带有 shuffle=False 的数据加载器，但图像顺序在每个纪元中都会发生变化

问：

即使我使用图像随机化每个时代。'shuffle=False'

下面是用于创建加载程序的代码：

data_set = dset.CIFAR10(root='./data/cifar10', train=True, transform=transform, download=True)
train_loader, test_loader = create_loader_from_data_set(data_set, n_samples, batch_size, num_workers)

def create_loader_from_data_set(data_set, n_samples, batch_size, num_workers, test_size=0.2):
    indices = list(range(len(data_set)))
    selected_indices = random.sample(indices, n_samples)

    train_indices, test_indices = train_test_split(selected_indices, test_size=test_size, random_state=42)

    train_sampler = SubsetRandomSampler(train_indices)
    test_sampler = SubsetRandomSampler(test_indices)

    train_loader = DataLoader(data_set, batch_size=batch_size, num_workers=num_workers, sampler=train_sampler, shuffle=False)
    test_loader = DataLoader(data_set, batch_size=batch_size, num_workers=num_workers, sampler=test_sampler, shuffle=False)
    return train_loader, test_loader

这是针对训练循环的：

def train_epoch(epoch, network, loader, optimizer, batch_size):
    network.train()
    for batch_index, sample_tensor in enumerate(loader):
        batch_images, _ = sample_tensor 

我在每个时期得到的图像顺序不同（批次也不相同）。 shuffle=False 不应该保持顺序不变吗？

谢谢！

我也尝试过使用发电机，但没有用：

gen = torch.Generator()

train_loader = DataLoader(data_set, batch_size=batch_size, num_workers=num_workers, sampler=train_sampler, generator=gen)