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深度学习Day-31：Pix2Pix理论与实战

2024/10/25 1:21:20 来源：https://blog.csdn.net/m0_51359915/article/details/141576377  浏览:  次   关键词：深度学习Day-31：Pix2Pix理论与实战

  🍨 本文为：[🔗365天深度学习训练营] 中的学习记录博客
 🍖 原作者：[K同学啊 | 接辅导、项目定制]

要求：

  1. 了解并学习Pix2Pix算法
  2. 画出本文代码中Pix2Pix的生成器网络结构

一、 基础配置

  • 语言环境：Python3.8
  • 编译器选择：Pycharm
  • 深度学习环境：
    • torch==1.12.1+cu113
    • torchvision==0.13.1+cu113

二、 相关代码

1. 文件结构

  • pix2pix 
    • data_facades
      • test
      • train
      • val
    • datasets.py  
    • models.py
    • pix2pix.py

2. 代码细节

datasets.py

import glob
import random
import os
import numpy as npfrom torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transformsclass ImageDataset(Dataset):def __init__(self, root, transforms_=None, mode="train"):self.transform = transforms.Compose(transforms_)self.files = sorted(glob.glob(os.path.join(root, mode) + "/*.*"))if mode == "train":self.files.extend(sorted(glob.glob(os.path.join(root, "test") + "/*.*")))def __getitem__(self, index):img = Image.open(self.files[index % len(self.files)])w, h = img.sizeimg_A = img.crop((0, 0, w / 2, h))img_B = img.crop((w / 2, 0, w, h))if np.random.random() < 0.5:img_A = Image.fromarray(np.array(img_A)[:, ::-1, :], "RGB")img_B = Image.fromarray(np.array(img_B)[:, ::-1, :], "RGB")img_A = self.transform(img_A)img_B = self.transform(img_B)return {"A": img_A, "B": img_B}def __len__(self):return len(self.files)

models.py

import torch.nn as nn
import torch.nn.functional as F
import torchdef weights_init_normal(m):classname = m.__class__.__name__if classname.find("Conv") != -1:torch.nn.init.normal_(m.weight.data, 0.0, 0.02)elif classname.find("BatchNorm2d") != -1:torch.nn.init.normal_(m.weight.data, 1.0, 0.02)torch.nn.init.constant_(m.bias.data, 0.0)##############################
#           U-NET
##############################class UNetDown(nn.Module):def __init__(self, in_size, out_size, normalize=True, dropout=0.0):super(UNetDown, self).__init__()layers = [nn.Conv2d(in_size, out_size, 4, 2, 1, bias=False)]if normalize:layers.append(nn.InstanceNorm2d(out_size))layers.append(nn.LeakyReLU(0.2))if dropout:layers.append(nn.Dropout(dropout))self.model = nn.Sequential(*layers)def forward(self, x):return self.model(x)class UNetUp(nn.Module):def __init__(self, in_size, out_size, dropout=0.0):super(UNetUp, self).