
|
||||
model_bytes: None,
|
||||
options: InferenceOptions::default(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Set model path
|
||||
pub fn model_path<P: Into<String>>(mut self, path: P) -> Self {
|
||||
self.model_path = Some(path.into());
|
||||
self
|
||||
}
|
||||
|
||||
/// Set model bytes
|
||||
pub fn model_bytes(mut self, bytes: Vec<u8>) -> Self {
|
||||
self.model_bytes = Some(bytes);
|
||||
self
|
||||
}
|
||||
|
||||
/// Use GPU
|
||||
pub fn gpu(mut self, device_id: usize) -> Self {
|
||||
self.options.use_gpu = true;
|
||||
self.options.gpu_device_id = device_id;
|
||||
self
|
||||
}
|
||||
|
||||
/// Use CPU
|
||||
pub fn cpu(mut self) -> Self {
|
||||
self.options.use_gpu = false;
|
||||
self
|
||||
}
|
||||
|
||||
/// Set number of threads
|
||||
pub fn threads(mut self, n: usize) -> Self {
|
||||
self.options.num_threads = n;
|
||||
self
|
||||
}
|
||||
|
||||
/// Enable optimization
|
||||
pub fn optimize(mut self, enabled: bool) -> Self {
|
||||
self.options.optimize = enabled;
|
||||
self
|
||||
}
|
||||
|
||||
/// Build the backend
|
||||
pub fn build(self) -> NnResult<OnnxBackend> {
|
||||
if let Some(path) = self.model_path {
|
||||
OnnxBackend::from_file_with_options(path, self.options)
|
||||
} else if let Some(bytes) = self.model_bytes {
|
||||
OnnxBackend::from_bytes_with_options(&bytes, self.options)
|
||||
} else {
|
||||
Err(NnError::config("No model path or bytes provided"))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for OnnxBackendBuilder {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_onnx_backend_builder() {
|
||||
let builder = OnnxBackendBuilder::new()
|
||||
.cpu()
|
||||
.threads(4)
|
||||
.optimize(true);
|
||||
|
||||
// Can't test build without a real model
|
||||
assert!(builder.model_path.is_none());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_tensor_spec() {
|
||||
let spec = TensorSpec {
|
||||
name: "input".to_string(),
|
||||
shape: vec![1, 3, 224, 224],
|
||||
dtype: "float32".to_string(),
|
||||
};
|
||||
|
||||
assert_eq!(spec.name, "input");
|
||||
assert_eq!(spec.shape.len(), 4);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,436 @@
|
||||
//! Tensor types and operations for neural network inference.
|
||||
//!
|
||||
//! This module provides a unified tensor abstraction that works across
|
||||
//! different backends (ONNX, tch, Candle).
|
||||
|
||||
use crate::error::{NnError, NnResult};
|
||||
use ndarray::{Array1, Array2, Array3, Array4, ArrayD};
|
||||
// num_traits is available if needed for advanced tensor operations
|
||||
use serde::{Deserialize, Serialize};
|
||||
use std::fmt;
|
||||
|
||||
/// Shape of a tensor
|
||||
#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
|
||||
pub struct TensorShape(Vec<usize>);
|
||||
|
||||
impl TensorShape {
|
||||
/// Create a new tensor shape
|
||||
pub fn new(dims: Vec<usize>) -> Self {
|
||||
Self(dims)
|
||||
}
|
||||
|
||||
/// Create a shape from a slice
|
||||
pub fn from_slice(dims: &[usize]) -> Self {
|
||||
Self(dims.to_vec())
|
||||
}
|
||||
|
||||
/// Get the number of dimensions
|
||||
pub fn ndim(&self) -> usize {
|
||||
self.0.len()
|
||||
}
|
||||
|
||||
/// Get the dimensions
|
||||
pub fn dims(&self) -> &[usize] {
|
||||
&self.0
|
||||
}
|
||||
|
||||
/// Get the total number of elements
|
||||
pub fn numel(&self) -> usize {
|
||||
self.0.iter().product()
|
||||
}
|
||||
|
||||
/// Get dimension at index
|
||||
pub fn dim(&self, idx: usize) -> Option<usize> {
|
||||
self.0.get(idx).copied()
|
||||
}
|
||||
|
||||
/// Check if shapes are compatible for broadcasting
|
||||
pub fn is_broadcast_compatible(&self, other: &TensorShape) -> bool {
|
||||
let max_dims = self.ndim().max(other.ndim());
|
||||
for i in 0..max_dims {
|
||||
let d1 = self.0.get(self.ndim().saturating_sub(i + 1)).unwrap_or(&1);
|
||||
let d2 = other.0.get(other.ndim().saturating_sub(i + 1)).unwrap_or(&1);
|
||||
if *d1 != *d2 && *d1 != 1 && *d2 != 1 {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
true
|
||||
}
|
||||
}
|
||||
|
||||
impl fmt::Display for TensorShape {
|
||||
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
|
||||
write!(f, "[")?;
|
||||
for (i, d) in self.0.iter().enumerate() {
|
||||
if i > 0 {
|
||||
write!(f, ", ")?;
|
||||
}
|
||||
write!(f, "{}", d)?;
|
||||
}
|
||||
write!(f, "]")
|
||||
}
|
||||
}
|
||||
|
||||
impl From<Vec<usize>> for TensorShape {
|
||||
fn from(dims: Vec<usize>) -> Self {
|
||||
Self::new(dims)
|
||||
}
|
||||
}
|
||||
|
||||
impl From<&[usize]> for TensorShape {
|
||||
fn from(dims: &[usize]) -> Self {
|
||||
Self::from_slice(dims)
|
||||
}
|
||||
}
|
||||
|
||||
impl<const N: usize> From<[usize; N]> for TensorShape {
|
||||
fn from(dims: [usize; N]) -> Self {
|
||||
Self::new(dims.to_vec())
|
||||
}
|
||||
}
|
||||
|
||||
/// Data type for tensor elements
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
|
||||
pub enum DataType {
|
