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| import torch
import syft as sy
from torch import nn
from torch import optim
from torchvision import datasets, transforms
import torch.nn.functional as F
hook = sy.TorchHook(torch)
Li = sy.VirtualWorker(hook, id='li')
Zhang = sy.VirtualWorker(hook, id='zhang')
class Arguments():
def __init__(self):
self.batch_size = 64
self.test_batch_size = 128
self.epochs = 3
self.lr = 0.01
self.momentum = 0.5
self.no_cuda = False
self.seed = 1
self.log_interval = 30
self.save_model = True
args = Arguments()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device('cuda' if use_cuda else 'cpu')
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
federated_train_loader = sy.FederatedDataLoader(
datasets.MNIST('../data', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,),
(0.3081,))
])).federate((Li, Zhang)),
batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
])),
batch_size=args.test_batch_size, shuffle=True, **kwargs)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 5)
self.conv2 = nn.Conv2d(32, 64, 5)
self.pool1 = nn.MaxPool2d(2, 2)
self.pool2 = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(1024, 512)
self.fc2 = nn.Linear(512, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool1(x)
x = F.relu(self.conv2(x))
x = self.pool2(x)
x = x.view(-1, 1024)
x = F.relu(self.fc1(x))
x = self.fc2(x)
x = F.log_softmax(x, dim=1)
return x
def train(args, model, device, federated_train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(federated_train_loader):
model.send(data.location)
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
model.get()
if batch_idx % args.log_interval == 0:
loss = loss.get()
print('Train Epoch : {} [ {} / {} ({:.0f}%)] \tLoss: {:.6f}'.format(
epoch, batch_idx * args.batch_size, len(federated_train_loader) *
args.batch_size,
100. * batch_idx / len(federated_train_loader), loss.item()))
def test(args, model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
dataset, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item()
pred = output.argmax(1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set : Average loss : {:.4f}, Accuracy: {}/{} ( {:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
model = Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=args.lr)
for epoch in range(1, args.epochs + 1):
train(args, model, device, federated_train_loader, optimizer, epoch)
test(args, model, device, test_loader)
if (args.save_model):
torch.save(model.state_dict(), "mnist_cnn.pt")
|