cycleGAN代码分析

本文浅析cycleGAN代码，实现对cycleGAN代码有一个基本认识
所分析的代码仓库：https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

装饰器

由于使用到了 abc 模块（一个抽象工具模块），顺带介绍一下python装饰器的使用。
装饰器的作用是在不改变原代码的情况下拓展函数功能，经常用于有切面需求的场景，比如：插入日志、性能测试、事务处理、缓存、权限校验等场景。装饰器包括函数装饰器和类装饰器，其中函数装饰器通过闭包实现（高阶函数的作用）。

def add_log(func):
    def use_logging(*args, **kwargs):
        print("add logging")
        return func(*args, **kwargs)
    return use_logging

@add_log
def bar():
    print("I am bar")

# bar = add_log(bar)
bar()

如上代码，通过装饰器add_log装饰后的bar() 等价为 add_log(bar)()
如果要使用带参数的装饰器，就需要嵌套一层以传入参数

def param_log(level):
    def decorator(func):
        def wrapper(*args, **kwargs):
            if level == "warn":
                print("level is warn")
            else:
                print("level is not warn")
            return func(*args, **kwargs)
        return wrapper
    return decorator


@param_log(level="warn")
def foo():
    print("I am foo")

foo()

类装饰器通过 init 函数初始化要装饰的函数，具体实现在 call 函数中

class Foo(object):
    def __init__(self, func):
        self._func = func

    def __call__(self):
        print('class decorator runing')
        self._func()
        print('class decorator ending')


@Foo
def bar():
    print('bar')

bar()

abc 模块中定义了抽象方法装饰器

def abstractmethod(funcobj):
    funcobj.__isabstractmethod__ = True
    return funcobj

cycleGAN

GAN网络的训练方式有点特殊，因此其整体框架稍有不同，下面对其进行分析

import torch
import itertools
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks


class CycleGANModel(BaseModel):
    """
    This class implements the CycleGAN model, for learning image-to-image translation without paired data.

    The model training requires '--dataset_mode unaligned' dataset.
    By default, it uses a '--netG resnet_9blocks' ResNet generator,
    a '--netD basic' discriminator (PatchGAN introduced by pix2pix),
    and a least-square GANs objective ('--gan_mode lsgan').

    CycleGAN paper: https://arxiv.org/pdf/1703.10593.pdf
    """
    @staticmethod
    def modify_commandline_options(parser, is_train=True):
        """Add new dataset-specific options, and rewrite default values for existing options.

        Parameters:
            parser          -- original option parser
            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.

        Returns:
            the modified parser.

        For CycleGAN, in addition to GAN losses, we introduce lambda_A, lambda_B, and lambda_identity for the following losses.
        A (source domain), B (target domain).
        Generators: G_A: A -> B; G_B: B -> A.
        Discriminators: D_A: G_A(A) vs. B; D_B: G_B(B) vs. A.
        Forward cycle loss:  lambda_A * ||G_B(G_A(A)) - A|| (Eqn. (2) in the paper)
        Backward cycle loss: lambda_B * ||G_A(G_B(B)) - B|| (Eqn. (2) in the paper)
        Identity loss (optional): lambda_identity * (||G_A(B) - B|| * lambda_B + ||G_B(A) - A|| * lambda_A) (Sec 5.2 "Photo generation from paintings" in the paper)
        Dropout is not used in the original CycleGAN paper.
        """
        parser.set_defaults(no_dropout=True)  # default CycleGAN did not use dropout
        if is_train:
            parser.add_argument('--lambda_A', type=float, default=10.0, help='weight for cycle loss (A -> B -> A)')
            parser.add_argument('--lambda_B', type=float, default=10.0, help='weight for cycle loss (B -> A -> B)')
            parser.add_argument('--lambda_identity', type=float, default=0.5, help='use identity mapping. Setting lambda_identity other than 0 has an effect of scaling the weight of the identity mapping loss. For example, if the weight of the identity loss should be 10 times smaller than the weight of the reconstruction loss, please set lambda_identity = 0.1')

        return parser

    def __init__(self, opt):
        """Initialize the CycleGAN class.

        Parameters:
            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
        """
        BaseModel.__init__(self, opt)
        # specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
        self.loss_names = ['D_A', 'G_A', 'cycle_A', 'idt_A', 'D_B', 'G_B', 'cycle_B', 'idt_B']
        # specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
        visual_names_A = ['real_A', 'fake_B', 'rec_A']
        visual_names_B = ['real_B', 'fake_A', 'rec_B']
        if self.isTrain and self.opt.lambda_identity > 0.0:  # if identity loss is used, we also visualize idt_B=G_A(B) ad idt_A=G_A(B)
            print("!!!!!!!!! use identity loss")
            visual_names_A.append('idt_B')
            visual_names_B.append('idt_A')

        self.visual_names = visual_names_A + visual_names_B  # combine visualizations for A and B
        # specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>.
        if self.isTrain:
            self.model_names = ['G_A', 'G_B', 'D_A', 'D_B']
        else:  # during test time, only load Gs
            self.model_names = ['G_A', 'G_B']

