Update discriminatorModel.py

discriminatorModel.py

@@ -1,22 +1,7 @@
 
 import torch
 import torch.nn as nn
-
-# The Embedding model
-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
+from discriminatorEmbedding import Embedding
 
 # The Discriminator model
 class Discriminator(nn.Module):