基于SRGAN的图像超分辨率重建

本文偏新手项，因此只是作为定性学习使用，因此不涉及最后的定量评估环节

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1 简要介绍

SRGAN的原论文发表于CVPR2017，即《Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network》

SRGAN使用了生成对抗的方式来进行图像的超分辨率重建，同时提出了一个由Adversarial Loss和Content Loss组成的损失函数。

更详细的介绍可以去看看这篇文章 传送门

2 代码实现

2.1 开发环境

pytorch == '1.7.0+cu101'
numpy == '1.19.4'
PIL == '8.0.1'
tqdm == '4.52.0'
matplotlib == '3.3.3'

对于开发文件的路径为

/root- /Urban100- img_001.png- img_002.png···- img_100.png- /Img- /model- /result- main.py  #主代码应该放在这里

2.2 主要流程

这次代码的主要流程为
$$构建数据集\to 构建生成模型\to 构建辨别模型\to 构建迭代器\to 构建训练循环$$

2.3 构建数据集

这次的数据集用的是Urban100数据集，当然使用其他数据集也没有太大的问题（不建议使用带有灰度图的数据集，会报错）

在这里使用的构造方法和我的上一篇博客相同 传送门

首先我们先把数据集预处理类构建好

import torchvision.transforms as transforms
import torch
from torch.utils.data import Dataset
import numpy as np
import os
from PIL import Image#图像处理操作，包括随机裁剪，转换张量
transform = transforms.Compose([transforms.RandomCrop(96),transforms.ToTensor()]) class PreprocessDataset(Dataset):"""预处理数据集类"""def __init__(self,imgPath = path,transforms = transform, ex = 10):"""初始化预处理数据集类"""self.transforms = transformfor _,_,files in os.walk(imgPath): self.imgs = [imgPath + file for file in files] * exnp.random.shuffle(self.imgs)  #随机打乱def __len__(self):"""获取数据长度"""return len(self.imgs)def __getitem__(self,index):"""获取数据"""tempImg = self.imgs[index]tempImg = Image.open(tempImg)sourceImg = self.transforms(tempImg)  #对原始图像进行处理cropImg = torch.nn.MaxPool2d(4,stride=4)(sourceImg)return cropImg,sourceImg

随后，我们只需要构造一个DataLoader就可以在后续训练中使用到我们的模型了

path = './Urban100/'
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
BATCH = 32
EPOCHS = 100#构建数据集
processDataset = PreprocessDataset(imgPath = path)
trainData = DataLoader(processDataset,batch_size=BATCH)#构造迭代器并取出其中一个样本
dataiter = iter(trainData)
testImgs,_ = dataiter.next()
testImgs = testImgs.to(device)  #testImgs的用处是为了可视化生成对抗的结果

2.4 构建生成模型（Generator）

在文章中的生成模型即为SRResNet，下图为他的网络结构图

该模型是可以单独用于进行超分辨率训练的，详情请看 $\to$ 传送门

模型的构造代码如下

import torch.nn as nn
import torch.nn.functional as Fclass ResBlock(nn.Module):"""残差模块"""def __init__(self,inChannals,outChannals):"""初始化残差模块"""super(ResBlock,self).__init__()self.conv1 = nn.Conv2d(inChannals,outChannals,kernel_size=1,bias=False)self.bn1 = nn.BatchNorm2d(outChannals)self.conv2 = nn.Conv2d(outChannals,outChannals,kernel_size=3,stride=1,padding=1,bias=False)self.bn2 = nn.BatchNorm2d(outChannals)self.conv3 = nn.Conv2d(outChannals,outChannals,kernel_size=1,bias=False)self.relu = nn.PReLU()def forward(self,x):"""前向传播过程"""resudial = x out = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out = self.relu(out)out = self.conv3(out)out += resudialout = self.relu(out)return outclass Generator(nn.Module):"""生成模型(4x)"""def __init__(self):"""初始化模型配置"""super(Generator,self).__init__()#卷积模块1self.conv1 = nn.Conv2d(3,64,kernel_size=9,padding=4,padding_mode='reflect',stride=1)self.relu = nn.PReLU()#残差模块self.resBlock = self._makeLayer_(ResBlock,64,64,5)#卷积模块2self.conv2 = nn.Conv2d(64,64,kernel_size=1,stride=1)self.bn2 = nn.BatchNorm2d(64)self.relu2 = nn.PReLU()#子像素卷积self.convPos1 = nn.Conv2d(64,256,kernel_size=3,stride=1,padding=2,padding_mode='reflect')self.pixelShuffler1 = nn.PixelShuffle(2)self.reluPos1 = nn.PReLU()self.convPos2 = nn.Conv2d(64,256,kernel_size=3,stride=1,padding=1,padding_mode='reflect')self.pixelShuffler2 = nn.PixelShuffle(2)self.reluPos2 = nn.PReLU()self.finConv = nn.Conv2d(64,3,kernel_size=9,stride=1)def _makeLayer_(self,block,inChannals,outChannals,blocks):"""构建残差层"""layers = []layers.append(block(inChannals,outChannals))for i in range(1,blocks):layers.append(block(outChannals,outChannals))return nn.Sequential(*layers)def forward(self,x):"""前向传播过程"""x = self.conv1(x)x = self.relu(x)residual = xout = self.resBlock(x)out = self.conv2(out)out = self.bn2(out)out += residualout = self.convPos1(out)   out = self.pixelShuffler1(out)out = self.reluPos1(out)out = self.convPos2(out)   out = self.pixelShuffler2(out)out = self.reluPos2(out)out = self.finConv(out)return out

