深度学习（34）—— StarGAN（2）

完整项目在这里：欢迎造访

使用数据集结构：

• data
  • train
    • domian 1
      • img 1
      • img 2
      • …
    • domain 2
      • img1
      • img2
      • …
    • domain n
  • val
    • domian 1
      • img 1
      • img 2
      • …
    • domain 2
      • img1
      • img2
      • …
    • domain n

1. build model

（1）generator

class Generator(nn.Module):def __init__(self, img_size=256, style_dim=64, max_conv_dim=512, w_hpf=1):super().__init__()dim_in = 2**14 // img_sizeself.img_size = img_sizeself.from_rgb = nn.Conv2d(3, dim_in, 3, 1, 1) #(in_channels,out_channels,kernel_size,stride,padding)self.encode = nn.ModuleList()self.decode = nn.ModuleList()self.to_rgb = nn.Sequential(nn.InstanceNorm2d(dim_in, affine=True),nn.LeakyReLU(0.2),nn.Conv2d(dim_in, 3, 1, 1, 0))# down/up-sampling blocksrepeat_num = int(np.log2(img_size)) - 4if w_hpf > 0:repeat_num += 1for _ in range(repeat_num):dim_out = min(dim_in*2, max_conv_dim)self.encode.append(ResBlk(dim_in, dim_out, normalize=True, downsample=True))self.decode.insert(0, AdainResBlk(dim_out, dim_in, style_dim,w_hpf=w_hpf, upsample=True))  # stack-likedim_in = dim_out# bottleneck blocksfor _ in range(2):self.encode.append(ResBlk(dim_out, dim_out, normalize=True))self.decode.insert(0, AdainResBlk(dim_out, dim_out, style_dim, w_hpf=w_hpf))if w_hpf > 0:device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')self.hpf = HighPass(w_hpf, device)def forward(self, x, s, masks=None):x = self.from_rgb(x)cache = {}for block in self.encode:if (masks is not None) and (x.size(2) in [32, 64, 128]):cache[x.size(2)] = xx = block(x)for block in self.decode:x = block(x, s)if (masks is not None) and (x.size(2) in [32, 64, 128]):mask = masks[0] if x.size(2) in [32] else masks[1]mask = F.interpolate(mask, size=x.size(2), mode='bilinear')x = x + self.hpf(mask * cache[x.size(2)])return self.to_rgb(x)

encoder 和decoder各6个ResBlk

（2）mapping network

class MappingNetwork(nn.Module):def __init__(self, latent_dim=16, style_dim=64, num_domains=2):super().__init__()layers = []layers += [nn.Linear(latent_dim, 512)]layers += [nn.ReLU()]for _ in range(3):layers += [nn.Linear(512, 512)]layers += [nn.ReLU()]self.shared = nn.Sequential(*layers)self.unshared = nn.ModuleList()for _ in range(num_domains):self.unshared += [nn.Sequential(nn.Linear(512, 512),nn.ReLU(),nn.Linear(512, 512),nn.ReLU(),nn.Linear(512, 512),nn.ReLU(),nn.Linear(512, style_dim))]def forward(self, z, y):h = self.shared(z)out = []for layer in self.unshared:out += [layer(h)]out = torch.stack(out, dim=1)  # (batch, num_domains, style_dim)idx = torch.LongTensor(range(y.size(0))).to(y.device)s = out[idx, y]  # (batch, style_dim)return s

unshared中有多个相同的分支，每个domain都有一个

（3）style encoder

class StyleEncoder(nn.Module):def __init__(self, img_size=256, style_dim=64, num_domains=2, max_conv_dim=512):super().__init__()dim_in = 2**14 // img_sizeblocks = []blocks += [nn.Conv2d(3, dim_in, 3, 1, 1)]repeat_num = int(np.log2(img_size)) - 2for _ in range(repeat_num):dim_out = min(dim_in*2, max_conv_dim)blocks += [ResBlk(dim_in, dim_out, downsample=True)]dim_in = dim_outblocks += [nn.LeakyReLU(0.2)]blocks += [nn.Conv2d(dim_out, dim_out, 4, 1, 0)]blocks += [nn.LeakyReLU(0.2)]self.shared = nn.Sequential(*blocks)self.unshared = nn.ModuleList()for _ in range(num_domains):self.unshared += [nn.Linear(dim_out, style_dim)]def forward(self, x, y):h = self.shared(x)h = h.view(h.size(0), -1)out = []for layer in self.unshared:out += [layer(h)]out = torch.stack(out, dim=1)  # (batch, num_domains, style_dim)idx = torch.LongTensor(range(y.size(0))).to(y.device)s = out[idx, y]  # (batch, style_dim)return s

