575 lines
16 KiB
Python
575 lines
16 KiB
Python
import mediapipe as mp
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import numpy as np
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import os
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import pandas as pd
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import json
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import LabelEncoder
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import pickle
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import Dataset, DataLoader
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import torch.nn.functional as F
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import math
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from pathlib import Path
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# GPU Configuration
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print("=" * 50)
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print("GPU CONFIGURATION")
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print("=" * 50)
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# Check CUDA availability
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if torch.cuda.is_available():
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print(f"✓ CUDA is available!")
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print(f"✓ GPU Device: {torch.cuda.get_device_name(0)}")
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print(f"✓ CUDA Version: {torch.version.cuda}")
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print(f"✓ Number of GPUs: {torch.cuda.device_count()}")
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print(f"✓ Current GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1024 ** 3:.2f} GB")
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# Set default GPU device
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torch.cuda.set_device(0)
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device = torch.device('cuda:0')
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# Enable cuDNN benchmark for better performance
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torch.backends.cudnn.benchmark = True
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torch.backends.cudnn.enabled = True
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print(f"✓ cuDNN benchmark mode: enabled")
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else:
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print("✗ CUDA is NOT available. Using CPU.")
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print(" Make sure you have:")
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print(" 1. NVIDIA GPU")
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print(" 2. CUDA toolkit installed")
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print(" 3. PyTorch with CUDA support")
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device = torch.device('cpu')
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print("=" * 50)
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print()
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# Load the dataset
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def load_kaggle_asl_data(base_path='asl_kaggle'):
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"""
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Load data from Kaggle ASL dataset format
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base_path should contain:
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- train.csv
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- train_landmark_files/ directory
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- sign_to_prediction_index_map.json
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"""
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train_df = pd.read_csv(os.path.join(base_path, 'train.csv'))
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with open(os.path.join(base_path, 'sign_to_prediction_index_map.json'), 'r') as f:
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sign_to_idx = json.load(f)
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print(f"Total sequences: {len(train_df)}")
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print(f"Unique signs: {len(sign_to_idx)}")
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print(f"Signs: {list(sign_to_idx.keys())[:10]}...")
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return train_df, sign_to_idx
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def extract_hand_landmarks_from_parquet(parquet_path):
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"""Extract hand landmarks from a parquet file"""
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df = pd.read_parquet(parquet_path)
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left_hand = df[df['type'] == 'left_hand']
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right_hand = df[df['type'] == 'right_hand']
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if len(left_hand) > len(right_hand):
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hand_df = left_hand
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elif len(right_hand) > 0:
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hand_df = right_hand
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else:
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return None
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landmarks_list = []
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for landmark_idx in range(21):
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landmark_data = hand_df[hand_df['landmark_index'] == landmark_idx]
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if len(landmark_data) == 0:
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landmarks_list.append([0.0, 0.0, 0.0])
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else:
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x = landmark_data['x'].mean()
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y = landmark_data['y'].mean()
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z = landmark_data['z'].mean()
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landmarks_list.append([x, y, z])
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return np.array(landmarks_list, dtype=np.float32)
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def get_optimized_features(landmarks_array):
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"""Extract optimally normalized relative coordinates from landmark array"""
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if landmarks_array is None:
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return None
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points = landmarks_array.copy()
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wrist = points[0].copy()
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points_centered = points - wrist
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palm_size = np.linalg.norm(points[9] - points[0])
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if palm_size < 1e-6:
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palm_size = 1.0
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points_normalized = points_centered / palm_size
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mean = np.mean(points_normalized, axis=0)
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std = np.std(points_normalized, axis=0) + 1e-8
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points_standardized = (points_normalized - mean) / std
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features = points_standardized.flatten()
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finger_tips = [4, 8, 12, 16, 20]
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tip_distances = []
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for i in range(len(finger_tips) - 1):
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dist = np.linalg.norm(points_normalized[finger_tips[i]] - points_normalized[finger_tips[i + 1]])
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tip_distances.append(dist)
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palm_center = np.mean(points_normalized[[0, 5, 9, 13, 17]], axis=0)
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tip_to_palm = []
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for tip in finger_tips:
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dist = np.linalg.norm(points_normalized[tip] - palm_center)
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tip_to_palm.append(dist)
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finger_curls = []
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finger_bases = [1, 5, 9, 13, 17]
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for base, tip in zip(finger_bases, finger_tips):
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curl = np.linalg.norm(points_normalized[tip] - points_normalized[base])
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finger_curls.append(curl)
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all_features = np.concatenate([
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features,
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tip_distances,
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tip_to_palm,
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finger_curls
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])
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return all_features.astype(np.float32)
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# Load dataset
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print("Loading Kaggle ASL dataset...")
