oxirs_embed/vision_language_graph/

encoders.rs

//! Module for vision-language-graph integration

use super::*;
use anyhow::Result;
use scirs2_core::ndarray_ext::{Array1, Array2, Array3, Array4, Axis};
use scirs2_core::random::{Random, Rng};
use std::collections::HashMap;

#[derive(Debug, Clone)]
pub struct VisionEncoder {
    pub config: VisionEncoderConfig,
    /// CNN backbone parameters
    pub cnn_parameters: HashMap<String, Array4<f32>>,
    /// Vision transformer parameters
    pub vit_parameters: HashMap<String, Array2<f32>>,
    /// Projection layer
    pub projection: Array2<f32>,
}

impl VisionEncoder {
    pub fn new(config: VisionEncoderConfig) -> Self {
        let mut cnn_parameters = HashMap::new();
        let mut vit_parameters = HashMap::new();

        // Initialize CNN parameters
        for (i, &filter_size) in config.cnn_config.filter_sizes.iter().enumerate() {
            let layer_name = format!("conv_{i}");
            let weight_shape = (
                filter_size,
                if i == 0 {
                    config.channels
                } else {
                    config.cnn_config.filter_sizes[i - 1]
                },
                3,
                3,
            );
            let mut random = Random::default();
            cnn_parameters.insert(
                layer_name,
                Array4::from_shape_fn(weight_shape, |_| (random.random::<f32>() - 0.5) * 0.1),
            );
        }

        // Initialize ViT parameters
        let mut random = Random::default();
        vit_parameters.insert(
            "patch_embedding".to_string(),
            Array2::from_shape_fn(
                (
                    config.channels * config.patch_size.0 * config.patch_size.1,
                    config.vision_dim,
                ),
                |_| (random.random::<f32>() - 0.5) * 0.1,
            ),
        );

        // Projection to unified dimension
        let mut random = Random::default();
        let projection = Array2::from_shape_fn((config.vision_dim, config.vision_dim), |_| {
            (random.random::<f32>() - 0.5) * 0.1
        });

        Self {
            config,
            cnn_parameters,
            vit_parameters,
            projection,
        }
    }

    /// Encode image to visual embeddings
    pub fn encode_image(&self, image: &Array3<f32>) -> Result<Array1<f32>> {
        match self.config.architecture {
            VisionArchitecture::VisionTransformer => self.encode_with_vit(image),
            VisionArchitecture::ResNet => self.encode_with_cnn(image),
            _ => self.encode_with_vit(image), // Default to ViT
        }
    }

    /// Encode with Vision Transformer
    fn encode_with_vit(&self, image: &Array3<f32>) -> Result<Array1<f32>> {
        // Simulate patch extraction and embedding
        let (h, w, c) = image.dim();
        let (patch_h, patch_w) = self.config.patch_size;

        let num_patches_h = h / patch_h;
        let num_patches_w = w / patch_w;
        let num_patches = num_patches_h * num_patches_w;

        // Extract patches and flatten
        let mut patch_embeddings = Array2::zeros((num_patches, self.config.vision_dim));

        for i in 0..num_patches_h {
            for j in 0..num_patches_w {
                let patch_idx = i * num_patches_w + j;

                // Extract patch
                let patch = image.slice(scirs2_core::ndarray_ext::s![
                    i * patch_h..(i + 1) * patch_h,
                    j * patch_w..(j + 1) * patch_w,
                    ..
                ]);

                // Flatten patch
                let patch_owned = patch.to_owned();
                let flattened_patch = patch_owned
                    .into_shape_with_order(c * patch_h * patch_w)
                    .expect("reshape should succeed for valid patch dimensions");

                // Project to embedding space
                if let Some(patch_embedding_matrix) = self.vit_parameters.get("patch_embedding") {
                    let embedding = flattened_patch.dot(patch_embedding_matrix);
                    patch_embeddings.row_mut(patch_idx).assign(&embedding);
                }
            }
        }

        // Global average pooling over patches
        let global_embedding = patch_embeddings
            .mean_axis(Axis(0))
            .expect("mean_axis should succeed for non-empty array");