__init__()layers = [nn.ConvTranspose2d(in_size, out_size, 4, 2, 1, bias=False),nn.InstanceNorm2d(out_size),nn.ReLU(inplace=True),]if dropout:layers.append(nn.Dropout(dropout))self.model = nn.Sequential(*layers)def forward(self, x, skip_input):x = self.model(x)x = torch.cat((x, skip_input), 1)return xclass GeneratorUNet(nn.Module):def __init__(self, in_channels=3, out_channels=3):super(GeneratorUNet, self).__init__()self.down1 = UNetDown(in_channels, 64, normalize=False)self.down2 = UNetDown(64, 128)self.down3 = UNetDown(128, 256)self.down4 = UNetDown(256, 512, dropout=0.5)self.down5 = UNetDown(512, 512, dropout=0.5)self.down6 = UNetDown(512, 512, dropout=0.5)self.down7 = UNetDown(512, 512, dropout=0.5)self.down8 = UNetDown(512, 512, normalize=False, dropout=0.5)self.up1 = UNetUp(512, 512, dropout=0.5)self.up2 = UNetUp(1024, 512, dropout=0.5)self.up3 = UNetUp(1024, 512, dropout=0.5)self.up4 = UNetUp(1024, 512, dropout=0.5)self.up5 = UNetUp(1024, 256)self.up6 = UNetUp(512, 128)self.up7 = UNetUp(256, 64)self.final = nn.Sequential(nn.Upsample(scale_factor=2),nn.ZeroPad2d((1, 0, 1, 0)),nn.Conv2d(128, out_channels, 4, padding=1),nn.Tanh(),)def forward(self, x):# U-Net generator with skip connections from encoder to decoderd1 = self.down1(x)d2 = self.down2(d1)d3 = self.down3(d2)d4 = self.down4(d3)d5 = self.down5(d4)d6 = self.down6(d5)d7 = self.down7(d6)d8 = self.down8(d7)u1 = self.up1(d8, d7)u2 = self.up2(u1, d6)u3 = self.up3(u2, d5)u4 = self.up4(u3, d4)u5 = self.up5(u4, d3)u6 = self.up6(u5, d2)u7 = self.up7(u6, d1)return self.final(u7)##############################
#        Discriminator
##############################class Discriminator(nn.Module):def __init__(self, in_channels=3):super(Discriminator, self).__init__()def discriminator_block(in_filters, out_filters, normalization=True):"""Returns downsampling layers of each discriminator block"""layers = [nn.Conv2d(in_filters, out_filters, 4, stride=2, padding=1)]if normalization:layers.append(nn.InstanceNorm2d(out_filters))layers.append(nn.LeakyReLU(0.2, inplace=True))return layersself.model = nn.Sequential(*discriminator_block(in_channels * 2, 64, normalization=False),*discriminator_block(64, 128),*discriminator_block(128, 256),*discriminator_block(256, 512),nn.ZeroPad2d((1, 0, 1, 0)),nn.Conv2d(512, 1, 4, padding=1, bias=False))def forward(self, img_A, img_B):# Concatenate image and condition image by channels to produce inputimg_input = torch.cat((img_A, img_B), 1)return self.model(img_input)

pix2pix.py

import argparse
import time
import datetime
import sysfrom torchvision.utils import save_image
from torch.utils.data import DataLoader
from torch.autograd import Variablefrom models import *
from datasets import *import torchimport warnings
warnings.filterwarnings('ignore')parser = argparse.ArgumentParser()
parser.add_argument("--epoch", type=int, default=0, help="epoch to start training from")
parser.add_argument("--n_epochs", type=int, default=50, help="number of epochs of training")
parser.add_argument("--dataset_name", type=str, default="data_facades", help="name of the dataset")
parser.add_argument("--batch_size", type=int, default=1, help="size of the batches")
parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
parser.add_argument("--decay_epoch", type=int, default=100, help="epoch from which to start lr decay")
parser.add_argument("--n_cpu", type=int, default=2, help="number of cpu threads to use during batch generation")
parser.add_argument("--img_height", type=int, default=256, help="size of image height")
parser.add_argument("--img_width", type=int, default=256, help="size of image width")
parser.add_argument("--channels", type=int, default=3, help="number of image channels")
parser.add_argument("--sample_interval", type=int, default=500, help="interval between sampling of images from generators"
)
parser.add_argument("--checkpoint_interval", type=int, default=-1, help="interval between model checkpoints")
opt = parser.parse_args()
print(opt)os.makedirs("images/%s" % opt.dataset_name, exist_ok=True)
os.makedirs("saved_models/%s" % opt.dataset_name, exist_ok=True)cuda = True if torch.cuda.is_available() else False# Loss functions
criterion_GAN = torch.nn.MSELoss()
criterion_pixelwise = torch.nn.L1Loss()# Loss weight of L1 pixel-wise loss between translated image and real image
lambda_pixel = 100# Calculate output of image discriminator (PatchGAN)
patch = (1, opt.img_height // 2 ** 4, opt.img_width // 2 ** 4)# Initialize generator and discriminator
generator = GeneratorUNet()
discriminator = Discriminator()if cuda:generator = generator.cuda()discriminator = discriminator.cuda()criterion_GAN.cuda()criterion_pixelwise.cuda()if opt.epoch != 0:# Load pretrained modelsgenerator.load_state_dict(torch.load("saved_models/%s/generator_%d.pth" % (opt.dataset_name, opt.epoch)))discriminator.load_state_dict(torch.load("saved_models/%s/discriminator_%d.pth" % (opt.dataset_name, opt.epoch)))
else:# Initialize weightsgenerator.apply(weights_init_normal)discriminator.apply(weights_init_normal)# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))# Configure dataloaders
transforms_ = [transforms.Resize((opt.img_height, opt.img_width), Image.BICUBIC),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]dataloader = DataLoader(ImageDataset("./%s" % opt.dataset_name, transforms_=transforms_),batch_size=opt.batch_size,shuffle=True,num_workers=opt.n_cpu,
)val_dataloader = DataLoader(ImageDataset("./%s" % opt.dataset_name, transforms_=transforms_, mode="val"),batch_size=10,shuffle=True,num_workers=1,
)# Tensor type
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensordef sample_images(batches_done):"""Saves a generated sample from the validation set"""imgs = next(iter(val_dataloader))real_A = Variable(imgs["B"].type(Tensor))real_B = Variable(imgs["A"].type(Tensor))fake_B = generator(real_A)img_sample = torch.cat((real_A.data, fake_B.data, real_B.data), -2)save_image(img_sample, "images/%s/%s.png" % (opt.dataset_name, batches_done), nrow=5, normalize=True)# ----------
#  Training
# ----------if __name__ == '__main__':prev_time = time.time()for epoch in range(opt.epoch, opt.n_epochs):for i, batch in enumerate(dataloader):# Model inputsreal_A = Variable(batch["B"].type(Tensor))real_B = Variable(batch["A"].type(Tensor))# Adversarial ground truthsvalid = Variable(Tensor(np.ones((real_A.size(0), *patch))), requires_grad=False)fake = Variable(Tensor(np.zeros((real_A.size(0), *patch))), requires_grad=False)# ------------------#  Train Generators# ------------------optimizer_G.zero_grad()# GAN lossfake_B = generator(real_A)pred_fake = discriminator(fake_B, real_A)loss_GAN = criterion_GAN(pred_fake, valid)# Pixel-wise lossloss_pixel = criterion_pixelwise(fake_B, real_B)# Total lossloss_G = loss_GAN + lambda_pixel * loss_pixelloss_G.backward()optimizer_G.step()# ---------------------#  Train Discriminator# ---------------------optimizer_D.zero_grad()# Real losspred_real = discriminator(real_B, real_A)loss_real = criterion_GAN(pred_real, valid)# Fake losspred_fake = discriminator(fake_B.detach(), real_A)loss_fake = criterion_GAN(pred_fake, fake)# Total lossloss_D = 0.5 * (loss_real + loss_fake)loss_D.backward()optimizer_D.step()# --------------#  Log Progress# --------------# Determine approximate time leftbatches_done = epoch * len(dataloader) + ibatches_left = opt.n_epochs * len(dataloader) - batches_donetime_left = datetime.timedelta(seconds=batches_left * (time.time() - prev_time))prev_time = time.time()# Print logsys.stdout.write("\r[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f, pixel: %f, adv: %f] ETA: %s"% (epoch,opt.n_epochs,i,len(dataloader),loss_D.item(),loss_G.item(),loss_pixel.item(),loss_GAN.item(),time_left,))# If at sample interval save imageif batches_done % opt.sample_interval == 0:sample_images(batches_done)if opt.checkpoint_interval != -1 and epoch % opt.checkpoint_interval == 0:# Save model checkpointstorch.save(generator.state_dict(), "saved_models/%s/generator_%d.pth" % (opt.dataset_name, epoch))torch.save(discriminator.state_dict(), "saved_models/%s/discriminator_%d.pth" % (opt.dataset_name, epoch))