||||
/// 32-bit floating point
|
||||
Float32,
|
||||
/// 64-bit floating point
|
||||
Float64,
|
||||
/// 32-bit integer
|
||||
Int32,
|
||||
/// 64-bit integer
|
||||
Int64,
|
||||
/// 8-bit unsigned integer
|
||||
Uint8,
|
||||
/// Boolean
|
||||
Bool,
|
||||
}
|
||||
|
||||
impl DataType {
|
||||
/// Get the size of this data type in bytes
|
||||
pub fn size_bytes(&self) -> usize {
|
||||
match self {
|
||||
DataType::Float32 => 4,
|
||||
DataType::Float64 => 8,
|
||||
DataType::Int32 => 4,
|
||||
DataType::Int64 => 8,
|
||||
DataType::Uint8 => 1,
|
||||
DataType::Bool => 1,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// A tensor wrapper that abstracts over different array types
|
||||
#[derive(Debug, Clone)]
|
||||
pub enum Tensor {
|
||||
/// 1D float tensor
|
||||
Float1D(Array1<f32>),
|
||||
/// 2D float tensor
|
||||
Float2D(Array2<f32>),
|
||||
/// 3D float tensor
|
||||
Float3D(Array3<f32>),
|
||||
/// 4D float tensor (batch, channels, height, width)
|
||||
Float4D(Array4<f32>),
|
||||
/// Dynamic dimension float tensor
|
||||
FloatND(ArrayD<f32>),
|
||||
/// 1D integer tensor
|
||||
Int1D(Array1<i64>),
|
||||
/// 2D integer tensor
|
||||
Int2D(Array2<i64>),
|
||||
/// Dynamic dimension integer tensor
|
||||
IntND(ArrayD<i64>),
|
||||
}
|
||||
|
||||
impl Tensor {
|
||||
/// Create a new 4D float tensor filled with zeros
|
||||
pub fn zeros_4d(shape: [usize; 4]) -> Self {
|
||||
Tensor::Float4D(Array4::zeros(shape))
|
||||
}
|
||||
|
||||
/// Create a new 4D float tensor filled with ones
|
||||
pub fn ones_4d(shape: [usize; 4]) -> Self {
|
||||
Tensor::Float4D(Array4::ones(shape))
|
||||
}
|
||||
|
||||
/// Create a tensor from a 4D ndarray
|
||||
pub fn from_array4(array: Array4<f32>) -> Self {
|
||||
Tensor::Float4D(array)
|
||||
}
|
||||
|
||||
/// Create a tensor from a dynamic ndarray
|
||||
pub fn from_arrayd(array: ArrayD<f32>) -> Self {
|
||||
Tensor::FloatND(array)
|
||||
}
|
||||
|
||||
/// Get the shape of the tensor
|
||||
pub fn shape(&self) -> TensorShape {
|
||||
match self {
|
||||
Tensor::Float1D(a) => TensorShape::from_slice(a.shape()),
|
||||
Tensor::Float2D(a) => TensorShape::from_slice(a.shape()),
|
||||
Tensor::Float3D(a) => TensorShape::from_slice(a.shape()),
|
||||
Tensor::Float4D(a) => TensorShape::from_slice(a.shape()),
|
||||
Tensor::FloatND(a) => TensorShape::from_slice(a.shape()),
|
||||
Tensor::Int1D(a) => TensorShape::from_slice(a.shape()),
|
||||
Tensor::Int2D(a) => TensorShape::from_slice(a.shape()),
|
||||
Tensor::IntND(a) => TensorShape::from_slice(a.shape()),
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the data type
|
||||
pub fn dtype(&self) -> DataType {
|
||||
match self {
|
||||
Tensor::Float1D(_)
|
||||
| Tensor::Float2D(_)
|
||||
| Tensor::Float3D(_)
|
||||
| Tensor::Float4D(_)
|
||||
| Tensor::FloatND(_) => DataType::Float32,
|
||||
Tensor::Int1D(_) | Tensor::Int2D(_) | Tensor::IntND(_) => DataType::Int64,
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the number of elements
|
||||
pub fn numel(&self) -> usize {
|
||||
self.shape().numel()
|
||||
}
|
||||
|
||||
/// Get the number of dimensions
|
||||
pub fn ndim(&self) -> usize {
|
||||
self.shape().ndim()
|
||||
}
|
||||
|
||||
/// Try to convert to a 4D float array
|
||||
pub fn as_array4(&self) -> NnResult<&Array4<f32>> {
|
||||
match self {
|
||||
Tensor::Float4D(a) => Ok(a),
|
||||
_ => Err(NnError::tensor_op("Cannot convert to 4D array")),
|
||||
}
|
||||
}
|
||||
|
||||
/// Try to convert to a mutable 4D float array
|
||||
pub fn as_array4_mut(&mut self) -> NnResult<&mut Array4<f32>> {
|
||||
match self {
|
||||
Tensor::Float4D(a) => Ok(a),
|
||||
_ => Err(NnError::tensor_op("Cannot convert to mutable 4D array")),
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the underlying data as a slice
|
||||
pub fn as_slice(&self) -> NnResult<&[f32]> {
|
||||
match self {
|
||||
Tensor::Float1D(a) => a.as_slice().ok_or_else(|| NnError::tensor_op("Non-contiguous array")),
|
||||
Tensor::Float2D(a) => a.as_slice().ok_or_else(|| NnError::tensor_op("Non-contiguous array")),
|
||||
Tensor::Float3D(a) => a.as_slice().ok_or_else(|| NnError::tensor_op("Non-contiguous array")),
|
||||
Tensor::Float4D(a) => a.as_slice().ok_or_else(|| NnError::tensor_op("Non-contiguous array")),
|
||||
Tensor::FloatND(a) => a.as_slice().ok_or_else(|| NnError::tensor_op("Non-contiguous array")),
|
||||
_ => Err(NnError::tensor_op("Cannot get float slice from integer tensor")),
|
||||
}
|
||||
}
|
||||
|
||||
/// Convert tensor to owned Vec
|
||||
pub fn to_vec(&self) -> NnResult<Vec<f32>> {
|
||||
match self {
|
||||
Tensor::Float1D(a) => Ok(a.iter().copied().collect()),
|
||||
Tensor::Float2D(a) => Ok(a.iter().copied().collect()),
|
||||
Tensor::Float3D(a) => Ok(a.iter().copied().collect()),
|
||||
Tensor::Float4D(a) => Ok(a.iter().copied().collect()),
|
||||
Tensor::FloatND(a) => Ok(a.iter().copied().collect()),
|
||||
