run_cnn_test_cifar10.py

import argparse
import os

import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from torch import optim
from torch.optim.lr_scheduler import MultiStepLR

# fix for python 3.6
try:
    import src
except:
    import sys

    sys.path.insert(0, './')

from utils import progress_bar

parser = argparse.ArgumentParser(description='PyTorch CIFAR100 Training')
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')
parser.add_argument('--method', '-m', default='mps', help='optimization method')
parser.add_argument('--net', '-n', default='resnet', help='network archtecture')
parser.add_argument('--partial', default=1 / 8, type=float, help='partially adaptive parameter p in Padam')
parser.add_argument('--wd', default=5e-4, type=float, help='weight decay')
parser.add_argument('--momentum', default=0.9, type=float, help='momentum')
parser.add_argument('--Nepoch', default=200, type=int, help='number of epoch')
parser.add_argument('--beta1', default=0.9, type=float, help='beta1')
parser.add_argument('--beta2', default=0.999, type=float, help='beta2')

args = parser.parse_args()

use_cuda = torch.cuda.is_available()
best_acc = 0  # best test accuracy
start_epoch = 0  # start from epoch 0 or last checkpoint epoch
train_errs = []
test_errs = []
train_losses = []
test_losses = []

# Data
print('==> Preparing data..')
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)),
])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

if args.resume:
    # Load checkpoint.
    print('==> Resuming from checkpoint..')
    assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
    checkpoint = torch.load('./checkpoint/cnn_cifar10_' + args.method)
    model = checkpoint['model']
    start_epoch = checkpoint['epoch']
    train_losses = checkpoint['train_losses']
    test_losses = checkpoint['test_losses']
    train_errs = checkpoint['train_errs']
    test_errs = checkpoint['test_errs']
else:
    print('==> Building model..')

if args.net == 'vggnet':
    from models import vgg

    model = vgg.VGG('VGG16', num_classes=10)
#     model = models.vgg16_bn(num_classes=10)
elif args.net == 'resnet':
    from models import resnet

    model = resnet.ResNet18(num_classes=10)
#     model = models.resnet18(num_classes=10)
elif args.net == 'wideresnet':
    from models import wideresnet

    model = wideresnet.WResNet_cifar10(num_classes=10, depth=16, multiplier=4)
else:
    print('Network undefined!')

if use_cuda:
    model.cuda()
    model = torch.nn.DataParallel(model, device_ids=range(torch.cuda.device_count()))
    cudnn.benchmark = True

criterion = nn.CrossEntropyLoss()

betas = (args.beta1, args.beta2)
if args.method == 'sgdm':
    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9, weight_decay=args.wd)
elif args.method == 'adam':
    import Adam

    optimizer = Adam.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd, betas=betas)
elif args.method == 'adamw':
    import AdamW

    optimizer = AdamW.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wd, betas=betas)
elif args.method == 'amsgrad':
    import Adam

    optimizer = Adam.Adam(model.parameters(), lr=args.lr, amsgrad=True, weight_decay=args.wd, betas=betas)
elif args.method == 'padam':
    import Padam

    optimizer = Padam.Padam(model.parameters(), lr=args.lr, partial=args.partial, weight_decay=args.wd, betas=betas)
elif args.method == 'mas':
    from MAS.adam_sgd_mix import AdamSGDWeighted

    optimizer = AdamSGDWeighted(model.parameters(), lr=args.lr,
                                adam_w=1, sgd_w=0, momentum=args.momentum,
                                weight_decay=args.wd, betas=betas)
elif args.method == 'mps':
    from MAS.padam_sgd_mix import PadamSGDWeighted

    optimizer = PadamSGDWeighted(model.parameters(), lr=args.lr,
                                 adam_w=1, sgd_w=0, partial=args.partial, momentum=0.9, weight_decay=args.wd,
                                 betas=betas)

elif args.method == 'map':
    from MAS.adam_padam_mix import AdamPadamWeighted

    optimizer = AdamPadamWeighted(model.parameters(), lr=args.lr,
                                 adam_w=1, sgd_w=0, momentum=args.momentum, partial=args.partial,
                                 weight_decay=args.wd, betas=betas)
else:
    print('Optimizer undefined!')

if args.method in ['mas', 'mps', 'map']:
    from MAS.mas_scheduler import MASScheduler
    # mas_scheduler = MASScheduler(optimizer, 1, 0, args.Nepoch)
    mas_scheduler = MASScheduler(optimizer, 1, 0, 100)

scheduler = MultiStepLR(optimizer, milestones=[100, 150], gamma=0.1)

for epoch in range(start_epoch + 1, args.Nepoch + 1):

    scheduler.step()
    print('\nEpoch: %d' % epoch, ' Learning rate:', scheduler.get_lr())
    model.train()  # Training

    train_loss = 0
    correct = 0
    total = 0
    for batch_idx, (inputs, targets) in enumerate(trainloader):
        if use_cuda:
            inputs, targets = inputs.cuda(), targets.cuda()


        def closure():
            outputs = model(inputs)
            loss = criterion(outputs, targets)
            return loss


        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step(closure)

        train_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        total += targets.size(0)
        correct += predicted.eq(targets.data).cpu().sum().item()

        progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
                     % (train_loss / (batch_idx + 1), 100.0 / total * (correct), correct, total))

    # Compute training error 
    train_loss = 0
    correct = 0
    total = 0
    for batch_idx, (inputs, targets) in enumerate(trainloader):
        if use_cuda:
            inputs, targets = inputs.cuda(), targets.cuda()
        outputs = model(inputs)
        loss = criterion(outputs, targets)

        train_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        total += targets.size(0)
        correct += predicted.eq(targets.data).cpu().sum().item()

        progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
                     % (train_loss / (batch_idx + 1), 100.0 / total * (correct), correct, total))
    train_errs.append(1 - correct / total)
    train_losses.append(train_loss / (batch_idx + 1))

    # do mas scheduler step
    if args.method in ['mas', 'mps', 'map']:
        mas_scheduler.step()

    model.eval()  # Testing

    test_loss = 0
    correct = 0
    total = 0
    for batch_idx, (inputs, targets) in enumerate(testloader):
        if use_cuda:
            inputs, targets = inputs.cuda(), targets.cuda()
        outputs = model(inputs)
        loss = criterion(outputs, targets)

        test_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        total += targets.size(0)
        correct += predicted.eq(targets.data).cpu().sum().item()

        progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
                     % (test_loss / (batch_idx + 1), 100.0 / total * (correct), correct, total))
    test_errs.append(1 - correct / total)
    test_losses.append(test_loss / (batch_idx + 1))

    # Save checkpoint
    acc = 100.0 / total * (correct)
    if acc > best_acc:
        print('Saving..')
        if not os.path.isdir('checkpoint'):
            os.mkdir('checkpoint')
        state = {
            'model': model,
            'epoch': epoch,
            'train_errs': train_errs,
            'test_errs': test_errs,
            'train_losses': train_losses,
            'test_losses': test_losses
        }
        torch.save(state, './checkpoint/cnn_cifar10_' + args.method)
        best_acc = acc