def get_dataset(args):
""" Returns train and test datasets and a user group which is a dict where
the keys are the user index and the values are the corresponding data for
each of those users.
"""
if args.dataset == 'cifar':
data_dir = '../data/cifar/'
apply_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = datasets.CIFAR10(data_dir, train=True, download=True,
transform=apply_transform)
test_dataset = datasets.CIFAR10(data_dir, train=False, download=True,
transform=apply_transform)
# sample training data amongst users
if args.iid:
# Sample IID user data from Mnist
user_groups = cifar_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Mnist
if args.unequal:
# Chose uneuqal splits for every user
user_groups = cifar_noniid_unequal(train_dataset, args.num_users)
elif args.unequalZipf:
user_groups = cifar_noniid_unequal_zipf(train_dataset, args.num_users)
else:
# Chose euqal splits for every user
user_groups = cifar_noniid(train_dataset, args.num_users)
elif args.dataset == 'mnist' or 'fmnist':
if args.dataset == 'mnist':
data_dir = '../data/mnist/'
else:
data_dir = '../data/fmnist/'
apply_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])
train_dataset = datasets.MNIST(data_dir, train=True, download=True,
transform=apply_transform)
test_dataset = datasets.MNIST(data_dir, train=False, download=True,
transform=apply_transform)
# sample training data amongst users
if args.iid:
# Sample IID user data from Mnist
user_groups = mnist_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Mnist
if args.unequal:
# Chose uneuqal splits for every user
user_groups = mnist_noniid_unequal(train_dataset, args.num_users)
else:
# Chose euqal splits for every user
user_groups = mnist_noniid(train_dataset, args.num_users)
return train_dataset, test_dataset, user_groups
def get_dataset(args):
""" Returns train and test datasets and a user group which is a dict where
the keys are the user index and the values are the corresponding data for
each of those users.
"""
if args.dataset == 'cifar10':
data_dir = '../data/cifar10/'
apply_transform_train = transforms.Compose([
transforms.RandomCrop(24),
transforms.RandomHorizontalFlip(0.5),
transforms.ColorJitter(brightness=0.2,
contrast=0.2,
saturation=0.2),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2470, 0.2435, 0.2616))
])
apply_transform_test = transforms.Compose([
transforms.CenterCrop(24),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2470, 0.2435, 0.2616))
])
train_dataset = datasets.CIFAR10(data_dir,
train=True,
download=True,
transform=apply_transform_train)
test_dataset = datasets.CIFAR10(data_dir,
train=False,
download=True,
transform=apply_transform_test)
# sample training data amongst users
if args.iid:
# Sample IID user data from Mnist
user_groups = cifar_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Mnist
if args.hard:
# Chose uneuqal splits for every user
raise NotImplementedError()
else:
# Chose euqal splits for every user
user_groups = cifar_noniid(train_dataset, args.num_users)
elif args.dataset == 'mnist' or args.dataset == 'fmnist':
if args.dataset == 'mnist':
data_dir = '../data/mnist/'
else:
data_dir = '../data/fmnist/'
#TODO:1 Accommodate FMNIST case (mean, var). This is the mean, var of MNIST; Fashion MNIST may have different set of params/
# shall we use the params from opt instead of setting hard params?
apply_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
train_dataset = datasets.MNIST(data_dir,
train=True,
download=True,
transform=apply_transform)
test_dataset = datasets.MNIST(data_dir,
train=False,
download=True,
transform=apply_transform)
# sample training data amongst users
if args.iid:
# Sample IID user data from Mnist
user_groups = mnist_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Mnist
if args.unequal:
# Chose uneuqal splits for every user
user_groups = mnist_noniid_unequal(train_dataset,
args.num_users)
else:
# Chose euqal splits for every user
user_groups = mnist_noniid(train_dataset, args.num_users)
elif args.dataset == 'cub200':
data_dir = '../data/cub200/'
apply_transform_train = transforms.Compose([
transforms.Resize(int(cf.imresize[args.net_type])),
transforms.RandomRotation(10),
transforms.RandomCrop(cf.imsize[args.net_type]),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(cf.mean[args.dataset], cf.std[args.dataset]),
])
apply_transform_test = transforms.Compose([
transforms.Resize(cf.imresize[args.net_type]),
transforms.CenterCrop(cf.imsize[args.net_type]),
transforms.ToTensor(),
transforms.Normalize(cf.mean[args.dataset], cf.std[args.dataset]),
])
train_dataset = cub.CUB200(data_dir,
year=2011,
train=True,
download=True,
transform=apply_transform_train)
test_dataset = cub.CUB200(data_dir,
year=2011,
train=False,
download=True,
transform=apply_transform_test)
# sample training data amongst users
if args.iid:
# Sample IID user data from Mnist
user_groups = cub_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Mnist
if args.hard:
# Chose uneuqal splits for every user
user_groups = cub_noniid_hard(train_dataset, args.num_users)
else:
# Chose euqal splits for every user
user_groups = cub_noniid(train_dataset, args.num_users)
return train_dataset, test_dataset, user_groups
def get_dataset(args):
""" Returns train and test datasets and a user group which is a dict where
the keys are the user index and the values are the corresponding data for
each of those users.
"""
if args.dataset == 'cifar':
data_dir = '../data/cifar/'
apply_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = datasets.MNIST(data_dir, train=True, download=True,
transform=apply_transform)
test_dataset = datasets.MNIST(data_dir, train=False, download=True,
transform=apply_transform)
# sample training data amongst users
if args.iid:
# Sample IID user data from Mnist
user_groups = cifar_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Mnist
if args.unequal:
# Chose uneuqal splits for every user
raise NotImplementedError()
else:
# Chose euqal splits for every user
user_groups = cifar_noniid(train_dataset, args.num_users)
elif args.dataset == 'medmnist':
train_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(mean=[.5], std=[.5])])
test_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(mean=[.5], std=[.5])])
input_root = args.input_root
train_dataset = BreastMNIST(root=input_root,
split='train',
transform=train_transform,
download=True)
test_dataset = BreastMNIST(root=input_root,
split='test',
transform=test_transform,
download=True)
user_groups = medmnist_iid(train_dataset, args.num_users)
print('--------------------------------')
print(train_dataset, test_dataset, user_groups)
print('--------------------------------')
elif args.dataset == 'mnist' or 'fmnist':
print('<<<<>>>>>')
if args.dataset == 'mnist':
data_dir = '../data/mnist/'
else:
data_dir = '../data/fmnist/'
apply_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])
train_dataset = datasets.MNIST(data_dir, train=True, download=True,
transform=apply_transform)
test_dataset = datasets.MNIST(data_dir, train=False, download=True,
transform=apply_transform)
# sample training data amongst users
if args.iid:
# Sample IID user data from Mnist
user_groups = mnist_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Mnist
if args.unequal:
# Chose uneuqal splits for every user
user_groups = mnist_noniid_unequal(train_dataset, args.num_users)
else:
# Chose euqal splits for every user
user_groups = mnist_noniid(train_dataset, args.num_users)
return train_dataset, test_dataset, user_groups
def get_dataset(args):
""" Returns train and test datasets and a user group which is a dict where
the keys are the user index and the values are the corresponding data for
each of those users.