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base_path = 'asl_kaggle'
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train_df, sign_to_idx = load_kaggle_asl_data(base_path)
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# Process landmarks with parallel processing
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from multiprocessing import Pool, cpu_count
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from functools import partial
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def process_single_sequence(row, base_path):
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"""Process a single sequence - designed for parallel execution"""
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parquet_path = os.path.join(base_path, row['path'])
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if not os.path.exists(parquet_path):
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return None, None
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try:
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landmarks = extract_hand_landmarks_from_parquet(parquet_path)
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if landmarks is None:
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return None, None
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features = get_optimized_features(landmarks)
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if features is None:
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return None, None
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return features, row['sign']
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except Exception as e:
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return None, None
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print("\nProcessing landmark files with parallel processing...")
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print(f"Using {cpu_count()} CPU cores")
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# Convert DataFrame rows to list for parallel processing
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rows_list = [row for _, row in train_df.iterrows()]
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# Create partial function with base_path
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process_func = partial(process_single_sequence, base_path=base_path)
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# Process in parallel with progress updates
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X = []
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y = []
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batch_size = 1000
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with Pool(processes=cpu_count()) as pool:
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for i in range(0, len(rows_list), batch_size):
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batch = rows_list[i:i + batch_size]
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results = pool.map(process_func, batch)
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for features, sign in results:
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if features is not None and sign is not None:
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X.append(features)
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y.append(sign)
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print(f"Processed {min(i + batch_size, len(rows_list))}/{len(rows_list)} sequences... (Valid: {len(X)})")
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print(f"\nSuccessfully processed {len(X)} sequences")
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if len(X) == 0:
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print("ERROR: No valid sequences found! Check your dataset path.")
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exit()
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X = np.array(X, dtype=np.float32)
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y = np.array(y)
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print(f"Feature vector size: {X.shape[1]} dimensions")
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# Clean data
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if np.isnan(X).any():
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print("WARNING: NaN values detected, removing affected samples...")
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mask = ~np.isnan(X).any(axis=1)
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X = X[mask]
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y = y[mask]
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if np.isinf(X).any():
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print("WARNING: Inf values detected, removing affected samples...")
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mask = ~np.isinf(X).any(axis=1)
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X = X[mask]
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y = y[mask]
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# Encode labels
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label_encoder = LabelEncoder()
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y_encoded = label_encoder.fit_transform(y)
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num_classes = len(label_encoder.classes_)
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print(f"\nNumber of classes: {num_classes}")
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print(f"Sample classes: {label_encoder.classes_[:20]}...")
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# Split data
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X_train, X_test, y_train, y_test = train_test_split(
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X, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded
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)
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# PyTorch Dataset
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class ASLDataset(Dataset):
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def __init__(self, X, y):
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self.X = torch.FloatTensor(X)
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self.y = torch.LongTensor(y)
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def __len__(self):
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return len(self.X)
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def __getitem__(self, idx):
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return self.X[idx], self.y[idx]
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train_dataset = ASLDataset(X_train, y_train)
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test_dataset = ASLDataset(X_test, y_test)
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# Optimized DataLoader settings for GPU
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num_workers = 4 if device.type == 'cuda' else 0
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pin_memory = True if device.type == 'cuda' else False
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batch_size = 128 if device.type == 'cuda' else 64 # Larger batch size for GPU
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train_loader = DataLoader(
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train_dataset,
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batch_size=batch_size,
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shuffle=True,
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num_workers=num_workers,
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pin_memory=pin_memory,
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persistent_workers=True if num_workers > 0 else False
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)
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test_loader = DataLoader(
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test_dataset,
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batch_size=batch_size,
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shuffle=False,
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num_workers=num_workers,
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pin_memory=pin_memory,
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persistent_workers=True if num_workers > 0 else False
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)
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print(f"\nDataLoader Configuration:")
