268 lines
12 KiB
Python
268 lines
12 KiB
Python
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import argparse
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import math
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import os
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import pickle
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import torchvision
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from torch import optim
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from tqdm import tqdm
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import torch
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import clip
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class CLIPLoss(torch.nn.Module):
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def __init__(self, opts):
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super(CLIPLoss, self).__init__()
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self.model, self.preprocess = clip.load("ViT-B/32", device="cuda")
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self.upsample = torch.nn.Upsample(scale_factor=7)
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self.avg_pool = torch.nn.AvgPool2d(kernel_size=opts.stylegan_size // 32)
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def forward(self, image, text):
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image = self.avg_pool(self.upsample(image))
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similarity = 1 - self.model(image, text)[0] / 100
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return similarity
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from torch import nn
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import sys
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sys.path.append('/home/ly/StyleCLIP-main/models/facial_recognition')
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from model_irse import Backbone
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class IDLoss(nn.Module):
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def __init__(self, opts):
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super(IDLoss, self).__init__()
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print('Loading ResNet ArcFace')
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self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')
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self.facenet.load_state_dict(torch.load(opts.ir_se50_weights))
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self.pool = torch.nn.AdaptiveAvgPool2d((256, 256))
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self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
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self.facenet.eval()
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self.facenet.cuda()
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self.opts = opts
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def extract_feats(self, x):
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if x.shape[2] != 256:
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x = self.pool(x)
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x = x[:, :, 35:223, 32:220] # Crop interesting region
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x = self.face_pool(x)
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x_feats = self.facenet(x)
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return x_feats
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def forward(self, y_hat, y):
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n_samples = y.shape[0]
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y_feats = self.extract_feats(y) # Otherwise use the feature from there
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y_hat_feats = self.extract_feats(y_hat)
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y_feats = y_feats.detach()
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loss = 0
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sim_improvement = 0
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count = 0
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for i in range(n_samples):
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diff_target = y_hat_feats[i].dot(y_feats[i])
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loss += 1 - diff_target
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count += 1
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return loss / count, sim_improvement / count
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sys.path.append('/home/ly/StyleCLIP-main/mapper/training')
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from train_utils import STYLESPACE_DIMENSIONS
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from model_3 import Generator
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from model_3 import SynthesisNetwork
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from model_3 import SynthesisLayer
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sys.path.append('/home/ly/StyleCLIP-main')
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from utils import ensure_checkpoint_exists
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STYLESPACE_INDICES_WITHOUT_TORGB = [i for i in range(len(STYLESPACE_DIMENSIONS)) if i not in list(range(1, len(STYLESPACE_DIMENSIONS), 3))]
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def get_lr(t, initial_lr, rampdown=0.25, rampup=0.05):
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lr_ramp = min(1, (1 - t) / rampdown)
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lr_ramp = 0.5 - 0.5 * math.cos(lr_ramp * math.pi)
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lr_ramp = lr_ramp * min(1, t / rampup)
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return initial_lr * lr_ramp
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def main(args):
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ensure_checkpoint_exists(args.ckpt)
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# 把描述加载进clip预训练模型里面去
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text_inputs = torch.cat([clip.tokenize(args.description)]).cuda()
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# print('text_input是： ', text_inputs)
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#tokenizer clip分词的机制 依据规则
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#以及词汇表的总量
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'''
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--description "a person with purple hair"
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tensor([[49406, 320, 2533, 593, 5496, 2225, 49407, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0]], device='cuda:0',
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dtype=torch.int32)
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--description "a person with red hair"
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tensor([[49406, 320, 2533, 593, 736, 2225, 49407, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0]], device='cuda:0',
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dtype=torch.int32)
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'''
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os.makedirs(args.results_dir, exist_ok=True)
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#改成stylegan3的输入
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# with open('/home/ly/StyleCLIP-main/models/stylegan3/torch_utils/stylegan3-r-afhqv2-512x512.pkl', 'rb') as f:
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# G = pickle.load(f)['G_ema'].cuda() # torch.nn.Module
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# z = torch.randn([1, G.z_dim]).cuda() # latent codes
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# c = None # class labels (not used in this example)
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# img = G(z, c) # NCHW, float32, dynamic range [-1, +1], no truncation
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# g_ema = Generator(512, 0, 512,args.stylegan_size, 3) #512,0,512,1024,3
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# with open('/home/ly/StyleCLIP-main/models/stylegan3/torch_utils/stylegan3-r-afhqv2-512x512.pkl', 'rb') as f:
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#stylegan3-r-ffhqu-1024x1024.pkl 生成图片的效果欠佳 别用
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#stylegan3-t-ffhq-1024x1024.pkl 生成效果一般 loss值较好
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#stylegan3-r-ffhq-1024x1024.pkl 折中
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#stylegan3-t-ffhqu-1024x1024.pkl 生成图片可以 loss较差
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with open('/home/ly/StyleCLIP-main/pretrained_models/stylegan3-t-ffhq-1024x1024.pkl', 'rb') as f: #stylespace_dimensions [512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 256, 256, 256, 128, 128, 128, 64, 64, 64, 32, 32]
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# new_p = pickle.load(f)
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# print(new_p)
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# print("new_p")
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# print(new_p.keys())
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# G_ema.load_state_dict(pickle.load(f)['G_ema'].cuda(), strict=False) 这种方式模型加载不进来
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g_ema = pickle.load(f)['G_ema'].cuda() # torch.nn.Module 这种方式推演三百步的图片平均要4分钟
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z = torch.randn([1, g_ema.z_dim]).cuda() # latent codes
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c = None # class labels (not used in this example)
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#g_ema.load_state_dict(torch.load(args.ckpt)["g_ema"], strict=False)
