change eval and shuffle dataset for training

src/federated-hierarchical_main.py

@@ -18,6 +18,7 @@ from update import LocalUpdate, test_inference
 from models import MLP, CNNMnist, CNNFashion_Mnist, CNNCifar
 from utils import get_dataset, average_weights, exp_details
 import math
+import random
 
 
 # BUILD MODEL
@@ -48,7 +49,7 @@ def build_model(args, train_dataset):
 # Defining the training function
 def fl_train(args, train_dataset, cluster_global_model, cluster, usergrp, epochs):
     
-    cluster_train_loss, cluster_train_accuracy = [], []
+    cluster_train_loss, cluster_train_acc = [], []
     cluster_val_acc_list, cluster_net_list = [], []
     cluster_cv_loss, cluster_cv_acc = [], []
     # print_every = 1
@@ -85,14 +86,16 @@ def fl_train(args, train_dataset, cluster_global_model, cluster, usergrp, epochs
         list_acc, list_loss = [], []
         cluster_global_model.eval()
         for c in range(len(cluster)):
+            # local_model = LocalUpdate(args=args, dataset=train_dataset,
+            #                           idxs=user_groups[c], logger=logger)
             local_model = LocalUpdate(args=args, dataset=train_dataset,
-                                      idxs=user_groups[c], logger=logger)
+                                      idxs=user_groups[idx], logger=logger)
             acc, loss = local_model.inference(model=global_model)
             list_acc.append(acc)
             list_loss.append(loss)
-        train_accuracy.append(sum(list_acc)/len(list_acc))
+        cluster_train_acc.append(sum(list_acc)/len(list_acc))
         # Add
-    print("Cluster accuracy: ", 100*train_accuracy[-1]) 
+    print("Cluster accuracy: ", 100*cluster_train_acc[-1]) 
 
     return cluster_global_model, cluster_global_weights, cluster_loss_avg
     
@@ -115,18 +118,31 @@ if __name__ == '__main__':
     device = 'cuda' if args.gpu else 'cpu'
 
     # load dataset and user groups
-    train_dataset, test_dataset, user_groups = get_dataset(args)
-
+    train_dataset, test_dataset, user_groupsold = get_dataset(args)
+
+    # ======= Shuffle dataset ======= 
+    keys =  list(user_groupsold.keys())
+    random.shuffle(keys)
+    user_groups = dict()
+    for key in keys:
+        user_groups.update({key:user_groupsold[key]})
+    # print(user_groups.keys()) 
+    keylist = list(user_groups.keys())
+    print("keylist: ", keylist)
     # ======= Splitting into clusters. FL groups ======= 
-    cluster_size = args.num_users / args.num_clusters
+    cluster_size = int(args.num_users / args.num_clusters)
     print("Each cluster size: ", cluster_size)
 
     # Cluster 1
-    A1 = np.arange(cluster_size, dtype=int)
+    # A1 = np.arange(cluster_size, dtaype=int)
+    A1 = keylist[:cluster_size]
+    print("A1: ", A1)
     user_groupsA = {k:user_groups[k] for k in A1 if k in user_groups}
     print("Size of cluster 1: ", len(user_groupsA))
     # Cluster 2
-    B1 = np.arange(cluster_size, cluster_size+cluster_size, dtype=int)
+    # B1 = np.arange(cluster_size, cluster_size+cluster_size, dtype=int)
+    B1 = keylist[cluster_size:2*cluster_size]
+    print("B1: ", B1)
     user_groupsB = {k:user_groups[k] for k in B1 if k in user_groups}
     print("Size of cluster 2: ", len(user_groupsB))
     # # Cluster 3
@@ -169,6 +185,7 @@ if __name__ == '__main__':
     cluster_modelB.train()
     # copy weights
     cluster_modelB_weights = cluster_modelB.state_dict()
+
     # # Cluster C
     # cluster_modelC = build_model(args, train_dataset)
     # cluster_modelC.to(device)
@@ -188,10 +205,10 @@ if __name__ == '__main__':
     cv_loss, cv_acc = [], []
     print_every = 1
     val_loss_pre, counter = 0, 0
-    testacc_check, epoch = 0, 0
+    testacc_check, epoch, idx = 0, 0, 0
 
-    # for epoch in tqdm(range(args.epochs)):
-    while testacc_check < args.test_acc:
+    for epoch in tqdm(range(args.epochs)):
+    # while testacc_check < args.test_acc:
         local_weights, local_losses, local_accuracies= [], [], []
         print(f'\n | Global Training Round : {epoch+1} |\n')
         
@@ -208,6 +225,7 @@ if __name__ == '__main__':
         local_weights.append(copy.deepcopy(B_weights))
         local_losses.append(copy.deepcopy(B_losses))
         cluster_modelB = B_model 
+
         # # Cluster C
         # C_weights, C_losses = fl_train(args, train_dataset, cluster_modelC, C1, user_groupsC, args.Cepochs)
         # local_weights.append(copy.deepcopy(C_weights))
@@ -231,9 +249,10 @@ if __name__ == '__main__':
         # Calculate avg training accuracy over all users at every epoch
         list_acc, list_loss = [], []
         global_model.eval()
+        # print("========== idx ========== ", idx)
         for c in range(args.num_users):
             local_model = LocalUpdate(args=args, dataset=train_dataset,
-                                      idxs=user_groups[c], logger=logger)
+                                      idxs=user_groups[idx], logger=logger)
             acc, loss = local_model.inference(model=global_model)
             list_acc.append(acc)
             list_loss.append(loss)

src/federated_main.py

@@ -116,11 +116,11 @@ if __name__ == '__main__':
         global_model.eval() # must set your model into evaluation mode when computing model output values if dropout or bach norm used for training.
 
         for c in range(args.num_users): # 0 to 99
-            local_model = LocalUpdate(args=args, dataset=train_dataset,
-                                      idxs=user_groups[idx], logger=logger)
+            # local_model = LocalUpdate(args=args, dataset=train_dataset,
+                                      # idxs=user_groups[idx], logger=logger)
             # Fix error idxs=user_groups[idx] to idxs=user_groups[c]                                      
             local_model = LocalUpdate(args=args, dataset=train_dataset,
-                                      idxs=user_groups[idx], logger=logger)
+                                      idxs=user_groups[c], logger=logger)
             acc, loss = local_model.inference(model=global_model)
             list_acc.append(acc)
             list_loss.append(loss)
@@ -145,7 +145,7 @@ if __name__ == '__main__':
 
     # Saving the objects train_loss and train_accuracy:
     file_name = '../save/objects/FL_{}_{}_{}_C[{}]_iid[{}]_E[{}]_B[{}].pkl'.\
-        format(args.dataset, args.model, args.epochs, args.frac, args.iid,
+        format(args.dataset, args.model, epoch, args.frac, args.iid,
                args.local_ep, args.local_bs)
 
     with open(file_name, 'wb') as f: