def init_train_loader(self, tl):
self.train_loader = tl
if __name__ == "__main__":
args = parse_args()
torch.manual_seed(args.seed)
num_users = 100
num_slices = num_users if args.client_selection else NUM_CLIENTS
server = CIFAR10ItPrServer(args, config, VGG11())
list_models, list_indices = server.init_clients()
sampler = FLSampler(list_indices, MAX_ROUND,
NUM_LOCAL_UPDATES * CLIENT_BATCH_SIZE,
args.client_selection, num_slices)
print("Sampler initialized")
train_loader = get_data_loader(EXP_NAME,
data_type="train",
batch_size=CLIENT_BATCH_SIZE,
shuffle=False,
sampler=sampler,
num_workers=8,
pin_memory=True)
client_list = [
CIFAR10ItPrClient(config, list_models[idx])
for idx in range(NUM_CLIENTS)
]
num_user_path = os.path.join("datasets", "FEMNIST", "processed",
"num_users.pt")
if not os.path.isfile(num_user_path):
get_data_loader(EXP_NAME,
data_type="train",
batch_size=CLIENT_BATCH_SIZE,
shuffle=False,
num_workers=8,
pin_memory=True)
num_users = torch.load(num_user_path)
server = FEMNISTItPrServer(args, config, Conv2())
list_models, list_users = server.init_clients()
sampler = FLSampler(get_indices_list(), MAX_ROUND,
NUM_LOCAL_UPDATES * CLIENT_BATCH_SIZE,
args.client_selection, NUM_CLIENTS)
print("Sampler initialized")
train_loader = get_data_loader(EXP_NAME,
data_type="train",
batch_size=CLIENT_BATCH_SIZE,
shuffle=False,
sampler=sampler,
num_workers=8,
pin_memory=True)
client_list = [
FEMNISTItPrClient(config, list_models[idx])
for idx in range(NUM_CLIENTS)
]