如何为PyTorch神经网络加载CSV数据？

import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset, DataLoader

data = pd.read_csv('data.csv')

train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)

class CustomDataset(Dataset):
    def __init__(self, data):
        self.data = data.values
    
    def __getitem__(self, index):
        sample = self.data[index]
        # 根据实际情况处理数据，例如转换为张量等
        return sample
    
    def __len__(self):
        return len(self.data)

batch_size = 64
train_dataset = CustomDataset(train_data)
test_dataset = CustomDataset(test_data)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size)

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

# 训练模型
for epoch in range(num_epochs):
    for batch_data in train_loader:
        inputs = batch_data
        labels = batch_data
        # 前向传播
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for batch_data in test_loader:
        inputs = batch_data
        labels = batch_data
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    accuracy = 100 * correct / total
    print('Test Accuracy: %.2f%%' % accuracy)