英伟达公司和加州大学伯克利分校于2018年发表的“基于有条件GAN的高分辨率图像合成及语义操控”项目，是本项目“让老照片重现光彩”的技术基础，算是一个前置开源项目。

“基于有条件GAN的高分辨率图像合成及语义操控”项目的技术核心是Pix2PixHD模型，我们在这里分享一下相关的源代码+中文注释，基于此可以加深对“让老照片重现光彩”项目的理解（尤其是，在老照片项目的模型与训练源代码尚未开源的情况下）。

“基于有条件GAN的高分辨率图像合成及语义操控”项目在GitHub上的链接是：https://github.com/NVIDIA/pix2pixHD

Pix2PixHD模型使用PyTorch构建，代码清晰、整齐，相关的源代码主要是3个文件，分别是：./models/models.py、 ./models/pix2pixHD_model.py 和  ./models/networks.py

说明如下：

（1）./models/models.py

调用 Pix2PixHDModel() 创建模型。

import torch# 创建模型，并返回模型
def create_model(opt):if opt.model == 'pix2pixHD':  # 选择pix2pixHD modelfrom .pix2pixHD_model import Pix2PixHDModel, InferenceModelif opt.isTrain:  # 若是训练，则为Truemodel = Pix2PixHDModel()else:  # 否则，若仅仅是前向传播用来演示，则为Falsemodel = InferenceModel()else:  # 选择 UIModel modelfrom .ui_model import UIModelmodel = UIModel()model.initialize(opt)  # 模型初始化参数if opt.verbose:  # 默认为false,表示之前并无模型保存print("model [%s] was created" % (model.name()))  # 打印label2city模型被创建if opt.isTrain and len(opt.gpu_ids) and not opt.fp16:model = torch.nn.DataParallel(model, device_ids=opt.gpu_ids)  # 多GPU训练return model