2.5 构建辨别模型（Discriminator）

辨别器采用了类似于VGG结构的模型，因此在实现上也没有很大难度

class ConvBlock(nn.Module):"""残差模块"""def __init__(self,inChannals,outChannals,stride = 1):"""初始化残差模块"""super(ConvBlock,self).__init__()self.conv = nn.Conv2d(inChannals,outChannals,kernel_size=3,stride = stride,padding=1,padding_mode='reflect',bias=False)self.bn = nn.BatchNorm2d(outChannals)self.relu = nn.LeakyReLU()def forward(self,x):"""前向传播过程"""out = self.conv(x)out = self.bn(out)out = self.relu(out)return outclass Discriminator(nn.Module):def __init__(self):super(Discriminator,self).__init__()self.conv1 = nn.Conv2d(3,64,kernel_size=3,stride=1,padding=1,padding_mode='reflect')self.relu1 = nn.LeakyReLU()self.convBlock1 = ConvBlock(64,64,stride = 2)self.convBlock2 = ConvBlock(64,128,stride = 1)self.convBlock3 = ConvBlock(128,128,stride = 2)self.convBlock4 = ConvBlock(128,256,stride = 1)self.convBlock5 = ConvBlock(256,256,stride = 2)self.convBlock6 = ConvBlock(256,512,stride = 1)self.convBlock7 = ConvBlock(512,512,stride = 2)self.avePool = nn.AdaptiveAvgPool2d(1)self.conv2 = nn.Conv2d(512,1024,kernel_size=1)self.relu2 = nn.LeakyReLU()self.conv3 = nn.Conv2d(1024,1,kernel_size=1)self.sigmoid = nn.Sigmoid()def forward(self,x):x = self.conv1(x)x = self.relu1(x)x = self.convBlock1(x)x = self.convBlock2(x)x = self.convBlock3(x)x = self.convBlock4(x)x = self.convBlock5(x)x = self.convBlock6(x)x = self.convBlock7(x)x = self.avePool(x)x = self.conv2(x)x = self.relu2(x)x = self.conv3(x)x = self.sigmoid(x)return x

（原谅我丑的一批的代码…）

2.6 初始化训练迭代器

在构建完数据集和两个网络之后，我们需要构造训练所需要的模型实例，损失函数，迭代器等。

这里迭代器使用的是Adam，两个网络的迭代器是互不相同的，为了保证网络之间对抗的稳定性，这里设置了两个模型的学习率相同。

SRGAN中使用了基于VGG提取的高级特征作为损失函数，因此需要使用到VGG预训练模型。

import torch.optim as optim
from torchvision.models.vgg import vgg16#构造模型
netD = Discriminator()
netG = Generator()
netD.to(device)
netG.to(device)#构造迭代器
optimizerG = optim.Adam(netG.parameters())
optimizerD = optim.Adam(netD.parameters())#构造损失函数
lossF = nn.MSELoss().to(device)#构造VGG损失中的网络模型
vgg = vgg16(pretrained=True).to(device)
lossNetwork = nn.Sequential(*list(vgg.features)[:31]).eval()
for param in lossNetwork.parameters():param.requires_grad = False  #让VGG停止学习

