use super::joint_embedding_spaces_aligner::JointEmbeddingSpace;
use super::joint_embedding_spaces_types::{
AudioAugmentation, ContrastiveOptimizer, ContrastivePairs, CurriculumLearning,
DataAugmentation, DifficultySchedule, ImageAugmentation, JointEmbeddingConfig,
LearningRateSchedule, PacingFunction, TextAugmentation,
};
use crate::cross_modal_embeddings::{
AudioData, ImageData, Modality, ModalityData, MultiModalContent, VideoData,
};
use crate::Vector;
use anyhow::{anyhow, Result};
use std::collections::HashMap;
pub struct CLIPAligner {
pub(crate) joint_space: JointEmbeddingSpace,
pub(crate) optimizer: ContrastiveOptimizer,
pub(crate) data_augmentation: DataAugmentation,
pub(crate) curriculum: CurriculumLearning,
}
impl CLIPAligner {
pub fn new(config: JointEmbeddingConfig) -> Self {
let joint_space = JointEmbeddingSpace::new(config.clone());
let optimizer = ContrastiveOptimizer::new(config.learning_rate, 0.9, config.weight_decay);
let data_augmentation = DataAugmentation::default();
let curriculum = CurriculumLearning::new();
Self {
joint_space,
optimizer,
data_augmentation,
curriculum,
}
}
pub fn train_alignment(
&mut self,
training_data: &[(MultiModalContent, MultiModalContent)],
epochs: usize,
) -> Result<Vec<f32>> {
let mut epoch_losses = Vec::new();
for epoch in 0..epochs {
let mut epoch_loss = 0.0;
let mut batch_count = 0;
for batch in training_data.chunks(self.joint_space.config.batch_size) {
let (positive_pairs, negative_pairs) = self.create_contrastive_pairs(batch)?;
let augmented_positive = self.augment_pairs(&positive_pairs)?;
let augmented_negative = self.augment_pairs(&negative_pairs)?;
let batch_loss = self
.joint_space
.contrastive_align(&augmented_positive, &augmented_negative)?;
epoch_loss += batch_loss;
batch_count += 1;
if self.curriculum.enabled {
self.curriculum.update_difficulty(batch_loss);
}
}
let avg_epoch_loss = epoch_loss / batch_count as f32;
epoch_losses.push(avg_epoch_loss);
self.optimizer.step_schedule();
tracing::info!(
"Epoch {}/{}: Average Loss = {:.4}, Temperature = {:.4}",
epoch + 1,
epochs,
avg_epoch_loss,
self.joint_space
.temperature_scheduler
.get_current_temperature()
);
}
Ok(epoch_losses)
}
fn create_contrastive_pairs(
&self,
batch: &[(MultiModalContent, MultiModalContent)],
) -> Result<ContrastivePairs> {
let mut positive_pairs = Vec::new();
let mut negative_pairs = Vec::new();
for (content1, content2) in batch {
for (mod1, data1) in &content1.modalities {
for (mod2, data2) in &content2.modalities {
if let (Ok(emb1), Ok(emb2)) = (
self.extract_embedding(*mod1, data1),
self.extract_embedding(*mod2, data2),
) {
positive_pairs.push((*mod1, emb1, *mod2, emb2));
}
}
}
}
let batch_size = batch.len();
for i in 0..batch_size {
for j in 0..batch_size {
if i != j {
let (content1, _) = &batch[i];
let (_, content2) = &batch[j];
for (mod1, data1) in &content1.modalities {
for (mod2, data2) in &content2.modalities {
if let (Ok(emb1), Ok(emb2)) = (
self.extract_embedding(*mod1, data1),
self.extract_embedding(*mod2, data2),
) {
negative_pairs.push((*mod1, emb1, *mod2, emb2));
}
}
}
}
}
}
let max_negatives = positive_pairs.len() * self.joint_space.config.negative_samples;
negative_pairs.truncate(max_negatives);
Ok((positive_pairs, negative_pairs))
}
fn extract_embedding(&self, modality: Modality, data: &ModalityData) -> Result<Vector> {
match (modality, data) {
(Modality::Text, ModalityData::Text(text)) => {
let words: Vec<&str> = text.split_whitespace().collect();
let embedding = self.create_text_embedding(&words);
Ok(embedding)
}
(Modality::Image, ModalityData::Image(image)) => {
let embedding = self.create_image_embedding(image);
Ok(embedding)
}
(Modality::Audio, ModalityData::Audio(audio)) => {
let embedding = self.create_audio_embedding(audio);
