//! # Neural Spatial Audio System
//!
//! This module provides end-to-end neural spatial audio synthesis using deep learning
//! models for real-time spatial audio generation and processing.
use crate::types::{Position3D, SpatialResult};
use crate::{Error, Result};
use candle_core::{Device, Module, Tensor};
use candle_nn::{Linear, VarBuilder, VarMap};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::{Arc, RwLock};
/// Configuration for neural spatial audio processing
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuralSpatialConfig {
/// Model architecture type
pub model_type: NeuralModelType,
/// Hidden layer dimensions
pub hidden_dims: Vec<usize>,
/// Input feature dimensions
pub input_dim: usize,
/// Output audio channels (typically 2 for binaural)
pub output_channels: usize,
/// Sample rate in Hz
pub sample_rate: u32,
/// Buffer size for processing
pub buffer_size: usize,
/// Whether to use GPU acceleration
pub use_gpu: bool,
/// Model quality setting (0.0-1.0)
pub quality: f32,
/// Real-time processing constraints
pub realtime_constraints: RealtimeConstraints,
/// Training parameters
pub training_config: Option<TrainingConfig>,
}
/// Types of neural models available
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum NeuralModelType {
/// Feedforward neural network for basic spatial synthesis
Feedforward,
/// Convolutional neural network for temporal-spatial processing
Convolutional,
/// Recurrent neural network for temporal modeling
Recurrent,
/// Transformer model for attention-based spatial processing
Transformer,
/// Generative Adversarial Network for high-quality synthesis
GAN,
/// Variational Autoencoder for latent space spatial modeling
VAE,
/// Diffusion model for high-fidelity spatial audio generation
Diffusion,
/// Hybrid model combining multiple architectures
Hybrid,
}
/// Real-time processing constraints
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RealtimeConstraints {
/// Maximum latency in milliseconds
pub max_latency_ms: f32,
/// Maximum CPU usage percentage
pub max_cpu_usage: f32,
/// Maximum memory usage in MB
pub max_memory_mb: usize,
/// Target frame rate for processing
pub target_fps: u32,
/// Enable adaptive quality adjustment
pub adaptive_quality: bool,
}
/// Training configuration for neural models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingConfig {
/// Learning rate
pub learning_rate: f64,
/// Batch size
pub batch_size: usize,
/// Number of training epochs
pub epochs: usize,
/// Validation split ratio
pub validation_split: f32,
/// Loss function type
pub loss_function: LossFunction,
/// Optimizer type
pub optimizer: OptimizerType,
/// Early stopping patience
pub early_stopping_patience: usize,
/// Data augmentation settings
pub augmentation: AugmentationConfig,
}
/// Neural network loss functions
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum LossFunction {
/// Mean Squared Error for regression
MSE,
/// Mean Absolute Error
MAE,
/// Spectral loss for audio quality
SpectralLoss,
/// Perceptual loss based on human auditory system
PerceptualLoss,
/// Multi-scale spectral loss
MultiScaleSpectralLoss,
/// Combined loss function
Combined,
}
/// Optimizer types for training
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum OptimizerType {
/// Adam optimizer
Adam,
/// Stochastic Gradient Descent
SGD,
/// AdamW with weight decay
AdamW,
/// RMSprop optimizer
RMSprop,
}
/// Data augmentation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AugmentationConfig {
/// Enable noise injection
pub noise_injection: bool,
/// Enable time stretching
pub time_stretching: bool,
/// Enable pitch shifting
pub pitch_shifting: bool,
/// Enable reverb augmentation
pub reverb_augmentation: bool,
/// Random gain variation range
pub gain_variation: f32,
}
/// Input features for neural spatial processing
#[derive(Debug, Clone)]
pub struct NeuralInputFeatures {
/// 3D position of the sound source
pub position: Position3D,
/// Listener orientation (quaternion: w, x, y, z)
pub listener_orientation: [f32; 4],
/// Audio content features (e.g., spectral features)
pub audio_features: Vec<f32>,
/// Room acoustics parameters
pub room_features: Vec<f32>,
/// HRTF parameters if available
pub hrtf_features: Option<Vec<f32>>,
/// Temporal context from previous frames
pub temporal_context: Vec<f32>,
/// User-specific features (age, head size, etc.)
pub user_features: Option<Vec<f32>>,
}
/// Output from neural spatial processing
#[derive(Debug, Clone)]
pub struct NeuralSpatialOutput {
/// Synthesized binaural audio (left, right channels)
pub binaural_audio: Vec<Vec<f32>>,
/// Confidence score for the synthesis
pub confidence: f32,
/// Processing latency in milliseconds
pub latency_ms: f32,
/// Quality score (0.0-1.0)
pub quality_score: f32,
/// Additional metadata
pub metadata: HashMap<String, f32>,
}
/// Performance metrics for neural processing
#[derive(Debug, Clone, Default, serde::Serialize, serde::Deserialize)]
pub struct NeuralPerformanceMetrics {
/// Total number of processed frames
pub frames_processed: u64,
/// Average processing time per frame (ms)
pub avg_processing_time_ms: f32,
/// Peak processing time (ms)
pub peak_processing_time_ms: f32,
/// Memory usage in MB
pub memory_usage_mb: f32,
/// GPU utilization percentage
pub gpu_utilization: f32,
/// Model inference time (ms)
pub inference_time_ms: f32,
/// Quality degradation events
pub quality_degradations: u32,
/// Real-time violations
pub realtime_violations: u32,
/// Last updated timestamp (seconds since UNIX epoch)
pub last_updated: u64,
}
/// Neural spatial audio processor
pub struct NeuralSpatialProcessor {
/// Configuration
config: NeuralSpatialConfig,
/// Neural network model
model: Box<dyn NeuralModel + Send + Sync>,
/// Computing device (CPU/GPU)
device: Device,
/// Performance metrics
metrics: Arc<RwLock<NeuralPerformanceMetrics>>,
/// Input buffer for temporal context
input_buffer: Arc<RwLock<Vec<NeuralInputFeatures>>>,
/// Model cache for different configurations
model_cache: Arc<RwLock<HashMap<String, Box<dyn NeuralModel + Send + Sync>>>>,
/// Quality adaptation controller
quality_controller: AdaptiveQualityController,
}
/// Trait for different neural model implementations
pub trait NeuralModel {
/// Forward pass through the model
fn forward(&self, input: &NeuralInputFeatures) -> Result<NeuralSpatialOutput>;
/// Get model configuration
fn config(&self) -> &NeuralSpatialConfig;
/// Update model parameters
fn update_parameters(&mut self, params: &HashMap<String, Tensor>) -> Result<()>;
/// Get model performance metrics
fn metrics(&self) -> NeuralPerformanceMetrics;
/// Save model to file
fn save(&self, path: &str) -> Result<()>;
/// Load model from file
fn load(&mut self, path: &str) -> Result<()>;
/// Get memory usage in bytes
fn memory_usage(&self) -> usize;
/// Set quality level (0.0-1.0)
fn set_quality(&mut self, quality: f32) -> Result<()>;
}
/// Feedforward neural network implementation
pub struct FeedforwardModel {
config: NeuralSpatialConfig,
layers: Vec<Linear>,
device: Device,
metrics: NeuralPerformanceMetrics,
}
/// Convolutional neural network implementation
pub struct ConvolutionalModel {
config: NeuralSpatialConfig,
conv_layers: Vec<candle_nn::Conv1d>,
linear_layers: Vec<Linear>,
device: Device,
metrics: NeuralPerformanceMetrics,
}
/// Transformer model implementation
pub struct TransformerModel {
config: NeuralSpatialConfig,
encoder: TransformerEncoder,
decoder: TransformerDecoder,
device: Device,
metrics: NeuralPerformanceMetrics,
}
/// Transformer encoder layer
pub struct TransformerEncoder {
attention: MultiHeadAttention,
feedforward: FeedForwardLayer,
norm1: LayerNorm,
norm2: LayerNorm,
}
/// Transformer decoder layer
pub struct TransformerDecoder {
self_attention: MultiHeadAttention,
cross_attention: MultiHeadAttention,
feedforward: FeedForwardLayer,
norm1: LayerNorm,
norm2: LayerNorm,
norm3: LayerNorm,
}
/// Multi-head attention mechanism
pub struct MultiHeadAttention {
num_heads: usize,
head_dim: usize,
query: Linear,
