Source code for monai.networks.nets.fullyconnectednet

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# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
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from __future__ import annotations

from collections.abc import Sequence

import torch
import torch.nn as nn

from monai.networks.blocks import ADN
from monai.networks.layers.factories import Act

__all__ = ["FullyConnectedNet", "VarFullyConnectedNet"]


def _get_adn_layer(act: tuple | str | None, dropout: tuple | str | float | None, ordering: str | None) -> ADN:
    if ordering:
        return ADN(act=act, dropout=dropout, dropout_dim=1, ordering=ordering)
    return ADN(act=act, dropout=dropout, dropout_dim=1)


[docs] class FullyConnectedNet(nn.Sequential): """ Simple full-connected layer neural network composed of a sequence of linear layers with PReLU activation and dropout. The network accepts input with `in_channels` channels, has output with `out_channels` channels, and hidden layer output channels given in `hidden_channels`. If `bias` is True then linear units have a bias term. Args: in_channels: number of input channels. out_channels: number of output channels. hidden_channels: number of output channels for each hidden layer. dropout: dropout ratio. Defaults to no dropout. act: activation type and arguments. Defaults to PReLU. bias: whether to have a bias term in linear units. Defaults to True. adn_ordering: order of operations in :py:class:`monai.networks.blocks.ADN`. Examples:: # accepts 4 values and infers 3 values as output, has 3 hidden layers with 10, 20, 10 values as output net = FullyConnectedNet(4, 3, [10, 20, 10], dropout=0.2) """
[docs] def __init__( self, in_channels: int, out_channels: int, hidden_channels: Sequence[int], dropout: tuple | str | float | None = None, act: tuple | str | None = Act.PRELU, bias: bool = True, adn_ordering: str | None = None, ) -> None: """ Defines a network accept input with `in_channels` channels, output of `out_channels` channels, and hidden layers with channels given in `hidden_channels`. If `bias` is True then linear units have a bias term. """ super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.hidden_channels = list(hidden_channels) self.act = act self.dropout = dropout self.adn_ordering = adn_ordering self.add_module("flatten", nn.Flatten()) prev_channels = self.in_channels for i, c in enumerate(hidden_channels): self.add_module("hidden_%i" % i, self._get_layer(prev_channels, c, bias)) prev_channels = c self.add_module("output", nn.Linear(prev_channels, out_channels, bias))
def _get_layer(self, in_channels: int, out_channels: int, bias: bool) -> nn.Sequential: seq = nn.Sequential( nn.Linear(in_channels, out_channels, bias), _get_adn_layer(self.act, self.dropout, self.adn_ordering) ) return seq
[docs] class VarFullyConnectedNet(nn.Module): """ Variational fully-connected network. This is composed of an encode layer, reparameterization layer, and then a decode layer. Args: in_channels: number of input channels. out_channels: number of output channels. latent_size: number of latent variables to use. encode_channels: number of output channels for each hidden layer of the encode half. decode_channels: number of output channels for each hidden layer of the decode half. dropout: dropout ratio. Defaults to no dropout. act: activation type and arguments. Defaults to PReLU. bias: whether to have a bias term in linear units. Defaults to True. adn_ordering: order of operations in :py:class:`monai.networks.blocks.ADN`. Examples:: # accepts inputs with 4 values, uses a latent space of 2 variables, and produces outputs of 3 values net = VarFullyConnectedNet(4, 3, 2, [5, 10], [10, 5]) """ def __init__( self, in_channels: int, out_channels: int, latent_size: int, encode_channels: Sequence[int], decode_channels: Sequence[int], dropout: tuple | str | float | None = None, act: tuple | str | None = Act.PRELU, bias: bool = True, adn_ordering: str | None = None, ) -> None: super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.latent_size = latent_size self.encode = nn.Sequential() self.decode = nn.Sequential() self.flatten = nn.Flatten() self.adn_layer = _get_adn_layer(act, dropout, adn_ordering) prev_channels = self.in_channels for i, c in enumerate(encode_channels): self.encode.add_module("encode_%i" % i, self._get_layer(prev_channels, c, bias)) prev_channels = c self.mu = nn.Linear(prev_channels, self.latent_size) self.logvar = nn.Linear(prev_channels, self.latent_size) self.decodeL = nn.Linear(self.latent_size, prev_channels) for i, c in enumerate(decode_channels): self.decode.add_module("decode%i" % i, self._get_layer(prev_channels, c, bias)) prev_channels = c self.decode.add_module("final", nn.Linear(prev_channels, out_channels, bias)) def _get_layer(self, in_channels: int, out_channels: int, bias: bool) -> nn.Sequential: seq = nn.Sequential(nn.Linear(in_channels, out_channels, bias)) seq.add_module("ADN", self.adn_layer) return seq def encode_forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: x = self.encode(x) x = self.flatten(x) mu = self.mu(x) logvar = self.logvar(x) return mu, logvar def decode_forward(self, z: torch.Tensor, use_sigmoid: bool = True) -> torch.Tensor: x: torch.Tensor x = self.decodeL(z) x = torch.relu(x) x = self.flatten(x) x = self.decode(x) if use_sigmoid: x = torch.sigmoid(x) return x def reparameterize(self, mu: torch.Tensor, logvar: torch.Tensor) -> torch.Tensor: std = torch.exp(0.5 * logvar) if self.training: # multiply random noise with std only during training std = torch.randn_like(std).mul(std) return std.add_(mu)
[docs] def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: mu, logvar = self.encode_forward(x) z = self.reparameterize(mu, logvar) return self.decode_forward(z), mu, logvar, z