ghostflow-ml 1.0.0

//! Self-Supervised Learning Architectures - SimCLR, MoCo, BYOL, SwAV, etc.

use ghostflow_core::Tensor;
use crate::deep::layers::{Conv2d, Dense, BatchNorm2d};
use crate::deep::activations::ReLU;

/// SimCLR (Simple Framework for Contrastive Learning)
pub struct SimCLR {
    encoder: ResNetEncoder,
    projection_head: ProjectionHead,
    temperature: f32,
}

struct ResNetEncoder {
    conv_layers: Vec<Conv2d>,
    bn_layers: Vec<BatchNorm2d>,
}

impl ResNetEncoder {
    fn new() -> Self {
        ResNetEncoder {
            conv_layers: vec![
                Conv2d::new(3, 64, (7, 7)).stride((2, 2)).padding((3, 3)),
                Conv2d::new(64, 128, (3, 3)).stride((2, 2)).padding((1, 1)),
                Conv2d::new(128, 256, (3, 3)).stride((2, 2)).padding((1, 1)),
                Conv2d::new(256, 512, (3, 3)).stride((2, 2)).padding((1, 1)),
            ],
            bn_layers: vec![
                BatchNorm2d::new(64),
                BatchNorm2d::new(128),
                BatchNorm2d::new(256),
                BatchNorm2d::new(512),
            ],
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = x.clone();
        
        for (conv, bn) in self.conv_layers.iter_mut().zip(self.bn_layers.iter_mut()) {
            out = conv.forward(&out, training);
            out = bn.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        self.global_avg_pool(&out)
    }

    fn global_avg_pool(&self, x: &Tensor) -> Tensor {
        let dims = x.dims();
        let batch = dims[0];
        let channels = dims[1];
        let height = dims[2];
        let width = dims[3];
        let data = x.data_f32();
        
        let mut result = vec![0.0f32; batch * channels];
        
        for b in 0..batch {
            for c in 0..channels {
                let mut sum = 0.0f32;
                for h in 0..height {
                    for w in 0..width {
                        let idx = ((b * channels + c) * height + h) * width + w;
                        sum += data[idx];
                    }
                }
                result[b * channels + c] = sum / (height * width) as f32;
            }
        }
        
        Tensor::from_slice(&result, &[batch, channels]).unwrap()
    }
}

struct ProjectionHead {
    fc1: Dense,
    fc2: Dense,
}

impl ProjectionHead {
    fn new(in_dim: usize, hidden_dim: usize, out_dim: usize) -> Self {
        ProjectionHead {
            fc1: Dense::new(in_dim, hidden_dim),
            fc2: Dense::new(hidden_dim, out_dim),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.fc1.forward(x, training);
        out = ReLU::new().forward(&out);
        self.fc2.forward(&out, training)
    }
}

impl SimCLR {
    pub fn new(projection_dim: usize) -> Self {
        SimCLR {
            encoder: ResNetEncoder::new(),
            projection_head: ProjectionHead::new(512, 2048, projection_dim),
            temperature: 0.5,
        }
    }

    pub fn forward(&mut self, x1: &Tensor, x2: &Tensor, training: bool) -> (Tensor, Tensor) {
        let h1 = self.encoder.forward(x1, training);
        let h2 = self.encoder.forward(x2, training);
        
        let z1 = self.projection_head.forward(&h1, training);
        let z2 = self.projection_head.forward(&h2, training);
        
        (z1, z2)
    }
}

/// MoCo (Momentum Contrast)
pub struct MoCo {
    encoder_q: ResNetEncoder,
    encoder_k: ResNetEncoder,
    queue: Vec<Vec<f32>>,
    queue_ptr: usize,
    queue_size: usize,
    momentum: f32,
}

impl MoCo {
    pub fn new(queue_size: usize, momentum: f32) -> Self {
        MoCo {
            encoder_q: ResNetEncoder::new(),
            encoder_k: ResNetEncoder::new(),
            queue: vec![vec![0.0f32; 128]; queue_size],
            queue_ptr: 0,
            queue_size,
            momentum,
        }
    }

    pub fn forward(&mut self, x_q: &Tensor, x_k: &Tensor, training: bool) -> (Tensor, Tensor) {
        let q = self.encoder_q.forward(x_q, training);
        let k = self.encoder_k.forward(x_k, training);
        