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print(f" Batch size: {batch_size}")
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print(f" Num workers: {num_workers}")
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print(f" Pin memory: {pin_memory}")
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# Positional Encoding for Transformer
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class PositionalEncoding(nn.Module):
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def __init__(self, d_model, max_len=100):
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super(PositionalEncoding, self).__init__()
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pe = torch.zeros(max_len, d_model)
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position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
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div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
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pe[:, 0::2] = torch.sin(position * div_term)
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pe[:, 1::2] = torch.cos(position * div_term)
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pe = pe.unsqueeze(0)
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self.register_buffer('pe', pe)
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def forward(self, x):
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return x + self.pe[:, :x.size(1), :]
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# Multi-Head Self-Attention Transformer + CNN Hybrid
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class TransformerCNN_ASL(nn.Module):
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def __init__(self, input_dim=77, num_classes=250, d_model=512, nhead=8, num_layers=6, dim_feedforward=2048):
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super(TransformerCNN_ASL, self).__init__()
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self.input_dim = input_dim
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self.d_model = d_model
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self.input_projection = nn.Linear(input_dim, d_model)
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self.input_norm = nn.LayerNorm(d_model)
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self.pos_encoder = PositionalEncoding(d_model, max_len=100)
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encoder_layer = nn.TransformerEncoderLayer(
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d_model=d_model,
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nhead=nhead,
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dim_feedforward=dim_feedforward,
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dropout=0.1,
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activation='gelu',
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batch_first=True,
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norm_first=True
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)
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self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
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self.conv1 = nn.Conv1d(d_model, 1024, kernel_size=3, padding=1)
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self.bn1 = nn.BatchNorm1d(1024)
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self.pool1 = nn.MaxPool1d(2)
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self.dropout1 = nn.Dropout(0.3)
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self.conv2 = nn.Conv1d(1024, 2048, kernel_size=3, padding=1)
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self.bn2 = nn.BatchNorm1d(2048)
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self.pool2 = nn.MaxPool1d(2)
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self.dropout2 = nn.Dropout(0.3)
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self.conv3 = nn.Conv1d(2048, 4096, kernel_size=3, padding=1)
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self.bn3 = nn.BatchNorm1d(4096)
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self.pool3 = nn.AdaptiveMaxPool1d(1)
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self.dropout3 = nn.Dropout(0.4)
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self.fc1 = nn.Linear(4096, 4096)
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self.bn_fc1 = nn.BatchNorm1d(4096)
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self.dropout_fc1 = nn.Dropout(0.5)
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self.fc2 = nn.Linear(4096, 2048)
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self.bn_fc2 = nn.BatchNorm1d(2048)
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self.dropout_fc2 = nn.Dropout(0.4)
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self.fc3 = nn.Linear(2048, 1024)
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self.bn_fc3 = nn.BatchNorm1d(1024)
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self.dropout_fc3 = nn.Dropout(0.3)
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self.fc4 = nn.Linear(1024, num_classes)
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def forward(self, x):
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batch_size = x.size(0)
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x = self.input_projection(x)
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x = self.input_norm(x)
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x = x.unsqueeze(1)
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x = self.pos_encoder(x)
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x = self.transformer_encoder(x)
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x = x.permute(0, 2, 1)
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x = F.gelu(self.bn1(self.conv1(x)))
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x = self.pool1(x)
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x = self.dropout1(x)
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x = F.gelu(self.bn2(self.conv2(x)))
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x = self.pool2(x)
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x = self.dropout2(x)
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x = F.gelu(self.bn3(self.conv3(x)))
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x = self.pool3(x)
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x = self.dropout3(x)
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x = x.view(batch_size, -1)
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x = F.gelu(self.bn_fc1(self.fc1(x)))
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x = self.dropout_fc1(x)
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x = F.gelu(self.bn_fc2(self.fc2(x)))
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x = self.dropout_fc2(x)
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x = F.gelu(self.bn_fc3(self.fc3(x)))
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x = self.dropout_fc3(x)
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x = self.fc4(x)
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return x
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# Initialize model
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print(f"\nInitializing model on {device}...")
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model = TransformerCNN_ASL(
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input_dim=X.shape[1],
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num_classes=num_classes,
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d_model=512,
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nhead=8,
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num_layers=6,
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dim_feedforward=2048
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).to(device)
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# Count parameters
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total_params = sum(p.numel() for p in model.parameters())
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trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
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print(f"Total parameters: {total_params:,}")
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print(f"Trainable parameters: {trainable_params:,}")
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if total_params > 50_000_000:
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print(f"WARNING: Model has {total_params:,} parameters, exceeding 50M limit!")