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# 将模型对象设置为评估模式
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g_ema.eval()
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#更改cuda卡号
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g_ema = g_ema.cuda()
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# device = torch.cuda.current_device()
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# print('cuda:',device)
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mean_latent = torch.randn([1, g_ema.z_dim]).cuda()
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torch.save(mean_latent,'/home/ly/StyleCLIP-main/pretrained_models/latent_code/style3.pt')
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# print('mean_latent: ', mean_latent)
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if args.latent_path:
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latent_code_init = torch.load(args.latent_path).cuda()
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# elif args.mode == "edit":
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# latent_code_init_not_trunc = torch.randn(1, 512).cuda()
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# with torch.no_grad():
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# _, latent_code_init, _ = g_ema([latent_code_init_not_trunc], return_latents=True,
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# truncation=args.truncation, truncation_latent=mean_latent)
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else:
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# latent_code_init = mean_latent.detach().clone().repeat(1, 18, 1) #在维度1上重复18次
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latent_code_init = mean_latent.detach().clone()
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# def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs):
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with torch.no_grad():
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print("mean_latent ", mean_latent.shape)
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# img_orig, _ = g_ema([latent_code_init], c, input_is_latent=True, randomize_noise=False)
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img_orig = g_ema(latent_code_init, c)
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if args.work_in_stylespace:
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with torch.no_grad():
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_, _, latent_code_init = g_ema([latent_code_init], input_is_latent=True, return_latents=True)
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latent = [s.detach().clone() for s in latent_code_init]
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for c, s in enumerate(latent):
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if c in STYLESPACE_INDICES_WITHOUT_TORGB:
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s.requires_grad = True
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else:
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latent = latent_code_init.detach().clone()
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latent.requires_grad = True
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clip_loss = CLIPLoss(args)
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id_loss = IDLoss(args)
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if args.work_in_stylespace:
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optimizer = optim.Adam(latent, lr=args.lr)
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else:
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optimizer = optim.Adam([latent], lr=args.lr)
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pbar = tqdm(range(args.step))
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for i in pbar:
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t = i / args.step
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lr = get_lr(t, args.lr)
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optimizer.param_groups[0]["lr"] = lr
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img_gen = g_ema(latent,c)
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c_loss = clip_loss(img_gen, text_inputs)
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if args.id_lambda > 0:
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#身份损失
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i_loss = id_loss(img_gen, img_orig)[0]
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else:
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i_loss = 0
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if args.mode == "edit":
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if args.work_in_stylespace:
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l2_loss = sum([((latent_code_init[c] - latent[c]) ** 2).sum() for c in range(len(latent_code_init))])
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else:
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#与潜在空间的L2距离
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l2_loss = ((latent_code_init - latent) ** 2).sum()
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loss = c_loss + args.l2_lambda * l2_loss + args.id_lambda * i_loss
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else:
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loss = c_loss
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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pbar.set_description(
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(
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f"loss: {loss.item():.4f};"
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)
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)
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if args.save_intermediate_image_every > 0 and i % args.save_intermediate_image_every == 0:
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with torch.no_grad():
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img_gen = g_ema(latent, c)
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torchvision.utils.save_image(img_gen, f"results/stygan3Clip/{str(i).zfill(5)}.jpg", normalize=True, range=(-1, 1))
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if args.mode == "edit":
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final_result = torch.cat([img_orig, img_gen])
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else:
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final_result = img_gen
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return final_result
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--description", type=str, default="a person with purple hair", help="the text that guides the editing/generation")
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parser.add_argument("--ckpt", type=str, default="../pretrained_models/stylegan2-ffhq-config-f.pt", help="pretrained StyleGAN2 weights")
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parser.add_argument("--stylegan_size", type=int, default=1024, help="StyleGAN resolution")
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parser.add_argument("--lr_rampup", type=float, default=0.05)
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parser.add_argument("--lr", type=float, default=0.1)
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parser.add_argument("--step", type=int, default=300, help="number of optimization steps")
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parser.add_argument("--mode", type=str, default="edit", choices=["edit", "free_generation"], help="choose between edit an image an generate a free one")
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parser.add_argument("--l2_lambda", type=float, default=0.008, help="weight of the latent distance (used for editing only)")
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parser.add_argument("--id_lambda", type=float, default=0.000, help="weight of id loss (used for editing only)")
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parser.add_argument("--latent_path", type=str, default=None, help="starts the optimization from the given latent code if provided. Otherwose, starts from"
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"the mean latent in a free generation, and from a random one in editing. "
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"Expects a .pt format")
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parser.add_argument("--truncation", type=float, default=1, help="used only for the initial latent vector, and only when a latent code path is"
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"not provided")
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parser.add_argument('--work_in_stylespace', default=False, action='store_true')
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parser.add_argument("--save_intermediate_image_every", type=int, default=20, help="if > 0 then saves intermidate results during the optimization")
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parser.add_argument("--results_dir", type=str, default="results")
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parser.add_argument('--ir_se50_weights', default='../pretrained_models/model_ir_se50.pth', type=str,
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help="Path to facial recognition network used in ID loss")
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args = parser.parse_args()
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result_image = main(args)
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torchvision.utils.save_image(result_image.detach().cpu(), os.path.join(args.results_dir, "final_result.jpg"), normalize=True, scale_each=True, range=(-1, 1))
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