        Ok(global_embedding)
    }

    /// Encode with CNN
    fn encode_with_cnn(&self, image: &Array3<f32>) -> Result<Array1<f32>> {
        // Simulate CNN forward pass
        let mut features = image.clone();

        // Apply multiple conv layers
        for i in 0..self.config.cnn_config.num_layers.min(2) {
            // Limit for simplicity
            // Simulate convolution + pooling
            let (h, w, c) = features.dim();
            let new_h = h / 2; // Simulate stride 2
            let new_w = w / 2;
            let new_c = self.config.cnn_config.filter_sizes[i];

            let mut new_features = Array3::zeros((new_h, new_w, new_c));

            // Simple downsampling simulation
            for new_i in 0..new_h {
                for new_j in 0..new_w {
                    for new_k in 0..new_c {
                        let old_i = new_i * 2;
                        let old_j = new_j * 2;

                        if old_i < h && old_j < w {
                            // Average over 2x2 region
                            let mut sum = 0.0;
                            let mut count = 0;
                            for di in 0..2 {
                                for dj in 0..2 {
                                    if old_i + di < h && old_j + dj < w {
                                        for k in 0..c.min(new_c) {
                                            sum += features[[old_i + di, old_j + dj, k]];
                                            count += 1;
                                        }
                                    }
                                }
                            }
                            new_features[[new_i, new_j, new_k]] = sum / count as f32;
                        }
                    }
                }
            }

            features = new_features;
        }

        // Global average pooling
        let features_len = features.len();
        let flattened = features
            .into_shape_with_order(features_len)
            .expect("reshape should succeed for valid features dimensions");
        let mut global_features = vec![0.0; self.config.vision_dim];

        for i in 0..global_features.len().min(flattened.len()) {
            global_features[i] = flattened[i];
        }

        Ok(Array1::from_vec(global_features))
    }
}

/// Language encoder
#[derive(Debug, Clone)]
pub struct LanguageEncoder {
    pub config: LanguageEncoderConfig,
    /// Token embeddings
    pub token_embeddings: Array2<f32>,
    /// Position embeddings
    pub position_embeddings: Array2<f32>,
    /// Transformer parameters
    pub transformer_parameters: HashMap<String, Array2<f32>>,
}

impl LanguageEncoder {
    pub fn new(config: LanguageEncoderConfig) -> Self {
        // Initialize embeddings
        let mut random = Random::default();
        let token_embeddings =
            Array2::from_shape_fn((config.vocab_size, config.language_dim), |_| {
                (random.random::<f32>() - 0.5) * 0.1
            });

        let mut random = Random::default();
        let position_embeddings =
            Array2::from_shape_fn((config.max_seq_length, config.language_dim), |_| {
                (random.random::<f32>() - 0.5) * 0.1
            });

        let mut transformer_parameters = HashMap::new();

        // Initialize transformer layers
        for layer in 0..config.transformer_config.num_layers {
            let mut random = Random::default();
            transformer_parameters.insert(
                format!("attention_weights_{layer}"),
                Array2::from_shape_fn((config.language_dim, config.language_dim), |_| {
                    (random.random::<f32>() - 0.5) * 0.1
                }),
            );

            let mut random = Random::default();
            transformer_parameters.insert(
                format!("feed_forward_{layer}"),
                Array2::from_shape_fn(
                    (
                        config.transformer_config.intermediate_dim,
                        config.language_dim,
                    ),
                    |_| (random.random::<f32>() - 0.5) * 0.1,
                ),
            );
        }

        Self {
            config,
            token_embeddings,
            position_embeddings,
            transformer_parameters,
        }
    }

    /// Encode text to language embeddings
    pub fn encode_text(&self, text: &str) -> Result<Array1<f32>> {
        // Simple tokenization (in real implementation would use proper tokenizer)
        let tokens = self.tokenize(text);

