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config.py

High level definition of CNN architectures.

@author: Nicola VIGANÒ, CEA-MEM, Grenoble, France

NetworkParams

Bases: ABC

Abstract base class for storing network parameters.

Source code in src/autoden/models/config.py 
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class NetworkParams(ABC):
    """Abstract base class for storing network parameters."""

    n_channels_in: int
    n_channels_out: int
    n_features: int

    def __init__(self, n_features: int, n_channels_in: int = 1, n_channels_out: int = 1) -> None:
        self.n_channels_in = n_channels_in
        self.n_channels_out = n_channels_out
        self.n_features = n_features

    def __repr__(self) -> str:
        """Produce the string representation of the object.

        Returns
        -------
        str
            The string representation.
        """
        return self.__class__.__name__ + " {\n" + ",\n".join([f"  {k} = {v}" for k, v in self.__dict__.items()]) + "\n}"

    @abstractmethod
    def get_model(self, device: str = "cuda" if is_cuda_available() else "cpu") -> Module:
        """Get the associated model with the selected parameters.

        Parameters
        ----------
        device : str, optional
            The device that the the model should run on, by default "cuda" if cuda is available, otherwise "cpu".

        Returns
        -------
        Module
            The model.
        """

__repr__()

Produce the string representation of the object.

Returns:

Type Description
str

The string representation.
Source code in src/autoden/models/config.py 
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def __repr__(self) -> str:
    """Produce the string representation of the object.

    Returns
    -------
    str
        The string representation.
    """
    return self.__class__.__name__ + " {\n" + ",\n".join([f"  {k} = {v}" for k, v in self.__dict__.items()]) + "\n}"

get_model(device='cuda' if is_cuda_available() else 'cpu') abstractmethod

Get the associated model with the selected parameters.

Parameters:

Name Type Description Default
device str

The device that the the model should run on, by default "cuda" if cuda is available, otherwise "cpu".

 'cuda' if is_available() else 'cpu'

Returns:

Type Description
Module

The model.
Source code in src/autoden/models/config.py 
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@abstractmethod
def get_model(self, device: str = "cuda" if is_cuda_available() else "cpu") -> Module:
    """Get the associated model with the selected parameters.

    Parameters
    ----------
    device : str, optional
        The device that the the model should run on, by default "cuda" if cuda is available, otherwise "cpu".

    Returns
    -------
    Module
        The model.
    """

NetworkParamsDnCNN

Bases: NetworkParams

Store DnCNN parameters.

Source code in src/autoden/models/config.py 
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class NetworkParamsDnCNN(NetworkParams):
    """Store DnCNN parameters."""

    n_layers: int

    def __init__(self, n_channels_in: int = 1, n_channels_out: int = 1, n_layers: int = 20, n_features: int = 64) -> None:
        """Initialize the DnCNN network parameters definition.

        Parameters
        ----------
        n_channels_in : int, optional
            Number of input channels. Default is 1.
        n_channels_out : int, optional
            Number of output channels. Default is 1.
        n_layers : int, optional
            Number of layers. Default is 20.
        n_features : int, optional
            Number of features. Default is 64.
        """
        super().__init__(n_features=n_features, n_channels_in=n_channels_in, n_channels_out=n_channels_out)
        self.n_layers = n_layers

    def get_model(self, device: str = "cuda" if is_cuda_available() else "cpu") -> Module:
        """Get a DnCNN model with the selected parameters.

        Parameters
        ----------
        device : str, optional
            The device that the the model should run on, by default "cuda" if cuda is available, otherwise "cpu".

        Returns
        -------
        Module
            The DnCNN model.
        """
        return DnCNN(
            n_channels_in=self.n_channels_in,
            n_channels_out=self.n_channels_out,
            n_layers=self.n_layers,
            n_features=self.n_features,
            device=device,
        )

__init__(n_channels_in=1, n_channels_out=1, n_layers=20, n_features=64)

Initialize the DnCNN network parameters definition.

Parameters:

Name Type Description Default
n_channels_in int

Number of input channels. Default is 1.

 1
n_channels_out int

Number of output channels. Default is 1.

 1
n_layers int

Number of layers. Default is 20.

 20
n_features int

Number of features. Default is 64.

 64
Source code in src/autoden/models/config.py 
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def __init__(self, n_channels_in: int = 1, n_channels_out: int = 1, n_layers: int = 20, n_features: int = 64) -> None:
    """Initialize the DnCNN network parameters definition.

    Parameters
    ----------
    n_channels_in : int, optional
        Number of input channels. Default is 1.
    n_channels_out : int, optional
        Number of output channels. Default is 1.
    n_layers : int, optional
        Number of layers. Default is 20.
    n_features : int, optional
        Number of features. Default is 64.
    """
    super().__init__(n_features=n_features, n_channels_in=n_channels_in, n_channels_out=n_channels_out)
    self.n_layers = n_layers

get_model(device='cuda' if is_cuda_available() else 'cpu')

Get a DnCNN model with the selected parameters.

Parameters:

Name Type Description Default
device str

The device that the the model should run on, by default "cuda" if cuda is available, otherwise "cpu".

 'cuda' if is_available() else 'cpu'

Returns:

Type Description
Module

The DnCNN model.
Source code in src/autoden/models/config.py 
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def get_model(self, device: str = "cuda" if is_cuda_available() else "cpu") -> Module:
    """Get a DnCNN model with the selected parameters.

    Parameters
    ----------
    device : str, optional
        The device that the the model should run on, by default "cuda" if cuda is available, otherwise "cpu".

    Returns
    -------
    Module
        The DnCNN model.
    """
    return DnCNN(
        n_channels_in=self.n_channels_in,
        n_channels_out=self.n_channels_out,
        n_layers=self.n_layers,
        n_features=self.n_features,
        device=device,
    )

NetworkParamsMSD

Bases: NetworkParams

Store MS-D net parameters.

Source code in src/autoden/models/config.py 
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class NetworkParamsMSD(NetworkParams):
    """Store MS-D net parameters."""

    dilations: Sequence[int] | NDArray[np.integer]
    n_layers: int

    def __init__(
        self,
        n_channels_in: int = 1,
        n_channels_out: int = 1,
        n_layers: int = 80,
        n_features: int = 1,
        dilations: Sequence[int] | NDArray[np.integer] = np.arange(1, 10),
    ) -> None:
        """Initialize the MS-D network parameters definition.

        Parameters
        ----------
        n_channels_in : int, optional
            Number of input channels, by default 1.
        n_channels_out : int, optional
            Number of output channels, by default 1.
        n_layers : int, optional
            Number of layers in the network, by default 80.
        n_features : int, optional
            Number of features, by default 1.
        dilations : Sequence[int] | NDArray[np.integer], optional
            Dilation values for the network, by default np.arange(1, 10).
        """
        super().__init__(n_features=n_features, n_channels_in=n_channels_in, n_channels_out=n_channels_out)
        self.n_layers = n_layers
        self.dilations = dilations

    def get_model(self, device: str = "cuda" if is_cuda_available() else "cpu") -> Module:
        """Get a MS-D net model with the selected parameters.

        Parameters
        ----------
        device : str, optional
            The device that the the model should run on, by default "cuda" if cuda is available, otherwise "cpu".

        Returns
        -------
        Module
            The model.
        """
        return MSDnet(
            self.n_channels_in,
            self.n_channels_out,
            n_layers=self.n_layers,
            n_features=self.n_features,
            dilations=list(self.dilations),
            device=device,
        )

__init__(n_channels_in=1, n_channels_out=1, n_layers=80, n_features=1, dilations=np.arange(1, 10))

Initialize the MS-D network parameters definition.

Parameters:

Name Type Description Default
n_channels_in int

Number of input channels, by default 1.

 1
n_channels_out int

Number of output channels, by default 1.

 1
n_layers int

Number of layers in the network, by default 80.

 80
n_features int

Number of features, by default 1.

 1
dilations Sequence[int] | NDArray[integer]

Dilation values for the network, by default np.arange(1, 10).

 arange(1, 10)
Source code in src/autoden/models/config.py 
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def __init__(
    self,
    n_channels_in: int = 1,
    n_channels_out: int = 1,
    n_layers: int = 80,
    n_features: int = 1,
    dilations: Sequence[int] | NDArray[np.integer] = np.arange(1, 10),
) -> None:
    """Initialize the MS-D network parameters definition.

    Parameters
    ----------
    n_channels_in : int, optional
        Number of input channels, by default 1.
    n_channels_out : int, optional
        Number of output channels, by default 1.
    n_layers : int, optional
        Number of layers in the network, by default 80.
    n_features : int, optional
        Number of features, by default 1.
    dilations : Sequence[int] | NDArray[np.integer], optional
        Dilation values for the network, by default np.arange(1, 10).
    """
    super().__init__(n_features=n_features, n_channels_in=n_channels_in, n_channels_out=n_channels_out)
    self.n_layers = n_layers
    self.dilations = dilations

get_model(device='cuda' if is_cuda_available() else 'cpu')

Get a MS-D net model with the selected parameters.

Parameters:

Name Type Description Default
device str

The device that the the model should run on, by default "cuda" if cuda is available, otherwise "cpu".

 'cuda' if is_available() else 'cpu'

Returns:

Type Description
Module

The model.
Source code in src/autoden/models/config.py 
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def get_model(self, device: str = "cuda" if is_cuda_available() else "cpu") -> Module:
    """Get a MS-D net model with the selected parameters.

    Parameters
    ----------
    device : str, optional
        The device that the the model should run on, by default "cuda" if cuda is available, otherwise "cpu".

    Returns
    -------
    Module
        The model.
    """
    return MSDnet(
        self.n_channels_in,
        self.n_channels_out,
        n_layers=self.n_layers,
        n_features=self.n_features,
        dilations=list(self.dilations),
        device=device,
    )

NetworkParamsUNet

Bases: NetworkParams

Store UNet parameters.

