使用torch.nn.utils在Python中实现数据平行化

在PyTorch中实现数据并行处理可以使用torch.nn.DataParallel类和torch.nn.parallel.DistributedDataParallel类。这些类允许在多个GPU上同时训练模型，并可以在单个GPU上处理更大的批次。

下面是一个使用torch.nn.DataParallel的例子：

首先，导入所需的库和模块：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.nn.utils as utils
import torchvision.datasets as datasets
import torchvision.transforms as transforms

然后，定义一个简单的卷积神经网络模型：

class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3)
        self.fc1 = nn.Linear(128 * 5 * 5, 1024)
        self.fc2 = nn.Linear(1024, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = x.view(-1, 128 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

接下来，定义数据加载器和转换：

train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms.ToTensor())
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)

然后，定义训练函数：

def train(model, optimizer, criterion, data_loader):
    model.train()
    for inputs, targets in data_loader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

接下来，实例化模型、定义优化器和损失函数：

model = ConvNet()
model = nn.DataParallel(model)  # 使用torch.nn.DataParallel进行数据并行处理

optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

最后，进行训练和测试循环：

for epoch in range(10):
    train(model, optimizer, criterion, train_loader)

    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for inputs, targets in test_loader:
            outputs = model(inputs)
            test_loss += criterion(outputs, targets).item()
            _, predicted = outputs.max(1)
            correct += predicted.eq(targets).sum().item()

    test_loss /= len(test_loader.dataset)

    print(f'Epoch {epoch}: Test Loss: {test_loss}, Accuracy: {correct/len(test_loader.dataset)}')

上述例子展示了如何使用torch.nn.DataParallel在多个GPU上进行数据并行的训练和测试。你可以根据自己的需求调整模型结构和参数设置。