请提供pytorch的示例

以下是一个基于PyTorch实现的Faster R-CNN目标检测模型示例：

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim

# 定义模型
class FasterRCNN(nn.Module):
    def __init__(self):
        super(FasterRCNN, self).__init__()
        self.backbone = torchvision.models.vgg16(pretrained=True)
        self.rpn = nn.Sequential(
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(512, 9 * 4, kernel_size=1, stride=1, padding=0),
        )
        self.roi_pooling = nn.AdaptiveMaxPool2d((7, 7))
        self.classifier = nn.Sequential(
            nn.Linear(7 * 7 * 512, 4096),
            nn.ReLU(),
            nn.Linear(4096, 4096),
            nn.ReLU(),
            nn.Linear(4096, 21),
        )

    def forward(self, x):
        x = self.backbone.features(x)
        rpn_output = self.rpn(x)
        roi_boxes = self.convert_to_roi_boxes(rpn_output)
        roi_features = self.roi_pooling(x, roi_boxes)
        roi_features = roi_features.view(roi_features.size(0), -1)
        output = self.classifier(roi_features)
        return output

    def convert_to_roi_boxes(self, rpn_output):
        rpn_output = rpn_output.permute(0, 2, 3, 1).contiguous().view(-1, 4)
        roi_boxes = torch.zeros((rpn_output.size(0), 4))
        roi_boxes[:, 0] = rpn_output[:, 0] - 0.5 * rpn_output[:, 2]
        roi_boxes[:, 1] = rpn_output[:, 1] - 0.5 * rpn_output[:, 3]
        roi_boxes[:, 2] = rpn_output[:, 0] + 0.5 * rpn_output[:, 2]
        roi_boxes[:, 3] = rpn_output[:, 1] + 0.5 * rpn_output[:, 3]
        return roi_boxes

# 准备数据
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练模型
for epoch in range(2):  # 2个epoch

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data

        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 2000 == 1999:    # 每2000个小批量数据打印一次损失值
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

注意：以上示例中的模型和数据仅供参考，实际应用中需要根据具体场景进行调整和优化。