"""
if args.dataset == 'cifar':
data_dir = '../data/cifar/'
apply_transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])
test_apply_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])
train_dataset = datasets.CIFAR10(data_dir,
train=True,
download=True,
transform=apply_transform)
test_dataset = datasets.CIFAR10(data_dir,
train=False,
download=True,
transform=test_apply_transform)
# sample training data amongst users
if args.iid:
# Sample IID user data from Mnist
user_groups = cifar_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Mnist
if args.unequal:
# Chose uneuqal splits for every user
raise NotImplementedError()
else:
# Chose euqal splits for every user
user_groups = cifar_noniid(train_dataset, args.num_users)
elif args.dataset == 'mnist' or args.dataset == 'fmnist':
if args.dataset == 'mnist':
data_dir = '../data/mnist/'
else:
data_dir = '../data/fmnist/'
apply_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
train_dataset = datasets.MNIST(data_dir,
train=True,
download=True,
transform=apply_transform)
test_dataset = datasets.MNIST(data_dir,
train=False,
download=True,
transform=apply_transform)
# sample training data amongst users
if args.iid:
# Sample IID user data from Mnist
user_groups = mnist_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Mnist
if args.unequal:
# Chose uneuqal splits for every user
user_groups = mnist_noniid_unequal(train_dataset,
args.num_users)
else:
# Chose euqal splits for every user
user_groups = mnist_noniid(train_dataset, args.num_users)
elif args.dataset == 'brats2018':
from data.brats2018.dataset import BRATS2018Dataset, InstitutionWiseBRATS2018Dataset
# from torch.utils.data import random_split
from sampling import brats2018_iid, brats2018_unbalanced
data_dir = args.data_dir
test_dataset = None
if args.balanced:
train_dataset = BRATS2018Dataset(training_dir=data_dir,
img_dim=128)
user_groups = brats2018_iid(dataset=train_dataset,
num_users=args.num_users)
else:
# BRATS2018 得到的数据来自于 19家机构. 默认
train_dataset = InstitutionWiseBRATS2018Dataset(
training_dir=data_dir,
img_dim=128,
config_json='../data/brats2018/hgg_config.json')
user_groups = brats2018_unbalanced(dataset=train_dataset,
num_users=args.num_users)
elif args.dataset == 'brats2018_data_aug':
from data.brats2018.dataset import InstitutionWiseBRATS2018DatasetDataAugmentation
# from torch.utils.data import random_split
from sampling import brats2018_iid, brats2018_unbalanced
data_dir = args.data_dir
test_dataset = None
if args.balanced:
raise NotImplementedError
else:
# BRATS2018 得到的数据来自于 19家机构. 默认
train_dataset = InstitutionWiseBRATS2018DatasetDataAugmentation(
training_dir=data_dir,
img_dim=128,
config_json='../data/brats2018/hgg_config.json')
user_groups = brats2018_unbalanced(dataset=train_dataset,
num_users=args.num_users)
return train_dataset, test_dataset, user_groups
def get_dataset(args,
tokenizer=None,
max_seq_len=MAX_SEQUENCE_LENGTH,
custom_sampling=None):
""" Returns train and test datasets and a user group which is a dict where
the keys are the user index and the values are the corresponding data for
each of those users.
"""
if args.task == 'nlp':
assert args.dataset == "ade", "Parsed dataset not implemented."
[complete_dataset] = ds.load_dataset("ade_corpus_v2",
"Ade_corpus_v2_classification",
split=["train"])
# Rename column.
complete_dataset = complete_dataset.rename_column("label", "labels")
complete_dataset = complete_dataset.shuffle(seed=args.seed)
# Split into train and test sets.
split_examples = complete_dataset.train_test_split(
test_size=args.test_frac)
train_examples = split_examples["train"]
test_examples = split_examples["test"]
# Tokenize training set.
train_dataset = train_examples.map(
lambda examples: tokenizer(
examples["text"],
truncation=True,
max_length=max_seq_len,
padding="max_length",
),
batched=True,
remove_columns=["text"],
)
train_dataset.set_format(type="torch")
# Tokenize test set.
test_dataset = test_examples.map(
lambda examples: tokenizer(
examples["text"],
truncation=True,
max_length=max_seq_len,
padding="max_length",
),
batched=True,
remove_columns=["text"],
)
test_dataset.set_format(type="torch")
# sample training data amongst users
if args.iid:
# Sample IID user data from Ade_corpus
user_groups = ade_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Ade_corpus
if args.unequal:
# Chose unequal splits for every user
raise NotImplementedError()
else:
# Chose equal splits for every user
user_groups = ade_noniid(train_dataset, args.num_users)
elif args.task == 'cv':
if args.dataset == 'cifar':
data_dir = './data/cifar/'
apply_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = datasets.CIFAR10(data_dir,
train=True,
download=True,
transform=apply_transform)
test_dataset = datasets.CIFAR10(data_dir,
train=False,
download=True,
transform=apply_transform)
# sample training data amongst users
if custom_sampling is not None:
user_groups = custom_sampling(dataset=train_dataset,
num_users=args.num_users)
assert len(
user_groups
) == args.num_users, "Incorrect number of users generated."
check_client_sampled_data = []
for client_idx, client_samples in user_groups.items():
assert len(client_samples) == len(
train_dataset
) / args.num_users, "Incorrectly sampled client shard."