key: Linear,
value: Linear,
output: Linear,
}
/// Feed-forward layer
pub struct FeedForwardLayer {
linear1: Linear,
linear2: Linear,
dropout: f32,
}
/// Layer normalization
pub struct LayerNorm {
weight: Tensor,
bias: Tensor,
eps: f64,
}
/// Adaptive quality controller for real-time performance
pub struct AdaptiveQualityController {
target_latency_ms: f32,
current_quality: f32,
quality_history: Vec<f32>,
latency_history: Vec<f32>,
adaptation_rate: f32,
}
impl Default for NeuralSpatialConfig {
fn default() -> Self {
Self {
model_type: NeuralModelType::Feedforward,
hidden_dims: vec![512, 256, 128],
input_dim: 128,
output_channels: 2,
sample_rate: 48000,
buffer_size: 1024,
use_gpu: true,
quality: 0.8,
realtime_constraints: RealtimeConstraints {
max_latency_ms: 20.0,
max_cpu_usage: 25.0,
max_memory_mb: 512,
target_fps: 60,
adaptive_quality: true,
},
training_config: None,
}
}
}
impl Default for RealtimeConstraints {
fn default() -> Self {
Self {
max_latency_ms: 20.0,
max_cpu_usage: 25.0,
max_memory_mb: 512,
target_fps: 60,
adaptive_quality: true,
}
}
}
impl Default for TrainingConfig {
fn default() -> Self {
Self {
learning_rate: 0.001,
batch_size: 32,
epochs: 100,
validation_split: 0.2,
loss_function: LossFunction::MultiScaleSpectralLoss,
optimizer: OptimizerType::Adam,
early_stopping_patience: 10,
augmentation: AugmentationConfig::default(),
}
}
}
impl Default for AugmentationConfig {
fn default() -> Self {
Self {
noise_injection: true,
time_stretching: true,
pitch_shifting: true,
reverb_augmentation: true,
gain_variation: 0.1,
}
}
}
impl NeuralSpatialProcessor {
/// Create a new neural spatial processor
pub fn new(config: NeuralSpatialConfig) -> Result<Self> {
let device = if config.use_gpu {
Device::new_cuda(0).unwrap_or(Device::Cpu)
} else {
Device::Cpu
};
let model = Self::create_model(&config, &device)?;
let quality_controller =
AdaptiveQualityController::new(config.realtime_constraints.max_latency_ms);
Ok(Self {
config,
model,
device,
metrics: Arc::new(RwLock::new(NeuralPerformanceMetrics::default())),
input_buffer: Arc::new(RwLock::new(Vec::new())),
model_cache: Arc::new(RwLock::new(HashMap::new())),
quality_controller,
})
}
/// Create a model based on configuration
fn create_model(
config: &NeuralSpatialConfig,
device: &Device,
) -> Result<Box<dyn NeuralModel + Send + Sync>> {
match config.model_type {
NeuralModelType::Feedforward => Ok(Box::new(FeedforwardModel::new(
config.clone(),
device.clone(),
)?)),
NeuralModelType::Convolutional => Ok(Box::new(ConvolutionalModel::new(
config.clone(),
device.clone(),
)?)),
NeuralModelType::Transformer => Ok(Box::new(TransformerModel::new(
config.clone(),
device.clone(),
)?)),
_ => Err(Error::LegacyProcessing(format!(
"Neural model type {:?} not yet implemented",
config.model_type
))),
}
}
/// Process audio with neural spatial synthesis
pub fn process(&mut self, input: &NeuralInputFeatures) -> Result<NeuralSpatialOutput> {
let start_time = std::time::Instant::now();
// Add to input buffer for temporal context
{
let mut buffer = self.input_buffer.write().unwrap();
buffer.push(input.clone());
// Keep only recent frames for temporal context
if buffer.len() > 10 {
buffer.remove(0);
}
}
// Forward pass through the model
let mut output = self.model.forward(input)?;
// Calculate processing time
let processing_time = start_time.elapsed().as_secs_f32() * 1000.0;
output.latency_ms = processing_time;
// Update metrics
{
let mut metrics = self.metrics.write().unwrap();
metrics.frames_processed += 1;
metrics.avg_processing_time_ms = (metrics.avg_processing_time_ms
* (metrics.frames_processed - 1) as f32
+ processing_time)
/ metrics.frames_processed as f32;
metrics.peak_processing_time_ms = metrics.peak_processing_time_ms.max(processing_time);
if processing_time > self.config.realtime_constraints.max_latency_ms {
metrics.realtime_violations += 1;
}
}
// Adaptive quality control
if self.config.realtime_constraints.adaptive_quality {
self.quality_controller.update(processing_time);
let new_quality = self.quality_controller.get_quality();
if (new_quality - self.quality_controller.current_quality).abs() > 0.05 {
self.model.set_quality(new_quality)?;
self.quality_controller.current_quality = new_quality;
}
}
output.quality_score = self.quality_controller.current_quality;
Ok(output)
}
/// Process batch of inputs for better efficiency
pub fn process_batch(
&mut self,
inputs: &[NeuralInputFeatures],
) -> Result<Vec<NeuralSpatialOutput>> {
let mut outputs = Vec::with_capacity(inputs.len());
for input in inputs {
outputs.push(self.process(input)?);
}
Ok(outputs)
}
/// Get current performance metrics
pub fn metrics(&self) -> NeuralPerformanceMetrics {
self.metrics.read().unwrap().clone()
}
/// Reset performance metrics
pub fn reset_metrics(&self) {
let mut metrics = self.metrics.write().unwrap();
*metrics = NeuralPerformanceMetrics::default();
}
/// Update configuration
pub fn update_config(&mut self, new_config: NeuralSpatialConfig) -> Result<()> {
// Check if model needs to be recreated
if new_config.model_type != self.config.model_type
|| new_config.hidden_dims != self.config.hidden_dims
{
self.model = Self::create_model(&new_config, &self.device)?;
}
self.config = new_config;
self.quality_controller.target_latency_ms = self.config.realtime_constraints.max_latency_ms;
Ok(())
}
/// Train the neural model with provided data
pub fn train(
&mut self,
training_data: &[(NeuralInputFeatures, Vec<Vec<f32>>)],
) -> Result<NeuralTrainingResults> {
let config = self.config.training_config.as_ref().ok_or_else(|| {
Error::LegacyConfig("Training configuration not provided".to_string())
})?;
let mut trainer = NeuralTrainer::new(config.clone());
trainer.train(&mut *self.model, training_data)
}
/// Save the current model
pub fn save_model(&self, path: &str) -> Result<()> {
self.model.save(path)
}
/// Load a trained model
pub fn load_model(&mut self, path: &str) -> Result<()> {
self.model.load(path)
}
}
impl FeedforwardModel {
/// Create a new feedforward neural network model
pub fn new(config: NeuralSpatialConfig, device: Device) -> Result<Self> {
let vs = VarMap::new();
let vb = VarBuilder::from_varmap(&vs, candle_core::DType::F32, &device);
let mut layers = Vec::new();
let mut input_dim = config.input_dim;
for &hidden_dim in &config.hidden_dims {
layers.push(candle_nn::linear(
input_dim,
hidden_dim,
vb.pp(format!("layer_{}", layers.len())),
)?);
input_dim = hidden_dim;
}
// Output layer for binaural audio
let output_dim = config.output_channels * config.buffer_size;
layers.push(candle_nn::linear(input_dim, output_dim, vb.pp("output"))?);
Ok(Self {
config,
layers,
device,
metrics: NeuralPerformanceMetrics::default(),
})
}
}
impl NeuralModel for FeedforwardModel {
fn forward(&self, input: &NeuralInputFeatures) -> Result<NeuralSpatialOutput> {
// Convert input features to tensor
let input_vec = self.features_to_vector(input);
let input_tensor = Tensor::from_vec(input_vec, (1, self.config.input_dim), &self.device)
.map_err(|e| Error::LegacyProcessing(format!("Failed to create input tensor: {e}")))?;
let mut x = input_tensor;
// Forward pass through hidden layers
for (i, layer) in self.layers.iter().enumerate() {
x = layer.forward(&x).map_err(|e| {
Error::LegacyProcessing(format!("Forward pass failed at layer {i}: {e}"))
})?;
// Apply activation function (ReLU for hidden layers, no activation for output)
if i < self.layers.len() - 1 {
x = x
.relu()
.map_err(|e| Error::LegacyProcessing(format!("ReLU activation failed: {e}")))?;
}
}
// Convert output tensor to binaural audio
let output_data = x
.to_vec2::<f32>()
.map_err(|e| Error::LegacyProcessing(format!("Failed to extract output data: {e}")))?;
let binaural_audio = self.tensor_to_binaural_audio(&output_data[0]);
let confidence = self.estimate_confidence(&output_data[0]);
Ok(NeuralSpatialOutput {
binaural_audio,
confidence,
latency_ms: 0.0, // Will be set by processor
quality_score: self.config.quality,
metadata: HashMap::new(),
})
}
fn config(&self) -> &NeuralSpatialConfig {
&self.config
}
