Method __init__

monai/networks/nets/densenet.py:125–149

Args: spatial_dims: number of spatial dimensions of the input image. in_channels: number of the input channel. out_channels: number of the output classes. act: activation type and arguments. Defaults to relu. norm: feature normaliz

(
        self,
        spatial_dims: int,
        in_channels: int,
        out_channels: int,
        act: str | tuple = ("relu", {"inplace": True}),
        norm: str | tuple = "batch",
    )

class _Transition(nn.Sequential):

    def __init__(
        self,
        spatial_dims: int,
        in_channels: int,
        out_channels: int,
        act: str | tuple = ("relu", {"inplace": True}),
        norm: str | tuple = "batch",
    ) -> None:
        """
        Args:
            spatial_dims: number of spatial dimensions of the input image.
            in_channels: number of the input channel.
            out_channels: number of the output classes.
            act: activation type and arguments. Defaults to relu.
            norm: feature normalization type and arguments. Defaults to batch norm.
        """
        super().__init__()

        conv_type: Callable = Conv[Conv.CONV, spatial_dims]
        pool_type: Callable = Pool[Pool.AVG, spatial_dims]

        self.add_module("norm", get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=in_channels))
        self.add_module("relu", get_act_layer(name=act))
        self.add_module("conv", conv_type(in_channels, out_channels, kernel_size=1, bias=False))
        self.add_module("pool", pool_type(kernel_size=2, stride=2))


class DenseNet(nn.Module):