Ok(embedding)
}
(Modality::Video, ModalityData::Video(video)) => {
let embedding = self.create_video_embedding(video);
Ok(embedding)
}
(Modality::Numeric, ModalityData::Numeric(values)) => Ok(Vector::new(values.clone())),
_ => Err(anyhow!("Modality-data type mismatch")),
}
}
pub(crate) fn create_text_embedding(&self, words: &[&str]) -> Vector {
let mut embedding = vec![0.0; 768];
for (i, word) in words.iter().enumerate().take(100) {
let hash = self.simple_hash(word) as usize;
let idx = hash % embedding.len();
embedding[idx] += 1.0 / (i + 1) as f32;
}
let norm: f32 = embedding.iter().map(|x| x * x).sum::<f32>().sqrt();
if norm > 0.0 {
for value in &mut embedding {
*value /= norm;
}
}
Vector::new(embedding)
}
fn create_image_embedding(&self, image: &ImageData) -> Vector {
let mut embedding = vec![0.0; 2048];
let color_features = self.extract_color_features(image);
for (i, &feature) in color_features.iter().enumerate().take(256) {
if i < embedding.len() {
embedding[i] = feature;
}
}
let texture_features = self.extract_texture_features(image);
for (i, &feature) in texture_features.iter().enumerate().take(256) {
if i + 256 < embedding.len() {
embedding[i + 256] = feature;
}
}
Vector::new(embedding)
}
fn create_audio_embedding(&self, audio: &AudioData) -> Vector {
let mut embedding = vec![0.0; 1024];
if let Some(ref features) = audio.features {
for (i, &feature) in features.iter().enumerate().take(embedding.len()) {
embedding[i] = feature;
}
} else {
let spectral_features = self.extract_spectral_features(audio);
for (i, &feature) in spectral_features.iter().enumerate().take(embedding.len()) {
embedding[i] = feature;
}
}
Vector::new(embedding)
}
fn create_video_embedding(&self, video: &VideoData) -> Vector {
let mut embedding = vec![0.0; 1536];
if !video.frames.is_empty() {
let frame_embedding = self.create_image_embedding(&video.frames[0]);
let frame_values = frame_embedding.as_f32();
for (i, &value) in frame_values.iter().enumerate().take(1024) {
if i < embedding.len() {
embedding[i] = value;
}
}
}
if let Some(ref audio) = video.audio {
let audio_embedding = self.create_audio_embedding(audio);
let audio_values = audio_embedding.as_f32();
for (i, &value) in audio_values.iter().enumerate().take(512) {
if i + 1024 < embedding.len() {
embedding[i + 1024] = value;
}
}
}
Vector::new(embedding)
}
fn simple_hash(&self, text: &str) -> u64 {
let mut hash = 5381u64;
for byte in text.bytes() {
hash = hash.wrapping_mul(33).wrapping_add(byte as u64);
}
hash
}
fn extract_color_features(&self, image: &ImageData) -> Vec<f32> {
let mut histogram = vec![0.0; 256];
match image.format {
crate::cross_modal_embeddings::ImageFormat::RGB => {
for chunk in image.data.chunks(3) {
if chunk.len() == 3 {
let intensity = (chunk[0] as f32 + chunk[1] as f32 + chunk[2] as f32) / 3.0;
let bin = (intensity as usize).min(255);
histogram[bin] += 1.0;
}
}
}
_ => {
for &pixel in &image.data {
let bin = (pixel as usize).min(255);
histogram[bin] += 1.0;
}
}
}
let total: f32 = histogram.iter().sum();
if total > 0.0 {
for value in &mut histogram {
*value /= total;
}
}
histogram
}
fn extract_texture_features(&self, image: &ImageData) -> Vec<f32> {
let mut features = vec![0.0; 256];
let width = image.width as usize;
let height = image.height as usize;
if width > 2 && height > 2 {
for y in 1..height - 1 {
for x in 1..width - 1 {
let center_idx = y * width + x;
if center_idx < image.data.len() {
let center = image.data[center_idx];
let mut pattern = 0u8;
let neighbors = [
(-1i32, -1i32),
(0, -1),
(1, -1),
(-1, 0),
(1, 0),
(-1, 1),
(0, 1),
(1, 1),
];
for (bit, (dx, dy)) in neighbors.iter().enumerate() {
let nx = (x as i32 + dx) as usize;
let ny = (y as i32 + dy) as usize;
let neighbor_idx = ny * width + nx;
