Update inference.py

inference.py

@@ -591,7 +591,7 @@ class GenerativeInferenceModel:
                             
                             # Create a new ResNet model with pretrained weights
                             resnet = models.resnet50(weights=ResNet50_Weights.IMAGENET1K_V1)
-                            model = nn.Sequential(self.normalizer, resnet)
+                            model = nn.Sequential(normalizer, resnet)
                             loading_report.append("✅ Successfully loaded PyTorch's pretrained ResNet50 model")
                         
                         # Show missing keys by layer type
@@ -642,7 +642,7 @@ class GenerativeInferenceModel:
                             
                             if norm_state_dict:
                                 try:
-                                    self.normalizer.load_state_dict(norm_state_dict, strict=False)
+                                    normalizer.load_state_dict(norm_state_dict, strict=False)
                                     print("Successfully loaded normalizer parameters")
                                 except Exception as e:
                                     print(f"Warning: Could not load normalizer parameters: {e}")
@@ -655,12 +655,12 @@ class GenerativeInferenceModel:
                 # Fallback to PyTorch's pretrained model
                 print("Falling back to PyTorch's pretrained model")
                 resnet = models.resnet50(weights=ResNet50_Weights.IMAGENET1K_V1)
-                model = nn.Sequential(self.normalizer, resnet)
+                model = nn.Sequential(normalizer, resnet)
         else:
             # Fallback to PyTorch's pretrained model
             print("No checkpoint available, using PyTorch's pretrained model")
             resnet = models.resnet50(weights=ResNet50_Weights.IMAGENET1K_V1)
-            model = nn.Sequential(self.normalizer, resnet)
+            model = nn.Sequential(normalizer, resnet)
         
         model = model.to(device)
         model.eval()  # Set to evaluation mode