model.py

import torch
from torch import nn

class SelfAttention(nn.Module):
    def __init__(self, in_channels):
        super(SelfAttention, self).__init__()
        self.query = nn.Conv2d(in_channels, in_channels//8, 1)
        self.key = nn.Conv2d(in_channels, in_channels//8, 1)
        self.value = nn.Conv2d(in_channels, in_channels, 1)
        self.gamma = nn.Parameter(torch.zeros(1))

    def forward(self, x):
        batch_size, C, H, W = x.size()
        
        q = self.query(x).view(batch_size, -1, H*W).permute(0, 2, 1)
        k = self.key(x).view(batch_size, -1, H*W)
        v = self.value(x).view(batch_size, -1, H*W)
        
        attention = torch.bmm(q, k)
        attention = torch.softmax(attention, dim=-1)
        
        out = torch.bmm(v, attention.permute(0, 2, 1))
        out = out.view(batch_size, C, H, W)
        
        return self.gamma * out + x

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

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

class aeModel(nn.Module):
    def __init__(self):
        super(aeModel, self).__init__()

        self.encoder = nn.ModuleList([
            nn.Sequential(
                nn.Conv2d(3, 32, 3, stride=2, padding=1),
                nn.BatchNorm2d(32),
                nn.ReLU(),
                ResidualBlock(32)
            ),
            nn.Sequential(
                nn.Conv2d(32, 64, 3, stride=2, padding=1),
                nn.BatchNorm2d(64),
                nn.ReLU(),
                ResidualBlock(64)
            ),
            nn.Sequential(
                nn.Conv2d(64, 128, 3, stride=2, padding=1),
                nn.BatchNorm2d(128),
                nn.ReLU(),
                ResidualBlock(128),
                SelfAttention(128)
            ),
            nn.Sequential(
                nn.Conv2d(128, 256, 3, stride=2, padding=1),
                nn.BatchNorm2d(256),
                nn.ReLU(),
                ResidualBlock(256),
                SelfAttention(256)
            )
        ])

        self.decoder = nn.ModuleList([
            nn.Sequential(
                nn.ConvTranspose2d(256, 128, 3, stride=2, padding=1, output_padding=1),
                nn.BatchNorm2d(128),
                nn.ReLU(),
                ResidualBlock(128),
                SelfAttention(128)
            ),
            nn.Sequential(
                nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1),
                nn.BatchNorm2d(64),
                nn.ReLU(),
                ResidualBlock(64)
            ),
            nn.Sequential(
                nn.ConvTranspose2d(64, 32, 3, stride=2, padding=1, output_padding=1),
                nn.BatchNorm2d(32),
                nn.ReLU(),
                ResidualBlock(32)
            ),
            nn.Sequential(
                nn.ConvTranspose2d(32, 3, 3, stride=2, padding=1, output_padding=1),
                nn.Sigmoid()
            )
        ])

    def forward(self, x):
        for encoder_block in self.encoder:
            x = encoder_block(x)
        
        for decoder_block in self.decoder:
            x = decoder_block(x)
        
        return x
    
    def encode(self, x):
        for encoder_block in self.encoder:
            x = encoder_block(x)
        return x
    
    def decode(self, x):
        for decoder_block in self.decoder:
            x = decoder_block(x)
        return x