def build_data_provider()

in flsim/utils/example_utils.py [0:0]

• 27 lines of code
• 4 McCabe index (conditional complexity)

def build_data_provider(local_batch_size, examples_per_user, image_size):

    # 1. Create training, eval, and test datasets like in non-federated learning.
    transform = transforms.Compose(
        [
            transforms.Resize(image_size),
            transforms.CenterCrop(image_size),
            transforms.ToTensor(),
            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
        ]
    )
    train_dataset = CIFAR10(
        root="./cifar10", train=True, download=True, transform=transform
    )
    test_dataset = CIFAR10(
        root="./cifar10", train=False, download=True, transform=transform
    )

    # 2. Create a sharder, which maps samples in the training data to clients.
    sharder = SequentialSharder(examples_per_shard=examples_per_user)

    # 3. Shard and batchify training, eval, and test data.
    fl_data_loader = DataLoader(
        train_dataset=train_dataset,
        eval_dataset=test_dataset,
        test_dataset=test_dataset,
        sharder=sharder,
        batch_size=local_batch_size,
        drop_last=False,
    )

    # 4. Wrap the data loader with a data provider.
    data_provider = DataProvider(fl_data_loader)
    print(f"Clients in total: {data_provider.num_users()}")
    return data_provider