Battle against a True Hero

rewrite_training.py

@@ -1,759 +1,209 @@
 import os
-import json
-import math
-import numpy as np
 import polars as pl
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import torch.optim as optim
+from torch.utils.data import TensorDataset, DataLoader
-
+from concurrent.futures import ProcessPoolExecutor
-from torch.utils.data import Dataset, DataLoader
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import LabelEncoder
-from multiprocessing import Pool, cpu_count
-from functools import partial
 from tqdm import tqdm
-from collections import Counter
+from sklearn.model_selection import train_test_split
 
-# ===============================
+# --- CONFIGURATION ---
-# GPU CONFIGURATION
+BASE_PATH = "asl_kaggle"
-# ===============================
+TARGET_FRAMES = 22
-print("=" * 60)
+# Hand landmarks + Lip landmarks (approximate indices for high-value face points)
-print("GPU CONFIGURATION")
+LIPS = [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, 308, 324, 318, 402, 317, 14, 87, 178, 88, 95]
-print("=" * 60)
+HANDS = list(range(468, 543))
-
+SELECTED_INDICES = LIPS + HANDS
-if torch.cuda.is_available():
+NUM_FEATS = len(SELECTED_INDICES) * 3  # X, Y, Z for each selected point
-    print(f"✓ CUDA available!")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    print(f"✓ GPU: {torch.cuda.get_device_name(0)}")
-    device = torch.device('cuda:0')
-    torch.backends.cudnn.benchmark = True
-    torch.backends.cudnn.enabled = True
-else:
-    print("✗ CUDA not available, using CPU")
-    device = torch.device('cpu')
-
-print("=" * 60)
-
-# ===============================
-# SELECTED LANDMARK INDICES
-# ===============================
-IMPORTANT_FACE_INDICES = sorted(list(set([
-    0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
-    55, 65, 66, 105, 107, 336, 296, 334,
-    33, 133, 160, 159, 158, 144, 145, 153,
-    362, 263, 387, 386, 385, 373, 374, 380,
-    1, 2, 98, 327,
-    61, 185, 40, 39, 37, 0, 267, 269, 270, 409,
-    291, 146, 91, 181, 84, 17, 314, 405, 321, 375,
-    78, 191, 80, 81, 82, 13, 312, 311, 310, 415,
-    308, 324, 318, 402, 317, 14, 87, 178, 88, 95
-])))
-
-NUM_FACE_POINTS = len(IMPORTANT_FACE_INDICES)
-NUM_HAND_POINTS = 21 * 2
-TOTAL_POINTS_PER_FRAME = NUM_HAND_POINTS + NUM_FACE_POINTS
 
 
-# ===============================
+# --- DATA PROCESSING ---
-# DATA AUGMENTATION
-# ===============================
-def augment_sequence(x, modality_mask):
-    """Apply random augmentations to training data"""
-    x = x.copy()
 
-    # Random temporal cropping (simulate different signing speeds)
+def load_kaggle_metadata(base_path):
-    if np.random.rand() < 0.3 and len(x) > 20:
+    return pl.read_csv(os.path.join(base_path, "train.csv"))
-        start = np.random.randint(0, max(1, len(x) // 4))
-        x = x[start:]
-        modality_mask = modality_mask[start:]
-
-    # Random spatial scaling
-    if np.random.rand() < 0.5:
-        scale = np.random.uniform(0.85, 1.15)
-        x = x * scale
-
-    # Random rotation (around z-axis for x,y coordinates)
-    if np.random.rand() < 0.5:
-        angle = np.random.uniform(-0.3, 0.3)
-        cos_a, sin_a = np.cos(angle), np.sin(angle)
-
-        # Reshape to get xyz coordinates
-        x_reshaped = x.reshape(len(x), -1, 3)
-        x_rot = x_reshaped.copy()
-        x_rot[..., 0] = x_reshaped[..., 0] * cos_a - x_reshaped[..., 1] * sin_a
-        x_rot[..., 1] = x_reshaped[..., 0] * sin_a + x_reshaped[..., 1] * cos_a
-        x = x_rot.reshape(x.shape)
-
-    # Random masking (simulate occlusion) - only for some frames
-    if np.random.rand() < 0.3:
-        n_mask = int(len(x) * 0.15)  # mask 15% of frames
-        mask_indices = np.random.choice(len(x), n_mask, replace=False)
-        x[mask_indices] *= 0.1  # dim but don't completely zero
-
-    # Random noise
-    if np.random.rand() < 0.4:
-        noise = np.random.normal(0, 0.02, x.shape)
-        x = x + noise
-
-    # Random time warping (speed up or slow down)
-    if np.random.rand() < 0.3 and len(x) > 20:
-        speed = np.random.uniform(0.8, 1.2)
-        new_len = int(len(x) * speed)
-        new_len = min(new_len, len(x))
-        indices = np.linspace(0, len(x) - 1, new_len).astype(int)
-        x = x[indices]
-        modality_mask = modality_mask[indices]
-
-    return x, modality_mask
 
 
-# ===============================
+def load_and_preprocess(path, base_path=BASE_PATH, target_frames=TARGET_FRAMES):
-# ENHANCED DATA EXTRACTION (POLARS)
+    parquet_path = os.path.join(base_path, path)
-# ===============================
+    df = pl.read_parquet(parquet_path)
-def extract_multi_landmarks(path, min_valid_frames=3):
-    """
-    Extract both hands + selected face landmarks with modality flags
-    Returns: dict with 'landmarks', 'left_hand_valid', 'right_hand_valid', 'face_valid'
-    """
-    try:
-        df = pl.read_parquet(path)
-        seq = []
-        left_valid_frames = []
-        right_valid_frames = []
-        face_valid_frames = []
 
-        all_types = df.select("type").unique().to_series().to_list()
+    # 1. Spatial Normalization (Nose Anchor)
-        has_data = any(t in all_types for t in ["left_hand", "right_hand", "face"])
+    anchors = (
+        df.filter((pl.col("type") == "face") & (pl.col("landmark_index") == 0))
+        .select([pl.col("frame"), pl.col("x").alias("nx"), pl.col("y").alias("ny"), pl.col("z").alias("nz")])
+    )
 
-        if not has_data:
+    processed = (
-            return None
+        df.join(anchors, on="frame", how="left")
+        .with_columns([
+            (pl.col("x") - pl.col("nx")).fill_null(0.0),
+            (pl.col("y") - pl.col("ny")).fill_null(0.0),
+            (pl.col("z") - pl.col("nz")).fill_null(0.0),
+        ])
+        .sort(["frame", "type", "landmark_index"])
+    )
 
-        frames = sorted(df.select("frame").unique().to_series().to_list())
+    # 2. Reshape & Feature Selection
+    # Get unique frames and total landmarks (543)
+    raw_tensor = processed.select(["x", "y", "z"]).to_numpy().reshape(-1, 543, 3)
 
-        if len(frames) < min_valid_frames:
+    # Slice to keep only Hands and Lips
-            return None
+    reduced_tensor = raw_tensor[:, SELECTED_INDICES, :]
 
-        for frame in frames:
+    # 3. Temporal Normalization (Resample to fixed frame count)
-            frame_df = df.filter(pl.col("frame") == frame)
+    curr_len = reduced_tensor.shape[0]
-            frame_points = np.full((TOTAL_POINTS_PER_FRAME, 3), np.nan, dtype=np.float32)
+    indices = np.linspace(0, curr_len - 1, num=target_frames).round().astype(int)
-
+    return reduced_tensor[indices]
-            pos = 0
-            left_valid = False
-            right_valid = False
-            face_valid = False
-
-            # Left hand
-            left = frame_df.filter(pl.col("type") == "left_hand")
-            if left.height > 0:
-                valid_count = 0
-                for i in range(21):
-                    row = left.filter(pl.col("landmark_index") == i)
-                    if row.height > 0:
-                        coords = row.select(["x", "y", "z"]).row(0)
-                        if all(c is not None for c in coords):
-                            frame_points[pos] = coords
-                            valid_count += 1
-                    pos += 1
-                left_valid = (valid_count >= 10)
-            else:
-                pos += 21
-
-            # Right hand
-            right = frame_df.filter(pl.col("type") == "right_hand")
-            if right.height > 0:
-                valid_count = 0
-                for i in range(21):
-                    row = right.filter(pl.col("landmark_index") == i)
-                    if row.height > 0:
-                        coords = row.select(["x", "y", "z"]).row(0)
-                        if all(c is not None for c in coords):
-                            frame_points[pos] = coords
-                            valid_count += 1
-                    pos += 1
-                right_valid = (valid_count >= 10)
-            else:
-                pos += 21
-
-            # Face
-            face = frame_df.filter(pl.col("type") == "face")
-            if face.height > 0:
-                valid_count = 0
-                for idx in IMPORTANT_FACE_INDICES:
-                    row = face.filter(pl.col("landmark_index") == idx)
-                    if row.height > 0:
-                        coords = row.select(["x", "y", "z"]).row(0)
-                        if all(c is not None for c in coords):
-                            frame_points[pos] = coords
-                            valid_count += 1
-                    pos += 1
-                face_valid = (valid_count >= len(IMPORTANT_FACE_INDICES) * 0.3)
-
-            valid_ratio = 1 - np.isnan(frame_points).mean()
-            if valid_ratio >= 0.20:
-                frame_points = np.nan_to_num(frame_points, nan=0.0)
-                seq.append(frame_points)
-                left_valid_frames.append(left_valid)
-                right_valid_frames.append(right_valid)
-                face_valid_frames.append(face_valid)
-
-        if len(seq) < min_valid_frames:
-            return None
-
-        return {
-            'landmarks': np.stack(seq),
-            'left_hand_valid': np.array(left_valid_frames),
-            'right_hand_valid': np.array(right_valid_frames),
-            'face_valid': np.array(face_valid_frames)
-        }
-
-    except Exception as e:
-        return None
 
 
-def get_features_sequence(landmarks_data, max_frames=100):
+# --- MODEL ARCHITECTURE ---
-    """Enhanced feature extraction with separate modality processing"""
-    if landmarks_data is None:
-        return None, None, None
 
-    landmarks_3d = landmarks_data['landmarks']
+class ASLClassifier(nn.Module):
-    if len(landmarks_3d) == 0:
+    def __init__(self, num_classes, target_frames=TARGET_FRAMES, num_feats=NUM_FEATS):
-        return None, None, None
-
-    T, N, _ = landmarks_3d.shape
-
-    # Separate modalities for independent normalization
-    left_hand = landmarks_3d[:, :21, :]
-    right_hand = landmarks_3d[:, 21:42, :]
-    face = landmarks_3d[:, 42:, :]
-
-    features_list = []
-
-    for modality, valid_mask in [
-        (left_hand, landmarks_data['left_hand_valid']),
-        (right_hand, landmarks_data['right_hand_valid']),
-        (face, landmarks_data['face_valid'])
-    ]:
-        valid_frames = modality[valid_mask] if valid_mask.any() else modality
-        if len(valid_frames) > 0:
-            center = np.mean(valid_frames, axis=(0, 1), keepdims=True)
-            spread = np.std(valid_frames, axis=(0, 1), keepdims=True).max()
-        else:
-            center = 0
-            spread = 1
-
-        modality_norm = (modality - center) / max(spread, 1e-6)
-        flat = modality_norm.reshape(T, -1)
-
-        # Deltas
-        deltas = np.zeros_like(flat)
-        if T > 1:
-            deltas[1:] = flat[1:] - flat[:-1]
-
-        features_list.append(flat)
-        features_list.append(deltas)
-
-    features = np.concatenate(features_list, axis=1)
-
-    modality_mask = np.stack([
-        landmarks_data['left_hand_valid'],
-        landmarks_data['right_hand_valid'],
-        landmarks_data['face_valid']
-    ], axis=1).astype(np.float32)
-
-    # Pad/truncate
-    if T < max_frames:
-        pad = np.zeros((max_frames - T, features.shape[1]), dtype=np.float32)
-        features = np.concatenate([features, pad], axis=0)
-
-        mask_pad = np.zeros((max_frames - T, 3), dtype=np.float32)
-        modality_mask = np.concatenate([modality_mask, mask_pad], axis=0)
-
-        frame_mask = np.zeros(max_frames, dtype=bool)
-        frame_mask[:T] = True
-    else:
-        features = features[:max_frames]
-        modality_mask = modality_mask[:max_frames]
-        frame_mask = np.ones(max_frames, dtype=bool)
-
-    features = np.nan_to_num(features, nan=0.0, posinf=0.0, neginf=0.0)
-    features = np.clip(features, -30, 30)
-
-    return features.astype(np.float32), frame_mask, modality_mask
-
-
-def process_row(row_data, base_path, max_frames=100):
-    """Process a single row"""
-    path_rel, sign = row_data
-    path = os.path.join(base_path, path_rel)
-    if not os.path.exists(path):
-        return None, None, None, None
-
-    try:
-        lm_data = extract_multi_landmarks(path)
-        if lm_data is None:
-            return None, None, None, None
-
-        feat, frame_mask, modality_mask = get_features_sequence(lm_data, max_frames)
-        if feat is None:
-            return None, None, None, None
-
-        return feat, frame_mask, modality_mask, sign
-
-    except Exception:
-        return None, None, None, None
-
-
-# ===============================
-# MIXUP AUGMENTATION
-# ===============================
-def mixup_data(x, frame_mask, modality_mask, y, alpha=0.2):
-    """Mixup augmentation"""
-    if alpha > 0:
-        lam = np.random.beta(alpha, alpha)
-    else:
-        lam = 1
-
-    batch_size = x.size(0)
-    index = torch.randperm(batch_size).to(x.device)
-
-    mixed_x = lam * x + (1 - lam) * x[index]
-    mixed_frame_mask = frame_mask | frame_mask[index]  # Union of valid frames
-    mixed_modality_mask = torch.max(modality_mask, modality_mask[index])
-
-    y_a, y_b = y, y[index]
-    return mixed_x, mixed_frame_mask, mixed_modality_mask, y_a, y_b, lam
-
-
-# ===============================
-# ENHANCED MODEL WITH ATTENTION POOLING
-# ===============================
-class PositionalEncoding(nn.Module):
-    def __init__(self, d_model, max_len=128):
         super().__init__()
-        pe = torch.zeros(max_len, d_model)
+        self.conv1 = nn.Conv1d(num_feats, 256, kernel_size=3, padding=1)
-        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        self.bn1 = nn.BatchNorm1d(256)
-        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
+        self.conv2 = nn.Conv1d(256, 512, kernel_size=3, padding=1)
-        pe[:, 0::2] = torch.sin(position * div_term)
+        self.bn2 = nn.BatchNorm1d(512)
-        pe[:, 1::2] = torch.cos(position * div_term)
+        self.pool = nn.MaxPool1d(2)
-        self.register_buffer('pe', pe.unsqueeze(0))
+        self.dropout = nn.Dropout(0.5)
+        self.fc = nn.Linear(512, num_classes)
 
     def forward(self, x):
-        return x + self.pe[:, :x.size(1)]
+        # x: (Batch, Frames, Selected_Landmarks, 3)
+        x = x.view(x.shape[0], x.shape[1], -1)  # Flatten landmarks/coords
+        x = x.transpose(1, 2)  # (Batch, Features, Time)
+
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = self.pool(x)
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = self.pool(x)
+
+        x = F.adaptive_avg_pool1d(x, 1).squeeze(-1)
+        x = self.dropout(x)
+        return self.fc(x)
 
 
-class ModalityAwareTransformer(nn.Module):
+# --- TRAINING FUNCTION ---
-    def __init__(self, input_dim, num_classes, d_model=512, nhead=8, num_layers=6, dropout=0.15):
-        super().__init__()
 
-        # Main projection
+def train_model(model, train_loader, val_loader, epochs=20, lr=0.001):
-        self.proj = nn.Linear(input_dim, d_model)
+    criterion = nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(model.parameters(), lr=lr)
+    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', patience=3, factor=0.5)
 
-        # Modality embedding (3 modalities: left_hand, right_hand, face)
+    best_val_acc = 0.0
-        self.modality_embed = nn.Linear(3, d_model)
-
-        self.norm_in = nn.LayerNorm(d_model)
-        self.pos = PositionalEncoding(d_model)
-
-        enc_layer = nn.TransformerEncoderLayer(
-            d_model=d_model,
-            nhead=nhead,
-            dim_feedforward=d_model * 4,
-            dropout=dropout,
-            activation='gelu',
-            batch_first=True,
-            norm_first=True
-        )
-        self.encoder = nn.TransformerEncoder(enc_layer, num_layers=num_layers)
-
-        # Attention pooling
-        self.attention_pool = nn.Linear(d_model, 1)
-
-        self.head = nn.Sequential(
-            nn.LayerNorm(d_model),
-            nn.Dropout(0.3),
-            nn.Linear(d_model, d_model // 2),
-            nn.GELU(),
-            nn.Dropout(0.2),
-            nn.Linear(d_model // 2, num_classes)
-        )
-
-    def forward(self, x, modality_mask=None, key_padding_mask=None):
-        # Project features
-        x = self.proj(x)
-
-        # Add modality information
-        if modality_mask is not None:
-            mod_embed = self.modality_embed(modality_mask)
-            x = x + mod_embed
-
-        x = self.norm_in(x)
-        x = self.pos(x)
-        x = self.encoder(x, src_key_padding_mask=key_padding_mask)
-
-        # Attention-based pooling
-        attn_weights = self.attention_pool(x)  # (B, T, 1)
-        if key_padding_mask is not None:
-            attn_weights = attn_weights.masked_fill(key_padding_mask.unsqueeze(-1), -1e9)
-        attn_weights = F.softmax(attn_weights, dim=1)
-        x = (x * attn_weights).sum(dim=1)
-
-        return self.head(x)
-
-
-def load_kaggle_asl_data(base_path):
-    """Load training metadata using Polars"""
-    train_path = os.path.join(base_path, "train.csv")
-    train_df = pl.read_csv(train_path)
-    return train_df, None
-
-
-# ===============================
-# DATASET WITH AUGMENTATION
-# ===============================
-class ASLMultiDataset(Dataset):
-    def __init__(self, X, frame_masks, modality_masks, y, training=False, max_frames=100):
-        self.X = X
-        self.frame_masks = frame_masks
-        self.modality_masks = modality_masks
-        self.y = y
-        self.training = training
-        self.max_frames = max_frames
-
-    def __len__(self):
-        return len(self.X)
-
-    def __getitem__(self, idx):
-        x = self.X[idx].copy()
-        frame_mask = self.frame_masks[idx].copy()
-        modality_mask = self.modality_masks[idx].copy()
-        y = self.y[idx]
-
-        if self.training:
-            # Apply augmentation
-            x, modality_mask = augment_sequence(x, modality_mask)
-
-            # Re-pad if needed after augmentation
-            if len(x) < self.max_frames:
-                pad = np.zeros((self.max_frames - len(x), x.shape[1]), dtype=np.float32)
-                x = np.concatenate([x, pad], axis=0)
-
-                mask_pad = np.zeros((self.max_frames - len(x), 3), dtype=np.float32)
-                modality_mask = np.concatenate([modality_mask, mask_pad], axis=0)
-
-                frame_mask = np.zeros(self.max_frames, dtype=bool)
-                frame_mask[:len(x)] = True
-            else:
-                x = x[:self.max_frames]
-                modality_mask = modality_mask[:self.max_frames]
-                frame_mask = np.ones(self.max_frames, dtype=bool)
-
-        return (
-            torch.from_numpy(x).float(),
-            torch.from_numpy(frame_mask).bool(),
-            torch.from_numpy(modality_mask).float(),
-            torch.tensor(y, dtype=torch.long)
-        )
-
-
-# ===============================
-# TRAINING SINGLE MODEL
-# ===============================
-def train_model(X_tr, fm_tr, mm_tr, y_tr, X_te, fm_te, mm_te, y_te,
-                num_classes, input_dim, model_idx=0, epochs=80):
-    """Train a single model"""
-
-    # Set different seed for each model
-    torch.manual_seed(42 + model_idx)
-    np.random.seed(42 + model_idx)
-
-    batch_size = 64 if device.type == 'cuda' else 32
-
-    train_dataset = ASLMultiDataset(X_tr, fm_tr, mm_tr, y_tr, training=True, max_frames=100)
-    test_dataset = ASLMultiDataset(X_te, fm_te, mm_te, y_te, training=False, max_frames=100)
-
-    train_loader = DataLoader(
-        train_dataset,
-        batch_size=batch_size, shuffle=True,
-        num_workers=4, pin_memory=device.type == 'cuda'
-    )
-
-    test_loader = DataLoader(
-        test_dataset,
-        batch_size=batch_size * 2, shuffle=False,
-        num_workers=4, pin_memory=device.type == 'cuda'
-    )
-
-    # Enhanced model
-    model = ModalityAwareTransformer(
-        input_dim=input_dim,
-        num_classes=num_classes,
-        d_model=512,
-        nhead=8,
-        num_layers=6,
-        dropout=0.15
-    ).to(device)
-
-    print(f"\n[Model {model_idx + 1}] Parameters: {sum(p.numel() for p in model.parameters()):,}")
-
-    criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
-    optimizer = optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4)
-
-    # OneCycleLR scheduler
-    scheduler = optim.lr_scheduler.OneCycleLR(
-        optimizer,
-        max_lr=1e-3,
-        steps_per_epoch=len(train_loader),
-        epochs=epochs,
-        pct_start=0.1,
-        anneal_strategy='cos'
-    )
-
-    best_acc = 0.0
-    save_path = f"best_asl_model_{model_idx}.pth"
-
-    print(f"\n{'=' * 60}")
-    print(f"TRAINING MODEL {model_idx + 1}")
-    print(f"{'=' * 60}")
 