unshared和上面的mapping network一样有两个domain所以有两个linear

（4）discriminator

class Discriminator(nn.Module):def __init__(self, img_size=256, num_domains=2, max_conv_dim=512):super().__init__()dim_in = 2**14 // img_sizeblocks = []blocks += [nn.Conv2d(3, dim_in, 3, 1, 1)]repeat_num = int(np.log2(img_size)) - 2for _ in range(repeat_num):dim_out = min(dim_in*2, max_conv_dim)blocks += [ResBlk(dim_in, dim_out, downsample=True)]dim_in = dim_outblocks += [nn.LeakyReLU(0.2)]blocks += [nn.Conv2d(dim_out, dim_out, 4, 1, 0)]blocks += [nn.LeakyReLU(0.2)]blocks += [nn.Conv2d(dim_out, num_domains, 1, 1, 0)]self.main = nn.Sequential(*blocks)def forward(self, x, y):out = self.main(x)out = out.view(out.size(0), -1)  # (batch, num_domains)idx = torch.LongTensor(range(y.size(0))).to(y.device)out = out[idx, y]  # (batch)return out

和style_encoder只有后面一点点不同
build完model之后就有权重加载权重，没有略过。下面打印了每个subnet的模型参数量

2. 加载数据dataloader

def get_train_loader(root, which='source', img_size=256,batch_size=8, prob=0.5, num_workers=4):print('Preparing DataLoader to fetch %s images ''during the training phase...' % which)crop = transforms.RandomResizedCrop(img_size, scale=[0.8, 1.0], ratio=[0.9, 1.1])rand_crop = transforms.Lambda(lambda x: crop(x) if random.random() < prob else x)transform = transforms.Compose([rand_crop,transforms.Resize([img_size, img_size]),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5, 0.5, 0.5]),])if which == 'source':dataset = ImageFolder(root, transform)elif which == 'reference':dataset = ReferenceDataset(root, transform)else:raise NotImplementedErrorsampler = _make_balanced_sampler(dataset.targets)return data.DataLoader(dataset=dataset,batch_size=batch_size,sampler=sampler,num_workers=num_workers,pin_memory=True,drop_last=True)

如果图片是train直接用ImageFold，如果是reference使用自定义的ReferenceDatabase

class ReferenceDataset(data.Dataset):def __init__(self, root, transform=None):self.samples, self.targets = self._make_dataset(root)self.transform = transformdef _make_dataset(self, root):domains = os.listdir(root)fnames, fnames2, labels = [], [], []for idx, domain in enumerate(sorted(domains)):class_dir = os.path.join(root, domain)cls_fnames = listdir(class_dir)fnames += cls_fnamesfnames2 += random.sample(cls_fnames, len(cls_fnames))labels += [idx] * len(cls_fnames)return list(zip(fnames, fnames2)), labelsdef __getitem__(self, index):fname, fname2 = self.samples[index]label = self.targets[index]img = Image.open(fname).convert('RGB')img2 = Image.open(fname2).convert('RGB')if self.transform is not None:img = self.transform(img)img2 = self.transform(img2)return img, img2, labeldef __len__(self):return len(self.targets)

reference 是在每个domain中选择两张图片，这两张图片有相同的label。fnames用于记录其中一张图片，fnames2记录另一张，label记录两者的标签

def get_test_loader(root, img_size=256, batch_size=32,shuffle=True, num_workers=4):print('Preparing DataLoader for the generation phase...')transform = transforms.Compose([transforms.Resize([img_size, img_size]),transforms.ToTensor(),transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5, 0.5, 0.5]),])dataset = ImageFolder(root, transform)return data.DataLoader(dataset=dataset,batch_size=batch_size,shuffle=shuffle,num_workers=num_workers,pin_memory=True)