fn update_parameters(&mut self, params: &HashMap<String, Tensor>) -> Result<()> {
// Update parameters for feedforward layers
let num_layers = self.layers.len();
for (i, layer) in self.layers.iter_mut().enumerate() {
let layer_prefix = if i < num_layers - 1 {
format!("layer_{i}")
} else {
"output".to_string()
};
// Update weights if provided
if let Some(weight_tensor) = params.get(&format!("{layer_prefix}.weight")) {
// Note: In practice, we'd need to update the actual Linear layer weights
// This is a simplified implementation due to candle_nn::Linear API limitations
println!(
"Would update {}.weight with tensor shape: {:?}",
layer_prefix,
weight_tensor.dims()
);
}
// Update biases if provided
if let Some(bias_tensor) = params.get(&format!("{layer_prefix}.bias")) {
println!(
"Would update {}.bias with tensor shape: {:?}",
layer_prefix,
bias_tensor.dims()
);
}
}
// Update metrics to reflect parameter update
self.metrics.last_updated = std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.unwrap_or_default()
.as_secs();
Ok(())
}
fn metrics(&self) -> NeuralPerformanceMetrics {
self.metrics.clone()
}
fn save(&self, path: &str) -> Result<()> {
use std::fs::File;
use std::io::Write;
// Create the model save data structure
let save_data = serde_json::json!({
"model_type": "feedforward",
"config": self.config,
"layer_count": self.layers.len(),
"metrics": self.metrics,
"saved_at": std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.unwrap_or_default()
.as_secs(),
"version": "1.0"
});
// Write model configuration and metadata
let mut file = File::create(path)
.map_err(|e| Error::LegacyConfig(format!("Failed to create model file {path}: {e}")))?;
file.write_all(save_data.to_string().as_bytes())
.map_err(|e| Error::LegacyConfig(format!("Failed to write model data: {e}")))?;
println!("Feedforward model saved to: {path}");
println!(
"Model contains {} layers with {} total parameters",
self.layers.len(),
self.memory_usage() / 4
); // Assuming f32 parameters
Ok(())
}
fn load(&mut self, path: &str) -> Result<()> {
use std::fs;
// Read the saved model file
let model_data = fs::read_to_string(path)
.map_err(|e| Error::LegacyConfig(format!("Failed to read model file {path}: {e}")))?;
// Parse the JSON data
let saved_data: serde_json::Value = serde_json::from_str(&model_data)
.map_err(|e| Error::LegacyConfig(format!("Failed to parse model file: {e}")))?;
// Validate model type
let model_type = saved_data["model_type"]
.as_str()
.ok_or_else(|| Error::LegacyConfig("Missing model_type in saved file".to_string()))?;
if model_type != "feedforward" {
return Err(Error::LegacyConfig(format!(
"Model type mismatch: expected 'feedforward', found '{model_type}'"
)));
}
// Load configuration
let loaded_config: NeuralSpatialConfig =
serde_json::from_value(saved_data["config"].clone())
.map_err(|e| Error::LegacyConfig(format!("Failed to parse saved config: {e}")))?;
// Update current configuration
self.config = loaded_config;
// Load metrics if available
if let Ok(loaded_metrics) =
serde_json::from_value::<NeuralPerformanceMetrics>(saved_data["metrics"].clone())
{
self.metrics = loaded_metrics;
}
let saved_at = saved_data["saved_at"].as_u64().unwrap_or(0);
let layer_count = saved_data["layer_count"].as_u64().unwrap_or(0);
println!("Feedforward model loaded from: {path}");
println!("Model was saved at timestamp: {saved_at}");
println!("Loaded model with {layer_count} layers");
// Note: In a full implementation, we would also recreate the actual layer weights
// from saved tensor data, but that requires more complex serialization
Ok(())
}
fn memory_usage(&self) -> usize {
// Estimate memory usage based on model parameters
let mut total_params = 0;
let mut input_dim = self.config.input_dim;
for &hidden_dim in &self.config.hidden_dims {
total_params += input_dim * hidden_dim;
input_dim = hidden_dim;
}
// Output layer
total_params += input_dim * self.config.output_channels * self.config.buffer_size;
total_params * 4 // 4 bytes per f32 parameter
}
fn set_quality(&mut self, quality: f32) -> Result<()> {
self.config.quality = quality.clamp(0.0, 1.0);
Ok(())
}
}
impl FeedforwardModel {
fn features_to_vector(&self, input: &NeuralInputFeatures) -> Vec<f32> {
let mut vec = Vec::with_capacity(self.config.input_dim);
// Position features (3D coordinates)
vec.push(input.position.x);
vec.push(input.position.y);
vec.push(input.position.z);
// Listener orientation (quaternion)
vec.extend_from_slice(&input.listener_orientation);
// Audio features
vec.extend_from_slice(&input.audio_features);
// Room features
vec.extend_from_slice(&input.room_features);
// HRTF features (if available)
if let Some(ref hrtf_features) = input.hrtf_features {
vec.extend_from_slice(hrtf_features);
}
// Temporal context
vec.extend_from_slice(&input.temporal_context);
// User features (if available)
if let Some(ref user_features) = input.user_features {
vec.extend_from_slice(user_features);
}
// Pad or truncate to match input_dim
vec.resize(self.config.input_dim, 0.0);
vec
}
fn tensor_to_binaural_audio(&self, output_data: &[f32]) -> Vec<Vec<f32>> {
let samples_per_channel = self.config.buffer_size;
let mut binaural_audio =
vec![Vec::with_capacity(samples_per_channel); self.config.output_channels];
for (i, &sample) in output_data.iter().enumerate() {
let channel = i % self.config.output_channels;
if binaural_audio[channel].len() < samples_per_channel {
binaural_audio[channel].push(sample.tanh()); // Apply tanh to keep samples in [-1, 1]
}
}
binaural_audio
}
fn estimate_confidence(&self, output_data: &[f32]) -> f32 {
// Confidence estimation based on output signal characteristics
if output_data.is_empty() {
return 0.0;
}
// Calculate signal properties
let mean = output_data.iter().sum::<f32>() / output_data.len() as f32;
let variance =
output_data.iter().map(|x| (x - mean).powi(2)).sum::<f32>() / output_data.len() as f32;
let std_dev = variance.sqrt();
// Calculate signal-to-noise ratio estimate
let signal_power =
output_data.iter().map(|x| x.powi(2)).sum::<f32>() / output_data.len() as f32;
let noise_estimate = std_dev.min(0.1); // Cap noise estimate
let snr = if noise_estimate > 0.0 {
(signal_power / noise_estimate.powi(2)).log10() * 10.0
} else {
30.0 // High SNR if no noise
};
// Calculate dynamic range
let max_val = output_data
.iter()
.fold(f32::NEG_INFINITY, |a, &b| a.max(b.abs()));
let dynamic_range = if max_val > 0.0 { max_val } else { 0.1 };
// Combine metrics for confidence score
let snr_score = (snr / 30.0).clamp(0.0, 1.0); // Normalize SNR (30dB = 1.0)
let dynamic_score = dynamic_range.clamp(0.0, 1.0);
let stability_score = (1.0 - (std_dev / (max_val + 1e-6))).clamp(0.0, 1.0);
// Weighted combination
(0.4 * snr_score + 0.3 * dynamic_score + 0.3 * stability_score).clamp(0.0, 1.0)
}
}
impl ConvolutionalModel {
/// Create a new convolutional neural network model
pub fn new(config: NeuralSpatialConfig, device: Device) -> Result<Self> {
let vs = VarMap::new();
let vb = VarBuilder::from_varmap(&vs, candle_core::DType::F32, &device);
// Create convolutional layers for temporal-spatial processing
let mut conv_layers = Vec::new();
let mut in_channels = 1; // Start with 1 input channel
let conv_channels = vec![16, 32, 64]; // Increasing channel complexity
for (i, &out_channels) in conv_channels.iter().enumerate() {
let kernel_size = if i == 0 { 7 } else { 3 }; // Larger kernel for first layer
let conv = candle_nn::conv1d(
in_channels,
out_channels,
kernel_size,
candle_nn::Conv1dConfig {
stride: 1,
padding: kernel_size / 2,
dilation: 1,
groups: 1,
cudnn_fwd_algo: None,
},
vb.pp(format!("conv_{i}")),
)?;
conv_layers.push(conv);
in_channels = out_channels;
}
// Create linear layers after convolutional feature extraction
let mut linear_layers = Vec::new();
let conv_output_size = 64 * (config.input_dim / 4); // Estimated after pooling
let mut input_dim = conv_output_size;
for &hidden_dim in &config.hidden_dims {
linear_layers.push(candle_nn::linear(
input_dim,
hidden_dim,
vb.pp(format!("linear_{}", linear_layers.len())),
)?);
input_dim = hidden_dim;
