ghostflow-ml 1.0.0

//! Specialized Architectures - 3D CNNs, Video, Audio, Point Cloud, etc.

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

/// 3D Convolutional Layer (simplified)
pub struct Conv3d {
    weight: Dense,
    in_channels: usize,
    out_channels: usize,
    kernel_size: (usize, usize, usize),
}

impl Conv3d {
    pub fn new(in_channels: usize, out_channels: usize, kernel_size: (usize, usize, usize)) -> Self {
        let kernel_volume = kernel_size.0 * kernel_size.1 * kernel_size.2;
        Conv3d {
            weight: Dense::new(in_channels * kernel_volume, out_channels),
            in_channels,
            out_channels,
            kernel_size,
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        // Simplified 3D convolution
        self.weight.forward(x, training)
    }
}

/// C3D (3D Convolutional Network for Video)
pub struct C3D {
    conv1: Conv3d,
    conv2: Conv3d,
    conv3a: Conv3d,
    conv3b: Conv3d,
    conv4a: Conv3d,
    conv4b: Conv3d,
    conv5a: Conv3d,
    conv5b: Conv3d,
    fc6: Dense,
    fc7: Dense,
    fc8: Dense,
}

impl C3D {
    pub fn new(num_classes: usize) -> Self {
        C3D {
            conv1: Conv3d::new(3, 64, (3, 3, 3)),
            conv2: Conv3d::new(64, 128, (3, 3, 3)),
            conv3a: Conv3d::new(128, 256, (3, 3, 3)),
            conv3b: Conv3d::new(256, 256, (3, 3, 3)),
            conv4a: Conv3d::new(256, 512, (3, 3, 3)),
            conv4b: Conv3d::new(512, 512, (3, 3, 3)),
            conv5a: Conv3d::new(512, 512, (3, 3, 3)),
            conv5b: Conv3d::new(512, 512, (3, 3, 3)),
            fc6: Dense::new(512, 4096),
            fc7: Dense::new(4096, 4096),
            fc8: Dense::new(4096, num_classes),
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.conv1.forward(x, training);
        out = ReLU::new().forward(&out);
        
        out = self.conv2.forward(&out, training);
        out = ReLU::new().forward(&out);
        
        out = self.conv3a.forward(&out, training);
        out = ReLU::new().forward(&out);
        out = self.conv3b.forward(&out, training);
        out = ReLU::new().forward(&out);
        
        out = self.conv4a.forward(&out, training);
        out = ReLU::new().forward(&out);
        out = self.conv4b.forward(&out, training);
        out = ReLU::new().forward(&out);
        
        out = self.conv5a.forward(&out, training);
        out = ReLU::new().forward(&out);
        out = self.conv5b.forward(&out, training);
        out = ReLU::new().forward(&out);
        
        out = self.fc6.forward(&out, training);
        out = ReLU::new().forward(&out);
        
        out = self.fc7.forward(&out, training);
        out = ReLU::new().forward(&out);
        
        self.fc8.forward(&out, training)
    }
}

/// I3D (Inflated 3D ConvNet)
pub struct I3D {
    conv3d_1a: Conv3d,
    conv3d_2a: Conv3d,
    conv3d_2b: Conv3d,
    conv3d_3a: Conv3d,
    conv3d_3b: Conv3d,
    conv3d_4a: Conv3d,
    conv3d_4b: Conv3d,
    conv3d_5a: Conv3d,
    conv3d_5b: Conv3d,
    fc: Dense,
}

impl I3D {
    pub fn new(num_classes: usize) -> Self {
        I3D {
            conv3d_1a: Conv3d::new(3, 64, (7, 7, 7)),
            conv3d_2a: Conv3d::new(64, 64, (1, 1, 1)),
            conv3d_2b: Conv3d::new(64, 192, (3, 3, 3)),
            conv3d_3a: Conv3d::new(192, 192, (1, 1, 1)),
            conv3d_3b: Conv3d::new(192, 480, (3, 3, 3)),
            conv3d_4a: Conv3d::new(480, 480, (1, 1, 1)),
            conv3d_4b: Conv3d::new(480, 832, (3, 3, 3)),
            conv3d_5a: Conv3d::new(832, 832, (1, 1, 1)),
            conv3d_5b: Conv3d::new(832, 1024, (3, 3, 3)),
            fc: Dense::new(1024, num_classes),
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.conv3d_1a.forward(x, training);
        out = ReLU::new().forward(&out);
        
        out = self.conv3d_2a.forward(&out, training);
        out = ReLU::new().forward(&out);
        out = self.conv3d_2b.forward(&out, training);
        out = ReLU::new().forward(&out);
        
