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autoencoder

CustomAE

Bases: AutoEncoder

A custom AutoEncoder class.

Inherits from AutoEncoder.

Attributes:

Name Type Description
padding int

The padding size for the convolutional layers.
decoder bool

Determines if the decoder part of the network is included.
kwargs bool

Additional keyword arguments passed to the parent class.

Methods:

Name Description
_get_encode_layer

Returns a single layer of the encoder part of the network.
_get_decode_layer

Returns a single layer of the decoder part of the network.
Source code in fmcib/models/autoencoder.py 
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class CustomAE(AutoEncoder):
    """
    A custom AutoEncoder class.

    Inherits from AutoEncoder.

    Attributes:
        padding (int): The padding size for the convolutional layers.
        decoder (bool, optional): Determines if the decoder part of the network is included.
        kwargs: Additional keyword arguments passed to the parent class.

    Methods:
        _get_encode_layer(in_channels, out_channels, strides, is_last): Returns a single layer of the encoder part of the network.
        _get_decode_layer(in_channels, out_channels, strides, is_last): Returns a single layer of the decoder part of the network.
    """

    def __init__(self, padding, decoder=True, **kwargs):
        """
        Initialize the object.

        Args:
            padding (int): Padding value.
            decoder (bool, optional): If True, use a decoder. Defaults to True.
            **kwargs: Additional keyword arguments.

        Attributes:
            padding (int): Padding value.

        Raises:
            None
        """
        self.padding = padding
        super().__init__(**kwargs)
        if not decoder:
            self.decode = nn.Sequential(nn.AvgPool3d(3), nn.Flatten())

    def _get_encode_layer(self, in_channels: int, out_channels: int, strides: int, is_last: bool) -> nn.Module:
        """
        Returns a single layer of the encoder part of the network.
        """
        mod: nn.Module
        if self.num_res_units > 0:
            mod = ResidualUnit(
                spatial_dims=self.dimensions,
                in_channels=in_channels,
                out_channels=out_channels,
                strides=strides,
                kernel_size=self.kernel_size,
                padding=self.padding,
                subunits=self.num_res_units,
                act=self.act,
                norm=self.norm,
                dropout=self.dropout,
                bias=self.bias,
                last_conv_only=is_last,
            )
            return mod
        mod = Convolution(
            spatial_dims=self.dimensions,
            in_channels=in_channels,
            out_channels=out_channels,
            strides=strides,
            kernel_size=self.kernel_size,
            padding=self.padding,
            act=self.act,
            norm=self.norm,
            dropout=self.dropout,
            bias=self.bias,
            conv_only=is_last,
        )
        return mod

    def _get_decode_layer(self, in_channels: int, out_channels: int, strides: int, is_last: bool) -> nn.Sequential:
        """
        Returns a single layer of the decoder part of the network.
        """
        decode = nn.Sequential()

        conv = Convolution(
            spatial_dims=self.dimensions,
            in_channels=in_channels,
            out_channels=out_channels,
            strides=strides,
            kernel_size=self.up_kernel_size,
            padding=self.padding,
            act=self.act,
            norm=self.norm,
            dropout=self.dropout,
            bias=self.bias,
            conv_only=is_last and self.num_res_units == 0,
            is_transposed=True,
        )

        decode.add_module("conv", conv)

        if self.num_res_units > 0:
            ru = ResidualUnit(
                spatial_dims=self.dimensions,
                in_channels=out_channels,
                out_channels=out_channels,
                padding=self.padding,
                strides=strides,
                kernel_size=self.kernel_size,
                subunits=1,
                act=self.act,
                norm=self.norm,
                dropout=self.dropout,
                bias=self.bias,
                last_conv_only=is_last,
            )

            decode.add_module("resunit", ru)

        return decode

__init__(padding, decoder=True, **kwargs)

Initialize the object.

Parameters:

Name Type Description Default
padding int

Padding value.

 required
decoder bool

If True, use a decoder. Defaults to True.

 True
**kwargs

Additional keyword arguments.

 {}

Attributes:

Name Type Description
padding int

Padding value.
Source code in fmcib/models/autoencoder.py 
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def __init__(self, padding, decoder=True, **kwargs):
    """
    Initialize the object.

    Args:
        padding (int): Padding value.
        decoder (bool, optional): If True, use a decoder. Defaults to True.
        **kwargs: Additional keyword arguments.

    Attributes:
        padding (int): Padding value.

    Raises:
        None
    """
    self.padding = padding
    super().__init__(**kwargs)
    if not decoder:
        self.decode = nn.Sequential(nn.AvgPool3d(3), nn.Flatten())

_get_decode_layer(in_channels, out_channels, strides, is_last)

Returns a single layer of the decoder part of the network.

Source code in fmcib/models/autoencoder.py 
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def _get_decode_layer(self, in_channels: int, out_channels: int, strides: int, is_last: bool) -> nn.Sequential:
    """
    Returns a single layer of the decoder part of the network.
    """
    decode = nn.Sequential()

    conv = Convolution(
        spatial_dims=self.dimensions,
        in_channels=in_channels,
        out_channels=out_channels,
        strides=strides,
        kernel_size=self.up_kernel_size,
        padding=self.padding,
        act=self.act,
        norm=self.norm,
        dropout=self.dropout,
        bias=self.bias,
        conv_only=is_last and self.num_res_units == 0,
        is_transposed=True,
    )

    decode.add_module("conv", conv)

    if self.num_res_units > 0:
        ru = ResidualUnit(
            spatial_dims=self.dimensions,
            in_channels=out_channels,
            out_channels=out_channels,
            padding=self.padding,
            strides=strides,
            kernel_size=self.kernel_size,
            subunits=1,
            act=self.act,
            norm=self.norm,
            dropout=self.dropout,
            bias=self.bias,
            last_conv_only=is_last,
        )

        decode.add_module("resunit", ru)

    return decode

_get_encode_layer(in_channels, out_channels, strides, is_last)

Returns a single layer of the encoder part of the network.

Source code in fmcib/models/autoencoder.py 
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def _get_encode_layer(self, in_channels: int, out_channels: int, strides: int, is_last: bool) -> nn.Module:
    """
    Returns a single layer of the encoder part of the network.
    """
    mod: nn.Module
    if self.num_res_units > 0:
        mod = ResidualUnit(
            spatial_dims=self.dimensions,
            in_channels=in_channels,
            out_channels=out_channels,
            strides=strides,
            kernel_size=self.kernel_size,
            padding=self.padding,
            subunits=self.num_res_units,
            act=self.act,
            norm=self.norm,
            dropout=self.dropout,
            bias=self.bias,
            last_conv_only=is_last,
        )
        return mod
    mod = Convolution(
        spatial_dims=self.dimensions,
        in_channels=in_channels,
        out_channels=out_channels,
        strides=strides,
        kernel_size=self.kernel_size,
        padding=self.padding,
        act=self.act,
        norm=self.norm,
        dropout=self.dropout,
        bias=self.bias,
        conv_only=is_last,
    )
    return mod