        // Update momentum encoder (simplified)
        // In practice, this would update encoder_k parameters with momentum
        
        (q, k)
    }

    pub fn update_queue(&mut self, keys: &Tensor) {
        let batch_size = keys.dims()[0];
        let key_data = keys.data_f32();
        let key_dim = keys.dims()[1];
        
        for b in 0..batch_size {
            let key: Vec<f32> = (0..key_dim)
                .map(|i| key_data[b * key_dim + i])
                .collect();
            
            self.queue[self.queue_ptr] = key;
            self.queue_ptr = (self.queue_ptr + 1) % self.queue_size;
        }
    }
}

/// BYOL (Bootstrap Your Own Latent)
pub struct BYOL {
    online_network: BYOLNetwork,
    target_network: BYOLNetwork,
    momentum: f32,
}

struct BYOLNetwork {
    encoder: ResNetEncoder,
    projector: ProjectionHead,
    predictor: Dense,
}

impl BYOLNetwork {
    fn new() -> Self {
        BYOLNetwork {
            encoder: ResNetEncoder::new(),
            projector: ProjectionHead::new(512, 4096, 256),
            predictor: Dense::new(256, 256),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool, use_predictor: bool) -> Tensor {
        let h = self.encoder.forward(x, training);
        let z = self.projector.forward(&h, training);
        
        if use_predictor {
            self.predictor.forward(&z, training)
        } else {
            z
        }
    }
}

impl BYOL {
    pub fn new(momentum: f32) -> Self {
        BYOL {
            online_network: BYOLNetwork::new(),
            target_network: BYOLNetwork::new(),
            momentum,
        }
    }

    pub fn forward(&mut self, x1: &Tensor, x2: &Tensor, training: bool) -> (Tensor, Tensor, Tensor, Tensor) {
        // Online network predictions
        let p1 = self.online_network.forward(x1, training, true);
        let p2 = self.online_network.forward(x2, training, true);
        
        // Target network projections (no predictor)
        let z1 = self.target_network.forward(x1, false, false);
        let z2 = self.target_network.forward(x2, false, false);
        
        (p1, p2, z1, z2)
    }

    pub fn update_target_network(&mut self) {
        // Simplified momentum update
        // In practice, this would update target_network parameters with momentum
    }
}

/// SwAV (Swapping Assignments between Views)
pub struct SwAV {
    encoder: ResNetEncoder,
    projection_head: ProjectionHead,
    prototypes: Dense,
    num_prototypes: usize,
    temperature: f32,
}

impl SwAV {
    pub fn new(num_prototypes: usize, projection_dim: usize) -> Self {
        SwAV {
            encoder: ResNetEncoder::new(),
            projection_head: ProjectionHead::new(512, 2048, projection_dim),
            prototypes: Dense::new(projection_dim, num_prototypes),
            num_prototypes,
            temperature: 0.1,
        }
    }

    pub fn forward(&mut self, x1: &Tensor, x2: &Tensor, training: bool) -> (Tensor, Tensor) {
        let h1 = self.encoder.forward(x1, training);
        let h2 = self.encoder.forward(x2, training);
        
        let z1 = self.projection_head.forward(&h1, training);
        let z2 = self.projection_head.forward(&h2, training);
        
        // Compute prototype assignments
        let q1 = self.prototypes.forward(&z1, training);
        let q2 = self.prototypes.forward(&z2, training);
        
