def setup_clients(self):
users = []
trainloaders, testloaders = [], []
if self.dataset_name == 'cifar10':
# data augmentation
# train_transform = transforms.Compose([
# # transforms.RandomCrop(size=24, padding=8, fill=0, padding_mode='constant'),
# transforms.RandomHorizontalFlip(p=0.5),
# transforms.RandomApply([
# transforms.ColorJitter(brightness=0.8, contrast=0.8, saturation=0.8, hue=0.2)], p=0.8),
#
# transforms.ToTensor(),
# transforms.Normalize(mean=[0.485, 0.456, 0.406],
# std=[0.229, 0.224, 0.225])
# ])
train_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
# TODO(specify the num of all clients: default 100 for cifar10 dataset)
users, trainloaders, testloaders = get_cifar10_dataloaders(
batch_size=self.batch_size,
train_transform=train_transform,
test_transform=test_transform)
elif self.dataset_name == 'mnist':
train_transform = None
test_transform = None
users, trainloaders, testloaders = get_mnist_dataloaders(
batch_size=self.batch_size,
train_transform=train_transform,
test_transform=test_transform)
elif self.dataset_name == 'femnist':
users, trainloaders, testloaders = get_femnist_dataloaders(
batch_size=self.batch_size)
clients = [
Client(user_id=user_id,
seed=self.seed,
trainloader=trainloaders[user_id],
testloader=testloaders[user_id],
model_name=self.model_name,
lr=self.lr,
epoch=self.epoch,
lr_decay=self.lr_decay,
decay_step=self.decay_step) for user_id in users
]
return clients
def setup_datasets(self):
users = []
trainloaders, testloaders = [], []
if self.dataset_name == 'cifar10':
train_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
users, trainloaders, testloaders = get_cifar10_dataloaders(
batch_size=self.batch_size,
train_transform=train_transform,
test_transform=test_transform)
elif self.dataset_name == 'cifar100':
train_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
users, trainloaders, testloaders = get_cifar100_dataloaders(
batch_size=self.batch_size,
train_transform=train_transform,
test_transform=test_transform)
elif self.dataset_name == 'mnist':
train_transform = None
test_transform = None
users, trainloaders, testloaders = get_mnist_dataloaders(
batch_size=self.batch_size,
train_transform=train_transform,
test_transform=test_transform)
elif self.dataset_name == 'femnist':
users, trainloaders, testloaders = get_femnist_dataloaders(
batch_size=self.batch_size)
elif self.dataset_name == 'flickr':
users, trainloaders, testloaders = get_flickr_dataloaders(
split_ratio=0.9, batch_size=10)
elif self.dataset_name == 'celeba':
users, trainloaders, testloaders = get_celeba_dataloaders(
batch_size=self.batch_size)
return users, trainloaders, testloaders
def setup_datasets(self):
users = []
trainloaders, testloaders = [], []
if self.dataset_name == 'cifar10':
train_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
users, trainloaders, testloaders = get_cifar10_dataloaders(
batch_size=self.batch_size,
train_transform=train_transform,
test_transform=test_transform)
elif self.dataset_name == 'cifar10_dirichlet':
train_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
users, trainloaders, testloaders = get_cifar10_dirichlet_dataloaders(
users_num=30,
alpha=self.alpha,
batch_size=self.batch_size,
train_transform=train_transform,
test_transform=test_transform)
elif self.dataset_name == 'cifar10_ld':
users, trainloaders, testloaders = get_cifar10_ld_dataloaders()
elif self.dataset_name == 'cifar100':
train_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
users, trainloaders, testloaders = get_cifar100_dataloaders(
batch_size=self.batch_size,
train_transform=train_transform,
test_transform=test_transform)
elif self.dataset_name == 'cifar100_superclass':
train_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
users, trainloaders, testloaders = get_cifar100_superclass_dataloaders(
batch_size=self.batch_size,
train_transform=train_transform,
test_transform=test_transform)
elif self.dataset_name == 'mnist_malposition':
train_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
users, trainloaders, testloaders = get_mnist_malposition_dataloaders(
batch_size=self.batch_size,
train_transform=train_transform,
test_transform=test_transform)
elif self.dataset_name == 'mnist_wo_malposition':
train_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
users, trainloaders, testloaders = get_mnist_wo_malposition_dataloaders(
batch_size=self.batch_size,
train_transform=train_transform,
test_transform=test_transform)
elif self.dataset_name == 'mnist_ld':
users, trainloaders, testloaders = get_mnist_ld_dataloaders(
batch_size=self.batch_size)
elif self.dataset_name == 'mnist_fedml':
users, trainloaders, testloaders = get_mnist_fedml_dataloaders(
batch_size=self.batch_size)
elif self.dataset_name == 'femnist':
users, trainloaders, testloaders = get_femnist_dataloaders(
batch_size=self.batch_size)
elif self.dataset_name == 'flickr':
users, trainloaders, testloaders = get_flickr_dataloaders(
split_ratio=0.9, batch_size=10)
elif self.dataset_name == 'celeba':
users, trainloaders, testloaders = get_celeba_dataloaders(
batch_size=self.batch_size)
elif self.dataset_name == 'har':
users, trainloaders, testloaders = get_har_dataloaders(
batch_size=self.batch_size)
return users, trainloaders, testloaders