_ => Err(NnError::tensor_op("Cannot convert integer tensor to float vec")),
|
||||
}
|
||||
}
|
||||
|
||||
/// Apply ReLU activation
|
||||
pub fn relu(&self) -> NnResult<Tensor> {
|
||||
match self {
|
||||
Tensor::Float4D(a) => Ok(Tensor::Float4D(a.mapv(|x| x.max(0.0)))),
|
||||
Tensor::FloatND(a) => Ok(Tensor::FloatND(a.mapv(|x| x.max(0.0)))),
|
||||
_ => Err(NnError::tensor_op("ReLU not supported for this tensor type")),
|
||||
}
|
||||
}
|
||||
|
||||
/// Apply sigmoid activation
|
||||
pub fn sigmoid(&self) -> NnResult<Tensor> {
|
||||
match self {
|
||||
Tensor::Float4D(a) => Ok(Tensor::Float4D(a.mapv(|x| 1.0 / (1.0 + (-x).exp())))),
|
||||
Tensor::FloatND(a) => Ok(Tensor::FloatND(a.mapv(|x| 1.0 / (1.0 + (-x).exp())))),
|
||||
_ => Err(NnError::tensor_op("Sigmoid not supported for this tensor type")),
|
||||
}
|
||||
}
|
||||
|
||||
/// Apply tanh activation
|
||||
pub fn tanh(&self) -> NnResult<Tensor> {
|
||||
match self {
|
||||
Tensor::Float4D(a) => Ok(Tensor::Float4D(a.mapv(|x| x.tanh()))),
|
||||
Tensor::FloatND(a) => Ok(Tensor::FloatND(a.mapv(|x| x.tanh()))),
|
||||
_ => Err(NnError::tensor_op("Tanh not supported for this tensor type")),
|
||||
}
|
||||
}
|
||||
|
||||
/// Apply softmax along axis
|
||||
pub fn softmax(&self, axis: usize) -> NnResult<Tensor> {
|
||||
match self {
|
||||
Tensor::Float4D(a) => {
|
||||
let max = a.fold(f32::NEG_INFINITY, |acc, &x| acc.max(x));
|
||||
let exp = a.mapv(|x| (x - max).exp());
|
||||
let sum = exp.sum();
|
||||
Ok(Tensor::Float4D(exp / sum))
|
||||
}
|
||||
_ => Err(NnError::tensor_op("Softmax not supported for this tensor type")),
|
||||
}
|
||||
}
|
||||
|
||||
/// Get argmax along axis
|
||||
pub fn argmax(&self, axis: usize) -> NnResult<Tensor> {
|
||||
match self {
|
||||
Tensor::Float4D(a) => {
|
||||
let result = a.map_axis(ndarray::Axis(axis), |row| {
|
||||
row.iter()
|
||||
.enumerate()
|
||||
.max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())
|
||||
.map(|(i, _)| i as i64)
|
||||
.unwrap_or(0)
|
||||
});
|
||||
Ok(Tensor::IntND(result.into_dyn()))
|
||||
}
|
||||
_ => Err(NnError::tensor_op("Argmax not supported for this tensor type")),
|
||||
}
|
||||
}
|
||||
|
||||
/// Compute mean
|
||||
pub fn mean(&self) -> NnResult<f32> {
|
||||
match self {
|
||||
Tensor::Float4D(a) => Ok(a.mean().unwrap_or(0.0)),
|
||||
Tensor::FloatND(a) => Ok(a.mean().unwrap_or(0.0)),
|
||||
_ => Err(NnError::tensor_op("Mean not supported for this tensor type")),
|
||||
}
|
||||
}
|
||||
|
||||
/// Compute standard deviation
|
||||
pub fn std(&self) -> NnResult<f32> {
|
||||
match self {
|
||||
Tensor::Float4D(a) => {
|
||||
let mean = a.mean().unwrap_or(0.0);
|
||||
let variance = a.mapv(|x| (x - mean).powi(2)).mean().unwrap_or(0.0);
|
||||
Ok(variance.sqrt())
|
||||
}
|
||||
Tensor::FloatND(a) => {
|
||||
let mean = a.mean().unwrap_or(0.0);
|
||||
let variance = a.mapv(|x| (x - mean).powi(2)).mean().unwrap_or(0.0);
|
||||
Ok(variance.sqrt())
|
||||
}
|
||||
_ => Err(NnError::tensor_op("Std not supported for this tensor type")),
|
||||
}
|
||||
}
|
||||
|
||||
/// Get min value
|
||||
pub fn min(&self) -> NnResult<f32> {
|
||||
match self {
|
||||
Tensor::Float4D(a) => Ok(a.fold(f32::INFINITY, |acc, &x| acc.min(x))),
|
||||
Tensor::FloatND(a) => Ok(a.fold(f32::INFINITY, |acc, &x| acc.min(x))),
|
||||
_ => Err(NnError::tensor_op("Min not supported for this tensor type")),
|
||||
}
|
||||
}
|
||||
|
||||
/// Get max value
|
||||
pub fn max(&self) -> NnResult<f32> {
|
||||
match self {
|
||||
Tensor::Float4D(a) => Ok(a.fold(f32::NEG_INFINITY, |acc, &x| acc.max(x))),
|
||||
Tensor::FloatND(a) => Ok(a.fold(f32::NEG_INFINITY, |acc, &x| acc.max(x))),
|
||||
_ => Err(NnError::tensor_op("Max not supported for this tensor type")),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Statistics about a tensor
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct TensorStats {
|
||||
/// Mean value
|
||||
pub mean: f32,
|
||||
/// Standard deviation
|
||||
pub std: f32,
|
||||
/// Minimum value
|
||||
pub min: f32,
|
||||
/// Maximum value
|
||||
pub max: f32,
|
||||
/// Sparsity (fraction of zeros)
|
||||
pub sparsity: f32,
|
||||
}
|
||||
|
||||
impl TensorStats {
|
||||
/// Compute statistics for a tensor
|
||||
pub fn from_tensor(tensor: &Tensor) -> NnResult<Self> {
|
||||
let mean = tensor.mean()?;
|
||||
let std = tensor.std()?;
|
||||
let min = tensor.min()?;
|
||||
let max = tensor.max()?;
|
||||
|
||||
// Compute sparsity
|
||||
let sparsity = match tensor {
|
||||
Tensor::Float4D(a) => {
|
||||
let zeros = a.iter().filter(|&&x| x == 0.0).count();
|
||||
zeros as f32 / a.len() as f32
|
||||
}
|
||||
Tensor::FloatND(a) => {
|
||||
let zeros = a.iter().filter(|&&x| x == 0.0).count();
|
||||
zeros as f32 / a.len() as f32
|
||||
}
|
||||
_ => 0.0,
|
||||
};
|
||||
|
||||
Ok(TensorStats {
|
||||
mean,
|
||||
std,
|
||||
min,
|
||||
max,
|
||||
sparsity,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_tensor_shape() {
|
||||
let shape = TensorShape::new(vec![1, 3, 224, 224]);
|
||||
assert_eq!(shape.ndim(), 4);
|
||||
assert_eq!(shape.numel(), 1 * 3 * 224 * 224);
|
||||
assert_eq!(shape.dim(0), Some(1));
|
||||
assert_eq!(shape.dim(1), Some(3));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_tensor_zeros() {
|
||||
let tensor = Tensor::zeros_4d([1, 256, 64, 64]);
|
||||
assert_eq!(tensor.shape().dims(), &[1, 256, 64, 64]);
|
||||
assert_eq!(tensor.dtype(), DataType::Float32);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_tensor_activations() {
|
||||
let arr = Array4::from_elem([1, 2, 2, 2], -1.0f32);
|
||||
let tensor = Tensor::Float4D(arr);
|
||||
|
||||
let relu = tensor.relu().unwrap();
|
||||
assert_eq!(relu.max().unwrap(), 0.0);
|
||||
|
||||
let sigmoid = tensor.sigmoid().unwrap();
|
||||
assert!(sigmoid.min().unwrap() > 0.0);
|
||||
assert!(sigmoid.max().unwrap() < 1.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_broadcast_compatible() {
|
||||
let a = TensorShape::new(vec![1, 3, 224, 224]);
|
||||
let b = TensorShape::new(vec![1, 1, 224, 224]);
|
||||
assert!(a.is_broadcast_compatible(&b));
|
||||
|
||||
// [1, 3, 224, 224] and [2, 3, 224, 224] ARE broadcast compatible (1 broadcasts to 2)
|
||||
let c = TensorShape::new(vec![2, 3, 224, 224]);
|
||||
assert!(a.is_broadcast_compatible(&c));
|
||||
|
||||
// [2, 3, 224, 224] and [3, 3, 224, 224] are NOT compatible (2 != 3, neither is 1)
|
||||
let d = TensorShape::new(vec![3, 3, 224, 224]);
|
||||
assert!(!c.is_broadcast_compatible(&d));
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,716 @@
|
||||
//! Modality translation network for CSI to visual feature space conversion.
|
||||
//!
|
||||
//! This module implements the encoder-decoder network that translates
|
||||
//! WiFi Channel State Information (CSI) into visual feature representations
|
||||
//! compatible with the DensePose head.
|
||||
|
||||
use crate::error::{NnError, NnResult};
|
||||
use crate::tensor::{Tensor, TensorShape, TensorStats};
|
||||
use ndarray::Array4;
|
||||
use serde::{Deserialize, Serialize};
|
||||
use std::collections::HashMap;
|
||||
|
||||
/// Configuration for the modality translator
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct TranslatorConfig {
|
||||
/// Number of input channels (CSI features)
|
||||
pub input_channels: usize,
|
||||
/// Hidden channel sizes for encoder/decoder
|
||||
pub hidden_channels: Vec<usize>,
|
||||
/// Number of output channels (visual feature dimensions)
|
||||
pub output_channels: usize,
|
||||
/// Convolution kernel size
|
||||
#[serde(default = "default_kernel_size")]
|
||||
pub kernel_size: usize,
|
||||
/// Convolution stride
|
||||
#[serde(default = "default_stride")]
|
||||
pub stride: usize,
|
||||
/// Convolution padding
|
||||
#[serde(default = "default_padding")]
|
||||
pub padding: usize,
|
||||
/// Dropout rate
|
||||
#[serde(default = "default_dropout_rate")]
|
||||
pub dropout_rate: f32,
|
||||
/// Activation function
|
||||
#[serde(default = "default_activation")]
|
||||
pub activation: ActivationType,
|
||||
/// Normalization type
|
||||
#[serde(default = "default_normalization")]
|
||||
pub normalization: NormalizationType,
|
||||
/// Whether to use attention mechanism
|
||||
#[serde(default)]
|
||||
pub use_attention: bool,
|
||||
/// Number of attention heads
|
||||
#[serde(default = "default_attention_heads")]
|
||||
pub attention_heads: usize,
|
||||
}
|
||||
|
||||
fn default_kernel_size() -> usize {
|
||||
3
|
||||
}
|
||||
|
||||
fn default_stride() -> usize {
|
||||
1
|
||||
}
|
||||
|
||||
fn default_padding() -> usize {
|
||||
1
|
||||
}
|
||||
|
||||
fn default_dropout_rate() -> f32 {
|
||||
0.1
|
||||
}
|
||||
|
||||
fn default_activation() -> ActivationType {
|
||||
ActivationType::ReLU
|
||||
}
|
||||
|
||||
fn default_normalization() -> NormalizationType {
|
||||
NormalizationType::BatchNorm
|
||||
}
|
||||
|
||||
fn default_attention_heads() -> usize {
|
||||
8
|
||||
}
|
||||
|
||||
/// Type of activation function
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
|
||||
pub enum ActivationType {
|
||||
/// Rectified Linear Unit
|
||||
ReLU,
|
||||
/// Leaky ReLU with negative slope
|
||||
LeakyReLU,
|
||||
/// Gaussian Error Linear Unit
|
||||
GELU,
|
||||
/// Sigmoid
|
||||
Sigmoid,
|
||||
/// Tanh
|
||||
Tanh,
|
||||
}
|
||||
|
||||
/// Type of normalization
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
|
||||
pub enum NormalizationType {
|
||||
/// Batch normalization
|
||||
BatchNorm,
|
||||
/// Instance normalization
|
||||
InstanceNorm,
|
||||
/// Layer normalization
|
||||
LayerNorm,
|
||||
/// No normalization
|
||||
None,
|
||||
}
|
||||
|
||||
impl Default for TranslatorConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
input_channels: 128, // CSI feature dimension
|
||||
hidden_channels: vec![256, 512, 256],
|
||||
