add cw loss

targetedAttack.py

@@ -19,7 +19,26 @@ import hydra
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
 
+def generate_labels(labels,NUM_CLASSES=307):
 
+    targets = torch.zeros_like(labels)
+    for i in range(len(labels)):
+        rand_v = torch.randint(0, NUM_CLASSES-1)
+        while labels[i]==rand_v:
+            rand_v = torch.randint(0, NUM_CLASSES-1)
+        targets[i] = rand_v
+    # targets = targets.astype(np.int32)
+
+    return targets
+
+def cw_loss(outputs, labels):
+    one_hot_labels = torch.eye(outputs.shape[1]).to(device)[labels]
+
+        # find the max logit other than the target class
+    other = torch.max((1 - one_hot_labels) * outputs, dim=1)[0]
+        # get the target class's logit
+    real = torch.max(one_hot_labels * outputs, dim=1)[0]
+    return torch.clamp((other - real), min=-0.)
 
 def get_stylegan_generator(cfg):
     generator=Generator(1024, 512, 8)
@@ -53,13 +72,7 @@ def main(cfg: DictConfig) -> None:
     max_loss=nn.MarginRankingLoss(0.1)
     clip_loss=CLIPLoss().to(device)
     loss_fn_vgg = lpips.LPIPS(net='vgg').to(device)
-    # vgg_loss=VggLoss().to(device)
+    
-#     summary(model, input_size = (3, 256, 256), batch_size = 5)
-#     set_requires_grad(model.mlp.parameters())
-    # for p in (model.mlp.parameters()):
-    #     p.requires_grad =True
-    # for p in (model.noise_mlp.parameters()):
-    #     p.requires_grad =True
     model.train()
     optimizer = optim.SGD(model.parameters(), lr=cfg.classifier.lr, momentum=cfg.classifier.momentum)
     start_time = time.time()
@@ -73,22 +86,21 @@ def main(cfg: DictConfig) -> None:
         for i, (inputs, labels,detail_code) in enumerate(train_dataloader):
             inputs = inputs.to(device)
             labels = labels.to(device)
+            target=generate_labels(labels)
             # base_code=base_code.to(device)
             detail_code=detail_code.to(device)
             # codes = model.net.encoder(inputs)
-            generated_img,adv_latent_codes=model(inputs,detail_code)
+            generated_img,adv_latent_codes=model(inputs,detail_code,target)
-            # _, _, _, clean_refine_images, clean_latent_codes, _=inverter(inputs,img_path)
+
             optimizer.zero_grad()
-            # generated_img,adv_latent_codes=model(inputs)
-            # loss_vgg=vgg_loss(inputs,generated_img)
-            # loss_l1=F.l1_loss(base_code,adv_latent_codes)
             loss_vgg=loss_fn_vgg(inputs,generated_img)
             loss_clip=clip_loss(generated_img,prompt)
             adv_outputs = classifier(generated_img)
-            clean_outputs=classifier(inputs)
+            loss_classifier=cw_loss(adv_outputs,target)
-            adv_preds=criterion(adv_outputs,labels)
+            # clean_outputs=classifier(inputs)
-            clean_preds=criterion(clean_outputs,labels)
+            # adv_preds=criterion(adv_outputs,labels)
-            loss_classifier=max_loss(clean_preds,adv_preds,torch.ones_like(preds))
+            # clean_preds=criterion(clean_outputs,labels)
+            # loss_classifier=max_loss(clean_preds,adv_preds,torch.ones_like(preds))
             # _, preds = torch.max(classifier(generated_img), 1)
             # loss_classifier=max_loss(torch.ones_like(criterion(outputs, labels)),criterion(outputs, labels),criterion(outputs, labels))
             

untargetedAttack.py

@@ -31,6 +31,15 @@ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 #     mean_latent = g_ema.mean_latent(4096)
 
 #     return g_ema, mean_latent
+def cw_loss(outputs, labels):
+    one_hot_labels = torch.eye(outputs.shape[1]).to(device)[labels]
+
+        # find the max logit other than the target class
+    other = torch.max((1 - one_hot_labels) * outputs, dim=1)[0]
+        # get the target class's logit
+    real = torch.max(one_hot_labels * outputs, dim=1)[0]
+    return torch.clamp((real - other), min=-0.)
+
 
 
 
@@ -59,10 +68,7 @@ def main(cfg: DictConfig) -> None:
     # vgg_loss=VggLoss().to(device)
 #     summary(model, input_size = (3, 256, 256), batch_size = 5)
 #     set_requires_grad(model.mlp.parameters())
-    for p in (model.mlp.parameters()):
+    
-        p.requires_grad =True
-    for p in (model.noise_mlp.parameters()):
-        p.requires_grad =True
     optimizer = optim.SGD(model.mlp.parameters()+model.noise_mlp.parameters(), lr=cfg.classifier.lr, momentum=cfg.classifier.momentum)
     start_time = time.time()
 
@@ -79,23 +85,12 @@ def main(cfg: DictConfig) -> None:
             detail_code=detail_code.to(device)
             # codes = model.net.encoder(inputs)
             generated_img,adv_latent_codes=model(inputs,detail_code)
-            # _, _, _, clean_refine_images, clean_latent_codes, _=inverter(inputs,img_path)
             optimizer.zero_grad()
-            # generated_img,adv_latent_codes=model(inputs)
-            # loss_vgg=vgg_loss(inputs,generated_img)
-            # loss_l1=F.l1_loss(base_code,adv_latent_codes)
             loss_vgg=loss_fn_vgg(inputs,generated_img)
             loss_clip=clip_loss(generated_img,prompt)
             adv_outputs = classifier(generated_img)
-            clean_outputs=classifier(inputs)
+            loss_classifier=cw_loss(adv_outputs,labels)
-            adv_preds=criterion(adv_outputs,labels)
-            clean_preds=criterion(clean_outputs,labels)
-            loss_classifier=max_loss(clean_preds,adv_preds,torch.ones_like(preds))
-            # _, preds = torch.max(classifier(generated_img), 1)
-            # loss_classifier=max_loss(torch.ones_like(criterion(outputs, labels)),criterion(outputs, labels),criterion(outputs, labels))
             
-#             loss=loss_vgg+cfg.optim.alpha*loss_l1+cfg.optim.beta*loss_clip+cfg.optim.delta*loss_classifier
-#             loss=loss_vgg+cfg.optim.beta*loss_clip+cfg.optim.delta*loss_classifier
             loss=loss_vgg+cfg.optim.beta*loss_clip+cfg.optim.delta*loss_classifier
             loss.backward()
             optimizer.step()