Battle against a True Hero pt 2

rewrite_training.py

@@ -4,25 +4,33 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from torch.utils.data import TensorDataset, DataLoader
+from torch.utils.data import Dataset, DataLoader
-from concurrent.futures import ProcessPoolExecutor
 from tqdm import tqdm
 from sklearn.model_selection import train_test_split
+from concurrent.futures import ProcessPoolExecutor
 
 # --- CONFIG ---
 BASE_PATH = "asl_kaggle"
+CACHE_DIR = "asl_cache"
 TARGET_FRAMES = 22
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
-# --- DATA LOADING WITH RELATIVE FEATURES ---
+# --- PREPROCESSING (RUN ONCE) ---
+
+def process_single_file(args):
+    """Process a single file - designed for multiprocessing"""
+    i, path, base_path, cache_dir = args
+    cache_path = os.path.join(cache_dir, f"sample_{i}.npy")
+
+    if os.path.exists(cache_path):
+        return  # Skip if already cached
 
-def load_file_to_memory(path, base_path=BASE_PATH):
     try:
         parquet_path = os.path.join(base_path, path)
         df = pl.read_parquet(parquet_path)
 
-        # 1. Global Anchor (Nose)
+        # Global Anchor (Nose)
         anchors = (
             df.filter((pl.col("type") == "face") & (pl.col("landmark_index") == 0))
             .select([
@@ -33,8 +41,7 @@ def load_file_to_memory(path, base_path=BASE_PATH):
             ])
         )
 
-        # 2. Local Anchors (Wrists)
+        # Local Anchors (Wrists)
-        # Left: 468, Right: 522
         wrists = (
             df.filter(pl.col("landmark_index").is_in([468, 522]))
             .select([
@@ -47,40 +54,83 @@ def load_file_to_memory(path, base_path=BASE_PATH):
 
         processed = df.join(anchors, on="frame", how="left")
 
-        # Join wrist data to the main frame
-        # We use a left join on frame and landmark_index to align wrist coords with their rows
         processed = (
             processed.join(wrists, on=["frame", "landmark_index"], how="left")
             .with_columns([
-                # Global (Nose-relative)
                 (pl.col("x") - pl.col("nx")).alias("x_g"),
                 (pl.col("y") - pl.col("ny")).alias("y_g"),
                 (pl.col("z") - pl.col("nz")).alias("z_g"),
-                # Local (Wrist-relative - defaults to global if not a hand point)
                 (pl.col("x") - pl.col("wx")).fill_null(pl.col("x") - pl.col("nx")).alias("x_l"),
                 (pl.col("y") - pl.col("wy")).fill_null(pl.col("y") - pl.col("ny")).alias("y_l"),
             ])
             .sort(["frame", "type", "landmark_index"])
         )
 
-        # We now have 5 channels: (x_g, y_g, z_g, x_l, y_l)
         n_frames = processed["frame"].n_unique()
-        # Reshape to (Frames, 543 landmarks, 5 features)
         tensor = processed.select(["x_g", "y_g", "z_g", "x_l", "y_l"]).to_numpy().reshape(n_frames, 543, 5)
 
         # Temporal Resampling
         indices = np.linspace(0, n_frames - 1, num=TARGET_FRAMES).round().astype(int)
-        return tensor[indices]
+        result = tensor[indices]
+
+        # Save to cache
+        np.save(cache_path, result)
     except Exception:
-        return np.zeros((TARGET_FRAMES, 543, 5))
+        # Save zero tensor for failed files
+        np.save(cache_path, np.zeros((TARGET_FRAMES, 543, 5)))
 