输出结果为： 

C:\Users\nemop\.conda\envs\P113\python.exe C:\NNStudy\Study\Pix2Pix\pix2pix.py 
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 0/50] [Batch 0/506] [D loss: 2.120547] [G loss: 46.905365, pixel: 0.439291, adv: 2.976255] ETA: 2 days, 1:52:14.432769Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 0/50] [Batch 500/506] [D loss: 0.178138] [G loss: 29.325426, pixel: 0.281019, adv: 1.223516] ETA: 0:23:27.389641Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 0/50] [Batch 505/506] [D loss: 0.144148] [G loss: 44.965244, pixel: 0.445120, adv: 0.453239] ETA: 0:21:45.184193Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 1/50] [Batch 494/506] [D loss: 0.058155] [G loss: 29.411367, pixel: 0.287625, adv: 0.648884] ETA: 0:18:30.059881Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 1/50] [Batch 505/506] [D loss: 0.019859] [G loss: 34.564346, pixel: 0.335710, adv: 0.993333] ETA: 0:19:49.136604Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 2/50] [Batch 488/506] [D loss: 0.019303] [G loss: 32.974598, pixel: 0.318522, adv: 1.122382] ETA: 0:21:46.913424Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 2/50] [Batch 505/506] [D loss: 0.032963] [G loss: 60.382744, pixel: 0.591032, adv: 1.279571] ETA: 0:19:27.992943Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 3/50] [Batch 482/506] [D loss: 0.058160] [G loss: 38.912128, pixel: 0.377501, adv: 1.162022] ETA: 0:18:53.856702Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 3/50] [Batch 505/506] [D loss: 0.024793] [G loss: 39.273933, pixel: 0.382994, adv: 0.974544] ETA: 0:19:56.469846Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 4/50] [Batch 476/506] [D loss: 0.068410] [G loss: 32.155556, pixel: 0.306792, adv: 1.476340] ETA: 0:20:15.607452Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 4/50] [Batch 505/506] [D loss: 0.017099] [G loss: 39.535625, pixel: 0.387563, adv: 0.779331] ETA: 0:19:00.660807Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 5/50] [Batch 470/506] [D loss: 0.020615] [G loss: 45.147377, pixel: 0.438832, adv: 1.264170] ETA: 0:20:10.960674Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 5/50] [Batch 505/506] [D loss: 0.046003] [G loss: 22.988733, pixel: 0.223286, adv: 0.660156] ETA: 0:19:02.641492Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 6/50] [Batch 464/506] [D loss: 0.012475] [G loss: 33.149433, pixel: 0.323028, adv: 0.846636] ETA: 0:17:38.616304Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 6/50] [Batch 505/506] [D loss: 0.068318] [G loss: 43.630173, pixel: 0.430630, adv: 0.567191] ETA: 0:16:49.759197Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 7/50] [Batch 458/506] [D loss: 0.014546] [G loss: 33.989143, pixel: 0.330266, adv: 0.962536] ETA: 0:17:26.452975Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 7/50] [Batch 505/506] [D loss: 0.043874] [G loss: 34.019508, pixel: 0.333941, adv: 0.625449] ETA: 0:18:10.340743Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 8/50] [Batch 452/506] [D loss: 0.105170] [G loss: 32.545296, pixel: 0.314449, adv: 1.100438] ETA: 0:19:11.048279Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 8/50] [Batch 505/506] [D loss: 0.011343] [G loss: 28.931974, pixel: 0.280629, adv: 0.869117] ETA: 0:15:15.186035Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 9/50] [Batch 446/506] [D loss: 0.033980] [G loss: 49.197403, pixel: 0.477602, adv: 1.437211] ETA: 0:16:25.291743Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 9/50] [Batch 505/506] [D loss: 0.109652] [G loss: 27.869932, pixel: 0.274658, adv: 0.404089] ETA: 0:15:46.128191Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 10/50] [Batch 440/506] [D loss: 0.087159] [G loss: 30.811535, pixel: 0.303785, adv: 0.433008] ETA: 0:17:33.459263Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 10/50] [Batch 505/506] [D loss: 0.031147] [G loss: 38.411480, pixel: 0.375912, adv: 0.820262] ETA: 0:14:21.026365Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 11/50] [Batch 434/506] [D loss: 0.007330] [G loss: 35.891857, pixel: 0.349066, adv: 0.985206] ETA: 0:17:00.203018Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 11/50] [Batch 505/506] [D loss: 0.098203] [G loss: 37.015667, pixel: 0.365276, adv: 0.488087] ETA: 0:15:37.925753Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 