lets try this architecture

rewrite_training.py

@@ -15,13 +15,21 @@ CACHE_DIR = "asl_cache"
 TARGET_FRAMES = 22
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+# Landmark indices for each body part (based on MediaPipe Holistic)
+LANDMARK_RANGES = {
+    'left_hand': list(range(468, 489)),  # 21 landmarks
+    'right_hand': list(range(522, 543)),  # 21 landmarks
+    'pose': list(range(489, 522)),  # 33 landmarks
+    'face': list(range(0, 468))  # 468 landmarks
+}
+
 
 # --- 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")
+    cache_path = os.path.join(cache_dir, f"sample_{i}.npz")
 
     if os.path.exists(cache_path):
         return  # Skip if already cached
@@ -41,7 +49,7 @@ def process_single_file(args):
             ])
         )
 
-        # Local Anchors (Wrists)
+        # Local Anchors (Wrists - landmark_index 468 and 522)
         wrists = (
             df.filter(pl.col("landmark_index").is_in([468, 522]))
             .select([
@@ -67,17 +75,37 @@ def process_single_file(args):
         )
 
         n_frames = processed["frame"].n_unique()
-        tensor = processed.select(["x_g", "y_g", "z_g", "x_l", "y_l"]).to_numpy().reshape(n_frames, 543, 5)
+
+        # Full tensor for indexing
+        full_tensor = processed.select(["landmark_index", "x_g", "y_g", "z_g", "x_l", "y_l"]).to_numpy()
+        full_tensor = full_tensor.reshape(n_frames, 543, 6)  # 6 = 1 (index) + 5 (features)
+
+        # Extract landmark_index and features separately
+        full_data = full_tensor[:, :, 1:]  # Remove landmark_index column (features only)
 
         # Temporal Resampling
         indices = np.linspace(0, n_frames - 1, num=TARGET_FRAMES).round().astype(int)
-        result = tensor[indices]
+        resampled = full_data[indices]  # (22, 543, 5)
 
-        # Save to cache
+        # Split by body part
-        np.save(cache_path, result)
+        left_hand = resampled[:, LANDMARK_RANGES['left_hand'], :]  # (22, 21, 5)
-    except Exception:
+        right_hand = resampled[:, LANDMARK_RANGES['right_hand'], :]  # (22, 21, 5)
-        # Save zero tensor for failed files
+        pose = resampled[:, LANDMARK_RANGES['pose'], :]  # (22, 33, 5)
-        np.save(cache_path, np.zeros((TARGET_FRAMES, 543, 5)))
+        face = resampled[:, LANDMARK_RANGES['face'], :]  # (22, 468, 5)
+
+        # Save as compressed npz
+        np.savez_compressed(cache_path,
+                            left_hand=left_hand.astype(np.float32),
+                            right_hand=right_hand.astype(np.float32),
+                            pose=pose.astype(np.float32),
+                            face=face.astype(np.float32))
+    except Exception as e:
+        # Save zero tensors for failed files
+        np.savez_compressed(cache_path,
+                            left_hand=np.zeros((TARGET_FRAMES, 21, 5), dtype=np.float32),
+                            right_hand=np.zeros((TARGET_FRAMES, 21, 5), dtype=np.float32),
+                            pose=np.zeros((TARGET_FRAMES, 33, 5), dtype=np.float32),
+                            face=np.zeros((TARGET_FRAMES, 468, 5), dtype=np.float32))
 
 
 def preprocess_and_cache(paths, base_path=BASE_PATH, cache_dir=CACHE_DIR):
@@ -85,7 +113,7 @@ def preprocess_and_cache(paths, base_path=BASE_PATH, cache_dir=CACHE_DIR):
     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)))
+    all_cached = all(os.path.exists(os.path.join(cache_dir, f"sample_{i}.npz")) for i in range(len(paths)))
     if all_cached:
         print("All files already cached, skipping preprocessing...")
         return
@@ -117,50 +145,122 @@ class CachedASLDataset(Dataset):
 
     def __getitem__(self, idx):
         sample_idx = self.indices[idx]
-        cache_path = os.path.join(self.cache_dir, f"sample_{sample_idx}.npy")
+        cache_path = os.path.join(self.cache_dir, f"sample_{sample_idx}.npz")
 
         # 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)
+        # Load each body part
+        left_hand = torch.tensor(data['left_hand'], dtype=torch.float32)
+        right_hand = torch.tensor(data['right_hand'], dtype=torch.float32)
+        pose = torch.tensor(data['pose'], dtype=torch.float32)
+        face = torch.tensor(data['face'], dtype=torch.float32)
+
+        label = torch.tensor(self.labels[idx], dtype=torch.long)
+
+        return (left_hand, right_hand, pose, face), label
 
