AI, we don't believe that you can fly bro

training.py

@@ -1,6 +1,3 @@
-# ===============================
-# IMPORTS
-# ===============================
 import os
 import json
 import math
@@ -20,7 +17,26 @@ from tqdm import tqdm
 from collections import Counter
 
 # ===============================
-# DATA LOADING
+# GPU CONFIGURATION
+# ===============================
+print("=" * 60)
+print("GPU CONFIGURATION")
+print("=" * 60)
+
+if torch.cuda.is_available():
+    print(f"✓ CUDA available!")
+    print(f"✓ GPU: {torch.cuda.get_device_name(0)}")
+    device = torch.device('cuda:0')
+    torch.backends.cudnn.benchmark = True
+else:
+    print("✗ CUDA not available, using CPU")
+    device = torch.device('cpu')
+
+print("=" * 60)
+
+
+# ===============================
+# DATA LOADING WITH NaN HANDLING
 # ===============================
 def load_kaggle_asl_data(base_path):
     train_df = pd.read_csv(os.path.join(base_path, "train.csv"))
@@ -30,39 +46,70 @@ def load_kaggle_asl_data(base_path):
 
 
 def extract_hand_landmarks_from_parquet(path):
+    """Extract hand landmarks, handling NaN values properly"""
     try:
         df = pd.read_parquet(path)
+
+        # Get hand data
         left = df[df["type"] == "left_hand"]
         right = df[df["type"] == "right_hand"]
 
-        hand = left if len(left) >= len(right) else right
+        # Choose hand with more non-NaN data
+        left_valid = left[['x', 'y', 'z']].notna().all(axis=1).sum()
+        right_valid = right[['x', 'y', 'z']].notna().all(axis=1).sum()
+
+        if left_valid == 0 and right_valid == 0:
+            return None  # No valid hand data
+
+        hand = left if left_valid >= right_valid else right
+
         if len(hand) == 0:
             return None
 
+        # Get frames with valid data
         frames = sorted(hand['frame'].unique())
         landmarks_seq = []
 
         for frame in frames:
             lm_frame = hand[hand['frame'] == frame]
+
+            # Check if this frame has valid data
+            valid_rows = lm_frame[['x', 'y', 'z']].notna().all(axis=1)
+            if valid_rows.sum() < 10:  # Need at least 10 valid landmarks
+                continue
+
             lm_list = []
+            frame_has_data = False
+
             for i in range(21):
                 lm = lm_frame[lm_frame['landmark_index'] == i]
                 if len(lm) == 0:
                     lm_list.append([0.0, 0.0, 0.0])
                 else:
-                    lm_list.append([
+                    x = lm['x'].iloc[0]
-                        float(lm['x'].iloc[0]),
+                    y = lm['y'].iloc[0]
-                        float(lm['y'].iloc[0]),
+                    z = lm['z'].iloc[0]
-                        float(lm['z'].iloc[0])
+
-                    ])
+                    # Check for NaN
+                    if pd.isna(x) or pd.isna(y) or pd.isna(z):
+                        lm_list.append([0.0, 0.0, 0.0])
+                    else:
+                        lm_list.append([float(x), float(y), float(z)])
+                        frame_has_data = True
+
+            if frame_has_data:
                 landmarks_seq.append(lm_list)
 
+        if len(landmarks_seq) == 0:
+            return None
+
         return np.array(landmarks_seq, dtype=np.float32)
-    except:
+    except Exception as e:
         return None
 
 
 def get_features_sequence(landmarks_seq, max_frames=100):
+    """Extract features from landmark sequence"""
     if landmarks_seq is None or len(landmarks_seq) == 0:
         return None, None
 
@@ -74,23 +121,27 @@ def get_features_sequence(landmarks_seq, max_frames=100):
     scale = np.maximum(scale, 1e-6)
     landmarks_seq = landmarks_seq / scale[:, np.newaxis, :]
 
+    # Replace any remaining NaN/Inf with 0
+    landmarks_seq = np.nan_to_num(landmarks_seq, nan=0.0, posinf=0.0, neginf=0.0)
+
     # Finger curl distances
     tips = [4, 8, 12, 16, 20]
     bases = [1, 5, 9, 13, 17]
     curl_features = []
     for b, t in zip(bases, tips):
-        curl_features.append(np.linalg.norm(landmarks_seq[:, t] - landmarks_seq[:, b], axis=1))
+        curl = np.linalg.norm(landmarks_seq[:, t] - landmarks_seq[:, b], axis=1)
-    curl_features = np.stack(curl_features, axis=1)  # (T,5)
+        curl_features.append(curl)
+    curl_features = np.stack(curl_features, axis=1)
 
