PyTorch DataLoader如何与PyTorch数据集交互以转换批处理？

import torch
from torch.utils.data import Dataset, DataLoader

# 自定义数据集类
class CustomDataset(Dataset):
    def __init__(self, data):
        self.data = data

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

    def __getitem__(self, idx):
        return self.data[idx]

# 创建数据集实例
data = [torch.randn(3, 32, 32) for _ in range(100)]  # 示例数据
dataset = CustomDataset(data)

# 创建DataLoader实例
dataloader = DataLoader(dataset, batch_size=4, shuffle=True, num_workers=2)

# 使用DataLoader进行迭代
for batch in dataloader:
    print(batch.shape)  # 输出批次形状

dataloader = DataLoader(dataset, batch_size=4, shuffle=True, num_workers=4)

dataset = CustomDataset(data)
dataset = torch.utils.data.Subset(dataset, range(100))  # 示例子集
dataset = torch.utils.data.DataLoader(dataset, batch_size=4, shuffle=True, num_workers=4, pin_memory=True)