12/50] [Batch 428/506] [D loss: 0.005904] [G loss: 23.991585, pixel: 0.229150, adv: 1.076538] ETA: 0:15:41.832352Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 12/50] [Batch 505/506] [D loss: 0.012911] [G loss: 40.356361, pixel: 0.395494, adv: 0.807002] ETA: 0:12:49.569338Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 13/50] [Batch 422/506] [D loss: 0.005118] [G loss: 39.827080, pixel: 0.388467, adv: 0.980332] ETA: 0:17:49.804859Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 13/50] [Batch 505/506] [D loss: 0.015462] [G loss: 38.131855, pixel: 0.372946, adv: 0.837252] ETA: 0:17:08.521379Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 14/50] [Batch 416/506] [D loss: 0.007606] [G loss: 23.220619, pixel: 0.222119, adv: 1.008741] ETA: 0:14:34.513340Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 14/50] [Batch 505/506] [D loss: 0.007736] [G loss: 20.776545, pixel: 0.197760, adv: 1.000570] ETA: 0:15:06.299166Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 15/50] [Batch 410/506] [D loss: 0.015369] [G loss: 35.453518, pixel: 0.345131, adv: 0.940383] ETA: 0:14:44.620094Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 15/50] [Batch 505/506] [D loss: 0.010403] [G loss: 37.952179, pixel: 0.368552, adv: 1.097019] ETA: 0:13:31.045790Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 16/50] [Batch 404/506] [D loss: 0.030206] [G loss: 45.571011, pixel: 0.442926, adv: 1.278415] ETA: 0:14:52.738724Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 16/50] [Batch 505/506] [D loss: 0.007322] [G loss: 36.920830, pixel: 0.359818, adv: 0.939081] ETA: 0:12:36.365281Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 17/50] [Batch 398/506] [D loss: 0.003598] [G loss: 26.794537, pixel: 0.258169, adv: 0.977672] ETA: 0:13:09.913654Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 17/50] [Batch 505/506] [D loss: 0.006808] [G loss: 47.424892, pixel: 0.463348, adv: 1.090045] ETA: 0:13:58.948172Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 18/50] [Batch 392/506] [D loss: 0.008659] [G loss: 29.037411, pixel: 0.281504, adv: 0.887019] ETA: 0:12:55.831509Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 18/50] [Batch 505/506] [D loss: 0.002096] [G loss: 44.415039, pixel: 0.433716, adv: 1.043489] ETA: 0:12:21.708688Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 19/50] [Batch 386/506] [D loss: 0.004421] [G loss: 29.186646, pixel: 0.281626, adv: 1.024012] ETA: 0:10:29.330134Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 19/50] [Batch 505/506] [D loss: 0.008713] [G loss: 36.611286, pixel: 0.355981, adv: 1.013227] ETA: 0:12:10.513671Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 20/50] [Batch 380/506] [D loss: 0.008213] [G loss: 29.808434, pixel: 0.287584, adv: 1.050004] ETA: 0:12:46.601372Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 20/50] [Batch 505/506] [D loss: 0.007512] [G loss: 35.687508, pixel: 0.346671, adv: 1.020363] ETA: 0:11:58.375605Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 21/50] [Batch 374/506] [D loss: 0.007330] [G loss: 29.338795, pixel: 0.282488, adv: 1.089955] ETA: 0:11:22.657886Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 21/50] [Batch 505/506] [D loss: 0.005063] [G loss: 36.397511, pixel: 0.354302, adv: 0.967319] ETA: 0:12:32.686236Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 22/50] [Batch 368/506] [D loss: 0.003426] [G loss: 37.780121, pixel: 0.367079, adv: 1.072234] ETA: 0:12:07.161884Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 22/50] [Batch 505/506] [D loss: 0.003645] [G loss: 37.703381, pixel: 0.367614, adv: 0.941956] ETA: 0:10:48.209264Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 23/50] [Batch 362/506] [D loss: 0.040089] [G loss: 26.172892, pixel: 0.252225, adv: 0.950376] ETA: 0:11:37.577882Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 23/50] [Batch 505/506] [D loss: 0.004438] [G loss: 28.593239, pixel: 0.275601, adv: 1.033159] ETA: 0:10:32.343456Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 24/50] [Batch 356/506] [D loss: 0.001854] [G loss: 31.993052, pixel: 0.309705, adv: 1.022520] ETA: 0:09:48.711548Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 24/50] [Batch 505/506] [D loss: 0.003472] [G loss: 28.758240, pixel: 0.277524, adv: 1.005792] ETA: 