本文用于对DCLGAN网络进行代码对照讲解，仍是新手，如有错误，请指正

论文地址：https://arxiv.org/abs/2104.07689

代码地址：https://github.com/JunlinHan/DCLGAN

DCLGAN 介绍

        一种基于对比学习（contrastive learning）和双学习设置（dual learning setting）的新方法，用于无监督的图像到图像翻译任务。这种方法被称为DCLGAN（Dual Contrastive Learning for Unsupervised Image-to-Image Translation）

方法：对比学习；双GAN，

任务：图像翻译，

优势：非对称，对比学习来最大化输入和输出图像块之间的互信息，两个不同的编码器（encoders）来学习不同域的特征

G：X->Y 任务  ；F：Y->X 任务

:用于后续组合使用的编码半部分

：组合用于编码X；：组合用于编码Y

GAN损失：绿线 ；基于patch的多NCE损失 ：紫线 ；相似度损失：橙线

任务很简单：real A-> fake B; real B ->fake A 

损失有三个

1、GAN loss（绿）：loss(real A,fakeA) 常规GAN损失，调整生成器的

2、PatchNCE loss（紫）：loss(patch(realA),patch(fake B))，让红框和黄框越像越好，篮框越不像越好

3、sim loss （橙）：loss(sim(real A),sim(fake A)) ,sim 用来提取领域的特征，用来学习领域相似性的，这个是simDCL的改进

是不是蛮简单的，咱们看代码。

代码对照讲解

因为至少讲网络，训练部分大部分就带过去了，如果有不懂的可以留言

看代码的顺序是根据个人习惯来的，仍是菜鸟，请见谅

官方代码给出了一种比较完善的网络封装框架，与其直接去解读不如从train 入手

dataset

from data import create_dataset
dataset = create_dataset(opt)

 初始化给出了这个函数

def create_dataset(opt):
    data_loader = CustomDatasetDataLoader(opt)
    dataset = data_loader.load_data()
    return dataset

继续看下去,CustomDatasetDataLoader函数里面初始化给出了dataset 和dataloader： 

class CustomDatasetDataLoader():
    def __init__(self, opt):
        self.opt = opt
        dataset_class = find_dataset_using_name(opt.dataset_mode)
        self.dataset = dataset_class(opt)

        self.dataloader = torch.utils.data.DataLoader(
            self.dataset,
            batch_size=opt.batch_size,
            shuffle=not opt.serial_batches,
            num_workers=int(opt.num_threads),
            drop_last=True if opt.isTrain else False,
        )

find_dataset_using_name(opt.dataset_mode)是base里面根据数据集名字寻找dataset构建方法的一个中转站，导入“data/[dataset_name]_dataset.py”模块，比如默认的是“unaligned”（你可以在base_options ctil+f 查找参数名），就定位到 data/unaligned_dataset.py这个dataset 文件。

def find_dataset_using_name(dataset_name):
    dataset_filename = "data." + dataset_name + "_dataset"
    datasetlib = importlib.import_module(dataset_filename)

    dataset = None
    target_dataset_name = dataset_name.replace('_', '') + 'dataset'
    for name, cls in datasetlib.__dict__.items():
        if name.lower() == target_dataset_name.lower() \
           and issubclass(cls, BaseDataset):
            dataset = cls

    return dataset

unaligned_dataset的数据集类可以加载未对齐/未配对的数据集。

它需要两个目录分别存放域A和域B的训练图像，如果你要训练自己数据集，首先要做的，也是照着他的数据集给是进行修改。

class UnalignedDataset(BaseDataset):

首先，初始化给出了文件的路径：

    def __init__(self, opt):
        """Initialize this dataset class.

        Parameters:
            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
        """
        BaseDataset.__init__(self, opt)
        self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A')  # create a path '/path/to/data/trainA'
        self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B')  # create a path '/path/to/data/trainB'

        if opt.phase == "test" and not os.path.exists(self.dir_A) \
           and os.path.exists(os.path.join(opt.dataroot, "valA")):
            self.dir_A = os.path.join(opt.dataroot, "valA")
            self.dir_B = os.path.join(opt.dataroot, "valB")

        self.A_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size))   # load images from '/path/to/data/trainA'
        self.B_paths = sorted(make_dataset(self.dir_B, opt.max_dataset_size))    # load images from '/path/to/data/trainB'
        self.A_size = len(self.A_paths)  # get the size of dataset A
        self.B_size = len(self.B_paths)  # get the size of dataset B

接下来主要看如何取数据的，

    def __getitem__(self, index):

