def main(args):
import sys
sys.path.append('/home/kp600168/semi')
from allinone import SEMI_DATASET_REGISTRY, SEMI_TRAINER_REGISTRY
print(args)
mnist = SEMI_DATASET_REGISTRY('split')(MNIST,
args.dataset,
10, [
tf.Resize((32, 32)),
tf.ToTensor(),
tf.Normalize((0.1307, ),
(0.3081, ))
],
semi_size=args.semi_size)
train_loader, test_loader, _, num_classes = mnist(args.batch_size,
num_workers=0,
return_num_classes=True)
lenet = MODEL_REGISTRY(args.model)(num_classes=num_classes)
kwargs = {
'bn_list': [lenet.bn1, lenet.bn2, lenet.bn3],
'sigma_list': [0.3, 0.3, 0.3],
'v_list':
[ConvTranspose2d(16, 6, 10, 2),
ConvTranspose2d(120, 16, 10)],
'lam_list': [0.1, 0.01, 0.01],
}
trainer = SEMI_TRAINER_REGISTRY('Ladder')(lenet,
SGD(lr=args.lr_256 *
args.batch_size / 256,
momentum=0.9),
F.cross_entropy,
**kwargs,
reporters=[TQDMReporter()])
# for _ in trainer.epoch_range(args.epochs):
# trainer.train(train_loader)
# trainer.test(test_loader)
trainer = SupervisedTrainer(lenet,
SGD(lr=args.lr_256 * args.batch_size / 256,
momentum=0.9),
F.cross_entropy,
reporters=[TQDMReporter()])
for _ in trainer.epoch_range(args.epochs):
trainer.train(train_loader)
trainer.test(test_loader)
print(f"Max Accuracy={max(trainer.history['accuracy/test'])}")
def main(args):
cifar = SemiVisionSet(CIFAR10,
args.dataset,
10, [
tf.ToTensor(),
tf.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
],
semi_size=40000)
train_loader, test_loader, num_classes = cifar(args.batch_size,
num_workers=4,
return_num_classes=True,
use_prefetcher=True)
model_dict = {
'generator_x': Generator_x(),
'generator_z': Generator_z(),
'discriminator_x': Discriminator_x(),
'discriminator_z': Discriminator_z(),
'discriminator_x_z': Discriminator_x_z(num_classes),
}
trainer = AdversariallyLearnedInferenceTrainerV2(
model_dict,
Adam(lr=args.lr_100 * args.batch_size / 100, betas=(0.5, 1 - 1e-3)),
F.cross_entropy,
1,
reporters=[TQDMReporter(), TensorboardReporter('.')])
for _ in trainer.epoch_range(args.epochs):
trainer.train(train_loader)
trainer.test(test_loader)
print(f"Max Accuracy={max(trainer.history['accuracy/test'])}")
def main(args):
change_val_to_unlabel()
mnist = VisionSet(MNIST,
args.dataset,
10,
[tf.ToTensor(), tf.Normalize((0.1307,), (0.3081,))],
[tf.RandomResizedCrop(
(32, 32), (0.9, 1.0), (0.9, 1.1)), ],
[tf.Resize((32, 32)), ],
)
mnist.unlabel_transform = TransformManyTimes(
[tf.RandomResizedCrop((32, 32), (0.9, 1.0), (0.9, 1.1))],
[tf.ToTensor(), tf.Normalize((0.1307,), (0.3081,))], 2)
train_loader, test_loader, unlabel_loader, num_classes = mnist(
args.batch_size, num_workers=0, return_num_classes=True, val_size=59900)
model = LeNet5(num_classes=num_classes)
trainer = MixMatchTrainer(model,
SGD(lr=args.lr_256 * args.batch_size /
256, momentum=0.9),
F.cross_entropy,
0.5,
0.2,
1,
reporters=[TQDMReporter()],
)
for _ in trainer.epoch_range(args.epochs):
trainer.train(zip(train_loader, cycle(unlabel_loader)))
trainer.test(test_loader)
def main(args):
mnist = SemiVisionSet(MNIST,
args.dataset,
10, [
tf.Resize((32, 32)),
