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taps.apps.fedlearn.modules

CifarModule 

CifarModule(num_classes: int)

Bases: Module

Cifar model.

Source: https://www.kaggle.com/code/shadabhussain/cifar-10-cnn-using-pytorch

Source code in taps/apps/fedlearn/modules.py 
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def __init__(self, num_classes: int):
    super().__init__()
    self.num_classes = num_classes
    self.network = nn.Sequential(
        nn.Conv2d(3, 32, kernel_size=3, padding=1),
        nn.ReLU(),
        nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
        nn.ReLU(),
        nn.MaxPool2d(2, 2),  # output: 64 x 16 x 16
        nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
        nn.ReLU(),
        nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
        nn.ReLU(),
        nn.MaxPool2d(2, 2),  # output: 128 x 8 x 8
        nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
        nn.ReLU(),
        nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
        nn.ReLU(),
        nn.MaxPool2d(2, 2),  # output: 256 x 4 x 4
        nn.Flatten(),
        nn.Linear(256 * 4 * 4, 1024),
        nn.ReLU(),
        nn.Linear(1024, 512),
        nn.ReLU(),
        nn.Linear(512, num_classes),
    )

forward 

forward(x: Tensor) -> Tensor

Forward pass.

Source code in taps/apps/fedlearn/modules.py 
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def forward(self, x: torch.Tensor) -> torch.Tensor:
    """Forward pass."""
    return self.network(x)

MnistModule 

MnistModule()

Bases: Module

Model for MNIST and FashionMNIST data.

Source code in taps/apps/fedlearn/modules.py 
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def __init__(self) -> None:
    super().__init__()
    self.flattener = nn.Flatten()
    self.fc1 = nn.Linear(28 * 28, 56 * 56)
    self.fc2 = nn.Linear(56 * 56, 28 * 28)
    self.fc3 = nn.Linear(28 * 28, 14 * 14)
    self.classifier = nn.Linear(14 * 14, 10)

forward 

forward(x: Tensor) -> Tensor

Forward pass.

Source code in taps/apps/fedlearn/modules.py 
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def forward(self, x: torch.Tensor) -> torch.Tensor:
    """Forward pass."""
    x = self.flattener(x)
    x = F.relu(self.fc1(x))
    x = F.relu(self.fc2(x))
    x = F.relu(self.fc3(x))
    x = self.classifier(x)
    return x

create_model 

create_model(data: DataChoices) -> Module

Create a model suitable for the dataset choice.

Note

The currently supported dataset options are MNIST, FashionMNIST, CIFAR10, and CIFAR100.

Parameters:

  • data (DataChoices) –

    Name of dataset that will be used for training (and testing).

Returns:

  • Module –

    PyTorch model.

Raises:

  • ValueError –

    If an unsupported value for data is provided.

Source code in taps/apps/fedlearn/modules.py 
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def create_model(data: DataChoices) -> nn.Module:
    """Create a model suitable for the dataset choice.

    Note:
        The currently supported dataset options are `MNIST`, `FashionMNIST`,
        `CIFAR10`, and `CIFAR100`.

    Args:
        data: Name of dataset that will be used for training (and testing).

    Returns:
        PyTorch model.

    Raises:
        ValueError: If an unsupported value for `data` is provided.
    """
    name = data.value.lower()

    if name == 'cifar10':
        return CifarModule(10)
    elif name == 'cifar100':
        return CifarModule(100)
    elif name in ('fmnist', 'mnist'):
        return MnistModule()
    else:
        raise ValueError(
            f'Unknown dataset "{data.value}". Supported options are '
            "'cifar10', 'cifar100', 'fmnist', and 'mnist'.",
        )

load_data 

load_data(
    data_name: DataChoices,
    root: Path,
    train: bool,
    download: bool = False,
) -> Dataset

Load dataset for training.

Parameters:

  • data_name (DataChoices) –

    Dataset choice.

  • root (Path) –

    Root dataset directory.

  • train (bool) –

    Flag for if training.

  • download (bool, default: False ) –

    Should the dataset be downloaded.

Returns:

  • Dataset ( Dataset ) –

    description

Source code in taps/apps/fedlearn/modules.py 
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def load_data(
    data_name: DataChoices,
    root: pathlib.Path,
    train: bool,
    download: bool = False,
) -> Dataset:
    """Load dataset for training.

    Args:
        data_name: Dataset choice.
        root: Root dataset directory.
        train: Flag for if training.
        download: Should the dataset be downloaded.

    Returns:
        Dataset: _description_
    """
    kwargs = {
        'root': root,
        'train': train,
        'transform': transforms.ToTensor(),
        'download': download,
    }
    name = data_name.value.lower()
    if name == 'cifar10':
        return torchvision.datasets.CIFAR10(**kwargs)
    elif name == 'cifar100':
        return torchvision.datasets.CIFAR100(**kwargs)
    elif name == 'fmnist':
        return torchvision.datasets.FashionMNIST(**kwargs)
    elif name == 'mnist':
        return torchvision.datasets.MNIST(**kwargs)
    else:
        raise ValueError(f'Unknown dataset: {data_name}.')