图片直接读取进来，保存为RGB格式，如果B类图片数量比A少，那就要注意下标

        A_path = self.A_paths[index % self.A_size]  # make sure index is within then range
        if self.opt.serial_batches:   # make sure index is within then range
            index_B = index % self.B_size
        else:   # randomize the index for domain B to avoid fixed pairs.
            index_B = random.randint(0, self.B_size - 1)
        B_path = self.B_paths[index_B]
        A_img = Image.open(A_path).convert('RGB')
        B_img = Image.open(B_path).convert('RGB')

应用图像变换，如果是在微调，不需要变换

        modified_opt = util.copyconf(self.opt, load_size=self.opt.crop_size if is_finetuning else self.opt.load_size)
        transform = get_transform(modified_opt)
        A = transform(A_img)
        B = transform(B_img)

主要有裁剪等操作，参数可以在option调整：

parser.add_argument('--crop_size', type=int, default=256, help='then crop to this size')

返回的是一个字典，里面包括 A领域图像，B领域图像，和各自路径

return {'A': A, 'B': B, 'A_paths': A_path, 'B_paths': B_path}

model

重头戏来了，数据集没啥特殊，自己建就行，看看model部分

from models import create_model
model = create_model(opt)

def create_model(opt):
    model = find_model_using_name(opt.model)
    instance = model(opt)
    print("model [%s] was created" % type(instance).__name__)
    return instance

def find_model_using_name(model_name):
    model_filename = "models." + model_name + "_model"
    modellib = importlib.import_module(model_filename)
    model = None
    target_model_name = model_name.replace('_', '') + 'model'
    for name, cls in modellib.__dict__.items():
        if name.lower() == target_model_name.lower() \
           and issubclass(cls, BaseModel):
            model = cls

    return model

跟dataset 同理，不赘述了，直接跳到 网络里,以simDCL举例，simdcl_model.py 举例

要直接看原网络，会发现有些看不懂，因为写法不一样，还是看回train，我只复制主要的网络部分啦。

for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1): 
        for i, data in enumerate(dataset):
            if epoch == opt.epoch_count and i == 0:
                model.data_dependent_initialize(data)
                model.setup(opt)               # regular setup: load and print networks; create schedulers
                model.parallelize()
            model.set_input(data)  # unpack data from dataset and apply preprocessing
            model.optimize_parameters()   # calculate loss functions, get gradients, update network weights

首先万物开始的data_dependent_initialize，见simdcl_model.py，特征网络netF是根据netG的编码器部分的中间提取特征的形状来定义的。因此，netF的权重在第一次前馈传递时初始化一些输入图像。

因为网络一个循环的结构，所以要构建一个圆，必须要有起点，这个函数里面便可以理解为是第一个forward，但是咱们先不看这个，后面的流程跟这个一样。

    def set_input(self, input):

        AtoB = self.opt.direction == 'AtoB'
        self.real_A = input['A' if AtoB else 'B'].to(self.device)
        self.real_B = input['B' if AtoB else 'A'].to(self.device)

set_input，加载数据到class 中，方向是A 2B ，简单易懂

接下来是训练部分，也就是优化环节 !!

    def optimize_parameters(self):
        # forward
        self.forward()

        # update D
        self.set_requires_grad([self.netD_A, self.netD_B], True)
        self.optimizer_D.zero_grad()
        self.backward_D_A()  # calculate gradients for D_A
        self.backward_D_B()  # calculate graidents for D_B
        self.optimizer_D.step()
        # update G
        self.set_requires_grad([self.netD_A, self.netD_B], False)
        self.optimizer_G.zero_grad()
        if self.opt.netF == 'mlp_sample':
            self.optimizer_F.zero_grad()
        self.loss_G = self.compute_G_loss()
        self.loss_G.backward()
        self.optimizer_G.step()
        if self.opt.netF == 'mlp_sample':
            self.optimizer_F.step()

一开始就是forward， 在model 内部进行计算，

    def forward(self):
        """Run forward pass; called by both functions <optimize_parameters> and <test>."""
        self.fake_B = self.netG_A(self.real_A)  # G_A(A)
        self.fake_A = self.netG_B(self.real_B)  # G_B(B)

        if self.opt.nce_idt:
            self.idt_A = self.netG_A(self.real_B)
            self.idt_B = self.netG_B(self.real_A)

可以看到，只用到了两个网络，其实只用到了一种生成器，看向netG

        self.netG_A = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.normG,
                                        not opt.no_dropout, opt.init_type, opt.init_gain, opt.no_antialias,
                                        opt.no_antialias_up, self.gpu_ids, opt)