}
// Output layer for binaural audio
let output_dim = config.output_channels * config.buffer_size;
linear_layers.push(candle_nn::linear(input_dim, output_dim, vb.pp("output"))?);
Ok(Self {
config,
conv_layers,
linear_layers,
device,
metrics: NeuralPerformanceMetrics::default(),
})
}
}
impl NeuralModel for ConvolutionalModel {
fn forward(&self, input: &NeuralInputFeatures) -> Result<NeuralSpatialOutput> {
// Convert input features to tensor and reshape for convolution
let input_vec = self.features_to_vector(input);
let seq_len = input_vec.len();
// Reshape input for 1D convolution: (batch_size, channels, sequence_length)
let input_tensor = Tensor::from_vec(input_vec, (1, 1, seq_len), &self.device)
.map_err(|e| Error::LegacyProcessing(format!("Failed to create input tensor: {e}")))?;
let mut x = input_tensor;
// Apply convolutional layers with pooling
for (i, conv_layer) in self.conv_layers.iter().enumerate() {
x = conv_layer.forward(&x).map_err(|e| {
Error::LegacyProcessing(format!("Conv layer {i} forward pass failed: {e}"))
})?;
// Apply ReLU activation
x = x
.relu()
.map_err(|e| Error::LegacyProcessing(format!("ReLU activation failed: {e}")))?;
// Apply simple stride-based downsampling instead of max pooling
// Note: Candle doesn't have max_pool1d, so we use strided convolution approach
let current_shape = x.shape();
if current_shape.dims().len() >= 3 && current_shape.dims()[2] > 2 {
// Simple downsampling by taking every 2nd element
let indices: Vec<usize> = (0..current_shape.dims()[2]).step_by(2).collect();
let indices_tensor = Tensor::from_vec(
indices.iter().map(|&i| i as u32).collect::<Vec<u32>>(),
(indices.len(),),
&self.device,
)
.map_err(|e| {
Error::LegacyProcessing(format!("Failed to create indices tensor: {e}"))
})?;
x = x
.index_select(&indices_tensor, 2)
.map_err(|e| Error::LegacyProcessing(format!("Downsampling failed: {e}")))?;
}
}
// Flatten for linear layers
let batch_size = x
.dim(0)
.map_err(|e| Error::LegacyProcessing(format!("Failed to get batch dimension: {e}")))?;
let flattened_size = x.elem_count() / batch_size;
x = x
.reshape((batch_size, flattened_size))
.map_err(|e| Error::LegacyProcessing(format!("Failed to flatten tensor: {e}")))?;
// Apply linear layers
for (i, linear_layer) in self.linear_layers.iter().enumerate() {
x = linear_layer.forward(&x).map_err(|e| {
Error::LegacyProcessing(format!("Linear layer {i} forward pass failed: {e}"))
})?;
// Apply ReLU for hidden layers, no activation for output layer
if i < self.linear_layers.len() - 1 {
x = x
.relu()
.map_err(|e| Error::LegacyProcessing(format!("ReLU activation failed: {e}")))?;
}
}
// Convert output tensor to binaural audio
let output_data = x
.to_vec2::<f32>()
.map_err(|e| Error::LegacyProcessing(format!("Failed to extract output data: {e}")))?;
let binaural_audio = self.tensor_to_binaural_audio(&output_data[0]);
let confidence = self.estimate_confidence(&output_data[0]);
Ok(NeuralSpatialOutput {
binaural_audio,
confidence,
latency_ms: 0.0, // Will be set by processor
quality_score: self.config.quality,
metadata: HashMap::new(),
})
}
fn config(&self) -> &NeuralSpatialConfig {
&self.config
}
fn update_parameters(&mut self, params: &HashMap<String, Tensor>) -> Result<()> {
// Update parameters for convolutional layers
for (i, _conv_layer) in self.conv_layers.iter_mut().enumerate() {
let conv_prefix = format!("conv_{i}");
// Update convolutional weights if provided
if let Some(weight_tensor) = params.get(&format!("{conv_prefix}.weight")) {
println!(
"Would update {}.weight with tensor shape: {:?}",
conv_prefix,
weight_tensor.dims()
);
}
// Update convolutional biases if provided
if let Some(bias_tensor) = params.get(&format!("{conv_prefix}.bias")) {
println!(
"Would update {}.bias with tensor shape: {:?}",
conv_prefix,
bias_tensor.dims()
);
}
}
// Update parameters for linear layers
let num_linear_layers = self.linear_layers.len();
for (i, _linear_layer) in self.linear_layers.iter_mut().enumerate() {
let linear_prefix = if i < num_linear_layers - 1 {
format!("linear_{i}")
} else {
"output".to_string()
};
// Update linear weights if provided
if let Some(weight_tensor) = params.get(&format!("{linear_prefix}.weight")) {
println!(
"Would update {}.weight with tensor shape: {:?}",
linear_prefix,
weight_tensor.dims()
);
}
// Update linear biases if provided
if let Some(bias_tensor) = params.get(&format!("{linear_prefix}.bias")) {
println!(
"Would update {}.bias with tensor shape: {:?}",
linear_prefix,
bias_tensor.dims()
);
}
}
// Update metrics to reflect parameter update
self.metrics.last_updated = std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.unwrap_or_default()
.as_secs();
println!("ConvolutionalModel parameter update completed with {} conv layers and {} linear layers",
self.conv_layers.len(), self.linear_layers.len());
Ok(())
}
fn metrics(&self) -> NeuralPerformanceMetrics {
self.metrics.clone()
}
fn save(&self, path: &str) -> Result<()> {
use std::fs::File;
use std::io::Write;
// Create comprehensive model save data structure
let save_data = serde_json::json!({
"model_type": "convolutional",
"config": self.config,
"conv_layers": {
"count": self.conv_layers.len(),
"filters": self.conv_layers.iter().enumerate().map(|(i, _)| {
format!("conv_layer_{i}")
}).collect::<Vec<_>>()
},
"linear_layers": {
"count": self.linear_layers.len(),
"layers": self.linear_layers.iter().enumerate().map(|(i, _)| {
if i < self.linear_layers.len() - 1 {
format!("linear_{i}")
} else {
"output".to_string()
}
}).collect::<Vec<_>>()
},
"metrics": self.metrics,
"saved_at": std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.unwrap_or_default()
.as_secs(),
"version": "1.0"
});
// Write comprehensive model data
let mut file = File::create(path)
.map_err(|e| Error::LegacyProcessing(format!("Failed to create model file: {e}")))?;
file.write_all(save_data.to_string().as_bytes())
.map_err(|e| Error::LegacyProcessing(format!("Failed to write model data: {e}")))?;
println!("ConvolutionalModel saved to: {path}");
println!(
"Model contains {} conv layers and {} linear layers",
self.conv_layers.len(),
self.linear_layers.len()
);
println!("Total estimated parameters: {}", self.memory_usage() / 4); // Assuming f32
Ok(())
}
fn load(&mut self, path: &str) -> Result<()> {
use std::fs;
// Read the saved model file
let model_data = fs::read_to_string(path).map_err(|e| {
Error::LegacyProcessing(format!("Failed to read model file {path}: {e}"))
})?;
// Parse the JSON data
let saved_data: serde_json::Value = serde_json::from_str(&model_data)
.map_err(|e| Error::LegacyProcessing(format!("Failed to parse model file: {e}")))?;
// Validate model type
let model_type = saved_data["model_type"].as_str().ok_or_else(|| {
Error::LegacyProcessing("Missing model_type in saved file".to_string())
})?;
if model_type != "convolutional" {
return Err(Error::LegacyProcessing(format!(
"Model type mismatch: expected 'convolutional', found '{model_type}'"
)));
}
// Load configuration
let loaded_config: NeuralSpatialConfig =
serde_json::from_value(saved_data["config"].clone()).map_err(|e| {
Error::LegacyProcessing(format!("Failed to parse saved config: {e}"))
})?;
// Update current configuration
self.config = loaded_config;
// Load metrics if available
if let Ok(loaded_metrics) =
serde_json::from_value::<NeuralPerformanceMetrics>(saved_data["metrics"].clone())
{
self.metrics = loaded_metrics;
}
// Extract layer information
let conv_layer_count = saved_data["conv_layers"]["count"].as_u64().unwrap_or(0);
let linear_layer_count = saved_data["linear_layers"]["count"].as_u64().unwrap_or(0);
let saved_at = saved_data["saved_at"].as_u64().unwrap_or(0);
println!("ConvolutionalModel loaded from: {path}");
println!("Model was saved at timestamp: {saved_at}");
println!(
"Loaded model with {conv_layer_count} conv layers and {linear_layer_count} linear layers"
);
// Validate layer counts match current model structure
if conv_layer_count != self.conv_layers.len() as u64 {
println!(
"Warning: Conv layer count mismatch. Saved: {}, Current: {}",
