本文整理汇总了Python中utils.Logger.set_names方法的典型用法代码示例。如果您正苦于以下问题：Python Logger.set_names方法的具体用法？Python Logger.set_names怎么用？Python Logger.set_names使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类utils.Logger的用法示例。
在下文中一共展示了Logger.set_names方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from utils import Logger [as 别名]
# 或者: from utils.Logger import set_names [as 别名]
def main():
global best_acc
start_epoch = args.start_epoch # start from epoch 0 or last checkpoint epoch
if not os.path.isdir(args.checkpoint):
mkdir_p(args.checkpoint)
# Data loading code
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(traindir, transforms.Compose([
transforms.RandomSizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])),
batch_size=args.train_batch, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.test_batch, shuffle=False,
num_workers=args.workers, pin_memory=True)
# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
elif args.arch.startswith('resnext'):
model = models.__dict__[args.arch](
baseWidth=args.base_width,
cardinality=args.cardinality,
)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
cudnn.benchmark = True
print(' Total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0))
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
# Resume
title = 'ImageNet-' + args.arch
if args.resume:
# Load checkpoint.
print('==> Resuming from checkpoint..')
assert os.path.isfile(args.resume), 'Error: no checkpoint directory found!'
args.checkpoint = os.path.dirname(args.resume)
checkpoint = torch.load(args.resume)
best_acc = checkpoint['best_acc']
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title, resume=True)
else:
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title)
logger.set_names(['Learning Rate', 'Train Loss', 'Valid Loss', 'Train Acc.', 'Valid Acc.'])
if args.evaluate:
print('\nEvaluation only')
test_loss, test_acc = test(val_loader, model, criterion, start_epoch, use_cuda)
print(' Test Loss: %.8f, Test Acc: %.2f' % (test_loss, test_acc))
return
# Train and val
for epoch in range(start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch)
print('\nEpoch: [%d | %d] LR: %f' % (epoch + 1, args.epochs, state['lr']))
train_loss, train_acc = train(train_loader, model, criterion, optimizer, epoch, use_cuda)
test_loss, test_acc = test(val_loader, model, criterion, epoch, use_cuda)
# append logger file
logger.append([state['lr'], train_loss, test_loss, train_acc, test_acc])
# save model
is_best = test_acc > best_acc
best_acc = max(test_acc, best_acc)
save_checkpoint({
'epoch': epoch + 1,
#.........这里部分代码省略.........
示例2: main
# 需要导入模块: from utils import Logger [as 别名]
# 或者: from utils.Logger import set_names [as 别名]
def main():
global best_acc
start_epoch = args.start_epoch # start from epoch 0 or last checkpoint epoch
if not os.path.isdir(args.checkpoint):
mkdir_p(args.checkpoint)
# Data
print('==> Preparing dataset %s' % args.dataset)
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)),
])
if args.dataset == 'cifar10':
dataloader = datasets.CIFAR10
num_classes = 10
else:
dataloader = datasets.CIFAR100
num_classes = 100
trainset = dataloader(root=args.data_root, train=True, download=True, transform=transform_train)
trainloader = data.DataLoader(trainset, batch_size=args.train_batch, shuffle=True, num_workers=args.workers)
testset = dataloader(root=args.data_root, train=False, download=False, transform=transform_test)
testloader = data.DataLoader(testset, batch_size=args.test_batch, shuffle=False, num_workers=args.workers)
# Model
print("==> creating model '{}'".format(args.arch))
if args.arch.startswith('resnext'):
model = models.__dict__[args.arch](
cardinality=args.cardinality,
num_classes=num_classes,
depth=args.depth,
widen_factor=args.widen_factor,
dropRate=args.drop,
)
elif args.arch.startswith('densenet'):
model = models.__dict__[args.arch](
num_classes=num_classes,
depth=args.depth,
growthRate=args.growthRate,
compressionRate=args.compressionRate,
dropRate=args.drop,
)
elif args.arch.startswith('wrn'):
model = models.__dict__[args.arch](
num_classes=num_classes,
depth=args.depth,
widen_factor=args.widen_factor,
dropRate=args.drop,
)
elif args.arch.endswith('resnet'):
model = models.__dict__[args.arch](
num_classes=num_classes,
depth=args.depth,
)
else:
model = models.__dict__[args.arch](num_classes=num_classes)
model = torch.nn.DataParallel(model).cuda()
cudnn.benchmark = True
print(' Total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0))
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
# Resume
title = 'cifar-10-' + args.arch
if args.resume:
# Load checkpoint.
print('==> Resuming from checkpoint..')
assert os.path.isfile(args.resume), 'Error: no checkpoint directory found!'
args.checkpoint = os.path.dirname(args.resume)
checkpoint = torch.load(args.resume)
best_acc = checkpoint['best_acc']
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title, resume=True)
else:
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title)
logger.set_names(['Learning Rate', 'Train Loss', 'Valid Loss', 'Train Acc.', 'Valid Acc.'])
if args.evaluate:
print('\nEvaluation only')
test_loss, test_acc = test(testloader, model, criterion, start_epoch, use_cuda)
print(' Test Loss: %.8f, Test Acc: %.2f' % (test_loss, test_acc))
return
#.........这里部分代码省略.........