Create discriminatorEmbedding.py

discriminatorEmbedding.py

@@ -0,0 +1,17 @@
+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