src/models/encoder.py

import torch.nn as nn
from torchvision.models import vgg19, VGG19_Weights

class VGGEncoder(nn.Module):
    def __init__(self):
        super().__init__()
        vgg = vgg19(weights=VGG19_Weights.DEFAULT).features
        self.slice1 = nn.Sequential()
        self.slice2 = nn.Sequential()
        self.slice3 = nn.Sequential()
        self.slice4 = nn.Sequential()

        for x in range(2):
            self.slice1.add_module(str(x), vgg[x])
        for x in range(2, 7):
            self.slice2.add_module(str(x), vgg[x])
        for x in range(7, 12):
            self.slice3.add_module(str(x), vgg[x])
        for x in range(12, 21):
            self.slice4.add_module(str(x), vgg[x])
        
        for param in self.parameters():
            param.requires_grad = False

    def forward(self, x):
        h1 = self.slice1(x)
        h2 = self.slice2(h1)
        h3 = self.slice3(h2)
        h4 = self.slice4(h3)
        return h1, h2, h3, h4