3. train

    def train(self, loaders):args = self.argsnets = self.netsnets_ema = self.nets_emaoptims = self.optims# fetch random validation images for debuggingfetcher = InputFetcher(loaders.src, loaders.ref, args.latent_dim, 'train')fetcher_val = InputFetcher(loaders.val, None, args.latent_dim, 'val')inputs_val = next(fetcher_val)# resume training if necessaryif args.resume_iter > 0:self._load_checkpoint(args.resume_iter)# remember the initial value of ds weightinitial_lambda_ds = args.lambda_dsprint('Start training...')start_time = time.time()for i in range(args.resume_iter, args.total_iters):# fetch images and labelsinputs = next(fetcher)x_real, y_org = inputs.x_src, inputs.y_srcx_ref, x_ref2, y_trg = inputs.x_ref, inputs.x_ref2, inputs.y_refz_trg, z_trg2 = inputs.z_trg, inputs.z_trg2masks = nets.fan.get_heatmap(x_real) if args.w_hpf > 0 else None# train the discriminatord_loss, d_losses_latent = compute_d_loss(nets, args, x_real, y_org, y_trg, z_trg=z_trg, masks=masks)self._reset_grad()d_loss.backward()optims.discriminator.step()d_loss, d_losses_ref = compute_d_loss(nets, args, x_real, y_org, y_trg, x_ref=x_ref, masks=masks)self._reset_grad()d_loss.backward()optims.discriminator.step()# train the generatorg_loss, g_losses_latent = compute_g_loss(nets, args, x_real, y_org, y_trg, z_trgs=[z_trg, z_trg2], masks=masks)self._reset_grad()g_loss.backward()optims.generator.step()optims.mapping_network.step()optims.style_encoder.step()g_loss, g_losses_ref = compute_g_loss(nets, args, x_real, y_org, y_trg, x_refs=[x_ref, x_ref2], masks=masks)self._reset_grad()g_loss.backward()optims.generator.step()# compute moving average of network parametersmoving_average(nets.generator, nets_ema.generator, beta=0.999)moving_average(nets.mapping_network, nets_ema.mapping_network, beta=0.999)moving_average(nets.style_encoder, nets_ema.style_encoder, beta=0.999)# decay weight for diversity sensitive lossif args.lambda_ds > 0:args.lambda_ds -= (initial_lambda_ds / args.ds_iter)# print out log infoif (i+1) % args.print_every == 0:elapsed = time.time() - start_timeelapsed = str(datetime.timedelta(seconds=elapsed))[:-7]log = "Elapsed time [%s], Iteration [%i/%i], " % (elapsed, i+1, args.total_iters)all_losses = dict()for loss, prefix in zip([d_losses_latent, d_losses_ref, g_losses_latent, g_losses_ref],['D/latent_', 'D/ref_', 'G/latent_', 'G/ref_']):for key, value in loss.items():all_losses[prefix + key] = valueall_losses['G/lambda_ds'] = args.lambda_dslog += ' '.join(['%s: [%.4f]' % (key, value) for key, value in all_losses.items()])print(log)# generate images for debuggingif (i+1) % args.sample_every == 0:os.makedirs(args.sample_dir, exist_ok=True)utils.debug_image(nets_ema, args, inputs=inputs_val, step=i+1)# save model checkpointsif (i+1) % args.save_every == 0:self._save_checkpoint(step=i+1)# compute FID and LPIPS if necessaryif (i+1) % args.eval_every == 0:calculate_metrics(nets_ema, args, i+1, mode='latent')calculate_metrics(nets_ema, args, i+1, mode='reference')

