然后是model的搭建：

在creat_model函数中：

import torch
def create_model(opt):
    if opt.model == 'pix2pixHD':
        from .pix2pixHD_model import Pix2PixHDModel, InferenceModel
        if opt.isTrain:
            model = Pix2PixHDModel()
        else:
            model = InferenceModel()
    else:
    	from .ui_model import UIModel
    	model = UIModel()
    model.initialize(opt)
    if opt.verbose:
        print("model [%s] was created" % (model.name()))

    if opt.isTrain and len(opt.gpu_ids) and not opt.fp16:
        model = torch.nn.DataParallel(model, device_ids=opt.gpu_ids)

    return model

首先判断model：


train的时候用第一个，否则用第二个：

先看第一个：

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 networks
class Pix2PixHDModel(BaseModel):
    def name(self):
        return 'Pix2PixHDModel'
    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_filter
    
    def initialize(self, opt):
        BaseModel.initialize(self, opt)
        if opt.resize_or_crop != 'none' or not opt.isTrain: # when training at full res this causes OOM
            torch.backends.cudnn.benchmark = True
        self.isTrain = opt.isTrain
        self.use_features = opt.instance_feat or opt.label_feat
        self.gen_features = self.use_features and not self.opt.load_features
        input_nc = opt.label_nc if opt.label_nc != 0 else opt.input_nc

        ##### define networks        
        # Generator network
        netG_input_nc = input_nc        
        if not opt.no_instance:
            netG_input_nc += 1
        if self.use_features:
            netG_input_nc += opt.feat_num                  
        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
        if self.isTrain:
            use_sigmoid = opt.no_lsgan
            netD_input_nc = input_nc + opt.output_nc
            if not opt.no_instance:
                netD_input_nc += 1
            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
        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
        if not self.isTrain or opt.continue_train or opt.load_pretrain:
            pretrained_path = '' if not self.isTrain else opt.load_pretrain
            self.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 optimizers
        if 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)
            self.old_lr = opt.lr

            # define loss functions
            self.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
            self.loss_names = self.loss_filter('G_GAN','G_GAN_Feat','G_VGG','D_real', 'D_fake')

            # initialize optimizers
            # optimizer G
            if opt.niter_fix_global > 0:                
                import sys
                if sys.version_info >= (3,0):
                    finetune_list = set()
                else:
                    from sets import Set
                    finetune_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))

    def encode_input(self, label_map, inst_map=None, real_image=None, feat_map=None, infer=False):             
        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)
            if self.opt.data_type == 16:
                input_label = input_label.half()

        # get edges from instance map
        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 training
        if real_image is not None:
            real_image = Variable(real_image.data.cuda())

        # instance map for feature encoding
        if self.use_features:
            # get precomputed feature maps
            if 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

    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)
            return self.netD.forward(fake_query)
        else:
            return self.netD.forward(input_concat)

    def forward(self, label, inst, image, feat, infer=False):
        # Encode Inputs
        input_label, inst_map, real_image, feat_map = self.encode_input(label, inst, image, feat)  

        # Fake Generation
        if self.use_features:
            if not self.opt.load_features:
                feat_map = self.netE.forward(real_image, inst_map)                     
            input_concat = torch.cat((input_label, feat_map), dim=1)                        
        else:
            input_concat = input_label
        fake_image = self.netG.forward(input_concat)

        # Fake Detection and Loss
        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        
        pred_real = self.discriminate(input_label, real_image)
        loss_D_real = self.criterionGAN(pred_real, True)

        # GAN loss (Fake Passability Loss)        
        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
        loss_G_GAN_Feat = 0
        if not self.opt.no_ganFeat_loss:
            feat_weights = 4.0 / (self.opt.n_layers_D + 1)
            D_weights = 1.0 / self.opt.num_D
            for i in range(self.opt.num_D):
                for j in range(len(pred_fake[i])-1):
                    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
        loss_G_VGG = 0
        if not self.opt.no_vgg_loss:
            loss_G_VGG = self.criterionVGG(fake_image, real_image) * self.opt.lambda_feat
        
