ghostflow-ml 1.0.0

//! Neural Architecture Search (NAS) Architectures - DARTS, ENAS, NASNet, etc.

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

/// DARTS (Differentiable Architecture Search) Cell
pub struct DARTSCell {
    operations: Vec<Vec<MixedOp>>,
    num_nodes: usize,
}

struct MixedOp {
    ops: Vec<Operation>,
    weights: Vec<f32>,
}

enum Operation {
    SepConv3x3(Conv2d, BatchNorm2d),
    SepConv5x5(Conv2d, BatchNorm2d),
    DilConv3x3(Conv2d, BatchNorm2d),
    MaxPool3x3,
    AvgPool3x3,
    Identity,
    Zero,
}

impl Operation {
    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        match self {
            Operation::SepConv3x3(conv, bn) => {
                let mut out = conv.forward(x, training);
                out = bn.forward(&out, training);
                ReLU::new().forward(&out)
            }
            Operation::SepConv5x5(conv, bn) => {
                let mut out = conv.forward(x, training);
                out = bn.forward(&out, training);
                ReLU::new().forward(&out)
            }
            Operation::DilConv3x3(conv, bn) => {
                let mut out = conv.forward(x, training);
                out = bn.forward(&out, training);
                ReLU::new().forward(&out)
            }
            Operation::MaxPool3x3 => self.max_pool(x),
            Operation::AvgPool3x3 => self.avg_pool(x),
            Operation::Identity => x.clone(),
            Operation::Zero => self.zeros_like(x),
        }
    }

    fn max_pool(&self, x: &Tensor) -> Tensor {
        x.clone() // Simplified
    }

    fn avg_pool(&self, x: &Tensor) -> Tensor {
        x.clone() // Simplified
    }

    fn zeros_like(&self, x: &Tensor) -> Tensor {
        Tensor::from_slice(&vec![0.0f32; x.data_f32().len()], x.dims()).unwrap()
    }
}

impl MixedOp {
    fn new(channels: usize) -> Self {
        MixedOp {
            ops: vec![
                Operation::SepConv3x3(Conv2d::new(channels, channels, (3, 3)).padding((1, 1)), BatchNorm2d::new(channels)),
                Operation::SepConv5x5(Conv2d::new(channels, channels, (5, 5)).padding((2, 2)), BatchNorm2d::new(channels)),
                Operation::DilConv3x3(Conv2d::new(channels, channels, (3, 3)).padding((2, 2)), BatchNorm2d::new(channels)),
                Operation::MaxPool3x3,
                Operation::AvgPool3x3,
                Operation::Identity,
                Operation::Zero,
            ],
            weights: vec![1.0 / 7.0; 7],
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut result = vec![0.0f32; x.data_f32().len()];
        
        for (op, &weight) in self.ops.iter_mut().zip(self.weights.iter()) {
            let op_out = op.forward(x, training);
            let op_data = op_out.data_f32();
            
            for (i, &val) in op_data.iter().enumerate() {
                result[i] += weight * val;
            }
        }
        
        Tensor::from_slice(&result, x.dims()).unwrap()
    }
}

impl DARTSCell {
    pub fn new(channels: usize, num_nodes: usize) -> Self {
        let mut operations = Vec::new();
        
        for i in 0..num_nodes {
            let mut node_ops = Vec::new();
            for _ in 0..=i+1 {
                node_ops.push(MixedOp::new(channels));
            }
            operations.push(node_ops);
        }
        
        DARTSCell {
            operations,
            num_nodes,
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut states = vec![x.clone()];
        
        for node_ops in &mut self.operations {
            let mut node_out = vec![0.0f32; x.data_f32().len()];
            
            for (i, op) in node_ops.iter_mut().enumerate() {
                let op_result = op.forward(&states[i], training);
                let op_data = op_result.data_f32();
                
                for (j, &val) in op_data.iter().enumerate() {
                    node_out[j] += val;
                }
            }
            
            states.push(Tensor::from_slice(&node_out, x.dims()).unwrap());
        }
        
        // Concatenate all intermediate nodes
        states[1..].iter().fold(states[1].clone(), |acc, s| acc)
    }
}

/// DARTS Network
pub struct DARTS {
    stem: Conv2d,
    cells: Vec<DARTSCell>,
    fc: Dense,
}

impl DARTS {
    pub fn new(num_classes: usize, num_cells: usize) -> Self {
        DARTS {
            stem: Conv2d::new(3, 48, (3, 3)).padding((1, 1)),
            cells: (0..num_cells).map(|_| DARTSCell::new(48, 4)).collect(),
            fc: Dense::new(48, num_classes),
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.stem.forward(x, training);
        
        for cell in &mut self.cells {
            out = cell.forward(&out, training);
        }
        
        self.fc.forward(&out, training)
    }
}

/// ENAS (Efficient Neural Architecture Search) Controller
pub struct ENASController {
    lstm: Dense, // Simplified LSTM
    classifier: Dense,
}

impl ENASController {
    pub fn new(hidden_size: usize, num_ops: usize) -> Self {
        ENASController {
            lstm: Dense::new(hidden_size, hidden_size),
            classifier: Dense::new(hidden_size, num_ops),
        }
    }

    pub fn sample_architecture(&mut self, num_layers: usize, training: bool) -> Vec<usize> {
        let mut architecture = Vec::new();
        let mut hidden = Tensor::from_slice(&vec![0.1f32; 128], &[1, 128]).unwrap();
        
        for _ in 0..num_layers {
            hidden = self.lstm.forward(&hidden, training);
            let logits = self.classifier.forward(&hidden, training);
            
