如何在联合学习中选择“数据”和“目标”？(PySyft)

Question

我不能理解在下面的函数train()中，变量(data，target)是如何选择的。

def train(args, model, device, federated_train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(federated_train_loader): # <-- now it is a distributed dataset
        model.send(data.location) # <-- NEW: send the model to the right location`

我猜它们是2个张量，代表数据集训练的2个随机图像，但是损失函数

loss = F.nll_loss(output, target)

在与不同目标的每次交互中都会进行计算？

我也有不同的问题:我用猫的图像训练网络，然后用汽车的图像测试它，准确率达到了97%。这怎麽可能？是一个适当的值，还是我做错了什么？

下面是完整的代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms

import syft as sy  # <-- NEW: import the Pysyft library
hook = sy.TorchHook(torch)  # <-- NEW: hook PyTorch ie add extra functionalities to support Federated Learning
bob = sy.VirtualWorker(hook, id="bob")  # <-- NEW: define remote worker bob
alice = sy.VirtualWorker(hook, id="alice")  # <-- NEW: and alice

class Arguments():
    def __init__(self):
        self.batch_size = 64
        self.test_batch_size = 1000
        self.epochs = 2
        self.lr = 0.01
        self.momentum = 0.5
        self.no_cuda = False
        self.seed = 1
        self.log_interval = 30
        self.save_model = False

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( # <-- this is now a FederatedDataLoader
    datasets.MNIST("C:\\users...\\train", train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ]))
    .federate((bob, alice)), # <-- NEW: we distribute the dataset across all the workers, it's now a FederatedDataset
    batch_size=args.batch_size, shuffle=True, **kwargs)

test_loader = torch.utils.data.DataLoader(
    datasets.MNIST("C:\\Users...\\test", train=False, download=True, transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    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, 20, 5, 1)
        self.conv2 = nn.Conv2d(20, 50, 5, 1)
        self.fc1 = nn.Linear(4*4*50, 500)
        self.fc2 = nn.Linear(500, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(-1, 4*4*50)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

def train(args, model, device, federated_train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(federated_train_loader): # <-- now it is a distributed dataset
        model.send(data.location) # <-- NEW: send the model to the right 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() # <-- NEW: get the model back
        if batch_idx % args.log_interval == 0:
            loss = loss.get() # <-- NEW: get the loss back
            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:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
            pred = output.argmax(1, keepdim=True) # get the index of the max log-probability
            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) # TODO momentum is not supported at the moment

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")   

Answer 1

你可以这样想。当你钩住torch时，所有的torch张量都将获得额外的功能--像.send()，.federate()这样的方法，以及像.location和._objects这样的属性。由于.federate((bob, alice))，曾经是火炬张量的数据和目标变成了指向驻留在不同VirtualWorker对象中的张量的指针。

现在data和target有了额外的属性，包括.location，它将返回名为data/target的指针所指向的张量数据/目标的位置。

联邦学习将全局模型发送到此位置，如model.send(data.location)中所示。

现在，model是驻留在同一位置的指针，而data也是驻留在那里的指针。因此，当您将输出作为output = model(data)时，输出也将驻留在那里，所有我们(中央服务器或换句话说，称为'me'的VirtualWorker )将得到的是指向该输出的指针。

现在，关于您对损失计算的疑问，由于输出和目标都位于同一位置，因此loss的计算也将在那里进行。backprop和step也是如此。

最后，您可以看到model.get()，这里是中央服务器使用名为model的指针拉取远程模型的地方。(不过，我不确定它是否应该是model = model.get() )。

因此，任何与.get()相关的内容都将从该worker中提取出来，并将在我们的python语句中返回。还要注意的是，当被调用时，.get()将从它所在的位置移除该对象。因此，如果您还需要.copy().get()，请使用它。