        # Only return the fake_B image if necessary to save BW
        return [ 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 ]

    def inference(self, label, inst, image=None):
        # Encode Inputs        
        image = Variable(image) if image is not None else None
        input_label, inst_map, real_image, _ = self.encode_input(Variable(label), Variable(inst), image, infer=True)

        # Fake Generation
        if self.use_features:
            if self.opt.use_encoded_image:
                # encode the real image to get feature map
                feat_map = self.netE.forward(real_image, inst_map)
            else:
                # sample clusters from precomputed features             
                feat_map = self.sample_features(inst_map)
            input_concat = torch.cat((input_label, feat_map), dim=1)                        
        else:
            input_concat = input_label        
           
        if torch.__version__.startswith('0.4'):
            with torch.no_grad():
                fake_image = self.netG.forward(input_concat)
        else:
            fake_image = self.netG.forward(input_concat)
        return fake_image

    def 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
        inst_np = inst.cpu().numpy().astype(int)                                      
        feat_map = self.Tensor(inst.size()[0], self.opt.feat_num, inst.size()[2], inst.size()[3])
        for i in np.unique(inst_np):    
            label = i if i < 1000 else i//1000
            if label in features_clustered:
                feat = features_clustered[label]
                cluster_idx = np.random.randint(0, feat.shape[0]) 
                                            
                idx = (inst == int(i)).nonzero()
                for k in range(self.opt.feat_num):                                    
                    feat_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_map

    def encode_features(self, image, inst):
        image = Variable(image.cuda(), volatile=True)
        feat_num = self.opt.feat_num
        h, w = inst.size()[2], inst.size()[3]
        block_num = 32
        feat_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//1000
            idx = (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

    def get_edges(self, t):
        edge = torch.cuda.ByteTensor(t.size()).zero_()
        edge[:,:,:,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()

    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 it
        params = 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 -----------')

    def update_learning_rate(self):
        lrd = self.opt.lr / self.opt.niter_decay
        lr = self.old_lr - lrd        
        for param_group in self.optimizer_D.param_groups:
            param_group['lr'] = lr
        for param_group in self.optimizer_G.param_groups:
            param_group['lr'] = lr
        if self.opt.verbose:
            print('update learning rate: %f -> %f' % (self.old_lr, lr))
        self.old_lr = lr

class InferenceModel(Pix2PixHDModel):
    def forward(self, inp):
        label, inst = inp
        return self.inference(label, inst)

        

首先看forward函数：将标签，实例，RGB，feat输入到encoder中。

如果标签通道不为0，对标签进行one-hot编码。CItyscapes有35个通道，oneHot_size=(b,35,1024,512)。初始化一个oneHot_size大小全为0的tensor。然后通过scatter_进行编码。

接着对实例图提边。输入的是示例图得到的是边界图。将边界图和输入label拼接在一起作为输入label。

默认为False。

将label输入到生成器中。

接着看netG。

接着看define_G:很多参数，首先是输入通道数，接着是输出通道数=3，ngf=64，netG=gloabl。

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

看第一个参数：如果label通道不为0，input_nc=label_nc,否则input_nc=0.如果使用instance，netG的输入通道数再加1.如果使用feature encoder network的话，则输入通道再加3.

根据norm_type确定归一化类型：

接着输入到global生成器中：

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)        

        model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
        ### downsample
        for i in range(n_downsampling):
            mult = 2**i
            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
                      norm_layer(ngf * mult * 2), activation]

        ### resnet blocks
        mult = 2**n_downsampling
        for i in range(n_blocks):
            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer)]
        
        ### upsample         
        for 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]
        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)    

首先对原图pad=3,接着输入经过一个7x7卷积，将输入通道变为64。图片大小不变，通道变为64。

接着进行连续的三次下采样：

step1：i=0，mult=1，nn.conv2d(64,642,k=3,s=2,p=1)，norm，act。(1,64,512,1024)—>(1,128,256,512)
step2：i=1，mul=2，nn.conv2d(642,644,k=3,s=2,p=1)，norm，act。(1,128,256,512)—>(1,256,128,256)
step3：i=2，mul=4，nn.conv2d(644,64*8,k=3,s=2,p=1)，norm，act。(1,256,128,256)—>(1,512,64,128)

mul=8,有连续9个block。每一个block调用一次Resnetblock。

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 = 0
        if padding_type == 'reflect':
            conv_block += [nn.ReflectionPad2d(1)]
        elif padding_type == 'replicate':
            conv_block += [nn.ReplicationPad2d(1)]
        elif padding_type == 'zero':
            p = 1
        else:
            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 = 0
        if padding_type == 'reflect':
            conv_block += [nn.ReflectionPad2d(1)]
        elif padding_type == 'replicate':
            conv_block += [nn.ReplicationPad2d(1)]
        elif padding_type == 'zero':
            p = 1
        else:
            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

