[FIX] fix a crucial bug in _calculate_intermediate_K_and_C

examples/dataset_pfs_workflow/main.py

@@ -76,8 +76,7 @@ class PFSDatasetWorkflow:
             semantic_spec = semantic_specs[0]
             semantic_spec["Name"]["Values"] = "learnware_%d" % (idx)
             semantic_spec["Description"]["Values"] = "test_learnware_number_%d" % (idx)
-            x = easy_market.add_learnware(zip_path, semantic_spec)
-            print(x)
+            easy_market.add_learnware(zip_path, semantic_spec)
 
         print("Total Item:", len(easy_market))
         # curr_inds = easy_market._get_ids()

learnware/market/easy/searcher.py

@@ -279,7 +279,6 @@ class EasyStatSearcher(BaseSearcher):
         weight, obj = rkme_solve_qp(K, C)
         weight = weight.double().to(user_rkme.device)
         score = user_rkme.inner_prod(user_rkme) + 2 * obj
-
         return weight.detach().cpu().numpy().reshape(-1), score
 
     def _calculate_intermediate_K_and_C(
@@ -311,7 +310,7 @@ class EasyStatSearcher(BaseSearcher):
         num = intermediate_K.shape[0] - 1
         RKME_list = [learnware.specification.get_stat_spec_by_name(self.stat_spec_type) for learnware in learnware_list]
         for i in range(intermediate_K.shape[0]):
-            intermediate_K[num, i] = RKME_list[-1].inner_prod(RKME_list[i])
+            intermediate_K[num, i] = intermediate_K[i, num] = RKME_list[-1].inner_prod(RKME_list[i])
         intermediate_C[num, 0] = user_rkme.inner_prod(RKME_list[-1])
         return intermediate_K, intermediate_C
 

learnware/specification/regular/table/rkme.py

@@ -578,6 +578,7 @@ def rkme_solve_qp(K: np.ndarray, C: np.ndarray):
         K = K.cpu().numpy()
     if torch.is_tensor(C):
         C = C.cpu().numpy()
+    C = C.reshape(-1)
 
     n = K.shape[0]
     P = np.array(K)

tests/test_learnware_client/test_check_learnware.py

@@ -32,3 +32,4 @@ class TestCheckLearnware(unittest.TestCase):
 
 if __name__ == "__main__":
     unittest.main()
+