update other

GanAttack.py

@@ -105,26 +105,26 @@ def main(cfg: DictConfig) -> None:
         print('[Train #{}] Loss: {:.4f} Acc: {:.4f}% Time: {:.4f}s'.format(epoch, epoch_loss, epoch_acc, time.time() - start_time))
 
     
-        model.eval()
-        print('Evaluating!')
-        with torch.no_grad():
-            running_loss = 0. #test_dataloader
-            running_corrects = 0
+    model.eval()
+    print('Evaluating!')
+    with torch.no_grad():
+        running_loss = 0. #test_dataloader
+        running_corrects = 0
 
-            for i, (inputs, labels) in enumerate(test_dataloader):
-                inputs = inputs.to(device)
-                labels = labels.to(device)
+        for i, (inputs, labels) in enumerate(test_dataloader):
+            inputs = inputs.to(device)
+            labels = labels.to(device)
 
-                generated_img,adv_latent_codes=model(inputs)
-                outputs = classifier(generated_img)
-                preds=criterion(outputs ,labels)
+            generated_img,adv_latent_codes=model(inputs)
+            outputs = classifier(generated_img)
+            preds=criterion(outputs ,labels)
 
                 # running_loss += loss.item() * inputs.size(0)
-                running_corrects += torch.sum(preds == labels.data)
+            running_corrects += torch.sum(preds == labels.data)
 
             # epoch_loss = running_loss / len(test_dataset)
-            epoch_acc = running_corrects / len(test_dataset) * 100.
-            print('[Test #{}] Acc: {:.4f}% Time: {:.4f}s'.format(epoch,  epoch_acc, time.time() - start_time))
+        epoch_acc = running_corrects / len(test_dataset) * 100.
+        print('[Test ] Acc: {:.4f}% Time: {:.4f}s'.format(  epoch_acc, time.time() - start_time))
 
     save_path = '{}/stylegan_{}_{}_{}.pth'.format(cfg.paths.pretrained_models, cfg.classifier.model, cfg.dataset,cfg.prompt)
     torch.save(model.state_dict(), save_path)

classifier_training.py

@@ -16,6 +16,7 @@ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 @hydra.main(version_base=None, config_path="./config", config_name="config")
 def main(cfg: DictConfig) -> None:
     model=get_model(cfg)
+    model.train()
     train_dataloader,test_dataloader,train_dataset,test_dataset=get_dataset(cfg)
     num_epochs = cfg.classifier.num_epochs
     criterion = nn.CrossEntropyLoss()

config/config.yaml

@@ -3,14 +3,14 @@ classifier:
   model:  resnet18
   lr: 0.01
   momentum: 0.9
-  num_epochs: 20
+  num_epochs: 120
   num_workers : 4
-  batch_size: 64
+  batch_size: 128
 
 
 paths:
   gender_dataset: ./dataset/CelebA_HQ_face_gender_dataset
-  identity_dataset: /dataset/face_identity/CelebA_HQ_facial_identity_dataset
+  identity_dataset: dataset/CelebA_HQ_facial_identity_dataset
   inverter_cfg: /dataset/face_identity/psp_ffhq_encode.pt
   classifier: checkpoint
   stylegan: checkpoint/stylegan2-ffhq-config-f.pt