Source code for monai.networks.blocks.denseblock

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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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from __future__ import annotations

from import Sequence

import torch
import torch.nn as nn

from monai.networks.blocks import Convolution, ResidualUnit
from monai.networks.layers.factories import Act, Norm

__ALL__ = ["DenseBlock", "ConvDenseBlock"]

[docs]class DenseBlock(nn.Sequential): """ A DenseBlock is a sequence of layers where each layer's outputs are concatenated with their inputs. This has the effect of accumulating outputs from previous layers as inputs to later ones and as the final output of the block. Args: layers: sequence of nn.Module objects to define the individual layers of the dense block """ def __init__(self, layers: Sequence[nn.Module]): super().__init__() for i, l in enumerate(layers): self.add_module(f"layers{i}", l)
[docs] def forward(self, x): for l in self.children(): result = l(x) x =[x, result], 1) return x
class ConvDenseBlock(DenseBlock): """ This dense block is defined as a sequence of `Convolution` or `ResidualUnit` blocks. The `_get_layer` method returns an object for each layer and can be overridden to change the composition of the block. Args: spatial_dims: number of spatial dimensions. in_channels: number of input channels. channels: output channels for each layer. dilations: dilation value for each layer. kernel_size: convolution kernel size. Defaults to 3. num_res_units: number of convolutions. Defaults to 2. adn_ordering: a string representing the ordering of activation, normalization, and dropout. Defaults to "NDA". act: activation type and arguments. Defaults to PReLU. norm: feature normalization type and arguments. Defaults to instance norm. dropout: dropout ratio. Defaults to no dropout. bias: whether to have a bias term. Defaults to True. """ def __init__( self, spatial_dims: int, in_channels: int, channels: Sequence[int], dilations: Sequence[int] | None = None, kernel_size: Sequence[int] | int = 3, num_res_units: int = 0, adn_ordering: str = "NDA", act: tuple | str | None = Act.PRELU, norm: tuple | str | None = Norm.INSTANCE, dropout: tuple | str | float | None = None, bias: bool = True, ): self.spatial_dims = spatial_dims self.kernel_size = kernel_size self.num_res_units = num_res_units self.adn_ordering = adn_ordering self.act = act self.norm = norm self.dropout = dropout self.bias = bias l_channels = in_channels dilations = dilations if dilations is not None else ([1] * len(channels)) layers = [] if len(channels) != len(dilations): raise ValueError("Length of `channels` and `dilations` must match") for c, d in zip(channels, dilations): layer = self._get_layer(l_channels, c, d) layers.append(layer) l_channels += c super().__init__(layers) def _get_layer(self, in_channels, out_channels, dilation): if self.num_res_units > 0: return ResidualUnit( spatial_dims=self.spatial_dims, in_channels=in_channels, out_channels=out_channels, strides=1, kernel_size=self.kernel_size, subunits=self.num_res_units, adn_ordering=self.adn_ordering, act=self.act, norm=self.norm, dropout=self.dropout, dilation=dilation, bias=self.bias, ) else: return Convolution( spatial_dims=self.spatial_dims, in_channels=in_channels, out_channels=out_channels, strides=1, kernel_size=self.kernel_size, act=self.act, norm=self.norm, dropout=self.dropout, dilation=dilation, bias=self.bias, )