本文整理汇总了Python中model.NetworkCIFAR方法的典型用法代码示例。如果您正苦于以下问题:Python model.NetworkCIFAR方法的具体用法?Python model.NetworkCIFAR怎么用?Python model.NetworkCIFAR使用的例子?那么, 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model
的用法示例。
在下文中一共展示了model.NetworkCIFAR方法的9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: import model [as 别名]
# 或者: from model import NetworkCIFAR [as 别名]
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled=True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
genotype = eval("genotypes.%s" % args.arch)
model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
model = model.cuda()
utils.load(model, args.model_path)
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
_, test_transform = utils._data_transforms_cifar10(args)
test_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=test_transform)
test_queue = torch.utils.data.DataLoader(
test_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)
model.drop_path_prob = args.drop_path_prob
test_acc, test_obj = infer(test_queue, model, criterion)
logging.info('test_acc %f', test_acc)
示例2: main
# 需要导入模块: import model [as 别名]
# 或者: from model import NetworkCIFAR [as 别名]
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled=True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
genotype = eval("genotypes.%s" % args.arch)
model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
model = model.cuda()
utils.load(model, args.model_path)
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
_, test_transform = utils.data_transforms_cifar10(args)
test_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=test_transform)
test_queue = torch.utils.data.DataLoader(
test_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)
model.drop_path_prob = args.drop_path_prob
test_acc, test_obj = infer(test_queue, model, criterion)
logging.info('test_acc %f', test_acc)
示例3: main
# 需要导入模块: import model [as 别名]
# 或者: from model import NetworkCIFAR [as 别名]
def main():
image_shape = [3, image_size, image_size]
devices = os.getenv("CUDA_VISIBLE_DEVICES") or ""
devices_num = len(devices.split(","))
logging.info("args = %s", args)
genotype = eval("genotypes.%s" % args.arch)
model = Network(args.init_channels, CIFAR_CLASSES, args.layers,
args.auxiliary, genotype)
steps_one_epoch = math.ceil(dataset_train_size /
(devices_num * args.batch_size))
train(model, args, image_shape, steps_one_epoch)
示例4: main
# 需要导入模块: import model [as 别名]
# 或者: from model import NetworkCIFAR [as 别名]
def main():
image_shape = [3, image_size, image_size]
devices = os.getenv("CUDA_VISIBLE_DEVICES") or ""
devices_num = len(devices.split(","))
logging.info("args = %s", args)
genotype = eval("genotypes.%s" % args.arch)
model = Network(args.init_channels, CIFAR_CLASSES, args.layers,
args.auxiliary, genotype)
test(model, args, image_shape)
示例5: main
# 需要导入模块: import model [as 别名]
# 或者: from model import NetworkCIFAR [as 别名]
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled=True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
genotype = eval("genotypes.%s" % args.arch)
model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
model = model.cuda()
utils.load(model, args.model_path)
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
_, test_transform = utils._data_transforms_cifar10(args)
test_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=test_transform)
test_queue = torch.utils.data.DataLoader(
test_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)
model.drop_path_prob = args.drop_path_prob
with torch.no_grad():
test_acc, test_obj = infer(test_queue, model, criterion)
logging.info('test_acc %f', test_acc)
示例6: main
# 需要导入模块: import model [as 别名]
# 或者: from model import NetworkCIFAR [as 别名]
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled=True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
genotype = eval("genotypes.%s" % args.arch)
model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(
model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay
)
train_transform, valid_transform = utils._data_transforms_cifar10(args)
train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=valid_transform)
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=2)
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs))
for epoch in range(args.epochs):
scheduler.step()
logging.info('epoch %d lr %e', epoch, scheduler.get_lr()[0])
model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('train_acc %f', train_acc)
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('valid_acc %f', valid_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))
示例7: main
# 需要导入模块: import model [as 别名]
# 或者: from model import NetworkCIFAR [as 别名]
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled=True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
genotype = eval("genotypes.%s" % args.arch)
model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(
model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay
)
train_transform, valid_transform = utils._data_transforms_cifar10(args)
train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=valid_transform)
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=2)
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs))
for epoch in range(args.epochs):
scheduler.step()
logging.info('epoch %d lr %e', epoch, scheduler.get_lr()[0])
model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('train_acc %f', train_acc)
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('valid_acc %f', valid_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))
示例8: main
# 需要导入模块: import model [as 别名]
# 或者: from model import NetworkCIFAR [as 别名]
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
torch.cuda.set_device(args.gpu)
cudnn.enabled=True
logging.info("args = %s", args)
genotype = eval("genotypes.%s" % args.arch)
if args.dataset in LARGE_DATASETS:
model = NetworkLarge(args.init_channels, CLASSES, args.layers, args.auxiliary, genotype)
else:
model = Network(args.init_channels, CLASSES, args.layers, args.auxiliary, genotype)
model = model.cuda()
utils.load(model, args.model_path)
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
_, test_transform = utils.data_transforms(args.dataset,args.cutout,args.cutout_length)
if args.dataset=="CIFAR100":
test_data = dset.CIFAR100(root=args.data, train=False, download=True, transform=test_transform)
elif args.dataset=="CIFAR10":
test_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=test_transform)
elif args.dataset=="sport8":
dset_cls = dset.ImageFolder
val_path = '%s/Sport8/test' %args.data
test_data = dset_cls(root=val_path, transform=test_transform)
elif args.dataset=="mit67":
dset_cls = dset.ImageFolder
val_path = '%s/MIT67/test' %args.data
test_data = dset_cls(root=val_path, transform=test_transform)
elif args.dataset == "flowers102":
dset_cls = dset.ImageFolder
val_path = '%s/flowers102/test' % args.tmp_data_dir
test_data = dset_cls(root=val_path, transform=test_transform)
test_queue = torch.utils.data.DataLoader(
test_data, batch_size=args.batch_size, shuffle=False, pin_memory=False, num_workers=2)
model.drop_path_prob = 0.0
test_acc, test_obj = infer(test_queue, model, criterion)
logging.info('Test_acc %f', test_acc)
示例9: main
# 需要导入模块: import model [as 别名]
# 或者: from model import NetworkCIFAR [as 别名]
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled=True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
genotype = eval("genotypes.%s" % args.arch)
model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(
model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay
)
train_transform, valid_transform = utils._data_transforms_cifar10(args)
train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=valid_transform)
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=2)
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs))
best_val_acc = 0.
for epoch in range(args.epochs):
scheduler.step()
logging.info('epoch %d lr %e', epoch, scheduler.get_lr()[0])
model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('train_acc %f', train_acc)
with torch.no_grad():
valid_acc, valid_obj = infer(valid_queue, model, criterion)
if valid_acc > best_val_acc:
best_val_acc = valid_acc
utils.save(model, os.path.join(args.save, 'best_weights.pt'))
logging.info('valid_acc %f\tbest_val_acc %f', valid_acc, best_val_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))