（2）./models/pix2pixHD_model.py 

构建模型的核心内容：

定义有条件GAN（Pix2PixHDModel）的生成器、鉴别器、编码器（用于生成实例的低维特征）；

定义损失函数（包括：GANloss，VGGloss、特征匹配损失函数）；

定义生成器和鉴别器的优化器（optimizer）；

定义各模块的输入；

定义forward函数。

import numpy as np
import torch
import os
from torch.autograd import Variable
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networksclass Pix2PixHDModel(BaseModel):def name(self):return 'Pix2PixHDModel'# loss滤波器：其中g_gan、d_real、d_fake三个loss值是肯定返回的# 这里的g_gan_feat即论文中的“特征匹配损失函数”（论文中的等式（4））# g_vgg为论文中的VGG感知损失函数，稍微改善了输出结果# g_gan_feat、g_vgg两个loss值根据train_options的opt.no_ganFeat_loss, not opt.no_vgg_loss而定（默认是需要返回的）def init_loss_filter(self, use_gan_feat_loss, use_vgg_loss):flags = (True, use_gan_feat_loss, use_vgg_loss, True, True)def loss_filter(g_gan, g_gan_feat, g_vgg, d_real, d_fake):return [l for (l,f) in zip((g_gan,g_gan_feat,g_vgg,d_real,d_fake),flags) if f]return loss_filterdef initialize(self, opt):BaseModel.initialize(self, opt)if opt.resize_or_crop != 'none' or not opt.isTrain: # when training at full res this causes OOMtorch.backends.cudnn.benchmark = Trueself.isTrain = opt.isTrainself.use_features = opt.instance_feat or opt.label_featself.gen_features = self.use_features and not self.opt.load_featuresinput_nc = opt.label_nc if opt.label_nc != 0 else opt.input_nc##### define networks        # Generator network# 生成器网络netG_input_nc = input_nc        if not opt.no_instance:netG_input_nc += 1  # 添加instance通道（区分不同实例）if self.use_features:netG_input_nc += opt.feat_num  # 添加feature_map通道（使用encoder）self.netG = networks.define_G(netG_input_nc, opt.output_nc, opt.ngf, opt.netG, opt.n_downsample_global, opt.n_blocks_global, opt.n_local_enhancers, opt.n_blocks_local, opt.norm, gpu_ids=self.gpu_ids)        # Discriminator network# 鉴别器网络if self.isTrain:use_sigmoid = opt.no_lsgannetD_input_nc = input_nc + opt.output_nc  # real_images + fake_imagesif not opt.no_instance:netD_input_nc += 1  # 添加instance通道（区分不同实例）self.netD = networks.define_D(netD_input_nc, opt.ndf, opt.n_layers_D, opt.norm, use_sigmoid, opt.num_D, not opt.no_ganFeat_loss, gpu_ids=self.gpu_ids)### Encoder network# 编码器网络（是define_G()中的一个子函数）if self.gen_features:          self.netE = networks.define_G(opt.output_nc, opt.feat_num, opt.nef, 'encoder', opt.n_downsample_E, norm=opt.norm, gpu_ids=self.gpu_ids)  if self.opt.verbose:print('---------- Networks initialized -------------')# load networks# 加载网络（模型）if not self.isTrain or opt.continue_train or opt.load_pretrain:pretrained_path = '' if not self.isTrain else opt.load_pretrainself.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)            if self.isTrain:self.load_network(self.netD, 'D', opt.which_epoch, pretrained_path)  if self.gen_features:self.load_network(self.netE, 'E', opt.which_epoch, pretrained_path)              # set loss functions and optimizersif self.isTrain:if opt.pool_size > 0 and (len(self.gpu_ids)) > 1:raise NotImplementedError("Fake Pool Not Implemented for MultiGPU")self.fake_pool = ImagePool(opt.pool_size)  # 初始化fake_pool：num_imgs = 0，images = []self.old_lr = opt.lr# define loss functions# 定义损失函数，在.forward()中使用# 默认使用ganfeat_loss和vgg_lossself.loss_filter = self.init_loss_filter(not opt.no_ganFeat_loss, not opt.no_vgg_loss)self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor)   self.criterionFeat = torch.nn.L1Loss()if not opt.no_vgg_loss:             self.criterionVGG = networks.VGGLoss(self.gpu_ids)# Names so we can breakout loss# 给损失函数命名self.loss_names = self.loss_filter('G_GAN','G_GAN_Feat','G_VGG','D_real', 'D_fake')# initialize optimizers# 初始化优化器# optimizer G（含：encoder）if opt.niter_fix_global > 0:                import sysif sys.version_info >= (3,0):finetune_list = set()else:from sets import Setfinetune_list = Set()params_dict = dict(self.netG.named_parameters())params = []for key, value in params_dict.items():       if key.startswith('model' + str(opt.n_local_enhancers)):                    params += [value]finetune_list.add(key.split('.')