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GanAttack
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leewlving 2023-12-10 11:44:38 +08:00
parent 63def0a779
commit cc87c09e86
1 changed files with 8 additions and 6 deletions
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GanAttack.py
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@ -76,7 +76,7 @@ def main(cfg: DictConfig) -> None:
criterion = nn.CrossEntropyLoss() criterion = nn.CrossEntropyLoss()
max_loss=nn.MarginRankingLoss(0.1) max_loss=nn.MarginRankingLoss(0.1)
clip_loss=CLIPLoss().to(device) clip_loss=CLIPLoss().to(device)
# vgg_loss=VggLoss().to(device) # vgg_loss=VggLoss().to(device)
# summary(model, input_size = (3, 256, 256), batch_size = 5) # summary(model, input_size = (3, 256, 256), batch_size = 5)
# set_requires_grad(model.mlp.parameters()) # set_requires_grad(model.mlp.parameters())
for p in (model.mlp.parameters()): for p in (model.mlp.parameters()):
@ -97,12 +97,14 @@ def main(cfg: DictConfig) -> None:
# _, _, _, clean_refine_images, clean_latent_codes, _=inverter(inputs,img_path) # _, _, _, clean_refine_images, clean_latent_codes, _=inverter(inputs,img_path)
optimizer.zero_grad() optimizer.zero_grad()
generated_img,adv_latent_codes=model(inputs) generated_img,adv_latent_codes=model(inputs)
loss_vgg=vgg_loss(inputs,generated_img) # loss_vgg=vgg_loss(inputs,generated_img)
# loss_l1=F.l1_loss(codes,adv_latent_codes) loss_l1=F.l1_loss(codes,adv_latent_codes)
loss_clip=clip_loss(generated_img,prompt) loss_clip=clip_loss(generated_img,prompt)
outputs = classifier(generated_img) adv_outputs = classifier(generated_img)
preds=criterion(outputs,labels) clean_outputs=classifier(inputs)
loss_classifier=max_loss(torch.ones_like(preds),preds,-torch.ones_like(preds)) 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) # _, preds = torch.max(classifier(generated_img), 1)
# loss_classifier=max_loss(torch.ones_like(criterion(outputs, labels)),criterion(outputs, labels),criterion(outputs, labels)) # loss_classifier=max_loss(torch.ones_like(criterion(outputs, labels)),criterion(outputs, labels),criterion(outputs, labels))