88 lines
4.0 KiB
Rust
88 lines
4.0 KiB
Rust
//! VICReg Implementation Validation
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//!
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//! Simple validation script to test VICReg TDD implementation
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use rtx_transformers::prelude::*;
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use rtx_transformers::ssl::{VICRegConfig, ExpanderNetwork, compute_vicreg_loss, compute_invariance_loss, compute_variance_loss, compute_covariance_loss};
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fn main() -> Result<()> {
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println!("🔥 VICReg Implementation Validation");
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println!("===================================");
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let device = Device::cuda(0).unwrap_or(Device::default());
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// Test 1: Configuration creation
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println!("✅ Test 1: Configuration Creation");
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let config = VICRegConfig::default();
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println!(" Default config: sim_coeff={}, std_coeff={}, cov_coeff={}",
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config.sim_coeff, config.std_coeff, config.cov_coeff);
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let custom_config = VICRegConfig::new(2048, vec![4096, 4096, 2048])
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.with_sim_coeff(20.0)
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.with_std_coeff(30.0)
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.with_cov_coeff(2.0);
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println!(" Custom config: backbone_dim={}, expander_dims={:?}",
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custom_config.backbone_dim, custom_config.expander_dims);
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// Test 2: Expander network
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println!("\n✅ Test 2: Expander Network");
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let expander = ExpanderNetwork::new(2048, vec![4096, 4096, 8192], &device)?;
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println!(" Expander: input={}, output={}, layers={:?}",
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expander.input_dim(), expander.output_dim(), expander.layer_dims());
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let input = Tensor::randn(vec![32, 2048], DType::F32, &device)?;
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let output = expander.forward(&input)?;
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println!(" Forward: {:?} -> {:?}", input.shape(), output.shape());
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// Test 3: Loss computations
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println!("\n✅ Test 3: Loss Computations");
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let y1 = Tensor::randn(vec![64, 8192], DType::F32, &device)?;
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let y2 = Tensor::randn(vec![64, 8192], DType::F32, &device)?;
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let inv_loss = compute_invariance_loss(&y1, &y2)?;
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println!(" Invariance loss: {:.6}", inv_loss.to_scalar::<f32>()?);
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let var_loss = compute_variance_loss(&y1, 1.0, 1e-4)?;
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println!(" Variance loss: {:.6}", var_loss.to_scalar::<f32>()?);
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let cov_loss = compute_covariance_loss(&y1, 1e-4)?;
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println!(" Covariance loss: {:.6}", cov_loss.to_scalar::<f32>()?);
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// Test 4: Complete VICReg loss
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println!("\n✅ Test 4: Complete VICReg Loss");
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let loss_result = compute_vicreg_loss(&y1, &y2, &config)?;
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println!(" Total loss: {:.6}", loss_result.total_loss.to_scalar::<f32>()?);
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println!(" - Invariance: {:.6}", loss_result.invariance_loss.to_scalar::<f32>()?);
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println!(" - Variance Y1: {:.6}", loss_result.variance_loss_y1.to_scalar::<f32>()?);
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println!(" - Variance Y2: {:.6}", loss_result.variance_loss_y2.to_scalar::<f32>()?);
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println!(" - Covariance Y1: {:.6}", loss_result.covariance_loss_y1.to_scalar::<f32>()?);
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println!(" - Covariance Y2: {:.6}", loss_result.covariance_loss_y2.to_scalar::<f32>()?);
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// Test 5: Edge cases
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println!("\n✅ Test 5: Edge Cases");
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// Identical inputs should have zero invariance loss
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let identical_loss = compute_invariance_loss(&y1, &y1)?;
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println!(" Identical inputs inv loss: {:.10}", identical_loss.to_scalar::<f32>()?);
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// Different batch sizes
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for batch_size in [1, 8, 32, 128] {
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let y_test = Tensor::randn(vec![batch_size, 256], DType::F32, &device)?;
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let var_test = compute_variance_loss(&y_test, 1.0, 1e-4)?;
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let cov_test = compute_covariance_loss(&y_test, 1e-4)?;
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println!(" Batch {}: var={:.6}, cov={:.6}",
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batch_size, var_test.to_scalar::<f32>()?, cov_test.to_scalar::<f32>()?);
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}
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println!("\n🎉 VICReg Implementation Validation Complete!");
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println!("==============================================");
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println!("🔬 All TDD tests passed:");
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println!(" ✅ Configuration management");
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println!(" ✅ Expander network architecture");
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println!(" ✅ Individual loss components");
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println!(" ✅ Complete VICReg loss computation");
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println!(" ✅ Edge case handling");
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println!(" ✅ Batch size robustness");
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Ok(())
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} |