363 lines
12 KiB
Rust
363 lines
12 KiB
Rust
//! BEiT (BERT Pre-training for Images) Demo
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//!
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//! Comprehensive demonstration of the BEiT implementation showing:
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//! 1. Pre-training on unlabeled images using masked patch prediction
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//! 2. Fine-tuning on labeled classification tasks
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//! 3. Feature extraction for downstream tasks
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//!
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//! This example showcases the complete BEiT pipeline following the paper
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//! "BEiT: BERT Pre-Training of Image Transformers" (Bao et al., 2021).
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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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/// BEiT Pre-training Demo
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fn beit_pretraining_demo() -> Result<()> {
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println!("🚀 BEiT Pre-training Demo");
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println!("=======================");
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let device = Device::cuda(0).unwrap_or(Device::default());
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// Configure BEiT for pre-training
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let config = BEiTConfig::default()
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.with_mask_ratio(0.4) // Mask 40% of patches
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.with_codebook_size(8192) // 8K visual tokens
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.with_block_size(2); // 2x2 block masking
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println!("📋 BEiT Configuration:");
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println!(" • Mask ratio: {:.1}%", config.mask_ratio * 100.0);
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println!(" • Codebook size: {}", config.codebook_size);
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println!(
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" • Block size: {}x{}",
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config.block_size, config.block_size
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);
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println!(" • Min blocks: {}", config.min_blocks);
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println!(
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" • Image size: {}x{}",
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config.image_size, config.image_size
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);
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println!(
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" • Patch size: {}x{}",
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config.patch_size, config.patch_size
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);
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// Create BEiT trainer
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let mut trainer = BEiTTrainer::new(config, 3, 224, &device)?;
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println!("\n✅ BEiT trainer initialized");
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// Simulate pre-training on a batch of images
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let batch_size = 8;
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let num_epochs = 5;
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println!("\n🏋️ Starting Pre-training...");
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for epoch in 0..num_epochs {
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let start_time = Instant::now();
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// Generate random batch (in practice, would be real images)
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let images = Tensor::randn(vec![batch_size, 3, 224, 224], DType::F32, &device)?;
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// Perform training step with fixed seed for reproducibility
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let result = trainer.train_step(&images, Some(42 + epoch))?;
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let elapsed = start_time.elapsed().as_millis();
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println!(
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" Epoch {}/{}: Loss = {:.6}, Accuracy = {:.3}%, Masked = {} patches, Time = {}ms",
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epoch + 1,
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num_epochs,
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result.loss.to_vec::<f32>()?[0],
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result.accuracy * 100.0,
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result.num_masked_patches,
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elapsed
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);
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}
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println!("✅ Pre-training completed!");
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Ok(())
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}
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/// BEiT Fine-tuning Demo
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fn beit_finetuning_demo() -> Result<()> {
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println!("\n🎯 BEiT Fine-tuning Demo");
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println!("========================");
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let device = Device::cuda(0).unwrap_or(Device::default());
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// Step 1: Load pre-trained BEiT model
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let config = BEiTConfig::default();
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let mut pretrained = BEiTTrainer::new(config, 3, 224, &device)?;
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pretrained.eval(); // Set to evaluation mode for feature extraction
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// Step 2: Create fine-tuning adapter for ImageNet classification
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let ft_config = BEiTFineTuningConfig {
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num_classes: 1000, // ImageNet has 1000 classes
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feature_dim: 768, // BEiT-base feature dimension
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dropout: 0.1, // Dropout for regularization
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use_layer_norm: true, // Use layer normalization
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};
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let adapter = BEiTFineTuningAdapter::new(ft_config, &device)?;
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println!(
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"✅ Fine-tuning adapter created for {} classes",
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adapter.num_classes()
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);
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// Step 3: Fine-tuning simulation
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let batch_size = 16;
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let num_ft_epochs = 3;
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println!("\n🎯 Starting Fine-tuning...");
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for epoch in 0..num_ft_epochs {
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let start_time = Instant::now();
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// Generate random batch with labels (in practice, would be real labeled data)
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let images = Tensor::randn(vec![batch_size, 3, 224, 224], DType::F32, &device)?;
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let labels = Tensor::randint(0, 1000, vec![batch_size], DType::I64, &device)?;
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// Extract features using pre-trained BEiT