if neighbor_idx < image.data.len() && image.data[neighbor_idx] > center
{
pattern |= 1 << bit;
}
}
features[pattern as usize] += 1.0;
}
}
}
}
let total: f32 = features.iter().sum();
if total > 0.0 {
for value in &mut features {
*value /= total;
}
}
features
}
fn extract_spectral_features(&self, audio: &AudioData) -> Vec<f32> {
let mut features = vec![0.0; 128];
if !audio.samples.is_empty() {
let chunk_size = audio.samples.len() / features.len();
for (i, feature) in features.iter_mut().enumerate() {
let start = i * chunk_size;
let end = ((i + 1) * chunk_size).min(audio.samples.len());
if start < end {
let chunk = &audio.samples[start..end];
let energy: f32 = chunk.iter().map(|x| x * x).sum();
*feature = energy.sqrt() / (chunk.len() as f32).sqrt();
}
}
}
features
}
fn augment_pairs(
&self,
pairs: &[(Modality, Vector, Modality, Vector)],
) -> Result<Vec<(Modality, Vector, Modality, Vector)>> {
let mut augmented = Vec::new();
for (mod1, emb1, mod2, emb2) in pairs {
let aug_emb1 = self.add_noise(emb1, 0.01)?;
let aug_emb2 = self.add_noise(emb2, 0.01)?;
augmented.push((*mod1, aug_emb1, *mod2, aug_emb2));
}
Ok(augmented)
}
fn add_noise(&self, embedding: &Vector, noise_std: f32) -> Result<Vector> {
let values = embedding.as_f32();
let mut noisy_values = Vec::with_capacity(values.len());
for (i, &value) in values.iter().enumerate() {
let noise = ((i as f32 * 0.1234).sin() * noise_std).clamp(-0.1, 0.1);
noisy_values.push(value + noise);
}
Ok(Vector::new(noisy_values))
}
}
impl ContrastiveOptimizer {
pub fn new(learning_rate: f32, momentum: f32, weight_decay: f32) -> Self {
Self {
learning_rate,
momentum,
weight_decay,
gradient_history: HashMap::new(),
adaptive_lr: true,
lr_schedule: LearningRateSchedule::CosineAnnealing {
min_lr: learning_rate * 0.01,
max_epochs: 100,
},
}
}
pub fn step_schedule(&mut self) {
match self.lr_schedule {
LearningRateSchedule::StepDecay {
step_size: _,
gamma,
} => {
self.learning_rate *= gamma;
}
LearningRateSchedule::ExponentialDecay { gamma } => {
self.learning_rate *= gamma;
}
LearningRateSchedule::CosineAnnealing {
min_lr,
max_epochs: _,
} => {
let progress = 0.01;
let lr_range = self.learning_rate - min_lr;
self.learning_rate =
min_lr + lr_range * (1.0 + (std::f32::consts::PI * progress).cos()) / 2.0;
}
LearningRateSchedule::Constant => {}
}
}
}
impl Default for DataAugmentation {
fn default() -> Self {
Self {
text_augmentations: vec![
TextAugmentation::RandomWordDropout(0.1),
TextAugmentation::SynonymReplacement(0.1),
],
image_augmentations: vec![
ImageAugmentation::RandomFlip {
horizontal: true,
vertical: false,
},
ImageAugmentation::ColorJitter {
brightness: 0.2,
contrast: 0.2,
saturation: 0.2,
},
],
audio_augmentations: vec![
AudioAugmentation::AddNoise { snr_db: 20.0 },
AudioAugmentation::TimeStretch { factor: 1.1 },
],
cross_modal_mixup: false,
augmentation_probability: 0.5,
}
}
}
impl Default for CurriculumLearning {
fn default() -> Self {
Self::new()
}
}
impl CurriculumLearning {
pub fn new() -> Self {
Self {
enabled: false,
current_difficulty: 0.0,
difficulty_schedule: DifficultySchedule::Linear {
start: 0.0,
end: 1.0,
epochs: 50,
},
pacing_function: PacingFunction::Root,
competence_threshold: 0.8,
}
}
pub fn update_difficulty(&mut self, loss: f32) {
if self.enabled {
if loss < self.competence_threshold {
self.current_difficulty = (self.current_difficulty + 0.01).min(1.0);
} else {
self.current_difficulty = (self.current_difficulty - 0.005).max(0.0);
}
}
}
}
pub fn zero_shot_retrieval(
space: &JointEmbeddingSpace,
query_modality: Modality,
query_embedding: &Vector,
target_modality: Modality,
target_embeddings: &[Vector],
top_k: usize,
) -> Result<Vec<(usize, f32)>> {
space.cross_modal_search(
query_modality,
query_embedding,
target_modality,
target_embeddings,
top_k,
)
}