🔧 Fix JIT compilation error in Mish activation function

breakthrough_unet_v6_simple.py

@@ -104,28 +104,17 @@ class EnhancedMultiScaleFusion(nn.Module):
         feat_1x1 = self.mish_1x1(self.conv_1x1(x))
         feat_3x3 = self.mish_3x3(self.conv_3x3(x))
 
-        # 🔬 RESEARCH: Conditional processing for CPU efficiency
-        if self.training or H > 256:  # Full processing for training or large images
-            feat_5x5 = self.mish_5x5(self.conv_5x5(x))
-            feat_dilated = self.mish_dilated(self.conv_dilated(x))
+        # 🔬 RESEARCH: Always use full processing for stability (conditional optimization removed)
+        feat_5x5 = self.mish_5x5(self.conv_5x5(x))
+        feat_dilated = self.mish_dilated(self.conv_dilated(x))
 
-            # Global context with optimized interpolation
-            global_feat = self.global_pool(x)
-            global_conv = self.mish_global(self.global_conv(global_feat))
-            global_upsampled = F.interpolate(global_conv, size=(H, W), mode='bilinear', align_corners=False)
-
-            # Full feature concatenation
-            all_features = torch.cat([feat_1x1, feat_3x3, feat_5x5, feat_dilated, global_upsampled], dim=1)
-        else:
-            # 🔬 RESEARCH: Simplified processing for CPU inference on smaller images
-            feat_dilated = self.mish_dilated(self.conv_dilated(x))
-
-            # Skip expensive 5x5 conv and global context for speed
-            # Pad to maintain expected channel count
-            feat_5x5_placeholder = torch.zeros_like(feat_1x1)
-            global_placeholder = torch.zeros_like(feat_1x1)
+        # Global context
+        global_feat = self.global_pool(x)
+        global_conv = self.mish_global(self.global_conv(global_feat))
+        global_upsampled = F.interpolate(global_conv, size=(H, W), mode='bilinear', align_corners=False)
 
-            all_features = torch.cat([feat_1x1, feat_3x3, feat_5x5_placeholder, feat_dilated, global_placeholder], dim=1)
+        # Concatenate all features
+        all_features = torch.cat([feat_1x1, feat_3x3, feat_5x5, feat_dilated, global_upsampled], dim=1)
 
         # Fuse features
         fused = self.fusion(all_features)