Class MBConvBlock

monai/networks/nets/efficientnet.py:75–227

class MBConvBlock(nn.Module):

    def __init__(
        self,
        spatial_dims: int,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int,
        image_size: list[int],
        expand_ratio: int,
        se_ratio: float | None,
        id_skip: bool | None = True,
        norm: str | tuple = ("batch", {"eps": 1e-3, "momentum": 0.01}),
        drop_connect_rate: float | None = 0.2,
    ) -> None:
        """
        Mobile Inverted Residual Bottleneck Block.

        Args:
            spatial_dims: number of spatial dimensions.
            in_channels: number of input channels.
            out_channels: number of output channels.
            kernel_size: size of the kernel for conv ops.
            stride: stride to use for conv ops.
            image_size: input image resolution.
            expand_ratio: expansion ratio for inverted bottleneck.
            se_ratio: squeeze-excitation ratio for se layers.
            id_skip: whether to use skip connection.
            norm: feature normalization type and arguments. Defaults to batch norm.
            drop_connect_rate: dropconnect rate for drop connection (individual weights) layers.

        References:
            [1] https://arxiv.org/abs/1704.04861 (MobileNet v1)
            [2] https://arxiv.org/abs/1801.04381 (MobileNet v2)
            [3] https://arxiv.org/abs/1905.02244 (MobileNet v3)
        """
        super().__init__()

        # select the type of N-Dimensional layers to use
        # these are based on spatial dims and selected from MONAI factories
        conv_type = Conv["conv", spatial_dims]
        adaptivepool_type = Pool["adaptiveavg", spatial_dims]

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.id_skip = id_skip
        self.stride = stride
        self.expand_ratio = expand_ratio
        self.drop_connect_rate = drop_connect_rate

        if (se_ratio is not None) and (0.0 < se_ratio <= 1.0):
            self.has_se = True
            self.se_ratio = se_ratio
        else:
            self.has_se = False

        # Expansion phase (Inverted Bottleneck)