[MNT] Update image example

examples/example_image/example_files/example_init.py

@@ -8,6 +8,7 @@ import torch
 
 class Model(BaseModel):
     def __init__(self):
+        super().__init__(input_shape=(3, 32, 32), output_shape=(10,))
         dir_path = os.path.dirname(os.path.abspath(__file__))
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         self.model = ConvModel(channel=3, n_random_features=10).to(self.device)

examples/example_image/main.py

@@ -167,13 +167,12 @@ def test_search(gamma=0.1, load_market=True):
             acc_list.append(acc)
             logger.info("search rank: %d, score: %.3f, learnware_id: %s, acc: %.3f" % (idx, score, learnware.id, acc))
         # test reuse
-        """
+
         reuse_baseline = JobSelectorReuser(learnware_list=mixture_learnware_list)
         reuse_predict = reuse_baseline.predict(user_data=user_data)
         reuse_score = eval_prediction(reuse_predict, user_label)
         job_selector_score_list.append(reuse_score)
         print(f"mixture reuse loss: {reuse_score}\n")
-        """
 
         reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode="vote")
         ensemble_predict_y = reuse_ensemble.predict(user_data=user_data)
@@ -188,10 +187,10 @@ def test_search(gamma=0.1, load_market=True):
         % (np.mean(select_list), np.std(select_list), np.mean(avg_list), np.std(avg_list))
     )
     logger.info("Average performance improvement: %.3f" % (np.mean(improve_list)))
-    # logger.info(
-    #     "Average Job Selector Reuse Performance: %.3f +/- %.3f"
-    #     % (np.mean(job_selector_score_list), np.std(job_selector_score_list))
-    # )
+    logger.info(
+        "Average Job Selector Reuse Performance: %.3f +/- %.3f"
+        % (np.mean(job_selector_score_list), np.std(job_selector_score_list))
+    )
     logger.info(
         "Ensemble Reuse Performance: %.3f +/- %.3f" % (np.mean(ensemble_score_list), np.std(ensemble_score_list))
     )
@@ -199,5 +198,5 @@ def test_search(gamma=0.1, load_market=True):
 
 if __name__ == "__main__":
     # prepare_data()
-    # prepare_model()
-    test_search(load_market=True)
+    prepare_model()
+    test_search(load_market=False)

learnware/learnware/reuse.py

@@ -91,7 +91,7 @@ class JobSelectorReuser(BaseReuser):
                 task_spec = learnware_rkme_spec_list[i]
                 task_herding_num = max(5, int(self.herding_num * task_mixture_weight[i]))
                 task_val_num = task_herding_num // 5
-                
+
                 if self.use_herding:
                     herding_X_i = task_spec.herding(task_herding_num).detach().cpu().numpy()
                 else:

learnware/market/easy.py

@@ -476,7 +476,7 @@ class EasyMarket(BaseMarket):
         """
         learnware_num = len(learnware_list)
         if learnware_num == 0:
-            return [], []
+            return None, [], []
         if learnware_num < max_search_num:
             logger.warning("Available Learnware num less than search_num!")
             max_search_num = learnware_num