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moe_evaluation_results.json

@@ -1,801 +1,801 @@
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     }
 }

requirements.txt

@@ -1,8 +1,8 @@
-torch>=2.0.0
-torchaudio>=2.0.0
-torchvision>=0.15.0
-gradio>=3.50.0
-numpy>=1.20.0
-pillow>=9.0.0
-tensorboard>=2.12.0
+torch==2.6.0
+torchaudio==2.6.0
+torchvision==0.21.0
+gradio==5.9.1
+numpy==2.0.2
+pillow==10.4.0
+tensorboard==2.19.0
 pydantic==2.10.6

test_moe_model.py

@@ -37,7 +37,7 @@ TOP_MODELS = [
 
 # Define class for the MoE model
 class WatermelonMoEModel(torch.nn.Module):
-    def __init__(self, model_configs, model_dir="test_models", weights=None):
+    def __init__(self, model_configs, model_dir="models", weights=None):
         """
         Mixture of Experts model that combines multiple backbone models.
         
@@ -92,13 +92,16 @@ class WatermelonMoEModel(torch.nn.Module):
         with torch.no_grad():
             for i, model in enumerate(self.models):
                 output = model(mfcc, image)
+                print(f"DEBUG: Model {i} output: {output}")
                 outputs.append(output * self.weights[i])
         
         # Return weighted average
-        return torch.sum(torch.stack(outputs), dim=0)
+        final_output = torch.sum(torch.stack(outputs), dim=0)
+        print(f"DEBUG: Raw prediction: {final_output}")
+        return final_output
 
 
-def evaluate_moe_model(data_dir, model_dir="test_models", weights=None):
+def evaluate_moe_model(data_dir, model_dir="models", weights=None):
     """
     Evaluate the MoE model on the test set.
     """
@@ -153,10 +156,12 @@ def evaluate_moe_model(data_dir, model_dir="test_models", weights=None):
                     model_name = f"{config['image_backbone']}_{config['audio_backbone']}"
                     output = model(mfcc, image)
                     individual_predictions[model_name].extend(output.view(-1).cpu().numpy())
+                    print(f"DEBUG: Model {j} output: {output}")
                 
                 # Get MoE prediction
                 output = moe_model(mfcc, image)
                 moe_predictions.extend(output.view(-1).cpu().numpy())
+                print(f"DEBUG: MoE prediction: {output}")
                 
                 # Store true labels
                 label = label.view(-1, 1).float()
@@ -243,7 +248,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "--model_dir", 
         type=str, 
-        default="test_models", 
+        default="models", 
         help="Directory containing model checkpoints"
     )
     parser.add_argument(