     for epoch in range(epochs):
+        # Training phase
         model.train()
-        total_loss = correct = total = 0
+        train_loss = 0.0
+        train_correct = 0
+        train_total = 0
 
-        for x, frame_mask, modality_mask, yb in tqdm(train_loader, desc=f"Epoch {epoch + 1}", leave=False):
+        pbar = tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs} [Train]")
-            x = x.to(device)
+        for inputs, labels in pbar:
-            frame_mask = frame_mask.to(device)
+            inputs, labels = inputs.to(device), labels.to(device)
-            modality_mask = modality_mask.to(device)
-            yb = yb.to(device)
-
-            # Apply mixup
-            if np.random.rand() < 0.5:
-                x, frame_mask, modality_mask, y_a, y_b, lam = mixup_data(
-                    x, frame_mask, modality_mask, yb, alpha=0.2
-                )
-
-                key_padding_mask = ~frame_mask
-                optimizer.zero_grad(set_to_none=True)
-                logits = model(x, modality_mask=modality_mask, key_padding_mask=key_padding_mask)
-                loss = lam * criterion(logits, y_a) + (1 - lam) * criterion(logits, y_b)
-
-                # Use original labels for accuracy
-                correct += (logits.argmax(-1) == yb).sum().item()
-            else:
-                key_padding_mask = ~frame_mask
-                optimizer.zero_grad(set_to_none=True)
-                logits = model(x, modality_mask=modality_mask, key_padding_mask=key_padding_mask)
-                loss = criterion(logits, yb)
-                correct += (logits.argmax(-1) == yb).sum().item()
 
+            optimizer.zero_grad()
+            outputs = model(inputs)
+            loss = criterion(outputs, labels)
             loss.backward()
-            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
             optimizer.step()
-            scheduler.step()
 
-            total_loss += loss.item()
+            train_loss += loss.item()
-            total += yb.size(0)
+            _, predicted = torch.max(outputs, 1)
+            train_total += labels.size(0)
+            train_correct += (predicted == labels).sum().item()
 
-        train_acc = correct / total * 100
+            pbar.set_postfix({'loss': f'{loss.item():.4f}', 'acc': f'{100 * train_correct / train_total:.2f}%'})
 
-        # Eval
+        train_acc = 100 * train_correct / train_total
+        avg_train_loss = train_loss / len(train_loader)
+
+        # Validation phase
         model.eval()
-        correct = total = 0
+        val_loss = 0.0
+        val_correct = 0
+        val_total = 0
+
         with torch.no_grad():
-            for x, frame_mask, modality_mask, yb in test_loader:
+            for inputs, labels in tqdm(val_loader, desc=f"Epoch {epoch + 1}/{epochs} [Val]"):
-                x = x.to(device)
+                inputs, labels = inputs.to(device), labels.to(device)
-                frame_mask = frame_mask.to(device)
+                outputs = model(inputs)
-                modality_mask = modality_mask.to(device)
+                loss = criterion(outputs, labels)
-                yb = yb.to(device)
 
-                key_padding_mask = ~frame_mask
+                val_loss += loss.item()
-                logits = model(x, modality_mask=modality_mask, key_padding_mask=key_padding_mask)
+                _, predicted = torch.max(outputs, 1)
-                correct += (logits.argmax(-1) == yb).sum().item()
+                val_total += labels.size(0)
-                total += yb.size(0)
+                val_correct += (predicted == labels).sum().item()
 
-        test_acc = correct / total * 100
+        val_acc = 100 * val_correct / val_total
+        avg_val_loss = val_loss / len(val_loader)
 