前两个参数很简单，输入输出尺度

第三个第四个参数，代表着生成器使用的网络,归一化等参数，我找找

parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in the last conv layer')

parser.add_argument('--netG', type=str, default='resnet_9blocks', choices=['resnet_9blocks', 'resnet_6blocks', 'unet_256', 'unet_128', 'stylegan2', 'smallstylegan2', 'resnet_cat'], help='specify generator architecture')

parser.add_argument('--normG', type=str, default='instance', choices=['instance', 'batch', 'none'], help='instance normalization or batch normalization for G')

使用的网络是resnet，残差网络，看向define_G（）函数中

if netG == 'resnet_9blocks':
        net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, no_antialias=no_antialias, no_antialias_up=no_antialias_up, n_blocks=9, opt=opt)

千辛万苦，终于找到了使用的网络了

直接从 ResnetGenerator的forward 开始看

    def forward(self, input, layers=[], encode_only=False):
        if -1 in layers:
            layers.append(len(self.model))
        if len(layers) > 0:
            feat = input
            feats = []
            for layer_id, layer in enumerate(self.model):
                # print(layer_id, layer)
                feat = layer(feat)
                if layer_id in layers:
                    # print("%d: adding the output of %s %d" % (layer_id, layer.__class__.__name__, feat.size(1)))
                    feats.append(feat)
                else:
                    # print("%d: skipping %s %d" % (layer_id, layer.__class__.__name__, feat.size(1)))
                    pass
                if layer_id == layers[-1] and encode_only:
                    # print('encoder only return features')
                    return feats  # return intermediate features alone; stop in the last layers

            return feat, feats  # return both output and intermediate features
        else:
            """Standard forward"""
            fake = self.model(input)
            return fake

很经典的残差结构，关注一个bolck块就行，也就是看一个layer 是怎么运行的，看向

    def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect', no_antialias=False, no_antialias_up=False, opt=None):
        model = [nn.ReflectionPad2d(3),
                 nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
                 norm_layer(ngf),
                 nn.ReLU(True)]

可以看到，model中首先获得了一个卷积模块,

添加下采样层,两层

        n_downsampling = 2
        for i in range(n_downsampling):  # add downsampling layers
            mult = 2 ** i
            if(no_antialias):
                model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias),
                          norm_layer(ngf * mult * 2),
                          nn.ReLU(True)]
            else:
                model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=1, padding=1, bias=use_bias),
                          norm_layer(ngf * mult * 2),
                          nn.ReLU(True),
                          Downsample(ngf * mult * 2)]

添加残差层

        mult = 2 ** n_downsampling
        for i in range(n_blocks):       # add ResNet blocks

            model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]

然后上采用回来,顺便激活一下

        for i in range(n_downsampling):  # add upsampling layers
            mult = 2 ** (n_downsampling - i)
            if no_antialias_up:
                model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
                                             kernel_size=3, stride=2,
                                             padding=1, output_padding=1,
                                             bias=use_bias),
                          norm_layer(int(ngf * mult / 2)),
                          nn.ReLU(True)]
            else:
                model += [Upsample(ngf * mult),
                          nn.Conv2d(ngf * mult, int(ngf * mult / 2),
                                    kernel_size=3, stride=1,
                                    padding=1,  # output_padding=1,
                                    bias=use_bias),
                          norm_layer(int(ngf * mult / 2)),
                          nn.ReLU(True)]
        model += [nn.ReflectionPad2d(3)]
        model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
        model += [nn.Tanh()]

        self.model = nn.Sequential(*model)

一个残差网络就结束啦，simdcl forward完美结束

获得了 fake B ,和fake A

loss

接下俩就是损失了

def optimize_parameters 大家还记得吗，第一步是forward的哪个，下一步进行辨别器的优化，也就是传统的GAN损失

        # update D
        self.set_requires_grad([self.netD_A, self.netD_B], True)
        self.optimizer_D.zero_grad()
        self.backward_D_A()  # calculate gradients for D_A
        self.backward_D_B()  # calculate graidents for D_B
        self.optimizer_D.step()

    def backward_D_A(self):
        """Calculate GAN loss for discriminator D_A"""
        fake_B = self.fake_B_pool.query(self.fake_B)
        self.loss_D_A = self.backward_D_basic(self.netD_A, self.real_B, fake_B)

    def backward_D_B(self):
        """Calculate GAN loss for discriminator D_B"""
        fake_A = self.fake_A_pool.query(self.fake_A)
        self.loss_D_B = self.backward_D_basic(self.netD_B, self.real_A, fake_A)

