diff --git a/training.py b/training.py index a8ffa99..702c7f8 100644 --- a/training.py +++ b/training.py @@ -28,6 +28,7 @@ if torch.cuda.is_available(): 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') @@ -36,7 +37,7 @@ print("=" * 60) # =============================== -# DATA LOADING WITH NaN HANDLING +# DATA LOADING - HANDLES PARTIAL NaN # =============================== def load_kaggle_asl_data(base_path): train_df = pd.read_csv(os.path.join(base_path, "train.csv")) @@ -46,7 +47,7 @@ def load_kaggle_asl_data(base_path): def extract_hand_landmarks_from_parquet(path): - """Extract hand landmarks, handling NaN values properly""" + """Extract hand landmarks - ONLY uses frames with valid (non-NaN) data""" try: df = pd.read_parquet(path) @@ -54,56 +55,70 @@ def extract_hand_landmarks_from_parquet(path): left = df[df["type"] == "left_hand"] right = df[df["type"] == "right_hand"] - # 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: + if len(left) == 0 and len(right) == 0: return None - # Get frames with valid data + # Count valid (non-NaN) rows for each hand + left_valid = 0 + right_valid = 0 + + if len(left) > 0: + left_valid = left[['x', 'y', 'z']].notna().all(axis=1).sum() + if len(right) > 0: + right_valid = right[['x', 'y', 'z']].notna().all(axis=1).sum() + + # No valid data at all + if left_valid == 0 and right_valid == 0: + return None + + # Choose hand with more valid data + hand = left if left_valid >= right_valid else right + + # Get unique frames 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 + # Count how many valid landmarks this frame has + valid_count = lm_frame[['x', 'y', 'z']].notna().all(axis=1).sum() + + # Skip frames with too few valid landmarks + if valid_count < 10: continue - lm_list = [] - frame_has_data = False + # Extract landmarks for this frame + frame_landmarks = [] + valid_landmarks_in_frame = 0 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]) + frame_landmarks.append([0.0, 0.0, 0.0]) else: - x = lm['x'].iloc[0] - y = lm['y'].iloc[0] - z = lm['z'].iloc[0] + x = float(lm['x'].iloc[0]) + y = float(lm['y'].iloc[0]) + z = 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]) + # Check if valid + if pd.notna(x) and pd.notna(y) and pd.notna(z): + frame_landmarks.append([x, y, z]) + valid_landmarks_in_frame += 1 else: - lm_list.append([float(x), float(y), float(z)]) - frame_has_data = True + frame_landmarks.append([0.0, 0.0, 0.0]) - if frame_has_data: - landmarks_seq.append(lm_list) + # Only add frame if it has enough valid landmarks + if valid_landmarks_in_frame >= 10: + landmarks_seq.append(frame_landmarks) - if len(landmarks_seq) == 0: + # Need at least 3 valid frames + if len(landmarks_seq) < 3: return None return np.array(landmarks_seq, dtype=np.float32) + except Exception as e: return None @@ -113,43 +128,57 @@ def get_features_sequence(landmarks_seq, max_frames=100): if landmarks_seq is None or len(landmarks_seq) == 0: return None, None - # Center on wrist - landmarks_seq = landmarks_seq - landmarks_seq[:, 0:1, :] + # Center on wrist (landmark 0) + wrist = landmarks_seq[:, 0:1, :].copy() + landmarks_seq = landmarks_seq - wrist - # Scale using wrist → middle finger MCP distance - scale = np.linalg.norm(landmarks_seq[:, 0] - landmarks_seq[:, 9], axis=1, keepdims=True) - scale = np.maximum(scale, 1e-6) + # Scale normalization using wrist to middle finger MCP (landmark 9) + scale = np.linalg.norm(landmarks_seq[:, 9, :] - np.zeros(3), axis=1, keepdims=True) + scale = np.maximum(scale, 1e-6) # Avoid division by zero landmarks_seq = landmarks_seq / scale[:, np.newaxis, :] - # Replace any remaining NaN/Inf