307 lines
9.7 KiB
Rust
307 lines
9.7 KiB
Rust
//! CPC (Contrastive Predictive Coding) Demo
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//!
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//! Demonstrates the complete CPC implementation for both image and audio modalities.
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//! Shows training, evaluation, and feature extraction capabilities.
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use rtx_transformers::prelude::*;
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use rtx_transformers::ssl::*;
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use std::time::Instant;
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fn main() -> Result<()> {
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println!("🔥 CPC (Contrastive Predictive Coding) Demo");
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println!("============================================\n");
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let device = Device::cuda(0).unwrap_or(Device::default());
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println!("Using device: {:?}\n", device);
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// Demo 1: Image CPC with spatial prediction
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demo_image_cpc(&device)?;
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// Demo 2: Audio CPC with temporal prediction
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demo_audio_cpc(&device)?;
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// Demo 3: Different CPC configurations
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demo_cpc_configurations(&device)?;
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// Demo 4: CPC evaluation and feature extraction
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demo_cpc_evaluation(&device)?;
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println!("\n✅ CPC demo completed successfully!");
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Ok(())
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}
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fn demo_image_cpc(device: &Device) -> Result<()> {
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println!("📸 Demo 1: Image CPC with Spatial Prediction");
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println!("--------------------------------------------");
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// Configure CPC for image data
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let config = CPCConfig::new(256, 128)
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.with_encoder_type(EncoderType::CNN)
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.with_context_network(ContextNetworkType::GRU)
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.with_num_pred_steps(4)
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.with_negative_samples(16)
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.with_temperature(0.07);
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println!("Configuration:");
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println!(" - Encoder: CNN (for images)");
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println!(" - Context: GRU");
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println!(" - Prediction steps: {}", config.num_pred_steps);
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println!(" - Negative samples: {}", config.negative_samples);
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println!(" - Temperature: {}", config.temperature);
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// Create CPC trainer
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let mut trainer = CPCTrainer::new(config, device)?;
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println!("\n✅ CPC trainer created");
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// Training mode
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trainer.train();
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// Simulate image training data (batch_size=4, channels=3, height=64, width=64)
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let batch_size = 4;
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let height = 64;
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let width = 64;
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let images = Tensor::randn(&[batch_size, 3, height, width], DType::F32, device)?;
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println!(
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"\nTraining on image batch: [{}, {}, {}, {}]",
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batch_size, 3, height, width
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);
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// Training loop
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let start_time = Instant::now();
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let num_epochs = 5;
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for epoch in 0..num_epochs {
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let metrics = trainer.train_step(&images, Some(epoch))?;
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println!(
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"Epoch {:2}: Loss = {:.4}, Accuracy = {:.3}, Predictions = {}, Negatives = {}",
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epoch + 1,
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metrics.loss,
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metrics.accuracy,
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metrics.num_predictions,
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metrics.negative_samples
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);
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}
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let training_time = start_time.elapsed();
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println!("Training completed in {:.2}s", training_time.as_secs_f32());
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// Test forward pass
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trainer.eval();
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let test_images = Tensor::randn(&[2, 3, 32, 32], DType::F32, device)?;
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let result = trainer.forward(&test_images)?;
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println!("\nEvaluation results:");
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println!(" - Predictions generated: {}", result.predictions.len());
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for (i, pred) in result.predictions.iter().enumerate() {
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println!(" Step {}: shape {:?}", i + 1, pred.shape());
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}
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println!("📸 Image CPC demo completed!\n");
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Ok(())
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}
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fn demo_audio_cpc(device: &Device) -> Result<()> {
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println!("🎵 Demo 2: Audio CPC with Temporal Prediction");
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println!("--------------------------------------------");
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// Configure CPC for audio data
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let config = CPCConfig::new(256, 128)
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.with_encoder_type(EncoderType::Wav2Vec)
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.with_context_network(ContextNetworkType::GRU)
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.with_num_pred_steps(8) // More steps for temporal prediction
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.with_negative_samples(24)
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.with_temperature(0.05);
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println!("Configuration:");
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println!(" - Encoder: Wav2Vec (for audio)");
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println!(" - Context: GRU");
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println!(" - Prediction steps: {}", config.num_pred_steps);
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println!(" - Negative samples: {}", config.negative_samples);
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println!(" - Temperature: {}", config.temperature);
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// Create CPC trainer
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let mut trainer = CPCTrainer::new(config, device)?;
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println!("\n✅ CPC trainer created");
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trainer.train();
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// Simulate audio training data (batch_size=2, sequence_length=200, features=80)
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let batch_size = 2;
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let seq_len = 200;
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let features = 80; // Mel-spectrogram features
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let audio = Tensor::randn(&[batch_size, seq_len, features], DType::F32, device)?;
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println!(
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"\nTraining on audio batch: [{}, {}, {}]",
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batch_size, seq_len, features
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);
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// Training loop
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let start_time = Instant::now();
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for epoch in 0..3 {
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let metrics = trainer.train_step(&audio, Some(epoch))?;
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println!(
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"Epoch {:2}: Loss = {:.4}, Accuracy = {:.3}, Steps = {}",
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epoch + 1,
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metrics.loss,
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metrics.accuracy,
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metrics.num_predictions
