diff --git a/training.py b/training.py index 0f2c267..b9f7021 100644 --- a/training.py +++ b/training.py @@ -1,3 +1,6 @@ +# =============================== +# IMPORTS +# =============================== import os import json import math @@ -11,13 +14,21 @@ import torch.optim as optim from torch.utils.data import Dataset, DataLoader from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder +from collections import Counter from multiprocessing import Pool, cpu_count from functools import partial from tqdm import tqdm -from collections import Counter # =============================== -# DATA LOADING +# DEVICE +# =============================== +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +print(f"Using device: {device}") +if device.type == "cuda": + print("GPU:", torch.cuda.get_device_name(0)) + +# =============================== +# DATA LOADING / FEATURES # =============================== def load_kaggle_asl_data(base_path): train_df = pd.read_csv(os.path.join(base_path, "train.csv")) @@ -28,46 +39,48 @@ def load_kaggle_asl_data(base_path): def extract_hand_landmarks_from_parquet(path): try: df = pd.read_parquet(path) - left = df[df["type"] == "left_hand"] - right = df[df["type"] == "right_hand"] + left = df[df["type"]=="left_hand"] + right = df[df["type"]=="right_hand"] hand = left if len(left) >= len(right) else right if len(hand) == 0: return None frames = sorted(hand['frame'].unique()) landmarks_seq = [] - for frame in frames: - lm_frame = hand[hand['frame'] == frame] + for f in frames: + lm_frame = hand[hand['frame']==f] lm_list = [] for i in range(21): - lm = lm_frame[lm_frame['landmark_index'] == i] + lm = lm_frame[lm_frame['landmark_index']==i] if len(lm) == 0: - lm_list.append([0.0, 0.0, 0.0]) + lm_list.append([0.0,0.0,0.0]) else: - lm_list.append([ - float(lm['x'].iloc[0]), - float(lm['y'].iloc[0]), - float(lm['z'].iloc[0]) - ]) + lm_list.append([float(lm['x'].iloc[0]), float(lm['y'].iloc[0]), float(lm['z'].iloc[0])]) landmarks_seq.append(lm_list) return np.array(landmarks_seq, dtype=np.float32) except: return None -def get_features_sequence(landmarks_seq, max_frames=100): - if landmarks_seq is None or len(landmarks_seq) == 0: - return None - +def get_features_sequence_augmented(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[:, 0:1, :] - - # Robust scale: wrist → middle finger MCP + landmarks_seq -= landmarks_seq[:,0:1,:] + # Scale using wrist → middle finger MCP scale = np.linalg.norm(landmarks_seq[:,0] - landmarks_seq[:,9], axis=1, keepdims=True) scale = np.maximum(scale, 1e-6) landmarks_seq /= scale[:, np.newaxis, :] - + # 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_features = np.stack(curl_features, axis=1) # (T,5) + # Temporal deltas + deltas = np.zeros_like(landmarks_seq) + deltas[1:] = landmarks_seq[1:] - landmarks_seq[:-1] # Flatten - seq = landmarks_seq.reshape(landmarks_seq.shape[0], -1) - + seq = np.concatenate([landmarks_seq, deltas, curl_features], axis=1) # Pad / truncate T = seq.shape[0] if T < max_frames: @@ -75,67 +88,64 @@ def get_features_sequence(landmarks_seq, max_frames=100): seq = np.concatenate([seq, pad]) else: seq = seq[:max_frames] - - # Mask for valid frames + # Mask valid_mask = np.zeros(max_frames, dtype=bool) - valid_mask[:min(T, max_frames)] = True - + valid_mask[:min(T,max_frames)] = True return seq, valid_mask def process_row(row, base_path, max_frames=100): 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 - return feat, mask, row["sign"] - except: + lm = extract_hand_landmarks_from_parquet(path) + if lm is None: return None, None, None + seq, mask = get_features_sequence_augmented(lm, max_frames) + if seq is None: + return None, None, None + return seq, mask, row["sign"] + +# =============================== +# DATASET +# =============================== +class ASLSequenceDataset(Dataset): + def __init__(self, X, masks, y): + self.X = torch.from_numpy(X).float() + self.masks = torch.from_numpy(masks) + self.y = torch.from_numpy(y).long() + def __len__(self): + return len(self.X) + def __getitem__(self, idx): + return self.X[idx], self.masks[idx], self.y[idx] # =============================== # TRANSFORMER MODEL # =============================== class PositionalEncoding(nn.Module): - def __init__(self, d_model, max_len=128): + def __init__(self,d_model,max_len=128): super().