num_workers=2)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False,
pin_memory=True,
num_workers=2)
model = WideResNet(depth=depth,
num_classes=num_classes,
widen_factor=widen_factor,
dropRate=drop_rate)
model = model.cuda()
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(),
lr=learning_rate,
momentum=0.9,
nesterov=True,
weight_decay=0.0005)
scheduler = MultiStepLR(optimizer, milestones=LR_MILESTONES, gamma=gamma)
try:
checkpoint_fpath = 'cifar-10/cifar10_wideresnet79.pt'
checkpoint = torch.load(checkpoint_fpath)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler = MultiStepLR(optimizer,
milestones=LR_MILESTONES,
gamma=0.2,
logits = model(x)
y = paddle.reshape(y, (-1, 1))
loss = loss_fn(logits, y)
acc = acc_fn(logits, y)
accuracies.append(np.mean(acc.numpy()))
losses.append(np.mean(loss.numpy()))
return np.mean(accuracies) * 100, np.mean(losses)
import warnings
warnings.filterwarnings("ignore", category=Warning)
model = WideResNet(28, 10, 20, 0.3)
model = paddle.DataParallel(model)
epochs = 400
save_every = 5
loss_fn = paddle.nn.CrossEntropyLoss()
acc_fn = paddle.metric.accuracy
scheduler = paddle.optimizer.lr.PiecewiseDecay(
boundaries=[60, 120, 160, 200, 240, 260, 280],
values=[0.05, 0.01, 0.002, 0.0004, 0.0002, 0.0001, 0.00005],
verbose=True)
opt = paddle.optimizer.Momentum(parameters=model.parameters(),
learning_rate=scheduler,
momentum=0.9,
weight_decay=0.0005)
save_dir = '/home/aistudio/models/cifar10/ResNet_wide'
training()
def main():
args = parse_args()
if args.name is None:
args.name = '%s_WideResNet%s-%s' %(args.dataset, args.depth, args.width)
if args.cutout:
args.name += '_wCutout'
if args.auto_augment:
args.name += '_wAutoAugment'
if not os.path.exists('models/%s' %args.name):
os.makedirs('models/%s' %args.name)
print('Config -----')
for arg in vars(args):
print('%s: %s' %(arg, getattr(args, arg)))
print('------------')
with open('models/%s/args.txt' %args.name, 'w') as f:
for arg in vars(args):
print('%s: %s' %(arg, getattr(args, arg)), file=f)
joblib.dump(args, 'models/%s/args.pkl' %args.name)
criterion = nn.CrossEntropyLoss().cuda()
cudnn.benchmark = True
# data loading code
if args.dataset == 'cifar10':
transform_train = [
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
]
if args.auto_augment:
transform_train.append(AutoAugment())
if args.cutout:
transform_train.append(Cutout())
transform_train.extend([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
transform_train = transforms.Compose(transform_train)
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5071, 0.4867, 0.4408),
(0.2675, 0.2565, 0.2761)),
])
train_set = datasets.CIFAR10(
root='~/data',
train=True,
download=True,
transform=transform_train)
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=128,
shuffle=True,
num_workers=8)
test_set = datasets.CIFAR10(
root='~/data',
train=False,
download=True,
transform=transform_test)
test_loader = torch.utils.data.DataLoader(
test_set,
batch_size=128,
shuffle=False,
num_workers=8)
num_classes = 10
elif args.dataset == 'cifar100':
transform_train = [
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
]
if args.auto_augment:
transform_train.append(AutoAugment())
if args.cutout:
transform_train.append(Cutout())
transform_train.extend([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
transform_train = transforms.Compose(transform_train)
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
train_set = datasets.CIFAR100(
root='~/data',
train=True,
download=True,
transform=transform_train)
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=128,
shuffle=True,
num_workers=8)
test_set = datasets.CIFAR100(
root='~/data',
train=False,
download=True,
transform=transform_test)
test_loader = torch.utils.data.DataLoader(
test_set,
batch_size=128,
shuffle=False,
num_workers=8)
num_classes = 100
# create model
model = WideResNet(args.depth, args.width, num_classes=num_classes)
model = model.cuda()
optimizer = optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr,
momentum=args.momentum, weight_decay=args.weight_decay)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=[int(e) for e in args.milestones.split(',')], gamma=args.gamma)
log = pd.DataFrame(index=[], columns=[
'epoch', 'lr', 'loss', 'acc', 'val_loss', 'val_acc'
])
best_acc = 0
for epoch in range(args.epochs):
print('Epoch [%d/%d]' %(epoch+1, args.epochs))
scheduler.step()
# train for one epoch
train_log = train(args, train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
val_log = validate(args, test_loader, model, criterion)
print('loss %.4f - acc %.4f - val_loss %.4f - val_acc %.4f'
%(train_log['loss'], train_log['acc'], val_log['loss'], val_log['acc']))
tmp = pd.Series([
epoch,
scheduler.get_lr()[0],
train_log['loss'],
train_log['acc'],
val_log['loss'],
val_log['acc'],
], index=['epoch', 'lr', 'loss', 'acc', 'val_loss', 'val_acc'])
log = log.append(tmp, ignore_index=True)
log.to_csv('models/%s/log.csv' %args.name, index=False)
if val_log['acc'] > best_acc:
torch.save(model.state_dict(), 'models/%s/model.pth' %args.name)
best_acc = val_log['acc']
print("=> saved best model")