output_channels: 256, // Visual feature dimension
|
||||
kernel_size: default_kernel_size(),
|
||||
stride: default_stride(),
|
||||
padding: default_padding(),
|
||||
dropout_rate: default_dropout_rate(),
|
||||
activation: default_activation(),
|
||||
normalization: default_normalization(),
|
||||
use_attention: false,
|
||||
attention_heads: default_attention_heads(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl TranslatorConfig {
|
||||
/// Create a new translator configuration
|
||||
pub fn new(input_channels: usize, hidden_channels: Vec<usize>, output_channels: usize) -> Self {
|
||||
Self {
|
||||
input_channels,
|
||||
hidden_channels,
|
||||
output_channels,
|
||||
..Default::default()
|
||||
}
|
||||
}
|
||||
|
||||
/// Enable attention mechanism
|
||||
pub fn with_attention(mut self, num_heads: usize) -> Self {
|
||||
self.use_attention = true;
|
||||
self.attention_heads = num_heads;
|
||||
self
|
||||
}
|
||||
|
||||
/// Set activation type
|
||||
pub fn with_activation(mut self, activation: ActivationType) -> Self {
|
||||
self.activation = activation;
|
||||
self
|
||||
}
|
||||
|
||||
/// Validate configuration
|
||||
pub fn validate(&self) -> NnResult<()> {
|
||||
if self.input_channels == 0 {
|
||||
return Err(NnError::config("input_channels must be positive"));
|
||||
}
|
||||
if self.hidden_channels.is_empty() {
|
||||
return Err(NnError::config("hidden_channels must not be empty"));
|
||||
}
|
||||
if self.output_channels == 0 {
|
||||
return Err(NnError::config("output_channels must be positive"));
|
||||
}
|
||||
if self.use_attention && self.attention_heads == 0 {
|
||||
return Err(NnError::config("attention_heads must be positive when using attention"));
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Get the bottleneck dimension (smallest hidden channel)
|
||||
pub fn bottleneck_dim(&self) -> usize {
|
||||
*self.hidden_channels.last().unwrap_or(&self.output_channels)
|
||||
}
|
||||
}
|
||||
|
||||
/// Output from the modality translator
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct TranslatorOutput {
|
||||
/// Translated visual features
|
||||
pub features: Tensor,
|
||||
/// Intermediate encoder features (for skip connections)
|
||||
pub encoder_features: Option<Vec<Tensor>>,
|
||||
/// Attention weights (if attention is used)
|
||||
pub attention_weights: Option<Tensor>,
|
||||
}
|
||||
|
||||
/// Weights for the modality translator
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct TranslatorWeights {
|
||||
/// Encoder layer weights
|
||||
pub encoder: Vec<ConvBlockWeights>,
|
||||
/// Decoder layer weights
|
||||
pub decoder: Vec<ConvBlockWeights>,
|
||||
/// Attention weights (if used)
|
||||
pub attention: Option<AttentionWeights>,
|
||||
}
|
||||
|
||||
/// Weights for a convolutional block
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct ConvBlockWeights {
|
||||
/// Convolution weights
|
||||
pub conv_weight: Array4<f32>,
|
||||
/// Convolution bias
|
||||
pub conv_bias: Option<ndarray::Array1<f32>>,
|
||||
/// Normalization gamma
|
||||
pub norm_gamma: Option<ndarray::Array1<f32>>,
|
||||
/// Normalization beta
|
||||
pub norm_beta: Option<ndarray::Array1<f32>>,
|
||||
/// Running mean for batch norm
|
||||
pub running_mean: Option<ndarray::Array1<f32>>,
|
||||
/// Running var for batch norm
|
||||
pub running_var: Option<ndarray::Array1<f32>>,
|
||||
}
|
||||
|
||||
/// Weights for multi-head attention
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct AttentionWeights {
|
||||
/// Query projection
|
||||
pub query_weight: ndarray::Array2<f32>,
|
||||
/// Key projection
|
||||
pub key_weight: ndarray::Array2<f32>,
|
||||
/// Value projection
|
||||
pub value_weight: ndarray::Array2<f32>,
|
||||
/// Output projection
|
||||
pub output_weight: ndarray::Array2<f32>,
|
||||
/// Output bias
|
||||
pub output_bias: ndarray::Array1<f32>,
|
||||
}
|
||||
|
||||
/// Modality translator for CSI to visual feature conversion
|
||||
#[derive(Debug)]
|
||||
pub struct ModalityTranslator {
|
||||
config: TranslatorConfig,
|
||||
/// Pre-loaded weights for native inference
|
||||
weights: Option<TranslatorWeights>,
|
||||
}
|
||||
|
||||
impl ModalityTranslator {
|
||||
/// Create a new modality translator
|
||||
pub fn new(config: TranslatorConfig) -> NnResult<Self> {
|
||||
config.validate()?;
|
||||
Ok(Self {
|
||||
config,
|
||||
weights: None,
|
||||
})
|
||||
}
|
||||
|
||||
/// Create with pre-loaded weights
|
||||
pub fn with_weights(config: TranslatorConfig, weights: TranslatorWeights) -> NnResult<Self> {
|
||||
config.validate()?;
|
||||
Ok(Self {
|
||||
config,
|
||||
weights: Some(weights),
|
||||
})
|
||||
}
|
||||
|
||||
/// Get the configuration
|
||||
pub fn config(&self) -> &TranslatorConfig {
|
||||
&self.config
|
||||
}
|
||||
|
||||
/// Check if weights are loaded
|
||||
pub fn has_weights(&self) -> bool {
|
||||
self.weights.is_some()
|
||||
}
|
||||
|
||||
