142 lines
4.4 KiB
Python
142 lines
4.4 KiB
Python
import sys
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import os
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torchvision import models
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from omegaconf import DictConfig, OmegaConf
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from data.dataset import get_dataset,get_adv_dataset
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from utils import get_model,set_requires_grad,unnormalize
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from model import UnTargetedGanAttack,CLIPLoss
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import lpips
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from prompt import get_prompt
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import torch.nn.functional as F
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import time
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import hydra
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# def get_stylegan_generator(cfg):
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# # ensure_checkpoint_exists(ckpt_path)
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# ckpt_path=cfg.paths.stylegan
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# g_ema = Generator(1024, 512, 8)
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# g_ema.load_state_dict(torch.load(ckpt_path)["g_ema"], strict=False)
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# g_ema.eval()
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# g_ema = g_ema.cuda()
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# mean_latent = g_ema.mean_latent(4096)
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# return g_ema, mean_latent
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def cw_loss(outputs, labels):
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one_hot_labels = torch.eye(outputs.shape[1]).to(device)[labels]
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# find the max logit other than the target class
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other = torch.max((1 - one_hot_labels) * outputs, dim=1)[0]
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# get the target class's logit
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real = torch.max(one_hot_labels * outputs, dim=1)[0]
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return torch.clamp((real - other), min=-0.)
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@hydra.main(version_base=None, config_path="./config", config_name="config")
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def main(cfg: DictConfig) -> None:
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model=get_model(cfg)
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train_dataloader,test_dataloader,train_dataset,test_dataset=get_adv_dataset(cfg)
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classifier=get_model(cfg)
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classifier.load_state_dict(torch.load('{}/{}_{}.pth'.format(cfg.paths.classifier, cfg.classifier.model, cfg.dataset)))
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classifier.eval()
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# g_ema, _=get_stylegan_generator(cfg)
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prompt=get_prompt(cfg)
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# net=get_stylegan_inverter(cfg)
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model=UnTargetedGanAttack(cfg,prompt).to(device)
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num_epochs = cfg.optim.num_epochs
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criterion = nn.CrossEntropyLoss()
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clip_loss=CLIPLoss().to(device)
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loss_fn_vgg = lpips.LPIPS(net='vgg').to(device)
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optimizer = optim.SGD(model.mlp.parameters()+model.noise_mlp.parameters(), lr=cfg.classifier.lr, momentum=cfg.classifier.momentum)
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start_time = time.time()
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for epoch in range(num_epochs):
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model.train()
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running_loss = 0
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running_corrects = 0
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for i, (inputs, labels,detail_code) in enumerate(train_dataloader):
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inputs = inputs.to(device)
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labels = labels.to(device)
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# base_code=base_code.to(device)
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detail_code=detail_code.to(device)
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# codes = model.net.encoder(inputs)
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generated_img,adv_latent_codes=model(inputs,detail_code)
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optimizer.zero_grad()
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loss_vgg=loss_fn_vgg(inputs,generated_img)
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loss_clip=clip_loss(generated_img,prompt)
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adv_outputs = classifier(generated_img)
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loss_classifier=cw_loss(adv_outputs,labels)
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loss=loss_vgg+cfg.optim.beta*loss_clip+cfg.optim.delta*loss_classifier
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loss.backward()
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optimizer.step()
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running_loss += loss.item() * inputs.size(0)
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running_corrects += torch.sum(preds == labels.data)
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epoch_loss = running_loss / len(train_dataset)
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epoch_acc = running_corrects / len(train_dataset) * 100.
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print('[Train #{}] Loss: {:.4f} Acc: {:.4f}% Time: {:.4f}s'.format(epoch, epoch_loss, epoch_acc, time.time() - start_time))
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model.eval()
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print('Evaluating!')
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with torch.no_grad():
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running_loss = 0. #test_dataloader
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running_corrects = 0
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for i, (inputs, labels,detail_code) in enumerate(test_dataloader):
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inputs = inputs.to(device)
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labels = labels.to(device)
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# base_code=base_code.to(device)
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detail_code=detail_code.to(device)
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generated_img,adv_latent_codes=model(inputs,detail_code)
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outputs = classifier(generated_img)
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preds=criterion(outputs ,labels)
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# running_loss += loss.item() * inputs.size(0)
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running_corrects += torch.sum(preds == labels.data)
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# epoch_loss = running_loss / len(test_dataset)
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epoch_acc = running_corrects / len(test_dataset) * 100.
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print('[Test ] Acc: {:.4f}% Time: {:.4f}s'.format( epoch_acc, time.time() - start_time))
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save_path = '{}/stylegan_{}_{}_{}.pth'.format(cfg.paths.pretrained_models, cfg.classifier.model, cfg.dataset,cfg.prompt)
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torch.save(model.state_dict(), save_path)
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if __name__ == "__main__":
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main() |