0:10:51.829161Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 25/50] [Batch 350/506] [D loss: 0.002718] [G loss: 25.189060, pixel: 0.242021, adv: 0.986952] ETA: 0:10:28.905559Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 25/50] [Batch 505/506] [D loss: 0.002969] [G loss: 36.854740, pixel: 0.358645, adv: 0.990274] ETA: 0:10:06.852725Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 26/50] [Batch 344/506] [D loss: 0.004154] [G loss: 27.227598, pixel: 0.262473, adv: 0.980309] ETA: 0:10:28.170824Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 26/50] [Batch 505/506] [D loss: 0.002406] [G loss: 25.096933, pixel: 0.241094, adv: 0.987580] ETA: 0:09:30.366788Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 27/50] [Batch 338/506] [D loss: 0.006147] [G loss: 25.924360, pixel: 0.250126, adv: 0.911718] ETA: 0:09:50.154552Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 27/50] [Batch 505/506] [D loss: 0.002861] [G loss: 36.782192, pixel: 0.357660, adv: 1.016201] ETA: 0:09:28.514253Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 28/50] [Batch 332/506] [D loss: 0.018754] [G loss: 19.939678, pixel: 0.190832, adv: 0.856480] ETA: 0:08:28.994865Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 28/50] [Batch 505/506] [D loss: 0.007137] [G loss: 41.722542, pixel: 0.406709, adv: 1.051621] ETA: 0:09:46.079391Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 29/50] [Batch 326/506] [D loss: 0.003144] [G loss: 25.841633, pixel: 0.247965, adv: 1.045161] ETA: 0:08:26.613255Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 29/50] [Batch 505/506] [D loss: 0.020236] [G loss: 25.448471, pixel: 0.245079, adv: 0.940563] ETA: 0:08:13.788889Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 30/50] [Batch 320/506] [D loss: 0.003752] [G loss: 23.666348, pixel: 0.226330, adv: 1.033345] ETA: 0:07:48.139219Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 30/50] [Batch 505/506] [D loss: 0.002398] [G loss: 29.302134, pixel: 0.282130, adv: 1.089153] ETA: 0:08:10.026863Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 31/50] [Batch 314/506] [D loss: 0.001211] [G loss: 25.154154, pixel: 0.241748, adv: 0.979381] ETA: 0:08:12.784452Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 31/50] [Batch 505/506] [D loss: 0.000945] [G loss: 32.078442, pixel: 0.310825, adv: 0.995949] ETA: 0:08:04.210115Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 32/50] [Batch 308/506] [D loss: 0.002173] [G loss: 27.432039, pixel: 0.263771, adv: 1.054988] ETA: 0:07:25.970726Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 32/50] [Batch 505/506] [D loss: 0.002896] [G loss: 25.211002, pixel: 0.241745, adv: 1.036515] ETA: 0:08:23.751802Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 33/50] [Batch 302/506] [D loss: 0.001799] [G loss: 19.686071, pixel: 0.186818, adv: 1.004259] ETA: 0:07:07.021265Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 33/50] [Batch 505/506] [D loss: 0.002455] [G loss: 25.911329, pixel: 0.248348, adv: 1.076557] ETA: 0:07:02.146672Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 34/50] [Batch 296/506] [D loss: 0.002308] [G loss: 23.641624, pixel: 0.226509, adv: 0.990705] ETA: 0:06:31.028023Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 34/50] [Batch 505/506] [D loss: 0.002152] [G loss: 24.272091, pixel: 0.232874, adv: 0.984653] ETA: 0:06:05.284515Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 35/50] [Batch 290/506] [D loss: 0.001247] [G loss: 21.595200, pixel: 0.205871, adv: 1.008100] ETA: 0:05:48.232102Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 35/50] [Batch 505/506] [D loss: 0.019104] [G loss: 24.502350, pixel: 0.233517, adv: 1.150635] ETA: 0:06:40.481257Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 36/50] [Batch 284/506] [D loss: 0.011365] [G loss: 30.808340, pixel: 0.299603, adv: 0.848016] ETA: 0:05:46.416569Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 36/50] [Batch 505/506] [D loss: 0.003670] [G loss: 25.197220, pixel: 0.241211, adv: 1.076138] ETA: 0:05:33.357328Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 37/50] [Batch 278/506] [D loss: 0.006345] [G loss: 22.263044, pixel: 0.212978, adv: 0.965199] ETA: 0:06:00.042787Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 37/50] [Batch 505/506] [D loss: 0.000763] [G