image_autoencoder/model.py at main · filippodz/image_autoencoder · GitHub

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import torch
import torch.nn as nn
import torch.nn.functional as F

# Residual Block
class ResidualBlock(nn.Module):
    def __init__(self, channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(channels)
        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(channels)

    def forward(self, x):
        residual = x
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += residual
        out = F.relu(out)
        return out

# Encoder
class Encoder(nn.Module):
    def __init__(self):
        super(Encoder, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1)
        self.res1 = ResidualBlock(32)
        self.res2 = ResidualBlock(32)
        self.res3 = ResidualBlock(32)
        self.conv2 = nn.Conv2d(32, 32, kernel_size=3, stride=2, padding=1)
        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
        self.conv4 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1)
        self.conv5 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1)
        self.conv6 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.res1(x)
        x = self.res2(x)
        x = self.res3(x)
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = F.relu(self.conv4(x))
        x = F.relu(self.conv5(x))
        x = F.relu(self.conv6(x))
        return x

# Decoder
class Decoder(nn.Module):
    def __init__(self):
        super(Decoder, self).__init__()
        self.conv1 = nn.ConvTranspose2d(64, 64, kernel_size=3, stride=2, padding=1, output_padding=1)
        self.conv2 = nn.ConvTranspose2d(64, 64, kernel_size=3, stride=2, padding=1, output_padding=1)
        self.conv3 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, output_padding=1)
        self.conv4 = nn.ConvTranspose2d(32, 32, kernel_size=3, stride=2, padding=1, output_padding=1)
        self.res1 = ResidualBlock(32)
        self.res2 = ResidualBlock(32)
        self.res3 = ResidualBlock(32)
        self.conv4 = nn.ConvTranspose2d(32, 32, kernel_size=3, stride=2, padding=1, output_padding=1)
        self.conv5 = nn.ConvTranspose2d(32, 3, kernel_size=3, stride=2, padding=1, output_padding=1)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = F.relu(self.conv4(x))
        x = self.res1(x)
        x = self.res2(x)
        x = self.res3(x)
        x = F.relu(self.conv4(x))
        x = torch.sigmoid(self.conv5(x))
        return x

# Autoencoder
class Autoencoder(nn.Module):
    def __init__(self):
        super(Autoencoder, self).__init__()
        self.encoder = Encoder()
        self.decoder = Decoder()

    def encode(self, x): return self.encoder(x)
    def decode(self, x): return self.decoder(x)

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