Source code in src/autoden/models/config.py 
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class NetworkParamsUNet(NetworkParams):
    """Store UNet parameters."""

    n_levels: int

    DEFAULT_LEVELS: int = 3
    DEFAULT_FEATURES: int = 32

    def __init__(
        self,
        n_channels_in: int = 1,
        n_channels_out: int = 1,
        n_levels: int = DEFAULT_LEVELS,
        n_features: int = DEFAULT_FEATURES,
        n_channels_skip: int | None = None,
        bilinear: bool = True,
        pad_mode: str = "replicate",
    ) -> None:
        """Initialize the UNet network parameters definition.

        Parameters
        ----------
        n_channels_in : int, optional
            Number of input channels. Default is 1.
        n_channels_out : int, optional
            Number of output channels. Default is 1.
        n_levels : int, optional
            Number of levels in the UNet. Default is 3.
        n_features : int, optional
            Number of features in the UNet. Default is 32.
        n_channels_skip : int, optional
            Number of skip connections channels. Default is None.
        bilinear : bool, optional
            Whether to use bilinear interpolation. Default is True.
        pad_mode : str, optional
            Padding mode for convolutional layers. Default is "replicate".
        """
        super().__init__(n_features=n_features, n_channels_in=n_channels_in, n_channels_out=n_channels_out)
        self.n_levels = n_levels
        self.n_channels_skip = n_channels_skip
        self.bilinear = bilinear
        self.pad_mode = pad_mode

    def get_model(self, device: str = "cuda" if is_cuda_available() else "cpu") -> Module:
        """Get a U-net model with the selected parameters.

        Parameters
        ----------
        device : str, optional
            The device that the the model should run on, by default "cuda" if cuda is available, otherwise "cpu".

        Returns
        -------
        Module
            The U-net model.
        """
        return UNet(
            n_channels_in=self.n_channels_in,
            n_channels_out=self.n_channels_out,
            n_features=self.n_features,
            n_levels=self.n_levels,
            n_channels_skip=self.n_channels_skip,
            bilinear=self.bilinear,
            pad_mode=self.pad_mode,
            device=device,
        )

__init__(n_channels_in=1, n_channels_out=1, n_levels=DEFAULT_LEVELS, n_features=DEFAULT_FEATURES, n_channels_skip=None, bilinear=True, pad_mode='replicate')

Initialize the UNet network parameters definition.

Parameters:

Name Type Description Default
n_channels_in int

Number of input channels. Default is 1.

 1
n_channels_out int

Number of output channels. Default is 1.

 1
n_levels int

Number of levels in the UNet. Default is 3.

 DEFAULT_LEVELS
n_features int

Number of features in the UNet. Default is 32.

 DEFAULT_FEATURES
n_channels_skip int

Number of skip connections channels. Default is None.

 None
bilinear bool

Whether to use bilinear interpolation. Default is True.

 True
pad_mode str

Padding mode for convolutional layers. Default is "replicate".

 'replicate'
Source code in src/autoden/models/config.py 
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def __init__(
    self,
    n_channels_in: int = 1,
    n_channels_out: int = 1,
    n_levels: int = DEFAULT_LEVELS,
    n_features: int = DEFAULT_FEATURES,
    n_channels_skip: int | None = None,
    bilinear: bool = True,
    pad_mode: str = "replicate",
) -> None:
    """Initialize the UNet network parameters definition.

    Parameters
    ----------
    n_channels_in : int, optional
        Number of input channels. Default is 1.
    n_channels_out : int, optional
        Number of output channels. Default is 1.
    n_levels : int, optional
        Number of levels in the UNet. Default is 3.
    n_features : int, optional
        Number of features in the UNet. Default is 32.
    n_channels_skip : int, optional
        Number of skip connections channels. Default is None.
    bilinear : bool, optional
        Whether to use bilinear interpolation. Default is True.
    pad_mode : str, optional
        Padding mode for convolutional layers. Default is "replicate".
    """
    super().__init__(n_features=n_features, n_channels_in=n_channels_in, n_channels_out=n_channels_out)
    self.n_levels = n_levels
    self.n_channels_skip = n_channels_skip
    self.bilinear = bilinear
    self.pad_mode = pad_mode

get_model(device='cuda' if is_cuda_available() else 'cpu')

Get a U-net model with the selected parameters.

Parameters:

Name Type Description Default
device str

The device that the the model should run on, by default "cuda" if cuda is available, otherwise "cpu".

 'cuda' if is_available() else 'cpu'

Returns:

Type Description
Module

The U-net model.
Source code in src/autoden/models/config.py 
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def get_model(self, device: str = "cuda" if is_cuda_available() else "cpu") -> Module:
    """Get a U-net model with the selected parameters.

    Parameters
    ----------
    device : str, optional
        The device that the the model should run on, by default "cuda" if cuda is available, otherwise "cpu".

    Returns
    -------
    Module
        The U-net model.
    """
    return UNet(
        n_channels_in=self.n_channels_in,
        n_channels_out=self.n_channels_out,
        n_features=self.n_features,
        n_levels=self.n_levels,
        n_channels_skip=self.n_channels_skip,
        bilinear=self.bilinear,
        pad_mode=self.pad_mode,
        device=device,
    )