那么损失是怎么计算的呢，看这个函数，跟上面流程一样，我就不一步步讲了

    def backward_D_basic(self, netD, real, fake):

        # Real
        pred_real = netD(real)
        loss_D_real = self.criterionGAN(pred_real, True)
        # Fake
        pred_fake = netD(fake.detach())
        loss_D_fake = self.criterionGAN(pred_fake, False)
        # Combined loss and calculate gradients
        loss_D = (loss_D_real + loss_D_fake) * 0.5
        loss_D.backward()
        return loss_D

self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device)

        parser.add_argument('--gan_mode', type=str, default='hinge', help='the type of GAN objective. [vanilla| lsgan | wgangp| hinge]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')

默认使用的hinge哦

        elif self.gan_mode == 'hinge':
            if target_is_real:
                minvalue = torch.min(prediction - 1, torch.zeros(prediction.shape).to(prediction.device))
                loss = -torch.mean(minvalue)
            else:
                minvalue = torch.min(-prediction - 1,torch.zeros(prediction.shape).to(prediction.device))
                loss = -torch.mean(minvalue)
        return loss

大家可以了解下hinge损失，也可以使用其他几个哦

ok，知道了两个辨别器器损失，然后平均一下就得到了整体的辨别器损失

赶紧back

然后看第二个损失

        self.loss_G = self.compute_G_loss()

    def compute_G_loss(self):
        """Calculate GAN and NCE loss for the generator"""
        fakeB = self.fake_B
        fakeA = self.fake_A

        # First, G(A) should fake the discriminator
        if self.opt.lambda_GAN > 0.0:
            pred_fakeB = self.netD_A(fakeB)
            pred_fakeA = self.netD_B(fakeA)
            self.loss_G_A = self.criterionGAN(pred_fakeB, True).mean() * self.opt.lambda_GAN
            self.loss_G_B = self.criterionGAN(pred_fakeA, True).mean() * self.opt.lambda_GAN
        else:
            self.loss_G_A = 0.0
            self.loss_G_B = 0.0
        # L1 IDENTICAL LOSS
        self.loss_idt_A = self.criterionIdt(self.idt_A, self.real_B)
        self.loss_idt_B = self.criterionIdt(self.idt_B, self.real_A)
        # Similarity Loss and NCE losses
        self.loss_Sim, self.loss_NCE1, self.loss_NCE2 = self.calculate_Sim_loss_all \
            (self.real_A, self.fake_B, self.real_B, self.fake_A)
        loss_NCE_both = (self.loss_NCE1 + self.loss_NCE2) * 0.5 + (self.loss_idt_A + self.loss_idt_B) * 0.5 \
                        + self.loss_Sim
        self.loss_G = (self.loss_G_A + self.loss_G_B) * 0.5 + loss_NCE_both
        return self.loss_G

计算生成器的GAN和NCE损失

里面主要的不同有一个计算L1 IDENTICAL LOSS和计算相似性损失，前者就是一个简单的l1,后者如下：

    def calculate_Sim_loss_all(self, src1, tgt1, src2, tgt2):
        n_layers = len(self.nce_layers)
        feat_q1 = self.netG_B(tgt1, self.nce_layers, encode_only=True)
        feat_k1 = self.netG_A(src1, self.nce_layers, encode_only=True)
        feat_q2 = self.netG_A(tgt2, self.nce_layers, encode_only=True)
        feat_k2 = self.netG_B(src2, self.nce_layers, encode_only=True)
        feat_k_pool1, sample_ids1 = self.netF1(feat_k1, self.opt.num_patches, None)
        feat_q_pool1, _ = self.netF2(feat_q1, self.opt.num_patches, sample_ids1)
        feat_q_pool1_noid, _ = self.netF2(feat_q1, self.opt.num_patches, None)
        feat_k_pool2, sample_ids2 = self.netF2(feat_k2, self.opt.num_patches, None)
        feat_q_pool2, _ = self.netF1(feat_q2, self.opt.num_patches, sample_ids2)
        feat_q_pool2_noid, _ = self.netF1(feat_q2, self.opt.num_patches, None)

        nce_loss1 = 0.0
        for f_q, f_k, crit in zip(feat_q_pool1, feat_k_pool1, self.criterionNCE):
            loss = crit(f_q, f_k)
            nce_loss1 += loss.mean()