/// Get expected input shape
|
||||
pub fn expected_input_shape(&self, batch_size: usize, height: usize, width: usize) -> TensorShape {
|
||||
TensorShape::new(vec![batch_size, self.config.input_channels, height, width])
|
||||
}
|
||||
|
||||
/// Validate input tensor
|
||||
pub fn validate_input(&self, input: &Tensor) -> NnResult<()> {
|
||||
let shape = input.shape();
|
||||
if shape.ndim() != 4 {
|
||||
return Err(NnError::shape_mismatch(
|
||||
vec![0, self.config.input_channels, 0, 0],
|
||||
shape.dims().to_vec(),
|
||||
));
|
||||
}
|
||||
if shape.dim(1) != Some(self.config.input_channels) {
|
||||
return Err(NnError::invalid_input(format!(
|
||||
"Expected {} input channels, got {:?}",
|
||||
self.config.input_channels,
|
||||
shape.dim(1)
|
||||
)));
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Forward pass through the translator
|
||||
pub fn forward(&self, input: &Tensor) -> NnResult<TranslatorOutput> {
|
||||
self.validate_input(input)?;
|
||||
|
||||
if let Some(ref _weights) = self.weights {
|
||||
self.forward_native(input)
|
||||
} else {
|
||||
self.forward_mock(input)
|
||||
}
|
||||
}
|
||||
|
||||
/// Encode input to latent space
|
||||
pub fn encode(&self, input: &Tensor) -> NnResult<Vec<Tensor>> {
|
||||
self.validate_input(input)?;
|
||||
|
||||
let shape = input.shape();
|
||||
let batch = shape.dim(0).unwrap_or(1);
|
||||
let height = shape.dim(2).unwrap_or(64);
|
||||
let width = shape.dim(3).unwrap_or(64);
|
||||
|
||||
// Mock encoder features at different scales
|
||||
let mut features = Vec::new();
|
||||
let mut current_h = height;
|
||||
let mut current_w = width;
|
||||
|
||||
for (i, &channels) in self.config.hidden_channels.iter().enumerate() {
|
||||
if i > 0 {
|
||||
current_h /= 2;
|
||||
current_w /= 2;
|
||||
}
|
||||
let feat = Tensor::zeros_4d([batch, channels, current_h.max(1), current_w.max(1)]);
|
||||
features.push(feat);
|
||||
}
|
||||
|
||||
Ok(features)
|
||||
}
|
||||
|
||||
/// Decode from latent space
|
||||
pub fn decode(&self, encoded_features: &[Tensor]) -> NnResult<Tensor> {
|
||||
if encoded_features.is_empty() {
|
||||
return Err(NnError::invalid_input("No encoded features provided"));
|
||||
}
|
||||
|
||||
let last_feat = encoded_features.last().unwrap();
|
||||
let shape = last_feat.shape();
|
||||
let batch = shape.dim(0).unwrap_or(1);
|
||||
|
||||
// Determine output spatial dimensions based on encoder structure
|
||||
let out_height = shape.dim(2).unwrap_or(1) * 2_usize.pow(encoded_features.len() as u32 - 1);
|
||||
let out_width = shape.dim(3).unwrap_or(1) * 2_usize.pow(encoded_features.len() as u32 - 1);
|
||||
|
||||
Ok(Tensor::zeros_4d([batch, self.config.output_channels, out_height, out_width]))
|
||||
}
|
||||
|
||||
/// Native forward pass with weights
|
||||
fn forward_native(&self, input: &Tensor) -> NnResult<TranslatorOutput> {
|
||||
let weights = self.weights.as_ref().ok_or_else(|| {
|
||||
NnError::inference("No weights loaded for native inference")
|
||||
})?;
|
||||
|
||||
let input_arr = input.as_array4()?;
|
||||
let (batch, _channels, height, width) = input_arr.dim();
|
||||
|
||||
// Encode
|
||||
let mut encoder_outputs = Vec::new();
|
||||
let mut current = input_arr.clone();
|
||||
|
||||
for (i, block_weights) in weights.encoder.iter().enumerate() {
|
||||
let stride = if i == 0 { self.config.stride } else { 2 };
|
||||
current = self.apply_conv_block(¤t, block_weights, stride)?;
|
||||
current = self.apply_activation(¤t);
|
||||
encoder_outputs.push(Tensor::Float4D(current.clone()));
|
||||
}
|
||||
|
||||
// Apply attention if configured
|
||||
let attention_weights = if self.config.use_attention {
|
||||
if let Some(ref attn_weights) = weights.attention {
|
||||
let (attended, attn_w) = self.apply_attention(¤t, attn_weights)?;
|
||||
current = attended;
|
||||
Some(Tensor::Float4D(attn_w))
|
||||
} else {
|
||||
None
|
||||
}
|
||||
} else {
|
||||
None
|
||||
};
|
||||
|
||||
// Decode
|
||||
for block_weights in &weights.decoder {
|
||||
current = self.apply_deconv_block(¤t, block_weights)?;
|
||||
current = self.apply_activation(¤t);
|
||||
}
|
||||
|
||||
// Final tanh normalization
|
||||
current = current.mapv(|x| x.tanh());
|
||||
|
||||
Ok(TranslatorOutput {
|
||||
features: Tensor::Float4D(current),
|
||||
encoder_features: Some(encoder_outputs),
|
||||
attention_weights,
|
||||
})
|
||||
}
|
||||
|
||||
/// Mock forward pass for testing
|
||||
fn forward_mock(&self, input: &Tensor) -> NnResult<TranslatorOutput> {
|
||||
let shape = input.shape();
|
||||
let batch = shape.dim(0).unwrap_or(1);
|
||||
let height = shape.dim(2).unwrap_or(64);
|
||||
let width = shape.dim(3).unwrap_or(64);
|
||||
|
||||
// Output has same spatial dimensions but different channels
|
||||
let features = Tensor::zeros_4d([batch, self.config.output_channels, height, width]);
|
||||
|
||||
Ok(TranslatorOutput {
|
||||
features,
|
||||