        // Get token embeddings
        let mut sequence_embeddings = Array2::zeros((tokens.len(), self.config.language_dim));

        for (i, &token_id) in tokens.iter().enumerate() {
            if token_id < self.token_embeddings.nrows() {
                let token_emb = self.token_embeddings.row(token_id);
                let pos_emb = self
                    .position_embeddings
                    .row(i.min(self.config.max_seq_length - 1));

                // Add token and position embeddings
                let combined = &token_emb + &pos_emb;
                sequence_embeddings.row_mut(i).assign(&combined);
            }
        }

        // Apply transformer layers (simplified)
        let mut hidden_states = sequence_embeddings;

        for layer in 0..self.config.transformer_config.num_layers.min(2) {
            // Limit for performance
            if let Some(attention_weights) = self
                .transformer_parameters
                .get(&format!("attention_weights_{layer}"))
            {
                // Apply self-attention (simplified)
                hidden_states = hidden_states.dot(attention_weights);

                // Apply layer norm (simplified)
                for mut row in hidden_states.rows_mut() {
                    let mean = row.mean().unwrap_or(0.0);
                    let var = row.var(0.0);
                    row.mapv_inplace(|x| (x - mean) / (var + 1e-8).sqrt());
                }
            }
        }

        // Pool to sentence-level representation (mean pooling)
        let sentence_embedding = hidden_states
            .mean_axis(Axis(0))
            .expect("mean_axis should succeed for non-empty array");

        Ok(sentence_embedding)
    }

    /// Simple tokenization
    fn tokenize(&self, text: &str) -> Vec<usize> {
        text.split_whitespace()
            .map(|word| {
                // Simple hash-based token ID
                let mut hash = 0usize;
                for byte in word.bytes() {
                    hash = hash.wrapping_mul(31).wrapping_add(byte as usize);
                }
                hash % self.config.vocab_size
            })
            .collect()
    }
}

/// Graph encoder
#[derive(Debug, Clone)]
pub struct GraphEncoder {
    pub config: GraphEncoderConfig,
    /// Node transformation parameters
    pub node_parameters: HashMap<String, Array2<f32>>,
    /// Edge transformation parameters  
    pub edge_parameters: HashMap<String, Array2<f32>>,
    /// Graph-level parameters
    pub graph_parameters: HashMap<String, Array2<f32>>,
}

impl GraphEncoder {
    pub fn new(config: GraphEncoderConfig) -> Self {
        let mut node_parameters = HashMap::new();
        let mut edge_parameters = HashMap::new();
        let mut graph_parameters = HashMap::new();

        // Initialize node transformation layers
        for layer in 0..config.num_layers {
            let mut random = Random::default();
            node_parameters.insert(
                format!("node_transform_{layer}"),
                Array2::from_shape_fn((config.node_dim, config.node_dim), |_| {
                    (random.random::<f32>() - 0.5) * 0.1
                }),
            );
        }

        // Initialize edge transformation layers
        for layer in 0..config.num_layers {
            let mut random = Random::default();
            edge_parameters.insert(
                format!("edge_transform_{layer}"),
                Array2::from_shape_fn((config.edge_dim, config.edge_dim), |_| {
                    (random.random::<f32>() - 0.5) * 0.1
                }),
            );
        }

        // Graph readout parameters (for attention mechanism)
        let mut random = Random::default();
        graph_parameters.insert(
            "readout".to_string(),
            Array2::from_shape_fn(
                (config.node_dim, 1), // Single attention score per node
                |_| (random.random::<f32>() - 0.5) * 0.1,
            ),
        );

        // Graph projection parameters (from node_dim to graph_dim)
        let mut random = Random::default();
        graph_parameters.insert(
            "graph_projection".to_string(),
            Array2::from_shape_fn((config.node_dim, config.graph_dim), |_| {
                (random.random::<f32>() - 0.5) * 0.1
            }),
        );

        Self {
            config,
            node_parameters,
            edge_parameters,
            graph_parameters,
        }
    }

    /// Encode graph to graph embeddings
    pub fn encode_graph(
        &self,
        node_features: &Array2<f32>,
        edge_features: &Array2<f32>,
        adjacency_matrix: &Array2<f32>,
    ) -> Result<Array1<f32>> {
        let mut node_embeddings = node_features.clone();