__init__() - pe = torch.zeros(max_len, d_model) - position = torch.arange(0, max_len, dtype=torch.float32).unsqueeze(1) - div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) - pe[:, 0::2] = torch.sin(position * div_term) - pe[:, 1::2] = torch.cos(position * div_term) + pe = torch.zeros(max_len,d_model) + pos = torch.arange(0,max_len,dtype=torch.float).unsqueeze(1) + div_term = torch.exp(torch.arange(0,d_model,2).float()*(-math.log(10000.0)/d_model)) + pe[:,0::2] = torch.sin(pos*div_term) + pe[:,1::2] = torch.cos(pos*div_term) self.register_buffer('pe', pe.unsqueeze(0)) - - def forward(self, x): - return x + self.pe[:, :x.size(1)] + def forward(self,x): + return x + self.pe[:,:x.size(1)] class TransformerASL(nn.Module): - def __init__(self, input_dim=63, num_classes=250, d_model=128, nhead=4, num_layers=2): + def __init__(self, input_dim=131, num_classes=250, d_model=192, nhead=6, num_layers=4): super().__init__() - self.proj = nn.Linear(input_dim, d_model) + self.proj = nn.Linear(input_dim,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=0.1, - activation='gelu', - batch_first=True, - norm_first=True + d_model=d_model, nhead=nhead, dim_feedforward=d_model*4, + dropout=0.2, activation='gelu', batch_first=True, norm_first=True ) - self.encoder = nn.TransformerEncoder(enc_layer, num_layers=num_layers) - self.head = nn.Sequential( - nn.LayerNorm(d_model), - nn.Dropout(0.2), - nn.Linear(d_model, num_classes) - ) - - def forward(self, x, key_padding_mask=None): + self.encoder = nn.TransformerEncoder(enc_layer,num_layers=num_layers) + self.head = nn.Sequential(nn.LayerNorm(d_model), nn.Dropout(0.25), nn.Linear(d_model,num_classes)) + def forward(self,x,key_padding_mask=None): x = self.proj(x) x = self.norm_in(x) x = self.pos(x) @@ -143,159 +153,135 @@ class TransformerASL(nn.Module): x = x.mean(dim=1) return self.head(x) -def create_padding_mask(valid_masks): - # valid_masks: (B,T) bool, True for valid - return ~valid_masks # True in mask = positions to ignore +def create_padding_mask(lengths,max_len): + return torch.arange(max_len,device=lengths.device)[None,:] >= lengths[:,None] # =============================== # MAIN # =============================== def main(): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - print(f"Using device: {device}") - if device.type == "cuda": - print("GPU:", torch.cuda.get_device_name(0)) - - base_path = "asl_kaggle" + base_path = "asl_kaggle" # adjust path max_frames = 100 MIN_SAMPLES_PER_CLASS = 6 - # --- LOAD DATA --- + # Load metadata + print("Loading metadata...") train_df, sign_to_idx = load_kaggle_asl_data(base_path) rows = [row for _, row in train_df.iterrows()] + # Extract features + print("Extracting features...") with Pool(cpu_count()) as pool: - results = list(tqdm( - pool.imap(partial(process_row, base_path=base_path, max_frames=max_frames), rows), - total=len(rows), - desc="Processing" - )) - - X_list, mask_list, y_list = [], [], [] - for feat, mask, sign in results: - if feat is not None: - X_list.append(feat) - mask_list.append(mask) + results = list(tqdm(pool.imap(partial(process_row,base_path=base_path,max_frames=max_frames),rows), + total=len(rows))) + X_list, masks_list, y_list = [], [], [] + for seq, mask, sign in results: + if seq is not None: + X_list.append(seq) + masks_list.append(mask) y_list.append(sign) - if not X_list: - print("No valid sequences found.") + print("No valid sequences found") return X = np.stack(X_list) - masks = np.stack(mask_list) - print(f"Loaded {len(X)} sequences | shape: {X.shape}") + masks = np.stack(masks_list) + print(f"{len(X)} sequences | shape: {X.shape}") - # --- NORMALIZE only valid frames --- - for i in range(X.shape[0]): - valid_idx = masks[i] - X[i, valid_idx] = (X[i, valid_idx] - X[i, valid_idx].mean(0)) / (X[i, valid_idx].std(0) + 1e-8) + # Normalize + mean = X.mean(axis=(0,1),keepdims=True) + std = X.std(axis=(0,1),keepdims=True)+1e-8 + X = (X - mean)/std - # --- LABELS --- + # Labels le = LabelEncoder() y = le.fit_transform(y_list) - # Remove rare classes + # Remove classes with too few samples counts = Counter(y) - valid_classes = [cls for cls, cnt in counts.items() if cnt >= MIN_SAMPLES_PER_CLASS] - mask_keep = np.isin(y, valid_classes) - X, masks, y = X[mask_keep], masks[mask_keep], y[mask_keep] - le = LabelEncoder() - y = le.fit_transform(y) - print(f"{len(X)} samples remain | {len(le.classes_)} classes") + 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] + le = LabelEncoder(); y = le.fit_transform(y) + print(f"{len(X)} samples | {len(le.classes_)} classes") - # --- SPLIT --- + # 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 ) - # --- DATASETS --- - class ASLSequenceDataset(Dataset): - def __init__(self, X, masks, y): - self.X = torch.from_numpy(X).float() - self.masks = torch.from_numpy(masks) - self.y = torch.from_numpy(y).long() + # Datasets / loaders + train_dataset = ASLSequenceDataset(X_train, masks_train, y_train) + test_dataset = ASLSequenceDataset(X_test, masks_test, y_test) + train_loader = DataLoader(train_dataset, batch_size=64,shuffle=True,num_workers=4,pin_memory=True) + test_loader = DataLoader(test_dataset, batch_size=96,shuffle=False,num_workers=4,pin_memory=True) - def __len__(self): - return len(self.X) - - def __getitem__(self, idx): - return self.X[idx], self.masks[idx], self.y[idx] - - train_loader = DataLoader(ASLSequenceDataset(X_train, masks_train, y_train), - batch_size=64, 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) - - # --- MODEL --- + # Model model = TransformerASL(input_dim=X.shape[2], num_classes=len(le.classes_)).to(device) print(f"Model params: {sum(p.numel() for p in model.parameters()):,}") - criterion = nn.CrossEntropyLoss(label_smoothing=0.05) - optimizer = optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-4) + # Class weights + counts = Counter(y_train) + class_weights = np.array([len(y_train)/ (len(counts)*counts[i]) for i in range(len(counts))],dtype=np.float32) + criterion = nn.CrossEntropyLoss(weight=torch.tensor(class_weights).to(device),label_smoothing=0.05) + optimizer = optim.AdamW(model.parameters(), lr=3e-4, weight_decay=1e-4) scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10) - # --- TRAINING --- - best_acc = 0.0 - patience = 15 - wait = 0 - epochs = 70 - + # Training / eval def train_epoch(): model.train() - total_loss = 0 - correct = total = 0 - for x, m, yb in tqdm(train_loader, desc="Train"): - x, m, yb = x.to(device), m.to(device), yb.to(device) - mask = create_padding_mask(m) - + total_loss=0; correct=total=0 + for x, mask, yb in tqdm(train_loader, desc="Train"): + x, mask, yb = x.to(device), mask.to(device), yb.to(device) + lengths = mask.sum(dim=1) + pad_mask = create_padding_mask(lengths, x.size(1)) optimizer.zero_grad(set_to_none=True) - logits = model(x, key_padding_mask=mask) - loss = criterion(logits, yb) + logits = model(x,key_padding_mask=pad_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(),0.8) optimizer.step() - total_loss += loss.item() - correct += (logits.argmax(-1) == yb).sum().item() + correct += (logits.argmax(-1)==yb).sum().item() total += yb.size(0) - return total_loss / len(train_loader), correct / total * 100 + return total_loss/len(train_loader), correct/total*100 @torch.no_grad() def evaluate(): model.eval() - correct = total = 0 - for x, m, yb in test_loader: - x, m, yb = x.to(device), m.to(device), yb.to(device) - mask = create_padding_mask(m) - logits = model(x, key_padding_mask=mask) - correct += (logits.argmax(-1) == yb).sum().item() + correct=total=0 + for x, mask, yb in test_loader: + x, mask, yb = x.to(device), mask.to(device), yb.to(device) + lengths = mask.sum(dim=1) + pad_mask = create_padding_mask(lengths, x.size(1)) + logits = model(x,key_padding_mask=pad_mask) + correct += (logits.argmax(-1)==yb).sum().item() total += yb.size(0) - return correct / total * 100 if total > 0 else 0.0 + return correct/total*100 if total>0 else 0.0 + # Train loop + best_acc=0.0; patience=15; wait=0; epochs=70 for epoch in range(epochs): loss, train_acc = train_epoch() test_acc = evaluate() - print(f"[{epoch+1}/{epochs}] loss: {loss:.4f} | train: {train_acc:.2f}% | test: {test_acc:.2f}%") + print(f"[{epoch+1:2d}/{epochs}] loss:{loss:.4f} train:{train_acc:.2f}% test:{test_acc:.2f}%") scheduler.step() - if test_acc > best_acc: - best_acc = test_acc - wait = 0 + if test_acc>best_acc: + best_acc=test_acc; wait=0 torch.save({ - 'model': model.state_dict(), - 'optimizer': optimizer.state_dict(), - 'label_encoder_classes': le.classes_, - 'acc': best_acc, - 'epoch': epoch - }, "best_asl_transformer.pth") - print(" → New best saved") + 'model':model.state_dict(), + 'optimizer':optimizer.state_dict(), + 'label_encoder_classes':le.classes_, + 'acc':best_acc, + 'epoch':epoch + },"best_asl_transformer.pth") + print(" → New best saved") else: - wait += 1 - if wait >= patience: + wait+=1 + if wait>=patience: print("Early stopping") break + print(f"\nBest test accuracy reached: {best_acc:.2f}%") - print(f"\nBest test accuracy: {best_acc:.2f}%") - -if __name__ == '__main__': +if __name__=="__main__": main()