loss: 24.089008, pixel: 0.230851, adv: 1.003909] ETA: 0:04:44.011625Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 38/50] [Batch 272/506] [D loss: 0.003851] [G loss: 22.189091, pixel: 0.213258, adv: 0.863312] ETA: 0:04:25.276146Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 38/50] [Batch 505/506] [D loss: 0.003049] [G loss: 30.102287, pixel: 0.290727, adv: 1.029616] ETA: 0:04:39.111591Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 39/50] [Batch 266/506] [D loss: 0.002462] [G loss: 22.286646, pixel: 0.213051, adv: 0.981535] ETA: 0:05:33.599067Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 39/50] [Batch 505/506] [D loss: 0.000776] [G loss: 25.155338, pixel: 0.241427, adv: 1.012646] ETA: 0:03:58.492897Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 40/50] [Batch 260/506] [D loss: 0.022566] [G loss: 28.759075, pixel: 0.278023, adv: 0.956751] ETA: 0:03:23.925705Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 40/50] [Batch 505/506] [D loss: 0.116388] [G loss: 21.571711, pixel: 0.200463, adv: 1.525447] ETA: 0:03:39.560282Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 41/50] [Batch 254/506] [D loss: 0.002298] [G loss: 26.076492, pixel: 0.250504, adv: 1.026126] ETA: 0:03:47.567720Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 41/50] [Batch 505/506] [D loss: 0.002343] [G loss: 24.009888, pixel: 0.229965, adv: 1.013357] ETA: 0:03:00.066078Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 42/50] [Batch 248/506] [D loss: 0.000630] [G loss: 21.811567, pixel: 0.208105, adv: 1.001038] ETA: 0:02:50.806408Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 42/50] [Batch 505/506] [D loss: 0.001527] [G loss: 26.589022, pixel: 0.256072, adv: 0.981805] ETA: 0:02:43.424869Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 43/50] [Batch 242/506] [D loss: 0.001914] [G loss: 27.548643, pixel: 0.266138, adv: 0.934835] ETA: 0:02:50.773244Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 43/50] [Batch 505/506] [D loss: 0.000972] [G loss: 22.799290, pixel: 0.217626, adv: 1.036651] ETA: 0:02:19.564437Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 44/50] [Batch 236/506] [D loss: 0.001705] [G loss: 31.411657, pixel: 0.304165, adv: 0.995185] ETA: 0:02:09.138470Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 44/50] [Batch 505/506] [D loss: 0.006793] [G loss: 25.012074, pixel: 0.240233, adv: 0.988776] ETA: 0:02:00.164304Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 45/50] [Batch 230/506] [D loss: 0.001323] [G loss: 24.693291, pixel: 0.237133, adv: 0.979945] ETA: 0:01:53.185906Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 45/50] [Batch 505/506] [D loss: 0.001237] [G loss: 31.724941, pixel: 0.307451, adv: 0.979858] ETA: 0:01:44.043370Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 46/50] [Batch 224/506] [D loss: 0.000983] [G loss: 26.760288, pixel: 0.257485, adv: 1.011809] ETA: 0:01:33.053341Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 46/50] [Batch 505/506] [D loss: 0.001785] [G loss: 16.132702, pixel: 0.151278, adv: 1.004861] ETA: 0:01:10.738114Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 47/50] [Batch 218/506] [D loss: 0.008509] [G loss: 29.800434, pixel: 0.286709, adv: 1.129509] ETA: 0:01:09.161558Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 47/50] [Batch 505/506] [D loss: 0.005529] [G loss: 22.623722, pixel: 0.216991, adv: 0.924638] ETA: 0:00:51.798611Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 48/50] [Batch 212/506] [D loss: 0.000450] [G loss: 21.556780, pixel: 0.205656, adv: 0.991187] ETA: 0:00:36.760712Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 48/50] [Batch 505/506] [D loss: 0.001802] [G loss: 25.394165, pixel: 0.243527, adv: 1.041436] ETA: 0:00:25.297877Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 49/50] [Batch 206/506] [D loss: 0.001160] [G loss: 24.300886, pixel: 0.232767, adv: 1.024143] ETA: 0:00:14.331865Namespace(b1=0.5, b2=0.999, batch_size=1, channels=3, checkpoint_interval=-1, dataset_name='data_facades', decay_epoch=100, epoch=0, img_height=256, img_width=256, lr=0.0002, n_cpu=2, n_epochs=50, sample_interval=500)
[Epoch 49/50] [Batch 505/506] [D loss: 0.005092] [G loss: 21.434053, pixel: 0.204980, adv: 0.936065] ETA: 0:00:00.044938
Process finished with exit code 0

