基于resnet完成4关键点检测的模型代码

你是pytorch专家，请基于resnet完成4关键点检测的模型代码

好的，我可以帮您基于ResNet完成4关键点检测的模型代码。在这个问题中，我将假设您的任务是在给定图像中检测四个特定点的位置，例如人脸关键点检测。

首先，我们需要导入PyTorch和必要的模块。在这个例子中，我们将使用ResNet18作为我们的基础模型，并在其之上添加一个全连接层，该层将产生四个输出，对应于每个关键点的x和y坐标。我们将使用MSE损失函数来训练模型。在代码中，我们还将使用GPU加速模型的训练过程。以下是完整的模型代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import models

class KeyPointsModel(nn.Module):
    def __init__(self, num_keypoints):
        super(KeyPointsModel, self).__init__()
        self.base_model = models.resnet18(pretrained=True)
        self.fc = nn.Sequential(
            nn.Linear(512, 256),
            nn.ReLU(inplace=True),
            nn.Linear(256, num_keypoints*2)
        )
        
    def forward(self, x):
        x = self.base_model.conv1(x)
        x = self.base_model.bn1(x)
        x = self.base_model.relu(x)
        x = self.base_model.maxpool(x)

        x = self.base_model.layer1(x)
        x = self.base_model.layer2(x)
        x = self.base_model.layer3(x)
        x = self.base_model.layer4(x)

        x = self.base_model.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)
        return x

model = KeyPointsModel(num_keypoints=4)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

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

# 训练代码
def train(model, train_loader, criterion, optimizer, device):
    model.train()
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        inputs = inputs.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()

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

        running_loss += loss.item()
    return running_loss / len(train_loader)

# 测试代码
def test(model, test_loader, criterion, device):
    model.eval()
    test_loss = 0.0
    with torch.no_grad():
        for i, data in enumerate(test_loader, 0):
            inputs, labels = data
            inputs = inputs.to(device)
            labels = labels.to(device)

            outputs = model(inputs)
            loss = criterion(outputs, labels)

            test_loss += loss.item()
    return test_loss / len(test_loader)

# 数据集和数据加载器
class KeyPointsDataset(torch.utils.data.Dataset):
    def __init__(self, data, transform=None):
        self.data = data
        self.transform = transform

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index):
        image_path, keypoints = self.data[index]
        image = Image.open(image_path).convert('RGB')

        if self.transform is not