深度学习（34）—— StarGAN（2）

柚子味的羊2023-08-05 22:38

深度学习（34）------ StarGAN（2）

完整项目在这里：欢迎造访

文章目录

  • [深度学习（34）------ StarGAN（2）](#深度学习（34）—— StarGAN（2）)
    • [1. build model](#1. build model)
    • [2. 加载数据dataloader](#2. 加载数据dataloader)
    • [3. train](#3. train)
    • [4. 训练 discriminator](#4. 训练 discriminator)
      • [(1)real image loss](#(1)real image loss)
      • [(2)fake image loss](#(2)fake image loss)
    • [5. 训练generator](#5. 训练generator)
      • [(1) adversarial loss](#(1) adversarial loss)
      • [(2) style restruction loss](#(2) style restruction loss)
      • [(3) diversity sensitive loss](#(3) diversity sensitive loss)
      • [(4)cycle-consistency loss](#(4)cycle-consistency loss)
    • 重点关注`！！！！！`
    • [debug processing](#debug processing)

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

python 复制代码 
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_size
        self.img_size = img_size
        self.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 blocks
        repeat_num = int(np.log2(img_size)) - 4
        if w_hpf > 0:
            repeat_num += 1
        for _ 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-like
            dim_in = dim_out

        # bottleneck blocks
        for _ 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)] = x
            x = 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

python 复制代码 
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

python 复制代码 
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_size
        blocks = []
        blocks += [nn.Conv2d(3, dim_in, 3, 1, 1)]

        repeat_num = int(np.log2(img_size)) - 2
        for _ in range(repeat_num):
            dim_out = min(dim_in*2, max_conv_dim)
            blocks += [ResBlk(dim_in, dim_out, downsample=True)]
            dim_in = dim_out

        blocks += [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

python 复制代码 
class Discriminator(nn.Module):
    def __init__(self, img_size=256, num_domains=2, max_conv_dim=512):
        super().__init__()
        dim_in = 2**14 // img_size
        blocks = []
        blocks += [nn.Conv2d(3, dim_in, 3, 1, 1)]

        repeat_num = int(np.log2(img_size)) - 2
        for _ in range(repeat_num):
            dim_out = min(dim_in*2, max_conv_dim)
            blocks += [ResBlk(dim_in, dim_out, downsample=True)]
            dim_in = dim_out

        blocks += [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

python 复制代码 
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 NotImplementedError

    sampler = _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

python 复制代码 
class ReferenceDataset(data.Dataset):
    def __init__(self, root, transform=None):
        self.samples, self.targets = self._make_dataset(root)
        self.transform = transform

    def _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_fnames
            fnames2 += random.sample(cls_fnames, len(cls_fnames))
            labels += [idx] * len(cls_fnames)
        return list(zip(fnames, fnames2)), labels

    def __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, label

    def __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

python 复制代码 
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

python 复制代码 
    def train(self, loaders):
        args = self.args
        nets = self.nets
        nets_ema = self.nets_ema
        optims = self.optims

        # fetch random validation images for debugging
        fetcher = 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 necessary
        if args.resume_iter > 0:
            self._load_checkpoint(args.resume_iter)

        # remember the initial value of ds weight
        initial_lambda_ds = args.lambda_ds

        print('Start training...')
        start_time = time.time()
        for i in range(args.resume_iter, args.total_iters):
            # fetch images and labels
            inputs = next(fetcher)
            x_real, y_org = inputs.x_src, inputs.y_src
            x_ref, x_ref2, y_trg = inputs.x_ref, inputs.x_ref2, inputs.y_ref
            z_trg, z_trg2 = inputs.z_trg, inputs.z_trg2

            masks = nets.fan.get_heatmap(x_real) if args.w_hpf > 0 else None

            # train the discriminator
            d_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 generator
            g_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 parameters
            moving_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 loss
            if args.lambda_ds > 0:
                args.lambda_ds -= (initial_lambda_ds / args.ds_iter)

            # print out log info
            if (i+1) % args.print_every == 0:
                elapsed = time.time() - start_time
                elapsed = 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] = value
                all_losses['G/lambda_ds'] = args.lambda_ds
                log += ' '.join(['%s: [%.4f]' % (key, value) for key, value in all_losses.items()])
                print(log)

            # generate images for debugging
            if (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 checkpoints
            if (i+1) % args.save_every == 0:
                self._save_checkpoint(step=i+1)

            # compute FID and LPIPS if necessary
            if (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

python 复制代码 
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 images
    x_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 images
    with torch.no_grad():
        if z_trg is not None:
            s_trg = nets.mapping_network(z_trg, y_trg)
        else:  # x_ref is not None
            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_fake = adv_loss(out, 0)

    loss = loss_real + loss_fake + args.lambda_reg * loss_reg
    return 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
python 复制代码 
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)
python 复制代码 
def r1_reg(d_out, x_in):
    # zero-centered gradient penalty for real images
    batch_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p
    grad_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
python 复制代码 
with torch.no_grad():
    if z_trg is not None:
        s_trg = nets.mapping_network(z_trg, y_trg)
    else:  # x_ref is not None
        s_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

python 复制代码 
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_trgs
    if x_refs is not None:
        x_ref, x_ref2 = x_refs

    # adversarial loss
    if 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 loss
    s_pred = nets.style_encoder(x_fake, y_trg)
    loss_sty = torch.mean(torch.abs(s_pred - s_trg))

    # diversity sensitive loss
    if 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 loss
    masks = nets.fan.get_heatmap(x_fake) if args.w_hpf > 0 else None
    s_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_cyc
    return 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

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