     # Temporal deltas
     deltas = np.zeros_like(landmarks_seq)
     deltas[1:] = landmarks_seq[1:] - landmarks_seq[:-1]
 
-    # Flatten features along last axis
+    # Flatten features
     seq = np.concatenate([landmarks_seq, deltas, curl_features[:, :, np.newaxis]], axis=2)
-    seq = seq.reshape(seq.shape[0], -1)  # (T, feature_dim)
+    seq = seq.reshape(seq.shape[0], -1)
 
-    # Pad or truncate to max_frames
+    # Pad or truncate
     T, F = seq.shape
     if T < max_frames:
         pad = np.zeros((max_frames - T, F), dtype=np.float32)
@@ -98,7 +149,7 @@ def get_features_sequence(landmarks_seq, max_frames=100):
     elif T > max_frames:
         seq = seq[:max_frames, :]
 
-    # Mask
+    # Create mask
     valid_mask = np.zeros(max_frames, dtype=bool)
     valid_mask[:min(T, max_frames)] = True
 
@@ -106,20 +157,29 @@ def get_features_sequence(landmarks_seq, max_frames=100):
 
 
 def process_row(row, base_path, max_frames=100):
+    """Process a single row"""
     path = os.path.join(base_path, row["path"])
     if not os.path.exists(path):
         return None, None, None
+
     try:
         lm = extract_hand_landmarks_from_parquet(path)
         if lm is None:
             return None, None, None
+
         feat, mask = get_features_sequence(lm, max_frames)
         if feat is None:
             return None, None, None
+
+        # Final NaN check
+        if np.isnan(feat).any() or np.isinf(feat).any():
+            return None, None, None
+
         return feat, mask, row["sign"]
     except:
         return None, None, None
 
+
 # ===============================
 # TRANSFORMER MODEL
 # ===============================
@@ -138,7 +198,7 @@ class PositionalEncoding(nn.Module):
 
 
 class TransformerASL(nn.Module):
-    def __init__(self, input_dim=63, num_classes=250, d_model=192, nhead=6, num_layers=4):
+    def __init__(self, input_dim=68, num_classes=250, d_model=256, nhead=8, num_layers=4):
         super().__init__()
         self.proj = nn.Linear(input_dim, d_model)
         self.norm_in = nn.LayerNorm(d_model)
@@ -170,37 +230,33 @@ class TransformerASL(nn.Module):
         return self.head(x)
 
 
-def create_padding_mask(lengths, max_len):
-    return torch.arange(max_len, device=lengths.device)[None, :] >= lengths[:, None]
-
 # ===============================
 # MAIN TRAINING
 # ===============================
 def main():
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    base_path = "asl_kaggle"
-    print(f"Using device: {device}")
-    if device.type == "cuda":
-        print("GPU:", torch.cuda.get_device_name(0))
-
-    base_path = "asl_kaggle"  # ← set your dataset path
     max_frames = 100
     MIN_SAMPLES_PER_CLASS = 6
 
-    print("Loading metadata...")
+    print("\nLoading metadata...")
     train_df, sign_to_idx = load_kaggle_asl_data(base_path)
+    print(f"Total sequences: {len(train_df)}")
+
     rows = [row for _, row in train_df.iterrows()]
 
-    print("Processing landmark sequences...")
+    print("\nProcessing sequences with NaN handling...")
     with Pool(cpu_count()) as pool:
         results = list(tqdm(
             pool.imap(
                 partial(process_row, base_path=base_path, max_frames=max_frames),
-                rows
+                rows,
+                chunksize=100
             ),
             total=len(rows),
             desc="Extracting landmarks"
         ))
 
+    # Filter valid results
     X_list, masks_list, y_list = [], [], []
     for feat, mask, sign in results:
         if feat is not None and feat.shape[0] == max_frames:
@@ -208,13 +264,19 @@ def main():
             masks_list.append(mask)
             y_list.append(sign)
 
+    print(f"\n✓ Valid sequences: {len(X_list)} out of {len(train_df)}")
+
     if not X_list:
-        print("No valid sequences found. Check parquet files / paths.")
+        print("❌ No valid sequences found!")
+        print("\nPossible issues:")
+        print("  1. Most files contain only NaN hand landmarks")
+        print("  2. Hand detection failed in most videos")
+        print("  3. Dataset might be corrupted")
         return
 
     X = np.stack(X_list)
     masks = np.stack(masks_list)
-    print(f"Loaded {len(X)} sequences | shape: {X.shape}")
+    print(f"Data shape: {X.shape}")
 