4. 训练 discriminator

def compute_d_loss(nets, args, x_real, y_org, y_trg, z_trg=None, x_ref=None, masks=None):assert (z_trg is None) != (x_ref is None)   #X_real 为原图，y_org为原图的label。y_trg 为reference的label，z_trg 为reference随机生成的向量# with real imagesx_real.requires_grad_()out = nets.discriminator(x_real, y_org)loss_real = adv_loss(out, 1)loss_reg = r1_reg(out, x_real)# with fake imageswith torch.no_grad():if z_trg is not None:s_trg = nets.mapping_network(z_trg, y_trg)else:  # x_ref is not Nones_trg = nets.style_encoder(x_ref, y_trg)x_fake = nets.generator(x_real, s_trg, masks=masks)out = nets.discriminator(x_fake, y_trg)loss_fake = adv_loss(out, 0)loss = loss_real + loss_fake + args.lambda_reg * loss_regreturn loss, Munch(real=loss_real.item(),fake=loss_fake.item(),reg=loss_reg.item())

---

latent 得到style 向量

(1)real image loss

• 需要先将real image输入discriminator得到结果out（batch*domain_num）
• 然后根据real image的label取真正label的结果(batch)
• 使用out计算与label的BCEloss

def adv_loss(logits, target):assert target in [1, 0]targets = torch.full_like(logits, fill_value=target)loss = F.binary_cross_entropy_with_logits(logits, targets)return loss

• 使用out计算与real image的回归loss (regression loss)

def r1_reg(d_out, x_in):# zero-centered gradient penalty for real imagesbatch_size = x_in.size(0)grad_dout = torch.autograd.grad(outputs=d_out.sum(), inputs=x_in,create_graph=True, retain_graph=True, only_inputs=True)[0] # 输入是image，属于这一类的pgrad_dout2 = grad_dout.pow(2)assert(grad_dout2.size() == x_in.size())reg = 0.5 * grad_dout2.view(batch_size, -1).sum(1).mean(0)return reg

(2)fake image loss

• 首先需要根据上面生成的随机向量经过mapping network生成每个风格风格向量

with torch.no_grad():if z_trg is not None:s_trg = nets.mapping_network(z_trg, y_trg)else:  # x_ref is not Nones_trg = nets.style_encoder(x_ref, y_trg)

• mapping network 的输入是随机生成的latent 向量和label，因为mapping network是多分支的，所以有几个domain在network的结尾就有几个分支，之后根据label选择这个分支的结果作为最后的风格向量s_trg。
• 使用得到的风格向量s_trg和当前真实的图进入generator【希望real image转换为inference那样的风格】
• generator在decoder的过程中encoder得到的向量连同风格向量s_trg一起作为decoder的输入生成属于该风格的fake image
• 将fake image和其对应的label输入discriminator【为什么还要输入对应的label，又不是计算loss?—— 因为discriminator也是多分支的，要根据真实的label取出预测的这个分支的value】
• 因为是fake image，所以是和0做lossloss_fake = adv_loss(out, 0)

到这里我们已经计算了三个loss,分别是real image的loss, fake image 的loss 和real image得到的regeression loss,三者加权相加做为最后的discriminator的loss
loss = loss_real + loss_fake + args.lambda_reg * loss_reg

---

reference image 得到style 向量

latent向量：d_loss, d_losses_latent = compute_d_loss(nets, args, x_real, y_org, y_trg, z_trg=z_trg, masks=masks)
reference image：d_loss, d_losses_ref = compute_d_loss(nets, args, x_real, y_org, y_trg, x_ref=x_ref, masks=masks)

• 【有reference的时候相当于有图像了，不需要根据latent向量经过mapping network生成风格向量，而是使用reference image经过style encoder生成属于该style的风格向量】
• style encoder： reference image经过encoder生成一个向量，该向量再经过多分支得到style 向量，之后根据reference image的label得到最终的style 向量
• real image 根据reference image经过style encoder生成的style向量生成fake image
• 后面的过程和上面相同