-        print(f"[{epoch + 1:2d}/{epochs}] Loss: {total_loss / len(train_loader):.4f} | "
+        scheduler.step(avg_val_loss)
-              f"Train: {train_acc:.2f}% | Test: {test_acc:.2f}%", end="")
+
+        print(f"\nEpoch {epoch + 1}/{epochs}:")
+        print(f"  Train Loss: {avg_train_loss:.4f} | Train Acc: {train_acc:.2f}%")
+        print(f"  Val Loss: {avg_val_loss:.4f} | Val Acc: {val_acc:.2f}%")
+
+        # Save best model
+        if val_acc > best_val_acc:
+            best_val_acc = val_acc
+            torch.save(model.state_dict(), 'best_asl_model.pth')
+            print(f"  ✓ New best model saved! (Val Acc: {val_acc:.2f}%)")
 
-        if test_acc > best_acc:
-            best_acc = test_acc
-            torch.save(model.state_dict(), save_path)
-            print("  → saved")
-        else:
         print()
 
-    print(f"\nModel {model_idx + 1} - Best test accuracy: {best_acc:.2f}%")
+    print(f"Training complete! Best validation accuracy: {best_val_acc:.2f}%")
-    return save_path, best_acc
 
 
-# ===============================
+# --- EXECUTION ---
-# ENSEMBLE PREDICTION
-# ===============================
-def ensemble_predict(model_paths, test_loader, num_classes, input_dim):
-    """Make predictions using ensemble of models"""
-    all_preds = []
-
-    for model_path in model_paths:
-        model = ModalityAwareTransformer(
-            input_dim=input_dim,
-            num_classes=num_classes,
-            d_model=512,
-            nhead=8,
-            num_layers=6
-        ).to(device)
-
-        model.load_state_dict(torch.load(model_path))
-        model.eval()
-
-        preds = []
-        with torch.no_grad():
-            for x, frame_mask, modality_mask, _ in test_loader:
-                x = x.to(device)
-                frame_mask = frame_mask.to(device)
-                modality_mask = modality_mask.to(device)
-
-                key_padding_mask = ~frame_mask
-                logits = model(x, modality_mask=modality_mask, key_padding_mask=key_padding_mask)
-                preds.append(F.softmax(logits, dim=-1))
-
-        all_preds.append(torch.cat(preds, dim=0))
-
-    # Average predictions
-    ensemble_pred = torch.stack(all_preds).mean(0)
-    return ensemble_pred.argmax(-1).cpu().numpy()
-
-
-# ===============================
-# MAIN
-# ===============================
-def main():
-    base_path = "asl_kaggle"
-    max_frames = 100
-    MIN_SAMPLES_PER_CLASS = 3  # Relaxed from 5
-    NUM_ENSEMBLE_MODELS = 3
-    EPOCHS = 80
-
-    print("\nLoading metadata...")
-    train_df, _ = load_kaggle_asl_data(base_path)
-    print(f"Total samples in train.csv: {train_df.height}")
-
-    rows = [(row[0], row[1]) for row in train_df.select(["path", "sign"]).iter_rows()]
-
-    print("\nProcessing sequences with BOTH hands + FACE (enhanced)...")
-    print("This may take a few minutes...")
-
-    with Pool(cpu_count()) as pool:
-        results = list(tqdm(
-            pool.imap(
-                partial(process_row, base_path=base_path, max_frames=max_frames),
-                rows,
-                chunksize=80
-            ),
-            total=len(rows),
-            desc="Landmarks extraction"
-        ))