     # Global normalization
     X = np.clip(X, -5.0, 5.0)
@@ -222,9 +284,11 @@ def main():
     std = X.std(axis=(0, 1), keepdims=True) + 1e-8
     X = (X - mean) / std
 
+    # Encode labels
     le = LabelEncoder()
     y = le.fit_transform(y_list)
 
+    # Filter classes with too few samples
     counts = Counter(y)
     valid_classes = [cls for cls, cnt in counts.items() if cnt >= MIN_SAMPLES_PER_CLASS]
     mask_valid = np.isin(y, valid_classes)
@@ -232,9 +296,11 @@ def main():
     masks = masks[mask_valid]
     y = y[mask_valid]
 
+    # Re-encode
     le = LabelEncoder()
     y = le.fit_transform(y)
-    print(f"{len(X)} samples | {len(le.classes_)} classes after filtering")
+
+    print(f"Final dataset: {len(X)} samples | {len(le.classes_)} classes")
 
     # Train-test split
     X_train, X_test, masks_train, masks_test, y_train, y_test = train_test_split(
@@ -254,23 +320,28 @@ def main():
         def __getitem__(self, idx):
             return self.X[idx], self.masks[idx], self.y[idx]
 
+    batch_size = 128 if device.type == 'cuda' else 64
+
     train_loader = DataLoader(
         ASLSequenceDataset(X_train, masks_train, y_train),
-        batch_size=64, shuffle=True, num_workers=4, pin_memory=True
+        batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True
     )
     test_loader = DataLoader(
         ASLSequenceDataset(X_test, masks_test, y_test),
-        batch_size=96, shuffle=False, num_workers=4, pin_memory=True
+        batch_size=batch_size * 2, shuffle=False, num_workers=4, pin_memory=True
     )
 
+    # Model
     model = TransformerASL(
         input_dim=X.shape[2],
         num_classes=len(le.classes_),
-        d_model=192,
+        d_model=256,
-        nhead=6,
+        nhead=8,
         num_layers=4
     ).to(device)
-    print(f"Model parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"\nModel parameters: {total_params:,}")
 
     criterion = nn.CrossEntropyLoss(label_smoothing=0.05)
     optimizer = optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-4)
@@ -282,22 +353,29 @@ def main():
     wait = 0
     epochs = 70
 
+    print("\nStarting training...")
+    print("=" * 60)
+
     for epoch in range(epochs):
         model.train()
         total_loss = 0
         correct = total = 0
-        for x, mask, yb in tqdm(train_loader, desc="Train"):
+
+        for x, mask, yb in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs}"):
             x, mask, yb = x.to(device), mask.to(device), yb.to(device)
-            key_mask = ~mask  # True where padding
+            key_mask = ~mask
+
             optimizer.zero_grad(set_to_none=True)
             logits = model(x, key_padding_mask=key_mask)
             loss = criterion(logits, yb)
             loss.backward()
             torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=0.8)
             optimizer.step()
+
             total_loss += loss.item()
             correct += (logits.argmax(-1) == yb).sum().item()
             total += yb.size(0)
+
         train_acc = correct / total * 100
 
         # Eval
@@ -310,12 +388,14 @@ def main():
                 logits = model(x, key_padding_mask=key_mask)
                 correct += (logits.argmax(-1) == yb).sum().item()
                 total += yb.size(0)
+
         test_acc = correct / total * 100
 
-        print(f"[{epoch+1:2d}/{epochs}] Loss: {total_loss/len(train_loader):.4f} | "
+        print(f"[{epoch + 1:2d}/{epochs}] Loss: {total_loss / len(train_loader):.4f} | "
               f"Train: {train_acc:.2f}% | Test: {test_acc:.2f}%")
 
         scheduler.step()
+
         if test_acc > best_acc:
             best_acc = test_acc
             wait = 0
@@ -324,16 +404,22 @@ def main():
                 'optimizer': optimizer.state_dict(),
                 'label_encoder_classes': le.classes_,
                 'acc': best_acc,
-                'epoch': epoch
+                'epoch': epoch,
+                'input_dim': X.shape[2],
+                'num_classes': len(le.classes_)
             }, "best_asl_transformer.pth")
-            print("  → New best saved")
+            print(f"  → New best: {best_acc:.2f}%")
         else:
             wait += 1
             if wait >= patience:
                 print("Early stopping")
                 break
 
-    print(f"\nBest test accuracy: {best_acc:.2f}%")
+    print("=" * 60)
+    print(f"\n✓ Training complete!")
+    print(f"✓ Best test accuracy: {best_acc:.2f}%")
+    print(f"✓ Model saved: best_asl_transformer.pth")
+
 
 if __name__ == "__main__":
     main()