# 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 Optional, Sequence, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
from monai.networks.layers.convutils import calculate_out_shape, same_padding
from monai.networks.layers.factories import Act, Norm
from monai.networks.nets import AutoEncoder
[docs]class VarAutoEncoder(AutoEncoder):
"""Variational Autoencoder based on the paper - https://arxiv.org/abs/1312.6114"""
def __init__(
self,
dimensions: int,
in_shape: Sequence[int],
out_channels: int,
latent_size: 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:
self.in_channels, *self.in_shape = in_shape
self.latent_size = latent_size
self.final_size = np.asarray(self.in_shape, dtype=int)
super().__init__(
dimensions,
self.in_channels,
out_channels,
channels,
strides,
kernel_size,
up_kernel_size,
num_res_units,
inter_channels,
inter_dilations,
num_inter_units,
act,
norm,
dropout,
)
padding = same_padding(self.kernel_size)
for s in strides:
self.final_size = calculate_out_shape(self.final_size, self.kernel_size, s, padding) # type: ignore
linear_size = int(np.product(self.final_size)) * self.encoded_channels
self.mu = nn.Linear(linear_size, self.latent_size)
self.logvar = nn.Linear(linear_size, self.latent_size)
self.decodeL = nn.Linear(self.latent_size, linear_size)
def encode_forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
x = self.encode(x)
x = self.intermediate(x)
x = x.view(x.shape[0], -1)
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 = F.relu(self.decodeL(z))
x = x.view(x.shape[0], self.channels[-1], *self.final_size)
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