[0])  print('------------- Only training the local enhancer network (for %d epochs) ------------' % opt.niter_fix_global)print('The layers that are finetuned are ', sorted(finetune_list))                         else:params = list(self.netG.parameters())if self.gen_features:              params += list(self.netE.parameters())         self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))                            # optimizer D                        params = list(self.netD.parameters())    self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))# feat=feature（特征），inst=instance（实例）# label_map（标签图）每个像素值代表像素的对象类，inst_map（实例图）每个像素包含每个单独对象的唯一对象ID# 获取实例图的边界（边缘），将edge_map与label_map的one-hot向量拼接在一起，封装为Variable，赋值给input_label# real_image和feat_map，封装为Variable，赋值给real_image和feat_map；label_map赋值给inst_mapdef encode_input(self, label_map, inst_map=None, real_image=None, feat_map=None, infer=False):# label_map 数据类型转化if self.opt.label_nc == 0:input_label = label_map.data.cuda()else:# create one-hot vector for label map size = label_map.size()oneHot_size = (size[0], self.opt.label_nc, size[2], size[3])input_label = torch.cuda.FloatTensor(torch.Size(oneHot_size)).zero_()input_label = input_label.scatter_(1, label_map.data.long().cuda(), 1.0)  # 将列表转成one-hot编码的形式if self.opt.data_type == 16:input_label = input_label.half()# get edges from instance map# 获取实例图的边界（边缘），将edge_map与input_label拼接在一起if not self.opt.no_instance:inst_map = inst_map.data.cuda()edge_map = self.get_edges(inst_map)input_label = torch.cat((input_label, edge_map), dim=1)         input_label = Variable(input_label, volatile=infer)# real images for trainingif real_image is not None:real_image = Variable(real_image.data.cuda())# instance map for feature encodingif self.use_features:# get precomputed feature mapsif self.opt.load_features:feat_map = Variable(feat_map.data.cuda())if self.opt.label_feat:inst_map = label_map.cuda()return input_label, inst_map, real_image, feat_map# 定义判别器def discriminate(self, input_label, test_image, use_pool=False):input_concat = torch.cat((input_label, test_image.detach()), dim=1)if use_pool:            fake_query = self.fake_pool.query(input_concat)  # 读取fake imagesreturn self.netD.forward(fake_query)else:return self.netD.forward(input_concat)# 前向传播，使用输入数据运行模型# PyTorch 允许在前向传播过程中进行动态操作（如：跳跃连接等）def forward(self, label, inst, image, feat, infer=False):# Encode Inputs# 获取实例图的边界（边缘），将edge_map与label_map的one-hot向量拼接在一起，封装为Variable，赋值给input_labelinput_label, inst_map, real_image, feat_map = self.encode_input(label, inst, image, feat)  # Fake Generation# 调用生成器生成fake imagesif self.use_features:# 调用netE（即：encoder）对输入图片进行encoder-decoder运算，提取feature_mapif not self.opt.load_features:feat_map = self.netE.forward(real_image, inst_map)                     input_concat = torch.cat((input_label, feat_map), dim=1)  # 将input_label与特征图拼接在一起，作为生成器netG的输入else:input_concat = input_labelfake_image = self.netG.forward(input_concat)# Fake Detection and Loss# 输入为input_label和fake_image，鉴别器生成fake images pool（假图片池）的预测（prediction）、D_fake损失函数pred_fake_pool = self.discriminate(input_label, fake_image, use_pool=True)loss_D_fake = self.criterionGAN(pred_fake_pool, False)# Real Detection and Loss# 输入为input_label和real_image，鉴别器生成real images的预测（prediction）、D_real损失函数pred_real = self.discriminate(input_label, real_image)loss_D_real = self.criterionGAN(pred_real, True)# GAN loss (Fake Passability Loss)# 将输入标签与假图片拼接后作为输入，鉴别器生成假图片预测（prediction）、G_GAN损失函数pred_fake = self.netD.forward(torch.cat((input_label, fake_image), dim=1))        loss_G_GAN = self.criterionGAN(pred_fake, True)# GAN feature matching loss# 计算GAN的特征匹配损失函数，每一个尺度的鉴别器（num_D）、鉴别器的每层特征提取器（pred_fake）分别加权计算并求和loss_G_GAN_Feat = 0if not self.opt.no_ganFeat_loss:feat_weights = 4.0 / (self.opt.n_layers_D + 1)  # 4.0/(鉴别器的层数+1)D_weights = 1.0 / self.opt.num_D  # 1.0/(多尺度的个数，论文中是3）for i in range(self.opt.num_D):for j in range(len(pred_fake[i])-1):# 计算：L1Loss()，lambda_feat为（输入的）调节系数loss_G_GAN_Feat += D_weights * feat_weights * \self.criterionFeat(pred_fake[i][j], pred_real[i][j].detach()) * self.opt.lambda_feat# VGG feature matching loss# VGG特征匹配损失函数loss_G_VGG = 0if not self.opt.no_vgg_loss:# 计算fake_image和real_image之间的VGGLoss，lambda_feat为输入的调节系数# real_image不进行梯度计算loss_G_VGG = self.criterionVGG(fake_image, real_image) * self.opt.lambda_feat# Only return the fake_B image if necessary to save BWreturn [ self.loss_filter( loss_G_GAN, loss_G_GAN_Feat, loss_G_VGG, loss_D_real, loss_D_fake ), None if not infer else fake_image ]# 推理# 将标签、实例边界、特征图作为输入，生成假图片def inference(self, label, inst, image=None):# Encode Inputs        image = Variable(image) if image is not None else None# 将实例边界与label的one-hot向量拼接在一起，返回给input_labelinput_label, inst_map, real_image, _ = self.encode_input(Variable(label), Variable(inst), image, infer=True)# Fake Generationif self.use_features:if self.opt.use_encoded_image:# encode the real image to get feature map# 用encoder计算真实图像的特征图feat_map = self.netE.forward(real_image, inst_map)else:# sample clusters from precomputed features# 随机选取实例图中的某个特征作为编码特征，用于训练feat_map = self.sample_features(inst_map)input_concat = torch.cat((input_label, feat_map), dim=1)  # 把feat_map和input_label拼接在一起，作为生成器的输入else:input_concat = input_label        if torch.