        out = self.conv3d_3a.forward(&out, training);
        out = ReLU::new().forward(&out);
        out = self.conv3d_3b.forward(&out, training);
        out = ReLU::new().forward(&out);
        
        out = self.conv3d_4a.forward(&out, training);
        out = ReLU::new().forward(&out);
        out = self.conv3d_4b.forward(&out, training);
        out = ReLU::new().forward(&out);
        
        out = self.conv3d_5a.forward(&out, training);
        out = ReLU::new().forward(&out);
        out = self.conv3d_5b.forward(&out, training);
        out = ReLU::new().forward(&out);
        
        self.fc.forward(&out, training)
    }
}

/// SlowFast Network for Video Understanding
pub struct SlowFast {
    slow_pathway: SlowPathway,
    fast_pathway: FastPathway,
    fusion: Dense,
    fc: Dense,
}

struct SlowPathway {
    conv1: Conv3d,
    conv2: Conv3d,
    conv3: Conv3d,
}

impl SlowPathway {
    fn new() -> Self {
        SlowPathway {
            conv1: Conv3d::new(3, 64, (1, 7, 7)),
            conv2: Conv3d::new(64, 128, (1, 3, 3)),
            conv3: Conv3d::new(128, 256, (1, 3, 3)),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.conv1.forward(x, training);
        out = ReLU::new().forward(&out);
        
        out = self.conv2.forward(&out, training);
        out = ReLU::new().forward(&out);
        
        out = self.conv3.forward(&out, training);
        ReLU::new().forward(&out)
    }
}

struct FastPathway {
    conv1: Conv3d,
    conv2: Conv3d,
    conv3: Conv3d,
}

impl FastPathway {
    fn new() -> Self {
        FastPathway {
            conv1: Conv3d::new(3, 8, (5, 7, 7)),
            conv2: Conv3d::new(8, 16, (3, 3, 3)),
            conv3: Conv3d::new(16, 32, (3, 3, 3)),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.conv1.forward(x, training);
        out = ReLU::new().forward(&out);
        
        out = self.conv2.forward(&out, training);
        out = ReLU::new().forward(&out);
        
        out = self.conv3.forward(&out, training);
        ReLU::new().forward(&out)
    }
}

impl SlowFast {
    pub fn new(num_classes: usize) -> Self {
        SlowFast {
            slow_pathway: SlowPathway::new(),
            fast_pathway: FastPathway::new(),
            fusion: Dense::new(256 + 32, 512),
            fc: Dense::new(512, num_classes),
        }
    }

    pub fn forward(&mut self, x_slow: &Tensor, x_fast: &Tensor, training: bool) -> Tensor {
        let slow_out = self.slow_pathway.forward(x_slow, training);
        let fast_out = self.fast_pathway.forward(x_fast, training);
        
        let fused = self.concatenate(&slow_out, &fast_out);
        let mut out = self.fusion.forward(&fused, training);
        out = ReLU::new().forward(&out);
        
        self.fc.forward(&out, training)
    }

    fn concatenate(&self, a: &Tensor, b: &Tensor) -> Tensor {
        let a_data = a.data_f32();
        let b_data = b.data_f32();
        
        let mut result = Vec::new();
        result.extend_from_slice(a_data);
        result.extend_from_slice(b_data);
        
        Tensor::from_slice(&result, &[a.dims()[0], a.dims()[1] + b.dims()[1]]).unwrap()
    }
}

/// WaveNet for Audio Generation
pub struct WaveNet {
    causal_conv: Conv2d,
    residual_blocks: Vec<WaveNetBlock>,
    output_conv: Conv2d,
}

struct WaveNetBlock {
    dilated_conv: Conv2d,
    residual_conv: Conv2d,
    skip_conv: Conv2d,
    dilation: usize,
}

impl WaveNetBlock {
    fn new(channels: usize, dilation: usize) -> Self {
        WaveNetBlock {
            dilated_conv: Conv2d::new(channels, channels, (2, 1)),
            residual_conv: Conv2d::new(channels, channels, (1, 1)),
            skip_conv: Conv2d::new(channels, channels, (1, 1)),
            dilation,
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> (Tensor, Tensor) {
        let dilated = self.dilated_conv.forward(x, training);
        let gated = self.gated_activation(&dilated);
        
        let residual = self.residual_conv.forward(&gated, training);
        let skip = self.skip_conv.forward(&gated, training);
        
        let output = self.add_tensors(x, &residual);
        (output, skip)
    }

    fn gated_activation(&self, x: &Tensor) -> Tensor {
        let data = x.data_f32();
        let half = data.len() / 2;
        
        let tanh_part: Vec<f32> = data[..half].iter().map(|&v| v.tanh()).collect();
        let sigmoid_part: Vec<f32> = data[half..].iter().map(|&v| 1.0 / (1.0 + (-v).exp())).collect();
        
        let result: Vec<f32> = tanh_part.iter()
            .zip(sigmoid_part.iter())
            .map(|(&t, &s)| t * s)
            .collect();
        
