Class DownSample

monai/networks/blocks/downsample.py:69–223

Downsamples data by `scale_factor`. Supported modes are: - "conv": uses a strided convolution for learnable downsampling. - "convgroup": uses a grouped strided convolution for efficient feature reduction. - "nontrainable": uses :py:class:`torch.nn.Upsample` with inverse scale

class DownSample(nn.Sequential):
    """
    Downsamples data by `scale_factor`.

    Supported modes are:

    - "conv": uses a strided convolution for learnable downsampling.
    - "convgroup": uses a grouped strided convolution for efficient feature reduction.
    - "nontrainable": uses :py:class:`torch.nn.Upsample` with inverse scale factor.
    - "pixelunshuffle": uses :py:class:`monai.networks.blocks.PixelUnshuffle` for channel-space rearrangement.

    This operation will cause non-deterministic behavior when ``mode`` is ``DownsampleMode.NONTRAINABLE``.
    Please check the link below for more details:
    https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms

    This module can optionally take a pre-convolution
    (often used to map the number of features from `in_channels` to `out_channels`).
    """

    def __init__(
        self,
        spatial_dims: int,
        in_channels: int | None = None,
        out_channels: int | None = None,
        scale_factor: Sequence[float] | float = 2,
        kernel_size: Sequence[float] | float | None = None,
        mode: DownsampleMode | str = DownsampleMode.CONV,
        pre_conv: nn.Module | str | None = "default",
        post_conv: nn.Module | None = None,
        bias: bool = True,
    ) -> None:
        """
        Downsamples data by `scale_factor`.
        Supported modes are:

            - DownsampleMode.CONV: uses a strided convolution for learnable downsampling.
            - DownsampleMode.CONVGROUP: uses a grouped strided convolution for efficient feature reduction.
            - DownsampleMode.MAXPOOL: uses maxpooling for non-learnable downsampling.
            - DownsampleMode.AVGPOOL: uses average pooling for non-learnable downsampling.
            - DownsampleMode.PIXELUNSHUFFLE: uses :py:class:`monai.networks.blocks.SubpixelDownsample`.

        This operation will cause non-deterministic behavior when ``mode`` is ``DownsampleMode.NONTRAINABLE``.
        Please check the link below for more details:
        https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms

        This module can optionally take a pre-convolution and post-convolution
        (often used to map the number of features from `in_channels` to `out_channels`).

        Args:
            spatial_dims: number of spatial dimensions of the input image.
            in_channels: number of channels of the input image.
            out_channels: number of channels of the output image. Defaults to `in_channels`.
            scale_factor: multiplier for spatial size reduction. Has to match input size if it is a tuple. Defaults to 2.
            kernel_size: kernel size used during convolutions. Defaults to `scale_factor`.
            mode: {``DownsampleMode.CONV``, ``DownsampleMode.CONVGROUP``, ``DownsampleMode.MAXPOOL``, ``DownsampleMode.AVGPOOL``,
                ``DownsampleMode.PIXELUNSHUFFLE``}. Defaults to ``DownsampleMode.CONV``.
            pre_conv: a conv block applied before downsampling. Defaults to "default".
                When ``conv_block`` is ``"default"``, one reserved conv layer will be utilized.