__version__.startswith('0.4'):with torch.no_grad():fake_image = self.netG.forward(input_concat)  # 调用generator生成假图片else:fake_image = self.netG.forward(input_concat)return fake_imagedef sample_features(self, inst): # read precomputed feature clusters cluster_path = os.path.join(self.opt.checkpoints_dir, self.opt.name, self.opt.cluster_path)        features_clustered = np.load(cluster_path, encoding='latin1').item()# randomly sample from the feature clusters# 从特征簇中随机采样inst_np = inst.cpu().numpy().astype(int)                                      feat_map = self.Tensor(inst.size()[0], self.opt.feat_num, inst.size()[2], inst.size()[3])  # feat_map.sizefor i in np.unique(inst_np):  # 对于一维数组或者列表，unique()去除其中重复的元素，并按元素由大到小返回一个新的无元素重复的元组或者列表# 确定具有唯一性的特征代码，并将特征代码排序label = i if i < 1000 else i//1000if label in features_clustered:feat = features_clustered[label]  # 从特征簇中取出当前特征代码对应的特征向量cluster_idx = np.random.randint(0, feat.shape[0])   # 任取一个随机数，用于抽取feat[]的某一行数据idx = (inst == int(i)).nonzero()  # nonzero()返回非零的位置，即特征图中与排序后的特征代码一致的所有非零位置for k in range(self.opt.feat_num):  # feat_num，特征的个数# feat_map[channel, feature_num, hight, width]# 任意抽取feat[]中某一行中的数据，赋值给feat_mapfeat_map[idx[:,0], idx[:,1] + k, idx[:,2], idx[:,3]] = feat[cluster_idx, k]if self.opt.data_type==16:feat_map = feat_map.half()return feat_mapdef encode_features(self, image, inst):image = Variable(image.cuda(), volatile=True)feat_num = self.opt.feat_numh, w = inst.size()[2], inst.size()[3]block_num = 32feat_map = self.netE.forward(image, inst.cuda())inst_np = inst.cpu().numpy().astype(int)feature = {}for i in range(self.opt.label_nc):feature[i] = np.zeros((0, feat_num+1))for i in np.unique(inst_np):label = i if i < 1000 else i//1000idx = (inst == int(i)).nonzero()num = idx.size()[0]idx = idx[num//2,:]val = np.zeros((1, feat_num+1))                        for k in range(feat_num):val[0, k] = feat_map[idx[0], idx[1] + k, idx[2], idx[3]].data[0]            val[0, feat_num] = float(num) / (h * w // block_num)feature[label] = np.append(feature[label], val, axis=0)return feature# 获得instance的边界（边缘），t是inst_map# 如果实例边界图中的一个像素的对象ID与它的4个邻居中的任何一个不同，那么该像素为1，否则为0def get_edges(self, t):edge = torch.cuda.ByteTensor(t.size()).zero_()  # 初始化为0edge[:,:,:,1:] = edge[:,:,:,1:] | (t[:,:,:,1:] != t[:,:,:,:-1])edge[:,:,:,:-1] = edge[:,:,:,:-1] | (t[:,:,:,1:] != t[:,:,:,:-1])edge[:,:,1:,:] = edge[:,:,1:,:] | (t[:,:,1:,:] != t[:,:,:-1,:])edge[:,:,:-1,:] = edge[:,:,:-1,:] | (t[:,:,1:,:] != t[:,:,:-1,:])if self.opt.data_type==16:return edge.half()else:return edge.float()# 保存模型参数def save(self, which_epoch):self.save_network(self.netG, 'G', which_epoch, self.gpu_ids)self.save_network(self.netD, 'D', which_epoch, self.gpu_ids)if self.gen_features:self.save_network(self.netE, 'E', which_epoch, self.gpu_ids)def update_fixed_params(self):# after fixing the global generator for a number of iterations, also start finetuning itparams = list(self.netG.parameters())if self.gen_features:params += list(self.netE.parameters())           self.optimizer_G = torch.optim.Adam(params, lr=self.opt.lr, betas=(self.opt.beta1, 0.999))if self.opt.verbose:print('------------ Now also finetuning global generator -----------')# 更新学习率def update_learning_rate(self):lrd = self.opt.lr / self.opt.niter_decaylr = self.old_lr - lrd        for param_group in self.optimizer_D.param_groups:param_group['lr'] = lrfor param_group in self.optimizer_G.param_groups:param_group['lr'] = lrif self.opt.verbose:print('update learning rate: %f -> %f' % (self.old_lr, lr))self.old_lr = lr# 推理模型，前向传播
class InferenceModel(Pix2PixHDModel):def forward(self, inp):label, inst = inpreturn self.inference(label, inst)

