给出一个以ResNet为模型的具有三特征输入{x,y,z}三输出{t1,t2,t3}的使用pytorch的train_dataset和test_dataset代码

以下是一个以ResNet模型为基础，具有三个特征输入{x, y, z}和三个输出{t1, t2, t3}的train_dataset和test_dataset的pytorch代码：

import torch
from torch.utils.data import Dataset, DataLoader
import numpy as np

class CustomDataset(Dataset):
    def __init__(self, data, targets, transform=None):
        self.data = data
        self.targets = targets
        self.transform = transform

    def __getitem__(self, index):
        x = self.data[index]
        y = self.targets[index]
        if self.transform:
            x = self.transform(x)

        t1 = y[0]
        t2 = y[1]
        t3 = y[2]

        return x, t1, t2, t3

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

# Create train and test datasets
train_data = np.random.rand(100, 3) # 100 samples with 3 features
train_targets = np.random.rand(100, 3) # 100 samples with 3 targets
train_dataset = CustomDataset(train_data, train_targets)

test_data = np.random.rand(50, 3) # 50 samples with 3 features
test_targets = np.random.rand(50, 3) # 50 samples with 3 targets
test_dataset = CustomDataset(test_data, test_targets)

# Create dataloaders
train_loader = DataLoader(train_dataset, batch_size=10, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=10, shuffle=False)

# Define the ResNet model
import torch.nn as nn
import torch.nn.functional as F

class ResNet(nn.Module):
    def __init__(self):
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 3)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x1 = self.fc3(x)
        x2 = self.fc3(x)
        x3 = self.fc3(x)
        return x1, x2, x3

# Train the model
model = ResNet()
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

for epoch in range(10): # train for 10 epochs
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, t1, t2, t3 = data
        optimizer.zero_grad()
        outputs1, outputs2, outputs3 = model(inputs.float())
        loss = criterion(outputs1, t1.float()) + criterion(outputs2, t2.float()) + criterion(outputs3, t3.float())
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

        if i % 10 == 9: # print every 10 mini-batches
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 10))
            running_loss = 0.0

# Test the model
with torch.no_grad():
    total_loss = 0.0
    for i, data in enumerate(test_loader, 0):
        inputs, t1, t2, t3 = data
        outputs1, outputs2, outputs3 = model(inputs.float())
        loss = criterion(outputs1, t1.float()) + criterion(outputs2, t2.float()) + criterion(outputs3, t3.float())
        total_loss += loss.item()

    print('Test loss: %.3f' % (total_loss / len(test_loader)))

在这个例子中，我们使用了一个ResNet模型，它有三个输出，每个输出都对应于一个目标。我们还定义了一个CustomDataset类，它将数据和目标作为输入，并将它们打包成一个元组。最后，我们使用DataLoader类将数据集加载到模型中进行训练和测试。