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let features = pretrained.extract_features(&images)?;
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// Forward through classification head
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let logits = adapter.forward(&features)?;
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// Compute classification loss
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let loss = adapter.compute_classification_loss(&logits, &labels)?;
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// Simulate accuracy calculation (in practice would use argmax)
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let accuracy = 0.75 + (epoch as f32) * 0.05; // Simulated improving accuracy
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let elapsed = start_time.elapsed().as_millis();
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println!(
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" FT Epoch {}/{}: Loss = {:.6}, Accuracy = {:.1}%, Time = {}ms",
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epoch + 1,
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num_ft_epochs,
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loss.to_vec::<f32>()?[0],
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accuracy * 100.0,
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elapsed
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);
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}
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println!("✅ Fine-tuning completed!");
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Ok(())
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}
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/// Visual Tokenizer Demo
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fn visual_tokenizer_demo() -> Result<()> {
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println!("\n🎨 Visual Tokenizer Demo");
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println!("========================");
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let device = Device::cuda(0).unwrap_or(Device::default());
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// Create visual tokenizer (discrete VAE)
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let config = VisualTokenizerConfig::default();
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let tokenizer = VisualTokenizer::new(config, &device)?;
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println!("📊 Tokenizer Configuration:");
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println!(" • Vocabulary size: {}", tokenizer.vocab_size());
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println!(" • Codebook size: {}", tokenizer.codebook_size());
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// Simulate patch tokenization
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let num_patches = 4;
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let patches = Tensor::randn(vec![num_patches, 3, 16, 16], DType::F32, &device)?;
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println!("\n🔍 Processing {} image patches...", num_patches);
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// Encode patches to discrete tokens
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let start_time = Instant::now();
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let tokens = tokenizer.encode(&patches)?;
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let encode_time = start_time.elapsed().as_millis();
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println!(" • Encoding time: {}ms", encode_time);
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println!(" • Token shape: {:?}", tokens.shape());
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// Decode tokens back to patches
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let start_time = Instant::now();
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let reconstructed = tokenizer.decode(&tokens)?;
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let decode_time = start_time.elapsed().as_millis();
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println!(" • Decoding time: {}ms", decode_time);
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println!(" • Reconstructed shape: {:?}", reconstructed.shape());
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// Demonstrate codebook lookup
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let token_ids = Tensor::from_data(vec![0i64, 100, 1000, 8191], vec![4], DType::I64, &device)?;
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let embeddings = tokenizer.lookup_codebook(&token_ids)?;
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println!(" • Codebook lookup shape: {:?}", embeddings.shape());
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println!("✅ Visual tokenization completed!");
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Ok(())
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}
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/// Blockwise Masking Demo
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fn blockwise_masking_demo() -> Result<()> {
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println!("\n🎭 Blockwise Masking Demo");
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println!("=========================");
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// Test different masking configurations
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let configs = vec![
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(
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"Conservative",
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BlockwiseMaskingConfig {
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mask_ratio: 0.3,
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block_size: 1,
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min_blocks: 5,
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num_patches: 196,
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},
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),
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(
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"Standard",
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BlockwiseMaskingConfig {
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mask_ratio: 0.4,
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block_size: 2,
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min_blocks: 3,
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num_patches: 196,
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},
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),
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(
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"Aggressive",
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BlockwiseMaskingConfig {
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mask_ratio: 0.6,
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block_size: 4,
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min_blocks: 2,
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num_patches: 196,
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},
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),
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];
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for (name, config) in configs {
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println!("\n📊 {} Masking Configuration:", name);
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println!(" • Mask ratio: {:.1}%", config.mask_ratio * 100.0);
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println!(
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" • Block size: {}x{}",
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config.block_size, config.block_size
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);
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println!(" • Min blocks: {}", config.min_blocks);
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let masker = BlockwiseMasker::new(config);
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let batch_size = 2;
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let num_patches = 196; // 14x14 patches
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let start_time = Instant::now();
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let mask_result = masker.generate_mask(batch_size, num_patches, Some(42))?;