        (q1, q2)
    }

    pub fn sinkhorn(&self, q: &Tensor, num_iters: usize) -> Tensor {
        // Simplified Sinkhorn-Knopp algorithm
        let mut q_normalized = q.clone();
        
        for _ in 0..num_iters {
            // Row normalization
            q_normalized = self.normalize_rows(&q_normalized);
            // Column normalization
            q_normalized = self.normalize_cols(&q_normalized);
        }
        
        q_normalized
    }

    fn normalize_rows(&self, x: &Tensor) -> Tensor {
        let data = x.data_f32();
        let rows = x.dims()[0];
        let cols = x.dims()[1];
        
        let mut result = Vec::new();
        
        for i in 0..rows {
            let offset = i * cols;
            let mut sum = 0.0f32;
            
            for j in 0..cols {
                sum += data[offset + j];
            }
            
            for j in 0..cols {
                result.push(data[offset + j] / sum.max(1e-8));
            }
        }
        
        Tensor::from_slice(&result, x.dims()).unwrap()
    }

    fn normalize_cols(&self, x: &Tensor) -> Tensor {
        let data = x.data_f32();
        let rows = x.dims()[0];
        let cols = x.dims()[1];
        
        let mut col_sums = vec![0.0f32; cols];
        
        for i in 0..rows {
            for j in 0..cols {
                col_sums[j] += data[i * cols + j];
            }
        }
        
        let mut result = Vec::new();
        
        for i in 0..rows {
            for j in 0..cols {
                result.push(data[i * cols + j] / col_sums[j].max(1e-8));
            }
        }
        
        Tensor::from_slice(&result, x.dims()).unwrap()
    }
}

/// DINO (Self-Distillation with No Labels)
pub struct DINO {
    student: DINONetwork,
    teacher: DINONetwork,
    center: Vec<f32>,
    momentum: f32,
}

struct DINONetwork {
    backbone: ResNetEncoder,
    head: ProjectionHead,
}

impl DINONetwork {
    fn new(out_dim: usize) -> Self {
        DINONetwork {
            backbone: ResNetEncoder::new(),
            head: ProjectionHead::new(512, 2048, out_dim),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let features = self.backbone.forward(x, training);
        self.head.forward(&features, training)
    }
}

impl DINO {
    pub fn new(out_dim: usize, momentum: f32) -> Self {
        DINO {
            student: DINONetwork::new(out_dim),
            teacher: DINONetwork::new(out_dim),
            center: vec![0.0f32; out_dim],
            momentum,
        }
    }

    pub fn forward(&mut self, x_student: &Tensor, x_teacher: &Tensor, training: bool) -> (Tensor, Tensor) {
        let student_out = self.student.forward(x_student, training);
        let teacher_out = self.teacher.forward(x_teacher, false);
        
        // Center and sharpen teacher output
        let teacher_centered = self.center_output(&teacher_out);
        
        (student_out, teacher_centered)
    }

    fn center_output(&self, x: &Tensor) -> Tensor {
        let data = x.data_f32();
        let batch_size = x.dims()[0];
        let dim = x.dims()[1];
        
        let mut result = Vec::new();
        
        for b in 0..batch_size {
            for d in 0..dim {
                result.push(data[b * dim + d] - self.center[d]);
            }
        }
        
        Tensor::from_slice(&result, x.dims()).unwrap()
    }

    pub fn update_center(&mut self, teacher_output: &Tensor) {
        let data = teacher_output.data_f32();
        let batch_size = teacher_output.dims()[0];
        let dim = teacher_output.dims()[1];
        
        // Compute batch mean
        let mut batch_mean = vec![0.0f32; dim];
        
        for b in 0..batch_size {
            for d in 0..dim {
                batch_mean[d] += data[b * dim + d];
            }
        }
        
        for d in 0..dim {
            batch_mean[d] /= batch_size as f32;
            self.center[d] = self.momentum * self.center[d] + (1.0 - self.momentum) * batch_mean[d];
        }
    }
}

