add adv model

GanAttack.py

@@ -7,6 +7,7 @@ from data.dataset import get_dataset
 from utils import get_model
 from model.GanInverter.models.stylegan2.model import Generator
 from model.GanInverter.inference.two_stage_inference import TwoStageInference
+from model import GanAttack
 import time
 
 import hydra
@@ -33,3 +34,84 @@ def get_stylegan_inverter(cfg):
     inverter=TwoStageInference(opts=path)
 
     return inverter.inverse
+
+@hydra.main(version_base=None, config_path="./config", config_name="config")
+def main(cfg: DictConfig) -> None:
+    model=get_model(cfg)
+    train_dataloader,test_dataloader,train_dataset,test_dataset=get_dataset(cfg)
+
+    classifier=get_model(cfg)
+    g_ema, _=get_stylegan_generator(cfg)
+
+    inverter=get_stylegan_inverter(cfg)
+    model=GanAttack(g_ema,inverter,images_resize=cfg.optim.images_resize,prompt=cfg.prompt).to(device)
+
+    num_epochs = cfg.optim.num_epochs
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.SGD(model.parameters(), lr=cfg.classifier.lr, momentum=cfg.classifier.momentum)
+    start_time = time.time()
+
+    for epoch in range(num_epochs):
+        model.train()
+
+        running_loss = 0
+        running_corrects = 0
+
+        for i, (inputs, labels) in enumerate(train_dataloader):
+            inputs = inputs.to(device)
+            labels = labels.to(device)
+
+            optimizer.zero_grad()
+            outputs = model(inputs)
+            _, preds = torch.max(outputs, 1)
+
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+
+            running_loss += loss.item() * inputs.size(0)
+            running_corrects += torch.sum(preds == labels.data)
+
+        epoch_loss = running_loss / len(train_dataset)
+        epoch_acc = running_corrects / len(train_dataset) * 100.
+        print('[Train #{}] Loss: {:.4f} Acc: {:.4f}% Time: {:.4f}s'.format(epoch, epoch_loss, epoch_acc, time.time() - start_time))
+
+    
+        model.eval()
+
+        with torch.no_grad():
+            running_loss = 0.
+            running_corrects = 0
+
+            for inputs, labels in test_dataloader:
+                inputs = inputs.to(device)
+                labels = labels.to(device)
+
+                outputs = model(inputs)
+                _, preds = torch.max(outputs, 1)
+                loss = criterion(outputs, labels)
+
+                running_loss += loss.item() * inputs.size(0)
+                running_corrects += torch.sum(preds == labels.data)
+
+            epoch_loss = running_loss / len(test_dataset)
+            epoch_acc = running_corrects / len(test_dataset) * 100.
+            print('[Test #{}] Loss: {:.4f} Acc: {:.4f}% Time: {:.4f}s'.format(epoch, epoch_loss, epoch_acc, time.time() - start_time))
+
+    save_path = '{}/{}_{}.pth'.format(cfg.paths.classifier, cfg.classifier.model, cfg.dataset)
+    torch.save(model.state_dict(), save_path)
+
+
+
+
+
+
+
+
+
+
+
+
+
+if __name__ == "__main__":
+    main()

config/config.yaml

@@ -23,3 +23,4 @@ optim:
   batch_size: 8
   num_epochs: 200
   num_workers : 4
+  images_resize: 256

model.py

@@ -6,7 +6,15 @@ import os
 import clip
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+def get_prompt(cfg):
     model, preprocess = clip.load("ViT-B/32", device=device)
+    prompt=cfg.prompt
+    text=clip.tokenize(prompt).to(device)
+    with torch.no_grad():
+            prompt = model.encode_text(text)
+    return prompt
 # class GanAttack(nn.Module):
     
 class GanAttack(nn.Module):
@@ -17,9 +25,7 @@ class GanAttack(nn.Module):
         self.generator.eval()
         self.inverter=inverter
         self.images_resize=images_resize
-        text=clip.tokenize(prompt).to(device)
-        with torch.no_grad():
-            self.prompt = model.encode_text(text)
+        self.prompt=prompt
         text_len=self.prompt.shape[0]
         self.mlp=nn.Sequential(
             nn.Linear(text_len+512, 4096),

utils.py

@@ -46,3 +46,38 @@ def get_model(config):
     model = model.to('cuda')
     
     return model
+
+def unnormalize(image):
+    mean = torch.tensor([0.5, 0.5, 0.5]).view(-1, 3, 1, 1).float()
+    std = torch.tensor([0.5, 0.5, 0.5]).view(-1, 3, 1, 1).float()
+    
+    image = image.detach().cpu()
+    image *= std
+    image += mean
+    image[image < 0] = 0
+    image[image > 1] = 1
+
+    return image
+
+def normalize(image):
+    mean = torch.tensor([0.5, 0.5, 0.5]).view(-1, 3, 1, 1).float().cuda()
+    std = torch.tensor([0.5, 0.5, 0.5]).view(-1, 3, 1, 1).float().cuda()
+    
+    image = image.clone()
+    image -= mean
+    image /= std
+    
+    return image
+
+def set_requires_grad( nets, requires_grad=False):
+    """Set requies_grad=Fasle for all the networks to avoid unnecessary computations
+        Parameters:
+            nets (network list)   -- a list of networks
+            requires_grad (bool)  -- whether the networks require gradients or not
+    """
+    if not isinstance(nets, list):
+        nets = [nets]
+    for net in nets:
+        if net is not None:
+            for param in net.parameters():
+                param.requires_grad = requires_grad