        Tensor::from_slice(&result, &[x.dims()[0], x.dims()[1] / 2]).unwrap()
    }

    fn add_tensors(&self, a: &Tensor, b: &Tensor) -> Tensor {
        let a_data = a.data_f32();
        let b_data = b.data_f32();
        let result: Vec<f32> = a_data.iter()
            .zip(b_data.iter())
            .map(|(&x, &y)| x + y)
            .collect();
        Tensor::from_slice(&result, a.dims()).unwrap()
    }
}

impl WaveNet {
    pub fn new(num_channels: usize, num_blocks: usize) -> Self {
        let mut residual_blocks = Vec::new();
        for i in 0..num_blocks {
            let dilation = 2_usize.pow(i as u32 % 10);
            residual_blocks.push(WaveNetBlock::new(num_channels, dilation));
        }
        
        WaveNet {
            causal_conv: Conv2d::new(1, num_channels, (2, 1)),
            residual_blocks,
            output_conv: Conv2d::new(num_channels, 256, (1, 1)),
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.causal_conv.forward(x, training);
        let mut skip_connections = Vec::new();
        
        for block in &mut self.residual_blocks {
            let (new_out, skip) = block.forward(&out, training);
            out = new_out;
            skip_connections.push(skip);
        }
        
        let skip_sum = self.sum_tensors(&skip_connections);
        self.output_conv.forward(&skip_sum, training)
    }

    fn sum_tensors(&self, tensors: &[Tensor]) -> Tensor {
        if tensors.is_empty() {
            return Tensor::from_slice(&[0.0f32], &[1, 1]).unwrap();
        }
        
        let mut result = tensors[0].data_f32().to_vec();
        
        for tensor in &tensors[1..] {
            let data = tensor.data_f32();
            for (i, &val) in data.iter().enumerate() {
                result[i] += val;
            }
        }
        
        Tensor::from_slice(&result, tensors[0].dims()).unwrap()
    }
}

/// PointNet for Point Cloud Processing
pub struct PointNet {
    mlp1: Vec<Dense>,
    mlp2: Vec<Dense>,
    fc: Dense,
}

impl PointNet {
    pub fn new(num_points: usize, num_classes: usize) -> Self {
        PointNet {
            mlp1: vec![
                Dense::new(3, 64),
                Dense::new(64, 64),
            ],
            mlp2: vec![
                Dense::new(64, 128),
                Dense::new(128, 1024),
            ],
            fc: Dense::new(1024, num_classes),
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = x.clone();
        
        // First MLP
        for layer in &mut self.mlp1 {
            out = layer.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        // Second MLP
        for layer in &mut self.mlp2 {
            out = layer.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        // Global max pooling
        out = self.global_max_pool(&out);
        
        // Classification head
        self.fc.forward(&out, training)
    }

    fn global_max_pool(&self, x: &Tensor) -> Tensor {
        let data = x.data_f32();
        let batch_size = x.dims()[0];
        let num_points = x.dims()[1];
        let features = x.dims()[2];
        
        let mut result = vec![f32::MIN; batch_size * features];
        
        for b in 0..batch_size {
            for f in 0..features {
                for p in 0..num_points {
                    let idx = (b * num_points + p) * features + f;
                    let out_idx = b * features + f;
                    result[out_idx] = result[out_idx].max(data[idx]);
                }
            }
        }
        
        Tensor::from_slice(&result, &[batch_size, features]).unwrap()
    }
}

/// PointNet++ for Point Cloud Processing
pub struct PointNetPlusPlus {
    sa_modules: Vec<SetAbstractionModule>,
    fc_layers: Vec<Dense>,
}

struct SetAbstractionModule {
    mlp: Vec<Dense>,
}

impl SetAbstractionModule {
    fn new(in_features: usize, out_features: usize) -> Self {
        SetAbstractionModule {
            mlp: vec![
                Dense::new(in_features, out_features / 2),
                Dense::new(out_features / 2, out_features),
            ],
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = x.clone();
        
        for layer in &mut self.mlp {
            out = layer.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        out
    }
}

impl PointNetPlusPlus {
    pub fn new(num_classes: usize) -> Self {
        PointNetPlusPlus {
            sa_modules: vec![
                SetAbstractionModule::new(3, 128),
                SetAbstractionModule::new(128, 256),
                SetAbstractionModule::new(256, 512),
            ],
            fc_layers: vec![
                Dense::new(512, 256),
                Dense::new(256, 128),
                Dense::new(128, num_classes),
            ],
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = x.clone();
        