tf.ToTensor(),
tf.Normalize((0.1307, ), (0.3081, ))
],
semi_size=59900)
# mnist.get_dataset = func
train_loader, test_loader, num_classes = mnist(args.batch_size,
num_workers=4,
return_num_classes=True)
# val_loader.transform = train_loader.transform
lenet = LeNet5(num_classes=num_classes)
trainer = InterpolationConsistencyTrainerV2(lenet,
SGD(lr=args.lr_256 *
args.batch_size / 256,
momentum=0.9),
F.cross_entropy,
1e-5,
0.99,
0.02,
reporters=[TQDMReporter()])
for _ in trainer.epoch_range(args.epochs):
trainer.train(train_loader)
trainer.test(test_loader)
# trainer = SupervisedTrainer(lenet, SGD(lr=args.lr_256 * args.batch_size /
# 256, momentum=0.9), F.cross_entropy, reporters=[TQDMReporter()])
# for _ in trainer.epoch_range(args.epochs):
# trainer.train(train_loader)
# trainer.test(test_loader)
print(f"Max Accuracy={max(trainer.history['accuracy/test'])}")
def main(args):
import sys
sys.path.append('/home/kp600168/semi')
from allinone import SEMI_DATASET_REGISTRY, SEMI_TRAINER_REGISTRY, SCHEDULER_REGISTRY
print(args)
cifar = SEMI_DATASET_REGISTRY('mix')(CIFAR10,
args.dataset,
10, [
tf.RandomResizedCrop((32, 32)),
tf.ToTensor(),
tf.Normalize(
(0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
],
semi_size=args.semi_size)
train_loader, test_loader, num_classes = cifar(
args.batch_size, num_workers=args.num_workers, return_num_classes=True)
model_dict = {
'generator_x': MODEL_REGISTRY('Generator_x')(),
'generator_z': MODEL_REGISTRY('Generator_z')(),
'discriminator_x': MODEL_REGISTRY('Discriminator_x')(),
'discriminator_z': MODEL_REGISTRY('Discriminator_z')(),
'discriminator_x_z': MODEL_REGISTRY('Discriminator_x_z')(num_classes),
}
kwargs = {
'model_dict':
model_dict,
'optimizer':
Adam(lr=args.lr_100 * args.batch_size / 100, betas=(0.5, 1 - 1e-3)),
'loss_f':
F.cross_entropy,
'consistency_weight':
SCHEDULER_REGISTRY('identity')(1.),
}
trainer = SEMI_TRAINER_REGISTRY('AdversariallyLearnedInference')(
**kwargs, reporters=[TQDMReporter(),
TensorboardReporter('.')])
for _ in trainer.epoch_range(args.epochs):
trainer.train(train_loader)
trainer.test(test_loader)
# trainer = SupervisedTrainer(lenet, SGD(lr=args.lr_256 * args.batch_size /
# 256, momentum=0.9), F.cross_entropy, reporters=[TQDMReporter()])
# for _ in trainer.epoch_range(args.epochs):
# trainer.train(train_loader)
# trainer.test(test_loader)
print(f"Max Accuracy={max(trainer.history['accuracy/test'])}")
def main(args):
import sys
sys.path.append('/home/kp600168/semi')
from allinone import SEMI_DATASET_REGISTRY, SEMI_TRAINER_REGISTRY, SCHEDULER_REGISTRY
print(args)
mnist = SEMI_DATASET_REGISTRY('mix')(MNIST,
args.dataset,
10, [
tf.Resize((32, 32)),
tf.ToTensor(),
tf.Normalize((0.1307, ),
(0.3081, ))
],
semi_size=args.semi_size)
train_loader, test_loader, num_classes = mnist(
args.batch_size, num_workers=args.num_workers, return_num_classes=True)
lenet = MODEL_REGISTRY(args.model)(num_classes=num_classes)
kwargs = {
'model':
MODEL_REGISTRY(args.model)(num_classes=num_classes),
'optimizer':