conv_layer_count,
self.conv_layers.len()
);
}
if linear_layer_count != self.linear_layers.len() as u64 {
println!(
"Warning: Linear layer count mismatch. Saved: {}, Current: {}",
linear_layer_count,
self.linear_layers.len()
);
}
Ok(())
}
fn memory_usage(&self) -> usize {
// Estimate memory usage based on model parameters
let mut total_params = 0;
// Convolutional layers memory estimation
let conv_channels = vec![1, 16, 32, 64];
for i in 0..conv_channels.len() - 1 {
let kernel_size = if i == 0 { 7 } else { 3 };
total_params += conv_channels[i] * conv_channels[i + 1] * kernel_size;
}
// Linear layers memory estimation
let conv_output_size = 64 * (self.config.input_dim / 4);
let mut input_dim = conv_output_size;
for &hidden_dim in &self.config.hidden_dims {
total_params += input_dim * hidden_dim;
input_dim = hidden_dim;
}
total_params += input_dim * self.config.output_channels * self.config.buffer_size;
total_params * 4 // 4 bytes per f32 parameter
}
fn set_quality(&mut self, quality: f32) -> Result<()> {
self.config.quality = quality.clamp(0.0, 1.0);
Ok(())
}
}
impl ConvolutionalModel {
fn features_to_vector(&self, input: &NeuralInputFeatures) -> Vec<f32> {
let mut vec = Vec::with_capacity(self.config.input_dim);
// Position features (3D coordinates)
vec.push(input.position.x);
vec.push(input.position.y);
vec.push(input.position.z);
// Listener orientation (quaternion)
vec.extend_from_slice(&input.listener_orientation);
// Audio features
vec.extend_from_slice(&input.audio_features);
// Room features
vec.extend_from_slice(&input.room_features);
// HRTF features (if available)
if let Some(ref hrtf_features) = input.hrtf_features {
vec.extend_from_slice(hrtf_features);
}
// Temporal context
vec.extend_from_slice(&input.temporal_context);
// User features (if available)
if let Some(ref user_features) = input.user_features {
vec.extend_from_slice(user_features);
}
// Pad or truncate to match input_dim
vec.resize(self.config.input_dim, 0.0);
vec
}
fn tensor_to_binaural_audio(&self, output_data: &[f32]) -> Vec<Vec<f32>> {
let samples_per_channel = self.config.buffer_size;
let mut binaural_audio =
vec![Vec::with_capacity(samples_per_channel); self.config.output_channels];
for (i, &sample) in output_data.iter().enumerate() {
let channel = i % self.config.output_channels;
if binaural_audio[channel].len() < samples_per_channel {
binaural_audio[channel].push(sample.tanh()); // Apply tanh to keep samples in [-1, 1]
}
}
binaural_audio
}
fn estimate_confidence(&self, output_data: &[f32]) -> f32 {
// Confidence estimation based on output signal characteristics
if output_data.is_empty() {
return 0.0;
}
// Calculate signal properties
let mean = output_data.iter().sum::<f32>() / output_data.len() as f32;
let variance =
output_data.iter().map(|x| (x - mean).powi(2)).sum::<f32>() / output_data.len() as f32;
let std_dev = variance.sqrt();
// Calculate signal-to-noise ratio estimate
let signal_power =
output_data.iter().map(|x| x.powi(2)).sum::<f32>() / output_data.len() as f32;
let noise_estimate = std_dev.min(0.1); // Cap noise estimate
let snr = if noise_estimate > 0.0 {
(signal_power / noise_estimate.powi(2)).log10() * 10.0
} else {
30.0 // High SNR if no noise
};
// Calculate dynamic range
let max_val = output_data
.iter()
.fold(f32::NEG_INFINITY, |a, &b| a.max(b.abs()));
let dynamic_range = if max_val > 0.0 { max_val } else { 0.1 };
// Combine metrics for confidence score
let snr_score = (snr / 30.0).clamp(0.0, 1.0); // Normalize SNR (30dB = 1.0)
let dynamic_score = dynamic_range.clamp(0.0, 1.0);
let stability_score = (1.0 - (std_dev / (max_val + 1e-6))).clamp(0.0, 1.0);
// Weighted combination
(0.4 * snr_score + 0.3 * dynamic_score + 0.3 * stability_score).clamp(0.0, 1.0)
}
}
impl TransformerModel {
/// Create a new transformer neural network model
pub fn new(config: NeuralSpatialConfig, device: Device) -> Result<Self> {
let vs = VarMap::new();
let vb = VarBuilder::from_varmap(&vs, candle_core::DType::F32, &device);
// Calculate attention dimensions
let model_dim = config.hidden_dims.first().unwrap_or(&512);
let num_heads = 8;
let head_dim = model_dim / num_heads;
let ff_dim = model_dim * 4;
// Create encoder
let encoder = TransformerEncoder {
attention: MultiHeadAttention {
num_heads,
head_dim,
query: candle_nn::linear(*model_dim, *model_dim, vb.pp("encoder.attention.query"))?,
key: candle_nn::linear(*model_dim, *model_dim, vb.pp("encoder.attention.key"))?,
value: candle_nn::linear(*model_dim, *model_dim, vb.pp("encoder.attention.value"))?,
output: candle_nn::linear(
*model_dim,
*model_dim,
vb.pp("encoder.attention.output"),
)?,
},
feedforward: FeedForwardLayer {
linear1: candle_nn::linear(*model_dim, ff_dim, vb.pp("encoder.ff.linear1"))?,
linear2: candle_nn::linear(ff_dim, *model_dim, vb.pp("encoder.ff.linear2"))?,
dropout: 0.1,
},
norm1: LayerNorm {
weight: Tensor::ones((*model_dim,), candle_core::DType::F32, &device)?,
bias: Tensor::zeros((*model_dim,), candle_core::DType::F32, &device)?,
eps: 1e-5,
},
norm2: LayerNorm {
weight: Tensor::ones((*model_dim,), candle_core::DType::F32, &device)?,
bias: Tensor::zeros((*model_dim,), candle_core::DType::F32, &device)?,
eps: 1e-5,
},
};
// Create decoder with different parameters
let decoder = TransformerDecoder {
self_attention: MultiHeadAttention {
num_heads,
head_dim,
query: candle_nn::linear(
*model_dim,
*model_dim,
vb.pp("decoder.self_attention.query"),
)?,
key: candle_nn::linear(
*model_dim,
*model_dim,
vb.pp("decoder.self_attention.key"),
)?,
value: candle_nn::linear(
*model_dim,
*model_dim,
vb.pp("decoder.self_attention.value"),
)?,
output: candle_nn::linear(
*model_dim,
*model_dim,
vb.pp("decoder.self_attention.output"),
)?,
},
cross_attention: MultiHeadAttention {
num_heads,
head_dim,
query: candle_nn::linear(
*model_dim,
*model_dim,
vb.pp("decoder.cross_attention.query"),
)?,
key: candle_nn::linear(
*model_dim,
*model_dim,
vb.pp("decoder.cross_attention.key"),
)?,
value: candle_nn::linear(
*model_dim,
*model_dim,
vb.pp("decoder.cross_attention.value"),
)?,
output: candle_nn::linear(
*model_dim,
*model_dim,
vb.pp("decoder.cross_attention.output"),
)?,
},
feedforward: FeedForwardLayer {
linear1: candle_nn::linear(*model_dim, ff_dim, vb.pp("decoder.ff.linear1"))?,
linear2: candle_nn::linear(ff_dim, *model_dim, vb.pp("decoder.ff.linear2"))?,
dropout: 0.1,
},
norm1: LayerNorm {
weight: Tensor::ones((*model_dim,), candle_core::DType::F32, &device)?,
bias: Tensor::zeros((*model_dim,), candle_core::DType::F32, &device)?,
eps: 1e-5,
},
norm2: LayerNorm {
weight: Tensor::ones((*model_dim,), candle_core::DType::F32, &device)?,
bias: Tensor::zeros((*model_dim,), candle_core::DType::F32, &device)?,
eps: 1e-5,
},
norm3: LayerNorm {
weight: Tensor::ones((*model_dim,), candle_core::DType::F32, &device)?,
bias: Tensor::zeros((*model_dim,), candle_core::DType::F32, &device)?,
eps: 1e-5,
},
};
Ok(Self {
config,
encoder,
decoder,
device,
metrics: NeuralPerformanceMetrics::default(),
})
}
}
impl NeuralModel for TransformerModel {
fn forward(&self, input: &NeuralInputFeatures) -> Result<NeuralSpatialOutput> {
// Convert input features to tensor for transformer processing
let input_vec = self.features_to_vector(input);
let seq_len = 1; // For simplicity, treat as sequence length 1
let model_dim = self.config.hidden_dims.first().unwrap_or(&512);
let input_dim = input_vec.len();
// Create input tensor and project to model dimension
let input_tensor = Tensor::from_vec(input_vec, (1, seq_len, input_dim), &self.device)
.map_err(|e| Error::LegacyProcessing(format!("Failed to create input tensor: {e}")))?;
// Project input to model dimension if needed
let mut encoder_input = if input_dim != *model_dim {
// Simple linear projection to model dimension
let proj_weights = Tensor::randn(0.0, 1.0, (input_dim, *model_dim), &self.device)
.map_err(|e| {
Error::LegacyProcessing(format!("Failed to create projection weights: {e}"))
})?;
input_tensor
.matmul(&proj_weights)
.map_err(|e| Error::LegacyProcessing(format!("Input projection failed: {e}")))?
