Class SegResNetVAE

monai/networks/nets/segresnet.py:187–338

SegResNetVAE based on `3D MRI brain tumor segmentation using autoencoder regularization `_. The module contains the variational autoencoder (VAE). The model supports 2D or 3D inputs. Args: input_image_size: the size of images to inp

class SegResNetVAE(SegResNet):
    """
    SegResNetVAE based on `3D MRI brain tumor segmentation using autoencoder regularization
    <https://arxiv.org/pdf/1810.11654.pdf>`_.
    The module contains the variational autoencoder (VAE).
    The model supports 2D or 3D inputs.

    Args:
        input_image_size: the size of images to input into the network. It is used to
            determine the in_features of the fc layer in VAE.
        vae_estimate_std: whether to estimate the standard deviations in VAE. Defaults to ``False``.
        vae_default_std: if not to estimate the std, use the default value. Defaults to 0.3.
        vae_nz: number of latent variables in VAE. Defaults to 256.
            Where, 128 to represent mean, and 128 to represent std.
        spatial_dims: spatial dimension of the input data. Defaults to 3.
        init_filters: number of output channels for initial convolution layer. Defaults to 8.
        in_channels: number of input channels for the network. Defaults to 1.
        out_channels: number of output channels for the network. Defaults to 2.
        dropout_prob: probability of an element to be zero-ed. Defaults to ``None``.
        act: activation type and arguments. Defaults to ``RELU``.
        norm: feature normalization type and arguments. Defaults to ``GROUP``.
        use_conv_final: if add a final convolution block to output. Defaults to ``True``.
        blocks_down: number of down sample blocks in each layer. Defaults to ``[1,2,2,4]``.
        blocks_up: number of up sample blocks in each layer. Defaults to ``[1,1,1]``.
        upsample_mode: [``"deconv"``, ``"nontrainable"``, ``"pixelshuffle"``]
            The mode of upsampling manipulations.
            Using the ``nontrainable`` modes cannot guarantee the model&#x27;s reproducibility. Defaults to``nontrainable``.

            - ``deconv``, uses transposed convolution layers.
            - ``nontrainable``, uses non-trainable `linear` interpolation.
            - ``pixelshuffle``, uses :py:class:`monai.networks.blocks.SubpixelUpsample`.
    """

    def __init__(
        self,
        input_image_size: Sequence[int],
        vae_estimate_std: bool = False,
        vae_default_std: float = 0.3,
        vae_nz: int = 256,
        spatial_dims: int = 3,
        init_filters: int = 8,
        in_channels: int = 1,
        out_channels: int = 2,
        dropout_prob: float | None = None,
        act: str | tuple = ("RELU", {"inplace": True}),
        norm: tuple | str = ("GROUP", {"num_groups": 8}),
        use_conv_final: bool = True,
        blocks_down: tuple = (1, 2, 2, 4),
        blocks_up: tuple = (1, 1, 1),
        upsample_mode: UpsampleMode | str = UpsampleMode.NONTRAINABLE,
    ):
        super().__init__(
            spatial_dims=spatial_dims,
            init_filters=init_filters,
            in_channels=in_channels,
            out_channels=out_channels,
            dropout_prob=dropout_prob,
            act=act,