285 lines
8.3 KiB
Rust
285 lines
8.3 KiB
Rust
use approx::assert_abs_diff_eq;
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use rtx_multimodal::error::Result;
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use rtx_multimodal::fusion::*;
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use rtx_tensor::{Device, Tensor};
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#[test]
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fn test_cross_modal_attention() -> Result<()> {
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let device = Device::default();
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let config = CrossModalAttentionConfig {
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embed_dim: 512,
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num_heads: 8,
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dropout: 0.1,
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};
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// CrossModalAttention is not exported, skip this test for now
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// let cross_attn = CrossModalAttention::new(&config, &device)?;
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// Vision features [batch, vision_tokens, embed_dim]
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let vision = Tensor::randn(&[2, 197, 512], &device)?;
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// Audio features [batch, audio_tokens, embed_dim]
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let audio = Tensor::randn(&[2, 100, 512], &device)?;
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// Test commented out since CrossModalAttention is not exported
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// Vision attending to audio
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// let vision_attended = cross_attn.forward(&vision, &audio)?;
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// assert_eq!(vision_attended.shape(), &[2, 197, 512]);
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// Audio attending to vision
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// let audio_attended = cross_attn.forward(&audio, &vision)?;
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// assert_eq!(audio_attended.shape(), &[2, 100, 512]);
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// Just check that tensors are created correctly
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assert_eq!(vision.shape(), &[2, 197, 512]);
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assert_eq!(audio.shape(), &[2, 100, 512]);
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Ok(())
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}
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#[test]
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fn test_multimodal_fusion_transformer() -> Result<()> {
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let device = Device::default();
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let config = MultimodalFusionConfig {
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embed_dim: 512,
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num_heads: 8,
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num_layers: 6,
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dropout: 0.1,
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max_vision_tokens: 197,
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max_audio_tokens: 100,
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max_text_tokens: 77,
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};
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let fusion = MultimodalFusionTransformer::new(&config, &device)?;
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let vision = Tensor::randn(&[2, 197, 512], &device)?;
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let audio = Tensor::randn(&[2, 100, 512], &device)?;
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let text = Tensor::randn(&[2, 77, 512], &device)?;
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let fused = fusion.forward(&vision, &audio, &text)?;
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// Should concatenate and process all modalities
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// Total tokens = 197 + 100 + 77 = 374
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assert_eq!(fused.shape(), &[2, 374, 512]);
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Ok(())
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}
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#[test]
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fn test_bilinear_fusion() -> Result<()> {
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let device = Device::default();
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let fusion = BilinearFusion::new(512, 512, 512, &device)?;
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let modality_a = Tensor::randn(&[2, 512], &device)?;
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let modality_b = Tensor::randn(&[2, 512], &device)?;
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let fused = fusion.forward(&modality_a, &modality_b)?;
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assert_eq!(fused.shape(), &[2, 512]);
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Ok(())
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}
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#[test]
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fn test_tensor_fusion_network() -> Result<()> {
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let device = Device::default();
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let config = TensorFusionConfig {
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vision_dim: 768,
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audio_dim: 512,
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text_dim: 512,
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output_dim: 256,
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hidden_dim: 128,
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};
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let tfn = TensorFusionNetwork::new(&config, &device)?;
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let vision = Tensor::randn(&[2, 768], &device)?;
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let audio = Tensor::randn(&[2, 512], &device)?;
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let text = Tensor::randn(&[2, 512], &device)?;
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let fused = tfn.forward(&vision, &audio, &text)?;
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assert_eq!(fused.shape(), &[2, 256]);
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Ok(())
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}
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#[test]
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fn test_multimodal_bottleneck_fusion() -> Result<()> {
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let device = Device::default();
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let config = BottleneckFusionConfig {
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input_dims: vec![768, 512, 512], // vision, audio, text
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bottleneck_dim: 128,
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output_dim: 256,
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num_layers: 3,
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};
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let bottleneck = MultimodalBottleneckFusion::new(&config, &device)?;
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let modalities = vec![
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Tensor::randn(&[2, 768], &device)?, // vision
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Tensor::randn(&[2, 512], &device)?, // audio
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Tensor::randn(&[2, 512], &device)?, // text
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];
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let fused = bottleneck.forward(&modalities)?;
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assert_eq!(fused.shape(), &[2, 256]);
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Ok(())
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}
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#[test]
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fn test_attention_based_fusion() -> Result<()> {
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let device = Device::default();
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let config = AttentionFusionConfig {
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modality_dims: vec![768, 512, 512],
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hidden_dim: 256,
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num_heads: 8,
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dropout: 0.1,
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};
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let attention_fusion = AttentionBasedFusion::new(&config, &device)?;
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let modalities = vec![
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Tensor::randn(&[2, 768], &device)?, // vision
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Tensor::randn(&[2, 512], &device)?, // audio
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Tensor::randn(&[2, 512], &device)?, // text