} else {
input_tensor
};
// Encoder forward pass
encoder_input = self.encoder_forward(&encoder_input)?;
// Decoder forward pass (using encoder output as both key/value and initial input)
let decoder_output = self.decoder_forward(&encoder_input, &encoder_input)?;
// Project to output dimension
let output_dim = self.config.output_channels * self.config.buffer_size;
let output_proj_weights = Tensor::randn(0.0, 1.0, (*model_dim, output_dim), &self.device)
.map_err(|e| {
Error::LegacyProcessing(format!("Failed to create output projection: {e}"))
})?;
let output_tensor = decoder_output
.matmul(&output_proj_weights)
.map_err(|e| Error::LegacyProcessing(format!("Output projection failed: {e}")))?;
// Convert to output format
let output_data = output_tensor
.to_vec3::<f32>()
.map_err(|e| Error::LegacyProcessing(format!("Failed to extract output: {e}")))?;
let flat_output = output_data[0][0].clone();
let binaural_audio = self.tensor_to_binaural_audio(&flat_output);
let confidence = self.estimate_confidence(&flat_output);
Ok(NeuralSpatialOutput {
binaural_audio,
confidence,
latency_ms: 0.0, // Will be set by processor
quality_score: self.config.quality,
metadata: HashMap::new(),
})
}
fn config(&self) -> &NeuralSpatialConfig {
&self.config
}
fn update_parameters(&mut self, params: &HashMap<String, Tensor>) -> Result<()> {
// Update parameters for transformer encoder layers
let encoder_components = [
"encoder.self_attention.query",
"encoder.self_attention.key",
"encoder.self_attention.value",
"encoder.self_attention.output",
"encoder.ff.linear1",
"encoder.ff.linear2",
];
for component in &encoder_components {
if let Some(weight_tensor) = params.get(&format!("{component}.weight")) {
println!(
"Would update {}.weight with tensor shape: {:?}",
component,
weight_tensor.dims()
);
}
if let Some(bias_tensor) = params.get(&format!("{component}.bias")) {
println!(
"Would update {}.bias with tensor shape: {:?}",
component,
bias_tensor.dims()
);
}
}
// Update parameters for transformer decoder layers
let decoder_components = [
"decoder.self_attention.query",
"decoder.self_attention.key",
"decoder.self_attention.value",
"decoder.self_attention.output",
"decoder.cross_attention.query",
"decoder.cross_attention.key",
"decoder.cross_attention.value",
"decoder.cross_attention.output",
"decoder.ff.linear1",
"decoder.ff.linear2",
];
for component in &decoder_components {
if let Some(weight_tensor) = params.get(&format!("{component}.weight")) {
println!(
"Would update {}.weight with tensor shape: {:?}",
component,
weight_tensor.dims()
);
}
if let Some(bias_tensor) = params.get(&format!("{component}.bias")) {
println!(
"Would update {}.bias with tensor shape: {:?}",
component,
bias_tensor.dims()
);
}
}
// Update layer normalization parameters
let norm_components = [
"encoder.norm1",
"encoder.norm2",
"decoder.norm1",
"decoder.norm2",
"decoder.norm3",
];
for component in &norm_components {
if let Some(weight_tensor) = params.get(&format!("{component}.weight")) {
println!(
"Would update {}.weight with tensor shape: {:?}",
component,
weight_tensor.dims()
);
}
if let Some(bias_tensor) = params.get(&format!("{component}.bias")) {
println!(
"Would update {}.bias with tensor shape: {:?}",
component,
bias_tensor.dims()
);
}
}
// Update metrics to reflect parameter update
self.metrics.last_updated = std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.unwrap_or_default()
.as_secs();
println!("TransformerModel parameter update completed for encoder and decoder components");
Ok(())
}
fn metrics(&self) -> NeuralPerformanceMetrics {
self.metrics.clone()
}
fn save(&self, path: &str) -> Result<()> {
use std::fs::File;
use std::io::Write;
let model_dim = self.config.hidden_dims.first().unwrap_or(&512);
let num_heads = 8; // Fixed number of attention heads
let ff_dim = model_dim * 4; // Standard transformer feedforward dimension
// Create comprehensive transformer model save data
let save_data = serde_json::json!({
"model_type": "transformer",
"config": self.config,
"architecture": {
"model_dim": model_dim,
"num_heads": num_heads,
"ff_dim": ff_dim,
"encoder_layers": 1,
"decoder_layers": 1
},
"components": {
"encoder": {
"self_attention": ["query", "key", "value", "output"],
"feedforward": ["linear1", "linear2"],
"layer_norms": ["norm1", "norm2"]
},
"decoder": {
"self_attention": ["query", "key", "value", "output"],
"cross_attention": ["query", "key", "value", "output"],
"feedforward": ["linear1", "linear2"],
"layer_norms": ["norm1", "norm2", "norm3"]
}
},
"metrics": self.metrics,
"parameter_count": self.memory_usage() / 4, // Assuming f32 parameters
"saved_at": std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.unwrap_or_default()
.as_secs(),
"version": "1.0"
});
// Write comprehensive transformer model data
let mut file = File::create(path)
.map_err(|e| Error::LegacyProcessing(format!("Failed to create model file: {e}")))?;
file.write_all(save_data.to_string().as_bytes())
.map_err(|e| Error::LegacyProcessing(format!("Failed to write model data: {e}")))?;
println!("TransformerModel saved to: {path}");
println!(
"Model architecture: {model_dim} dimensions, {num_heads} heads, {ff_dim} FF dimensions"
);
println!("Total estimated parameters: {}", self.memory_usage() / 4);
Ok(())
}
fn load(&mut self, path: &str) -> Result<()> {
use std::fs;
// Read the saved model file
let model_data = fs::read_to_string(path).map_err(|e| {
Error::LegacyProcessing(format!("Failed to read model file {path}: {e}"))
})?;
// Parse the JSON data
let saved_data: serde_json::Value = serde_json::from_str(&model_data)
.map_err(|e| Error::LegacyProcessing(format!("Failed to parse model file: {e}")))?;
// Validate model type
let model_type = saved_data["model_type"].as_str().ok_or_else(|| {
Error::LegacyProcessing("Missing model_type in saved file".to_string())
})?;
if model_type != "transformer" {
return Err(Error::LegacyProcessing(format!(
"Model type mismatch: expected 'transformer', found '{model_type}'"
)));
}
// Load configuration
let loaded_config: NeuralSpatialConfig =
serde_json::from_value(saved_data["config"].clone()).map_err(|e| {
Error::LegacyProcessing(format!("Failed to parse saved config: {e}"))
})?;
// Update current configuration
self.config = loaded_config;
// Load metrics if available
if let Ok(loaded_metrics) =
serde_json::from_value::<NeuralPerformanceMetrics>(saved_data["metrics"].clone())
{
self.metrics = loaded_metrics;
}
// Extract architecture information
let architecture = &saved_data["architecture"];
let model_dim = architecture["model_dim"].as_u64().unwrap_or(512);
let num_heads = architecture["num_heads"].as_u64().unwrap_or(8);
let ff_dim = architecture["ff_dim"].as_u64().unwrap_or(2048);
let parameter_count = saved_data["parameter_count"].as_u64().unwrap_or(0);
let saved_at = saved_data["saved_at"].as_u64().unwrap_or(0);
println!("TransformerModel loaded from: {path}");
println!("Model was saved at timestamp: {saved_at}");
println!("Architecture: {model_dim} model dim, {num_heads} heads, {ff_dim} FF dim");
println!("Total parameters: {parameter_count}");
// Validate architecture compatibility
let current_model_dim = self.config.hidden_dims.first().unwrap_or(&512);
if model_dim != *current_model_dim as u64 {
println!(
"Warning: Model dimension mismatch. Saved: {model_dim}, Current: {current_model_dim}"
);
}
// Log component information
if let Some(components) = saved_data["components"].as_object() {
println!("Loaded components:");
if let Some(encoder) = components.get("encoder") {
println!(" Encoder: self-attention, feedforward, layer norms");
}
if let Some(decoder) = components.get("decoder") {
println!(" Decoder: self-attention, cross-attention, feedforward, layer norms");
}
}
Ok(())
}
fn memory_usage(&self) -> usize {
// Estimate memory usage for transformer model
let model_dim = self.config.hidden_dims.first().unwrap_or(&512);
let num_heads = 8;
let ff_dim = model_dim * 4;
// Attention layers: Q, K, V, Output projections
let attention_params = (model_dim * model_dim) * 4 * 2; // encoder + decoder
// Feed-forward layers
let ff_params = (model_dim * ff_dim + ff_dim * model_dim) * 2; // encoder + decoder
// Layer norm parameters
let norm_params = model_dim * 2 * 5; // 5 layer norms total
let total_params = attention_params + ff_params + norm_params;
total_params * 4 // 4 bytes per f32 parameter
}
fn set_quality(&mut self, quality: f32) -> Result<()> {
self.config.quality = quality.clamp(0.0, 1.0);
Ok(())
}
}
impl TransformerModel {
fn features_to_vector(&self, input: &NeuralInputFeatures) -> Vec<f32> {
let mut vec = Vec::with_capacity(self.config.input_dim);
// Position features (3D coordinates)
vec.push(input.position.x);
vec.push(input.position.y);
vec.push(input.position.z);
// Listener orientation (quaternion)
vec.extend_from_slice(&input.listener_orientation);
// Audio features
vec.extend_from_slice(&input.audio_features);
// Room features
vec.extend_from_slice(&input.room_features);
// HRTF features (if available)
if let Some(ref hrtf_features) = input.hrtf_features {
vec.extend_from_slice(hrtf_features);
}
// Temporal context
vec.extend_from_slice(&input.temporal_context);
// User features (if available)
if let Some(ref user_features) = input.user_features {
vec.extend_from_slice(user_features);
}
// Pad or truncate to match input_dim
vec.resize(self.config.input_dim, 0.0);
vec
}
fn tensor_to_binaural_audio(&self, output_data: &[f32]) -> Vec<Vec<f32>> {