 
-# --- DUAL-STREAM MODEL ---
+def preprocess_and_cache(paths, base_path=BASE_PATH, cache_dir=CACHE_DIR):
+    """Preprocess all files in parallel and save as numpy arrays"""
+    os.makedirs(cache_dir, exist_ok=True)
+
+    # Check if already cached
+    all_cached = all(os.path.exists(os.path.join(cache_dir, f"sample_{i}.npy")) for i in range(len(paths)))
+    if all_cached:
+        print("All files already cached, skipping preprocessing...")
+        return
+
+    print(f"Preprocessing {len(paths)} files in parallel...")
+
+    # Create arguments for each file
+    args_list = [(i, path, base_path, cache_dir) for i, path in enumerate(paths)]
+
+    # Process in parallel
+    with ProcessPoolExecutor() as executor:
+        list(tqdm(executor.map(process_single_file, args_list), total=len(args_list)))
+
+    print("Preprocessing complete!")
+
+
+# --- FAST DATASET (LOADS FROM CACHE) ---
+
+class CachedASLDataset(Dataset):
+    """Fast dataset that loads from preprocessed numpy files"""
+
+    def __init__(self, indices, labels, cache_dir=CACHE_DIR):
+        self.indices = indices
+        self.labels = labels
+        self.cache_dir = cache_dir
+
+    def __len__(self):
+        return len(self.indices)
+
+    def __getitem__(self, idx):
+        sample_idx = self.indices[idx]
+        cache_path = os.path.join(self.cache_dir, f"sample_{sample_idx}.npy")
+
+        # Fast numpy load
+        data = np.load(cache_path)
+        label = self.labels[idx]
+
+        return torch.tensor(data, dtype=torch.float32), torch.tensor(label, dtype=torch.long)
+
+
+# --- MODEL ---
 
 class ASLClassifier(nn.Module):
     def __init__(self, num_classes):
         super().__init__()
-        # 543 landmarks * 5 features per landmark = 2715
         self.feat_dim = 543 * 5
 
         self.conv1 = nn.Conv1d(self.feat_dim, 512, kernel_size=3, padding=1)
@@ -99,10 +149,8 @@ class ASLClassifier(nn.Module):
         )
 
     def forward(self, x):
-        # x shape: (Batch, 22, 543, 5)
         b, t, l, f = x.shape
-        # Flatten landmarks and features into one vector, then transpose for Conv1d
+        x = x.view(b, t, -1).transpose(1, 2)
-        x = x.view(b, t, -1).transpose(1, 2)  # (Batch, 2715, 22)
 
         x = F.relu(self.bn1(self.conv1(x)))
         x = self.pool(x)
@@ -110,7 +158,6 @@ class ASLClassifier(nn.Module):
         x = F.relu(self.bn2(self.conv2(x)))
         x = self.pool(x)
 
-        # Global Average Pool across the time dimension
         x = F.adaptive_avg_pool1d(x, 1).squeeze(-1)
 
         return self.fc(self.dropout(x))
@@ -119,38 +166,51 @@ class ASLClassifier(nn.Module):
 # --- EXECUTION ---
 
 if __name__ == "__main__":
-    # 1. Setup Data
+    # 1. Setup Metadata
     metadata = pl.read_csv(os.path.join(BASE_PATH, "train.csv"))
     unique_signs = sorted(metadata["sign"].unique().to_list())
     sign_to_idx = {sign: i for i, sign in enumerate(unique_signs)}
-    labels = [sign_to_idx[s] for s in metadata["sign"].to_list()]
 
     paths = metadata["path"].to_list()
+    labels = [sign_to_idx[s] for s in metadata["sign"].to_list()]
 
-    # 2. Load to RAM (Parallelized)
+    # 2. Preprocess and cache (parallelized, only runs if cache doesn't exist)
-    print(f"Loading {len(paths)} files into RAM with 5-channel features...")
+    preprocess_and_cache(paths)
-    with ProcessPoolExecutor() as executor:
-        data_list = list(tqdm(executor.map(load_file_to_memory, paths), total=len(paths)))
 
-    X = torch.tensor(np.array(data_list), dtype=torch.float32)
+    # 3. Create index mapping for train/val split
-    y = torch.tensor(labels, dtype=torch.long)
+    all_indices = list(range(len(paths)))
+    train_indices, val_indices, train_labels, val_labels = train_test_split(
+        all_indices, labels, test_size=0.1, stratify=labels, random_state=42
+    )
 