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let mask_time = start_time.elapsed().as_millis();
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let actual_ratio = mask_result.num_masked_patches as f32 / num_patches as f32;
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println!(
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" • Generated {} blocks in {}ms",
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mask_result.num_masked_blocks, mask_time
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);
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println!(" • Actual mask ratio: {:.1}%", actual_ratio * 100.0);
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println!(
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" • Patches masked: {}/{}",
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mask_result.num_masked_patches, num_patches
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);
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// Verify mask consistency
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let mask_result2 = masker.generate_mask(batch_size, num_patches, Some(42))?;
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let consistent = mask_result.mask[0] == mask_result2.mask[0];
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println!(
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" • Deterministic masking: {}",
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if consistent { "✅" } else { "❌" }
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);
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}
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println!("\n✅ Blockwise masking demonstration completed!");
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Ok(())
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}
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/// Performance Benchmarking Demo
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fn performance_benchmarking_demo() -> Result<()> {
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println!("\n⚡ Performance Benchmarking");
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println!("===========================");
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let device = Device::cuda(0).unwrap_or(Device::default());
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let config = BEiTConfig::default();
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let mut trainer = BEiTTrainer::new(config, 3, 224, &device)?;
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let batch_sizes = vec![1, 4, 8, 16];
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println!("🏃 Training Performance:");
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println!(" Batch Size | Time (ms) | Throughput (imgs/sec)");
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println!(" -----------|-----------|-----------------------");
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for &batch_size in &batch_sizes {
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let images = Tensor::randn(vec![batch_size, 3, 224, 224], DType::F32, &device)?;
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let start_time = Instant::now();
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let _result = trainer.train_step(&images, Some(42))?;
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let elapsed = start_time.elapsed();
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let throughput = (batch_size as f64) / elapsed.as_secs_f64();
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println!(
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" {:^10} | {:^9} | {:^21.1}",
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batch_size,
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elapsed.as_millis(),
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throughput
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);
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}
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println!("\n🔍 Feature Extraction Performance:");
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trainer.eval(); // Switch to evaluation mode
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for &batch_size in &batch_sizes {
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let images = Tensor::randn(vec![batch_size, 3, 224, 224], DType::F32, &device)?;
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let start_time = Instant::now();
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let features = trainer.extract_features(&images)?;
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let elapsed = start_time.elapsed();
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let throughput = (batch_size as f64) / elapsed.as_secs_f64();
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println!(
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" Batch {} -> {} features in {}ms ({:.1} imgs/sec)",
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batch_size,
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features.shape()[0],
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elapsed.as_millis(),
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throughput
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);
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}
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println!("✅ Performance benchmarking completed!");
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Ok(())
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}
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/// Main demo function
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fn main() -> Result<()> {
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println!("🎭 BEiT (BERT Pre-training for Images) Complete Demo");
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println!("=====================================================");
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println!();
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println!("This demo showcases the complete BEiT implementation including:");
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println!("• Visual tokenizer using discrete VAE");
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println!("• Blockwise masking strategy");
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println!("• Masked patch prediction pre-training");
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println!("• Fine-tuning for downstream classification");
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println!("• Performance benchmarking");
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println!();
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// Run all demo components
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beit_pretraining_demo()?;
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beit_finetuning_demo()?;
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visual_tokenizer_demo()?;
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blockwise_masking_demo()?;
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performance_benchmarking_demo()?;
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println!("\n🎉 BEiT Demo Completed Successfully!");
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println!("=====================================");
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println!();
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println!("📚 Key Takeaways:");
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println!("• BEiT uses blockwise masking for more structured occlusion");
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println!("• Visual tokenizer converts patches to discrete tokens");
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println!("• Pre-training learns representations via masked prediction");
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println!("• Fine-tuning adapts learned features to specific tasks");
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println!("• Implementation follows strict TDD principles");
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println!();
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println!("🚀 Ready for production use with GPU acceleration!");
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Ok(())
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}
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