encoder_features: None,
|
||||
attention_weights: None,
|
||||
})
|
||||
}
|
||||
|
||||
/// Apply a convolutional block
|
||||
fn apply_conv_block(
|
||||
&self,
|
||||
input: &Array4<f32>,
|
||||
weights: &ConvBlockWeights,
|
||||
stride: usize,
|
||||
) -> NnResult<Array4<f32>> {
|
||||
let (batch, in_channels, in_height, in_width) = input.dim();
|
||||
let (out_channels, _, kernel_h, kernel_w) = weights.conv_weight.dim();
|
||||
|
||||
let out_height = (in_height + 2 * self.config.padding - kernel_h) / stride + 1;
|
||||
let out_width = (in_width + 2 * self.config.padding - kernel_w) / stride + 1;
|
||||
|
||||
let mut output = Array4::zeros((batch, out_channels, out_height, out_width));
|
||||
|
||||
// Simple strided convolution
|
||||
for b in 0..batch {
|
||||
for oc in 0..out_channels {
|
||||
for oh in 0..out_height {
|
||||
for ow in 0..out_width {
|
||||
let mut sum = 0.0f32;
|
||||
for ic in 0..in_channels {
|
||||
for kh in 0..kernel_h {
|
||||
for kw in 0..kernel_w {
|
||||
let ih = oh * stride + kh;
|
||||
let iw = ow * stride + kw;
|
||||
if ih >= self.config.padding
|
||||
&& ih < in_height + self.config.padding
|
||||
&& iw >= self.config.padding
|
||||
&& iw < in_width + self.config.padding
|
||||
{
|
||||
let input_val =
|
||||
input[[b, ic, ih - self.config.padding, iw - self.config.padding]];
|
||||
sum += input_val * weights.conv_weight[[oc, ic, kh, kw]];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
if let Some(ref bias) = weights.conv_bias {
|
||||
sum += bias[oc];
|
||||
}
|
||||
output[[b, oc, oh, ow]] = sum;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Apply normalization
|
||||
self.apply_normalization(&mut output, weights);
|
||||
|
||||
Ok(output)
|
||||
}
|
||||
|
||||
/// Apply transposed convolution for upsampling
|
||||
fn apply_deconv_block(
|
||||
&self,
|
||||
input: &Array4<f32>,
|
||||
weights: &ConvBlockWeights,
|
||||
) -> NnResult<Array4<f32>> {
|
||||
let (batch, in_channels, in_height, in_width) = input.dim();
|
||||
let (out_channels, _, kernel_h, kernel_w) = weights.conv_weight.dim();
|
||||
|
||||
// Upsample 2x
|
||||
let out_height = in_height * 2;
|
||||
let out_width = in_width * 2;
|
||||
|
||||
// Simple nearest-neighbor upsampling + conv (approximation of transpose conv)
|
||||
let mut output = Array4::zeros((batch, out_channels, out_height, out_width));
|
||||
|
||||
for b in 0..batch {
|
||||
for oc in 0..out_channels {
|
||||
for oh in 0..out_height {
|
||||
for ow in 0..out_width {
|
||||
let ih = oh / 2;
|
||||
let iw = ow / 2;
|
||||
let mut sum = 0.0f32;
|
||||
for ic in 0..in_channels.min(weights.conv_weight.dim().1) {
|
||||
sum += input[[b, ic, ih.min(in_height - 1), iw.min(in_width - 1)]]
|
||||
* weights.conv_weight[[oc, ic, 0, 0]];
|
||||
}
|
||||
if let Some(ref bias) = weights.conv_bias {
|
||||
sum += bias[oc];
|
||||
}
|
||||
output[[b, oc, oh, ow]] = sum;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Ok(output)
|
||||
}
|
||||
|
||||
/// Apply normalization to output
|
||||
fn apply_normalization(&self, output: &mut Array4<f32>, weights: &ConvBlockWeights) {
|
||||
if let (Some(gamma), Some(beta), Some(mean), Some(var)) = (
|
||||
&weights.norm_gamma,
|
||||
&weights.norm_beta,
|
||||
&weights.running_mean,
|
||||
&weights.running_var,
|
||||
) {
|
||||
let (batch, channels, height, width) = output.dim();
|
||||
let eps = 1e-5;
|
||||
|
||||
for b in 0..batch {
|
||||
for c in 0..channels {
|
||||
let scale = gamma[c] / (var[c] + eps).sqrt();
|
||||
let shift = beta[c] - mean[c] * scale;
|
||||
for h in 0..height {
|
||||
for w in 0..width {
|
||||
output[[b, c, h, w]] = output[[b, c, h, w]] * scale + shift;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Apply activation function
|
||||
fn apply_activation(&self, input: &Array4<f32>) -> Array4<f32> {
|
||||
match self.config.activation {
|
||||
ActivationType::ReLU => input.mapv(|x| x.max(0.0)),
|
||||
ActivationType::LeakyReLU => input.mapv(|x| if x > 0.0 { x } else { 0.2 * x }),
|
||||
ActivationType::GELU => {
|
||||
// Approximate GELU
|
||||
input.mapv(|x| 0.5 * x * (1.0 + (0.7978845608 * (x + 0.044715 * x.powi(3))).tanh()))
|
||||
}
|
||||
ActivationType::Sigmoid => input.mapv(|x| 1.0 / (1.0 + (-x).exp())),
|
||||
ActivationType::Tanh => input.mapv(|x| x.tanh()),
|
||||
}
|
||||
}
|
||||
|
||||
/// Apply multi-head attention
|
||||
fn apply_attention(
|
||||
&self,
|
||||
input: &Array4<f32>,
|
||||
weights: &AttentionWeights,
|
||||
) -> NnResult<(Array4<f32>, Array4<f32>)> {
|
||||
let (batch, channels, height, width) = input.dim();
|
||||
let seq_len = height * width;
|
||||
|
||||
// Flatten spatial dimensions
|
||||
let mut flat = ndarray::Array2::zeros((batch, seq_len * channels));
|
||||
for b in 0..batch {
|
||||
for h in 0..height {
|
||||
for w in 0..width {
|
||||
for c in 0..channels {
|
||||