5. 训练generator

def compute_g_loss(nets, args, x_real, y_org, y_trg, z_trgs=None, x_refs=None, masks=None):assert (z_trgs is None) != (x_refs is None)if z_trgs is not None:z_trg, z_trg2 = z_trgsif x_refs is not None:x_ref, x_ref2 = x_refs# adversarial lossif z_trgs is not None:s_trg = nets.mapping_network(z_trg, y_trg)else:s_trg = nets.style_encoder(x_ref, y_trg)x_fake = nets.generator(x_real, s_trg, masks=masks)out = nets.discriminator(x_fake, y_trg)loss_adv = adv_loss(out, 1)# style reconstruction losss_pred = nets.style_encoder(x_fake, y_trg)loss_sty = torch.mean(torch.abs(s_pred - s_trg))# diversity sensitive lossif z_trgs is not None:s_trg2 = nets.mapping_network(z_trg2, y_trg)else:s_trg2 = nets.style_encoder(x_ref2, y_trg)x_fake2 = nets.generator(x_real, s_trg2, masks=masks)x_fake2 = x_fake2.detach()loss_ds = torch.mean(torch.abs(x_fake - x_fake2))# cycle-consistency lossmasks = nets.fan.get_heatmap(x_fake) if args.w_hpf > 0 else Nones_org = nets.style_encoder(x_real, y_org)x_rec = nets.generator(x_fake, s_org, masks=masks)loss_cyc = torch.mean(torch.abs(x_rec - x_real))loss = loss_adv + args.lambda_sty * loss_sty \- args.lambda_ds * loss_ds + args.lambda_cyc * loss_cycreturn loss, Munch(adv=loss_adv.item(),sty=loss_sty.item(),ds=loss_ds.item(),cyc=loss_cyc.item())

---

latent 向量 生成style 向量

(1) adversarial loss

• 将real image和style向量输入generator生成fake image
• fake image 和 他的label经过discriminator辨别得到结果out
• 和上面一样计算BCEloss,但是这里虽然是生成的图，但是我们希望generator生成的fake image骗过discriminator，所以这里是和1做BCEloss:loss_adv = adv_loss(out, 1)

(2) style restruction loss

• fake image 是我们根据real image 得到的希望的style的图片。
• 现在将fake image输入style encoder 得到这个image的style向量
• 这个向量和前面的真实style之间做lossloss_sty = torch.mean(torch.abs(s_pred - s_trg))

(3) diversity sensitive loss

之前我们不是reference image都有两个嘛，现在排上用场了，前面我们处理的都是第一个reference，无论是latent 向量还是reference image

• 将第二个latent向量输入mapping network得到style 向量
• 将real image和这个style 向量输入generator生成第二个fake image
• 计算两个fake image之间的lossloss_ds = torch.mean(torch.abs(x_fake - x_fake2))

我们希望同一张图片被转化为另一个风格都是不一样的，不是每次都是一样的，所以这个loss 我们希望是越大越好的

(4)cycle-consistency loss

• 我们希望real image生成的指定style的fake image经过指定real style 可以返回real image’，所以这里设置了cyclegan-consistency loss
• 根据fake image生成mask
• 使用style encoder得到real image的style向量
• generator根据fake image和real image的style向量生成rec_image
• 计算real image 和 recovery image之间做lossloss_cyc = torch.mean(torch.abs(x_rec - x_real))

到这里generator的loss全部计算完，一共有四个，分别是对抗loss (loss_adv)，风格loss(loss_sty)，多样性loss(loss_ds)，循环loss(loss_cyc),最终generator的loss为：loss = loss_adv + args.lambda_sty * loss_sty - args.lambda_ds * loss_ds + args.lambda_cyc * loss_cyc

---

reference image 生成style 向量

latent 向量：g_loss, g_losses_latent = compute_g_loss(nets, args, x_real, y_org, y_trg, z_trgs=[z_trg, z_trg2], masks=masks)
reference image：g_loss, g_losses_ref = compute_g_loss(nets, args, x_real, y_org, y_trg, x_refs=[x_ref, x_ref2], masks=masks)

重点关注！！！！！

• 无论是discriminator 还是generator都有两个过程：

  1. 使用latent向量经过mapping network生成的style 向量作为最终要转化的style
  2. 使用reference image经过style encoder生成的style向量作为最终要转化的style

• 无论latent向量还是reference image都是有两个的

debug processing

• build_model

  • generator
  • mapping network
  • style_encoder
  • discriminator

• data

• train

okk，又是脑细胞死亡的一天，好饿好饿，886~
完整项目在这里：欢迎造访