在内部调用：
首先在convblock里面添加padding。

接着在里面添加卷积，k=3，p=0，因为事前已经添加了pad，所以这里不指定pad，大小也不发生变换。如过使用dropout，添加dropout。

接着上述的操作再执行一遍：大小还是不变。

最后再来一个indentity相加。

小结：Convblock一个里面有两个卷积，不改变大小，不改变通道，有9个Convblock，那么就有18个卷积，不改变通道和大小。
接着进行上采样:

step1:输入通道为512，输出256，大小上采样两倍。norm，act。
step2:输入通道为256，输出128，大小上采样两倍。norm，act。
step3:输入通道为128，输出64，大小上采样两倍。norm，act。
最后再加一个卷积，输出通道为3，大小不变。
小结：GlobalGenerator：首先下采样三次，再经过一个残差卷积由18个卷积组成，最后上采样到原图大小，输出通道为3.因为输入是经过one-hot编码后的标签，输出是image通道为3.
如果生成器是LocalEnhancer：

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 #####           
        ngf_global = ngf * (2**n_local_enhancers)
        model_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        
        self.model = nn.Sequential(*model_global)                

        ###### local enhancer layers #####
        for n in range(1, n_local_enhancers+1):
            ### 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 = []
            for i in range(n_blocks_local):
                model_upsample += [ResnetBlock(ngf_global * 2, padding_type=padding_type, norm_layer=norm_layer)]

            ### upsample
            model_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 convolution
            if n == n_local_enhancers:                
                model_upsample += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]                       
            
            setattr(self, 'model'+str(n)+'_1', nn.Sequential(*model_downsample))
            setattr(self, 'model'+str(n)+'_2', nn.Sequential(*model_upsample))                  
        
        self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)

    def forward(self, input): 
        ### create input pyramid
        input_downsampled = [input]
        for i in range(self.n_local_enhancers):#1
            input_downsampled.append(self.downsample(input_downsampled[-1]))

        ### output at coarest level
        output_prev = self.model(input_downsampled[-1])        
        ### build up one layer at a time
        for n_local_enhancers in range(1, self.n_local_enhancers+1):
            model_downsample = getattr(self, 'model'+str(n_local_enhancers)+'_1')
            model_upsample = getattr(self, 'model'+str(n_local_enhancers)+'_2')            
            input_i = input_downsampled[self.n_local_enhancers-n_local_enhancers]            
            output_prev = model_upsample(model_downsample(input_i) + output_prev)
        return output_prev

将输入首先进行平均池化：大小变为一半，通道不变。

这样input_downsampled列表中就有两个值了，分辨是输入和下采样一半的值。

接着将下采样一半的值输入到model中：还是调用的GlobalGenerator。

model_global包含了17个卷积层：则len(model)=19,i从0到[(17-3)-1]。

则model_global包含前14层(0到13)。

接着：假设local_enhancer实例化为A，则getattr获得A的model1_1属性对应的值。getattr函数

我们去上面找：setattr是A设置model1_1属性，对应的值为nn.Sequential(*model_downsample)，然后通过getattr获得属性值即model_downsample = nn.Sequential(*model_downsample)。

接着看nn.Sequential(*model_downsample)组成：

这个for循环只循环一次：model_down由两个卷积组成，输出通道变为64，下采样4倍，model_up由三个ResNetBlock和一个转置卷积和一个输出通道为3的卷积组成。
最后给A赋予属性值：

回到forward中:input_i = input_downsampled列表中第0个值，即输入。将输入下采样两倍再和经过GlobalGenerator前14层的值住像素相加。在上采样。

对应于原文：

回到pix2pixHD_model:self.netG就是指定的网络，然后调用forward函数。最后生成假的图片。