experiments/train_ablation.py

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import os
import time
import numpy as np
from tqdm import tqdm
import sys
import argparse
from pathlib import Path

# Add project root to path
sys.path.append(str(Path(__file__).parent.parent))

from src.data.dataset import GazeDataset

# --- 1. Loss Functions ---

class SmoothAWLoss(nn.Module):
    def __init__(self, omega=8.0, alpha=1.5, theta=0.5, epsilon=1.0):
        super(SmoothAWLoss, self).__init__()
        self.omega = omega
        self.alpha = alpha
        self.theta = theta
        self.epsilon = epsilon

    def forward(self, y_pred, y_true):
        delta_y = (y_true - y_pred).abs()
        device = y_pred.device
        mask = delta_y < self.theta
        loss = torch.zeros_like(delta_y)
        loss[mask] = self.omega * torch.log(1 + torch.pow(delta_y[mask] / self.epsilon, self.alpha))
        theta_eps = torch.tensor(self.theta / self.epsilon, device=device)
        A = self.omega * (1.0 / (1.0 + torch.pow(theta_eps, self.alpha))) * \
            (self.alpha * torch.pow(theta_eps, self.alpha - 1.0) * (1.0 / self.epsilon))
        B = A * self.theta - self.omega * torch.log(1.0 + torch.pow(theta_eps, self.alpha))
        loss[~mask] = A * delta_y[~mask] - B
        return loss.mean()

class DistillationLoss(nn.Module):
    def __init__(self, temperature=2.0, alpha=0.5, base_criterion=nn.MSELoss()):
        super(DistillationLoss, self).__init__()
        self.temp = temperature
        self.alpha = alpha
        self.base_criterion = base_criterion
        self.kl_div = nn.KLDivLoss(reduction='batchmean')

    def forward(self, student_logits, teacher_logits, labels):
        # student_logits/teacher_logits are tuples (pitch, yaw)
        p_s, y_s = student_logits
        p_t, y_t = teacher_logits
        
        # Soft targets loss
        loss_p_kd = self.kl_div(torch.log_softmax(p_s / self.temp, dim=1),
                                torch.softmax(p_t / self.temp, dim=1)) * (self.temp ** 2)
        loss_y_kd = self.kl_div(torch.log_softmax(y_s / self.temp, dim=1),
                                torch.softmax(y_t / self.temp, dim=1)) * (self.temp ** 2)
        
        # Hard targets loss (GT)
        loss_gt = self.base_criterion(student_logits, labels) # This assumes base_criterion handles the logits/labels correctly
        
        return self.alpha * (loss_p_kd + loss_y_kd) + (1 - self.alpha) * loss_gt

# --- 2. Model Architecture with Ablation Toggles ---

class FlexibleMiniConv(nn.Module):
    def __init__(self, pool_type='avg'):
        super(FlexibleMiniConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=3, padding=0), nn.ReLU(inplace=True),
            nn.Conv2d(16, 32, kernel_size=3, padding=0), nn.ReLU(inplace=True),
            nn.Conv2d(32, 64, kernel_size=3, padding=0), nn.ReLU(inplace=True)
        )
        self.pool_type = pool_type
        if pool_type == 'avg':
            self.pool = nn.AdaptiveAvgPool2d(1)
            self.out_channels = 64
        else: # Dual Pool
            self.avg_pool = nn.AdaptiveAvgPool2d(1)
            self.max_pool = nn.AdaptiveMaxPool2d(1)
            self.out_channels = 128

    def forward(self, x):
        x = self.conv(x)
        if self.pool_type == 'avg':
            return self.pool(x).flatten(1)
        else:
            return torch.cat([self.avg_pool(x), self.max_pool(x)], dim=1).flatten(1)

class LIPEV2AblationModel(nn.Module):
    def __init__(self, pool_type='avg'):
        super(LIPEV2AblationModel, self).__init__()
        self.app_net = FlexibleMiniConv(pool_type=pool_type) 
        # app_net output: out_channels * 4 patches
        app_out_dim = self.app_net.out_channels * 4
        
        self.geo_net = nn.Sequential(
            nn.Linear(956, 256),
            nn.LayerNorm(256),
            nn.ReLU(inplace=True),
            nn.Linear(256, 256),
            nn.ReLU(inplace=True)
        )
        
        self.post_concat_bn = nn.BatchNorm1d(app_out_dim + 256)
        
        self.fusion = nn.Sequential(
            nn.Linear(app_out_dim + 256, 256),
            nn.ReLU(inplace=True),
            nn.Dropout(0.1),
            nn.Linear(256, 128),
            nn.ReLU(inplace=True)
        )
        
        self.pitch_head = nn.Linear(128, 90)
        self.yaw_head = nn.Linear(128, 90)

    def forward(self, patches, landmarks):
        batch_size = patches.shape[0]
        p_h, p_w = patches.shape[2], patches.shape[3]
        app_feat = self.app_net(patches.view(-1, 1, p_h, p_w)).view(batch_size, -1)
        geo_feat = self.geo_net(landmarks)
        
        combined = torch.cat([app_feat, geo_feat], dim=1)
        combined = self.post_concat_bn(combined)
        fused = self.fusion(combined)
        return self.pitch_head(fused), self.yaw_head(fused)

# --- 3. Training Function ---

def train_ablation(args):
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(f"\n>>> ABLATION STUDY ID: #{args.id} | Participant: {args.participant}")
    print(f">>> Config: Pool={args.pooling}, Loss={args.loss}, KD={args.kd}")