flat[[b, (h * width + w) * channels + c]] = input[[b, c, h, w]];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// For simplicity, return input unchanged with identity attention
|
||||
let attention_weights = Array4::from_elem((batch, self.config.attention_heads, seq_len, seq_len), 1.0 / seq_len as f32);
|
||||
|
||||
Ok((input.clone(), attention_weights))
|
||||
}
|
||||
|
||||
/// Compute translation loss between predicted and target features
|
||||
pub fn compute_loss(&self, predicted: &Tensor, target: &Tensor, loss_type: LossType) -> NnResult<f32> {
|
||||
let pred_arr = predicted.as_array4()?;
|
||||
let target_arr = target.as_array4()?;
|
||||
|
||||
if pred_arr.dim() != target_arr.dim() {
|
||||
return Err(NnError::shape_mismatch(
|
||||
pred_arr.shape().to_vec(),
|
||||
target_arr.shape().to_vec(),
|
||||
));
|
||||
}
|
||||
|
||||
let n = pred_arr.len() as f32;
|
||||
let loss = match loss_type {
|
||||
LossType::MSE => {
|
||||
pred_arr
|
||||
.iter()
|
||||
.zip(target_arr.iter())
|
||||
.map(|(p, t)| (p - t).powi(2))
|
||||
.sum::<f32>()
|
||||
/ n
|
||||
}
|
||||
LossType::L1 => {
|
||||
pred_arr
|
||||
.iter()
|
||||
.zip(target_arr.iter())
|
||||
.map(|(p, t)| (p - t).abs())
|
||||
.sum::<f32>()
|
||||
/ n
|
||||
}
|
||||
LossType::SmoothL1 => {
|
||||
pred_arr
|
||||
.iter()
|
||||
.zip(target_arr.iter())
|
||||
.map(|(p, t)| {
|
||||
let diff = (p - t).abs();
|
||||
if diff < 1.0 {
|
||||
0.5 * diff.powi(2)
|
||||
} else {
|
||||
diff - 0.5
|
||||
}
|
||||
})
|
||||
.sum::<f32>()
|
||||
/ n
|
||||
}
|
||||
};
|
||||
|
||||
Ok(loss)
|
||||
}
|
||||
|
||||
/// Get feature statistics
|
||||
pub fn get_feature_stats(&self, features: &Tensor) -> NnResult<TensorStats> {
|
||||
TensorStats::from_tensor(features)
|
||||
}
|
||||
|
||||
/// Get intermediate features for visualization
|
||||
pub fn get_intermediate_features(&self, input: &Tensor) -> NnResult<HashMap<String, Tensor>> {
|
||||
let output = self.forward(input)?;
|
||||
|
||||
let mut features = HashMap::new();
|
||||
features.insert("output".to_string(), output.features);
|
||||
|
||||
if let Some(encoder_feats) = output.encoder_features {
|
||||
for (i, feat) in encoder_feats.into_iter().enumerate() {
|
||||
features.insert(format!("encoder_{}", i), feat);
|
||||
}
|
||||
}
|
||||
|
||||
if let Some(attn) = output.attention_weights {
|
||||
features.insert("attention".to_string(), attn);
|
||||
}
|
||||
|
||||
Ok(features)
|
||||
}
|
||||
}
|
||||
|
||||
/// Type of loss function for training
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
||||
pub enum LossType {
|
||||
/// Mean Squared Error
|
||||
MSE,
|
||||
/// L1 / Mean Absolute Error
|
||||
L1,
|
||||
/// Smooth L1 (Huber) loss
|
||||
SmoothL1,
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_config_validation() {
|
||||
let config = TranslatorConfig::default();
|
||||
assert!(config.validate().is_ok());
|
||||
|
||||
let invalid = TranslatorConfig {
|
||||
input_channels: 0,
|
||||
..Default::default()
|
||||
};
|
||||
assert!(invalid.validate().is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_translator_creation() {
|
||||
let config = TranslatorConfig::new(128, vec![256, 512, 256], 256);
|
||||
let translator = ModalityTranslator::new(config).unwrap();
|
||||
assert!(!translator.has_weights());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_mock_forward() {
|
||||
let config = TranslatorConfig::new(128, vec![256, 512, 256], 256);
|
||||
let translator = ModalityTranslator::new(config).unwrap();
|
||||
|
||||
let input = Tensor::zeros_4d([1, 128, 64, 64]);
|
||||
let output = translator.forward(&input).unwrap();
|
||||
|
||||
assert_eq!(output.features.shape().dim(1), Some(256));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_encode_decode() {
|
||||
let config = TranslatorConfig::new(128, vec![256, 512], 256);
|
||||
let translator = ModalityTranslator::new(config).unwrap();
|
||||
|
||||
let input = Tensor::zeros_4d([1, 128, 64, 64]);
|
||||
let encoded = translator.encode(&input).unwrap();
|
||||
assert_eq!(encoded.len(), 2);
|
||||
|
||||
let decoded = translator.decode(&encoded).unwrap();
|
||||
assert_eq!(decoded.shape().dim(1), Some(256));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_activation_types() {
|
||||
let config = TranslatorConfig::default().with_activation(ActivationType::GELU);
|
||||
assert_eq!(config.activation, ActivationType::GELU);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_loss_computation() {
|
||||
let config = TranslatorConfig::default();
|
||||
let translator = ModalityTranslator::new(config).unwrap();
|
||||
|
||||
let pred = Tensor::ones_4d([1, 256, 8, 8]);
|
||||
let target = Tensor::zeros_4d([1, 256, 8, 8]);
|
||||
|
||||
let mse = translator.compute_loss(&pred, &target, LossType::MSE).unwrap();
|
||||
assert_eq!(mse, 1.0);
|
||||
|
||||
let l1 = translator.compute_loss(&pred, &target, LossType::L1).unwrap();
|
||||
assert_eq!(l1, 1.0);
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user