        // Apply GNN layers
        for layer in 0..self.config.num_layers.min(2) {
            // Limit for performance
            node_embeddings =
                self.apply_gnn_layer(&node_embeddings, edge_features, adjacency_matrix, layer)?;
        }

        // Graph-level readout
        let graph_embedding = self.graph_readout(&node_embeddings)?;

        Ok(graph_embedding)
    }

    /// Apply a single GNN layer
    fn apply_gnn_layer(
        &self,
        node_embeddings: &Array2<f32>,
        _edge_features: &Array2<f32>,
        adjacency_matrix: &Array2<f32>,
        layer: usize,
    ) -> Result<Array2<f32>> {
        let transform_key = format!("node_transform_{layer}");

        if let Some(transform_matrix) = self.node_parameters.get(&transform_key) {
            // Message passing: aggregate neighbor features
            let aggregated = adjacency_matrix.dot(node_embeddings);

            // Apply transformation
            let transformed = aggregated.dot(transform_matrix);

            // Apply activation (ReLU)
            let activated = transformed.mapv(|x| x.max(0.0));

            Ok(activated)
        } else {
            Ok(node_embeddings.clone())
        }
    }

    /// Graph-level readout
    fn graph_readout(&self, node_embeddings: &Array2<f32>) -> Result<Array1<f32>> {
        let node_level_embedding = match self.config.readout {
            ReadoutFunction::GlobalMean => node_embeddings
                .mean_axis(Axis(0))
                .expect("mean_axis should succeed for non-empty array"),
            ReadoutFunction::GlobalMax => {
                node_embeddings.fold_axis(Axis(0), f32::NEG_INFINITY, |&a, &b| a.max(b))
            }
            ReadoutFunction::GlobalSum => node_embeddings.sum_axis(Axis(0)),
            ReadoutFunction::GlobalAttention => {
                if let Some(readout_matrix) = self.graph_parameters.get("readout") {
                    // Attention-based readout
                    let attention_scores = node_embeddings.dot(readout_matrix); // (num_nodes, 1)
                    let attention_scores_1d = attention_scores.column(0).to_owned(); // (num_nodes,)
                    let attention_weights = self.softmax_1d(&attention_scores_1d); // (num_nodes,)

                    // Weighted average of node embeddings
                    let mut weighted_sum = Array1::zeros(node_embeddings.ncols());
                    for (i, &weight) in attention_weights.iter().enumerate() {
                        let node_emb = node_embeddings.row(i);
                        weighted_sum = weighted_sum + weight * &node_emb;
                    }
                    weighted_sum
                } else {
                    node_embeddings
                        .mean_axis(Axis(0))
                        .expect("mean_axis should succeed for non-empty array")
                }
            }
            _ => node_embeddings
                .mean_axis(Axis(0))
                .expect("mean_axis should succeed for non-empty array"),
        };

        // Project from node_dim to graph_dim
        if let Some(projection_matrix) = self.graph_parameters.get("graph_projection") {
            Ok(projection_matrix.t().dot(&node_level_embedding))
        } else {
            Ok(node_level_embedding)
        }
    }

    /// Apply softmax to 2D array
    fn softmax_2d(&self, x: &Array2<f32>) -> Array2<f32> {
        let mut result = x.clone();
        for mut row in result.rows_mut() {
            let max_val = row.fold(f32::NEG_INFINITY, |a, &b| a.max(b));
            row.mapv_inplace(|v| (v - max_val).exp());
            let sum = row.sum();
            if sum > 0.0 {
                row /= sum;
            }
        }
        result
    }

    fn softmax_1d(&self, x: &Array1<f32>) -> Array1<f32> {
        let max_val = x.fold(f32::NEG_INFINITY, |a, &b| a.max(b));
        let mut result = x.mapv(|v| (v - max_val).exp());
        let sum = result.sum();
        if sum > 0.0 {
            result /= sum;
        }
        result
    }
}