def FederatedTrain(args):
if args.dataset == 'MNIST':
dataset = data.load_mnist()
dataloaders_train, dataloader_test = data.create_split_dataloaders(
dataset = dataset,
args=args
)
dataiters_train = [iter(loader) for loader in dataloaders_train]
dataiters_test = iter(dataloader_test)
n_channels = 1
else:
print 'Dataset Is Not Supported'
exit(1)
n_clients = args.n_clients
global_net = net.LeNet(n_channels = n_channels)
print global_net
learner = FederatedLearner(net = global_net, args = args)
learner.gpu = args.gpu
model_dir = args.model_dir
global_model_name = args.global_model_name
global_optim_name = args.global_optimizor_name
global_model_suffix = global_model_suffix = '_init_.pth'
if not os.path.exists(model_dir):
os.makedirs(model_dir)
torch.save(learner.net.state_dict(), model_dir + global_model_name + global_model_suffix)
print "Model saved"
for t in range(args.epochs):
if t == 0:
global_model_suffix = '_init_.pth'
else:
global_model_suffix = '_{cur}.pth'.format(cur=t-1)
learner.load_model(model_path = model_dir + global_model_name + global_model_suffix)
for i in range(n_clients):
print 't=', t, 'client model idx=', i
try:
batchX, batchY = next(dataiters_train[i])
except StopIteration:
dataiters_train[i] = iter(dataloaders_train[i])
batchX, batchY = next(dataiters_train[i])
learner.comp_grad(i, batchX, batchY)
learner._update_model()
global_model_suffix = '_{cur}.pth'.format(cur=t)
torch.save(learner.net.state_dict(), model_dir + global_model_name + global_model_suffix)
if (t+1) % args.n_eval_iters == 0:
learner._evalTest(test_loader = dataloader_test)
# 标准化
data_tf = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])])
train_dataset = datasets.MNIST(root='F:/shujuji/',
train=True,
transform=data_tf,
download=True)
test_dataset = datasets.MNIST(root='F:/shujuji/',
train=False,
transform=data_tf)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
model = net.LeNet()
if torch.cuda.is_available():
model = model.cuda()
# 定义loss函数和优化方法
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)
for epoch in range(num_epoches):
model.train()
for data in train_loader: # 每次取一个batch_size张图片
img, label = data # img.size:128*1*28*28
# img = img.view(img.size(0), -1) # 展开成128 *784（28*28）
if torch.cuda.is_available():
img = img.cuda()
label = label.cuda()