（3） ./models/networks.py

定义底层的神经网络模块：

定义生成器define_G()，以及生成器中的核心模块：全局生成器GlobalGenerator()、局部增强器LocalEnhancer()、残差块ResnetBlock()、编码器Encoder()；

定义鉴别器define_D()，以及鉴别器的核心模块：多尺度鉴别器MultiscaleDiscriminator()、PactchGAN N层鉴别器NLayerDiscriminator()；

定义损失函数GANLoss()、VGGLoss()；

定义网络模型Vgg19()。

import torch
import torch.nn as nn
import functools
from torch.autograd import Variable
import numpy as np###############################################################################
# Functions
###############################################################################
def weights_init(m):classname = m.__class__.__name__if classname.find('Conv') != -1:m.weight.data.normal_(0.0, 0.02)elif classname.find('BatchNorm2d') != -1:m.weight.data.normal_(1.0, 0.02)m.bias.data.fill_(0)# 数据的归一化处理
def get_norm_layer(norm_type='instance'):if norm_type == 'batch':norm_layer = functools.partial(nn.BatchNorm2d, affine=True  # 对NHW做归一化elif norm_type == 'instance':norm_layer = functools.partial(nn.InstanceNorm2d, affine=False)  # 对HW做归一化，用在风格化迁移else:raise NotImplementedError('normalization layer [%s] is not found' % norm_type)return norm_layer# 在Pix2PixHD中，G分为两部分，一部分是global net，另一部分是local net，即：define_G()前两个if语句对应的分支
# 第三个if语句对应的是论文中E的部分，用来预先计算类别特征，区分相同语义标签（semantic label）的多个实例
# input_nc = 3，number of input channels（不含instance和feature map通道）
# output_nc = 3，number of output channels（不含instance和feature map通道）
# ngf = 64 第一层卷积核数
def define_G(input_nc, output_nc, ngf, netG, n_downsample_global=3, n_blocks_global=9, n_local_enhancers=1, n_blocks_local=3, norm='instance', gpu_ids=[]):    norm_layer = get_norm_layer(norm_type=norm)     if netG == 'global':    netG = GlobalGenerator(input_nc, output_nc, ngf, n_downsample_global, n_blocks_global, norm_layer)       elif netG == 'local':        netG = LocalEnhancer(input_nc, output_nc, ngf, n_downsample_global, n_blocks_global, n_local_enhancers, n_blocks_local, norm_layer)elif netG == 'encoder':netG = Encoder(input_nc, output_nc, ngf, n_downsample_global, norm_layer)else:raise('generator not implemented!')print(netG)if len(gpu_ids) > 0:assert(torch.cuda.is_available())netG.cuda(gpu_ids[0])netG.apply(weights_init)return netG# 按照论文的说法，Pix2PixHD的D有多个（3个）
# input_nc = 3+3 (real_images+fake_images，不含instance通道）
def define_D(input_nc, ndf, n_layers_D, norm='instance', use_sigmoid=False, num_D=1, getIntermFeat=False, gpu_ids=[]):        norm_layer = get_norm_layer(norm_type=norm)   netD = MultiscaleDiscriminator(input_nc, ndf, n_layers_D, norm_layer, use_sigmoid, num_D, getIntermFeat)   print(netD)if len(gpu_ids) > 0:assert(torch.cuda.is_available())netD.cuda(gpu_ids[0])netD.apply(weights_init)return netDdef print_network(net):if isinstance(net, list):net = net[0]num_params = 0for param in net.parameters():num_params += param.numel()print(net)print('Total number of parameters: %d' % num_params)##############################################################################
# Losses
##############################################################################
class GANLoss(nn.Module):def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0,tensor=torch.FloatTensor):super(GANLoss, self).__init__()self.real_label = target_real_labelself.fake_label = target_fake_labelself.real_label_var = Noneself.fake_label_var = Noneself.Tensor = tensor# lsgan: Least Squares GAN, 最小二乘GANif use_lsgan:self.loss = nn.MSELoss()  #  均方差 MSE(Mean Square Error)else:self.loss = nn.BCELoss()  # 二元交叉熵 BCE(Binary Cross Entropy)，xlog(p(x)) + (1-x)log(1-p(x))# Pytorch中基本的变量类型是FloatTensor# Variable是FloatTensor的封装，除了包含FloatTensor还包含有梯度信息def get_target_tensor(self, input, target_is_real):target_tensor = Noneif target_is_real:create_label = ((self.real_label_var is None) or(self.real_label_var.numel() != input.numel()))if create_label:real_tensor = self.Tensor(input.size()).fill_(self.real_label)self.real_label_var = Variable(real_tensor, requires_grad=False)target_tensor = self.real_label_varelse:create_label = ((self.fake_label_var is None) or(self.fake_label_var.numel() != input.numel()))if