            // Sample operation (simplified - just take argmax)
            let op_idx = self.argmax(&logits);
            architecture.push(op_idx);
        }
        
        architecture
    }

    fn argmax(&self, x: &Tensor) -> usize {
        let data = x.data_f32();
        let mut max_idx = 0;
        let mut max_val = data[0];
        
        for (i, &val) in data.iter().enumerate() {
            if val > max_val {
                max_val = val;
                max_idx = i;
            }
        }
        
        max_idx
    }
}

/// NASNet Cell
pub struct NASNetCell {
    operations: Vec<NASNetOp>,
}

struct NASNetOp {
    conv: Conv2d,
    bn: BatchNorm2d,
}

impl NASNetOp {
    fn new(in_channels: usize, out_channels: usize, kernel_size: usize) -> Self {
        NASNetOp {
            conv: Conv2d::new(in_channels, out_channels, (kernel_size, kernel_size)).padding((kernel_size / 2, kernel_size / 2)),
            bn: BatchNorm2d::new(out_channels),
        }
    }

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

impl NASNetCell {
    pub fn new(channels: usize) -> Self {
        NASNetCell {
            operations: vec![
                NASNetOp::new(channels, channels, 3),
                NASNetOp::new(channels, channels, 5),
                NASNetOp::new(channels, channels, 3),
                NASNetOp::new(channels, channels, 5),
            ],
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut outputs = Vec::new();
        
        for op in &mut self.operations {
            outputs.push(op.forward(x, training));
        }
        
        // Combine outputs
        self.combine_outputs(&outputs)
    }

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

/// NASNet
pub struct NASNet {
    stem: Conv2d,
    cells: Vec<NASNetCell>,
    fc: Dense,
}

impl NASNet {
    pub fn new(num_classes: usize, num_cells: usize) -> Self {
        NASNet {
            stem: Conv2d::new(3, 96, (3, 3)).padding((1, 1)),
            cells: (0..num_cells).map(|_| NASNetCell::new(96)).collect(),
            fc: Dense::new(96, num_classes),
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.stem.forward(x, training);
        
        for cell in &mut self.cells {
            out = cell.forward(&out, training);
        }
        
        self.fc.forward(&out, training)
    }
}

/// AmoebaNet (Evolution-based NAS)
pub struct AmoebaNet {
    stem: Conv2d,
    cells: Vec<AmoebaCell>,
    fc: Dense,
}

struct AmoebaCell {
    ops: Vec<(Conv2d, BatchNorm2d)>,
}

impl AmoebaCell {
    fn new(channels: usize) -> Self {
        AmoebaCell {
            ops: vec![
                (Conv2d::new(channels, channels, (3, 3)).padding((1, 1)), BatchNorm2d::new(channels)),
                (Conv2d::new(channels, channels, (5, 5)).padding((2, 2)), BatchNorm2d::new(channels)),
                (Conv2d::new(channels, channels, (7, 7)).padding((3, 3)), BatchNorm2d::new(channels)),
            ],
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut outputs = Vec::new();
        
        for (conv, bn) in &mut self.ops {
            let mut out = conv.forward(x, training);
            out = bn.forward(&out, training);
            out = ReLU::new().forward(&out);
            outputs.push(out);
        }
        
        self.combine_outputs(&outputs)
    }

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

impl AmoebaNet {
    pub fn new(num_classes: usize, num_cells: usize) -> Self {
        AmoebaNet {
            stem: Conv2d::new(3, 128, (3, 3)).padding((1, 1)),
            cells: (0..num_cells).map(|_| AmoebaCell::new(128)).collect(),
            fc: Dense::new(128, num_classes),
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.stem.forward(x, training);
        
        for cell in &mut self.cells {
            out = cell.forward(&out, training);
        }
        
        self.fc.forward(&out, training)
    }
}

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

    #[test]
    fn test_darts() {
        let mut model = DARTS::new(10, 8);
        let input = Tensor::from_slice(&vec![0.5f32; 1 * 3 * 32 * 32], &[1, 3, 32, 32]).unwrap();
        let output = model.forward(&input, false);
        assert_eq!(output.dims()[1], 10);
    }

    #[test]
    fn test_nasnet() {
        let mut model = NASNet::new(1000, 6);
        let input = Tensor::from_slice(&vec![0.5f32; 1 * 3 * 224 * 224], &[1, 3, 224, 224]).unwrap();
        let output = model.forward(&input, false);
        assert_eq!(output.dims()[1], 1000);
    }
}