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else:
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print(f"Model is within 50M parameter limit ✓")
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# Display GPU memory usage
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if device.type == 'cuda':
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print(f"\nGPU Memory after model initialization:")
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print(f" Allocated: {torch.cuda.memory_allocated(0) / 1024 ** 2:.2f} MB")
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print(f" Cached: {torch.cuda.memory_reserved(0) / 1024 ** 2:.2f} MB")
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# Loss and optimizer
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criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
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optimizer = optim.AdamW(model.parameters(), lr=0.001, weight_decay=1e-4)
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# Cosine annealing learning rate scheduler
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scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=2)
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# Training function
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def train_epoch(model, loader, criterion, optimizer, device):
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model.train()
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total_loss = 0
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correct = 0
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total = 0
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for X_batch, y_batch in loader:
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X_batch, y_batch = X_batch.to(device, non_blocking=True), y_batch.to(device, non_blocking=True)
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optimizer.zero_grad(set_to_none=True) # More efficient than zero_grad()
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outputs = model(X_batch)
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loss = criterion(outputs, y_batch)
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
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optimizer.step()
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total_loss += loss.item()
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_, predicted = outputs.max(1)
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total += y_batch.size(0)
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correct += predicted.eq(y_batch).sum().item()
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return total_loss / len(loader), 100. * correct / total
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# Evaluation function
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def evaluate(model, loader, device):
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model.eval()
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correct = 0
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total = 0
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with torch.no_grad():
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for X_batch, y_batch in loader:
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X_batch, y_batch = X_batch.to(device, non_blocking=True), y_batch.to(device, non_blocking=True)
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outputs = model(X_batch)
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_, predicted = outputs.max(1)
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total += y_batch.size(0)
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correct += predicted.eq(y_batch).sum().item()
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return 100. * correct / total
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# Dynamic epoch calculation
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def calculate_epochs(dataset_size):
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if dataset_size < 1000:
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return 200
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elif dataset_size < 5000:
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return 150
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elif dataset_size < 10000:
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return 100
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elif dataset_size < 50000:
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return 75
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else:
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return 50
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num_epochs = calculate_epochs(len(X_train))
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print(f"\nDynamic epoch calculation: {num_epochs} epochs for {len(X_train)} training samples")
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# Early stopping
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patience = 20
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best_acc = 0
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patience_counter = 0
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print("\nStarting training with Transformer + CNN architecture...")
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print("=" * 50)
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|
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# Track training time
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import time
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|
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start_time = time.time()
|
|
|
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for epoch in range(num_epochs):
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epoch_start = time.time()
|
|
|
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train_loss, train_acc = train_epoch(model, train_loader, criterion, optimizer, device)
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test_acc = evaluate(model, test_loader, device)
|
|
|
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scheduler.step()
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|
|
|
epoch_time = time.time() - epoch_start
|
|
|
|
if test_acc > best_acc:
|
|
best_acc = test_acc
|
|
patience_counter = 0
|
|
# Save best model
|
|
torch.save({
|
|
'model_state_dict': model.state_dict(),
|
|
'label_encoder': label_encoder,
|
|
'num_classes': num_classes,
|
|
'input_dim': X.shape[1],
|
|
'sign_to_idx': sign_to_idx,
|
|
'model_config': {
|
|
'd_model': 512,
|
|
'nhead': 8,
|
|
'num_layers': 6,
|
|
'dim_feedforward': 2048
|
|
}
|
|
}, 'asl_kaggle_transformer.pth')
|
|
else:
|
|
patience_counter += 1
|
|
|
|
if (epoch + 1) % 5 == 0:
|
|
current_lr = optimizer.param_groups[0]['lr']
|
|
print(f"Epoch {epoch + 1}/{num_epochs} | Loss: {train_loss:.4f} | "
|
|
f"Train: {train_acc:.2f}% | Test: {test_acc:.2f}% | "
|
|
f"Best: {best_acc:.2f}% | LR: {current_lr:.6f} | "
|
|
f"Time: {epoch_time:.2f}s")
|
|
|
|
if device.type == 'cuda':
|
|
print(f" GPU Memory: {torch.cuda.memory_allocated(0) / 1024 ** 2:.2f} MB")
|
|
|
|
# Early stopping
|
|
if patience_counter >= patience:
|
|
print(f"\nEarly stopping triggered at epoch {epoch + 1}")
|
|
break
|
|
|
|
total_time = time.time() - start_time
|
|
|
|
print("=" * 50)
|
|
print(f"\nTraining complete! Best test accuracy: {best_acc:.2f}%")
|
|
print(f"Total training time: {total_time / 60:.2f} minutes")
|
|
print(f"Average time per epoch: {total_time / (epoch + 1):.2f} seconds")
|
|
print("Model saved to asl_kaggle_transformer.pth")
|
|
|
|
# Final GPU memory stats
|
|
if device.type == 'cuda':
|
|
print(f"\nFinal GPU Memory Usage:")
|
|
print(f" Allocated: {torch.cuda.memory_allocated(0) / 1024 ** 2:.2f} MB")
|
|
print(f" Cached: {torch.cuda.memory_reserved(0) / 1024 ** 2:.2f} MB")
|
|
print(f" Max Allocated: {torch.cuda.max_memory_allocated(0) / 1024 ** 2:.2f} MB") |