with 0 + # Clean up any remaining NaN/Inf 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] + # Clip extreme values + landmarks_seq = np.clip(landmarks_seq, -10, 10) + + # Calculate finger curl features + tips = [4, 8, 12, 16, 20] # Thumb, index, middle, ring, pinky tips + bases = [1, 5, 9, 13, 17] # Corresponding base joints + curl_features = [] for b, t in zip(bases, tips): curl = np.linalg.norm(landmarks_seq[:, t] - landmarks_seq[:, b], axis=1) curl_features.append(curl) - curl_features = np.stack(curl_features, axis=1) + curl_features = np.stack(curl_features, axis=1) # (T, 5) - # Temporal deltas + # Temporal deltas (motion) deltas = np.zeros_like(landmarks_seq) - deltas[1:] = landmarks_seq[1:] - landmarks_seq[:-1] + if len(landmarks_seq) > 1: + deltas[1:] = landmarks_seq[1:] - landmarks_seq[:-1] - # Flatten features - seq = np.concatenate([landmarks_seq, deltas, curl_features[:, :, np.newaxis]], axis=2) + # Combine all features + seq = np.concatenate([ + landmarks_seq, # (T, 21, 3) + deltas, # (T, 21, 3) + curl_features[:, :, np.newaxis] # (T, 5, 1) + ], axis=2) + + # Flatten spatial dimensions: (T, 21*3 + 21*3 + 5) = (T, 131) seq = seq.reshape(seq.shape[0], -1) - # Pad or truncate + # Pad or truncate to max_frames T, F = seq.shape if T < max_frames: + # Pad with zeros pad = np.zeros((max_frames - T, F), dtype=np.float32) seq = np.concatenate([seq, pad], axis=0) elif T > max_frames: + # Truncate seq = seq[:max_frames, :] - # Create mask + # Create attention mask (True for valid positions) valid_mask = np.zeros(max_frames, dtype=bool) valid_mask[:min(T, max_frames)] = True @@ -157,26 +186,30 @@ def get_features_sequence(landmarks_seq, max_frames=100): def process_row(row, base_path, max_frames=100): - """Process a single row""" + """Process a single row - worker function for multiprocessing""" path = os.path.join(base_path, row["path"]) + if not os.path.exists(path): return None, None, None try: + # Extract landmarks lm = extract_hand_landmarks_from_parquet(path) if lm is None: return None, None, None + # Get features feat, mask = get_features_sequence(lm, max_frames) if feat is None: return None, None, None - # Final NaN check + # Final safety check if np.isnan(feat).any() or np.isinf(feat).any(): return None, None, None return feat, mask, row["sign"] - except: + + except Exception as e: return None, None, None @@ -198,12 +231,15 @@ class PositionalEncoding(nn.Module): class TransformerASL(nn.Module): - def __init__(self, input_dim=68, num_classes=250, d_model=256, nhead=8, num_layers=4): + def __init__(self, input_dim, num_classes, d_model=256, nhead=8, num_layers=4): super().__init__() + + # Input projection self.proj = nn.Linear(input_dim, d_model) self.norm_in = nn.LayerNorm(d_model) - self.pos = PositionalEncoding(d_model) + self.pos = PositionalEncoding(d_model, max_len=128) + # Transformer encoder enc_layer = nn.TransformerEncoderLayer( d_model=d_model, nhead=nhead, @@ -215,6 +251,7 @@ class TransformerASL(nn.Module): ) self.encoder = nn.TransformerEncoder(enc_layer, num_layers=num_layers) + # Classification head self.head = nn.Sequential( nn.LayerNorm(d_model), nn.Dropout(0.25), @@ -222,11 +259,17 @@ class TransformerASL(nn.Module): ) def forward(self, x, key_padding_mask=None): + # x: (batch, seq_len, input_dim) + # key_padding_mask: (batch, seq_len) - True for padding positions + x = self.proj(x) x = self.norm_in(x) x = self.pos(x) x = self.encoder(x, src_key_padding_mask=key_padding_mask) + + # Global average pooling over valid positions x = x.mean(dim=1) + return self.head(x) @@ -236,7 +279,7 @@ class TransformerASL(nn.Module): def