成了

FLAlgorithms/servers/serveravg.py

@@ -95,7 +95,7 @@ class FedAvg(Server):
             for item in topKdict:
                 topK.append(int(item))
 
-            self.selected_users = self.select_users(glob_iter, self.num_users)
+            self.selected_users = self.select_users_(glob_iter, self.num_users,topK)
 
             self.aggregate_parameters()
             # for param in self.model.parameters():

FLAlgorithms/users/useravg.py

@@ -59,6 +59,18 @@ class UserAVG(User):
         # print(loss)
         sys.stdout.flush()
         return LOSS
+    
+    def test_others(self):
+        self.mount.eval()
+        test_acc = 0
+        for x, y in self.testloaderfull:
+            x, y = x.to(self.device), y.to(self.device)
+            output = self.model(x)
+            test_acc += (torch.sum(torch.argmax(output, dim=1) == y)).item()
+            #@loss += self.loss(output, y)
+            #print(self.id + ", Test Accuracy:", test_acc / y.shape[0] )
+            #print(self.id + ", Test Loss:", loss)
+        return test_acc, y.shape[0]
 
     #训练完之后跑
     def anothertest(self, gradients,glob_iter):
@@ -70,9 +82,9 @@ class UserAVG(User):
         scores = []
         for gradient,id in gradients:
             # self.set_parameters(gradient)
-            model = self.model
-            self.model = copy.deepcopy(gradient)
-            acc, n = self.test()
+            # model = self.model
+            self.mount = copy.deepcopy(gradient)
+            acc, n = self.test_others()
             score = acc/n
             r=requests.get(url="http://10.134.153.83:8899/score",params={
                 "cid":self.id,
@@ -83,5 +95,5 @@ class UserAVG(User):
             # print(r)
             sys.stdout.flush()
             # scores.append(acc)
-        self.model = copy.deepcopy(model)
+        # self.model = copy.deepcopy(model)
         # return scores

FLAlgorithms/users/userbase.py

@@ -34,6 +34,8 @@ class User:
         self.local_model = copy.deepcopy(list(self.model.parameters()))
         self.persionalized_model = copy.deepcopy(list(self.model.parameters()))
         self.persionalized_model_bar = copy.deepcopy(list(self.model.parameters()))
+
+        self.mount = 0
     
     def set_parameters(self, model):
         for old_param, new_param, local_param in zip(self.model.parameters(), model.parameters(), self.local_model):