benchmarkexp/dl-main.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision import datasets, transforms, models
import matplotlib.pyplot as plt
from dmadam import DMAdam
import os

# ResNet-34 for CIFAR-10 (adapted from torchvision)
def get_resnet34():
    model = models.resnet34(weights=None)
    model.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
    model.maxpool = nn.Identity()
    model.fc = nn.Linear(512, 10)
    return model

# VGG-13 for CIFAR-10 (adapted from torchvision)
def get_vgg13():
    model = models.vgg13(weights=None)
    model.classifier[6] = nn.Linear(4096, 10)
    return model

def train_epoch(model, loader, optimizer, criterion, device):
    model.train()
    correct = 0
    total = 0
    for data, target in loader:
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        pred = output.argmax(dim=1)
        correct += pred.eq(target).sum().item()
        total += target.size(0)
    return 100. * correct / total

def test(model, loader, device):
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for data, target in loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            pred = output.argmax(dim=1)
            correct += pred.eq(target).sum().item()
            total += target.size(0)
    return 100. * correct / total

def create_optimizer(name, params, config):
    if name == 'SGD':
        return torch.optim.SGD(params, lr=config['lr'], weight_decay=config.get('weight_decay', 0))
    elif name == 'SGDM':
        return torch.optim.SGD(params, lr=config['lr'], momentum=config['momentum'], weight_decay=config.get('weight_decay', 0))
    elif name == 'Adagrad':
        return torch.optim.Adagrad(params, lr=config['lr'], weight_decay=config.get('weight_decay', 0))
    elif name == 'Adam':
        return torch.optim.Adam(params, lr=config['lr'], betas=config['betas'], weight_decay=config.get('weight_decay', 0))
    elif name == 'AdamW':
        return torch.optim.AdamW(params, lr=config['lr'], betas=config['betas'], weight_decay=config['weight_decay'])
    elif name == 'DMAdam':
        return DMAdam(params, lr=config['lr'], eta0=config['eta0'], eps=config.get('eps', 1e-8))

def run_cifar10(model_name='ResNet-34'):
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(f"Using device: {device}")

    transform_train = transforms.Compose([
        transforms.RandomCrop(32, padding=4),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
    ])
    transform_test = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
    ])

    train_dataset = datasets.CIFAR10('./data', train=True, download=True, transform=transform_train)
    test_dataset = datasets.CIFAR10('./data', train=False, transform=transform_test)
    train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=2)
    test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False, num_workers=2)

    # DMAdam parameters from Table 2
    # VGG-13: η_k=0.9, α_k=0.001, β_k=0.999, ε=10^-8, weight_decay=0.0005
    # ResNet-34: η_k=1.4, α_k=0.001, β_k=0.999, ε=10^-8, weight_decay=0.0005
    dmadam_eta0 = 0.9 if model_name == 'VGG-13' else 1.4

    optimizers_config = {
        'SGD': {'lr': 0.1, 'weight_decay': 0.0005},
        'SGDM': {'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0005},
        'Adagrad': {'lr': 0.01, 'weight_decay': 0.0005},
        'Adam': {'lr': 0.001, 'betas': (0.9, 0.999), 'weight_decay': 0.0005},
        'AdamW': {'lr': 0.001, 'betas': (0.9, 0.999), 'weight_decay': 0.0005},
        'DMAdam': {'lr': 0.001, 'eta0': dmadam_eta0, 'eps': 1e-8}
    }

    results = {}
    criterion = nn.CrossEntropyLoss()

    for opt_name, config in optimizers_config.items():
        print(f"\nTraining CIFAR-10 ({model_name}) with {opt_name}")

        if model_name == 'ResNet-34':
            model = get_resnet34().to(device)
        elif model_name == 'VGG-13':
            model = get_vgg13().to(device)

        optimizer = create_optimizer(opt_name, model.parameters(), config)
        scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[100, 150], gamma=0.1)

        test_accs = []

        for epoch in range(200):
            train_acc = train_epoch(model, train_loader, optimizer, criterion, device)
            test_acc = test(model, test_loader, device)
            test_accs.append(test_acc)
            scheduler.step()

            if (epoch + 1) % 10 == 0:
                print(f"Epoch {epoch+1}/200: Train Acc={train_acc:.2f}%, Test Acc={test_acc:.2f}%")

        results[opt_name] = test_accs
        print(f"{opt_name} Final Test Acc: {test_accs[-1]:.2f}%")

    plt.figure(figsize=(10, 5))
    for name, accs in results.items():
        plt.plot(range(1, 201), accs, label=name)
    plt.xlabel('Epoch')
    plt.ylabel('Test Accuracy (%)')
    plt.title(f'CIFAR-10 Test Accuracy ({model_name})')
    plt.legend()
    plt.grid(True)
    os.makedirs('results', exist_ok=True)
    plt.savefig(f'results/cifar10_{model_name.lower().replace("-", "")}_accuracy.png', dpi=140, bbox_inches='tight')
    plt.close()

    return results

if __name__ == '__main__':
    print("Running CIFAR-10 experiment with ResNet-34...")
    run_cifar10('ResNet-34')

    print("\n" + "="*50)
    print("Running CIFAR-10 experiment with VGG-13...")
    run_cifar10('VGG-13')