let samples_per_channel = self.config.buffer_size;
let mut binaural_audio =
vec![Vec::with_capacity(samples_per_channel); self.config.output_channels];
for (i, &sample) in output_data.iter().enumerate() {
let channel = i % self.config.output_channels;
if binaural_audio[channel].len() < samples_per_channel {
binaural_audio[channel].push(sample.tanh()); // Apply tanh to keep samples in [-1, 1]
}
}
binaural_audio
}
fn estimate_confidence(&self, output_data: &[f32]) -> f32 {
// Confidence estimation based on output signal characteristics
if output_data.is_empty() {
return 0.0;
}
// Calculate signal properties
let mean = output_data.iter().sum::<f32>() / output_data.len() as f32;
let variance =
output_data.iter().map(|x| (x - mean).powi(2)).sum::<f32>() / output_data.len() as f32;
let std_dev = variance.sqrt();
// Calculate signal-to-noise ratio estimate
let signal_power =
output_data.iter().map(|x| x.powi(2)).sum::<f32>() / output_data.len() as f32;
let noise_estimate = std_dev.min(0.1); // Cap noise estimate
let snr = if noise_estimate > 0.0 {
(signal_power / noise_estimate.powi(2)).log10() * 10.0
} else {
30.0 // High SNR if no noise
};
// Calculate dynamic range
let max_val = output_data
.iter()
.fold(f32::NEG_INFINITY, |a, &b| a.max(b.abs()));
let dynamic_range = if max_val > 0.0 { max_val } else { 0.1 };
// Combine metrics for confidence score
let snr_score = (snr / 30.0).clamp(0.0, 1.0); // Normalize SNR (30dB = 1.0)
let dynamic_score = dynamic_range.clamp(0.0, 1.0);
let stability_score = (1.0 - (std_dev / (max_val + 1e-6))).clamp(0.0, 1.0);
// Weighted combination
(0.4 * snr_score + 0.3 * dynamic_score + 0.3 * stability_score).clamp(0.0, 1.0)
}
fn encoder_forward(&self, input: &Tensor) -> Result<Tensor> {
// Simplified encoder forward pass
// In a full implementation, this would include:
// 1. Multi-head self-attention
// 2. Residual connection and layer norm
// 3. Feed-forward network
// 4. Another residual connection and layer norm
// For now, apply a simple linear transformation
let batch_size = input
.dim(0)
.map_err(|e| Error::LegacyProcessing(format!("Failed to get batch dimension: {e}")))?;
let seq_len = input.dim(1).map_err(|e| {
Error::LegacyProcessing(format!("Failed to get sequence dimension: {e}"))
})?;
let model_dim = input
.dim(2)
.map_err(|e| Error::LegacyProcessing(format!("Failed to get model dimension: {e}")))?;
// Apply ReLU activation and return (placeholder for full attention mechanism)
let output = input
.relu()
.map_err(|e| Error::LegacyProcessing(format!("ReLU activation failed: {e}")))?;
Ok(output)
}
fn decoder_forward(&self, encoder_output: &Tensor, decoder_input: &Tensor) -> Result<Tensor> {
// Simplified decoder forward pass
// In a full implementation, this would include:
// 1. Masked multi-head self-attention
// 2. Residual connection and layer norm
// 3. Multi-head cross-attention with encoder output
// 4. Residual connection and layer norm
// 5. Feed-forward network
// 6. Final residual connection and layer norm
// For now, combine encoder and decoder inputs with a simple operation
let combined = decoder_input.add(encoder_output).map_err(|e| {
Error::LegacyProcessing(format!("Failed to combine encoder and decoder: {e}"))
})?;
let output = combined
.relu()
.map_err(|e| Error::LegacyProcessing(format!("ReLU activation failed: {e}")))?;
Ok(output)
}
}
impl AdaptiveQualityController {
/// Create a new adaptive quality controller
pub fn new(target_latency_ms: f32) -> Self {
Self {
target_latency_ms,
current_quality: 0.8,
quality_history: Vec::new(),
latency_history: Vec::new(),
adaptation_rate: 0.1,
}
}
/// Update the controller with new latency measurement
pub fn update(&mut self, latency_ms: f32) {
self.latency_history.push(latency_ms);
if self.latency_history.len() > 10 {
self.latency_history.remove(0);
}
// Calculate average latency over recent frames
let avg_latency =
self.latency_history.iter().sum::<f32>() / self.latency_history.len() as f32;
// Adjust quality based on latency performance
if avg_latency > self.target_latency_ms * 1.2 {
// Latency too high, reduce quality
self.current_quality = (self.current_quality - self.adaptation_rate).max(0.1);
} else if avg_latency < self.target_latency_ms * 0.8 {
// Latency good, can increase quality
self.current_quality = (self.current_quality + self.adaptation_rate * 0.5).min(1.0);
}
self.quality_history.push(self.current_quality);
if self.quality_history.len() > 10 {
self.quality_history.remove(0);
}
}
/// Get the current quality level
pub fn get_quality(&self) -> f32 {
self.current_quality
}
}
/// Training results from neural model training
#[derive(Debug, Clone)]
pub struct NeuralTrainingResults {
/// Training loss per epoch
pub training_loss: Vec<f32>,
/// Validation loss per epoch
pub validation_loss: Vec<f32>,
/// Final training accuracy
pub final_accuracy: f32,
/// Training duration in seconds
pub training_duration_secs: f32,
/// Number of epochs completed
pub epochs_completed: usize,
/// Whether early stopping was triggered
pub early_stopped: bool,
}
/// Neural model trainer
pub struct NeuralTrainer {
config: TrainingConfig,
}
impl NeuralTrainer {
/// Create a new neural trainer
pub fn new(config: TrainingConfig) -> Self {
Self { config }
}
/// Train a neural model
pub fn train(
&mut self,
model: &mut dyn NeuralModel,
training_data: &[(NeuralInputFeatures, Vec<Vec<f32>>)],
) -> Result<NeuralTrainingResults> {
let start_time = std::time::Instant::now();
let mut training_loss_history = Vec::new();
let mut validation_loss_history = Vec::new();
let mut best_validation_loss = f32::INFINITY;
let mut patience_counter = 0;
let mut early_stopped = false;
// Split data into training and validation sets
let split_index =
(training_data.len() as f32 * (1.0 - self.config.validation_split)) as usize;
let (train_data, val_data) = training_data.split_at(split_index);
println!(
"Starting neural model training with {} training samples, {} validation samples",
train_data.len(),
val_data.len()
);
for epoch in 0..self.config.epochs {
let epoch_start = std::time::Instant::now();
// Training phase
let train_loss = self.train_epoch(model, train_data)?;
training_loss_history.push(train_loss);
// Validation phase
let val_loss = self.validate_epoch(model, val_data)?;
validation_loss_history.push(val_loss);
println!(
"Epoch {}/{}: train_loss={:.6}, val_loss={:.6}, time={:.2}s",
epoch + 1,
self.config.epochs,
train_loss,
val_loss,
epoch_start.elapsed().as_secs_f32()
);
// Early stopping check
if val_loss < best_validation_loss {
best_validation_loss = val_loss;
patience_counter = 0;
println!("New best validation loss: {val_loss:.6}");
} else {
patience_counter += 1;
if patience_counter >= self.config.early_stopping_patience {
println!(
"Early stopping triggered after {patience_counter} epochs of no improvement"
);
early_stopped = true;
break;
}
}
// Learning rate scheduling (simple decay)
if (epoch + 1) % 10 == 0 {
// Would implement learning rate decay here
println!("Learning rate decay would be applied here");
}
}
let training_duration = start_time.elapsed().as_secs_f32();
let epochs_completed = training_loss_history.len();
// Calculate final accuracy based on final validation loss
let final_accuracy = if !validation_loss_history.is_empty() {
let final_val_loss = validation_loss_history[validation_loss_history.len() - 1];
// Convert loss to accuracy estimate (simple heuristic)
(1.0 - final_val_loss.min(1.0)).max(0.0)
} else {
0.0
};
println!(
"Training completed: {epochs_completed} epochs, {training_duration:.2}s total, final accuracy: {final_accuracy:.3}"
);
Ok(NeuralTrainingResults {
training_loss: training_loss_history,
validation_loss: validation_loss_history,
final_accuracy,
training_duration_secs: training_duration,
epochs_completed,
early_stopped,
})
}
fn train_epoch(
&self,
model: &mut dyn NeuralModel,
train_data: &[(NeuralInputFeatures, Vec<Vec<f32>>)],
) -> Result<f32> {
let mut total_loss = 0.0;
let mut batch_count = 0;
// Process in batches
for batch_start in (0..train_data.len()).step_by(self.config.batch_size) {
let batch_end = (batch_start + self.config.batch_size).min(train_data.len());
let batch = &train_data[batch_start..batch_end];
let batch_loss = self.train_batch(model, batch)?;
total_loss += batch_loss;
batch_count += 1;
}
Ok(total_loss / batch_count as f32)
}
fn validate_epoch(
&self,
model: &mut dyn NeuralModel,
val_data: &[(NeuralInputFeatures, Vec<Vec<f32>>)],
) -> Result<f32> {
let mut total_loss = 0.0;
let mut batch_count = 0;
// Process validation data in batches
for batch_start in (0..val_data.len()).step_by(self.config.batch_size) {
let batch_end = (batch_start + self.config.batch_size).min(val_data.len());
let batch = &val_data[batch_start..batch_end];
let batch_loss = self.validate_batch(model, batch)?;
total_loss += batch_loss;
batch_count += 1;
}
Ok(total_loss / batch_count as f32)
}
fn train_batch(
&self,
model: &mut dyn NeuralModel,
batch: &[(NeuralInputFeatures, Vec<Vec<f32>>)],
) -> Result<f32> {
let mut batch_loss = 0.0;
for (input, target) in batch {
// Forward pass
let output = model.forward(input)?;
// Compute loss
let loss = self.compute_loss(&output.binaural_audio, target)?;
batch_loss += loss;
// Apply data augmentation if enabled
if self.config.augmentation.noise_injection {
// Would apply noise injection here
}
// Backward pass and parameter updates would be implemented here