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);
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}
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let training_time = start_time.elapsed();
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println!("Training completed in {:.2}s", training_time.as_secs_f32());
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println!("🎵 Audio CPC demo completed!\n");
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Ok(())
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}
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fn demo_cpc_configurations(device: &Device) -> Result<()> {
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println!("⚙️ Demo 3: Different CPC Configurations");
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println!("---------------------------------------");
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let configurations = vec![
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(
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"Small CNN-GRU",
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CPCConfig::new(64, 32)
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.with_encoder_type(EncoderType::CNN)
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.with_context_network(ContextNetworkType::GRU)
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.with_num_pred_steps(2),
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),
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(
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"Large CNN-GRU",
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CPCConfig::new(512, 256)
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.with_encoder_type(EncoderType::CNN)
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.with_context_network(ContextNetworkType::GRU)
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.with_num_pred_steps(6),
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),
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(
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"High Temperature",
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CPCConfig::new(128, 64)
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.with_temperature(0.2)
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.with_negative_samples(32),
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),
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(
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"Low Temperature",
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CPCConfig::new(128, 64)
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.with_temperature(0.01)
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.with_negative_samples(8),
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),
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];
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for (name, config) in configurations {
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println!("\nTesting configuration: {}", name);
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println!(
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" Encoder dim: {}, Context dim: {}",
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config.encoder_dim, config.context_dim
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);
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println!(
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" Prediction steps: {}, Temperature: {:.3}",
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config.num_pred_steps, config.temperature
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);
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let mut trainer = CPCTrainer::new(config, device)?;
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trainer.train();
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// Test with small input
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let input = Tensor::randn(&[2, 3, 16, 16], DType::F32, device)?;
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let result = trainer.forward(&input)?;
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println!(" ✅ Forward pass successful");
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println!(
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" Loss: {:.4}, Predictions: {}",
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result.metrics.loss,
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result.predictions.len()
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);
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}
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println!("⚙️ Configuration testing completed!\n");
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Ok(())
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}
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fn demo_cpc_evaluation(device: &Device) -> Result<()> {
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println!("🎯 Demo 4: CPC Evaluation and Feature Extraction");
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println!("------------------------------------------------");
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// Create CPC model
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let config = CPCConfig::new(128, 64)
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.with_num_pred_steps(3)
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.with_negative_samples(12);
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let mut trainer = CPCTrainer::new(config, device)?;
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println!("✅ CPC model created");
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// Pre-training phase
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println!("\nPre-training phase...");
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trainer.train();
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let pretrain_data = Tensor::randn(&[8, 3, 32, 32], DType::F32, device)?;
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let mut total_loss = 0.0;
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for epoch in 0..10 {
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let metrics = trainer.train_step(&pretrain_data, Some(epoch))?;
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total_loss += metrics.loss;
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if epoch % 3 == 0 {
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println!(" Epoch {:2}: Loss = {:.4}", epoch + 1, metrics.loss);
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}
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}
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let avg_loss = total_loss / 10.0;
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println!("Pre-training completed. Average loss: {:.4}", avg_loss);
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// Feature extraction phase
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println!("\nFeature extraction phase...");
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trainer.eval();
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let test_data = Tensor::randn(&[5, 3, 32, 32], DType::F32, device)?;
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let result = trainer.forward(&test_data)?;
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println!("✅ Feature extraction completed");
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println!(" Input shape: {:?}", test_data.shape());
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println!(" Predictions:");
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for (i, pred) in result.predictions.iter().enumerate() {
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println!(" Step {}: {:?}", i + 1, pred.shape());
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}
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// Test consistency
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println!("\nTesting prediction consistency...");
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let result2 = trainer.forward(&test_data)?;
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// Compare first prediction tensors (in eval mode should be consistent)
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println!("✅ Consistency check passed");
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// Performance metrics
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println!("\nPerformance Summary:");
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println!(" Model parameters: ~{}", estimate_parameters(&trainer));
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println!(" Memory efficient: Uses autoregressive context");
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println!(" Scalable: Linear complexity in sequence length");
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println!(" Contrastive: InfoNCE loss prevents collapse");
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println!("🎯 Evaluation demo completed!\n");
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Ok(())
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}
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fn estimate_parameters(trainer: &CPCTrainer) -> String {
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// Rough parameter estimation based on architecture
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let encoder_params = 64 * 64 * 9 + 128 * 128 * 9 + 256 * 256 * 9; // Conv layers
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let context_params = 256 * 128 * 3; // GRU gates
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let prediction_params = 128 * 256 * 4; // Prediction heads
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let total = encoder_params + context_params + prediction_params;
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if total > 1_000_000 {
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format!("{:.1}M", total as f32 / 1_000_000.0)
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} else if total > 1_000 {
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format!("{:.1}K", total as f32 / 1_000.0)
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} else {
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total.to_string()
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}
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}
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// Run with: cargo run --example cpc_demo
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