2.7 构造训练循环

训练的循环如下

from tqdm import tqdm
import torchvision.utils as vutils
import matplotlib.pyplot as pltfor epoch in range(EPOCHS):netD.train()netG.train()processBar = tqdm(enumerate(trainData,1))for i,(cropImg,sourceImg) in processBar:cropImg,sourceImg = cropImg.to(device),sourceImg.to(device)fakeImg = netG(cropImg).to(device)#迭代辨别器网络netD.zero_grad()realOut = netD(sourceImg).mean()fakeOut = netD(fakeImg).mean()dLoss = 1 - realOut + fakeOutdLoss.backward(retain_graph=True)optimizerD.step()#迭代生成器网络netG.zero_grad()gLossSR = lossF(fakeImg,sourceImg) gLossGAN = 0.001 * torch.mean(1 - fakeOut)gLossVGG = 0.006 * lossF(lossNetwork(fakeImg),lossNetwork(sourceImg))gLoss = gLossSR + gLossGAN + gLossVGGgLoss.backward()optimizerG.step()#数据可视化processBar.set_description(desc='[%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f' % (epoch, EPOCHS, dLoss.item(),gLoss.item(),realOut.item(),fakeOut.item()))#将文件输出到目录中with torch.no_grad():fig = plt.figure(figsize=(10,10))plt.axis("off")fakeImgs = netG(testImgs).detach().cpu()plt.imshow(np.transpose(vutils.make_grid(fakeImgs,padding=2,normalize=True),(1,2,0)), animated=True)plt.savefig('./Img/Result_epoch % 05d.jpg' % epoch, bbox_inches='tight', pad_inches = 0)print('[INFO] Image saved successfully!')#保存模型路径文件torch.save(netG.state_dict(), 'model/netG_epoch_%d_%d.pth' % (4, epoch))torch.save(netD.state_dict(), 'model/netD_epoch_%d_%d.pth' % (4, epoch))

[0/100] Loss_D: 1.0737 Loss_G: 0.0360 D(x): 0.1035 D(G(z)): 0.1772: : 33it [00:32,  1.02it/s]
0it [00:00, ?it/s]
[INFO] Image saved successfully!
[1/100] Loss_D: 0.8497 Loss_G: 0.0216 D(x): 0.6464 D(G(z)): 0.4960: : 33it [00:31,  1.04it/s]
0it [00:00, ?it/s]
[INFO] Image saved successfully!
[2/100] Loss_D: 0.9925 Loss_G: 0.0235 D(x): 0.5062 D(G(z)): 0.4987: : 33it [00:31,  1.05it/s]
0it [00:00, ?it/s]
[INFO] Image saved successfully!
[3/100] Loss_D: 0.9907 Loss_G: 0.0277 D(x): 0.4948 D(G(z)): 0.4856: : 33it [00:31,  1.06it/s]
0it [00:00, ?it/s]
[INFO] Image saved successfully!
[4/100] Loss_D: 0.9936 Loss_G: 0.0180 D(x): 0.0127 D(G(z)): 0.0062: : 33it [00:31,  1.06it/s]
0it [00:00, ?it/s]
[INFO] Image saved successfully!
[5/100] Loss_D: 1.0636 Loss_G: 0.0300 D(x): 0.2553 D(G(z)): 0.3188: : 33it [00:31,  1.06it/s]
0it [00:00, ?it/s]
[INFO] Image saved successfully!
[6/100] Loss_D: 1.0000 Loss_G: 0.0132 D(x): 0.1667 D(G(z)): 0.1667: : 33it [00:31,  1.06it/s]
0it [00:00, ?it/s]
[INFO] Image saved successfully!
[7/100] Loss_D: 1.1650 Loss_G: 0.0227 D(x): 0.1683 D(G(z)): 0.3333: : 33it [00:31,  1.06it/s]
0it [00:00, ?it/s]
[INFO] Image saved successfully!
[8/100] Loss_D: 1.0000 Loss_G: 0.0262 D(x): 0.1667 D(G(z)): 0.1667: : 33it [00:31,  1.05it/s]
0it [00:00, ?it/s]
[INFO] Image saved successfully!
···
[56/100] Loss_D: 1.0000 Loss_G: 0.0119 D(x): 1.0000 D(G(z)): 1.0000: : 33it [00:32,  1.01it/s]
0it [00:00, ?it/s]
[INFO] Image saved successfully!
[57/100] Loss_D: 1.0000 Loss_G: 0.0084 D(x): 1.0000 D(G(z)): 1.0000: : 33it [00:32,  1.03it/s]
0it [00:00, ?it/s]
[INFO] Image saved successfully!
[58/100] Loss_D: 1.0000 Loss_G: 0.0065 D(x): 1.0000 D(G(z)): 1.0000: : 33it [00:32,  1.03it/s]
0it [00:00, ?it/s]
[INFO] Image saved successfully!