main(): base_path = "asl_kaggle" max_frames = 100 - MIN_SAMPLES_PER_CLASS = 6 + MIN_SAMPLES_PER_CLASS = 5 print("\nLoading metadata...") train_df, sign_to_idx = load_kaggle_asl_data(base_path) @@ -244,7 +287,10 @@ def main(): rows = [row for _, row in train_df.iterrows()] - print("\nProcessing sequences with NaN handling...") + print("\nProcessing sequences (this will take a few minutes)...") + print("Expected: ~36,000 valid sequences based on diagnostic") + + # Process with multiprocessing with Pool(cpu_count()) as pool: results = list(tqdm( pool.imap( @@ -259,27 +305,30 @@ def main(): # 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: - X_list.append(feat) - masks_list.append(mask) - y_list.append(sign) + if feat is not None and mask is not None and sign is not None: + if feat.shape[0] == max_frames: + X_list.append(feat) + masks_list.append(mask) + y_list.append(sign) - print(f"\n✓ Valid sequences: {len(X_list)} out of {len(train_df)}") + print(f"\n✓ Successfully extracted: {len(X_list)} valid sequences") + print(f" Success rate: {len(X_list) / len(train_df) * 100:.1f}%") - if not X_list: - 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") + if len(X_list) < 100: + print("❌ Too few valid sequences found!") + print(" This shouldn't happen - please share this output for debugging") return + # Stack into arrays X = np.stack(X_list) masks = np.stack(masks_list) - print(f"Data shape: {X.shape}") + + print(f"\nData shape: {X.shape}") + print(f"Feature dimension: {X.shape[2]}") # Global normalization - X = np.clip(X, -5.0, 5.0) + print("Normalizing features...") + X = np.clip(X, -10.0, 10.0) mean = X.mean(axis=(0, 1), keepdims=True) std = X.std(axis=(0, 1), keepdims=True) + 1e-8 X = (X - mean) / std @@ -292,22 +341,29 @@ def main(): 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) + X = X[mask_valid] masks = masks[mask_valid] y = y[mask_valid] - # Re-encode + # Re-encode after filtering le = LabelEncoder() y = le.fit_transform(y) - print(f"Final dataset: {len(X)} samples | {len(le.classes_)} classes") + print(f"\nFinal dataset after filtering:") + print(f" Samples: {len(X):,}") + print(f" Classes: {len(le.classes_)}") + print(f" Sign examples: {list(le.classes_[:10])}") # Train-test split X_train, X_test, masks_train, masks_test, y_train, y_test = train_test_split( X, masks, y, test_size=0.15, stratify=y, random_state=42 ) - # Dataset + print(f"\nTrain set: {len(X_train):,} samples") + print(f"Test set: {len(X_test):,} samples") + + # Dataset wrapper class ASLSequenceDataset(Dataset): def __init__(self, X, masks, y): self.X = torch.from_numpy(X).float() @@ -320,18 +376,27 @@ def main(): def __getitem__(self, idx): return self.X[idx], self.masks[idx], self.y[idx] + # DataLoaders batch_size = 128 if device.type == 'cuda' else 64 train_loader = DataLoader( ASLSequenceDataset(X_train, masks_train, y_train), - batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True - ) - test_loader = DataLoader( - ASLSequenceDataset(X_test, masks_test, y_test), - batch_size=batch_size * 2, shuffle=False, num_workers=4, pin_memory=True + batch_size=batch_size, + shuffle=True, + num_workers=4, + pin_memory=True if device.type == 'cuda' else False ) - # Model + test_loader = DataLoader( + ASLSequenceDataset(X_test, masks_test, y_test), + batch_size=batch_size * 2, + shuffle=False, + num_workers=4, + pin_memory=True if device.type == 'cuda' else False + ) + + # Initialize model + print("\nInitializing model...") model = TransformerASL( input_dim=X.shape[2], num_classes=len(le.classes_), @@ -341,35 +406,41 @@ def main(): ).to(device) total_params = sum(p.numel() for p in model.parameters()) - print(f"\nModel parameters: {total_params:,}") + print(f"Model parameters: {total_params:,}") + # Training setup criterion = nn.CrossEntropyLoss(label_smoothing=0.05) optimizer = optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-4) - scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10) + scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=2) - # Training + # Training loop best_acc = 0.0 patience = 15 wait = 0 - epochs = 70 + epochs = 60 - print("\nStarting training...") + print("\n" + "=" * 60) + print("STARTING TRAINING") print("=" * 60) for epoch in range(epochs): + # Train model.train() total_loss = 0 correct = total = 0 - for x, mask, yb in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs}"): + for x, mask, yb in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs}", leave=False): x, mask, yb = x.to(device), mask.to(device), yb.to(device) + + # Invert mask: True for padding positions 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) + + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) optimizer.step() total_loss += loss.item() @@ -378,7 +449,7 @@ def main(): train_acc = correct / total * 100 - # Eval + # Evaluate model.eval() correct = total = 0 with torch.no_grad(): @@ -391,11 +462,13 @@ def main(): test_acc = correct / total * 100 + # Print progress print(f"[{epoch + 1:2d}/{epochs}] Loss: {total_loss / len(train_loader):.4f} | " - f"Train: {train_acc:.2f}% | Test: {test_acc:.2f}%") + f"Train: {train_acc:.2f}% | Test: {test_acc:.2f}%", end="") scheduler.step() + # Save best model if test_acc > best_acc: best_acc = test_acc wait = 0 @@ -406,19 +479,25 @@ def main(): 'acc': best_acc, 'epoch': epoch, 'input_dim': X.shape[2], - 'num_classes': len(le.classes_) + 'num_classes': len(le.classes_), + 'd_model': 256, + 'nhead': 8, + 'num_layers': 4 }, "best_asl_transformer.pth") - print(f" → New best: {best_acc:.2f}%") + print(f" → New best: {best_acc:.2f}% ✓") else: wait += 1 + print() + if wait >= patience: - print("Early stopping") + print(f"\nEarly stopping triggered at epoch {epoch + 1}") break - print("=" * 60) - print(f"\n✓ Training complete!") + print("\n" + "=" * 60) + print(f"✓ Training complete!") print(f"✓ Best test accuracy: {best_acc:.2f}%") print(f"✓ Model saved: best_asl_transformer.pth") + print("=" * 60) if __name__ == "__main__": diff --git a/valid_files.json b/valid_files.json new file mode 100644 index 0000000..86d5f4b --- /dev/null +++ b/valid_files.json @@ -0,0 +1,626 @@ +[ + { + "path": "train_landmark_files/16069/100015657.parquet", + "sign": "cloud", + "hand": "left", + "total_frames": 105, + "valid_frames": 28, + "nan_ratio": 0.7333333333333333 + }, + { + "path": "train_landmark_files/32319/1000278229.parquet", + "sign": "lips", + "hand": "left", + "total_frames": 57, + "valid_frames": 36, + "nan_ratio": 0.3684210526315789 + }, + { + "path": "train_landmark_files/36257/1000536928.parquet", + "sign": "apple", + "hand": "left", + "total_frames": 13, + "valid_frames": 10, + "nan_ratio": 0.23076923076923078 + }, + { + "path": "train_landmark_files/22343/1000638205.parquet", + "sign": "puzzle", + "hand": "left", + "total_frames": 19, + "valid_frames": 11, + "nan_ratio": 0.42105263157894735 + }, + { + "path": "train_landmark_files/27610/1000697904.parquet", + "sign": "there", + "hand": "left", + "total_frames": 43, + "valid_frames": 15, + "nan_ratio": 0.6511627906976745 + }, + { + "path": "train_landmark_files/61333/1000909322.parquet", + "sign": "shirt", + "hand": "left", + "total_frames": 22, + "valid_frames": 22, + "nan_ratio": 0.0 + }, + { + "path": "train_landmark_files/27610/1000956928.parquet", + "sign": "owl", + "hand": "left", + "total_frames": 100, + "valid_frames": 98, + "nan_ratio": 0.02 + }, + { + "path": "train_landmark_files/22343/1001223069.parquet", + "sign": "not", + "hand": "left", + "total_frames": 18, + "valid_frames": 6, + "nan_ratio": 0.6666666666666666 + }, + { + "path": "train_landmark_files/32319/1001258102.parquet", + "sign": "zipper", + "hand": "left", + "total_frames": 13, + "valid_frames": 8, + "nan_ratio": 0.38461538461538464 + }, + { + "path": "train_landmark_files/55372/1001471195.parquet", + "sign": "cheek", + "hand": "left", + "total_frames": 17, + "valid_frames": 17, + "nan_ratio": 0.0 + }, + { + "path": "train_landmark_files/36257/1001560021.parquet", + "sign": "shoe", + "hand": "left", + "total_frames": 63, + "valid_frames": 63, + "nan_ratio": 0.0 + }, + { + "path": "train_landmark_files/34503/1001685690.parquet", + "sign": "empty", + "hand": "left", + "total_frames": 8, + "valid_frames": 8, + "nan_ratio": 0.0 + }, + { + "path": "train_landmark_files/61333/1001819372.parquet", + "sign": "balloon", + "hand": "left", + "total_frames": 27, + "valid_frames": 16, + "nan_ratio": 0.4074074074074074 + }, + { + "path": "train_landmark_files/36257/1001899025.parquet", + "sign": "same", + "hand": "left", + "total_frames": 44, + "valid_frames": 22, + "nan_ratio": 0.5 + }, + { + "path": "train_landmark_files/28656/1001919956.parquet", + "sign": "orange", + "hand": "left", + "total_frames": 53, + "valid_frames": 48, + "nan_ratio": 0.09433962264150944 + }, + { + "path": "train_landmark_files/32319/1001958254.parquet", + "sign": "go", + "hand": "left", + "total_frames": 24, + "valid_frames": 23, + "nan_ratio": 0.041666666666666664 + }, + { + "path": "train_landmark_files/61333/1002052130.parquet", + "sign": "TV", + "hand": "left", + "total_frames": 116, + "valid_frames": 109, + "nan_ratio": 0.0603448275862069 + }, + { + "path": "train_landmark_files/16069/1002113535.parquet", + "sign": "another", + "hand": "left", + "total_frames": 6, + "valid_frames": 3, + "nan_ratio": 0.5 + }, + { + "path": "train_landmark_files/55372/1002129762.parquet", + "sign": "giraffe", + "hand": "left", + "total_frames": 23, + "valid_frames": 14, + "nan_ratio": 0.391304347826087 + }, + { + "path": "train_landmark_files/29302/1002284514.parquet", + "sign": "can", + "hand": "left", + "total_frames": 41, + "valid_frames": 8, + "nan_ratio": 0.8048780487804879 + }, + { + "path": "train_landmark_files/55372/100230619.parquet", + "sign": "say", + "hand": "left", + "total_frames": 20, + "valid_frames": 19, + "nan_ratio": 0.05 + }, + { + "path": "train_landmark_files/55372/1002734054.parquet", + "sign": "that", + "hand": "left", + "total_frames": 6, + "valid_frames": 6, + "nan_ratio": 0.0 + }, + { + "path": "train_landmark_files/22343/1002776784.parquet", + "sign": "black", + "hand": "left", + "total_frames": 15, + "valid_frames": 9, + "nan_ratio": 0.4 + }, + { + "path": "train_landmark_files/37055/1003007869.parquet", + "sign": "moon", + "hand": "left", + "total_frames": 62, + "valid_frames": 4, + "nan_ratio": 0.9354838709677419 + }, + { + "path": "train_landmark_files/61333/1003093029.parquet", + "sign": "pizza", + "hand": "left", + "total_frames": 23, + "valid_frames": 18, + "nan_ratio": 0.21739130434782608 + }, + { + "path": "train_landmark_files/37055/1003109377.parquet", + "sign": "shhh", + "hand": "left", + "total_frames": 23, + "valid_frames": 20, + "nan_ratio": 0.13043478260869565 + }, + { + "path": "train_landmark_files/36257/1003335907.parquet", + "sign": 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