        # define networks (both Generators and discriminators)
        # The naming is different from those used in the paper.
        # Code (vs. paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X)
        self.netG_A = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm,
                                        not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
        self.netG_B = networks.define_G(opt.output_nc, opt.input_nc, opt.ngf, opt.netG, opt.norm,
                                        not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)

        if self.isTrain:  # define discriminators
            self.netD_A = networks.define_D(opt.output_nc, opt.ndf, opt.netD,
                                            opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids)
            self.netD_B = networks.define_D(opt.input_nc, opt.ndf, opt.netD,
                                            opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids)

        if self.isTrain:
            if opt.lambda_identity > 0.0:  # only works when input and output images have the same number of channels
                assert(opt.input_nc == opt.output_nc)
            self.fake_A_pool = ImagePool(opt.pool_size)  # create image buffer to store previously generated images
            self.fake_B_pool = ImagePool(opt.pool_size)  # create image buffer to store previously generated images
            # define loss functions
            self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device)  # define GAN loss.
            self.criterionCycle = torch.nn.L1Loss()
            self.criterionIdt = torch.nn.L1Loss()
            # initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
            self.optimizer_G = torch.optim.Adam(itertools.chain(self.netG_A.parameters(), self.netG_B.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999))
            self.optimizer_D = torch.optim.Adam(itertools.chain(self.netD_A.parameters(), self.netD_B.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999))
            self.optimizers.append(self.optimizer_G)
            self.optimizers.append(self.optimizer_D)

    def set_input(self, input):
        """Unpack input data from the dataloader and perform necessary pre-processing steps.

        Parameters:
            input (dict): include the data itself and its metadata information.

        The option 'direction' can be used to swap domain A and domain B.
        """
        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)
        self.image_paths = input['A_paths' if AtoB else 'B_paths']

    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.rec_A = self.netG_B(self.fake_B)   # G_B(G_A(A))
        self.fake_A = self.netG_B(self.real_B)  # G_B(B)
        self.rec_B = self.netG_A(self.fake_A)   # G_A(G_B(B))

    def backward_D_basic(self, netD, real, fake):
        """Calculate GAN loss for the discriminator

        Parameters:
            netD (network)      -- the discriminator D
            real (tensor array) -- real images
            fake (tensor array) -- images generated by a generator

        Return the discriminator loss.
        We also call loss_D.backward() to calculate the gradients.
        """
        # 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

    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)

    def backward_G(self):
        """Calculate the loss for generators G_A and G_B"""
        lambda_idt = self.opt.lambda_identity
        lambda_A = self.opt.lambda_A
        lambda_B = self.opt.lambda_B
        # Identity loss
        if lambda_idt > 0:
            # G_A should be identity if real_B is fed: ||G_A(B) - B||
            self.idt_A = self.netG_A(self.real_B)
            self.loss_idt_A = self.criterionIdt(self.idt_A, self.real_B) * lambda_B * lambda_idt
            # G_B should be identity if real_A is fed: ||G_B(A) - A||
            self.idt_B = self.netG_B(self.real_A)
            self.loss_idt_B = self.criterionIdt(self.idt_B, self.real_A) * lambda_A * lambda_idt
        else:
            self.loss_idt_A = 0
            self.loss_idt_B = 0

        # GAN loss D_A(G_A(A))
        self.loss_G_A = self.criterionGAN(self.netD_A(self.fake_B), True)
        # GAN loss D_B(G_B(B))
        self.loss_G_B = self.criterionGAN(self.netD_B(self.fake_A), True)
        # Forward cycle loss || G_B(G_A(A)) - A||
        self.loss_cycle_A = self.criterionCycle(self.rec_A, self.real_A) * lambda_A
        # Backward cycle loss || G_A(G_B(B)) - B||
        self.loss_cycle_B = self.criterionCycle(self.rec_B, self.real_B) * lambda_B
        # combined loss and calculate gradients
        self.loss_G = self.loss_G_A + self.loss_G_B + self.loss_cycle_A + self.loss_cycle_B + self.loss_idt_A + self.loss_idt_B
        self.loss_G.backward()

    def optimize_parameters(self):
        """Calculate losses, gradients, and update network weights; called in every training iteration"""
        # forward
        self.forward()      # compute fake images and reconstruction images.
        # G_A and G_B
        self.set_requires_grad([self.netD_A, self.netD_B], False)  # Ds require no gradients when optimizing Gs
        self.optimizer_G.zero_grad()  # set G_A and G_B's gradients to zero
        self.backward_G()             # calculate gradients for G_A and G_B
        self.optimizer_G.step()       # update G_A and G_B's weights
        # D_A and D_B
        self.set_requires_grad([self.netD_A, self.netD_B], True)
        self.optimizer_D.zero_grad()   # set D_A and D_B's gradients to zero
        self.backward_D_A()      # calculate gradients for D_A
        self.backward_D_B()      # calculate graidents for D_B
        self.optimizer_D.step()  # update D_A and D_B's weights

从forward函数，init函数和optimize_parameters函数进行分析。
init函数初始化一些变量，forward函数构建pipeline，具体需要清楚个命名代表什么。
real_A –> netG_A –> fake_B –> netG_B –> rec_A
real_B –> netG_B –> fake_A –> netG_A –> rec_B
每个epoch调用的是 optimize_parameters。

for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1): 
    for i, data in enumerate(dataset):  # inner loop within one epoch
        model.set_input(data)         # unpack data from dataset and apply preprocessing
        model.optimize_parameters()   # calculate loss functions, get gradients, update network weights

关于生成器和判别器是如何搭建的，如无深入研究，建议直接看论文。