create_label:fake_tensor = self.Tensor(input.size()).fill_(self.fake_label)self.fake_label_var = Variable(fake_tensor, requires_grad=False)target_tensor = self.fake_label_varreturn target_tensordef __call__(self, input, target_is_real):if isinstance(input[0], list):loss = 0for input_i in input:pred = input_i[-1]target_tensor = self.get_target_tensor(pred, target_is_real)loss += self.loss(pred, target_tensor)return losselse:            target_tensor = self.get_target_tensor(input[-1], target_is_real)return self.loss(input[-1], target_tensor)# VGG19输出的特征图的5个切片的L1Loss()，权重分别为[1/32, 1/16, 1/8, 1/4, 1]，加权求和
class VGGLoss(nn.Module):def __init__(self, gpu_ids):super(VGGLoss, self).__init__()        self.vgg = Vgg19().cuda()self.criterion = nn.L1Loss()  # L1Loss, 平均绝对误差（Mean Absolute Error,MAE)self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]        # 计算 x 和 y 的 L1Lossdef forward(self, x, y):              x_vgg, y_vgg = self.vgg(x), self.vgg(y)loss = 0for i in range(len(x_vgg)):# .detach()返回一个新的从当前图中分离的 Variable，返回的 Variable 永远不会需要梯度# 可以用于以该变量为输入部分网络求梯度，而不影响y_vgg[]本身loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())return loss##############################################################################
# Generator
##############################################################################
# 局部增强器（论文中的G2）
class LocalEnhancer(nn.Module):def __init__(self, input_nc, output_nc, ngf=32, n_downsample_global=3, n_blocks_global=9, n_local_enhancers=1, n_blocks_local=3, norm_layer=nn.BatchNorm2d, padding_type='reflect'):        super(LocalEnhancer, self).__init__()self.n_local_enhancers = n_local_enhancers###### global generator model ###### G1 modelngf_global = ngf * (2**n_local_enhancers)  # =64model_global = GlobalGenerator(input_nc, output_nc, ngf_global, n_downsample_global, n_blocks_global, norm_layer).model        model_global = [model_global[i] for i in range(len(model_global)-3)] # get rid of final convolution layers# 最后一层的输出[64,512,512]self.model = nn.Sequential(*model_global)                ###### local enhancer layers #####for n in range(1, n_local_enhancers+1): # =2### downsample            ngf_global = ngf * (2**(n_local_enhancers-n))model_downsample = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf_global, kernel_size=7, padding=0), norm_layer(ngf_global), nn.ReLU(True),nn.Conv2d(ngf_global, ngf_global * 2, kernel_size=3, stride=2, padding=1), norm_layer(ngf_global * 2), nn.ReLU(True)]### residual blocks# model_upsample在此处定义，在 .forward 里使用model_upsample = []for i in range(n_blocks_local):  # =3model_upsample += [ResnetBlock(ngf_global * 2, padding_type=padding_type, norm_layer=norm_layer)]### upsamplemodel_upsample += [nn.ConvTranspose2d(ngf_global * 2, ngf_global, kernel_size=3, stride=2, padding=1, output_padding=1), norm_layer(ngf_global), nn.ReLU(True)]      ### final convolutionif n == n_local_enhancers:                model_upsample += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]                       # 为中间层命名setattr(self, 'model'+str(n)+'_1', nn.Sequential(*model_downsample))setattr(self, 'model'+str(n)+'_2', nn.Sequential(*model_upsample))# 平均池化，输出 y = (x+2*1-3)/2 + 1，下采样self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)def forward(self, input): ### create input pyramid# （缺省）构建二组不同的输入# 通过平均池化，第二组输入尺寸降低1/2input_downsampled = [input]for i in range(self.n_local_enhancers):  # =1input_downsampled.append(self.downsample(input_downsampled[-1])) # [-1]取最后一个元素### output at coarest level# 论文中G1输出的特征图output_prev = self.model(input_downsampled[-1])        ### build up one layer at a time# coarse to fine，G1输出的特征图与G2(F)输出的特征图求和，作为model_upsample()的输入# G2（F）缺省为只有一层，即：n_local_enhancers=1for n_local_enhancers in range(1, self.n_local_enhancers+1):  # =2# 取出各中间层model_downsample = getattr(self, 'model'+str(n_local_enhancers)+'_1')model_upsample = getattr(self, 'model'+str(n_local_enhancers)+'_2')# 确定输入input_i = input_downsampled[self.n_local_enhancers-n_local_enhancers]  # 1-1 = 0# 生成输出output_prev = model_upsample(model_downsample(input_i) + output_prev)return output_prev# 全局生成器（论文中的G1）
