Source code for monai.networks.nets.autoencoder

# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Any, Optional, Sequence, Tuple, Union

import torch
import torch.nn as nn

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


[docs]class AutoEncoder(nn.Module): def __init__( self, dimensions: int, in_channels: int, out_channels: int, channels: Sequence[int], strides: Sequence[int], kernel_size: Union[Sequence[int], int] = 3, up_kernel_size: Union[Sequence[int], int] = 3, num_res_units: int = 0, inter_channels: Optional[list] = None, inter_dilations: Optional[list] = None, num_inter_units: int = 2, act: Optional[Union[Tuple, str]] = Act.PRELU, norm: Union[Tuple, str] = Norm.INSTANCE, dropout: Optional[Union[Tuple, str, float]] = None, ) -> None: super().__init__() self.dimensions = dimensions self.in_channels = in_channels self.out_channels = out_channels self.channels = list(channels) self.strides = list(strides) self.kernel_size = kernel_size self.up_kernel_size = up_kernel_size self.num_res_units = num_res_units self.act = act self.norm = norm self.dropout = dropout self.num_inter_units = num_inter_units self.inter_channels = inter_channels if inter_channels is not None else [] self.inter_dilations = list(inter_dilations or [1] * len(self.inter_channels)) # The number of channels and strides should match if len(channels) != len(strides): raise ValueError("Autoencoder expects matching number of channels and strides") self.encoded_channels = in_channels decode_channel_list = list(channels[-2::-1]) + [out_channels] self.encode, self.encoded_channels = self._get_encode_module(self.encoded_channels, channels, strides) self.intermediate, self.encoded_channels = self._get_intermediate_module(self.encoded_channels, num_inter_units) self.decode, _ = self._get_decode_module(self.encoded_channels, decode_channel_list, strides[::-1] or [1]) def _get_encode_module( self, in_channels: int, channels: Sequence[int], strides: Sequence[int] ) -> Tuple[nn.Sequential, int]: encode = nn.Sequential() layer_channels = in_channels for i, (c, s) in enumerate(zip(channels, strides)): layer = self._get_encode_layer(layer_channels, c, s, False) encode.add_module("encode_%i" % i, layer) layer_channels = c return encode, layer_channels def _get_intermediate_module(self, in_channels: int, num_inter_units: int) -> Tuple[nn.Module, int]: # Define some types intermediate: nn.Module unit: nn.Module intermediate = nn.Identity() layer_channels = in_channels if self.inter_channels: intermediate = nn.Sequential() for i, (dc, di) in enumerate(zip(self.inter_channels, self.inter_dilations)): if self.num_inter_units > 0: unit = ResidualUnit( dimensions=self.dimensions, in_channels=layer_channels, out_channels=dc, strides=1, kernel_size=self.kernel_size, subunits=self.num_inter_units, act=self.act, norm=self.norm, dropout=self.dropout, dilation=di, ) else: unit = Convolution( dimensions=self.dimensions, in_channels=layer_channels, out_channels=dc, strides=1, kernel_size=self.kernel_size, act=self.act, norm=self.norm, dropout=self.dropout, dilation=di, ) intermediate.add_module("inter_%i" % i, unit) layer_channels = dc return intermediate, layer_channels def _get_decode_module( self, in_channels: int, channels: Sequence[int], strides: Sequence[int] ) -> Tuple[nn.Sequential, int]: decode = nn.Sequential() layer_channels = in_channels for i, (c, s) in enumerate(zip(channels, strides)): layer = self._get_decode_layer(layer_channels, c, s, i == (len(strides) - 1)) decode.add_module("decode_%i" % i, layer) layer_channels = c return decode, layer_channels def _get_encode_layer(self, in_channels: int, out_channels: int, strides: int, is_last: bool) -> nn.Module: if self.num_res_units > 0: return ResidualUnit( dimensions=self.dimensions, in_channels=in_channels, out_channels=out_channels, strides=strides, kernel_size=self.kernel_size, subunits=self.num_res_units, act=self.act, norm=self.norm, dropout=self.dropout, last_conv_only=is_last, ) return Convolution( dimensions=self.dimensions, in_channels=in_channels, out_channels=out_channels, strides=strides, kernel_size=self.kernel_size, act=self.act, norm=self.norm, dropout=self.dropout, conv_only=is_last, ) def _get_decode_layer(self, in_channels: int, out_channels: int, strides: int, is_last: bool) -> nn.Sequential: decode = nn.Sequential() conv = Convolution( dimensions=self.dimensions, in_channels=in_channels, out_channels=out_channels, strides=strides, kernel_size=self.up_kernel_size, act=self.act, norm=self.norm, dropout=self.dropout, conv_only=is_last and self.num_res_units == 0, is_transposed=True, ) decode.add_module("conv", conv) if self.num_res_units > 0: ru = ResidualUnit( dimensions=self.dimensions, in_channels=out_channels, out_channels=out_channels, strides=1, kernel_size=self.kernel_size, subunits=1, act=self.act, norm=self.norm, dropout=self.dropout, last_conv_only=is_last, ) decode.add_module("resunit", ru) return decode
[docs] def forward(self, x: torch.Tensor) -> Any: x = self.encode(x) x = self.intermediate(x) x = self.decode(x) return x