SGD(lr=args.lr_256 * args.batch_size / 256, momentum=0.9),
'loss_f':
F.cross_entropy,
'consistency_weight':
SCHEDULER_REGISTRY('identity')(1e-5),
'alpha':
SCHEDULER_REGISTRY('lambda')(lambda epoch: min(1 - 1 /
(1 + epoch), 0.99)),
'beta':
0.02
}
trainer = SEMI_TRAINER_REGISTRY('InterpolationConsistency')(
**kwargs, reporters=[TQDMReporter()])
for _ in trainer.epoch_range(args.epochs):
trainer.train(train_loader)
trainer.test(test_loader)
# trainer = SupervisedTrainer(lenet, SGD(lr=args.lr_256 * args.batch_size /
# 256, momentum=0.9), F.cross_entropy, reporters=[TQDMReporter()])
# for _ in trainer.epoch_range(args.epochs):
# trainer.train(train_loader)
# trainer.test(test_loader)
print(f"Max Accuracy={max(trainer.history['accuracy/test'])}")
def main(args):
import sys
sys.path.append('/home/kp600168/semi')
from allinone import SEMI_DATASET_REGISTRY, SEMI_TRAINER_REGISTRY, SCHEDULER_REGISTRY, TRANSFORM_REGISTRY
print(args)
unlabel_transform = TRANSFORM_REGISTRY('ManyTimes')(tf.Compose([
tf.RandomResizedCrop(((32, 32)), (0.9, 1.0), (0.9, 1.1)),
tf.ToTensor(),
tf.Normalize((0.1307, ), (0.3081, ))
]), 4)
mnist = SEMI_DATASET_REGISTRY('split')(MNIST,
args.dataset,
10, [],
[unlabel_transform.transform], [
tf.Resize((32, 32)),
tf.ToTensor(),
tf.Normalize((0.1307, ),
(0.3081, ))
],
semi_size=args.semi_size,
unlabel_transform=unlabel_transform)
train_loader, test_loader, unlabel_loader, num_classes = mnist(
args.batch_size, num_workers=0, return_num_classes=True)
kwargs = {
'model': MODEL_REGISTRY(args.model)(num_classes=num_classes),
'optimizer': SGD(lr=args.lr_256 * args.batch_size / 256, momentum=0.9),
'loss_f': F.cross_entropy,
'temperature': 0.5,
'beta': 0.2,
'consistency_weight': SCHEDULER_REGISTRY('identity')(1.),
'dataset_type': 'split',
}
trainer = SEMI_TRAINER_REGISTRY('MixMatch')(**kwargs,
reporters=[TQDMReporter()])
for _ in trainer.epoch_range(args.epochs):
trainer.train(zip(train_loader, unlabel_loader))
trainer.test(test_loader)
print(f"Max Accuracy={max(trainer.history['accuracy/test'])}")
def main(args):
mnist = SemiVisionSet(MNIST,
args.dataset,
10, [
tf.Resize((32, 32)),
tf.ToTensor(),
tf.Normalize((0.1307, ), (0.3081, ))
],
semi_size=59900)
train_loader, test_loader, num_classes = mnist(args.batch_size,
num_workers=4,
return_num_classes=True)
lenet = LeNet5(num_classes=num_classes)
bn_list = [lenet.bn1, lenet.bn2, lenet.bn3]
sigma_list = [0.3, 0.3, 0.3]
v_list = [ConvTranspose2d(16, 6, 10, 2), ConvTranspose2d(120, 16, 10)]
lam_list = [0.1, 0.01, 0.01]
trainer = LadderTrainerV2(lenet,
SGD(lr=args.lr_256 * args.batch_size / 256,
momentum=0.9),
F.cross_entropy,
bn_list,
sigma_list,
v_list,
lam_list,
reporters=[TQDMReporter()])
for _ in trainer.epoch_range(args.epochs):
trainer.train(train_loader)
trainer.test(test_loader)
# trainer = SupervisedTrainer(lenet, SGD(lr=args.lr_256 * args.batch_size /
# 256, momentum=0.9), F.cross_entropy, reporters=[TQDMReporter()])
# for _ in trainer.epoch_range(args.epochs):
# trainer.train(train_loader)
# trainer.test(test_loader)
print(f"Max Accuracy={max(trainer.history['accuracy/test'])}")