class GlobalGenerator(nn.Module):def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, padding_type='reflect'):assert(n_blocks >= 0)super(GlobalGenerator, self).__init__()        activation = nn.ReLU(True)        # 第一层，用的是zero_padding# 因为第一层用的是7x7的卷积核、padding=0，而 512%7 = 1，因此需要补充6个像素，镜像填充ReflectionPad2d(3)# [3,512,512]->[64,512,512]，ngf=64model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]# 下采样，每一层卷积的stride都是2，n_downsampling=3### downsample，stride=2# [64,512,512]->[512,64,64]for i in range(n_downsampling):mult = 2**imodel += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),norm_layer(ngf * mult * 2), activation]# 残差块，残差块不改变分辨率### resnet blocks# dim=512mult = 2**n_downsamplingfor i in range(n_blocks):model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer)]# 和下采样数目一样的上采样部分，上采样部分不像Unet结构，没有用到下采样得到的特征图### upsample，使用转置卷积函数ConvTranspoese2d()，stride=2for i in range(n_downsampling):mult = 2**(n_downsampling - i)model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1),norm_layer(int(ngf * mult / 2)), activation]# 模型的输出层。这里没有使用归一化# [64,512,512]->[3,512,512]model += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]        self.model = nn.Sequential(*model)def forward(self, input):return self.model(input)             # Define a resnet block
# 定义残差块
class ResnetBlock(nn.Module):def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False):super(ResnetBlock, self).__init__()self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout)def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout):conv_block = []p = 0if padding_type == 'reflect':conv_block += [nn.ReflectionPad2d(1)]elif padding_type == 'replicate':conv_block += [nn.ReplicationPad2d(1)]elif padding_type == 'zero':p = 1else:raise NotImplementedError('padding [%s] is not implemented' % padding_type)conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p),norm_layer(dim),activation]if use_dropout:conv_block += [nn.Dropout(0.5)]p = 0if padding_type == 'reflect':conv_block += [nn.ReflectionPad2d(1)]elif padding_type == 'replicate':conv_block += [nn.ReplicationPad2d(1)]elif padding_type == 'zero':p = 1else:raise NotImplementedError('padding [%s] is not implemented' % padding_type)conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p),norm_layer(dim)]return nn.Sequential(*conv_block)def forward(self, x):out = x + self.conv_block(x)return out# 编码器网络E，生成低维特征，作为生成器网络的输入
# 这是一个标准的编解码器网络，添加了一个实例级平均池层，以计算对象实例的平均特性（找到每一类对象的多个实例）
class Encoder(nn.Module):def __init__(self, input_nc, output_nc, ngf=32, n_downsampling=4, norm_layer=nn.BatchNorm2d):super(Encoder, self).__init__()        self.output_nc = output_nc        model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), nn.ReLU(True)]             ### downsample，stride=2for i in range(n_downsampling):mult = 2**imodel += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),norm_layer(ngf * mult * 2), nn.ReLU(True)]### upsample，使用转置卷积函数ConvTranspose2d()，stride=2for i in range(n_downsampling):mult = 2**(n_downsampling - i)model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1),norm_layer(int(ngf * mult / 2)), nn.ReLU(True)]        model += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]self.model = nn.Sequential(*model) def forward(self, input, inst):outputs = self.model(input)# instance-wise average poolingoutputs_mean = outputs.clone()inst_list = np.unique(inst.cpu().numpy().astype(int))  # instance listfor i in inst_list:for b in range(input.size()[0]):  # 对HW做平均池化indices = (inst[b:b+1] == int(i)).nonzero() # n x 4，nonzero()返回的是数组中非零元素的位置for j in range(self.output_nc): # 每个feature map单独计算output_ins = outputs[indices[:,0] + b, indices[:,1] + j, indices[:,2], indices[:,3]]                    mean_feat = torch.mean(output_ins).expand_as(output_ins)                                        outputs_mean[indices[:,0] + b, indices[:,1] + j, indices[:,2], indices[:,3]] = mean_feat                       return outputs_mean#  多尺度判别器，基于鉴别器的特征匹配损失函数，用来改善GAN损失函数（提高稳定型和优化效率）