3. 总结

        Pix2Pix是图像翻译必读的文章之一，它的核心技术有三点：基于CGAN的损失函数，基于U-Net的生成器和基于PatchGAN的判别器。Pix2Pix 能够在诸多图像翻译任务上取得令人惊艳的效果，但因为它的输入是图像对，因此它得到的模型还是有偏的。这里的有偏指的是模型能够在与数据集近似的 x 的情况下得到令人满意的生成内容，但是如果输入 x 与训练集的偏差过大，Pix2Pix 得到的结果便不那么理想了。

三、理论基础

1. 图像翻译

        图像翻译指的是将图像从源域转换到目标域的过程，同时保持图像内容的一致性。具体解释如下：

图像内容（Content）：这是图像的固有属性，指的是图像展示的对象、场景或任何其他可视化信息。图像内容是区分不同图像的主要依据。
图像域（Domain）：在图像翻译的背景下，一个域可以被认为是一组具有共同特征的图像。例如，所有带有蓝色天空的照片可以属于同一个域。在图像翻译中，通常涉及至少两个域：源域和目标域。域内的图像可以认为其内容被赋予了某些相同的风格、纹理或其他视觉特性。
图像翻译（Image-to-Image Translation, I2I）：这是一个过程，目的是将图像从一个域（源域）转换到另一个域（目标域），同时尽可能保留原始图像的内容。这涉及到一系列复杂的算法和模型，如生成对抗网络（GANs），它们能够捕捉并学习不同域之间的映射关系。这个过程在计算机视觉和图像处理领域有着广泛的应用，包括图像风格转换、草图着色、照片卡通化等。

2. U-Net

        U-Net是一种专为图像分割任务设计的深度学习网络结构，具有以下特点：

编码器-解码器（Encoder-Decoder）结构：U-Net由一个收缩路径（编码器）和一个对称的扩展路径（解码器）组成。编码器部分主要负责通过卷积层提取特征，而解码器部分则用于上采样特征图，逐步恢复到原始图像的尺寸。
跳跃连接（Skip Connections）：在编码和解码阶段之间存在跳跃连接，即从编码器到解码器的深层特征图会与解码器相应层次的输出进行拼接。这种设计可以帮助保持图像的细节信息，并有助于更好地进行精确的分割。
多层次特征融合：U-Net结构允许在不同层级的特征之间进行融合，这样可以让网络同时学习到浅层次的细节特征和深层次的语义特征，从而增强模型对不同尺度结构的识别能力。

3. Pix2Pix解析

        Pix2Pix是一种基于条件生成对抗网络（cGAN）的图像翻译模型。它能够将输入的图像转换为对应的输出图像，通常用于解决图像到图像的转换问题。

原理结构：
        编码器-解码器结构（U-Net）：Pix2Pix的生成器G采用的是U-Net结构，这种结构通过跳跃连接（skip connections）使得网络能够更好地学习输入图像和输出图像之间的对应关系，保留图像的细节信息。
        条件判别器（PatchGAN）：判别器D使用的是PatchGAN结构，它的作用是在给定输入图像的条件下，判断输出图像是真实图像还是生成图像。PatchGAN不是对整个图像进行判别，而是对图像的局部区域（patches）进行判别，这样可以提高判别的效率和准确性。
        条件对抗性网络（cGAN）：Pix2Pix是基于cGAN的扩展，它在传统的GAN基础上增加了条件变量，使得生成器在生成图像时能够参考额外的信息，如输入图像或者其他条件信息。
优势：
        端到端训练：Pix2Pix可以实现端到端的训练，不需要复杂的图像预处理或后处理步骤。
        通用性强：虽然Pix2Pix是为特定的图像转换任务设计的，但由于其基于cGAN的结构，它具有很好的通用性，可以应用于多种图像到图像的转换任务。
        高质量的输出：Pix2Pix能够生成高分辨率、高质量的图像，这得益于其精细的网络结构和训练过程。
劣势：
        计算资源要求高：由于Pix2Pix使用了深度学习模型，尤其是GAN，它的训练过程需要大量的计算资源和时间。
        模型调优难度大：GAN类模型通常较难训练，需要精心设计的网络架构和合适的超参数设置。
        可能的模式崩溃（Mode Collapse）：在某些情况下，GAN可能会生成非常相似的输出，而忽略了输入数据中的多样性。
        综上所述，Pix2Pix以其强大的图像转换能力和较高的通用性在图像处理领域得到了广泛的应用，但同时也面临着计算资源要求高和模型调优难度大等挑战。

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