        nce_loss2 = 0.0
        for f_q, f_k, crit in zip(feat_q_pool2, feat_k_pool2, self.criterionNCE):
            loss = crit(f_q, f_k)
            nce_loss2 += loss.mean()

        m, n = self.opt.num_patches, self.opt.netF_nc
        nce_loss1 = nce_loss1 / n_layers
        nce_loss2 = nce_loss2 / n_layers
        feature_realA = torch.zeros([n_layers, m, n])
        feature_fakeB = torch.zeros([n_layers, m, n])
        feature_realB = torch.zeros([n_layers, m, n])
        feature_fakeA = torch.zeros([n_layers, m, n])
        for i in range(n_layers):
            feature_realA[i] = feat_k_pool1[i]
            feature_fakeB[i] = feat_q_pool1_noid[i]
            feature_realB[i] = feat_k_pool2[i]
            feature_fakeA[i] = feat_q_pool2_noid[i]
        feature_realA_out = self.netF3(feature_realA.to(self.device))
        feature_fakeB_out = self.netF4(feature_fakeB.to(self.device))
        feature_realB_out = self.netF5(feature_realB.to(self.device))
        feature_fakeA_out = self.netF6(feature_fakeA.to(self.device))
        sim_loss = self.criterionSim(feature_realA_out, feature_fakeA_out) + \
                   self.criterionSim(feature_fakeB_out, feature_realB_out)

        return sim_loss * self.opt.lambda_SIM, nce_loss1, nce_loss2

        self.netF1 = networks.define_F(opt.input_nc, opt.netF, opt.normG,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, opt.no_antialias, self.gpu_ids,
                                       opt)
        self.netF2 = networks.define_F(opt.input_nc, opt.netF, opt.normG,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, opt.no_antialias, self.gpu_ids,
                                       opt)
        n_layers = len(self.nce_layers)
        self.netF3 = networks.define_F(n_layers, 'mapping', opt.normG,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, opt.no_antialias, self.gpu_ids,
                                       opt)
        self.netF4 = networks.define_F(n_layers, 'mapping', opt.normG,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, opt.no_antialias, self.gpu_ids,
                                       opt)
        self.netF5 = networks.define_F(n_layers, 'mapping', opt.normG,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, opt.no_antialias, self.gpu_ids,
                                       opt)
        self.netF6 = networks.define_F(n_layers, 'mapping', opt.normG,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, opt.no_antialias, self.gpu_ids,
                                       opt)

def define_F(input_nc, netF, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, no_antialias=False, gpu_ids=[], opt=None):
    if netF == 'global_pool':
        net = PoolingF()
    elif netF == 'reshape':
        net = ReshapeF()
    elif netF == 'mapping':
        net = MappingF(input_nc, gpu_ids=gpu_ids)
    elif netF == 'sample':
        net = PatchSampleF(use_mlp=False, init_type=init_type, init_gain=init_gain, gpu_ids=gpu_ids, nc=opt.netF_nc)
    elif netF == 'mlp_sample':
        net = PatchSampleF(use_mlp=True, init_type=init_type, init_gain=init_gain, gpu_ids=gpu_ids, nc=opt.netF_nc)
    elif netF == 'strided_conv':
        net = StridedConvF(init_type=init_type, init_gain=init_gain, gpu_ids=gpu_ids)
    else:
        raise NotImplementedError('projection model name [%s] is not recognized' % netF)
    return init_net(net, init_type, init_gain, gpu_ids)

class ReshapeF(nn.Module):
    def __init__(self):
        super(ReshapeF, self).__init__()
        model = [nn.AdaptiveAvgPool2d(4)]
        self.model = nn.Sequential(*model)
        self.l2norm = Normalize(2)

    def forward(self, x):
        x = self.model(x)
        x_reshape = x.permute(0, 2, 3, 1).flatten(0, 2)
        return self.l2norm(x_reshape)

简单描述下这一段的意义，就是通过池化和转换维度，使得领域之间的特征映射出来，然后对各个领域的特征进行l1 loss

然后将idt 和sim 损失合在一起作为NCE损失再加上生成器损失获得了整体的生成器损失

        loss_NCE_both = (self.loss_NCE1 + self.loss_NCE2) * 0.5 + (self.loss_idt_A + self.loss_idt_B) * 0.5 \
                        + self.loss_Sim
        self.loss_G = (self.loss_G_A + self.loss_G_B) * 0.5 + loss_NCE_both

backwardbackwardbackward

就此终于结束了

损失部分大家可以继续看看，三个损失对应着生成器的三个损失，loss_G_A/B,loss_NCE,loss_Sim