def main(args):
import sys
sys.path.append('/home/kp600168/semi')
from allinone import SEMI_DATASET_REGISTRY, SEMI_TRAINER_REGISTRY, TRANSFORM_REGISTRY, SCHEDULER_REGISTRY
print(args)
unlabel_transform = TRANSFORM_REGISTRY('twice')(tf.Compose([
tf.RandomResizedCrop(((32, 32)), (0.9, 1.0), (0.9, 1.1)),
tf.ToTensor(),
tf.Normalize((0.1307, ), (0.3081, ))
]))
mnist = SEMI_DATASET_REGISTRY('mix')(MNIST,
args.dataset,
10, [], [unlabel_transform], [
tf.Resize((32, 32)),
tf.ToTensor(),
tf.Normalize((0.1307, ),
(0.3081, ))
],
semi_size=args.semi_size)
train_loader, test_loader, num_classes = mnist(args.batch_size,
num_workers=4,
return_num_classes=True)
kwargs = {
'model':
MODEL_REGISTRY(args.model)(num_classes=num_classes),
'optimizer':
SGD(lr=args.lr_256 * args.batch_size / 256, momentum=0.9),
'loss_f':
F.cross_entropy,
'consistency_weight':
SCHEDULER_REGISTRY('identity')(0.01),
'alpha':
SCHEDULER_REGISTRY('lambda')(lambda epoch: min(1 - 1 /
(1 + epoch), 0.99)),
'dataset_type':
'mix',
}
trainer = SEMI_TRAINER_REGISTRY('meanteacher')(**kwargs,
reporters=[TQDMReporter()])
for _ in trainer.epoch_range(args.epochs):
trainer.train(train_loader)
trainer.test(test_loader)
# mnist = SEMI_DATASET_REGISTRY('split')(MNIST, args.dataset, 10, [], [unlabel_transform.transform], [tf.Resize(
# (32, 32)), tf.ToTensor(), tf.Normalize((0.1307,), (0.3081,))], semi_size=args.semi_size, unlabel_transform=unlabel_transform)
# train_loader, test_loader, unlabel_loader, num_classes = mnist(
# args.batch_size, num_workers=0, return_num_classes=True)
# kwargs = {
# 'model': MODEL_REGISTRY(args.model)(num_classes=num_classes),
# 'optimizer': SGD(lr=args.lr_256 * args.batch_size / 256, momentum=0.9),
# 'loss_f': F.cross_entropy,
# 'consistency_weight': SCHEDULER_REGISTRY('identity')(0.01),
# 'alpha': SCHEDULER_REGISTRY('lambda')(lambda epoch: min(1 - 1 / (1 + epoch), 0.99)),
# 'dataset_type': 'split',
# }
# trainer = SEMI_TRAINER_REGISTRY('meanteacher')(
# **kwargs, reporters=[TQDMReporter()])
# for _ in trainer.epoch_range(args.epochs):
# trainer.train(zip(train_loader, unlabel_loader))
# trainer.test(test_loader)
print(f"Max Accuracy={max(trainer.history['accuracy/test'])}")
def main(args):
mnist = SemiVisionSet(MNIST, args.dataset, 10, [], [TransformTwice(tf.Compose([tf.RandomResizedCrop((32, 32), (0.9, 1.0), (0.9, 1.1)), tf.ToTensor(
), tf.Normalize((0.1307,), (0.3081,))]))], [tf.Resize((32, 32)), tf.ToTensor(), tf.Normalize((0.1307,), (0.3081,))], semi_size=59900)
train_loader, test_loader, num_classes = mnist(
args.batch_size, num_workers=4, return_num_classes=True)
# val_loader.transform = train_loader.transform
lenet = LeNet5(num_classes=num_classes)
trainer = MeanTeacherTrainerV2(lenet, SGD(lr=args.lr_256 * args.batch_size /
256, momentum=0.9), F.cross_entropy, 0.01, 0.99, reporters=[TQDMReporter()])
for _ in trainer.epoch_range(args.epochs):
trainer.train(train_loader)
trainer.test(test_loader)
print(f"Max Accuracy={max(trainer.history['accuracy/test'])}")