 
-# --- MODEL ---
+# --- IMPROVED MODEL WITH SEPARATE STREAMS ---
 
-class ASLClassifier(nn.Module):
+class ASLClassifierSeparateStreams(nn.Module):
-    def __init__(self, num_classes):
+    def __init__(self, num_classes, dropout_rate=0.3):
         super().__init__()
-        self.feat_dim = 543 * 5
 
-        self.conv1 = nn.Conv1d(self.feat_dim, 512, kernel_size=3, padding=1)
+        # Feature dimensions for each body part
-        self.bn1 = nn.BatchNorm1d(512)
+        self.left_hand_dim = 21 * 5  # 21 landmarks * 5 features
-        self.conv2 = nn.Conv1d(512, 512, kernel_size=3, padding=1)
+        self.right_hand_dim = 21 * 5  # 21 landmarks * 5 features
-        self.bn2 = nn.BatchNorm1d(512)
+        self.pose_dim = 33 * 5  # 33 landmarks * 5 features
+        self.face_dim = 468 * 5  # 468 landmarks * 5 features
 
-        self.pool = nn.MaxPool1d(2)
+        # Separate convolutional streams for each body part
-        self.dropout = nn.Dropout(0.4)
+        self.left_hand_stream = self._make_conv_stream(self.left_hand_dim, 128, dropout_rate)
+        self.right_hand_stream = self._make_conv_stream(self.right_hand_dim, 128, dropout_rate)
+        self.pose_stream = self._make_conv_stream(self.pose_dim, 128, dropout_rate)
+        self.face_stream = self._make_conv_stream(self.face_dim, 256, dropout_rate)
 
-        self.fc = nn.Sequential(
+        # Combined features dimension
-            nn.Linear(512, 1024),
+        combined_dim = 128 + 128 + 128 + 256  # 640
+
+        # Bidirectional LSTM for temporal modeling
+        self.lstm = nn.LSTM(
+            input_size=combined_dim,
+            hidden_size=256,
+            num_layers=2,
+            batch_first=True,
+            bidirectional=True,
+            dropout=dropout_rate
+        )
+
+        # Attention mechanism
+        self.attention = nn.Sequential(
+            nn.Linear(512, 128),  # 512 from bidirectional LSTM (256*2)
+            nn.Tanh(),
+            nn.Linear(128, 1)
+        )
+
+        # Classification head
+        self.classifier = nn.Sequential(
+            nn.Linear(512, 512),
             nn.ReLU(),
-            nn.Dropout(0.2),
+            nn.Dropout(dropout_rate),
-            nn.Linear(1024, num_classes)
+            nn.Linear(512, 256),
+            nn.ReLU(),
+            nn.Dropout(dropout_rate),
+            nn.Linear(256, num_classes)
+        )
+
+    def _make_conv_stream(self, input_dim, output_dim, dropout_rate):
+        """Create a convolutional stream for processing a body part"""
+        return nn.Sequential(
+            nn.Conv1d(input_dim, output_dim * 2, kernel_size=3, padding=1),
+            nn.BatchNorm1d(output_dim * 2),
+            nn.ReLU(),
+            nn.Dropout(dropout_rate * 0.5),
+            nn.Conv1d(output_dim * 2, output_dim, kernel_size=3, padding=1),
+            nn.BatchNorm1d(output_dim),
+            nn.ReLU(),
+            nn.Dropout(dropout_rate * 0.5)
         )
 
     def forward(self, x):
-        b, t, l, f = x.shape
+        # x is a tuple: (left_hand, right_hand, pose, face)
-        x = x.view(b, t, -1).transpose(1, 2)
+        left_hand, right_hand, pose, face = x
 
-        x = F.relu(self.bn1(self.conv1(x)))
+        b = left_hand.shape[0]  # batch size
-        x = self.pool(x)
 
-        x = F.relu(self.bn2(self.conv2(x)))
+        # Flatten landmarks and features for each body part
-        x = self.pool(x)
+        # Shape: (batch, time, landmarks, features) -> (batch, landmarks*features, time)
+        left_hand = left_hand.view(b, TARGET_FRAMES, -1).transpose(1, 2)
+        right_hand = right_hand.view(b, TARGET_FRAMES, -1).transpose(1, 2)
+        pose = pose.view(b, TARGET_FRAMES, -1).transpose(1, 2)
+        face = face.view(b, TARGET_FRAMES, -1).transpose(1, 2)
 