for record in client_samples:
check_client_sampled_data.append(record)
assert len(set(check_client_sampled_data)) == len(
train_dataset), "Client shards are not i.i.d"
print("Congratulations! You've got it :)")
else:
# sample training data amongst users
if args.iid:
# Sample IID user data from Mnist
user_groups = cifar_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Mnist
if args.unequal:
# Chose uneuqal splits for every user
raise NotImplementedError()
else:
# Chose euqal splits for every user
user_groups = cifar_noniid(train_dataset,
args.num_users)
elif args.dataset == 'mnist' or 'fmnist':
if args.dataset == 'mnist':
data_dir = './data/mnist/'
else:
data_dir = './data/fmnist/'
apply_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
train_dataset = datasets.MNIST(data_dir,
train=True,
download=True,
transform=apply_transform)
test_dataset = datasets.MNIST(data_dir,
train=False,
download=True,
transform=apply_transform)
if args.iid:
# Sample IID user data from Mnist
user_groups = mnist_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Mnist
if args.unequal:
# Chose unequal splits for every user
user_groups = mnist_noniid_unequal(train_dataset,
args.num_users)
else:
# Chose equal splits for every user
user_groups = mnist_noniid(train_dataset, args.num_users)
else:
raise NotImplementedError(f"""Unrecognized dataset {args.dataset}.
Options are: `cifar`, `mnist`, `fmnist`.
""")
else:
raise NotImplementedError(f"""Unrecognised task {args.task}.
Options are: `nlp` and `cv`.
""")
return train_dataset, test_dataset, user_groups
def get_dataset(args):
""" Returns train and test datasets and a user group which is a dict where
the keys are the user index and the values are the corresponding data for
each of those users.
"""
if args.dataset == 'cifar':
data_dir = '../data/cifar/'
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
transforms_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)),
])
transforms_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
train_dataset = datasets.CIFAR10(data_dir,
train=True,
download=True,
transform=transforms_train)
test_dataset = datasets.CIFAR10(data_dir,
train=False,
download=True,
transform=transforms_test)
"""
apply_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = datasets.CIFAR10(data_dir, train=True, download=True,
transform=apply_transform)
test_dataset = datasets.CIFAR10(data_dir, train=False, download=True,
transform=apply_transform)
"""
# sample training data amongst users
if args.iid == 1:
# Sample IID user data from Mnist
# user_groups = cifar_iid(train_dataset, args.num_users)
user_groups = cifar10_iid(train_dataset, args.num_users, args=args)
else:
# Sample Non-IID user data from Mnist
if args.unequal:
# Chose uneuqal splits for every user
raise NotImplementedError()
else:
# Chose euqal splits for every user
if args.iid == 2:
# user_groups = partition_data(train_dataset, 'noniid-#label2', num_uers=args.num_users, alpha=1, args=args)
user_groups = cifar_noniid(train_dataset,
num_users=args.num_users,
args=args)
else:
user_groups = partition_data(train_dataset,
'dirichlet',
num_uers=args.num_users,
alpha=1,
args=args)
# 분류된 index와 train dataset로 client train dataloder 생성
client_loader_dict = client_loader(train_dataset, user_groups, args)
elif args.dataset == 'cifar100':
data_dir = '../data/fed_cifar100'
train_dataset, test_dataset, client_loader_dict = load_partition_data_federated_cifar100(
data_dir=data_dir, batch_size=args.local_bs)
elif args.dataset == 'mnist' or 'fmnist':
if args.dataset == 'mnist':
data_dir = '../data/mnist/'
else:
data_dir = '../data/fmnist/'
apply_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
train_dataset = datasets.MNIST(data_dir,
train=True,
download=True,
transform=apply_transform)
test_dataset = datasets.MNIST(data_dir,
train=False,
download=True,
transform=apply_transform)
# sample training data amongst users
if args.iid:
# Sample IID user data from Mnist
user_groups = mnist_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Mnist
if args.unequal:
# Chose uneuqal splits for every user
user_groups = mnist_noniid_unequal(train_dataset,
args.num_users)
else:
# Chose euqal splits for every user
user_groups = mnist_noniid(train_dataset, args.num_users)
# 분류된 index와 train dataset로 client train data loader 생성
client_loader_dict = client_loader(train_dataset, user_groups, args)
return train_dataset, test_dataset, client_loader_dict
def get_dataset(args, n_list, k_list):
""" Returns train and test datasets and a user group which is a dict where
the keys are the user index and the values are the corresponding data for
each of those users.