-
-    X_list, frame_masks_list, modality_masks_list, y_list = [], [], [], []
-    failed_count = 0
-    for feat, frame_mask, modality_mask, sign in results:
-        if feat is not None and frame_mask is not None:
-            X_list.append(feat)
-            frame_masks_list.append(frame_mask)
-            modality_masks_list.append(modality_mask)
-            y_list.append(sign)
-        else:
-            failed_count += 1
-
-    if not X_list:
-        print(f"\n❌ No valid sequences extracted!")
-        print(f"Failed to process: {failed_count}/{len(results)} files")
-        return
-
-    print(f"\n✓ Successfully processed: {len(X_list)}/{len(results)} files")
-    print(f"✗ Failed: {failed_count}/{len(results)} files")
-
-    X = np.stack(X_list)
-    frame_masks = np.stack(frame_masks_list)
-    modality_masks = np.stack(modality_masks_list)
-
-    print(f"\nExtracted {len(X):,} sequences")
-    print(f"Feature shape: {X.shape[1:]}   (input_dim = {X.shape[2]})")
-
-    # Global normalization
-    X = np.clip(X, -30, 30)
-    mean = X.mean(axis=(0, 1), keepdims=True)
-    std = X.std(axis=(0, 1), keepdims=True) + 1e-8
-    X = (X - mean) / std
-
-    # Labels
-    le = LabelEncoder()
-    y = le.fit_transform(y_list)
-
-    # Filter rare classes
-    counts = Counter(y)
-    valid = [k for k, v in counts.items() if v >= MIN_SAMPLES_PER_CLASS]
-    mask = np.isin(y, valid)
-
-    X = X[mask]
-    frame_masks = frame_masks[mask]
-    modality_masks = modality_masks[mask]
-    y = y[mask]
-
-    le = LabelEncoder()
-    y = le.fit_transform(y)
-
-    print(f"After filtering: {len(X):,} samples | {len(le.classes_)} classes")
-
-    # Split
-    X_tr, X_te, fm_tr, fm_te, mm_tr, mm_te, y_tr, y_te = train_test_split(
-        X, frame_masks, modality_masks, y, test_size=0.15, stratify=y, random_state=42
-    )
-
-    # Train ensemble of models
-    model_paths = []
-    best_accs = []
-
-    for i in range(NUM_ENSEMBLE_MODELS):
-        model_path, best_acc = train_model(
-            X_tr, fm_tr, mm_tr, y_tr,
-            X_te, fm_te, mm_te, y_te,
-            num_classes=len(le.classes_),
-            input_dim=X.shape[2],
-            model_idx=i,
-            epochs=EPOCHS
-        )
-        model_paths.append(model_path)
-        best_accs.append(best_acc)
-
-    # Ensemble evaluation
-    print("\n" + "=" * 60)
-    print("ENSEMBLE EVALUATION")
-    print("=" * 60)
-
-    test_dataset = ASLMultiDataset(X_te, fm_te, mm_te, y_te, training=False)
-    test_loader = DataLoader(
-        test_dataset,
-        batch_size=128,
-        shuffle=False,
-        num_workers=4,
-        pin_memory=device.type == 'cuda'
-    )
-
-    ensemble_preds = ensemble_predict(model_paths, test_loader, len(le.classes_), X.shape[2])
-    ensemble_acc = (ensemble_preds == y_te).mean() * 100
-
-    print(f"\nIndividual model accuracies:")
-    for i, acc in enumerate(best_accs):
-        print(f"  Model {i + 1}: {acc:.2f}%")
-
-    print(f"\n🎯 Ensemble accuracy: {ensemble_acc:.2f}%")
-    print(f"   Improvement: +{ensemble_acc - max(best_accs):.2f}% over best single model")
-
-    print("\n" + "=" * 60)
-    print(f"TRAINING COMPLETE")
-    print("=" * 60)
-
 