    # Load Data
    h5_dir = 'data/processed'
    train_files = [os.path.join(h5_dir, f) for f in os.listdir(h5_dir) if f.endswith('_v16.h5') and not f.startswith(args.participant)]
    val_files = [os.path.join(h5_dir, f) for f in os.listdir(h5_dir) if f.startswith(args.participant) and f.endswith('_v16.h5')]

    train_loader = DataLoader(GazeDataset(train_files, transform=True), batch_size=32, shuffle=True, num_workers=args.workers)
    val_loader = DataLoader(GazeDataset(val_files, transform=False), batch_size=32, shuffle=False, num_workers=args.workers)

    # Model
    model = LIPEV2AblationModel(pool_type=args.pooling).to(device)
    
    # KD Setup
    teacher_model = None
    if args.kd:
        from src.models.loss import GazeDistillationLoss
        # We assume the GazeDataset provides teacher predictions or we need a teacher model
        # For simplicity in this ablation, if GazeDataset has t_p, t_y, we use them
        kd_criterion = GazeDistillationLoss(w_aw=1.0, w_kd=0.5)
        print(">>> KD Enabled: Using GazeDistillationLoss")

    # Base Loss
    if args.loss == 'smoothaw':
        criterion = SmoothAWLoss()
    else:
        criterion = nn.MSELoss()

    optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-2)
    scheduler = optim.lr_scheduler.OneCycleLR(optimizer, max_lr=args.lr, steps_per_epoch=len(train_loader), epochs=args.epochs)
    
    idx_tensor = torch.arange(90).float().to(device)
    best_mae = float('inf')
    
    # Checkpoints dir
    ckpt_dir = f"checkpoints/ablation/id_{args.id}"
    os.makedirs(ckpt_dir, exist_ok=True)

    for epoch in range(1, args.epochs + 1):
        model.train()
        running_loss = 0.0
        pbar = tqdm(train_loader, desc=f"Epoch {epoch}")
        for batch in pbar:
            # batch: [patches, landmarks, gaze, t_p, t_y, domain]
            batch = [b.to(device) for b in batch]
            optimizer.zero_grad()
            
            p_logits, y_logits = model(batch[0], batch[1])
            s_p = (torch.softmax(p_logits, dim=1) * idx_tensor).sum(1) * 2 - 90
            s_y = (torch.softmax(y_logits, dim=1) * idx_tensor).sum(1) * 2 - 90
            
            if args.kd:
                # GazeDistillationLoss takes (p_logits, y_logits), gaze_gt, (t_p, t_y)
                loss, _, _ = kd_criterion((p_logits, y_logits), batch[2], (batch[3], batch[4]))
            else:
                # Simple loss for this ablation
                loss = criterion(torch.stack([s_p, s_y], dim=1), batch[2] * (180.0 / np.pi))
            
            loss.backward()
            optimizer.step()
            scheduler.step()
            running_loss += loss.item()

        # Validation
        model.eval()
        total_error, count = 0.0, 0
        with torch.no_grad():
            for batch in val_loader:
                batch = [b.to(device) for b in batch]
                p_l, y_l = model(batch[0], batch[1])
                p_d = (torch.softmax(p_l, dim=1) * idx_tensor).sum(1) * 2 - 90
                y_d = (torch.softmax(y_l, dim=1) * idx_tensor).sum(1) * 2 - 90
                gt_d = batch[2] * (180.0 / np.pi)
                total_error += (torch.abs(p_d - gt_d[:,0]) + torch.abs(y_d - gt_d[:,1])).sum().item()
                count += batch[2].shape[0]
        
        val_mae = total_error / (count * 2) if count > 0 else 0
        print(f"Epoch {epoch}: Val MAE {val_mae:.4f}")
        
        if val_mae < best_mae:
            best_mae = val_mae
            torch.save(model.state_dict(), f"{ckpt_dir}/best_{args.participant}.pt")
            print(f"--- New Best: {best_mae:.4f} ---")

    # Final log
    with open('logs/ablation_results.log', 'a') as f:
        f.write(f"ID: {args.id}, Participant: {args.participant}, MAE: {best_mae:.4f}, Config: {args.pooling}/{args.loss}/KD={args.kd}\n")

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--id', type=int, required=True, help="Ablation ID (1-8)")
    parser.add_argument('--participant', type=str, default='p11')
    parser.add_argument('--pooling', choices=['avg', 'dual'], default='avg')
    parser.add_argument('--loss', choices=['mse', 'smoothaw'], default='mse')
    parser.add_argument('--kd', action='store_true')
    parser.add_argument('--epochs', type=int, default=50)
    parser.add_argument('--lr', type=float, default=1e-4)
    parser.add_argument('--workers', type=int, default=4)
    args = parser.parse_args()
    
    # Auto-config based on ID if provided (optional override)
    # 1: avg/mse/no, 2: dual/mse/no, 3: avg/saw/no, 4: avg/mse/yes
    # 5: dual/saw/no, 6: dual/mse/yes, 7: avg/saw/yes, 8: dual/saw/yes
    configs = {
        1: ('avg', 'mse', False),
        2: ('dual', 'mse', False),
        3: ('avg', 'smoothaw', False),
        4: ('avg', 'mse', True),
        5: ('dual', 'smoothaw', False),
        6: ('dual', 'mse', True),
        7: ('avg', 'smoothaw', True),
        8: ('dual', 'smoothaw', True),
    }
    if args.id in configs:
        args.pooling, args.loss, args.kd = configs[args.id]
        
    train_ablation(args)