150 lines
5.3 KiB
Python
150 lines
5.3 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 GanInverter.inference.two_stage_inference import TwoStageInference
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# from GanInverter.models.stylegan2.model import Generator
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# from models import GanAttack
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# from pixel2style2pixel.scripts.align_all_parallel import align_face
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# from pixel2style2pixel.models.stylegan2.model import Generator
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from model import GanAttack,CLIPLoss,VggLoss
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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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# from torchsummary import summary
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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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@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_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=GanAttack(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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max_loss=nn.MarginRankingLoss(0.1)
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clip_loss=CLIPLoss().to(device)
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# vgg_loss=VggLoss().to(device)
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# summary(model, input_size = (3, 256, 256), batch_size = 5)
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# set_requires_grad(model.mlp.parameters())
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for p in (model.mlp.parameters()):
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p.requires_grad =True
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optimizer = optim.SGD(model.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,base_code,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,base_code,detail_code)
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# _, _, _, clean_refine_images, clean_latent_codes, _=inverter(inputs,img_path)
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optimizer.zero_grad()
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# generated_img,adv_latent_codes=model(inputs)
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# loss_vgg=vgg_loss(inputs,generated_img)
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loss_l1=F.l1_loss(base_code,adv_latent_codes)
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loss_clip=clip_loss(generated_img,prompt)
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adv_outputs = classifier(generated_img)
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clean_outputs=classifier(inputs)
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adv_preds=criterion(adv_outputs,labels)
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clean_preds=criterion(clean_outputs,labels)
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loss_classifier=max_loss(clean_preds,adv_preds,torch.ones_like(preds))
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# _, preds = torch.max(classifier(generated_img), 1)
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# loss_classifier=max_loss(torch.ones_like(criterion(outputs, labels)),criterion(outputs, labels),criterion(outputs, labels))
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# loss=loss_vgg+cfg.optim.alpha*loss_l1+cfg.optim.beta*loss_clip+cfg.optim.delta*loss_classifier
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# loss=loss_vgg+cfg.optim.beta*loss_clip+cfg.optim.delta*loss_classifier
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loss=loss_l1+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,base_code,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,base_code,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() |