fix calculate the average accuracy and loss in fusion_bench/fusion_be…

…nch/taskpool/clip_vision/taskpool.py

fusion_bench/method/adamerging/task_wise_adamerging.py

@@ -5,6 +5,7 @@
 import lightning as L
 import numpy as np
 import torch
+import torch.nn as nn
 from omegaconf import DictConfig
 from torch import Tensor
 from torch.utils.data import DataLoader

fusion_bench/taskpool/clip_vision/taskpool.py

@@ -288,17 +288,18 @@ def evaluate(self, model: Union[CLIPVisionModel, CLIPVisionTransformer]):
             self.on_task_evaluation_end()
 
         # calculate the average accuracy and loss
-        report["average"] = {}
-        accuracies = [
-            value["accuracy"] for key, value in report.items() if "accuracy" in value
-        ]
-        if len(accuracies) > 0:
-            average_accuracy = sum(accuracies) / len(accuracies)
-            report["accuracy"] = average_accuracy
-        losses = [value["loss"] for key, value in report.items() if "loss" in value]
-        if len(losses) > 0:
-            average_loss = sum(losses) / len(losses)
-            report["loss"] = average_loss
+        if "average" not in report:
+            report["average"] = {}
+            accuracies = [
+                value["accuracy"] for key, value in report.items() if "accuracy" in value
+            ]
+            if len(accuracies) > 0:
+                average_accuracy = sum(accuracies) / len(accuracies)
+                report["average"]["accuracy"] = average_accuracy
+            losses = [value["loss"] for key, value in report.items() if "loss" in value]
+            if len(losses) > 0:
+                average_loss = sum(losses) / len(losses)
+                report["average"]["loss"] = average_loss
 
         log.info(f"Evaluation Result: {report}")
         if self.fabric.is_global_zero and len(self.fabric._loggers) > 0: