Class BasicUNet

monai/networks/nets/basic_unet.py:177–278

class BasicUNet(nn.Module):

    def __init__(
        self,
        spatial_dims: int = 3,
        in_channels: int = 1,
        out_channels: int = 2,
        features: Sequence[int] = (32, 32, 64, 128, 256, 32),
        act: str | tuple = ("LeakyReLU", {"negative_slope": 0.1, "inplace": True}),
        norm: str | tuple = ("instance", {"affine": True}),
        bias: bool = True,
        dropout: float | tuple = 0.0,
        upsample: str = "deconv",
    ):
        """
        A UNet implementation with 1D/2D/3D supports.

        Based on:

            Falk et al. "U-Net – Deep Learning for Cell Counting, Detection, and
            Morphometry". Nature Methods 16, 67–70 (2019), DOI:
            http://dx.doi.org/10.1038/s41592-018-0261-2

        Args:
            spatial_dims: number of spatial dimensions. Defaults to 3 for spatial 3D inputs.
            in_channels: number of input channels. Defaults to 1.
            out_channels: number of output channels. Defaults to 2.
            features: six integers as numbers of features.
                Defaults to ``(32, 32, 64, 128, 256, 32)``,

                - the first five values correspond to the five-level encoder feature sizes.
                - the last value corresponds to the feature size after the last upsampling.

            act: activation type and arguments. Defaults to LeakyReLU.
            norm: feature normalization type and arguments. Defaults to instance norm.
            bias: whether to have a bias term in convolution blocks. Defaults to True.
                According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_,
                if a conv layer is directly followed by a batch norm layer, bias should be False.
            dropout: dropout ratio. Defaults to no dropout.
            upsample: upsampling mode, available options are
                ``"deconv"``, ``"pixelshuffle"``, ``"nontrainable"``.

        Examples::

            # for spatial 2D
            >>> net = BasicUNet(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128))

            # for spatial 2D, with group norm
            >>> net = BasicUNet(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128), norm=("group", {"num_groups": 4}))

            # for spatial 3D
            >>> net = BasicUNet(spatial_dims=3, features=(32, 32, 64, 128, 256, 32))

        See Also

            - :py:class:`monai.networks.nets.DynUNet`
            - :py:class:`monai.networks.nets.UNet`