🐛 | Fixed discriminator outputting nonsense.

discriminator.py

@@ -1,23 +1,25 @@
 import torch.nn as nn
+import torch
 
 class SISUDiscriminator(nn.Module):
     def __init__(self):
         super(SISUDiscriminator, self).__init__()
         self.model = nn.Sequential(
-            nn.Conv1d(2, 64, kernel_size=4, stride=2, padding=1),  # Now accepts 2 input channels
-            nn.LeakyReLU(0.2),
-            nn.Conv1d(64, 128, kernel_size=4, stride=2, padding=1),
-            nn.BatchNorm1d(128),
-            nn.LeakyReLU(0.2),
-            nn.Conv1d(128, 256, kernel_size=4, stride=2, padding=1),
-            nn.BatchNorm1d(256),
-            nn.LeakyReLU(0.2),
-            nn.Conv1d(256, 512, kernel_size=4, stride=2, padding=1),
-            nn.BatchNorm1d(512),
-            nn.LeakyReLU(0.2),
-            nn.Conv1d(512, 1, kernel_size=4, stride=1, padding=0),
-            nn.Sigmoid()
+            nn.Conv1d(2, 128, kernel_size=3, padding=1),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Conv1d(128, 256, kernel_size=3, padding=1),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Conv1d(256, 128, kernel_size=3, padding=1),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Conv1d(128, 64, kernel_size=3, padding=1),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Conv1d(64, 1, kernel_size=3, padding=1),
+            nn.LeakyReLU(0.2, inplace=True),
         )
+        self.global_avg_pool = nn.AdaptiveAvgPool1d(1)  # Output size (1,)
 
     def forward(self, x):
-        return self.model(x)
+        x = self.model(x)
+        x = self.global_avg_pool(x)
+        x = x.view(-1, 1)  # Flatten to (batch_size, 1)
+        return x