-        x = F.adaptive_avg_pool1d(x, 1).squeeze(-1)
+        # Process each body part through its stream
+        left_hand_feat = self.left_hand_stream(left_hand)  # (batch, 128, time)
+        right_hand_feat = self.right_hand_stream(right_hand)  # (batch, 128, time)
+        pose_feat = self.pose_stream(pose)  # (batch, 128, time)
+        face_feat = self.face_stream(face)  # (batch, 256, time)
 
-        return self.fc(self.dropout(x))
+        # Transpose back: (batch, features, time) -> (batch, time, features)
+        left_hand_feat = left_hand_feat.transpose(1, 2)
+        right_hand_feat = right_hand_feat.transpose(1, 2)
+        pose_feat = pose_feat.transpose(1, 2)
+        face_feat = face_feat.transpose(1, 2)
+
+        # Concatenate all features
+        combined = torch.cat([left_hand_feat, right_hand_feat, pose_feat, face_feat], dim=2)
+        # combined shape: (batch, time, 640)
+
+        # LSTM processing
+        lstm_out, _ = self.lstm(combined)  # (batch, time, 512)
+
+        # Attention mechanism
+        attention_weights = F.softmax(self.attention(lstm_out), dim=1)  # (batch, time, 1)
+        attended = torch.sum(attention_weights * lstm_out, dim=1)  # (batch, 512)
+
+        # Classification
+        return self.classifier(attended)
 
 
 # --- EXECUTION ---
@@ -187,20 +287,21 @@ if __name__ == "__main__":
     train_dataset = CachedASLDataset(train_indices, train_labels)
     val_dataset = CachedASLDataset(val_indices, val_labels)
 
-    # Increase batch size and workers since loading is now fast
+    # Adjust batch size based on GPU memory (separate streams use more memory)
-    train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=4, pin_memory=True)
+    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4, pin_memory=True)
-    val_loader = DataLoader(val_dataset, batch_size=64, num_workers=4, pin_memory=True)
+    val_loader = DataLoader(val_dataset, batch_size=32, num_workers=4, pin_memory=True)
 
     print(f"Train samples: {len(train_dataset)}, Val samples: {len(val_dataset)}")
 
     # 5. Train
-    model = ASLClassifier(len(unique_signs)).to(device)
+    model = ASLClassifierSeparateStreams(len(unique_signs)).to(device)
     optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
     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}...")
+    print(f"Model parameters: {sum(p.numel() for p in model.parameters()):,}")
 
     for epoch in range(25):
         # Training
@@ -210,11 +311,13 @@ if __name__ == "__main__":
         train_total = 0
 
         pbar = tqdm(train_loader, desc=f"Epoch {epoch + 1}/25 [Train]")
-        for batch_x, batch_y in pbar:
+        for batch_data, batch_y in pbar:
-            batch_x, batch_y = batch_x.to(device), batch_y.to(device)
+            # Move all body parts to device
+            batch_data = tuple(d.to(device) for d in batch_data)
+            batch_y = batch_y.to(device)
 
             optimizer.zero_grad()
-            output = model(batch_x)
+            output = model(batch_data)
             loss = criterion(output, batch_y)
             loss.backward()
             optimizer.step()
@@ -232,9 +335,11 @@ if __name__ == "__main__":
         val_loss = 0
 
         with torch.no_grad():
-            for vx, vy in tqdm(val_loader, desc=f"Epoch {epoch + 1}/25 [Val]"):
+            for batch_data, vy in tqdm(val_loader, desc=f"Epoch {epoch + 1}/25 [Val]"):
-                vx, vy = vx.to(device), vy.to(device)
+                batch_data = tuple(d.to(device) for d in batch_data)
-                output = model(vx)
+                vy = vy.to(device)
+
+                output = model(batch_data)
                 val_loss += criterion(output, vy).item()
                 pred = output.argmax(1)
                 val_correct += (pred == vy).sum().item()
@@ -254,11 +359,11 @@ if __name__ == "__main__":
         # Save best model
         if val_acc > best_acc:
             best_acc = val_acc
-            torch.save(model.state_dict(), "best_asl_model.pth")
+            torch.save(model.state_dict(), "best_asl_model_separate.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_e{epoch + 1}.pth")
+            torch.save(model.state_dict(), f"asl_model_separate_e{epoch + 1}.pth")
 
     print(f"\nTraining complete! Best validation accuracy: {best_acc:.2f}%")