网络结构

涉及4个网络，生成器A2B,生成器B2A，判别器A判别器B

4种损失函数：G网络(用MSELoss计算)、D网络(用MSELoss计算)、Cycle(原始输入和Cycl输出，用L1Loss计算)、identify(对于生成器A2B用B作为输入，用L1Loss计算；对于生成器B2A用A作为输入，用L1Loss计算)

self.loss_names = ['D_A', 'G_A', 'cycle_A', 'idt_A', 'D_B', 'G_B', 'cycle_B', 'idt_B']

生成器网络结构：输入-->普通卷积-->图像越来越小，特征图越来越多-->反卷积-->图像越来越大，特征图越来越少-->输出(与输入相同大小)

判别器网络结构：输入-->普通卷积-->升维操作特征图越来越多-->假如得到MxNx512特征图，再经过卷积到得MxNx1的矩阵(后面需要针对每个像素点计算Loss)-->输出

Cycle_gan_model.py

import torch
import itertools
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networksclass 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"""@staticmethoddef modify_commandline_options(parser, is_train=True):"""Add new dataset-specific options, and rewrite default values for existing options.Parameters:parser          -- original option parseris_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 dropoutif 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 parserdef __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)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 Gsself.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 discriminatorsself.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 channelsassert(opt.input_nc == opt.output_nc)self.fake_A_pool = ImagePool(opt.pool_size)  # create image buffer to store previously generated imagesself.fake_B_pool = ImagePool(opt.pool_size)  # create image buffer to store previously generated images# define loss functionsself.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 discriminatorParameters:netD (network)      -- the discriminator Dreal (tensor array) -- real imagesfake (tensor array) -- images generated by a generatorReturn the discriminator loss.We also call loss_D.backward() to calculate the gradients."""# Realpred_real = netD(real)loss_D_real = self.criterionGAN(pred_real, True)# Fakepred_fake = netD(fake.detach())loss_D_fake = self.criterionGAN(pred_fake, False)# Combined loss and calculate gradientsloss_D = (loss_D_real + loss_D_fake) * 0.5loss_D.backward()return loss_Ddef 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_identitylambda_A = self.opt.lambda_Alambda_B = self.opt.lambda_B# Identity lossif 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_idtelse:self.loss_idt_A = 0self.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 gradientsself.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_Bself.loss_G.backward()def optimize_parameters(self):"""Calculate losses, gradients, and update network weights; called in every training iteration"""# forwardself.forward()      # compute fake images and reconstruction images.# G_A and G_Bself.set_requires_grad([self.netD_A, self.netD_B], False)  # Ds require no gradients when optimizing Gsself.optimizer_G.zero_grad()  # set G_A and G_B's gradients to zeroself.backward_G()             # calculate gradients for G_A and G_Bself.optimizer_G.step()       # update G_A and G_B's weights# D_A and D_Bself.set_requires_grad([self.netD_A, self.netD_B], True)self.optimizer_D.zero_grad()   # set D_A and D_B's gradients to zeroself.backward_D_A()      # calculate gradients for D_Aself.backward_D_B()      # calculate graidents for D_Bself.optimizer_D.step()  # update D_A and D_B's weights