-    # 3. Split
+    # 4. Create datasets from cached files
-    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.1, stratify=y, random_state=42)
+    train_dataset = CachedASLDataset(train_indices, train_labels)
+    val_dataset = CachedASLDataset(val_indices, val_labels)
 
-    train_loader = DataLoader(TensorDataset(X_train, y_train), batch_size=64, shuffle=True)
+    # Increase batch size and workers since loading is now fast
-    val_loader = DataLoader(TensorDataset(X_val, y_val), batch_size=64)
+    train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=4, pin_memory=True)
+    val_loader = DataLoader(val_dataset, batch_size=64, num_workers=4, pin_memory=True)
 
-    # 4. Train
+    print(f"Train samples: {len(train_dataset)}, Val samples: {len(val_dataset)}")
+
+    # 5. Train
     model = ASLClassifier(len(unique_signs)).to(device)
     optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
-    criterion = nn.CrossEntropyLoss(label_smoothing=0.1)  # Helps prevent over-confidence
+    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, factor=0.5)
+    criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
 
+    best_acc = 0.0
     print(f"Starting training on {device}...")
+
     for epoch in range(25):
+        # Training
         model.train()
         train_loss = 0
-        for batch_x, batch_y in tqdm(train_loader, desc=f"Epoch {epoch + 1}"):
+        train_correct = 0
+        train_total = 0
+
+        pbar = tqdm(train_loader, desc=f"Epoch {epoch + 1}/25 [Train]")
+        for batch_x, batch_y in pbar:
             batch_x, batch_y = batch_x.to(device), batch_y.to(device)
 
             optimizer.zero_grad()
@@ -158,19 +218,47 @@ if __name__ == "__main__":
             loss = criterion(output, batch_y)
             loss.backward()
             optimizer.step()
+
             train_loss += loss.item()
+            _, predicted = torch.max(output, 1)
+            train_total += batch_y.size(0)
+            train_correct += (predicted == batch_y).sum().item()
+
+            pbar.set_postfix({'loss': f'{loss.item():.4f}', 'acc': f'{100 * train_correct / train_total:.1f}%'})
 
         # Validation
         model.eval()
-        correct, total = 0, 0
+        val_correct, val_total = 0, 0
+        val_loss = 0
+
         with torch.no_grad():
-            for vx, vy in val_loader:
+            for vx, vy in tqdm(val_loader, desc=f"Epoch {epoch + 1}/25 [Val]"):
                 vx, vy = vx.to(device), vy.to(device)
-                pred = model(vx).argmax(1)
+                output = model(vx)
-                correct += (pred == vy).sum().item()
+                val_loss += criterion(output, vy).item()
-                total += vy.size(0)
+                pred = output.argmax(1)
+                val_correct += (pred == vy).sum().item()
+                val_total += vy.size(0)
 
-        print(f"Epoch {epoch + 1} | Loss: {train_loss / len(train_loader):.4f} | Val Acc: {100 * correct / total:.2f}%")
+        avg_train_loss = train_loss / len(train_loader)
+        avg_val_loss = val_loss / len(val_loader)
+        train_acc = 100 * train_correct / train_total
+        val_acc = 100 * val_correct / val_total
 
+        scheduler.step(avg_val_loss)
+
+        print(f"\nEpoch {epoch + 1}/25:")
+        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_acc:
+            best_acc = val_acc
+            torch.save(model.state_dict(), "best_asl_model.pth")
+            print(f"  ✓ Best model saved! (Val Acc: {val_acc:.2f}%)\n")
+
+        # Checkpoint every 5 epochs
         if (epoch + 1) % 5 == 0:
-            torch.save(model.state_dict(), f"asl_model_v2_e{epoch + 1}.pth")
+            torch.save(model.state_dict(), f"asl_model_e{epoch + 1}.pth")
+
+    print(f"\nTraining complete! Best validation accuracy: {best_acc:.2f}%")