model.py

import torch.nn as nn


class AutoEncoder(nn.Module):
    def __init__(self):
        super(AutoEncoder, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv3d(1, 32, 3, stride=2, padding=1),
            nn.ReLU(),  # or SELU()
            nn.Conv3d(32, 64, 3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv3d(64, 128, 3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv3d(128, 256, 3, stride=2, padding=1),
            nn.ReLU()
        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose3d(256, 128, 3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose3d(128, 64, 3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose3d(64, 32, 3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose3d(32, 1, 3, stride=2, padding=1, output_padding=1),
            nn.ReLU()
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x