discriminatorEmbedding.py

import torch
import torch.nn as nn

class Embedding(nn.Module):
    def __init__(self, size_in, size_out):
        super(Embedding, self).__init__()
        self.text_embedding = nn.Sequential(
            nn.Linear(size_in, size_out),
            nn.BatchNorm1d(1),
            nn.LeakyReLU(0.2, inplace=True)
        )

    def forward(self, x, text):
        embed_out = self.text_embedding(text)
        embed_out_resize = embed_out.repeat(4, 1, 4, 1).permute(1, 3, 0, 2)  # Resize to match the discriminator input size
        out = torch.cat([x, embed_out_resize], 1)  # Concatenate text embedding with the input feature map
        return out