Update script.py

script.py

@@ -49,47 +49,39 @@ class PytorchWorker:
                                     #  T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
 
 
-    def predict_image(self, image: np.ndarray) -> list():
+    def predict_image(self, image: np.ndarray) -> list:
         """Run inference using ONNX runtime.
         :param image: Input image as numpy array.
         :return: A list with logits and confidences.
         """
-
-        # logits = self.model(self.transforms(image).unsqueeze(0).to(self.device))
-        
         self.model.eval()
-        
         outputs = self.model(self.transforms(image).unsqueeze(0).to(self.device))
-        
-        _, preds = torch.max(outputs, 1)
-        
-        preds = preds.cpu()  # Move tensor to CPU    
-
-        # post process
-        # max_value = torch.max(outputs)
-        # if max_value < -20:
-        #     preds[0]=1604
-        
-        print("preds: ", preds)
+        return outputs.cpu()  # Convert tensor to list
 
-        return preds.tolist()  # Convert tensor to list
 
-
-def make_submission(test_metadata, model_path, model_name, output_csv_path="./submission.csv", images_root_path="/tmp/data/private_testset"):
+def make_submission(test_metadata, model_path, model_path2, model_name, output_csv_path="./submission.csv", images_root_path="/tmp/data/private_testset"):
     """Make submission with given """
 
     model = PytorchWorker(model_path, model_name)
-
+    model2 = PytorchWorker(model_path2, model_name)
+    
     predictions = []
+    correct_max_values = []
+    incorrect_max_values = []
 
     for _, row in tqdm(test_metadata.iterrows(), total=len(test_metadata)):
         image_path = os.path.join(images_root_path, row.image_path)
-
         test_image = Image.open(image_path).convert("RGB")
-
-        logits = model.predict_image(test_image)
         
-        pred_class_id = logits[0] if logits[0] !=1604 else -1
+        outputs = model.predict_image(test_image)
+        outputs2 = model2.predict_image(test_image)
+        
+        # max_value = torch.max(outputs+outputs2)
+        _, preds = torch.max(outputs+outputs2, 1)
+        pred_class_id = preds.tolist()
+        # max_value2 = torch.max(outputs2)
+        
+        pred_class_id = pred_class_id[0] if pred_class_id[0] != 1604 else -1
         
         predictions.append(pred_class_id)
 
@@ -113,7 +105,8 @@ if __name__ == "__main__":
     # MODEL_PATH = './efficientnet_b3_epoch_21_trick1.2.5_a0.7237_l17.1662.pth'
     # MODEL_PATH = './efficientnet_b3_epoch_21_trcik1.5.2.pth'
     # MODEL_PATH = './efficientnet_b3_epoch_28_1.4.3.pth'
-    MODEL_PATH = './efficientnet_b3_epoch_28_trick1.4.3.2.pth'
+    MODEL_PATH = './efficientnet_b3_epoch_28_1.4.3.pth'
+    MODEL_PATH2 = './efficientnet_b3_epoch_23_trick1.4.1.pth'
     MODEL_NAME = 'tf_efficientnet_b3_ns' #"tf_efficientnet_b1.ap_in1k"
 
     metadata_file_path = "./FungiCLEF2024_TestMetadata.csv"
@@ -122,5 +115,6 @@ if __name__ == "__main__":
     make_submission(
         test_metadata=test_metadata,
         model_path=MODEL_PATH,
+        model_path2=MODEL_PATH2,
         model_name=MODEL_NAME
     )