        // Set abstraction modules
        for module in &mut self.sa_modules {
            out = module.forward(&out, training);
        }
        
        // Global pooling
        out = self.global_max_pool(&out);
        
        // FC layers
        for (i, layer) in self.fc_layers.iter_mut().enumerate() {
            out = layer.forward(&out, training);
            if i < self.fc_layers.len() - 1 {
                out = ReLU::new().forward(&out);
            }
        }
        
        out
    }

    fn global_max_pool(&self, x: &Tensor) -> Tensor {
        let data = x.data_f32();
        let batch_size = x.dims()[0];
        let num_points = x.dims()[1];
        let features = x.dims()[2];
        
        let mut result = vec![f32::MIN; batch_size * features];
        
        for b in 0..batch_size {
            for f in 0..features {
                for p in 0..num_points {
                    let idx = (b * num_points + p) * features + f;
                    let out_idx = b * features + f;
                    result[out_idx] = result[out_idx].max(data[idx]);
                }
            }
        }
        
        Tensor::from_slice(&result, &[batch_size, features]).unwrap()
    }
}

/// Tacotron 2 for Text-to-Speech
pub struct Tacotron2 {
    encoder: TacotronEncoder,
    decoder: TacotronDecoder,
}

struct TacotronEncoder {
    embedding: Dense,
    convolutions: Vec<Conv2d>,
    lstm: Dense, // Simplified LSTM
}

impl TacotronEncoder {
    fn new(vocab_size: usize, embedding_dim: usize) -> Self {
        TacotronEncoder {
            embedding: Dense::new(vocab_size, embedding_dim),
            convolutions: vec![
                Conv2d::new(embedding_dim, 512, (5, 1)).padding((2, 0)),
                Conv2d::new(512, 512, (5, 1)).padding((2, 0)),
                Conv2d::new(512, 512, (5, 1)).padding((2, 0)),
            ],
            lstm: Dense::new(512, 512),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.embedding.forward(x, training);
        
        for conv in &mut self.convolutions {
            out = conv.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        self.lstm.forward(&out, training)
    }
}

struct TacotronDecoder {
    prenet: Vec<Dense>,
    attention_rnn: Dense,
    decoder_rnn: Dense,
    linear_projection: Dense,
}

impl TacotronDecoder {
    fn new(mel_dim: usize) -> Self {
        TacotronDecoder {
            prenet: vec![
                Dense::new(mel_dim, 256),
                Dense::new(256, 256),
            ],
            attention_rnn: Dense::new(256, 1024),
            decoder_rnn: Dense::new(1024, 1024),
            linear_projection: Dense::new(1024, mel_dim),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = x.clone();
        
        for layer in &mut self.prenet {
            out = layer.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        out = self.attention_rnn.forward(&out, training);
        out = self.decoder_rnn.forward(&out, training);
        self.linear_projection.forward(&out, training)
    }
}

impl Tacotron2 {
    pub fn new(vocab_size: usize, mel_dim: usize) -> Self {
        Tacotron2 {
            encoder: TacotronEncoder::new(vocab_size, 512),
            decoder: TacotronDecoder::new(mel_dim),
        }
    }

    pub fn forward(&mut self, text: &Tensor, mel: &Tensor, training: bool) -> Tensor {
        let _encoder_out = self.encoder.forward(text, training);
        self.decoder.forward(mel, training)
    }
}

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

    #[test]
    fn test_c3d() {
        let mut model = C3D::new(101);
        let input = Tensor::from_slice(&vec![0.5f32; 1 * 3 * 16], &[1, 3, 16]).unwrap();
        let output = model.forward(&input, false);
        assert_eq!(output.dims()[1], 101);
    }

    #[test]
    fn test_pointnet() {
        let mut model = PointNet::new(1024, 40);
        let input = Tensor::from_slice(&vec![0.5f32; 1 * 1024 * 3], &[1, 1024, 3]).unwrap();
        let output = model.forward(&input, false);
        assert_eq!(output.dims()[1], 40);
    }
}