ASLTranslator/training.py

# ===============================
# IMPORTS
# ===============================
import os
import json
import math
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
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

# ===============================
# 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"))
    with open(os.path.join(base_path, "sign_to_prediction_index_map.json")) as f:
        sign_to_idx = json.load(f)
    return train_df, sign_to_idx

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"]
        hand = left if len(left) >= len(right) else right
        if len(hand) == 0:
            return None
        frames = sorted(hand['frame'].unique())
        landmarks_seq = []
        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]
                if len(lm) == 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])])
            landmarks_seq.append(lm_list)
        return np.array(landmarks_seq, dtype=np.float32)
    except:
        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,:]
    # 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 = np.concatenate([landmarks_seq, deltas, curl_features], axis=1)
    # Pad / truncate
    T = seq.shape[0]
    if T < max_frames:
        pad = np.zeros((max_frames - T, seq.shape[1]), dtype=np.float32)
        seq = np.concatenate([seq, pad])
    else:
        seq = seq[:max_frames]
    # Mask
    valid_mask = np.zeros(max_frames, dtype=bool)
    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
    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):
        super().__init__()
        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)]

class TransformerASL(nn.Module):
    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.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=0.2, 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(0.25), 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)
        x = self.encoder(x, src_key_padding_mask=key_padding_mask)
        x = x.mean(dim=1)
        return self.head(x)

def create_padding_mask(lengths,max_len):
    return torch.arange(max_len,device=lengths.device)[None,:] >= lengths[:,None]

# ===============================
# MAIN
# ===============================
def main():
    base_path = "asl_kaggle"  # adjust path
    max_frames = 100
    MIN_SAMPLES_PER_CLASS = 6

    # 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)))
    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")
        return

    X = np.stack(X_list)
    masks = np.stack(masks_list)
    print(f"{len(X)} sequences | shape: {X.shape}")

    # Normalize
    mean = X.mean(axis=(0,1),keepdims=True)
    std = X.std(axis=(0,1),keepdims=True)+1e-8
    X = (X - mean)/std

    # Labels
    le = LabelEncoder()
    y = le.fit_transform(y_list)

    # 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_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
    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 / 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)

    # 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()):,}")

    # 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 / eval
    def train_epoch():
        model.train()
        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=pad_mask)
            loss = criterion(logits,yb)
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(),0.8)
            optimizer.step()
            total_loss += loss.item()
            correct += (logits.argmax(-1)==yb).sum().item()
            total += yb.size(0)
        return total_loss/len(train_loader), correct/total*100

    @torch.no_grad()
    def evaluate():
        model.eval()
        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

    # 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+1:2d}/{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
            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")
        else:
            wait+=1
            if wait>=patience:
                print("Early stopping")
                break
    print(f"\nBest test accuracy reached: {best_acc:.2f}%")

if __name__=="__main__":
    main()