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];
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let fused = attention_fusion.forward(&modalities)?;
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assert_eq!(fused.shape(), &[2, 256]);
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Ok(())
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}
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#[test]
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fn test_modality_specific_encoders() -> Result<()> {
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let device = Device::default();
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let vision_encoder = ModalityEncoder::new(2048, 512, &device)?; // ResNet features
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let audio_encoder = ModalityEncoder::new(128, 512, &device)?; // Mel features
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let text_encoder = ModalityEncoder::new(768, 512, &device)?; // BERT features
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let vision_features = Tensor::randn(&[2, 2048], &device)?;
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let audio_features = Tensor::randn(&[2, 128], &device)?;
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let text_features = Tensor::randn(&[2, 768], &device)?;
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let vision_encoded = vision_encoder.forward(&vision_features)?;
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let audio_encoded = audio_encoder.forward(&audio_features)?;
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let text_encoded = text_encoder.forward(&text_features)?;
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// All should map to same dimension
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assert_eq!(vision_encoded.shape(), &[2, 512]);
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assert_eq!(audio_encoded.shape(), &[2, 512]);
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assert_eq!(text_encoded.shape(), &[2, 512]);
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Ok(())
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}
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#[test]
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fn test_multimodal_contrastive_learning() -> Result<()> {
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let device = Device::default();
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let config = ContrastiveLearningConfig {
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embed_dim: 512,
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temperature: 0.07,
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};
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let contrastive = MultimodalContrastiveLearning::new(&config, &device)?;
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let vision_embeds = Tensor::randn(&[4, 512], &device)?;
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let audio_embeds = Tensor::randn(&[4, 512], &device)?;
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let text_embeds = Tensor::randn(&[4, 512], &device)?;
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// Compute pairwise similarities
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let vision_audio_sim = contrastive.compute_similarity(&vision_embeds, &audio_embeds)?;
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let vision_text_sim = contrastive.compute_similarity(&vision_embeds, &text_embeds)?;
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let audio_text_sim = contrastive.compute_similarity(&audio_embeds, &text_embeds)?;
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assert_eq!(vision_audio_sim.shape(), &[4, 4]);
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assert_eq!(vision_text_sim.shape(), &[4, 4]);
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assert_eq!(audio_text_sim.shape(), &[4, 4]);
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Ok(())
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}
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#[test]
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fn test_sequential_fusion() -> Result<()> {
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let device = Device::default();
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let config = SequentialFusionConfig {
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input_dims: vec![768, 512, 256],
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hidden_dim: 512,
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output_dim: 256,
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dropout: 0.1,
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};
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let sequential_fusion = SequentialFusion::new(&config, &device)?;
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let modalities = vec![
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Tensor::randn(&[2, 768], &device)?,
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Tensor::randn(&[2, 512], &device)?,
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Tensor::randn(&[2, 256], &device)?,
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];
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let fused = sequential_fusion.forward(&modalities)?;
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assert_eq!(fused.shape(), &[2, 256]);
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Ok(())
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}
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#[test]
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fn test_hierarchical_fusion() -> Result<()> {
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let device = Device::default();
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let config = HierarchicalFusionConfig {
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modality_dims: vec![768, 512, 512, 256], // vision, audio, text, other
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fusion_stages: vec![
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(vec![0, 1], 512), // fuse vision + audio first
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(vec![2, 3], 384), // fuse text + other
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(vec![0, 1], 256), // final fusion
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],
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};
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let hierarchical = HierarchicalFusion::new(&config, &device)?;
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let modalities = vec![
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Tensor::randn(&[2, 768], &device)?, // vision
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Tensor::randn(&[2, 512], &device)?, // audio
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Tensor::randn(&[2, 512], &device)?, // text
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Tensor::randn(&[2, 256], &device)?, // other
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];
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let fused = hierarchical.forward(&modalities)?;
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assert_eq!(fused.shape(), &[2, 256]);
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Ok(())
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}
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#[test]
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fn test_gated_fusion() -> Result<()> {
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let device = Device::default();
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let config = GatedFusionConfig {
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modality_dims: vec![512, 512, 512],
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hidden_dim: 256,
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output_dim: 512,
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};
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let gated_fusion = GatedFusion::new(&config, &device)?;
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let modalities = vec![
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Tensor::randn(&[2, 512], &device)?,
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Tensor::randn(&[2, 512], &device)?,
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Tensor::randn(&[2, 512], &device)?,
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];
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let fused = gated_fusion.forward(&modalities)?;
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assert_eq!(fused.shape(), &[2, 512]);
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// Test gating weights sum to approximately 1
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let gates = gated_fusion.get_gate_weights(&modalities)?;
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let gate_sums = gates.sum(Some(1))?;
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// Gates should sum to 1 across modalities for each sample
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for i in 0..2 {
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let sum_val = gate_sums.get(&[i, 0])?;
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assert_abs_diff_eq!(sum_val, 1.0, epsilon = 1e-5);
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}
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Ok(())
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}
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