 if __name__ == "__main__":
-    main()
+    asl_data = load_kaggle_metadata(BASE_PATH)
+
+    # Process all files
+    paths = asl_data["path"].to_list()
+    labels = asl_data["sign"].to_list()
+
+    # Create label mapping
+    unique_signs = sorted(set(labels))
+    sign_to_idx = {sign: idx for idx, sign in enumerate(unique_signs)}
+    label_indices = [sign_to_idx[sign] for sign in labels]
+
+    print(f"Processing {len(paths)} files in parallel...")
+    with ProcessPoolExecutor() as executor:
+        results = list(tqdm(executor.map(load_and_preprocess, paths), total=len(paths)))
+
+    # Create tensors
+    X = torch.tensor(np.array(results), dtype=torch.float32)
+    y = torch.tensor(label_indices, dtype=torch.long)
+
+    print(f"Dataset Tensor Shape: {X.shape}")
+    print(f"Labels Tensor Shape: {y.shape}")
+    print(f"Number of unique signs: {len(unique_signs)}")
+
+    # Train/Val split
+    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
+
+    # Create DataLoaders
+    train_dataset = TensorDataset(X_train, y_train)
+    val_dataset = TensorDataset(X_val, y_val)
+
+    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+    val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
+
+    print(f"Train samples: {len(train_dataset)}")
+    print(f"Val samples: {len(val_dataset)}")
+
+    # Initialize and train model
+    model = ASLClassifier(num_classes=len(unique_signs))
+    model.to(device)
+
+    print(f"\nModel initialized with {sum(p.numel() for p in model.parameters()):,} parameters")
+    print("Starting training...\n")
+
+    train_model(model, train_loader, val_loader, epochs=20, lr=0.002)