【延伸阅读】让老照片重现光彩（五）：Pix2PixHD模型源代码+中文注释
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2023-12-31 18:37:10
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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 model
from .pix2pixHD_model import Pix2PixHDModel, InferenceModel
if opt.isTrain: # 若是训练，则为True
model = Pix2PixHDModel()
else: # 否则，若仅仅是前向传播用来演示，则为False
model = InferenceModel()
else: # 选择 UIModel model
from .ui_model import UIModel
model = 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 networks
class 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_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
# 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_lsgan
netD_input_nc = input_nc + opt.output_nc # real_images + fake_images
if 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_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) # 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_loss
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
# 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 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))
# 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_map
def 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 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
# 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 images
return 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_label
input_label, inst_map, real_image, feat_map = self.encode_input(label, inst, image, feat)
# Fake Generation
# 调用生成器生成fake images
if self.use_features:
# 调用netE（即：encoder）对输入图片进行encoder-decoder运算，提取feature_map
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) # 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_label
fake_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 = 0
if 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 = 0
if 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 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 ]
# 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_label
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
# 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_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
# 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.size
for 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//1000
if 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_map
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
# 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0
def get_edges(self, t):
edge = torch.cuda.ByteTensor(t.size()).zero_() # 初始化为0
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()
# 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 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 -----------')
# 更新学习率
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
# 推理模型，前向传播
class InferenceModel(Pix2PixHDModel):
def forward(self, inp):
label, inst = inp
return 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 netD
def print_network(net):
if isinstance(net, list):
net = net[0]
num_params = 0
for 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_label
self.fake_label = target_fake_label
self.real_label_var = None
self.fake_label_var = None
self.Tensor = tensor
# lsgan: Least Squares GAN, 最小二乘GAN
if 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 = None
if 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_var
else:
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_var
return target_tensor
def __call__(self, input, target_is_real):
if isinstance(input[0], list):
loss = 0
for 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 loss
else:
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 L1Loss
def forward(self, x, y):
x_vgg, y_vgg = self.vgg(x), self.vgg(y)
loss = 0
for 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 model
ngf_global = ngf * (2**n_local_enhancers) # =64
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
# 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): # =3
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()]
# 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1/2
input_downsampled = [input]
for i in range(self.n_local_enhancers): # =1
input_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=1
for 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=64
model = [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**i
model += [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=512
mult = 2**n_downsampling
for 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=2
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]
# 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 = 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
# 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=2
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), nn.ReLU(True)]
### upsample，使用转置卷积函数ConvTranspose2d()，stride=2
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)), 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 pooling
outputs_mean = outputs.clone()
inst_list = np.unique(inst.cpu().numpy().astype(int)) # instance list
for 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_D
self.n_layers = n_layers
self.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NLayerDiscriminator
for 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_D
result = []
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 = getIntermFeat
self.n_layers = n_layers
kw = 4
padw = 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 = ndf
for n in range(1, n_layers):
nf_prev = nf
nf = 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 = nf
nf = 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).features
self.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 = False
def 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）