// This requires implementing automatic differentiation or using a framework
// For now, we simulate the training process
}
Ok(batch_loss / batch.len() as f32)
}
fn validate_batch(
&self,
model: &mut dyn NeuralModel,
batch: &[(NeuralInputFeatures, Vec<Vec<f32>>)],
) -> Result<f32> {
let mut batch_loss = 0.0;
for (input, target) in batch {
// Forward pass only (no gradient computation)
let output = model.forward(input)?;
// Compute loss
let loss = self.compute_loss(&output.binaural_audio, target)?;
batch_loss += loss;
}
Ok(batch_loss / batch.len() as f32)
}
fn compute_loss(&self, predicted: &[Vec<f32>], target: &[Vec<f32>]) -> Result<f32> {
if predicted.len() != target.len() {
return Err(Error::LegacyProcessing(
"Predicted and target channel counts don't match".to_string(),
));
}
let mut total_loss = 0.0;
let mut sample_count = 0;
match self.config.loss_function {
LossFunction::MSE => {
for (pred_channel, target_channel) in predicted.iter().zip(target.iter()) {
let min_len = pred_channel.len().min(target_channel.len());
for i in 0..min_len {
let diff = pred_channel[i] - target_channel[i];
total_loss += diff * diff;
sample_count += 1;
}
}
}
LossFunction::MAE => {
for (pred_channel, target_channel) in predicted.iter().zip(target.iter()) {
let min_len = pred_channel.len().min(target_channel.len());
for i in 0..min_len {
let diff = (pred_channel[i] - target_channel[i]).abs();
total_loss += diff;
sample_count += 1;
}
}
}
LossFunction::SpectralLoss => {
// Simplified spectral loss - would implement FFT-based comparison
for (pred_channel, target_channel) in predicted.iter().zip(target.iter()) {
let min_len = pred_channel.len().min(target_channel.len());
for i in 0..min_len {
let diff = pred_channel[i] - target_channel[i];
total_loss += diff * diff; // Simplified spectral approximation
sample_count += 1;
}
}
total_loss *= 1.2; // Weight spectral loss slightly higher
}
LossFunction::PerceptualLoss => {
// Simplified perceptual loss based on psychoacoustic principles
for (pred_channel, target_channel) in predicted.iter().zip(target.iter()) {
let min_len = pred_channel.len().min(target_channel.len());
for i in 0..min_len {
let diff = pred_channel[i] - target_channel[i];
// Apply perceptual weighting (simplified)
let perceptual_weight = 1.0 + 0.5 * (i as f32 / min_len as f32);
total_loss += diff * diff * perceptual_weight;
sample_count += 1;
}
}
}
LossFunction::MultiScaleSpectralLoss => {
// Multi-scale analysis at different time scales
for (pred_channel, target_channel) in predicted.iter().zip(target.iter()) {
let min_len = pred_channel.len().min(target_channel.len());
// Multiple scales: full, half, quarter
for scale in [1, 2, 4] {
for i in (0..min_len).step_by(scale) {
let diff = pred_channel[i] - target_channel[i];
total_loss += diff * diff / (scale as f32);
sample_count += 1;
}
}
}
}
LossFunction::Combined => {
// Combination of MSE and spectral loss
let mse_loss = self.compute_mse_loss(predicted, target)?;
let spectral_loss = self.compute_spectral_loss(predicted, target)?;
total_loss = 0.7 * mse_loss + 0.3 * spectral_loss;
sample_count = 1; // Already normalized
}
}
if sample_count > 0 {
Ok(total_loss / sample_count as f32)
} else {
Ok(0.0)
}
}
fn compute_mse_loss(&self, predicted: &[Vec<f32>], target: &[Vec<f32>]) -> Result<f32> {
let mut total_loss = 0.0;
let mut sample_count = 0;
for (pred_channel, target_channel) in predicted.iter().zip(target.iter()) {
let min_len = pred_channel.len().min(target_channel.len());
for i in 0..min_len {
let diff = pred_channel[i] - target_channel[i];
total_loss += diff * diff;
sample_count += 1;
}
}
Ok(if sample_count > 0 {
total_loss / sample_count as f32
} else {
0.0
})
}
fn compute_spectral_loss(&self, predicted: &[Vec<f32>], target: &[Vec<f32>]) -> Result<f32> {
// Simplified spectral loss computation
// In a full implementation, this would use FFT to compare frequency domain representations
let mut total_loss = 0.0;
let mut sample_count = 0;
for (pred_channel, target_channel) in predicted.iter().zip(target.iter()) {
let min_len = pred_channel.len().min(target_channel.len());
// Simple approximation: compare signal energy at different scales
for window_size in [16, 32, 64, 128] {
for start in (0..min_len).step_by(window_size / 2) {
let end = (start + window_size).min(min_len);
if end > start {
let pred_energy: f32 = pred_channel[start..end].iter().map(|x| x * x).sum();
let target_energy: f32 =
target_channel[start..end].iter().map(|x| x * x).sum();
let diff = pred_energy - target_energy;
total_loss += diff * diff;
sample_count += 1;
}
}
}
}
Ok(if sample_count > 0 {
total_loss / sample_count as f32
} else {
0.0
})
}
}
/// Builder for neural spatial processor configuration
pub struct NeuralSpatialConfigBuilder {
config: NeuralSpatialConfig,
}
impl NeuralSpatialConfigBuilder {
/// Create a new configuration builder
pub fn new() -> Self {
Self {
config: NeuralSpatialConfig::default(),
}
}
/// Set the neural model type
pub fn model_type(mut self, model_type: NeuralModelType) -> Self {
self.config.model_type = model_type;
self
}
/// Set the hidden layer dimensions
pub fn hidden_dims(mut self, dims: Vec<usize>) -> Self {
self.config.hidden_dims = dims;
self
}
/// Set the input dimension
pub fn input_dim(mut self, dim: usize) -> Self {
self.config.input_dim = dim;
self
}
/// Set the number of output channels
pub fn output_channels(mut self, channels: usize) -> Self {
self.config.output_channels = channels;
self
}
/// Set the audio sample rate
pub fn sample_rate(mut self, rate: u32) -> Self {
self.config.sample_rate = rate;
self
}
/// Set the audio buffer size
pub fn buffer_size(mut self, size: usize) -> Self {
self.config.buffer_size = size;
self
}
/// Enable or disable GPU usage
pub fn use_gpu(mut self, use_gpu: bool) -> Self {
self.config.use_gpu = use_gpu;
self
}
/// Set the quality level (0.0-1.0)
pub fn quality(mut self, quality: f32) -> Self {
self.config.quality = quality.clamp(0.0, 1.0);
self
}
/// Set the maximum latency in milliseconds
pub fn max_latency_ms(mut self, latency: f32) -> Self {
self.config.realtime_constraints.max_latency_ms = latency;
self
}
/// Set the training configuration
pub fn training_config(mut self, training_config: TrainingConfig) -> Self {
self.config.training_config = Some(training_config);
self
}
/// Build the neural spatial configuration
pub fn build(self) -> NeuralSpatialConfig {
self.config
}
}
impl Default for NeuralSpatialConfigBuilder {
fn default() -> Self {
Self::new()
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_neural_config_creation() {
let config = NeuralSpatialConfig::default();
assert_eq!(config.model_type, NeuralModelType::Feedforward);
assert_eq!(config.output_channels, 2);
assert_eq!(config.sample_rate, 48000);
}
#[test]
fn test_neural_config_builder() {
let config = NeuralSpatialConfigBuilder::new()
.model_type(NeuralModelType::Transformer)
.hidden_dims(vec![256, 128])
.input_dim(64)
.sample_rate(44100)
.quality(0.9)
.build();
assert_eq!(config.model_type, NeuralModelType::Transformer);
assert_eq!(config.hidden_dims, vec![256, 128]);
assert_eq!(config.input_dim, 64);
assert_eq!(config.sample_rate, 44100);
assert_eq!(config.quality, 0.9);
}
#[test]
fn test_neural_processor_creation() {
let config = NeuralSpatialConfig::default();
let processor = NeuralSpatialProcessor::new(config);
assert!(processor.is_ok());
}
#[test]
fn test_adaptive_quality_controller() {
let mut controller = AdaptiveQualityController::new(20.0);
assert_eq!(controller.get_quality(), 0.8);
// Simulate high latency - should reduce quality
for _ in 0..5 {
controller.update(30.0);
}
let quality_after_high_latency = controller.get_quality();
assert!(quality_after_high_latency < 0.8);
// Simulate low latency - should increase quality
for _ in 0..15 {
controller.update(10.0);
}
let quality_after_low_latency = controller.get_quality();
// Quality should improve from the degraded state, but may not go above 0.5 due to conservative adaptation
assert!(quality_after_low_latency > quality_after_high_latency);
}
#[test]
fn test_feedforward_model_creation() {
let config = NeuralSpatialConfig::default();
let device = Device::Cpu;
let model = FeedforwardModel::new(config, device);
assert!(model.is_ok());
}
#[test]
fn test_neural_input_features() {
let features = NeuralInputFeatures {
position: Position3D::new(1.0, 2.0, 3.0),
listener_orientation: [1.0, 0.0, 0.0, 0.0],
audio_features: vec![0.5; 64],
room_features: vec![0.3; 32],
hrtf_features: Some(vec![0.7; 16]),
temporal_context: vec![0.1; 8],
user_features: Some(vec![0.9; 4]),
};
assert_eq!(features.position.x, 1.0);
assert_eq!(features.audio_features.len(), 64);
assert!(features.hrtf_features.is_some());
}
#[test]
fn test_neural_spatial_output() {
let output = NeuralSpatialOutput {
binaural_audio: vec![vec![0.1; 1024], vec![0.2; 1024]],
confidence: 0.9,
latency_ms: 15.0,
quality_score: 0.8,
metadata: HashMap::new(),
};
assert_eq!(output.binaural_audio.len(), 2);
assert_eq!(output.binaural_audio[0].len(), 1024);
assert_eq!(output.confidence, 0.9);
assert_eq!(output.latency_ms, 15.0);
}
#[test]
fn test_performance_metrics() {
let mut metrics = NeuralPerformanceMetrics::default();
metrics.frames_processed = 100;
metrics.avg_processing_time_ms = 12.5;
metrics.peak_processing_time_ms = 25.0;
assert_eq!(metrics.frames_processed, 100);
assert_eq!(metrics.avg_processing_time_ms, 12.5);
assert_eq!(metrics.peak_processing_time_ms, 25.0);
}
}