在Img文件夹中保存了每次训练的可视化结果，在训练中，第一轮的结果如下所示：

而在第58轮中的结果为：

3 结果可视化

接下来将构造结果可视化的代码。
该代码的头文件为

import torch.nn as nn
import torch.nn.functional as F
import torch
from PIL import Image
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt

首先我们需要引入生成模型

class ResBlock(nn.Module):"""残差模块"""def __init__(self,inChannals,outChannals):"""初始化残差模块"""super(ResBlock,self).__init__()self.conv1 = nn.Conv2d(inChannals,outChannals,kernel_size=1,bias=False)self.bn1 = nn.BatchNorm2d(outChannals)self.conv2 = nn.Conv2d(outChannals,outChannals,kernel_size=3,stride=1,padding=1,bias=False)self.bn2 = nn.BatchNorm2d(outChannals)self.conv3 = nn.Conv2d(outChannals,outChannals,kernel_size=1,bias=False)self.relu = nn.PReLU()def forward(self,x):"""前向传播过程"""resudial = x out = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(x)out = self.bn2(out)out = self.relu(out)out = self.conv3(x)out += resudialout = self.relu(out)return outclass Generator(nn.Module):"""生成模型(4x)"""def __init__(self):"""初始化模型配置"""super(Generator,self).__init__()#卷积模块1self.conv1 = nn.Conv2d(3,64,kernel_size=9,padding=4,padding_mode='reflect',stride=1)self.relu = nn.PReLU()#残差模块self.resBlock = self._makeLayer_(ResBlock,64,64,5)#卷积模块2self.conv2 = nn.Conv2d(64,64,kernel_size=1,stride=1)self.bn2 = nn.BatchNorm2d(64)self.relu2 = nn.PReLU()#子像素卷积self.convPos1 = nn.Conv2d(64,256,kernel_size=3,stride=1,padding=2,padding_mode='reflect')self.pixelShuffler1 = nn.PixelShuffle(2)self.reluPos1 = nn.PReLU()self.convPos2 = nn.Conv2d(64,256,kernel_size=3,stride=1,padding=1,padding_mode='reflect')self.pixelShuffler2 = nn.PixelShuffle(2)self.reluPos2 = nn.PReLU()self.finConv = nn.Conv2d(64,3,kernel_size=9,stride=1)def _makeLayer_(self,block,inChannals,outChannals,blocks):"""构建残差层"""layers = []layers.append(block(inChannals,outChannals))for i in range(1,blocks):layers.append(block(outChannals,outChannals))return nn.Sequential(*layers)def forward(self,x):"""前向传播过程"""x = self.conv1(x)x = self.relu(x)residual = xout = self.resBlock(x)out = self.conv2(out)out = self.bn2(out)out += residualout = self.convPos1(out)   out = self.pixelShuffler1(out)out = self.reluPos1(out)out = self.convPos2(out)   out = self.pixelShuffler2(out)out = self.reluPos2(out)out = self.finConv(out)return out

随后，我们初始化并构建可视化函数

device = torch.device("cpu")
net = Generator()
net.load_state_dict(torch.load("你的模型pth文件路径"))def imshow(path,sourceImg = True):"""展示结果"""preTransform = transforms.Compose([transforms.ToTensor()]) pilImg = Image.open(path)img = preTransform(pilImg).unsqueeze(0).to(device)source = net(img)[0,:,:,:]source = source.cpu().detach().numpy()  #转为numpysource = source.transpose((1,2,0)) #切换形状source = np.clip(source,0,1)  #修正图片if sourceImg:temp = np.clip(img[0,:,:,:].cpu().detach().numpy().transpose((1,2,0)),0,1)shape = temp.shapesource[-shape[0]:,:shape[1],:] = tempplt.imshow(source)img = Image.fromarray(np.uint8(source*255))img.save('./result/' + path.split('/')[-1][:-4] + '_result_with_source.jpg')  # 将数组保存为图片returnplt.imshow(source)img = Image.fromarray(np.uint8(source*255))img.save(path[:-4] + '_result.jpg')  # 将数组保存为图片

最后，只需要简单调用就好

imshow("你的图片路径",sourceImg = True)

以本次训练模型为例，拿一张从未见过的图片作为测试

能够看出细节问题还是很多的，因此可以考虑一下增加模型的训练时间，或者是修改一下模型的结构。