"""
data_dir = args.data_dir + args.dataset
if args.dataset == 'mnist':
apply_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
train_dataset = datasets.MNIST(data_dir,
train=True,
download=True,
transform=apply_transform)
test_dataset = datasets.MNIST(data_dir,
train=False,
download=True,
transform=apply_transform)
# sample training data amongst users
if args.iid:
# Sample IID user data from Mnist
user_groups = mnist_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Mnist
if args.unequal:
# Chose uneuqal splits for every user
user_groups = mnist_noniid_unequal(args, train_dataset,
args.num_users)
else:
# Chose euqal splits for every user
user_groups, classes_list = mnist_noniid(
args, train_dataset, args.num_users, n_list, k_list)
user_groups_lt = mnist_noniid_lt(args, test_dataset,
args.num_users, n_list,
k_list, classes_list)
classes_list_gt = classes_list
elif args.dataset == 'femnist':
apply_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
train_dataset = femnist.FEMNIST(args,
data_dir,
train=True,
download=True,
transform=apply_transform)
test_dataset = femnist.FEMNIST(args,
data_dir,
train=False,
download=True,
transform=apply_transform)
# sample training data amongst users
if args.iid:
# Sample IID user data from Mnist
user_groups = femnist_iid(train_dataset, args.num_users)
# print("TBD")
else:
# Sample Non-IID user data from Mnist
if args.unequal:
# Chose uneuqal splits for every user
# user_groups = mnist_noniid_unequal(train_dataset, args.num_users)
user_groups = femnist_noniid_unequal(args, train_dataset,
args.num_users)
# print("TBD")
else:
# Chose euqal splits for every user
user_groups, classes_list, classes_list_gt = femnist_noniid(
args, args.num_users, n_list, k_list)
user_groups_lt = femnist_noniid_lt(args, args.num_users,
classes_list)
elif args.dataset == 'cifar10':
train_dataset = datasets.CIFAR10(data_dir,
train=True,
download=True,
transform=trans_cifar10_train)
test_dataset = datasets.CIFAR10(data_dir,
train=False,
download=True,
transform=trans_cifar10_val)
# sample training data amongst users
if args.iid:
# Sample IID user data from Mnist
user_groups = cifar_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Mnist
if args.unequal:
# Chose uneuqal splits for every user
raise NotImplementedError()
else:
# Chose euqal splits for every user
user_groups, classes_list, classes_list_gt = cifar10_noniid(
args, train_dataset, args.num_users, n_list, k_list)
user_groups_lt = cifar10_noniid_lt(args, test_dataset,
args.num_users, n_list,
k_list, classes_list)
elif args.dataset == 'cifar100':
train_dataset = datasets.CIFAR100(data_dir,
train=True,
download=True,
transform=trans_cifar100_train)
test_dataset = datasets.CIFAR100(data_dir,
train=False,
download=True,
transform=trans_cifar100_val)
# sample training data amongst users
if args.iid:
# Sample IID user data from Mnist
user_groups = cifar_iid(train_dataset, args.num_users)
else:
# Sample Non-IID user data from Mnist
if args.unequal:
# Chose uneuqal splits for every user
raise NotImplementedError()
else:
# Chose euqal splits for every user
user_groups, classes_list = cifar100_noniid(
args, train_dataset, args.num_users, n_list, k_list)
user_groups_lt = cifar100_noniid_lt(test_dataset,
args.num_users,
classes_list)
return train_dataset, test_dataset, user_groups, user_groups_lt, classes_list, classes_list_gt
# load dataset and split users
if args.dataset == 'mnist':
dataset_train = datasets.MNIST('../data/mnist/',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ),
(0.3081, ))
]))
# sample users
if args.iid:
dict_users = mnist_iid(dataset_train, args.num_users)
elif args.unequal:
dict_users = mnist_noniid_unequal(dataset_train, args.num_users)
else:
dict_users = mnist_noniid(dataset_train, args.num_users)
elif args.dataset == 'cifar':
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset_train = datasets.CIFAR10('../data/cifar',
train=True,
transform=transform,
target_transform=None,
download=True)
if args.iid:
dict_users = cifar_iid(dataset_train, args.num_users)