135 lines
4.3 KiB
Python
135 lines
4.3 KiB
Python
import torch
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import torchvision
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from torchvision import datasets, models, transforms
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import torch.nn as nn
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import torch.nn.functional as F
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import os
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import clip
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# from pixel2style2pixel.models.psp import pSp
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from argparse import Namespace
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from utils import normalize
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from stylegan.stylegan2_generator import StyleGAN2Generator
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import hydra
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from omegaconf import DictConfig, OmegaConf
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import sys
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import os
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sys.path.append('./pixel2style2pixel')
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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def get_prompt(cfg):
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model, preprocess = clip.load("RN50", device=device)
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prompt=cfg.prompt
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text=clip.tokenize(prompt).to(device)
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with torch.no_grad():
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prompt = model.encode_text(text)
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return prompt
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def get_stylegan_generator(cfg):
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# model, preprocess = clip.load("RN50", device=device)
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resolution=cfg.resolution
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generator=StyleGAN2Generator(resolution=resolution)
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checkpoint = torch.load(cfg.paths.stylegan, map_location=device)
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generator.load_state_dict(checkpoint['generator'])
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generator.to(device)
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generator.eval()
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return generator
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# class GanAttack(nn.Module):
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# def get_stylegan_inverter(cfg):
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# # ensure_checkpoint_exists(ckpt_path)
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# path=cfg.paths.inverter_cfg
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# ckpt = torch.load(path, map_location='cuda:0')
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# opts = ckpt['opts']
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# opts['checkpoint_path'] = path
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# if 'learn_in_w' not in opts:
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# opts['learn_in_w'] = False
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# if 'output_size' not in opts:
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# opts['output_size'] = 1024
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# net = pSp(Namespace(**opts))
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# net.eval()
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# net.cuda()
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# return net
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class GanAttack(nn.Module):
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def __init__(self, cfg,prompt):
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super().__init__()
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# self.net= get_stylegan_inverter(cfg)
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# self.generator.eval()
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# self.inverter=inverter
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# self.images_resize=images_resize
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self.generator=get_stylegan_generator(cfg)
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self.prompt=prompt
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text_len=self.prompt.shape[1]
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self.mlp=nn.Sequential(
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nn.Linear(text_len+512, 4096),
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nn.ReLU(inplace=True),
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nn.Linear(4096, 512)
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)
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def forward(self, img,basecode,detailcode):
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batch_size=img.shape[0]
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prompt=self.prompt.repeat(batch_size,18,1).to(device)
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# print(prompt.shape)
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x_prompt=torch.cat([basecode,prompt],dim=2)
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x_prompt=self.mlp(x_prompt)
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x=x_prompt+x
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result_images=self.generator.synthesis(detailcode,randomize_noise=False,basecode=x)['image']
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return result_images,x
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class CLIPLoss(torch.nn.Module):
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def __init__(self):
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super(CLIPLoss, self).__init__()
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self.model, self.preprocess = clip.load("RN50", device="cuda")
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self.model.eval()
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self.face_pool = torch.nn.AdaptiveAvgPool2d((224, 224))
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# self.mean = torch.tensor([0.48145466, 0.4578275, 0.40821073], device="cuda").view(1,3,1,1)
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# self.std = torch.tensor([0.26862954, 0.26130258, 0.27577711], device="cuda").view(1,3,1,1)
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def forward(self, image, text):
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image=normalize(image)
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image = self.face_pool(image)
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similarity = 1 - self.model(image, text)[0]/ 100
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return similarity
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class VggLoss(torch.nn.Module):
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def __init__(self):
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super(VggLoss, self).__init__()
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self.model=models.vgg11(pretrained=True)
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self.model.features=nn.Sequential()
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# self.mean = torch.tensor([0.48145466, 0.4578275, 0.40821073], device="cuda").view(1,3,1,1)
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# self.std = torch.tensor([0.26862954, 0.26130258, 0.27577711], device="cuda").view(1,3,1,1)
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def forward(self, image1, image2):
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# image=normalize(image)
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with torch.no_grad:
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feature1=self.model(image1)
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feature2=self.model(image2)
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feature1=torch.flatten(feature1)
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feature2=torch.flatten(feature2)
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similarity = F.cosine_similarity(feature1,feature2)
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return similarity
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@hydra.main(version_base=None, config_path="./config", config_name="config")
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def test(cfg):
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prompt=torch.randn([1,1024]).to(device)
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model=GanAttack(cfg,prompt).to(device)
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data=torch.randn([2,3,256,256]).to(device)
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result_images,x=model(data)
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print(result_images.shape)
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print(x.shape)
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if __name__ == "__main__":
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test()
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