class MultiscaleDiscriminator(nn.Module):def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, num_D=3, getIntermFeat=False):super(MultiscaleDiscriminator, self).__init__()self.num_D = num_Dself.n_layers = n_layersself.getIntermFeat = getIntermFeat# 生成的NLayerDiscriminator类，被设置（恰当地说，是“命名”）为当前类(self)的一个属性# 生成num_D个NLayerDiscriminatorfor i in range(num_D):netD = NLayerDiscriminator(input_nc, ndf, n_layers, norm_layer, use_sigmoid, getIntermFeat)if getIntermFeat:                                for j in range(n_layers+2):# setattr() 函数对应函数 getattr()，用于设置属性值setattr(self, 'scale'+str(i)+'_layer'+str(j), getattr(netD, 'model'+str(j)))                                   else:setattr(self, 'layer'+str(i), netD.model)self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)  # 平均池化，下采样def singleD_forward(self, model, input):if self.getIntermFeat:result = [input]for i in range(len(model)):result.append(model[i](result[-1]))return result[1:]else:return [model(input)]# D的前向传播def forward(self, input):        num_D = self.num_Dresult = []input_downsampled = input# 逐一下采样，生成多个不同尺度的输入，并经singleD_forward()生成不同尺度的输出for i in range(num_D):if self.getIntermFeat:model = [getattr(self, 'scale'+str(num_D-1-i)+'_layer'+str(j)) for j in range(self.n_layers+2)]else:model = getattr(self, 'layer'+str(num_D-1-i))result.append(self.singleD_forward(model, input_downsampled))if i != (num_D-1):input_downsampled = self.downsample(input_downsampled)return result# 用指定的参数定义PatchGAN鉴别器（只定义网络，loss函数在class Pix2PixHDModel()中定义）
# Defines the PatchGAN discriminator with the specified arguments.
class NLayerDiscriminator(nn.Module):def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, getIntermFeat=False):super(NLayerDiscriminator, self).__init__()self.getIntermFeat = getIntermFeatself.n_layers = n_layerskw = 4padw = int(np.ceil((kw-1.0)/2))  # =2，np.ceil()计算大于等于该值的最小整数# [3, 512, 512] -> [64, 257, 257]sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]]nf = ndffor n in range(1, n_layers):nf_prev = nfnf = min(nf * 2, 512)sequence += [[nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw),norm_layer(nf), nn.LeakyReLU(0.2, True)]]nf_prev = nfnf = min(nf * 2, 512)sequence += [[nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),norm_layer(nf),nn.LeakyReLU(0.2, True)]]sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]if use_sigmoid:sequence += [[nn.Sigmoid()]]# 命名，以方便取出每一个中间层（计算feature mapping loss会用到）if getIntermFeat:for n in range(len(sequence)):setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))else:sequence_stream = []for n in range(len(sequence)):sequence_stream += sequence[n]self.model = nn.Sequential(*sequence_stream)def forward(self, input):if self.getIntermFeat:res = [input]for n in range(self.n_layers+2):model = getattr(self, 'model'+str(n))res.append(model(res[-1]))return res[1:]else:return self.model(input)        from torchvision import models
# VGG19，定义模型的5个切片（只用到0--29层）
class Vgg19(torch.nn.Module):def __init__(self, requires_grad=False):super(Vgg19, self).__init__()vgg_pretrained_features = models.vgg19(pretrained=True).featuresself.slice1 = torch.nn.Sequential()self.slice2 = torch.nn.Sequential()self.slice3 = torch.nn.Sequential()self.slice4 = torch.nn.Sequential()self.slice5 = torch.nn.Sequential()for x in range(2):self.slice1.add_module(str(x), vgg_pretrained_features[x])for x in range(2, 7):self.slice2.add_module(str(x), vgg_pretrained_features[x])for x in range(7, 12):self.slice3.add_module(str(x), vgg_pretrained_features[x])for x in range(12, 21):self.slice4.add_module(str(x), vgg_pretrained_features[x])for x in range(21, 30):self.slice5.add_module(str(x), vgg_pretrained_features[x])if not requires_grad:for param in self.parameters():param.requires_grad = Falsedef forward(self, X):h_relu1 = self.slice1(X)h_relu2 = self.slice2(h_relu1)        h_relu3 = self.slice3(h_relu2)        h_relu4 = self.slice4(h_relu3)        h_relu5 = self.slice5(h_relu4)                out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]return out

（完）