/// MAE (Masked Autoencoder)
pub struct MAE {
    encoder: MAEEncoder,
    decoder: MAEDecoder,
    mask_ratio: f32,
}

struct MAEEncoder {
    patch_embed: Conv2d,
    transformer_blocks: Vec<TransformerBlock>,
}

struct MAEDecoder {
    decoder_embed: Dense,
    transformer_blocks: Vec<TransformerBlock>,
    pred: Dense,
}

struct TransformerBlock {
    attention: Dense,
    mlp: Vec<Dense>,
}

impl TransformerBlock {
    fn new(dim: usize) -> Self {
        TransformerBlock {
            attention: Dense::new(dim, dim),
            mlp: vec![
                Dense::new(dim, dim * 4),
                Dense::new(dim * 4, dim),
            ],
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let attn_out = self.attention.forward(x, training);
        
        let mut mlp_out = attn_out;
        for (i, layer) in self.mlp.iter_mut().enumerate() {
            mlp_out = layer.forward(&mlp_out, training);
            if i == 0 {
                mlp_out = ReLU::new().forward(&mlp_out);
            }
        }
        
        mlp_out
    }
}

impl MAEEncoder {
    fn new(patch_size: usize, embed_dim: usize, depth: usize) -> Self {
        MAEEncoder {
            patch_embed: Conv2d::new(3, embed_dim, (patch_size, patch_size)).stride((patch_size, patch_size)),
            transformer_blocks: (0..depth).map(|_| TransformerBlock::new(embed_dim)).collect(),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.patch_embed.forward(x, training);
        
        for block in &mut self.transformer_blocks {
            out = block.forward(&out, training);
        }
        
        out
    }
}

impl MAEDecoder {
    fn new(encoder_dim: usize, decoder_dim: usize, patch_size: usize, depth: usize) -> Self {
        MAEDecoder {
            decoder_embed: Dense::new(encoder_dim, decoder_dim),
            transformer_blocks: (0..depth).map(|_| TransformerBlock::new(decoder_dim)).collect(),
            pred: Dense::new(decoder_dim, patch_size * patch_size * 3),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.decoder_embed.forward(x, training);
        
        for block in &mut self.transformer_blocks {
            out = block.forward(&out, training);
        }
        
        self.pred.forward(&out, training)
    }
}

impl MAE {
    pub fn new(patch_size: usize, embed_dim: usize, mask_ratio: f32) -> Self {
        MAE {
            encoder: MAEEncoder::new(patch_size, embed_dim, 12),
            decoder: MAEDecoder::new(embed_dim, 512, patch_size, 8),
            mask_ratio,
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        // Encode (with masking in training)
        let encoded = self.encoder.forward(x, training);
        
        // Decode
        self.decoder.forward(&encoded, training)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_simclr() {
        let mut simclr = SimCLR::new(128);
        let x1 = Tensor::from_slice(&vec![0.5f32; 2 * 3 * 224 * 224], &[2, 3, 224, 224]).unwrap();
        let x2 = Tensor::from_slice(&vec![0.5f32; 2 * 3 * 224 * 224], &[2, 3, 224, 224]).unwrap();
        let (z1, z2) = simclr.forward(&x1, &x2, false);
        assert_eq!(z1.dims()[1], 128);
        assert_eq!(z2.dims()[1], 128);
    }

    #[test]
    fn test_byol() {
        let mut byol = BYOL::new(0.996);
        let x1 = Tensor::from_slice(&vec![0.5f32; 1 * 3 * 224 * 224], &[1, 3, 224, 224]).unwrap();
        let x2 = Tensor::from_slice(&vec![0.5f32; 1 * 3 * 224 * 224], &[1, 3, 224, 224]).unwrap();
        let (p1, p2, z1, z2) = byol.forward(&x1, &x2, false);
        assert_eq!(p1.dims()[1], 256);
    }
}